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Genetic Dissection of Complex Traits
Abstract
Medical genetics was revolutionized during the 1980s by the application of genetic mapping to locate the genes responsible for simple Mendelian diseases. Most diseases and traits, however, do not follow simple inheritance patterns. Genetics have thus begun taking up the even greater challenge of the genetic dissection of complex traits. Four major approaches have been developed: linkage analysis, allele-sharing methods, association studies, and polygenic analysis of experimental crosses. This article synthesizes the current state of the genetic dissection of complex traits--describing the methods, limitations, and recent applications to biological problems.
... MPP+ is released by glia into the extracellular space and taken up by dopaminergic neurons through dopamine transporters (Slc6a3), which are expressed on and near the dopaminergic synapses [5]. Once inside the dopaminergic neurons, MPP+ enters the mitochondria where it inhibits predominantly Complex I [29,39], and to a lower degree Complexes III and IV of the electron transport chain [35]. This inhibition results in a decrease in adenosine triphosphate (ATP) production, paired with an increase in the production of reactive oxygen species resulting in the death of the metabolically vulnerable neurons. ...
... Why are retinal ganglion cells vulnerable to intravitreal MPTP injection? MPP+ inhibits mitochondrial Complex I, III and IV resulting in mitochondrial and metabolic dysfunction [29,35,39]. Retinal ganglion cells are the susceptible neurons in many diseases characterized by mutations in mitochondrial genes or genes encoding mitochondrial or metabolic pathway proteins. ...
Neurodegenerative diseases have common underlying pathological mechanisms including progressive neuronal dysfunction, axonal and dendritic retraction, and mitochondrial dysfunction resulting in neuronal death. The retina is often affected in common neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease. Studies have demonstrated that the retina in patients with Parkinson’s disease undergoes changes that parallel the dysfunction in the brain. These changes classically include decreased levels of dopamine, accumulation of alpha-synuclein in the brain and retina, and death of dopaminergic nigral neurons and retinal amacrine cells leading to gross neuronal loss. Exploring this disease's retinal phenotype and vision-related symptoms is an important window for elucidating its pathophysiology and progression, and identifying novel ways to diagnose and treat Parkinson’s disease. 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is commonly used to model Parkinson’s disease in animal models. MPTP is a neurotoxin converted to its toxic form by astrocytes, transported to neurons through the dopamine transporter, where it causes mitochondrial Complex I inhibition and neuron degeneration. Systemic administration of MPTP induces retinal changes in different animal models. In this study, we assessed the effects of MPTP on the retina directly via intravitreal injection in mice (5 mg/mL and 50 mg/mL to 7, 14 and 21 days post-injection). MPTP treatment induced the reduction of retinal ganglion cells—a sensitive neuron in the retina—at all time points investigated. This occurred without a concomitant loss of dopaminergic amacrine cells or neuroinflammation at any of the time points or concentrations tested. The observed neurodegeneration which initially affected retinal ganglion cells indicated that this method of MPTP administration could yield a fast and straightforward model of retinal ganglion cell neurodegeneration. To assess whether this model could be amenable to neuroprotection, mice were treated orally with nicotinamide (a nicotinamide adenine dinucleotide precursor) which has been demonstrated to be neuroprotective in several retinal ganglion cell injury models. Nicotinamide was strongly protective following intravitreal MPTP administration, further supporting intravitreal MPTP use as a model of retinal ganglion cell injury. As such, this model could be utilized for testing neuroprotective treatments in the context of Parkinson’s disease and retinal ganglion cell injury.
Supplementary Information
The online version contains supplementary material available at 10.1186/s40478-024-01782-3.
... When a population GWAS draws samples from individuals of dissimilar ancestries, differences in the distribution of causal genotypes, and potentially of environmental exposures, can confound the association study [1,4]. Correcting for confounds due to population structure has therefore been an important pursuit in the GWAS literature [14,23,61]. ...
... It has long been recognized that population GWASs in humans can be biased by environmental and genetic confounding [1,4]. Currently, population GWASs attempt to control for these confounds by focusing on sets of individuals that are genetically more similar and by controlling for population stratification. ...
A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual’s phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the “indirect” genetic effects of relatives’ genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.
... To achieve this purpose, theoretical principles used here for the identification and organization of relevant measurement do-mains and related issues need to be inclusive. The multifactorial model of complex diseases (Falconer, 1965;Lander & Schork, 1994) provides a theoretical framework sufficiently broad as to be applicable to most prevention research paradigms examining childhood risks and SUDs. In this model, genetic and environmental influences determine phenotypes. ...
... In pursuing genetic as well as environmental influences on complex traits, characterization of the phenotype of interest is an assessment activity centrally important to generating interpretable results. Several dimensions have proven useful in defining phenotypes, including family history, age of onset, clinical characteristics, and severity (Lander & Schork, 1994). ...
Assessment planning in substance use disorder prevention research entails the identification of measurement domains and the selection of corresponding instruments needed to fulfill specific project goals. The study design, developmental periods examined, feasibility constraints, and anticipated statistical analyses are important considerations in optimally designing the assessment protocol. As a conceptual framework to organize the domains considered here as examples, the multifactorial model of complex disorders with elaborations emphasized by the discipline of developmental psychopathology is applied. Risks reviewed include family history, childhood maltreatment, peer relationships, and psychopathology. The substance involvement dimensions germane as outcomes include substance type, consumption quantity and frequency, and substance-related problems. Comprehensive diachronic evaluation over critical developmental periods provides the technical foundation for etiology and intervention research.
... To cover the LD between alleles in various genomic regions, GWAS, as a genetic technique, needed a large population. In spite of the advantages of GWAS for revealing genetic polymorphisms underlying agronomic traits, this approach is prone to the introduction of false positives due to population structure (Lander and Schork, 1994;Kang et al., 2008;Zhang et al., 2010). In order to avoid false-positive associations, a model based on the enhanced version of BLINK was used to exhibit significant population structure and relatedness as used by Zhou et al. (2016); Gapare et al. (2017), and Crowell et al. (2016). ...
Early cassava storage root formation and bulking is a medium of escape that farmers and processors tend to adopt in cases of abiotic and biotic stresses like drought, flood, and destruction by domestic animals. In this study, 220 cassava genotypes from the International Institute of Tropical Agriculture (IITA), National Root Crops Research Institute (NRCRI), International Center for Tropical Agriculture (CIAT), local farmers (from farmer’s field), and NextGen project were evaluated in three locations (Umudike, Benue, and Ikenne). The trials were laid out using a split plot in a randomized incomplete block design (alpha lattice) with two replications in 2 years. The storage roots for each plant genotype were sampled or harvested at 3, 6, 9, and 12 month after planting (MAP). All data collected were analyzed using the R-statistical package. The result showed moderate to high heritability among the traits, and there were significant differences (p< 0.05) among the performances of the genotypes. The genome-wide association mapping using the BLINK model detected 45 single-nucleotide polymorphism (SNP) markers significantly associated with the four early storage root bulking and formation traits on Chromosomes 1, 2, 3, 4, 5, 6, 8, 9, 10, 13, 14, 17, and 18. A total of 199 putative candidate genes were found to be directly linked to early storage root bulking and formation. The functions of these candidate genes were further characterized to regulate i) phytohormone biosynthesis, ii) cellular growth and development, and iii) biosynthesis of secondary metabolites for accumulation of starch and defense. Genome-wide association study (GWAS) also revealed the presence of four pleiotropic SNPs, which control starch content, dry matter content, dry yield, and bulking and formation index. The information on the GWAS could be used to develop improved cassava cultivars by breeders. Five genotypes (W940006, NR090146, TMS982123, TMS13F1060P0014, and NR010161) were selected as the best early storage root bulking and formation genotypes across the plant age. These selected cultivars should be used as sources of early storage root bulking and formation in future breeding programs.
... However, deciphering the genetics of complex traits is challenging due to the underlying complex gene interactions. [55][56][57] In this study, a total of 23 strains were constructed and characterized to map a single generelated trait of MANT biosynthesis. For multiple gene-related traits, the workload of genetic dissection would dramatically increase. ...
Synthetic biology confers new functions to hosts by introducing exogenous genetic elements, yet rebuilding complex traits that are based on large-scale genetic information remains challenging. Here, we developed a CRISPR/Cas9-mediated haploidization method that bypasses the natural process of meiosis. Based on the programmed haploidization in yeast, we further developed an easy-to-use method designated HAnDy (Haploidization-based DNA Assembly and Delivery in yeast) that enables efficient assembly and delivery of large DNA, with no need for any fussy in vitro manipulations. Using HAnDy, a de novo designed 1.024 Mb synthetic accessory chromosome (synAC) encoding 542 exogenous genes was parallelly assembled and then directly transferred to six phylogenetically diverse yeasts. The synAC significantly promotes hosts’ adaptations and increases the scope of the metabolic network, which allows the emergence of valuable compounds. Our approach should facilitate the assembly and delivery of large-scale DNA for expanding and deciphering complex biological functions.
... According to Lander and Schork [38], the genetic study of adult glaucoma is complex since its transmission mechanisms are sometimes unclear and have variable penetrance and late onset. Adult glaucoma is a multifactorial genetic disease whose outcome is also influenced by a number of environmental factors, many of them unknown. ...
Primary open-angle glaucoma (POAG) is a complex disease with a strong hereditably component. Several genetic variants have recently been associated with POAG, partially due to technological improvements such as next-generation sequencing (NGS). The aim of this study was to genetically analyze patients with POAG to determine the contribution of rare variants and hypomorphic alleles associated with glaucoma as a future method of diagnosis and early treatment. Seventy-two genes potentially associated with adult glaucoma were studied in 61 patients with POAG. Additionally, we sequenced the coding sequence of CYP1B1 gene in 13 independent patients to deep analyze the potential association of hypomorphic CYP1B1 alleles in the pathogenesis of POAG. We detected nine rare variants in 16% of POAG patients studied by NGS. Those rare variants are located in CYP1B1 , SIX6 , CARD10 , MFN1 , OPTC , OPTN , and WDR36 glaucoma-related genes. Hypomorphic variants in CYP1B1 and SIX6 genes have been identified in 8% of the total POAG patient assessed. Our findings suggest that NGS could be a valuable tool to clarify the impact of genetic component on adult glaucoma. However, in order to demonstrate the contribution of these rare variants and hypomorphic alleles to glaucoma, segregation and functional studies would be necessary. The identification of new variants and hypomorphic alleles in glaucoma patients will help to configure the genetic identity of these patients, in order to make an early and precise molecular diagnosis.
... Results suggest this is partly due to the fact that humans are not randomized in their environments, called population structure, which can cause spurious correlations between genotype and phenotype. This is best embodied by the "chopstick gene" example (Lander & Schork, 1994): If a GWAS were done on chopstick use, the analysis would identify genetic variants, but they would have nothing to do with chopstick skills and instead be those that happened to differ in frequency between East Asia and the rest of the world. Researchers thought this issue was previously addressed in GWAS studies, but it has returned with how population structure can lead to systematic biases in polygenic scores (Bird, 2021;Rosenberg et al., 2018). ...
Some psychologists claim “new evidence” that genes, not racism, are primarily responsible for racial differences in education, income, and incarceration, a claim that is taken up by those promoting racial inequality and White nationalism. This “new evidence” does not meet established scientific and ethical standards of genetics and evolutionary biology. By adopting the standards of genetics and evolutionary biology, psychologists can help eliminate the harm caused by “scientific racism” while preserving academic freedom.
... Variable phenotypes of complex life-history traits are often maintained through polygenic inheritance, with many genes of small effect contributing to phenotypic variance, often in a population-specific pattern (Lander & Schork, 1994). However, over the last several decades, studies of both plants and animals have found that life-history variation can be maintained in populations largely through one or several chromosomal inversions that create non-recombining haploblocks (Kirubakaran et al., 2016;Kunte et al., 2014;Pearse et al., 2019;Todesco et al., 2020). ...
Life‐history variation is the raw material of adaptation, and understanding its genetic and environmental underpinnings is key to designing effective conservation strategies. We used large‐scale genetic pedigree reconstruction of anadromous steelhead trout ( Oncorhynchus mykiss ) from the Russian River, CA, USA, to elucidate sex‐specific patterns of life‐history traits and their heritability. SNP data from adults returning from sea over a 14‐year period were used to identify 13,474 parent–offspring trios. These pedigrees were used to determine age structure, size distributions and family sizes for these fish, as well as to estimate the heritability of two key life‐history traits, spawn date and age at maturity (first reproduction). Spawn date was highly heritable ( h ² = 0.73) and had a cross‐sex genetic correlation near unity. We provide the first estimate of heritability for age at maturity in ocean‐going fish from this species and found it to be highly heritable ( h ² from 0.29 to 0.62, depending on sex and method), with a much lower genetic correlation across sexes. We also evaluated genotypes at a migration‐associated inversion polymorphism and found sex‐specific correlations with age at maturity. The significant heritability of these two key reproductive traits in these imperiled fish, and their patterns of inheritance in the two sexes, is consistent with predictions of both natural and sexually antagonistic selection (sexes experience opposing selection pressures). This emphasizes the importance of anthropogenic factors, including hatchery practices and ecosystem modifications, in shaping the fitness of this species, thus providing important guidance for management and conservation efforts.
... First, they provide a lens through which researchers can observe how functional variation is shaped and maintained by the effects of mutation, selection, recombination, genetic drift and demography. Beyond contextualizing the adaptive qualities of functional variation, population genomic studies quantify the genetic relatedness among individuals and characterize population structure, which can influence GWAS performance ( 82 ). For instance, in C. elegans , the optimal GWAS mapping panel is dependent on trade-offs between the amount of natural variation surveyed, the statistical power to detect QTL and the false discovery rate ( 56 ). ...
Studies of model organisms have provided important insights into how natural genetic differences shape trait variation. These discoveries are driven by the growing availability of genomes and the expansive experimental toolkits afforded to researchers using these species. For example, Caenorhabditis elegans is increasingly being used to identify and measure the effects of natural genetic variants on traits using quantitative genetics. Since 2016, the C. elegans Natural Diversity Resource (CeNDR) has facilitated many of these studies by providing an archive of wild strains, genome-wide sequence and variant data for each strain, and a genome-wide association (GWA) mapping portal for the C. elegans community. Here, we present an updated platform, the Caenorhabditis Natural Diversity Resource (CaeNDR), that enables quantitative genetics and genomics studies across the three Caenorhabditis species: C. elegans, C. briggsae and C. tropicalis. The CaeNDR platform hosts several databases that are continually updated by the addition of new strains, whole-genome sequence data and annotated variants. Additionally, CaeNDR provides new interactive tools to explore natural variation and enable GWA mappings. All CaeNDR data and tools are accessible through a freely available web portal located at caendr.org.
... Surveying a large number of genotypes in the existing germplasm of sugarcane can be helpful in finding associations between the markers and traits, using association mapping (Wei et al., 2006;Banerjee et al., 2015). To avoid spurious associations, the population structure and kinship of the association map population were employed to elucidate inferences (Lander and Schork, 2006). Validation of all those markers linked with QTL will have been identified by means of association mapping in a diverse population (Korir et al., 2013;Picañol et al., 2013;Ukoskit et al., 2019). ...
Sugarcane (Saccharum spp.) is a widely cultivated crop that fulfils approximately 75% of the sucrose demand worldwide. Owing to its polyploidy and complex genetic nature, it is difficult to identify and map genes related to complex traits, such as sucrose content. However, association mapping is one of the alternatives for identifying genes or markers for marker-assisted selection. In the present study, EST-SSR primers were obtained from in silico studies. The functionality of each primer was tested using Blast2Go software, and 30 EST-SSR primers related to sugar content were selected. These markers were validated using association analysis. A total of 70 F1 diverse genotypes for sugar content were phenotypes with two check lines. All parameters related to sugar content were recorded. The results showed a significant variation between the genotypes for sugar yield traits such as Brix value, purity, and sucrose content, etc. Correlation studies revealed that the Brix%, sucrose content, and sucrose recovery were significantly correlated. An association analysis was performed using mixed linear model to avoid false positive associations. The association analysis revealed that the SEM 407 marker was significantly associated with Brix% and sucrose content. The SEM 407 primers are putatively related to diphosphate-fructose-6-phosphate 1-phosphotransferase which is associated with Brix% and sucrose content. This functional marker can be used for marker-assisted selection for sugar yield traits in sugarcane that could accelerate the sugarcane breeding program.
... The Human Genome Project will be crucial to the future of behavioral genetics. Linkage-and association-based molecular genetic methods can detect specific alleles associated with behavioral outcomes (Lander & Schork, 1994). Early molecular and statistical methods assumed that the relevant alleles were genes of large effect; more modern methods offer the power to detect quantitative trait loci (QTLs) accounting for less than 10% of the variability in outcome. ...
Modern neuroscientific and genetic technologies have provoked intense disagreement between scientists who envision a future in which biogenetic theories will enrich or even replace psychological theories, and others who consider biogenetic theories exaggerated, dehumanizing, and dangerous. Both sides of the debate about the role of genes and brains in the genesis of human behavior have missed an important point: All human behavior that varies among individuals is partially heritable and correlated with measurable aspects of brains, but the very ubiquity of these findings makes them a poor basis for reformulating scientists’ conceptions of human behavior. Materialism requires psychological processes to be physically instantiated, but more crucial for psychology is the occasional empirical discovery of behavioral phenomena that are specific manifestations of low-level biological variables. Heritability and psychobiological association cannot be the basis for establishing whether behavior is genetic or biological, because to do so leads only to the banal tautology that all behavior is ultimately based in the genotype and brain.
... However, since UC affects less than 1% of Canadians 38 and that only 5 to 15% of these will be using a biologic during the course of their treatment 39 , the number of patients included in the study was a convenient sample among all possible cases in our SLSJ region (282,330 inhabitants in 2023 40 , meaning that ~ 2,800 prevalent cases are expected, including ~ 230 cases on biotherapies, all followed in one central hospital deserving gastroenterology specialty). Thus, despite the small sample size of our cohort, we found signi cant association even when applying stringent correction for multiple testing, which highlights the strength of our deep phenotyping strategy 11 combined with a targeted candidate-gene approach and the advantages of the use of a well-known founder population in genetic studies [41][42][43][44][45][46][47] . Grouping all anti-TNF agents together was another limitation of the study because these drugs, despite having the same action mechanism, may be affected by different PGx variants. ...
For severe forms of ulcerative colitis (UC), a chronic inflammatory bowel disease (IBD), biological therapies, including tumor necrosis factor inhibitors (anti-TNF), are often used. However, these drugs have a high variability in treatment response. Multiple factors, such as genetic variants, can affect this variability. The goal of the study was to verify if selected candidate variants could affect response to anti-TNF in UC treatment. This association study included 76 participants suffering from UC and past or current users of anti-TNF. Clinical data for phenotyping was collected through a single visit with the participant and a medical chart review. Blood or saliva samples were collected to extract DNA and to genotype eight selected candidate variants in genes TNF, TNFAIP3, TNFRSF1A and TNFRSF1B. For anti-TNF users, 30% of individuals were non-responders, 70% suffered from AE and none of the studied variants was associated with the response’s phenotype. However, for infliximab users only (n = 44), the TNFRSF1B-rs1061622 variant was associated with nonresponse to infliximab for the first time in a cohort of UC patients (p-value = 0.028). Next steps are to replicate this association in independent cohorts and to perform functional studies to gain more evidence on the variant.
... Candidate gene discovery through genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping is prolific in plant and animal populations (Lander and Schork 1994;Visscher et al. 2012Visscher et al. , 2017. Despite decades of directional selection in many plant populations, loci impacting traits of interest still segregate, even in advanced breeding materials. ...
Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model (LMM) analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction (MAP) and its successor, genomic prediction (GP), have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.
... Bu nedenle poligenik yapıdaki ve çevredeki değişim, kantitatif özelliklerle ilgili çalışmaları monogenik özelliklerle ilgili çalışmalara göre daha zor hale getirmektedir. Bu özellikler, fenotip olarak ölçülmesi kolay olmasına rağmen genetik olarak karmaşıktır ve bu nedenle bazı araştırmacılar tarafından karmaşık özelliklerin ifadesi kullanılmaktadır (55). Poligenliği ve çevresel değişikliklere duyarlılığı nedeniyle, büyük popülasyonlarda bu özelliklerin genetik mimarisini incelemek için istatistiksel araçlar kullanmak gerekir (53). ...
... single-nucleotide polymorphisms (SNPs), insertion-deletions (indels), etc.) is associated with a trait (e.g. disease, plant height, etc.) [2]. A typical GWA study will require genotyping a group of individuals, variant detection and imputation, phenotyping the same group, trait data extraction, and association mapping. ...
We demonstrated a flexible Genome-Wide Association Study (GWAS) platform built upon the iPlant Collaborative Cyber-infrastructure. The platform supports big data management, sharing, and large scale study of both genotype and phenotype data on clusters. End users can add their own analysis tools, and create customized analysis workflows through the graphical user interfaces in both iPlant Discovery Environment and BioExtract server.
Type 1 diabetes (T1D) results from a complex interplay of genetic predisposition, immunological dysregulation, and environmental triggers, that culminate in the destruction of insulin‐secreting pancreatic β cells. This review provides a comprehensive examination of the multiple factors underpinning T1D pathogenesis, to elucidate key mechanisms and potential therapeutic targets. Beginning with an exploration of genetic risk factors, we dissect the roles of human leukocyte antigen (HLA) haplotypes and non‐HLA gene variants associated with T1D susceptibility. Mechanistic insights gleaned from the NOD mouse model provide valuable parallels to the human disease, particularly immunological intricacies underlying β cell–directed autoimmunity. Immunological drivers of T1D pathogenesis are examined, highlighting the pivotal contributions of both effector and regulatory T cells and the multiple functions of B cells and autoantibodies in β‐cell destruction. Furthermore, the impact of environmental risk factors, notably modulation of host immune development by the intestinal microbiome, is examined. Lastly, the review probes human longitudinal studies, unveiling the dynamic interplay between mucosal immunity, systemic antimicrobial antibody responses, and the trajectories of T1D development. Insights garnered from these interconnected factors pave the way for targeted interventions and the identification of biomarkers to enhance T1D management and prevention strategies.
El objetivo de este proyecto fue explorar y analizar la presencia de la medicina genómica en las creencias, las prácticas y las políticas en Costa Rica en mi propio campo de investigación, la genética de los trastornos mentales mayores, desde mi propia mirada como participante y observadora. Para lo anterior, se realizó un estudio cualitativo utilizando un diseño fenomenológico con entrevistas a profundidad a doce personas directamente involucradas en el campo de la salud mental, ya sea por sus experiencias vividas desde su padecimiento psiquiátrico mayor, su práctica y experiencia profesional en clínica o su participación en la definición de políticas públicas en salud mental. Estas personas fueron seleccionadas deliberadamente para obtener el mayor rango de posiciones. Los resultados de las entrevistas se complementaron y contrastaron con las respuestas a un cuestionario corto en línea sobre prioridades percibidas en Salud Mental, el documento de la Política Nacional de Salud Mental 2012-20 y mis reflexiones críticas.
Se encontró que el modelo explicativo mayoritario fue el de la genetización ilustrada en que las explicaciones genéticas son balanceadas, sin posiciones extremas de determinismo, esencialismo o fatalismo genético. También se determinó que este modelo tiene repercusiones positivas para las personas padecientes por la reducción en la percepción de culpa y que mejoraría posiblemente con el acceso a un buen servicio de consejo genético para condiciones de herencia compleja. Con excepción de algunos clínicos a nivel privado, no se encontró que las prácticas estuvieran permeadas con el uso de pruebas genéticas. Tampoco se encontró que las políticas públicas estén permeadas por tendencias genómicas. Otros hallazgos ancilares fueron: una pobre atención a nivel público a las personas que no tienen acceso a servicios privados; maltrato y violencia por las mismas instituciones que están para cuidar, aliviar y curar; ausencia de políticas sociales dirigidas a grupos que por su situación socioeconómica están más predispuestos a la enfermedad; y
que las voces de las personas padecientes no han sido ni son escuchadas. Estos hallazgos se enmarcaron en la teoría del sufrimiento social que permite agrupar, interpretar y reflexionar sobre los malestares y problemas humanos, individuales y colectivos, que se expresaron en las narraciones de las personas entrevistadas.
Hereditary angioedema (HAE) due to C1 inhibitor protein (C1-INH) deficiency was recently shown to be associated with increased risk of venous thromboembolism (VTE). This is the first national family study of HAE with the aim to determine the familial risk of VTE. The Swedish Multi-Generation Register was linked to the Swedish National Patient Register during the period 1964-2018. Only HAE patients with a validated diagnosis were included in the study and were linked to their family members. Hazard ratios (HRs) and 95% confidence intervals (CIs) for VTE were calculated for HAE patients compared with relatives without HAE. Among 2,006 individuals (from 276 pedigrees of 365 patients with HAE), 103 individuals were affected by VTE. In total 35 (9.6%) of HAE patients compared to 68 (4.1%) of non-HAE relatives were affected by VTE (p<0.001). The adjusted HR for VTE among HAE patients was 2.51 (95% CI 1.67-3.77). HAE patients were younger at the first VTE than their non-HAE relatives (mean age 51 versus 63 years, p<0.001). Before the age of 70 years the HR for VTE among HAE patients was 3.62 (95%CI 2.26-5.80). The HR for VTE for HAE patients born after 1964 was 8.29 (95%CI 2.90-23.71). The HR for VTE for HAE patients born 1964 or earlier was 1.82 (95%CI 1.14-2.91). HAE is associated with VTE among young and middle-aged individuals in Swedish families with HAE. The effect size of the association is in the order of other thrombophilias. We suggest that HAE may be considered a new rare thrombophilia.
Disorders of behavior represent some of the most common and disabling diseases affecting humankind; however, despite their worldwide distribution, genetic influences on these illnesses are often overlooked by families and mental health professionals. Psychiatric genetics is a rapidly advancing field, elucidating the varied roles of specific genes and their interactions in brain development and dysregulation. Principles of Psychiatric Genetics includes 22 disorder-based chapters covering, amongst other conditions, schizophrenia, mood disorders, anxiety disorders, Alzheimer's disease, learning and developmental disorders, eating disorders and personality disorders. Supporting chapters focus on issues of genetic epidemiology, molecular and statistical methods, pharmacogenetics, epigenetics, gene expression studies, online genetic databases and ethical issues. Written by an international team of contributors, and fully updated with the latest results from genome-wide association studies, this comprehensive text is an indispensable reference for psychiatrists, neurologists, psychologists and anyone involved in psychiatric genetic studies.
In population neuroscience, three disciplines come together to advance our knowledge of factors that shape the human brain: neuroscience, genetics, and epidemiology (Paus, Human Brain Mapping 31:891–903, 2010). Here, I will come back to some of the background material reviewed in more detail in our previous book (Paus, Population Neuroscience, 2013), followed by a brief overview of current advances and challenges faced by this integrative approach.
Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women’s Women’s Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.
Author Summary
Principal component analysis (PCA) is a widely used technique in human genetics research. One of its most frequent applications is in the context of genetic association studies, wherein researchers use PCA to infer, and then adjust for, the genetic ancestry of study participants. Although a powerful approach, prior work has shown that PCA sometimes captures other features or data quality issues, and pre-processing steps have been suggested to address these concerns. However, the utility and downstream implications of this recommended preprocessing are not fully understood, nor are these steps universally implemented. Moreover, the vast majority of prior work in this area was conducted in studies that exclusively included individuals of European ancestry. Here, we revisit this work in the context of admixed populations—populations with diverse, mixed ancestry that have been largely underrepresented in genetics research to date. We demonstrate the unique concerns that can arise in this context and illustrate the detrimental effects that including principal components in genetic association study models can have when not implemented carefully. Altogether, we hope our work serves as a reminder of the care that must be taken—including careful pre-processing, diagnostics, and modeling choices—when implementing PCA in admixed populations and beyond.
Basic relevant information on methodologies used in neurological disease models can be extremely hard to find. Originally published in 2006, this important reference work contains 30 chapters from over 60 internationally recognized scientists and covers every major methodology and disease model used in neuroscience research. Divided into two major sections, the first deals with general methodologies in neuroscience research covering topics from animal welfare and ethical issues to surgical procedures, post-operative care and behavioral testing. Section two covers every major disease model including traumatic brain injury, ischemia and stroke, to Parkinson's, motor neurone disease, epilepsy and sleep disorders. Delivering critical methodological information and describing small animal models for almost all major neurological diseases, this book forms an essential reference for anyone working in neuroscience, from beginning students to experienced researchers and medical professionals.
Polygenic indices (PGI)—the new recommended label for polygenic scores in social science applications—are genetic summary scales often used to represent an individual’s liability for a disease, trait, or behavior on the basis of the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science data sets have facilitated increased uptake of PGIs in social science research, a trend that will likely continue. Yet most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, I provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. I summarize recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. I conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.
Basic relevant information on methodologies used in neurological disease models can be extremely hard to find. Originally published in 2006, this important reference work contains 30 chapters from over 60 internationally recognized scientists and covers every major methodology and disease model used in neuroscience research. Divided into two major sections, the first deals with general methodologies in neuroscience research covering topics from animal welfare and ethical issues to surgical procedures, post-operative care and behavioral testing. Section two covers every major disease model including traumatic brain injury, ischemia and stroke, to Parkinson's, motor neurone disease, epilepsy and sleep disorders. Delivering critical methodological information and describing small animal models for almost all major neurological diseases, this book forms an essential reference for anyone working in neuroscience, from beginning students to experienced researchers and medical professionals.
Objectives Vasospastic angina (VSA) is a complex coronary vasomotor disorder associated with an increased risk of myocardial infarction and sudden death. Despite considerable advances in understanding VSA pathophysiology, the interplay between genetic and environmental factors remains elusive. Accordingly, we aimed to determine the familial VSA risk among first-degree relatives of affected individuals.
Methods A population-based multigenerational cohort study was conducted, including full-sibling pairs born to Swedish parents between 1932 and 2018. Register-based diagnoses were ascertained through linkage to the Swedish Multigeneration Register and National Patient Register. Incidence rate ratios (IRRs) and adjusted HRs were calculated for relatives of individuals with VSA compared with relatives of individuals without VSA.
Results The total study population included 5 764 770 individuals. Overall, 3461 (0.06%) individuals (median age at disease onset 59 years, IQR: 63–76) were diagnosed with VSA. Of these, 2236 (64.61%) were women. The incidence rate of VSA for individuals with an affected sibling was 0.31 (95% CI: 0.24 to 0.42) per 1000 person-years compared with 0.04 (95% CI: 0.04 to 0.04) per 1000 person-years for those without an affected sibling, yielding an IRR of 7.58 (95% CI: 5.71 to 10.07). The risk of VSA for siblings with an affected sibling was significantly increased in the fully adjusted model (HR: 2.56; 95% CI: 1.73 to 3.79). No increased risk of VSA was observed in spouses of affected individuals (HR: 0.63; 95% CI: 0.19 to 2.09).
Conclusions In this nationwide family study, we identified high familial risk for VSA independent of shared environmental risk factors. Our findings indicate that VSA tends to cluster in families, emphasising the need to explore genetic and non-genetic factors that may contribute.
Journal of genetics and Genomics : Accepted on "29-01-2023".
This comprehensive review paper explores the profound impact of genetics in pathology and the transformative role of molecular science research in the healthcare landscape. Genetics, a fundamental field of science, has long been at the forefront of understanding the hereditary basis of diseases. It encompasses both rare Mendelian disorders and common complex conditions, providing insights into the genetic underpinnings of ailments that afflict humanity. The integration of genetics with advances in molecular science research has revolutionized the practice of pathology, offering innovative avenues for diagnosis, prognosis, and treatment. In this review, we discuss the implications, challenges, and future prospects of this dynamic relationship. The implications are far-reaching, influencing diagnostics, personalized medicine, and risk assessment. Genetic testing has become a standard practice for identifying inherited disorders, and molecular pathology has transformed disease diagnosis and treatment. Challenges, including ethical concerns and the interpretation of genetic data, must be addressed to fully realize the potential of genetics in pathology. Effective interdisciplinary collaboration is paramount to translating genetic insights into practical clinical applications. The future of genetics and molecular science research in pathology is marked by great promise. As technology advances and costs decrease, personalized medicine will become more accessible, while emerging trends, such as exploring non-coding regions of the genome and the role of the microbiome, open new avenues for research and treatment. This review provides a comprehensive and informative resource for healthcare professionals, researchers, and policymakers, shedding light on the fascinating interplay of genetics and molecular science in the realm of medicine and the transformative potential it holds for the future of healthcare. Abstract: This comprehensive review paper explores the profound impact of genetics in pathology and the transformative role of molecular science research in the healthcare landscape. Genetics, a fundamental field of science, has long been at the forefront of understanding the hereditary basis of diseases. It encompasses both rare Mendelian disorders and common complex conditions, providing insights into the genetic underpinnings of ailments that afflict humanity. The integration of genetics with advances in molecular science research has revolutionized the practice of pathology, offering innovative avenues for diagnosis, prognosis, and treatment. In this review, we discuss the implications, challenges, and future prospects of this dynamic relationship. The implications are far-reaching, influencing diagnostics, personalized medicine, and risk assessment. Genetic testing has become a standard practice for identifying inherited disorders, and molecular pathology has transformed disease diagnosis and treatment. Challenges, including ethical concerns and the interpretation of genetic data, must be addressed to fully realize the potential of genetics in pathology. Effective interdisciplinary collaboration is paramount to translating genetic insights into practical clinical applications. The future of genetics and molecular science research in pathology is marked by great promise. As technology advances and costs decrease, personalized medicine will become more accessible, while emerging trends, such as exploring non-coding regions of the genome and the role of the microbiome, open new avenues for research and treatment. This review provides a comprehensive and informative resource for healthcare professionals, researchers, and Manuscript Click here to view linked References policymakers, shedding light on the fascinating interplay of genetics and molecular science in the realm of medicine and the transformative potential it holds for the future of healthcare.
Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to di seases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which – among transcriptomes of homozygotes and heterozygotes for its various alleles – there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.
Alcoholism is transmitted in families. The complexity and heterogeneity of this disorder has made it difficult to identify specific genetic correlates. One design with the potential to do so is the family-based association study, in which the frequencies of genetic polymorphisms are compared between affected and nonaffected members. Reduced central serotonin neurotransmission is associated with features of an antisocial subtype of alcoholism, although a primary deficit has not been traced to a particular component. Genetic markers related to the sertonergic system have been identified, located, and cloned. If associations can be discovered, the development process for pharmacotherapy could be facilitated. In this review, the evidence for the involvement of the serotonergic system in antisocial alcoholism is examined, and the potential for family-based association studies to identify specific components that may be involved is discussed.
Genome-wide association studies (GWAS) are powerful for identifying genomic regions, or even directly the causal loci, controlling the variation of quantitative traits impacted by multiple loci. First proposed for the discovery of genetic loci controlling human diseases, GWAS rapidly became a method of choice in plant genetic studies, once the number of markers covering the genome became sufficient. Based on the study of a large panel of unrelated accessions, the principle is simple: it consists of screening significant associations between the values of a trait assessed in each accession of the panel and their genotypes for markers covering the whole genome in a sufficiently dense manner. Several parameters may impact GWAS results and must be considered when starting a new study. They concern (i) the panel composition (size and composition), (ii) the phenotypes (quality of measurement, heritability, genotype × environment interaction) and (iii) genotyping (type and number of markers, possibility to perform imputation). Then several methods and software have been proposed to perform GWAS, considering (or not) the structure of the population, the kinship or other covariates and performing the analysis one marker at a time or adding multiple loci in the model. In this chapter, we will review all these aspects, illustrating them with a few examples. Finally, we will present the most recent developments in the domain.KeywordsGenome-wide association studyCropsLinkage disequilibriumPopulation structureGenotyping imputation
Objectives
To examine whether multimorbidity aggregates in families in Sweden.
Design
National explorative family study.
Setting
Swedish Multigeneration Register linked to the National Patient Register, 1997-2015. Multimorbidity was assessed with a modified counting method of 45 chronic non-communicable diseases according to ICD-10 (international classification of diseases, 10th revision) diagnoses.
Participants
2 694 442 Swedish born individuals (48.73% women) who could be linked to their Swedish born first, second, and third degree relatives. Twins were defined as full siblings born on the same date.
Main outcome measures
Multimorbidity was defined as two or more non-communicable diseases. Familial associations for one, two, three, four, and five or more non-communicable diseases were assessed to examine risks depending on the number of non-communicable diseases. Familial adjusted odds ratios for multimorbidity were calculated for individuals with a diagnosis of multimorbidity compared with relatives of individuals unaffected by multimorbidity (reference). An initial principal component decomposition followed by a factor analysis with a principal factor method and an oblique promax rotation was used on the correlation matrix of tetrachoric correlations between 45 diagnoses in patients to identify disease clusters.
Results
The odds ratios for multimorbidity were 2.89 in twins (95% confidence interval 2.56 to 3.25), 1.81 in full siblings (1.78 to 1.84), 1.26 in half siblings (1.24 to 1.28), and 1.13 in cousins (1.12 to 1.14) of relatives with a diagnosis of multimorbidity. The odds ratios for multimorbidity increased with the number of diseases in relatives. For example, among twins, the odds ratios for multimorbidity were 1.73, 2.84, 4.09, 4.63, and 6.66 for an increasing number of diseases in relatives, from one to five or more, respectively. Odds ratios were highest at younger ages: in twins, the odds ratio was 3.22 for those aged ≤20 years, 3.14 for those aged 21-30 years, and 2.29 for those aged >30 years at the end of follow-up. Nine disease clusters (factor clusters 1-9) were identified, of which seven aggregated in families. The first three disease clusters in the principal component decomposition were cardiometabolic disease (factor 1), mental health disorders (factor 2), and disorders of the digestive system (factor 3). Odds ratios for multimorbidity in twins, siblings, half siblings, and cousins for the factor 1 cluster were 2.79 (95% confidence interval 0.97 to 8.06), 2.62 (2.39 to 2.88), 1.52 (1.34 to 1.73), and 1.31 (1.23 to 1.39), and for the factor 2 cluster, 5.79 (4.48 to 7.48) 3.24 (3.13 to 3.36), 1.51 (1.45 to 1.57), and 1.37 (1.341.40).
Conclusions
The results of this explorative family study indicated that multimorbidity aggregated in Swedish families. The findings suggest that map clusters of diseases should be used for the genetic study of common diseases to show new genetic patterns of non-communicable diseases.
Context:
It's well-documented that most economic traits have a complex genetic structure that is controlled by additive and non-additive gene actions. Hence, knowledge of the underlying genetic architecture of such complex traits could aid in understanding how these traits respond to the selection in breeding and mating programs. Computing and having estimates of the non-additive effect for economic traits in sheep using genome-wide information can be important because; non-additive genes play an important role in the prediction accuracy of genomic breeding values and the genetic response to the selection.
Aim:
This study aimed to assess the impact of non-additive effects (dominance and epistasis) on the estimation of genetic parameters for body weight traits in sheep.
Methods:
This study used phenotypic and genotypic belonging to 752 Scottish Blackface lambs. Three live weight traits considered in this study were included in body weight at 16, 20, and 24 weeks). Three genetic models including additive (AM), additive + dominance (ADM), and additive + dominance + epistasis (ADEM), were used.
Key results:
The narrow sense heritability for weight at 16 weeks of age (BW16) were 0.39, 0.35, and 0.23, for 20 weeks of age (BW20) were 0.55, 0.54, and 0.42, and finally for 24 weeks of age (BW24) were 0.16, 0.12, and 0.02, using the AM, ADM, and ADEM models, respectively. The additive genetic model significantly outperformed the non-additive genetic model (p < 0.01). The dominance variance of the BW16, BW20, and BW24 accounted for 38, 6, and 30% of the total phenotypic, respectively. Moreover, the epistatic variance accounted for 39, 0.39, and 47% of the total phenotypic variances of these traits, respectively. In addition, our results indicated that the most important SNPs for live weight traits are on chromosomes 3 (three SNPS including s12606.1, OAR3_221188082.1, and OAR3_4106875.1), 8 (OAR8_16468019.1, OAR8_18067475.1, and OAR8_18043643.1), and 19 (OAR19_18010247.1), according to the genome-wide association analysis using additive and non-additive genetic model.
Conclusions:
The results emphasized that the non-additive genetic effects play an important role in controlling body weight variation at the age of 16-24 weeks in Scottish Blackface lambs.
Implications:
It is expected that using a high-density SNP panel and the joint modeling of both additive and non-additive effects can lead to better estimation and prediction of genetic parameters.
Genome-wide association studies (GWAS) have yielded tremendous insight into the genetic architecture of trait variation. However, the collections of loci they uncover are far from exhaustive. As many of the complicating factors that confound or limit the efficacy of GWAS are exaggerated over broad geographic scales, a shift toward more analyses using mapping panels sampled from narrow geographic localities ("local" populations) could provide novel, complementary insights. Here, we present an overview of the major complicating factors, review mounting evidence from genomic analyses that these factors are pervasive, and synthesize theoretical and empirical evidence for the power of GWAS in local populations.
Basic relevant information on methodologies used in neurological disease models can be extremely hard to find. Originally published in 2006, this important reference work contains 30 chapters from over 60 internationally recognized scientists and covers every major methodology and disease model used in neuroscience research. Divided into two major sections, the first deals with general methodologies in neuroscience research covering topics from animal welfare and ethical issues to surgical procedures, post-operative care and behavioral testing. Section two covers every major disease model including traumatic brain injury, ischemia and stroke, to Parkinson's, motor neurone disease, epilepsy and sleep disorders. Delivering critical methodological information and describing small animal models for almost all major neurological diseases, this book forms an essential reference for anyone working in neuroscience, from beginning students to experienced researchers and medical professionals.
Basic relevant information on methodologies used in neurological disease models can be extremely hard to find. Originally published in 2006, this important reference work contains 30 chapters from over 60 internationally recognized scientists and covers every major methodology and disease model used in neuroscience research. Divided into two major sections, the first deals with general methodologies in neuroscience research covering topics from animal welfare and ethical issues to surgical procedures, post-operative care and behavioral testing. Section two covers every major disease model including traumatic brain injury, ischemia and stroke, to Parkinson's, motor neurone disease, epilepsy and sleep disorders. Delivering critical methodological information and describing small animal models for almost all major neurological diseases, this book forms an essential reference for anyone working in neuroscience, from beginning students to experienced researchers and medical professionals.
Basic relevant information on methodologies used in neurological disease models can be extremely hard to find. Originally published in 2006, this important reference work contains 30 chapters from over 60 internationally recognized scientists and covers every major methodology and disease model used in neuroscience research. Divided into two major sections, the first deals with general methodologies in neuroscience research covering topics from animal welfare and ethical issues to surgical procedures, post-operative care and behavioral testing. Section two covers every major disease model including traumatic brain injury, ischemia and stroke, to Parkinson's, motor neurone disease, epilepsy and sleep disorders. Delivering critical methodological information and describing small animal models for almost all major neurological diseases, this book forms an essential reference for anyone working in neuroscience, from beginning students to experienced researchers and medical professionals.
Basic relevant information on methodologies used in neurological disease models can be extremely hard to find. Originally published in 2006, this important reference work contains 30 chapters from over 60 internationally recognized scientists and covers every major methodology and disease model used in neuroscience research. Divided into two major sections, the first deals with general methodologies in neuroscience research covering topics from animal welfare and ethical issues to surgical procedures, post-operative care and behavioral testing. Section two covers every major disease model including traumatic brain injury, ischemia and stroke, to Parkinson's, motor neurone disease, epilepsy and sleep disorders. Delivering critical methodological information and describing small animal models for almost all major neurological diseases, this book forms an essential reference for anyone working in neuroscience, from beginning students to experienced researchers and medical professionals.
Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations—the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.
Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
In an age of competing medical priorities, why should healthcare professionals who are already overloaded with information develop core competencies in genetics and genomics? [1]
The genomic architecture of kidney disease has fascinated developmental biologists and human geneticists for over four decades. Seminal discoveries of note include the discovery of genes implicated in autosomal dominant/recessive polycystic kidney disease, nephronophthisis, and nephrotic syndrome. Uncovering disease-causing genes has helped refine our pathogenetic understanding of many renal diseases, and in many cases, it has directly translated into a concrete improvement of patient care. The recent emergence of next-generation sequencing strategies has dramatically sped up the discovery process and constitutes the cornerstone towards the realization of personalized medicine. This chapter summarizes basic genomic/genetic concepts before delving into recent advances that are pertinent to the practice of contemporary pediatric nephrologists. From laboratory methods to the interpretation of genetic variants, we present every topic within a clinical framework enriched with many examples from the pediatric nephrology literature. The major benefits of genomics to the day-to-day practice of busy clinicians are: it will expedite diagnosis, clarify prognosis, and guide therapeutic choices. Our overarching goals for this chapter were two-fold: to first convince clinicians “already overloaded with information” that learning about genomics is a worthwhile investment that will pay dividends in the short-term, while providing an accessible port of entry into this complex field.KeywordsGenetic testingMendelian conditionPathogenic variantFamily historyNext-generation sequencing
Polygenic scores have become an important tool in human genetics, enabling the prediction of individual phenotypes from their genotypes. Understanding how the pattern of differences in polygenic score predictions across individuals intersects with variation in ancestry can provide insights into the evolutionary forces acting on the trait in question, and is important for understanding health disparities. However, because most polygenic scores are computed using effect estimates from population samples, they are susceptible to confounding by both genetic and environmental effects that are correlated with ancestry. The extent to which this confounding drives patterns in the distribution of polygenic scores depends on patterns of population structure in both the original estimation panel and in the prediction/test panel. Here, we use theory from population and statistical genetics, together with simulations, to study the procedure of testing for an association between polygenic scores and axes of ancestry variation in the presence of confounding. We use a simple model of genetic relatedness to describe how confounding in the estimation panel biases the distribution of polygenic scores in a way that depends on the degree of overlap in population structure between panels. We then show how this confounding can bias tests for associations between polygenic scores and important axes of ancestry variation in the test panel. We then use the understanding gained from this analysis to develop a simple method that leverages the patterns of genetic similarity between the two panels to guard against these biases, and show that this method can provide better protection against confounding than the standard PCA-based approach.
Down Syndrome (DS), caused by triplication of human chromosome 21 (Hsa21) is the most common form of intellectual disability worldwide. Recent progress in healthcare has resulted in a dramatic increase in the lifespan of individuals with DS. Unfortunately, most will develop Alzheimer’s disease like dementia (DS-AD) as they age. Understanding similarities and differences between DS-AD and the other forms of the disease – i.e., late-onset AD (LOAD) and autosomal dominant AD (ADAD) – will provide important clues for the treatment of DS-AD. In addition to the APP gene that codes the precursor of the main component of amyloid plaques found in the brain of AD patients, other genes on Hsa21 are likely to contribute to disease initiation and progression. This review focuses on SYNJ1, coding the phosphoinositide phosphatase synaptojanin 1 (SYNJ1). First, we highlight the function of SYNJ1 in the brain. We then summarize the involvement of SYNJ1 in the different forms of AD at the genetic, transcriptomic, proteomic and neuropathology levels in humans. We further examine whether results in humans correlate with what has been described in murine and cellular models of the disease and report possible mechanistic links between SYNJ1 and the progression of the disease. Finally, we propose a set of questions that would further strengthen and clarify the role of SYNJ1 in the different forms of AD.
Rice is subjected to numerous biotic and abiotic stress. Widespread genetic variation for such stress tolerance has spurred their detection and exploitation in breeding facilitated by molecular markers. Gene discovery through QTL mapping approaches has been developed with the recent advances in molecular genomics. Two approaches that have been commonly used for genetic mapping, e.g., linkage analysis and association or linkage disequilibrium (LD) analysis, are the most widely used approaches in rice. The use of these two approaches has resulted in the identification of QTLs and their allelic variation for use in breeding quite successfully. This has been discussed in detail here.
Background
Despite the many insights gleaned from GWAS, polygenic predictions of complex traits have had limited success, particularly when these predictions are applied to individuals of non-European descent. A deeper understanding of the genetic architecture of complex traits may inform why some traits are easier to predict than others.
Methods
Examining 163 complex traits from the UK Biobank, we compared and contrasted three aspects of genetic architecture (SNP heritability, LD variability, and genomic inequality) with three aspects of polygenic score performance (prediction accuracy in the source population, portability across populations, and trait divergence across populations). Here, genomic inequality refers to how unequally the genetic variance of each trait is distributed across the top trait-associated SNPs, as quantified via a novel application of Gini coefficients.
Results
Consistent with reduced statistical power, polygenic predictions of binary traits performed worse than predictions of quantitative traits. Traits with low Gini coefficients (i.e., highly polygenic architectures) include hip circumference as well as systolic and diastolic blood pressure. Traits with large population-level differences in polygenic scores include skin pigmentation and hair color. Focusing on 96 quantitative traits, we found that highly heritable traits were easier to predict and had predictions that were more portable to other ancestries. Traits with highly divergent polygenic score distributions across populations were less likely to have portable predictions. Intriguingly, LD variability was largely uninformative regarding the portability of polygenic predictions. This suggests that factors other than the differential tagging of causal SNPs drive the reduction in polygenic score accuracy across populations. Subsequent analyses identified suites of traits with similar genetic architecture and polygenic score performance profiles. Importantly, lifestyle and psychological traits tended to have low heritability, as well as poor predictability and portability.
Conclusions
Novel metrics capture different aspects of trait-specific genetic architectures and polygenic score performance. Our findings also caution against the application of polygenic scores to traits like general happiness, alcohol frequency, and average income, especially when polygenic scores are applied to individuals who have an ancestry that differs from the original source population.
This volume represents a burgeoning perspective on the origins of psychopathology, one that focuses on the development of the human central nervous system. The contemporary neurodevelopmental perspective assumes that mental disorders result from etiologic factors that alter the normal course of brain development. Defined here in its broadest sense, neurodevelopment is a process that begins at conception and extends throughout the life span. We now know that it is a complex process, and that its course can be altered by a host of factors, ranging from inherited genetic liabilities to psychosocial stressors. This book features the very best thinking in the converging fields of developmental neuroscience and developmental psychopathology. The developmental window represented is broad, extending from the prenatal period through adulthood, and the authors cover a broad range of etiologic factors and a spectrum of clinical disorders. Moreover, the contributors did not hesitate to use the opportunity to hypothesize about underlying mechanisms and to speculate on research directions.
A general method is proposed for calculating approximate thresholds of interval mapping tests for quantitative trait loci (QTL) detection. Simulation results show that this method, when applied to backcross and F2 populations, gives good approximations and is useful for any situation. Programs which calculate these thresholds for backcross, recombinant inbreds and F2 for any given level and any chromosome with any given distribution of codominant markers were written in Fortran 77 and are available under request. The approach presented here could be used to obtain, after suitable calculations, thresholds for most segregating populations used in QTL mapping experiments.
A graph theoretic definition of pedigrees is given and a distinction drawn between simple and complex pedigrees. Algorithms are presented to calculate the likelihood of any kind of pedigree, assuming only segregation at a finite set of loci, nonassortative mating and no environmental correlations; multiple births and consanguineous marriages are explicitly allowed for. The formulation given can lead to more powerful genetic counselling, segregation analysis and linkage analysis.
An analytical study is conducted of the properties of statistical tests to detect linkage between a disease locus and a very polymorphic marker locus when data on sib pairs are available. In most instances the most powerful test is the test based on the mean number of marker alleles shared identical by descent by the two members of a sib pair, and the most efficient sampling strategy is almost always to sample only pairs with both sibs affected. We show it is valid to use the information from all possible sib pairs as though they came from separate families when data on sibships of size three or larger are available, though more power may be obtained if different weights are given to the different sibship sizes.
Recent studies suggest that one or more genes on chromosome 5q21 are important for the development of colorectal cancers,
particularly those associated with familial adenomatous polyposis (FAP). To facilitate the identification of genes from this
locus, a portion of the region that is tightly linked to FAP was cloned. Six contiguous stretches of sequence (contigs) containing
approximately 5.5 Mb of DNA were isolated. Subclones from these contigs were used to identify and position six genes, all
of which were expressed in normal colonic mucosa. Two of these genes (APC and MCC) are likely to contribute to colorectal
tumorigenesis. The MCC gene had previously been identified by virtue of its mutation in human colorectal tumors. The APC gene
was identified in a contig initiated from the MCC gene and was found to encode an unusually large protein. These two closely
spaced genes encode proteins predicted to contain coiled-coil regions. Both genes were also expressed in a wide variety of
tissues. Further studies of MCC and APC and their potential interaction should prove useful for understanding colorectal neoplasia.
We investigate the power and robustness of Haseman and Elstonfs sib-pair test for genetic linkage between a marker locus and a locus affecting a quantitative trait, and compare the test to that of Penrose. The Haseman-Elston test is more powerful than Penrrose's test; its power is acceptable for cases of tight linkage and high heritability due to the hypothesized quantitative trait locus, but is quite low in other situations. Computer simulations indicate that both tests are valid for normally distributed trait values, and that the Haseman-Elston test is robust for a variety of continuous distributions of the trait values. Several linkage tests are developed for sib trios that are much more powerful , for the same total number of sibs, than the test on independent sib pairs. The Haseman-Elston test on all possible sib pairs is suggested for sibships of size larger than three and for samples including sibships of various sizes.
Small (100-260 kb), nested deletions were characterized in DNA from two unrelated patients with familial adenomatous polyposis coli (APC). Three candidate genes located within the deleted region were ascertained and a previous candidate gene, MCC, was shown to be located outside the deleted region. One of the new genes contained sequence identical to SRP19, the gene coding for the 19 kd component of the ribosomal signal recognition particle. The second, provisionally designated DP1 (deleted in polyposis 1), was found to be transcribed in the same orientation as MCC. Two other cDNAs, DP2 and DP3, were found to overlap, forming a single gene, DP2.5, that is transcribed in the same orientation as SRP19.
The advent of complete genetic linkage maps consisting of codominant DNA markers [typically restriction fragment length polymorphisms (RFLPs)] has stimulated interest in the systematic genetic dissection of discrete Mendelian factors underlying quantitative traits in experimental organisms. We describe here a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs). These include: (i) a method of identifying promising crosses for QTL mapping by exploiting a classical formula of SEWALL WRIGHT; (ii) a method (interval mapping) for exploiting the full power of RFLP linkage maps by adapting the approach of LOD score analysis used in human genetics, to obtain accurate estimates of the genetic location and phenotypic effect of QTLs; and (iii) a method (selective genotyping) that allows a substantial reduction in the number of progeny that need to be scored with the DNA markers. In addition to the exposition of the methods, explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.
Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.
A simulation study was carried out on a backcross population in order to determine the effect of marker spacing, gene effect and population size on the power of marker-quantitative trait loci (QTL) linkage experiments and on the standard error of maximum likelihood estimates (MLE) of QTL gene effect and map location. Power of detecting a QTL was virtually the same for a marker spacing of 10 cM as for an infinite number of markers and was only slightly decreased for marker spacing of 20 or even 50 cM. The advantage of using interval mapping as compared to single-marker analysis was slight. "Resolving power" of a marker-QTL linkage experiment was defined as the 95% confidence interval for the QTL map location that would be obtained when scoring an infinite number of markers. It was found that reducing marker spacing below the resolving power did not add appreciably to narrowing the confidence interval. Thus, the 95% confidence interval with infinite markers sets the useful marker spacing for estimating QTL map location for a given population size and estimated gene effect.
Fetal hemoglobin (Hb F) production in sickle cell (SS) disease and in normal individuals varies over a 20-fold range and is under genetic control. Previous studies suggested that variant Hb F levels might be controlled by genetic loci separate from the beta-globin complex on chromosome 11. Using microscopic radial immunodiffusion and flow cytometric immunofluorescent assays to determine the percentage of F reticulocytes and F cells in SS and nonanemic individuals, we observed that F-cell levels were significantly higher in nonanemic females than males (mean +/- SD, 3.8% +/- 3.2% v 2.7% +/- 2.3%). F-cell production as determined by F reticulocyte levels in SS females was also higher than in SS males (17% +/- 10% v 13% +/- 8%). We tested the hypothesis that F-cell production in both normal and anemic SS individuals was controlled by an X-linked locus with two alleles, high (H) and low (L). Using an algorithm to determine the 99.8% confidence interval of a normal distribution in nonanemic individuals, we estimated that males and females with at least one H allele had greater than 3.3% F cells. Comparisons of male-male or female-female SS sib pairs with discordant F reticulocyte levels distinguished two phenotypes in SS males (L, less than 12%; H, greater than 12%) and three phenotypes in SS females (LL, less than 12%; HL, 12% to 24%, HH greater than 24%). Linkage analysis using polymorphic restriction sites along the X chromosome in eight SS and one AA family localized the F-cell production (FCP) locus to Xp22.2, with a maximum lod score (logarithm of odds of linkage v independent assortment) of 4.6 at a recombination fraction of 0.04.
The familial risk of breast cancer was investigated in a large population-based, case-control study conducted by the Centers for Disease Control. The data set included 4,730 histologically confirmed breast cancer cases aged 20–54 years and 4,688 controls who were frequency matched to cases by geographic region and 5-year categories of age. Family history of breast cancer among first-degree female relatives of cases and controls was utilized. To identify factors associated with familial risk of breast cancer, a Cox proportional hazards model was used, modeling time to onset of breast cancer among mothers and sisters. Case relatives were at greater risk than control relatives. Among relatives of cases, a significant increase in the risk of breast cancer was associated with decreasing age at onset of the case and with having an additional relative affected with breast cancer. The hazard ratio for the mother of a case with breast cancer diagnosed at 50 years of age was 1.7 (95% confidence interval (Cl) 1.4–2.0), compared with 2.7 (95% Cl 2.2–3.2) and 4.3 (95% Cl 3.3–5.6) for the mother of a case whose diagnosis occurred at 40 and 30 years of age, respectively. The hazard ratio for the sister of a case with an unaffected mother and at least one affected sister in addition to the case was 3.6 (95% Cl 2.1–6.1) when the case was diagnosed at age 50, compared with 5.8 (95% Cl 3.4–10.0) and 9.4 (95% Cl 5.3–16.7) when the case was diagnosed at 40 and 30 years of age, respectively. The hazard ratio for the sister of a case with an affected mother and no additional affected sisters was 5.9 (95% Cl 3.9–8.9) when the case was diagnosed at age 50, compared with 9.4 (95% Cl 6.2–14.4) and 15.1 (95% Cl 9.4–24.3) when the case was diagnosed at 40 and 30 years of age, respectively. The hazard ratio for the sister of a case with both an affected mother and at least one affected sister aside from the case was 17.1 (95% Cl 9.4–31.3) when the case was diagnosed at age 50, compared with 27.5 (95% Cl 15.0–50.3) and 44.2 (95% Cl 23.5–83.2) when the case was diagnosed at 40 and 30 years of age, respectively. No effect of case's menopausal status and bilaterality was found, indicating that in addition to a positive family history, age at onset is the strongest indicator of a possible genetic subtype of breast cancer in these data.
Preface. List of Figures. List of Tables. 1. The Scope of Genetic Analyses. 2. Data Summary. 3. Biometrical Genetics. 4. Matrix Algebra. 5. Path Analysis and Structural Equations. 6. LISREL Models and Methods. 7. Model Fitting Functions and Optimization. 8. Univariate Analysis. 9. Power and Sample Size. 10. Social Interaction. 11. Sex Limitation and GE Interaction. 12. Multivariate Analysis. 13. Direction of Causation. 14. Repeated Measures. 15. Longitudinal Mean Trends. 16. Observer Ratings. 17. Assortment and Cultural Transmission. 18. Future Directions. Appendices: A. List of Participants. B. The Greek Alphabet. C. LISREL Scripts for Univariate Models. D. LISREL Script for Power Calculation. E. LISREL Scripts for Multivariate Models. F. LISREL Script for Sibling Interaction Model. G. LISREL Scripts for Sex and GE Interaction. H. LISREL Script for Rater Bias Model. I. LISREL Scripts for Direction of Causation. J. LISREL Script and Data for Simplex Model. K. LISREL Scripts for Assortment Models. Bibliography. Index.
In a blinded experiment, we report the first allelic association of the dopamine D2 receptor gene in alcoholism. From 70 brain samples of alcoholics and nonalcoholics, DNA was digested with restriction endonucleases and probed with a clone that contained the entire 3' coding exon, the polyadenylation signal, and approximately 16.4 kilobases of noncoding 3' sequence of the human dopamine D2 receptor gene (λhD2G1). In the present samples, the presence of A1 allele of the dopamine D2 receptor gene correctly classified 77% of alcoholics, and its absence classified 72% of nonalcoholics. The polymorphic pattern of this receptor gene suggests that a gene that confers susceptibility to at least one form of alcoholism is located on the q22-q23 region of chromosome 11.
Approximately 70 percent of the mutations in cystic fibrosis patients correspond to a specific deletion of three base pairs, which results in the loss of a phenylalanine residue at amino acid position 508 of the putative product of the cystic fibrosis gene. Extended haplotype data based on DNA markers closely linked to the putative disease gene locus suggest that the remainder of the cystic fibrosis mutant gene pool consists of multiple, different mutations. A small set of these latter mutant alleles (about 8 percent) may confer residual pancreatic exocrine function in a subgroup of patients who are pancreatic sufficient. The ability to detect mutations in the cystic fibrosis gene at the DNA level has important implications for genetic diagnosis.
Objective.
—We attempted to replicate a positive allelic association between the A1 allele of DRD2 (the D2 dopamine receptor locus) and alcoholism that has been reported.
Design.
—We compared allele frequencies at the previously described Taq I restriction fragment length polymorphism system of DRD2 in alcoholics and random population controls.
Subjects.
—The alcoholic subjects were 44 unrelated white individuals, diagnosed by direct structured interview to have alcohol dependence (by the Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition, criteria). The subjects in our random population control group (N = 68) were also white.
Results.
—For the control group, allele frequencies at DRD2 were 0.20 (A1) and 0.80 (A2). For the alcoholic group overall, allele frequencies were 0.23 (A1) and 0.77 (A2). There were no significant differences in allele frequencies at the DRD2 locus between alcoholics and controls. The allele frequencies in both groups agreed closely with those observed in most previously described control populations. Subtyping the alcoholic group according to presence or absence of family history of alcoholism, presence or absence of antisocial personality disorder, age of onset, presence or absence of physical withdrawal symptoms, or recent alcohol consumption (as a measure of severity) did not in any case reveal significant differences in allele frequencies.
Conclusion.
—We were not able to replicate the results previously reported. We conclude that our data do not support an allelic association between the A1 allele at DRD2 and alcoholism.(JAMA. 1991;266:1801-1807)
Objective.
—To assess the relationship between the GCT repeat number in the myotonic dystrophy gene and the clinical phenotype and examine its predictive utility in prenatal testing.Design.
—DNA from patients was examined for the length of the myotonic dystrophy GCT repeat region, using both Southern blot analysis and polymerase chain reaction. The results were compared with the clinical onset of disease, as well as with pregnancy outcomes.Setting.
—Patient samples were referred to the Kleberg DNA Diagnostic Laboratory at the Baylor College of Medicine for DNA analysis by geneticists and genetic counselors (84%), neurologists (10%), and obstetricians and other specialists (6%). Clinical features including onset of disease and family pedigrees were determined by the referring centers.Patients.
—A total of 241 patient samples from 118 families referred from primarily genetic or neurological centers for genetic linkage analysis or mutation analysis for myotonic dystrophy. This included 44 families referred for prenatal diagnosis.Main Outcome Measures.
—A relationship between myotonic dystrophy disease onset and length of the GCT repeat allele, parental origin of the disease allele, and results of prenatal diagnosis predictions of disease status were measured.Results.
—There is a relationship between increasing repeat length and earlier clinical onset of disease. Essentially all (>99%) myotonic mutations causing myotonic dystrophy are accounted for by GCT repeat amplification. Congenital myotonic dystrophy occurs with as few as 730 GCT repeats but only with alleles of maternal origin. Maternal GCT repeats were found as low as 75 (asymptomatic) that were amplified to result in a child with congenital myotonic dystrophy. Application of DNA diagnosis to 32 pregnancies provided an accurate method for identification of at-risk fetuses and allele enlargement.Conclusions.
—The GCT repeat in myotonic dystrophy is highly mutable. The triplet repeat amplification is highly specific for mutations involving the myotonin protein kinase gene accounting for myotonic dystrophy. The quantitation of triplet repeats can be more sensitive than physical, ophthalmologic, and electromyography examinations since the mutation can be detected in patients without evidence of myotonic dystrophy clinical findings. The length of the triplet expansion is influenced by the sex of the transmitting parent and is related to the clinical onset of disease features. Prenatal measurement of the GCT triplet repeat has utility for families with myotonic dystrophy risk since mutant and normal repeats are distinguishable and the length of mutant repeat alleles is associated with clinical severity. Thus, GCT triplet measurement provides a highly accurate means of detecting the myotonic dystrophy mutation in patients and offers a new reproductive option for families at risk for myotonic dystrophy.(JAMA 1993;269:1960-1965)
Sixty cases of dyslipidemic hypertension were identified in the 1028 middleaged, white, male twin participants in the first examination of the National Heart, Lung, and Blood Institute Twin Study (1969 to 1973). The prevalence of dyslipidemic hypertension was similar by zygosity but proband concordance was three times greater in monozygotic than dizygotic twins (0.44 [seven concordant and 18 discordant pairs] vs 0.14 [two concordant and 24 discordant pairs]), suggesting a genetic effect on the condition. Low high-density lipoprotein cholesterol level was the most common lipid abnormality in concordant pairs. Mortality from ischemic heart disease was significantly higher in individuals with dyslipidemic hypertension. Obesity and glucose intolerance were closely associated with the syndrome. Moreover, within the 18 discordant monozygotic twin pairs, the twins with dyslipidemic hypertension had gained significantly more weight as adults and were significantly heavier than their unaffected cotwins. Thus, although genetic factors may influence development of dyslipidemic hypertension, nongenetic, potentially modifiable aspects of obesity are also closely related to expression of this clinically important syndrome.
(JAMA. 1991;265:2079-2084)
• The association of the A1 allele of the D2 dopamine receptor gene with alcoholism was examined by comparing 32 unrelated white alcoholics with 25 unrelated white controls and by analysis of 17 nuclear families in multigenerational pedigrees of alcoholics in whom the A1 allele was segregating. All subjects had structured psychiatric interviews. Clinical assessment and genotyping were carried out independently. Thirteen (41%) of the 32 alcoholics carried the A1 allele compared with three (12%) of the 25 controls. The association with the A1 allele was significant when controls were compared with a subset of 10 alcoholics with severe medical problems (60% vs 12%), but not less severe cases. However, regardless of clinical severity or subtype, there was no evidence of linkage or cosegregation of the A1 allele and increased susceptibility to alcoholism in informative pedigrees. The possible association in the general population without linkage in families may be explained either by chance variation in our small samples or a modifying effect of the A1 allele that increases severity. Further study of the role of the D2 receptor gene in alcoholism is warranted.
• The frequency of familial dyslipidemia syndromes was determined from blood tests in 33 objectively ascertained families with early coronary heart disease (CHD) (two or more siblings with CHD by the age of 55 years). Three fourths of persons with early CHD in these families had 90th percentile lipid abnormalities (cholesterol level at or above the 90th percentile, triglyceride level at or above the 90th percentile, and/or high-density lipoprotein cholesterol (HDL-C) level at or less than the 10th percentile). The HDL-C and triglyceride abnormalities were twice as common as low-density lipoprotein–cholesterol abnormalities. The most common syndromes found were familial combined hyperlipidemia (36% to 48% of families with CHD), familial dyslipidemic hypertension (21% to 54% of families with CHD), and isolated low levels of HDL-C (15%), with overlapping familial dyslipidemic hypertension with familial combined hyperlipidemia and low-level HDL-C. Well-defined monogenic syndromes were uncommon: familial hypercholesterolemia being 3% and familial type III hyperlipidemia, 3%. Another 15% of families with CHD had no lipid abnormalities at the 90th percentile. Physicians should learn to recognize and treat these common familial syndromes before the onset of CHD by evaluating family history and all three standard blood lipid determinations. Failure to recognize and treat them leaves affected family members at high risk of premature CHD.
(Arch Intern Med. 1990;150:582-588)
We consider the analysis of a continuous trait measured on human families and pedigrees to elucidate the mechanism of underlying major genes. Thinking in terms of the naturally Markovian structure of the dependencies in pedigrees rather than variance components, we describe natural classes of regressive models that are computationally feasible. These models can accommodate wide generalizations of the residual variation, and so should provide a stronger basis than other models. proposed to date for inferring the segregation and linkage relationships of major genes.
To identify genes responsible for the susceptibility for schizophrenia, and to test the hypothesis that schizophrenia is etiologically heterogeneous, we have studied 39 multiplex families from a systematic sample of schizophrenic patients. Using a complex autosomal dominant model, which considers only those with a diagnosis of schizophrenia or schizoaffective disorder as affected, a random search of the genome for detection of linkage was undertaken. Pairwise linkage analyses suggest a potential linkage (LRH = 34.7 or maximum lod score = 1.54) for one region (22q12-q13.1). Reanalyses, varying parameters in the dominant model, maximized the LRH at 660.7 (maximum lod score 2.82). This finding is of sufficient interest to warrant further investigation through collaborative studies. © 1994 Wiley-Liss, Inc.
A statistical model that uses an iterative maximum likelihood estimation procedure is proposed for measuring and testing the association between polymorhphic genetic markers and quantitative traits in human pedigrees, after adjusting for covariates such as age and sex. The model allows the quantitative trait to have a familial correlation structure among the individuals in the sample and to follow one of a broad class of skewed or kurtotic underlying distributions. The use of the model is illustrated, and the results are compared to those using models that assume normality without any transformation and do not incorporate familial correlations.
Some investigators have expressed concern—specially for psychiatric disorders—that bilineal pedigrees should not be included in linkage studies. This study compares the ‘informativeness’ of bilineal and unilineal families for a homogeneous single-gene disorder. Three approaches were used: (1) simulation studies of three-generation pedigrees, ( 2 ) calculation of expected lod scores (ELODs) in nuclear families, and ( 3 ) calculation of Fisher's information number I(θ) in nuclear families. The simulation studies in (1) permitted a realistic comparison between bilineal datasets and purely unilineal ones. The calculations in nuclear families in ( 2 ) and (3) then made it possible to analyze the sources of information loss in bilineal families.
Overall, in datasets of five three-generation pedigrees each, the drop in mean maximum lod score was approximately 50% from purely unilineal datasets to extremely bilineal ones. In less-extreme bilineal datasets, which are closer to most real data than the extremely bilineal ones, the drops in lod score were very small—less than 10% in some, and practically zero in others. The details will vary, depending on size and structure of the pedigree, genetic model, true value of the recombination fraction, and informativeness of the marker. However, these results imply that the information loss due to bilineality is not necessarily very great.
The nuclear-family calculations showed that for phase-known matings there is relatively little information loss in bilineal families, but for phase-unknown matings the loss is much greater.
In conclusion, for single-gene disorders with no genetic heterogeneity, whereas bilineal families can be less informative than comparable unilineal families, they are not so much less informative that they should automatically be discarded from linkage datasets. The implications of bilineal pedigrees for linkage studies of heterogeneous disorders are also discussed.
In a segregating population a quantitative trait may be considered to follow a mixture of (normal) distributions, the mixing proportions being based on Mendelian segregation rules. A general and flexible mixture model is proposed for mapping quantitative trait loci (QTLs) by using molecular markers. A method is discribed to fit the model to data. The model makes it possible to (1) analyse non-normally distributed traits such as lifetimes, counts or percentages in addition to normally distributed traits, (2) reduce environmental variation by taking into account the effects of experimental design factors and interaction between genotype and environment, (3) reduce genotypic variation by taking into account the effects of two or more QTLs simultaneously, (4) carry out a (combined) analysis of different population types, (5) estimate recombination frequencies between markers or use known marker distances, (6) cope with missing marker observations, (7) use markers as covariables in detection and mapping of QTLs, and finally to (8) implement the mapping in standard statistical packages.
The affected sib-pair method can be applied to investigate linkage between a marker locus and a disease. Several statistics have been proposed to test if the observed pattern of marker alleles shared identically by descent (ibd) is compatible with the null hypothesis of no linkage. Here, we consider different optimality criteria for sib-pair linkage tests. While for recessive inherited diseases the mean test is found to be uniformly (in θ) most powerful, it can also be shown that, irrespective of the mode of inheritance, the mean test is the locally optimal test.Copyright © 1994 S. Karger AG, Basel
If a genetic association between the D2 dopamine receptor genotype and alcoholism is mediated by altered dopamine function, then a stronger association might be found in alcoholics who are deviant in indices of dopamine function and by comparing alcoholics to nonalcoholics matched for ethnic origin. Therefore, we evaluated the D2/TaqI polymorphism in 29 impulsive violent alcoholic Finns, 17 nonimpulsive violent alcoholic Finns and 36 Finnish controls free of mental disorders, alcoholism and substance abuse. In 37 of the alcoholics, we measured cerebrospinal fluid (CSF) homovanillic acid (HVA), 5-hydroxyindoleacetic acid (5-HIAA) and 3-methoxy-4-hydroxyphenylglycol. There was no relationship between D2/Taq 1 genotype and concentrations of these monoamine metabolites in this group, which exhibits lower CSF HVA and 5-HIAA as compared to controls. There was also no genotypic difference between Finnish alcoholics and nonalcoholic controls. The lack of relationship between D2/Taq1 genotype and HVA concentration was replicated in 24 Caucasian alcoholics in the United States.
A method for detecting the linkage between an interval defined by a pair of markers and a nonadditive quantitative trait locus for F2 populations is presented. The method uses the EM algorithm to estimate the biometrical parameters and the locations of the quantitative trait loci. A computationally efficient approach suitable for implementation on a personal computer (PC) has been developed. The method has been applied to 100 replicated sets of 250 simulated individuals. Two criteria, the LOD score and the Fisher-Snedecor F, are compared in terms of their power and Type I error.
Germline mutations in a gene on chromosome 17q known as BRCA1 are responsible for a large proportion of inherited predispositions to breast and ovarian cancer. In 33 families with evidence of linkage to BRCA1, we estimated the risks of breast and ovarian cancer from the occurrence of second cancers in individuals with breast cancer, and examined the risks of other cancers in BRCA1carriers. 26 contralateral primary breast cancers occurring more than 3 years after a first breast cancer were observed before age 70, giving an estimated cumulative risk of breast cancer in gene carriers of 87% by age 70. 23 primary ovarian cancers occurred in women with a previous breast cancer, resulting in an estimated cumulative risk of ovarian cancer of 44% by age 70. 87 cancers other than breast or ovarian cancer were observed in individuals with breast or ovarian cancer and their first-degree relatives compared with 69·3 expected, based on national incidence rates. Significant excesses were observed for colon cancer (estimated relative risk [RR] to gene carriers 4·11 [95% Cl 2·36-7·15]) and prostate cancer (3·33 [1·78-6·20]). No significant excesses (or deficits) were noted for cancers of other sites. Our study provides estimates of breast and ovarian cancer risks which are useful for counselling BRCA1-mutation carriers. It also shows that carriers are at increased risk of colon and prostate cancer, which may be of clinical significance in certain families if the risks are associated with specific mutations.
The calculation of probabilities on pedigrees of arbitrary complexity is discussed for a basic model of transmission and penetrance (encompassing Mendelian inheritance, and certain environmental influences).
The structure of pedigrees, and the types of loops occurring, is discussed. Some results in graph theory are obtained and, using these, a recurrence relation derived for certain probabilities. The recursive procedure enables the successive peeling off of certain members of the pedigree, and the condensation of the information on those individuals into a function on a subset of those remaining. The underlying theory is set out, and examples given of the utilization of the resulting algorithm.
A problem of interest in genetics is that of testing whether a mixture of two binomial distributions Bi(k, p) and Bi(k, 12) is simply the pure distribution Bi(k, 12). This problem arises in determining whether we have a genetic marker for a gene responsible for a heterogeneous trait, that is a trait which is caused by any one of several genes. In that event we would have a nontrivial mixture involving 0 < p < 0.5 where p is a recombination probability.Standard asymptotic theory breaks down for such problems which belong to a class of problems where a natural parametrization represents a single distribution, under the hypothesis to be tested, by infinitely many possible parameter points. That difficulty may be eliminated by a transformation of parameters. But in that case a second problem appears. The regularity conditions demanded by the applicability of the Fisher Information fails when k > 2. We present an approach where use is made of the Kullback Leibler information, of which the Fisher information is a limiting case.Several versions of the binomial mixture problem are studied. The asymptotic analysis is supplemented by the results of simulations. It is shown that as n → ∞, the asymptotic distribution of twice the logarithm of the likelihood ratio corresponds to the square of the supremum of a Gaussian stochastic process with mean 0, variance 1 and a well behaved covariance function. As k → ∞ this limiting distribution grows stochastically as log k.
A maximum likelihood method is presented for the detection of quantitative trait loci (QTL) using flanking markers in full-sib families. This method incorporates a random component for common family effects due to additional QTL or the environment. Simulated data have been used to investigate this method. With a fixed total number of full sibs power of detection decreased substantially with decreasing family size. Increasing the number of alleles at the marker loci (i.e., polymorphism information content) and decreasing the interval size about the QTL increased power. Flanking markers were more powerful than single markers. In testing for a linked QTL the test must be made against a model which allows for between family variation (i.e., including an unlinked QTL or a between family variance component) or the test statistic may be grossly inflated. Mean parameter estimates were close to the simulated values in all situations when fitting the full model (including a linked QTL and common family effect). If the common family component was omitted the QTL effect was overestimated in data in which additional genetic variance was simulated and when compared with an unlinked QTL model there was reduced power. The test statistic curves, reflecting the likelihood of the QTL at each position along the chromosome, have discontinuities at the markers caused by adjacent pairs of markers providing different amounts of information. This must be accounted for when using flanking markers to search for a QTL in an outbred population.
Objective.
—An allelic association between the Taq I "A" system A1 allele at the D2 dopamine receptor locus (DRD2) and either alcoholism or severe alcoholism has been proposed. Our purpose was to evaluate whether, based on all of the accumulated evidence, this association could be considered to be proven.
An investigation of the genetic epidemiology of breast cancer involving complex segregation analysis of 200 breast cancer pedigrees of Danish extraction is presented. The observed distribution of breast cancer is compatible with transmission of an autosomal-dominant gene with no evidence for residual family resemblance. The gene frequency of the abnormal allele is 0.00756, and the displacement between the homozygous genotype means is 1.695. The gene frequency accounts for a significant proportion of breast cancer in young women, whereas by an advanced age a majority (87%) of affected women are phenocopies. Genetic modeling of other breast cancer families and results of linkage studies are reviewed.
Assessing the genetic causation of a disease, which is of prime importance in medical genetics, is usually done by analysing pedigree data. When gathering such data, it is often more practical to adopt a non-random sampling strategy. However, unless suitable corrections for non-random sampling are made at the time of data analysis, inferences may be grossly affected. For pedigree data ascertained through multiple probands, various correction schemes have been suggested, although the efficiencies of these schemes are unknown. This paper compares such schemes, using Monte Carlo simulation techniques, under a simple genetic model, for pedigrees of fixed sizes and structures and for probands of two types of relationship-parent-offspring, and a pair of siblings. It is found that gene frequencies are grossly overestimated and the penetrance value of heterozygotes slightly underestimated whether or not any correction for non-random sampling of pedigrees is made. Knowledge of the population value of the gene frequency improves the estimate of the penetrance parameter.
A computer-simulation method is presented for determining and correcting for the effect of maximizing the lod score over disease definitions, penetrance values, and perhaps other model parameters. The method consists of simulating the complete analysis using marker genotypes randomly generated under the assumption of free recombination. It is applicable as a “post-treatment” to linkage analyses of any trait with an uncertain mode of inheritance and/or disease definition. When the method is applied to a linkage analysis of schizophrenia versus chromosome 5 markers, we find that, in this specific case, the P-value associated with a maximum lod score of 3 is equal to 0.0003. We also find that a lod score of 3.0 should be “deflated” by approximately 0.3 to 1 units, and, by tentative extrapolation, the observed lod score of 6.5 should be “deflated” by 0.7 to 1.5 units.
Eight recent studies have focused on the putative association of the dopamine D2 receptor (DRD2) gene and alcoholism. In this report, these studies are reviewed and the data and findings are examined in a meta-analysis. Four reports find a statistically significant increased risk for alcoholism in subjects carrying the A1 allele and 4 failed to observe a significant increase in risk. Overall, our metaanalysis of the results from all 8 studies supported a statistically significant association between the A1 allele of DRD2 and alcoholism, with an apparent increase in relative risk associated with increased severity of alcoholism. These results must be interpreted cautiously because the A1 allele of DRD2 varies significantly in frequency from one population to another. This variability in the population frequency of the A1 allele could result in an apparent association resulting from unrelated population differences. These findings support the need for carefully designed studies that minimize the ethnic heterogeneity of the subject and control populations. © 1993 Wiley-Liss, Inc.
Overt computational constraints in the formation of mixed models for the analysis of large extended-pedigree quantitative trait data which allow one to reliably characterize and partition sources of variation resulting from a variety sources have proven difficult to overcome. The present paper suggests that by combining a restricted patterned covariance matrix approach to modeling and partitioning the variation arising from polygenic and environmental forces with an Elston–Stewart like algorithmic approach to modeling variation resulting from a single genetic locus with large phenotypic effects one can produce a model that is at once intuitively appealing, efficient computationally, and reliable numerically. Extensions and variations of this approach are also discussed, as are some simulation and timing studies carried out in an effort to validate the accuracy and computational efficiency of the proposed methodology. © 1992 Wiley-Liss, Inc.
The use of patterned covariance matrices in forming pedigree-based mixed models for quantitative traits is discussed. It is suggested that patterned covariance matrix models provide intuitive, theoretically appealing, and flexible genetic modeling devices for pedigree data. It is suggested further that the very great computational burden assumed in the implementation of covariance matrix-dependent mixed models can be overcome through the use of recent architectural breakthroughs in computing machinery. A brief and nontechnical overview of these architectures is offered, as are numerical and timing studies on various aspects of their use in evaluating mixed models. As the kinds of computers discussed in this paper are becoming more prevalent and easier to access and use, it is emphasized that it behooves geneticists to consider their use to combat needless approximation and time constraints necessitated by smaller, scalar computation oriented, machines.
Statistically characterizing factors responsible for quantitative phenotype expression (e.g., polygenes, major genes, shared household factors, etc.) through model selection strategies is a difficult task. A great deal of effort has been expended on refining mathematical and computational aspects of various segregation models used to characterize unique expressions of quantitative phenotypes in an effort to make these models easier to implement and evaluate for a given set of data. In this paper a slightly different angle is emphasized: namely, the explicit modeling of the potentially numerous heterogeneous genetic and environmental processes (i.e., segregation patterns, household aggregations, etiologic processes, etc.) that could contribute to the overall variation of a quantitative trait. As such, this paper describes tools for detecting quantitative trait heterogeneity that are meant to answer such questions as, ‘are there pedigress among a great many that show a pattern consistent with a possibly very specific single locus segregation pattern while the rest show compatibility with a polygenic or purely environmental pattern?’ Methods for determing the significance of such heterogeneity are also discussed, as are the results of numerous examples and simulation studies carried out in an effort to validate and further elaborate aspects of the proposed techniques. © 1992 Wiley-Liss, Inc.