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Recombination is due to an odd number of crossovers. After the homologous chromosomes pair up, a crossover takes place in the relatively large interval between loci B and D. This results in recombination between the locus pairs A-D and B-D. In contrast, no recombination is observed between A and B, which are separated by a smaller distance.
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Genetic mapping by linkage analysis has been an invaluable tool in the positional strategy to identify the molecular basis of many rare Mendelian disorders. With the attention of the scientific and medical community shifting towards the analysis of more common, complex traits, it has become necessary to develop new approaches that take into account...
Citations
... In addition, big data can have limitations in study design that impact interpretation. For example, genome-wide association studies (GWAS) seek to understand disease pathogenesis by correlating human sequence variation with disease phenotypes, [6,7] however, multiple hypothesis testing, linkage disequilibrium, and limited or heterogeneous disease populations limit the resolution of these studies. These issues lead to an overemphasis on variations with relatively rare minor allele frequencies in diseases. ...
The repurposing of biomedical data is inhibited by its fragmented and multi-formatted nature that requires redundant investment of time and resources by data scientists. This is particularly true for Type 1 Diabetes (T1D), one of the most intensely studied common childhood diseases. Intense investigation of the contribution of pancreatic β-islet and T-lymphocytes in T1D has been made. However, genetic contributions from B-lymphocytes, which are known to play a role in a subset of T1D patients, remain relatively understudied. We have addressed this issue through the creation of Biomedical Data Commons (BMDC), a knowledge graph that integrates data from multiple sources into a single queryable format. This increases the speed of analysis by multiple orders of magnitude. We develop a pipeline using B-lymphocyte multi-dimensional epigenome and connectome data and deploy BMDC to assess genetic variants in the context of Type 1 Diabetes (T1D). Pipeline-identified variants are primarily common, non-coding, poorly conserved, and are of unknown clinical significance. While variants and their chromatin connectivity are cell-type specific, they are associated with well-studied disease genes in T-lymphocytes. Candidates include established variants in the HLA-DQB1 and HLA-DRB1 and IL2RA loci that have previously been demonstrated to protect against T1D in humans and mice providing validation for this method. Others are included in the well-established T1D GRS2 genetic risk scoring method. More intriguingly, other prioritized variants are completely novel and form the basis for future mechanistic and clinical validation studies The BMDC community-based platform can be expanded and repurposed to increase the accessibility, reproducibility, and productivity of biomedical information for diverse applications including the prioritization of cell type-specific disease alleles from complex phenotypes.
... IBD is defined as the proportion of 0, 1, or 2 identical by descent alleles between two individuals; the higher the estimate, the greater the probability of relatedness. IBS represents the proportion of shared DNA segments, identical from the molecular point of view, but without sharing a common origin, or in which their common origin cannot be unequivocally determined (Forabosco et al., 2005). Nevertheless, hidden or unrecorded relations may cause bias in the estimates. ...
... Several algorithms have been proposed and implanted in linkage analysis tools to calculate the likelihood of observed pedigrees. Among them, the Elston-Stewart algorithm and the Lander-Green algorithm are most commonly used [15,33]. For the Elston-Stewart algorithm, the time for computation increases linearly with the size of the pedigree but increases exponentially with the number of biomarkers. ...
With the advances of next-generation sequencing technology, the field of disease research has been revolutionized. However, pinpointing the disease-causing variants from millions of revealed variants is still a tough task. Here, we have reviewed the existing linkage analysis tools and presented PedMiner, a web-based application designed to narrow down candidate variants from family based whole-exome sequencing (WES) data through linkage analysis. PedMiner integrates linkage analysis, variant annotation and prioritization in one automated pipeline. It provides graphical visualization of the linked regions along with comprehensive annotation of variants and genes within these linked regions. This efficient and comprehensive application will be helpful for the scientific community working on Mendelian inherited disorders using family based WES data.
... The dominance effects observed indicates MLN resistance is possibly due to interactions between individual alleles at specific loci. Other factors may include recombination between QTL and markers, leading to change in number of expected resistant alleles in the progeny hence reduced penetrance in QTL effects [53,54]. For QTL to be useful in a breeding program, it is important to first carry out validation studies to confirm their repeatability in different genetic backgrounds and environments. ...
Maize lethal necrosis (MLN) occurs when maize chlorotic mottle virus (MCMV) and sugarcane mosaic virus (SCMV) co-infect maize plant. Yield loss of up to 100% can be experienced under severe infections. Identification and validation of genomic regions and their flanking markers can facilitate marker assisted breeding for resistance to MLN. To understand the status of previously identified quantitative trait loci (QTL)in diverse genetic background, F3 progenies derived from seven bi-parental populations were genotyped using 500 selected kompetitive allele specific PCR (KASP) SNPs. The F3 progenies were evaluated under artificial MLN inoculation for three seasons. Phenotypic analyses revealed significant variability (P ≤ 0.01) among genotypes for responses to MLN infections, with high heritability estimates (0.62 to 0.82) for MLN disease severity and AUDPC values. Linkage mapping and joint linkage association mapping revealed at least seven major QTL (qMLN3_130 and qMLN3_142, qMLN5_190 and qMLN5_202, qMLN6_85 and qMLN6_157 qMLN8_10 and qMLN9_142) spread across the 7-biparetal populations, for resistance to MLN infections and were consistent with those reported previously. The seven QTL appeared to be stable across genetic backgrounds and across environments. Therefore, these QTL could be useful for marker assisted breeding for resistance to MLN.
... With the development of the human genome project, the medical attention has been shifted toward the analysis of complex traits. The increase in studies toward the molecular biological aspect of diseases enhances the need to develop new approaches that take into account the complexity of the genetic basis of conditions and the possible interaction with other, nongenetic factors [41]. Development in this area benefits also genomic research on biogenic amines, here histamine, to establish a better association between diseases, biological markers, and drug treatments. ...
Histamine is a biogenic amine that has an inherent biological importance in many physiological functions. With the new genomic era we are facing, personalized care and treatment is becoming one of the major focal points in research. This chapter will focus on the tools available to assess polymorphisms and genetic variations in the human histamine receptor family. The genetic composition of this receptor family is discussed and explained. Methodologies in genetic analysis are described, the use of bioinformatics information available is explored, and the use of Hapmap data and how it can be used in genome-wide association studies and linkage analysis is explained. Bioinformatics and molecular biology prove to be essential tools in investigating genetic associations between genotype and disease. This area of research is of utmost importance for identification of biological markers and is essential in the search to develop personalized medication.
... In this case the penetrance of each disease gene is in fact one, although not every individual exhibiting the disease will possess the same genotype at any one of the disease loci. There is also the related phenomenon of phenocopies, whereby individuals (even within the same family) may exhibit the same trait due to different, and not necessarily genetic, causes (Forabosco et al., 2005). ...
... For example, some analyses allow a more generalised family structure and are now capable of including complex pedigrees, i.e., pedigrees containing multiple generations and inbreeding loops (where it is possible to trace a route in the pedigree between two individuals, and return back to the first individual by a different route). Other ASP design extensions allow missing data, and some allow data from multiple markers to be used simultaneously (Forabosco et al., 2005). ...
... While VC analysis and other linkage techniques have proved extremely useful for detecting high risk variants, linkage tests do not have sufficient power to detect small to moderate effects, certainly not less than 10% of total phenotypic variance (Forabosco et al., 2005). This is because power to detect effects of a given magnitude is a function of sample size, and for smaller effects the sample size required to reach significance is far beyond that generally available to collect within families, or even across multiple families. ...
Data from a Croatian isolate population are analysed in a genome-wide association
study (GWAS) for a variety of disease-related quantitative traits. A novel genomewide
approach to analysing pedigree-based association data called GRAMMAR is
utilised. One of the significant findings, for uric acid, is followed up in greater detail,
and is replicated in another isolate population, from Orkney. The associated SNPs are
located in the SLC2A9 gene, coding for a known glucose transporter, which leads to
identification of SLC2A9 as a urate transporter too (Vitart et al., 2008). These SNPs
are later implicated in affecting gout, a disease known to be linked with high serum
uric acid levels, in an independent study (Dehghan et al., 2008). Subsequently,
investigation into different ways in which to use SNP data to identify quantitative
trait loci (QTL) for genome-wide association (GWA) studies is performed. Several
multi-marker approaches are compared to single SNP analysis using simulated
phenotypes and real genotype data, and results show that for rare variants haplotype
analysis is the most effective method of detection. Finally, the multi-marker methods
are compared with single SNP analysis on the real uric acid data. Interpretation of
real data results was complicated due to low sample size, since only founder and
unrelated individuals may be used for population-based haplotype analysis,
nonetheless, results of the prior analyses of simulated data indicate that multi-marker
methods, in particular haplotypes, may greatly facilitate detection of QTL with low
minor allele frequency in GWA studies.
... Linkage disequilibrium, the non-uniform association of alleles at two loci, has been successfully employed for mapping both Mendelian disease genes [1][2][3][4] and QTL [5][6][7]. Interested readers can also refer to reviews by [8][9][10][11]. For all chromosomal loci, including those that are physically unlinked, linkage disequilibrium can be generated or influenced by various evolutionary forces such as mutation, natural or artificial selection, genetic drift, population admixture, changes in population size (exponential growth or bottleneck, for instance). Most methods using the linkage disequilibrium concept for QTL fine-mapping are based on the genetic history of the population. ...
Since 2001, the use of more and more dense maps has made researchers aware that combining linkage and linkage disequilibrium enhances the feasibility of fine-mapping genes of interest. So, various method types have been derived to include concepts of population genetics in the analyses. One major drawback of many of these methods is their computational cost, which is very significant when many markers are considered. Recent advances in technology, such as SNP genotyping, have made it possible to deal with huge amount of data. Thus the challenge that remains is to find accurate and efficient methods that are not too time consuming. The study reported here specifically focuses on the half-sib family animal design. Our objective was to determine whether modelling of linkage disequilibrium evolution improved the mapping accuracy of a quantitative trait locus of agricultural interest in these populations. We compared two methods of fine-mapping. The first one was an association analysis. In this method, we did not model linkage disequilibrium evolution. Therefore, the modelling of the evolution of linkage disequilibrium was a deterministic process; it was complete at time 0 and remained complete during the following generations. In the second method, the modelling of the evolution of population allele frequencies was derived from a Wright-Fisher model. We simulated a wide range of scenarios adapted to animal populations and compared these two methods for each scenario.
Our results indicated that the improvement produced by probabilistic modelling of linkage disequilibrium evolution was not significant. Both methods led to similar results concerning the location accuracy of quantitative trait loci which appeared to be mainly improved by using four flanking markers instead of two.
Therefore, in animal half-sib designs, modelling linkage disequilibrium evolution using a Wright-Fisher model does not significantly improve the accuracy of the QTL location when compared to a simpler method assuming complete and constant linkage between the QTL and the marker alleles. Finally, given the high marker density available nowadays, the simpler method should be preferred as it gives accurate results in a reasonable computing time.
... As a result methods have been developed that correct for structure. These methods and the software that implements them have been reviewed previously [3]. One of the most successful approaches incorporates both major population structure and the relatedness from all pairs of individuals within those populations in a linear mixed model to remove spurious associations [4,5]. ...
Mixed models improve the ability to detect phenotype-genotype associations in the presence of population stratification and multiple levels of relatedness in genome-wide association studies (GWAS), but for large data sets the resource consumption becomes impractical. At the same time, the sample size and number of markers used for GWAS is increasing dramatically, resulting in greater statistical power to detect those associations. The use of mixed models with increasingly large data sets depends on the availability of software for analyzing those models. While multiple software packages implement the mixed model method, no single package provides the best combination of fast computation, ability to handle large samples, flexible modeling and ease of use. Key elements of association analysis with mixed models are reviewed, including modeling phenotype-genotype associations using mixed models, population stratification, kinship and its estimation, variance component estimation, use of best linear unbiased predictors or residuals in place of raw phenotype, improving efficiency and software-user interaction. The available software packages are evaluated, and suggestions made for future software development.
... Transmission analysis of single or multiple molecular markers in affected sibling pairs and in trios (simple families composed of parents and one affected child) is an emerging technique in complex traits, in addition to comparison of unrelated patients and controls for population-based association studies. So far, only limited application of these techniques has been reported (for reviews see Forabosco et al. [6]). The logical basis for genetic mapping lies in the occurrence of genetic recombination of maternal and paternal homologues, which results from crossing over during meiosis. ...
... It is clear, however, that the choice of using SNP or microsatellites in linkage analysis depends on their availability in the genomic region of interest and on the variability among the members of families under study [1]. When an association is observed, linkage analysis defines by statistical probability whether the association occurs by chance or because the disease gene and the tested genetic markers are closely located in the same genomic segment [1, 6]. The principal method of linkage analysis is the calculation of the logarithm of the odds (lod) score, which is based on the genetic recombination frequencies generated by meiotic cross-overs. ...
... In other words, the lod score analysis tests the hypothesis that the recombination fractions between the genetic markers and the clinical trait observed are significantly different from 50%, which is the value predicted by the hypothesis of random association. Non-random association is defined by values of the lod score equal to or higher than 3.3, a value actually considered to be the threshold for accepting linkage with 5% probability of false positive results [6]. In general, linkage analysis indicates the interval in which the disease gene is localised in close proximity with the associated markers. ...
Positional cloning is the approach of choice for the identification of genetic mutations underlying the pathological development of diseases with simple Mendelian inheritance. It consists of different consecutive steps, starting with recruitment of patients and DNA collection, that are critical to the overall process. A genetic analysis of the enrolled patients and their families is performed, based on genetic recombination frequencies generated by meiotic cross-overs and on genome-wide molecular studies, to define a critical DNA region of interest. This analysis culminates in a statistical estimate of the probability that disease features may segregate in the families independently or in association with specific molecular markers located in known regions. In this latter case, a marker can be defined as being linked to the disease manifestations. The genetic markers define an interval that is a function of their recombination frequencies with the disease, in which the disease gene is localised. The identification and characterisation of chromosome abnormalities as translocations, deletions and duplications by classical cytogenetic methods or by the newly developed microarray-based comparative genomic hybridisation (array CGH) technique may define extensions and borders of the genomic regions involved. The step following the definition of a critical genomic region is the identification of candidate genes that is based on the analysis of available databases from genome browsers. Positional cloning culminates in the identification of the causative gene mutation, and the definition of its functional role in the pathogenesis of the disorder, by the use of cell-based or animal-based experiments. More often, positional cloning ends with the generation of mice with homologous mutations reproducing the human clinical phenotype. Altogether, positional cloning has represented a fundamental step in the research on genetic renal disorders, leading to the definition of several disease mechanisms and allowing a proper diagnostic approach to many conditions.
... In recent years, remarkable methodological and technical progress has been achieved in the area of LD mapping. Much emphasis has been placed on LD mapping in unrelated cases and controls coming from the general population and LD mapping of binary and quantitative traits, using family data; see Forabosco et al. (2005) for review. For pedigree-based QTL association analysis, a range of methods and software that utilize information about transmission of alleles, such as the orthogonal test for within-family variation (quantitative trait transmission disequilibrium test, QTDT) (Abecasis et al. 2000) and the family-based association test (FBAT) (Lange et al. 2002; Horvath et al. 2004) have been developed. ...
For pedigree-based quantitative trait loci (QTL) association analysis, a range of methods utilizing within-family variation such as transmission-disequilibrium test (TDT)-based methods have been developed. In scenarios where stratification is not a concern, methods exploiting between-family variation in addition to within-family variation, such as the measured genotype (MG) approach, have greater power. Application of MG methods can be computationally demanding (especially for large pedigrees), making genomewide scans practically infeasible. Here we suggest a novel approach for genomewide pedigree-based quantitative trait loci (QTL) association analysis: genomewide rapid association using mixed model and regression (GRAMMAR). The method first obtains residuals adjusted for family effects and subsequently analyzes the association between these residuals and genetic polymorphisms using rapid least-squares methods. At the final step, the selected polymorphisms may be followed up with the full measured genotype (MG) analysis. In a simulation study, we compared type 1 error, power, and operational characteristics of the proposed method with those of MG and TDT-based approaches. For moderately heritable (30%) traits in human pedigrees the power of the GRAMMAR and the MG approaches is similar and is much higher than that of TDT-based approaches. When using tabulated thresholds, the proposed method is less powerful than MG for very high heritabilities and pedigrees including large sibships like those observed in livestock pedigrees. However, there is little or no difference in empirical power of MG and the proposed method. In any scenario, GRAMMAR is much faster than MG and enables rapid analysis of hundreds of thousands of markers.
