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Denaturing high-performance liquid chromatography chromatogram of CAPN10 SNP44 copy number in male and female individuals. Each panel includes genotypes with three peaks corresponding to polymerase chain reaction amplification of CAPN10 SNP44, DMD and CTLA-4, respectively, obtained from (A) a normal male control or (B) female with two copies of CAPN10 SNP44. (a) Male and (b) female individuals with gene deletions are shown. Peaks corresponding to deletions are shown with an asterisk on top of the peak.
Source publication
Aims/IntroductionA combination of multiple genetic and environmental factors contribute to pathogenesis of type 2 diabetes. Copy number variations (CNVs) are associated with complex human diseases. However, CNVs can cause genotype deviation from Hardy Weinberg Equilibrium (HWE). A genetic case control association study in 216 Thai diabetic patients...
Contexts in source publication
Context 1
... peak heights from individuals carrying two nor- mal copies of CAPN10 SNP44 were equal to CTLA-4 peak heights (Figure 1a,b). Conversely, chromatogram peak heights reflecting heterozygous deletion (1 copy) of SNP44 were only half CTLA-4 peak heights (Figure 1a,b). ...
Context 2
... peak heights from individuals carrying two nor- mal copies of CAPN10 SNP44 were equal to CTLA-4 peak heights (Figure 1a,b). Conversely, chromatogram peak heights reflecting heterozygous deletion (1 copy) of SNP44 were only half CTLA-4 peak heights (Figure 1a,b). We carried out real-time quantitative PCR using SYBR Green in the same cohort to confirm our DHPLC results. ...
Context 3
... CNVs reported in Database of Genomic Variants to cover CAPN10 were shown by Redon et al. 21 , Wang et al. 22 Jakobsson et al. 23 and Dogan et al. 24 ( Figure S1). Several studies have attempted to identify CNVs spe- cific for certain populations, such as European, Korean and Chi- nese [25][26][27] . ...
Citations
... Each SYBR Green qPCR assay was run on a QuantStudio 5 Real-Time PCR System (Thermo Fisher). For data analysis, the relative copy numbers of selected regions were determined by comparing the target sequences to the reference (GAPDH), in patient and normal control DNA samples, and applying the formula 2 × 2 (−∆∆Ct) [52]. ...
Neurofibromatosis type 2 is an autosomal dominant tumor-prone disorder mainly caused by NF2 point mutations or intragenic deletions. Few individuals with a complex phenotype and 22q12 microdeletions have been described. The 22q12 microdeletions’ pathogenic effects at the genetic and epigenetic levels are currently unknown. We here report on 22q12 microdeletions’ characterization in three NF2 patients with different phenotype complexities. A possible effect of the position was investigated by in silico analysis of 22q12 topologically associated domains (TADs) and regulatory elements, and by expression analysis of 12 genes flanking patients’ deletions. A 147 Kb microdeletion was identified in the patient with the mildest phenotype, while two large deletions of 561 Kb and 1.8 Mb were found in the other two patients, showing a more severe symptomatology. The last two patients displayed intellectual disability, possibly related to AP1B1 gene deletion. The microdeletions change from one to five TADs, and the 22q12 chromatin regulatory landscape, according to the altered expression levels of four deletion-flanking genes, including PIK3IP1, are likely associated with an early ischemic event occurring in the patient with the largest deletion. Our results suggest that the identification of the deletion extent can provide prognostic markers, predictive of NF2 phenotypes, and potential therapeutic targets, thus overall improving patient management.
... A "B" genotype indicates C57BL/6J-derived. Al-Sinani et al. 2015;Plengvidhya et al. 2015;Zhang et al. 2019). It is tempting to speculate that one potential reason for the inconsistent results could be an incomplete understanding of epistatic interactions between CAPN10 and other loci. ...
The genetic contribution of additive versus non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate approach for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTLs) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTLs regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTLs for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTLs; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.
... Deviations from HWE usually show genotyping errors, population stratification, or some other artifact. However, the existence of a copy number variation (CNV) can also cause genotype deviation from the HWE (Lee et al., 2008;Plengvidhya et al., 2015). DEFB4 gene exhibits CNV, which could explain why our cohort of subjects does not meet with the HWE. ...
Background:
Human β-defensin-2 is an antimicrobial weapon peptide with antibiotic properties secreted by the oral cavity to protect the host against microbial attack. The interindividual differences of defensin expression profiles due to genetic variation might be partly responsible for differences in disease susceptibility.
Aims:
The objective of this study was to examine whether variation in the human β-defensin-2 gene (DEFB4A) is associated with chronic periodontitis (CP).
Materials and Methods:
This case-control study analyzed two promoter polymorphisms in DEFB4A with potential functional consequences in DNA samples collected from 200 unrelated individuals using Sanger sequencing.
Results:
The DEFB4A rs1339258595 promoter polymorphism is associated with CP risk and clinical attachment level (CAL) but the rs3762040 polymorphism is not. Carriers of the T allele (rs1339258595) were approximately three times less likely to develop periodontitis compared with noncarriers (p = 0.0004, odds ratio = 0.35). Consistent with a protective role, the carriers of T allele had a lower CAL compared with the wild-type (G) allele. Moreover, the wild-type diplotype (GGGG) had a significantly higher risk of tooth loss compared with other diplotypes (p = 0.016).
Conclusion:
This study demonstrates that genetic variation in the promoter region of DEFB4A likely affects resistance to periodontal infection and might be a potential marker for CP risk and severity.
https://www.liebertpub.com/doi/full/10.1089/gtmb.2019.0218?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed
... It was the first T2D susceptibility gene identified in linkage studies (Horikawa et al. 2000), with multiple subsequent GWAS or candidate gene studies having also identified an association between variants near CAPN10 and T2D risk and other diabetes-related phenotypes (Castro-Martinez et al. 2018;Bayramci et al. 2017;Hou et al. 2017;Zhao et al. 2016;Cui et al. 2016;Ibrahim et al. 2015;Orozco et al. 2014;Baier et al. 2000;Evans et al. 2001). However, many other studies have failed to replicate these findings Plengvidhya et al. 2015;Al-Sinani et al. 2015;Khan et al. 2014;Hegele et al. 2001;Tsai et al. 2001). It is tempting to speculate that one potential reason for the inconsistent results could be an incomplete understanding of epistatic interactions between CAPN10 and other loci. ...
The genetic contribution of additive versus non-additive (epistasis) effects in the regulation of hematologic and other complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate and powerful model for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTLs) were identified using a modified backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. By analyzing the transcriptomes of offspring from this cross, we identified and mapped additive QTLs regulating the expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTLs for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTLs was approximately equal to that of the additive QTLs, demonstrating the importance of identifying genetic interactions to understand the genetic architecture of complex traits. Of particular note, even the epistatic QTLs that accounted for the largest variances were undetected in our single loci GWAS-like association analyses, highlighting the need to account for epistasis in association studies.
... Therefore, early detection and prevention of gestational diabetes mellitus will benefit from the identification of susceptible women. The calpain 10 (CAPN10) gene has a role in the control of blood glucose concentrations and structural changes in the calpain 10 protein encoded by this gene affect the increase in blood glucose [14,15]. Gene polymorphisms are an important manifestation of genetic changes and genetic differences between people [16]. ...
Background
This study aimed to investigate the association between the rs2975760 and rs3792267 single nucleotide polymorphisms (SNPs) of the calpain 10 (CAPN10) gene and gestational diabetes mellitus.
Material/Methods
The study included 138 patients with gestational diabetes mellitus and 152 healthy pregnant women. Venous blood was separated, and the DNA was extracted. The rs2975760 and rs3792267SNP polymorphisms of CAPN10 were detected using polymerase chain reaction (PCR). The frequencies of different genotypes in patients with gestational diabetes mellitus and healthy pregnant women were determined, and the relationship between different SNP genotypes and the risk of gestational diabetes mellitus was analyzed.
Results
There were no significant differences in the frequencies of the TT, CT and CC genotypes of rs2975760 and the frequencies of the GG, AG and AA genotypes of rs3792267 between the women with gestational diabetes and the controls. Expression of rs2975760 and rs3792267 were not associated with the risk of gestational diabetes in the dominant model, recessive model, and additive model. However, grade B and grade D diabetes in the CC and TC genotypes of rs2975760 were significantly different from those in the TT genotype (P<0.05). Grade B and grade D diabetes in the AA and AG genotypes of rs3792267 were significantly different compared with those in the GG genotype (P<0.05), and allele A was significantly increased compared with allele G (P<0.05).
Conclusions
The rs2975760 and rs3792267 SNP polymorphisms of CAPN10 showed no significant association with the incidence of gestational diabetes mellitus and only a mild association with the severity.
... KCNIP1 CNV associates with risk to T2D lipoprotein cholesterol and high density lipoprotein cholesterol, which corresponded with the effect analysis of the CAPN10 gene on T2D [30,40]. This finding has a possible interpretation that KCNIP1 may not be the major regulator among the key gene networks in signaling pathway (e.g. ...
Copy number variation (CNV) has emerged as another important genetic marker in addition to SNP for understanding etiology of complex disease. Kv channel interacting protein 1 (KCNIP1) is a Ca2+-dependent transcriptional modulator that contributes to the regulation of insulin secretion. Previous genome-wide CNV assay identified the KCNIP1 gene encompassing a CNV region, however, its further effect and risk rate on type 2 diabetes (T2D) have rarely been addressed, especially in Chinese population. The current study aims to detect and excavate genetic distribution profile of KCNIP1 CNV in Chinese T2D and control populations, and further to investigate the associations with clinical characteristics. Divergent patterns of the KCNIP1 CNV were identified (p < 0.01), in which the copy number gain was predominant in T2D, while the copy number normal accounted for the most in control group. Consistently, the individuals with copy number gain showed significant risk on T2D (OR = 4.550, p < 0.01). The KCNIP1 copy numbers presented significantly positive correlations with fasting plasma glucose and glycated hemoglobin in T2D. For OGTT test, the T2D patients with copy number gain had remarkably elevated glucose contents (60, 120, 180-min, p < 0.05 or p < 0.01) and diminished insulin levels (60, 120-min, p < 0.05) than those with copy number loss and normal, which suggested that the KCNIP1 CNV was correlated with the glucose and insulin action. This is the first CNV association study of the KCNIP1 gene in Chinese population, and these data indicated that KCNIP1 might function as a T2D-susceptibility gene whose dysregulation alters insulin production.
Introduction:
Approximately half of patients with severe haemophilia A are caused by structural variants in the F8 gene. Unlike inversions or deletions directly impairing the integrity of F8, some duplications do not completely disrupt the open reading frame or even retain an intact F8 copy. Currently, only a few duplication breakpoints were precisely characterized, and the corresponding rearrangement mechanisms and clinical outcomes remain to be further investigated.
Aim:
Establishing an effective strategy for breakpoint characterization of duplications and revealing their rearrangement mechanisms.
Methods:
AccuCopy is used for the detection of duplications, long-distance PCR for the characterization of tandem duplications, genome walking technique and whole genome sequencing for the characterization of inverted duplications.
Results:
Four F8 duplication rearrangements were successfully characterized at the nucleotide level: one tandem duplication (exons 7-11) and three inverted duplications (exons 7-22, exons 2-26, and exons 15-22). Two shared features of inverted duplication were found after carefully analysing our results and breakpoint information in the literature: 1, an inverted fragment was inserted into the original chromosome via two junctions; 2, one junction is mediated by a pair of inverted repetitive elements, while the other consists of two breakpoints with microhomology.
Conclusion:
Similar breakpoint features motivated us to propose a DNA replication-based model to explain the formation of duplication rearrangements. Based on our model, we further divide the inverted duplications into three basic types: type I with a DEL-NOR/INV-DUP pattern, type II with a DUP-NOR/INV-DUP pattern and type III with a DUP-TRP/INV-DUP pattern.
Thalassemia is caused by complex mechanisms, including copy number variants (CNVs) and single nucleotide variants (SNVs). The CNV types of α-thalassemia are typically detected by gap-polymerase chain reaction (PCR). The SNV types are detected by Sanger sequencing. In the present study, a novel method was developed that simultaneously detects CNVs and SNVs by multiplex PCR and next-generation sequencing (NGS). To detect CNVs, 33 normal samples were used as a cluster of control values to build a baseline, and the A, B, C, and D ratios were developed to evaluate- SEA , -α 4.2 , -α 3.7 , and compound or homozygous CNVs, respectively. To detect other SNVs, sequencing data were analyzed using the system's software and annotated using Annovar software. In a test of performance, 128 patients with thalassemia were detected using the method developed and were confirmed by Sanger sequencing and gap-PCR. Four different CNV types were clearly distinguished by the developed algorithm, with - SEA , -α 3.7 , -α 4.2 , and compound or homozygous deletions. The sensitivities for each CNV type were 96.72% (59/61), 97.37% (37/38), 83.33% (10/12) and 95% (19/20), and the specificities were 93.94% (32/33), 93.94% (32/33), 100% (33/33) and 100% (33/33), respectively. The SNVs detected were consistent with those of the Sanger sequencing.
