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Can whole-exome sequencing data be used for linkage analysis?

ABSTRACT: Whole-exome sequencing (WES) has become the strategy of choice to identify causal variants in monogenic disorders. However, the list of candidate variants can be quite large, including false positives generated by sequencing errors. To reduce this list of candidate variants to the most relevant ones, a cost-effective strategy would be to focus on regions of linkage identified through linkage analysis conducted with common polymorphisms present in WES data. However, the non-uniform exon coverage of the genome and the lack of knowledge on the power of this strategy have largely precluded its use so far. To compare the performance of linkage analysis conducted with WES and SNP chip data in different situations, we performed simulations on two pedigree structures with, respectively, a dominant and a recessive trait segregating. We found that the performance of the two sets of markers at excluding regions of the genome were very similar, and there was no real gain at using SNP chip data compared with using the common SNPs extracted from WES data. When analyzing the real WES data available for these two pedigrees, we found that the linkage information derived from the WES common polymorphisms was able to reduce by half the list of candidate variants identified by a simple filtering approach. Conducting linkage analysis with WES data available on pedigrees and excluding among the candidate variants those that fall in excluded linkage regions is thus a powerful and cost-effective strategy to reduce the number of false-positive candidate variants.

SUBMITTER: Gazal S

PROVIDER: S-EPMC4929867 | biostudies-other | 2016 Apr

REPOSITORIES: biostudies-other

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Can whole-exome sequencing data be used for linkage analysis?

Gazal Steven S Gosset Simon S Verdura Edgard E Bergametti Françoise F Guey Stéphanie S Babron Marie-Claude MC Tournier-Lasserve Elisabeth E

European journal of human genetics : EJHG 20150715 4

Whole-exome sequencing (WES) has become the strategy of choice to identify causal variants in monogenic disorders. However, the list of candidate variants can be quite large, including false positives generated by sequencing errors. To reduce this list of candidate variants to the most relevant ones, a cost-effective strategy would be to focus on regions of linkage identified through linkage analysis conducted with common polymorphisms present in WES data. However, the non-uniform exon coverage ...[more]

PMID: 26173971

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Project description:Type 1 diabetes (T1D) is characterized by the progressive destruction of pancreatic β-cells, leading to insulin deficiency and lifelong dependency on exogenous insulin. Higher estimates of heritability rates in monozygotic twins, followed by dizygotic twins and sib-pairs, indicate the role of genetics in the pathogenesis of T1D. The incidence and prevalence of T1D are alarmingly high in Kuwait. Consanguineous marriages account for 50-70% of all marriages in Kuwait, leading to an excessive burden of recessive allele enrichment and clustering of familial disorders. Thus, genetic studies from this Arab region are expected to lead to the identification of novel gene loci for T1D. In this study, we performed linkage analyses to identify the recurrent genetic variants segregating in high-risk Kuwaiti families with T1D. We studied 18 unrelated Kuwaiti native T1D families using whole exome sequencing data from 86 individuals, of whom 37 were diagnosed with T1D. The study identified three potential loci with a LOD score of ≥ 3, spanning across four candidate genes, namely SLC17A1 (rs1165196:pT269I), SLC17A3 (rs942379: p.S370S), TATDN2 (rs394558:p.V256I), and TMEM131L (rs6848033:p.R190R). Upon examination of missense variants from these genes in the familial T1D dataset, we observed a significantly increased enrichment of the genotype homozygous for the minor allele at SLC17A3 rs56027330_p.G279R accounting for 16.2% in affected children from 6 unrelated Kuwaiti T1D families compared to 1000 genomes Phase 3 data (0.9%). Data from the NephQTL database revealed that the rs1165196, rs942379, rs394558, and rs56027330 SNPs exhibited genotype-based differential expression in either glomerular or tubular tissues. Data from the GTEx database revealed rs942379 and rs394558 as QTL variants altering the expression of TRIM38 and IRAK2 respectively. Global genome-wide association studies indicated that SLC17A1 rs1165196 and other variants from SLC17A3 are associated with uric acid concentrations and gout. Further evidence from the T1D Knowledge portal supported the role of shortlisted variants in T1D pathogenesis and urate metabolism. Our study suggests the involvement of SLC17A1, SLC17A3, TATDN2, and TMEM131L genes in familial T1D in Kuwait. An enrichment selection of genotype homozygous for the minor allele is observed at SLC17A3 rs56027330_p.G279R variant in affected members of Kuwaiti T1D families. Future studies may focus on replicating the findings in a larger T1D cohort and delineate the mechanistic details of the impact of these novel candidate genes on the pathophysiology of T1D.

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Can whole-exome sequencing data be used for linkage analysis?

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Can whole-exome sequencing data be used for linkage analysis?

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