Dataset Information

Rare variant detection using family-based sequencing analysis.

ABSTRACT: Next-generation sequencing is revolutionizing genomic analysis, but this analysis can be compromised by high rates of missing true variants. To develop a robust statistical method capable of identifying variants that would otherwise not be called, we conducted sequence data simulations and both whole-genome and targeted sequencing data analysis of 28 families. Our method (Family-Based Sequencing Program, FamSeq) integrates Mendelian transmission information and raw sequencing reads. Sequence analysis using FamSeq reduced the number of false negative variants by 14-33% as assessed by HapMap sample genotype confirmation. In a large family affected with Wilms tumor, 84% of variants uniquely identified by FamSeq were confirmed by Sanger sequencing. In children with early-onset neurodevelopmental disorders from 26 families, de novo variant calls in disease candidate genes were corrected by FamSeq as mendelian variants, and the number of uniquely identified variants in affected individuals increased proportionally as additional family members were included in the analysis. To gain insight into maximizing variant detection, we studied factors impacting actual improvements of family-based calling, including pedigree structure, allele frequency (common vs. rare variants), prior settings of minor allele frequency, sequence signal-to-noise ratio, and coverage depth (∼20× to >200×). These data will help guide the design, analysis, and interpretation of family-based sequencing studies to improve the ability to identify new disease-associated genes.

SUBMITTER: Peng G

PROVIDER: S-EPMC3593912 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Rare variant detection using family-based sequencing analysis.

Peng Gang G Fan Yu Y Palculict Timothy B TB Shen Peidong P Ruteshouser E Cristy EC Chi Aung-Kyaw AK Davis Ronald W RW Huff Vicki V Scharfe Curt C Wang Wenyi W

Proceedings of the National Academy of Sciences of the United States of America 20130220 10

Next-generation sequencing is revolutionizing genomic analysis, but this analysis can be compromised by high rates of missing true variants. To develop a robust statistical method capable of identifying variants that would otherwise not be called, we conducted sequence data simulations and both whole-genome and targeted sequencing data analysis of 28 families. Our method (Family-Based Sequencing Program, FamSeq) integrates Mendelian transmission information and raw sequencing reads. Sequence ana ...[more]

PMID: 23426633

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Project description:Extraordinary progress has been made in our understanding of common variants in many diseases, including melanoma. Because the contribution of rare coding variants is not as well characterized, we performed an exome-wide, gene-based association study of familial cutaneous melanoma (CM) and ocular melanoma (OM). Using 11 990 jointly processed individual DNA samples, whole-exome sequencing was performed, followed by large-scale joint variant calling using GATK (Genome Analysis ToolKit). PLINK/SEQ was used for statistical analysis of genetic variation. Four models were used to estimate the association among different types of variants. In vitro functional validation was performed using three human melanoma cell lines in 2D and 3D proliferation assays. In vivo tumor growth was assessed using xenografts of human melanoma A375 melanoma cells in nude mice (eight mice per group). All statistical tests were two-sided. Strong signals were detected for CDKN2A (Pmin = 6.16 × 10-8) in the CM cohort (n = 273) and BAP1 (Pmin = 3.83 × 10-6) in the OM (n = 99) cohort. Eleven genes that exhibited borderline association (P < 10-4) were independently validated using The Cancer Genome Atlas melanoma cohort (379 CM, 47 OM) and a matched set of 3563 European controls with CDKN2A (P = .009), BAP1 (P = .03), and EBF3 (P = 4.75 × 10-4), a candidate risk locus, all showing evidence of replication. EBF3 was then evaluated using germline data from a set of 132 familial melanoma cases and 4769 controls of UK origin (joint P = 1.37 × 10-5). Somatically, loss of EBF3 expression correlated with progression, poorer outcome, and high MITF tumors. Functionally, induction of EBF3 in melanoma cells reduced cell growth in vitro, retarded tumor formation in vivo, and reduced MITF levels. The results of this large rare variant germline association study further define the mutational landscape of hereditary melanoma and implicate EBF3 as a possible CM predisposition gene.

Project description:A large proportion of genetic variations involved in complex diseases are rare and located within noncoding regions, making the interpretation of underlying biological mechanisms a daunting task. Although technical and methodological progress has been made to annotate the genome, current disease-rare-variant association tests incorporating such annotations suffer from two major limitations. First, they are generally restricted to case-control designs of unrelated individuals, which often require tens or hundreds of thousands of individuals to achieve sufficient power. Second, they were not evaluated with region-based annotations needed to interpret the causal regulatory mechanisms. In this work, we propose RetroFun-RVS, a new retrospective family-based score test, incorporating functional annotations. A critical feature of the proposed method is to aggregate genotypes to compare against rare variant-sharing expectations among affected family members. Through extensive simulations, we have demonstrated that RetroFun-RVS integrating networks based on 3D genome contacts as functional annotations reach greater power over the region-wide test, other strategies to include subregions and competing methods. Also, the proposed framework shows robustness to non-informative annotations, maintaining its power when causal variants are spread across regions. Asymptotic p-values are susceptible to Type I error inflation when the number of families with rare variants is small, and a bootstrap procedure is recommended in these instances. Application of RetroFun-RVS is illustrated on whole genome sequence in the Eastern Quebec Schizophrenia and Bipolar Disorder Kindred Study with networks constructed from 3D contacts and epigenetic data on neurons. In summary, the integration of functional annotations corresponding to regions or networks with transcriptional impacts in rare variant tests appears promising to highlight regulatory mechanisms involved in complex diseases.

Dataset Information

Rare variant detection using family-based sequencing analysis.

Publications

Rare variant detection using family-based sequencing analysis.

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