title: 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.
authors: Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors: Gang
authors: G
authors: Peng
authors: Yu
authors: Y
authors: Fan
authors: Timothy B
authors: TB
authors: Palculict
authors: Peidong
authors: P
authors: Shen
authors: E Cristy
authors: EC
authors: Ruteshouser
authors: Aung-Kyaw
authors: AK
authors: Chi
authors: Ronald W
authors: RW
authors: Davis
authors: Vicki
authors: V
authors: Huff
authors: Curt
authors: C
authors: Scharfe
authors: Wenyi
authors: W
authors: Wang
mesh_terms: D001499:Bayes Theorem; D005190:Family; D005260:Female; D014644:Genetic Variation; D015894:Genome, Human; D006801:Humans; D007680:Kidney Neoplasms / Q000235:genetics; D016013:Likelihood Functions; D008297:Male; D028361:Mitochondrial Diseases / Q000235:genetics; D008957:Models, Genetic; D019636:Neurodegenerative Diseases / Q000235:genetics; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software; D009396:Wilms Tumor / Q000235:genetics