Leveraging Identity-by-Descent for Accurate Genotype Inference in Family Sequencing Data

Sequencing family DNA samples provides an attractive alternative to population based designs to identify rare variants associated with human disease due to the enrichment of causal variants in pedigrees. Previous studies showed that genotype calling accuracy can be improved by modeling family relatedness compared to standard calling algorithms. Current family-based variant calling methods use sequencing data on single variants and ignore the identity-by-descent (IBD) sharing along the genome. In this study we describe a new computational framework to accurately estimate the IBD sharing from the sequencing data, and to utilize the inferred IBD among family members to jointly call genotypes in pedigrees. Through simulations and application to real data, we showed that IBD can be reliably estimated across the genome, even at very low coverage (e.g. 2X), and genotype accuracy can be dramatically improved. Moreover, the improvement is more pronounced for variants with low frequencies, especially at low to intermediate coverage (e.g. 10X to 20X), making our approach effective in studying rare variants in cost-effective whole genome sequencing in pedigrees. We hope that our tool is useful to the research community for identifying rare variants for human disease through family-based sequencing.Author Summary: To identify disease variants that occur less frequently in population, sequencing families in which multiple individuals are affected is more powerful due to the enrichment of causal variants. An important step in such studies is to infer individual genotypes from sequencing data. Existing methods do not utilize full familial transmission information and therefore result in reduced accuracy of inferred genotypes. In this study we describe a new method that infers shared genetic materials among family members and then incorporate the shared genomic information in a novel algorithm that can accurately infer genotypes. Our method is particularly advantageous when inferring low frequency variants with fewer sequence data, making it effective in analyzing genome-wide sequence data. We implemented the algorithm in a computationally efficient tool to facilitate cost-effective sequencing in families for identifying disease genetic variants.