Improving sequence-based genotype calls with linkage disequilibrium and pedigree information

TL;DR

This paper introduces likelihood-based methods that enhance genotype calling accuracy from sequencing data by leveraging linkage disequilibrium and pedigree information, outperforming traditional methods that treat loci independently.

Contribution

The paper presents novel likelihood-based approaches that incorporate LD and pedigree data into genotype calling, improving accuracy over existing independent-locus methods.

Findings

Methods outperform traditional approaches in simulations

Incorporating LD improves genotype accuracy

Pedigree information further enhances calls

Abstract

Whole and targeted sequencing of human genomes is a promising, increasingly feasible tool for discovering genetic contributions to risk of complex diseases. A key step is calling an individual's genotype from the multiple aligned short read sequences of his DNA, each of which is subject to nucleotide read error. Current methods are designed to call genotypes separately at each locus from the sequence data of unrelated individuals. Here we propose likelihood-based methods that improve calling accuracy by exploiting two features of sequence data. The first is the linkage disequilibrium (LD) between nearby SNPs. The second is the Mendelian pedigree information available when related individuals are sequenced. In both cases the likelihood involves the probabilities of read variant counts given genotypes, summed over the unobserved genotypes. Parameters governing the prior genotype distribution and the read error rates can be estimated either from the sequence data itself or from external reference data. We use simulations and synthetic read data based on the 1000 Genomes Project to evaluate the performance of the proposed methods. An R-program to apply the methods to small families is freely available at http://med.stanford.edu/epidemiology/PHGC/.