Leveraging ancestry to improve causal variant identification in exome sequencing for monogenic disorders

Abstract

Recent breakthroughs in exome-sequencing technology have made possible the identification of many causal variants of monogenic disorders. Although extremely powerful when closely related individuals (eg, child and parents) are simultaneously sequenced, sequencing of a single case is often unsuccessful due to the large number of variants that need to be followed up for functional validation. Many approaches filter out common variants above a given frequency threshold (eg, 1%), and then prioritize the remaining variants according to their functional, structural and conservation properties. Here we present methods that leverage the genetic structure across different populations to improve filtering performance while accounting for the finite sample size of the reference panels. We show that leveraging genetic structure reduces the number of variants that need to be followed up by 16% in simulations and by up to 38% in empirical data of 20 exomes from individuals with monogenic disorders for which the causal variants are known.

Whole exome and genome sequencing for Mendelian immune disorders: from molecular diagnostics to new disease variant and gene discovery

“…The 1000 Genomes project (totalling 2504 individuals in the latest release) is the main resource covering the whole human genome and different continental population (Europeans, Africans, Eastern Asians, Southern Asians, admixed Latin Americans) (1000Genomes Project Consortium et al 2015. It is a common practice to filter based on the maximum frequency observed across the 5 continental populations (Bamshad et al 2011;Lek et al 2016); while the earlier release of 1000 Genomes presented excessively small continental population sizes for accurate allele frequency estimate, the current size is satisfactory (Brown et al 2016).…”

SNVstory: inferring genetic ancestry from genome sequencing data

“…In addition to clinical importance, knowing the ancestral composition of an individual or a population is essential in the genetic research setting. For example, signals from genome-wide association studies (GWAS) or whole genome sequencing cohorts can be reassessed based on population stratification, whereby loci associated with disease may be more accurately identified by discarding rare variants associated with an individual’s ancestry rather than with the disease in question [ 9 , 10 ].…”

SNVstory: A dockerized algorithm for rapid and accurate inference of sub-continental ancestry

“…Therefore, developing methods to report ancestry accurately and consistently is essential.In addition to clinical importance, knowing the ancestral composition of an individual or a population is essential in the genetic research setting. For example, signals from genome-wide association studies (GWAS) or whole genome sequencing cohorts can be reassessed based on population stratification, whereby loci associated with disease may be more accurately identified by discarding rare variants associated with an individual's ancestry rather than with the disease in question 9,10 .Given the importance of ancestry, several ancestry inference algorithms that operate on genomic data have been developed that can be divided into two broad types: parametric and non-parametric.Parametric learning algorithms estimate a finite set of parameters from the data to establish a relationship between the independent and dependent variables. Two widely-used parametric tools are STRUCTURE 11 and ADMIXTURE 12 , which estimate the proportions of different ancestries (or ancestral populations) for each individual, known as admixture.…”