Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage

Abstract

Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Compared with GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome-wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions.exome sequencing | genotyping array | population structure | homozygosity mapping | linkage analysis W hole-exome sequencing (WES) has become the leading strategy for uncovering germ-line exome variants in humans. A number of gene-and variant-level methods have been proposed for the analysis of WES data to select candidate variants in rare Mendelian disorders and more common traits (1-13). These analyses benefit from the use of additional information, such as familial linkage, homozygosity rate, and ethnic background, which are commonly used in the study of inherited diseases (14-17). Genomewide single-nucleotide variant array (GWSAs) are the gold standard method for linkage analysis, because they provide maximal linkage information for the whole genome (18). GWSAs are also classically used to estimate homozygosity rate in patients, confirming or sometimes, revealing parental consanguinity through the inbreeding coefficient parameter F in particular (19,20). Population stratification can be an issue in the analysis of population-based genetic data, including WES, particularly for association studies (21-24). Population structures have been widely determined by GWSA (25, 26) in European (27), African (28, 29), Asian (30), Jewish (31), Mexican (32), and other populations (33). These analyses are mostly based on principal component analysis (PCA) (34), which can also be used to confirm or reveal the ethnicity of an individual patient (or his or her parents).U...

A recessive form of hyper-IgE syndrome by disruption of ZNF341-dependent STAT3 transcription and activity

“…Kindreds A, B, and C, which carried R302X, belonged to three different ethnic groups, as confirmed by principal component analysis (Fig. S1U) (32). The mutation was recurrent due to a hotspot rather than a founder effect, as the haplotypes encompassing ZNF341 differed between the three families (Fig.…”

“…S1A-J, Table S1–2 and case reports). Four families were known to be consanguineous, whereas the other two families were shown to be consanguineous by whole-exome sequencing (WES), which revealed a high percentage of homozygosity in the patients (32) (Fig. S1K).…”

An ancestral informative marker panel design for individual ancestry estimation of Hispanic population using whole exome sequencing data

“…In this study, we developed, validated and tested the pipeline for designing AIM panels within the evolutionary-conserved exome regions to distinguish genetic ancestry descendants base on three continental populations (African, European, and East Asian). Although WES could be applied to analyze population structure using all variants [24], it will be problematic since variants will be influenced by the number of somatic mutations in tumor samples, which typically are significantly different on germline and tumor [42–44]. By using UT-AIM250 panel and we acquired the satisfactory result as we expected (by removing low-frequency MAFs and constraint with only biallelic SNPs) even with the tumor samples.…”

“…Particularly in translational research, WES offers an unbiased view than conventional targeted molecular diagnostics approach, commonly available in many of large genomic studies such as genomic data at the Cancer Genome Atlas (TCGA) [23]. Previous studies showed that admixture proportions could be determined by using principal component analysis (PCA) with all variants [24], or using allele frequency for pooled DNA [25], and using off-target sequence reads [26]. However, using a panel of AIM within exome regions, if feasible, will allow rapid determination of a patient’s ancestry admixture from sequencing data and thus validate self-reported race/ethnicity categories.…”

Obesity genetics: insights from the Pakistani population

“…The Pakistani population is highly relevant for the identification of recessive forms of monogenic forms of obesity, using homozygosity mapping, runs of homozygosity and whole‐exome sequencing strategies . The large, consanguineous, genetically diverse nature of this population, combined with the presence of large pedigrees, is likely to elucidate considerably more mutations responsible for heterogeneous recessive Mendelian obesity disorders than those found in small isolated and/or endogamous populations .…”