2019
DOI: 10.1159/000507576
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Unbalanced Sample Size Introduces Spurious Correlations to Genome-Wide Heterozygosity Analyses

Abstract: Excess of heterozygosity (H) is a widely used measure of genetic diversity of a population. As high-throughput sequencing and genotyping data become readily available, it has been applied to investigating the associations of genome-wide genetic diversity with human diseases and traits. However, these studies often report contradictory results. In this paper, we present a meta-analysis of five whole-exome studies to examine the association of H scores with Alzheimer’s disease. We show that the mean H score of a… Show more

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Cited by 2 publications
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“…The distributions of within‐stand, per‐locus expected heterozygosity values showed a moderate excess in all species, although with quite large variation among loci (Results R6). The extent of heterozygote excess was correlated to MAF (Results R6), with more negative inbreeding coefficient values for higher MAFs, suggesting that departure from Hardy–Weinberg equilibrium may be caused by some technical bias; heterozygote excess in high‐throughput sequence data has been previously reported as partially, but not entirely, caused by paralogous alleles (Gayral et al, 2013), or as an effect of small sample sizes (Liu & Caselli, 2019). Note, however, that we applied filters to remove paralogues.…”
Section: Resultsmentioning
confidence: 89%
“…The distributions of within‐stand, per‐locus expected heterozygosity values showed a moderate excess in all species, although with quite large variation among loci (Results R6). The extent of heterozygote excess was correlated to MAF (Results R6), with more negative inbreeding coefficient values for higher MAFs, suggesting that departure from Hardy–Weinberg equilibrium may be caused by some technical bias; heterozygote excess in high‐throughput sequence data has been previously reported as partially, but not entirely, caused by paralogous alleles (Gayral et al, 2013), or as an effect of small sample sizes (Liu & Caselli, 2019). Note, however, that we applied filters to remove paralogues.…”
Section: Resultsmentioning
confidence: 89%