Journal: medRxiv

Article Title: A reassessment of Hardy-Weinberg equilibrium filtering in large sample Genomic studies

doi: 10.1101/2024.02.07.24301951

Figure Lengend Snippet: Manhattan Plots for Gallstone disease in WES data, European Only Ancestry with various Hardy Weinberg equilibrium thresholds: p<1e-15 top, p<1e-300 middle and no threshold bottom. Manhattan plots truncated to log(p)<=40 due to very significant results in ABCG8 and MHC .

Article Snippet: Hardy Weinberg equilibrium (HWE) is a fundamental principle of population genetics stating that a given set of genotypes AA, AB, and BB are expected to occur at relative frequencies of p , 2pq, and q , where p and q are the allele frequencies of A and B respectively. ( ) As part of the recommended data quality control step prior to analysis, variants are filtered using the p-value of the HWE test (a chi squared test comparing expected frequencies based on HWE vs observed based on the data) to remove potential genotyping errors ( ) This has become standard practice in genome wide association studies (GWAS) despite several known factors that may also cause departure from HWE including population structure, non-random mating, unstable genomic regions, effects of natural selection, and genetic linked disease states. ( , ) In the advent of GWAS, the majority of studies consisted of fewer than 10,000 subjects and it was common to filter variants for deviation from HWE at p<1e-5.

Techniques:

Journal: medRxiv

Article Title: A reassessment of Hardy-Weinberg equilibrium filtering in large sample Genomic studies

doi: 10.1101/2024.02.07.24301951

Figure Lengend Snippet: Manhattan Plots for Gallstone disease in WES data, all ancestry with various Hardy Weinberg equilibrium thresholds: p<1e-15 top, p<1e-300 middle and no threshold bottom. Manhattan plots truncated to log(p)<=40 due to very significant results in ABCG8 and MHC .

Article Snippet: Hardy Weinberg equilibrium (HWE) is a fundamental principle of population genetics stating that a given set of genotypes AA, AB, and BB are expected to occur at relative frequencies of p , 2pq, and q , where p and q are the allele frequencies of A and B respectively. ( ) As part of the recommended data quality control step prior to analysis, variants are filtered using the p-value of the HWE test (a chi squared test comparing expected frequencies based on HWE vs observed based on the data) to remove potential genotyping errors ( ) This has become standard practice in genome wide association studies (GWAS) despite several known factors that may also cause departure from HWE including population structure, non-random mating, unstable genomic regions, effects of natural selection, and genetic linked disease states. ( , ) In the advent of GWAS, the majority of studies consisted of fewer than 10,000 subjects and it was common to filter variants for deviation from HWE at p<1e-5.

Techniques:

Journal: medRxiv

Article Title: A reassessment of Hardy-Weinberg equilibrium filtering in large sample Genomic studies

doi: 10.1101/2024.02.07.24301951

Figure Lengend Snippet: Whole Exome Sequencing Ternary Plots for European Only ancestry (n=362198), left, and all ancestry (n=463605), right, cohorts at various Hardy Weinberg equilibrium thresholds (p<1e-15 top, p<1e-300 middle and +/- 20% difference bottom.

Article Snippet: Hardy Weinberg equilibrium (HWE) is a fundamental principle of population genetics stating that a given set of genotypes AA, AB, and BB are expected to occur at relative frequencies of p , 2pq, and q , where p and q are the allele frequencies of A and B respectively. ( ) As part of the recommended data quality control step prior to analysis, variants are filtered using the p-value of the HWE test (a chi squared test comparing expected frequencies based on HWE vs observed based on the data) to remove potential genotyping errors ( ) This has become standard practice in genome wide association studies (GWAS) despite several known factors that may also cause departure from HWE including population structure, non-random mating, unstable genomic regions, effects of natural selection, and genetic linked disease states. ( , ) In the advent of GWAS, the majority of studies consisted of fewer than 10,000 subjects and it was common to filter variants for deviation from HWE at p<1e-5.

Techniques: Sequencing

Journal: medRxiv

Article Title: A reassessment of Hardy-Weinberg equilibrium filtering in large sample Genomic studies

doi: 10.1101/2024.02.07.24301951

Figure Lengend Snippet: Ternary Plots for imputed chromosome 1 data from the all ancestry cohort at sample size=300,000 with various Hardy Weinberg equilibrium thresholds: p<1e-15 top, p<1e-300 middle and +/- 20% difference bottom.

Article Snippet: Hardy Weinberg equilibrium (HWE) is a fundamental principle of population genetics stating that a given set of genotypes AA, AB, and BB are expected to occur at relative frequencies of p , 2pq, and q , where p and q are the allele frequencies of A and B respectively. ( ) As part of the recommended data quality control step prior to analysis, variants are filtered using the p-value of the HWE test (a chi squared test comparing expected frequencies based on HWE vs observed based on the data) to remove potential genotyping errors ( ) This has become standard practice in genome wide association studies (GWAS) despite several known factors that may also cause departure from HWE including population structure, non-random mating, unstable genomic regions, effects of natural selection, and genetic linked disease states. ( , ) In the advent of GWAS, the majority of studies consisted of fewer than 10,000 subjects and it was common to filter variants for deviation from HWE at p<1e-5.

Techniques: