Scatter plots of spouses for PC1 and PC2, by generational cohort.

<p><b>Scatter plots of spouse-pair male versus female:</b> Top left: Fig 4a, PC1 in the original cohort. Top right: Fig 4b, PC2 in the original cohort. Bottom left: Fig 4c, PC1 in the offspring cohort. Bottom right: Fig 4d, PC2 in the offspring cohort.</p

Probability density functions of spouse kinship estimates comparing results from GCTA (unadjusted for genetic ancestry) to those from PC-Relate (adjusted for genetic ancestry) in the FHS original cohort and offspring cohort.

<p>Probability density functions of spouse kinship estimates comparing results from GCTA (unadjusted for genetic ancestry) to those from PC-Relate (adjusted for genetic ancestry) in the FHS original cohort and offspring cohort.</p

Principal components analyses on FHS participants.

<p>Principal components analysis on FHS participants with: Top left: Fig 3a, FHS and HGDP participants projected. Top right: Fig 3b, original cohort participants projected. Bottom left: Fig 3c, offspring cohort participants projected. Bottom right: Fig 3d, third generation cohort participants projected.</p

Comparison of kinship estimates without ancestry adjustment (using GCTA) versus with ancestry adjustment (using PC-Relate).

<p>(a) Original cohort. Blue color represents Ashkenazi spouse pairs; red color represents Northwestern European spouse pairs; green color represents Southern European spouse pairs; black color represents spouse pairs of different ancestry. (b) Offspring cohort. Blue color represents Ashkenazi spouse pairs; red color represents Northwestern European spouse pairs; green color represents Southern European spouse pairs; black color represents spouse pairs of different ancestry.</p

Regression of Hardy–Weinberg disequilibrium parameter F on squared SNP PC Loadings.

<p>Regression of Hardy–Weinberg disequilibrium parameter F on squared SNP PC Loadings.</p

Flow diagram illustrating SNP selection for each analysis.

<p>Black box—SNPs used to characterize global genetic ancestry and identify continentally admixed and non-white individuals; SNPs used in GCTA analysis; SNPs used for regression analysis using F; SNPs used to calculate spousal genetic correlations. Blue box—SNPs used to characterize European/West Asian ancestry. Red box—SNPs used for regression analysis using D.</p

Regression analysis of linkage disequilibrium parameter, D on the product of the PC loadings for unlinked SNPs.

<p>Regression analysis of linkage disequilibrium parameter, D on the product of the PC loadings for unlinked SNPs.</p

Spouse Correlations in the FHS cohorts.

<p>Spouse Correlations in the FHS cohorts.</p

Systematic Cell-Based Phenotyping of Missense Alleles Empowers Rare Variant Association Studies: A Case for <i>LDLR</i> and Myocardial Infarction

<div><p>A fundamental challenge to contemporary genetics is to distinguish rare missense alleles that disrupt protein functions from the majority of alleles neutral on protein activities. High-throughput experimental tools to securely discriminate between disruptive and non-disruptive missense alleles are currently missing. Here we establish a scalable cell-based strategy to profile the biological effects and likely disease relevance of rare missense variants <i>in vitro</i>. We apply this strategy to systematically characterize missense alleles in the low-density lipoprotein receptor (<i>LDLR</i>) gene identified through exome sequencing of 3,235 individuals and exome-chip profiling of 39,186 individuals. Our strategy reliably identifies disruptive missense alleles, and disruptive-allele carriers have higher plasma LDL-cholesterol (LDL-C). Importantly, considering experimental data refined the risk of rare <i>LDLR</i> allele carriers from 4.5- to 25.3-fold for high LDL-C, and from 2.1- to 20-fold for early-onset myocardial infarction. Our study generates proof-of-concept that systematic functional variant profiling may empower rare variant-association studies by orders of magnitude.</p></div

Workflow of this study to determine the functional impact of 70 rare missense variants on LDLR protein activities and improve rare variant association testing for plasma LDL-C and the risk for early-onset MI.

<p>Variants were identified through whole-exome sequencing of 3,235 individuals from the Italian Study of Early-onset Myocardial Infarction (ATVB) cohort.</p