Genetic Diversity and Association Studies in US Hispanic/Latino Populations: Applications in the Hispanic Community Health Study/Study of Latinos

Abstract

US Hispanic/Latino individuals are diverse in genetic ancestry, culture, and environmental exposures. Here, we characterized and controlled for this diversity in genome-wide association studies (GWASs) for the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We simultaneously estimated population-structure principal components (PCs) robust to familial relatedness and pairwise kinship coefficients (KCs) robust to population structure, admixture, and Hardy-Weinberg departures. The PCs revealed substantial genetic differentiation within and among six self-identified background groups (Cuban, Dominican, Puerto Rican, Mexican, and Central and South American). To control for variation among groups, we developed a multi-dimensional clustering method to define a "genetic-analysis group" variable that retains many properties of self-identified background while achieving substantially greater genetic homogeneity within groups and including participants with non-specific self-identification. In GWASs of 22 biomedical traits, we used a linear mixed model (LMM) including pairwise empirical KCs to account for familial relatedness, PCs for ancestry, and genetic-analysis groups for additional group-associated effects. Including the genetic-analysis group as a covariate accounted for significant trait variation in 8 of 22 traits, even after we fit 20 PCs. Additionally, genetic-analysis groups had significant heterogeneity of residual variance for 20 of 22 traits, and modeling this heteroscedasticity within the LMM reduced genomic inflation for 19 traits. Furthermore, fitting an LMM that utilized a genetic-analysis group rather than a self-identified background group achieved higher power to detect previously reported associations. We expect that the methods applied here will be useful in other studies with multiple ethnic groups, admixture, and relatedness.

Figure 1

Hyper-ellipsoids Capturing Most of the Genetic Variation within Each Self-Identified Background Group To illustrate the definition of genetic-analysis groups, here we use only PCs 1–3, although the full definition used PCs 1–5. These PCs are from PCA of all individuals except those with high East Asian ancestry. 3D hyper-ellipsoids, containing the highest density of points (defined by MCD), are shown for self-identified Mexican, Central American, and Puerto Rican background groups (Cuban, Dominican, and South American background groups are omitted for visual clarity).

Figure 2

Continental-Ancestry Proportions for the Autosomes and X Chromosome (A) Estimates of continental-ancestry proportions on the autosomes for an unrelated set of HCHS/SOL individuals are grouped by self-identified background, and the number of unrelated individuals is shown for each group. Each vertical bar represents a single individual, and the three color-coded segments represent the three ancestry fractions. (B) Boxplots show distributions of estimates of continental-ancestry proportions within each self-identified background group for the autosomes (from A) and the X chromosome. The same individuals (excluding 15 individuals with X chromosome anomalies) were used for calculating X chromosome estimates and autosome estimates.

Figure 3

PCs of HCHS/SOL Participants PCs were calculated with all individuals except for outliers with high East Asian ancestry. Color coding is by self-identified background. “Other” includes subjects who self-identified as having multiple or other backgrounds or had missing values. (A) Scatter plot of PCs 1 and 2. Each point represents one individual. Regions representing high proportions of one continental ancestry are labeled. The three vertices of the triangle represent a high fraction of ancestry from each of the three continents, as determined by projecting control samples genotyped with the study samples (data not shown). (B) Plot of parallel coordinates for the first 12 PCs. The 12 parallel vertical lines of equal length correspond to the first 12 PCs. Each individual is represented by a set of line segments connecting his or her PC values. The percentage of variance accounted for is given for each PC in the abscissa labels.

Figure 4

3D Plots of PCs of HCHS/SOL Participants The PCA included all study participants except for outliers with high East Asian ancestry. (A) PCs 1–3 with color coding by self-identified background. (B) PCs 3–5 with color coding as in (A). (C) PCs 3–5 with color coding by country of grandparent origin for individuals who self-identified as having a South American background. (D–F) PCs 1, 2, and 3 with color coding based on the ADMIXTURE estimates of continental-ancestry proportions from Africa (D), America (E), and Europe (F), respectively.

Figure 5

PCs by Country of Grandparental Origin PCs were calculated with unrelated individuals whose four grandparents originated from the same country (according to participant reporting). The SNP set used for PCA was identical to that used for the overall PCA that excluded individuals with high East Asian ancestry. (A) PCA of individuals with grandparents from Central America, Colombia, or Venezuela. A group of 37 outliers with high proportions of African ancestry (see Subjects and Methods) were excluded. (B) Map showing geographical location of countries with grandparent origins of the individuals in (A). (C) PCA of individuals with grandparents from the given South American countries. Two outliers were excluded. (D) Map showing geographical location of countries with grandparent origins of the individuals in (C). The two PCs that most clearly separate the countries are shown in (A) and (C). Pairwise plots of other PCs are shown in Figures S6 and S7.

Figure 6

PCs Showing Genetic Similarity between Individuals with and without a Specific Self-Identified Background Group The same PCs were calculated with all individuals except for outliers with high East Asian ancestry. Individuals self-identifying as one of the six specific background groups are tan, and individuals in the “other” group are red. (A) PCs 1 and 2 and all of the “other” individuals (i.e., those with multiple, other, or missing responses) are plotted on top of those who self-identified with one of the six specific backgrounds. (B) Plot of parallel coordinates for the first 12 PCs, including transparent colors and a random plot order. The percentage of variance accounted for is given for each PC in the abscissa labels.

Figure 7

More Genetic Homogeneity in the Mexican Genetic-Analysis Group Than in the Mexican Self-Identified Background Group (A) Distributions of the Mahalanobis distances between an individual in the five-dimensional PC space and the center of the Mexican hyper-ellipsoid. The four boxplots include individuals who belong to (1) both the Mexican self-identified background group and the Mexican genetic-analysis group, (2) the Mexican self-identified background group and another (not Mexican) genetic-analysis group, (3) one of the other (not Mexican) specific self-identified background groups and the Mexican genetic-analysis group, and (4) the “other” (i.e., multiple, other, or missing values) self-identified background group and the Mexican genetic-analysis group. The red line indicates the distance from the Mexican hyper-ellipsoid boundary to its center, which was one of the criteria for defining genetic-analysis groups. (B and C) Plots of parallel coordinates for individuals in either the Mexican self-identified background or the Mexican genetic-analysis group (PCs are from the PCA of all individuals except the outliers with high East Asian ancestry). The scaling of PCs is the same for both plots. Panel (B) shows only individuals in the Mexican self-identified background group and distinguishes those who are also in the Mexican genetic-analysis group from those who are not. Panel (C) shows only individuals who are in the Mexican genetic-analysis group and distinguishes those who are also in the Mexican self-identified background group from those who are not. Panel (B) shows that individuals who are in the self-identified Mexican background group but not the Mexican genetic-analysis group (red) tend to be outliers for one or a combination of PCs, whereas panel (C) shows that individuals who are in the self-identified non-Mexican background group and the Mexican genetic-analysis group are not outliers.

Figure 8

Relationship between Genomic Inflation Factor and Heteroscedasticity among Genetic-Analysis Groups in a Pooled GWAS Each point in these plots is from a GWAS of 1 of 22 biomedical traits (see Table S1). (A) A measure of the degree of heteroscedasticity (the CV of residual variance by group) versus a measure of genomic inflation, λ_GC. Both were calculated from data pooled over the six genetic-analysis groups in an LMM analylsis assuming homoscedasticity. (B) λ_GC is compared between a pooled LMM analysis that assumed homoscedasticity and a pooled LMM analysis that modeled the heterogeneous variances among the six genetic-analysis groups. (See Table S5 for the plotted data labeled by trait name.) All λ_GC values were calculated with autosomal SNPs filtered by an effective minor allele count, N_eff > 120, as described in Subjects and Methods. All models included age, sex, center, sampling weight, genetic-analysis group, and other trait-specific fixed effects, plus random effects for household, block group, and polygenic effects due to relatedness. The same sample set was used for both the homoscedastic and heteroscedastic models for each trait. Medians of 1,898,000 genotyped SNPs and 12,030,000 imputed SNPs were used in the λ_GC calculations.

Figure 9

Residual Variance Components in LMM Regression with Samples Pooled over Genetic-Analysis Groups For each of 22 biomedical traits, the “pooled” residual variance component was estimated with an LMM that included fixed effects for sex, age, center, sampling weight, genetic-analysis group, and PCs 1–5 and random effects for block group, household, and polygenic effects due to relatedness. In some cases, trait-specific fixed-effect covariates were also included. A second LMM that allowed for heterogeneous residual variance by background group was run. In each panel, the gray box shows the estimate for the residual variance component from the model that assumed homoscedasticity. The colored boxes show the estimated residual variance components by group in the heteroscedastic model in relation to the residual variance component from the homoscedastic model. The range of each box shows the 95% confidence interval. The p value from a likelihood-ratio test, with a null hypothesis of no heterogeneity, is also given for each trait.

Figure 10

Using Models with Different Ethnic-Group Definitions to Compare Wald Test Statistics for the Effects of SNPs with Previously Published Trait Associations For 12 biomedical traits, we performed association tests to assess power to detect known hits from the literature for models utilizing genetic-analysis groups, self-identified background groups, or no group variable. See Table S7 for SNP numbers and citations for each trait. All models included adjustment for sex, center, age, sampling weight, PCs 1–5, and trait-specific covariates. Random effects included block group, household, and genetic relatedness. The models also included genetic-analysis groups (“gengrp,” up to 12,747 subjects), self-reported background groups (“background,” up to 12,359 subjects), or no group variable (“no group,” up to 12,784 subjects) as a covariate. For models using a group variable (“gengrp” and “background”), heterogeneous residual variance was fit by that group. (A) For example, for log(BMI), the test statistics for 104 previously published SNP effects for the “no group” and “background” models are each plotted against the test statistics for the “gengrp” model. The test statistics for each model were divided by λ_GC from that model to provide genomic control. The solid lines show the linear regression (through the origin) of the “other” (i.e., “background” or “no group”) test statistics on the “gengrp” test statistics, and the gray bands around the lines show the 95% confidence intervals. The thin gray line is x = y. The text on the plot gives the fitted slopes and their SEs. A slope less than 1 indicates a higher likelihood of detecting hits in the “gengrp” model than in the “other” model. (B) Slopes from the linear regression of test statistics (as in A) for 12 different traits. The label “background ∼ gengrp” refers to regression of the test statistic from the background-group model on that for the genetic-analysis- group model. Similarly, “no group ∼ gengrp” refers to regression of the test statistic from the no-group model on that for the genetic-analysis-group model. Each point represents the slope for one trait and one model comparison, and its error bar represents the 95% confidence interval. The colored box for each model comparison shows the mean of the slopes from all 12 traits and its 95% confidence interval (see Table S7 for numeric values). The red points all have a slope less than 1.0 (mean 0.964), indicating that the background-group model has less power to detect previously detected GWAS hits than the genetic-analysis-group model. The blue points are scattered above and below a slope of 1.0 (mean 0.998), indicating no consistent difference in power between the no-group and genetic-analysis-group models.