A new test for trait mean and variance detects unreported loci for blood-pressure variation.

Variability in quantitative traits has clinical, ecological, and evolutionary significance. Most genetic variants identified for complex quantitative traits have only a detectable effect on the mean of trait. We have developed the mean-variance test (MVtest) to simultaneously model the mean and log-variance of a quantitative trait as functions of genotypes and covariates by using estimating equations. The advantages of MVtest include the facts that it can detect effect modification, that multiple testing can follow conventional thresholds, that it is robust to non-normal outcomes, and that association statistics can be meta-analyzed. In simulations, we show control of type I error of MVtest over several alternatives. We identified 51 and 37 previously unreported associations for effects on blood-pressure variance and mean, respectively, in the UK Biobank. Transcriptome-wide association studies revealed 633 significant unique gene associations with blood-pressure mean variance. MVtest is broadly applicable to studies of complex quantitative traits and provides an important opportunity to detect novel loci.

Genome-Wide Association Study of CKD Progression.

SIGNIFICANCE STATEMENT: Rapid progression of CKD is associated with poor clinical outcomes. Most previous studies looking for genetic factors associated with low eGFR have used cross-sectional data. The authors conducted a meta-analysis of genome-wide association studies of eGFR decline among 116,870 participants with CKD, focusing on longitudinal data. They identified three loci (two of them novel) associated with longitudinal eGFR decline. In addition to the known UMOD/PDILT locus, variants within BICC1 were associated with significant differences in longitudinal eGFR slope. Variants within HEATR4 also were associated with differences in eGFR decline, but only among Black/African American individuals without diabetes. These findings help characterize molecular mechanisms of eGFR decline in CKD and may inform new therapeutic approaches for progressive kidney disease. BACKGROUND: Rapid progression of CKD is associated with poor clinical outcomes. Despite extensive study of the genetics of cross-sectional eGFR, only a few loci associated with eGFR decline over time have been identified. METHODS: We performed a meta-analysis of genome-wide association studies of eGFR decline among 116,870 participants with CKD-defined by two outpatient eGFR measurements of <60 ml/min per 1.73 m 2 , obtained 90-365 days apart-from the Million Veteran Program and Vanderbilt University Medical Center's DNA biobank. The primary outcome was the annualized relative slope in outpatient eGFR. Analyses were stratified by ethnicity and diabetes status and meta-analyzed thereafter. RESULTS: In cross-ancestry meta-analysis, the strongest association was rs77924615, near UMOD / PDILT ; each copy of the G allele was associated with a 0.30%/yr faster eGFR decline ( P = 4.9×10 -27 ). We also observed an association within BICC1 (rs11592748), where every additional minor allele was associated with a 0.13%/yr slower eGFR decline ( P = 5.6×10 -9 ). Among participants without diabetes, the strongest association was the UMOD/PDILT variant rs36060036, associated with a 0.27%/yr faster eGFR decline per copy of the C allele ( P = 1.9×10 -17 ). Among Black participants, a significantly faster eGFR decline was associated with variant rs16996674 near APOL1 (R 2 =0.29 with the G1 high-risk genotype); among Black participants with diabetes, lead variant rs11624911 near HEATR4 also was associated with a significantly faster eGFR decline. We also nominally replicated loci with known associations with eGFR decline, near PRKAG2, FGF5, and C15ORF54. CONCLUSIONS: Three loci were significantly associated with longitudinal eGFR change at genome-wide significance. These findings help characterize molecular mechanisms of eGFR decline and may contribute to the development of new therapeutic approaches for progressive CKD.

Uterine leiomyomata and keloids fibrosis origins: a mini-review of fibroproliferative diseases.

Diseases such as uterine leiomyomata (fibroids and benign tumors of the uterus) and keloids (raised scars) may share common etiology. Fibroids and keloids can co-occur in individuals, and both are highly heritable, suggesting they may share common genetic risk factors. Fibroproliferative diseases are common and characterized by scarring and overgrowth of connective tissue, impacting multiple organ systems. These conditions both have racial disparities in prevalence, with the highest prevalence observed among individuals of African ancestry. Several fibroproliferative diseases are more severe and common in populations of sub-Saharan Africa. This mini-review aims to provide a broad overview of the current knowledge of the evolutionary origins and causes of fibroproliferative diseases. We also discuss current hypotheses proposing that the increased prevalence of these diseases in African-derived populations is due to the selection for profibrotic alleles that are protective against helminth infections and provide examples from knowledge of uterine fibroid and keloid research.

Uterine fibroid polygenic risk score (PRS) associates and predicts risk for uterine fibroid.

Uterine fibroids (UF) are common pelvic tumors in women, heritable, and genome-wide association studies (GWAS) have identified ~ 30 loci associated with increased risk in UF. Using summary statistics from a previously published UF GWAS performed in a non-Hispanic European Ancestry (NHW) female subset from the Electronic Medical Records and Genomics (eMERGE) Network, we constructed a polygenic risk score (PRS) for UF. UF-PRS was developed using PRSice and optimized in the separate clinical population of BioVU. PRS was validated using parallel methods of 10-fold cross-validation logistic regression and phenome-wide association study (PheWAS) in a seperate subset of eMERGE NHW females (validation set), excluding samples used in GWAS. PRSice determined pt < 0.001 and after linkage disequilibrium pruning (r2 < 0.2), 4458 variants were in the PRS which was significant (pseudo-R2 = 0.0018, p = 0.041). 10-fold cross-validation logistic regression modeling of validation set revealed the model had an area under the curve (AUC) value of 0.60 (95% confidence interval [CI] 0.58-0.62) when plotted in a receiver operator curve (ROC). PheWAS identified six phecodes associated with the PRS with the most significant phenotypes being 218 'benign neoplasm of uterus' and 218.1 'uterine leiomyoma' (p = 1.94 × 10-23, OR 1.31 [95% CI 1.26-1.37] and p = 3.50 × 10-23, OR 1.32 [95% CI 1.26-1.37]). We have developed and validated the first PRS for UF. We find our PRS has predictive ability for UF and captures genetic architecture of increased risk for UF that can be used in further studies.

Association Between Genetic Variation in Blood Pressure and Increased Lifetime Risk of Peripheral Artery Disease.

[Figure: see text].

Evidence that geographic variation in genetic ancestry associates with uterine fibroids.

Uterine fibroids disproportionately impact Black women. Evidence suggests Black women have earlier onset and higher cumulative risk. This risk disparity may be due an imbalance of risk alleles in one parental geographic ancestry subgroup relative to others. We investigated ancestry proportions for the 1000 Genomes phase 3 populations clustered into six geographic groups for association with fibroid traits in Black women (n = 583 cases, 797 controls) and White women (n = 1195 cases, 1164 controls). Global ancestry proportions were estimated using ADMIXTURE. Dichotomous (fibroids status and multiple fibroid status) and continuous outcomes (volume and largest dimension) were modeled for association with ancestry proportions using logistic and linear regression adjusting for age. Effect estimates are reported per 10% increase in genetically inferred ancestry proportion. Among Black women, West African (WAFR) ancestry was associated with fibroid risk, East African ancestry was associated with risk of multiple fibroids, Northern European (NEUR) ancestry was protective for multiple fibroids, Southern European ancestry was protective for fibroids and multiple fibroids, and South Asian (SAS) ancestry was positively associated with volume and largest dimension. In White women, NEUR ancestry was protective for fibroids, SAS ancestry was associated with fibroid risk, and WAFR ancestry was positively associated with volume and largest dimension. These results suggest that a proportion of fibroid risk and fibroid trait racial disparities are due to genetic differences between geographic groups. Further investigation at the local ancestry and single variant levels may yield novel insights into disease architecture and genetic mechanisms underlying ethnic disparities in fibroid risk.

Genome-wide interaction with the insulin secretion locus MTNR1B reveals CMIP as a novel type 2 diabetes susceptibility gene in African Americans.

Although type 2 diabetes (T2D) results from metabolic defects in insulin secretion and insulin sensitivity, most of the genetic risk loci identified to date relates to insulin secretion. We reported that T2D loci influencing insulin sensitivity may be identified through interactions with insulin secretion loci, thereby leading to T2D. Here, we hypothesize that joint testing of variant main effects and interaction effects with an insulin secretion locus increases power to identify genetic interactions leading to T2D. We tested this hypothesis with an intronic MTNR1B SNP, rs10830963, which is associated with acute insulin response to glucose, a dynamic measure of insulin secretion. rs10830963 was tested for interaction and joint (main + interaction) effects with genome-wide data in African Americans (2,452 cases and 3,772 controls) from five cohorts. Genome-wide genotype data (Affymetrix Human Genome 6.0 array) was imputed to a 1000 Genomes Project reference panel. T2D risk was modeled using logistic regression with rs10830963 dosage, age, sex, and principal component as predictors. Joint effects were captured using the Kraft two degrees of freedom test. Genome-wide significant (P < 5 × 10-8 ) interaction with MTNR1B and joint effects were detected for CMIP intronic SNP rs17197883 (Pinteraction  = 1.43 × 10-8 ; Pjoint  = 4.70 × 10-8 ). CMIP variants have been nominally associated with T2D, fasting glucose, and adiponectin in individuals of East Asian ancestry, with high-density lipoprotein, and with waist-to-hip ratio adjusted for body mass index in Europeans. These data support the hypothesis that additional genetic factors contributing to T2D risk, including insulin sensitivity loci, can be identified through interactions with insulin secretion loci.

Genetic Risk Score Is Associated With Prevalence of Advanced Neoplasms in a Colorectal Cancer Screening Population.

BACKGROUND & AIMS: The presence of specific single nucleotide polymorphisms (SNPs) can be used to calculate an individual's risk for colorectal cancer (CRC), called a genetic risk score (GRS). We investigated whether GRS can identify individuals with clinically relevant neoplasms in a screening colonoscopy population. METHODS: We derived a GRS based on 48 SNPs associated with CRC, identified in a comprehensive literature search. We obtained genetic data from 1043 participants (50-79 years old) in a screening colonoscopy study in Germany, recruited from 2005 through 2013 (294 with advanced neoplasms, 249 with non-advanced adenoma (NAAs), and 500 without neoplasms). Each participant was assigned a GRS by aggregating their risk alleles (0, 1, or 2). Risk of advanced neoplasms and NAA according to GRS was calculated by multiple logistic regression. Risk advancement periods were calculated. We replicated our findings using data from a subset of the Tennessee Colorectal Polyp Study. RESULTS: An increased GRS was associated with higher prevalence of advanced neoplasms, but not NAAs. Participants in the middle and upper tertiles of GRS had a 2.2-fold and 2.7-fold increase in risk, respectively, of advanced neoplasms compared to those in the lower tertile. Adjusted odds ratios (ORs) were 1.09 (95% confidence interval [CI], 0.76-1.57) for NAA in the middle tertile and 1.05 (95% CI, 0.70-1.55) for NAA in the upper tertile. The ORs were largest for proximal advanced neoplasms for participants in the middle tertile (OR, 3.55; 95% CI 1.85-6.82) and the upper tertile (OR, 3.61; 95% CI 1.84-7.10). The risk advancement period for medium vs low GRS was 13.4 years (95% CI 4.8-22.0) and for high vs low GRS was 17.5 years (95% CI, 7.8-27.3). CONCLUSIONS: In a genetic analysis of participants in a CRC screening study in Germany, an increased GRS (based on CRC-associated SNPs) was associated with increased prevalence of advanced neoplasms. These findings might be used in defining risk-adapted screening ages.

Gene-based evaluation of low-frequency variation and genetically-predicted gene expression impacting risk of keloid formation.

Keloids are benign dermal tumors occurring approximately 20 times more often in individuals of African descent as compared to individuals of European descent. While most keloids occur sporadically, a genetic predisposition is supported by both familial aggregation of some keloids and large differences in risk among populations. Despite Africans and African Americans being at increased risk over lighter-skinned individuals, little genetic research exists into this phenotype. Using a combination of admixture mapping and exome analysis, we reported multiple common variants within chr15q21.2-22.3 associated with risk of keloid formation in African Americans. Here we describe a gene-based association analysis using 478 African American samples with exome genotyping data to identify genes containing low-frequency variants associated with keloids, with evaluation of genetically-predicted gene expression in skin tissues using association summary statistics. The strongest signal from gene-based association was located in C15orf63 (P-value = 6.6 × 10-6 ) located at 15q15.3. The top result from gene expression was increased predicted DCAF4 expression (P-value = 5.5 × 10-4 ) in non-sun-exposed skin, followed by increased predicted OR10A3 expression in sun-exposed skin (P-value = 6.9 × 10-4 ). Our findings identify variation with putative roles in keloid formation, enhanced by the use of predicted gene expression to support the biological roles of variation identified only though genetic association studies.

Race/Ethnicity and the Pharmacogenetics of Reported Suicidality With Efavirenz Among Clinical Trials Participants.

Background: We examined associations between suicidality and genotypes that predict plasma efavirenz exposure among AIDS Clinical Trials Group study participants in the United States. Methods: Four clinical trials randomly assigned treatment-naive participants to efavirenz-containing regimens; suicidality was defined as reported suicidal ideation or attempted or completed suicide. Genotypes that predict plasma efavirenz exposure were defined by CYP2B6 and CYP2A6 polymorphisms. Associations were evaluated with weighted Cox proportional hazards models stratified by race/ethnicity. Additional analyses adjusted for genetic ancestry and selected covariates. Results: Among 1833 participants, suicidality was documented in 41 in exposed analyses, and 34 in on-treatment analyses. In unadjusted analyses based on 12 genotype levels, suicidality increased per level in exposed (hazard ratio, 1.11; 95% confidence interval, .96-1.27) and on-treatment 1.16; 1.01-1.34) analyses. In the on-treatment analysis, the association was strongest among white but nearly null among black participants. Considering 3 metabolizer levels (extensive, intermediate and slow), slow metabolizers were at increased risk. Results were similar after baseline covariate-adjustment for genetic ancestry, sex, age, weight, injection drug use history, and psychiatric history or recent psychoactive medication. Conclusions: Genotypes that predict higher plasma efavirenz exposure were associated with increased risk of suicidality. Strength of association varied by race/ethnicity.

Analysis of Whole Exome Sequencing with Cardiometabolic Traits Using Family-Based Linkage and Association in the IRAS Family Study.

Family-based methods are a potentially powerful tool to identify trait-defining genetic variants in extended families, particularly when used to complement conventional association analysis. We utilized two-point linkage analysis and single variant association analysis to evaluate whole exome sequencing (WES) data from 1205 Hispanic Americans (78 families) from the Insulin Resistance Atherosclerosis Family Study. WES identified 211,612 variants above the minor allele frequency threshold of ≥0.005. These variants were tested for linkage and/or association with 50 cardiometabolic traits after quality control checks. Two-point linkage analysis yielded 10,580,600 logarithm of the odds (LOD) scores with 1148 LOD scores ≥3, 183 LOD scores ≥4, and 29 LOD scores ≥5. The maximal novel LOD score was 5.50 for rs2289043:T>C, in UNC5C with subcutaneous adipose tissue volume. Association analysis identified 13 variants attaining genome-wide significance (P < 5 × 10-08 ), with the strongest association between rs651821:C>T in APOA5 and triglyceride levels (P = 3.67 × 10-10 ). Overall, there was a 5.2-fold increase in the number of informative variants detected by WES compared to exome chip analysis in this population, nearly 30% of which were novel variants relative to the Database of Single Nucleotide Polymorphisms (dbSNP) build 138. Thus, integration of results from two-point linkage and single-variant association analysis from WES data enabled identification of novel signals potentially contributing to cardiometabolic traits.

A multi-stage genome-wide association study of uterine fibroids in African Americans.

Uterine fibroids are benign tumors of the uterus affecting up to 77% of women by menopause. They are the leading indication for hysterectomy, and account for $34 billion annually in the United States. Race/ethnicity and age are the strongest known risk factors. African American (AA) women have higher prevalence, earlier onset, and larger and more numerous fibroids than European American women. We conducted a multi-stage genome-wide association study (GWAS) of fibroid risk among AA women followed by in silico genetically predicted gene expression profiling of top hits. In Stage 1, cases and controls were confirmed by pelvic imaging, genotyped and imputed to 1000 Genomes. Stage 2 used self-reported fibroid and GWAS data from 23andMe, Inc. and the Black Women's Health Study. Associations with fibroid risk were modeled using logistic regression adjusted for principal components, followed by meta-analysis of results. We observed a significant association among 3399 AA cases and 4764 AA controls at rs739187 (risk-allele frequency = 0.27) in CYTH4 (OR (95% confidence interval) = 1.23 (1.16-1.30), p value = 7.82 × 10-9). Evaluation of the genetic association results with MetaXcan identified lower predicted gene expression of CYTH4 in thyroid tissue as significantly associated with fibroid risk (p value = 5.86 × 10-8). In this first multi-stage GWAS for fibroids among AA women, we identified a novel risk locus for fibroids within CYTH4 that impacts gene expression in thyroid and has potential biological relevance for fibroids.

Erratum to: A multi-stage genome-wide association study of uterine fibroids in African Americans.

The article "A multi-stage genome-wide association study of uterine fibroids in African Americans", written by Jacklyn N. Hellwege, was originally published Online First without open access. After publication in volume 136, issue 10, page 1363-1373 the author decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to

Genome-wide linkage and association analysis of cardiometabolic phenotypes in Hispanic Americans.

Linkage studies of complex genetic diseases have been largely replaced by genome-wide association studies, due in part to limited success in complex trait discovery. However, recent interest in rare and low-frequency variants motivates re-examination of family-based methods. In this study, we investigated the performance of two-point linkage analysis for over 1.6 million single-nucleotide polymorphisms (SNPs) combined with single variant association analysis to identify high impact variants, which are both strongly linked and associated with cardiometabolic traits in up to 1414 Hispanics from the Insulin Resistance Atherosclerosis Family Study (IRASFS). Evaluation of all 50 phenotypes yielded 83 557 000 LOD (logarithm of the odds) scores, with 9214 LOD scores ⩾3.0, 845 ⩾4.0 and 89 ⩾5.0, with a maximal LOD score of 6.49 (rs12956744 in the LAMA1 gene for tumor necrosis factor-α (TNFα) receptor 2). Twenty-seven variants were associated with P<0.005 as well as having an LOD score >4, including variants in the NFIB gene under a linkage peak with TNFα receptor 2 levels on chromosome 9. Linkage regions of interest included a broad peak (31 Mb) on chromosome 1q with acute insulin response (max LOD=5.37). This region was previously documented with type 2 diabetes in family-based studies, providing support for the validity of these results. Overall, we have demonstrated the utility of two-point linkage and association in comprehensive genome-wide array-based SNP genotypes.

Obesity and pelvic organ prolapse: a systematic review and meta-analysis of observational studies.

BACKGROUND: Studies evaluating the association between obesity and pelvic organ prolapse report estimates that range from negative to positive associations. Heterogeneous definitions for pelvic organ prolapse and variable choices for categorizing obesity measures have made it challenging to conduct meta-analysis. OBJECTIVE: We systematically evaluated evidence to provide quantitative summaries of association between degrees of obesity and pelvic organ prolapse, and identify sources of heterogeneity. STUDY DESIGN: We searched for all indexed publications relevant to pelvic organ prolapse up until June 18, 2015, in PubMed/MEDLINE to identify analytical observational studies published in English that reported risk ratios (relative risk, odds ratio, or hazard ratio) for body mass index categories in relation to pelvic organ prolapse. Random effects meta-analyses were conducted to report associations with pelvic organ prolapse for overweight and obese body mass index categories compared with women in the normal-weight category (referent: body mass index <25 kg/m2). RESULTS: Of the 70 studies that reported evidence on obesity and pelvic organ prolapse, 22 eligible studies provided effect estimates for meta-analysis of the overweight and obese body mass index categories. Compared with the referent category, women in the overweight and obese categories had meta-analysis risk ratios of at least 1.36 (95% confidence interval, 1.20-1.53) and at least 1.47 (95% confidence interval, 1.35-1.59), respectively. Subgroup analyses showed effect estimates for objectively measured clinically significant pelvic organ prolapse were higher than for self-reported pelvic organ prolapse. Other potential sources of heterogeneity included proportion of postmenopausal women in study and reported study design. CONCLUSION: Overweight and obese women are more likely to have pelvic organ prolapse compared with women with body mass index in the normal range. The finding that the associations for obesity measures were strongest for objectively measured, clinically significant pelvic organ prolapse further strengthens this evidence. However, prospective investigations evaluating obesity and pelvic organ prolapse are few.

Genome-wide family-based linkage analysis of exome chip variants and cardiometabolic risk.

Linkage analysis of complex traits has had limited success in identifying trait-influencing loci. Recently, coding variants have been implicated as the basis for some biomedical associations. We tested whether coding variants are the basis for linkage peaks of complex traits in 42 African-American (n = 596) and 90 Hispanic (n = 1,414) families in the Insulin Resistance Atherosclerosis Family Study (IRASFS) using Illumina HumanExome Beadchips. A total of 92,157 variants in African Americans (34%) and 81,559 (31%) in Hispanics were polymorphic and tested using two-point linkage and association analyses with 37 cardiometabolic phenotypes. In African Americans 77 LOD scores greater than 3 were observed. The highest LOD score was 4.91 with the APOE SNP rs7412 (MAF = 0.13) with plasma apolipoprotein B (ApoB). This SNP was associated with ApoB (P-value = 4 × 10(-19)) and accounted for 16.2% of the variance in African Americans. In Hispanic families, 104 LOD scores were greater than 3. The strongest evidence of linkage (LOD = 4.29) was with rs5882 (MAF = 0.46) in CETP with HDL. CETP variants were strongly associated with HDL (0.00049 < P-value <4.6 × 10(-12)), accounting for up to 4.5% of the variance. These loci have previously been shown to have effects on the biomedical traits evaluated here. Thus, evidence of strong linkage in this genome wide survey of primarily coding variants was uncommon. Loci with strong evidence of linkage was characterized by large contributions to the variance, and, in these cases, are common variants. Less compelling evidence of linkage and association was observed with additional loci that may require larger family sets to confirm.

Empirical characteristics of family-based linkage to a complex trait: the ADIPOQ region and adiponectin levels.

We previously identified a low-frequency (1.1 %) coding variant (G45R; rs200573126) in the adiponectin gene (ADIPOQ) which was the basis for a multipoint microsatellite linkage signal (LOD = 8.2) for plasma adiponectin levels in Hispanic families. We have empirically evaluated the ability of data from targeted common variants, exome chip genotyping, and genome-wide association study data to detect linkage and association to adiponectin protein levels at this locus. Simple two-point linkage and association analyses were performed in 88 Hispanic families (1,150 individuals) using 10,958 SNPs on chromosome 3. Approaches were compared for their ability to map the functional variant, G45R, which was strongly linked (two-point LOD = 20.98) and powerfully associated (p value = 8.1 × 10(-50)). Over 450 SNPs within a broad 61 Mb interval around rs200573126 showed nominal evidence of linkage (LOD > 3) but only four other SNPs in this region were associated with p values < 1.0 × 10(-4). When G45R was accounted for, the maximum LOD score across the interval dropped to 4.39 and the best p value was 1.1 × 10(-5). Linked and/or associated variants ranged in frequency (0.0018-0.50) and type (coding, non-coding) and had little detectable linkage disequilibrium with rs200573126 (r (2) < 0.20). In addition, the two-point linkage approach empirically outperformed multipoint microsatellite and multipoint SNP analysis. In the absence of data for rs200573126, family-based linkage analysis using a moderately dense SNP dataset, including both common and low-frequency variants, resulted in stronger evidence for an adiponectin locus than association data alone. Thus, linkage analysis can be a useful tool to facilitate identification of high-impact genetic variants.

Session Introduction: Drug-repurposing and discovery in the era of "big" real-world data: how the incorporation of observational data, genetics, and other -omic technologies can move us forward.

This PSB 2024 session discusses the many broad biological, computational, and statistical approaches currently being used for therapeutic drug target identification and repurposing of existing treatments. Drug repurposing efforts have the potential to dramatically improve the treatment landscape by more rapidly identifying drug targets and alternative strategies for untreated or poorly managed diseases. The overarching theme for this session is the use and integration of real-world data to identify drug-disease pairs with potential therapeutic use. These drug-disease pairs may be identified through genomic, proteomic, biomarkers, protein interaction analyses, electronic health records, and chemical profiling. Taken together, this session combines novel applications of methods and innovative modeling strategies with diverse real-world data to suggest new pharmaceutical treatments for human diseases.

EVALUATING THE RELATIONSHIPS BETWEEN GENETIC ANCESTRY AND THE CLINICAL PHENOME.

There is a desire in research to move away from the concept of race as a clinical factor because it is a societal construct used as an imprecise proxy for geographic ancestry. In this study, we leverage the biobank from Vanderbilt University Medical Center, BioVU, to investigate relationships between genetic ancestry proportion and the clinical phenome. For all samples in BioVU, we calculated six ancestry proportions based on 1000 Genomes references: eastern African (EAFR), western African (WAFR), northern European (NEUR), southern European (SEUR), eastern Asian (EAS), and southern Asian (SAS). From PheWAS, we found phecode categories significantly enriched neoplasms for EAFR, WAFR, and SEUR, and pregnancy complication in SEUR, NEUR, SAS, and EAS (p < 0.003). We then selected phenotypes hypertension (HTN) and atrial fibrillation (AFib) to further investigate the relationships between these phenotypes and EAFR, WAFR, SEUR, and NEUR using logistic regression modeling and non-linear restricted cubic spline modeling (RCS). For EAS and SAS, we chose renal failure (RF) for further modeling. The relationships between HTN and AFib and the ancestries EAFR, WAFR, and SEUR were best fit by the linear model (beta p < 1x10-4 for all) while the relationships with NEUR were best fit with RCS (HTN ANOVA p = 0.001, AFib ANOVA p < 1x10-4). For RF, the relationship with SAS was best fit with a linear model (beta p < 1x10-4) while RCS model was a better fit for EAS (ANOVA p < 1x10-4). In this study, we identify relationships between genetic ancestry and phenotypes that are best fit with non-linear modeling techniques. The assumption of linearity for regression modeling is integral for proper fitting of a model and there is no knowing a priori to modeling if the relationship is truly linear.

Evidence of recent and ongoing admixture in the U.S. and influences on health and disparities.

Many researchers in genetics and social science incorporate information about race in their work. However, migrations (historical and forced) and social mobility have brought formerly separated populations of humans together, creating younger generations of individuals who have more complex and diverse ancestry and race profiles than older age groups. Here, we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and impact health outcomes. We evaluated variation in genetic ancestry over 100 birth years in a cohort of 35,842 individuals with electronic health record (EHR) information in the Southeastern United States. Using the software STRUCTURE, we analyzed 2,678 ancestrally informative markers relative to three ancestral clusters (African, East Asian, and European) and observed rising levels of admixture for all clinically-defined race groups since 1990. Most race groups also exhibited increases in heterozygosity and long-range linkage disequilibrium over time, further supporting the finding of increasing admixture in young individuals in our cohort. These data are consistent with United States Census information from broader geographic areas and highlight the changing demography of the population. This increased diversity challenges classic approaches to studies of genotype-phenotype relationships which motivated us to explore the relationship between heterozygosity and disease diagnosis. Using a phenome-wide association study approach, we explored the relationship between admixture and disease risk and found that increased admixture resulted in protective associations with female reproductive disorders and increased risk for diseases with links to autoimmune dysfunction. These data suggest that tendencies in the United States population are increasing ancestral complexity over time. Further, these observations imply that, because both prevalence and severity of many diseases vary by race groups, complexity of ancestral origins influences health and disparities.