Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies

Genomic ancestry and cancer among Latin Americans

Latin American populations, characterized by intricate admixture patterns resulting from the intermingling of ancestries from European, Native American (NA) Asian, and African ancestries which result in a vast and complex genetic landscape, harboring unique combinations of novel variants. This genetic diversity not only poses challenges in traditional population genetics methods but also opens avenues for a deeper understanding of its implications in health. In cancer, the interplay between genetic ancestry, lifestyle factors, and healthcare disparities adds a layer of complexity to the varying incidence and mortality rates observed across different Latin American subpopulations. This complex interdependence has been unveiled through numerous studies, whether conducted on Latin American patients residing on the continent or abroad, revealing discernible differences in germline composition that influence divergent disease phenotypes such as higher incidence of Luminal B and Her2 breast tumors, EGFR and KRAS mutated lung adenocarcinomas in addition to an enrichment in BRCA1/2 pathogenic variants and a higher than expected prevalence of variants in colorectal cancer associated genes such as APC and MLH1. In prostate cancer novel risk variants have also been solely identified in Latin American populations. Due to the complexity of genetic divergence, inputs from each individual ancestry seem to carry independent contributions that interplay in the development of these complex disease phenotypes. By understanding these unique population characteristics, genomic ancestries hold a promising avenue for tailoring prognostic assessments and optimizing responses to oncological interventions.

Identification of Genetic Variants for Diabetic Retinopathy Risk Applying Exome Sequencing in Extreme Phenotypes

Background

Diabetic retinopathy (DR) risk has been shown to vary depending on ethnic backgrounds, and thus, it is worthy that underrepresented populations are analyzed for the potential identification of DR-associated genetic variants. We conducted a case-control study for the identification of DR-risk variants in Mexican population.

Methods

We ascertained 60 type 2 diabetes mellitus (T2DM) patients. Cases (n = 30) were patients with advanced proliferative DR (PDR) with less than 15 years after a T2DM diagnosis while controls (n = 30) were patients with no DR 15 years after the diagnosis of T2DM. Exome sequencing was performed in all patients, and the frequency of rare variants was compared. In addition, the frequency of variants occurring in a set of 169 DR-associated genes were compared.

Results

Statistically significant differences were identified for rare missense and splice variants and for rare splice variants occurring more than once in either group. A strong statistical difference was observed when the number of rare missense variants with an aggregated prediction of pathogenicity and occurring more than once in either group was compared (p = 0.0035). Moreover, 8 variants identified more than once in either group, occurring in previously identified DR-associated genes were recognized. The p.Pro234Ser KIR2DS4 variant showed a strong protective effect (OR = 0.04 [0.001-0.36]; p = 0.04).

Conclusions

Our study showed an enrichment of rare splice acceptor/donor variants in patients with PDR and identified a potential protective variant in KIR2DS4. Although statistical significance was not reached, our results support the replication of 8 previously identified DR-associated genes.

Joint multi-ancestry and admixed GWAS reveals the complex genetics behind human cranial vault shape

The cranial vault in humans is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conduct a joint multi-ancestry and admixed multivariate genome-wide association study on 3D cranial vault shape extracted from magnetic resonance images of 6772 children from the ABCD study cohort yielding 30 genome-wide significant loci. Follow-up analyses indicate that these loci overlap with genomic risk loci for sagittal craniosynostosis, show elevated activity cranial neural crest cells, are enriched for processes related to skeletal development, and are shared with the face and brain. We present supporting evidence of regional localization for several of the identified genes based on expression patterns in the cranial vault bones of E15.5 mice. Overall, our study provides a comprehensive overview of the genetics underlying normal-range cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations.

Interpreting SNP heritability in admixed populations

SNP heritability is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability ( h 2 ), being equal to it if all causal variants are known. Despite the simple intuition behind , its interpretation and equivalence to h 2 is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation in estimates. Here we use analytical theory and simulations to demonstrate that estimated with genome-wide restricted maximum likelihood (GREML) can be biased in admixed populations, even in the absence of confounding and even if all causal variants are known. This is because admixture generates linkage disequilibrium (LD), which contributes to the genetic variance, and therefore to heritability. GREML implicitly assumes this component is zero, which may not be true, particularly for traits under divergent or stabilizing selection in the source populations, leading under- or over-estimates of relative to h 2 . For the same reason, GREML estimates of local ancestry heritability will also be biased. We describe the bias in and as a function of admixture history and the genetic architecture of the trait and discuss its implications for genome-wide association and polygenic prediction.

Genotyping, sequencing and analysis of 140,000 adults from Mexico City

The Mexico City Prospective Study is a prospective cohort of more than 150,000 adults recruited two decades ago from the urban districts of Coyoacán and Iztapalapa in Mexico City1. Here we generated genotype and exome-sequencing data for all individuals and whole-genome sequencing data for 9,950 selected individuals. We describe high levels of relatedness and substantial heterogeneity in ancestry composition across individuals. Most sequenced individuals had admixed Indigenous American, European and African ancestry, with extensive admixture from Indigenous populations in central, southern and southeastern Mexico. Indigenous Mexican segments of the genome had lower levels of coding variation but an excess of homozygous loss-of-function variants compared with segments of African and European origin. We estimated ancestry-specific allele frequencies at 142 million genomic variants, with an effective sample size of 91,856 for Indigenous Mexican ancestry at exome variants, all available through a public browser. Using whole-genome sequencing, we developed an imputation reference panel that outperforms existing panels at common variants in individuals with high proportions of central, southern and southeastern Indigenous Mexican ancestry. Our work illustrates the value of genetic studies in diverse populations and provides foundational imputation and allele frequency resources for future genetic studies in Mexico and in the United States, where the Hispanic/Latino population is predominantly of Mexican descent.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Strategies for the Genomic Analysis of Admixed Populations

Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations-the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.

Impact of cross-ancestry genetic architecture on GWASs in admixed populations

Genome-wide association studies (GWASs) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWASs in admixed populations, such as the need to correctly adjust for population stratification. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing a GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes, we find that controlling for and conditioning effect sizes on local ancestry can reduce statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs, HetLanc is not large enough for GWASs to benefit from modeling heterogeneity in this way.

Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture

We explored ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans. We found that heritability of gene expression significantly increased with greater proportions of African genetic ancestry and decreased with higher proportions of Indigenous American ancestry, reflecting the relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most anc-eQTLs (89%) were driven by population differences in allele frequency. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait associations using transcriptome prediction models trained in our admixed population than models trained using data from the Genotype-Tissue Expression project. Our study highlights the importance of measuring gene expression across large and ancestrally diverse populations for enabling new discoveries and reducing disparities.

Genome-wide admixture and association analysis identifies African ancestry-specific risk loci of eosinophilic esophagitis in African Americans

BACKGROUND

Eosinophilic esophagitis (EoE), a chronic allergic inflammatory disease, is linked to multiple genetic risk factors, but studies have focused on populations of European ancestry. Few studies have assessed Black or African American (AA) populations for loci involved in EoE susceptibility.

OBJECTIVE

We performed admixture mapping (AM) and genome-wide association study (GWAS) of EoE using participants from AA populations.

METHODS

We conducted AM and GWAS of EoE using 137 EoE cases and 1465 healthy controls from the AA population. Samples were genotyped using molecular evolutionary genetics analysis (MEGA). Genotype imputation was carried out with the Consortium on Asthma Among African-Ancestry Populations in the Americas (CAAPA) reference panel using the Michigan Imputation Server. Global and local ancestry inference was carried out, followed by fine mapping and RNA sequencing. After quality control filtering, over 6,000,000 variants were tested by logistic regression adjusted for sex, age, and global ancestry.

RESULTS

The global African ancestry proportion was found to be significantly lower among cases than controls (0.751 vs 0.786, P = .012). Case-only AM identified 3 significant loci (9p13.3, 12q24.22-23, and 15q11.2) associated with EoE, of which 12q24.22-23 and 9p13.3 were further replicated in the case-control analysis, with associations observed with African ancestry. Fine mapping and multiomic functional annotations prioritized the variants rs11068264 (FBXW8) and rs7307331 (VSIG10) at 12q24.23 and rs2297879 (ARHGEF39) at 9p13.3. GWAS identified 1 genome-wide significant locus at chromosome 1p22.3 (rs17131726, DDAH1) and 10 other suggestive loci. Most GWAS variants were low-frequency African ancestry-specific variants. RNA sequencing revealed that esophageal DDAH1 and VSIG10 were downregulated and ARHGEF39 upregulated among EoE cases.

CONCLUSIONS

GWAS and AM for EoE in AA revealed that African ancestry-specific genetic susceptibility loci exist at 1p22.3, 9p13.3, and 12q24.23, providing evidence of ancestry-specific inheritance of EoE. More independent genetic studies of different ancestries for EoE are needed.

Impact of cross-ancestry genetic architecture on GWAS in admixed populations

Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity.

Redundancy analysis, genome-wide association studies and the pigmentation of brown trout (Salmo trutta L.)

The association of molecular variants with phenotypic variation is a main issue in biology, often tackled with genome-wide association studies (GWAS). GWAS are challenging, with increasing, but still limited, use in evolutionary biology. We used redundancy analysis (RDA) as a complimentary ordination approach to single- and multitrait GWAS to explore the molecular basis of pigmentation variation in brown trout (Salmo trutta) belonging to wild populations impacted by hatchery fish. Based on 75,684 single nucleotide polymorphic (SNP) markers, RDA, single- and multitrait GWAS allowed the extraction of 337 independent colour patterning loci (CPLs) associated with trout pigmentation traits, such as the number of red and black spots on flanks. Collectively, these CPLs (i) mapped onto 35 out of 40 brown trout linkage groups indicating a polygenic genomic architecture of pigmentation, (ii) were found to be associated with 218 candidate genes, including 197 genes formerly mentioned in the literature associated to skin pigmentation, skin patterning, differentiation or structure notably in a close relative, the rainbow trout (Onchorhynchus mykiss), and (iii) related to functions relevant to pigmentation variation (e.g., calcium- and ion-binding, cell adhesion). Annotated CPLs include genes with well-known pigmentation effects (e.g., PMEL, SLC45A2, SOX10), but also markers associated with genes formerly found expressed in rainbow or brown trout skins. RDA was also shown to be useful to investigate management issues, especially the dynamics of trout pigmentation submitted to several generations of hatchery introgression.

Ethnic Diversity and Warfarin Pharmacogenomics

Warfarin has remained the most commonly prescribed vitamin K oral anticoagulant worldwide since its approval in 1954. Dosing challenges including having a narrow therapeutic window and a wide interpatient variability in dosing requirements have contributed to making it the most studied drug in terms of genotype-phenotype relationships. However, most of these studies have been conducted in Whites or Asians which means the current pharmacogenomics evidence-base does not reflect ethnic diversity. Due to differences in minor allele frequencies of key genetic variants, studies conducted in Whites/Asians may not be applicable to underrepresented populations such as Blacks, Hispanics/Latinos, American Indians/Alaska Natives and Native Hawaiians/other Pacific Islanders. This may exacerbate health inequalities when Whites/Asians have better anticoagulation profiles due to the existence of validated pharmacogenomic dosing algorithms which fail to perform similarly in the underrepresented populations. To examine the extent to which individual races/ethnicities are represented in the existing body of pharmacogenomic evidence, we review evidence pertaining to published pharmacogenomic dosing algorithms, including clinical utility studies, cost-effectiveness studies and clinical implementation guidelines that have been published in the warfarin field.

Polygenic risk, population structure and ongoing difficulties with race in human genetics

‘The Apportionment of Human Diversity’ stands as a noteworthy intervention, both for the field of human population genetics as well as in the annals of public communication of science. Despite the widespread uptake of Lewontin's conclusion that racial classification is of ‘virtually no genetic or taxonomic significance’, the biomedical research community continues to grapple with whether and how best to account for race in its work. Nowhere is this struggle more apparent than in the latest attempts to translate genetic associations with complex disease risk to clinical use in the form of polygenic risk scores, or PRS. In this perspective piece, we trace current challenges surrounding the appropriate development and clinical application of PRS in diverse patient cohorts to ongoing difficulties deciding which facets of population structure matter, and for what reasons, to human health. Despite numerous analytical innovations, there are reasons that emerge from Lewontin's work to remain sceptical that accounting for population structure in the context of polygenic risk estimation will allow us to more effectively identify and intervene on the significant health disparities which plague marginalized populations around the world.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

Human genetic admixture through the lens of population genomics

Over the past 50 years, geneticists have made great strides in understanding how our species' evolutionary history gave rise to current patterns of human genetic diversity classically summarized by Lewontin in his 1972 paper, ‘The Apportionment of Human Diversity’. One evolutionary process that requires special attention in both population genetics and statistical genetics is admixture: gene flow between two or more previously separated source populations to form a new admixed population. The admixture process introduces ancestry-based structure into patterns of genetic variation within and between populations, which in turn influences the inference of demographic histories, identification of genetic targets of selection and prediction of complex traits. In this review, we outline some challenges for admixture population genetics, including limitations of applying methods designed for populations without recent admixture to the study of admixed populations. We highlight recent studies and methodological advances that aim to overcome such challenges, leveraging genomic signatures of admixture that occurred in the past tens of generations to gain insights into human history, natural selection and complex trait architecture.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.