The Effects of Migration and Assortative Mating on Admixture Linkage Disequilibrium

Circum-Mediterranean influence in the Y-chromosome lineages associated with prostate cancer in Mexican men: A Converso heritage founder effect?

Prostate cancer is the second most common neoplasia amongst men worldwide. Hereditary susceptibility and ancestral heritage are well-established risk factors that explain the disparity trends across different ethnicities, populations, and regions even within the same country. The Y-chromosome has been considered a prototype biomarker for male health. African, European, Middle Eastern, and Hispanic ancestries exhibit the highest incidences of such neoplasia; Asians have the lowest rates. Nonetheless, the contribution of ancestry patterns has been scarcely explored among Latino males. The Mexican population has an extremely diverse genetic architecture where all the aforementioned ancestral backgrounds converge. Trans-ethnic research could illuminate the aetiology of prostate cancer, involving the migratory patterns, founder effects, and the ethnic contributions to its disparate incidence rates. The contribution of the ancestral heritage to prostate cancer risk were explored through a case-control study (152 cases and 372 controls) study in Mexican Mestizo males. Seventeen microsatellites were used to trace back the ancestral heritage using two Bayesian predictor methods. The lineage R1a seems to contribute to prostate cancer (ORadjusted:8.04, 95%CI:1.41-45.80) development, whereas E1b1a/E1b1b and GHIJ contributed to well-differentiated (Gleason ≤ 7), and late-onset prostate cancer. Meta-analyses reinforced our findings. The mentioned lineages exhibited a connection with the Middle Eastern and North African populations that enriched the patrilineal diversity to the southeast region of the Iberian Peninsula. This ancestral legacy arrived at the New World with the Spanish and Sephardim migrations. Our findings reinforced the contribution of family history and ethnic background to prostate cancer risk, although should be confirmed using a large sample size. Nonetheless, given its complex aetiology, in addition to the genetic component, the lifestyle and xenobiotic exposition could also influence the obtained results.

Interpreting SNP heritability in admixed populations

SNP heritabilityh s n p 2 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 behindh s n p 2 , its interpretation and equivalence toh 2 is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation inh ˆ s n p 2 estimates because of confounding due to linkage disequilibrium (LD) or shared environment. Here we use analytical theory and simulations to demonstrate thath s n p 2 estimates 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 LD, which contributes to the genetic variance, and therefore to heritability. Genome-wide restricted maximum likelihood (GREML) does not capture this contribution leading to under- or over-estimates ofh s n p 2 relative toh 2 , depending on the genetic architecture. In contrast, Haseman-Elston (HE) regression exaggerates the LD contribution leading to biases in the opposite direction. For the same reason, GREML and HE estimates of local ancestry heritabilityh γ 2 are also biased. We describe this bias inh ˆ s n p 2 andh ˆ γ 2 as a function of admixture history and the genetic architecture of the trait and show that it can be recovered under some conditions. We clarify the interpretation ofh ˆ s n p 2 in admixed populations and discuss its implication for genome-wide association studies and polygenic prediction.

Differentiating mechanism from outcome for ancestry-assortative mating in admixed human populations

Population genetic theory, and the empirical methods built upon it, often assume that individuals pair randomly for reproduction. However, natural populations frequently violate this assumption, which may potentially confound genome-wide association studies, selection scans, and demographic inference. Within several recently admixed human populations, empirical genetic studies have reported a correlation in global ancestry proportion between spouses, referred to as ancestry-assortative mating. Here, we use forward genomic simulations to link correlations in ancestry between mates to the underlying mechanistic mate-choice process. We consider the impacts of two types of mate-choice model, using either ancestry-based preferences or social groups as the basis for mate pairing. We find that multiple mate-choice models can produce the same correlations in ancestry proportion between spouses; however, we also highlight alternative analytic approaches and circumstances in which these models may be distinguished. With this work, we seek to highlight potential pitfalls when interpreting correlations in empirical data as evidence for a particular model of human mating practices, as well as to offer suggestions toward development of new best practices for analysis of human ancestry-assortative mating.

Interpreting population- and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

The genomic footprint of social stratification in admixing American populations

Cultural and socioeconomic differences stratify human societies and shape their genetic structure beyond the sole effect of geography. Despite mating being limited by sociocultural stratification, most demographic models in population genetics often assume random mating. Taking advantage of the correlation between sociocultural stratification and the proportion of genetic ancestry in admixed populations, we sought to infer the former process in the Americas. To this aim, we define a mating model where the individual proportions of the genome inherited from Native American, European, and sub-Saharan African ancestral populations constrain the mating probabilities through ancestry-related assortative mating and sex bias parameters. We simulate a wide range of admixture scenarios under this model. Then, we train a deep neural network and retrieve good performance in predicting mating parameters from genomic data. Our results show how population stratification, shaped by socially constructed racial and gender hierarchies, has constrained the admixture processes in the Americas since the European colonization and the subsequent Atlantic slave trade.

On the number of genealogical ancestors tracing to the source groups of an admixed population

Members of genetically admixed populations possess ancestry from multiple source groups, and studies of human genetic admixture frequently estimate ancestry components corresponding to fractions of individual genomes that trace to specific ancestral populations. However, the same numerical ancestry fraction can represent a wide array of admixture scenarios within an individual's genealogy. Using a mechanistic model of admixture, we consider admixture genealogically: how many ancestors from the source populations does the admixture represent? We consider African-Americans, for whom continent-level estimates produce a 75-85% value for African ancestry on average and 15-25% for European ancestry. Genetic studies together with key features of African-American demographic history suggest ranges for parameters of a simple three-epoch model. Considering parameter sets compatible with estimates of current ancestry levels, we infer that if all genealogical lines of a random African-American born during 1960-1965 are traced back until they reach members of source populations, the mean over parameter sets of the expected number of genealogical lines terminating with African individuals is 314 (interquartile range 240-376), and the mean of the expected number terminating in Europeans is 51 (interquartile range 32-69). Across discrete generations, the peak number of African genealogical ancestors occurs in birth cohorts from the early 1700s, and the probability exceeds 50% that at least one European ancestor was born more recently than 1835. Our genealogical perspective can contribute to further understanding the admixture processes that underlie admixed populations. For African-Americans, the results provide insight both on how many of the ancestors of a typical African-American might have been forcibly displaced in the Transatlantic Slave Trade and on how many separate European admixture events might exist in a typical African-American genealogy.

Interpreting population and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding, and can also absorb the 'indirect' genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of Mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect size estimates are used in polygenic scores. We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. In addition to known biases that can arise in family-based GWASs when interactions between family members are ignored, we show that biases can also arise from gene-by-environment (G×E) interactions when parental genotypes are not distributed identically across interacting environmental and genetic backgrounds. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding and interactions.

Rapid Evolution of the Fine-scale Recombination Landscape in Wild House Mouse (Mus musculus) Populations

Meiotic recombination is an important evolutionary force and an essential meiotic process. In many species, recombination events concentrate into hotspots defined by the site-specific binding of PRMD9. Rapid evolution of Prdm9's zinc finger DNA-binding array leads to remarkably abrupt shifts in the genomic distribution of hotspots between species, but the question of how Prdm9 allelic variation shapes the landscape of recombination between populations remains less well understood. Wild house mice (Mus musculus) harbor exceptional Prdm9 diversity, with >150 alleles identified to date, and pose a particularly powerful system for addressing this open question. We employed a coalescent-based approach to construct broad- and fine-scale sex-averaged recombination maps from contemporary patterns of linkage disequilibrium in nine geographically isolated wild house mouse populations, including multiple populations from each of three subspecies. Comparing maps between wild mouse populations and subspecies reveals several themes. First, we report weak fine- and broad-scale recombination map conservation across subspecies and populations, with genetic divergence offering no clear prediction for recombination map divergence. Second, most hotspots are unique to one population, an outcome consistent with minimal sharing of Prdm9 alleles between surveyed populations. Finally, by contrasting aggregate hotspot activity on the X versus autosomes, we uncover evidence for population-specific differences in the degree and direction of sex dimorphism for recombination. Overall, our findings illuminate the variability of both the broad- and fine-scale recombination landscape in M. musculus and underscore the functional impact of Prdm9 allelic variation in wild mouse populations.

Sex-biased admixture and assortative mating shape genetic variation and influence demographic inference in admixed Cabo Verdeans

Genetic data can provide insights into population history, but first, we must understand the patterns that complex histories leave in genomes. Here, we consider the admixed human population of Cabo Verde to understand the patterns of genetic variation left by social and demographic processes. First settled in the late 1400s, Cabo Verdeans are admixed descendants of Portuguese colonizers and enslaved West African people. We consider Cabo Verde's well-studied historical record alongside genome-wide SNP data from 563 individuals from 4 regions within the archipelago. We use genetic ancestry to test for patterns of nonrandom mating and sex-specific gene flow, and we examine the consequences of these processes for common demographic inference methods and genetic patterns. Notably, multiple population genetic tools that assume random mating underestimate the timing of admixture, but incorporating nonrandom mating produces estimates more consistent with historical records. We consider how admixture interrupts common summaries of genomic variation such as runs of homozygosity. While summaries of runs of homozygosity may be difficult to interpret in admixed populations, differentiating runs of homozygosity by length class shows that runs of homozygosity reflect historical differences between the islands in their contributions from the source populations and postadmixture population dynamics. Finally, we find higher African ancestry on the X chromosome than on the autosomes, consistent with an excess of European males and African females contributing to the gene pool. Considering these genomic insights into population history in the context of Cabo Verde's historical record, we can identify how assumptions in genetic models impact inference of population history more broadly.

Simultaneous inference of parental admixture proportions and admixture times from unphased local ancestry calls

Population genetic analyses of local ancestry tracts routinely assume that the ancestral admixture process is identical for both parents of an individual, an assumption that may be invalid when considering recent admixture. Here, we present Parental Admixture Proportion Inference (PAPI), a Bayesian tool for inferring the admixture proportions and admixture times for each parent of a single admixed individual. PAPI analyzes unphased local ancestry tracts and has two components: a binomial model that leverages genome-wide ancestry fractions to infer parental admixture proportions and a hidden Markov model (HMM) that infers admixture times from tract lengths. Crucially, the HMM accounts for unobserved within-ancestry recombination by approximating the pedigree crossover dynamics, enabling inference of parental admixture times. In simulations, we find that PAPI's admixture proportion estimates deviate from the truth by 0.047 on average, outperforming ANCESTOR and PedMix by 46.0% and 57.6%, respectively. Moreover, PAPI's admixture time estimates were strongly correlated with the truth (R=0.76) but have an average downward bias of 1.01 generations that is partly attributable to inaccuracies in local ancestry inference. As an illustration of its utility, we ran PAPI on African American genotypes from the PAGE study (N = 5,786) and found strong evidence of assortative mating by ancestry proportion: couples' ancestry proportions are highly correlated (R = 0.87) and are closer to each other than expected under random mating (p < 10-6). We anticipate that PAPI will be useful in studying the population dynamics of admixture and will also be of interest to individuals seeking to learn about their personal genealogies.

Assortative mating enhances postzygotic barriers to gene flow via ancestry bundling

Hybridization and subsequent genetic introgression are now known to be common features of the histories of many species, including our own. Following hybridization, selection often purges introgressed DNA genome-wide. While assortative mating can limit hybridization in the first place, it is also known to play an important role in postzygotic selection against hybrids and, thus, the purging of introgressed DNA. However, this role is usually thought of as a direct one: a tendency for mates to be conspecific reduces the sexual fitness of hybrids, reducing the transmission of introgressed ancestry. Here, we explore a second, indirect role of assortative mating as a postzygotic barrier to gene flow. Under assortative mating, parents covary in their ancestry, causing ancestry to be "bundled" in their offspring and later generations. This bundling effect increases ancestry variance in the population, enhancing the efficiency with which postzygotic selection purges introgressed DNA. Using whole-genome simulations, we show that the bundling effect can comprise a substantial portion of mate choice's overall effect as a postzygotic barrier to gene flow. We then derive a simple method for estimating the impact of the bundling effect from standard metrics of assortative mating. Applying this method to data from a diverse set of hybrid zones, we find that the bundling effect increases the purging of introgressed DNA by between 1.2-fold (in a baboon system with weak assortative mating) and 14-fold (in a swordtail system with strong assortative mating). Thus, assortative mating's bundling effect contributes substantially to the genetic isolation of species.

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'.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Skin deep: The decoupling of genetic admixture levels from phenotypes that differed between source populations

OBJECTIVES

In genetic admixture processes, source groups for an admixed population possess distinct patterns of genotype and phenotype at the onset of admixture. Particularly in the context of recent and ongoing admixture, such differences are sometimes taken to serve as markers of ancestry for individuals-that is, phenotypes initially associated with the ancestral background in one source population are assumed to continue to reflect ancestry in that population. Such phenotypes might possess ongoing significance in social categorizations of individuals, owing in part to perceived continuing correlations with ancestry. However, genotypes or phenotypes initially associated with ancestry in one specific source population have been seen to decouple from overall admixture levels, so that they no longer serve as proxies for genetic ancestry. Here, we aim to develop an understanding of the joint dynamics of admixture levels and phenotype distributions in an admixed population.

METHODS

We devise a mechanistic model, consisting of an admixture model, a quantitative trait model, and a mating model. We analyze the behavior of the mechanistic model in relation to the model parameters.

RESULTS

We find that it is possible for the decoupling of genetic ancestry and phenotype to proceed quickly, and that it occurs faster if the phenotype is driven by fewer loci. Positive assortative mating attenuates the process of dissociation relative to a scenario in which mating is random with respect to genetic admixture and with respect to phenotype.

CONCLUSIONS

The mechanistic framework suggests that in an admixed population, a trait that initially differed between source populations might serve as a reliable proxy for ancestry for only a short time, especially if the trait is determined by few loci. It follows that a social categorization based on such a trait is increasingly uninformative about genetic ancestry and about other traits that differed between source populations at the onset of admixture.

Human genetic admixture

Throughout human history, large-scale migrations have facilitated the formation of populations with ancestry from multiple previously separated populations. This process leads to subsequent shuffling of genetic ancestry through recombination, producing variation in ancestry between populations, among individuals in a population, and along the genome within an individual. Recent methodological and empirical developments have elucidated the genomic signatures of this admixture process, bringing previously understudied admixed populations to the forefront of population and medical genetics. Under this theme, we present a collection of recent PLOS Genetics publications that exemplify recent progress in human genetic admixture studies, and we discuss potential areas for future work.

Inclusion of variants discovered from diverse populations improves polygenic risk score transferability

The majority of polygenic risk scores (PRSs) have been developed and optimized in individuals of European ancestry and may have limited generalizability across other ancestral populations. Understanding aspects of PRSs that contribute to this issue and determining solutions is complicated by disease-specific genetic architecture and limited knowledge of sharing of causal variants and effect sizes across populations. Motivated by these challenges, we undertook a simulation study to assess the relationship between ancestry and the potential bias in PRSs developed in European ancestry populations. Our simulations show that the magnitude of this bias increases with increasing divergence from European ancestry, and this is attributed to population differences in linkage disequilibrium and allele frequencies of European-discovered variants, likely as a result of genetic drift. Importantly, we find that including into the PRS variants discovered in African ancestry individuals has the potential to achieve unbiased estimates of genetic risk across global populations and admixed individuals. We confirm our simulation findings in an analysis of hemoglobin A1c (HbA1c), asthma, and prostate cancer in the UK Biobank. Given the demonstrated improvement in PRS prediction accuracy, recruiting larger diverse cohorts will be crucial—and potentially even necessary—for enabling accurate and equitable genetic risk prediction across populations.

Assortative mating by population of origin in a mechanistic model of admixture

Populations whose mating pairs have levels of similarity in phenotypes or genotypes that differ systematically from the level expected under random mating are described as experiencing assortative mating. Excess similarity in mating pairs is termed positive assortative mating, and excess dissimilarity is negative assortative mating. In humans, empirical studies suggest that mating pairs from various admixed populations - whose ancestry derives from two or more source populations - possess correlated ancestry components that indicate the occurrence of positive assortative mating on the basis of ancestry. Generalizing a two-sex mechanistic admixture model, we devise a model of one form of ancestry-assortative mating that occurs through preferential mating based on source population. Under the model, we study the moments of the admixture fraction distribution for different assumptions about mating preferences, including both positive and negative assortative mating by population. We demonstrate that whereas the mean admixture under assortative mating is equivalent to that of a corresponding randomly mating population, the variance of admixture depends on the level and direction of assortative mating. We consider two special cases of assortative mating by population: first, a single admixture event, and second, constant contributions to the admixed population over time. In contrast to standard settings in which positive assortment increases variation within a population, certain assortative mating scenarios allow the variance of admixture to decrease relative to a corresponding randomly mating population: with the three populations we consider, the variance-increasing effect of positive assortative mating within a population might be overwhelmed by a variance-decreasing effect emerging from mating preferences involving other pairs of populations. The effect of assortative mating is smaller on the X chromosome than on the autosomes because inheritance of the X in males depends only on the mother's ancestry, not on the mating pair. Because the variance of admixture is informative about the timing of admixture and possibly about sex-biased admixture contributions, the effects of assortative mating are important to consider in inferring features of population history from distributions of admixture values. Our model provides a framework to quantitatively study assortative mating under flexible scenarios of admixture over time.

Assortative Mating on Ancestry-Variant Traits in Admixed Latin American Populations

Assortative mating is a universal feature of human societies, and individuals from ethnically diverse populations are known to mate assortatively based on similarities in genetic ancestry. However, little is currently known regarding the exact phenotypic cues, or their underlying genetic architecture, which inform ancestry-based assortative mating. We developed a novel approach, using genome-wide analysis of ancestry-specific haplotypes, to evaluate ancestry-based assortative mating on traits whose expression varies among the three continental population groups – African, European, and Native American – that admixed to form modern Latin American populations. Application of this method to genome sequences sampled from Colombia, Mexico, Peru, and Puerto Rico revealed widespread ancestry-based assortative mating. We discovered a number of anthropometric traits (body mass, height, and facial development) and neurological attributes (educational attainment and schizophrenia) that serve as phenotypic cues for ancestry-based assortative mating. Major histocompatibility complex (MHC) loci show population-specific patterns of both assortative and disassortative mating in Latin America. Ancestry-based assortative mating in the populations analyzed here appears to be driven primarily by African ancestry. This study serves as an example of how population genomic analyses can yield novel insights into human behavior.

Glioma risk associated with extent of estimated European genetic ancestry in African Americans and Hispanics

Glioma incidence is highest in non-Hispanic Whites, and to date, glioma genome-wide association studies (GWAS) to date have only included European ancestry (EA) populations. African Americans and Hispanics in the US have varying proportions of EA, African (AA) and Native American ancestries (NAA). It is unknown if identified GWAS loci or increased EA is associated with increased glioma risk. We assessed whether EA was associated with glioma in African Americans and Hispanics. Data were obtained for 832 cases and 675 controls from the Glioma International Case-Control Study and GliomaSE Case-Control Study previously estimated to have <80% EA, or self-identify as non-White. We estimated global and local ancestry using fastStructure and RFMix, respectively, using 1,000 genomes project reference populations. Within groups with ≥40% AA (AFR_≥0.4 ), and ≥15% NAA (AMR_≥0.15 ), genome-wide association between local EA and glioma was evaluated using logistic regression conditioned on global EA for all gliomas. We identified two regions (7q21.11, p = 6.36 × 10^-4 ; 11p11.12, p = 7.0 × 10^-4 ) associated with increased EA, and one associated with decreased EA (20p12.13, p = 0.0026) in AFR_≥0.4 . In addition, we identified a peak at rs1620291 (p = 4.36 × 10^-6 ) in 7q21.3. Among AMR_≥0.15 , we found an association between increased EA in one region (12q24.21, p = 8.38 × 10^-4 ), and decreased EA in two regions (8q24.21, p = 0. 0010; 20q13.33, p = 6.36 × 10^-4 ). No other significant associations were identified. This analysis identified an association between glioma and two regions previously identified in EA populations (8q24.21, 20q13.33) and four novel regions (7q21.11, 11p11.12, 12q24.21 and 20p12.13). The identifications of novel association with EA suggest regions to target for future genetic association studies.

Genome-wide Significance Thresholds for Admixture Mapping Studies

Admixture mapping studies have become more common in recent years, due in part to technological advances and growing international efforts to increase the diversity of genetic studies. However, many open questions remain about appropriate implementation of admixture mapping studies, including how best to control for multiple testing, particularly in the presence of population structure. In this study, we develop a theoretical framework to characterize the correlation of local ancestry and admixture mapping test statistics in admixed populations with contributions from any number of ancestral populations and arbitrary population structure. Based on this framework, we develop an analytical approach for obtaining genome-wide significance thresholds for admixture mapping studies. We validate our approach via analysis of simulated traits with real genotype data for 8,064 unrelated African American and 3,425 Hispanic/Latina women from the Women's Health Initiative SNP Health Association Resource (WHI SHARe). In an application to these WHI SHARe data, our approach yields genome-wide significant p value thresholds of 2.1 × 10-5 and 4.5 × 10-6 for admixture mapping studies in the African American and Hispanic/Latina cohorts, respectively. Compared to other commonly used multiple testing correction procedures, our method is fast, easy to implement (using our publicly available R package), and controls the family-wise error rate even in structured populations. Importantly, we note that the appropriate admixture mapping significance threshold depends on the number of ancestral populations, generations since admixture, and population structure of the sample; as a result, significance thresholds are not, in general, transferable across studies.

Racial and ethnic differences in preterm birth: A complex, multifactorial problem

Preterm birth remains the leading cause of morbidity and mortality among nonanomalous neonates, and is a major public health problem. Non-Hispanic black women have a 2-fold greater risk for preterm birth compared with non-Hispanic white race. The reasons for this disparity are poorly understood and cannot be explained solely by sociodemographic factors. Underlying factors including a complex interaction between maternal, paternal, and fetal genetics, epigenetics, the microbiome, and these sociodemographic risk factors likely underlies the differences between racial groups, but these relationships are currently poorly understood. This article reviews the epidemiology of disparities in preterm birth rates and adverse pregnancy outcomes and discuss possible explanations for the racial and ethnic differences, while examining potential solutions to this major public health problem.