Gene-environment interactions in human health.

Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.

A hepatitis B virus (HBV) sequence variation graph improves alignment and sample-specific consensus sequence construction.

Nearly 300 million individuals live with chronic hepatitis B virus (HBV) infection (CHB), for which no curative therapy is available. As viral diversity is associated with pathogenesis and immunological control of infection, improved methods to characterize this diversity could aid drug development efforts. Conventionally, viral sequencing data are mapped/aligned to a reference genome, and only the aligned sequences are retained for analysis. Thus, reference selection is critical, yet selecting the most representative reference a priori remains difficult. We investigate an alternative pangenome approach which can combine multiple reference sequences into a graph which can be used during alignment. Using simulated short-read sequencing data generated from publicly available HBV genomes and real sequencing data from an individual living with CHB, we demonstrate alignment to a phylogenetically representative 'genome graph' can improve alignment, avoid issues of reference ambiguity, and facilitate the construction of sample-specific consensus sequences more genetically similar to the individual's infection. Graph-based methods can, therefore, improve efforts to characterize the genetics of viral pathogens, including HBV, and have broader implications in host-pathogen research.

Advancing genomics to improve health equity.

Health equity is the state in which everyone has fair and just opportunities to attain their highest level of health. The field of human genomics has fallen short in increasing health equity, largely because the diversity of the human population has been inadequately reflected among participants of genomics research. This lack of diversity leads to disparities that can have scientific and clinical consequences. Achieving health equity related to genomics will require greater effort in addressing inequities within the field. As part of the commitment of the National Human Genome Research Institute (NHGRI) to advancing health equity, it convened experts in genomics and health equity research to make recommendations and performed a review of current literature to identify the landscape of gaps and opportunities at the interface between human genomics and health equity research. This Perspective describes these findings and examines health equity within the context of human genomics and genomic medicine.

Genetic Susceptibility to Astrovirus Diarrhea in Bangladeshi Infants.

Background: Astroviral infections commonly cause acute nonbacterial gastroenteritis in children globally. However, these infections often go undiagnosed outside of research settings. There is no treatment available for astrovirus, and Astroviridae strain diversity presents a challenge to potential vaccine development. Methods: To address our hypothesis that host genetic risk factors are associated with astrovirus disease susceptibility, we performed a genome-wide association study of astrovirus infection in the first year of life from children enrolled in 2 Bangladeshi birth cohorts. Results: We identified a novel region on chromosome 1 near the loricrin gene (LOR) associated with astrovirus diarrheal infection (rs75437404; meta-analysis P = 8.82 × 10-9; A allele odds ratio, 2.71) and on chromosome 10 near the prolactin releasing hormone receptor gene (PRLHR) (rs75935441; meta-analysis P = 1.33 × 10-8; C allele odds ratio, 4.17). The prolactin-releasing peptide has been shown to influence feeding patterns and energy balance in mice. In addition, several single-nucleotide polymorphisms in the chromosome 1 locus have previously been associated with expression of innate immune system genes PGLYRP4, S100A9, and S100A12. Conclusions: This study identified 2 significant host genetic regions that may influence astrovirus diarrhea susceptibility and should be considered in further studies.

Mexican Biobank advances population and medical genomics of diverse ancestries.

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories and complex trait architectures remain hidden owing to insufficient data1. To fill this gap, the Mexican Biobank project genotyped 6,057 individuals from 898 rural and urban localities across all 32 states in Mexico at a resolution of 1.8 million genome-wide markers with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Here, using ancestry deconvolution and inference of identity-by-descent segments, we inferred ancestral population sizes across Mesoamerican regions over time, unravelling Indigenous, colonial and postcolonial demographic dynamics2-6. We observed variation in runs of homozygosity among genomic regions with different ancestries reflecting distinct demographic histories and, in turn, different distributions of rare deleterious variants. We conducted genome-wide association studies (GWAS) for 22 complex traits and found that several traits are better predicted using the Mexican Biobank GWAS compared to the UK Biobank GWAS7,8. We identified genetic and environmental factors associating with trait variation, such as the length of the genome in runs of homozygosity as a predictor for body mass index, triglycerides, glucose and height. This study provides insights into the genetic histories of individuals in Mexico and dissects their complex trait architectures, both crucial for making precision and preventive medicine initiatives accessible worldwide.

Admixture mapping of peripheral artery disease in a Dominican population reveals a putative risk locus on 2q35.

Peripheral artery disease (PAD) is a form of atherosclerotic cardiovascular disease, affecting ∼8 million Americans, and is known to have racial and ethnic disparities. PAD has been reported to have a significantly higher prevalence in African Americans (AAs) compared to non-Hispanic European Americans (EAs). Hispanic/Latinos (HLs) have been reported to have lower or similar rates of PAD compared to EAs, despite having a paradoxically high burden of PAD risk factors; however, recent work suggests prevalence may differ between sub-groups. Here, we examined a large cohort of diverse adults in the BioMe biobank in New York City. We observed the prevalence of PAD at 1.7% in EAs vs. 8.5% and 9.4% in AAs and HLs, respectively, and among HL sub-groups, the prevalence was found at 11.4% and 11.5% in Puerto Rican and Dominican populations, respectively. Follow-up analysis that adjusted for common risk factors demonstrated that Dominicans had the highest increased risk for PAD relative to EAs [OR = 3.15 (95% CI 2.33-4.25), p < 6.44 × 10-14]. To investigate whether genetic factors may explain this increased risk, we performed admixture mapping by testing the association between local ancestry and PAD in Dominican BioMe participants (N = 1,813) separately from European, African, and Native American (NAT) continental ancestry tracts. The top association with PAD was an NAT ancestry tract at chromosome 2q35 [OR = 1.96 (SE = 0.16), p < 2.75 × 10-05) with 22.6% vs. 12.9% PAD prevalence in heterozygous NAT tract carriers versus non-carriers, respectively. Fine-mapping at this locus implicated tag SNP rs78529201 located within a long intergenic non-coding RNA (lincRNA) LINC00607, a gene expression regulator of key genes related to thrombosis and extracellular remodeling of endothelial cells, suggesting a putative link of the 2q35 locus to PAD etiology. Efforts to reproduce the signal in other Hispanic cohorts were unsuccessful. In summary, we showed how leveraging health system data helped understand nuances of PAD risk across HL sub-groups and admixture mapping approaches elucidated a putative risk locus in a Dominican population.

Differences in the Circulating Proteome in Individuals with versus without Sickle Cell Trait.

BACKGROUND: Sickle cell trait affects approximately 8% of Black individuals in the United States, along with many other individuals with ancestry from malaria-endemic regions worldwide. While traditionally considered a benign condition, recent evidence suggests that sickle cell trait is associated with lower eGFR and higher risk of kidney diseases, including kidney failure. The mechanisms underlying these associations remain poorly understood. We used proteomic profiling to gain insight into the pathobiology of sickle cell trait. METHODS: We measured proteomics ( N =1285 proteins assayed by Olink Explore) using baseline plasma samples from 592 Black participants with sickle cell trait and 1:1 age-matched Black participants without sickle cell trait from the prospective Women's Health Initiative cohort. Age-adjusted linear regression was used to assess the association between protein levels and sickle cell trait. RESULTS: In age-adjusted models, 35 proteins were significantly associated with sickle cell trait after correction for multiple testing. Several of the sickle cell trait-protein associations were replicated in Black participants from two independent cohorts (Atherosclerosis Risk in Communities study and Jackson Heart Study) assayed using an orthogonal aptamer-based proteomic platform (SomaScan). Many of the validated sickle cell trait-associated proteins are known biomarkers of kidney function or injury ( e.g. , hepatitis A virus cellular receptor 1 [HAVCR1]/kidney injury molecule-1 [KIM-1], uromodulin [UMOD], ephrins), related to red cell physiology or hemolysis (erythropoietin [EPO], heme oxygenase 1 [HMOX1], and α -hemoglobin stabilizing protein) and/or inflammation (fractalkine, C-C motif chemokine ligand 2/monocyte chemoattractant protein-1 [MCP-1], and urokinase plasminogen activator surface receptor [PLAUR]). A protein risk score constructed from the top sickle cell trait-associated biomarkers was associated with incident kidney failure among those with sickle cell trait during Women's Health Initiative follow-up (odds ratio, 1.32; 95% confidence interval, 1.10 to 1.58). CONCLUSIONS: We identified and replicated the association of sickle cell trait with a number of plasma proteins related to hemolysis, kidney injury, and inflammation.

Targeting hepatitis B vaccine escape using immunogenetics in Bangladeshi infants.

Hepatitis B virus (HBV) vaccine escape mutants (VEM) are increasingly described, threatening progress in control of this virus worldwide. Here we studied the relationship between host genetic variation, vaccine immunogenicity and viral sequences implicating VEM emergence. In a cohort of 1,096 Bangladeshi children, we identified human leukocyte antigen (HLA) variants associated with response vaccine antigens. Using an HLA imputation panel with 9,448 south Asian individuals DPB1*04:01 was associated with higher HBV antibody responses (p=4.5×10-30). The underlying mechanism is a result of higher affinity binding of HBV surface antigen epitopes to DPB1*04:01 dimers. This is likely a result of evolutionary pressure at the HBV surface antigen 'a-determinant' segment incurring VEM specific to HBV. Prioritizing pre-S isoform HBV vaccines may tackle the rise of HBV vaccine evasion.

Genetic distance informs polygenic score predictive accuracy.

Polygenic scores (PGSs) aggregate the effects of variants across the genome to estimate genetic liability, but have lower performance in external study populations. A new study by Ding et al. has applied a novel framework to estimate the individual-level predictive accuracy of PGSs, and demonstrates that performance reduction occurs linearly with genetic distance.

Admixture Mapping of Peripheral Artery Disease in a Dominican Population Reveals a Novel Risk Locus on 2q35.

Peripheral artery disease (PAD) is a form of atherosclerotic cardiovascular disease, affecting ∼8 million Americans, and is known to have racial and ethnic disparities. PAD has been reported to have significantly higher prevalence in African Americans (AAs) compared to non-Hispanic European Americans (EAs). Hispanic/Latinos (HLs) have been reported to have lower or similar rates of PAD compared to EAs, despite having a paradoxically high burden of PAD risk factors, however recent work suggests prevalence may differ between sub-groups. Here we examined a large cohort of diverse adults in the Bio Me biobank in New York City (NYC). We observed the prevalence of PAD at 1.7% in EAs vs 8.5% and 9.4% in AAs and HLs, respectively; and among HL sub-groups, at 11.4% and 11.5% in Puerto Rican and Dominican populations, respectively. Follow-up analysis that adjusted for common risk factors demonstrated that Dominicans had the highest increased risk for PAD relative to EAs (OR=3.15 (95% CI 2.33-4.25), P <6.44×10 -14 ). To investigate whether genetic factors may explain this increased risk, we performed admixture mapping by testing the association between local ancestry (LA) and PAD in Dominican Bio Me participants (N=1,940) separately for European (EUR), African (AFR) and Native American (NAT) continental ancestry tracts. We identified a NAT ancestry tract at chromosome 2q35 that was significantly associated with PAD (OR=2.05 (95% CI 1.51-2.78), P <4.06×10 -6 ) with 22.5% vs 12.5% PAD prevalence in heterozygous NAT tract carriers versus non-carriers, respectively. Fine-mapping at this locus implicated tag SNP rs78529201 located within a long intergenic non-coding RNA (lincRNA) LINC00607 , a gene expression regulator of key genes related to thrombosis and extracellular remodeling of endothelial cells, suggesting a putative link of the 2q35 locus to PAD etiology. In summary, we showed how leveraging health systems data helped understand nuances of PAD risk across HL sub-groups and admixture mapping approaches elucidated a novel risk locus in a Dominican population.

Genome-Wide Association Studies of Diarrhea Frequency and Duration in the First Year of Life in Bangladeshi Infants.

BACKGROUND: Diarrhea is the second leading cause of death in children under 5 years old worldwide. Known diarrhea risk factors include sanitation, water sources, and pathogens but do not fully explain the heterogeneity in frequency and duration of diarrhea in young children. We evaluated the role of host genetics in diarrhea. METHODS: Using 3 well-characterized birth cohorts from an impoverished area of Dhaka, Bangladesh, we compared infants with no diarrhea in the first year of life to those with an abundance, measured by either frequency or duration. We performed a genome-wide association analysis for each cohort under an additive model and then meta-analyzed across the studies. RESULTS: For diarrhea frequency, we identified 2 genome-wide significant loci associated with not having any diarrhea, on chromosome 21 within the noncoding RNA AP000959 (C allele odds ratio [OR] = 0.31, P = 4.01 × 10-8), and on chromosome 8 within SAMD12 (T allele OR = 0.35, P = 4.74 × 10-7). For duration of diarrhea, we identified 2 loci associated with no diarrhea, including the same locus on chromosome 21 (C allele OR = 0.31, P = 1.59 × 10-8) and another locus on chromosome 17 near WSCD1 (C allele OR = 0.35, P = 1.09 × 10-7). CONCLUSIONS: These loci are in or near genes involved in enteric nervous system development and intestinal inflammation and may be potential targets for diarrhea therapeutics.

Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals.

Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93-0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.

Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects.

Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.

A hepatitis B virus (HBV) sequence variation graph improves sequence alignment and sample-specific consensus sequence construction for genetic analysis of HBV.

Hepatitis B virus (HBV) remains a global public health concern, with over 250 million individuals living with chronic HBV infection (CHB) and no curative therapy currently available. Viral diversity is associated with CHB pathogenesis and immunological control of infection. Improved methods to characterize the viral genome at both the population and intra-host level could aid drug development efforts. Conventionally, HBV sequencing data are aligned to a linear reference genome and only sequences capable of aligning to the reference are captured for analysis. Reference selection has additional consequences, including sample-specific 'consensus' sequence construction. It remains unclear how to select a reference from available sequences and whether a single reference is sufficient for genetic analyses. Using simulated short-read sequencing data generated from full-length publicly available HBV genome sequences and HBV sequencing data from a longitudinally sampled individual with CHB, we investigate alternative graph-based alignment approaches. We demonstrate that using a phylogenetically representative 'genome graph' for alignment, rather than linear reference sequences, avoids issues of reference ambiguity, improves alignment, and facilitates the construction of sample-specific consensus sequences genetically similar to an individual's infection. Graph-based methods can therefore improve efforts to characterize the genetics of viral pathogens, including HBV, and may have broad implications in host pathogen research.

By their powers combined, global initiative joins forces for genomic research.

Genome-wide association studies (GWASs) can require immense sample sizes to identify variants associated with human health across the frequency spectrum. As the Global Biobank Meta-analysis Initiative (GBMI), Zhou et al. describe a collaborative network across 23 biobanks and 2.2 million participants to address challenges of underrepresentation of diversity in genomic research.

Large-scale genome-wide association study of coronary artery disease in genetically diverse populations.

We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.

Opportunities and challenges for the use of common controls in sequencing studies.

Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

Clotting factor genes are associated with preeclampsia in high-altitude pregnant women in the Peruvian Andes.

Preeclampsia is a multi-organ complication of pregnancy characterized by sudden hypertension and proteinuria that is among the leading causes of preterm delivery and maternal morbidity and mortality worldwide. The heterogeneity of preeclampsia poses a challenge for understanding its etiology and molecular basis. Intriguingly, risk for the condition increases in high-altitude regions such as the Peruvian Andes. To investigate the genetic basis of preeclampsia in a population living at high altitude, we characterized genome-wide variation in a cohort of preeclamptic and healthy Andean families (n = 883) from Puno, Peru, a city located above 3,800 meters of altitude. Our study collected genomic DNA and medical records from case-control trios and duos in local hospital settings. We generated genotype data for 439,314 SNPs, determined global ancestry patterns, and mapped associations between genetic variants and preeclampsia phenotypes. A transmission disequilibrium test (TDT) revealed variants near genes of biological importance for placental and blood vessel function. The top candidate region was found on chromosome 13 of the fetal genome and contains clotting factor genes PROZ, F7, and F10. These findings provide supporting evidence that common genetic variants within coagulation genes play an important role in preeclampsia. A selection scan revealed a potential adaptive signal around the ADAM12 locus on chromosome 10, implicated in pregnancy disorders. Our discovery of an association in a functional pathway relevant to pregnancy physiology in an understudied population of Native American origin demonstrates the increased power of family-based study design and underscores the importance of conducting genetic research in diverse populations.

Disentangling Signatures of Selection Before and After European Colonization in Latin Americans.

Throughout human evolutionary history, large-scale migrations have led to intermixing (i.e., admixture) between previously separated human groups. Although classical and recent work have shown that studying admixture can yield novel historical insights, the extent to which this process contributed to adaptation remains underexplored. Here, we introduce a novel statistical model, specific to admixed populations, that identifies loci under selection while determining whether the selection likely occurred post-admixture or prior to admixture in one of the ancestral source populations. Through extensive simulations, we show that this method is able to detect selection, even in recently formed admixed populations, and to accurately differentiate between selection occurring in the ancestral or admixed population. We apply this method to genome-wide SNP data of ∼4,000 individuals in five admixed Latin American cohorts from Brazil, Chile, Colombia, Mexico, and Peru. Our approach replicates previous reports of selection in the human leukocyte antigen region that are consistent with selection post-admixture. We also report novel signals of selection in genomic regions spanning 47 genes, reinforcing many of these signals with an alternative, commonly used local-ancestry-inference approach. These signals include several genes involved in immunity, which may reflect responses to endemic pathogens of the Americas and to the challenge of infectious disease brought by European contact. In addition, some of the strongest signals inferred to be under selection in the Native American ancestral groups of modern Latin Americans overlap with genes implicated in energy metabolism phenotypes, plausibly reflecting adaptations to novel dietary sources available in the Americas.