Generalization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study

Genomic landscape of cancer in racially and ethnically diverse populations

Cancer incidence and mortality rates can vary widely among different racial and ethnic groups, attributed to a complex interplay of genetic, environmental and social factors. Recently, substantial progress has been made in investigating hereditary genetic risk factors and in characterizing tumour genomes. However, most research has been conducted in individuals of European ancestries and, increasingly, in individuals of Asian ancestries. The study of germline and somatic genetics in cancer across racial and ethnic groups using omics technologies offers opportunities to identify similarities and differences in both heritable traits and the molecular features of cancer genomes. An improved understanding of population-specific cancer genomics, as well as translation of those findings across populations, will help reduce cancer disparities and ensure that personalized medicine and public health approaches are equitable across racial and ethnic groups.

Evaluating Multi-Ancestry Genome-Wide Association Methods: Statistical Power, Population Structure, and Practical Implications

The increasing availability of diverse biobanks has enabled multi-ancestry genome-wide association studies (GWAS), enhancing the discovery of genetic variants across traits and diseases. However, the choice of an optimal method remains debated due to challenges in statistical power differences across ancestral groups and approaches to account for population structure. Two primary strategies exist: (1) Pooled analysis, which combines individuals from all genetic backgrounds into a single dataset while adjusting for population stratification using principal components, increasing the sample size and statistical power but requiring careful control of population stratification. (2) Meta-analysis, which performs ancestry-group-specific GWAS and subsequently combines summary statistics, potentially capturing fine-scale population structure, but facing limitations in handling admixed individuals. Using large-scale simulations with varying sample sizes and ancestry compositions, we compare these methods alongside real data analyses of eight continuous and five binary traits from the UK Biobank ( N ≈ 324,000 ) and All of Us Research Program ( N ≈ 207,000 ). Our results demonstrate that pooled analysis generally exhibits better statistical power while effectively adjusting for population stratification. We further present a theoretical framework linking power differences to allele frequency variations across populations. These findings, validated across both biobanks, highlight pooled analysis as a robust and scalable strategy for multi-ancestry GWAS, improving genetic discovery while maintaining rigorous population structure control.

Fine-scale population structure and widespread conservation of genetic effect sizes between human groups across traits

Understanding genetic differences between populations is essential for avoiding confounding in genome-wide association studies and improving polygenic score (PGS) portability. We developed a statistical pipeline to infer fine-scale Ancestry Components and applied it to UK Biobank data. Ancestry Components identify population structure not captured by widely used principal components, improving stratification correction for geographically correlated traits. To estimate the similarity of genetic effect sizes between groups, we developed ANCHOR, which estimates changes in the predictive power of an existing PGS in distinct local ancestry segments. ANCHOR infers highly similar (estimated correlation 0.98 ± 0.07) effect sizes between UK Biobank participants of African and European ancestry for 47 of 53 quantitative phenotypes, suggesting that gene-environment and gene-gene interactions do not play major roles in poor cross-ancestry PGS transferability for these traits in the United Kingdom, and providing optimism that shared causal mutations operate similarly in different populations.

Polygenic scores and social determinants of health: Their correlations and potential biases

The use of polygenic scores (PGS) for personalized medicine has gained momentum, along with caution to avoid accentuating health disparities. Greater ancestral diversity in genetic studies is needed, as well as close attention to the social determinants of health (SDoH).We measured the correlations between 3,030 PGS from the PGS Catalog and SDoH among participants in the Personalized Environment and Genes Study (PEGS). Correlations mainly ranged from -0.05 to 0.05, yet there was a heterogeneity of correlations across SDoH themes, with the largest amount of heterogeneity for PGS predicting body measures and smoking, as well as some common diseases. We also quantify the expected bias of PGS effect size on disease risk when strong predictors, such as SDoH, are omitted from models, emphasizing the importance of including SDoH with PGS to avoid biased estimates of PGS risk and to achieve equitable precision medicine.

Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups

Transcriptome-wide association studies (TWAS) investigate the links between genetically regulated gene expression and complex traits. TWAS involves imputing gene expression using expression quantitative trait loci (eQTL) as predictors and testing the association between the imputed expression and the trait. The effectiveness of TWAS depends on the accuracy of these imputation models, which require genotype and gene expression data from the same samples. However, publicly accessible resources, such as the Genotype Tissue Expression (GTEx) Project, are biased toward individuals of European ancestry, potentially reducing prediction accuracy into other ancestry groups. This study explored eQTL transferability across ancestry groups by comparing two imputation models: PrediXcan (tissue-specific) and UTMOST (cross-tissue). Both models were trained on tissues from the GTEx Project using European ancestry individuals and then tested on data sets of European ancestry and African American individuals. Results showed that both models performed best when the training and testing data sets were from the same ancestry group, with the cross-tissue approach generally outperforming the tissue-specific approach. This study underscores that eQTL detection is influenced by ancestry and tissue context. Developing ancestry-specific reference panels across tissues can improve prediction accuracy, enhancing TWAS analysis and our understanding of the biological processes contributing to complex traits.

Disparities in ABO blood type determination across diverse ancestries: a systematic review and validation in the All of Us Research Program

OBJECTIVES

ABO blood types have widespread clinical use and robust associations with disease. The purpose of this study is to evaluate the portability and suitability of tag single-nucleotide polymorphisms (tSNPs) used to determine ABO alleles and blood types across diverse populations in published literature.

MATERIALS AND METHODS

Bibliographic databases were searched for studies using tSNPs to determine ABO alleles. We calculated linkage between tSNPs and functional variants across inferred continental ancestry groups from 1000 Genomes. We compared r2 across ancestry and assessed real-world consequences by comparing tSNP-derived blood types to serology in a diverse population from the All of Us Research Program.

RESULTS

Linkage between functional variants and O allele tSNPs was significantly lower in African (median r2 = 0.443) compared to East Asian (r2 = 0.946, P = 1.1 × 10-5) and European (r2 = 0.869, P = .023) populations. In All of Us, discordance between tSNP-derived blood types and serology was high across all SNPs in African ancestry individuals and linkage was strongly correlated with discordance across all ancestries (ρ = -0.90, P = 3.08 × 10-23).

DISCUSSION

Many studies determine ABO blood types using tSNPs. However, tSNPs with low linkage disequilibrium promote misinference of ABO blood types, particularly in diverse populations. We observe common use of inappropriate tSNPs to determine ABO blood type, particularly for O alleles and with some tSNPs mistyping up to 58% of individuals.

CONCLUSION

Our results highlight the lack of transferability of tSNPs across ancestries and potential exacerbation of disparities in genomic research for underrepresented populations. This is especially relevant as more diverse cohorts are made publicly available.

Systematic Review and Phenome-Wide Scans of Genetic Associations with Vascular Cognitive Impairment

Vascular cognitive impairment (VCI) is a heterogenous form of cognitive impairment that results from cerebrovascular disease. It is a result of both genetic and non-genetic factors. Although much research has been conducted on the genetic contributors to other forms of cognitive impairment (e.g. Alzheimer's disease), knowledge is lacking on the genetic factors associated with VCI. A better understanding of the genetics of VCI will be critical for prevention and treatment. To begin to fill this gap, the genetic contributors are reviewed with VCI from the literature. Phenome-wide scans of the identified genes are conducted and genetic variants identified in the review in large-scale resources displaying genetic variant-trait association information. Gene set are also carried out enrichment analysis using the genes identified from the review. Thirty one articles are identified meeting the search criteria and filters, from which 107 unique protein-coding genes are noted related to VCI. The phenome-wide scans and gene set enrichment analysis identify pathways associated with a diverse set of biological systems. This results indicate that genes with evidence of involvement in VCI are involved in a diverse set of biological functions. This information can facilitate downstream research to better dissect possible shared biological mechanisms for future therapies.

Investigation of genetic markers associated to type 2 diabetes mellitus in Santarém-Pará

Genetic, epigenetic and environmental factors play an important role in the genesis of Type 2 Diabetes Mellitus (T2D). In the genetic context, one of the strategies used to investigate possible associations with diabetes is the search for Single Nucleotide Polymorphisms (SNPs), involving the comparison of alelle frequencies, the phenotypic variations and other relevant factors, such as environmental influences and lifestyle choices, Thus, the aim of this study was to find the relationship of risk variants for T2D in SNPs (rs4994) in the ADRB3 gene; (rs1799854) in the ABCC8 gene; (rs7901695 and rs12255372) in the TCF7L2 gene; and (rs8050136) in the FTO gene in a sample of the population of the municipality of Santarém (PA), Brazilian Amazon, in the northern region of Brazil. ABCC8 (rs1799854 C>T) showed a statistically significant association with T2D. Each chosen gene and SNP has been previously implicated in T2D risk according to existing scientific literature, owing to their roles in glucose regulation and body fat.

Estimation of genetic variation in vitiligo associated genes: Population genomics perspective

BACKGROUND

Vitiligo is an auto-immune progressive depigmentation disorder of the skin due to loss of melanocytes. Genetic risk is one of the important factors for development of vitiligo. Preponderance of vitiligo in certain ethnicities is known which can be analysed by understanding the distribution of allele frequencies across normal populations. Earlier GWAS identified 108 risk alleles for vitiligo in Europeans and East Asians. In this study, 64 of these risk alleles were used for analysing their enrichment and depletion across populations (1000 Genomes Project and IndiGen) with reference to 1000 Genomes dataset. Genetic risk scores were calculated and Fisher's exact test was performed to understand statistical significance of their variation in each population with respect to 1000 Genomes dataset as reference. In addition to SNPs reported in GWAS, significant variation in allele frequencies of 1079 vitiligo-related genes were also analysed. Two-tailed Chi-square test and Bonferroni's multiple adjustment values along with fixation index (≥ 0.5) and minimum allele frequency (≥ 0.05) were calculated and used to prioritise the variants based on pairwise comparison across populations.

RESULTS

Risk alleles rs1043101 and rs10768122 belong to 3 prime UTR of glutamate receptor gene SLC1A2 are found to be highly enriched in the South Asian population when compared with the 'global normal' population. Intron variant rs4766578 (ATXN2) was found to be deleted in SAS, EAS and AFR and enriched in EUR and AMR1. This risk allele is found to be under positive selection in SAS, AMR1 and EUR. From the ancillary vitiligo gene list, nonsynonymous variant rs16891982 was found to be enriched in the European and the Admixed American populations and depleted in all others. rs2279238 and rs11039155 belonging to the LXR-α gene involved in regulation of metalloproteinase 2 and 9 (melanocyte precursors) were found to be associated with vitiligo in the North Indian population (in earlier study).

CONCLUSION

The differential enrichment/depletion profile of the risk alleles provides insight into the underlying inter-population variations. This would provide clues towards prioritisation of SNPs associated with vitiligo thereby elucidating its preponderance in different ethnic groups.

Validity of European-centric cardiometabolic polygenic scores in multi-ancestry populations

Polygenic scores (PGSs) provide an individual level estimate of genetic risk for any given disease. Since most PGSs have been derived from genome wide association studies (GWASs) conducted in populations of White European ancestry, their validity in other ancestry groups remains unconfirmed. This is especially relevant for cardiometabolic diseases which are known to disproportionately affect people of non-European ancestry. Thus, we aimed to evaluate the performance of PGSs for glycaemic traits (glycated haemoglobin, and type 1 and type 2 diabetes mellitus), cardiometabolic risk factors (body mass index, hypertension, high- and low-density lipoproteins, and total cholesterol and triglycerides) and cardiovascular diseases (including stroke and coronary artery disease) in people of White European, South Asian, and African Caribbean ethnicity in the UK Biobank. Whilst PGSs incorporated some GWAS data from multi-ethnic populations, the vast majority originated from White Europeans. For most outcomes, PGSs derived mostly from European populations had an overall better performance in White Europeans compared to South Asians and African Caribbeans. Thus, multi-ancestry GWAS data are needed to derive ancestry stratified PGSs to tackle health inequalities.

Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

BACKGROUND

Approximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores.

METHODS

Here, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), and South-East Asian (SAS)).

RESULTS

We detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome p.vivax induced malaria.

CONCLUSIONS

Multi-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.

Systematic exploration of a decade of publications on psychiatric genetics in Latin America

Psychiatric disorders have a great impact in terms of mortality, morbidity, and disability across the lifespan. Considerable effort has been devoted to understanding their complex and heterogeneous genetic architecture, including diverse ancestry populations. Our aim was to review the psychiatric genetics research published with Latin American populations from 2010 to 2019, and classify it according to country of origin, type of analysis, source of funding, and other variables. We found that most publications came from Brazil, Mexico, and Colombia. Also, local funds are generally not large enough for genome-wide studies in Latin America, with the exception of Brazil and Mexico; larger studies are often done in collaboration with international partners, mostly funded by US agencies. In most of the larger studies, the participants are individuals of Latin American ancestry living in the United States, which limits the potential for exploring the complex gene-environment interaction. Family studies, traditionally strong in Latin America, represent about 30% of the total research publications. Scarce local resources for research in Latin America have probably been an important limitation for conducting bigger and more complex studies, contributing to the reduced representation of these populations in global psychiatric genetics studies. Increasing diversity must be a goal to improve generalizability and applicability in clinical settings.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework

BACKGROUND

The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies.

METHODS

We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies.

RESULTS

We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue.

CONCLUSION

Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Genetic and molecular architecture of complex traits

Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.

Transcriptome-wide association studies associated with Crohn's disease: challenges and perspectives

Crohn's disease (CD) is regarded as a lifelong progressive disease affecting all segments of the intestinal tract and multiple organs. Based on genome-wide association studies (GWAS) and gene expression data, transcriptome-wide association studies (TWAS) can help identify susceptibility genes associated with pathogenesis and disease behavior. In this review, we overview seven reported TWASs of CD, summarize their study designs, and discuss the key methods and steps used in TWAS, which affect the prioritization of susceptibility genes. This article summarized the screening of tissue-specific susceptibility genes for CD, and discussed the reported potential pathological mechanisms of overlapping susceptibility genes related to CD in a certain tissue type. We observed that ileal lipid-related metabolism and colonic extracellular vesicles may be involved in the pathogenesis of CD by performing GO pathway enrichment analysis for susceptibility genes. We further pointed the low reproducibility of TWAS associated with CD and discussed the reasons for these issues, strategies for solving them. In the future, more TWAS are needed to be designed into large-scale, unified cohorts, unified analysis pipelines, and fully classified databases of expression trait loci.

Transfer learning with false negative control improves polygenic risk prediction

Polygenic risk score (PRS) is a quantity that aggregates the effects of variants across the genome and estimates an individual's genetic predisposition for a given trait. PRS analysis typically contains two input data sets: base data for effect size estimation and target data for individual-level prediction. Given the availability of large-scale base data, it becomes more common that the ancestral background of base and target data do not perfectly match. In this paper, we treat the GWAS summary information obtained in the base data as knowledge learned from a pre-trained model, and adopt a transfer learning framework to effectively leverage the knowledge learned from the base data that may or may not have similar ancestral background as the target samples to build prediction models for target individuals. Our proposed transfer learning framework consists of two main steps: (1) conducting false negative control (FNC) marginal screening to extract useful knowledge from the base data; and (2) performing joint model training to integrate the knowledge extracted from base data with the target training data for accurate trans-data prediction. This new approach can significantly enhance the computational and statistical efficiency of joint-model training, alleviate over-fitting, and facilitate more accurate trans-data prediction when heterogeneity level between target and base data sets is small or high.

Genetic diet interactions of ACE: the increased hypertension predisposition in the Latin American population

Hypertension is one of the primary risk factors associated with cardiovascular diseases (CVDs). It is a condition that affects people worldwide, and its prevalence is increasing due to several factors, such as lack of physical activity, population aging, and unhealthy diets. Notably, this increase has primarily occurred in low and middle-income countries (LMICs). In Latin America, approximately 40% of adults have been diagnosed with hypertension. Moreover, reports have shown that the Latin American genetic composition is highly diverse, and this genetic background can influence various biological processes, including disease predisposition and treatment effectiveness. Research has shown that Western dietary patterns, which include increased consumption of red meat, refined grains, sugar, and ultra-processed food, have spread across the globe, including Latin America, due to globalization processes. Furthermore, a higher than recommended sodium consumption, which has been associated with hypertension, has been identified across different regions, including Asia, Europe, America, Oceania, and Africa. In conclusion, hypertension is a multifactorial disease involving environmental and genetic factors. In Latin America, hypertension prevalence is increasing due to various factors, including age, the adoption of a "Westernized" diet, and potential genetic predisposition factors involving the ACE gene. Furthermore, identifying the genetic and molecular mechanisms of the disease, its association with diet, and how they interact is essential for the development of personalized treatments to increase its efficacy and reduce side effects.

Improving genetic risk prediction across diverse population by disentangling ancestry representations

Risk prediction models using genetic data have seen increasing traction in genomics. However, most of the polygenic risk models were developed using data from participants with similar (mostly European) ancestry. This can lead to biases in the risk predictors resulting in poor generalization when applied to minority populations and admixed individuals such as African Americans. To address this issue, largely due to the prediction models being biased by the underlying population structure, we propose a deep-learning framework that leverages data from diverse population and disentangles ancestry from the phenotype-relevant information in its representation. The ancestry disentangled representation can be used to build risk predictors that perform better across minority populations. We applied the proposed method to the analysis of Alzheimer's disease genetics. Comparing with standard linear and nonlinear risk prediction methods, the proposed method substantially improves risk prediction in minority populations, including admixed individuals, without needing self-reported ancestry information.

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.

Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants

Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.

Optimal strategies for learning multi-ancestry polygenic scores vary across traits

Polygenic scores (PGSs) are individual-level measures that aggregate the genome-wide genetic predisposition to a given trait. As PGS have predominantly been developed using European-ancestry samples, trait prediction using such European ancestry-derived PGS is less accurate in non-European ancestry individuals. Although there has been recent progress in combining multiple PGS trained on distinct populations, the problem of how to maximize performance given a multiple-ancestry cohort is largely unexplored. Here, we investigate the effect of sample size and ancestry composition on PGS performance for fifteen traits in UK Biobank. For some traits, PGS estimated using a relatively small African-ancestry training set outperformed, on an African-ancestry test set, PGS estimated using a much larger European-ancestry only training set. We observe similar, but not identical, results when considering other minority-ancestry groups within UK Biobank. Our results emphasise the importance of targeted data collection from underrepresented groups in order to address existing disparities in PGS performance.

Genome-wide Association Study for AKI

Key Points

Two genetic variants in the DISP1-TLR5 gene locus were associated with risk of AKI. DISP1 and TLR5 were differentially regulated in kidney biopsy tissue from patients with AKI compared with no AKI.

Background

Although common genetic risks for CKD are well established, genetic factors influencing risk for AKI in hospitalized patients are poorly understood.

Methods

We conducted a genome-wide association study in 1369 participants in the Assessment, Serial Evaluation, and Subsequent Sequelae of AKI Study; a multiethnic population of hospitalized participants with and without AKI matched on demographics, comorbidities, and kidney function before hospitalization. We then completed functional annotation of top-performing variants for AKI using single-cell RNA sequencing data from kidney biopsies in 12 patients with AKI and 18 healthy living donors from the Kidney Precision Medicine Project.

Results

No genome-wide significant associations with AKI risk were found in Assessment, Serial Evaluation, and Subsequent Sequelae of AKI (P < 5×10 −8 ). The top two variants with the strongest association with AKI mapped to the dispatched resistance-nodulation-division (RND) transporter family member 1 (DISP1) gene and toll-like receptor 5 (TLR5) gene locus, rs17538288 (odds ratio, 1.55; 95% confidence interval, 1.32 to 182; P = 9.47×10 −8 ) and rs7546189 (odds ratio, 1.53; 95% confidence interval, 1.30 to 1.81; P = 4.60×10 −7 ). In comparison with kidney tissue from healthy living donors, kidney biopsies in patients with AKI showed differential DISP1 expression in proximal tubular epithelial cells (adjusted P = 3.9× 10−2) and thick ascending limb of the loop of Henle (adjusted P = 8.7× 10−3) and differential TLR5 gene expression in thick ascending limb of the loop of Henle (adjusted P = 4.9× 10−30).

Conclusions

AKI is a heterogeneous clinical syndrome with various underlying risk factors, etiologies, and pathophysiology that may limit the identification of genetic variants. Although no variants reached genome-wide significance, we report two variants in the intergenic region between DISP1 and TLR5 , suggesting this region as a novel risk for AKI susceptibility.

Evaluating significance of European-associated index SNPs in the East Asian population for 31 complex phenotypes

BACKGROUND

Genome-wide association studies (GWASs) have identified many single-nucleotide polymorphisms (SNPs) associated with complex phenotypes in the European (EUR) population; however, the extent to which EUR-associated SNPs can be generalized to other populations such as East Asian (EAS) is not clear.

RESULTS

By leveraging summary statistics of 31 phenotypes in the EUR and EAS populations, we first evaluated the difference in heritability between the two populations and calculated the trans-ethnic genetic correlation. We observed the heritability estimates of some phenotypes varied substantially across populations and 53.3% of trans-ethnic genetic correlations were significantly smaller than one. Next, we examined whether EUR-associated SNPs of these phenotypes could be identified in EAS using the trans-ethnic false discovery rate method while accounting for winner's curse for SNP effect in EUR and difference of sample sizes in EAS. We found on average 54.5% of EUR-associated SNPs were also significant in EAS. Furthermore, we discovered non-significant SNPs had higher effect heterogeneity, and significant SNPs showed more consistent linkage disequilibrium and allele frequency patterns between the two populations. We also demonstrated non-significant SNPs were more likely to undergo natural selection.

CONCLUSIONS

Our study revealed the extent to which EUR-associated SNPs could be significant in the EAS population and offered deep insights into the similarity and diversity of genetic architectures underlying phenotypes in distinct ancestral groups.

Similarity and diversity of genetic architecture for complex traits between East Asian and European populations

BACKGROUND

Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently.

RESULTS

By leveraging summary statistics of 37 traits from East Asian (N_max=254,373) or European (N_max=693,529) populations, we first evaluated the trans-ethnic genetic correlation (ρ_g) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with [Formula: see text] ranging from 0.53 (se = 0.11) for adult-onset asthma to 0.98 (se = 0.17) for hemoglobin A1c. However, 88.9% of the genetic correlation estimates were significantly less than one, indicating potential heterogeneity in genetic effect across populations. We next identified common associated SNPs using the conjunction conditional false discovery rate method and observed 21.7% of trait-associated SNPs can be identified simultaneously in both populations. Among these shared associated SNPs, 20.8% showed heterogeneous influence on traits between the two ancestral populations. Moreover, we demonstrated that population-common associated SNPs often exhibited more consistent linkage disequilibrium and allele frequency pattern across ancestral groups compared to population-specific or null ones. We also revealed population-specific associated SNPs were much likely to undergo natural selection compared to population-common associated SNPs.

CONCLUSIONS

Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping.

Intelligence Polygenic Score Is More Predictive of Crystallized Measures: Evidence From the Adolescent Brain Cognitive Development (ABCD) Study

Findings in adults have shown that crystallized measures of intelligence, which are more culturally sensitive than fluid intelligence measures, have greater heritability; however, these results have not been found in children. The present study used data from 8,518 participants between 9 and 11 years old from the Adolescent Brain Cognitive Development (ABCD) Study. We found that polygenic predictors of intelligence test performance (based on genome-wide association meta-analyses of data from 269,867 individuals) and of educational attainment (based on data from 1.1 million individuals) predicted neurocognitive performance. We found that crystallized measures were more strongly associated with both polygenic predictors than were fluid measures. This mirrored heritability differences reported previously in adults and suggests similar associations in children. This may be consistent with a prominent role of gene-environment correlation in cognitive development measured by crystallized intelligence tests. Environmental and experiential mediators may represent malleable targets for improving cognitive outcomes.

Assessment of multi-population polygenic risk scores for lipid traits in African Americans

Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip (n = 3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRSLDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.

A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data

Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to anthropometric and other complex traits from the UK Biobank data across ancestry groups. For obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestries.

Comparison of Risk Allele Frequencies of Psoriasis-Associated Single-Nucleotide Polymorphisms in Different Population Groups

BACKGROUND

The prevalence of psoriasis differs by population, and it appears to be more common among Europeans than in East Asians. Recent genome-wide association studies (GWAS) have identified alleles that increase the risk of psoriasis, and these alleles may present different frequencies in different geographic regions.

OBJECTIVE

We aimed to gain insights into the causes of differences in disease frequencies according to populations and the factors affecting prevalence and pattern differences.

METHODS

We collected a total of 147 psoriasis-associated single-nucleotide polymorphisms (SNPs) from the GWAS catalog and compared the allele frequency differences in 27 populations using public population frequency in the 1000 Genomes Project phase 3 (n=2,504) and the Korean Reference Genome Database (n=1,722). Additionally, we calculated the composited genetic risk scores across the population groups.

RESULTS

There were distinct patterns of allele frequencies in different population groups. In many cases, East Asians exhibited allele frequencies opposite to that of Europeans. The genetic risk score was higher in Europeans (average: 0.487) and Americans (average: 0.492) than in East Asians (average: 0.471). The prevalence of psoriasis correlated with the average genetic risk score of the population.

CONCLUSION

We observed a difference in the allele frequencies of psoriasis-associated SNPs between the studied populations. This result suggests that the difference in the prevalence of psoriasis between population groups can be interpreted to some extent by the genotype.

Sequencing-based fine-mapping and in silico functional characterization of the 10q24.32 arsenic metabolism efficiency locus across multiple arsenic-exposed populations

Inorganic arsenic is highly toxic and carcinogenic to humans. Exposed individuals vary in their ability to metabolize arsenic, and variability in arsenic metabolism efficiency (AME) is associated with risks of arsenic-related toxicities. Inherited genetic variation in the 10q24.32 region, near the arsenic methyltransferase (AS3MT) gene, is associated with urine-based measures of AME in multiple arsenic-exposed populations. To identify potential causal variants in this region, we applied fine mapping approaches to targeted sequencing data generated for exposed individuals from Bangladeshi, American Indian, and European American populations (n = 2,357, 557, and 648 respectively). We identified three independent association signals for Bangladeshis, two for American Indians, and one for European Americans. The size of the confidence sets for each signal varied from 4 to 85 variants. There was one signal shared across all three populations, represented by the same SNP in American Indians and European Americans (rs191177668) and in strong linkage disequilibrium (LD) with a lead SNP in Bangladesh (rs145537350). Beyond this shared signal, differences in LD patterns, minor allele frequency (MAF) (e.g., rs12573221 ~13% in Bangladesh ~0.2% among American Indians), and/or heterogeneity in effect sizes across populations likely contributed to the apparent population specificity of the additional identified signals. One of our potential causal variants influences AS3MT expression and nearby DNA methylation in numerous GTEx tissue types (with rs4919690 as a likely causal variant). Several SNPs in our confidence sets overlap transcription factor binding sites and cis-regulatory elements (from ENCODE). Taken together, our analyses reveal multiple potential causal variants in the 10q24.32 region influencing AME, including a variant shared across populations, and elucidate potential biological mechanisms underlying the impact of genetic variation on AME.

Genetics, genomics, and diet interactions in obesity in the Latin American environment

Obesity is a chronic disease characterized by abnormal or excessive fat accumulation that could impact an individual's health; moreover, the World Health Organization (WHO) has declared obesity a global epidemic since 1997. In Latin America, in 2016, reports indicated that 24.2% of the adult population was obese. The environmental factor or specific behaviors like dietary intake or physical activity have a vital role in the development of a condition like obesity, but the interaction of genes could contribute to that predisposition. Hence, it is vital to understand the relationship between genes and disease. Indeed, genetics in nutrition studies the genetic variations and their effect on dietary response; while genomics in nutrition studies the role of nutrients in gene expression. The present review represents a compendium of the dietary behaviors in the Latin American environment and the interactions of genes with their single nucleotide polymorphisms (SNPs) associated with obesity, including the risk allele frequencies in the Latin American population. Additionally, a bibliographical selection of several studies has been included; these studies examined the impact that dietary patterns in Latin American environments have on the expression of numerous genes involved in obesity-associated metabolic pathways.

Addressing the challenges of polygenic scores in human genetic research

The genotyping of millions of human samples has made it possible to evaluate variants across the human genome for their possible association with risks for numerous diseases and other traits by using genome-wide association studies (GWASs). The associations between phenotype and genotype found in GWASs make possible the construction of polygenic scores (PGSs), which aim to predict a trait or disease outcome in an individual on the basis of their genotype (in the disease case, the term polygenic risk score [PRS] is often used). PGSs have shown promise for studying the biology of complex traits and as a tool for evaluating individual disease risks in clinical settings. Although the quantity and quality of data to compute PGSs are increasing, challenges remain in the technical aspects of developing PGSs and in the ethical and social issues that might arise from their use. This ASHG Guidance emphasizes three major themes for researchers working with or interested in the application of PGSs in their own research: (1) developing diverse research cohorts; (2) fostering robustness in the development, application, and interpretation of PGSs; and (3) improving the communication of PGS results and their implications to broad audiences.

Population-based meta-analysis and gene-set enrichment identifies FXR/RXR pathway as common to fatty liver disease and serum lipids

Nonalcoholic fatty liver disease (NAFLD) is prevalent worldwide. NAFLD is associated with elevated serum triglycerides (TG), low-density lipoprotein cholesterol (LDL), and reduced high-density lipoprotein cholesterol (HDL). Both NAFLD and blood lipid levels are genetically influenced and may share a common genetic etiology. We used genome-wide association studies (GWAS)-ranked genes and gene-set enrichment analysis to identify pathways that affect serum lipids and NAFLD. We identified credible genes in these pathways and characterized missense variants in these for effects on serum traits. We used MAGENTA to identify 58 enriched pathways from publicly available TG, LDL, and HDL GWAS (n = 99,000). Three of these pathways were also enriched for associations with European-ancestry NAFLD GWAS (n = 7176). One pathway, farnesoid X receptor (FXR)/retinoid X receptor (RXR) activation, was replicated for association in an African-ancestry NAFLD GWAS (n = 3214) and plays a role in serum lipids and NAFLD. Credible genes (proteins) in FXR/RXR activation include those associated with cholesterol/bile/bilirubin transport/absorption (ABCC2 (MRP2) [ATP binding cassette subfamily C member (multidrug resistance-associated protein 2)], ABCG5, ABCG8 [ATP-binding cassette (ABC) transporters G5 and G8], APOB (APOB) [apolipoprotein B], FABP6 (ILBP) [fatty acid binding protein 6 (ileal lipid-binding protein)], MTTP (MTP) [microsomal triglyceride transfer protein], SLC4A2 (AE2) [solute carrier family 4 member 2 (anion exchange protein 2)]), nuclear hormone-mediated control of metabolism (NR0B2 (SHP) [nuclear receptor subfamily 0 group B member 2 (small heterodimer partner)], NR1H4 (FXR) [nuclear receptor subfamily 1 group H member 4 (FXR)], PPARA (PPAR) [peroxisome proliferator activated receptor alpha], FOXO1 (FOXO1A) [forkhead box O1]), or other pathways (FETUB (FETUB) [fetuin B]). Missense variants in ABCC2 (MRP2), ABCG5 (ABCG5), ABCG8 (ABCG8), APOB (APOB), MTTP (MTP), NR0B2 (SHP), NR1H4 (FXR), and PPARA (PPAR) that associate with serum LDL levels also associate with serum liver function tests in UK Biobank. Conclusion: Genetic variants in NR1H4 (FXR) that protect against liver steatosis increase serum LDL cholesterol while variants in other members of the family have congruent effects on these traits. Human genetic pathway enrichment analysis can help guide therapeutic development by identifying effective targets for NAFLD/serum lipid manipulation while minimizing side effects. In addition, missense variants could be used in companion diagnostics to determine their influence on drug effectiveness.

Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing

Background

Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking.

Methods

By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement.

Results

Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones.

Conclusion

Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03637-8.

Genetic determinants of metabolic biomarkers and their associations with cardiometabolic traits in Hispanic/Latino adolescents

BACKGROUND

Metabolic regulation plays a significant role in energy homeostasis, and adolescence is a crucial life stage for the development of cardiometabolic disease (CMD). This study aims to investigate the genetic determinants of metabolic biomarkers-adiponectin, leptin, ghrelin, and orexin-and their associations with CMD risk factors.

METHODS

We characterized the genetic determinants of the biomarkers among Hispanic/Latino adolescents of the Santiago Longitudinal Study (SLS) and identified the cumulative effects of genetic variants on adiponectin and leptin using biomarker polygenic risk scores (PRS). We further investigated the direct and indirect effect of the biomarker PRS on downstream body fat percent (BF%) and glycemic traits using structural equation modeling.

RESULTS

We identified putatively novel genetic variants associated with the metabolic biomarkers. A substantial amount of biomarker variance was explained by SLS-specific PRS, and the prediction was improved by including the putatively novel loci. Fasting blood insulin and insulin resistance were associated with PRS for adiponectin, leptin, and ghrelin, and BF% was associated with PRS for adiponectin and leptin. We found evidence of substantial mediation of these associations by the biomarker levels.

CONCLUSIONS

The genetic underpinnings of metabolic biomarkers can affect the early development of CMD, partly mediated by the biomarkers.

IMPACT

This study characterized the genetic underpinnings of four metabolic hormones and investigated their potential influence on adiposity and insulin biology among Hispanic/Latino adolescents. Fasting blood insulin and insulin resistance were associated with polygenic risk score (PRS) for adiponectin, leptin, and ghrelin, with evidence of some degree of mediation by the biomarker levels. Body fat percent (BF%) was also associated with PRS for adiponectin and leptin. This provides important insight on biological mechanisms underlying early metabolic dysfunction and reveals candidates for prevention efforts. Our findings also highlight the importance of ancestrally diverse populations to facilitate valid studies of the genetic architecture of metabolic biomarker levels.

Microbiome-associated human genetic variants impact phenome-wide disease risk

Human genetic variation associates with the composition of the gut microbiome, yet its influence on clinical traits remains largely unknown. We analyzed the consequences of nearly a thousand gut microbiome-associated variants (MAVs) on phenotypes reported in electronic health records from tens of thousands of individuals. We discovered and replicated associations of MAVs with neurological, metabolic, digestive, and circulatory diseases. Five significant MAVs in these categories correlate with the relative abundance of microbes down to the strain level. We also demonstrate that these relationships are independently observed and concordant with microbe by disease associations reported in case-control studies. Moreover, a selective sweep and population differentiation impacted some disease-linked MAVs. Combined, these findings establish triad relationships among the human genome, microbiome, and disease. Consequently, human genetic influences may offer opportunities for precision diagnostics of microbiome-associated diseases but also highlight the relevance of genetic background for microbiome modulation and therapeutics.

Common BMI and diabetes-related genetic variants: A pilot study among indigenous people in the Brazilian Amazon

This study was carried out to investigate the frequency of genetic variants related to body mass index (BMI) and type 2 diabetes (T2D) and evaluating the potential impact of risk alleles on susceptibility to these disorders in six indigenous peoples from Brazilian Amazon region. The majority of Fst values for pairwise population comparisons among the indigenous groups are low or moderate. The indigenous people show high values of differentiation with Africans, Europeans and Southeast Asians and moderate values with East Asian and American populations, as expected. The allelic frequencies among indigenous indicate that the majority of associations observed with T2D in continental populations can be replicated in native Amazonians. The genetic risk scores calculated for T2D in indigenous are high and similar to those calculated for Americans and East Asians, while the estimates obtained for obesity are low, probably due to the low frequencies of the risk allele of the FTO gene found in our samples. ADRB3-rs4994 and ABCC8-rs1799854 genes showed a significant association with BMI and waist circumference, and the KCNJ11-rs5219 gene with hyperglycemia. These results emphasize the importance of knowing the genetic variability underlying complex genetic diseases in indigenous peoples and the search for particular or rare variants.

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

Polygenic score accuracy in ancient samples: Quantifying the effects of allelic turnover

Polygenic scores link the genotypes of ancient individuals to their phenotypes, which are often unobservable, offering a tantalizing opportunity to reconstruct complex trait evolution. In practice, however, interpretation of ancient polygenic scores is subject to numerous assumptions. For one, the genome-wide association (GWA) studies from which polygenic scores are derived, can only estimate effect sizes for loci segregating in contemporary populations. Therefore, a GWA study may not correctly identify all loci relevant to trait variation in the ancient population. In addition, the frequencies of trait-associated loci may have changed in the intervening years. Here, we devise a theoretical framework to quantify the effect of this allelic turnover on the statistical properties of polygenic scores as functions of population genetic dynamics, trait architecture, power to detect significant loci, and the age of the ancient sample. We model the allele frequencies of loci underlying trait variation using the Wright-Fisher diffusion, and employ the spectral representation of its transition density to find analytical expressions for several error metrics, including the expected sample correlation between the polygenic scores of ancient individuals and their true phenotypes, referred to as polygenic score accuracy. Our theory also applies to a two-population scenario and demonstrates that allelic turnover alone may explain a substantial percentage of the reduced accuracy observed in cross-population predictions, akin to those performed in human genetics. Finally, we use simulations to explore the effects of recent directional selection, a bias-inducing process, on the statistics of interest. We find that even in the presence of bias, weak selection induces minimal deviations from our neutral expectations for the decay of polygenic score accuracy. By quantifying the limitations of polygenic scores in an explicit evolutionary context, our work lays the foundation for the development of more sophisticated statistical procedures to analyze both temporally and geographically resolved polygenic scores.

Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits-The Hispanic/Latino Anthropometry Consortium

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

Current progress and open challenges for applying deep learning across the biosciences

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.

Cross-ancestry Genome-wide Association Studies of Sex Hormone Concentrations in Pre- and Postmenopausal Women

OBJECTIVE

Concentrations of circulating sex hormones have been associated with a variety of diseases in women and are strongly influenced by menopausal status. We investigated the genetic architectures of circulating concentrations of estradiol, testosterone, and SHBG by menopausal status in women of European and African ancestry.

METHODS

Using data on 229 966 women from the UK Biobank, we conducted genome-wide association studies (GWASs) of circulating concentrations of estradiol, testosterone, and SHBG in premenopausal and postmenopausal women. We tested for evidence of heterogeneity of genetic effects by menopausal status and genetic ancestry. We conducted gene-based enrichment analyses to identify tissues in which genes with GWAS-enriched signals were expressed.

RESULTS

We identified 4 loci (5q35.2, 12q14.3, 19q13.42, 20p12.3) that were associated with detectable concentrations of estradiol in both pre- and postmenopausal women of European ancestry. Heterogeneity analysis identified 1 locus for testosterone (7q22.1) in the CYP3A7 gene and 1 locus that was strongly associated with concentrations of SHBG in premenopausal women only (10q15.1) near the AKR1C4 gene. Gene-based analysis of testosterone revealed evidence of enrichment of GWAS signals in genes expressed in adipose tissue for postmenopausal women. We did not find any evidence of ancestry-specific genetic effects for concentrations of estradiol, testosterone, or SHBG.

CONCLUSIONS

We identified specific loci that showed genome-wide significant evidence of heterogeneity by menopausal status for testosterone and SHBG. We also observed support for a more prominent role of genetic variants located near genes expressed in adipose tissue in determining testosterone concentrations among postmenopausal women.

From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

Epidemiology of Type 2 Diabetes in Indigenous Communities in the United States

PURPOSE OF REVIEW

The present review focuses on the epidemiology of type 2 diabetes (T2D) in Indigenous communities in the continental United States (U.S.)-including disease prevention and management-and discusses special considerations in conducting research with Indigenous communities.

RECENT FINDINGS

Previous studies have reported the disparately high prevalence of diabetes, especially T2D, among Indigenous peoples in the U.S. The high prevalence and incidence of early-onset T2D in Indigenous youth relative to that of all youth in the U.S. population pose challenges to the prevention of complications of diabetes. Behavioral, dietary, lifestyle, and genetic factors associated with T2D in Indigenous communities are often investigated. More limited is the discussion of the historical and ongoing consequences of colonization and displacement that impact the aforementioned risk factors. Future research is necessary to assess community-specific needs with respect to diabetes prevention and management across the diversity of Indigenous communities in the U.S.

Human immune polymorphisms associated with the risk of cryptococcal disease

Cryptococcus neoformans is an opportunistic fungal pathogen that can cause lethal cryptococcal meningitis in immunocompromised individuals such as those with HIV/AIDS. In addition, cryptococcal infections occasionally arise in immunocompetent individuals or those with previously undiagnosed immunodeficiencies. The course of cryptococcosis is highly variable in both patient groups, and there is rapidly growing evidence that genetic polymorphisms may have a significant impact on the trajectory of disease. Here, we review what is currently known about the nature of these polymorphisms and their impact on host response to C. neoformans infection. Thus far, polymorphisms in Fc gamma receptors, mannose‐binding lectin, Dectin‐2, Toll‐like receptors and macrophage colony‐stimulating factor have been associated with susceptibility to cryptococcal disease. Notably, however, in some cases the impact of these polymorphisms depends on the genetic background of the population; for example, the FCGR3A 158 F/V polymorphism was associated with an increased risk of cryptococcal disease in both HIV‐positive and HIV‐negative white populations, but not in Han Chinese patients. In most cases, the precise mechanism by which the identified polymorphisms influence disease progression remains unclear, although impaired fungal recognition and phagocytosis by innate immune cells appears to play a major role. Finally, we highlight outstanding questions in the field and emphasize the need for future research to include more diverse populations in their genetic association studies.

Continuity of Genetic Risk for Aggressive Behavior Across the Life-Course

We test whether genetic influences that explain individual differences in aggression in early life also explain individual differences across the life-course. In two cohorts from The Netherlands (N = 13,471) and Australia (N = 5628), polygenic scores (PGSs) were computed based on a genome-wide meta-analysis of childhood/adolescence aggression. In a novel analytic approach, we ran a mixed effects model for each age (Netherlands: 12-70 years, Australia: 16-73 years), with observations at the focus age weighted as 1, and decaying weights for ages further away. We call this approach a 'rolling weights' model. In The Netherlands, the estimated effect of the PGS was relatively similar from age 12 to age 41, and decreased from age 41-70. In Australia, there was a peak in the effect of the PGS around age 40 years. These results are a first indication from a molecular genetics perspective that genetic influences on aggressive behavior that are expressed in childhood continue to play a role later in life.

The Need for a Human Pangenome Reference Sequence

The reference human genome sequence is inarguably the most important and widely used resource in the fields of human genetics and genomics. It has transformed the conduct of biomedical sciences and brought invaluable benefits to the understanding and improvement of human health. However, the commonly used reference sequence has profound limitations, because across much of its span, it represents the sequence of just one human haplotype. This single, monoploid reference structure presents a critical barrier to representing the broad genomic diversity in the human population. In this review, we discuss the modernization of the reference human genome sequence to a more complete reference of human genomic diversity, known as a human pangenome.

Candidate Genes and Pathways Associated with Gilles de la Tourette Syndrome-Where Are We?

Gilles de la Tourette syndrome (GTS) is a childhood-onset neurodevelopmental and -psychiatric tic-disorder of complex etiology which is often comorbid with obsessive-compulsive disorder (OCD) and/or attention deficit hyperactivity disorder (ADHD). Twin and family studies of GTS individuals have shown a high level of heritability suggesting, that genetic risk factors play an important role in disease etiology. However, the identification of major GTS susceptibility genes has been challenging, presumably due to the complex interplay between several genetic factors and environmental influences, low penetrance of each individual factor, genetic diversity in populations, and the presence of comorbid disorders. To understand the genetic components of GTS etiopathology, we conducted an extensive review of the literature, compiling the candidate susceptibility genes identified through various genetic approaches. Even though several strong candidate genes have hitherto been identified, none of these have turned out to be major susceptibility genes yet.

Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population

BACKGROUND

Genome-wide association studies (GWAS) have identified a number of genetic variants for Alzheimer's disease (AD). However, most GWAS were conducted in individuals of European ancestry, and non-European populations are still underrepresented in genetic discovery efforts. Here, we performed GWAS to identify single nucleotide polymorphisms (SNPs) associated with amyloid β (Aβ) positivity using a large sample of Korean population.

METHODS

One thousand four hundred seventy-four participants of Korean ancestry were recruited from multicenters in South Korea. Discovery dataset consisted of 1190 participants (383 with cognitively unimpaired [CU], 330 with amnestic mild cognitive impairment [aMCI], and 477 with AD dementia [ADD]) and replication dataset consisted of 284 participants (46 with CU, 167 with aMCI, and 71 with ADD). GWAS was conducted to identify SNPs associated with Aβ positivity (measured by amyloid positron emission tomography). Aβ prediction models were developed using the identified SNPs. Furthermore, bioinformatics analysis was conducted for the identified SNPs.

RESULTS

In addition to APOE, we identified nine SNPs on chromosome 7, which were associated with a decreased risk of Aβ positivity at a genome-wide suggestive level. Of these nine SNPs, four novel SNPs (rs73375428, rs2903923, rs3828947, and rs11983537) were associated with a decreased risk of Aβ positivity (p < 0.05) in the replication dataset. In a meta-analysis, two SNPs (rs7337542 and rs2903923) reached a genome-wide significant level (p < 5.0 × 10-8). Prediction performance for Aβ positivity increased when rs73375428 were incorporated (area under curve = 0.75; 95% CI = 0.74-0.76) in addition to clinical factors and APOE genotype. Cis-eQTL analysis demonstrated that the rs73375428 was associated with decreased expression levels of FGL2 in the brain.

CONCLUSION

The novel genetic variants associated with FGL2 decreased risk of Aβ positivity in the Korean population. This finding may provide a candidate therapeutic target for AD, highlighting the importance of genetic studies in diverse populations.