Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations

Trans-ethnic GWAS meta-analysis of idiopathic spermatogenic failure highlights the immune-mediated nature of Sertoli cell-only syndrome

Non-obstructive azoospermia, a severe form of male infertility caused by spermatogenic failure (SPGF), has a largely unknown genetic basis across ancestries. To our knowledge, this is the first trans-ethnic meta-analysis of genome-wide association studies on SPGF, involving 2255 men with idiopathic SPGF and 3608 controls from European and Asian populations. Using logistic regression and inverse variance methods, we identify two significant genetic associations with Sertoli cell-only (SCO) syndrome, the most extreme SPGF phenotype. The G allele of rs34915133, in the major histocompatibility complex class II region, significantly increases SCO risk (P = 5.25E-10, OR = 1.57), supporting a potential immune-related cause. Additionally, the rs10842262 variant in the SOX5 gene region is also a genetic marker of SCO (P = 5.29E-09, OR = 0.72), highlighting the key role of this gene in the male reproductive function. Our findings reveal shared genetic factors in male infertility across ancestries and provide insights into the molecular mechanisms underlying SCO.

Seven loci associated with schizophrenia and bipolar I disorder in selected southern African population groups

Two major psychiatric disorders, schizophrenia and bipolar I disorder, are regarded as distinct disorder entities; however, they share intricate connections through characteristic overlap and underlying genetic aetiology, challenging the traditional dichotomy. This convergence emerged as an essential area of investigation in understanding the genetic determinants of schizophrenia and bipolar I disorder. Moreover, psychiatric genetic research has revealed demographic disparities, with South African population groups notably underrepresented. Therefore, this preliminary targeted candidate gene association study of 20 single nucleotide polymorphisms implicated in schizophrenia and bipolar I disorder aimed to investigate association and overlap. Candidate loci for schizophrenia and bipolar I disorder were selected through an exploratory Illumina® Infinium PsychArray-24 analysis combined with literature and database searches. Genotyping of the selected loci was performed with the Agena Bioscience MassARRAY® platform on 96 cases (58 schizophrenia and 38 bipolar I disorder patients) and 44 controls of Afrikaner, Sotho, and Tswana descent. Association analysis was performed by comparing and combining population and phenotype groups. Significant (p < 0.05) loci in the ADAMTSL1, CACNA1B, CACNA1C, CDH13, CTNNA2, RBFOX1, and TRIO genes were identified as possible susceptibility factors, and differences were observed with the association between population and phenotype groups. Through further pathway analysis, the calcium and cadherin-catenin pathways were identified as possible role players in the aetiology of schizophrenia and bipolar I disorder. The study represented an essential step towards understanding the genetic contribution towards schizophrenia and bipolar I disorder in distinct population groups and has the potential to contribute towards the knowledge base and inform future research efforts.

Update on the genetics of allergic diseases

The field of genetic etiology of allergic diseases has advanced significantly in recent years. Shared risk loci reflect the contribution of genetic factors to the sequential development of allergic conditions across the atopic march, while unique risk loci provide opportunities to understand tissue specific manifestations of allergic disease. Most identified risk variants are noncoding, indicating that they likely influence gene expression through gene regulatory mechanisms. Despite recent advances, challenges persist, particularly regarding the need for increased ancestral diversity in research populations. Further, while polygenic risk scores show promise for identifying individuals at higher genetic risk for allergic diseases, their predictive accuracy varies across different ancestries and can be difficult to translate to an individual's absolute risk of developing a disease. Methodologies, including "nearest gene," 3D chromatin interaction analysis, expression quantitative trait locus analysis, experimental screens, and integrative bioinformatic models, have established connections between genetic variants and their regulatory targets, enhancing our understanding of disease risk and phenotypic variability. In this review, we focus on the state of knowledge of allergic sensitization and 5 allergic diseases: asthma, atopic dermatitis, allergic rhinitis, food allergy, and eosinophilic esophagitis. We summarize recent progress and highlight opportunities for advancing our understanding of their genetic etiology.

The Interplay of Genetic Predisposition, Circadian Misalignment, and Metabolic Regulation in Obesity

PURPOSE OF REVIEW

This review explores the complex interplay between genetic predispositions to obesity, circadian rhythms, metabolic regulation, and sleep. It highlights how genetic factors underlying obesity exacerbate metabolic dysfunction through circadian misalignment and examines promising interventions to mitigate these effects.

RECENT FINDINGS

Genome-wide association Studies (GWAS) have identified numerous Single Nucleotide Polymorphisms (SNPs) associated with obesity traits, attributing 40-75% heritability to body mass index (BMI). These findings illuminate critical links between genetic obesity, circadian clocks, and metabolic processes. SNPs in clock-related genes influence metabolic pathways, with disruptions in circadian rhythms-driven by poor sleep hygiene or erratic eating patterns-amplifying metabolic dysfunction. Circadian clocks, synchronized with the 24-h light-dark cycle, regulate key metabolic activities, including glucose metabolism, lipid storage, and energy utilization. Genetic mutations or external disruptions, such as irregular sleep or eating habits, can destabilize circadian rhythms, promoting weight gain and metabolic disorders. Circadian misalignment in individuals with genetic predispositions to obesity disrupts the release of key metabolic hormones, such as leptin and insulin, impairing hunger regulation and fat storage. Interventions like time-restricted feeding (TRF) and structured physical activity offer promising strategies to restore circadian harmony, improve metabolic health, and mitigate obesity-related risks.

TEMR: Trans-ethnic mendelian randomization method using large-scale GWAS summary datasets

Available large-scale genome-wide association study (GWAS) summary datasets predominantly stem from European populations, while sample sizes for other ethnicities, notably Central/South Asian, East Asian, African, Hispanic, etc., remain comparatively limited, resulting in low precision of causal effect estimations within these ethnicities when using Mendelian randomization (MR). In this paper, we propose a trans-ethnic MR method, TEMR, to improve the statistical power and estimation precision of MR in a target population that is underrepresented, using trans-ethnic large-scale GWAS summary datasets. TEMR incorporates trans-ethnic genetic correlation coefficients through a conditional likelihood-based inference framework, producing calibrated p values with substantially improved MR power. In the simulation study, compared with other existing MR methods, TEMR exhibited superior precision and statistical power in causal effect estimation within the target populations. Finally, we applied TEMR to infer causal relationships between concentrations of 16 blood biomarkers and the risk of developing five diseases (hypertension, ischemic stroke, type 2 diabetes, schizophrenia, and major depression disorder) in East Asian, African, and Hispanic/Latino populations, leveraging biobank-scale GWAS summary data obtained from individuals of European descent. We found that the causal biomarkers were mostly validated by previous MR methods, and we also discovered 17 causal relationships that were not identified using previously published MR methods.

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.

Understanding the Genetic Architecture of Vitamin Status Biomarkers in the Genome-Wide Association Study Era: Biological Insights and Clinical Significance

Vitamins play an intrinsic role in human health and are targets for clinical intervention through dietary or pharmacological approaches. Biomarkers of vitamin status are complex traits, measurable phenotypes that arise from an interplay between dietary and other environmental factors with a genetic component that is polygenic, meaning many genes are plausibly involved. Studying these genetic influences will improve our knowledge of fundamental vitamin biochemistry, refine estimates of the effects of vitamins on human health, and may in future prove clinically actionable. Here, we evaluate genetic studies of circulating and excreted biomarkers of vitamin status in the era of hypothesis-free genome-wide association studies (GWAS) that have provided unprecedented insights into the genetic architecture of these traits. We found that the most comprehensive and well-powered GWAS currently available were for circulating status biomarkers of vitamin A, C, D, and a subset of the B vitamins (B9 and B12). The biology implicated by GWAS of measured biomarkers of each vitamin is then discussed, both in terms of key genes and higher-order processes. Across all major vitamins, there were genetic signals revealed by GWAS that could be directly linked with known vitamin biochemistry. We also outline how genetic variants associated with vitamin status biomarkers have been already extensively used to estimate causal effects of vitamins on human health outcomes, which is particularly important given the large number of randomized control trials of vitamin related interventions with null findings. Finally, we discuss the current evidence for the clinical applicability of findings from vitamin GWAS, along with future directions for the field to maximize the utility of these data.

DNA Methylation Signatures of Cardiovascular Health Provide Insights into Diseases

Background

The association of overall cardiovascular health (CVH) with changes in DNA methylation (DNAm) has not been well characterized.

Methods

We calculated the American Heart Association's Life's Essential 8 (LE8) score to reflect CVH in five cohorts with diverse ancestry backgrounds. Epigenome-wide association studies (EWAS) for LE8 score were conducted, followed by bioinformatic analyses. DNAm loci significantly associated with LE8 score were used to calculate a CVH DNAm score. We examined the association of the CVH DNAm score with incident CVD, CVD-specific mortality, and all-cause mortality.

Results

We identified 609 CpGs associated with LE8 score at false discovery rate (FDR) < 0.05 in the discovery analysis and at Bonferroni corrected P < 0.05 in the multi-cohort replication stage. Most had low-to-moderate heterogeneity (414 CpGs [68.0%] with I2 < 0.2) in replication analysis. Pathway enrichment analyses and phenome-wide association study (PheWAS) search associated these CpGs with inflammatory or autoimmune phenotypes. We observed enrichment for phenotypes in the EWAS catalog, with 29-fold enrichment for stroke (P = 2.4e-15) and 21-fold for ischemic heart disease (P = 7.4e-38). Two-sample Mendelian randomization (MR) analysis showed significant association between 141 CpGs and ten phenotypes (261 CpG-phenotype pairs) at FDR < 0.05. For example, hypomethylation at cg20544516 (MIR33B; SREBF1) associated with lower risk of stroke (P = 8.1e-6). In multivariable prospective analyses, the CVH DNAm score was consistently associated with clinical outcomes across participating cohorts, the reduction in risk of incident CVD, CVD mortality, and all-cause mortality per standard deviation increase in the DNAm score ranged from 19% to 32%, 28% to 40%, and 27% to 45%, respectively.

Conclusions

We identified new DNAm signatures for CVH across diverse cohorts. Our analyses indicate that immune response-related pathways may be the key mechanism underpinning the association between CVH and clinical outcomes.

Novel risk loci for COVID-19 hospitalization among admixed American populations

The genetic basis of severe COVID-19 has been thoroughly studied, and many genetic risk factors shared between populations have been identified. However, reduced sample sizes from non-European groups have limited the discovery of population-specific common risk loci. In this second study nested in the SCOURGE consortium, we conducted a genome-wide association study (GWAS) for COVID-19 hospitalization in admixed Americans, comprising a total of 4702 hospitalized cases recruited by SCOURGE and seven other participating studies in the COVID-19 Host Genetic Initiative. We identified four genome-wide significant associations, two of which constitute novel loci and were first discovered in Latin American populations (BAZ2B and DDIAS). A trans-ethnic meta-analysis revealed another novel cross-population risk locus in CREBBP. Finally, we assessed the performance of a cross-ancestry polygenic risk score in the SCOURGE admixed American cohort. This study constitutes the largest GWAS for COVID-19 hospitalization in admixed Latin Americans conducted to date. This allowed to reveal novel risk loci and emphasize the need of considering the diversity of populations in genomic research.

Amygdala connectivity is associated with withdrawn/depressed behavior in a large sample of children from the Adolescent Brain Cognitive Development (ABCD) Study®

Many psychopathologies tied to internalizing symptomatology emerge during adolescence, therefore identifying neural markers of internalizing behavior in childhood may allow for early intervention. We utilized data from the Adolescent Brain and Cognitive Development (ABCD) Study® to evaluate associations between cortico-amygdalar functional connectivity, polygenic risk for depression (PRSD), traumatic events experienced, internalizing behavior, and internalizing subscales: withdrawn/depressed behavior, somatic complaints, and anxious/depressed behaviors. Data from 6371 children (ages 9-11) were used to analyze amygdala resting-state fMRI connectivity to Gordon parcellation based whole-brain regions of interest (ROIs). Internalizing behaviors were measured using the parent-reported Child Behavior Checklist. Linear mixed-effects models were used to identify patterns of cortico-amygdalar connectivity associated with internalizing behaviors. Results indicated left amygdala connections to auditory, frontoparietal network (FPN), and dorsal attention network (DAN) ROIs were significantly associated with withdrawn/depressed symptomatology. Connections relevant for withdrawn/depressed behavior were linked to social behaviors. Specifically, amygdala connections to DAN were associated with social anxiety, social impairment, and social problems. Additionally, an amygdala connection to the FPN ROI and the auditory network ROI was associated with social anxiety and social problems, respectively. Therefore, it may be important to account for social behaviors when looking for brain correlates of depression.

Association study reveals a susceptibility locus with male pattern baldness in the Han Chinese population

Introduction

Male pattern baldness (MPB), also known as androgenetic alopecia, represents the most prevalent form of progressive hair loss in humans. It is characterized by a distinctive pattern of hair loss progression from the scalp; however, its underlying mechanism remains elusive and is influenced by hereditary, immune, and environmental factors. Genome-wide association studies (GWASs) have uncovered numerous risk genes/loci among European individuals with MPB. However, the validation of these susceptibility genes/loci within Han Chinese men remains largely unexplored. The aim of this study was to investigate whether the 71 susceptibility loci identified in a recent GWAS among European men also confer risk for MPB in Chinese men.

Methods

Forty-seven single nucleotide polymorphisms (SNPs) previously reported in GWASs of MPB were selected and genotyped in independent individuals comprising 499 Han Chinese cases and 1,489 controls using the Sequenom MassArray system. After stringent quality control measures, 25 SNPs were subjected to statistical analyses. Cochran-Armitage trend test was used to evaluate the association between SNPs and disease susceptibility. To address multiple tests, Bonferroni correction was conducted, setting the threshold for statistical significance at a p-value <2 × 10-3 (0.05/25).

Results

The rs13405699 SNP located at 2q31.1 exhibited a significant association with MPB in Han Chinese men (p = 4.84 × 10-5, OR = 1.37, 95% CI: 1.18-1.59). Moreover, the difference in rs13405699 genotype distribution between MPB cases and controls was statistically significant (p = 7.00 × 10-5). Genotype-based association analysis suggested that the recessive model provided the best fit for the rs13405699 polymorphism.

Conclusion

This study represents the first confirmation of the association between the rs13405699 SNP at 2q31.1 and MPB within the Han Chinese population, thereby enhancing our understanding of the genetic underpinnings of MPB.

Genetic Risk Factors for Bortezomib-induced Neuropathic Pain in an Asian Population: A Genome-wide Association Study in South Korea

Bortezomib-induced neuropathic pain (BINP) poses a challenge in multiple myeloma (MM) treatment. Genetic factors play a key role in BINP susceptibility, but research has predominantly focused on Caucasian populations. This research explored novel genetic risk loci and pathways associated with BINP development in Korean MM patients while evaluating the reproducibility of variants from Caucasians. Clinical data and buffy coat samples from 185 MM patients on bortezomib were collected. The cohort was split into discovery and validation cohorts through random stratification of clinical risk factors for BINP. Genome-wide association study was performed on the discovery cohort (n = 74) with Infinium Global Screening Array-24 v3.0 BeadChip (654,027 single nucleotide polymorphism [SNPs]). Relevant biological pathways were identified using the pathway scoring algorithm. The top 20 SNPs were validated in the validation cohort (n = 111). Previously reported SNPs were validated in the entire cohort (n = 185). Pathway analysis of the genome-wide association study results identified 31 relevant pathways, including immune systems and endosomal vacuolar pathways. Among the top 20 SNPs from the discovery cohort, 16 were replicated, which included intronic variants in ASIC2 and SMOC2, recently implicated in nociception, as well as intergenic variants or long noncoding RNAs. None of the 17 previously reported SNPs remained significant in our cohort (rs2274578, P = .085). This study represents the first investigation of novel genetic loci and biological pathways associated with BINP occurrence. Our findings, in conjunction with existing Caucasian studies, expand the understanding of personalized risk prediction and disease mechanisms. PERSPECTIVE: This article is the first to explore novel genetic loci and pathways linked to BINP in Korean MM patients, offering novel insights beyond the existing research focused on Caucasian populations into personalized risk assessment and therapeutic strategies of BINP.

The polygenic implication of clopidogrel responsiveness: Insights from platelet reactivity analysis and next-generation sequencing

Clopidogrel is widely used worldwide as an antiplatelet therapy in patients with acute coronary disease. Genetic factors influence interindividual variability in response. Some studies have explored the polygenic contributions in the drug response, generating pharmacogenomic risk scores (PgxPRS). Importantly, these factors are less explored in underrepresented populations, such as Latin-American countries. Identifying patients at risk of high-on-treatment platelet reactivity (HTPR) is highly valuable in translational medicine. In this study we used a custom next-generation sequencing (NGS) panel composed of 91 single nucleotide polymorphisms (SNPs) and 28 genes related to clopidogrel metabolism, to analyze 70 patients with platelet reactivity values, assessed through closure time (CT). Our results demonstrated the association of SNPs with HTPR and non-HTPR, revealing the strongest associations with rs2286823 (OR: 5,0; 95% CI: 1,02-24,48; p: 0,03), rs2032582 (OR: 4,41; 95% CI: 1,20-16,12; p: 0,019), and rs1045642 (OR: 3,38; 95% CI: 0,96-11,9; p: 0,05). Bivariate regression analysis demonstrated the significant association of several SNPs with the CT value, a "surrogate" biomarker of clopidogrel response. Exploratory results from the LASSO regression model showed a high discriminatory capacity between HTPR and non-HTPR patients (AUC: 0,955), and the generated PgxPRS demonstrated a significant negative association between the risk score, CT value, and the condition of HTPR and non-HTPR. To our knowledge, our study addresses for the first time the analysis of the polygenic contribution in platelet reactivity using NGS and establishes PgxPRS derived from the LASSO model. Our results demonstrate the polygenic implication of clopidogrel response and offer insights applicable to the translational medicine of antiplatelet therapy in an understudied population.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Low-frequency and rare genetic variants associated with rheumatoid arthritis risk

Rheumatoid arthritis (RA) has an estimated heritability of nearly 50%, which is particularly high in seropositive RA. HLA alleles account for a large proportion of this heritability, in addition to many common single-nucleotide polymorphisms with smaller individual effects. Low-frequency and rare variants, such as those captured by next-generation sequencing, can also have a large role in heritability in some individuals. Rare variant discovery has informed the development of drugs such as inhibitors of PCSK9 and Janus kinases. Some 34 low-frequency and rare variants are currently associated with RA risk. One variant (19:10352442G>C in TYK2) was identified in five separate studies, and might therefore represent a promising therapeutic target. Following a set of best practices in future studies, including studying diverse populations, using large sample sizes, validating RA and serostatus, replicating findings, adjusting for other variants and performing functional assessment, could help to ensure the relevance of identified variants. Exciting opportunities are now on the horizon for genetics in RA, including larger datasets and consortia, whole-genome sequencing and direct applications of findings in the management, and especially treatment, of RA.

Meta-analysis of ACE inhibitor-induced angioedema identifies novel risk locus

BACKGROUND

Angioedema is a rare but potentially life-threatening adverse drug reaction in patients receiving angiotensin-converting enzyme inhibitors (ACEis). Research suggests that susceptibility to ACEi-induced angioedema (ACEi-AE) involves both genetic and nongenetic risk factors. Genome- and exome-wide studies of ACEi-AE have identified the first genetic risk loci. However, understanding of the underlying pathophysiology remains limited.

OBJECTIVE

We sought to identify further genetic factors of ACEi-AE to eventually gain a deeper understanding of its pathophysiology.

METHODS

By combining data from 8 cohorts, a genome-wide association study meta-analysis was performed in more than 1000 European patients with ACEi-AE. Secondary bioinformatic analyses were conducted to fine-map associated loci, identify relevant genes and pathways, and assess the genetic overlap between ACEi-AE and other traits. Finally, an exploratory cross-ancestry analysis was performed to assess shared genetic factors in European and African-American patients with ACEi-AE.

RESULTS

Three genome-wide significant risk loci were identified. One of these, located on chromosome 20q11.22, has not been implicated previously in ACEi-AE. Integrative secondary analyses highlighted previously reported genes (BDKRB2 [bradykinin receptor B2] and F5 [coagulation factor 5]) as well as biologically plausible novel candidate genes (PROCR [protein C receptor] and EDEM2 [endoplasmic reticulum degradation enhancing alpha-mannosidase like protein 2]). Lead variants at the risk loci were found with similar effect sizes and directions in an African-American cohort.

CONCLUSIONS

The present results contributed to a deeper understanding of the pathophysiology of ACEi-AE by (1) providing further evidence for the involvement of bradykinin signaling and coagulation pathways and (2) suggesting, for the first time, the involvement of the fibrinolysis pathway in this adverse drug reaction. An exploratory cross-ancestry comparison implicated the relevance of the associated risk loci across diverse ancestries.

Genetic and brain similarity independently predict childhood anthropometrics and neighborhood socioeconomic conditions

Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ an integrative approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, associations between cortical thickness similarity and traits were limited to anthropometrics such as height, weight, and birth weight, as well as a marker of neighborhood socioeconomic conditions. Common genetic variants explained significant proportions of variance across nearly all included outcomes, although estimates were somewhat lower than previous reports. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as neighborhood socioeconomic conditions and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample.

South Asia: The Missing Diverse in Diversity

South Asia, making up around 25% of the world's population, encompasses a wide range of individuals with tremendous genetic and environmental diversity. This region, which spans eight countries, is home to over 4500 anthropologically defined groups that speak numerous languages and have an array of religious beliefs and cultures, making it one of the most diverse places in the world. Much of the region's rich genetic diversity and structure is the result of a complex combination of population history, migration patterns, and endogamous practices. Despite the overwhelming size and diversity, South Asians have often been underrepresented in genetic research, making up less than 2% of the participants in genetic studies. This has led to a lack of population specific understanding of genetic disease risks. We aim to raise awareness about underlying genetic diversity in this ancestry group, call attention to the lack of representation of the group, and to highlight strategies for future studies in South Asians.

Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels

KEY MESSAGE

An original GWAS model integrating the ancestry of alleles was proposed and allowed the detection of background specific additive and dominance QTLs involved in heterotic group complementarity and hybrid performance. Maize genetic diversity is structured into genetic groups selected and improved relative to each other. This process increases group complementarity and differentiation over time and ensures that the hybrids produced from inter-group crosses exhibit high performances and heterosis. To identify loci involved in hybrid performance and heterotic group complementarity, we introduced an original association study model that disentangles allelic effects from the heterotic group origin of the alleles and compared it with a conventional additive/dominance model. This new model was applied on a factorial between Dent and Flint lines and a diallel between Dent-Flint admixed lines with two different layers of analysis: within each environment and in a multiple-environment context. We identified several strong additive QTLs for all traits, including some well-known additive QTLs for flowering time (in the region of Vgt1/2 on chromosome 8). Yield trait displayed significant non-additive effects in the diallel panel. Most of the detected Yield QTLs exhibited overdominance or, more likely, pseudo-overdominance effects. Apparent overdominance at these QTLs contributed to a part of the genetic group complementarity. The comparison between environments revealed a higher stability of additive QTL effects than non-additive ones. Several QTLs showed variations of effects according to the local heterotic group origin. We also revealed large chromosomic regions that display genetic group origin effects. Altogether, our results illustrate how admixed panels combined with dedicated GWAS modeling allow the identification of new QTLs that could not be revealed by a classical hybrid panel analyzed with traditional modeling.

Modeling of horizontal pleiotropy identifies possible causal gene expression in systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is a chronic autoimmune condition with complex causes involving genetic and environmental factors. While genome-wide association studies (GWASs) have identified genetic loci associated with SLE, the functional genomic elements responsible for disease development remain largely unknown. Mendelian Randomization (MR) is an instrumental variable approach to causal inference based on data from observational studies, where genetic variants are employed as instrumental variables (IVs).

Methods

This study utilized a two-step strategy to identify causal genes for SLE. In the first step, the classical MR method was employed, assuming the absence of horizontal pleiotropy, to estimate the causal effect of gene expression on SLE. In the second step, advanced probabilistic MR methods (PMR-Egger, MRAID, and MR-MtRobin) were applied to the genes identified in the first step, considering horizontal pleiotropy, to filter out false positives. PMR-Egger and MRAID analyses utilized whole blood expression quantitative trait loci (eQTL) and SLE GWAS summary data, while MR-MtRobin analysis used an independent eQTL dataset from multiple immune cell types along with the same SLE GWAS data.

Results

The initial MR analysis identified 142 genes, including 43 outside of chromosome 6. Subsequently, applying the advanced MR methods reduced the number of genes with significant causal effects on SLE to 66. PMR-Egger, MRAID, and MR-MtRobin, respectively, identified 13, 7, and 16 non-chromosome 6 genes with significant causal effects. All methods identified expression of PHRF1 gene as causal for SLE. A comprehensive literature review was conducted to enhance understanding of the functional roles and mechanisms of the identified genes in SLE development.

Conclusions

The findings from the three MR methods exhibited overlapping genes with causal effects on SLE, demonstrating consistent results. However, each method also uncovered unique genes due to different modelling assumptions and technical factors, highlighting the complementary nature of the approaches. Importantly, MRAID demonstrated a reduced percentage of causal genes from the Major Histocompatibility complex (MHC) region on chromosome 6, indicating its potential in minimizing false positive findings. This study contributes to unraveling the mechanisms underlying SLE by employing advanced probabilistic MR methods to identify causal genes, thereby enhancing our understanding of SLE pathogenesis.

Disentangling the complexity of psoriasis in the post-genome-wide association era

In recent years, genome-wide association studies (GWAS) have been instrumental in unraveling the genetic architecture of complex diseases, including psoriasis. The application of large-scale GWA studies in psoriasis has illustrated several associated loci that participate in the cutaneous inflammation, however explaining a fraction of the disease heritability. With the advent of high-throughput sequencing technologies and functional genomics approaches, the post-GWAS era aims to unravel the functional mechanisms underlying the inter-individual variability in psoriasis patients. In this review, we present the key advances of psoriasis GWAS in under-represented populations, rare, non-coding and structural variants and epistatic phenomena that orchestrate the interplay between different cell types. We further review the gene-gene and gene-environment interactions contributing to the disease predisposition and development of comorbidities through Mendelian randomization studies and pleiotropic effects of psoriasis-associated loci. We finally examine the holistic approaches conducted in psoriasis through system genetics and state-of-the-art transcriptomic analyses, discussing their potential implication in the expanding field of precision medicine and characterization of comorbidities.

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.

Reduced Vrk2 expression is associated with higher risk of depression in humans and mediates depressive-like behaviors in mice

BACKGROUND

Genome-wide association studies (GWAS) have reported single-nucleotide polymorphisms (SNPs) in the VRK serine/threonine kinase 2 gene (VRK2) showing genome-wide significant associations with major depression, but the regulation effect of the risk SNPs on VRK2 as well as their roles in the illness are yet to be elucidated.

METHODS

Based on the summary statistics of major depression GWAS, we conducted population genetic analyses, epigenome bioinformatics analyses, dual luciferase reporter assays, and expression quantitative trait loci (eQTL) analyses to identify the functional SNPs regulating VRK2; we also carried out behavioral assessments, dendritic spine morphological analyses, and phosphorylated 4D-label-free quantitative proteomics analyses in mice with Vrk2 repression.

RESULTS

We identified a SNP rs2678907 located in the 5' upstream of VRK2 gene exhibiting large spatial overlap with enhancer regulatory marks in human neural cells and brain tissues. Using luciferase reporter gene assays and eQTL analyses, the depression risk allele of rs2678907 decreased enhancer activities and predicted lower VRK2 mRNA expression, which is consistent with the observations of reduced VRK2 level in the patients with major depression compared with controls. Notably, Vrk2-/- mice exhibited depressive-like behaviors compared to Vrk2+/+ mice and specifically repressing Vrk2 in the ventral hippocampus using adeno-associated virus (AAV) lead to consistent and even stronger depressive-like behaviors in mice. Compared with Vrk2+/+ mice, the density of mushroom and thin spines in the ventral hippocampus was significantly altered in Vrk2-/- mice, which is in line with the phosphoproteomic analyses showing dysregulated synapse-associated proteins and pathways in Vrk2-/- mice.

CONCLUSIONS

Vrk2 deficiency mice showed behavioral abnormalities that mimic human depressive phenotypes, which may serve as a useful murine model for studying the pathophysiology of depression.

Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases

Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestries has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping, which builds on the single-population fine-mapping framework, Sum of Single Effects (SuSiE). SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and LD patterns, accounts for multiple causal variants in a genomic region, and can be applied to GWAS summary statistics. We comprehensively evaluated SuSiEx using simulations, a range of quantitative traits measured in both UK Biobank and Taiwan Biobank, and schizophrenia GWAS across East Asian and European ancestries. In all evaluations, SuSiEx fine-mapped more association signals, produced smaller credible sets and higher posterior inclusion probability (PIP) for putative causal variants, and captured population-specific causal variants.

Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes

The hippocampus is critical for memory and cognition and neuropsychiatric disorders, and its subfields differ in architecture and function. Genome-wide association studies on hippocampal and subfield volumes are mainly conducted in European populations; however, other ancestral populations are under-represented. Here we conduct cross-ancestry genome-wide association meta-analyses in 65,791 individuals for hippocampal volume and 38,977 for subfield volumes, including 7,009 individuals of East Asian ancestry. We identify 339 variant-trait associations at P < 1.13 × 10^-9 for 44 hippocampal traits, including 23 new associations. Common genetic variants have similar effects on hippocampal traits across ancestries, although ancestry-specific associations exist. Cross-ancestry analysis improves the fine-mapping precision and the prediction performance of polygenic scores in under-represented populations. These genetic variants are enriched for Wnt signaling and neuron differentiation and affect cognition, emotion and neuropsychiatric disorders. These findings may provide insight into the genetic architectures of hippocampal and subfield volumes.

Leveraging trans-ethnic genetic risk scores to improve association power for complex traits in underrepresented populations

Trans-ethnic genome-wide association studies have revealed that many loci identified in European populations can be reproducible in non-European populations, indicating widespread trans-ethnic genetic similarity. However, how to leverage such shared information more efficiently in association analysis is less investigated for traits in underrepresented populations. We here propose a statistical framework, trans-ethnic genetic risk score informed gene-based association mixed model (GAMM), by hierarchically modeling single-nucleotide polymorphism effects in the target population as a function of effects of the same trait in well-studied populations. GAMM powerfully integrates genetic similarity across distinct ancestral groups to enhance power in understudied populations, as confirmed by extensive simulations. We illustrate the usefulness of GAMM via the application to 13 blood cell traits (i.e. basophil count, eosinophil count, hematocrit, hemoglobin concentration, lymphocyte count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, mean corpuscular volume, monocyte count, neutrophil count, platelet count, red blood cell count and total white blood cell count) in Africans of the UK Biobank (n = 3204) while utilizing genetic overlap shared in Europeans (n = 746 667) and East Asians (n = 162 255). We discovered multiple new associated genes, which had otherwise been missed by existing methods, and revealed that the trans-ethnic information indirectly contributed much to the phenotypic variance. Overall, GAMM represents a flexible and powerful statistical framework of association analysis for complex traits in underrepresented populations by integrating trans-ethnic genetic similarity across well-studied populations, and helps attenuate health inequities in current genetics research for people of minority populations.

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.

PIK3R3 is a candidate regulator of platelet count in people of Bangladeshi ancestry

Background

Blood platelets are mediators of atherothrombotic disease and are regulated by complex sets of genes. Association studies in European ancestry populations have already detected informative platelet regulatory loci. Studies in other ancestries can potentially reveal new associations because of different allele frequencies, linkage structures, and variant effects.

Objectives

To reveal new regulatory genes for platelet count (PLT).

Methods

Genome-wide association studies (GWAS) were performed in 20,218 Bangladeshi and 9198 Pakistani individuals from the Genes & Health study. Loci significantly associated with PLT underwent fine-mapping to identify candidate genes.

Results

Of 1588 significantly associated variants (P < 5 × 10-8) at 20 loci in the Bangladeshi analysis, most replicated findings in prior transancestry GWAS and in the Pakistani analysis. However, the Bangladeshi locus defined by rs946528 (chr1:46019890) did not associate with PLT in the Pakistani analysis but was in the same linkage disequilibrium block (r2 ≥ 0.5) as PLT-associated variants in prior East Asian GWAS. The single independent association signal was refined to a 95% credible set of 343 variants spanning 8 coding genes. Functional annotation, mapping to megakaryocyte regulatory regions, and colocalization with blood expression quantitative trait loci identified the likely mediator of the PLT phenotype to be PIK3R3 encoding a regulator of phosphoinositol 3-kinase (PI3K).

Conclusion

Abnormal PI3K activity in the vessel wall is already implicated in the pathogenesis of atherothrombosis. Our identification of a new association between PIK3R3 and PLT provides further mechanistic insights into the contribution of the PI3K pathway to platelet biology.

Seeing humans through an evolutionary lens

A collection of mammalian genomes provides insights into human biology and evolution.

A genome-wide association study identifies a possible role for cannabinoid signalling in the pathogenesis of diabetic kidney disease

Diabetic kidney disease (DKD), also known as diabetic nephropathy, is the leading cause of renal impairment and end-stage renal disease. Patients with diabetes are at risk for DKD because of poor control of their blood glucose, as well as nonmodifiable risk factors including age, ethnicity, and genetics. This genome-wide association study (GWAS) was conducted for the first time in the Emirati population to investigate possible genetic factors associated with the development and progression of DKD. We included data on 7,921,925 single nucleotide polymorphism (SNPs) in 258 cases of type 2 diabetes mellitus (T2DM) who developed DKD and 938 control subjects with T2DM who did not develop DKD. GWAS suggestive results (P < 1 × 10-5) were further replicated using summary statistics from three cohorts with T2DM-induced DKD (Bio Bank Japan data, UK Biobank, and FinnGen Project data) and T1DM-induced DKD (UK-ROI cohort data from Belfast, UK). When conducting a multiple linear regression model for gene-set analyses, the CNR2 gene demonstrated genome-wide significance at 1.46 × 10-6. SNPs in CNR2 gene, encodes cannabinoid receptor 2 or CB2, were replicated in Japanese samples with the leading SNP rs2501391 showing a Pcombined = 9.3 × 10-7, and odds ratio = 0.67 in association with DKD associated with T2DM, but not with T1DM, without any significant association with T2DM itself. The allele frequencies of our cohort and those of the replication cohorts were in most cases markedly different. In addition, we replicated the association between rs1564939 in the GLRA3 gene and DKD in T2DM (P = 0.016, odds ratio = 0.54 per allele C). Our findings suggest evidence that cannabinoid signalling may be involved in the development of DKD through CB2, which is expressed in different kidney regions and known to be involved in insulin resistance, inflammation, and the development of kidney fibrosis.

The genetic and evolutionary basis of gene expression variation in East Africans

BACKGROUND

Mapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases, though most studies have focused on individuals of European descent. While important progress has been made to study a greater diversity of human populations, many groups remain unstudied, particularly among indigenous populations within Africa. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations.

RESULTS

We find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine-mapping of causal variation. Integrating our QTL scans with signatures of natural selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation.

CONCLUSION

Extending QTL mapping studies beyond European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.

From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies?

Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.

Multinational Genome-Wide Association Study and Functional Genomics Analysis Implicates Decreased SIRT3 Expression Underlying Intracranial Aneurysm Risk

BACKGROUND

The genetic mechanisms regulating intracranial aneurysm (IA) formation and rupture are largely unknown. To identify germline-genetic risk factors for IA, we perform a multinational genome-wide association study (GWAS) of individuals from the United Kingdom, Finland, and Japan.

OBJECTIVE

To identify a shared, multinational genetic basis of IA.

METHODS

Using GWAS summary statistics from UK Biobank, FinnGen, and Biobank Japan, we perform a meta-analysis of IA, containing ruptured and unruptured IA cases. Logistic regression was used to identify IA-associated single-nucleotide polymorphisms. Effect size was calculated using the coefficient r , estimating the contribution of the single-nucleotide polymorphism to the genetic variance of the trait. Genome-wide significance was set at 5.0 × 10 -8 . Expression quantitative trait loci mapping and functional genomics approaches were used to infer mechanistic consequences of implicated variants.

RESULTS

Our cohort contained 155 154 individuals (3132 IA cases and 152 022 controls). We identified 4 genetic loci reaching genome-wide: rs73392700 ( SIRT3 , effect size = 0.28, P = 4.3 × 10 -12 ), rs58721068 ( EDNRA , effect size = -0.20, P = 4.8 × 10 -12 ), rs4977574 ( AL359922.1 , effect size = 0.18, P = 7.9 × 10 -12 ), and rs11105337 ( ATP2B1 , effect size = -0.15, P = 3.4 × 10 -8 ). Expression quantitative trait loci mapping suggests that rs73392700 has a large effect size on SIRT3 gene expression in arterial and muscle, but not neurological, tissues. Functional genomics analysis suggests that rs73392700 causes decreased SIRT3 gene expression.

CONCLUSION

We perform a multinational GWAS of IA and identify 4 genetic risk loci, including 2 novel IA risk loci ( SIRT3 and AL359922.1 ). Identification of high-risk genetic loci across ancestries will enable population-genetic screening approaches to identify patients with IA.

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.

Towards a global view of multiple sclerosis genetics

Multiple sclerosis (MS) is a neuroimmunological disorder of the CNS with a strong heritable component. The genetic architecture of MS susceptibility is well understood in populations of European ancestry. However, the extent to which this architecture explains MS susceptibility in populations of non-European ancestry remains unclear. In this Perspective article, we outline the scientific arguments for studying MS genetics in ancestrally diverse populations. We argue that this approach is likely to yield insights that could benefit individuals with MS from all ancestral groups. We explore the logistical and theoretical challenges that have held back this field to date and conclude that, despite these challenges, inclusion of participants of non-European ancestry in MS genetics studies will ultimately be of value to all patients with MS worldwide.

Longitudinal association of atopic dermatitis progression and keratin 6A

Atopic dermatitis is a common skin disease characterized by loss of skin integrity. Risk and severity have been associated with genetic variation especially with respect to the filaggrin gene, suggesting the importance of skin barrier function in atopic dermatitis pathogenesis. The keratin protein plays a role in epithelial health but its relationship with disease severity would benefit from further exploration. In this study, we evaluate the association between common keratin 6 variants and severity of atopic dermatitis over time using a Bayesian generalized linear mixed model to account for repeated measures. We identify groups of variants within which individual variants have similar effects on skin repair. Further assessment of the biological mechanisms by which these contribute to repair of epidermis may inform treatment of atopic dermatitis.

Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

Causality of abdominal obesity on cognition: a trans-ethnic Mendelian randomization study

BACKGROUND

Obesity has been associated with cognition in observational studies; however, whether its effect is confounding or a reverse causality remains inconclusive. This study aimed to investigate the causal relationships of overall obesity, measured by body mass index (BMI), and abdominal adiposity, measured by waist-hip ratio adjusted for BMI (WHRadjBMI), and cognition across European and Asian populations using Mendelian randomization (MR) analysis.

METHODS

We used publicly available genome-wide association study (GWAS) summary data of European ancestry, including BMI (n = 322,154) and WHRadjBMI (n = 210,088) from the GIANT consortium, and cognition performance (n = 257,828) from the UK Biobank and COGENT consortium. Data for individuals of Asian ancestry were retrieved from Taiwan Biobank to perform GWAS for BMI (n = 65,689), WHRadjBMI (n = 65,683), and Mini-Mental State Examination (MMSE, n = 21,273). MR analysis was carried out using the inverse-variance weighted method for the main results. Further, we examined the overall pleiotropy by MR-Egger intercept, and detected and adjusted for possible outliers using MR PRESSO.

RESULTS

No causal effect of BMI on cognition performance (beta [95% CI] = 0.00 [-0.07, 0.07], p value = 0.91) was found for Europeans; however, a 1-SD increase in WHRadjBMI was associated with a 0.07 standardized score decrease in cognition performance (beta [95% CI] = -0.07 [-0.12, -0.02], p value = 0.006). Further, no causal effect of BMI on MMSE (beta [95% CI] = 0.01 [-0.08, 0.10], p = 0.91) was found for Asians; however, a 1-SD increase in WHRadjBMI was associated with a 0.17 standardized score decrease in MMSE (beta [95% CI] = -0.17 [-0.30, -0.03], p = 0.02). In both populations, overall pleiotropy was not detected, and outliers did not affect the robustness of the main findings.

CONCLUSIONS

This trans-ethnic MR study reveals that abdominal adiposity, as measured by WHR adjusted for BMI, impairs cognition, whereas weak evidence suggests that BMI impairs cognition.

Underrepresented Populations in Parkinson's Genetics Research: Current Landscape and Future Directions

BACKGROUND

Human genetics research lacks diversity; over 80% of genome-wide association studies have been conducted on individuals of European ancestry. In addition to limiting insights regarding disease mechanisms, disproportionate representation can create disparities preventing equitable implementation of personalized medicine.

OBJECTIVE

This systematic review provides an overview of research involving Parkinson's disease (PD) genetics in underrepresented populations (URP) and sets a baseline to measure the future impact of current efforts in those populations.

METHODS

We searched PubMed and EMBASE until October 2021 using search strings for "PD," "genetics," the main "URP," and and the countries in Latin America, Caribbean, Africa, Asia, and Oceania (excluding Australia and New Zealand). Inclusion criteria were original studies, written in English, reporting genetic results on PD from non-European populations. Two levels of independent reviewers identified and extracted information.

RESULTS

We observed imbalances in PD genetic studies among URPs. Asian participants from Greater China were described in the majority of the articles published (57%), but other populations were less well studied; for example, Blacks were represented in just 4.0% of the publications. Also, although idiopathic PD was more studied than monogenic forms of the disease, most studies analyzed a limited number of genetic variants. We identified just nine studies using a genome-wide approach published up to 2021, including URPs.

CONCLUSION

This review provides insight into the significant lack of population diversity in PD research highlighting the immediate need for better representation. The Global Parkinson's Genetics Program (GP2) and similar initiatives aim to impact research in URPs, and the early metrics presented here can be used to measure progress in the field of PD genetics in the future. © 2022 International Parkinson and Movement Disorder Society.

Leveraging the local genetic structure for trans-ancestry association mapping

Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.

Evaluating the power and limitations of genome-wide association studies in Caenorhabditis elegans

Quantitative genetics in Caenorhabditis elegans seeks to identify naturally segregating genetic variants that underlie complex traits. Genome-wide association studies scan the genome for individual genetic variants that are significantly correlated with phenotypic variation in a population, or quantitative trait loci. Genome-wide association studies are a popular choice for quantitative genetic analyses because the quantitative trait loci that are discovered segregate in natural populations. Despite numerous successful mapping experiments, the empirical performance of genome-wide association study has not, to date, been formally evaluated in C. elegans. We developed an open-source genome-wide association study pipeline called NemaScan and used a simulation-based approach to provide benchmarks of mapping performance in collections of wild C. elegans strains. Simulated trait heritability and complexity determined the spectrum of quantitative trait loci detected by genome-wide association studies. Power to detect smaller-effect quantitative trait loci increased with the number of strains sampled from the C. elegans Natural Diversity Resource. Population structure was a major driver of variation in mapping performance, with populations shaped by recent selection exhibiting significantly lower false discovery rates than populations composed of more divergent strains. We also recapitulated previous genome-wide association studies of experimentally validated quantitative trait variants. Our simulation-based evaluation of performance provides the community with critical context to pursue quantitative genetic studies using the C. elegans Natural Diversity Resource to elucidate the genetic basis of complex traits in C. elegans natural populations.

Genetic Architecture of Plasma Alpha-Aminoadipic Acid Reveals a Relationship With High-Density Lipoprotein Cholesterol

Background Elevated plasma levels of alpha-aminoadipic acid (2-AAA) have been associated with the development of type 2 diabetes and atherosclerosis. However, the nature of the association remains unknown. Methods and Results We identified genetic determinants of plasma 2-AAA through meta-analysis of genome-wide association study data in 5456 individuals of European, African, and Asian ancestry from the Framingham Heart Study, Diabetes Prevention Program, Jackson Heart Study, and Shanghai Women's and Men's Health Studies. No single nucleotide polymorphisms reached genome-wide significance across all samples. However, the top associations from the meta-analysis included single-nucleotide polymorphisms in the known 2-AAA pathway gene DHTKD1, and single-nucleotide polymorphisms in genes involved in mitochondrial respiration (NDUFS4) and macrophage function (MSR1). We used a Mendelian randomization instrumental variable approach to evaluate relationships between 2-AAA and cardiometabolic phenotypes in large disease genome-wide association studies. Mendelian randomization identified a suggestive inverse association between increased 2-AAA and lower high-density lipoprotein cholesterol (P=0.005). We further characterized the genetically predicted relationship through measurement of plasma 2-AAA and high-density lipoprotein cholesterol in 2 separate samples of individuals with and without cardiometabolic disease (N=98), and confirmed a significant negative correlation between 2-AAA and high-density lipoprotein (rs=-0.53, P<0.0001). Conclusions 2-AAA levels in plasma may be regulated, in part, by common variants in genes involved in mitochondrial and macrophage function. Elevated plasma 2-AAA associates with reduced levels of high-density lipoprotein cholesterol. Further mechanistic studies are required to probe this as a possible mechanism linking 2-AAA to future cardiometabolic risk.

Population differentiation of polygenic score predictions under stabilizing selection

Given the many small-effect loci uncovered by genome-wide association studies (GWAS), polygenic scores have become central to genomic medicine, and have found application in diverse settings including evolutionary studies of adaptation. Despite their promise, polygenic scores have been found to suffer from limited portability across human populations. This at first seems in conflict with the observation that most common genetic variation is shared among populations. We investigate one potential cause of this discrepancy: stabilizing selection on complex traits. Counterintuitively, while stabilizing selection constrains phenotypic evolution, it accelerates the loss and fixation of alleles underlying trait variation within populations (GWAS loci). Thus even when populations share an optimum phenotype, stabilizing selection erodes the variance contributed by their shared GWAS loci, such that predictions from GWAS in one population explain less of the phenotypic variation in another. We develop theory to quantify how stabilizing selection is expected to reduce the prediction accuracy of polygenic scores in populations not represented in GWAS samples. In addition, we find that polygenic scores can substantially overstate average genetic differences of phenotypes among populations. We emphasize stabilizing selection around a common optimum as a useful null model to connect patterns of allele frequency and polygenic score differentiation. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.

Clarifying the causes of consistent and inconsistent findings in genetics

As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family‐ and genome‐wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV₁] and its ratio to forced vital capacity [FEV₁/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV₁ and FEV₁/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10^-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

Stability of polygenic scores across discovery genome-wide association studies

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

Estimating SNP heritability in presence of population substructure in biobank-scale datasets

Single nucleotide polymorphism heritability of a trait is measured as the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms. Linear mixed models are routinely used to estimate single nucleotide polymorphism heritability for many complex traits, which requires estimation of a genetic relationship matrix among individuals. Heritability is usually estimated by the restricted maximum likelihood or method of moments approaches such as Haseman-Elston regression. The common practice of accounting for such population substructure is to adjust for the top few principal components of the genetic relationship matrix as covariates in the linear mixed model. This can get computationally very intensive on large biobank-scale datasets. Here, we propose a method of moments approach for estimating single nucleotide polymorphism heritability in presence of population substructure. Our proposed method is computationally scalable on biobank datasets and gives an asymptotically unbiased estimate of heritability in presence of discrete substructures. It introduces the adjustments for population stratification in a second-order estimating equation. It allows these substructures to vary in their single nucleotide polymorphism allele frequencies and in their trait distributions (means and variances) while the heritability is assumed to be the same across these substructures. Through extensive simulation studies and the application on 7 quantitative traits in the UK Biobank cohort, we demonstrate that our proposed method performs well in the presence of population substructure and much more computationally efficient than existing approaches.

PGS-server: accuracy, robustness and transferability of polygenic score methods for biobank scale studies

Polygenic scores (PGS) are important tools for carrying out genetic prediction of common diseases and disease related complex traits, facilitating the development of precision medicine. Unfortunately, despite the critical importance of PGS and the vast number of PGS methods recently developed, few comprehensive comparison studies have been performed to evaluate the effectiveness of PGS methods. To fill this critical knowledge gap, we performed a comprehensive comparison study on 12 different PGS methods through internal evaluations on 25 quantitative and 25 binary traits within the UK Biobank with sample sizes ranging from 147 408 to 336 573, and through external evaluations via 25 cross-study and 112 cross-ancestry analyses on summary statistics from multiple genome-wide association studies with sample sizes ranging from 1415 to 329 345. We evaluate the prediction accuracy, computational scalability, as well as robustness and transferability of different PGS methods across datasets and/or genetic ancestries, providing important guidelines for practitioners in choosing PGS methods. Besides method comparison, we present a simple aggregation strategy that combines multiple PGS from different methods to take advantage of their distinct benefits to achieve stable and superior prediction performance. To facilitate future applications of PGS, we also develop a PGS webserver (http://www.pgs-server.com/) that allows users to upload summary statistics and choose different PGS methods to fit the data directly. We hope that our results, method and webserver will facilitate the routine application of PGS across different research areas.

Transethnic analysis of psoriasis susceptibility in South Asians and Europeans enhances fine-mapping in the MHC and genomewide

Because transethnic analysis may facilitate prioritization of causal genetic variants, we performed a genomewide association study (GWAS) of psoriasis in South Asians (SAS), consisting of 2,590 cases and 1,720 controls. Comparison with our existing European-origin (EUR) GWAS showed that effect sizes of known psoriasis signals were highly correlated in SAS and EUR (Spearman ρ = 0.78; p < 2 × 10-14). Transethnic meta-analysis identified two non-MHC psoriasis loci (1p36.22 and 1q24.2) not previously identified in EUR, which may have regulatory roles. For these two loci, the transethnic GWAS provided higher genetic resolution and reduced the number of potential causal variants compared to using the EUR sample alone. We then explored multiple strategies to develop reference panels for accurately imputing MHC genotypes in both SAS and EUR populations and conducted a fine-mapping of MHC psoriasis associations in SAS and the largest such effort for EUR. HLA-C*06 was the top-ranking MHC locus in both populations but was even more prominent in SAS based on odds ratio, disease liability, model fit and predictive power. Transethnic modeling also substantially boosted the probability that the HLA-C*06 protein variant is causal. Secondary MHC signals included coding variants of HLA-C and HLA-B, but also potential regulatory variants of these two genes as well as HLA-A and several HLA class II genes, with effects on both chromatin accessibility and gene expression. This study highlights the shared genetic basis of psoriasis in SAS and EUR populations and the value of transethnic meta-analysis for discovery and fine-mapping of susceptibility loci.