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

Bench Research Informed by GWAS Results

Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.

Genetic Risk Prediction of COVID-19 Susceptibility and Severity in the Indian Population

Host genetic variants can determine their susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS). Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we compute genetic risk scores in diverse Indian sub-populations that may predict differences in the severity of COVID-19 outcomes. We utilized the top 100 most significantly associated single-nucleotide polymorphisms (SNPs) from a GWAS by Pairo-Castineira et al. determining the genetic susceptibility to severe COVID-19 infection, to compute population-wise polygenic risk scores (PRS) for populations represented in the Indian Genome Variation Consortium (IGVC) database. Using a generalized linear model accounting for confounding variables, we found that median PRS was significantly associated (p < 2 x 10⁻¹⁶) with COVID-19 mortality in each district corresponding to the population studied and had the largest effect on mortality (regression coefficient = 10.25). As a control we repeated our analysis on randomly selected 100 non-associated SNPs several times and did not find significant association. Therefore, we conclude that genetic susceptibility may play a major role in determining the differences in COVID-19 outcomes and mortality across the Indian sub-continent. We suggest that combining PRS with other observed risk-factors in a Bayesian framework may provide a better prediction model for ascertaining high COVID-19 risk groups and to design more effective public health resource allocation and vaccine distribution schemes.

False discovery rate control in genome-wide association studies with population structure

We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing interpretable findings while controlling the false discovery rate. In contrast with standard approaches, our method can leverage sophisticated multivariate algorithms but makes no parametric assumptions about the unknown relation between genotypes and phenotype. Instead, we recognize that genotypes can be considered as a random sample from an appropriate model, encapsulating our knowledge of genetic inheritance and human populations. This allows the generation of imperfect copies (knockoffs) of these variables that serve as ideal negative controls, correcting for linkage disequilibrium and accounting for unknown population structure, which may be due to diverse ancestries or familial relatedness. The validity and effectiveness of our method are demonstrated by extensive simulations and by applications to the UK Biobank data. These analyses confirm our method is powerful relative to state-of-the-art alternatives, while comparisons with other studies validate most of our discoveries. Finally, fast software is made available for researchers to analyze Biobank-scale datasets.

False discovery rate control in genome-wide association studies with population structure

We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing interpretable findings while controlling the false discovery rate. In contrast with standard approaches, our method can leverage sophisticated multivariate algorithms but makes no parametric assumptions about the unknown relation between genotypes and phenotype. Instead, we recognize that genotypes can be considered as a random sample from an appropriate model, encapsulating our knowledge of genetic inheritance and human populations. This allows the generation of imperfect copies (knockoffs) of these variables that serve as ideal negative controls, correcting for linkage disequilibrium and accounting for unknown population structure, which may be due to diverse ancestries or familial relatedness. The validity and effectiveness of our method are demonstrated by extensive simulations and by applications to the UK Biobank data. These analyses confirm our method is powerful relative to state-of-the-art alternatives, while comparisons with other studies validate most of our discoveries. Finally, fast software is made available for researchers to analyze Biobank-scale datasets.

Genetic architecture of schizophrenia: a review of major advancements

Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

Genomewide Association Studies in Pharmacogenomics

The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.

The genetic architecture of temperature adaptation is shaped by population ancestry and not by selection regime

BACKGROUND

Understanding the genetic architecture of temperature adaptation is key for characterizing and predicting the effect of climate change on natural populations. One particularly promising approach is Evolve and Resequence, which combines advantages of experimental evolution such as time series, replicate populations, and controlled environmental conditions, with whole genome sequencing. Recent analysis of replicate populations from two different Drosophila simulans founder populations, which were adapting to the same novel hot environment, uncovered very different architectures-either many selection targets with large heterogeneity among replicates or fewer selection targets with a consistent response among replicates.

RESULTS

Here, we expose the founder population from Portugal to a cold temperature regime. Although almost no selection targets are shared between the hot and cold selection regime, the adaptive architecture was similar. We identify a moderate number of targets under strong selection (19 selection targets, mean selection coefficient = 0.072) and parallel responses in the cold evolved replicates. This similarity across different environments indicates that the adaptive architecture depends more on the ancestry of the founder population than the specific selection regime.

CONCLUSIONS

These observations will have broad implications for the correct interpretation of the genomic responses to a changing climate in natural populations.

Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals

Genes underneath signals from genome-wide association studies (GWAS) for kidney function are promising targets for functional studies, but prioritizing variants and genes is challenging. By GWAS meta-analysis for creatinine-based estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics Consortium and UK Biobank (n = 1,201,909), we expand the number of eGFRcrea loci (424 loci, 201 novel; 9.8% eGFRcrea variance explained by 634 independent signal variants). Our increased sample size in fine-mapping (n = 1,004,040, European) more than doubles the number of signals with resolved fine-mapping (99% credible sets down to 1 variant for 44 signals, ≤5 variants for 138 signals). Cystatin-based eGFR and/or blood urea nitrogen association support 348 loci (n = 460,826 and 852,678, respectively). Our customizable tool for Gene PrioritiSation reveals 23 compelling genes including mechanistic insights and enables navigation through genes and variants likely relevant for kidney function in human to help select targets for experimental follow-up.

Genomic Association vs. Serological Determination of ABO Blood Types in a Chinese Cohort, with Application in Mendelian Randomization

ABO blood system is an inborn trait determined by the ABO gene. The genetic-phenotypic mechanism underneath the four mutually exclusive and collectively exhaustive types of O, A, B and AB could theoretically be elucidated. However, genetic polymorphisms in the human populations render the link elusive, and importantly, past studies using genetically determined rather than biochemically determined ABO types were not and could not be evaluated for the inference errors. Upon both blood-typing and genotyping a cohort of 1008 people of the Han Chinese population, we conducted a genome-wide association study in parallel with both binomial and multinomial log-linear models. Significant genetic variants are all mapped to the ABO gene, and are quantitatively evaluated for binary and multi-class classification performances. Three single nucleotide polymorphisms of rs8176719, rs635634 and rs7030248 would together be sufficient to establish a multinomial predictive model that achieves high accuracy (0.98) and F1 scores (micro 0.99 and macro 0.97). Using the set of identified ABO-associated genetic variants as instrumental variables, we demonstrate the application in causal analysis by Mendelian randomization (MR) studies on blood pressures (one-sample MR) and severe COVID-19 with respiratory failure (two-sample MR).

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.

Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations

Genome-wide association studies (GWASs) have successfully identified a large amount of single-nucleotide polymorphisms associated with many complex phenotypes in diverse populations. However, a comprehensive understanding of the genetic correlation of associated loci of phenotypes across populations remains lacking and the extent to which associations discovered in one population can be generalized to other populations or can be utilized for trans-ethnic genetic prediction is also unclear. By leveraging summary statistics, we proposed MAGIC to evaluate the trans-ethnic marginal genetic correlation (r_m) of per-allele effect sizes for associated SNPs (P < 5E-8) under the framework of measurement error models. We confirmed the methodological advantage of MAGIC over general approaches through simulations and demonstrated its utility by analyzing 34 GWAS summary statistics of phenotypes from the East Asian (N_max = 254,373) and European (N_max = 1,220,901) populations. Among these phenotypes, r_m was estimated to range from 0.584 (se = 0.140) for breast cancer to 0.949 (se = 0.035) for age of menarche, with an average of 0.835 (se = 0.045). We also uncovered that the trans-ethnic genetic prediction accuracy for phenotypes in the target population would substantially become low when using associated SNPs identified in non-target populations, indicating that associations discovered in the one population cannot be simply generalized to another population and that the accuracy of trans-ethnic phenotype prediction is generally dissatisfactory. Overall, our study provides in-depth insight into trans-ethnic genetic correlation and prediction for complex phenotypes across diverse populations.

5-HTTLPR-rs25531 and Antidepressant Treatment Outcomes in Korean Patients with Major Depression

INTRODUCTION

Despite the ethnic differences in 5-HTTLPR (S allele relates to better antidepressant response in Korean and Japanese people, while L allele with better response in Caucasians), it is unclear whether 5-HTTLPR and its high expression locus rs25531 are interactively associated with antidepressant treatment outcome. We investigated the individual and interaction effects of these polymorphisms on antidepressant treatment outcomes in the Korean population.

METHODS

A total of 464 Korean subjects with major depressive disorder completed 6 weeks of antidepressant monotherapy. Venous blood was extracted for genotyping 5-HTTLPR and rs25531 by polymerase chain reaction and DNA sequencing. We used logistic regression analyses to verify the main and interaction effects of 5-HTTLPR and rs25531 on response and remission after antidepressant treatment.

RESULTS

After adjusting for covariates, the SS genotype of 5-HTTLPR was significantly associated with better treatment outcomes (p<0.001, odds ratio [OR] [95% confidence interval (CI)]=2.435 [1.551, 3.823] in response; p<0.001, OR [95% CI]=2.912 [1.730, 4.903] in remission), while G-containing genotype (AG+GG) of rs25531 was only associated with remission (p=0.034, OR [95% CI]=2.104 [1.058, 4.181]). The interaction effect of 5-HTTLPR and rs25531 on response and remission was insignificant (all p>0.05).

DISCUSSION

Our findings suggest variations in allelic frequency and functionality of 5-HTTLPR and rs25531 among the different ethnicities. We found a minor advantage of rs25531 in achieving remission. However, there was no interaction effect with 5-HTTLPR.

Association of Blood Pressure Genetic Risk Score with Cardiovascular Disease and CKD Progression: Findings from the CRIC Study

Background

In the general population, genetic risk for high BP has been associated with cardiovascular disease, but not kidney function or incident CKD. These relationships have not been studied longitudinally in participants with CKD. We examined whether BP genetic risk predicts cardiovascular disease and kidney disease progression in patients with CKD.

Methods

We included 1493 African- and 1581 European-ancestry participants from the Chronic Renal Insufficiency Cohort who were followed for 12 years. We examined associations of BP genetic risk scores with development of cardiovascular disease (myocardial infarction, congestive heart failure, or stroke) and CKD progression (incident ESKD or halving of eGFR) using Cox proportional hazards models. Analyses were stratified by race and included adjustment for age, sex, study site, and ancestry principal components.

Results

Among European-ancestry participants, each SD increase in systolic BP and pulse pressure genetic risk score conferred a 15% (95% CI, 4% to 27%) and 11% (95% CI, 1% to 23%), respectively, higher risk of cardiovascular disease, with a similar, marginally significant trend for diastolic BP. Among African-ancestry participants, each SD increase in systolic and diastolic BP genetic risk score conferred a 10% (95% CI, 1% to 20%) and 9% (95% CI, 0% to 18%), respectively, higher risk of cardiovascular disease. Higher genetic risk was not associated with CKD progression.

Conclusions

Genetic risk for elevation in BP was associated with increased risk of cardiovascular disease, but not CKD progression.

A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), DEEP*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. DEEP*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply DEEP*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10^-120). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.

Fine-mapping of two differentiated thyroid carcinoma susceptibility loci at 2q35 and 8p12 in Europeans, Melanesians and Polynesians

Differentiated thyroid carcinoma (DTC) incidence is characterized by wide ethnic and geographic variations, with high incidence rates observed in Oceanian populations. Genome-wide association studies (GWAS) identified mainly four DTC susceptibility loci at 9q22.33, 14q13.3, 2q35 and 8p12. Here we performed fine-mapping of the 2q35 and 8p12 loci in the population of the EPITHYR consortium that includes Europeans, Melanesians and Polynesians to identify likely causal variants for DTC risk. We conducted a colocalization analysis using eQTLs data to determine the SNPs with the highest probability of causality. At 2q35, we highlighted rs16857609 located in DIRC3. This SNP has a high probability of causality in the three populations, and a significant association in Europeans (OR = 1.4, p = 1.9 x 10-10). It is also associated with expression of DIRC3 and of the nearby gene IGFBP5 in thyroid tumour cells. At 8p12, we identified rs7844425 which was significantly associated with DTC in Europeans (OR = 1.32, p = 7.6 x 10-8) and rs2439304, which was highlighted by the colocalization analysis but only moderately associated with DTC in our dataset (OR = 1.2, p = 0.001). These SNPs are linked to the expression of NRG1 in thyroid tissue. Hence, our study identified novel variants at 2q35 and 8p12 to be prioritized for further functional studies.

Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.

Diversity matters: opportunities in the study of the genetics of psychotic disorders in low- and middle-income countries in Latin America

Lack of diversity regarding genetic and environmental backgrounds weakens the generalization and clinical applicability of research findings on psychotic disorders. Notably, Latin Americans have been generally neglected in genetic studies, comprising less than 2% of genome-wide association study samples. But Latin American populations represent a unique opportunity for research, given the exceptionally high ethnic admixture of this group. Increasing genetic diversity is essential to improve the fine mapping of known regions associated with psychotic disorders, discover novel genetic associations, and replicate studies. Additionally, Latin America is characterized by massive social, political, and economic inequalities, all known risk factors for mental health issues, including psychotic disorders. This article aims to 1) discuss the challenges and advantages of studying Latin America’s particular genetic makeup and environmental context; 2) review previous studies conducted in the region; and 3) describe three Latin American research initiatives in progress: the Neuropsychiatric Genetics of Psychosis in Mexican Populations (NeuroMEX), the Paisa, and the Latin American Network for the Study of Early Psychosis (ANDES) studies.

Convergent lines of evidence support NOTCH4 as a schizophrenia risk gene

The association between NOTCH4 and schizophrenia has been repeatedly reported. However, the results from different genetic studies are inconsistent, and the role of NOTCH4 in schizophrenia pathogenesis remains unknown. Here, we provide convergent lines of evidence that support NOTCH4 as a schizophrenia risk gene. We first performed a meta-analysis and found that a genetic variant (rs2071287) in NOTCH4 was significantly associated with schizophrenia (a total of 125 848 subjects, p=8.31×10^-17), with the same risk allele across all tested samples. Expression quantitative trait loci (eQTL) analysis showed that rs2071287 was significantly associated with NOTCH4 expression (p=1.08×10^-14) in human brain tissues, suggesting that rs2071287 may confer schizophrenia risk through regulating NOTCH4 expression. Sherlock integrative analysis using a large-scale schizophrenia GWAS and eQTL data from human brain tissues further revealed that NOTCH4 was significantly associated with schizophrenia (p=4.03×10^-7 in CMC dataset and p=3.06×10^-6 in xQTL dataset), implying that genetic variants confer schizophrenia risk through modulating NOTCH4 expression. Consistently, we found that NOTCH4 was significantly downregulated in brains of schizophrenia patients compared with controls (p=2.53×10^-3), further suggesting that dysregulation of NOTCH4 may have a role in schizophrenia. Finally, we showed that NOTCH4 regulates proliferation, self-renewal, differentiation and migration of neural stem cells, suggesting that NOTCH4 may confer schizophrenia risk through affecting neurodevelopment. Our study provides convergent lines of evidence that support the involvement of NOTCH4 in schizophrenia. In addition, our study also elucidates a possible mechanism for the role of NOTCH4 in schizophrenia pathogenesis.

Genome-wide landscape establishes novel association signals for metabolic traits in the Arab population

While the Arabian population has a high prevalence of metabolic disorders, it has not been included in global studies that identify genetic risk loci for metabolic traits. Determining the transferability of such largely Euro-centric established risk loci is essential to transfer the research tools/resources, and drug targets generated by global studies to a broad range of ethnic populations. Further, consideration of populations such as Arabs, that are characterized by consanguinity and a high level of inbreeding, can lead to identification of novel risk loci. We imputed published GWAS data from two Kuwaiti Arab cohorts (n = 1434 and 1298) to the 1000 Genomes Project haplotypes and performed meta-analysis for associations with 13 metabolic traits. We compared the observed association signals with those established for metabolic traits. Our study highlighted 70 variants from 9 different genes, some of which have established links to metabolic disorders. By relaxing the genome-wide significance threshold, we identified ‘novel’ risk variants from 11 genes for metabolic traits. Many novel risk variant association signals were observed at or borderline to genome-wide significance. Furthermore, 349 previously established variants from 187 genes were validated in our study. Pleiotropic effect of risk variants on multiple metabolic traits were observed. Fine-mapping illuminated rs7838666/CSMD1 rs1864163/CETP and rs112861901/[INTS10,LPL] as candidate causal variants influencing fasting plasma glucose and high-density lipoprotein levels. Computational functional analysis identified a variety of gene regulatory signals around several variants. This study enlarges the population ancestry diversity of available GWAS and elucidates new variants in an ethnic group burdened with metabolic disorders.

Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

Genetic architecture of Alzheimer's disease

Advances in genetic and genomic technologies over the last thirty years have greatly enhanced our knowledge concerning the genetic architecture of Alzheimer's disease (AD). Several genes including APP, PSEN1, PSEN2, and APOE have been shown to exhibit large effects on disease susceptibility, with the remaining risk loci having much smaller effects on AD risk. Notably, common genetic variants impacting AD are not randomly distributed across the genome. Instead, these variants are enriched within regulatory elements active in human myeloid cells, and to a lesser extent liver cells, implicating these cell and tissue types as critical to disease etiology. Integrative approaches are emerging as highly effective for identifying the specific target genes through which AD risk variants act and will likely yield important insights related to potential therapeutic targets in the coming years. In the future, additional consideration of sex- and ethnicity-specific contributions to risk as well as the contribution of complex gene-gene and gene-environment interactions will likely be necessary to further improve our understanding of AD genetic architecture.

Multiple ancestral haplotypes harboring regulatory mutations cumulatively contribute to a QTL affecting chicken growth traits

In depth studies of quantitative trait loci (QTL) can provide insights to the genetic architectures of complex traits. A major effect QTL at the distal end of chicken chromosome 1 has been associated with growth traits in multiple populations. This locus was fine-mapped in a fifteen-generation chicken advanced intercross population including 1119 birds and explored in further detail using 222 sequenced genomes from 10 high/low body weight chicken stocks. We detected this QTL that, in total, contributed 14.4% of the genetic variance for growth. Further, nine mosaic precise intervals (Kb level) which contain ancestral regulatory variants were fine-mapped and we chose one of them to demonstrate the key regulatory role in the duodenum. This is the first study to break down the detail genetic architectures for the well-known QTL in chicken and provides a good example of the fine-mapping of various of quantitative traits in any species.

On the cross-population generalizability of gene expression prediction models

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

Germline genetic landscape of pediatric central nervous system tumors

Central nervous system (CNS) tumors are the second most common type of cancer among children. Depending on histopathology, anatomic location, and genomic factors, specific subgroups of brain tumors have some of the highest cancer-related mortality rates or result in considerable lifelong morbidity. Pediatric CNS tumors often occur in patients with genetic predisposition, at times revealing underlying cancer predisposition syndromes. Advances in next-generation sequencing (NGS) have resulted in the identification of an increasing number of cancer predisposition genes. In this review, the literature on genetic predisposition to pediatric CNS tumors is evaluated with a discussion of potential future targets for NGS and clinical implications. Furthermore, we explore potential strategies for enhancing the understanding of genetic predisposition of pediatric CNS tumors, including evaluation of non-European populations, pan-genomic approaches, and large collaborative studies.

Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data

Despite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze nine complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8× enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWASs due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes

Diabetes mellitus, also known simply as diabetes, has been described as a chronic and complex endocrine metabolic disorder that is a leading cause of death across the globe. It is considered a key public health problem worldwide and one of four important non-communicable diseases prioritized for intervention through world health campaigns by various international foundations. Among its four categories, Type 2 diabetes (T2D) is the commonest form of diabetes accounting for over 90% of worldwide cases. Unlike monogenic inherited disorders that are passed on in a simple pattern, T2D is a multifactorial disease with a complex etiology, where a mixture of genetic and environmental factors are strong candidates for the development of the clinical condition and pathology. The genetic factors are believed to be key predisposing determinants in individual susceptibility to T2D. Therefore, identifying the predisposing genetic variants could be a crucial step in T2D management as it may ameliorate the clinical condition and preclude complications. Through an understanding the unique genetic and environmental factors that influence the development of this chronic disease individuals can benefit from personalized approaches to treatment. We searched the literature published in three electronic databases: PubMed, Scopus and ISI Web of Science for the current status of T2D and its associated genetic risk variants and discus promising approaches toward a personalized management of this chronic, non-communicable disorder.

Genetic and epigenetic analyses of panic disorder in the post-GWAS era

Panic disorder (PD) is a common and debilitating neuropsychiatric disorder characterized by panic attacks coupled with excessive anxiety. Both genetic factors and environmental factors play an important role in PD pathogenesis and response to treatment. However, PD is clinically heterogeneous and genetically complex, and the exact genetic or environmental causes of this disorder remain unclear. Various approaches for detecting disease-causing genes have recently been made available. In particular, genome-wide association studies (GWAS) have attracted attention for the identification of disease-associated loci of multifactorial disorders. This review introduces GWAS of PD, followed by a discussion about the limitations of GWAS and the major challenges facing geneticists in the post-GWAS era. Alternative strategies to address these challenges are then proposed, such as epigenome-wide association studies (EWAS) and rare variant association studies (RVAS) using next-generation sequencing. To date, however, few reports have described these analyses, and the evidence remains insufficient to confidently identify or exclude rare variants or epigenetic changes in PD. Further analyses are therefore required, using sample sizes in the tens of thousands, extensive functional annotations, and highly targeted hypothesis testing.

Genetic Epidemiology in Latin America: Identifying Strong Genetic Proxies for Complex Disease Risk Factors

Epidemiology seeks to determine the causal effects of exposures on outcomes related to the health and wellbeing of populations. Observational studies, one of the most commonly used designs in epidemiology, can be biased due to confounding and reverse causation, which makes it difficult to establish causal relationships. In recent times, genetically informed methods, like Mendelian randomization (MR), have been developed in an attempt to overcome these disadvantages. MR relies on the association of genetic variants with outcomes of interest, where the genetic variants are proxies or instruments for modifiable exposures. Because genotypes are sorted independently and at random at the time of conception, they are less prone to confounding and reverse causation. Implementation of MR depends on, among other things, a strong association of the genetic variants with the exposure, which has usually been defined via genome-wide association studies (GWAS). Because GWAS have been most often carried out in European populations, the limited identification of strong instruments in other populations poses a major problem for the application of MR in Latin America. We suggest potential solutions that can be realized with the resources at hand and others that will have to wait for increased funding and access to technology.

Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data

Genome-wide association studies (GWAS) of major depression and its relevant biological phenotypes have been extensively conducted in large samples, and transcriptome-wide analyses in the tissues of brain regions relevant to pathogenesis of depression, e.g., dorsolateral prefrontal cortex (DLPFC), have also been widely performed recently. Integrating these multi-omics data will enable unveiling of depression risk genes and even underlying pathological mechanisms. Here, we employ summary data-based Mendelian randomization (SMR) and integrative risk gene selector (iRIGS) approaches to integrate multi-omics data from GWAS, DLPFC expression quantitative trait loci (eQTL) analyses and enhancer-promoter physical link studies to prioritize high-confidence risk genes for depression, followed by independent replications across distinct populations. These integrative analyses identify multiple high-confidence depression risk genes, and numerous lines of evidence supporting pivotal roles of the netrin 1 receptor (DCC) gene in this illness across different populations. Our subsequent explorative analyses further suggest that DCC significantly predicts neuroticism, well-being spectrum, cognitive function and putamen structure in general populations. Gene expression correlation and pathway analyses in DLPFC further show that DCC potentially participates in the biological processes and pathways underlying synaptic plasticity, axon guidance, circadian entrainment, as well as learning and long-term potentiation. These results are in agreement with the recent findings of this gene in neurodevelopment and psychiatric disorders, and we thus further confirm that DCC is an important susceptibility gene for depression, and might be a potential target for new antidepressants.

Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics

BACKGROUND

Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population.

RESULTS

We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP.

CONCLUSION

This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering

When handling a structured population in association mapping, group-specific allele effects may be observed at quantitative trait loci (QTLs) for several reasons: (i) a different linkage disequilibrium (LD) between SNPs and QTLs across groups, (ii) group-specific genetic mutations in QTL regions, and/or (iii) epistatic interactions between QTLs and other loci that have differentiated allele frequencies between groups. We present here a new genome-wide association (GWAS) approach to identify QTLs exhibiting such group-specific allele effects. We developed genetic materials including admixed progeny from different genetic groups with known genome-wide ancestries (local admixture). A dedicated statistical methodology was developed to analyze pure and admixed individuals jointly, allowing one to disentangle the factors causing the heterogeneity of allele effects across groups. This approach was applied to maize by developing an inbred "Flint-Dent" panel including admixed individuals that was evaluated for flowering time. Several associations were detected revealing a wide range of configurations of allele effects, both at known flowering QTLs (Vgt1, Vgt2 and Vgt3) and new loci. We found several QTLs whose effect depended on the group ancestry of alleles while others interacted with the genetic background. Our GWAS approach provides useful information on the stability of QTL effects across genetic groups and can be applied to a wide range of species.

Effect of READ1 on latent profiles of reading disorder and comorbid attention and language impairment subtypes

Recent studies of co-occurring reading disorder (RD) and attention deficit/hyperactivity disorder (ADHD), and co-occurring RD and language impairment (LI), support a core disability plus co-occurrence model focused on language and attention. Genetic factors have been associated with poor reading performance. However, little is known about whether different genetic variants independently contribute to RD co-occurrence subtypes. We aimed to identify subgroups of struggling readers using a latent profile analysis (LPA) in a sample of 1,432 Hispanic American and African American youth. RD classes were then tested for association with variants of READ1, a regulatory element within the candidate RD risk gene, DCDC2. Six groups were identified in the LPA using RD designation as a known-class variable. The three RD classes identified groups of subjects with neurocognitive profiles representing RD+ADHD, specific phonological deficit RD, and RD+LI. Genetic associations across RD subtypes were investigated against functional groupings of READ1. The RU1-1 group of READ1 alleles was associated with RD cases that were marked by deficits in both processing speed and attention (RD+ADHD). The DCDC2 microdeletion that encompasses READ1 was associated with RD cases showing a phonological deficit RD profile. These findings provide evidence for differential genetic contribution to RD subtypes, and that previously implicated genetic variants for RD may share an underlying genetic architecture across population groups for reading disorder.

Radiogenomics Consortium Genome-Wide Association Study Meta-Analysis of Late Toxicity After Prostate Cancer Radiotherapy

BACKGROUND

A total of 10%-20% of patients develop long-term toxicity following radiotherapy for prostate cancer. Identification of common genetic variants associated with susceptibility to radiotoxicity might improve risk prediction and inform functional mechanistic studies.

METHODS

We conducted an individual patient data meta-analysis of six genome-wide association studies (n = 3871) in men of European ancestry who underwent radiotherapy for prostate cancer. Radiotoxicities (increased urinary frequency, decreased urinary stream, hematuria, rectal bleeding) were graded prospectively. We used grouped relative risk models to test associations with approximately 6 million genotyped or imputed variants (time to first grade 2 or higher toxicity event). Variants with two-sided Pmeta less than 5 × 10-8 were considered statistically significant. Bayesian false discovery probability provided an additional measure of confidence. Statistically significant variants were evaluated in three Japanese cohorts (n = 962). All statistical tests were two-sided.

RESULTS

Meta-analysis of the European ancestry cohorts identified three genomic signals: single nucleotide polymorphism rs17055178 with rectal bleeding (Pmeta = 6.2 × 10-10), rs10969913 with decreased urinary stream (Pmeta = 2.9 × 10-10), and rs11122573 with hematuria (Pmeta = 1.8 × 10-8). Fine-scale mapping of these three regions was used to identify another independent signal (rs147121532) associated with hematuria (Pconditional = 4.7 × 10-6). Credible causal variants at these four signals lie in gene-regulatory regions, some modulating expression of nearby genes. Previously identified variants showed consistent associations (rs17599026 with increased urinary frequency, rs7720298 with decreased urinary stream, rs1801516 with overall toxicity) in new cohorts. rs10969913 and rs17599026 had similar effects in the photon-treated Japanese cohorts.

CONCLUSIONS

This study increases the understanding of the architecture of common genetic variants affecting radiotoxicity, points to novel radio-pathogenic mechanisms, and develops risk models for testing in clinical studies. Further multinational radiogenomics studies in larger cohorts are worthwhile.

Is population structure in the genetic biobank era irrelevant, a challenge, or an opportunity?

Replicable genetic association signals have consistently been found through genome-wide association studies in recent years. The recent dramatic expansion of study sizes improves power of estimation of effect sizes, genomic prediction, causal inference, and polygenic selection, but it simultaneously increases susceptibility of these methods to bias due to subtle population structure. Standard methods using genetic principal components to correct for structure might not always be appropriate and we use a simulation study to illustrate when correction might be ineffective for avoiding biases. New methods such as trans-ethnic modeling and chromosome painting allow for a richer understanding of the relationship between traits and population structure. We illustrate the arguments using real examples (stroke and educational attainment) and provide a more nuanced understanding of population structure, which is set to be revisited as a critical aspect of future analyses in genetic epidemiology. We also make simple recommendations for how problems can be avoided in the future. Our results have particular importance for the implementation of GWAS meta-analysis, for prediction of traits, and for causal inference.

Comparative genetic architectures of schizophrenia in East Asian and European populations

Schizophrenia is a debilitating psychiatric disorder with approximately 1% lifetime risk globally. Large-scale schizophrenia genetic studies have reported primarily on European ancestry samples, potentially missing important biological insights. Here, we report the largest study to date of East Asian participants (22,778 schizophrenia cases and 35,362 controls), identifying 21 genome-wide-significant associations in 19 genetic loci. Common genetic variants that confer risk for schizophrenia have highly similar effects between East Asian and European ancestries (genetic correlation = 0.98 ± 0.03), indicating that the genetic basis of schizophrenia and its biology are broadly shared across populations. A fixed-effect meta-analysis including individuals from East Asian and European ancestries identified 208 significant associations in 176 genetic loci (53 novel). Trans-ancestry fine-mapping reduced the sets of candidate causal variants in 44 loci. Polygenic risk scores had reduced performance when transferred across ancestries, highlighting the importance of including sufficient samples of major ancestral groups to ensure their generalizability across populations.

International Society of Psychiatric Genetics Ethics Committee: Issues facing us

Psychiatric genetics research is improving our understanding of the biological underpinnings of neurodiversity and mental illness. Using psychiatric genetics in ways that maximize benefits and minimize harms to individuals and society depends largely on how the ethical, legal, and social implications (ELSI) of psychiatric genetics are managed. The International Society of Psychiatric Genetics (ISPG) is the largest international organization dedicated to psychiatric genetics. Given its history, membership, and international reach, we believe the ISPG is well-equipped to contribute to the resolution of these ELSI challenges. As such, we recently created the ISPG Ethics Committee, an interdisciplinary group comprised of psychiatric genetics researchers, clinical geneticists, genetic counselors, mental health professionals, patients, patient advocates, bioethicists, and lawyers. This article highlights key ELSI challenges identified by the ISPG Ethics Committee to be of paramount importance for the ethical translation of psychiatric research into society in three contexts: research settings, clinical settings, and legal proceedings. For each of these arenas, we identify and discuss pressing psychiatric genetics ELSI dilemmas that merit attention and require action. The goal is to increase awareness about psychiatric genetics ELSI issues and encourage dialogue and action among stakeholders.

Archaic hominin introgression into modern human genomes

Ancient genomes from multiple Neanderthal and the Denisovan individuals, along with DNA sequence data from diverse contemporary human populations strongly support the prevalence of gene flow among different hominins. Recent studies now provide evidence for multiple gene flow events that leave genetic signatures in extant and ancient human populations. These events include older gene flow from an unknown hominin in Africa predating out-of-Africa migrations, and in the last 50,000-100,000 years, multiple gene flow events from Neanderthals into ancestral Eurasian human populations, and at least three distinct introgression events from a lineage close to Denisovans into ancestors of extant Southeast Asian and Oceanic populations. Some of these introgression events may have happened as late as 20,000 years before present and reshaped the way in which we think about human evolution. In this review, I aim to answer anthropologically relevant questions with regard to recent research on ancient hominin introgression in the human lineage. How have genomic data from archaic hominins changed our view of human evolution? Is there any doubt about whether introgression from ancient hominins to the ancestors of present-day humans occurred? What is the current view of human evolutionary history from the genomics perspective? What is the impact of introgression on human phenotypes?

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

GlobAl Distribution of GEnetic Traits (GADGET) web server: polygenic trait scores worldwide

Human populations from around the world show striking phenotypic variation across a wide variety of traits. Genome-wide association studies (GWAS) are used to uncover genetic variants that influence the expression of heritable human traits; accordingly, population-specific distributions of GWAS-implicated variants may shed light on the genetic basis of human phenotypic diversity. With this in mind, we developed the GlobAl Distribution of GEnetic Traits web server (GADGET http://gadget.biosci.gatech.edu). The GADGET web server provides users with a dynamic visual platform for exploring the relationship between worldwide genetic diversity and the genetic architecture underlying numerous human phenotypes. GADGET integrates trait-implicated single nucleotide polymorphisms (SNPs) from GWAS, with population genetic data from the 1000 Genomes Project, to calculate genome-wide polygenic trait scores (PTS) for 818 phenotypes in 2504 individual genomes. Population-specific distributions of PTS are shown for 26 human populations across 5 continental population groups, with traits ordered based on the extent of variation observed among populations. Users of GADGET can also upload custom trait SNP sets to visualize global PTS distributions for their own traits of interest.

A critical evaluation of results from genome-wide association studies of micronutrient status and their utility in the practice of precision nutrition

Rapid advances in ‘omics’ technologies have paved the way forward to an era where more ‘precise’ approaches – ‘precision’ nutrition – which leverage data on genetic variability alongside the traditional indices, have been put forth as the state-of-the-art solution to redress the effects of malnutrition across the life course. We purport that this inference is premature and that it is imperative to first review and critique the existing evidence from large-scale epidemiological findings. We set out to provide a critical evaluation of findings from genome-wide association studies (GWAS) in the roadmap to precision nutrition, focusing on GWAS of micronutrient disposition. We found that a large number of loci associated with biomarkers of micronutrient status have been identified. Mean estimates of heritability of micronutrient status ranged between 20 and 35 % for minerals, 56–59 % for water-soluble and 30–70 % for fat-soluble vitamins. With some exceptions, the majority of the identified genetic variants explained little of the overall variance in status for each micronutrient, ranging between 1·3 and 8 % (minerals), <0·1–12 % (water-soluble) and 1·7–2·3 % for (fat-soluble) vitamins. However, GWAS have provided some novel insight into mechanisms that underpin variability in micronutrient status. Our findings highlight obvious gaps that need to be addressed if the full scope of precision nutrition is ever to be realised, including research aimed at (i) dissecting the genetic basis of micronutrient deficiencies or ‘response’ to intake/supplementation (ii) identifying trans-ethnic and ethnic-specific effects (iii) identifying gene–nutrient interactions for the purpose of unravelling molecular ‘behaviour’ in a range of environmental contexts.

Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations

Using genetic data to predict gene expression has garnered significant attention in recent years. PrediXcan has become one of the most widely used gene-based methods for testing associations between predicted gene expression values and a phenotype, which has facilitated novel insights into the relationship between complex traits and the component of gene expression that can be attributed to genetic variation. The gene expression prediction models for PrediXcan were developed using supervised machine learning methods and training data from the Depression Genes and Networks (DGN) study and the Genotype-Tissue Expression (GTEx) project, where the majority of subjects are of European descent. Many genetic studies, however, include samples from multi-ethnic populations, and in this paper we evaluate the accuracy of PrediXcan for predicting gene expression in diverse populations. Using transcriptomic data from the GEUVADIS (Genetic European Variation in Disease) RNA sequencing project and whole genome sequencing data from the 1000 Genomes project, we evaluate and compare the predictive performance of PrediXcan in an African population (Yoruban) and four European ancestry populations for thousands of genes. We evaluate a range of models from the PrediXcan weight databases and use Pearson's correlation coefficient to assess gene expression prediction accuracy with PrediXcan. From our evaluation, we find that the predictive performance of PrediXcan varies substantially among populations from different continents (F-test p-value < 2.2 × 10-16), where prediction accuracy is lower in the Yoruban population from West Africa compared to the European-ancestry populations. Moreover, not only do we find differences in predictive performance between populations from different continents, we also find highly significant differences in prediction accuracy among the four European ancestry populations considered (F-test p-value < 2.2 × 10-16). Finally, while there is variability in prediction accuracy across different PrediXcan weight databases, we also find consistency in the qualitative performance of PrediXcan for the five populations considered, with the African ancestry population having the lowest accuracy across databases.

Modeling Heterogeneity in the Genetic Architecture of Ethnically Diverse Groups Using Random Effect Interaction Models

In humans, most genome-wide association studies have been conducted using data from Caucasians and many of the reported findings have not replicated in other populations. This lack of replication may be due to statistical issues (small sample sizes or confounding) or perhaps more fundamentally to differences in the genetic architecture of traits between ethnically diverse subpopulations. What aspects of the genetic architecture of traits vary between subpopulations and how can this be quantified? We consider studying effect heterogeneity using Bayesian random effect interaction models. The proposed methodology can be applied using shrinkage and variable selection methods, and produces useful information about effect heterogeneity in the form of whole-genome summaries (e.g., the proportions of variance of a complex trait explained by a set of SNPs and the average correlation of effects) as well as SNP-specific attributes. Using simulations, we show that the proposed methodology yields (nearly) unbiased estimates when the sample size is not too small relative to the number of SNPs used. Subsequently, we used the methodology for the analyses of four complex human traits (standing height, high-density lipoprotein, low-density lipoprotein, and serum urate levels) in European-Americans (EAs) and African-Americans (AAs). The estimated correlations of effects between the two subpopulations were well below unity for all the traits, ranging from 0.73 to 0.50. The extent of effect heterogeneity varied between traits and SNP sets. Height showed less differences in SNP effects between AAs and EAs whereas HDL, a trait highly influenced by lifestyle, exhibited a greater extent of effect heterogeneity. For all the traits, we observed substantial variability in effect heterogeneity across SNPs, suggesting that effect heterogeneity varies between regions of the genome.

Established and emerging strategies to crack the genetic code of obesity

Tremendous progress has been made in the genetic elucidation of obesity over the past two decades, driven largely by technological, methodological and organizational innovations. Current strategies for identifying obesity-predisposing loci/genes, including cytogenetics, linkage analysis, homozygosity mapping, admixture mapping, candidate gene studies, genome-wide association studies, custom genotyping arrays, whole-exome sequencing and targeted exome sequencing, have achieved differing levels of success, and the identified loci in aggregate explain only a modest fraction of the estimated heritability of obesity. This review outlines the successes and limitations of these approaches and proposes novel strategies, including the use of exceptionally large sample sizes, the study of diverse ethnic groups and deep phenotypes and the application of innovative methods and study designs, to identify the remaining obesity-predisposing genes. The use of both established and emerging strategies has the potential to crack the genetic code of obesity in the not-too-distant future. The resulting knowledge is likely to yield improvements in obesity prediction, prevention and care.

Trans-ethnic kidney function association study reveals putative causal genes and effects on kidney-specific disease aetiologies

Chronic kidney disease (CKD) affects ~10% of the global population, with considerable ethnic differences in prevalence and aetiology. We assemble genome-wide association studies of estimated glomerular filtration rate (eGFR), a measure of kidney function that defines CKD, in 312,468 individuals of diverse ancestry. We identify 127 distinct association signals with homogeneous effects on eGFR across ancestries and enrichment in genomic annotations including kidney-specific histone modifications. Fine-mapping reveals 40 high-confidence variants driving eGFR associations and highlights putative causal genes with cell-type specific expression in glomerulus, and in proximal and distal nephron. Mendelian randomisation supports causal effects of eGFR on overall and cause-specific CKD, kidney stone formation, diastolic blood pressure and hypertension. These results define novel molecular mechanisms and putative causal genes for eGFR, offering insight into clinical outcomes and routes to CKD treatment development.

Estimated glomerular filtration rate (eGFR) is a measure of kidney function used to define chronic kidney disease. Here, Morris et al. perform trans-ethnic genome-wide meta-analyses for eGFR in 312,468 individuals and identify novel loci and downstream putative causal genes.

Genetics of ulcerative colitis: putting into perspective the incremental gains from Indian studies

Ulcerative colitis (UC), one of the two clinical subtypes of inflammatory bowel disease is perceived as a potential 'sleeping giant' in the Indian subcontinent. Clinical manifestation is overall believed to be the same across ethnic groups but overwhelming genetics from large European and fewer non-European studies have revealed shared as well as unique disease susceptibly signaturesbetween them, pointing to population specific differences at genomic and environmental levels. A systematic recount of the four major eras in UC genetics spanning earliest linkage analysis, cherry picked candidate gene association studies, unbiased genomewide association studies, their logical extension in trans-ethnic setting (Immunochip study), lastly whole exome sequencing efforts forrare variant burden; and lessons learnt thereof in context of genetically distinct Indian population was attempted in this review. Genetic heterogeneity manifesting at allelic/locus level across these approaches has been the consistent finding through the range of pan India studies. On the other hand, these salient findings also highlight the limitations of even the best of these genetic leadsfor prognostic/clinical application. The imminent need, therefore, for the UC research community to adopt newer approaches/tools with improved study design to (i) gain better insight into genetic/mechanistic basis of disease; (ii) identify biomarkers of immediate translational value; and (iii) develop new/alternate therapeutic options is emphasized at the end.

Host ancestry and dengue fever: from mapping of candidate genes to prediction of worldwide genetic risk

Prevalence of many complex diseases, including dengue disease, is not even across the world. Dengue is endemic in several overpopulated urban centers in poor Asian and American countries, representing a risk to two-thirds of the human population. High-throughput genomic screenings and new algorithms of admixture mapping are allowing confirming that ancestry plays a major role in dengue disease. Allele and haplotype candidate frequencies display high heterogeneity between population groups, especially in comparison with Africans. Genetic risk evaluation testifies African ancestry protection against the more severe forms of the disease, most probably due to natural selection of protective variants. European genetic predisposition is identical to the Asian for severer forms, but lower for mild dengue fever.