Widespread signatures of natural selection across human complex traits and functional genomic categories

Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics

Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.

Genome-wide study of half a million individuals with major depression identifies 697 independent associations, infers causal neuronal subtypes and biological targets for novel pharmacotherapies

In a genome-wide association study (GWAS) of 685,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries and across diverse and admixed ancestries, we identify 697 independent associations at 636 genetic loci, 293 of which are novel. Using fine-mapping and functional genomic datasets, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. Leveraging new single-cell gene expression data, we conducted a causal neural cell type enrichment analysis that implicated excitatory and inhibitory midbrain and forebrain neurons, peptidergic neurons, and medium spiny neurons in MD. Critically, our findings are enriched for the targets of antidepressants and provide potential antidepressant repurposing opportunities (e.g., pregabalin and modafinil). Polygenic scores (PGS) from European ancestries explained up to 5.7% of the variance in liability to MD in European samples and PGS trained using either European or multi-ancestry data significantly predicted case control status across all four diverse ancestries. These findings represent a major advance in our understanding of MD across global populations. We provide evidence that MD GWAS reveals known and novel biological targets that may be used to target and develop pharmacotherapies addressing the considerable unmet need for effective treatment.

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

Genome-wide association studies in a large Korean cohort identify novel quantitative trait loci for 36 traits and illuminates their genetic architectures

Genome-wide association studies (GWAS) have been predominantly conducted in populations of European ancestry, limiting opportunities for biological discovery in diverse populations. We report GWAS findings from 153,950 individuals across 36 quantitative traits in the Korean Cancer Prevention Study-II (KCPS2) Biobank. We discovered 616 novel genetic loci in KCPS2, including an association between thyroid-stimulating hormone and CD36. Meta-analysis with the Korean Genome and Epidemiology Study, Biobank Japan, Taiwan Biobank, and UK Biobank identified 3,524 loci that were not significant in any contributing GWAS. We describe differences in genetic architectures across these East Asian and European samples. We also highlight East Asian specific associations, including a known pleiotropic missense variant in ALDH2, which fine-mapping identified as a likely causal variant for a diverse set of traits. Our findings provide insights into the genetic architecture of complex traits in East Asian populations and highlight how broadening the population diversity of GWAS samples can aid discovery.

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.

Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits

We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

Genetic architecture of the structural connectome

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear.

METHODS

To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk.

RESULTS

We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks.

CONCLUSION

Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

HOPX-associated molecular programs control cardiomyocyte cell states underpinning cardiac structure and function

Genomic regulation of cardiomyocyte differentiation is central to heart development and function. This study uses genetic loss-of-function human-induced pluripotent stem cell-derived cardiomyocytes to evaluate the genomic regulatory basis of the non-DNA-binding homeodomain protein HOPX. We show that HOPX interacts with and controls cardiac genes and enhancer networks associated with diverse aspects of heart development. Using perturbation studies in vitro, we define how upstream cell growth and proliferation control HOPX transcription to regulate cardiac gene programs. We then use cell, organoid, and zebrafish regeneration models to demonstrate that HOPX-regulated gene programs control cardiomyocyte function in development and disease. Collectively, this study mechanistically links cell signaling pathways as upstream regulators of HOPX transcription to control gene programs underpinning cardiomyocyte identity and function.

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

Inferring disease architecture and predictive ability with LDpred2-auto

LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

East Asian-specific and cross-ancestry genome-wide meta-analyses provide mechanistic insights into peptic ulcer disease

Peptic ulcer disease (PUD) refers to acid-induced injury of the digestive tract, occurring mainly in the stomach (gastric ulcer (GU)) or duodenum (duodenal ulcer (DU)). In the present study, we conducted a large-scale, cross-ancestry meta-analysis of PUD combining genome-wide association studies with Japanese and European studies (52,032 cases and 905,344 controls), and discovered 25 new loci highly concordant across ancestries. An examination of GU and DU genetic architecture demonstrated that GUs shared the same risk loci as DUs, although with smaller genetic effect sizes and higher polygenicity than DUs, indicating higher heterogeneity of GUs. Helicobacter pylori (HP)-stratified analysis found an HP-related host genetic locus. Integrative analyses using bulk and single-cell transcriptome profiles highlighted the genetic factors of PUD being enriched in the highly expressed genes in stomach tissues, especially in somatostatin-producing D cells. Our results provide genetic evidence that gastrointestinal cell differentiations and hormone regulations are critical in PUD etiology.

Genome-wide Association Analysis of Schizophrenia and Vitamin D Levels Shows Shared Genetic Architecture and Identifies Novel Risk Loci

Low vitamin D (vitD) levels have been consistently reported in schizophrenia (SCZ) suggesting a role in the etiopathology. However, little is known about the role of underlying shared genetic mechanisms. We applied a conditional/conjunctional false discovery rate approach (FDR) on large, nonoverlapping genome-wide association studies for SCZ (N cases = 53 386, N controls = 77 258) and vitD serum concentration (N = 417 580) to evaluate shared common genetic variants. The identified genomic loci were characterized using functional analyses and biological repositories. We observed cross-trait SNP enrichment in SCZ conditioned on vitD and vice versa, demonstrating shared genetic architecture. Applying the conjunctional FDR approach, we identified 72 loci jointly associated with SCZ and vitD at conjunctional FDR < 0.05. Among the 72 shared loci, 40 loci have not previously been reported for vitD, and 9 were novel for SCZ. Further, 64% had discordant effects on SCZ-risk and vitD levels. A mixture of shared variants with concordant and discordant effects with a predominance of discordant effects was in line with weak negative genetic correlation (rg = -0.085). Our results displayed shared genetic architecture between SCZ and vitD with mixed effect directions, suggesting overlapping biological pathways. Shared genetic variants with complex overlapping mechanisms may contribute to the coexistence of SCZ and vitD deficiency and influence the clinical picture.

The shared genetic architecture and evolution of human language and musical rhythm

Rhythm and language-related traits are phenotypically correlated, but their genetic overlap is largely unknown. Here, we leveraged two large-scale genome-wide association studies performed to shed light on the shared genetics of rhythm (N=606,825) and dyslexia (N=1,138,870). Our results reveal an intricate shared genetic and neurobiological architecture, and lay groundwork for resolving longstanding debates about the potential co-evolution of human language and musical traits.

Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology

Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRSmulti, hold promise for improving PRS accuracy and generalizability across populations. To establish best practices for leveraging the increasing diversity of genomic studies, we investigated how various factors affect the performance of PRSmulti compared with PRSs constructed from single-ancestry GWASs (PRSsingle). Through extensive simulations and empirical analyses, we showed that PRSmulti overall outperformed PRSsingle in understudied populations, except when the understudied population represented a small proportion of the multi-ancestry GWAS. Furthermore, integrating PRSs based on local ancestry-informed GWASs and large-scale, European-based PRSs improved predictive performance in understudied African populations, especially for less polygenic traits with large-effect ancestry-enriched variants. Our work highlights the importance of diversifying genomic studies to achieve equitable PRS performance across ancestral populations and provides guidance for developing PRSs from multiple studies.

Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes

Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.

Up-Regulation of S100 Gene Family in Brain Samples of a Subgroup of Individuals with Schizophrenia: Meta-analysis

The S100 proteins family is known to affect neuroinflammation and astrocyte activation, which have been suggested to be contributors to the pathogenesis of schizophrenia. We conducted a systematic meta-analysis of S100 genes differential expression in postmortem samples of patients with schizophrenia vs. healthy controls, following the commonly used Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Twelve microarray datasets met the inclusion criteria (overall 511 samples, 253 schizophrenia and 258 controls were analyzed). Nine out of 21 genes were significantly up-regulated or with tendency for up-regulation. A per-sample fold change analysis indicated that the S100 genes' up-regulation was concentrated in a subgroup of the patients. None of the genes have been found to be down-regulated. ANXA3, which encodes Annexin 3 protein and was associated with neuroinflammation, was up-regulated and positively correlated with the S100 genes' expression pattern. In addition, astrocytes and endothelial cell markers were significantly correlated with S100A8 expression. S100 correlation with ANXA3 and endothelial cell markers suggests that the up-regulation we detected reflects increased inflammation. However, it might also reflect astrocytes abundance or activation. The fact that S100 proteins were shown to be up-regulated in blood samples and other body fluids of patients with schizophrenia suggests a potential role as biomarkers, which might help disease subtyping, and the development of etiological treatments for immune dysregulation in schizophrenia.

Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes

Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements underpinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we introduce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n = 20,833-766,345) and xQTL data from seven omics layers (n = 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mechanisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular intermediates to GWAS signals.

Fast and accurate Bayesian polygenic risk modeling with variational inference

The advent of large-scale genome-wide association studies (GWASs) has motivated the development of statistical methods for phenotype prediction with single-nucleotide polymorphism (SNP) array data. These polygenic risk score (PRS) methods use a multiple linear regression framework to infer joint effect sizes of all genetic variants on the trait. Among the subset of PRS methods that operate on GWAS summary statistics, sparse Bayesian methods have shown competitive predictive ability. However, most existing Bayesian approaches employ Markov chain Monte Carlo (MCMC) algorithms, which are computationally inefficient and do not scale favorably to higher dimensions, for posterior inference. Here, we introduce variational inference of polygenic risk scores (VIPRS), a Bayesian summary statistics-based PRS method that utilizes variational inference techniques to approximate the posterior distribution for the effect sizes. Our experiments with 36 simulation configurations and 12 real phenotypes from the UK Biobank dataset demonstrated that VIPRS is consistently competitive with the state-of-the-art in prediction accuracy while being more than twice as fast as popular MCMC-based approaches. This performance advantage is robust across a variety of genetic architectures, SNP heritabilities, and independent GWAS cohorts. In addition to its competitive accuracy on the "White British" samples, VIPRS showed improved transferability when applied to other ethnic groups, with up to 1.7-fold increase in R2 among individuals of Nigerian ancestry for low-density lipoprotein (LDL) cholesterol. To illustrate its scalability, we applied VIPRS to a dataset of 9.6 million genetic markers, which conferred further improvements in prediction accuracy for highly polygenic traits, such as height.

Single Nucleotide Polymorphism rs9277336 Controls the Nuclear Alpha Actinin 4-Human Leukocyte Antigen-DPA1 Axis and Pulmonary Endothelial Pathophenotypes in Pulmonary Arterial Hypertension

Background Pulmonary arterial hypertension (PAH) is a complex, fatal disease where disease severity has been associated with the single nucleotide polymorphism (SNP) rs2856830, located near the human leukocyte antigen DPA1 (HLA-DPA1) gene. We aimed to define the genetic architecture of functional variants associated with PAH disease severity by identifying allele-specific binding transcription factors and downstream targets that control endothelial pathophenotypes and PAH. Methods and Results Electrophoretic mobility shift assays of oligonucleotides containing SNP rs2856830 and 8 SNPs in linkage disequilibrium revealed functional SNPs via allele-imbalanced binding to human pulmonary arterial endothelial cell nuclear proteins. DNA pulldown proteomics identified SNP-binding proteins. SNP genotyping and clinical correlation analysis were performed in 84 patients with PAH at University of Pittsburgh Medical Center and in 679 patients with PAH in the All of Us database. SNP rs9277336 was identified as a functional SNP in linkage disequilibrium (r2>0.8) defined by rs2856830, and the minor allele was associated with decreased hospitalizations and improved cardiac output in patients with PAH, an index of disease severity. SNP pulldown proteomics showed allele-specific binding of nuclear ACTN4 (alpha actinin 4) protein to rs9277336 minor allele. Both ACTN4 and HLA-DPA1 were downregulated in pulmonary endothelium in human patients and rodent models of PAH. Via transcriptomic and phenotypic analyses, knockdown of HLA-DPA1 phenocopied knockdown of ACTN4, both similarly controlling cell structure pathways, immune pathways, and endothelial dysfunction. Conclusions We defined the pathogenic activity of functional SNP rs9277336, entailing the allele-specific binding of ACTN4 and controlling expression of the neighboring HLA-DPA1 gene. Through inflammatory or genetic means, downregulation of this ACTN4-HLA-DPA1 regulatory axis promotes endothelial pathophenotypes, providing a mechanistic explanation for the association between this SNP and PAH outcomes.

Genetic correlates of vitamin D-binding protein and 25-hydroxyvitamin D in neonatal dried blood spots

The vitamin D binding protein (DBP), encoded by the group-specific component (GC) gene, is a component of the vitamin D system. In a genome-wide association study of DBP concentration in 65,589 neonates we identify 26 independent loci, 17 of which are in or close to the GC gene, with fine-mapping identifying 2 missense variants on chromosomes 12 and 17 (within SH2B3 and GSDMA, respectively). When adjusted for GC haplotypes, we find 15 independent loci distributed over 10 chromosomes. Mendelian randomization analyses identify a unidirectional effect of higher DBP concentration and (a) higher 25-hydroxyvitamin D concentration, and (b) a reduced risk of multiple sclerosis and rheumatoid arthritis. A phenome-wide association study confirms that higher DBP concentration is associated with a reduced risk of vitamin D deficiency. Our findings provide valuable insights into the influence of DBP on vitamin D status and a range of health outcomes.

15 years of GWAS discovery: Realizing the promise

It has been 15 years since the advent of the genome-wide association study (GWAS) era. Here, we review how this experimental design has realized its promise by facilitating an impressive range of discoveries with remarkable impact on multiple fields, including population genetics, complex trait genetics, epidemiology, social science, and medicine. We predict that the emergence of large-scale biobanks will continue to expand to more diverse populations and capture more of the allele frequency spectrum through whole-genome sequencing, which will further improve our ability to investigate the causes and consequences of human genetic variation for complex traits and diseases.

VDAC genes down-regulation in brain samples of individuals with schizophrenia is revealed by a systematic meta-analysis

Mitochondrial dysfunction was shown to be involved in schizophrenia pathophysiology. Abnormal energy states can lead to alterations in neural function and thereby to the cognitive and behavioral aberrations characteristics of schizophrenia. Voltage-dependent anion-selective channels (VDAC) are located in the outer mitochondrial membrane and are involved in mitochondrial energy production. Only few studies explored VDAC genes' expression in schizophrenia, and their results were not consistent. We conducted a systematic meta-analysis of ten brain samples gene expression datasets (overall 368 samples, 179 schizophrenia, 189 controls). In addition, we conducted a meta-analysis of three blood samples datasets (overall 300 samples, 167 schizophrenia, 133 controls). Pairwise correlation analysis was conducted between the VDAC and proteasome subunit genes' expression patterns. VDAC1, VDAC2 and VDAC3 showed significant down-regulation in brain samples of patients with schizophrenia. They also showed significant positive correlations with the proteasome subunit genes' expression levels. Our findings suggest that VDAC genes might play a role in mitochondrial dysfunction in schizophrenia. VDAC1 was down-regulated also in blood samples, which suggests its potential role as a biomarker for schizophrenia. The correlation with proteasome subunits, which were previously shown to be down-regulated in a subgroup of the patients, suggests that our findings might characterize a subgroup of the patients. This direction has the potential to lead to patients' stratification and more precisely-targeted therapy and necessitates further study.

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ² statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

Correlation between protein abundance and sequence conservation: what do recent experiments say?

Cells evolve in a space of parameter values set by physical and chemical forces. These constraints create associations among cellular properties. A particularly strong association is the negative correlation between the rate of evolution of proteins and their abundance in the cell. Highly expressed proteins evolve slower than lowly expressed ones. Multiple hypotheses have been put forward to explain this relationship, including, for instance, the requirement for higher mRNA stability, misfolding avoidance, and misinteraction avoidance for highly expressed proteins. Here, we review some of these hypotheses, their predictions, and how they are supported to finally discuss recent experiments that have been performed to test these predictions.

Assessing polygenic risk score models for applications in populations with under-represented genomics data: an example of Vietnam

Most polygenic risk score (PRS)models have been based on data from populations of European origins (accounting for the majority of the large genomics datasets, e.g. >78% in the UK Biobank and >85% in the GTEx project). Although several large-scale Asian biobanks were initiated (e.g. Japanese, Korean, Han Chinese biobanks), most other Asian countries have little or near-zero genomics data. To implement PRS models for under-represented populations, we explored transfer learning approaches, assuming that information from existing large datasets can compensate for the small sample size that can be feasibly obtained in developing countries, like Vietnam. Here, we benchmark 13 common PRS methods in meta-population strategy (combining individual genotype data from multiple populations) and multi-population strategy (combining summary statistics from multiple populations). Our results highlight the complementarity of different populations and the choice of methods should depend on the target population. Based on these results, we discussed a set of guidelines to help users select the best method for their datasets. We developed a robust and comprehensive software to allow for benchmarking comparisons between methods and proposed a computational framework for improving PRS performance in a dataset with a small sample size. This work is expected to inform the development of genomics applications in under-represented populations. PRSUP framework is available at: https://github.com/BiomedicalMachineLearning/VGP.

Denisovan and Neanderthal archaic introgression differentially impacted the genetics of complex traits in modern populations

BACKGROUND

Introgression from extinct Neanderthal and Denisovan human species has been shown to contribute to the genetic pool of modern human populations and their phenotypic spectrum. Evidence of how Neanderthal introgression shaped the genetics of human traits and diseases has been extensively studied in populations of European descent, with signatures of admixture reported for instance in genes associated with pigmentation, immunity, and metabolic traits. However, limited information is currently available about the impact of archaic introgression on other ancestry groups. Additionally, to date, no study has been conducted with respect to the impact of Denisovan introgression on the health and disease of modern populations. Here, we compare the way evolutionary pressures shaped the genetics of complex traits in East Asian and European populations, and provide evidence of the impact of Denisovan introgression on the health of East Asian and Central/South Asian populations.

RESULTS

Leveraging genome-wide association statistics from the Biobank Japan and UK Biobank, we assessed whether Denisovan and Neanderthal introgression together with other evolutionary genomic signatures were enriched for the heritability of physiological and pathological conditions in populations of East Asian and European descent. In EAS, Denisovan-introgressed loci were enriched for coronary artery disease heritability (1.69-fold enrichment, p=0.003). No enrichment for archaic introgression was observed in EUR. We also performed a phenome-wide association study of Denisovan and Neanderthal alleles in six ancestry groups available in the UK Biobank. In EAS, the Denisovan-introgressed SNP rs62391664 in the major histocompatibility complex region was associated with albumin/globulin ratio (beta=-0.17, p=3.57×10^-7). Neanderthal-introgressed alleles were associated with psychiatric and cognitive traits in EAS (e.g., "No Bipolar or Depression"-rs79043717 beta=-1.5, p=1.1×10^-7), and with blood biomarkers (e.g., alkaline phosphatase-rs11244089 beta=0.1, p=3.69×10^-116) and red hair color (rs60733936 beta=-0.86, p=4.49×10^-165) in EUR. In the other ancestry groups, Neanderthal alleles were associated with several traits, also including the use of certain medications (e.g., Central/South East Asia: indapamide - rs732632 beta=-2.38, p=5.22×10^-7).

CONCLUSIONS

Our study provides novel evidence regarding the impact of archaic introgression on the genetics of complex traits in worldwide populations, highlighting the specific contribution of Denisovan introgression in EAS populations.

Mosaic patterns of selection in genomic regions associated with diverse human traits

Natural selection shapes the genetic architecture of many human traits. However, the prevalence of different modes of selection on genomic regions associated with variation in traits remains poorly understood. To address this, we developed an efficient computational framework to calculate positive and negative enrichment of different evolutionary measures among regions associated with complex traits. We applied the framework to summary statistics from >900 genome-wide association studies (GWASs) and 11 evolutionary measures of sequence constraint, population differentiation, and allele age while accounting for linkage disequilibrium, allele frequency, and other potential confounders. We demonstrate that this framework yields consistent results across GWASs with variable sample sizes, numbers of trait-associated SNPs, and analytical approaches. The resulting evolutionary atlas maps diverse signatures of selection on genomic regions associated with complex human traits on an unprecedented scale. We detected positive enrichment for sequence conservation among trait-associated regions for the majority of traits (>77% of 290 high power GWASs), which included reproductive traits. Many traits also exhibited substantial positive enrichment for population differentiation, especially among hair, skin, and pigmentation traits. In contrast, we detected widespread negative enrichment for signatures of balancing selection (51% of GWASs) and absence of enrichment for evolutionary signals in regions associated with late-onset Alzheimer's disease. These results support a pervasive role for negative selection on regions of the human genome that contribute to variation in complex traits, but also demonstrate that diverse modes of evolution are likely to have shaped trait-associated loci. This atlas of evolutionary signatures across the diversity of available GWASs will enable exploration of the relationship between the genetic architecture and evolutionary processes in the human genome.

A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis

Genomics-driven drug discovery is indispensable for accelerating the development of novel therapeutic targets. However, the drug discovery framework based on evidence from genome-wide association studies (GWASs) has not been established, especially for cross-population GWAS meta-analysis. Here, we introduce a practical guideline for genomics-driven drug discovery for cross-population meta-analysis, as lessons from the Global Biobank Meta-analysis Initiative (GBMI). Our drug discovery framework encompassed three methodologies and was applied to the 13 common diseases targeted by GBMI (N _mean = 1,329,242). Individual methodologies complementarily prioritized drugs and drug targets, which were systematically validated by referring previously known drug-disease relationships. Integration of the three methodologies provided a comprehensive catalog of candidate drugs for repositioning, nominating promising drug candidates targeting the genes involved in the coagulation process for venous thromboembolism and the interleukin-4 and interleukin-13 signaling pathway for gout. Our study highlighted key factors for successful genomics-driven drug discovery using cross-population meta-analyses.

Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation

BACKGROUND

Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions.

METHODS

The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data.

RESULTS

Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by - 0.1 to ~ 6.9% for real traits with the medium-density panel.

CONCLUSIONS

Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation.

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

Polygenic adaptation after a sudden change in environment

Polygenic adaptation is thought to be ubiquitous, yet remains poorly understood. Here, we model this process analytically, in the plausible setting of a highly polygenic, quantitative trait that experiences a sudden shift in the fitness optimum. We show how the mean phenotype changes over time, depending on the effect sizes of loci that contribute to variance in the trait, and characterize the allele dynamics at these loci. Notably, we describe the two phases of the allele dynamics: The first is a rapid phase, in which directional selection introduces small frequency differences between alleles whose effects are aligned with or opposed to the shift, ultimately leading to small differences in their probability of fixation during a second, longer phase, governed by stabilizing selection. As we discuss, key results should hold in more general settings and have important implications for efforts to identify the genetic basis of adaptation in humans and other species.

Using singleton densities to detect recent selection in Bos taurus

Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the "Singleton Density Score" (SDS), to the Holstein breed of Bos taurus to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural-cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans-milk protein content, milk fat content, and stature-have been subject to directional selection. Simulations demonstrate that because B. taurus recently experienced a population bottleneck, singletons are depleted so the power of SDS methods is reduced. These results inform on which genes underlie recent genetic change in B. taurus, while providing information on how polygenic selection can be best investigated in future studies.

Maintenance of Complex Trait Variation: Classic Theory and Modern Data

Numerous studies have found evidence that GWAS loci experience negative selection, which increases in intensity with the effect size of identified variants. However, there is also accumulating evidence that this selection is not entirely mediated by the focal trait and contains a substantial pleiotropic component. Understanding how selective constraint shapes phenotypic variation requires advancing models capable of balancing these and other components of selection, as well as empirical analyses capable of inferring this balance and how it is generated by the underlying biology. We first review the classic theory connecting phenotypic selection to selection at individual loci as well as approaches and findings from recent analyses of negative selection in GWAS data. We then discuss geometric theories of pleiotropic selection with the potential to guide future modeling efforts. Recent findings revealing the nature of pleiotropic genetic variation provide clues to which genetic relationships are important and should be incorporated into analyses of selection, while findings that effect sizes vary between populations indicate that GWAS measurements could be misleading if effect sizes have also changed throughout human history.

Utility of polygenic embryo screening for disease depends on the selection strategy

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating 'virtual' couples and offspring based on real genomes from schizophrenia and Crohn's disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

Discovery and implications of polygenicity of common diseases

The sequencing of the human genome has allowed the study of the genetic architecture of common diseases: the number of genomic variants that contribute to risk of disease and their joint frequency and effect size distribution. Common diseases are polygenic, with many loci contributing to phenotype, and the cumulative burden of risk alleles determines individual risk in conjunction with environmental factors. Most risk loci occur in noncoding regions of the genome regulating cell- and context-specific gene expression. Although the effect sizes of most risk alleles are small, their cumulative effects in individuals, quantified as a polygenic (risk) score, can identify people at increased risk of disease, thereby facilitating prevention or early intervention.

From variant to function in human disease genetics

[Figure: see text].

New Polygenic Risk Score to Predict High Myopia in Singapore Chinese Children

Purpose

The purpose of this study was to develop an Asian polygenic risk score (PRS) to predict high myopia (HM) in Chinese children in the Singapore Cohort of Risk factors for Myopia (SCORM) cohort.

Methods

We included children followed from 6 to 11 years old until teenage years (12–18 years old). Cycloplegic autorefraction, ultrasound biometry, Illumina HumanHap 550, or 550 Duo Beadarrays, demographics, and environmental factors data were obtained. The PRS was generated from the Consortium for Refractive Error and Myopia genomewide association study (n = 542,934) and the Strabismus, Amblyopia, and Refractive Error in Singapore children Study (n = 500). The Growing Up in Singapore Towards healthy Outcomes Cohort study (n = 339) was the replication cohort. The outcome was teenage HM (≤ −5.00 D) with predictive performance assessed using the area under the curve (AUC).

Results

Mean baseline age ± SD was 7.85 ± 0.84 (n = 1004) and 571 attended the teenage visit; 23.3% had HM. In multivariate analysis, the PRS was associated with a myopic spherical equivalent with an incremental R² of 0.041 (95% confidence interval [CI] = 0.010, 0.073; P < 0.001). AUC for HM (0.77 [95% CI = 0.71–0.83]) performed better (P = 0.02) with the PRS compared with a model without (0.72 [95% CI = 0.65, 0.78]). Children at the top 25% PRS risk had a 2.34-fold-greater risk of HM (95% CI = 1.53, 3.55; P < 0.001).

Conclusions

The new Asian PRS improved the predictive performance to detect children at risk of HM.

Translational Relevance

Clinicians may use the PRS with other predictive factors to identify high risk children and guide interventions to reduce the risk of HM later in life.