Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

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.

Trans Effects on Gene Expression Can Drive Omnigenic Inheritance.

Early genome-wide association studies (GWASs) led to the surprising discovery that, for typical complex traits, most of the heritability is due to huge numbers of common variants with tiny effect sizes. Previously, we argued that new models are needed to understand these patterns. Here, we provide a formal model in which genetic contributions to complex traits are partitioned into direct effects from core genes and indirect effects from peripheral genes acting in trans. We propose that most heritability is driven by weak trans-eQTL SNPs, whose effects are mediated through peripheral genes to impact the expression of core genes. In particular, if the core genes for a trait tend to be co-regulated, then the effects of peripheral variation can be amplified such that nearly all of the genetic variance is driven by weak trans effects. Thus, our model proposes a framework for understanding key features of the architecture of complex traits.

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders.

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits.

Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.

An explanation for the prevalence of XY over ZW sex determination in species derived from hermaphroditism.

The many independent transitions from hermaphroditism to separate sexes (dioecy) in flowering plants and some animal clades must often have involved the emergence of a heterogametic sex-determining locus, the basis of XY and ZW sex determination (i.e., male and female heterogamety). Current estimates indicate that XY sex determination is much more frequent than ZW, but the reasons for this asymmetry are unclear. One proposition is that separate sexes evolve through the invasion of sterility mutations at closely linked loci, in which case XY sex determination evolves if the initial male sterility mutation is fully recessive. Alternatively, dioecy may evolve via the gradual divergence of male and female phenotypes, but the genetic basis of such divergence and its connection to XY and ZW systems remain poorly understood. Using mathematical modeling, we show how dioecy with XY or ZW sex determination can emerge from the joint evolution of resource allocation to male and female function with its genetic architecture. Our model reveals that whether XY or ZW sex determination evolves depends on the trade-off between allocation to male and female function, and on the mating system of the ancestral hermaphrodites, with selection for female specialization or inbreeding avoidance both favoring XY sex determination. Together, our results cast light on an important but poorly understood path from hermaphroditism to dioecy, and provide an adaptive hypothesis for the preponderance of XY systems. Beyond sex and sex determination, our model shows how ecology can influence the way selection shapes the genetic architecture of polymorphic traits.

Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits.

Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.

Understanding the Genetic Basis of Variation in Meiotic Recombination: Past, Present, and Future.

Meiotic recombination is a fundamental feature of sexually reproducing species. It is often required for proper chromosome segregation and plays important role in adaptation and the maintenance of genetic diversity. The molecular mechanisms of recombination are remarkably conserved across eukaryotes, yet meiotic genes and proteins show substantial variation in their sequence and function, even between closely related species. Furthermore, the rate and distribution of recombination shows a huge diversity within and between chromosomes, individuals, sexes, populations, and species. This variation has implications for many molecular and evolutionary processes, yet how and why this diversity has evolved is not well understood. A key step in understanding trait evolution is to determine its genetic basis-that is, the number, effect sizes, and distribution of loci underpinning variation. In this perspective, I discuss past and current knowledge on the genetic basis of variation in recombination rate and distribution, explore its evolutionary implications, and present open questions for future research.

Leveraging the local genetic structure for trans-ancestry association mapping.

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

Leveraging gene co-regulation to identify gene sets enriched for disease heritability.

Identifying gene sets that are associated to disease can provide valuable biological knowledge, but a fundamental challenge of gene set analyses of GWAS data is linking disease-associated SNPs to genes. Transcriptome-wide association studies (TWASs) detect associations between the genetically predicted expression of a gene and disease risk, thus implicating candidate disease genes. However, causal disease genes at TWAS-associated loci generally remain unknown due to gene co-regulation, which leads to correlations across genes in predicted expression. We developed a method, gene co-regulation score (GCSC) regression, to identify gene sets that are enriched for disease heritability explained by predicted expression. GCSC regresses TWAS chi-square statistics on gene co-regulation scores reflecting correlations in predicted gene expression; a gene set is enriched for heritability if genes with high co-regulation to the set have higher TWAS chi-square statistics than genes with low co-regulation to the set, beyond what is expected based on co-regulation to all genes. We verified via simulations that GCSC is well calibrated and well powered. We applied GCSC to gene expression data from GTEx (48 tissues) and GWAS summary statistics for 43 independent diseases and complex traits analyzing a broad set of biological pathways and specifically expressed gene sets. We identified many enriched sets, recapitulating known biology. For Alzheimer disease, we detected evidence of an immune basis, and specifically a role for antigen presentation, in analyses of both biological pathways and specifically expressed gene sets. Our results highlight the advantages of leveraging gene co-regulation within the TWAS framework to identify enriched gene sets.

The Genetics of Plant Metabolism.

Plant metabolic studies have traditionally focused on the role and regulation of the enzymes catalyzing key reactions within specific pathways. Within the past 20 years, reverse genetic approaches have allowed direct determination of the effects of the deficiency, or surplus, of a given protein on the biochemistry of a plant. In parallel, top-down approaches have also been taken, which rely on screening broad, natural genetic diversity for metabolic diversity. Here, we compare and contrast the various strategies that have been adopted to enhance our understanding of the natural diversity of metabolism. We also detail how these approaches have enhanced our understanding of both specific and global aspects of the genetic regulation of metabolism. Finally, we discuss how such approaches are providing important insights into the evolution of plant secondary metabolism.

Epidemiologic association and shared genetic architecture between cataract and hearing difficulties among middle-aged and older adults.

Age-related cataract and hearing difficulties are major sensory disorders that often co-exist in the global-wide elderly and have a tangible influence on the quality of life. However, the epidemiologic association between cataract and hearing difficulties remains unexplored, while little is known about whether the two share their genetic etiology. We first investigated the clinical association between cataract and hearing difficulties using the UK Biobank covering 502,543 individuals. Both unmatched analysis (adjusted for confounders) and a matched analysis (one control matched for each patient with cataract according to confounding factors) were undertaken and confirmed that cataract was associated with hearing difficulties (OR, 2.12; 95% CI, 1.98-2.27; OR, 2.03; 95% CI, 1.86-2.23, respectively). Furthermore, we explored and quantified the shared genetic architecture of these two complex sensory disorders at the common variant level using the bivariate causal mixture model (MiXeR) and conditional/conjunctional false discovery rate method based on the largest available genome-wide association studies of cataract (N = 585,243) and hearing difficulties (N = 323,978). Despite detecting only a negligible genetic correlation, we observe polygenic overlap between cataract and hearing difficulties and identify 6 shared loci with mixed directions of effects. Follow-up analysis of the shared loci implicates candidate genes QKI, STK17A, TYR, NSF, and TCF4 likely contribute to the pathophysiology of cataracts and hearing difficulties. In conclusion, this study demonstrates the presence of epidemiologic association between cataract and hearing difficulties and provides new insights into the shared genetic architecture of these two disorders at the common variant level.

An intermediate-effect size variant in UMOD confers risk for chronic kidney disease.

The kidney-specific gene UMOD encodes for uromodulin, the most abundant protein excreted in normal urine. Rare large-effect variants in UMOD cause autosomal dominant tubulointerstitial kidney disease (ADTKD), while common low-impact variants strongly associate with kidney function and the risk of chronic kidney disease (CKD) in the general population. It is unknown whether intermediate-effect variants in UMOD contribute to CKD. Here, candidate intermediate-effect UMOD variants were identified using large-population and ADTKD cohorts. Biological and phenotypical effects were investigated using cell models, in silico simulations, patient samples, and international databases and biobanks. Eight UMOD missense variants reported in ADTKD are present in the Genome Aggregation Database (gnomAD), with minor allele frequency (MAF) ranging from 10-5 to 10-3. Among them, the missense variant p.Thr62Pro is detected in ∼1/1,000 individuals of European ancestry, shows incomplete penetrance but a high genetic load in familial clusters of CKD, and is associated with kidney failure in the 100,000 Genomes Project (odds ratio [OR] = 3.99 [1.84 to 8.98]) and the UK Biobank (OR = 4.12 [1.32 to 12.85). Compared with canonical ADTKD mutations, the p.Thr62Pro carriers displayed reduced disease severity, with slower progression of CKD and an intermediate reduction of urinary uromodulin levels, in line with an intermediate trafficking defect in vitro and modest induction of endoplasmic reticulum (ER) stress. Identification of an intermediate-effect UMOD variant completes the spectrum of UMOD-associated kidney diseases and provides insights into the mechanisms of ADTKD and the genetic architecture of CKD.

The Evolutionary Importance of Intraspecific Variation in Sexual Communication Across Sensory Modalities.

The evolution of sexual communication is critically important in the diversity of arthropods, which are declining at a fast pace worldwide. Their environments are rapidly changing, with increasing chemical, acoustic, and light pollution. To predict how arthropod species will respond to changing climates, habitats, and communities, we need to understand how sexual communication systems can evolve. In the past decades, intraspecific variation in sexual signals and responses across different modalities has been identified, but never in a comparative way. In this review, we identify and compare the level and extent of intraspecific variation in sexual signals and responses across three different modalities, chemical, acoustic, and visual, focusing mostly on insects. By comparing causes and possible consequences of intraspecific variation in sexual communication among these modalities, we identify shared and unique patterns, as well as knowledge needed to predict the evolution of sexual communication systems in arthropods in a changing world.

Evaluation of heritability partitioning approaches in livestock populations.

BACKGROUND: Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium. RESULTS: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively). CONCLUSIONS: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

Threespine Stickleback: A Model System For Evolutionary Genomics.

The repeated adaptation of oceanic threespine sticklebacks to fresh water has made it a premier organism to study parallel evolution. These small fish have multiple distinct ecotypes that display a wide range of diverse phenotypic traits. Ecotypes are easily crossed in the laboratory, and families are large and develop quickly enough for quantitative trait locus analyses, positioning the threespine stickleback as a versatile model organism to address a wide range of biological questions. Extensive genomic resources, including linkage maps, a high-quality reference genome, and developmental genetics tools have led to insights into the genomic basis of adaptation and the identification of genomic changes controlling traits in vertebrates. Recently, threespine sticklebacks have been used as a model system to identify the genomic basis of highly complex traits, such as behavior and host-microbiome and host-parasite interactions. We review the latest findings and new avenues of research that have led the threespine stickleback to be considered a supermodel of evolutionary genomics.

Beyond the standard GWAS - a guide for plant biologists.

Classic genome-wide association studies (GWAS) look for associations between individual SNPs and phenotypes of interest. With the rapid progress of high-throughput genotyping and phenotyping technologies, GWAS have become increasingly powerful for detecting genetic determinants and their molecular mechanisms underpinning natural phenotypic variation. However, GWAS frequently yield results with neither expected nor promising loci, nor any significant associations. This is often because associations between SNPs and a single phenotype are confounded, for example with the environment, other traits, or complex genetic structures. Such confounding can mask true genotype-phenotype associations, or inflate spurious associations. To address these problems, numerous methods have been developed that go beyond the standard model. Such advanced GWAS models are flexible and can offer improved statistical power for understanding the genetics underlying complex traits. Despite this advantage, these models have not been widely adopted and implemented compared to the standard GWAS approach, partly because this literature is diverse and often technical. In this review, our aim is to provide an overview of the application and the benefits of various advanced GWAS models for handling complex traits and genetic structures, targeting plant biologists who wish to carry out GWAS more effectively.

Whole-genome sequencing of African Americans implicates differential genetic architecture in inflammatory bowel disease.

Whether or not populations diverge with respect to the genetic contribution to risk of specific complex diseases is relevant to understanding the evolution of susceptibility and origins of health disparities. Here, we describe a large-scale whole-genome sequencing study of inflammatory bowel disease encompassing 1,774 affected individuals and 1,644 healthy control Americans with African ancestry (African Americans). Although no new loci for inflammatory bowel disease are discovered at genome-wide significance levels, we identify numerous instances of differential effect sizes in combination with divergent allele frequencies. For example, the major effect at PTGER4 fine maps to a single credible interval of 22 SNPs corresponding to one of four independent associations at the locus in European ancestry individuals but with an elevated odds ratio for Crohn disease in African Americans. A rare variant aggregate analysis implicates Ca2+-binding neuro-immunomodulator CALB2 in ulcerative colitis. Highly significant overall overlap of common variant risk for inflammatory bowel disease susceptibility between individuals with African and European ancestries was observed, with 41 of 241 previously known lead variants replicated and overall correlations in effect sizes of 0.68 for combined inflammatory bowel disease. Nevertheless, subtle differences influence the performance of polygenic risk scores, and we show that ancestry-appropriate weights significantly improve polygenic prediction in the highest percentiles of risk. The median amount of variance explained per locus remains the same in African and European cohorts, providing evidence for compensation of effect sizes as allele frequencies diverge, as expected under a highly polygenic model of disease.

(Epi)mutation Rates and the Evolution of Composite Trait Architectures.

AbstractMutation rates vary widely along genomes and across inheritance systems. This suggests that complex traits-resulting from the contributions of multiple determinants-might be composite in terms of the underlying mutation rates. Here we investigate through mathematical modeling whether such a heterogeneity may drive changes in a trait's architecture, especially in fluctuating environments, where phenotypic instability can be beneficial. We first identify a convexity principle related to the shape of the trait's fitness function, setting conditions under which composite architectures should be adaptive or, conversely and more commonly, should be selected against. Simulations reveal, however, that applying this principle to realistic evolving populations requires taking into account pervasive epistatic interactions that take place in the system. Indeed, the fate of a mutation affecting the architecture depends on the (epi)genetic background, which itself depends on the current architecture in the population. We tackle this problem by borrowing the adaptive dynamics framework from evolutionary ecology-where it is routinely used to deal with such resident/mutant dependencies-and find that the principle excluding composite architectures generally prevails. Yet the predicted evolutionary trajectories will typically depend on the initial architecture, possibly resulting in historical contingencies. Finally, by relaxing the large population size assumption, we unexpectedly find that not only the strength of selection on a trait's architecture but also its direction depend on population size, revealing a new occurrence of the recently identified phenomenon coined "sign inversion."

Dissecting the shared genetic landscape of anxiety, depression, and schizophrenia.

BACKGROUND: Numerous studies highlight the genetic underpinnings of mental disorders comorbidity, particularly in anxiety, depression, and schizophrenia. However, their shared genetic loci are not well understood. Our study employs Mendelian randomization (MR) and colocalization analyses, alongside multi-omics data, to uncover potential genetic targets for these conditions, thereby informing therapeutic and drug development strategies. METHODS: We utilized the Consortium for Linkage Disequilibrium Score Regression (LDSC) and Mendelian Randomization (MR) analysis to investigate genetic correlations among anxiety, depression, and schizophrenia. Utilizing GTEx V8 eQTL and deCODE Genetics pQTL data, we performed a three-step summary-data-based Mendelian randomization (SMR) and protein-protein interaction analysis. This helped assess causal and comorbid loci for these disorders and determine if identified loci share coincidental variations with psychiatric diseases. Additionally, phenome-wide association studies, drug prediction, and molecular docking validated potential drug targets. RESULTS: We found genetic correlations between anxiety, depression, and schizophrenia, and under a meta-analysis of MR from multiple databases, the causal relationships among these disorders are supported. Based on this, three-step SMR and colocalization analyses identified ITIH3 and CCS as being related to the risk of developing depression, while CTSS and DNPH1 are related to the onset of schizophrenia. BTN3A1, PSMB4, and TIMP4 were identified as comorbidity loci for both disorders. Molecules that could not be determined through colocalization analysis were also presented. Drug prediction and molecular docking showed that some drugs and proteins have good binding affinity and available structural data. CONCLUSIONS: Our study indicates genetic correlations and shared risk loci between anxiety, depression, and schizophrenia. These findings offer insights into the underlying mechanisms of their comorbidities and aid in drug development.