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The genetic complexity of polygenic traits represents a captivating and intricate facet of biological inheritance. Unlike Mendelian traits controlled by a single gene, polygenic traits are influenced by multiple genetic loci, each exerting a modest effect on the trait. This cumulative impact of numerous genes, interactions among them, environmental factors, and epigenetic modifications results in a multifaceted architecture of genetic contributions to complex traits. Given the well-characterized genome, diverse traits, and range of genetic resources, chicken (Gallus gallus) was employed as a model organism to dissect the intricate genetic makeup of a previously identified major Quantitative Trait Loci (QTL) for body weight on chromosome 1. A multigenerational advanced intercross line (AIL) of 3215 chickens whose genomes had been sequenced to an average of 0.4x was analyzed using genome-wide association study (GWAS) and variance-heterogeneity GWAS (vGWAS) to identify markers associated with 8-week body weight. Additionally, epistatic interactions were studied using the natural and orthogonal interaction (NOIA) model. Six genetic modules, two from GWAS and four from vGWAS, were strongly associated with the studied trait. We found evidence of both additive- and non-additive interactions between these modules and constructed a putative local epistasis network for the region. Our screens for functional alleles revealed a missense variant in the gene ribonuclease H2 subunit B (RNASEH2B), which has previously been associated with growth-related traits in chickens and Darwin's finches. In addition, one of the most strongly associated SNPs identified is located in a non-coding region upstream of the long non-coding RNA, ENSGALG00000053256, previously suggested as a candidate gene for regulating chicken body weight. By studying large numbers of individuals from a family material using approaches to capture both additive and non-additive effects, this study advances our understanding of genetic complexities in a highly polygenic trait and has practical implications for poultry breeding and agriculture. [ABSTRACT FROM AUTHOR]

Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes and identified 14 loci with genome-wide significance (p<5×10−8 and intersection of hits from left and right eyes). We also did GWAS on the retina color, the average color of the center region of the retinal fundus photos. The GWAS of retina colors identified 34 loci, 7 are overlapping with GWAS of raw image phenotype. Our results establish the feasibility of this new framework of genomic study based on self-supervised phenotyping of medical images. Author summary: Imaging genetics is a research field focused on understanding how genetic variations influence observable traits or diseases that can be visualized through medical imaging. Previous studies in imaging genetics have mostly relied on traits identified by human experts. In this study, we used a self-supervised contrastive learning algorithm that automatically identifies features in fundus images that are unique to each individual yet consistent between their left and right eyes. These features are represented as a set of 128 numbers that can quantitatively describe the characteristics of each individual's fundus images without human labeling bias. We then conducted genome wide association studies (GWAS) to explore the associations between these features and genetic variations using paired fundus images and genetic data from the UK Biobank. Our findings indicate that these features are linked to genetic signals associated with eye measurements, eye diseases, pigmentation, and vessel development. Additionally, we found that GWAS of this set of features identified new genetic signals not detected in GWAS only using retina color. These results establish the feasibility of this self-supervised phenotyping approach for imaging genetics studies. [ABSTRACT FROM AUTHOR]

Background: Observational studies have previously shown a potential link between psycho-emotional disorders, such as mood swings, highly strung, anxious feelings, and gastroesophageal reflux disease (GERD). However, the credibility of these associations could be influenced by various confounding factors. Consequently, our study sought to employ a Mendelian randomization (MR) approach to elucidate a potential causal relationship between psycho-emotional disorders and GERD. Method: Information on independent genetic variants linked to mood swings, highly strung, and anxious feelings was gathered from European populations participating in the IEU Open GWAS research. The FinnGen Consortium provided the genome-wide association study (GWAS) summary statistics for GERD. Our analysis employed the inverse variance weighted (IVW) method under the random effects model as the main analytical method. To further bolster our findings, we employed the weighted median and MR Egger methods. In addition, we conducted a series of sensitivity analyses. Results: Our study supports the existence of a causal relationship between psycho-emotional disorders and GERD. Mood swings, highly strung, and anxious feelings adversely affected GERD risk (mood swings: OR 2.21, 95% CI 1.19–5.59, p = 3.09 × 10–2; highly strung: OR 5.63, 95% CI 1.77–17.94, p = 3.42 × 10–3; anxious feelings: OR 2.48, 95% CI 1.08–4.33, p = 2.89 × 10–2). Conclusion: This Mendelian randomization study provides robust support for the notion that mood swings, highly strung and anxious feelings, are associated with an increased risk of developing GERD. [ABSTRACT FROM AUTHOR]

Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes, EPDR1, PKDCC, and SPTBN1, which are highly expressed in muscle tissue and previously unlinked to bone metabolism. About 90% and 85% of filtered Single-Nucleotide Polymorphisms were in the intronic and intergenic regions for the threshold at P≤5×10−8 and P≤5×10−100, respectively. EPDR1 was highly expressed in multiple tissues, including muscles, adrenal glands, blood vessels, and the thyroid. SPTBN1 was highly expressed in all 30 tissue types except blood, while PKDCC was highly expressed in all 30 tissue types except the brain, pancreas, and skin. Our study provides a framework for using GWAS findings to highlight functional evidence of crosstalk between multiple tissues based on shared genetic architecture between muscle and bone. Further research should focus on functional validation, multi-omics data integration, gene-environment interactions, and clinical relevance in musculoskeletal disorders. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction. Author summary: Genome-wide association studies (GWAS) are powerful in identifying genetic variants influencing complex traits and diseases. However, adjusting for heritable covariates in GWAS may introduce collider bias when both genetic variants and confounders may causally influence these covariates. In this study, for the first time we derived the analytical form of the bias term in GWAS with multiple covariates, enabling bias estimation and correction using any MVMR method. On the other hand, many existing MVMR methods may not be robust to invalid IVs and are designed for independent samples. Since GWAS data of multiple traits are needed, overlapping samples become inevitable. Hence, while investigating the performance of many MVMR methods, we mainly adopt MVMR-cML, a novel MVMR approach robust to invalid IVs and sample overlap. Our simulations underscore that most MVMR methods effectively reduce collider bias across various scenarios. Furthermore, by accounting for correlations among GWAS statistics, as well as the linkage disequilibrium (LD) between the target SNP and IVs, we establish the consistency and asymptotic normality of the bias-corrected estimator based on MVMR-cML. The application of our bias-correction approach to two published GWAS data examples illustrates its utility and efficacy. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWASs) can be affected by confounding. Family-based GWAS uses random, within-family genetic variation to avoid this. A study in PLOS Biology details how different sources of confounding affect GWAS and whether family-based designs offer a solution. Genome-wide association studies (GWASs) can be affected by confounding, but family-based GWASs use random, within-family genetic variation to avoid this. This Primer explores a study in PLOS Biology which asks how different sources of confounding affect GWASs and whether family-based designs offer a solution. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphisms are the most common form of DNA alterations at the level of a single nucleotide in the genomic sequence. Genome-wide association studies (GWAS) were carried to identify potential risk genes or genomic regions by screening for SNPs associated with disease. Recent studies have shown that SCN9A comprises the NaV1.7 subunit, Na+ channels have a gene encoding of 1988 amino acids arranged into 4 domains, all with 6 transmembrane regions, and are mainly found in dorsal root ganglion (DRG) neurons and sympathetic ganglion neurons. Multiple forms of acute hypersensitivity conditions, such as primary erythermalgia, congenital analgesia, and paroxysmal pain syndrome have been linked to polymorphisms in the SCN9A gene. Under this study, we utilized a variety of computational tools to explore out nsSNPs that are potentially damaging to heath by modifying the structure or activity of the SCN9A protein. Over 14 potentially damaging and disease-causing nsSNPs (E1889D, L1802P, F1782V, D1778N, C1370Y, V1311M, Y1248H, F1237L, M936V, I929T, V877E, D743Y, C710W, D623H) were identified by a variety of algorithms, including SNPnexus, SNAP-2, PANTHER, PhD-SNP, SNP & GO, I-Mutant, and ConSurf. Homology modeling, structure validation, and protein-ligand interactions also were performed to confirm 5 notable substitutions (L1802P, F1782V, D1778N, V1311M, and M936V). Such nsSNPs may become the center of further studies into a variety of disorders brought by SCN9A dysfunction. Using in-silico strategies for assessing SCN9A genetic variations will aid in organizing large-scale investigations and developing targeted therapeutics for disorders linked to these variations. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine-mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By prioritizing functional variants within the candidate region using eQTL information, SuSiE2 improves SuSiE by increasing the detection rate of causal SNPs and reducing the average size of credible sets. We compared the performance of SuSiE2 with other multi-trait fine-mapping methods with respect to power, coverage, and precision through simulations and applications to the GWAS results of Alzheimer's disease (AD) and body mass index (BMI). Our results demonstrate the better performance of SuSiE2, both when the in-sample linkage disequilibrium (LD) matrix and an external reference panel is used in inference. Author summary: Genome-wide association studies (GWASs) have proven powerful in detecting genetic variants associated with complex traits. However, there are challenges in distinguishing the causal variants from other variants strongly correlated with them. To better identify causal SNPs, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants. We introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, which can improve the accuracy and efficiency of association studies by prioritizing functional variants within the risk genes before evaluating the causation. Our new fine-mapping framework, SuSiE2, connects two sum of single-effects (SuSiE) models, one for eQTL analysis and one for genetic fine-mapping. The posterior inclusion probabilities from an eQTL-based SuSiE model are utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. Through simulations and real data analyses focused on body mass index and Alzheimer's disease, we demonstrate that SuSiE2 improves fine-mapping results by increasing statistical power, having appropriate coverage, and reducing the average size of credible sets. [ABSTRACT FROM AUTHOR]

The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits. [ABSTRACT FROM AUTHOR]

Most deleterious variants are recessive and segregate at relatively low frequency. Therefore, high sample sizes are required to identify these variants. In this study we report a large-scale sequence based genome-wide association study (GWAS) in pigs, with a total of 120,000 Large White and 80,000 Synthetic breed animals imputed to sequence using a reference population of approximately 1,100 whole genome sequenced pigs. We imputed over 20 million variants with high accuracies (R2>0.9) even for low frequency variants (1–5% minor allele frequency). This sequence-based analysis revealed a total of 14 additive and 9 non-additive significant quantitative trait loci (QTLs) for growth rate and backfat thickness. With the non-additive (recessive) model, we identified a deleterious missense SNP in the CDHR2 gene reducing growth rate and backfat in homozygous Large White animals. For the Synthetic breed, we revealed a QTL on chromosome 15 with a frameshift variant in the OBSL1 gene. This QTL has a major impact on both growth rate and backfat, resembling human 3M-syndrome 2 which is related to the same gene. With the additive model, we confirmed known QTLs on chromosomes 1 and 5 for both breeds, including variants in the MC4R and CCND2 genes. On chromosome 1, we disentangled a complex QTL region with multiple variants affecting both traits, harboring 4 independent QTLs in the span of 5 Mb. Together we present a large scale sequence-based association study that provides a key resource to scan for novel variants at high resolution for breeding and to further reduce the frequency of deleterious alleles at an early stage in the breeding program. Author summary: In this study we investigated the effect of over 20 million genetic variants on the growth rate and backfat thickness of approximately 140,000 pigs across two commercial breeds, with specific focus on recessive harmful variation. We identified 14 regions with a significant additive effect and 9 regions with a significant recessive effect on these traits. By looking at recessive effects we identified several rare deleterious variants with high impacts on animal fitness. These include a deletion on chromosome 15 in the OBSL1 gene, which leads to a growth reduction of 100 grams a day on average. Interestingly, loss-of-function mutations in OBSL1 are associated with short stature in humans. Looking at additive effects with this high-resolution dataset allowed us to gain more insight into the locus around the MC4R gene on chromosome 1. Here we found a small complex region containing several independent variants affecting both growth rate and backfat. With this study we have shown that by using several gene models and a large dataset, we can identify novel genetic variants at high resolution (<0.01 frequency) with significant impact on animal fitness and production. These results can help us in further eradicating deleterious genetic variants from pig populations. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits. Author summary: In spite of continued success in developing statistical methodologies that integrate GWAS summary statistics and functional annotation data, existing methods are unable to pinpoint the interactions between functional annotations that influence one or more traits. Hence, the underlying interactions between biological mechanisms linking risk-associated SNPs to traits remain unknown. We propose multi-GPA-Tree to identify risk-associated SNPs and the combinations of functional annotations related to one or more trait risk-associated SNPs. Notably, multi-GPA-Tree requires only GWAS p-value summary statistics, instead of individual level genotype-phenotype data, making it more viable to implement. Compared to the existing state-of-the-art methods, multi-GPA-Tree showed improved performance in simulation studies and validated results for several auto-immune diseases in real data application. These combined results suggest that multi-GPA-Tree is an effective tool for integrative analysis and can potentially be valuable to clinical genomic researchers for hypothesis generation and validation. [ABSTRACT FROM AUTHOR]

Mammalian evolution has been influenced by viruses for millions of years, leaving signatures of adaptive evolution within genes encoding for viral interacting proteins. Synaptogyrin-2 (SYNGR2) is a transmembrane protein implicated in promoting bacterial and viral infections. A genome-wide association study of pigs experimentally infected with porcine circovirus type 2b (PCV2b) uncovered a missense mutation (SYNGR2 p.Arg63Cys) associated with viral load. In this study, CRISPR/Cas9-mediated gene editing of the porcine kidney 15 (PK15, wtSYNGR2+p.63Arg) cell line generated clones homozygous for the favorable SYNGR2 p.63Cys allele (emSYNGR2+p.63Cys). Infection of edited clones resulted in decreased PCV2 replication compared to wildtype PK15 (P<0.05), with consistent effects across genetically distinct PCV2b and PCV2d isolates. Sequence analyses of wild and domestic pigs (n>700) revealed the favorable SYNGR2 p.63Cys allele is unique to domestic pigs and more predominant in European than Asian breeds. A haplotype defined by the SYNGR2 p.63Cys allele was likely derived from an ancestral haplotype nearly fixed within European (0.977) but absent from Asian wild boar. We hypothesize that the SYNGR2 p.63Cys allele arose post-domestication in ancestral European swine. Decreased genetic diversity in homozygotes for the SYNGR2 p.63Cys allele compared to SYNGR2 p.63Arg, corroborates a rapid increase in frequency of SYGNR2 p.63Cys via positive selection. Signatures of adaptive evolution across mammalian species were also identified within SYNGR2 intraluminal loop domains, coinciding with the location of SYNGR2 p.Arg63Cys. Therefore, SYNGR2 may reflect a novel component of the host-virus evolutionary arms race across mammals with SYNGR2 p.Arg63Cys representing a species-specific example of putative adaptive evolution. Author summary: Our research provides direct evidence of the functional role of a missense substitution in the host SYNGR2 gene (SYNGR2 p.Arg63Cys) in the replication of porcine circovirus 2, the smallest virus known to infect mammalian cells. DNA sequences and haplotype analyses of SYNGR2 across domestic and wild pig populations suggests a likely origin and subsequent positive selection of the favorable SYNGR2 p.63Cys allele post-domestication in European swine. While SYNGR2 p.Arg63Cys represents a species-specific example of putative adaptive evolution, signatures of selection detected in SYNGR2 across mammalian species suggested that this gene is a new component of the host-virus evolutionary arms race. [ABSTRACT FROM AUTHOR]

Small ruminants, especially sheep, are essential for sustainable agricultural production systems, future food/nutrition security, and poverty reduction in developing countries. Within developed countries, the ability of sheep to survive on low-quality forage intake could act as buffer against climate change. Besides sheep's importance in sustainable agricultural production, there has been less ongoing work in terms of sheep genetics in Near East, Middle East and in Africa. For lamb meat production, body weight and average daily gain (ADG) until weaning are critical economic traits that affects the profitability of the industry. The current study aims to identify single nucleotide polymorphisms (SNPs) that are significantly associated with pre-weaning growth traits in fat tail Akkaraman lambs using a genome-wide association study (GWAS). A total of 196 Akkaraman lambs were selected for analysis. After quality control, a total of 31,936 SNPs and 146 lambs were used for subsequent analyses. PLINK 1.9 beta software was used for the analyses. Based on Bonferroni-adjusted p-values, one SNP (rs427117280) on chromosome 2 (OAR2) had significant associations with weaning weight at day 90 and ADG from day 0 to day 90, which jointly explains a 0.8% and 0.9% of total genetic variation respectively. The Ovis aries natriuretic peptide C (NPPC) could be considered as a candidate gene for the defined significant associations. The results of the current study will help to increase understanding of the variation in weaning weight and ADG until weaning of Akkaraman lambs and help enhance selection for lambs with improved weaning weight and ADG. However, further investigations are required for the identification of causal variants within the identified genomic regions. [ABSTRACT FROM AUTHOR]

Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free (i.e., k-mer) approach using raw whole-genome sequencing data in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen Zymoseptoria tritici. We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the k-mer approach explained a higher proportion of phenotypic variation than a reference genome-based approach and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens. Author summary: Mapping trait variation to polymorphism within species has become a cornerstone of modern biology. Applications in microbial pathogens (both bacteria and fungi) have produced major insights into their ecology, emergence of resistance, gains in virulence and climatic adaptation. However, microbial populations collected from the environment often express major adaptive traits governed by complex genetic variation. Standard genome-wide association studies (GWAS) based on single nucleotide polymorphisms have typically failed to reveal the full extent of loci contributing to such traits. We provide the first quantitative assessment of GWAS in a fungal pathogen comprehensively accounting for complex sequence variation. We used an environmental collection of the global fungal pathogen of wheat, Zymoseptoria tritici, as a case study. We analyzed a panel of 145 strains collected across the global distribution range and gathered trait variation data on 49 distinct traits. We integrated complex sequence variation among strains systematically into the association mapping and found that multiple approaches are needed to cover satisfactorily causal loci for trait variation. Our approach is generalizable to many microbial species and will uncover novel mechanisms driving rapid host adaptation in microbial populations. [ABSTRACT FROM AUTHOR]

Background: Preterm birth (<37 weeks of gestation) is a major cause of neonatal death and morbidity. Up to 40% of the variation in timing of birth results from genetic factors, mostly due to the maternal genome. Methods: We conducted a genome-wide meta-analysis of gestational duration and spontaneous preterm birth in 68,732 and 98,370 European mothers, respectively. Results: The meta-analysis detected 15 loci associated with gestational duration, and four loci associated with preterm birth. Seven of the associated loci were novel. The loci mapped to several biologically plausible genes, for example HAND2 whose expression was previously shown to decrease during gestation, associated with gestational duration, and GC (Vitamin D-binding protein), associated with preterm birth. Downstream in silico-analysis suggested regulatory roles as underlying mechanisms for the associated loci. LD score regression found birth weight measures as the most strongly correlated traits, highlighting the unique nature of spontaneous preterm birth phenotype. Tissue expression and colocalization analysis revealed reproductive tissues and immune cell types as the most relevant sites of action. Conclusion: We report novel genetic risk loci that associate with preterm birth or gestational duration, and reproduce findings from previous genome-wide association studies. Altogether, our findings provide new insight into the genetic background of preterm birth. Better characterization of the causal genetic mechanisms will be important to public health as it could suggest new strategies to treat and prevent preterm birth. Author summary: Annually, more than 15 million pregnancies are affected by preterm births all over the world. There are no effective ways to prevent preterm birth, and premature babies suffer from neonatal mortality and lifelong morbidities. Genetic factors of mother and fetus explain a large proportion, approximately 30–40%, of the of the variation in gestational age at delivery. To date, there have been just a few unbiased genome-wide investigations set out to locate these genes. Better characterization of the causal genetic mechanisms could suggest new strategies to treat and prevent preterm birth. In the current study, we aimed to identify maternal genetic factors that contribute to the timing of birth by meta-analyzing genome-wide association studies from European populations. We detected 17 independent loci that were associated with gestational duration and/or the risk of preterm birth. Ten of the loci replicated associations from previous studies, and seven were novel. The replicated associations provide strong evidence for the importance of these loci in the timing of birth, although the exact genes and causal variants and pathways still require further functional analysis. The seven novel associations provide further intriguing candidates that may account for the risk of preterm birth. Bioinformatics analysis proposed the associated loci have regulatory functions predominantly in immune cell types and reproductive tissues. The analysis further highlighted the unique nature or preterm birth as a phenotype, since the only traits with strong correlations were birth weight measures that are closely linked to the studied phenotypes. Our findings complement the knowledge of the genetic factors of preterm birth. [ABSTRACT FROM AUTHOR]

Background: The prevalence of substance use in people with HIV (PWH) in the United States is higher than in the general population and is an important driver of HIV-related outcomes. We sought to assess if previously identified genetic associations that contribute to substance use are also observed in a population of PWH. Methods: We performed genome-wide association studies (GWAS) of alcohol, smoking, and cannabis use phenotypes in a multi-ancestry population of 7,542 PWH from the Center for AIDS Research Network of Integrated Clinical Systems (CNICS). We conducted multi-ancestry GWAS for individuals of African (n = 3,748), Admixed American (n = 1,334), and European (n = 2,460) ancestry. Phenotype data were self-reported and collected using patient reported outcomes (PROs) and three questions from AUDIT-C, an alcohol screening tool. We analyzed nine phenotypes: 1) frequency of alcohol consumption, 2) typical number of drinks on a day when drinking alcohol, 3) frequency of five or more alcoholic drinks in a 30-day period, 4) smoking initiation, 5) smoking cessation, 6) cigarettes per day, 7) cannabis use initiation, 8) cannabis use cessation, 9) frequency of cannabis use during the previous 30 days. For each phenotype we considered a) variants previously identified as associated with a substance use trait and b) novel associations. Results: We observed evidence for effects of previously reported single nucleotide polymorphisms (SNPs) related to alcohol (rs1229984, p = 0.001), tobacco (rs11783093, p = 2.22E-4), and cannabis use (rs2875907, p = 0.005). We also report two novel loci (19p13.2, p = 1.3E-8; and 20p11.21, p = 2.1E-8) associated with cannabis use cessation. Conclusions: Our analyses contribute to understanding the genetic bases of substance use in a population with relatively higher rates of use compared to the general population. [ABSTRACT FROM AUTHOR]

Purpose: Stunting is known to be heavily influenced by environmental factors, so the genetic contribution has received little attention. Here we report an exploration of genetic influences in stunted Zambian children with environmental enteropathy. Method: Children with stunting (LAZ < -2) were enrolled and given nutritional therapy. Those that were non-responsive to therapy were designated as cases, and children with good growth (LAZ > -1) from the same community as controls. Blood and stool samples were taken to measure biomarkers of intestinal inflammation, epithelial damage, and microbial translocation. Single nucleotide polymorphism array genotyping was carried out on saliva samples using the H3Africa consortium array. Results: Genome wide associations were analysed in 117 cases and 41 controls. While no significant associations with stunting were observed at P<5x10-8, likely due to the small sample size, interesting associations were observed at lower thresholds. SNPs associated with stunting were in genomic regions known to modulate neuronal differentiation and fatty acid biosynthesis. SNPs associated with increased microbial translocation were associated with non-integrin membrane ECM interactions, tight junctions, hemostasis, and G-alpha signalling events. SNPs associated with increased inflammation were associated with, ECM interactions, purine metabolism, axon guidance, and cell motility. SNPs negatively associated with inflammation overlapped genes involved in semaphoring interactions. We explored the existing coeliac disease risk HLA genotypes and found present: DQ2.5 (7.5%), DQ8 (3.5%) and DQ2.2 (3.8%); however, no children were positive for coeliac antibodies. We detected HLA-DRB:1301 and HLA-C:1802 with high odds ratios and P<0.05 in stunted children compared to controls. Conclusion: Genetic variations associated with stunting and the enteropathy underlying it, include variants associated with multiple pathways relating to gene expression, glycosylation, nerve signalling, and sensing of the nutritional and microbiological milieu. [ABSTRACT FROM AUTHOR]

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol. We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL cholesterol and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions. Author summary: Human genetics is becoming increasingly useful to help predict human traits across a population owing to findings from large-scale genetic association studies and advances in the power of genetic predictors. This provides an opportunity to potentially identify individuals that deviate from genetic predictions for a common phenotype under investigation. For example, an individual may be genetically predicted to be tall, but be shorter than expected. It is potentially important to identify individuals who deviate from genetic predictions as this can facilitate further follow-up to assess likely causes. Using 158,951 unrelated individuals from the UK Biobank, with height and LDL cholesterol, as exemplar traits, we demonstrate that approximately 0.15% & 0.12% of individuals deviate from their genetically predicted phenotypes respectively. We observed these individuals to be enriched for a range of rare clinical diagnoses, as well as rare genetic factors that may be causal. Our analyses also demonstrate several methods for detecting individuals who deviate from genetic predictions that can be applied to a range of continuous human phenotypes. [ABSTRACT FROM AUTHOR]

Common genetic variants associated with lung cancer have been well studied in the past decade. However, only 12.3% heritability has been explained by these variants. In this study, we investigate the contribution of rare variants (RVs) (minor allele frequency <0.01) to lung cancer through two large whole exome sequencing case-control studies. We first performed gene-based association tests using a novel Bayes Factor statistic in the International Lung Cancer Consortium, the discovery study (European, 1042 cases vs. 881 controls). The top genes identified are further assessed in the UK Biobank (European, 630 cases vs. 172 864 controls), the replication study. After controlling for the false discovery rate, we found two genes, CTSL and APOE, significantly associated with lung cancer in both studies. Single variant tests in UK Biobank identified 4 RVs (3 missense variants) in CTSL and 2 RVs (1 missense variant) in APOE stongly associated with lung cancer (OR between 2.0 and 139.0). The role of these genetic variants in the regulation of CTSL or APOE expression remains unclear. If such a role is established, this could have important therapeutic implications for lung cancer patients. Author summary: Lung cancer (LC) is the leading cause of cancer death accounting for 18% of all cancer deaths. Previous studies have suggested genetic contribution to the disease. Common genetic variants associated with LC have been well studied through large, collaborative, genome-wide association studies (GWASs) in the past decade. However, they explained only about 12.3% of LC heritability. It is therefore hypothesized that the unexplained variability might be partially due to rare variants (RVs). In this study, we applied a novel gene-based test statistic based on a Bayes Factor approach, to whole exome sequencing data from the International Lung Cancer consortium (ILCCO). Independent replication of the top genes identified was performed using the UK Biobank data. We found two genes, CTSL and APOE, significantly associated with LC in both studies. Within these two genes, several RVs showed strong associations with lung cancer in the UK Biobank data. These findings could suggest potential molecular mechanisms leading to lung cancer and more importantly, possible therapeutic targets for personalized treatment. [ABSTRACT FROM AUTHOR]

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. Author summary: Genome-wide association studies have identified many non-coding loci associated with complex traits and diseases. While these variants are primarily expected to affect gene regulation, identifying the target genes as well as the tissue and cell type contexts where these variants are active has remained challenging. We have previously developed the eQTL Catalogue resource to systematically curate and reprocess all available RNA sequencing and genotype datasets. Here we present several updates to the resources that increase its utility for interpreting complex trait associations even further. In addition to increasing both the number of studies and datasets covered, we have also implemented statistical fine mapping to improve our ability to link multiple independent genetic variants influencing gene regulation with complex traits and diseases. We demonstrate the utility of these updates by focussing on interpreting genetic variants associated with a single well-characterised complex trait—circulating levels of vitamin D in human plasma. We believe that our publicly available analysis results will greatly facilitate the interpretation of complex trait associations identified by other large-scale human genetics efforts. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) are commonly used to identify genomic variants that are associated with complex traits, and estimate the magnitude of this association for each variant. However, it has been widely observed that the association estimates of variants tend to be lower in a replication study than in the study that discovered those associations. A phenomenon known as Winner's Curse is responsible for this upward bias present in association estimates of significant variants in the discovery study. We review existing Winner's Curse correction methods which require only GWAS summary statistics in order to make adjustments. In addition, we propose modifications to improve existing methods and propose a novel approach which uses the parametric bootstrap. We evaluate and compare methods, first using a wide variety of simulated data sets and then, using real data sets for three different traits. The metric, estimated mean squared error (MSE) over significant SNPs, was primarily used for method assessment. Our results indicate that widely used conditional likelihood based methods tend to perform poorly. The other considered methods behave much more similarly, with our proposed bootstrap method demonstrating very competitive performance. To complement this review, we have developed an R package, 'winnerscurse' which can be used to implement these various Winner's Curse adjustment methods to GWAS summary statistics. Author summary: A genome-wide association study is designed to analyse many common genetic variants in thousands of samples and identify which variants are associated with a trait of interest. It provides estimates of association strength for each variant and variants are classified as associated if their test statistics obtained in the study pass a chosen significance threshold. However, due to a phenomenon known as Winner's Curse, the association estimates of these significant variants tend to be upward biased and greater in magnitude than their true values. Naturally, this bias has adverse consequences for downstream statistical techniques which use these estimates. In this paper, we look at current methods which have been designed to combat Winner's Curse and propose modifications to these methods in order to improve performance. Using a wide variety of simulated data sets as well as real data, we perform a thorough evaluation of these methods. We use a metric which allows us to identify which methods, on average, produce adjusted estimates for significant variants that are closest to the true values. To accompany our work, we have created an R package, 'winnerscurse', which allows users to easily apply Winner's Curse correction methods to their data sets. [ABSTRACT FROM AUTHOR]

Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context. Author summary: The genome-wide association study (GWAS) is a method used to identify genetic variants which contribute to the risk of developing disease. These genetic variants are frequently shared between conditions, such that the study of the genetic basis of one disease can be informed by knowledge of another, similar disease. This approach can be productive where the disease in question is rare such that a GWAS has less power to associate variants with the disease, but there exist larger GWAS of similar diseases. Existing methods do not measure genetic similarity precisely when patients are few. Here we assess a previously published method of testing for genetic similarity between pairs of diseases using GWAS data, the 'GPS' test, against three other methods with the use of real and simulated data. We present a new computational procedure for carrying out the test and show that the GPS test is superior to its comparators in identifying genetic similarity when the sample size is small and when the genetic similarity signal is less strong. Use of the test will enable accurate detection of genetic similarity and the study of rarer conditions using data from better-characterised diseases. [ABSTRACT FROM AUTHOR]

Genome-wide association studies have revealed common genetic variants with small effect sizes associated with diverse lymphoid cancers. Family studies have uncovered rare variants with high effect sizes. However, these variants explain only a portion of the heritability of these cancers. Some of the missing heritability may be attributable to rare variants with small effect sizes. We aim to identify rare germline variants associated with familial lymphoid cancers using exome sequencing. One case per family was selected from 39 lymphoid cancer families based on early onset of disease or rarity of subtype. Control data was from Non-Finnish Europeans in gnomAD exomes (N = 56,885) or ExAC (N = 33,370). Gene and pathway-based burden tests for rare variants were performed using TRAPD. Five putatively pathogenic germline variants were found in four genes: INTU, PEX7, EHHADH, and ASXL1. Pathway-based association tests identified the innate and adaptive immune systems, peroxisomal pathway and olfactory receptor pathway as associated with lymphoid cancers in familial cases. Our results suggest that rare inherited defects in the genes involved in immune system and peroxisomal pathway may predispose individuals to lymphoid cancers. [ABSTRACT FROM AUTHOR]

Background: The lack of integrated analysis of genome-wide association studies (GWAS) and 3D epigenomics restricts a deep understanding of the genetic mechanisms of meat-related traits. With the application of techniques as ChIP-seq and Hi-C, the annotations of cis-regulatory elements in the pig genome have been established, which offers a new opportunity to elucidate the genetic mechanisms and identify major genetic variants and candidate genes that are significantly associated with important economic traits. Among these traits, loin muscle depth (LMD) is an important one as it impacts the lean meat content. In this study, we integrated cis-regulatory elements and genome-wide association studies (GWAS) to identify candidate genes and genetic variants regulating LMD. Results: Five single nucleotide polymorphisms (SNPs) located on porcine chromosome 17 were significantly associated with LMD in Yorkshire pigs. A 10 kb quantitative trait locus (QTL) was identified as a candidate functional genomic region through the integration of linkage disequilibrium and linkage analysis (LDLA) and high-throughput chromosome conformation capture (Hi-C) analysis. The BMP2 gene was identified as a candidate gene for LMD based on the integrated results of GWAS, Hi-C meta-analysis, and cis-regulatory element data. The identified QTL region was further verified through target region sequencing. Furthermore, through using dual-luciferase assays and electrophoretic mobility shift assays (EMSA), two SNPs, including SNP rs321846600, located in the enhancer region, and SNP rs1111440035, located in the promoter region, were identified as candidate SNPs that may be functionally related to the LMD. Conclusions: Based on the results of GWAS, Hi-C, and cis-regulatory elements, the BMP2 gene was identified as an important candidate gene regulating variation in LMD. The SNPs rs321846600 and rs1111440035 were identified as candidate SNPs that are functionally related to the LMD of Yorkshire pigs. Our results shed light on the advantages of integrating GWAS with 3D epigenomics in identifying candidate genes for quantitative traits. This study is a pioneering work for the identification of candidate genes and related genetic variants regulating one key production trait (LMD) in pigs by integrating genome-wide association studies and 3D epigenomics. Author summary: The loin muscle depth (LMD) is positively correlated with lean meat content and can therefore be used to estimate the lean meat content of pigs. The loin muscle depth is a quantitative trait that is influenced by whole-genome distributed polygenes. To identify the possible candidate genes and mutations impacting the LMD, we used genome-wide association study (GWAS), which is commonly used to identify candidate genomic regions associated with complex growth traits, to mapping the genomic regions. As over 90% of the identified variants have been localized to non-coding regions of the pig genome and their functions in phenotype regulation are poorly understood, we further combined 3D epigenomics to understand the mechanism of genetic variants regulating LMD. In combination with different types of data and analyses, we identified a 10 kb quantitative trait locus (QTL) on chromosome 17 that was significantly associated with LMD. We identified the BMP2 gene as a major candidate gene, and SNP rs1111440035 and rs321846600 were identified as likely candidate mutations affecting the LMD. Our study is unique in its attempt to identify candidate genetic variants by integrating GWAS and 3D epigenomics in pigs. [ABSTRACT FROM AUTHOR]

Background: Observational studies have suggested a relationship between type-1 diabetes mellitus (T1DM) and systemic lupus erythematosus (SLE). In both autoimmunities, 25-hydroxyvitamin D (25-OHD) deficiency is common. However, the causality between T1DM, 25-OHD level and SLE remains largely unknown. Methods: Independent genetic variants associated with T1DM, 25-OHD level, and SLE from the largest genome-wide association studies were used to conduct two-sample bidirectional Mendelian randomization (BIMR) and two-step Mendelian randomization (MR) analysis to estimate causal relationship between T1DM, 25-OHD level and SLE, and further multivariable Mendelian randomization (MVMR) was used to verify direct causality of T1DM and 25-OHD level on SLE. A series of sensitivity analysis as validation of primary MR results were performed. Results: Consistent with the results of BIMR, there was strong evidence for a direct causal effect of T1DM on the risk of SLE (ORMVMR-IVW = 1.249, 95% CI = 1.148–1.360, PMVMR-IVW = 1.25×10−5), and 25-OHD level was negatively associated with the risk of SLE (ORMVMR-IVW = 0.305, 95% CI = 0.109–0.857, PMVMR-IVW = 0.031). We also observed a negative causal effect of T1DM on 25-OHD level (ORBIMR-IVW = 0.995, 95% CI = 0.991–0.999, PBIMR-IVW = 0.030) while the causal effect of 25-OHD level on the risk of T1DM did not exist (PBIMR-IVW = 0.106). In BIMR analysis, there was no evidence for causal effects of SLE on the risk of T1DM and 25-OHD level (PBIMR-IVW > 0.05, respectively). Conclusion: Our MR analysis suggested that there was a network causal relationship between T1DM, 25-OHD level and SLE. T1DM and 25-OHD level both have causal associations with the risk of SLE, and 25-OHD level could be a mediator in the causality of T1DM and SLE. [ABSTRACT FROM AUTHOR]

Antithrombin resistance is a rare subtype of hereditary thrombophilia caused by prothrombin gene variants, leading to thrombotic disorders. Recently, the Prothrombin Belgrade variant has been reported as a specific variant that leads to antithrombin resistance in two Serbian families with thrombosis. However, due to clinical data scarcity and the inapplicability of traditional genome-wide association studies (GWAS), a broader perspective on molecular and phenotypic mechanisms associated with the Prothrombin Belgrade variant is yet to be uncovered. Here, we propose an integrative framework to address the lack of genomic samples and support the genomic signal from the full genome sequences of five heterozygous subjects by integrating it with subjects' phenotypes and the genes' molecular interactions. Our goal is to identify candidate thrombophilia-related genes for which our subjects possess germline variants by focusing on the resulting gene clusters of our integrative framework. We applied a Non-negative Matrix Tri-Factorization-based method to simultaneously integrate different data sources, taking into account the observed phenotypes. In other words, our data-integration framework reveals gene clusters involved with this rare disease by fusing different datasets. Our results are in concordance with the current literature about antithrombin resistance. We also found candidate disease-related genes that need to be further investigated. CD320, RTEL1, UCP2, APOA5 and PROZ participate in healthy-specific or disease-specific subnetworks involving thrombophilia-annotated genes and are related to general thrombophilia mechanisms according to the literature. Moreover, the ADRA2A and TBXA2R subnetworks analysis suggested that their variants may have a protective effect due to their connection with decreased platelet activation. The results show that our method can give insights into antithrombin resistance even if a small amount of genetic data is available. Our framework is also customizable, meaning that it applies to any other rare disease. [ABSTRACT FROM AUTHOR]

Lung cancer is a malignant tumor with high rates of mortality and shows significant hereditary predisposition. Previous genome-wide association studies suggest that rs748404, located at promoter of TGM5 (transglutaminase 5), is associated with lung carcinoma. By analysis of 1000 genomes project data for three representative populations in the world, another five SNPs are identified to be in strong linkage disequilibrium with rs748404, thus suggesting that they may also be associated with lung carcinoma risk. However, it is ambiguous about the actually causal SNP(s) and the mechanism for the association. Dual-luciferase assay indicates that the functional SNPs are not rs748404, rs12911132 or rs35535629 but another three SNPs (rs66651343, rs12909095 and rs17779494) in lung cell. By chromosome conformation capture, it is disclosed that the enhancer encompassing the two SNPs, rs66651343 and rs12909095, can interact with the promoter of CCNDBP1 (cyclin D1 binding protein 1). RNA-seq data analysis indicates that CCNDBP1 expression is dependent on the genotype of these two SNPs. Chromatin immunoprecipitation assay suggests that the fragments spanning rs66651343 and rs12909095 can bind with the transcription factors, cut like homeobox 1 and SRY-box transcription factor 9, respectively. Our results establish the connection between genetic variations at this locus and lung cancer susceptibility. [ABSTRACT FROM AUTHOR]

Treatment of parasitic nematode infections in humans and livestock relies on a limited arsenal of anthelmintic drugs that have historically reduced parasite burdens. However, anthelmintic resistance (AR) is increasing, and little is known about the molecular and genetic causes of resistance for most drugs. The free-living roundworm Caenorhabditis elegans has proven to be a tractable model to understand AR, where studies have led to the identification of molecular targets of all major anthelmintic drug classes. Here, we used genetically diverse C. elegans strains to perform dose-response analyses across 26 anthelmintic drugs that represent the three major anthelmintic drug classes (benzimidazoles, macrocyclic lactones, and nicotinic acetylcholine receptor agonists) in addition to seven other anthelmintic classes. First, we found that C. elegans strains displayed similar anthelmintic responses within drug classes and significant variation across drug classes. Next, we compared the effective concentration estimates to induce a 10% maximal response (EC10) and slope estimates of each dose-response curve of each strain to the laboratory reference strain, which enabled the identification of anthelmintics with population-wide differences to understand how genetics contribute to AR. Because genetically diverse strains displayed differential susceptibilities within and across anthelmintics, we show that C. elegans is a useful model for screening potential nematicides before applications to helminths. Third, we quantified the levels of anthelmintic response variation caused by genetic differences among individuals (heritability) to each drug and observed a significant correlation between exposure closest to the EC10 and the exposure that exhibited the most heritable responses. These results suggest drugs to prioritize in genome-wide association studies, which will enable the identification of AR genes. Author summary: Parasitic nematodes infect most animal species and significantly impact human and animal health. Control of parasitic nematodes in host species relies on a limited collection of anthelmintic drugs. However, anthelmintic resistance is widespread, which threatens our ability to control parasitic nematode populations. Here, we used the non-parasitic roundworm Caenorhabditis elegans as a model to study anthelmintic resistance across 26 anthelmintics that span ten drug classes. We leveraged the genetic diversity of C. elegans to quantify anthelmintic responses across a range of doses, estimate dose-response curves, fit strain-specific model parameters, and calculate the contributions of genetics to these parameters. We found that genetic variation within a species plays a considerable role in anthelmintic responses within and across drug classes. Our results emphasize how the incorporation of genetically diverse C. elegans strains is necessary to understand anthelmintic response variation found in natural populations. These results highlight drugs to prioritize in future mapping studies to identify genes involved in anthelmintic resistance. [ABSTRACT FROM AUTHOR]

Genetic variation in mitochondrial and nuclear genomes can perturb mitonuclear interactions and lead to phenotypic differences between individuals and populations. Despite their importance to most complex traits, it has been difficult to identify the interacting mitonuclear loci. Here, we present a novel advanced intercrossed population of Saccharomyces cerevisiae yeasts, called the Mitonuclear Recombinant Collection (MNRC), designed explicitly for detecting mitonuclear loci contributing to complex traits. For validation, we focused on mapping genes that contribute to the spontaneous loss of mitochondrial DNA (mtDNA) that leads to the petite phenotype in yeast. We found that rates of petite formation in natural populations are variable and influenced by genetic variation in nuclear DNA, mtDNA and mitonuclear interactions. We mapped nuclear and mitonuclear alleles contributing to mtDNA stability using the MNRC by integrating a term for mitonuclear epistasis into a genome-wide association model. We found that the associated mitonuclear loci play roles in mitotic growth most likely responding to retrograde signals from mitochondria, while the associated nuclear loci with main effects are involved in genome replication. We observed a positive correlation between growth rates and petite frequencies, suggesting a fitness tradeoff between mitotic growth and mtDNA stability. We also found that mtDNA stability was correlated with a mobile mitochondrial GC-cluster that is present in certain populations of yeast and that selection for nuclear alleles that stabilize mtDNA may be rapidly occurring. The MNRC provides a powerful tool for identifying mitonuclear interacting loci that will help us to better understand genotype-phenotype relationships and coevolutionary trajectories. Author summary: Mitochondrial functions require genes from nuclear and mitochondrial genomes that must work together. These interactions influence organismal fitness and coevolutionary processes yet it is difficult to identify the genes involved. Here, we created a novel collection of yeast designed explicitly for mapping mitonuclear genes. We used this collection to reveal genes influencing the maintenance of mitochondrial DNAs (mtDNAs), a trait important for human health. The mapping population presented here is an important new resource that will help to understand genotype-phenotype relationships and coevolutionary trajectories. Additionally, this work provides insight into mechanisms underlying mtDNA stability. [ABSTRACT FROM AUTHOR]

Photoreceptor cells (PRCs) are the light-detecting cells of the retina. Such cells can be non-invasively imaged using optical coherence tomography (OCT) which is used in clinical settings to diagnose and monitor ocular diseases. Here we present the largest genome-wide association study of PRC morphology to date utilising quantitative phenotypes extracted from OCT images within the UK Biobank. We discovered 111 loci associated with the thickness of one or more of the PRC layers, many of which had prior associations to ocular phenotypes and pathologies, and 27 with no prior associations. We further identified 10 genes associated with PRC thickness through gene burden testing using exome data. In both cases there was a significant enrichment for genes involved in rare eye pathologies, in particular retinitis pigmentosa. There was evidence for an interaction effect between common genetic variants, VSX2 involved in eye development and PRPH2 known to be involved in retinal dystrophies. We further identified a number of genetic variants with a differential effect across the macular spatial field. Our results suggest a continuum between common and rare variation which impacts retinal structure, sometimes leading to disease. Author summary: The photoreceptor cells (PRCs) are found at the back of the retina and are responsible for detecting light and converting this to an electrical signal. Examination of these cells using optical coherence tomography (OCT) is often used to aid diagnosis and monitor several eye diseases including age-related macular degeneration and retinal dystrophies. Here we harnessed the large scale of the UK Biobank to study genetic effects on the thickness of the PRC layers, a measure extracted from the OCT images. We found a large number of common genetic variants associated with these measures, many of which are in or near genes known to be involved in eye biology or eye diseases. In particular we found many genetic variants associated with PRC thickness that are in or near genes known to be involved in a set of rare diseases called retinal dystrophies. We also found that the interaction of two of these genetic variants has an effect on the PRC layer thickness. Overall, we provided evidence for a relationship between common genetic variation, and both common and rare variation in retinal biology. [ABSTRACT FROM AUTHOR]

Here in this study we adopted genome-wide association studies (GWAS) to investigate the genetic components of the personality constructs in the Chinese Personality Assessment Inventory 2 (CPAI-2) in Taiwanese Hakka populations, who are likely the descendants of a recent admixture between a group of Chinese immigrants with high emigration intention and a group of the Taiwanese aboriginal population generally without it. A total of 279 qualified participants were examined and genotyped by an Illumina array with 547,644 SNPs to perform the GWAS. Although our sample size is small and that unavoidably limits our statistical power (Type 2 error but not Type 1 error), we still found three genomic regions showing strong association with Enterprise, Diversity, and Logical vs. Affective Orientation, respectively. Multiple genes around the identified regions were reported to be nervous system related, which suggests that genetic variants underlying the certain personalities should indeed exist in the nearby areas. It is likely that the recent immigration and admixture history of the Taiwanese Hakka people created strong linkage disequilibrium between the emigration intention-related genetic variants and their neighboring genetic markers, so that we could identify them despite with only limited statistical power. [ABSTRACT FROM AUTHOR]

Genome-wide association studies have greatly increased the number of T2DM associated risk variants but most of them have focused on populations of European origin. There is scarcity of such studies in developing countries including Pakistan. High prevalence of T2DM in Pakistani population prompted us to design this study. We have devised a two stage (the discovery stage and validation stage) case-control study in Pashtun ethnic population in which 500 T2DM cases and controls each have been recruited to investigate T2DM genetic risk variants. In discovery stage Whole Exome Sequencing (WES) was used to identify and suggest T2DM pathogenic SNPs, based on SIFT and Polyphen scores; whereas in validation stage the selected variants were confirmed for T2DM association using MassARRAY genotyping and appropriate statistical tests. Results of the study showed the target positive association of rs1801282/PPARG (OR = 1.24, 95%Cl = 1.20–1.46, P = 0.010), rs745975/HNF4A (OR = 1.30, 95%Cl = 1.06–1.38, P = 0.004), rs806052/GLIS3 (OR = 1.32, 95%Cl = 1.07–1.66, P = 0.016), rs8192552/MTNR1B (OR = 1.53, 95%Cl = 0.56–1.95, P = 0.012) and rs1805097/IRS-2 (OR = 1.27, 95%Cl = 1.36–1.92, P = 0.045), with T2DM; whereas rs6415788/GLIS3, rs61788900/NOTCH2, rs61788901/NOTCH2 and rs11810554/NOTCH2 (P>0.05) showed no significant association. Identification of genetic risk factors/variants can be used in defining high risk subjects assessment, and disease prevention. [ABSTRACT FROM AUTHOR]

Tissue-specific transcription factors (TFs) control the transcriptome through an association with noncoding regulatory regions (cistromes). Identifying the combination of TFs that dictate specific cell fate, their specific cistromes and examining their involvement in complex human traits remain a major challenge. Here, we focus on the retinal pigmented epithelium (RPE), an essential lineage for retinal development and function and the primary tissue affected in age-related macular degeneration (AMD), a leading cause of blindness. By combining mechanistic findings in stem-cell-derived human RPE, in vivo functional studies in mice and global transcriptomic and proteomic analyses, we revealed that the key developmental TFs LHX2 and OTX2 function together in transcriptional module containing LDB1 and SWI/SNF (BAF) to regulate the RPE transcriptome. Importantly, the intersection between the identified LHX2-OTX2 cistrome with published expression quantitative trait loci, ATAC-seq data from human RPE, and AMD genome-wide association study (GWAS) data, followed by functional validation using a reporter assay, revealed a causal genetic variant that affects AMD risk by altering TRPM1 expression in the RPE through modulation of LHX2 transcriptional activity on its promoter. Taken together, the reported cistrome of LHX2 and OTX2, the identified downstream genes and interacting co-factors reveal the RPE transcription module and uncover a causal regulatory risk single-nucleotide polymorphism (SNP) in the multifactorial common blinding disease AMD. This study reveals that the transcription factors LHX2 and OTX2 function together in a transcriptional module containing LDB1 and SWI/SNF to regulate the retinal pigmented epithelium differentiation program; a causal regulatory risk SNP contributes to age-related macular degeneration by altering the binding of LHX2-OTX2 to the TRPM1 promoter. [ABSTRACT FROM AUTHOR]

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases. Author summary: Large genetic studies with hundreds of thousands of patients have been successful at finding genetic variants that associate with disease traits in humans. However, smaller-scale studies can often have inadequate power to discover new genetic associations. Here, we use a small genetic study of Obstructive Sleep Apnea (OSA), to introduce a strategy that both helps find genetic associations and proposes biological hypotheses for the mechanisms behind those associations. To achieve this, we use large genetic studies carried out on traits that are related to OSA, and look for genetic variants that affect both OSA in our small study and the trait in question in the large study. By linking two or more traits at select loci, we were able to, among other results, find a locus that affects the expression of a gene in immune cells (DNA primase subunit 1), a marker of thrombotic and inflammatory processes (plateletcrit) and OSA. This results in a novel genetic association to OSA and a corresponding biological hypothesis behind its effect on OSA. [ABSTRACT FROM AUTHOR]

Prescription of PCSK9-inhibitors has increased in recent years but not much is known about its off-target effects. PCSK9-expression is evident in non-hepatic tissues, notably the brain, and genetic variation in the PCSK9 locus has recently been shown to be associated with mood disorder-related traits. We investigated whether PCSK9 inhibition, proxied by a genetic reduction in expression of PCSK9 mRNA, might have a causal adverse effect on mood disorder-related traits. We used genetic variants in the PCSK9 locus associated with reduced PCSK9 expression (eQTLs) in the European population from GTEx v8 and examined the effect on PCSK9 protein levels and three mood disorder-related traits (major depressive disorder, mood instability, and neuroticism), using summary statistics from the largest European ancestry genome-wide association studies. We conducted summary-based Mendelian randomization analyses to estimate the causal effects, and attempted replication using data from eQTLGen, Brain-eMETA, and the CAGE consortium. We found that genetically reduced PCSK9 gene-expression levels were significantly associated with reduced PCSK9 protein levels but not with increased risk of mood disorder-related traits. Further investigation of nearby genes demonstrated that reduced USP24 gene-expression levels was significantly associated with increased risk of mood instability (p-value range = 5.2x10-5–0.03), and neuroticism score (p-value range = 2.9x10-5–0.02), but not with PCSK9 protein levels. Our results suggest that genetic variation in this region acts on mood disorders through a PCSK9-independent pathway, and therefore PCSK9-inhibitors are unlikely to have an adverse impact on mood disorder-related traits. [ABSTRACT FROM AUTHOR]

Walnut (Juglans) species are economically important hardwood trees cultivated worldwide for both edible nuts and high-quality wood. Broad-scale assessments of species diversity, evolutionary history, and domestication are needed to improve walnut breeding. In this study, we sequenced 309 walnut accessions from around the world, including 55 Juglans relatives, 98 wild Persian walnuts (J. regia), 70 J. regia landraces, and 86 J. regia cultivars. The phylogenetic tree indicated that J. regia samples (section Dioscaryon) were monophyletic within Juglans. The core areas of genetic diversity of J. regia germplasm were southwestern China and southern Asia near the Qinghai-Tibet Plateau and the Himalayas, and the uplift of the Himalayas was speculated to be the main factor leading to the current population dynamics of Persian walnut. The pattern of genomic variation in terms of nucleotide diversity, linkage disequilibrium, single nucleotide polymorphisms, and insertions/deletions revealed the domestication and selection footprints in Persian walnut. Selective sweep analysis, GWAS, and expression analysis further identified two transcription factors, JrbHLH and JrMYB6, that influence the thickness of the nut diaphragm as loci under selection during domestication. Our results elucidate the domestication and selection footprints in Persian walnuts and provide a valuable resource for the genomics-assisted breeding of this important crop. Author summary: Walnut (Juglans) species are economically important hardwood trees. The Persian walnut as a woody nut crop is limited by an incomplete understanding of the diversity and spatial genetic structure of the species' wild germplasm, and the relationship of the secondary germplasm pool of walnut (wild and landrace walnuts) to improved cultivars. To better understand walnut's breeding, we sequenced the genomes of 309 walnut accessions from around the world. The results revealed core areas of genetic diversity of Persian walnut germplasm were southwestern China and southern Asia near the Qinghai-Tibet Plateau and the Himalayas. The genome-wide scan for selective sweeps, genome-wide association studies (GWAS), and expression analysis identified JrbHLH and JrMYB6 transcript factors related to the thickness of the diaphragm that were under selection during domestication. Our results elucidate the genetic diversity of walnut and its evolution as a species and a crop. We identified centers of wild genetic diversity that could contribute to breeding and sustainable improvement. [ABSTRACT FROM AUTHOR]

Background: Studies on genome-wide associations help to determine the cause of many genetic diseases. Genome-wide associations typically focus on associations between single-nucleotide polymorphisms (SNPs). Genotyping every SNP in a chromosomal region for identifying genetic variation is computationally very expensive. A representative subset of SNPs, called tag SNPs, can be used to identify genetic variation. Small tag SNPs save the computation time of genotyping platform, however, there could be missing data or genotyping errors in small tag SNPs. This study aims to solve Tag SNPs selection problem using many-objective evolutionary algorithms. Methods: Tag SNPs selection can be viewed as an optimization problem with some trade-offs between objectives, e.g. minimizing the number of tag SNPs and maximizing tolerance for missing data. In this study, the tag SNPs selection problem is formulated as a many-objective problem. Nondominated Sorting based Genetic Algorithm (NSGA-III), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which are Many-Objective evolutionary algorithms, have been applied and investigated for optimal tag SNPs selection. This study also investigates different initialization methods like greedy and random initialization. optimization. Results: The evaluation measures used for comparing results for different algorithms are Hypervolume, Range, SumMin, MinSum, Tolerance rate, and Average Hamming distance. Overall MOEA/D algorithm gives superior results as compared to other algorithms in most cases. NSGA-III outperforms NSGA-II and other compared algorithms on maximum tolerance rate, and SPEA2 outperforms all algorithms on average hamming distance. Conclusion: Experimental results show that the performance of our proposed many-objective algorithms is much superior as compared to the results of existing methods. The outcomes show the advantages of greedy initialization over random initialization using NSGA-III, SPEA2, and MOEA/D to solve the tag SNPs selection as many-objective optimization problem. [ABSTRACT FROM AUTHOR]

The bumblebee Bombus terrestris is commonly infected by a trypanosomatid gut parasite Crithidia bombi. This system shows a striking degree of genetic specificity where host genotypes are susceptible to different genotypes of parasite. To a degree, variation in host gene expression underlies these differences, however, the effects of standing genetic variation has not yet been explored. Here we report on an extensive experiment where workers of twenty colonies of B. terrestris were each infected by one of twenty strains of C. bombi. To elucidate the host's genetic bases of susceptibility to infection (measured as infection intensity), we used a low-coverage (~2 x) genome-wide association study (GWAS), based on angsd, and a standard high-coverage (~15x) GWAS (with a reduced set from a 8 x 8 interaction matrix, selected from the full set of twenty). The results from the low-coverage approach remained ambiguous. The high-coverage approach suggested potentially relevant genetic variation in cell surface and adhesion processes. In particular, mucin, a surface mucoglycoprotein, potentially affecting parasite binding to the host gut epithelia, emerged as a candidate. Sequencing the gut microbial community of the same bees showed that the abundance of bacterial taxa, such as Gilliamella, Snodgrassella, or Lactobacillus, differed between 'susceptible' and 'resistant' microbiota, in line with earlier studies. Our study suggests that the constitutive microbiota and binding processes at the cell surface are candidates to affect infection intensity after the first response (captured by gene expression) has run its course. We also note that a low-coverage approach may not be powerful enough to analyse such complex traits. Furthermore, testing large interactions matrices (as with the full 20 x 20 combinations) for the effect of interaction terms on infection intensity seems to blur the specific host x parasite interaction effects, likely because the outcome of an infection is a highly non-linear process dominated by variation in individually different pathways of host defence (immune) responses. [ABSTRACT FROM AUTHOR]

Myopia most often develops during school age, with the highest incidence in countries with intensive education systems. Interactions between genetic variants and educational exposure are hypothesized to confer susceptibility to myopia, but few such interactions have been identified. Here, we aimed to identify genetic variants that interact with education level to confer susceptibility to myopia. Two groups of unrelated participants of European ancestry from UK Biobank were studied. A 'Stage-I' sample of 88,334 participants whose refractive error (avMSE) was measured by autorefraction and a 'Stage-II' sample of 252,838 participants who self-reported their age-of-onset of spectacle wear (AOSW) but who did not undergo autorefraction. Genetic variants were prioritized via a 2-step screening process in the Stage-I sample: Step 1 was a genome-wide association study for avMSE; Step 2 was a variance heterogeneity analysis for avMSE. Genotype-by-education interaction tests were performed in the Stage-II sample, with University education coded as a binary exposure. On average, participants were 58 years-old and left full-time education when they were 18 years-old; 35% reported University level education. The 2-step screening strategy in the Stage-I sample prioritized 25 genetic variants (GWAS P < 1e-04; variance heterogeneity P < 5e-05). In the Stage-II sample, 19 of the 25 (76%) genetic variants demonstrated evidence of variance heterogeneity, suggesting the majority were true positives. Five genetic variants located near GJD2, RBFOX1, LAMA2, KCNQ5 and LRRC4C had evidence of a genotype-by-education interaction in the Stage-II sample (P < 0.002) and consistent evidence of a genotype-by-education interaction in the Stage-I sample. For all 5 variants, University-level education was associated with an increased effect of the risk allele. In this cohort, additional years of education were associated with an enhanced effect of genetic variants that have roles including axon guidance and the development of neuronal synapses and neural circuits. Author summary: Myopia (short-sightedness) is a refractive error that typically develops during childhood. Uncorrected myopia causes blurred distance vision. Myopia is also associated with a range of eye disorders, making it a leading cause of irreversible visual impairment in older age. About 80% of children in East Asia develop myopia during school-age; in the West the corresponding figure is about 30%. Genetics, insufficient time spent outdoors, and years spent in education are risk factors for myopia. Genetics studies have identified more than 450 genetic variants associated with an increased risk of myopia. However, very few genetic variants have been found that confer an increased risk of myopia specifically in individuals exposed to higher levels of lifestyle risk factors. Here, we leverage statistical features expected for variants with gene-environment interaction effects and harness the large sample size of UK Biobank, to identify 5 genetic variants that confer a progressively increased risk of myopia in individuals who spent increasing years in education. Two of the variants replicate findings reported in East Asian cohorts, while the other 3 variants are novel. This work provides insight into the biological pathways through which genes and lifestyle interact to cause myopia. [ABSTRACT FROM AUTHOR]

We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS. Author summary: Genome-wide association studies have identified tens of thousands of genetic variants associated with complex traits. An ever increasing number of associated variants are shown to affect multiple traits, a phenomenon known as pleiotropy. We propose a method that leverages this genetic architecture and uses summary statistics to perform an omnibus association test between one genetic variant and a set of traits. Simulations show that the method properly controls for type-I errors and increases statistical power. In addition to a powerful omnibus test, we also enable a per trait interpretation of the associations by extending the m-value framework to account for the correlation structure between traits. This framework enables a significant increase in the identification of per trait effects. [ABSTRACT FROM AUTHOR]

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA's results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement. Author summary: Genome-wide association studies (GWAS) are commonly used in human and plant studies to identify genetic variants responsible for the phenotype of interest and provide foundations for studying disease mechanisms and crop improvement. Most GWAS models are developed and optimized using human datasets. However, the difference between human and plant datasets essentially limits their applications in plant studies, especially when mapping complex traits such as drought resistance and yield. In this study, we present a novel GWAS method, HapFM, tailored for plant datasets to overcome the difficulties of many conventional GWAS methods. HapFM resulted in higher statistical power than conventional GWAS methods for mapping complex traits in our simulation and real dataset analyses. In addition, HapFM reduced the mapping interval by prioritizing candidate causal regions in the genome, which benefits the downstream experimental studies. Last but not least, HapFM can incorporate biological annotations to increase statistical power further. Overall, HapFM balances statistical power, result interpretability, and downstream experimental verifiability. [ABSTRACT FROM AUTHOR]

Uterine fibroid is one of the most prevalent benign tumors in women, with high socioeconomic costs. Although genome-wide association studies (GWAS) have identified several loci associated with uterine fibroid risks, they could not successfully interpret the biological effects of genomic variants at the gene expression levels. To prioritize uterine fibroid susceptibility genes that are biologically interpretable, we conducted a transcriptome-wide association study (TWAS) by integrating GWAS data of uterine fibroid and expression quantitative loci data. We identified nine significant TWAS genes including two novel genes, RP11-282O18.3 and KBTBD7, which may be causal genes for uterine fibroid. We conducted functional enrichment network analyses using the TWAS results to investigate the biological pathways in which the overall TWAS genes were involved. The results demonstrated the immune system process to be a key pathway in uterine fibroid pathogenesis. Finally, we carried out chemical–gene interaction analyses using the TWAS results and the comparative toxicogenomics database to determine the potential risk chemicals for uterine fibroid. We identified five toxic chemicals that were significantly associated with uterine fibroid TWAS genes, suggesting that they may be implicated in the pathogenesis of uterine fibroid. In this study, we performed an integrative analysis covering the broad application of bioinformatics approaches. Our study may provide a deeper understanding of uterine fibroid etiologies and informative notifications about potential risk chemicals for uterine fibroid. [ABSTRACT FROM AUTHOR]

Proximal genetic variants are frequently correlated, implying that the corresponding effect sizes detected by genome-wide association studies (GWAS) are also not independent. Methods already exist to account for this when aggregating effects from a single GWAS across genes or pathways. Here we present a rigorous yet fast method for detecting genes with coherent association signals for two traits, facilitating cross-GWAS analyses. To this end, we devised a new significance test for the covariance of datapoints not drawn independently but with a known inter-sample covariance structure. We show that the distribution of its test statistic is a linear combination of χ2 distributions with positive and negative coefficients. The corresponding cumulative distribution function can be efficiently calculated with Davies' algorithm at high precision. We apply this general framework to test for dependence between SNP-wise effect sizes of two GWAS at the gene level. We extend this test to detect also gene-wise causal links. We demonstrate the utility of our method by uncovering potential shared genetic links between the severity of COVID-19 and (1) being prescribed class M05B medication (drugs affecting bone structure and mineralization), (2) rheumatoid arthritis, (3) vitamin D (25OHD), and (4) serum calcium concentrations. Our method detects a potential role played by chemokine receptor genes linked to TH1 versus TH2 immune response, a gene related to integrin beta-1 cell surface expression, and other genes potentially impacting the severity of COVID-19. Our approach will be useful for similar analyses involving datapoints with known auto-correlation structures. Author summary: Genome-wide association studies (GWAS) deliver effect size estimates of a given trait for millions of Single Nucleotide Polymorphisms (SNPs). Powerful tools already exist using these summary statistics to elucidate the global joint genetic contribution to a pair of traits, such as cross-trait LD-score regression, but these methods cannot reveal the joint contributions at the level of genes and pathways. Here we present a novel methodology to co-analyze the association data from a pair of GWAS to identify genes and pathways that may be relevant to both individual traits. Our test novelty is that a gene is considered co-relevant if the SNP-wise effects from both GWAS tend to have the same sign and magnitude in the gene window. This is different from the commonly used approach asking only for the aggregate signals from two GWAS to be jointly significant. Our method is feasible due to novel insight into the product-normal distribution. We apply our new method to test for co-significant genes for severe COVID-19 and conditions leading to the prescription of common medications. Of the 23 medication classes we tested for coherent co-significant genes, only one, M05B (drugs affecting bone structure and mineralization), yielded Bonferroni significant hits. We then searched for available GWAS data for related conditions and found that also rheumatoid arthritis, calcium concentration, and vitamin D are traits pointing to several co-relevant genes in our new coherence analysis. Furthermore, testing for anti-coherence showed that the medication classes H03A (thyroid preparations), R03A, and R03BA (drugs for obstructive airway diseases) feature Bonferroni co-significant genes. Our joint analysis provides new insights into potential COVID-19 disease mechanisms. [ABSTRACT FROM AUTHOR]

Purpose: Genetic variants identified through population-based genome-wide studies are generally of high frequency, exerting their action in the central part of the refractive error spectrum. However, the power to identify associations with variants of lower minor allele frequency is greatly reduced, requiring considerable sample sizes. Here we aim to assess the impact of rare variants on genetic variation of refractive errors in a very large general population cohort. Methods: Genetic association analyses of non-cyclopaedic autorefraction calculated as mean spherical equivalent (SPHE) used whole-exome sequence genotypic information from 50,893 unrelated participants in the UK Biobank of European ancestry. Gene-based analyses tested for association with SPHE using an optimised SNP-set kernel association test (SKAT-O) restricted to rare variants (minor allele frequency < 1%) within protein-coding regions of the genome. All models were adjusted for age, sex and common lead variants within the same locus reported by previous genome-wide association studies. Potentially causal markers driving association at significant loci were elucidated using sensitivity analyses by sequentially dropping the most associated variants from gene-based analyses. Results: We found strong statistical evidence for association of SPHE with the SIX6 (p-value = 2.15 x 10−10, or Bonferroni-Corrected p = 4.41x10-06) and the CRX gene (p-value = 6.65 x 10−08, or Bonferroni-Corrected p = 0.001). The SIX6 gene codes for a transcription factor believed to be critical to the eye, retina and optic disc development and morphology, while CRX regulates photoreceptor specification and expression of over 700 genes in the retina. These novel associations suggest an important role of genes involved in eye morphogenesis in refractive error. Conclusion: The results of our study support previous research highlighting the importance of rare variants to the genetic risk of refractive error. We explain some of the origins of the genetic signals seen in GWAS but also report for the first time a completely novel association with the CRX gene. [ABSTRACT FROM AUTHOR]

Objectives: To utilize the UK Biobank to identify genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease (CeVD). Background: Cerebrovascular disease occurs because of a complex interplay between vascular, environmental, and genetic factors. It is the second leading cause of disability worldwide. Understanding who may be genetically predisposed to cerebrovascular disease can help guide preventative efforts. Moreover, there is considerable interest in the use of real-world data, such as EHR (electronic health records) to better understand disease mechanisms and to discover new treatment strategies, but whether ICD10-based diagnosis can be used to study CeVD genetics is unknown. Methods: Using the UK Biobank, we conducted a genome-wide association study (GWAS) where we analyzed the genomes of 11,155 cases and 122,705 controls who were sex, age and ancestry-matched in a 1:11 case: control design. Genetic variants were identified by Plink's firth logistic regression and assessed for association with the ICD10 codes corresponding to CeVD. Results: We identified two groups of SNPs closely linked to PITX2 and LRRTM4 that were significantly associated with CeVD in this study (p < 5 x 10−8) and had a minor allele frequency of > 0.5%. Discussion: Disease assignment based on ICD10 codes may underestimate prevalence; however, for CeVD, this does not appear to be the case. Compared to the age- and sex-matched control population, individuals with CeVD were more frequently diagnosed with comorbid conditions, such as hypertension, hyperlipidemia & atrial fibrillation or flutter, confirming their contribution to CeVD. The UK Biobank based ICD10 study identified 2 groups of variants that were associated with CeVD. The association between PITX2 and CeVD is likely explained by the increased rates of atrial fibrillation and flutter. While the mechanism explaining the relationship between LRRTM4 and CeVD is unclear, this has been documented in previous studies. [ABSTRACT FROM AUTHOR]

Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only (attention-deficit/hyperactivity disorder, anxiety, depression, insomnia) (rG > 0.49), but not with autism or the less common adult disorders (schizophrenia, bipolar disorder, or eating disorders) (rG < 0.01). Importantly, the total psychiatric problem score also showed at least a moderate genetic correlation with intelligence, educational attainment, wellbeing, smoking, and body fat (rG > 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits. [ABSTRACT FROM AUTHOR]

Genetic diversity studies provide important details on target trait availability and its variability, for the success of breeding programs. In this study, GBS approach was used to reveal a new structuration of genetic diversity and population structure of pigeonpea in Benin. We used a total of 688 high-quality Single Nucleotide Polymorphism markers for a total of 44 pigeonpea genotypes. The distribution of SNP markers on the 11 chromosomes ranged from 14 on chromosome 5 to 133 on chromosome 2. The Polymorphism Information Content and gene diversity values were 0.30 and 0.34 respectively. The analysis of population structure revealed four clear subpopulations. The Weighted Neighbor Joining tree agreed with structure analyses by grouping the 44 genotypes into four clusters. The PCoA revealed that genotypes from subpopulations 1, 2 and 3 intermixed among themselves. The Analysis of Molecular Variance showed 7% of the total variation among genotypes while the rest of variation (93%) was within genotypes from subpopulations indicating a high gene exchange (Nm = 7.13) and low genetic differentiation (PhiPT = 0.07) between subpopulations. Subpopulation 2 presented the highest mean values of number of different alleles (Na = 1.57), number of loci with private alleles (Pa = 0.11) and the percentage of polymorphic loci (P = 57.12%). We discuss our findings and demonstrate how the genetic diversity and the population structure of this specie can be used through the Genome Wide Association Studies and Marker-Assisted Selection to enhance genetic gain in pigeonpea breeding programs in Benin. [ABSTRACT FROM AUTHOR]

A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which can carry as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG's applications to Alzheimer's disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits. Author summary: While genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits, it remains difficult to identify which genes they regulate and in which biological contexts. This interpretation challenge has motivated the development of computational methods to prioritize causal genes at GWAS loci. Most available methods have focused on linking risk variants with differential gene expression. However, genetic control of splicing and expression are comparable in their complex trait risk, and few studies have focused on identifying causal genes using splicing information. To study splicing mediated effects, one important statistical challenge is the large multiple testing burden generated from multidimensional splicing events. In this study, we develop a new approach, MSG, to test the mediating role of splicing variation on complex traits. We integrate multidimensional splicing data using sparse canonocial correlation analysis and then combine evidence for splicing-trait associations across features using a joint test. We show this approach has higher power to identify causal genes using splicing data than current state-of-art methods designed to model multidimensional expression data. We illustrate the benefits of our approach through extensive simulations and applications to real data sets of 14 complex traits. [ABSTRACT FROM AUTHOR]

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results. Author summary: Genome-wide association studies (GWASs) have yielded genetic variants associated with various complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. For many complex traits, however, the specific cell or tissue types leading to risk are unknown. Recent advances of single-cell RNA sequencing (scRNA-seq) provide unprecedented opportunities, alongside challenges, to systematically investigate the cell-type-specific enrichment of GWAS risk variants. We propose EPIC, a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific transcriptomic measurements from scRNA-seq data to prioritize trait-relevant cell types. We use known trait-relevant tissues and cell types as ground truths for benchmark, adopt independent GWAS and scRNA-seq datasets for reproducibility, and refer to PubMed keyword search and existing case-control studies for validation. Such an integrative analysis helps elucidate the underlying cell-type-specific disease etiology and prioritize important risk variants. [ABSTRACT FROM AUTHOR]

Penicillium roqueforti is a major food-spoilage fungus known for its high resistance to the food preservative sorbic acid. Here, we demonstrate that the minimum inhibitory concentration of undissociated sorbic acid (MICu) ranges between 4.2 and 21.2 mM when 34 P. roqueforti strains were grown on malt extract broth. A genome-wide association study revealed that the six most resistant strains contained the 180 kbp gene cluster SORBUS, which was absent in the other 28 strains. In addition, a SNP analysis revealed five genes outside the SORBUS cluster that may be linked to sorbic acid resistance. A partial SORBUS knock-out (>100 of 180 kbp) in a resistant strain reduced sorbic acid resistance to similar levels as observed in the sensitive strains. Whole genome transcriptome analysis revealed a small set of genes present in both resistant and sensitive P. roqueforti strains that were differentially expressed in the presence of the weak acid. These genes could explain why P. roqueforti is more resistant to sorbic acid when compared to other fungi, even in the absence of the SORBUS cluster. Together, the MICu of 21.2 mM makes P. roqueforti among the most sorbic acid-resistant fungi, if not the most resistant fungus, which is mediated by the SORBUS gene cluster. Author summary: Chemical preservatives, such as sorbic acid, are often used in food to prevent spoilage by fungi, yet some fungi are particularly well-suited to deal with these preservatives. First, we investigated the resistance of 34 Penicillium roqueforti strains to various food preservatives. This revealed that some strains were highly resistant to sorbic acid, while others are more sensitive. Next, we used DNA sequencing to compare the genetic variation between these strains and discovered a specific genetic region (SORBUS) that is unique to the resistant strains. Through comparative analysis with other fungal species the SORBUS region was studied in more detail and with the use of genetic engineering tools we removed this unique region. Finally, the mutant lacking the SORBUS region was confirmed to have lost its sorbic acid resistance. This finding is of particular interest as it suggests that only some, not all, P. roqueforti strains are potent spoilers and that specific genetic markers could help in the identification of resistant strains. [ABSTRACT FROM AUTHOR]