Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Abdominal aortic aneurysm and cardiometabolic traits share strong genetic susceptibility to lipid metabolism and inflammation

Abdominal aortic aneurysm has a high heritability and often co-occurs with other cardiometabolic disorders, suggesting shared genetic susceptibility. We investigate this commonality leveraging recent GWAS studies of abdominal aortic aneurysm and 32 cardiometabolic traits. We find significant genetic correlations between abdominal aortic aneurysm and 21 of the cardiometabolic traits investigated, including causal relationships with coronary artery disease, hypertension, lipid traits, and blood pressure. For each trait pair, we identify shared causal variants, genes, and pathways, revealing that cholesterol metabolism and inflammation are shared most prominently. Additionally, we show the tissue and cell type specificity in the shared signals, with strong enrichment across traits in the liver, arteries, adipose tissues, macrophages, adipocytes, and fibroblasts. Finally, we leverage drug-gene databases to identify several lipid-lowering drugs and antioxidants with high potential to treat abdominal aortic aneurysm with comorbidities. Our study provides insight into the shared genetic mechanism between abdominal aortic aneurysm and cardiometabolic traits, and identifies potential targets for pharmacological intervention.

Unraveling metabolic stress response in dairy cows: genetic control of plasma biomarkers throughout lactation and the transition period

Breeding animals able to effectively respond to stress could be a long-term, sustainable, and affordable strategy to improve resilience and welfare in livestock systems. In the present study, the concentrations of 29 plasma biomarkers were used as candidate endophenotypes for metabolic stress response in single-SNP, gene- and haplotype-based GWAS using 739 healthy lactating Italian Holstein cows and 88,271 variants. Significant genetic associations were found in all the 3 GWAS approaches for plasma γ-glutamyl transferase concentration on BTA17, for paraoxonase on BTA4, and for alkaline phosphatase and zinc on BTA2. On these chromosomes, single-SNP and gene-based chromosome-wide association studies were performed, confirming GWAS findings. The signals identified for paraoxonase, γ-glutamyl transferase, and alkaline phosphatase were in proximity of the genes coding for them. The heritability of these 4 biomarkers ranged from moderate to high (from 0.39 to 0.54). Plasma biomarkers are known to undergo large changes in concentration during metabolic stress in the transition period, with an inter-individual variability in the rate of change and recovery time. Genetics may account in part for these differences. To assess this, we studied a subset of 139 periparturient cows homozygous at 3 SNPs known to be respectively associated with concentration of plasma ceruloplasmin, paraoxonase and γ-glutamyl transferase. We compared the immune-metabolic profile measured in plasma at -7, +5 and +30 d relative to calving between groups of opposite homozygotes. A significant effect of the genotype was found on paraoxonase and γ-glutamyl transferase plasma concentration at all the 3 time points. No evidence for genotype effect was detected for ceruloplasmin. Understanding the genetic control underlying metabolic stress response may suggest new approaches to foster resilience in dairy cows.

Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data

OBJECTIVE

This study aims to identify BMI-associated genes by integrating aggregated summary information from different omics data.

METHODS

We conducted a meta-analysis to leverage information from a genome-wide association study (n = 339 224), a transcriptome-wide association study (n = 5619), and an epigenome-wide association study (n = 3743). We prioritized the significant genes with a machine learning-based method, netWAS, which borrows information from adipose tissue-specific interaction networks. We also used the brain-specific network in netWAS to investigate genes potentially involved in brain-adipose interaction.

RESULTS

We identified 195 genes that were significantly associated with BMI through meta-analysis. The netWAS analysis narrowed down the list to 21 genes in adipose tissue. Among these 21 genes, six genes, including FUS, STX4, CCNT2, FUBP1, NDUFS3, and RAPSN, were not reported to be BMI-associated in PubMed or GWAS Catalog. We also identified 11 genes that were significantly associated with BMI in both adipose and whole brain tissues.

CONCLUSION

This study integrated three types of omics data and identified a group of genes that have not previously been reported to be associated with BMI. This strategy could provide new insights for future studies to identify molecular mechanisms contributing to BMI regulation.

Sex effects on DNA methylation affect discovery in epigenome-wide association study of schizophrenia

Sex differences in the epidemiology and clinical characteristics of schizophrenia are well-known; however, the molecular mechanisms underlying these differences remain unclear. Further, the potential advantages of sex-stratified meta-analyses of epigenome-wide association studies (EWAS) of schizophrenia have not been investigated. Here, we performed sex-stratified EWAS meta-analyses to investigate whether sex stratification improves discovery, and to identify differentially methylated regions (DMRs) in schizophrenia. Peripheral blood-derived DNA methylation data from 1519 cases of schizophrenia (male n = 989, female n = 530) and 1723 controls (male n = 997, female n = 726) from three publicly available datasets, and the TOP cohort were meta-analyzed to compare sex-specific, sex-stratified, and sex-adjusted EWAS. The predictive power of each model was assessed by polymethylation score (PMS). The number of schizophrenia-associated differentially methylated positions identified was higher for the sex-stratified model than for the sex-adjusted one. We identified 20 schizophrenia-associated DMRs in the sex-stratified analysis. PMS from sex-stratified analysis outperformed that from sex-adjusted analysis in predicting schizophrenia. Notably, PMSs from the sex-stratified and female-only analyses, but not those from sex-adjusted or the male-only analyses, significantly predicted schizophrenia in males. The findings suggest that sex-stratified EWAS meta-analyses improve the identification of schizophrenia-associated epigenetic changes and highlight an interaction between sex and schizophrenia status on DNA methylation. Sex-specific DNA methylation may have potential implications for precision psychiatry and the development of stratified treatments for schizophrenia.

Identifying risk loci for obsessive-compulsive disorder and shared genetic component with schizophrenia: A large-scale multi-trait association analysis with summary statistics

Due to limited samples, no genetic loci have been identified for obsessive-compulsive disorder (OCD) in genome-wide association studies. Additionally, although co-morbidities between OCD and schizophrenia (SCZ) were observed, their common genetic etiology was not completely known. Here, we conducted a comprehensive investigation regarding the genetic architecture of OCD and the common genetic foundation shared by OCD and SCZ using summary statistics data (2688 cases and 7037 controls for OCD; 53,386 cases and 77,258 controls for SCZ). We discovered significant genetic correlation between OCD and SCZ (r̂g=0.296, P = 2.82 × 10-11). We then performed two multi-trait association analyses to detect OCD-associated loci and colocalization analysis to detect causal variants. Parallel gene-level analyses were also implemented. We identified 323 OCD-relevant variants located within 12 loci, with four loci shared the same causal variants between OCD and SCZ. Further, the gene-level analyses discovered 8 OCD-associated genes. Finally, multiple functional analyses at both SNP and gene levels showed that these genetic association signals had significant enrichments in the regions of left ventricle and anterior cingulate cortex, and suggested an important role of pathways involving regulation of telomere maintenance, histone phosphorylation, and GnRH secretion. Overall, this study identified new genetic loci for OCD and provided substantial evidence supporting common genetic foundation underlying OCD and SCZ. The findings advanced our understanding of genetic architecture and pathophysiology of OCD as well as shedding light on shared genetic etiology of the two disorders.

Identifying risk loci for FTD and shared genetic component with ALS: A large-scale multitrait association analysis

Current genome-wide association studies of frontotemporal dementia (FTD) are underpowered due to limited samples. Further, common genetic etiologies between FTD and amyotrophic lateral sclerosis (ALS) remain unknown. Using the largest summary statistics of FTD (3526 cases and 9402 controls) and ALS (27,205 cases and 110,881 controls), we found a significant genetic correlation between them (rˆg = 0.637, P = 0.032) and identified 190 FTD-related variants within 5 loci (3p22.1, 5q35.1, 9p21.2, 19p13.11, and 20q13.13). Among these, ALS and FTD had causal variants in 9p21.2 and 19p13.11. Moreover, MOBP (3p22.1), C9orf72 (9p21.2), MOB3B (9p21.2), UNC13A (19p13.11), SLC9A8 (20q13.13), SNAI1 (20q13.13), and SPATA2 (20q13.13) were discovered by both SNP- and gene-level analyses, which together discovered 15 FTD-associated genes, with 10 not detected before (IFNK, RNF114, SLC9A8, SPATA2, SNAI1, SCFD1, POLDIP2, TMEM97, G2E3, and PIGW). Functional analyses showed these genes were enriched in heart left ventricle, kidney cortex, and some brain regions. Overall, this study provides insights into genetic determinants of FTD and shared genetic etiology underlying FTD and ALS.

Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery

MOTIVATION

Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes.

RESULTS

We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.

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

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

Effects of genetics on odor perception: Can a quick smell test effectively screen everyone?

SCENTinel, a rapid smell test designed to screen for olfactory disorders, including anosmia (no ability to smell an odor) and parosmia (distorted sense of smell), measures 4 components of olfactory function: detection, intensity, identification, and pleasantness. Each test card contains one of 9 odorant mixtures. Some people born with genetic insensitivities to specific odorants (i.e. specific anosmia) may fail the test if they cannot smell an odorant but otherwise have a normal sense of smell. However, using odorant mixtures has largely been found to prevent this from happening. To better understand whether genetic differences affect SCENTinel test results, we asked genetically informative adult participants (twins or triplets, N = 630; singletons, N = 370) to complete the SCENTinel test. A subset of twins (n = 304) also provided a saliva sample for genotyping. We examined data for differences between the 9 possible SCENTinel odors; effects of age, sex, and race on SCENTinel performance, test-retest variability; and heritability using both structured equation modeling and SNP-based statistical methods. None of these strategies provided evidence for specific anosmia for any of the odors, but ratings of pleasantness were, in part, genetically determined (h2 = 0.40) and were nominally associated with alleles of odorant receptors (e.g. OR2T33 and OR1G1; P < 0.001). These results provide evidence that using odorant mixtures protected against effects of specific anosmia for ratings of intensity but that ratings of pleasantness showed effects of inheritance, possibly informed by olfactory receptor genotypes.

Sex bias in celiac disease: XWAS and monocyte eQTLs in women identify TMEM187 as a functional candidate gene

BACKGROUND

Celiac disease (CeD) is an immune-mediated disorder that develops in genetically predisposed individuals upon gluten consumption. HLA risk alleles explain 40% of the genetic component of CeD, so there have been continuing efforts to uncover non-HLA loci that can explain the remaining heritability. As in most autoimmune disorders, the prevalence of CeD is significantly higher in women. Here, we investigated the possible involvement of the X chromosome on the sex bias of CeD.

METHODS

We performed a X chromosome-wide association study (XWAS) and a gene-based association study in women from the CeD Immunochip (7062 cases, 5446 controls). We also constructed a database of X chromosome cis-expression quantitative trait loci (eQTLs) in monocytes from unstimulated (n = 226) and lipopolysaccharide (LPS)-stimulated (n = 130) female donors and performed a Summary-data-based MR (SMR) analysis to integrate XWAS and eQTL information. We interrogated the expression of the potentially causal gene (TMEM187) in peripheral blood mononuclear cells (PBMCs) from celiac patients at onset, on a gluten-free diet, potential celiac patients and non-celiac controls.

RESULTS

The XWAS and gene-based analyses identified 13 SNPs and 25 genes, respectively, 22 of which had not been previously associated with CeD. The X chromosome cis-eQTL analysis found 18 genes with at least one cis-eQTL in naïve female monocytes and 8 genes in LPS-stimulated female monocytes, 2 of which were common to both situations and 6 were unique to LPS stimulation. SMR identified a potentially causal association of TMEM187 expression in naïve monocytes with CeD in women, regulated by CeD-associated, eQTL-SNPs rs7350355 and rs5945386. The CeD-risk alleles were correlated with lower TMEM187 expression. These results were replicated using eQTLs from LPS-stimulated monocytes. We observed higher levels of TMEM187 expression in PBMCs from female CeD patients at onset compared to female non-celiac controls, but not in male CeD individuals.

CONCLUSION

Using X chromosome genotypes and gene expression data from female monocytes, SMR has identified TMEM187 as a potentially causal candidate in CeD. Further studies are needed to understand the implication of the X chromosome in the higher prevalence of CeD in women.

Biliary atresia is associated with polygenic susceptibility in ciliogenesis and planar polarity effector genes

BACKGROUND & AIMS

Biliary atresia (BA) is poorly understood and leads to liver transplantation (LT), with the requirement for and associated risks of lifelong immunosuppression, in most children. We performed a genome-wide association study (GWAS) to determine the genetic basis of BA.

METHODS

We performed a GWAS in 811 European BA cases treated with LT in US, Canadian and UK centers, and 4,654 genetically matched controls. Whole-genome sequencing of 100 cases evaluated synthetic association with rare variants. Functional studies included whole liver transcriptome analysis of 64 BA cases and perturbations in experimental models.

RESULTS

A GWAS of common single nucleotide polymorphisms (SNPs), i.e. allele frequencies >1%, identified intronic SNPs rs6446628 in AFAP1 with genome-wide significance (p = 3.93E-8) and rs34599046 in TUSC3 at sub-threshold genome-wide significance (p = 1.34E-7), both supported by credible peaks of neighboring SNPs. Like other previously reported BA-associated genes, AFAP1 and TUSC3 are ciliogenesis and planar polarity effectors (CPLANE). In gene-set-based GWAS, BA was associated with 6,005 SNPs in 102 CPLANE genes (p = 5.84E-15). Compared with non-CPLANE genes, more CPLANE genes harbored rare variants (allele frequency <1%) that were assigned Human Phenotype Ontology terms related to hepatobiliary anomalies by predictive algorithms, 87% vs. 40%, p <0.0001. Rare variants were present in multiple genes distinct from those with BA-associated common variants in most BA cases. AFAP1 and TUSC3 knockdown blocked ciliogenesis in mouse tracheal cells. Inhibition of ciliogenesis caused biliary dysgenesis in zebrafish. AFAP1 and TUSC3 were expressed in fetal liver organoids, as well as fetal and BA livers, but not in normal or disease-control livers. Integrative analysis of BA-associated variants and liver transcripts revealed abnormal vasculogenesis and epithelial tube formation, explaining portal vein anomalies that co-exist with BA.

CONCLUSIONS

BA is associated with polygenic susceptibility in CPLANE genes. Rare variants contribute to polygenic risk in vulnerable pathways via unique genes.

IMPACT AND IMPLICATIONS

Liver transplantation is needed to cure most children born with biliary atresia, a poorly understood rare disease. Transplant immunosuppression increases the likelihood of life-threatening infections and cancers. To improve care by preventing this disease and its progression to transplantation, we examined its genetic basis. We find that this disease is associated with both common and rare mutations in highly specialized genes which maintain normal communication and movement of cells, and their organization into bile ducts and blood vessels during early development of the human embryo. Because defects in these genes also cause other birth defects, our findings could lead to preventive strategies to lower the incidence of biliary atresia and potentially other birth defects.

Investigating the shared genetic architecture of post-traumatic stress disorder and gastrointestinal tract disorders: a genome-wide cross-trait analysis

BACKGROUND

Observational studies suggest a correlation between post-traumatic stress disorder (PTSD) and gastrointestinal tract (GIT) disorders. However, the genetic overlap, causal relationships, and underlining mechanisms between PTSD and GIT disorders were absent.

METHODS

We obtained genome-wide association study statistics for PTSD (23 212 cases, 151 447 controls), peptic ulcer disease (PUD; 16 666 cases, 439 661 controls), gastroesophageal reflux disease (GORD; 54 854 cases, 401 473 controls), PUD and/or GORD and/or medications (PGM; 90 175 cases, 366 152 controls), irritable bowel syndrome (IBS; 28 518 cases, 426 803 controls), and inflammatory bowel disease (IBD; 7045 cases, 449 282 controls). We quantified genetic correlations, identified pleiotropic loci, and performed multi-marker analysis of genomic annotation, fast gene-based association analysis, transcriptome-wide association study analysis, and bidirectional Mendelian randomization analysis.

RESULTS

PTSD globally correlates with PUD (rg = 0.526, p = 9.355 × 10-7), GORD (rg = 0.398, p = 5.223 × 10-9), PGM (rg = 0.524, p = 1.251 × 10-15), and IBS (rg = 0.419, p = 8.825 × 10-6). Cross-trait meta-analyses identify seven genome-wide significant loci between PTSD and PGM (rs13107325, rs1632855, rs1800628, rs2188100, rs3129953, rs6973700, and rs73154693); three between PTSD and GORD (rs13107325, rs1632855, and rs3132450); one between PTSD and IBS/IBD (rs4937872 and rs114969413, respectively). Proximal pleiotropic genes are mainly enriched in immune response regulatory pathways, and in brain, digestive, and immune systems. Gene-level analyses identify five candidates: ABT1, BTN3A2, HIST1H3J, ZKSCAN4, and ZKSCAN8. We found significant causal effects of GORD, PGM, IBS, and IBD on PTSD. We observed no reverse causality of PTSD with GIT disorders, except for GORD.

CONCLUSIONS

PTSD and GIT disorders share common genetic architectures. Our work offers insights into the biological mechanisms, and provides genetic basis for translational research studies.

Multi-omics cannot replace sample size in genome-wide association studies

The integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has been suggested that multi-omics could circumvent or greatly reduce the need to increase GWAS sample sizes for novel variant discovery. We tested whether incorporating multi-omics information in earlier and smaller-sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. We applied 10 different analytic approaches to integrating multi-omics data from 12 sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (alcohol use disorder/problematic alcohol use, major depression/depression, schizophrenia, and intracranial volume/brain volume) could detect genes that were revealed by a later and larger GWAS. Multi-omics data did not reliably identify novel genes in earlier less-powered GWAS (PPV <0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1-8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., intracranial volume and schizophrenia). Although multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), can help to prioritize genes within genome-wide significant loci (PPVs = 0.5-1.0) and translate them into information about disease biology, it does not reliably increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is required.

Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.

Insights from Mendelian randomization and genetic correlation analyses into the relationship between endometriosis and its comorbidities

BACKGROUND

Endometriosis remains a poorly understood disease, despite its high prevalence and debilitating symptoms. The overlap in symptoms and the increased risk of multiple other traits in women with endometriosis is becoming increasingly apparent through epidemiological data. Genetic studies offer a method of investigating these comorbid relationships through the assessment of causal relationships with Mendelian randomization (MR), as well as identification of shared genetic variants and genes involved across traits. This has the capacity to identify risk factors for endometriosis as well as provide insight into the aetiology of disease.

OBJECTIVE AND RATIONALE

We aim to review the current literature assessing the relationship between endometriosis and other traits using genomic data, primarily through the methods of MR and genetic correlation. We critically examine the limitations of these studies in accordance with the assumptions of the utilized methods.

SEARCH METHODS

The PubMed database was used to search for peer-reviewed original research articles using the terms 'Mendelian randomization endometriosis' and '"genetic correlation" endometriosis'. Additionally, a Google Scholar search using the terms '"endometriosis" "mendelian randomization" "genetic correlation"' was performed. All relevant publications (n = 21) published up until 7 October 2022 were included in this review. Upon compilation of all traits with published MR and/or genetic correlation with endometriosis, additional epidemiological and genetic information on their comorbidity with endometriosis was sourced by searching for the trait in conjunction with 'endometriosis' on Google Scholar.

OUTCOMES

The association between endometriosis and multiple pain, gynaecological, cancer, inflammatory, gastrointestinal, psychological, and anthropometric traits has been assessed using MR analysis and genetic correlation analysis. Genetic correlation analyses provide evidence that genetic factors contributing to endometriosis are shared with multiple traits: migraine, uterine fibroids, subtypes of ovarian cancer, melanoma, asthma, gastro-oesophageal reflux disease, gastritis/duodenitis, and depression, suggesting the involvement of multiple biological mechanisms in endometriosis. The assessment of causality with MR has revealed several potential causes (e.g. depression) and outcomes (e.g. ovarian cancer and uterine fibroids) of a genetic predisposition to endometriosis; however, interpretation of these results requires consideration of potential violations of the MR assumptions.

WIDER IMPLICATIONS

Genomic studies have demonstrated that there is a molecular basis for the co-occurrence of endometriosis with other traits. Dissection of this overlap has identified shared genes and pathways, which provide insight into the biology of endometriosis. Thoughtful MR studies are necessary to ascertain causality of the comorbidities of endometriosis. Given the significant diagnostic delay of endometriosis of 7-11 years, determining risk factors is necessary to aid diagnosis and reduce the disease burden. Identification of traits for which endometriosis is a risk factor is important for holistic treatment and counselling of the patient. The use of genomic data to disentangle the overlap of endometriosis with other traits has provided insights into the aetiology of endometriosis.

Genomic characterisation of the overlap of endometriosis with 76 comorbidities identifies pleiotropic and causal mechanisms underlying disease risk

Comorbid conditions can be driven by underlying pleiotropic and causal mechanisms that can provide insights into shared molecular and biological processes contributing to disease risk. Endometriosis is a chronic condition affecting one in nine women of reproductive age and poses many challenges including lengthy diagnostic delays and limited treatment efficacy owing to poor understanding of disease aetiology. To shed light on the underlying biological mechanisms and to identify potential risk factors, we examine the epidemiological and genomic relationship between endometriosis and its comorbidities. In the UK Biobank 292 ICD10 codes were epidemiologically correlated with endometriosis diagnosis, including gynaecological, immune, infection, pain, psychiatric, cancer, gastrointestinal, urinary, bone and cardiovascular traits. A subset of the identified comorbidities (n = 76) underwent follow-up genetic analysis. Whilst Mendelian randomisation suggested causality was not responsible for most comorbid relationships, 22 traits were genetically correlated with endometriosis, including pain, gynaecological and gastrointestinal traits, suggestive of a shared genetic background. Pleiotropic genetic variants and genes were identified using gene-based and colocalisation analysis. Shared genetic risk factors and potential target genes suggest a diverse collection of biological systems are involved in these comorbid relationships including coagulation factors, development of the female reproductive tract and cell proliferation. These findings highlight the diversity of traits with epidemiological and genomic overlap with endometriosis and implicate a key role for pleiotropy in the comorbid relationships.

Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat

Soil salinity can impose substantial stress on plant growth and cause significant yield losses. Crop varieties tolerant to salinity stress are needed to sustain yields in saline soils. This requires effective genotyping and phenotyping of germplasm pools to identify novel genes and QTL conferring salt tolerance that can be utilised in crop breeding schemes. We investigated a globally diverse collection of 580 wheat accessions for their growth response to salinity using automated digital phenotyping performed under controlled environmental conditions. The results show that digitally collected plant traits, including digital shoot growth rate and digital senescence rate, can be used as proxy traits for selecting salinity-tolerant accessions. A haplotype-based genome-wide association study was conducted using 58,502 linkage disequilibrium-based haplotype blocks derived from 883,300 genome-wide SNPs and identified 95 QTL for salinity tolerance component traits, of which 54 were novel and 41 overlapped with previously reported QTL. Gene ontology analysis identified a suite of candidate genes for salinity tolerance, some of which are already known to play a role in stress tolerance in other plant species. This study identified wheat accessions that utilise different tolerance mechanisms and which can be used in future studies to investigate the genetic and genic basis of salinity tolerance. Our results suggest salinity tolerance has not arisen from or been bred into accessions from specific regions or groups. Rather, they suggest salinity tolerance is widespread, with small-effect genetic variants contributing to different levels of tolerance in diverse, locally adapted germplasm.

Complex genetic architecture underlying the plasticity of maize agronomic traits

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture.

StocSum: stochastic summary statistics for whole genome sequencing studies

Genomic summary statistics, usually defined as single-variant test results from genome-wide association studies, have been widely used to advance the genetics field in a wide range of applications. Applications that involve multiple genetic variants also require their correlations or linkage disequilibrium (LD) information, often obtained from an external reference panel. In practice, it is usually difficult to find suitable external reference panels that represent the LD structure for underrepresented and admixed populations, or rare genetic variants from whole genome sequencing (WGS) studies, limiting the scope of applications for genomic summary statistics. Here we introduce StocSum, a novel reference-panel-free statistical framework for generating, managing, and analyzing stochastic summary statistics using random vectors. We develop various downstream applications using StocSum including single-variant tests, conditional association tests, gene-environment interaction tests, variant set tests, as well as meta-analysis and LD score regression tools. We demonstrate the accuracy and computational efficiency of StocSum using two cohorts from the Trans-Omics for Precision Medicine Program. StocSum will facilitate sharing and utilization of genomic summary statistics from WGS studies, especially for underrepresented and admixed populations.

Vascular cells improve functionality of human cardiac organoids

Crosstalk between cardiac cells is critical for heart performance. Here we show that vascular cells within human cardiac organoids (hCOs) enhance their maturation, force of contraction, and utility in disease modeling. Herein we optimize our protocol to generate vascular populations in addition to epicardial, fibroblast, and cardiomyocyte cells that self-organize into in-vivo-like structures in hCOs. We identify mechanisms of communication between endothelial cells, pericytes, fibroblasts, and cardiomyocytes that ultimately contribute to cardiac organoid maturation. In particular, (1) endothelial-derived LAMA5 regulates expression of mature sarcomeric proteins and contractility, and (2) paracrine platelet-derived growth factor receptor β (PDGFRβ) signaling from vascular cells upregulates matrix deposition to augment hCO contractile force. Finally, we demonstrate that vascular cells determine the magnitude of diastolic dysfunction caused by inflammatory factors and identify a paracrine role of endothelin driving dysfunction. Together this study highlights the importance and role of vascular cells in organoid models.

Discovery of genomic loci associated with sleep apnea risk through multi-trait GWAS analysis with snoring

STUDY OBJECTIVES

Despite its association with severe health conditions, the etiology of sleep apnea (SA) remains understudied. This study sought to identify genetic variants robustly associated with SA risk.

METHODS

We performed a genome-wide association study (GWAS) meta-analysis of SA across five cohorts (NTotal = 523 366), followed by a multi-trait analysis of GWAS (multi-trait analysis of genome-wide association summary statistics [MTAG]) to boost power, leveraging the high genetic correlation between SA and snoring. We then adjusted our results for the genetic effects of body mass index (BMI) using multi-trait-based conditional and joint analysis (mtCOJO) and sought replication of lead hits in a large cohort of participants from 23andMe, Inc (NTotal = 1 477 352; Ncases = 175 522). We also explored genetic correlations with other complex traits and performed a phenome-wide screen for causally associated phenotypes using the latent causal variable method.

RESULTS

Our SA meta-analysis identified five independent variants with evidence of association beyond genome-wide significance. After adjustment for BMI, only one genome-wide significant variant was identified. MTAG analyses uncovered 49 significant independent loci associated with SA risk. Twenty-nine variants were replicated in the 23andMe GWAS adjusting for BMI. We observed genetic correlations with several complex traits, including multisite chronic pain, diabetes, eye disorders, high blood pressure, osteoarthritis, chronic obstructive pulmonary disease, and BMI-associated conditions.

CONCLUSION

Our study uncovered multiple genetic loci associated with SA risk, thus increasing our understanding of the etiology of this condition and its relationship with other complex traits.

LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.

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

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

Genome-Wide Integration of Genetic and Genomic Studies of Atopic Dermatitis: Insights into Genetic Architecture and Pathogenesis

Atopic dermatitis (AD) is a common heterogeneous, chronic, itching, and inflammatory skin disease. Genetic studies have identified multiple AD susceptibility genes. However, the genetic architecture of AD has not been elucidated. In this study, we conducted a large-scale meta-analysis of AD (35,647 cases and 1,013,885 controls) to characterize the genetic basis of AD. The heritability of AD in different datasets varied from 0.6 to 7.1%. We identified 31 previously unreported genes by integrating multiomics data. Among the 31 genes, MCL1 was identified as a potential treatment target for AD by mediating gene‒drug interactions. Tissue enrichment analyses and phenome-wide association study provided strong support for the role of the hemic and immune systems in AD. Across 1,207 complex traits and diseases, genetic correlations indicated that AD shared links with multiple respiratory phenotypes. The phenome-wide Mendelian randomization analysis (Mendelian randomization‒phenome-wide association study) revealed that the age of onset of diabetes exhibited a positive causal effect on AD (inverse-variance weighted β = 0.39, SEM = 0.09, P = 2.77 × 10^-5). Overall, these results provide important insights into the genetic architecture of AD and will lead to a more thorough and complete understanding of the molecular mechanisms underlying AD.

Genome Wide Association Study with Imputed Whole Genome Sequence Data Identifies a 431 kb Risk Haplotype on CFA18 for Congenital Laryngeal Paralysis in Alaskan Sled Dogs

Congenital laryngeal paralysis (CLP) is an inherited disorder that affects the ability of the dog to exercise and precludes it from functioning as a working sled dog. Though CLP is known to occur in Alaskan sled dogs (ASDs) since 1986, the genetic mutation underlying the disease has not been reported. Using a genome-wide association study (GWAS), we identified a 708 kb region on CFA 18 harboring 226 SNPs to be significantly associated with CLP. The significant SNPs explained 47.06% of the heritability of CLP. We narrowed the region to 431 kb through autozygosity mapping and found 18 of the 20 cases to be homozygous for the risk haplotype. Whole genome sequencing of two cases and a control ASD, and comparison with the genome of 657 dogs from various breeds, confirmed the homozygous status of the risk haplotype to be unique to the CLP cases. Most of the dogs that were homozygous for the risk allele had blue eyes. Gene annotation and a gene-based association study showed that the risk haplotype encompasses genes implicated in developmental and neurodegenerative disorders. Pathway analysis showed enrichment of glycoproteins and glycosaminoglycans biosynthesis, which play a key role in repairing damaged nerves. In conclusion, our results suggest an important role for the identified candidate region in CLP.

Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction

Despite an estimated heritability of ~50%, genome-wide association studies of opioid use disorder (OUD) have revealed few genome-wide significant loci. We conducted a cross-ancestry meta-analysis of OUD in the Million Veteran Program (N = 425,944). In addition to known exonic variants in OPRM1 and FURIN, we identified intronic variants in RABEPK, FBXW4, NCAM1 and KCNN1. A meta-analysis including other datasets identified a locus in TSNARE1. In total, we identified 14 loci for OUD, 12 of which are novel. Significant genetic correlations were identified for 127 traits, including psychiatric disorders and other substance use-related traits. The only significantly enriched cell-type group was CNS, with gene expression enrichment in brain regions previously associated with substance use disorders. These findings increase our understanding of the biological basis of OUD and provide further evidence that it is a brain disease, which may help to reduce stigma and inform efforts to address the opioid epidemic.

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

BACKGROUND

Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods.

RESULTS

We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow.

CONCLUSION

In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .

Sodium-calcium exchanger-3 regulates pain "wind-up": From human psychophysics to spinal mechanisms

Repeated application of noxious stimuli leads to a progressively increased pain perception; this temporal summation is enhanced in and predictive of clinical pain disorders. Its electrophysiological correlate is "wind-up," in which dorsal horn spinal neurons increase their response to repeated nociceptor stimulation. To understand the genetic basis of temporal summation, we undertook a GWAS of wind-up in healthy human volunteers and found significant association with SLC8A3 encoding sodium-calcium exchanger type 3 (NCX3). NCX3 was expressed in mouse dorsal horn neurons, and mice lacking NCX3 showed normal, acute pain but hypersensitivity to the second phase of the formalin test and chronic constriction injury. Dorsal horn neurons lacking NCX3 showed increased intracellular calcium following repetitive stimulation, slowed calcium clearance, and increased wind-up. Moreover, virally mediated enhanced spinal expression of NCX3 reduced central sensitization. Our study highlights Ca2+ efflux as a pathway underlying temporal summation and persistent pain, which may be amenable to therapeutic targeting.

Pinpointing novel risk loci for Lewy body dementia and the shared genetic etiology with Alzheimer's disease and Parkinson's disease: a large-scale multi-trait association analysis

BACKGROUND

The current genome-wide association study (GWAS) of Lewy body dementia (LBD) suffers from low power due to a limited sample size. In addition, the genetic determinants underlying LBD and the shared genetic etiology with Alzheimer's disease (AD) and Parkinson's disease (PD) remain poorly understood.

METHODS

Using the largest GWAS summary statistics of LBD to date (2591 cases and 4027 controls), late-onset AD (86,531 cases and 676,386 controls), and PD (33,674 cases and 449,056 controls), we comprehensively investigated the genetic basis of LBD and shared genetic etiology among LBD, AD, and PD. We first conducted genetic correlation analysis using linkage disequilibrium score regression (LDSC), followed by multi-trait analysis of GWAS (MTAG) and association analysis based on SubSETs (ASSET) to identify the trait-specific SNPs. We then performed SNP-level functional annotation to identify significant genomic risk loci paired with Bayesian fine-mapping and colocalization analysis to identify potential causal variants. Parallel gene-level analysis including GCTA-fastBAT and transcriptome-wide association analysis (TWAS) was implemented to explore novel LBD-associated genes, followed by pathway enrichment analysis to understand underlying biological mechanisms.

RESULTS

Pairwise LDSC analysis found positive genome-wide genetic correlations between LBD and AD (rg = 0.6603, se = 0.2001; P = 0.0010), between LBD and PD (rg = 0.6352, se = 0.1880; P = 0.0007), and between AD and PD (rg = 0.2136, se = 0.0860; P = 0.0130). We identified 13 significant loci for LBD, including 5 previously reported loci (1q22, 2q14.3, 4p16.3, 4q22.1, and 19q13.32) and 8 novel biologically plausible genetic associations (5q12.1, 5q33.3, 6p21.1, 8p23.1, 8p21.1, 16p11.2, 17p12, and 17q21.31), among which APOC1 (19q13.32), SNCA (4q22.1), TMEM175 (4p16.3), CLU (8p21.1), MAPT (17q21.31), and FBXL19 (16p11.2) were also validated by gene-level analysis. Pathway enrichment analysis of 40 common genes identified by GCTA-fastBAT and TWAS implicated significant role of neurofibrillary tangle assembly (GO:1902988, adjusted P = 1.55 × 10^-2).

CONCLUSIONS

Our findings provide novel insights into the genetic determinants of LBD and the shared genetic etiology and biological mechanisms of LBD, AD, and PD, which could benefit the understanding of the co-pathology as well as the potential treatment of these diseases simultaneously.

Multi-Trait Genetic Analysis Identifies Autoimmune Loci Associated with Cutaneous Melanoma

Genome-wide association studies (GWAS) have identified a number of risk loci for cutaneous melanoma. Cutaneous melanoma shares overlapping genetic risk (genetic correlation) with a number of other traits, including its risk factors such as sunburn propensity. This genetic correlation can be exploited to identify additional cutaneous melanoma risk loci by multitrait analysis of GWAS (MTAG). We used bivariate linkage disequilibrium-score regression score regression to identify traits that are genetically correlated with clinically confirmed cutaneous melanoma and then used publicly available GWAS for these traits in a multitrait analysis of GWAS. Multitrait analysis of GWAS allows GWAS to be combined while accounting for sample overlap and incomplete genetic correlation. We identified a total of 74 genome-wide independent loci, 19 of them were not previously reported in the input cutaneous melanoma GWAS meta-analysis. Of these loci, 55 were replicated (P < 0.05/74, Bonferroni-corrected P-value in two independent cutaneous melanoma replication cohorts from Melanoma Institute Australia and 23andMe, Inc. Among the, to our knowledge, previously unreported cutaneous melanoma loci are ones that have also been associated with autoimmune traits including rs715199 near LPP and rs10858023 near AP4B1. Our analysis indicates genetic correlation between traits can be leveraged to identify new risk genes for cutaneous melanoma.

A multi-level investigation of the genetic relationship between endometriosis and ovarian cancer histotypes

Endometriosis is associated with increased risk of epithelial ovarian cancers (EOCs). Using data from large endometriosis and EOC genome-wide association meta-analyses, we estimate the genetic correlation and evaluate the causal relationship between genetic liability to endometriosis and EOC histotypes, and identify shared susceptibility loci. We estimate a significant genetic correlation (r_g) between endometriosis and clear cell (r_g = 0.71), endometrioid (r_g = 0.48), and high-grade serous (r_g = 0.19) ovarian cancer, associations supported by Mendelian randomization analyses. Bivariate meta-analysis identified 28 loci associated with both endometriosis and EOC, including 19 with evidence for a shared underlying association signal. Differences in the shared risk suggest different underlying pathways may contribute to the relationship between endometriosis and the different histotypes. Functional annotation using transcriptomic and epigenomic profiles of relevant tissues/cells highlights several target genes. This comprehensive analysis reveals profound genetic overlap between endometriosis and EOC histotypes with valuable genomic targets for understanding the biological mechanisms linking the diseases.

•

Endometriosis is genetically correlated with CCOC, ENOC, and HGSOC

•

Genetic liability to endometriosis confers risk of these EOC histotypes

•

Profound colocalization of genetic associations at endometriosis and EOC risk loci

•

Functional annotation highlights shared target genes elucidating the genetic link

Distinct biological ages of organs and systems identified from a multi-omics study

Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple "clocks" within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People's aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian.

A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus

Mucus obstruction is a central feature in the cystic fibrosis (CF) airways. A genome-wide association study (GWAS) of lung disease by the CF Gene Modifier Consortium (CFGMC) identified a significant locus containing two mucin genes, MUC20 and MUC4. Expression quantitative trait locus (eQTL) analysis using human nasal epithelia (HNE) from 94 CF-affected Canadians in the CFGMC demonstrated MUC4 eQTLs that mirrored the lung association pattern in the region, suggesting that MUC4 expression may mediate CF lung disease. Complications arose, however, with colocalization testing using existing methods: the locus is complex and the associated SNPs span a 0.2 Mb region with high linkage disequilibrium (LD) and evidence of allelic heterogeneity. We previously developed the Simple Sum (SS), a powerful colocalization test in regions with allelic heterogeneity, but SS assumed eQTLs to be present to achieve type I error control. Here we propose a two-stage SS (SS2) colocalization test that avoids a priori eQTL assumptions, accounts for multiple hypothesis testing and the composite null hypothesis, and enables meta-analysis. We compare SS2 to published approaches through simulation and demonstrate type I error control for all settings with the greatest power in the presence of high LD and allelic heterogeneity. Applying SS2 to the MUC20/MUC4 CF lung disease locus with eQTLs from CF HNE revealed significant colocalization with MUC4 (p = 1.31 × 10⁻⁵) rather than with MUC20. The SS2 is a powerful method to inform the responsible gene(s) at a locus and guide future functional studies. SS2 has been implemented in the application LocusFocus.

STXBP6 and B3GNT6 Genes are Associated With Selective IgA Deficiency

Immunoglobulin A Deficiency (IgAD) is a polygenic primary immune deficiency, with a strong genetic association to the human leukocyte antigen (HLA) region. Previous genome-wide association studies (GWAS) have identified five non-HLA risk loci (IFIH1, PVT1, ATG13-AMBRA1, AHI1 and CLEC16A). In this study, we investigated the genetic interactions between different HLA susceptibility haplotypes and non-MHC genes in IgAD. To do this, we stratified IgAD subjects and healthy controls based on HLA haplotypes (N = 10,993), and then performed GWAS to identify novel genetic regions contributing to IgAD susceptibility. After replicating previously published HLA risk haplotypes, we compared individuals carrying at least one HLA risk allele (HLA-B*08:01-DRB1*03:01-DQB1*02:01 or HLA-DRB1*07:01-DQB1*02:02 or HLA-DRB1*01-DQB1*05:01) with individuals lacking an HLA risk allele. Subsequently, we stratified subjects based on the susceptibility alleles/haplotypes and performed gene-based association analysis using 572,856 SNPs and 24,125 genes. A significant genome-wide association in STXBP6 (rs4097492; p = 7.63 × 10^-9) was observed in the cohort carrying at least one MHC risk allele. We also identified a significant gene-based association for B3GNT6 (P _Gene = 2.1 × 10^-6) in patients not carrying known HLA susceptibility alleles. Our findings indicate that the etiology of IgAD differs depending on the genetic background of HLA susceptibility haplotypes.

Genome-wide analysis of genetic predisposition to common polygenic cancers

Lung, breast, prostate, and colorectal cancers are among the most common and fatal malignancies worldwide. They are mainly caused by multifactorial mechanisms and are genetically heterogeneous. We investigated the genetic architecture of these cancers through genome-wide association, pathway-based, and summary-based transcriptome-/methylome-wide association analyses using three independent cohorts. Our genome-wide association analyses identified the associations of 33 single-nucleotide polymorphisms (SNPs) at P < 5E - 06, of which 32 SNPs were not previously reported and did not have proxy variants within their ± 1 Mb flanking regions. Moreover, other polymorphisms mapped to their closest genes were not previously associated with the same cancers at P < 5E - 06. Our pathway enrichment analyses revealed associations of 32 pathways; mainly related to the immune system, DNA replication/transcription, and chromosomal organization; with the studied cancers. Also, 60 probes were associated with these cancers in our transcriptome-wide and methylome-wide analyses. The ± 1 Mb flanking regions of most probes had not attained P < 5E - 06 in genome-wide association studies. The genes corresponding to the significant probes can be considered as potential targets for further functional studies. Two genes (i.e., CDC14A and PMEL) demonstrated stronger evidence of associations with lung cancer as they had significant probes in both transcriptome-wide and methylome-wide association analyses. The novel cancer-associated SNPs and genes identified here would advance our understanding of the genetic heterogeneity of the common cancers.

Glycosaminoglycan biosynthesis pathway in host genome is associated with Helicobacter pylori infection

Helicobacter pylori is a causative pathogen of many gastric and extra-gastric diseases. It has infected about half of the global population. There were no genome-wide association studies (GWAS) for H. pylori infection conducted in Chinese population, who carried different and relatively homogenous strain of H. pylori. In this work, we performed SNP (single nucleotide polymorphism)-based, gene-based and pathway-based genome-wide association analyses to investigate the genetic basis of host susceptibility to H. pylori infection in 480 Chinese individuals. We also profiled the composition and function of the gut microbiota between H. pylori infection cases and controls. We found several genes and pathways associated with H. pylori infection (P < 0.05), replicated one previously reported SNP rs10004195 in TLR1 gene region (P = 0.02). We also found that glycosaminoglycan biosynthesis related pathway was associated with both onset and progression of H. pylori infection. In the gut microbiome association study, we identified 2 species, 3 genera and several pathways had differential abundance between H. pylori infected cases and controls. This paper is the first GWAS for H. pylori infection in Chinese population, and we combined the genetic and microbial data to comprehensively discuss the basis of host susceptibility to H. pylori infection.

Genetic Susceptibility to Pneumonia: A GWAS Meta-Analysis Between the UK Biobank and FinnGen

Pneumonia is a respiratory condition with complex etiology. Host genetic variation is thought to contribute to individual differences in susceptibility and symptom manifestation. Here, we analyze pneumonia data from the UK Biobank (14,780 cases and 439,096 controls) and FinnGen (9980 cases and 86,519 controls) and perform a genomewide association study meta-analysis. We use gene-based tests, colocalization, genetic correlation, latent causal variable (LCV) and polygenic prediction in an independent Australian sample (N = 5595) to draw insights into the etiology of pneumonia risk. We identify two independent loci on chromosome 15 (lead single-nucleotide polymorphisms rs2009746 and rs76474922) to be associated with pneumonia (p < 5e-8). Gene-based tests revealed 18 genes in chromosomes 15, 16 and 9, including IL127, PBX3, ApoB receptor (APOBR) and smoking related genes CHRNA3/5, statistically associated with pneumonia. We observed genetic correlations between pneumonia and cardiorespiratory, psychiatric and inflammatory related traits. LCV analysis suggests a strong genetic causal relationship with cardiovascular health phenotypes. Polygenic risk scores for pneumonia significantly predicted self-reported pneumonia in an independent sample, albeit with a small effect size (OR = 1.11 95% CI [1.04, 1.19], p < .05). Sensitivity analyses suggested the associations in chromosome 15 are mediated by smoking history, but the associations in chromosomes 16 and 9, and polygenic prediction were robust to adjustment for smoking. Altogether, our results highlight common genetic variants, genes and potential pathways that contribute to individual differences in susceptibility to pneumonia, and advance our understanding of the genetic factors underlying heterogeneity in respiratory medical outcomes.

Genetic analyses of gynecological disease identify genetic relationships between uterine fibroids and endometrial cancer, and a novel endometrial cancer genetic risk region at the WNT4 1p36.12 locus

Endometriosis, polycystic ovary syndrome (PCOS) and uterine fibroids have been proposed as endometrial cancer risk factors; however, disentangling their relationships with endometrial cancer is complicated due to shared risk factors and comorbidities. Using genome-wide association study (GWAS) data, we explored the relationships between these non-cancerous gynecological diseases and endometrial cancer risk by assessing genetic correlation, causal relationships and shared risk loci. We found significant genetic correlation between endometrial cancer and PCOS, and uterine fibroids. Adjustment for genetically predicted body mass index (a risk factor for PCOS, uterine fibroids and endometrial cancer) substantially attenuated the genetic correlation between endometrial cancer and PCOS but did not affect the correlation with uterine fibroids. Mendelian randomization analyses suggested a causal relationship between only uterine fibroids and endometrial cancer. Gene-based analyses revealed risk regions shared between endometrial cancer and endometriosis, and uterine fibroids. Multi-trait GWAS analysis of endometrial cancer and the genetically correlated gynecological diseases identified a novel genome-wide significant endometrial cancer risk locus at 1p36.12, which replicated in an independent endometrial cancer dataset. Interrogation of functional genomic data at 1p36.12 revealed biologically relevant genes, including WNT4 which is necessary for the development of the female reproductive system. In summary, our study provides genetic evidence for a causal relationship between uterine fibroids and endometrial cancer. It further provides evidence that the comorbidity of endometrial cancer, PCOS and uterine fibroids may partly be due to shared genetic architecture. Notably, this shared architecture has revealed a novel genome-wide risk locus for endometrial cancer.

Therapeutic Inhibition of Acid Sensing Ion Channel 1a Recovers Heart Function After Ischemia-Reperfusion Injury

Background: Ischemia-reperfusion injury (IRI) is one of the major risk factors implicated in morbidity and mortality associated with cardiovascular disease. During cardiac ischemia, the build-up of acidic metabolites results in decreased intracellular and extracellular pH that can reach as low as 6.0-6.5. The resulting tissue acidosis exacerbates ischemic injury and significantly impacts cardiac function. Methods: We used genetic and pharmacological methods to investigate the role of acid sensing ion channel 1a (ASIC1a) in cardiac IRI at the cellular and whole organ level. Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) as well as ex vivo and in vivo models of IRI were used to test the efficacy of ASIC1a inhibitors as pre- and post-conditioning therapeutic agents. Results: Analysis of human complex trait genetics indicate that variants in the ASIC1 genetic locus are significantly associated with cardiac and cerebrovascular ischemic injuries. Using hiPSC-CMs in vitro and murine ex vivo heart models, we demonstrate that genetic ablation of ASIC1a improves cardiomyocyte viability after acute IRI. Therapeutic blockade of ASIC1a using specific and potent pharmacological inhibitors recapitulates this cardioprotective effect. We used an in vivo model of myocardial infarction (MI) and two models of ex vivo donor heart procurement and storage as clinical models to show that ASIC1a inhibition improves post-IRI cardiac viability. Use of ASIC1a inhibitors as pre- or post-conditioning agents provided equivalent cardioprotection to benchmark drugs, including the sodium-hydrogen exchange inhibitor zoniporide. At the cellular and whole organ level, we show that acute exposure to ASIC1a inhibitors has no impact on cardiac ion channels regulating baseline electromechanical coupling and physiological performance. Conclusions: Collectively, our data provide compelling evidence for a novel pharmacological strategy involving ASIC1a blockade as a cardioprotective therapy to improve the viability of hearts subjected to IRI.

Genome-Wide Analysis of Sex Disparities in the Genetic Architecture of Lung and Colorectal Cancers

Almost all complex disorders have manifested epidemiological and clinical sex disparities which might partially arise from sex-specific genetic mechanisms. Addressing such differences can be important from a precision medicine perspective which aims to make medical interventions more personalized and effective. We investigated sex-specific genetic associations with colorectal (CRCa) and lung (LCa) cancers using genome-wide single-nucleotide polymorphisms (SNPs) data from three independent datasets. The genome-wide association analyses revealed that 33 SNPs were associated with CRCa/LCa at P < 5.0 × 10⁻⁶ neither males or females. Of these, 26 SNPs had sex-specific effects as their effect sizes were statistically different between the two sexes at a Bonferroni-adjusted significance level of 0.0015. None had proxy SNPs within their ±1 Mb regions and the closest genes to 32 SNPs were not previously associated with the corresponding cancers. The pathway enrichment analyses demonstrated the associations of 35 pathways with CRCa or LCa which were mostly implicated in immune system responses, cell cycle, and chromosome stability. The significant pathways were mostly enriched in either males or females. Our findings provided novel insights into the potential sex-specific genetic heterogeneity of CRCa and LCa at SNP and pathway levels.

Joo JW. MARS: leveraging allelic heterogeneity to increase power of association testing

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

Genome-wide identification of cis DNA methylation quantitative trait loci in three Southeast Asian Populations

DNA methylation (DNAm) is an epigenetic modification that acts to regulate gene transcription, is essential for cellular processes and plays an important role in complex traits and disease. Variation in DNAm levels is influenced by both genetic and environmental factors. Several studies have examined the extent to which common genetic variation influences DNAm (i.e. mQTLs), however, an improved understanding of mQTLs across diverse human populations is needed to increase their utility in integrative genomic studies in order to further our understanding of complex trait and disease biology. Here, we systematically examine cis-mQTLs in three Southeast Asian populations in the Singapore Integrative Omics (iOmics) Study, comprised of Chinese (n = 93), Indians (n = 83) and Malays (n = 78). A total of 24 851 cis-mQTL probes were associated with at least one SNP in meta- and ethnicity-specific analyses at a stringent significance level. These cis-mQTL probes show significant differences in local SNP heritability between the ethnicities, enrichment in functionally relevant regions using data from the Roadmap Epigenomics Mapping Consortium and are associated with nearby genes and complex traits due to pleiotropy. Importantly, DNAm prediction performance and the replication of cis-mQTLs both within iOmics and between two independent mQTL studies in European and Bangladeshi individuals is best when the genetic distance between the ethnicities is small, with differences in cis-mQTLs likely due to differences in allele frequency and linkage disequilibrium. This study highlights the importance of, and opportunities from, extending investigation of the genetic control of DNAm to Southeast Asian populations.

A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding

Modern potato breeding methods following a genomic-led approach provide means for shortening breeding cycles and increasing breeding efficiency across selection cycles. Acquiring genetic data for large breeding populations remains expensive. We present a pipeline to reduce the number of single nucleotide polymorphisms (SNPs) to lower the cost of genotyping. First, we reduced the number of individuals to be genotyped with a high-throughput method according to the multi-trait variation as defined by principal component analysis of phenotypic characteristics. Next, we reduced the number of SNPs by pruning for linkage disequilibrium. By adjusting the square of the correlation coefficient between two adjacent loci, we obtained reduced subsets of SNPs. We subsequently tested these SNP subsets by two methods; (1) a genome-wide association study (GWAS) for marker identification, and (2) genomic selection (GS) to predict genomic estimated breeding values. The results indicate that both GWAS and GS can be done without loss of information after SNP reduction. The pipeline allows for creating custom SNP subsets to cover all variation found in any particular breeding population. Low-throughput genotyping will reduce the genotyping cost associated with large populations, thereby making genomic breeding methods applicable to large potato breeding populations by reducing genotyping costs.

Comparison of Genome-Wide Association Scans for Quantitative and Observational Measures of Human Hair Curvature

Previous genetic studies on hair morphology focused on the overall morphology of the hair using data collected by self-report or researcher observation. Here, we present the first genome-wide association study (GWAS) of a micro-level quantitative measure of hair curvature. We compare these results to GWAS results obtained using a macro-level classification of observable hair curvature performed in the same sample of twins and siblings of European descent. Observational data were collected by trained observers, while quantitative data were acquired using an Optical Fibre Diameter Analyser (OFDA). The GWAS for both the observational and quantitative measures of hair curvature resulted in genome-wide significant signals at chromosome 1q21.3 close to the trichohyalin (TCHH) gene, previously shown to harbor variants associated with straight hair morphology in Europeans. All genetic variants reaching genome-wide significance for both GWAS (quantitative measure lead single-nucleotide polymorphism [SNP] rs12130862, p = 9.5 × 10-09; observational measure lead SNP rs11803731, p = 2.1 × 10-17) were in moderate to very high linkage disequilibrium (LD) with each other (minimum r2 = .45), indicating they represent the same genetic locus. Conditional analyses confirmed the presence of only one signal associated with each measure at this locus. Results from the quantitative measures reconfirmed the accuracy of observational measures.

Genome-Wide Association Studies Detect Multiple QTLs for Productivity in Mesoamerican Diversity Panel of Common Bean Under Drought Stress

Drought stress is an important abiotic factor limiting common bean yield, with great impact on the production worldwide. Understanding the genetic basis regulating beans' yield and seed weight (SW) is a fundamental prerequisite for the development of superior cultivars. The main objectives of this work were to conduct genome-wide marker discovery by genotyping a Mesoamerican panel of common bean germplasm, containing cultivated and landrace accessions of broad origin, followed by the identification of genomic regions associated with productivity under two water regimes using different genome-wide association study (GWAS) approaches. A total of 11,870 markers were genotyped for the 339 genotypes, of which 3,213 were SilicoDArT and 8,657 SNPs derived from DArT and CaptureSeq. The estimated linkage disequilibrium extension, corrected for structure and relatedness (r ² _sv ), was 98.63 and 124.18 kb for landraces and breeding lines, respectively. Germplasm was structured into landraces and lines/cultivars. We carried out GWASs for 100-SW and yield in field environments with and without water stress for 3 consecutive years, using single-, segment-, and gene-based models. Higher number of associations at high stringency was identified for the SW trait under irrigation, totaling ∼185 QTLs for both single- and segment-based, whereas gene-based GWASs showed ∼220 genomic regions containing ∼650 genes. For SW under drought, 18 QTLs were identified for single- and segment-based and 35 genes by gene-based GWASs. For yield, under irrigation, 25 associations were identified, whereas under drought the total was 10 using both approaches. In addition to the consistent associations detected across experiments, these GWAS approaches provided important complementary QTL information (∼221 QTLs; 650 genes; r ² from 0.01% to 32%). Several QTLs were mined within or near candidate genes playing significant role in productivity, providing better understanding of the genetic mechanisms underlying these traits and making available molecular tools to be used in marker-assisted breeding. The findings also allowed the identification of genetic material (germplasm) with better yield performance under drought, promising to a common bean breeding program. Finally, the availability of this highly diverse Mesoamerican panel is of great scientific value for the analysis of any relevant traits in common bean.

The Chromosome Level Genome and Genome-wide Association Study for the Agronomic Traits of Panax Notoginseng

The Chinese ginseng Panax notoginseng is a domesticated herb with significant medicinal and economic value. Here we report a chromosome-level P. notoginseng genome assembly with a high (∼79%) repetitive sequence content. The juxtaposition with the widely distributed, closely related Korean ginseng (Panax ginseng) genome revealed contraction of plant defense genes (in particular R-genes) in the P. notoginseng genome. We also investigated the reasons for the larger genome size of Panax species, revealing contributions from two Panax-specific whole-genome duplication events and transposable element expansion. Transcriptome data and comparative genome analysis revealed the candidate genes involved in the ginsenoside synthesis pathway. We also performed a genome-wide association study on 240 cultivated P. notoginseng individuals and identified the associated genes with dry root weight (63 genes) and stem thickness (168 genes). The P. notoginseng genome represents a critical step toward harnessing the full potential of an economically important and enigmatic plant.

Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock

Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p = 0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p = 0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p = 0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.

Genetic control of temperament traits across species: association of autism spectrum disorder risk genes with cattle temperament

BACKGROUND

Temperament traits are of high importance across species. In humans, temperament or personality traits correlate with psychological traits and psychiatric disorders. In cattle, they impact animal welfare, product quality and human safety, and are therefore of direct commercial importance. We hypothesized that genetic factors that contribute to variation in temperament among individuals within a species will be shared between humans and cattle. Using imputed whole-genome sequence data from 9223 beef cattle from three cohorts, a series of genome-wide association studies was undertaken on cattle flight time, a temperament phenotype measured as the time taken for an animal to cover a short-fixed distance after release from an enclosure. We also investigated the association of cattle temperament with polymorphisms in bovine orthologs of risk genes for neuroticism, schizophrenia, autism spectrum disorders (ASD), and developmental delay disorders in humans.

RESULTS

Variants with the strongest associations were located in the bovine orthologous region that is involved in several behavioural and cognitive disorders in humans. These variants were also partially validated in independent cattle cohorts. Genes in these regions (BARHL2, NDN, SNRPN, MAGEL2, ABCA12, KIFAP3, TOPAZ1, FZD3, UBE3A, and GABRA5) were enriched for the GO term neuron migration and were differentially expressed in brain and pituitary tissues in humans. Moreover, variants within 100 kb of ASD susceptibility genes were associated with cattle temperament and explained 6.5% of the total additive genetic variance in the largest cattle cohort. The ASD genes with the most significant associations were GABRB3 and CUL3. Using the same 100 kb window, a weak association was found with polymorphisms in schizophrenia risk genes and no association with polymorphisms in neuroticism and developmental delay disorders risk genes.

CONCLUSIONS

Our analysis showed that genes identified in a meta-analysis of cattle temperament contribute to neuron development functions and are differentially expressed in human brain tissues. Furthermore, some ASD susceptibility genes are associated with cattle temperament. These findings provide evidence that genetic control of temperament might be shared between humans and cattle and highlight the potential for future analyses to leverage results between species.