Multi-trait Genome-wide Association Study Research Articles

A multi-trait GWAS identifies novel genes influencing albuminuria.

Albuminuria is common and associated with increased risks of end-stage kidney disease and cardiovascular diseases, yet its underlying mechanism remains obscure. Previous genome-wide association studies (GWAS) for albuminuria did not consider gene pleiotropy and primarily focused on European ancestry populations. This study adopted a multi-trait analysis of GWAS (MTAG) approach to jointly analyze two vital kidney traits, estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) to identify and prioritize the genes associated with UACR. Data from the Taiwan Biobank from 2012 to 2023 were analyzed. GWAS of UACR and eGFR were performed separately and the summary statistics from these GWAS were jointly analyzed using MTAG. The polygenic risk scores (PRS) of UACR were constructed for validation. The UACR-associated loci were further fine-mapped and prioritized based on their deleteriousness, eQTL associations, and relatedness to Mendelian kidney diseases. MTAG analysis of the UACR revealed 15 genetic loci, including 12 novel loci. The PRS for UACR was significantly associated with urinary albumin level (P<0.001) and microalbuminuria (P=0.001 ∼ 0.045). A list of priority genes was generated. Twelve genes with high priority included the albumin endocytic receptor gene LRP2 and ciliary genes IFT172. The findings of this multi-trait GWAS suggest that primary cilia play a role in sensing mechanical stimuli, leading to albumin endocytosis. The priority list of genes warrants further translational investigation to reduce albuminuria.

Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

BackgroundApproximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores.MethodsHere, we extend and assess the potential limitations and gains of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), and South-East Asian (SAS)).ResultsWe detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multi-trait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome p.vivax induced malaria.ConclusionsMulti-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.

Genome-wide association studies for loin muscle area, loin muscle depth and backfat thickness in DLY pigs.

This study aimed at identifying genes associated with loin muscle area (LMA), loin muscle depth (LMD) and backfat thickness (BFT). We performed single-trait and multi-trait genome-wide association studies (GWASs) after genotyping 685 Duroc × (Landrace × Yorkshire) (DLY) pigs using the Geneseek Porcine 50K SNP chip. In the single-trait GWASs, we identified two, eight and two significant SNPs associated with LMA, LMD and BFT, respectively, and searched genes within the 1 Mb region near the significant SNPs with relevant functions as candidate genes. Consequently, we identified one (DOCK5), three (PID1, PITX2, ELOVL6) and three (CCR1, PARP14, CASR) promising candidate genes for LMA, LMD and BFT, respectively. Moreover, the multi-trait GWAS identified four significant SNPs associated with the three traits. In conclusion, the GWAS analysis of LMA, LMD and BFT in a DLY pig population identified several associated SNPs and candidate genes, further deepening our understanding of the genetic basis of these traits, and they may be useful for marker-assisted selection to improve the three traits in DLY pigs.

Genetic linkage between swimming performance and disease resistance enables multitrait breeding strategies in large yellow croaker

Improving the robustness of fish stock has become a key issue in the advancement of aquaculture, as diseases and harsh aquatic conditions impact industry sustainability and yield. In this study, we identify several genetic loci that link swimming performance to disease resistance in large yellow croaker, and apply genomic selection (GS) for swimming performance to generate offspring with both enhanced swimming performance and disease resistance. First, we classified our reference population as superior swimmers (SS) or inferior swimmers (IS) by swimming tests and assessed inherent disease resistance. Consistent with previous research, SS displayed enhanced resistance to the parasite Cryptocaryon irritans. Through genotyping of parental reference and candidate populations, we generated a single nucleotide polymorphism (SNP) database, containing 45832 high-quality SNPs in total. We applied multitrait genome-wide association study analysis of swimming performance and disease resistance to this dataset and identified three linked SNPs, which were associated with 48 potential candidate genes, including pp3r1b, mapk12b, mapk11, and tnfrsf11a. Next, we generated a GS model using the BayesB method and calculated the candidate population's genetic estimated breeding value (GEBV). Individuals with GEBV's in the top 12 ​% were selected as broodstocks for breeding selective lines (SL), and the remaining individuals were used as the control group (CG). We found that SL exhibited both increased swimming performance and resistance to C. irritans compared to CG. Our study reveals the genetic basis related to swimming performance and disease resistance. This work has implications for designing multitrait breeding strategies and improving whole-fish fitness, both of which are critical in aquaculture.

Large-scale genetic and multi-omics analyses identify molecular pathways underlying dilated cardiomyopathy

Abstract Background Dilated cardiomyopathy (DCM) is a heart muscle disease that represents a major public health burden. While both common and rare genetic variants are known to affect DCM risk, previous genome-wide association studies (GWAS) have yielded only few loci and the molecular pathways underlying DCM remain largely unknown. Purpose We aimed to discover novel genetic loci for DCM by combining data from biobank and case-control studies, and by combining GWAS with multi-trait analyses. We also pursued multi-omics Mendelian randomization to nominate novel molecular pathways underlying DCM. Methods and results We first performed a GWAS of ICD-code defined DCM (N=1472 cases) from two large biobank datasets (UK Biobank and Mass General Brigham Biobank), and found good genetic correlation (rg=0.82, SE=0.22) and similar heritability estimates (h2=0.12, SE=0.03 vs. h2=0.14, SE=0.03) when compared with a previously published GWAS of recruited DCM cases. In a large meta-analysis across case-control and biobank datasets (N=4191 cases), we identified 10 loci significantly associated with DCM. We then integrated this novel meta-analysis into a multi-trait GWAS (MTAG) with MRI-derived left ventricular traits from the UK Biobank (LVEF and LVESV; N=36083), which yielded 44 lead variants at 36 significant loci. Novel loci overlap reported Mendelian cardiomyopathy genes (ACTN2, OBSCN, MYH7B), support previous mechanistic findings (HSPB8; heat shock protein family), and include genomic regions also arising from a recent GWAS of hypertrophic cardiomyopathy (notably, SVIL). We then performed Mendelian randomization analyses on the MTAG GWAS, using genetic instruments from published multi-omics datasets. Mendelian randomisation of the blood metabolome suggests potential causal roles for metabolites, notably those involved in the pentose phosphate pathway (ribitol; β=0.25, SE=0.04) and glycemic excursion (1,5-anhydroglucitol; β=0.08, SE=0.02). Analyses of the blood proteome suggest potential causal roles for several serum proteins, including proteins involved in redox balance (glutathione synthetase; β=-0.34, SE=0.05), mTORC1 signalling (LAMTOR3; β=0.08, SE=0.01), and the pentose phosphate pathway (G6PE; β=0.09, SE=0.02). Conclusion Our analyses link several new genomic regions with DCM, and implicate redox disbalance and alternative usage of (gluco)metabolic pathways as putative causal mechanisms of disease. Future functional studies could determine whether these represent actionable findings.

Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops

BackgroundWe consider two key problems in genomics involving multiple traits: multi-trait genome wide association studies (GWAS), where the goal is to detect genetic variants associated with the traits; and multi-trait genomic selection (GS), where the emphasis is on accurately predicting trait values. Multi-trait linear mixed models build on the linear mixed model to jointly model multiple traits. Existing estimation methods, however, are limited to the joint analysis of a small number of genotypes; in fact, most approaches consider one SNP at a time. Estimating multi-dimensional genetic and environment effects also results in considerable computational burden. Efficient approaches that incorporate regularization into multi-trait linear models (no random effects) have been recently proposed to identify genomic loci associated with multiple traits (Yu et al. in Multitask learning using task clustering with applications to predictive modeling and GWAS of plant varieties. arXiv:1710.01788, 2017; Yu et al in Front Big Data 2:27, 2019), but these ignore population structure and familial relatedness (Yu et al in Nat Genet 38:203–208, 2006).ResultsThis work addresses this gap by proposing a novel class of regularized multi-trait linear mixed models along with scalable approaches for estimation in the presence of high-dimensional genotypes and a large number of traits. We evaluate the effectiveness of the proposed methods using datasets in maize and sorghum diversity panels, and demonstrate benefits in both achieving high prediction accuracy in GS and in identifying relevant marker-trait associations.ConclusionsThe proposed regularized multivariate linear mixed models are relevant for both GWAS and GS. We hope that they will facilitate agronomy-related research in plant biology and crop breeding endeavors.

THU206 Constitutional Delay Of Puberty And Idiopathic Hypogonadotropic Hypogonadism: Differential Contributions Of Common Genetic Variants

Abstract Disclosure: M.F. Lippincott: None. E. Schafer: None. A.A. Hindman: None. R. Brauner: None. A. Delaney: None. K. McElreavey: None. J.E. Hall: None. J.N. Hirschhorn: None. M.R. Palmert: None. S.B. Seminara: None. R.M. Salem: None. Y. Chan: None. Introduction: Disorders of pubertal timing include self-limited constitutional delay of puberty (CDP) and longer lasting idiopathic hypogonadotropic hypogonadism (IHH). Both conditions have strong genetic influences. We sought to determine if common genetic variants that influence pubertal timing contribute to CDP and IHH. Hypothesis: IHH and CDP can sometimes be caused by the accumulation of common genetic variants that delay pubertal onset. Therefore, compared to controls, patients with CDP and IHH have higher polygenic risk scores (PRS) based on genome-wide association studies (GWAS) for pubertal timing. Methods: All research and data-sharing were conducted with participant/guardian consent and institutional ethical approval. We defined CDP as puberty starting between 12 and 18 y in girls and between 13.5 and 18 y in boys and IHH as low sex steroids and non-elevated gonadotropins at ≥18 y, both with no cause identified on standard clinical evaluation. We genotyped CDP cases (n=106) on the Illumina Infinium OmniExpress Exome 8-1v6 platform and retrieved array-matched control data from dbGaP (n=9,222). IHH cases (n=713) and controls from the Schizophrenia Psychiatric GWAS (n=1,868) were genotyped on the Infinium PsychArray. After data harmonization, quality control, and principal components analysis were performed on combined sets, genotype imputation was performed on all unrelated individuals of European ancestry using the Michigan Imputation Server. We downloaded summary statistics for: 1) GWAS meta-analysis for age at menarche (AAM), 2) multi-trait GWAS for male pubertal hallmarks, and 3) a negative control using GWAS for basal cell carcinoma (BCC). We calculated and compared PRS between cases and controls using PRS-CS and PRSice2, with the first 10 ancestral principal components and sex as covariates. We also performed sex-stratified analyses. Results: Both the CDP and the IHH cohorts had significantly higher PRS for male pubertal hallmarks compared to controls, but the difference was smaller for IHH (CDP Cohen’s d=0.59, p=2×10-10; IHH d=0.13, p=0.007). The CDP cohort also had a significantly higher PRS for AAM compared to controls (d=0.83, p=8×10-20), but the IHH cohort did not (d=0.08, p=0.06). Of note, sex-specific PRS provided greater discrimination between CDP cases and controls in stratified analyses of the corresponding sex (AAM in females: d=1.1, p=4×10-11; AAM in males d=0.72, p=8×10-11; male hallmarks in females: d=0.52, p=0.001; male hallmarks in males: d=0.63, p=2×10-8). No significant differences were observed between cases and controls for BCC PRS. Conclusions: Common genetic variants that influence pubertal timing in the general population contribute strongly to the genetics of CDP, with some sex-specificity, but only weakly to IHH. These findings support the hypothesis that the common-variant genetics of CDP and IHH are largely distinct. Presentation: Thursday, June 15, 2023

Uncovering the candidate genes related to sheep body weight using multi-trait genome-wide association analysis.

In sheep, body weight is an economically important trait. This study sought to map genetic loci related to weaning weight and yearling weight. To this end, a single-trait and multi-trait genome-wide association study (GWAS) was performed using a high-density 600 K single nucleotide polymorphism (SNP) chip. The results showed that 43 and 56 SNPs were significantly associated with weaning weight and yearling weight, respectively. A region associated with both weaning and yearling traits (OARX: 6.74-7.04 Mb) was identified, suggesting that the same genes could play a role in regulating both these traits. This region was found to contain three genes (TBL1X, SHROOM2 and GPR143). The most significant SNP was Affx-281066395, located at 6.94 Mb (p = 1.70 × 10-17), corresponding to the SHROOM2 gene. We also identified 93 novel SNPs elated to sheep weight using multi-trait GWAS analysis. A new genomic region (OAR10: 76.04-77.23 Mb) with 22 significant SNPs were discovered. Combining transcriptomic data from multiple tissues and genomic data in sheep, we found the HINT1, ASB11 and GPR143 genes may involve in sheep body weight. So, multi-omic anlaysis is a valuable strategy identifying candidate genes related to body weight.

Large-scale multitrait genome-wide association analyses identify hundreds of glaucoma risk loci

Glaucoma, a leading cause of irreversible blindness, is a highly heritable human disease. Previous genome-wide association studies have identified over 100 loci for the most common form, primary open-angle glaucoma. Two key glaucoma-associated traits also show high heritability: intraocular pressure and optic nerve head excavation damage quantified as the vertical cup-to-disc ratio. Here, since much of glaucoma heritability remains unexplained, we conducted a large-scale multitrait genome-wide association study in participants of European ancestry combining primary open-angle glaucoma and its two associated traits (total sample size over 600,000) to substantially improve genetic discovery power (263 loci). We further increased our power by then employing a multiancestry approach, which increased the number of independent risk loci to 312, with the vast majority replicating in a large independent cohort from 23andMe, Inc. (total sample size over 2.8 million; 296 loci replicated at P < 0.05, 240 after Bonferroni correction). Leveraging multiomics datasets, we identified many potential druggable genes, including neuro-protection targets likely to act via the optic nerve, a key advance for glaucoma because all existing drugs only target intraocular pressure. We further used Mendelian randomization and genetic correlation-based approaches to identify novel links to other complex traits, including immune-related diseases such as multiple sclerosis and systemic lupus erythematosus.

Genome-wide association study of multiyear dynamic growth traits in hybrid Liriodendron identifies robust genetic loci associated with growth trajectories.

The genetic factors underlying growth traits differ over time points or stages. However, most current studies of phenotypes at single time points do not capture all loci or explain the genetic differences underlying growth trajectories. Hybrid Liriodendron exhibits obvious heterosis and is widely cultivated, although its complex genetic mechanism underlying growth traits remains unknown. A genome-wide association study (GWAS) is an effective method for elucidating the genetic architecture by identifying genetic loci underlying complex quantitative traits. In the present study, using a GWAS, we identified robust loci associated with growth trajectories in hybrid Liriodendron populations. We selected 233 hybrid progenies derived from 25 crosses for resequencing, and measured their tree height (H) and diameter at breast height (DBH) for 11 consecutive years; 192 972 high-quality single nucleotide polymorphisms (SNPs) were obtained. The dynamics of the multiyear single-trait GWAS showed that year-specific SNPs predominated, and only five robust SNPs for DBH were identified in at least three different years. Multitrait GWAS analysis with model parameters as latent variables also revealed 62 SNPs for H and 52 for DBH associated with the growth trajectory, displaying different biomass accumulation patterns, among which four SNPs exerted pleiotropic effects. All identified SNPs also exhibited temporal variations in effect sizes and inheritance patterns potentially related to different growth and developmental stages. The haplotypes resulting from these significant SNPs might pyramid favorable loci, benefitting the selection of superior genotypes. The present study provides insights into the genetic architecture of dynamic growth traits and lays a basis for future molecular-assisted breeding.

Genome-wide multi-trait analysis of irritable bowel syndrome and related mental conditions identifies 38 new independent variants

BackgroundIrritable bowel syndrome (IBS) is a chronic disorder of gut-brain interaction frequently accompanied by mental conditions, including depression and anxiety. Despite showing substantial heritability and being partly determined by a genetic component, the genetic underpinnings explaining the high rates of comorbidity remain largely unclear and there are no conclusive data on the temporal relationship between them. Exploring the overlapping genetic architecture between IBS and mental conditions may help to identify novel genetic loci and biological mechanisms underlying IBS and causal relationships between them.MethodsWe quantified the genetic overlap between IBS, neuroticism, depression and anxiety, conducted a multi-trait genome-wide association study (GWAS) considering these traits and investigated causal relationships between them by using the largest GWAS to date.ResultsIBS showed to be a highly polygenic disorder with extensive genetic sharing with mental conditions. Multi-trait analysis of IBS and neuroticism, depression and anxiety identified 42 genome-wide significant variants for IBS, of which 38 are novel. Fine-mapping risk loci highlighted 289 genes enriched in genes upregulated during early embryonic brain development and gene-sets related with psychiatric, digestive and autoimmune disorders. IBS-associated genes were enriched for target genes of anti-inflammatory and antirheumatic drugs, anesthetics and opioid dependence pharmacological treatment. Mendelian-randomization analysis accounting for correlated pleiotropy identified bidirectional causal effects between IBS and neuroticism and depression and causal effects of the genetic liability of IBS on anxiety.ConclusionsThese findings provide evidence of the polygenic architecture of IBS, identify novel genome-wide significant variants for IBS and extend previous knowledge on the genetic overlap and relationship between gastrointestinal and mental disorders.

Genome-wide association study reveals candidate genes for pollution excreta traits in pigs.

Excreta traits comprise a very important characteristic in breeding that have been neglected for a long time. With the growth of intensive pig farming, plenty of environment problems have been raised, and people have begun to pay attention to pig excreta behaviors from genetics and breeding perspectives. However, the genetic architecture of excreta traits remains unclear. To investigate the genetic architecture of excreta traits in pigs, eight excreta traits and feed conversion ratio (FCR) were analyzed in this study. We performed genome-wide association studies (GWASs) on 213 Yorkshire pigs and estimated genetic parameters for a total number of 290 pigs, comprising 213 Yorkshire, 52 Landrace and 25 Duroc. After analysis, eight and 22 genome-wide significant SNPs were detected for FCR and the eight excreta traits in single-trait GWASs separately, and 18 were detected in a multi-trait meta-analysis for excreta traits, six of which were detected in both the single-trait and the multi-trait GWAS. Eighty, 182 and 133 genes were detected within 1 Mb of the genome-wide significant SNPs for FCR, excreta traits and multi-trait meta-analysis, respectively. Five candidate genes (BCKDC, DBT, ANKRD7, SHPRH and HCRT) with biochemical and physiological effects relevant to feed efficiency and excreta traits might be interesting markers for future breeding. Meanwhile, functional enrichment analysis indicates that most of the significant pathways are associated with the glutathione catabolic process, DNA topological change and replication fork protection complex. This study reveals the architecture of excreta traits in commercial pigs and offers an opportunity for decreasing the pollution from excreta using genomic selection in pigs.

Multi-trait genome-wide association study identifies a novel endometrial cancer risk locus that associates with testosterone levels

To detect novel endometrial cancer risk variants, we leveraged information from endometrial cancer risk factors in a multi-trait GWAS analysis. We first assessed causal relationships between established and suspected endometrial cancer risk factors, and endometrial cancer using Mendelian randomization. Following multivariable analysis, five independent risk factors (waist circumference, testosterone levels, sex hormone binding globulin levels, age at menarche, and age at natural menopause) were included in a multi-trait Bayesian GWAS analysis. We identified three potentially novel loci that associate with endometrial cancer risk, one of which (7q22.1) replicated in an independent endometrial cancer GWAS dataset and was genome-wide significant in a meta-analysis. This locus may affect endometrial cancer risk through altered testosterone levels. Consistent with this, we observed colocalization between the signals for endometrial cancer risk and expression of CYP3A7, a gene involved in testosterone metabolism. Thus, our findings suggest opportunities for hormone therapy to prevent or treat endometrial cancer.

Integrated Single-Trait and Multi-Trait GWASs Reveal the Genetic Architecture of Internal Organ Weight in Pigs

Internal organ weight is an essential indicator of growth status as it reflects the level of growth and development in pigs. However, the associated genetic architecture has not been well explored because phenotypes are difficult to obtain. Herein, we performed single-trait and multi-trait genome-wide association studies (GWASs) to map the genetic markers and genes associated with six internal organ weight traits (including heart weight, liver weight, spleen weight, lung weight, kidney weight, and stomach weight) in 1518 three-way crossbred commercial pigs. In summation, single-trait GWASs identified a total of 24 significant single- nucleotide polymorphisms (SNPs) and 5 promising candidate genes, namely, TPK1, POU6F2, PBX3, UNC5C, and BMPR1B, as being associated with the six internal organ weight traits analyzed. Multi-trait GWAS identified four SNPs with polymorphisms localized on the APK1, ANO6, and UNC5C genes and improved the statistical efficacy of single-trait GWASs. Furthermore, our study was the first to use GWASs to identify SNPs associated with stomach weight in pigs. In conclusion, our exploration of the genetic architecture of internal organ weights helps us better understand growth traits, and the key SNPs identified could play a potential role in animal breeding programs.

GWAS Explorer: an open-source tool to explore, visualize, and access GWAS summary statistics in the PLCO Atlas

The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial is a prospective cohort study of nearly 155,000 U.S. volunteers aged 55–74 at enrollment in 1993–2001. We developed the PLCO Atlas Project, a large resource for multi-trait genome-wide association studies (GWAS), by genotyping participants with available DNA and genomic consent. Genotyping on high-density arrays and imputation was performed, and GWAS were conducted using a custom semi-automated pipeline. Association summary statistics were generated from a total of 110,562 participants of European, African and Asian ancestry. Application programming interfaces (APIs) and open-source software development kits (SKDs) enable exploring, visualizing and open data access through the PLCO Atlas GWAS Explorer website, promoting Findable, Accessible, Interoperable, and Re-usable (FAIR) principles. Currently the GWAS Explorer hosts association data for 90 traits and >78,000,000 genomic markers, focusing on cancer and cancer-related phenotypes. New traits will be posted as association data becomes available. The PLCO Atlas is a FAIR resource of high-quality genetic and phenotypic data with many potential reuse opportunities for cancer research and genetic epidemiology.

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

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. 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. 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. 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.

Pleiotropic architectures of porcine immune and growth trait pairs revealed by a self-product-based transcriptome method.

Pleiotropy is an important biological phenomenon with complicated genetic architectures for multiple traits. To date, pleiotropy has been mainly identified by multi-trait genome-wide association studies, but this method has its disadvantages, and new developments for pleiotropy detection methods are needed. Here we define a novel metric, self-product, to measure individual-level co-variation of two traits, and develop a novel self-product-based transcriptome method to detect pleiotropic genes (PGs). Our method was tested using four immune-growth trait pairs and four immune-immune trait pairs in pigs. Comparative transcriptome analyses identified hundreds of candidate PGs related to eight trait pairs from two tails of self-product distribution. Gene Ontology enrichment analysis indicated that most of identified PGs were involved in immune- or growth-related biological processes. We established PG interaction networks to exhibit core genes shared by eight trait pairs, of which CCL5 and IL-10 genes were the hub genes. Genetic association analyses showed that SmaI-polymorphisms of CCL5 and IL-10 genes had significant associations with phenotypic co-variations of multiple trait pairs, indicating that the variants in pleiotropic genes were also pleiotropic variants. Taken together, the validity of our proposed method was preliminarily verified, and our findings provide new insights into the genetic basis of pleiotropic architectures of immune and growth trait pairs in pigs.

Redundancy analysis, genome-wide association studies and the pigmentation of brown trout (Salmo trutta L.).

The association of molecular variants with phenotypic variation is a main issue in biology, often tackled with genome-wide association studies (GWAS). GWAS are challenging, with increasing, but still limited, use in evolutionary biology. We used redundancy analysis (RDA) as a complimentary ordination approach to single- and multitrait GWAS to explore the molecular basis of pigmentation variation in brown trout (Salmo trutta) belonging to wild populations impacted by hatchery fish. Based on 75,684 single nucleotide polymorphic (SNP) markers, RDA, single- and multitrait GWAS allowed the extraction of 337 independent colour patterning loci (CPLs) associated with trout pigmentation traits, such as the number of red and black spots on flanks. Collectively, these CPLs (i) mapped onto 35 out of 40 brown trout linkage groups indicating a polygenic genomic architecture of pigmentation, (ii) were found to be associated with 218 candidate genes, including 197 genes formerly mentioned in the literature associated to skin pigmentation, skin patterning, differentiation or structure notably in a close relative, the rainbow trout (Onchorhynchus mykiss), and (iii) related to functions relevant to pigmentation variation (e.g., calcium- and ion-binding, cell adhesion). Annotated CPLs include genes with well-known pigmentation effects (e.g., PMEL, SLC45A2, SOX10), but also markers associated with genes formerly found expressed in rainbow or brown trout skins. RDA was also shown to be useful to investigate management issues, especially the dynamics of trout pigmentation submitted to several generations of hatchery introgression.

Quantitative trait loci related to growth and wood quality traits in Eucalyptus grandis W. Hill identified through single- and multi-trait genome-wide association studies

Quantitative trait loci related to growth and wood quality traits in Eucalyptus grandis W. Hill identified through single- and multi-trait genome-wide association studies

Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond

Opioid addiction (OA) is moderately heritable, yet only rs1799971, the A118G variant in OPRM1, has been identified as a genome-wide significant association with OA and independently replicated. We applied genomic structural equation modeling to conduct a GWAS of the new Genetics of Opioid Addiction Consortium (GENOA) data together with published studies (Psychiatric Genomics Consortium, Million Veteran Program, and Partners Health), comprising 23,367 cases and effective sample size of 88,114 individuals of European ancestry. Genetic correlations among the various OA phenotypes were uniformly high (rg > 0.9). We observed the strongest evidence to date for OPRM1: lead SNP rs9478500 (p = 2.56 × 10–9). Gene-based analyses identified novel genome-wide significant associations with PPP6C and FURIN. Variants within these loci appear to be pleiotropic for addiction and related traits.