SNP Effects Research Articles

85 Genomic regions associated with electronic measures indicative of structural soundness in pigs

Abstract The objective was to determine the genetic factors influencing mobility traits recorded from 5 mo-old gilts. Mobility was measured using a pressure-sensing mat (GAIT4) and 7 d of video recorded activity (NUtrack). Gilts (n = 3,659) were evaluated for the study, but only 2,172 gilts had data for both systems. GAIT4 system creates a series of measurements for each foot related to pressure, duration and step length of each foot generating a lameness score for each foot. Traits studied summarized values for all 4 feet: average stride length, average stance time, standard deviation of stance time, lameness score and total scaled pressure. NUtrack measurements were rotations, velocity, and distance walked as well as the time spent eating, sitting, standing, lying sternal and lying lateral. All NUtrack traits were standardized to a mean of zero and standard deviation of 1.0 based on each pen-day subgroup. Mixed model analyses were conducted in R fitting animal as a random effect and fixed effects of breed of sire and contemporary group, with day included for NUtrack data. ssGBLUP analyses were conducted in WOMBAT using animal effects from R models as phenotypes. Three generations of pedigree were included and genotypic data from the NeoGen Porcine 50K beadchip on 3,186 animals representing 60% of gilts with NUtrack data, 67% of gilts with GAIT4 data and most parents (>99%). Single trait analyses were conducted for all traits, gEBVs estimated and then SNP effects estimated by back-solving equations. Significance values were determined by 5,000 permutations and a Bonferroni adjustment factor applied. Estimates of heritability ranged from 0.08 to 0.41 and traits from the NUtrack system tended to have greater heritability than GAIT4 traits. There were 343 significant SNP effects in 39 unique chromosomal regions. There were 133 significant SNP associations located on chromosome 14 between 45-47 Mb associated with rotations, distance and times standing and lying. Pathway analysis identified proteoglycan and apelin pathways to be over-represented based on associations with SNP located at 2:45, 2:51, 13:79 and 14:40 (SSC:Mb). Proteoglycans are a subunit of cartilage and apelins affect blood flow and angiogenesis which make them candidates for the development of osteochondrosis, a leading cause of lameness in pigs. These results will facilitate selection of more robust animals capable of withstanding commercial production environments and group sow housing. USDA is an equal opportunity employer.

Weighed ssGBLUP enhances the genomic prediction accuracy for milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Previous studies have shown that milk citrate predicted by milk mid-infrared spectra (MIR) is strongly affected by a few genomic regions. This study aimed to explore the effect of the weighted single-step GBLUP on the accuracy of genomic prediction (GP) for MIR-predicted milk citrate in early lactation Holstein cows. A total of 134,517 test-day predicted milk citrate collected within the first 50 d in milk on 52,198 Holstein cows from the first 5 parities were used. There were 122,218 animals in the pedigree, of them 4,479 had genotypic data for 566,170 SNPs. Two data sets (partial and whole data sets) were used to verify whether the accuracy of GP is improved using the following different methods. The (genomic) estimated breeding values (EBV or GEBV) in the partial and whole data sets were estimated by pedigree-based BLUP (ABLUP), single-step GBLUP (ssGBLUP, pedigree-genomic combined using no weight for SNP), weighted ssGBLUP (WssGBLUP, pedigree-genomic combined using weighted SNP), respectively. The difference between the 2 data sets is that the phenotypic data from 2017 to 2019 in the partial data set were set as missing values. 181 youngest cows with genomic were selected as the validation population. A linear regression method was used to compare EBV (GEBV) predicted for partial and whole data sets. The accuracies of GP for ABLUP and ssGBLUP were 0.42 and 0.70, respectively. The accuracies of GP for WssGBLUP in the 5 iterations with different CT (constant) values (determines departure from normality for SNP effects) ranged from 0.70 to 0.86. This study showed that weighted SNP is beneficial in improving prediction accuracy for predicted milk citrate.

Marker effect p-values for single-step GWAS with the algorithm for proven and young in large genotyped populations

BackgroundSingle-nucleotide polymorphism (SNP) effects can be backsolved from ssGBLUP genomic estimated breeding values (GEBV) and used for genome-wide association studies (ssGWAS). However, obtaining p-values for those SNP effects relies on the inversion of dense matrices, which poses computational limitations in large genotyped populations. In this study, we present a method to approximate SNP p-values for ssGWAS with many genotyped animals. This method relies on the combination of a sparse approximation of the inverse of the genomic relationship matrix (GAPY-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{G}}_{\\mathbf{A}\\mathbf{P}\\mathbf{Y}}^\\mathbf{-1}$$\\end{document}) built with the algorithm for proven and young (APY\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{APY}$$\\end{document}) and an approximation of the prediction error variance of SNP effects which does not require the inversion of the left-hand side (LHS) of the mixed model equations. To test the proposed p-value computing method, we used a reduced genotyped population of 50K genotyped animals and compared the approximated SNP p-values with benchmark p-values obtained with the direct inverse of LHS built with an exact genomic relationship matrix (G-1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbf{G}}^\\mathbf{-1})$$\\end{document}. Then, we applied the proposed approximation method to obtain SNP p-values for a larger genotyped population composed of 450K genotyped animals.ResultsThe same genomic regions on chromosomes 7 and 20 were identified across all p-value computing methods when using 50K genotyped animals. In terms of computational requirements, obtaining p-values with the proposed approximation reduced the wall-clock time by 38 times and the memory requirement by ten times compared to using the exact inversion of the LHS. When the approximation was applied to a population of 450K genotyped animals, two new significant regions on chromosomes 6 and 14 were uncovered, indicating an increase in GWAS detection power when including more genotypes in the analyses. The process of obtaining p-values with the approximation and 450K genotyped individuals took 24.5 wall-clock hours and 87.66GB of memory, which is expected to increase linearly with the addition of noncore genotyped individuals.ConclusionsWith the proposed method, obtaining p-values for SNP effects in ssGWAS is computationally feasible in large genotyped populations. The computational cost of obtaining p-values in ssGWAS may no longer be a limitation in extensive populations with many genotyped animals.

Interpreting SNP heritability in admixed populations.

SNP heritability is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability ( ), being equal to it if all causal variants are known. Despite the simple intuition behind , its interpretation and equivalence to is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation in estimates because of confounding due to linkage disequilibrium (LD) or shared environment. Here we use analytical theory and simulations to demonstrate that estimates can be biased in admixed populations, even in the absence of confounding and even if all causal variants are known. This is because admixture generates LD, which contributes to the genetic variance, and therefore to heritability. Genome-wide restricted maximum likelihood (GREML) does not capture this contribution leading to under- or over-estimates of relative to , depending on the genetic architecture. In contrast, Haseman-Elston (HE) regression exaggerates the LD contribution leading to biases in the opposite direction. For the same reason, GREML and HE estimates of local ancestry heritability are also biased. We describe this bias in and as a function of admixture history and the genetic architecture of the trait and show that it can be recovered under some conditions. We clarify the interpretation of in admixed populations and discuss its implication for genome-wide association studies and polygenic prediction.

The contribution of silencer variants to human diseases

BackgroundAlthough disease-causal genetic variants have been found within silencer sequences, we still lack a comprehensive analysis of the association of silencers with diseases. Here, we profiled GWAS variants in 2.8 million candidate silencers across 97 human samples derived from a diverse panel of tissues and developmental time points, using deep learning models.ResultsWe show that candidate silencers exhibit strong enrichment in disease-associated variants, and several diseases display a much stronger association with silencer variants than enhancer variants. Close to 52% of candidate silencers cluster, forming silencer-rich loci, and, in the loci of Parkinson’s-disease-hallmark genes TRIM31 and MAL, the associated SNPs densely populate clustered candidate silencers rather than enhancers displaying an overall twofold enrichment in silencers versus enhancers. The disruption of apoptosis in neuronal cells is associated with both schizophrenia and bipolar disorder and can largely be attributed to variants within candidate silencers. Our model permits a mechanistic explanation of causative SNP effects by identifying altered binding of tissue-specific repressors and activators, validated with a 70% of directional concordance using SNP-SELEX. Narrowing the focus of the analysis to individual silencer variants, experimental data confirms the role of the rs62055708 SNP in Parkinson’s disease, rs2535629 in schizophrenia, and rs6207121 in type 1 diabetes.ConclusionsIn summary, our results indicate that advances in deep learning models for the discovery of disease-causal variants within candidate silencers effectively “double” the number of functionally characterized GWAS variants. This provides a basis for explaining mechanisms of action and designing novel diagnostics and therapeutics.

Viral hepatitis moderates the impact of TGFB1 on neurocognitive impairment.

Recent studies have identified a correlation between chronic viral hepatitis and cognitive impairment, yet the underlying mechanisms remain unclear. This study investigated the influence of TGFB1 genetic polymorphisms on cognitive function in individuals with and without hepatitis infections, hypothesizing that these polymorphisms and the viral hepatitis-induced inflammatory environment interact to affect cognitive abilities. Participants (173 with viral hepatitis and 258 healthy controls) were recruited. Genotyping of TGFB1 SNPs was performed using the C2-58 Axiom Genome-Wide TWB 2.0 Array Plate. Cognitive function was assessed using the MMSE and MoCA tests. Our results showed that healthy individuals carrying the C allele of rs2241715 displayed better performance in sentence writing (p = 0.020) and language tasks (p = 0.022). Notably, viral hepatitis was found to moderate the impact of the rs2241715 genotype on language function (p = 0.002). Similarly, those carrying the T allele of rs10417924 demonstrated superior orientation to time (p = 0.002), with viral hepatitis modifying the influence of the SNP on this particular cognitive function (p = 0.010). Our findings underscore the significant role of TGFβ1 in cognitive function and the moderating impact of viral hepatitis on TGFB1 SNP effects. These findings illuminate the potential of TGFB1 as a therapeutic target for cognitive impairment induced by viral hepatitis, thus broadening our understanding of TGFβ1 functionality in the pathogenesis of neurodegeneration.

Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework

BackgroundBiologically annotated neural networks (BANNs) are feedforward Bayesian neural network models that utilize partially connected architectures based on SNP-set annotations. As an interpretable neural network, BANNs model SNP and SNP-set effects in their input and hidden layers, respectively. Furthermore, the weights and connections of the network are regarded as random variables with prior distributions reflecting the manifestation of genetic effects at various genomic scales. However, its application in genomic prediction has yet to be explored.ResultsThis study extended the BANNs framework to the area of genomic selection and explored the optimal SNP-set partitioning strategies by using dairy cattle datasets. The SNP-sets were partitioned based on two strategies–gene annotations and 100 kb windows, denoted as BANN_gene and BANN_100kb, respectively. The BANNs model was compared with GBLUP, random forest (RF), BayesB and BayesCπ through five replicates of five-fold cross-validation using genotypic and phenotypic data on milk production traits, type traits, and one health trait of 6,558, 6,210 and 5,962 Chinese Holsteins, respectively. Results showed that the BANNs framework achieves higher genomic prediction accuracy compared to GBLUP, RF and Bayesian methods. Specifically, the BANN_100kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP, RF, BayesB and BayesCπ across all traits. The average accuracy improvements of BANN_100kb over GBLUP, RF, BayesB and BayesCπ were 4.86%, 3.95%, 3.84% and 1.92%, and the accuracy of BANN_gene was improved by 3.75%, 2.86%, 2.73% and 0.85% compared to GBLUP, RF, BayesB and BayesCπ, respectively across all seven traits. Meanwhile, both BANN_100kb and BANN_gene yielded lower overall mean square error values than GBLUP, RF and Bayesian methods.ConclusionOur findings demonstrated that the BANNs framework performed better than traditional genomic prediction methods in our tested scenarios, and might serve as a promising alternative approach for genomic prediction in dairy cattle.

Genomic Regions Associated with Resistance to Gastrointestinal Parasites in Australian Merino Sheep.

The objective of this study was to identify genomic regions and genes associated with resistance to gastrointestinal nematodes in Australian Merino sheep in Uruguay, using the single-step GWAS methodology (ssGWAS), which is based on genomic estimated breeding values (GEBVs) obtained from a combination of pedigree, genomic, and phenotypic data. This methodology converts GEBVs into SNP effects. The analysis included 26,638 animals with fecal egg count (FEC) records obtained in two independent parasitic cycles (FEC1 and FEC2) and 1700 50K SNP genotypes. The comparison of genomic regions was based on genetic variances (gVar(%)) explained by non-overlapping regions of 20 SNPs. For FEC1 and FEC2, 18 and 22 genomic windows exceeded the significance threshold (gVar(%) ≥ 0.22%), respectively. The genomic regions with strong associations with FEC1 were located on chromosomes OAR 2, 6, 11, 21, and 25, and for FEC2 on OAR 5, 6, and 11. The proportion of genetic variance attributed to the top windows was 0.83% and 1.9% for FEC1 and FEC2, respectively. The 33 candidate genes shared between the two traits were subjected to enrichment analysis, revealing a marked enrichment in biological processes related to immune system functions. These results contribute to the understanding of the genetics underlying gastrointestinal parasite resistance and its implications for other productive and welfare traits in animal breeding programs.

Single nucleotide polymorphism profile for quantitative trait nucleotide in populations with small effective size and its impact on mapping and genomic predictions.

Increasing SNP density by incorporating sequence information only marginally increases prediction accuracies of breeding values in livestock. To find out why, we used statistical models and simulations to investigate the shape of distribution of estimated SNP effects (a profile) around quantitative trait nucleotides (QTNs) in populations with a small effective population size (Ne). A QTN profile created by averaging SNP effects around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles. However, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN. The QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN was contained in ± 1/Ne Morgan interval around the QTN, corresponding to 2 Mb in cattle and 5 Mb in pigs and chickens. With such large intervals, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely spaced QTN, and spurious signals. However, small Ne allows for accurate predictions with large data even without QTN identification because QTNs are accounted for by QTN profiles if SNP density is sufficient to saturate the segments.

Genotypic and Phenotypic Spectrum of maple syrup urine disease in Zhejiang of China.

Maple Syrup Urine Disease (MSUD) is an autosomal recessive metabolic disorder originating from defects in the branched-chain α-ketoacid dehydrogenase (BCKDH) complex encoded by BCKDHA, BCKDHB, and DBT. This condition presents a spectrum of symptoms and potentially fatal outcomes. Although numerous mutations in the BCKDH complex genes associated with MSUD have been identified, the relationship between specific genotypes remains to be fully elucidated. Our objective was to predict the pathogenicity of these genetic mutations and establish potential links between genotypic alterations and the clinical phenotypes of MSUD. Retrospective population-based cohort. We analyzed 20 MSUD patients from the Children's Hospital at Zhejiang University School of Medicine (Hangzhou, China), recorded from January 2010 to May 2023. Patients' blood samples were collected by heel-stick through neonatal screening, and amino acid profiles were measured by tandem mass spectrometry. In silico methods were employed to assess the pathogenicity, stability, and biophysical properties. Various computation tools were utilized for assessment, namely PredictSNP, MAGPIE, iStable, Align GVGD, ConSurf and SNP effect. We detected 25 distinct mutations, including 12 novel mutations. The BCKDHB gene was the most commonly affected (53.3%) compared to the BCKDHA gene (20.0%) and DBT gene (26.7%). In silico webservers predicted all novel mutations were disease-causing. This study highlights the genetic complexity of MSUD and underscores the importance of early detection and intervention. Integrating neonatal screening with advanced sequencing methodologies is pivotal in ensuring precise diagnosis and effective management of MSUD, thereby significantly improving the prognosis for individuals afflicted with this condition.

Effects of COVID-19 on the cardiovascular system: A mendelian randomization study

Infections with the coronavirus disease 2019 (COVID-19) and disorders of the heart and blood vessels are causally related. To ascertain the causal relationship between COVID-19 and cardiovascular disease (CVD), we carried out a Mendelian randomization (MR) study through a method known as inverse variance weighting (IVW). When analyzing multiple SNPs, MR can meta-aggregate the effects of multiple loci by using IVW meta-pooling method. The weighted median (WM) is the median of the distribution function obtained by ranking all individual SNP effect values according to their weights. WM yields robust estimates when at least 50% of the information originates from valid instrumental variables (IVs). Directed gene pleiotropy in the included IVs is permitted because MR–Egger does not require a regression straight line through the origin. For MR estimation, IVW, WM and MR-Egger were employed. Sensitivity analysis was conducted using funnel plots, Cochran's Q test, MR–Egger intercept test, MR–PRESSO, and leave-one-out analysis. SNPs related to exposure to COVID-19 and CVD were compiled. CVD for COVID-19 infection, COVID-19 laboratory/self-reported negative, and other very severe respiratory diagnosis and population were randomly assigned using MR. The COVID-19 laboratory/self-reported negative results and other very severe respiratory confirmed cases versus MR analysis of CVD in the population (p ​> ​0.05); COVID-19 infection to CVD (p ​= ​0.033, OR ​= ​1.001, 95%CI: 1.000–1.001); and the MR–Egger results indicated that COVID-19 infection was associated with CVD risk. This MR study provides preliminary evidence for the validity of the causal link between COVID-19 infection and CVD.

Assessment of causal relationships between omega-3 and omega-6 polyunsaturated fatty acids in autoimmune rheumatic diseases: a brief research report from a Mendelian randomization study.

Currently, the association between the consumption of polyunsaturated fatty acids (PUFAs) and the susceptibility to autoimmune rheumatic diseases (ARDs) remains conflict and lacks substantial evidence in various clinical studies. To address this issue, we employed Mendelian randomization (MR) to establish causal links between six types of PUFAs and their connection to the risk of ARDs. We retrieved summary-level data on six types of PUFAs, and five different types of ARDs from publicly accessible GWAS statistics. Causal relationships were determined using a two-sample MR analysis, with the IVW approach serving as the primary analysis method. To ensure the reliability of our research findings, we used four complementary approaches and conducted multivariable MR analysis (MVMR). Additionally, we investigated reverse causality through a reverse MR analysis. Our results indicate that a heightened genetic predisposition for elevated levels of EPA (ORIVW: 0.924, 95% CI: 0.666-1.283, P IVW = 0.025) was linked to a decreased susceptibility to psoriatic arthritis (PsA). Importantly, the genetically predicted higher levels of EPA remain significantly associated with an reduced risk of PsA, even after adjusting for multiple testing using the FDR method (P IVW-FDR-corrected = 0.033) and multivariable MR analysis (P MV-IVW < 0.05), indicating that EPA may be considered as the risk-protecting PUFAs for PsA. Additionally, high levels of LA showed a positive causal relationship with a higher risk of PsA (ORIVW: 1.248, 95% CI: 1.013-1.538, P IVW = 0.037). It is interesting to note, however, that the effects of these associations were weakened in our MVMR analyses, which incorporated adjustment for lipid profiles (P MV-IVW > 0.05) and multiple testing using the FDR method (P IVW-FDR-corrected = 0.062). Moreover, effects of total omega-3 PUFAs, DHA, EPA, and LA on PsA, were massively driven by SNP effects in the FADS gene region. Furthermore, no causal association was identified between the concentrations of other circulating PUFAs and the risk of other ARDs. Further analysis revealed no significant horizontal pleiotropy and heterogeneity or reverse causality. Our comprehensive MR analysis indicated that EPA is a key omega-3 PUFA that may protect against PsA but not other ARDs. The FADS2 gene appears to play a central role in mediating the effects of omega-3 PUFAs on PsA risk. These findings suggest that EPA supplementation may be a promising strategy for preventing PsA onset. Further well-powered epidemiological studies and clinical trials are warranted to explore the potential mechanisms underlying the protective effects of EPA in PsA.

Association of whole-person eigen-polygenic risk scores with Alzheimer's disease.

Late-Onset Alzheimer's Disease (LOAD) is a heterogeneous neurodegenerative disorder with complex etiology and high heritability. Its multifactorial risk profile and large portions of unexplained heritability suggest the involvement of yet unidentified genetic risk factors. Here we describe the "whole person" genetic risk landscape of polygenic risk scores for 2218 traits in 2044 elderly individuals and test if novel eigen-PRSs derived from clustered subnetworks of single-trait PRSs can improve the prediction of LOAD diagnosis, rates of cognitive decline, and canonical LOAD neuropathology. Network analyses revealed distinct clusters of PRSs with clinical and biological interpretability. Novel eigen-PRSs (ePRS) from these clusters significantly improved LOAD-related phenotypes prediction over current state-of-the-art LOAD PRS models. Notably, an ePRS representing clusters of traits related to cholesterol levels was able to improve variance explained in a model of the brain-wide beta-amyloid burden by 1.7% (likelihood ratio test P = 9.02 × 10-7). All associations of ePRS with LOAD phenotypes were eliminated by the removal of APOE-proximal loci. However, our association analysis identified modules characterized by PRSs of high cholesterol and LOAD. We believe this is due to the influence of the APOE region from both PRSs. We found significantly higher mean SNP effects for LOAD in the intersecting APOE region SNPs. Combining genetic risk factors for vascular traits and dementia could improve current single-trait PRS models of LOAD, enhancing the use of PRS in risk stratification. Our results are catalogued for the scientific community, to aid in generating new hypotheses based on our maps of clustered PRSs and associations with LOAD-related phenotypes.

Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank.

Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP's main and SNP×sex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation.

Genome-wide association analyses reveal genotype-by-environment interactions of growth and organ weights in gilthead seabream (Sparus aurata)

Gilthead seabream is one of the most important fish species for Mediterranean aquaculture. More than 80% of the genetically improved fingerlings of this species originate from a single country, Greece. A previous study revealed moderate genotype-by-environment (G x E) interactions for growth traits and organ weights between commercial production sites in Greece and Spain. The main objectives of this study were to identify QTLs and genes associated with growth traits and organ weights in gilthead seabream and to identify the biological processes and pathways controlling these traits under different temperature environments. For growth traits, we found fourteen genome-wide suggestive SNP effects in Spain. A strong peak between 1.97 Mb to 4.69 Mb on chromosome (chr) 22 was shared among three growth traits (harvest weight, fillet weight and thermal growth coefficient). Fourteen genome-wide suggestive SNP effects were also identified in Greece. However, none overlap with those found in Spain. Two SNPs on chr5 (chr5:5729959, chr5:2336903) and two SNPs on chr6 (chr6:1957732, chr6:3659811) were associated with all three growth traits. For organ weights, a total of 15 SNPs were associated above the suggestive threshold in Spain. Four SNPs (chr22:1966940, chr22:6094383, chr22:67838, chr15:16798259) are shared between viscera weight and liver weight, while one SNP (chr22:67838) is shared among viscera, liver and heart weight. Interestingly, three of these four SNPs (chr22:1966940, chr22:67838, and chr15:16798259) also overlap with significant SNPs associated with growth traits. None of the SNPs associated with viscera weight, liver weight and heart weight are shared with Greece. GO and KEGG functional enrichment analyses showed that light absorption and ECM-receptor interaction are significantly enriched with growth traits QTLs in Spain but not in Greece. For organ weights, response to stimulus is the most prominent process in Spain, while cell adhesion, and regulation of phosphorylation are more prominent in Greece. Genomic architectures for growth traits and organ weights differ between environments. These findings not only explain part of the G x E interactions, but also give insight into how genetics determines differences in how environmental conditions affect growth and organ weights.

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization.

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction.

Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

Evaluating three strategies of genome-wide association analysis for integrating data from multiple populations.

In livestock, genome-wide association studies (GWAS) are usually conducted in a single population (single-GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta-analysis of GWAS (meta-GWAS) and mega-analysis of GWAS (mega-GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single-GWAS, meta-GWAS, and mega-GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p-values (pC ). In Yorkshire, with the largest sample size, mega-GWAS, meta-GWAS and single-GWAS detected 149, 38 and 20 significant SNPs (pC < 1E-5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, pC of SNPs from mega-GWAS was the lowest, followed by meta-GWAS and single-GWAS. The correlation of pC among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta-GWAS and mega-GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega-GWAS versus single-GWAS.

The RAAS system SNPs polymorphism is associated with essential hypertension risk in rural areas in northern China.

Objectives: Epidemiological evidence has shown that genetics and environment are associated with the risk of hypertension. However, the specific SNP effects of a cluster of crucial genes in the RAAS system on the risk of hypertension are unclear. Methods: A case-control study was performed on the baseline participants of Environment and Chronic Disease in Rural Areas of Heilongjiang China (ECDRAHC) study. According to the inclusion and exclusion criteria, 757 subjects (428 hypertensive patients) were enrolled. A total of 32 SNP sites and related haplotypes, involved in AGT (angiotensinogen), ACE (angiotensin-converting enzyme), AGTR1, CYP11B2 (aldosterone-synthase), LDLR (low-density lipoprotein receptor), LRP5 (low-density lipoprotein receptor associated protein 5), LRP6 (low-density lipoprotein receptor associated protein 6), PPARG (peroxisome proliferator-activated receptor gamma) and ACE2 (angiotensin-converting enzyme 2) genes which exert important roles in renin-angiotensin-aldosterone system (RAAS) system were analyzed. Furthermore, a polygenic scoring model was established to assess individual risk of developing hypertension based on the comprehensive SNPs effects in genes related the RAAS system. Results: After controlling the impact of confounding factors, multivariate logistic regression analysis revealed that the distribution of AGT/rs5046, LRP6/rs12823243 and ACE2/rs2285666 was associated with susceptibility to essential hypertension. In genetic score model, the score > -0.225 had a higher risk, the OR (95%CI) was 1.229 (1.110, 1.362). Conclusions: To the best of our knowledge, this is the first time a hypertension risk scoring model on RAAS associated gene cluster has been constructed, which will provide a novel approach for prevention and control of essential hypertension.