SNP Effects Research Articles

Correcting for volunteer bias in GWAS increases SNP effect sizes and heritability estimates

Selection bias in genome-wide association studies (GWASs) due to volunteer-based sampling (volunteer bias) is poorly understood. The UK Biobank (UKB), one of the largest and most widely used cohorts, is highly selected. Using inverse probability (IP) weights we estimate inverse probability weighted GWAS (WGWAS) to correct GWAS summary statistics in the UKB for volunteer bias. Our IP weights were estimated using UK Census data – the largest source of population-representative data – made representative of the UKB’s sampling population. These weights have a substantial SNP-based heritability of 4.8% (s.e. 0.8%), suggesting they capture volunteer bias in GWAS. Across ten phenotypes, WGWAS yields larger SNP effect sizes, larger heritability estimates, and altered gene-set tissue expression, despite decreasing the effective sample size by 62% on average, compared to GWAS. The impact of volunteer bias on GWAS results varies by phenotype. Traits related to disease, health behaviors, and socioeconomic status are most affected. We recommend that GWAS consortia provide population weights for their data sets, or use population-representative samples.

Erosion of estimated genomic breeding values with generations is due to long distance associations between markers and QTL

BackgroundMost validation studies of genomic evaluations on candidates (prior to observing phenotypes) present inflation of their predicted breeding values, i.e., regression coefficients of their later observed phenotypes on the early predictions are smaller than one. The aim of this study was to show that this inflation pattern reflects at least partly long-distance associations between markers and quantitative trait loci (QTL) in the reference population and to propose methods to estimate the corresponding “erosion” coefficient.ResultsAcross-chromosome linkage disequilibrium (LD) is observed in different dairy cattle breeds, being a result from limited effective population size and from relationships within the reference population. Due to this long distance LD, the estimated SNP effects capture non-zero contributions from distant QTLs, some located on other chromosomes than the SNP itself. Therefore, corresponding SNP effects are partly lost in the next generations and we refer to this loss as “erosion”. With the concept of QTL contribution to SNP effects derived from mixed model equations, we show with simulation that this long range LD explains 6–25% of the variance of the estimated genomic breeding values, a proportion that is unchanged when the evaluation model includes a residual polygenic effect. Two methods are proposed to predict this erosion factor assuming known simulated QTL effects. In Method 1, one generation of progeny is simulated from the reference population and the GEBV of these progeny based on SNP effects estimated in this newly simulated generation are regressed on the GEBV of the same progeny based on SNP effects estimated in the reference population. In Method 2 all the QTL contributions to SNP effects are regressed based on SNP-QTL recombination rates and summed to predict the GEBV at the next generation. The regression coefficient of the GEBV based on eroded contributions on the raw GEBV is also an estimate of erosion. An illustration is given with the French Normande female reference bovine population in 2021, showing erosion factors ranging from 0.84 to 0.87.ConclusionAccounting for erosion is important to avoid inflation and biased predictions. The ways to both reduce inflation and to correct for it in the prediction are discussed.

Low Stability and Specificity of Polygenic Risk Scores for Major Psychiatric Disorders Limit their Clinical Utility.

Low Stability and Specificity of Polygenic Risk Scores for Major Psychiatric Disorders Limit their Clinical Utility.

Contrasting genetic burden for bipolar disorder: Early onset versus late onset in an older adult bipolar disorder sample.

Older Adults with Bipolar Disorder (OABD) represent a heterogeneous group, including those with early and late onset of the disorder. Recent evidence shows both groups have distinct clinical, cognitive, and medical features, tied to different neurobiological profiles. This study explored the link between polygenic risk scores (PRS) for bipolar disorder (PRS-BD), schizophrenia (PRS-SCZ), and major depressive disorder (PRS-MDD) with age of onset in OABD. PRS-SCZ, PRS-BD, and PRS-MDD among early vs late onset were calculated. PRS was used to infer posterior SNP effect sizes using a fully Bayesian approach. Demographic, clinical, and cognitive variables were also analyzed. Logistic regression analysis was used to estimate the amount of variation of each group explained by standardized PRS-SCZ, PRS-MDD, and PRS-BD. A total of 207 OABD subjects were included (144 EOBD; 63 LOBD). EOBD showed higher PRS-BD compared to LOBD (p = 0.005), while no association was found between age of onset and PRS-SCZ or PRS-MDD. Compared to LOBD, EOBD individuals also showed a higher likelihood for suicide attempts (p = 0.01), higher presence of psychotic symptoms (p = 0.003), higher prevalence of BD-I (p = 0.002), higher rates of familiarity for any psychiatric disorder (p = 0.004), and lower processing speed measured with Trail-Making Test part A (p = 0.03). OABD subjects with an early onset showed a greater genetic burden for BD compared to subjects with a late onset. These findings contribute to the notion that EOBD and LOBD may represent different forms of OABD, particularly regarding the genetic predisposition to BD.

Development of a prototype genetic evaluation for teat and udder score in American Angus cattle.

Improving traits related to teat size (TS) and udder suspension score (US) can have long-term benefits for the health and productivity of beef herds. Well-structured udders and teats contribute to better calf health and growth, through un-inhibited suckling as well as improved cow longevity and milk production. No genetic evaluation is currently available for these traits in American Angus; the aim of the study was to investigate and develop a prototype genetic evaluation for TS and US. Teat and udder suspension scores were subjectively assessed on the farm following the American Angus Association guidelines. After quality control, the final dataset comprised 41,914 complete scores recorded on 23,886 Angus cows. Scores ranged from 1 to 9 for both teat and udder with similar mean (SD) scores of 7.1 (1.6), respectively. A series of multi-trait, animal models (pedigree size of 154,330 individuals) between TS, US, and growth traits were run to estimate trait heritability and genetic correlations. TS and US were found to be moderately heritable (0.31 and 0.34, respectively) and highly repeatable (0.51 and 0.47, respectively). TS and US had a high genetic correlation (0.76) between them and generally low negative genetic correlations with birth weight, weaning weight (WW), yearling gain, and yearling height (0 to -0.19). Udder score was moderately genetically correlated to the maternal genetic component of WW (MILK; -0.24). Mimicking a standard American Angus evaluation, model prediction accuracies for TS and US were estimated for best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP) scenarios. Accuracies ranged from 0.39 to 0.61 indicating that the starting set of phenotypes and models is sufficient to produce an accurate national evaluation. ssGWAS found that TS and US are likely polygenic, with no large single nucleotide polymorphism effects noted. Angus breeders are encouraged to submit annual teat and udder scores on their cows, to continue to improve model accuracy and expedite genetic improvement for these traits.

Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.

Initially introduced in 1909 by William Bateson, classic epistasis (genetic variant interaction) refers to the phenomenon that one variant prevents another variant from a different locus from manifesting its effects. The potential effects of genetic variant interactions on complex diseases have been recognized for the past decades. Moreover, It has been studied and demonstrated that leveraging the combined SNP effects within the genetic block can significantly increase calculation power, reducing background noise, ultimately leading to novel epistasis discovery that the single SNP statistical epistasis study might overlook. However, it is still an open question how we can best combine gene structure representation modelling and interaction learning into an end-to-end model for gene interaction searching. Here, in the current study, we developed a neural genetic block interaction searching model that can effectively process large SNP chip inputs and output the potential genetic block interaction heatmap. Our model augments a previously published hierarchical transformer architecture (Liu and Lapata, 2019) with the ability to model genetic blocks. The cross-block relationship mapping was achieved via a hierarchical attention mechanism which allows the sharing of information regarding specific phenotypes, as opposed to simple unsupervised dimensionality reduction methods e.g. PCA. Results on both simulation and UKBiobank studies show our model brings substantial improvements compared to traditional exhaustive searching and neural network methods.

Ovalbumin gene polymorphism: Implications for hatchability and egg quality changes during storage in Japanese quail.

Ovalbumin gene polymorphism: Implications for hatchability and egg quality changes during storage in Japanese quail.

Genomic analysis of mobility measures on 5-month-old gilts associated with structural soundness.

Sow lameness results in premature culling, causing economic loss and well-being issues. A study, utilizing a pressure-sensing mat (GAIT4) and video monitoring system (NUtrack), was conducted to identify objective measurements on gilts that are predictive of future lameness. Gilts (N = 3,656) were categorized to describe their lifetime soundness: SOUND, retained for breeding with no detected mobility issues; LAME_SOW, retained for breeding and detected lame as a sow; CULL_STR, not retained due to poor leg structure; LAME_GILT, not retained due to visible signs of lameness; and CULL, not retained due to reasons other than leg structure. The GAIT4 system creates a series of measurements for each hoof and a lameness score (GLS) while NUtrack records animal movement and posture durations each day. To determine if measurements from the GAIT4 and NUtrack systems were associated with lifetime soundness, mixed model analyses were conducted in R including fixed effects of breed of sire, contemporary group and lifetime soundness score, and random effect of animal. A second mixed model was run without lifetime soundness score and estimates of animal effects were then used to conduct ssGBLUP analyses using three generations of pedigree and genotypes from ~50k SNP on > 60% of phenotyped animals. Genomic heritabilities were estimated, SNP effects were back-solved and significance was based on Bonferroni-corrected permutation tests. GAIT4 traits indicative of lameness (LAME_GILT and CULL_STR vs. SOUND; P < 0.05) were the standard deviation of GLS, average stride length, and average stance time, while significant NUtrack measurements were eating, standing, lateral lying, total lying, speed, distance, and rotations. In addition, rotations differed (P < 0.05) between SOUND vs. LAME_SOW and distance tended to be different (P < 0.10). Estimates of heritability for predictive NUtrack traits were ~0.3 and GAIT4 traits were ~0.2. There were 382 significant SNP effects in 47 genomic regions, four regions on chromosomes 1, 4, 11, and 14 accounted for over 60% of the associations. Genome-level imputed genotypes linked several regions with possible causative genes. Objective measurements from the GAIT4 and NUtrack systems at 5 mo of age were heritable, able to detect unsound animals, and were associated with lifetime soundness.

All-breed single-step genomic best linear unbiased predictor evaluations for fertility traits in US dairy cattle.

All-breed single-step genomic best linear unbiased predictor evaluations for fertility traits in US dairy cattle.

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.

Genome-wide association studies and functional annotation of pre-weaning calf mortality and reproductive traits in Nellore cattle from experimental selection lines

BackgroundReproductive efficiency is crucial for the long-term economic sustainability of beef cattle production. Pregnancy loss and stillbirth are complex reproductive traits that do not yet have their genomic background fully understood, especially in zebu breeds (Bos taurus indicus). Hence, this study aimed to perform a genome-wide association study (GWAS) and functional annotation for conception success (CS), pregnancy loss (PL), stillbirth (SB), and pre-weaning calf mortality (PWM) in Nellore cattle. In this study, 3,728 cows with 17,094 reproductive records and 11,785 calves were evaluated. A total of 3,351 genotyped animals and 383,739 SNP markers were considered for GWAS analyses. SNP effects were estimated using the weighted single-step GWAS (WssGWAS), which considered two iterations. The top ten genomic windows with the highest contribution to the additive genetic variance of the traits were selected for gene annotation. Candidate genes were then analyzed for Gene Ontology terms (GO) and metabolic pathways.ResultsThe top ten genomic windows that explained the largest proportion of the direct additive genetic variance () for CS, PL, SB, and PWM accounted for 17.03% (overlapping with 79 genes), 16.76% (57 genes), 11.71% (73 genes), and 12.03% (65 genes) of the total , respectively. For CS, significant GO terms included Somitogenesis (GO:0001756), Somite Development (GO:0061053), and Chromosome Segregation (GO:0007059). Considering PL, the processes annotated were the Regulation of Hormone Secretion (GO:0046883), and Hormone Transport (GO:0009914), along with the Glucagon Signaling Pathway (bta04922). Embryonic Development (GO:0045995), and Cerebellum Development (GO:0021549) were the main biological processes found in the gene enrichment analysis for SB. For PWM, the Regulation of Glucose metabolic processes (GO:0010906), Zinc Ion Homeostasis (GO:0055069), Lactation (GO:0007595), and Regulation of Insulin Secretion (GO:0050796) were the most significant GO terms observed.ConclusionsThese findings provide valuable information on genomic regions, candidate genes, biological processes, and metabolic pathways that may significantly influence the expression of complex reproductive traits in Nellore cattle, offering potential contributions to breeding strategies and future genomic selection strategies.

The Evolution and Role of Molecular Tools in Measuring Diversity and Genomic Selection in Livestock Populations (Traditional and Up-to-Date Insights): A Comprehensive Exploration.

Distinctive molecular approaches and tools, particularly high-throughput SNP genotyping, have been applied to determine and discover SNPs, potential genes of interest, indicators of evolutionary selection, genetic abnormalities, molecular indicators, and loci associated with quantitative traits (QTLs) in various livestock species. These methods have also been used to obtain whole-genome sequencing (WGS) data, enabling the implementation of genomic selection. Genomic selection allows for selection decisions based on genomic-estimated breeding values (GEBV). The estimation of GEBV relies on the calculation of SNP effects using prediction equations derived from a subset of individuals in the reference population who possess both SNP genotypes and phenotypes for target traits. Compared to traditional methods, modern genomic selection methods offer advantages for sex-limited traits, low heritability traits, late-measured traits, and the potential to increase genetic gain by reducing generation intervals. The current availability of high-density genotyping and next-generation sequencing data allow for genome-wide scans for selection. This investigation provides an overview of the essential role of advanced molecular tools in studying genetic diversity and implementing genomic selection. It also highlights the significance of adaptive selection in light of new high-throughput genomic technologies and the establishment of selective comparisons between different genomes. Moreover, this investigation presents candidate genes and QTLs associated with various traits in different livestock species, such as body conformation, meat production and quality, carcass characteristics and composition, milk yield and composition, fertility, fiber production and characteristics, and disease resistance.

Integration of GWAS summary statistics with cell type‐specific eQTLs prioritizes potential causal genes for Alzheimer’s disease

AbstractBackgroundAnalyzing disease‐linked genetic variants via expression quantitative trait loci (eQTLs) is crucial for identifying disease‐causing genes. Previous research prioritized genes by integrating Genome‐Wide Association Study (GWAS) results with tissue‐level eQTLs. Recent studies explored brain cell type‐specific eQTLs, but they lack a systematic analysis across various AD GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type‐specific eQTL datasets. Here, we integrated brain cell type‐specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level.MethodTo prioritize disease‐causing genes, we used summary data‐based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the AD GWAS summary statistics with cell type‐specific eQTLs in human brain. We utilized five latest AD GWAS datasets and a cell type‐specific eQTL dataset comprising 424 participants of the Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP) cohort. We replicated our analysis using a cell type‐specific eQTL dataset of 192 participants from Bryois et al., 2021. For comparison, we utilized a previous tissue‐level metabrain eQTL dataset from a meta‐analysis of 14 datasets. Furthermore, we visualized the colocalization of novel candidate causal genes using eQTpLot.ResultWe identified 17 cell type‐specific candidate causal genes using the ROSMAP eQTL dataset. Our results showed that the largest number of candidate causal genes are identified in microglia, followed by astrocytes, oligodendrocytes, excitatory neurons, inhibitory neurons, and oligodendrocyte progenitor cells (OPCs). Four candidate causal genes were common across different cell types. Interestingly, JAZF1, detected as a candidate causal gene affected by the same leading variant in both microglia and OPCs, showed a congruous (same direction) colocalized SNP effect on the gene expression level and AD in OPCs, but an incongruous (opposite direction) colocalized SNP effect in microglia. After comparing our results with previously known prioritized causal genes, we identified PABPC1 in astrocyte as a novel potential causal gene.ConclusionWe systematically prioritized AD candidate causal genes based on cell type‐specific molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD‐related genetic variants and facilitates the interpretation of AD GWAS results.

Genome‐wide Interaction and Stratified Study with Smoking Identifies Association of APAF1 and MIXL1/LIN9 with Alzheimer’s Disease

AbstractBackgroundAlzheimer’s disease (AD) has both genetic and environmental risk factors. Gene‐environment interaction may help explain some missing heritability. There is strong evidence for cigarette smoking as a risk factor for AD. To identify genetic‐smoking‐related associations with AD, we conducted a genome‐wide association study (GWAS) assessing a SNP‐smoking interaction and stratified analysis by smoking status.MethodLifetime smoking data were available and analyzed among 22,030 non‐Hispanic White (NHW; 8,232 cases; 13,798 controls) and 3,126 African American (AFA; 921 cases; 2,205 controls) participants from the AD Genetic Consortium and the Framingham Heart Study. “Ever smoking” status was considered as a dichotomous exposure, defined by current smoking status or past history of smoking. Across 35 datasets, we conducted GWAS with two approaches: inclusion of a SNP‐by‐smoking interaction term and stratification by smoking status (12,080 smokers, 13,428 non‐smokers). MAGEE was used to estimate SNP‐by‐smoking interaction effects and SAIGE was used to estimate SNP effects in stratified analysis. Age, sex, and principal components for population structure were included as covariates. METAL was used for inverse‐variance weighted meta‐analysis across datasets to estimate within‐ and cross‐ancestry effects.ResultThe stratified analysis identified a genome‐wide significant association among smokers in APAF1 on chromosome 12 (top SNP: rs12368451; smokers: MAF = 0.44, p = 2.2 × 10‐8, OR = 1.20; non‐smokers: MAF = 0.44, p = 0.97, OR = 1.00). Effects were present in both ancestry groups (NHW: MAF = 0.45, p = 6.1 × 10‐6, OR = 1.16; AFA: MAF = 0.35, p = 6.6 × 10‐5, OR = 1.46). APAF1 has been linked to gene‐smoking interaction for non‐AD related outcomes. A neighboring gene, ANKS1B, is highly expressed in the brain, interacts with amyloid‐b precursor protein, and has shown GWAS signals for smoking initiation and cognitive ability. We also identified a genome‐wide significant SNP‐by‐smoking interaction in the MIXL1/LIN9 region on chromosome 1 (top SNP: rs1091961, MAF = 0.35, p = 4.9 × 10‐8, βSNP*smoking = 0.24; smokers: OR = 1.12, p = 0.0006; non‐smokers: OR = 0.89, p = 0.0001). Within LIN9, several SNPs in linkage disequilibrium with rs1091961 have shown sub‐genome wide association with nicotine dependence.ConclusionIn this gene‐smoking interaction and smoking‐stratified GWAS of AD, we identified two promising loci. These findings highlight the strength of utilizing cross‐ancestry datasets and considering both genetic and environmental factors together towards a personalized medicine approach to AD.

Advancing Gene Discovery for Substance Use Disorders Using Additional Traits Related to Behavioral Disinhibition.

Substance use disorders (SUDs) frequently co-occur with each other and with other traits related to behavioral disinhibition, a spectrum of outcomes referred to as externalizing. Nevertheless, genome-wide association studies (GWAS) typically study individual SUDs separately. This single-disorder approach ignores genetic covariance between SUDs and other traits and may contribute to the relatively limited genetic discoveries to date. To identify the most effective model for capturing genetic relationships between SUDs and externalizing phenotypes, optimizing the detection of genetic influences on SUDs while maintaining specificity. We used Genomic SEM to estimate SNP effects on a broad factor representing liability to externalizing and SUDs, on factors representing liability to behavioral disinhibition and SUDs separately, and on residualized SUDs. Subsequent gene-based, tissue expression, and polygenic score (PGS) analyses were used to compare the ability of these alternative approaches to identify genetic influences on SUDs. This study was carried out from May 2023 - September 2024. We used GWAS summary statistics based on samples of European ancestry from previous studies of externalizing and SUD phenotypes in the main multivariate GWAS (N > 2.2 million). We used two independent samples to estimate polygenic associations, a family-based sample enriched for substance use problems (COGA; N = 7,530) and a population-based sample representative of the United States, (All of Us; N = 77,442). N/A. Across the three factors (Externalizing; SUDs; Behavioral Disinhibition) and four residualized SUDs (alcohol, tobacco, opioid, and cannabis), we compared the number, putative function, previous associations of significant genomic risk loci and genes, and variance explained by polygenic scores in substance use outcomes. We identified genomic risk loci and genes uniquely associated with Externalizing that are relevant to the neurobiology of substance use. Genes identified for residual SUDs were involved in substance-specific processes (e.g., metabolism). The Externalizing PGS accounted for the most variance in substance outcomes relative to the PGS for the other factors and residual PGS appeared to capture substance specific signals. Our findings suggest that modeling both a broad genetic liability to externalizing behaviors and substance-specific liabilities enhances the detection of genetic effects related to SUDs and explains more variance in substance use outcomes.

An Investigation of the Sodium Nitroprusside Effects on Serum Lipids in an Animal Model of Schizophrenia by the Magnetic Resonance Study.

Schizophrenia (SCZ) is a multifactorial mental illness with limited knowledge concerning pathogenesis, contributing to the lack of effective therapies. More recently, the use of a nitric oxide donor named sodium nitroprusside (sNP) was suggested as a potential therapeutic drug for the treatment of SCZ. Despite the mixed results regarding the effectiveness of the sNP in reducing SCZ symptoms, successful trials on sNP in treatment-resistant SCZ were published. We have also demonstrated the power of evaluating the lipidic profiles of human clinical and animal model samples to identify the biomarkers of the pharmacological response to the diagnosis of mental disorders. Aim of this work is to evaluate the sNP effects in an animal model for SCZ studies through lipidomic profiles assessed by magnetic resonance spectroscopy (NMR). Lipidic profiling of serum from these animals indicated a more pronounced effect of sNP on lipids in the 0.50-6.00 ppm spectral region. Chemometric analysis also indicated an approximation of the lipidic profiling of SCZ animal model rats treated with sNP compared to that of the control group. In addition, we have compared the sNP treatment with other antipsychotics classically used in the clinic, such as haloperidol and clozapine, and the sNP treatment evaluated herein confirms the potential of sNP for the treatment of SCZ.

Examining genotype-phenotype associations of GRAM domain proteins using GWAS, PheWAS and literature review in cattle, human, pig, mouse and chicken

The GRAMD genes are involved in maintaining cholesterol homeostasis, apoptosis, cancer and production traits in livestock. A lipid-binding GRAM domain is implicated in lipid transport and metabolism. The functions of GRAMD proteins remain incompletely understood. The aim of the present study was therefore to investigate the associations between six GRAMD genes in cattle using data from the international genomic evaluation of the Interbull InterGenomics Centre and to evaluate genotype-phenotype associations in human, cattle, pig, mouse and, chicken. Genotyping of 55,013 bulls was performed using DNA microarrays and 11 SNPs were mapped to the five GRAMD genes. A phenome-wide association study (PheWAS) tested associations between the 11 SNPs and 36 traits. The integrated analysis of SNP effects, rankings, and clustering patterns revealed their potential for improving cattle productivity, health, and robustness, and established a baseline for the targeted improvement of cattle traits. This study lays the groundwork for functional experiments aimed at uncovering the mechanism of action of GRAMD genes and to evaluate the potential of using GRAMD sequence variants for selection programs in dairy cattle. The study presents an example of how the combination of GWAS and the PheWAS offers a promising toolbox for the systematic functional annotation of vertebrate genomes.

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 (&amp;gt;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.

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.