Subset Of Single Nucleotide Polymorphisms Research Articles

Accuracy of genomic prediction using multiple Atlantic salmon populations

BackgroundThe accuracy of genomic prediction is partly determined by the size of the reference population. In Atlantic salmon breeding programs, four parallel populations often exist, thus offering the opportunity to increase the size of the reference set by combining these populations. By allowing a reduction in the number of records per population, multi-population prediction can potentially reduce cost and welfare issues related to the recording of traits, particularly for diseases. In this study, we evaluated the accuracy of multi- and across-population prediction of breeding values for resistance to amoebic gill disease (AGD) using all single nucleotide polymorphisms (SNPs) on a 55K chip or a selected subset of SNPs based on the signs of allele substitution effect estimates across populations, using both linear and nonlinear genomic prediction (GP) models in Atlantic salmon populations. In addition, we investigated genetic distance, genetic correlation estimated based on genomic relationships, and persistency of linkage disequilibrium (LD) phase across these populations.ResultsThe genetic distance between populations ranged from 0.03 to 0.07, while the genetic correlation ranged from 0.19 to 0.99. Nonetheless, compared to within-population prediction, there was limited or no impact of combining populations for multi-population prediction across the various models used or when using the selected subset of SNPs. The estimates of across-population prediction accuracy were low and to some extent proportional to the genetic correlation estimates. The persistency of LD phase between adjacent markers across populations using all SNP data ranged from 0.51 to 0.65, indicating that LD is poorly conserved across the studied populations.ConclusionsOur results show that a high genetic correlation and a high genetic relationship between populations do not guarantee a higher prediction accuracy from multi-population genomic prediction in Atlantic salmon.

Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models

BackgroundMetabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches.ResultsThe results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed.ConclusionsOur results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

The use of SNP markers for cattle breed identification.

A potential application of single nucleotide polymorphisms (SNPs) in animal husbandry and production is identification of the animal breed. In this study, using chosen marker selection methods and genotypic data obtained with the use of Illumina Bovine SNP50 BeadChip for individuals belonging to ten cattle breeds, the reduced panels containing the most informative SNP markers were developed. The suitability of selected SNP panels for the effective and reliable assignment of the studied individuals to the breed of origin was checked by three allocation algorithms implemented in GeneClass 2. The studied breeds set included both Polish-native breeds under the genetic resources conservation programs and highly productive breeds with a global range. For all of the tested marker selection methods ("delta" and two FST-based variants), two separate methodological approaches of marker assortment were used and three marker panels were created with 96, 192, and 288 SNPs respectively, to determine the minimum number of markers required for effective differentiation of the studied breeds. Moreover, the usefulness of the most effective panels of markers to assess the population structure and genetic diversity of the analyzed breeds was examined. The conducted analyses showed the possibility of using SNP subsets from medium-density genotypic microarrays to distinguish breeds of cattle kept in Poland and to analyze their genetic structure.

Selection of informative markers using machine learning approaches and genome-wide association studies to improve genomic prediction in Hanwoo cattle: a simulation study

The present study deploys a comparison of Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Genome Wide Association Studies (GWAS) in selecting optimum subsets of single nucleotide polymorphisms (SNPs) to be used in genomic prediction in cattle. The data simulation was carried out for 6,000 animals and 47,841 SNPs which include 43,633 polygenic markers and 4208 quantitative trait loci (QTL) using QMSim software. The genomic prediction was conducted with the best linear unbiased prediction (BLUP) method using the BLUPF90 program. The accuracy of prediction was computed in three different types, namely, Empirical all SNPs, Empirical QTL, and theoretical accuracy, Accuracy PEV . Among the three models, the highest Empirical all SNPs accuracy 0.79 was derived for GBM followed by 0.77 for XGBoost and 0.76 for GWAS. The Empirical QTL accuracy was almost equal for all three models. The maximum theoretical accuracy was obtained for GWAS which was 0.93, whereas GBM and XGBoost obtained 0.86 and 0.85 accuracy levels respectively. Our results indicate that all three models comparably performed in genomic predictions; however, subsets selected by both GBM and GWAS reported higher prediction accuracies compared to the whole SNP set. The number of QTL selected as a proportion of the total number of SNPs was superior in GWAS. These observations can be validated using real data which could enable further optimization of the analysis process.

Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection.

Effects of individual single-nucleotide polymorphism (SNP) markers and the size of "training" and "test" populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of "training" to "test" population size was also evaluated. Fourteen alfalfa populations sampled from long-term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of "training" to "test" population size increased. However, the prediction accuracy of the six GS methods reduced to a range of -0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge-regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for "training" sets, but rrBLUP tended to outperform Bayesian methods in independent "test" sets when SNP subsets with high mean-squared-estimated-marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.

A machine learning approach to predict the most and the least feed–efficient groups in beef cattle

The present study evaluated three strategies to find the optimum subset of DNA markers from the 50 K Illumina Bovine panel to classify beef cattle into the most and the least feed-efficient groups without using individual feed intake and performance measures. Residual feed intake (RFI) and 50 K single nucleotide polymorphisms (SNPs) genotype data of 4,057 beef animals from research and commercial herds were included. Initially, all cattle were ranked based on their phenotypic RFI values. Then different datasets were created by selecting animals from the 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, and 45% range of top and bottom of the ranked RFI values. SNP subsets were selected based on the top-ranked SNPs contributing to the variance of RFI (first strategy), selecting SNPs from the SNP subsets created in the first strategy (strategy 2), and extracting SNPs from 50k SNPs (strategy 3). Then eleven ML algorithms were employed to classify the most and the least feed-efficient groups using 260 datasets generated by combinations of ten RFI phenotype percentage groups and 6, 18, and 2 SNP subsets in the first, second and third strategies, respectively. There was a high degree of accuracy (>69%) for classifying animals in the range of 1% for all ML algorithms under the three strategies and different SNP subsets. Implementing the linear Support Vector Machine algorithm for 15 K SNPs obtained in the first strategy predicted the 1% of the most and the least feed-efficient animals with an accuracy of 84%. In the second strategy, selecting 524 SNPs from the 15 K SNPs subset outperformed the other strategies with an accuracy of 81% for 1% of the population using the Naive Bayes algorithm. It was concluded that a smaller number of SNPs (524) could be used to predict the most and the least feed-efficient animals with an acceptable accuracy to reduce the cost of selection for RFI using genomic information.

A genomic estimated breeding value-assisted reduction method of single nucleotide polymorphism sets: a novel approach for determining the cutoff thresholds in genome-wide association studies and best linear unbiased prediction

ABSTRACT Traditionally, the p-value is the criterion for the cutoff threshold to determine significant markers in genome-wide association studies (GWASs). Choosing the best subset of markers for the best linear unbiased prediction (BLUP) for improved prediction ability (PA) has become an interesting issue. However, when dealing with many traits having the same marker information, the p-values’ themselves cannot be used as an obvious solution for having a confidence in GWAS and BLUP. We thus suggest a genomic estimated breeding value-assisted reduction method of the single nucleotide polymorphism (SNP) set (GARS) to address these difficulties. GARS is a BLUP-based SNP set decision presentation. The samples were Landrace pigs and the traits used were back fat thickness (BF) and daily weight gain (DWG). The prediction abilities (PAs) for BF and DWG for the entire SNP set were 0.8 and 0.8, respectively. By using the correlation between genomic estimated breeding values (GEBVs) and phenotypic values, selecting the cutoff threshold in GWAS and the best SNP subsets in BLUP was plausible as defined by GARS method. 6,000 SNPs in BF and 4,000 SNPs in DWG were considered as adequate thresholds. Gene Ontology (GO) analysis using the GARS results of the BF indicated neuron projection development as the notable GO term, whereas for the DWG, the main GO terms were nervous system development and cell adhesion.

Multivariate extension of penalized regression on summary statistics to construct polygenic risk scores for correlated traits.

Genetic correlations between human traits and disorders such as schizophrenia (SZ) and bipolar disorder (BD) diagnoses are well established. Improved prediction of individual traits has been obtained by combining predictors of multiple genetically correlated traits derived from summary statistics produced by genome-wide association studies, compared with single trait predictors. We extend this idea to penalized regression on summary statistics in Multivariate Lassosum, expressing regression coefficients for the multiple traits on single nucleotide polymorphisms (SNPs) as correlated random effects, similarly to multi-trait summary statistic best linear unbiased predictors (MT-SBLUPs). We also allow the SNP contributions to genetic covariance and heritability to depend on genomic annotations. We conducted simulations with two dichotomous traits having polygenic architecture similar to SZ and BD, using genotypes from 29,330 subjects from the CARTaGENE cohort. Multivariate Lassosum produced polygenic risk scores (PRSs) more strongly correlated with the true genetic risk predictor and had better discrimination power between affected and non-affected subjects than previously published sparse multi-trait (PANPRS) and univariate (Lassosum, sparse LDpred2, and the standard clumping and thresholding) methods in most simulation settings. Application of Multivariate Lassosum to predict SZ, BD, and related psychiatric traits in the Eastern Quebec SZ and BD kindred study revealed associations with every trait stronger than those obtained with univariate sparse PRSs, particularly when heritability and genetic covariance depended on genomic annotations. Multivariate Lassosum thus appears promising to improve prediction of genetically correlated traits with summary statistics for a selected subset of SNPs.

Effectiveness of SNPs for Sibship Assignment in Farmed Banana Shrimp (Penaeus merguiensis)

Pedigrees are essential components in selective breeding programs to manage genetic diversity and obtain accurate genetic parameter estimates to ensure long-term response to selection in captive populations. High throughput and cost-effective sequencing technologies has offered opportunities of using single nucleotide polymorphisms (SNPs) to resolve penaeid shrimp pedigrees from mass spawning cohorts and communal rearing. Effects of SNPs for sibship assignment were investigated on 546 shrimp using two software programs, Colony and Sequoia. Assignment rates and accuracies using SNP subsets with six different minor allele frequencies (MAFs), four sets of SNPs, and five genotyping error rates were compared to the microsatellite-based pedigree established in a previous study. High MAFs and numbers of SNPs contributed to significant increases in assignment rates and accuracies, whereas genotyping error rates showed negligible impacts on assignment results. Sibship assignments achieved rates and accuracies of 98% and 83%, respectively, with a minimum number of 91 SNPs (average MAF ≥ 0.14), and the two different programs exhibited similar resulting patterns for different SNP subsets. High consistencies between SNP-based and microsatellite-based pedigrees showed that accurate pedigrees could be achieved by using SNPs and thus contribute to the long-term response to selection in farmed banana shrimp.

Using whole genome sequence to compare variant callers and breed differences of US sheep.

As whole genome sequence (WGS) data sets have become abundant and widely available, so has the need for variant detection and scoring. The aim of this study was to compare the accuracy of commonly used variant calling programs, Freebayes and GATK HaplotypeCaller (GATK-HC), and to use U.S. sheep WGS data sets to identify novel breed-associated SNPs. Sequence data from 145 sheep consisting of 14 U.S. breeds were filtered and biallelic single nucleotide polymorphisms (SNPs) were retained for genotyping analyses. Genotypes from both programs were compared to each other and to genotypes from bead arrays. The SNPs from WGS were compared to the bead array data with breed heterozygosity, principal component analysis and identifying breed associated SNPs to analyze genetic diversity. The average sequence read depth was 2.78 reads greater with 6.11% more SNPs being identified in Freebayes compared to GATK-HC. The genotype concordance of the variant callers to bead array data was 96.0% and 95.5% for Freebayes and GATK-HC, respectively. Genotyping with WGS identified 10.5 million SNPs from all 145 sheep. This resulted in an 8% increase in measured heterozygosity and greater breed separation in the principal component analysis compared to the bead array analysis. There were 1,849 SNPs identified in only the Romanov sheep where all 10 rams were homozygous for one allele and the remaining 135 sheep from 13 breeds were homozygous for the opposite allele. Both variant calling programs had greater than 95% concordance of SNPs with bead array data, and either was suitably accurate for ovine WGS data sets. The use of WGS SNPs improved the resolution of PCA analysis and was critical for identifying Romanov breed-associated SNPs. Subsets of such SNPs could be used to estimate germplasm composition in animals without pedigree information.

Approximating prediction error variances and accuracies of estimated breeding values from a SNP–BLUP model for genotyped individuals

Context The accuracy of estimated breeding values (EBVs) is an important metric in genetic evaluation systems in Australia. With reduced costs for DNA genotyping due to advances in molecular technology, more and more animals have been genotyped for EBVs. The rapid increase in genotyped animals has grown beyond the capacity of the current genomic best linear unbiased prediction (GBLUP) method. Aims This study aimed to implement and evaluate a new single-nucleotide polymorphism (SNP)–BLUP model for the computation of prediction error variances (PEVs) to accommodate the increasing number of genotyped animals in beef and sheep single-step genetic evaluations in Australia. Methods First, the equivalence of PEV estimates obtained from both GBLUP and SNP-BLUP models was demonstrated. Second, the computing resources required by each model were compared. Third, within the SNP-BLUP model, the PEVs obtained from subsets of SNP were evaluated against those from the complete dataset. Fourth, the new model was tested in the Australian Merino sheep and Angus beef cattle datasets. Key results The PEVs of genotyped animals calculated from the SNP–BLUP model were equivalent to the PEVs derived from the GBLUP model. The SNP–BLUP model used much less time than did the GBLUP model when the number of genotyped animals was larger than the number of SNPs. Within the SNP–BLUP model, the running time could be further reduced using a subset of SNPs makers, with high correlations (>0.97) observed between the PEVs obtained from the complete dataset and subsets. However, it is important to exercise caution when selecting the size of the subsets in the SNP–BLUP model, as reducing the subset size may result in an increase in the bias of the PEVs. Conclusions The new SNP-BLUP model for PEV calculation for genotyped animals outperforms the current GBLUP model. A new accuracy program has been developed for the Australian genetic evaluation system which uses much less memory and time to compute accuracies. Implications The new model has been implemented in routine sheep and beef genetic evaluation systems in Australia. This development ensures that the calculation of accuracies is sustainable, with increasing numbers of animals with genotypes.

Brain structure and allelic associations in Alzheimer's disease.

Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic. This paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations. The participants, ages 65-85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD-associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample-major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta-GWAS study by Jansen and colleagues. We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models. In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM-NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC. This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.

Assessing effectiveness of many-objective evolutionary algorithms for selection of tag SNPs.

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

Brain Structure and Allelic Associations in Alzheimer’s Disease

AbstractBackgroundThis paper examines late‐onset dementia‐related cognitive impairment utilizing neuroimaging‐genetics biomarker associations.MethodThe participants, ages 65 to 85, included 266 normal controls (CN’s), 572 mild cognitive impairment (MCI’s), and 188 Alzheimer’s disease (AD) patients. Genotype dosage data for AD‐associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics dosage data using sample‐major additive coding. 29 such SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta‐GWAS study by Jansen et al.ResultThe expected significant correlations between the SNPs and the neuroimaging phenotypes were confirmed using the top 29 genomic markers (GMs) and 200 neuroimaging markers (NIMs). Relative to CN, odds ratios and relative risks for AD and MCI were employed for prediction using multinominal linear modeling of diagnosis. In the CN and MCI cohorts, mainly cortical thickness measures were associated with GMs among thickness and the other measures, even though it was not in the AD cohort. Network patterns within the CN and AD groups were shown as separated 3 group metrics including thickness, volume, and pct (the proportion of White to Gray Matter), but not in the MCI cohort. Multinominal linear model of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and CN and between MCI and CN.ConclusionAdvanced analytics provide mechanisms for exploring the interrelations between morphometric biomarkers and genomic indicators. This facilitates clinical investigation of phenotypic associations and facilitates deep systematic understanding of AD pathogenesis.

Potential application of elastic nets for shared polygenicity detection with adapted threshold selection.

Current research suggests that hundreds to thousands of single nucleotide polymorphisms (SNPs) with small to modest effect sizes contribute to the genetic basis of many disorders, a phenomenon labeled as polygenicity. Additionally, many such disorders demonstrate polygenic overlap, in which risk alleles are shared at associated genetic loci. A simple strategy to detect polygenic overlap between two phenotypes is based on rank-ordering the univariate p-values from two genome-wide association studies (GWASs). Although high-dimensional variable selection strategies such as Lasso and elastic nets have been utilized in other GWAS analysis settings, they are yet to be utilized for detecting shared polygenicity. In this paper, we illustrate how elastic nets, with polygenic scores as the dependent variable and with appropriate adaptation in selecting the penalty parameter, may be utilized for detecting a subset of SNPs involved in shared polygenicity. We provide theory to better understand our approaches, and illustrate their utility using synthetic datasets. Results from extensive simulations are presented comparing the elastic net approaches with the rank ordering approach, in various scenarios. Results from simulations studies exhibit one of the elastic net approaches to be superior when the correlations among the SNPs are high. Finally, we apply the methods on two real datasets to illustrate further the capabilities, limitations and differences among the methods.

PSIII-4 Late-Breaking: Genetic Marker Association Study of Heifer Pregnancy in Red Angus Cattle

Abstract Heifer pregnancy (HPG) is lowly heritable, yet economically relevant in beef cattle production systems. Hence, identifying single nucleotide polymorphisms (SNP) associated with phenotype could increase the amount of information used in the genetic evaluation of the trait. Using RNA-sequence data from lactating Holstein cows, a set of SNP (n = 69) were discovered among embryo mortality conceptuses (Conceptus-SNP) associated with maternal recognition of pregnancy on day 16 after artificial insemination (AI). To further study these SNP, our objective was to conduct analyses to identify markers associated with HPG in Red Angus. Phenotypic records and genotypes were provided by the Red Angus Association of America (RAAA). For phenotypes, a total of 59,387 cattle records, from the June 2022 RAAA genetic evaluation, were used in the analysis. For genotype data, a 47,860 SNP panel (excluding SNP on the X-chromosome) were formed by combining the historical RAAA genotype data (96,162 animals with genotypes) and genotypes from whole genome sequence of 30 prominent Red Angus AI sires. Markers were mapped to the ARS-UCD1.2 bovine reference genome and then imputed to 47,860 SNP panel using findhap software. A subset of SNP (n = 2,169 SNP; Conceptus-SNP and the multi-breed selected reduced panel [MSRP]) were extracted from the imputed genotypes file. Using PLINK software, genotype quality control procedure was performed so that animals with call rate < 0.9 were removed. A marker was removed if: it had minor allele frequency < 0.01, not in Hardy–Weinberg equilibrium (P < 0.00001), or in high linkage disequilibrium (r2 > 0.95). The final genotype file contained 96,667 animals with 2,137 SNP. A Bayes Cπ single-step super hybrid model (π = 0.99), using the BOLT software package, was used to estimate marker’s posterior probability of inclusion (PPI), the marker significance criterion in predicting HPG. A marker’s PPI was calculated as the average probability from four different Gibbs sampling chains of 75,000 iterations that included 5,000 iterations as burn-in. Markers rs42576394 (18:60583784) and rs42942055 (14:34249373) had PPI of 25.5 and 7.1%, respectively; suggesting association with HPG. None of the Conceptus-SNP had a detectable association with HPG; however, these Conceptus-SNP may be much more informative for genotype to phenotype association analyses for the trait of stayability. Such a trait would be measured with lactating mature beef cows and potentially more similar to the lactating Holstein cows from which the markers were discovered.

Design and validation of a 63K genome-wide SNP-genotyping platform for caribou/reindeer (Rangifer tarandus)

BackgroundDevelopment of large single nucleotide polymorphism (SNP) arrays can make genomic data promptly available for conservation problematic. Medium and high-density panels can be designed with sufficient coverage to offer a genome-wide perspective and the generated genotypes can be used to assess different genetic metrics related to population structure, relatedness, or inbreeding. SNP genotyping could also permit sexing samples with unknown associated metadata as it is often the case when using non-invasive sampling methods favored for endangered species. Genome sequencing of wild species provides the necessary information to design such SNP arrays. We report here the development of a SNP-array for endangered Rangifer tarandus using a multi-platform sequencing approach from animals found in diverse populations representing the entire circumpolar distribution of the species.ResultsFrom a very large comprehensive catalog of SNPs detected over the entire sample set (N = 894), a total of 63,336 SNPs were selected. SNP selection accounted for SNPs evenly distributed across the entire genome (~ every 50Kb) with known minor alleles across populations world-wide. In addition, a subset of SNPs was selected to represent rare and local alleles found in Eastern Canada which could be used for ecotype and population assignments - information urgently needed for conservation planning. In addition, heterozygosity from SNPs located in the X-chromosome and genotyping call-rate of SNPs located into the SRY gene of the Y-chromosome yielded an accurate and robust sexing assessment. All SNPs were validated using a high-throughput SNP-genotyping chip.ConclusionThis design is now integrated into the first genome-wide commercially available genotyping platform for Rangifer tarandus. This platform would pave the way to future genomic investigation of populations for this endangered species, including estimation of genetic diversity parameters, population assignments, as well as animal sexing from genetic SNP data for non-invasive samples.

Bayesian analysis of longitudinal traits in the Korea Association Resource (KARE) cohort.

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

ELSSI: parallel SNP-SNP interactions detection by ensemble multi-type detectors.

With the development of high-throughput genotyping technology, single nucleotide polymorphism (SNP)-SNP interactions (SSIs) detection has become an essential way for understanding disease susceptibility. Various methods have been proposed to detect SSIs. However, given the disease complexity and bias of individual SSI detectors, these single-detector-based methods are generally unscalable for real genome-wide data and with unfavorable results. We propose a novel ensemble learning-based approach (ELSSI) that can significantly reduce the bias of individual detectors and their computational load. ELSSI randomly divides SNPs into different subsets and evaluates them by multi-type detectors in parallel. Particularly, ELSSI introduces a four-stage pipeline (generate, score, switch and filter) to iteratively generate new SNP combination subsets from SNP subsets, score the combination subset by individual detectors, switch high-score combinations to other detectors for re-scoring, then filter out combinations with low scores. This pipeline makes ELSSI able to detect high-order SSIs from large genome-wide datasets. Experimental results on various simulated and real genome-wide datasets show the superior efficacy of ELSSI to state-of-the-art methods in detecting SSIs, especially for high-order ones. ELSSI is applicable with moderate PCs on the Internet and flexible to assemble new detectors. The code of ELSSI is available at https://www.sdu-idea.cn/codes.php?name=ELSSI.

Genome-Wide Association Study and Genomic Selection for Proteinogenic Methionine in Soybean Seeds.

Soybean [Glycine max (L.) Merr.] seeds have an amino acid profile that provides excellent viability as a food and feed protein source. However, low concentrations of an essential amino acid, methionine, limit the nutritional utility of soybean protein. The objectives of this study were to identify genomic associations and evaluate the potential for genomic selection (GS) for methionine content in soybean seeds. We performed a genome-wide association study (GWAS) that utilized 311 soybean accessions from maturity groups IV and V grown in three locations in 2018 and 2019. A total of 35,570 single nucleotide polymorphisms (SNPs) were used to identify genomic associations with proteinogenic methionine content that was quantified by high-performance liquid chromatography (HPLC). Across four environments, 23 novel SNPs were identified as being associated with methionine content. The strongest associations were found on chromosomes 3 (ss715586112, ss715586120, ss715586126, ss715586203, and ss715586204), 8 (ss715599541 and ss715599547) and 16 (ss715625009). Several gene models were recognized within proximity to these SNPs, such as a leucine-rich repeat protein kinase and a serine/threonine protein kinase. Identification of these linked SNPs should help soybean breeders to improve protein quality in soybean seeds. GS was evaluated using k-fold cross validation within each environment with two SNP sets, the complete 35,570 set and a subset of 248 SNPs determined to be associated with methionine through GWAS. Average prediction accuracy (r2) was highest using the SNP subset ranging from 0.45 to 0.62, which was a significant improvement from the complete set accuracy that ranged from 0.03 to 0.27. This indicated that GS utilizing a significant subset of SNPs may be a viable tool for soybean breeders seeking to improve methionine content.