Pre-selected Single Nucleotide Polymorphisms Research Articles

Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs.

The use of whole-genome sequence (WGS) data is expected to improve genomic prediction (GP) power of complex traits because it may contain mutations that in strong linkage disequilibrium pattern with causal mutations. However, a few previous studies have shown no or small improvement in prediction accuracy using WGS data. Incorporating prior biological information into GP seems to be an attractive strategy that might improve prediction accuracy. In this study, a total of 6334 pigs were genotyped using 50K chips and subsequently imputed to the WGS level. This cohort includes two prior discovery populations that comprise 294 Landrace pigs and 186 Duroc pigs, as well as two validation populations that consist of 3770 American Duroc pigs and 2084 Canadian Duroc pigs. Then we used annotation information and genome-wide association study (GWAS) from the WGS data to make GP for six growth traits in two Duroc pig populations. Based on variant annotation, we partitioned different genomic classes, such as intron, intergenic, and untranslated regions, for imputed WGS data. Based on GWAS results of WGS data, we obtained trait-associated single-nucleotide polymorphisms (SNPs). We then applied the genomic feature best linear unbiased prediction (GFBLUP) and genomic best linear unbiased prediction (GBLUP) models to estimate the genomic estimated breeding values for growth traits with these different variant panels, including six genomic classes and trait-associated SNPs. Compared with 50K chip data, GBLUP with imputed WGS data had no increase in prediction accuracy. Using only annotations resulted in no increase in prediction accuracy compared to GBLUP with 50K, but adding annotation information into the GFBLUP model with imputed WGS data could improve the prediction accuracy with increases of 0.00%-2.82%. In conclusion, a GFBLUP model that incorporated prior biological information might increase the advantage of using imputed WGS data for GP.

Prostate Cancer Mortality Associated with Aggregate Polymorphisms in Androgen-Regulating Genes: The Atherosclerosis Risk in the Communities (ARIC) Study

Simple SummaryThe growth of prostate cancer is driven by androgens through activation of the androgen receptors and its target genes in the prostate tumor tissue. Alterations in several genes involved in androgen regulation may influence the prognosis of prostate cancer, but have not been tested in a population-based study. In this epidemiological study, we confirmed that the alteration in the HSD3B1 gene that affected the progression of prostate cancer in clinical trials, is associated with increased risk of prostate cancer death in men diagnosed with metastatic disease. Additionally, we examined the cumulative effect of pre-specified alterations in the three critical androgen-regulating genes (including HSD3B1) and found that their combination predisposes to death from prostate cancer. Our findings suggest that an interplay between the androgen-regulating genes may influence the prognosis of prostate cancer and should be further examined for tailoring prostate cancer treatment.Genetic variations in androgen metabolism may influence prostate cancer (PC) prognosis. Clinical studies consistently linked PC prognosis with four single nucleotide polymorphisms (SNPs) in the critical androgen-regulating genes: 3-beta-hydroxysteroid dehydrogenase (HSD3B1) rs1047303, 5-alpha-reductase 2 (SRD5A2) rs523349, and solute carrier organic ion (SLCO2B1) rs1789693 and rs12422149. We tested the association of four androgen-regulating SNPs, individually and combined, with PC-specific mortality in the ARIC population-based prospective cohort. Men diagnosed with PC (N = 622; 79% White, 21% Black) were followed for death (N = 350) including PC death (N = 74). Cox proportional hazards regression was used to estimate hazard ratios (HR) and 95%CI adjusting for center, age, stage, and grade at diagnosis using separate hazards for races. A priori genetic risk score (GRS) was created as the unweighted sum of risk alleles in the four pre-selected SNPs. The gain-of-function rs1047303C allele was associated PC-specific mortality among men with metastatic PC at diagnosis (HR = 4.89 per risk allele, p = 0.01). Higher GRS was associated with PC-specific mortality (per risk allele: HR = 1.26, p = 0.03). We confirmed that the gain-of-function allele in HSD3B1 rs1047303 is associated with greater PC mortality in men with metastatic disease. Additionally, our findings suggest a cumulative effect of androgen-regulating genes on PC-specific mortality; however, further validation is required.

Genomic Prediction Based on SNP Functional Annotation Using Imputed Whole-Genome Sequence Data in Korean Hanwoo Cattle.

Whole-genome sequence (WGS) data are increasingly being applied into genomic predictions, offering a higher predictive ability by including causal mutations or single-nucleotide polymorphisms (SNPs) putatively in strong linkage disequilibrium with causal mutations affecting the trait. This study aimed to improve the predictive performance of the customized Hanwoo 50 k SNP panel for four carcass traits in commercial Hanwoo population by adding highly predictive variants from sequence data. A total of 16,892 Hanwoo cattle with phenotypes (i.e., backfat thickness, carcass weight, longissimus muscle area, and marbling score), 50 k genotypes, and WGS imputed genotypes were used. We partitioned imputed WGS data according to functional annotation [intergenic (IGR), intron (ITR), regulatory (REG), synonymous (SYN), and non-synonymous (NSY)] to characterize the genomic regions that will deliver higher predictive power for the traits investigated. Animals were assigned into two groups, the discovery set (7324 animals) used for predictive variant detection and the cross-validation set for genomic prediction. Genome-wide association studies were performed by trait to every genomic region and entire WGS data for the pre-selection of variants. Each set of pre-selected SNPs with different density (1000, 3000, 5000, or 10,000) were added to the 50 k genotypes separately and the predictive performance of each set of genotypes was assessed using the genomic best linear unbiased prediction (GBLUP). Results showed that the predictive performance of the customized Hanwoo 50 k SNP panel can be improved by the addition of pre-selected variants from the WGS data, particularly 3000 variants from each trait, which is then sufficient to improve the prediction accuracy for all traits. When 12,000 pre-selected variants (3000 variants from each trait) were added to the 50 k genotypes, the prediction accuracies increased by 9.9, 9.2, 6.4, and 4.7% for backfat thickness, carcass weight, longissimus muscle area, and marbling score compared to the regular 50 k SNP panel, respectively. In terms of prediction bias, regression coefficients for all sets of genotypes in all traits were close to 1, indicating an unbiased prediction. The strategy used to select variants based on functional annotation did not show a clear advantage compared to using whole-genome. Nonetheless, such pre-selected SNPs from the IGR region gave the highest improvement in prediction accuracy among genomic regions and the values were close to those obtained using the WGS data for all traits. We concluded that additional gain in prediction accuracy when using pre-selected variants appears to be trait-dependent, and using WGS data remained more accurate compared to using a specific genomic region.

Use of gene expression and whole-genome sequence information to improve the accuracy of genomic prediction for carcass traits in Hanwoo cattle

BackgroundIn this study, we assessed the accuracy of genomic prediction for carcass weight (CWT), marbling score (MS), eye muscle area (EMA) and back fat thickness (BFT) in Hanwoo cattle when using genomic best linear unbiased prediction (GBLUP), weighted GBLUP (wGBLUP), and a BayesR model. For these models, we investigated the potential gain from using pre-selected single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on imputed sequence data and from gene expression information. We used data on 13,717 animals with carcass phenotypes and imputed sequence genotypes that were split in an independent GWAS discovery set of varying size and a remaining set for validation of prediction. Expression data were used from a Hanwoo gene expression experiment based on 45 animals.ResultsUsing a larger number of animals in the reference set increased the accuracy of genomic prediction whereas a larger independent GWAS discovery dataset improved identification of predictive SNPs. Using pre-selected SNPs from GWAS in GBLUP improved accuracy of prediction by 0.02 for EMA and up to 0.05 for BFT, CWT, and MS, compared to a 50 k standard SNP array that gave accuracies of 0.50, 0.47, 0.58, and 0.47, respectively. Accuracy of prediction of BFT and CWT increased when BayesR was applied with the 50 k SNP array (0.02 and 0.03, respectively) and was further improved by combining the 50 k array with the top-SNPs (0.06 and 0.04, respectively). By contrast, using BayesR resulted in limited improvement for EMA and MS. wGBLUP did not improve accuracy but increased prediction bias. Based on the RNA-seq experiment, we identified informative expression quantitative trait loci, which, when used in GBLUP, improved the accuracy of prediction slightly, i.e. between 0.01 and 0.02. SNPs that were located in genes, the expression of which was associated with differences in trait phenotype, did not contribute to a higher prediction accuracy.ConclusionsOur results show that, in Hanwoo beef cattle, when SNPs are pre-selected from GWAS on imputed sequence data, the accuracy of prediction improves only slightly whereas the contribution of SNPs that are selected based on gene expression is not significant. The benefit of statistical models to prioritize selected SNPs for estimating genomic breeding values is trait-specific and depends on the genetic architecture of each trait.

Prediction of genomic breeding values based on pre-selected SNPs using ssGBLUP, WssGBLUP and BayesB for Edwardsiellosis resistance in Japanese flounder

BackgroundEdwardsiella tarda causes acute symptoms with ascites in Japanese flounder (Paralichthys olivaceus) and is a major problem for China’s aquaculture sector. Genomic selection (GS) has been widely adopted in breeding industries because it shortens generation intervals and results in the selection of individuals that have great breeding potential with high accuracy. Based on an artificial challenge test and re-sequenced data of 1099 flounders, the aims of this study were to estimate the genetic parameters of resistance to E. tarda in Japanese flounder and to evaluate the accuracy of single-step GBLUP (ssGBLUP), weighted ssGBLUP (WssGBLUP), and BayesB for improving resistance to E. tarda by using three subsets of pre-selected single nucleotide polymorphisms (SNPs). In addition, SNPs that are associated with this trait were identified using a single-SNP genome-wide association study (GWAS) and WssGBLUP.ResultsWe estimated a heritability of 0.13 ± 0.02 for resistance to E. tarda in Japanese flounder. One million SNPs at fixed intervals were selected from 4,978,724 SNPs that passed quality controls. GWAS identified significant SNPs on chromosomes 14 and 24. WssGBLUP revealed that the putative quantitative trait loci on chromosomes 1 and 14 contained SNPs that explained more than 1% of the genetic variance. Three 50 k-SNP subsets were pre-selected based on different criteria. Compared with pedigree-based prediction (ABLUP), the three genomic methods evaluated resulted in at least 7.7% greater accuracy of predictions. The accuracy of these genomic prediction methods was almost unchanged when pre-selected trait-related SNPs were used for prediction.ConclusionsResistance to E. tarda in Japanese flounder has a low heritability. GWAS and WssGBLUP revealed that the genetic architecture of this trait is polygenic. Genomic prediction of breeding values performed better than ABLUP. It is feasible to implement genomic selection to increase resistance to E. tarda in Japanese flounder with 50 k SNPs. Based on the criteria used here, pre-selection of SNPs was not beneficial and other criteria for pre-selection should be considered.

Applicability of Obesity-Related SNPs and their Effect Size Measures Defined on Populations with European Ancestry for Genetic Risk Estimation among Roma.

Investigations on the impact of genetic factors on the development of obesity have been limited regarding the Roma population—the largest and most vulnerable ethnic minority in Europe of Asian origin. Genetic variants identified from genetic association studies are primarily from European populations. With that in mind, we investigated the applicability of data on selected obesity-related single nucleotide polymorphisms (SNPs), obtained from the Hungarian general (HG) population of European origin, on the Hungarian Roma (HR) population. Twenty preselected SNPs in susceptible alleles, known to be significantly associated with obesity-related phenotypes, were used to estimate the effect of these SNPs on body mass index (BMI) and waist circumference (WC) in HG (N = 1783) and HR (N = 1225) populations. Single SNP associations were tested using linear and logistic regression models, adjusted for known covariates. Out of 20 SNPs, four located in FTO (rs1121980, rs1558902, rs9939609, and rs9941349) showed strong association with BMI and WC as continuous variables in both samples. Computations based on Adult Treatment Panel III (ATPIII) and the International Diabetes Federation’s (IDF) European and Asian criteria showed rs9941349 in FTO to be associated only with WC among both populations, and two SNPs (rs2867125, rs6548238) in TMEM18 associated with WC only in HG population. A substantial difference (both in direction and effect size) was observed only in the case of rs1801282 in PPARγ on WC as a continuous outcome. Findings suggest that genetic risk scores based on counting SNPs with relatively high effect sizes, defined based on populations with European ancestry, can sufficiently allow estimation of genetic susceptibility for Roma. Further studies are needed to clarify the role of SNP(s) with protective effect(s).

A deterministic equation to predict the accuracy of multi-population genomic prediction with multiple genomic relationship matrices

BackgroundA multi-population genomic prediction (GP) model in which important pre-selected single nucleotide polymorphisms (SNPs) are differentially weighted (MPMG) has been shown to result in better prediction accuracy than a multi-population, single genomic relationship matrix ({mathbf{GRM}}) GP model (MPSG) in which all SNPs are weighted equally. Our objective was to underpin theoretically the advantages and limits of the MPMG model over the MPSG model, by deriving and validating a deterministic prediction equation for its accuracy.MethodsUsing selection index theory, we derived an equation to predict the accuracy of estimated total genomic values of selection candidates from population A (r_{{{mathbf{EGV}}_{{A_{T} }} }}), when individuals from two populations, A and B, are combined in the training population and two {mathbf{GRM}}, made respectively from pre-selected and remaining SNPs, are fitted simultaneously in MPMG. We used simulations to validate the prediction equation in scenarios that differed in the level of genetic correlation between populations, heritability, and proportion of genetic variance explained by the pre-selected SNPs. Empirical accuracy of the MPMG model in each scenario was calculated and compared to the predicted accuracy from the equation.ResultsIn general, the derived prediction equation resulted in accurate predictions of r_{{{mathbf{EGV}}_{{A_{T} }} }} for the scenarios evaluated. Using the prediction equation, we showed that an important advantage of the MPMG model over the MPSG model is its ability to benefit from the small number of independent chromosome segments (M_{e}) due to the pre-selected SNPs, both within and across populations, whereas for the MPSG model, there is only a single value for M_{e}, calculated based on all SNPs, which is very large. However, this advantage is dependent on the pre-selected SNPs that explain some proportion of the total genetic variance for the trait.ConclusionsWe developed an equation that gives insight into why, and under which conditions the MPMG outperforms the MPSG model for GP. The equation can be used as a deterministic tool to assess the potential benefit of combining information from different populations, e.g., different breeds or lines for GP in livestock or plants, or different groups of people based on their ethnic background for prediction of disease risk scores.

Decanucleotide insertion polymorphism of F7 significantly influences the risk of thrombosis in patients with essential thrombocythemia

There is strong evidence that certain thrombophilic single nucleotide polymorphisms (SNPs) account for an increased risk of thrombosis. The additive impact of inherited thrombotic risk factors to a certain disease- immanent thrombotic risk is vastly unknown. Therefore, we aimed to investigate the influence of three novel, preselected SNPs on the risk of thrombosis in patients diagnosed with myeloproliferative neoplasm (MPN). In 167 patients with a diagnosis of essential thrombocythemia (ET) or prefibrotic primary myelofibrosis (PMF) thrombophilic SNPs in the genes of factor VII (F7), nitric oxide synthase 3 (NOS3) and FcɣRIIa (FCGR2A) were determined. Subsequently, the polymorphic variants were correlated with the incidence of major thrombosis after diagnosis. Decanucleotide insertion polymorphism of F7 emerged as an independent, significant risk factor for total thrombosis and arterial thrombosis in particular in the whole group of patients (P = 0.0007) as well as in the separate analysis of patients with ET (P = 0.0002). Our results illustrate that the risk of thrombosis in MPN is significantly multiplied by inherited thrombophilic SNPs. This result points to the importance of a combined consideration of the inherited and the acquired hypercoagulable state in patients with MPN. Larger studies are needed to confirm and extend these important findings.

Genotype – environment correlations in corals from the Great Barrier Reef

BackgroundKnowledge of genetic markers that are correlated to stress tolerance may improve spatial mapping of reef vulnerability and can inform restoration efforts, including the choice of genotypes for breeding and reseeding. In this manuscript we present two methods for screening transcriptome data for candidate genetic markers in two reef building corals, Acropora millepora and Pocillopora damicornis (types α and β). In A. millepora, Single Nucleotide Polymorphisms (SNPs) were pre-selected by targeting genes believed to be involved in the coral thermal stress responses. In P. damicornis (type α and β), SNPs showing varying allele frequencies between two populations from distinct environments were pre-selected. Allele frequencies at nine, five and eight of the pre-selected SNP loci were correlated against gradients of water clarity and temperature in a large number of populations along the Great Barrier Reef.ResultsA significant correlation between environmental category and SNP allele frequency was detected in up to 55% of the tested loci, which is an exceptional success rate for these types of tests. In P. damicornis, SNP allele frequencies of β-hexosaminidase and Elongation factor 1-α were significantly correlated to temperature in type α and to temperature and/or water clarity respectively in type β. Type α also showed a correlation between water clarity and SNP allele frequency in a gene of unknown function. In A. millepora, allele frequencies at five (β-gamma crystallin, Galaxin, Ubiquitin, Ligand of Numb X2 and Thioredoxin) SNP loci showed significant correlations.ConclusionsAfter validation of these candidate loci through laboratory or field assessment of relative stress tolerance of colonies harbouring different alleles, it is anticipated that a proportion of these markers may represent the first coral candidate Quantitative Trait Loci for environmental stress tolerance and provide an important genetic tool that can be incorporated into spatial management decisions and restoration efforts of coral reefs. One pertinent example would be to combine spatial data of tolerant populations with genetic connectivity and thus identify high priority conservation reefs and implement targeted coral husbandry and active restoration efforts that use locally- and stress-adapted genotypes.

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

BackgroundAt the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI).MethodsDense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length.ResultsRR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls.ConclusionsAccurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP.

Ischaemic stroke in hypertensive patients is associated with variations in the PDE4D genome region

Previous Icelandic studies reported that single nucleotide polymorphisms (SNPs) in the phosphodiesterase 4D (PDE4D) region and the 5-lipoxygenase activating protein ALOX5AP were associated with ischaemic stroke, whereas other studies reported ambiguous findings. We examined 932 ischaemic stroke patients from a Swedish population-based stroke register, and 396 control subjects. We assessed possible associations between ischaemic stroke and nine preselected SNPs in the chromosome regions of the PDE4D gene, including rs12188950 (SNP45) and rs3887175 (SNP39); the ALOX5AP gene, including rs17222814 (SG13S25) and the promoter region of the MHC class II transactivator, MHC2TA. The T allele of SNP45 showed negative association with ischaemic stroke (odds ratio, OR=0.72; 95% confidence interval (CI): 0.58-0.91; P=0.0055). Among hypertensive subjects, this influence of the T allele of SNP45, and the T allele of SNP39, were more pronounced (with OR=0.52; 95% CI: 0.37-0.73; P=0.0001 and OR=0.57; 95% CI: 0.41-0.79; P=0.0007, respectively). These SNPs also interacted with hypertension with a relative excess risk due to interaction of -1.66 (P=0.0002) for SNP45 and -1.65 (P=0.0005) for SNP39. The P-values remained significant after correction for multiple testing. Among nonhypertensives, the A allele of SG13S25 indicated increased stroke risk (OR=1.82; 95% CI: 1.21-2.74; P=0.0039; not significant after Bonferroni correction). SNP45 was associated with ischaemic stroke even when controlling for hypertension, diabetes, heart disease and smoking. Our meta-analysis of 13 studies (including ours) showed no overall influence of SNP45 on ischaemic stroke. However, the 13 studies may differ because of nonrandom causes, as suggested by the heterogeneity test (P=0.042). This might support previously undetected mechanisms causing fluctuating ischaemic stroke risk.