Supervised independent component analysis as an alternative method for genomic selection in pigs

The objective of this work was to evaluate the efficiency of the supervised independent component regression (SICR) method for the estimation of genomic values and the SNP marker effects for boar taint and carcass traits in pigs. The methods were evaluated via the agreement between the predicted genetic values and the corrected phenotypes observed by cross‐validation. These values were also compared with other methods generally used for the same purposes, such as RR‐BLUP, SPCR, SPLS, ICR, PCR and PLS. The SICR method was found to have the most accurate prediction values.     

Identification of differential selection traces in two Polish cattle breeds

Genetic improvement of animals based on artificial selection is leading to changes in the frequency of genes related to desirable production traits. The changes are reflected by the neutral, intergenic single nucleotide polymorphims (SNPs) being in long‐range linkage disequilibrium with functional polymorphisms. Genome‐wide SNP analysis tools designed for cattle, allow for scanning divergences in allelic frequencies between distinct breeds and thus for identification of genomic regions which were divergently selected in breeds' histories. In this study, by using Bovine SNP50 assay, we attempted to identify genomic regions showing the highest differences in allele frequencies between two distinct cattle breeds – preserved, unselected Polish Red breed and highly selected Holstein cattle. Our study revealed 19 genomic regions encompassing 55 protein‐coding genes and numerous quantitative trait loci which potentially may underlie some of the phenotypic traits distinguishing the breeds.     

Genomic selection in American mink (Neovison vison) using a single-step genomic best linear unbiased prediction model for size and quality traits graded on live mink

Genomic selection relies on single-nucleotide polymorphisms (SNPs), which are often collected using medium-density SNP arrays. In mink, no such array is available; instead, genotyping by sequencing (GBS) can be used to generate marker information. Here, we evaluated the effect of genomic selection for mink using GBS. We compared the estimated breeding values (EBVs) from single-step genomic best linear unbiased prediction (SSGBLUP) models to the EBV from ordinary pedigree-based BLUP models. We analyzed seven size and quality traits from the live grading of brown mink. The phenotype data consisted of ~20,600 records for the seven traits from the mink born between 2013 and 2016. Genotype data included 2,103 mink born between 2010 and 2014, mostly breeding animals. In total, 28,336 SNP markers from 391 scaffolds were available for genomic prediction. The pedigree file included 29,212 mink. The predictive ability was assessed by the correlation (r) between progeny trait deviation (PTD) and EBV, and the regression of PTD on EBV, using 5-fold cross-validation. For each fold, one-fifth of animals born in 2014 formed the validation set. For all traits, the SSGBLUP model resulted in higher accuracies than the BLUP model. The average increase in accuracy was 15% (between 3% for fur clarity and 28% for body weight). For three traits (body weight, silky appearance of the under wool, and guard hair thickness), the difference in r between the two models was significant (P < 0.05). For all traits, the regression slopes of PTD on EBV from SSGBLUP models were closer to 1 than regression slopes from BLUP models, indicating SSGBLUP models resulted in less bias of EBV for selection candidates than the BLUP models. However, the regression coefficients did not differ significantly. In conclusion, the SSGBLUP model is superior to conventional BLUP model in the accurate selection of superior animals, and, thus, it would increase genetic gain in a selective breeding program. In addition, this study shows that GBS data work well in genomic prediction in mink, demonstrating the potential of GBS for genomic selection in livestock species.     

Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers

Summary Genome‐wide association studies using single nucleotide polymorphisms (SNPs) can identify genetic variants related to complex traits. Typically thousands of SNPs are genotyped, whereas the number of phenotypes for which there is genomic information may be smaller. When predicting phenotypes, options for statistical model building range from incorporating all possible markers into the specification to including only sets of relevant SNPs (features). In the latter case, an efficient method of selecting influential features is required. A two‐step feature selection method for binary traits was developed, which consisted of filtering (using information gain), and wrapping (using naïve Bayesian classification). The filter reduces the large number of SNPs to a much smaller size, to facilitate the wrapper step. As the procedure is tailored for discrete outcomes, an approach based on discretization of phenotypic values was developed, to enable feature selection in a classification framework. The method was applied to chick mortality rates (0–14 days of age) on progeny from 201 sires in a commercial broiler line, with the goal of identifying SNPs (over 5000) related to progeny mortality. To mimic a case–control study, sires were clustered into two groups, low and high, according to two arbitrarily chosen mortality rate cut points. By varying these thresholds, 11 different ‘case–control’ samples were formed, and the SNP selection procedure was applied to each sample. To compare the 11 sets of chosen SNPs, predicted residual sum of squares (PRESS) from a linear model was used. The two‐step method improved naïve Bayesian classification accuracy over the case without feature selection (from around 50 to above 90% without and with feature selection in each case–control sample). The best case–control group (63 sires above or below the thresholds) had the smallest PRESS statistic among groups with model p‐values below 0.003. The 17 SNPs selected using this group accounted for 31% of the variation in raw mortality rates between sire families.     

Theoretical efficiency of multiple-trait quantitative trait loci-assisted selection

The effectiveness of five selection methods for genetic improvement of net merit comprising trait 1 of low heritability (h² = 0.1) and trait 2 of high heritability (h² = 0.4) was examined: (i) two‐trait quantitative trait loci (QTL)‐assisted selection; (ii) partial QTL‐assisted selection based on trait 1; (iii) partial QTL‐assisted selection based on trait 2; (iv) QTL‐only selection; and (v) conventional selection index without QTL information. These selection methods were compared under 72 scenarios with different combinations of the relative economic weights, the genetic correlations between traits, the ratio of QTL variance to total genetic variance of the trait, and the ratio of genetic variances between traits. The results suggest that the detection of QTL for multiple‐trait QTL‐assisted selection is more important when the index traits are negatively correlated than when they are positively correlated. In contrast to literature reports that single‐trait marker‐assisted selection (MAS) is the most efficient for low heritability traits, this study found that the identified QTL of the low heritability trait contributed negligibly to total response in net merit. This is because multiple‐trait QTL‐assisted selection is designed to maximize total net merit rather than the genetic response of the individual index trait as in the case of single‐trait MAS. Therefore, it is not economical to identify the QTL of the low heritability traits for the improvement of total net merit. The efficient, cost‐effective selection strategy is to identify the QTL of the moderate or high heritability traits of the QTL‐assisted selection index to facilitate total economic returns. Detection of the QTL of the low h² traits for the QTL‐assisted index selection is justified when the low h² traits have high negative genetic correlation with the other index traits and/or when both economic weights and genetic variances of the low h² traits are larger as compared to the other index traits of higher h². This study deals with theoretical efficiency of QTL‐assisted selection, but the same principle applies to SNP‐based genomic selection when the proportion of the genetic variance ‘explained by the identified QTLs’ in this study is replaced by ‘explained by SNPs’.     

Identification of the putative ancestral allele of bovine single‐nucleotide polymorphisms

Identifying the action of natural selection from patterns of standing genetic variation has long been of interest to the population genetic community. Thanks to the availability of large single‐nucleotide polymorphism (SNP) data sets for many species and of high‐throughput SNP genotyping methods, whole‐genomic surveys to detect selective sweeps are now possible. Knowing the ancestral allele increases the power to detect selection. We present here a comparative genomic approach to determine the putative ancestral allele of bovine SNPs deposited in public databases. We analysed 19 551 488 SNPs and identified the putative ancestral allele for 14 339 107 SNPs. Our predicted ancestral alleles were in agreement with ancestral alleles detected by genotyping outgroup species for 97% SNPs from the BovineSNP50 BeadChip. This comparison indicates that our comparative genomic‐based approach to identify putative ancestral alleles is reliable.     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

Genomic selection

Genomic selection is a form of marker-assisted selection in which genetic markers covering the whole genome are used so that all quantitative trait loci (QTL) are in linkage disequilibrium with at least one marker. This approach has become feasible thanks to the large number of single nucleotide polymorphisms (SNP) discovered by genome sequencing and new methods to efficiently genotype large number of SNP. Simulation results and limited experimental results suggest that breeding values can be predicted with high accuracy using genetic markers alone but more validation is required especially in samples of the population different from that in which the effect of the markers was estimated. The ideal method to estimate the breeding value from genomic data is to calculate the conditional mean of the breeding value given the genotype of the animal at each QTL. This conditional mean can only be calculated by using a prior distribution of QTL effects so this should be part of the research carried out to implement genomic selection. In practice, this method of estimating breeding values is approximated by using the marker genotypes instead of the QTL genotypes but the ideal method is likely to be approached more closely as more sequence and SNP data is obtained. Implementation of genomic selection is likely to have major implications for genetic evaluation systems and for genetic improvement programmes generally and these are discussed.     

Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

There is an increasing interest in using whole‐genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole‐genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole‐genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non‐synonymous and non‐coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non‐synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.     

Use of high‐density SNP data to identify patterns of diversity and signatures of selection in broiler chickens

The development of broiler chickens over the last 70 years has been accompanied by large phenotypic changes, so that the resulting genomic signatures of selection should be detectable by current statistical techniques with sufficiently dense genetic markers. Using two approaches, this study analysed high‐density SNP data from a broiler chicken line to detect low‐diversity genomic regions characteristic of past selection. Seven regions with zero diversity were identified across the genome. Most of these were very small and did not contain many genes. In addition, fifteen regions were identified with diversity increasing asymptotically from a low level. These regions were larger and thus generally included more genes. Several candidate genes for broiler traits were found within these ‘regression regions’, including IGF1, GPD2 and MTNR1AI. The results suggest that the identification of zero‐diversity regions is too restrictive for characterizing regions under selection, but that regions showing patterns of diversity along the chromosome that are consistent with selective sweeps contain a number of genes that are functional candidates for involvement in broiler development. Many regions identified in this study overlap or are close to regions identified in layer chicken populations, possibly due to their shared precommercialization history or to shared selection pressures between broilers and layers.     

Implications of SNP weighting on single‐step genomic predictions for different reference population sizes

We investigated the importance of SNP weighting in populations with 2,000 to 25,000 genotyped animals. Populations were simulated with two effective sizes (20 or 100) and three numbers of QTL (10, 50 or 500). Pedigree information was available for six generations; phenotypes were recorded for the four middle generations. Animals from the last three generations were genotyped for 45,000 SNP. Single‐step genomic BLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used to estimate genomic EBV using a genomic relationship matrix ( G ). The WssGBLUP performed better in small genotyped populations; however, any advantage for WssGBLUP was reduced or eliminated when more animals were genotyped. WssGBLUP had greater resolution for genome‐wide association (GWA) as did increasing the number of genotyped animals. For few QTL, accuracy was greater for WssGBLUP than ssGBLUP; however, for many QTL, accuracy was the same for both methods. The largest genotyped set was used to assess the dimensionality of genomic information (number of effective SNP). The number of effective SNP was considerably less in weighted G than in unweighted G . Once the number of independent SNP is well represented in the genotyped population, the impact of SNP weighting becomes less important.     

Application of imputation methods to genomic selection in Chinese Holstein cattle

Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection. This problem can be circumvented by imputing the missing genotypes with estimated genotypes. When implementing imputation, the criteria used for SNP data quality control and whether to perform imputation before or after data quality control need to consider. In this paper, we compared six strategies of imputation and quality control using different imputation methods, different quality control criteria and by changing the order of imputation and quality control, against a real dataset of milk production traits in Chinese Holstein cattle. The results demonstrated that, no matter what imputation method and quality control criteria were used, strategies with imputation before quality control performed better than strategies with imputation after quality control in terms of accuracy of genomic selection. The different imputation methods and quality control criteria did not significantly influence the accuracy of genomic selection. We concluded that performing imputation before quality control could increase the accuracy of genomic selection, especially when the rate of missing genotypes is high and the reference population is small.     

Detecting selection signatures on the X chromosome of the Chinese Debao pony

The X chromosome shows a special interaction between demographic factors and genetic variation, and the analysis of X‐linked genomic variation can therefore provide insights into the unique effects of demography and selection on the horse genome that cannot be readily detected by autosomal markers. Debao (DB) ponies have experienced intense selective pressure for the development of their small stature (<106 cm at adult height). To identify selective sweeps on the X chromosome of the DB pony, we performed a genome‐wide scan of three Chinese horse breeds using an Equine SNP70 BeadChip. Using Yili and Mongolian horses (>134 cm at adult height) as reference groups, both F_ST and XP‐EHH revealed that five regions on the X chromosome were under strong selection, resulting in 95 overlapping genes. Seven of these genes, SMS, PHEX, ACSL4, CHRDL1, CACNA1F, DKC1 and CDKL5, are involved in bone development, growth hormone secretion and fat deposition. The region showing the strongest selection pressure was located at the position of 86.6–87.5 Mb. The subsequent genome‐wide association analysis of the adult height of three Chinese horse breeds detected the two most significant SNPs in the same region, and these two SNPs overlapped with the gene CHRDL1. As a member of the bone morphogenetic protein (BMP) superfamily, CHRDL1 antagonizes the function of BMP4 and plays an important role in embryonic bone formation and cartilage generation. Our results provide new insights into the X‐linked selection in Chinese Debao pony.     

The impact of the rank of marker variance-covariance matrix in principal component evaluation for genomic selection applications

In genomic selection (GS) programmes, direct genomic values (DGV) are evaluated using information provided by high-density SNP chip. Being DGV accuracy strictly dependent on SNP density, it is likely that an increase in the number of markers per chip will result in severe computational consequences. Aim of present work was to test the effectiveness of principal component analysis (PCA) carried out by chromosome in reducing the marker dimensionality for GS purposes. A simulated data set of 5700 individuals with an equal number of SNP distributed over six chromosomes was used. PCs were extracted both genome-wide (ALL) and separately by chromosome (CHR) and used to predict DGVs. In the ALL scenario, the SNP variance-covariance matrix (S) was singular, positive semi-definite and contained null information which introduces 'spuriousness' in the derived results. On the contrary, the S matrix for each chromosome (CHR scenario) had a full rank. Obtained DGV accuracies were always better for CHR than ALL. Moreover, in the latter scenario, DGV accuracies became soon unsettled as the number of animals decreases, whereas in CHR, they remain stable till 900-1000 individuals. In real applications where a 54k SNP chip is used, the largest number of markers per chromosome is approximately 2500. Thus, a number of around 3000 genotyped animals could lead to reliable results when the original SNP variables are replaced by a reduced number of PCs.     

Signatures of selection in Charolais beef cattle identified by genome‐wide analysis

Charolais cattle are one of the most important breeds for meat production worldwide; in México, its selection is mainly made by live weight traits. One strategy for mapping important genomic regions that might influence productive traits is the identification of signatures of selection. This type of genomic features contains loci with extended linkage disequilibrium (LD) and homozygosity patterns that are commonly associated with sites of quantitative trait locus (QTL). Therefore, the objective of this study was to identify the signatures of selection in Charolais cattle genotyped with the GeneSeek Genomic Profiler Bovine HD panel consisting of 77 K single nucleotide polymorphisms (SNPs). A total 61,311 SNPs and 819 samples were used for the analysis. Identification of signatures of selection was carried out using the integrated haplotype score (iHS) methodology implemented in the rehh R package. The top ten SNPs with the highest piHS values were located on BTA 4, 5, 6 and 14. By identifying markers in LD with top ten SNPs, the candidate regions defined were mapped to 52.8–59.3 Mb on BTA 4; 67.5–69.3 on BTA 5; 39.5–41.0 Mb on BTA 6; and 26.4–29.6 Mb on BTA 14. The comparison of these candidate regions with the bovine QTLdb effectively confirmed the association (p < 0.05) with QTL related to growth traits and other important productive traits. The genomic regions identified in this study indicated selection for growth traits on the Charolais population via the conservation of haplotypes on various chromosomes. These genomic regions and their associated genes could serve as the basis for haplotype association studies and for the identification of causal genes related to growth traits.     

SNPpath: Characterizing cattle SNPs by enriched pathway terms

High‐density single nucleotide polymorphism (SNP) microarrays have made large‐scale genome‐wide association studies (GWAS) and genomic selection (GS) feasible. Valuable insight into the genetic basis underlying complex polygenic traits will likely be gained by considering functionally related sets of genes simultaneously. SNPpath, a suite of computer‐generated imagery‐based web servers has been developed to automatically annotate and characterize cattle SNPs by enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway terms. The SNPpath allows users to navigate and analysis large SNP sets and is the only web server currently providing pathway annotations of cattle SNPs in National Center for Biotechnology Information's dbSNP database and three commercial platforms. Hence, we describe SNPpath and provide details of the query options, as well as biological examples of use. The SNPpath may be favorable for the analysis of combining SNP association analysis with pathway‐driven gene set enrichment analysis and is freely available at http://klab.sjtu.edu.cn/SNPpath .     

Identification of divergently selected regions between Japanese Black and Holstein cattle using bovine 50k SNP array

Differences between average allelic frequencies of genes that relate to traits suggest that it would be evidence of artificial selections. Sliding window approach is a useful method to identify genomic regions that have been differently selected between two breeds. The objective of this study was to identify the divergently selected regions between Japanese Black (JB) and Japanese Holstein (JH) cattle based on genotypic information obtained through a high‐density single nucleotide polymorphism (SNP) panel. After genotyping of 54 001 SNP markers on 100 animals (50 JB and 50 JH), 40 635 SNPs were suitable for the analysis. For each of these SNPs, the absolute difference between allelic frequencies of JB and JH was calculated. In the current study, 10 consecutive SNPs were defined as components of a window. For each window, the average difference in allelic frequency was calculated. This was termed sliding window average difference (SWAD). Among 40 055 windows, we focused on 39 windows with the largest SWAD. This was equivalent to 0.1% of all windows and the SWAD was more than 0.435. Some of these windows overlapped and were distributed in 11 regions. These regions were in good agreement with reported quantitative trait locus, therefore would be selection signatures and good candidates that harbor the causative mutations.     

Effect of allele frequencies,effect sizes and number of markers on prediction of quantitative traits in chickens

The objective was to assess goodness of fit and predictive ability of subsets of single nucleotide polymorphism (SNP) markers constructed based on minor allele frequency (MAF), effect sizes and varying marker density. Target traits were body weight (BW), ultrasound measurement of breast muscle (BM) and hen house egg production (HHP) in broiler chickens. We used a 600 K Affymetrix platform with 1352 birds genotyped. The prediction method was genomic best linear unbiased prediction (GBLUP) with 354 564 single nucleotide polymorphisms (SNPs) used to derive a genomic relationship matrix ( G ). Predictive ability was assessed as the correlation between predicted genomic values and corrected phenotypes from a threefold cross‐validation. Predictive ability was 0.27 ± 0.002 for BW, 0.33 ± 0.001 for BM and 0.20 ± 0.002 for HHP. For the three traits studied, predictive ability decreased when SNPs with a higher MAF were used to construct G . Selection of the 20% SNPs with the largest absolute effect sizes induced a predictive ability equal to that from fitting all markers together. When density of markers increased from 5 K to 20 K, predictive ability enhanced slightly. These results provide evidence that designing a low‐density chip using low‐frequency markers with large effect sizes may be useful for commercial usage.     

Genome‐wide association study for fatty acid composition in Japanese Black cattle

Fatty acid composition is one of the important traits in beef. The aim of this study was to identify candidate genomic regions for fatty acid composition by genome‐wide association study with 50 K single nucleotide polymorphism (SNP) array in Japanese Black cattle. A total of 461 individuals and 40 657 SNPs were used in this study. We applied genome‐wide rapid association using mixed model and regression (GRAMMAR) and genomic control approaches to estimate the associations between genotypes and fatty acid composition. In addition, two SNPs in fatty acid synthase (FASN) (T1952A) and stearoyl‐CoA desaturase (SCD) (V293A) genes were also genotyped. Association analysis revealed that 30 significant SNPs for several fatty acids (C14:0, C14:1, C16:1 and C18:1) were located in the BTA19 FASN gene located within this region but the FASN mutation had no significant effect on any traits. We also detected one significant SNP for C18:1 on BTA23 and two SNPs for C16:0 on BTA25. The region around 17 Mb on BTA26 harbored two significant SNPs for C14:1 and SNP in SCD in this region showed the strongest association with C14:1. This study demonstrated novel candidate regions in BTA19, 23 and 25 for fatty acid composition.     

Estimation of variance and genomic prediction using genotypes imputed from low‐density marker subsets for carcass traits in Japanese black cattle

The influence of genotype imputation using low‐density single nucleotide polymorphism (SNP) marker subsets on the genomic relationship matrix (G matrix), genetic variance explained, and genomic prediction (GP) was investigated for carcass weight and marbling score in Japanese Black fattened steers, using genotype data of approximately 40,000 SNPs. Genotypes were imputed using equally spaced SNP subsets of different densities. Two different linear models were used. The first (model 1) incorporated one G matrix, while the second (model 2) used two different G matrices constructed using the selected and remaining SNPs. When using model 1, the estimated additive genetic variance was always larger when using all SNPs obtained via genotype imputation than when using only equally spaced SNP subsets. The correlations between the genomic estimated breeding values obtained using genotype imputation with at least 3,000 SNPs and those using all available SNPs without imputation were higher than 0.99 for both traits. While additive genetic variance was likely to be partitioned with model 2, it did not enhance the accuracy of GP compared with model 1. These results indicate that genotype imputation using an equally spaced low‐density panel of an appropriate size can be used to produce a cost‐effective, valid GP.