Recent artificial selection in U.S. Jersey cattle impacts autozygosity levels of specific genomic regions

Background

Genome signatures of artificial selection in U.S. Jersey cattle were identified by examining changes in haplotype homozygosity for a resource population of animals born between 1953 and 2007. Genetic merit of this population changed dramatically during this period for a number of traits, especially milk yield. The intense selection underlying these changes was achieved through extensive use of artificial insemination (AI), which also increased consanguinity of the population to a few superior Jersey bulls. As a result, allele frequencies are shifted for many contemporary animals, and in numerous cases to a homozygous state for specific genomic regions. The goal of this study was to identify those selection signatures that occurred after extensive use of AI since the 1960, using analyses of shared haplotype segments or Runs of Homozygosity. When combined with animal birth year information, signatures of selection associated with economically important traits were identified and compared to results from an extended haplotype homozygosity analysis.

Results

Overall, our results reveal that more recent selection increased autozygosity across the entire genome, but some specific regions increased more than others. A genome-wide scan identified more than 15 regions with a substantial change in autozygosity. Haplotypes found to be associated with increased milk, fat and protein yield in U.S. Jersey cattle also consistently increased in frequency.

Conclusions

The analyses used in this study was able to detect directional selection over the last few decades when individual production records for Jersey animals were available.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1500-x) contains supplementary material, which is available to authorized users.     

Consistency of linkage disequilibrium between Chinese and Nordic Holsteins and genomic prediction for Chinese Holsteins using a joint reference population

Background

In China, the reference population of genotyped Holstein cattle is relatively small with to date, 80 bulls and 2091 cows genotyped with the Illumina 54 K chip. Including genotyped Holstein cattle from other countries in the reference population could improve the accuracy of genomic prediction of the Chinese Holstein population. This study investigated the consistency of linkage disequilibrium between adjacent markers between the Chinese and Nordic Holstein populations, and compared the reliability of genomic predictions based on the Chinese reference population only or the combined Chinese and Nordic reference populations.

Methods

Genomic estimated breeding values of Chinese Holstein cattle were predicted using a single-trait GBLUP model based on the Chinese reference dataset, and using a two-trait GBLUP model based on a joint reference dataset that included both the Chinese and Nordic Holstein data.

Results

The extent of linkage disequilibrium was similar in the Chinese and Nordic Holstein populations and the consistency of linkage disequilibrium between the two populations was very high, with a correlation of 0.97. Genomic prediction using the joint versus the Chinese reference dataset increased reliabilities of genomic predictions of Chinese Holstein bulls in the test data from 0.22, 0.15 and 0.11 to 0.51, 0.47 and 0.36 for milk yield, fat yield and protein yield, respectively. Using five-fold cross-validation, reliabilities of genomic predictions of Chinese cows increased from 0.15, 0.12 and 0.15 to 0.26, 0.17 and 0.20 for milk yield, fat yield and protein yield, respectively.

Conclusions

The linkage disequilibrium between the two populations was very consistent and using the combined Nordic and Chinese reference dataset substantially increased reliabilities of genomic predictions for Chinese Holstein cattle.     

Genotyping strategies for genomic selection in small dairy cattle populations

This study evaluated different female-selective genotyping strategies to increase the predictive accuracy of genomic breeding values (GBVs) in populations that have a limited number of sires with a large number of progeny. A simulated dairy population was utilized to address the aims of the study. The following selection strategies were used: random selection, two-tailed selection by yield deviations, two-tailed selection by breeding value, top yield deviation selection and top breeding value selection. For comparison, two other strategies, genotyping of sires and pedigree indexes from traditional genetic evaluation, were included in the analysis. Two scenarios were simulated, low heritability (h² = 0.10) and medium heritability (h² = 0.30). GBVs were estimated using the Bayesian Lasso. The accuracy of predicted GBVs using the two-tailed strategies was better than the accuracy obtained using other strategies (0.50 and 0.63 for the two-tailed selection by yield deviations strategy and 0.48 and 0.63 for the two-tailed selection by breeding values strategy in low- and medium-heritability scenarios, respectively, using 1000 genotyped cows). When 996 genotyped bulls were used as the training population, the sire’ strategy led to accuracies of 0.48 and 0.55 for low- and medium-heritability traits, respectively. The Random strategies required larger training populations to outperform the accuracies of the pedigree index; however, selecting females from the top of the yield deviations or breeding values of the population did not improve accuracy relative to that of the pedigree index. Bias was found for all genotyping strategies considered, although the Top strategies produced the most biased predictions. Strategies that involve genotyping cows can be implemented in breeding programs that have a limited number of sires with a reliable progeny test. The results of this study showed that females that exhibited upper and lower extreme values within the distribution of yield deviations may be included as training population to increase reliability in small reference populations. The strategies that selected only the females that had high estimated breeding values or yield deviations produced suboptimal results.     

Invited review: efficient computation strategies in genomic selection

The purpose of this study is review and evaluation of computing methods used in genomic selection for animal breeding. Commonly used models include SNP BLUP with extensions (BayesA, etc), genomic BLUP (GBLUP) and single-step GBLUP (ssGBLUP). These models are applied for genomewide association studies (GWAS), genomic prediction and parameter estimation. Solving methods include finite Cholesky decomposition possibly with a sparse implementation, and iterative Gauss–Seidel (GS) or preconditioned conjugate gradient (PCG), the last two methods possibly with iteration on data. Details are provided that can drastically decrease some computations. For SNP BLUP especially with sampling and large number of SNP, the only choice is GS with iteration on data and adjustment of residuals. If only solutions are required, PCG by iteration on data is a clear choice. A genomic relationship matrix (GRM) has limited dimensionality due to small effective population size, resulting in infinite number of generalized inverses of GRM for large genotyped populations. A specific inverse called APY requires only a small fraction of GRM, is sparse and can be computed and stored at a low cost for millions of animals. With APY inverse and PCG iteration, GBLUP and ssGBLUP can be applied to any population. Both tools can be applied to GWAS. When the system of equations is sparse but contains dense blocks, a recently developed package for sparse Cholesky decomposition and sparse inversion called YAMS has greatly improved performance over packages where such blocks were treated as sparse. With YAMS, GREML and possibly single-step GREML can be applied to populations with >50 000 genotyped animals. From a computational perspective, genomic selection is becoming a mature methodology.     

Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.)

Molecular Breeding - As an important pollination system, male sterility has been used widely for broccoli (Brassica oleracea var. italica) hybrid production. New male sterile lines are important...     

Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs

One of the most important applications of genomic selection in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. Nineteen tropical maize biparental populations evaluated in multienvironment trials were used in this study to assess prediction accuracy of different quantitative traits using low-density (~200 markers) and genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs), respectively. An extension of the Genomic Best Linear Unbiased Predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross-validation methods were applied to quantify prediction accuracy. Our results showed that: (1) low-density SNPs (~200 markers) were largely sufficient to get good prediction in biparental maize populations for simple traits with moderate-to-high heritability, but GBS outperformed low-density SNPs for complex traits and simple traits evaluated under stress conditions with low-to-moderate heritability; (2) heritability and genetic architecture of target traits affected prediction performance, prediction accuracy of complex traits (grain yield) were consistently lower than those of simple traits (anthesis date and plant height) and prediction accuracy under stress conditions was consistently lower and more variable than under well-watered conditions for all the target traits because of their poor heritability under stress conditions; and (3) the prediction accuracy of GE models was found to be superior to that of non-GE models for complex traits and marginal for simple traits.     

Bivalve enhancement and restoration strategies in Florida,U.S.A.

In Florida, U.S.A. populations of recreationally and commercially important bivalve molluscs are stressed by a variety of factors, including habitat degradation, overfishing and development. Bivalves such as the bay scallop, Argopecten irradians, and the hard clam, Mercenaria spp., are uniquely positioned to benefit from restoration and enhancement activities because of the physical setting in which they live or because of recent efforts to restore habitat. For bay scallops, a major restoration effort has been implemented on the west coast of Florida. Adult scallops are collected from the target site and spawned in the laboratory. The offspring are cultured in ponds until they reach an average shell height of approximately 30 mm and then are planted in cages at the target site from which the parents were collected. Parents and offspring are genetically monitored, as are new recruits sampled at a variety of sites along the coast. The ultimate goal of this restoration program is to create concentrated patches of spawners that will supply recruits to west Florida seagrass beds. For hard clams, three different strategies are being tested to determine the most ecologically and economically feasible approach to use to enhance harvestable clam populations. Hard clams are occasionally abundant in the Indian River lagoon on the east central coast of the state, but transportation causeways fragment the lagoon into a series of basins that are largely isolated from one another. At any time, only a subset of the basins that compose the lagoon may be environmentally suitable for supporting the sensitive larval and juvenile stages of hard clams, and the members of that subset change constantly as environmental conditions change in the lagoon. Transplanting spawner stock, seeding juveniles under a variety of protective treatments, and injecting fertilized eggs directly into the lagoon are each being tested for their effectiveness in exploiting environmentally appropriate conditions and producing successful settlement events. Each strategy is being tested for its ecological and economic suitability, and the most appropriate strategy will be implemented in an effort to maintain clam abundance at a level where licensed clammers in the Indian River region can realize a minimum income from the fishery, even when naturally recruiting clam stocks are depleted.     

Effect of genomic prediction on response to selection in forest tree breeding

Through stochastic simulations, estimates of breeding values accuracies and response to selection were assessed under traditional pedigree-based and genomic-based evaluation methods. More specifically, several key parameters such as the trait’s heritability (0.2 and 0.6), the number of QTLs underlying the trait (100 to 200), and the marker density (1 to 10 SNPs/cM) were evaluated. Additionally, impact of two contrasting mating designs (partial diallel vs. single-pair mating) was investigated. Response to selection was then assessed in a seed production population (seed orchard consisting of unrelated selections) for different effective population sizes (N_e?=?5 to 25). The simulated candidate population comprised a fixed size of 2050 individuals with fast linkage disequilibrium decay, generally found in forest tree populations. Following the genetic/genomic evaluation, top-ranked individuals were selected to meeting the predetermined effective population size in target production population. The combination of low h², high N_e, and dense marker coverage resulted at maximum relative genomic prediction efficiency and the most efficient exploitation of the Mendelian sampling term (within-family additive genetic variance). Since genomic prediction of breeding values constitutes the methodological foundation of genomic selection, our results can be used to address important questions when similar scenarios are considered.     

Solvent selection strategies for extractive biocatalysis.

This report follows the development of systematic solvent screening strategies for the identification of superior pure solvents and introduces techniques for the identification of effective coextractants. Specifically, methods to predict the biocompatibility and extractant capability of solvents are discussed. Biocompatibility is predicted by using heuristic data or the correlations between bioactivity and the logarithm of the partition coefficient of the solvent or the concentration of solvent in the cell membrane. A computer program, known as the extractant screening program or ESP, has been developed to effectively predict the behavior of virtually any product in any solvent/aqueous system. It is demonstrated that a biocompatible yet poor solvent can be mixed with a toxic solvent that has better extractant properties to yield a mixture with improved solvent characteristics that is still biocompatible. The fact that solvents do not mix in an ideal manner is exploited by using ESP to identify solvent mixtures that are still biocompatible at relatively high concentrations of toxic solvent.     

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.Subject terms: Quantitative trait, Genetic models     

Comparing three classification strategies for use in ecology

Abstract. We compare three common types of clustering algorithms for use with community data. TWINSPAN is divisive hierarchical, flexible-UPGMA is agglomerative and hierarchical, and ALOC is non-hierarchical. A balanced design six-factor model was used to generate 480 data sets of known characteristics. Recovery of the embedded clusters suggests that both flexible UPGMA and ALOC are significantly better than TWINSPAN. No significant difference existed between flexible UPGMA and ALOC.     

4040 SNPs for genomic analysis in the rhesus macaque (Macaca mulatta)

Although the rhesus macaque (Macaca mulatta) is commonly used for biomedical research and becoming a preferred model for translational medicine, quantification of genome-wide variation has been slow to follow the publication of the genome in 2007. Here we report the properties of 4040 single nucleotide polymorphisms discovered and validated in Chinese and Indian rhesus macaques from captive breeding colonies in the United States. Frequency-matched measures of linkage disequilibrium were much greater in the Indian sample. Although the majority of polymorphisms were shared between the two populations, rare alleles were over twice as common in the Chinese sample. Indian rhesus had higher rates of heterozygosity, as well as previously undetected substructure, potentially due to admixture from Burma in wild populations and demographic events post-captivity.