Genomic selection using different marker types and densities

With the availability of high-density marker maps and cost-effective genotyping, genomic selection methods may provide faster genetic gain than can be achieved by current selection methods based on phenotypes and the pedigree. Here we investigate some of the factors driving the accuracy of genomic selection, namely marker density and marker type (i.e., microsatellite and SNP markers), and the use of marker haplotypes versus marker genotypes alone. Different densities were tested with marker densities equivalent to 2, 1, 0.5, and 0.25N(e) markers/morgan using microsatellites and 8, 4, 2, and 1N(e) markers/morgan using SNP, where 1N(e) markers/morgan means 100 markers per morgan, if effective size (N(e)) is 100. Marker characteristics and linkage disequilibria were obtained by simulating a population over 1,000 generations to achieve a mutation drift balance. The marker designs were evaluated for their accuracy of predicting breeding values from either estimating marker effects or estimating effects of haplotypes based upon combining 2 markers. Using microsatellites as direct marker effects, the accuracy of selection increased from 0.63 to 0.83 as the density increased from 0.25N(e)/morgan to 2N(e)/morgan. Using SNP markers as direct marker effects, the accuracy of selection increased from 0.69 to 0.86 as the density increased from 1N(e)/morgan to 8N(e)/morgan. The SNP markers required a 2 to 3 times greater density compared with using microsatellites to achieve a similar accuracy. The biases that genomic selection EBV often show are due to the prediction of marker effects instead of QTL effects, and hence, genomic selection EBV may need rescaling for practical use. Using haplotypes resulted in similar or reduced accuracies compared with using direct marker effects. In practical situations, this means that it is advantageous to use direct marker effects, because this avoids the estimation of marker phases with the associated errors. In general, the results showed that the accuracy remained responsive with small bias to increasing marker density at least up to 8N(e) SNP/morgan, where the effective population size was 100 and with the genomic model assumed. For a 30-morgan genome and N(e) = 100, this implies that about approximately 24,000 SNP are needed.     

Genomic prediction in a numerically small breed population using prioritized genetic markers from whole-genome sequence data

The objective of this study was to investigate the accuracy of genomic prediction of body weight and eating quality traits in a numerically small sheep population (Dorper sheep). Prediction was based on a large multi-breed/admixed reference population and using (a) 50k or 500k single nucleotide polymorphism (SNP) genotypes, (b) imputed whole-genome sequencing data (~31 million), (c) selected SNPs from whole genome sequence data and (d) 50k SNP genotypes plus selected SNPs from whole-genome sequence data. Furthermore, the impact of using a breed-adjusted genomic relationship matrix on accuracy of genomic breeding value was assessed. The selection of genetic variants was based on an association study performed on imputed whole-genome sequence data in an independent population, which was chosen either randomly from the base population or according to higher genetic proximity to the target population. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of genomic prediction was assessed according to the correlation between genomic breeding value and corrected phenotypes divided by the square root of trait heritability. The accuracy of genomic prediction was between 0.20 and 0.30 across different traits based on common 50k SNP genotypes, which improved on average by 0.06 (absolute value) on average based on using prioritized genetic markers from whole-genome sequence data. Using prioritized genetic markers from a genetically more related GWAS population resulted in slightly higher prediction accuracy (0.02 absolute value) compared to genetic markers derived from a random GWAS population. Using high-density SNP genotypes or imputed whole-genome sequence data in GBLUP showed almost no improvement in genomic prediction accuracy however, accounting for different marker allele frequencies in reference population according to a breed-adjusted GRM resulted to on average 0.024 (absolute value) increase in accuracy of genomic prediction.     

Using the genomic relationship matrix to predict the accuracy of genomic selection

Estimated breeding values (EBVs) using data from genetic markers can be predicted using a genomic relationship matrix, derived from animal's genotypes, and best linear unbiased prediction. However, if the accuracy of the EBVs is calculated in the usual manner (from the inverse element of the coefficient matrix), it is likely to be overestimated owing to sampling errors in elements of the genomic relationship matrix. We show here that the correct accuracy can be obtained by regressing the relationship matrix towards the pedigree relationship matrix so that it is an unbiased estimate of the relationships at the QTL controlling the trait. This method shows how the accuracy increases as the number of markers used increases because the regression coefficient (of genomic relationship towards pedigree relationship) increases. We also present a deterministic method for predicting the accuracy of such genomic EBVs before data on individual animals are collected. This method estimates the proportion of genetic variance explained by the markers, which is equal to the regression coefficient described above, and the accuracy with which marker effects are estimated. The latter depends on the variance in relationship between pairs of animals, which equals the mean linkage disequilibrium over all pairs of loci. The theory was validated using simulated data and data on fat concentration in the milk of Holstein cattle.     

Effect of marker‐data editing on the accuracy of genomic prediction

Genomic selection is a method to predict breeding values using genome‐wide single‐nucleotide polymorphism (SNP) markers. High‐quality marker data are necessary for genomic selection. The aim of this study was to investigate the effect of marker‐editing criteria on the accuracy of genomic predictions in the Nordic Holstein and Jersey populations. Data included 4429 Holstein and 1071 Jersey bulls. In total, 48 222 SNP for Holstein and 44 305 SNP for Jersey were polymorphic. The SNP data were edited based on (i) minor allele frequencies (MAF) with thresholds of no limit, 0.001, 0.01, 0.02, 0.05 and 0.10, (ii) deviations from Hardy–Weinberg proportions (HWP) with thresholds of no limit, chi‐squared p‐values of 0.001, 0.02, 0.05 and 0.10, and (iii) GenCall (GC) scores with thresholds of 0.15, 0.55, 0.60, 0.65 and 0.70. The marker data sets edited with different criteria were used for genomic prediction of protein yield, fertility and mastitis using a Bayesian variable selection and a GBLUP model. De‐regressed EBV were used as response variables. The result showed little difference between prediction accuracies based on marker data sets edited with MAF and deviation from HWP. However, accuracy decreased with more stringent thresholds of GC score. According to the results of this study, it would be appropriate to edit data with restriction of MAF being between 0.01 and 0.02, a p‐value of deviation from HWP being 0.05, and keeping all individual SNP genotypes having a GC score over 0.15.     

畜禽基因组选择方法研究进展

畜禽的选种选育在生产中至关重要,育种值估计是选种选育的核心。基因组选择（genomic selection,GS）是利用全基因组范围内的高密度标记估计个体基因组育种值的一种新型分子育种方法,目前已在牛、猪、鸡等畜禽育种中得到应用并取得了良好的效果。该方法可实现畜禽育种早期选择,降低测定费用,缩短世代间隔,提高育种值估计准确性,加快遗传进展。基因组选择主要是通过参考群体中每个个体的表型性状信息和单核苷酸多态性（single nucleotide polymorphism,SNP）基因型估计出每个SNP的效应值,然后测定候选群体中每个个体的SNP基因型,计算候选个体的基因组育种值,根据基因组育种值的高低对候选群体进行合理的选择。随着基因分型技术快速发展和检测成本不断降低,以及基因组选择方法不断优化,基因组选择已成为畜禽选种选育的重要手段。作者对一些常用的基因组选择方法进行了综述,比较了不同方法之间的差异,分析了基因组选择存在的问题与挑战,并展望了其在畜禽育种中的应用前景。     

Research Progress on Genomic Selection Methods in Livestock and Poultry

Selection and breeding are very important in production of livestock and poultry,and breeding value estimation is the core of selection and breeding.Genomic selection (GS) is a novel molecular breeding method to estimate genomic breeding value using high-density markers across the whole genome.At present,GS has been successfully applied in cattle,pig,chicken and so on,and made significant progress.This method can achieve early selection,decrease the testing costs,shorten generation interval,improve the accuracy of breeding value estimation and accelerate genomic progress.GS estimates the effect of SNP by phenotype information and SNP genotype of each individual in the reference population,and measures the SNP genotype to calculate the genomic estimated breeding value in the candidate population,then selects the best individuals according to the genomic estimated breeding value.With the rapid development of genotyping technology and the decrease of detection cost,and the continuous optimization and high efficiency of genomic selection methods,genomic selection has become an important research method in the selection and breeding of livestock and poultry.The authors reviewed some of the widely used genomic selection methods,compared the differences between different methods,analyzed the problems and challenges of genomic selection,and looked forward to its application prospects in breeding.     

Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions

Background: Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci(QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.Methods: Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect.A commonly used method for genome wide association studies is to fit a mixture model such as Bayes B that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a42 K SNP panel.Results: Using data from the first 7 generations, both Bayes B and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with Bayes B and less than 1 % with GBLUP. When predicting phenotypes in generation 8 Bayes B accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with Bayes B in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with Bayes B but only6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using Bayes B but had no predictive power with GBLUP.Conclusions: In conclusion, In the presence of large effect QTL, Bayes B did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.     

A method to detect recombination between a genetic marker and the QTL that it marks

Some individual genetic markers show strong and apparently consistent effects on trait merit and are taken as causative mutations that can be used directly as fixed effects in marker‐assisted selection programs. If the effect of such a marker is seen to decrease over time, key reasons include epistasis, where the effect depends on genetic background, and recombination, where the marker is in fact not causative, and strong linkage disequilibrium between the marker and the causative QTL is breaking down. This paper presents a method to detect the latter scenario, including calculation of the probability of a recombinant haplotype for each gamete contributing to each individual in a pedigree. This method requires only pedigree, phenotypes and genotypic information on the single marker. Missing marker genotypes are handled by the method, but with diminishing power. For biallelic markers, strong QTL effects are needed for the method to be of clear value. Given suitable results, breeders may chose to eliminate certain individuals from the breeding program in order to continue using the single genetic marker under high linkage disequilibrium with the causative QTL. Alternatively, other linked markers might be sought that can be used individually or in haplotype tests to restore strong LD for marker‐assisted selection.     

利用基因组信息提高畜禽生产性能研究进展

为了满足人们对畜产品需求的快速增长，必须在加快畜禽产业发展的同时把对环境的影响降到最低，提高畜禽遗传特性有望促进这一问题的解决。进入21世纪以来，以基因组选择为核心的分子育种技术迎来了发展机遇，利用该技术可实现早期准确选择，从而大幅度缩短世代间隔，加快群体遗传进展，并显著降低育种成本。虽然在某些畜种中（如奶牛），基因组选择取得了成功，群体也获得较大遗传进展，但仍无法满足快速增长的需求。因此，亟需寻找能够进一步加快遗传进展的方法。研究表明，在SNP标记数据中加入目标性状的已知功能基因信息，可以提高基因组育种值预测的准确性，进而加快遗传进展。而挖掘更多基因组信息的同时，开发更优化的分析方法可以更有助于目标的实现。文章总结了主要畜禽物种的可用基因组数据，包括牛、绵羊、山羊、猪和鸡以及这些数据是如何有助于鉴定影响重要性状的遗传标记和基因，从而进一步提高基因组选择的准确性。     

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.     

全基因组选择及其在奶牛育种中应用进展

全基因组选择是指基于基因组育种值（GEBV）的选择方法,指通过检测覆盖全基因组的分子标记,利用基因组水平的遗传信息对个体进行遗传评估,以期获得更高的育种值估计准确度。由于可显著缩短世代间隔,全基因组选择作为一项育种新技术在奶牛育种中具有广阔的应用前景,目前已经成为各国的研究热点。不同国家的试验结果表明,奶牛育种中基于GEBV的遗传评估可靠性在20%~67%之间,如果代替常规后裔测定体系,可节省92%的育种成本。本文综述了全基因组选择的基本原理及其在各国奶牛育种中的应用现状和所面临的问题。     

Detection of single nucleotide polymorphisms associated with ultrasonic backfat depth in a segregating Meishan x White Composite population

Multiple genomic scans have identified QTL for backfat deposition across the porcine genome. The objective of this study was to detect SNP and genomic regions associated with ultrasonic backfat. A total of 74 SNP across 5 chromosomes (SSC 1, 3, 7, 8, and 10) were selected based on their proximity to backfat QTL or to QTL for other traits of interest in the experimental population. Gilts were also genotyped for a SNP thought to influence backfat in the thyroxine-binding globulin gene (TBG) on SSC X. Genotypic data were collected on 298 gilts, divided between the F8 and F10 generations of the US Meat Animal Research Center Meishan resource population (composition, one-quarter Meishan). Backfat depths were recorded by ultrasound from 3 locations along the back at approximately 210 and 235 d of age in the F8 and F10 generations, respectively. Ultrasound measures were averaged for association analyses. Regressors for additive, dominant, and parent-of-origin effects of each SNP were calculated using genotypic probabilities computed by allelic peeling algorithms in GenoProb. The association model included the fixed effects of scan date and TBG genotype, the covariates of weight and SNP regressors, and random additive polygenic effects to account for genetic similarities between animals not explained by known genotypes. Variance components for polygenic effects and error were estimated using MTDFREML. Initially, each SNP was fitted (once with and once without parent-of-origin effects) separately due to potential multi-collinearity between regressions of closely linked markers. To form a final model, all significant SNP across chromosomes were included in a common model and were individually removed in successive iterations based on their significance. Across all analyses, TBG was significant, with an additive effect of approximately 1.2 to 1.6 mm of backfat. Three SNP on SSC3 remained in the final model even though few studies have identified QTL for backfat on this chromosome. Two of these SNP exhibited irregular parent-of-origin effects and may not have been detected in other genome scans. One significant SNP on SSC7 remained in the final, backward-selected model; the estimated effect of this marker was similar in magnitude and direction to previously identified QTL. This SNP can potentially be used to introgress the leaner Meishan allele into commercial swine populations.     

基于高通量测序技术挖掘分子遗传标记及其在蛋鸡生产中的精准应用

高通量测序技术是研究物种复杂生物性状遗传机制的基础,随着高通量测序技术的不断优化提升,一些与生物表型性状密切相关的基因组变异被精准地挖掘出来,其中包括单核苷酸多态位点(SNP)、小片段的插入或缺失(Indel)、拷贝数变异(CNV)以及结构变异(SV)为代表的分子标记。与传统遗传标记相比较,分子遗传标记具有多态性高、遍布整个基因组、检测手段简单快捷以及成本低廉的特点。通过检测覆盖全基因组范围内的分子标记,利用基因组水平的遗传信息对个体或群体遗传资源进行评估,能够缩短世代间隔、提高选种的准确性,进而在短期内取得较大的遗传进展。作者从高通量测序技术挖掘分子遗传标记角度入手,综述了三代测序技术发展历程和应用领域以及三代分子遗传标记检测技术在蛋鸡种业创新中的应用,并详细阐述了高通量测序技术与分子遗传标记相结合在蛋鸡群体遗传多样性及进化分类、群体遗传图谱的构建和功能基因定位、数量性状形成的遗传机制解析和质量性状形成的遗传机制解析等4个方面的精准应用,以期为蛋鸡基因组选择进入实质应用阶段提供科学依据和指导。     

畜禽基因组选择的研究进展

基因组选择（genomic selection,GS）是继标记辅助选择（marker-assisted selection,MAS）之后发展起来的新一代畜禽遗传评估的新方法。近年来,不少学者从各方面对GS在畜禽育种中的运用进行了研究,结果发现,与其他方法相比,GS优势明显,是当前畜禽遗传育种领域的研究热点。作者系统地阐述了GS估计染色体片段效应的方法及其准确性比较,详细介绍了影响GS准确性的因素、GS的经济效益,以及世界各国学者研究GS在实际育种中的应用情况,最后简述了中国畜禽育种开展GS策略所面临的挑战及其展望。     

Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low‐density SNP panels: Evidence that long‐range LD is a major contributing factor

Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium‐density single nucleotide polymorphism (SNP) array. Here, the impact of lower‐density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)‐flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single‐step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree‐based prediction (0.50–0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL‐flanking SNP (0.65–0.72) was similar to the panel with 35K SNP (0.65–0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r² ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long‐range LD likely contributed to the accurate genomic predictions with the low‐density SNP panels. Population structure analysis supported the hypothesis that long‐range LD in this population may be caused by admixture. Results suggest that lower‐cost, low‐density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs.     

Genes controlling vaccine responses and disease resistance to respiratory viral pathogens in cattle

Farm animals remain at risk of endemic, exotic and newly emerging viruses. Vaccination is often promoted as the best possible solution, and yet for many pathogens, either there are no appropriate vaccines or those that are available are far from ideal. A complementary approach to disease control may be to identify genes and chromosomal regions that underlie genetic variation in disease resistance and response to vaccination. However, identification of the causal polymorphisms is not straightforward as it generally requires large numbers of animals with linked phenotypes and genotypes. Investigation of genes underlying complex traits such as resistance or response to viral pathogens requires several genetic approaches including candidate genes deduced from knowledge about the cellular pathways leading to protection or pathology, or unbiased whole genome scans using markers spread across the genome. Evidence for host genetic variation exists for a number of viral diseases in cattle including bovine respiratory disease and anecdotally, foot and mouth disease virus (FMDV). We immunised and vaccinated a cattle cross herd with a 40-mer peptide derived from FMDV and a vaccine against bovine respiratory syncytial virus (BRSV). Genetic variation has been quantified. A candidate gene approach has grouped high and low antibody and T cell responders by common motifs in the peptide binding pockets of the bovine major histocompatibility complex (BoLA) DRB3 gene. This suggests that vaccines with a minimal number of epitopes that are recognised by most cattle could be designed. Whole genome scans using microsatellite and single nucleotide polymorphism (SNP) markers has revealed many novel quantitative trait loci (QTL) and SNP markers controlling both humoral and cell-mediated immunity, some of which are in genes of known immunological relevance including the toll-like receptors (TLRs). The sequencing, assembly and annotation of livestock genomes and is continuing apace. In addition, provision of high-density SNP chips should make it possible to link phenotypes with genotypes in field populations without the need for structured populations or pedigree information. This will hopefully enable fine mapping of QTL and ultimate identification of the causal gene(s). The research could lead to selection of animals that are more resistant to disease and new ways to improve vaccine efficacy.     

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.     

Regularized quantile regression for SNP marker estimation of pig growth curves

Background: Genomic growth curves are general y defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression(QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time(genomic growth curve) under different quantiles(levels).Results: The regularized quantile regression(RQR) enabled the discovery, at different levels of interest(quantiles), of the most relevant markers al owing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters(mature weight and maturity rate): two(ALGA0096701 and ALGA0029483)for RQR(0.2), one(ALGA0096701) for RQR(0.5), and one(ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others.Conclusions: RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest(quantiles), the most relevant markers for each trait(growth curve parameter estimates) and their respective chromosomal positions(identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.     

Genotypes and allele frequencies of buried SNPs in a bovine single‐nucleotide polymorphism array in Japanese Black cattle

Single nucleotide polymorphism (SNP) arrays are widely used for genetic and genomic analyses in cattle breeding; thus, data derived from SNP arrays have accumulated on a large scale nationwide. Commercial SNP arrays contain a considerable number of unassigned SNPs on the chromosome/position on the genome; these SNPs are excluded in subsequent analyses. Notably, the position‐unassigned SNPs, or “buried SNPs” include some of the markers associated with genetic disease. In this study, we identified the position of buried SNPs using the Basic Local Alignment Search Tool against the surrounding sequences and characterized the relationship between SNPs and genetic diseases in Online Mendelian Inheritance in Animals based on the genomic position. We determined the position of 285 buried SNPs on the genome and surveyed the genotype and allele frequencies of these SNPs in 5,955 individual Japanese Black cattle. Eleven SNPs associated with genetic disease, which contained five buried SNPs, were found in the population with the risk allele frequency ranging from 0.00008396 to 0.46. These results indicate that buried SNPs in the bovine SNP array can be utilized to identify associations with genetic disorders from large scale accumulated SNP genotype data in Japanese Black cattle.     

中国荷斯坦牛Toll样受体4基因的遗传多态性与体细胞评分的关联分析

试验旨在研究中国荷斯坦牛Toll样受体4(toll-like receptor 4,TLR4)基因的遗传多态性及其与体细胞评分(somatic cell score,SCS)的关联性,寻找与乳房炎相关的分子标记,加快中国荷斯坦牛的抗病育种。利用PCR-RFLP和PCR-SSCP技术对30个公牛家系的610头中国荷斯坦牛TLR4基因进行多态性检测,用最小二乘均数法对TLR4基因的多态位点与SCS进行相关分析。结果发现,试验共检测到2个单核苷酸多态(single nucleotide polymorphisms,SNPs),TLR4基因5'侧翼区存在SNP -226 G>C突变,经PCR-RFLP检测发现3种基因型:GG、GC和CC,基因型频率分别为0.208、0.482和0.310;外显子3存在SNP 1760 C>T突变,经PCR-SSCP检测发现3种基因型:CC、TC和TT,基因型频率分别为0.738、0.225和0.007,以上2位点均偏离Hardy-Weinberg 平衡。对于SNP -226 G>C位点,基因型个体的SCS差异不显著;对于SNP 1760 C>T位点,CC基因型个体的SCS最小二乘均值极显著低于TT和TC基因型个体(P<0.01)。SNP 1760 C>T的CC基因型对于中国荷斯坦牛的SCS有较大的遗传效应,可作为分子标记应用于奶牛乳房炎抗性筛选。