Combined use of principal component analysis and random forests identify population‐informative single nucleotide polymorphisms: application in cattle breeds

The genetic identification of the population of origin of individuals, including animals, has several practical applications in forensics, evolution, conservation genetics, breeding and authentication of animal products. Commercial high‐density single nucleotide polymorphism (SNP) genotyping tools that have been recently developed in many species provide information from a large number of polymorphic sites that can be used to identify population‐/breed‐informative markers. In this study, starting from Illumina BovineSNP50 v1 BeadChip array genotyping data available from 3711 cattle of four breeds (2091 Italian Holstein, 738 Italian Brown, 475 Italian Simmental and 407 Marchigiana), principal component analysis (PCA) and random forests (RFs) were combined to identify informative SNP panels useful for cattle breed identification. From a PCA preselected list of 580 SNPs, RFs were computed using ranking methods (Mean Decrease in the Gini Index and Mean Accuracy Decrease) to identify the most informative 48 and 96 SNPs for breed assignment. The out‐of‐bag (OOB) error rate for both ranking methods and SNP densities ranged from 0.0 to 0.1% in the reference population. Application of this approach in a test population (10% of individuals pre‐extracted from the whole data set) achieved 100% of correct assignment with both classifiers. Linkage disequilibrium between selected SNPs was relevant (r² > 0.6) only in few pairs of markers indicating that most of the selected SNPs captured different fractions of variance. Several informative SNPs were in genes/QTL regions that affect or are associated with phenotypes or production traits that might differentiate the investigated breeds. The combination of PCA and RF to perform SNP selection and breed assignment can be easily implemented and is able to identify subsets of informative SNPs useful for population assignment starting from a large number of markers derived by high‐throughput genotyping platforms.     

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.     

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.     

Dissection of additive genetic variability for quantitative traits in chickens using SNP markers

The aim of this study was to separate marked additive genetic variability for three quantitative traits in chickens into components associated with classes of minor allele frequency (MAF), individual chromosomes and marker density using the genomewide complex trait analysis (GCTA) approach. Data were from 1351 chickens measured for body weight (BW), ultrasound of breast muscle (BM) and hen house egg production (HHP), each bird with 354 364 SNP genotypes. Estimates of variance components show that SNPs on commercially available genotyping chips marked a large amount of genetic variability for all three traits. The estimated proportion of total variation tagged by all autosomal SNPs was 0.30 (SE 0.04) for BW, 0.33 (SE 0.04) for BM, and 0.19 (SE 0.05) for HHP. We found that a substantial proportion of this variation was explained by low frequency variants (MAF <0.20) for BW and BM, and variants with MAF 0.10–0.30 for HHP. The marked genetic variance explained by each chromosome was linearly related to its length (R² = 0.60) for BW and BM. However, for HHP, there was no linear relationship between estimates of variance and length of the chromosome (R² = 0.01). Our results suggest that the contribution of SNPs to marked additive genetic variability is dependent on the allele frequency spectrum. For the sample of birds analysed, it was found that increasing marker density beyond 100K SNPs did not capture additional additive genetic variance.     

A genome‐wide association study for fat‐related traits computed by image analysis in Japanese Black cattle

The objective of this study was to identify genomic regions associated with fat‐related traits using a Japanese Black cattle population in Hyogo. From 1836 animals, those with high or low values were selected on the basis of corrected phenotype and then pooled into high and low groups (n = 100 each), respectively. DNA pool‐based genome‐wide association study (GWAS) was performed using Illumina BovineSNP50 BeadChip v2 with three replicate assays for each pooled sample. GWAS detected that two single nucleotide polymorphisms (SNPs) on BTA7 (ARS‐BFGL‐NGS‐35463 and Hapmap23838‐BTA‐163815) and one SNP on BTA12 (ARS‐BFGL‐NGS‐2915) significantly affected fat percentage (FAR). The significance of ARS‐BFGL‐NGS‐35463 on BTA7 was confirmed by individual genotyping in all pooled samples. Moreover, association analysis between SNP and FAR in 803 Japanese Black cattle revealed a significant effect of SNP on FAR. Thus, further investigation of these regions is required to identify FAR‐associated genes and mutations, which can lead to the development of DNA markers for marker‐assisted selection for the genetic improvement of beef quality.     

Comparison between haplotype-based and individual snp-based genomic predictions for beef fatty acid profile in Nelore cattle

The aim of this study was to evaluate the genomic predictions using the single-step genomic best linear unbiased predictor (ssGBLUP) method based on SNPs and haplotype markers associated with beef fatty acids (FAs) profile in Nelore cattle. The data set contained records from 963 Nelore bulls finished in feedlot (±90 days) and slaughtered with approximately 24 months of age. Meat samples from the Longissimus dorsi muscle were taken for FAs profile measurement. FAs were quantified by gas chromatography using a SP-2560 capillary column. Animals were genotyped with the high-density SNP panel (BovineHD BeadChip assay) containing 777,962 markers. SNPs with a minor allele frequency and a call rate lower than 0.05 and 0.90, respectively, monomorphic, located on sex chromosomes, and with unknown position were removed from the data set. After genomic quality control, a total of 469,981 SNPs and 892 samples were available for subsequent analyses. Missing genotypes were imputed and phased using the FImpute software. Haplotype blocks were defined based on linkage disequilibrium using the Haploview software. The model to estimate variance components and genetic parameters and to predict the genomic values included the random genetic additive effects, fixed effects of the contemporary group and the age at slaughter as a linear covariate. Accuracies using the haplotype-based approach ranged from 0.07 to 0.31, and those SNP-based ranged from 0.06 to 0.33. Regression coefficients ranged from 0.07 to 0.74 and from 0.08 to 1.45 using the haplotype- and SNP-based approaches, respectively. Despite the low to moderate accuracies for the genomic values, it is possible to obtain genetic progress trough selection using genomic information based either on SNPs or haplotype markers. The SNP-based approach allows less biased genomic evaluations, and it is more feasible when taking into account the computational and operational cost underlying the haplotypes inference.     

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.     

Assessing single-nucleotide polymorphism selection methods for the development of a low-density panel optimized for imputation in South African Drakensberger beef cattle

A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single-nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger cattle using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen (1) at random, (2) with even genomic dispersion, (3) by maximizing the mean minor allele frequency (MAF), (4) using a combined score of MAF and linkage disequilibrium (LD), (5) using a partitioning-around-medoids (PAM) algorithm, and finally (6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen vs. a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) vs. 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01 < MAF ≤ 0.1) vs. high MAF (0.4 < MAF ≤ 0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the SA Drakensberger. Based on the results, a genotyping panel consisting of ~10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a <3% imputation error rate for a breed characterized by genomic admixture on the condition that these SNP are selected based on breed-specific selection criteria.     

Accuracy of imputation of single nucleotide polymorphism marker genotypes from low‐density panels in Japanese Black cattle

Using target and reference fattened steer populations, the performance of genotype imputation using lower‐density marker panels in Japanese Black cattle was evaluated. Population imputation was performed using BEAGLE software. Genotype information for approximately 40 000 single nucleotide polymorphism (SNP) markers by Illumina BovineSNP50 BeadChip was available, and imputation accuracy was assessed based on the average concordance rates of the genotypes, varying equally spaced SNP densities, and the number of individuals in the reference population. Two additional statistics were also calculated as indicators of imputation performance. The concordance rates tended to be lower for SNPs with greater minor allele frequencies, or those located near the ends of the chromosomes. Longer autosomes yielded greater imputation accuracies than shorter ones. When SNPs were selected based on linkage disequilibrium information, relative imputation accuracy was slightly improved. When 3000 and 10 000 equally spaced SNPs were used, the imputation accuracies were greater than 90% and approximately 97%, respectively. These results indicate that combining genotyping using a lower‐density SNP chip with genotype imputation based on a population of individuals genotyped using a higher‐density SNP chip is a cost‐effective and valid approach for genomic prediction.     

Consequences for diversity when animals are prioritized for conservation of the whole genome or of one specific allele

When animals are selected for one specific allele, for example for inclusion in a gene bank, this may result in the loss of diversity in other parts of the genome. The aim of this study was to quantify the risk of losing diversity across the genome when targeting a single allele for conservation when storing animals in a gene bank. From a small Holstein population, genotyped for 54 001 SNP loci, animals were prioritized for a single allele while maximizing the genomewide diversity using optimal contribution selection. Selection for a single allele was done for five different target frequencies: (i) no restriction on a target frequency; (ii) target frequency = original frequency in population; (iii) target frequency = 0.50; (iv) target frequency of the major allele = 1 (fixation); and (v) target frequency of the major allele = 0 (elimination). To do this, optimal contribution selection was extended with an extra constraint on the allele frequency of the target SNP marker. Results showed that elimination or fixation of alleles can result in substantial losses in genetic diversity around the targeted locus and also across the rest of the genome, depending on the allele frequency and the target frequency. It was concluded that losses of genetic diversity around the target allele are the largest when the target frequency is very different from the current allele frequency.     

Detection of candidate polymorphisms around the QTL for fat area ratio to rib eye area on BTA7 using whole‐genome resequencing in Japanese Black cattle

In our previous study, we performed genome‐wide association study (GWAS) to identify the genomic region associated with Fat area ratio to rib eye area (FAR) and detected a candidate in BTA7 at 10–30 Mbp. The present study aims to comprehensively detect all polymorphisms in the candidate region using whole‐genome resequencing data. Based on whole‐genome resequencing of eight animals, we detected 127,090 polymorphisms within the region. Of these, 31,945 were located within the genes. We further narrowed the polymorphisms to 6,044 with more than five allele differences between the high and low FAR groups that were located within 179 genes. We subsequently investigated the functions of these genes and selected 170 polymorphisms in eight genes as possible candidate polymorphisms. We focused on SLC27A6 K81M as a putative candidate polymorphism. We genotyped the SNP in a Japanese Black population (n = 904) to investigate the effect on FAR. Analysis of variance revealed that SLC27A6 K81M had a lower p‐value (p = .0009) than the most significant SNP in GWAS (p = .0049). Although only SLC27A6 K81M was verified in the present study, subsequent verification of the remaining candidate genes and polymorphisms could lead to the identification of genes and polymorphisms responsible for FAR.     

Genomic relationships computed from either next‐generation sequence or array SNP data

The use of sequence data in genomic prediction models is a topic of high interest, given the decreasing prices of current ‘next’‐generation sequencing technologies (NGS) and the theoretical possibility of directly interrogating the genomes for all causal mutations. Here, we compare by simulation how well genetic relationships (G) could be estimated using either NGS or ascertained SNP arrays. DNA sequences were simulated using the coalescence according to two scenarios: a ‘cattle’ scenario that consisted of a bottleneck followed by a split in two breeds without migration, and a ‘pig’ model where Chinese introgression into international pig breeds was simulated. We found that introgression results in a large amount of variability across the genome and between individuals, both in differentiation and in diversity. In general, NGS data allowed the most accurate estimates of G, provided enough sequencing depth was available, because shallow NGS (4×) may result in highly distorted estimates of G elements, especially if not standardized by allele frequency. However, high‐density genotyping can also result in accurate estimates of G . Given that genotyping is much less noisy than NGS data, it is suggested that specific high‐density arrays (~3M SNPs) that minimize the effects of ascertainment could be developed in the population of interest by sequencing the most influential animals and rely on those arrays for implementing genomic selection.     

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.     

Potential of preimplantation genomic selection using the blastomere separation technique in bovine in vitro fertilized embryos

Preimplantation genomic selection combined with an in vitro embryo production system is expected as a means of accelerating genetic improvement in cattle. While micromanipulation-based biopsy approaches are often used to collect embryonic cells for genetic testing, they require expensive equipment and sophisticated skills, hindering the adoption of this system. In the present study, to develop a simple method for preimplantation genomic selection using the blastomere separation (BS) technique in bovine in vitro fertilized embryos, we examined the accuracy of single nucleotide polymorphism (SNP) genotyping and optimal cryopreservation method in demi-blastocysts produced by the BS technique. We demonstrated reliable SNP genotyping using DNA derived from demi-blastocysts. We indicated a suitable equilibrium time in vitrification solution for demi-blastocysts and succeeded obtaining pregnancies by the transfer of vitrified demi-blastocysts. In conclusion, our findings suggest that the BS technique provides a simple method for preimplantation genomic selection in bovine in vitro fertilized embryos.     

Investigations on the pattern of linkage disequilibrium and selection signatures in the genomes of German Piétrain pigs

The aim of this study was to study the population structure, to characterize the LD structure and to define core regions based on low recombination rates among SNP pairs in the genome of Piétrain pigs using data from the PorcineSNP60 BeadChip. This breed is a European sire line and was strongly selected for lean meat content during the last decades. The data were used to map signatures of selection using the REHH test. In the first step, selection signatures were searched genome‐wide using only core haplotypes having a frequency above 0.25. In the second step, the results from the selection signature analysis were matched with the results from the recently conducted genome‐wide association study for economical relevant traits to investigate putative overlaps of chromosomal regions. A small subdivision of the population with regard to the geographical origin of the individuals was observed. The extent of LD was determined genome‐wide using r² values for SNP pairs with a distance ≤5 Mb and was on average 0.34. This comparable low r² value indicates a high genetic diversity in the Piétrain population. Six REHH values having a p‐value < 0.001 were genome‐wide detected. These were located on SSC1, 2, 6 and 17. Three positional candidate genes with potential biological roles were suggested, called LOC100626459, LOC100626014 and MIR1. The results imply that for genome‐wide analysis especially in this population, a higher marker density and higher sample sizes are required. For a number of nine SNPs, which were successfully annotated to core regions, the REHH test was applied. However, no selection signatures were found for those regions (p‐value < 0.1).     

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.     

Dimension reduction and variable selection for genomic selection: application to predicting milk yield in Holsteins

Genome‐assisted prediction of genetic merit of individuals for a quantitative trait requires building statistical models that can handle data sets consisting of a massive number of markers and many fewer observations. Numerous regression models have been proposed in which marker effects are treated as random variables. Alternatively, multivariate dimension reduction techniques [such as principal component regression (PCR) and partial least‐squares regression (PLS)] model a small number of latent components which are linear combinations of original variables, thereby reducing dimensionality. Further, marker selection has drawn increasing attention in genomic selection. This study evaluated two dimension reduction methods, namely, supervised PCR and sparse PLS, for predicting genomic breeding values (BV) of dairy bulls for milk yield using single‐nucleotide polymorphisms (SNPs). These two methods perform variable selection in addition to reducing dimensionality. Supervised PCR preselects SNPs based on the strength of association of each SNP with the phenotype. Sparse PLS promotes sparsity by imposing some penalty on the coefficients of linear combinations of original SNP variables. Two types of supervised PCR (I and II) were examined. Method I was based on single‐SNP analyses, whereas method II was based on multiple‐SNP analyses. Supervised PCR II was clearly better than supervised PCR I in predictive ability when evaluated on SNP subsets of various sizes, and sparse PLS was in between. Supervised PCR II and sparse PLS attained similar predictive correlations when the size of the SNP subset was below 1000. Supervised PCR II with 300 and 500 SNPs achieved correlations of 0.54 and 0.59, respectively, corresponding to 80 and 87% of the correlation (0.68) obtained with all 32 518 SNPs in a PCR model. The predictive correlation of supervised PCR II reached a plateau of 0.68 when the number of SNPs increased to 3500. Our results demonstrate the potential of combining dimension reduction and variable selection for accurate and cost‐effective prediction of genomic BV.     

全基因组SNP分型技术在畜禽遗传育种研究中的应用

随着畜禽资源分子鉴定、物种进化、全基因组育种等热点领域的逐渐兴起,准确的全基因组SNP分型成为了畜禽基因组研究的关键。基因芯片、重测序、简化基因组测序及靶向捕获测序等全基因组SNP分型技术已广泛应用于畜禽基因组研究中。本文概述了全基因组SNP分型技术的原理及其在全基因组关联分析、选择信号分析和畜禽遗传资源背景分析等方面的应用,以期为畜禽基因组研究和育种应用提供借鉴和参考。     

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.     

Power of a genome scan to detect and locate quantitative trait loci in cattle using dense single nucleotide polymorphisms

There is increasing use of dense single nucleotide polymorphisms (SNPs) for whole‐genome association studies (WGAS) in livestock to map and identify quantitative trait loci (QTL). These studies rely on linkage disequilibrium (LD) to detect an association between SNP genotypes and phenotypes. The power and precision of these WGAS are unknown, and will depend on the extent of LD in the experimental population. One complication for WGAS in livestock populations is that they typically consist of many paternal half‐sib families, and in some cases full‐sib families; unless this subtle population stratification is accounted for, many spurious associations may be reported. Our aim was to investigate the power, precision and false discovery rates of WGAS for QTL discovery, with a commercial SNP array, given existing patterns of LD in cattle. We also tested the efficiency of selective genotyping animals. A total of 365 cattle were genotyped for 9232 SNPs. We simulated a QTL effect as well as polygenic and environmental effects for all animals. One QTL was simulated on a randomly chosen SNP and accounted for 5%, 10% or 18% of the total variance. The power to detect a moderate‐sized additive QTL (5% of the phenotypic variance) with 365 animals genotyped was 37% (p < 0.001). Most importantly, if pedigree structure was not accounted for, the number of false positives significantly increased above those expected by chance alone. Selective genotyping also resulted in a significant increase in false positives, even when pedigree structure was accounted for.