Partitioning of the genetic trends of French dairy sheep in Mendelian samplings and long-term contributions.

The genetic trend of milk yield for 4 French dairy sheep breeds (Lacaune, Basco-Béarnaise, Manech Tête Noire, and Manech Tête Rousse) was partitioned in Mendelian sampling trends by categories of animals defined by sex and by selection pathways. Five categories were defined, as follows: (1) artificial insemination (AI) males (after progeny testing), (2) males discarded after progeny testing, (3) natural mating males, (4) dams of males, and (5) dams of females. Dams of males and AI males were the most important sources of genetic progress, as observed in the decomposition in Mendelian sampling trends. The yearly contributions were more erratic for AI males than for dams of males, as AI males are averaged across a smaller number of individuals. Natural mating males and discarded males did not contribute to the trend in terms of Mendelian sampling, as their estimated Mendelian sampling term is either null (natural mating males) or negative (discarded males). Overall, in terms of Mendelian sampling, females contributed more than males to the total genetic gain, and we interpret that this is because females constitute a larger pool of genetic diversity. In addition, we computed long-term contributions from each individual to the following pseudo-generations (one pseudo-generation spanning 4 years). With this information, we studied the selection decisions (selected or not selected) for females, and the contributions to the following generations. Mendelian sampling was more important than parent average to determine the selection of individuals and their long-term contributions. Long-term contributions were greater for AI males (with larger progeny sizes than females) and in Basco-Béarnaise than in Lacaune (with the latter being a larger population).

Genome-wide association studies for milk production traits in Valle del Belice sheep using repeated measures.

Genome-wide association studies (GWASes) have become a powerful tool for identifying genomic regions associated with important traits in livestock. Milk production traits in dairy sheep are measured at different time points during their life span. Using phenotypic data generated from longitudinal traits could improve the power of association studies but until now have received less attention in GWASes as a methodology and has not been implemented. The aim of this study was to carry out a GWAS for milk production traits in Valle del Belice sheep using repeated measures. After quality control, 469 ewes and 37 228 SNPs were retained for the analysis, and phenotypic data included 5586 test-day records for five milk production traits (milk yield, MY; fat yield and percentage, FY and F%; protein yield and percentage, PY and P%). Nine SNPs located within or close to known genes were found to be associated with milk production traits. In particular, rs398340969, associated with both milk yield and protein yield, is located within the DCPS gene. In addition, rs425417915 and rs417079368, both associated with both fat percentage and protein percentage, are located within the TTC7B gene and at 0.37 Mb within the SUCNR1 gene respectively. In summary, the use of repeated records was beneficial for mapping genomic regions affecting milk production traits in the Valle del Belice sheep.

An analysis of effects of heterozygosity in dairy cattle for bovine tuberculosis resistance.

Genetic selection of cattle more resistant to bovine tuberculosis (bTB) may offer a complementary control strategy. Hypothesising underlying non-additive genetic variation, we present an approach using genome-wide high density markers to identify genomic loci with dominance effects on bTB resistance and to test previously published regions with heterozygote advantage in bTB. Our data comprised 1151 Holstein-Friesian cows from Northern Ireland, confirmed bTB cases and controls, genotyped with the 700K Illumina BeadChip. Genome-wide markers were tested for associations between heterozygosity and bTB status using marker-based relationships. Results were tested for robustness against genetic structure, and the genotypic frequencies of a significant locus were tested for departures from Hardy-Weinberg equilibrium. Genomic regions identified in our study and in previous publications were tested for dominance effects. Genotypic effects were estimated through ASReml mixed models. A SNP (rs43032684) on chromosome 6 was significant at the chromosome-wide level, explaining 1.7% of the phenotypic variance. In the controls, there were fewer heterozygotes for rs43032684 (P < 0.01) with the genotypic values suggesting that heterozygosity confers a heterozygote disadvantage. The region surrounding rs43032684 had a significant dominance effect (P < 0.01). SNP rs43032684 resides within a pseudogene with a parental gene involved in macrophage response to infection and within a copy-number-variation region previously associated with nematode resistance. No dominance effect was found for the region on chromosome 11, as indicated by a previous candidate region bTB study. These findings require further validation with large-scale data.

Cow genotyping strategies for genomic selection in a small dairy cattle population.

This study compares how different cow genotyping strategies increase the accuracy of genomic estimated breeding values (EBV) in dairy cattle breeds with low numbers. In these breeds, few sires have progeny records, and genotyping cows can improve the accuracy of genomic EBV. The Guernsey breed is a small dairy cattle breed with approximately 14,000 recorded individuals worldwide. Predictions of phenotypes of milk yield, fat yield, protein yield, and calving interval were made for Guernsey cows from England and Guernsey Island using genomic EBV, with training sets including 197 de-regressed proofs of genotyped bulls, with cows selected from among 1,440 genotyped cows using different genotyping strategies. Accuracies of predictions were tested using 10-fold cross-validation among the cows. Genomic EBV were predicted using 4 different methods: (1) pedigree BLUP, (2) genomic BLUP using only bulls, (3) univariate genomic BLUP using bulls and cows, and (4) bivariate genomic BLUP. Genotyping cows with phenotypes and using their data for the prediction of single nucleotide polymorphism effects increased the correlation between genomic EBV and phenotypes compared with using only bulls by 0.163±0.022 for milk yield, 0.111±0.021 for fat yield, and 0.113±0.018 for protein yield; a decrease of 0.014±0.010 for calving interval from a low base was the only exception. Genetic correlation between phenotypes from bulls and cows were approximately 0.6 for all yield traits and significantly different from 1. Only a very small change occurred in correlation between genomic EBV and phenotypes when using the bivariate model. It was always better to genotype all the cows, but when only half of the cows were genotyped, a divergent selection strategy was better compared with the random or directional selection approach. Divergent selection of 30% of the cows remained superior for the yield traits in 8 of 10 folds.

Regional heritability mapping method helps explain missing heritability of blood lipid traits in isolated populations.

Single single-nucleotide polymorphism (SNP) genome-wide association studies (SSGWAS) may fail to identify loci with modest effects on a trait. The recently developed regional heritability mapping (RHM) method can potentially identify such loci. In this study, RHM was compared with the SSGWAS for blood lipid traits (high-density lipoprotein (HDL), low-density lipoprotein (LDL), plasma concentrations of total cholesterol (TC) and triglycerides (TG)). Data comprised 2246 adults from isolated populations genotyped using ∼300 000 SNP arrays. The results were compared with large meta-analyses of these traits for validation. Using RHM, two significant regions affecting HDL on chromosomes 15 and 16 and one affecting LDL on chromosome 19 were identified. These regions covered the most significant SNPs associated with HDL and LDL from the meta-analysis. The chromosome 19 region was identified in our data despite the fact that the most significant SNP in the meta-analysis (or any SNP tagging it) was not genotyped in our SNP array. The SSGWAS identified one SNP associated with HDL on chromosome 16 (the top meta-analysis SNP) and one on chromosome 10 (not reported by RHM or in the meta-analysis and hence possibly a false positive association). The results further confirm that RHM can have better power than SSGWAS in detecting causal regions including regions containing crucial ungenotyped variants. This study suggests that RHM can be a useful tool to explain some of the 'missing heritability' of complex trait variation.

Efficiency of genomic prediction for boar taint reduction in Danish Landrace pigs.

Genetic selection against boar taint, which is caused by high skatole and androstenone concentrations in fat, is a more acceptable alternative than is the current practice of castration. Genomic predictors offer an opportunity to overcome the limitations of such selection caused by the phenotype being expressed only in males at slaughter, and this study evaluated different approaches to obtain such predictors. Samples from 1000 pigs were included in a design which was dominated by 421 sib pairs, each pair having one animal with high and one with low skatole concentration (≥0.3 µg/g). All samples were measured for both skatole and androstenone and genotyped using the Illumina SNP60 porcine BeadChip for 62 153 single nucleotide polymorphisms. The accuracy of predicting phenotypes was assessed by cross-validation using six different genomic evaluation methods: genomic best linear unbiased prediction (GBLUP) and five Bayesian regression methods. In addition, this was compared to the accuracy of predictions using only QTL that showed genome-wide significance. The range of accuracies obtained by different prediction methods was narrow for androstenone, between 0.29 (Bayes Lasso) and 0.31 (Bayes B), and wider for skatole, between 0.21 (GBLUP) and 0.26 (Bayes SSVS). Relative accuracies, corrected for h(2) , were 0.54-0.56 and 0.75-0.94 for androstenone and skatole respectively. The whole-genome evaluation methods gave greater accuracy than using only the QTL detected in the data. The results demonstrate that GBLUP for androstenone is the simplest genomic technology to implement and was also close to the most accurate method. More specialised models may be preferable for skatole.

Application of high-dimensional feature selection: evaluation for genomic prediction in man.

In this study, we investigated the effect of five feature selection approaches on the performance of a mixed model (G-BLUP) and a Bayesian (Bayes C) prediction method. We predicted height, high density lipoprotein cholesterol (HDL) and body mass index (BMI) within 2,186 Croatian and into 810 UK individuals using genome-wide SNP data. Using all SNP information Bayes C and G-BLUP had similar predictive performance across all traits within the Croatian data, and for the highly polygenic traits height and BMI when predicting into the UK data. Bayes C outperformed G-BLUP in the prediction of HDL, which is influenced by loci of moderate size, in the UK data. Supervised feature selection of a SNP subset in the G-BLUP framework provided a flexible, generalisable and computationally efficient alternative to Bayes C; but careful evaluation of predictive performance is required when supervised feature selection has been used.

Polymorphisms in twelve candidate genes are associated with growth, muscle lipid profile and meat quality traits in eleven European cattle breeds.

Current customers' demands focus on the nutritional and sensory quality of cattle meat. Candidate gene approach allows identification of genetic polymorphisms that have a measurable effect on traits of interest. The aim of this work is to identify new molecular markers for beef production through an association study using 27 candidate genes and 314 purebred bulls from 11 European cattle breeds. Twelve genes were found associated with different lipid and meat quality traits, and among these stand out the considerable effect of CAST on fatness score, CGGBP1 on growth traits, HSPB1 on the percentage of lauric acid (12:0) and phospholipid docosahexaenoic acid (DHA 22:6 n - 3), RORA on the ratio of light absorption (K) to light scattering (S) (K/S), and TNFA on lightness (L*). Most of these traits are related to post-mortem muscle biochemical changes, which are key factors controlling meat quality and consumers' acceptance. Also, the variations produced on muscle fatty acid profiles, such as those of AANAT, CRH, CSN3, HSPB1, and TNFA, give insights into the genetic networks controlling these complex traits and the possibility of future improvement of meat nutritional quality.

Genome-wide association study identifies novel loci associated with resistance to bovine tuberculosis.

Tuberculosis (TB) caused by Mycobacterium bovis is a re-emerging disease of livestock that is of major economic importance worldwide, as well as being a zoonotic risk. There is significant heritability for host resistance to bovine TB (bTB) in dairy cattle. To identify resistance loci for bTB, we undertook a genome-wide association study in female Holstein-Friesian cattle with 592 cases and 559 age-matched controls from case herds. Cases and controls were categorised into distinct phenotypes: skin test and lesion positive vs skin test negative on multiple occasions, respectively. These animals were genotyped with the Illumina BovineHD 700K BeadChip. Genome-wide rapid association using linear and logistic mixed models and regression (GRAMMAR), regional heritability mapping (RHM) and haplotype-sharing analysis identified two novel resistance loci that attained chromosome-wise significance, protein tyrosine phosphatase receptor T (PTPRT; P=4.8 × 10(-7)) and myosin IIIB (MYO3B; P=5.4 × 10(-6)). We estimated that 21% of the phenotypic variance in TB resistance could be explained by all of the informative single-nucleotide polymorphisms, of which the region encompassing the PTPRT gene accounted for 6.2% of the variance and a further 3.6% was associated with a putative copy number variant in MYO3B. The results from this study add to our understanding of variation in host control of infection and suggest that genetic marker-based selection for resistance to bTB has the potential to make a significant contribution to bTB control.

A joint analysis to identify loci underlying variation in nematode resistance in three European sheep populations.

Gastrointestinal nematode infections are one of the main health/economic issues in sheep industries, worldwide. Indicator traits for resistance such as faecal egg count (FEC) are commonly used in genomic studies; however, published results are inconsistent among breeds. Meta (or joint)-analysis is a tool for aggregating information from multiple independent studies. The aim of this study was to identify loci underlying variation in FEC, as an indicator of nematode resistance, in a joint analysis using data from three populations (Scottish Blackface, Sarda × Lacaune and Martinik Black-Belly × Romane), genotyped with the ovine 50k SNP chip. The trait analysed was the average animal effect for Strongyles and Nematodirus FEC data. Analyses were performed with regional heritability mapping (RHM), fitting polygenic effects with either the whole genomic relationship matrix or matrices excluding the chromosome being interrogated. Across-population genomic covariances were set to zero. After quality control, 4123 animals and 38 991 SNPs were available for the analysis. RHM identified genome-wide significant regions on OAR4, 12, 14, 19 and 20, with the latter being the most significant. The OAR20 region is close to the major histocompatibility complex, which has often been proposed as a functional candidate for nematode resistance. This region was significant only in the Sarda × Lacaune population. Several other regions, on OAR1, 3, 4, 5, 7, 12, 19, 20 and 24, were significant at the suggestive level.

Genetic factors controlling wool shedding in a composite Easycare sheep flock.

Historically, sheep have been selectively bred for desirable traits including wool characteristics. However, recent moves towards extensive farming and reduced farm labour have seen a renewed interest in Easycare breeds. The aim of this study was to quantify the underlying genetic architecture of wool shedding in an Easycare flock. Wool shedding scores were collected from 565 pedigreed commercial Easycare sheep from 2002 to 2010. The wool scoring system was based on a 10-point (0-9) scale, with score 0 for animals retaining full fleece and 9 for those completely shedding. DNA was sampled from 200 animals of which 48 with extreme phenotypes were genotyped using a 50-k SNP chip. Three genetic analyses were performed: heritability analysis, complex segregation analysis to test for a major gene hypothesis and a genome-wide association study to map regions in the genome affecting the trait. Phenotypes were treated as a continuous or binary variable and categories. High estimates of heritability (0.80 when treated as a continuous, 0.65-0.75 as binary and 0.75 as categories) for shedding were obtained from linear mixed model analyses. Complex segregation analysis gave similar estimates (0.80 ± 0.06) to those above with additional evidence for a major gene with dominance effects. Mixed model association analyses identified four significant (P < 0.05) SNPs. Further analyses of these four SNPs in all 200 animals revealed that one of the SNPs displayed dominance effects similar to those obtained from the complex segregation analyses. In summary, we found strong genetic control for wool shedding, demonstrated the possibility of a single putative dominant gene controlling this trait and identified four SNPs that may be in partial linkage disequilibrium with gene(s) controlling shedding.

Genome-wide association and regional heritability mapping to identify loci underlying variation in nematode resistance and body weight in Scottish Blackface lambs.

The genetic architecture underlying nematode resistance and body weight in Blackface lambs was evaluated comparing genome-wide association (GWA) and regional heritability mapping (RHM) approaches. The traits analysed were faecal egg count (FEC) and immunoglobulin A activity against third-stage larvae from Teladorsagia circumcincta, as indicators of nematode resistance, and body weight in a population of 752 Scottish Blackface lambs, genotyped with the 50k single-nucleotide polymorphism (SNP) chip. FEC for both Nematodirus and Strongyles nematodes (excluding Nematodirus), as well as body weight were collected at approximately 16, 20 and 24 weeks of age. In addition, a weighted average animal effect was estimated for both FEC and body weight traits. After quality control, 44 388 SNPs were available for the GWA analysis and 42 841 for the RHM, which utilises only mapped SNPs. The same fixed effects were used in both analyses: sex, year, management group, litter size and age of dam, with day of birth as covariate. Some genomic regions of interest for both nematode resistance and body weight traits were identified, using both GWA and RHM approaches. For both methods, strong evidence for association was found on chromosome 14 for Nematodirus average animal effect, chromosome 6 for Strongyles FEC at 16 weeks and chromosome 6 for body weight at 16 weeks. Across the entire data set, RHM identified more regions reaching the suggestive level than GWA, suggesting that RHM is capable of capturing some of the variation not detected by GWA analyses.

Bias, accuracy, and impact of indirect genetic effects in infectious diseases.

Selection for improved host response to infectious disease offers a desirable alternative to chemical treatment but has proven difficult in practice, due to low heritability estimates of disease traits. Disease data from field studies is often binary, indicating whether an individual has become infected or not following exposure to an infectious disease. Numerous studies have shown that from this data one can infer genetic variation in individuals' underlying susceptibility. In a previous study, we showed that with an indirect genetic effect (IGE) model it is possible to capture some genetic variation in infectivity, if present, as well as in susceptibility. Infectivity is the propensity of transmitting infection upon contact with a susceptible individual. It is an important factor determining the severity of an epidemic. However, there are severe shortcomings with the Standard IGE models as they do not accommodate the dynamic nature of disease data. Here we adjust the Standard IGE model to (1) make expression of infectivity dependent on the individuals' disease status (Case Model) and (2) to include timing of infection (Case-ordered Model). The models are evaluated by comparing impact of selection, bias, and accuracy of each model using simulated binary disease data. These were generated for populations with known variation in susceptibility and infectivity thus allowing comparisons between estimated and true breeding values. Overall the Case Model provided better estimates for host genetic susceptibility and infectivity compared to the Standard Model in terms of bias, impact, and accuracy. Furthermore, these estimates were strongly influenced by epidemiological characteristics. However, surprisingly, the Case-Ordered model performed considerably worse than the Standard and the Case Models, pointing toward limitations in incorporating disease dynamics into conventional variance component estimation methodology and software used in animal breeding.

Characterization of OAR1 and OAR18 QTL associated with muscle depth in British commercial terminal sire sheep.

This study aimed at verifying previously identified QTL affecting growth and carcass traits on ovine chromosome 18 (OAR18) in Texel sheep (n = 1844), and on OAR1 in Charollais (n = 851) and Suffolk (n = 998) sheep. The QTL were investigated using regression and variance component mapping (VCA) of body weight, muscle and fat depth measurements. In addition, the mode of inheritance of the Texel OAR18 QTL was explored, using data from 4376 Texel sheep, fitting VCA models testing for additive and imprinting effects. We also simulated a 480-sheep population with different QTL imprinting models and various available levels of marker information to understand the behaviour of the VCA results under different assumed genetic models. In summary, the previously identified QTL were successfully verified using both interval mapping and VCA in the three breeds. We propose a polar overdominance mode of inheritance for the OAR18 QTL in Texel sheep, and we present methods to dissect the QTL mode of inheritance, using the Texel OAR18 QTL as an example.

Confirmation of two quantitative trait loci regions for nematode resistance in commercial British terminal sire breeds.

Sheep internal parasites (nematodes) remain a major health challenge and are costly for pasture-based production systems. Most current breeding programmes for nematode resistance are based on indicator traits such as faecal egg counts (FEC), which are costly and laborious to collect. Hence, genetic markers for resistance would be advantageous. However, although some quantitative trait loci (QTL) have been identified, these QTL are often not consistent across breeds and few breeding strategies for nematode resistance in sheep are currently using molecular information. In this study, QTL for nematode resistance on ovine chromosomes (OAR) 3 and 14, previously identified in the Blackface breed, were explored using commercial Suffolk (n = 336) and Texel lambs (n = 879) sampled from terminal sire breeder flocks in the United Kingdom. FEC were used as the indicator trait for nematode resistance, and these were counted separately for Nematodirus and Strongyles genera. Microsatellite markers were used to map the QTL and the data were analysed using interval mapping regression techniques and variance component analysis. QTL for Nematodirus and Strongyles FEC were found to be segregating on OAR3 at 5% chromosome region-wide significance threshold in both Suffolk and Texel sheep, and Nematodirus FEC QTL were segregating on OAR14 in both breeds. In addition, QTL for growth traits were also found to be segregating at 5% chromosome region-wide on OAR3 and OAR14. The confirmation that FEC QTL segregate in the same position in three widely used breeds widens their potential applicability to purebred Blackface, Suffolk and Texel sheep, with benefits likely to be observed in their commercial crossbred progeny.

Restricting coancestry and inbreeding at a specific position on the genome by using optimized selection.

Over recent years, selection methodologies have been developed to allow the maximization of genetic gain whilst constraining the rate of inbreeding. The desired rate of inbreeding is achieved by constraining the group coancestry using the numerator relationship matrix computed from pedigree. It is shown that when the method is applied to mixed inheritance models, where a QTL is segregating together with polygenes, the rate of inbreeding achieved in the region around a QTL is greater than the desired level. The constraint on group coancestry at specific positions around the QTL is achieved by using a relationship matrix computed from pedigree and genetic markers. However, the rate of inbreeding realized at the position of constraint is lower than that expected given the assumed relationship between group coancestry and the subsequent rate of inbreeding. The use of markers in the calculation of the relationship matrix allows the selection of candidates with very low or zero relationships because they are homozygous for alternative alleles, which results in a heterozygosity amongst their offspring higher than would be expected given their allele frequencies. A generation of random selection restored the expected relationship between group coancestry and inbreeding.

Benefits from marker-assisted selection under an additive polygenic genetic model.

This study investigated, through stochastic computer simulation, the extra gains expected from marker-assisted selection (MAS) in an infinitesimal model with linkage. The trait under selection was assumed to be controlled by 2,000 loci of additive small effect and evenly distributed in c chromosomes of one Morgan each (and c = 5, 10, 20, or 30). This approach differs from previous studies on the benefits of MAS that have considered mixed inheritance models. Marker information was used together with pedigree information to compute the relationship matrix used in BLUP genetic evaluations. The MAS schemes were compared with schemes where genetic evaluations were performed using standard BLUP (i.e., the relationship matrix is obtained using pedigree information only). When the number of markers was large enough (approximately one marker every 10 cM), there were increases in the accuracy of selection with MAS, and this led to extra gains compared with standard BLUP for all genome sizes considered. The benefit from MAS increased over generations. At the last generation of selection (Generation 10), the response from MAS was 11, 9, 7, and 5% greater than with standard BLUP for genomes with 5, 10, 20, and 30 chromosomes, respectively. Thus, although small, gains from MAS were nonetheless detectable for genome sizes typical of livestock populations.

Full pedigree quantitative trait locus analysis in commercial pigs using variance components.

In commercial livestock populations, QTL detection methods often use existing half-sib family structures and ignore additional relationships within and between families. We reanalyzed the data from a large QTL confirmation experiment with 10 pig lines and 10 chromosome regions using identity-by-descent (IBD) scores and variance component analyses. The IBD scores were obtained using a Monte Carlo Markov Chain method, as implemented in the LOKI software, and were used to model a putative QTL in a mixed animal model. The analyses revealed 61 QTL at a nominal 5% level (out of 650 tests). Twenty-seven QTL mapped to areas where QTL have been reported, and eight of these exceeded the threshold to claim confirmed linkage (P < 0.01). Forty-two of the putative QTL were detected previously using half-sib analyses, whereas 46 QTL previously identified by half-sib analyses could not be confirmed using the variance component approach. Some of the differences could be traced back to the underlying assumptions between the two methods. Using a deterministic approach to estimate IBD scores on a subset of the data gave very similar results to LOKI. We have demonstrated the feasibility of applying variance component QTL analysis to a large amount of data, equivalent to a genome scan. In many situations, the deterministic IBD approach offers a fast alternative to LOKI.

A simple and rapid method for calculating identity-by-descent matrices using multiple markers.

A fast, partly recursive deterministic method for calculating Identity-by-Descent (IBD) probabilities was developed with the objective of using IBD in Quantitative Trait Locus (QTL) mapping. The method combined a recursive method for a single marker locus with a method to estimate IBD between sibs using multiple markers. Simulated data was used to compare the deterministic method developed in the present paper with a stochastic method (LOKI) for precision in estimating IBD probabilities and performance in the task of QTL detection with the variance component approach. This comparison was made in a variety of situations by varying family size and degree of polymorphism among marker loci. The following were observed for the deterministic method relative to MCMC: (i) it was an order of magnitude faster; (ii) its estimates of IBD probabilities were found to agree closely, even though it does not extract information when haplotypes are not known with certainty; (iii) the shape of the profile for the QTL test statistic as a function of location was similar, although the magnitude of the test statistic was slightly smaller; and (iv) the estimates of QTL variance was similar. It was concluded that the method proposed provided a rapid means of calculating the IBD matrix with only a small loss in precision, making it an attractive alternative to the use of stochastic MCMC methods. Furthermore, developments in marker technology providing denser maps would enhance the relative advantage of this method.

Estimating major gene effects with partial information using Gibbs sampling.

A method for estimating major gene effects using Gibbs sampling to infer genotype of individuals with unknown values, was compared with a standard mixed-model analysis. The purpose of this study was to evaluate the effect of including information of individuals with unknown genotypes on the estimates and their error variances (Ve) of the single-gene effects. When genotypes were known for all the individuals, results using the Gibbs method (GS) were similar to those obtained with the mixed model (MM). In the absence of selection, when information from individuals with unknown genotypes was included, GS yielded unbiased estimates of the major gene effects while reducing the Ve associated with them. This reduction in Ve depended on the gene frequency and mode of action of the major locus. For the additive effect, the reduction in Ve ranged from 29 to 69% of the total reduction which would have been obtained if all individuals had had a known genotype. Similarly the reduction in Ve found for the dominance effect ranged from 12 to 58%. Estimates using GS generally had small detectable biases when the polygenic heritability used in the analysis was inflated or estimated simultaneously. However, the benefit of using information from individuals with unknown genotypes was still maintained when comparing the mean square error of the estimates using either GS or MM when genotypes are only known for a subset of the population. When the population has been under selection, the use of Gibbs sampling to incorporate information of individuals without genotypes reduced substantially the bias and mean square error found for MM analysis on partial data. Nevertheless, there was some bias detected using Gibbs sampling. The gene frequency of the major gene in the base population was also well estimated despite its change over generations due to selection.