The number of publications performing genome-wide association studies (GWAS) has increased dramatically. Penalized regression approaches have been developed to overcome the challenges caused by the high dimensional data, but these methods are relatively new in the GWAS field. In this study we have compared the statistical performance of two methods (the least absolute shrinkage and selection operator—lasso and the elastic net) on two simulated data sets and one real data set from a 50 K genome-wide single nucleotide polymorphism (SNP) panel of 5570 Fleckvieh bulls. The first simulated data set displays moderate to high linkage disequilibrium between SNPs, whereas the second simulated data set from the QTLMAS 2010 workshop is biologically more complex. We used cross-validation to find the optimal value of regularization parameter λ with both minimum MSE and minimum MSE + 1SE of minimum MSE. The optimal λ values were used for variable selection. Based on the first simulated data, we found that the minMSE in general picked up too many SNPs. At minMSE + 1SE, the lasso didn't acquire any false positives, but selected too few correct SNPs. The elastic net provided the best compromise between few false positives and many correct selections when the penalty weight α was around 0.1. However, in our simulation setting, this α value didn't result in the lowest minMSE + 1SE. The number of selected SNPs from the QTLMAS 2010 data was after correction for population structure 82 and 161 for the lasso and the elastic net, respectively. In the Fleckvieh data set after population structure correction lasso and the elastic net identified from 1291 to 1966 important SNPs for milk fat content, with major peaks on chromosomes 5, 14, 15, and 20. Hence, we can conclude that it is important to analyze GWAS data with both the lasso and the elastic net and an alternative tuning criterion to minimum MSE is needed for variable selection.

Genetic variants underlying reduced male reproductive performance have been identified in humans and model organisms, most of them compromising semen quality. Occasionally, male fertility is severely compromised although semen analysis remains without any apparent pathological findings (i.e., idiopathic subfertility). Artificial insemination (AI) in most cattle populations requires close examination of all ejaculates before insemination. Although anomalous ejaculates are rejected, insemination success varies considerably among AI bulls. In an attempt to identify genetic causes of such variation, we undertook a genome-wide association study (GWAS). Imputed genotypes of 652,856 SNPs were available for 7962 AI bulls of the Fleckvieh (FV) population. Male reproductive ability (MRA) was assessed based on 15.3 million artificial inseminations. The GWAS uncovered a strong association signal on bovine chromosome 19 (P = 4.08×10−59). Subsequent autozygosity mapping revealed a common 1386 kb segment of extended homozygosity in 40 bulls with exceptionally poor reproductive performance. Only 1.7% of 35,671 inseminations with semen samples of those bulls were successful. None of the bulls with normal reproductive performance was homozygous, indicating recessive inheritance. Exploiting whole-genome re-sequencing data of 43 animals revealed a candidate causal nonsense mutation (rs378652941, c.483C>A, p.Cys161X) in the transmembrane protein 95 encoding gene TMEM95 which was subsequently validated in 1990 AI bulls. Immunohistochemical investigations evidenced that TMEM95 is located at the surface of spermatozoa of fertile animals whereas it is absent in spermatozoa of subfertile animals. These findings imply that integrity of TMEM95 is required for an undisturbed fertilisation. Our results demonstrate that deficiency of TMEM95 severely compromises male reproductive performance in cattle and reveal for the first time a phenotypic effect associated with genomic variation in TMEM95.

Three breeds (Fleckvieh, Holstein, and Jersey) were included in a reference population, separately and together, to assess the accuracy of prediction of genomic breeding values in single-breed validation populations. The accuracy of genomic selection was defined as the correlation between estimated breeding values, calculated using phenotypic data, and genomic breeding values. The Holstein and Jersey populations were from Australia, whereas the Fleckvieh population (dual-purpose Simmental) was from Austria and Germany. Both a BLUP with a multi-breed genomic relationship matrix (GBLUP) and a Bayesian method (BayesA) were used to derive the prediction equations. The hypothesis tested was that having a multi-breed reference population increased the accuracy of genomic selection. Minimal advantage existed of either GBLUP or BayesA multi-breed genomic evaluations over single-breed evaluations. However, when the goal was to predict genomic breeding values for a breed with no individuals in the reference population, using 2 other breeds in the reference was generally better than only 1 breed.

BackgroundHaplotypes with reduced or missing homozygosity may harbor deleterious alleles that compromise juvenile survival. A scan for homozygous haplotype deficiency revealed a short segment on bovine chromosome 19 (Braunvieh haplotype 2, BH2) that was associated with high juvenile mortality in Braunvieh cattle. However, the molecular genetic underpinnings and the pathophysiology of BH2 remain to be elucidated.ResultsThe frequency of BH2 was 6.5 % in 8,446 Braunvieh animals from the national bovine genome databases. Both perinatal and juvenile mortality of BH2 homozygous calves were higher than the average in Braunvieh cattle resulting in a depletion of BH2 homozygous adult animals (P = 9.3x10−12). The analysis of whole-genome sequence data from 54 Braunvieh animals uncovered a missense mutation in TUBD1 (rs383232842, p.H210R) that was compatible with recessive inheritance of BH2. The availability of sequence data of 236 animals from diverse bovine populations revealed that the missense mutation also segregated at a low frequency (1.7 %) in the Fleckvieh breed. A validation study in 37,314 Fleckvieh animals confirmed high juvenile mortality of homozygous calves (P = 2.2x10−15). Our findings show that the putative disease allele is located on an ancestral haplotype that segregates in Braunvieh and Fleckvieh cattle. To unravel the pathophysiology of BH2, six homozygous animals were examined at the animal clinic. Clinical and pathological findings revealed that homozygous calves suffered from chronic airway disease possibly resulting from defective cilia in the respiratory tract.ConclusionsA missense mutation in TUBD1 is associated with high perinatal and juvenile mortality in Braunvieh and Fleckvieh cattle. The mutation is located on a common haplotype likely originating from an ancient ancestor of Braunvieh and Fleckvieh cattle. Our findings demonstrate for the first time that deleterious alleles may segregate across closed cattle breeds without recent admixture. Homozygous calves suffer from chronic airway disease resulting in poor growth performance and high juvenile mortality. The respiratory manifestations resemble key features of diseases resulting from impaired function of airway cilia.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2742-y) contains supplementary material, which is available to authorized users.

Additive and nonadditive genetic variances were estimated for yield traits and fertility for three subsequent lactations and for lifetime performance traits of purebred and crossbred dairy cattle populations. Traits were milk yield, energy-corrected milk yield, fat percentage, protein percentage, calving interval, length of productive life, and lifetime FCM of purebred Simmental, Simmental including crossbreds, and Braunvieh crossed with Brown Swiss. Data files ranged from 66,740 to 375,093 records. An approach based on pedigree information for sire and maternal grandsire was used and included additive, dominance, and additive by additive genetic effects. Variances were estimated using the tildehat approximation to REML. Heritability estimated without nonadditive effects in the model was overestimated, particularly in presence of additive by additive variance. Dominance variance was important for most traits; for the lifetime performance traits, dominance was clearly higher than additive variance. Additive by additive variance was very high for milk yield and energy-corrected milk yield, especially for data including crossbreds. Effect of inbreeding was low in most cases. Inclusion of nonadditive effects in genetic evaluation models might improve estimation of additive effects and may require consideration for dairy cattle breeding programs.