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Current breeding tools aiming to improve feed efficiency use definitions based on total dry matter intake (DMI); for example, residual feed intake or feed saved. This research aimed to define alternative traits using existing data that differentiate between feed intake capacity and roughage or concentrate intake, and to investigate the phenotypic and genetic relationships among these traits. The data set contained 39,017 weekly milk yield, live weight, and DMI records of 3,164 cows. The 4 defined traits were as follows: (1) Feed intake capacity (FIC), defined as the difference between how much a cow ate and how much she was expected to eat based on diet satiety value and status of the cow (parity and lactation stage); (2) feed saved (FS), defined as the difference between the measured and the predicted DMI, based on the regression of DMI on milk components within experiment; (3) residual roughage intake (RRI), defined as the difference between the measured and the predicted roughage intake, based on the regression of roughage intake on milk components and concentrate intake within experiment; and (4) residual concentrate intake (RCI), defined as the difference between the measured and the predicted concentrate intake, based on the regression of concentrate intake on milk components and roughage intake within experiment. The phenotypic correlations were -0.72 between FIC and FS, -0.84 between FS and RRI, and -0.53 between FS and RCI. Heritability of FIC, FS, RRI, and RCI were estimated to be 0.21, 0.12, 0.15, and 0.03, respectively. The genetic correlations were -0.81 between FS and FIC, -0.96 between FS and RRI, and -0.25 between FS and RCI. Concentrate intake and RCI had low heritability. Genetic correlation between DMI and FIC was 0.98. Although the defined traits had moderate phenotypic correlations, the genetic correlations between DMI, FS, FIC, and RRI were above 0.79 (in absolute terms), suggesting that these traits are genetically similar. Therefore, selecting for FIC is expected to simply increase DMI and RRI, and there seems to be little advantage in separating concentrate and roughage intake in the genetic evaluation, because measured concentrate intake was determined by the feeding system in our data and not by the genetics of the cow.

International audience; To optimize breeding for feed efficiency, dry matter intake data from 3,967 Holstein cows with 131,234 weekly records from Denmark, France, The Netherlands, and Canada were combined. A random regression model was fitted to 1 st and 2 nd parity data for dry matter intake, energy corrected milk and body weight. Results showed that genetic residual feed intake is heritable in both parities, with an average heritability of 0.21. Genetic correlations across lactation and parities showed moderate to high estimates between mid and late lactation within parity. Early lactation had low genetic correlation with mid and late lactation. The genetic correlations between parities were around zero at all lactation stages. These findings show the importance of data collection and modelling at all lactation stages and parities.

Copy Number Variations (CNVs) are DNA sequences of 50 bp up to several Mb long, which can vary in number of copies in comparison with a reference genome. CNVs can be used in association studies to disclose genetic basis of quantitative traits phenotypic variation. Up to date, no genome-wide association study (GWAS) with CNVs and quantitative traits in such a large Brown Swiss population (i.e. with 1116 samples) has been described. The purpose of this study was to perform a GWAS using CNVs with functional, health and productive traits and to asses the impact on farming and breeding practices. The CNV – association studies were performed with the Golden Helix SVS 8.4.4 software using a correlation-trend test model. Genes within significant associated CNVs for each trait were annotated with a GO analysis using the DAVID Bioinformatics Resources 6.7. A total of 56 CNVs were significantly associated with one or more of the eight evaluated traits. The greatest association signals were given by three CNVs on chromosome 12 for the fat yield trait and on BTA23 for udder traits. The associated CNVs overlap with 23 different genes annotated on the Bos taurus genome assembly (UMD3.1).

Currently, various attempts are being made to implement breeding schemes aimed at producing low methane (CH4) emitting cows. We investigated the persistence of differences in CH4 emission between groups of cows categorized as either low or high emitters over a 5-mo period. Two feeding regimens (pasture vs. indoors) were used. Early- to mid-lactation Holstein Friesian cows were categorized as low or high emitters (n = 10 each) retrospectively, using predictions from milk mid-infrared (MIR) spectra, before the start of the experiment. Data from MIR estimates and from measurements with the GreenFeed (GF; C-Lock Technology Inc., Rapid City, SD) system over the 5-mo experiment were combined into 7-, 14-, and 28-d periods. Feed intake, eating and ruminating behavior, and ruminal fluid traits were determined in two 7-d measurement periods in the grazing season. The CH4 emission data were analyzed using a split-plot ANOVA, and the repeatability of each of the applied methods for determining CH4 emission was calculated. Traits other than CH4 emission were analyzed for differences between low and high emitters using a linear mixed model. The initial category-dependent differences in daily CH4 production persisted over the subsequent 5 mo and across 2 feeding regimens with both methods. The repeatability analysis indicated that the biweekly milk control scheme, and even a monthly scheme as practiced on farms, might be sufficient for confirming category differences. However, the relationship between CH4 data estimated by MIR and measured with GF for individual cows was weak (R2 = 0.26). The categorization based on CH4 production also generated differences in CH4 emission per kilogram of milk; differentiation between cow categories was not persistent based on milk MIR spectra and GF. Compared with the high emitters, low emitters tended to show a lower acetate-to-propionate ratio in ruminal volatile fatty acids, whereas feed intake and ruminating time did not differ. Interestingly, the low emitters spent less time eating than the high emitters. In conclusion, the CH4 estimation from analyzing the milk MIR spectra is an appropriate proxy to form and regularly control categories of cows with different CH4 production levels. The categorization was also sufficient to secure similar and persistent differences in emission intensity when estimated by MIR spectra of the milk. Further studies are needed to determine whether MIR data from individual cows are sufficiently accurate for breeding., Journal of Dairy Science, 102 (12), ISSN:0022-0302, ISSN:1525-3198

Supernumerary teats (SNT) are any abnormal teats found on a calf in addition to the usual and functional 4 teats. The presence of SNT has also been termed “hyperthelia” since the end of the 19th century. Supernumerary teats can act as an incubator for bacteria, infecting the whole udder, and can interfere with the positioning of the milking machine, and consequently, have economic relevance. Different types of SNT are observed at different positions on the udder. Caudal teats are in the rear, ramal teats are attached to another teat, and intercalary teats are found between 2 regular teats. Not all teats are equally developed; some are completely functional but most are rudimentary and not attached to any mammary gland tissue. Recently, different studies showed the poly/oligogenic character of these malformations in cattle as well as in other mammalian species. The objective of this study was to analyze the genetic architecture and incidence of hyperthelia in Swiss Brown Swiss cattle using both traditional genetic evaluation as well as imputed whole genome sequence variant information. First, phenotypes collected over the last 20 yr were used together with pedigree information for estimation of genetic variance. Second, breeding values of Brown Swiss bulls were estimated applying the BLUP algorithm. The BLUP-EBV were deregressed and used as phenotypes in genome-wide association studies. The gene LGR5 on chromosome 5 was identified as a candidate for the presence of SNT. Using alternative trait coding, genomic regions on chromosome 17 and 20 were also identified as being involved in the development of SNT with their own supernumerary mammary gland tissue. Implementing knowledge gained in this study as a routine application allows a more accurate evaluation of the trait and reduction of SNT prevalence in the Swiss Brown Swiss cattle population.

Detecting genetic variation such as Copy Number Variants (CNVs) in cattle provides the opportunity to study their association with quantitative traits. CNVs are DNA sequences of 50 bp up to several Mb long, which can vary in copy number in comparison with a reference genome. The aim of this study was to investigate CNVs in 1410 samples of the Brown Swiss cattle breed using Illumina Bovine HD SNP chip information, which includes 777,962 SNPs. After stringent quality control, CNVs were called with the Golden Helix SVS 8.3.1 (SVS) and PennCNV software and were summarized to CNV regions (CNVRs) at a population level (i.e. overlapping CNVs), using BEDTools. Additionally, common CNVRs between the two software were set as consensus regions. Genes within consensus CNVRs were annotated with a GO analysis using the DAVID Bioinformatics Resources 6.7. In order to validate these results, quantitative PCRs were executed on 15 selected CNVRs. The SVS software identified 25,030 CNVs summarized to 398 CNVRs, which comprised 30 gains, 344 losses and 24 complex CNVRs (i.e. containing both losses and gains), covering 3.92% of the bovine genome. The PennCNV software identified 62,341 CNVs summarized to 5578 CNVRs, which comprised 2638 gains, 2404 losses and 537 complex CNVRs, covering 7.68% of the bovine genome. The length of these CNVRs ranged from 1244 bp to 1,381,355 bp. A total of 563 consensus CNVRs were found covering 2.29% of the UMD 3.1 bovine genome assembly. Of these, 24 were gains, 300 were losses and 239 were complex CNVRs. A total of 775 official gene IDs were annotated in the consensus CNVRs. Among the 537 genes with functional information, the GO and pathway analysis was reported for those who clustered with a p-value

Feed efficiency has been widely studied in many areas of dairy science and is currently seeing renewed interest in the field of breeding and genetics. A critical part of determining how efficiently an animal utilizes feed is accurately measuring individual dry matter (DM) intake. Currently, multiple methods are used to measure feed intake or determine the DM content of that feed, resulting in different levels of accuracy of measurement. Furthermore, the scale at which data need to be collected for use in genetic analyses makes some methodologies impractical. This systematic review aims to provide an overview of the current methodologies used to measure both feed intake in ruminants and DM content of feedstuffs, current methods to predict individual DM intake, and applications of large-scale intake measurements. Overall, advances in milk spectral data analysis present a promising method of estimating individual DM intake on a herd scale with further validation of prediction models. Although measurements of individual feed intake rely on the same underlying principle, the methods selected are largely dictated by the costs of capital, labor, and necessary analyses. Finally, DM methodologies were synthesized into a comprehensive protocol for use in a variety of feedstuffs.

In this study we estimated levels of local ancestry for individuals of the Swiss Fleckvieh dairy cattle population. It is a composite breed descending from two pure breeds, Simmental (SIM) and Red Holstein Friesian (RHF). Illumina BovineSNP50 Beadchip genotyping data for a total of 500 pure and admixed animals were used for the analysis. The global ancestries estimated by Hidden Markov model were 0.68 and 0.32 for RHF and SIM respectively. Local ancestry levels investigated along chromosomes 2, 3 and 13 indicated that there were some regions across the chromosomes exhibiting substantial fluctuations in admixture. On chromosome 2, in the range of 28 to 31, 41 to 46 and 54 to 56 Mb RHF ancestry is substantially higher than average (0.77-0.78). These regions on chromosome 2 are wide, indicating recent admixture. Along the segments on chromosome 2, many QTLs related to dairy, conformation, reproduction, health and carcass traits were found. We observed sharper excess in favour of SIM on chromosome 3, whereas different regions with excess of RHF and SIM were found out on Chromosome 13. At the first part of chromosome 13, an excess of RHF was observed. Moreover, in regions between 40 and 57 Mb excess of SIM, referred to recent admixture was detected. In respect of RHF chromosome segments in admixed animals, dairy, reproduction and health QTLs were found. In positions where more Simmental segments were detected, QTLs related to meat and carcass traits as well as udder health traits were found. In conclusion, the authors believe that estimation of local admixture levels in crossbred populations can add information to the composite breeds history of selection.

peer-reviewed H.D.D., A.J.C., P.J.B. and B.J.H. would like to acknowledge the Dairy Futures Cooperative Research Centre for funding. H.P. and R.F. acknowledge funding from the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr ‘Synbreed—Synergistic Plant and Animal Breeding’ (grant 0315527B). H.P., R.F., R.E. and K.-U.G. acknowledge the Arbeitsgemeinschaft Süddeutscher Rinderzüchter, the Arbeitsgemeinschaft Österreichischer Fleckviehzüchter and ZuchtData EDV Dienstleistungen for providing genotype data. A. Bagnato acknowledges the European Union (EU) Collaborative Project LowInputBreeds (grant agreement 222623) for providing Brown Swiss genotypes. Braunvieh Schweiz is acknowledged for providing Brown Swiss phenotypes. H.P. and R.F. acknowledge the German Holstein Association (DHV) and the Confederación de Asociaciones de Frisona Española (CONCAFE) for sharing genotype data. H.P. was financially supported by a postdoctoral fellowship from the Deutsche Forschungsgemeinschaft (DFG) (grant PA 2789/1-1). D.B. and D.C.P. acknowledge funding from the Research Stimulus Fund (11/S/112) and Science Foundation Ireland (14/IA/2576). M.S. and F.S.S. acknowledge the Canadian Dairy Network (CDN) for providing the Holstein genotypes. P.S. acknowledges funding from the Genome Canada project entitled ‘Whole Genome Selection through Genome Wide Imputation in Beef Cattle’ and acknowledges WestGrid and Compute/Calcul Canada for providing computing resources. J.F.T. was supported by the National Institute of Food and Agriculture, US Department of Agriculture, under awards 2013-68004-20364 and 2015-67015-23183. A. Bagnato, F.P., M.D. and J.W. acknowledge EU Collaborative Project Quantomics (grant 516 agreement 222664) for providing Brown Swiss and Finnish Ayrshire sequences and genotypes. A.C.B. and R.F.V. acknowledge funding from the public–private partnership ‘Breed4Food’ (code BO-22.04-011- 001-ASG-LR) and EU FP7 IRSES SEQSEL (grant 317697). A.C.B. and R.F.V. acknowledge CRV (Arnhem, the Netherlands) for providing data on Dutch and New Zealand Holstein and Jersey bulls. Stature is affected by many polymorphisms of small effect in humans1. In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10−8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP–seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.

Background Genotyping and whole-genome sequencing data have been generated for hundreds of thousands of cattle. International consortia used these data to compile imputation reference panels that facilitate the imputation of sequence variant genotypes for animals that have been genotyped using dense microarrays. Association studies with imputed sequence variant genotypes allow for the characterization of quantitative trait loci (QTL) at nucleotide resolution particularly when individuals from several breeds are included in the mapping populations. Results We imputed genotypes for 28 million sequence variants in 17,229 cattle of the Braunvieh, Fleckvieh and Holstein breeds in order to compile large mapping populations that provide high power to identify QTL for milk production traits. Association tests between imputed sequence variant genotypes and fat and protein percentages in milk uncovered between six and thirteen QTL (P < 1e-8) per breed. Eight of the detected QTL were significant in more than one breed. We combined the results across breeds using meta-analysis and identified a total of 25 QTL including six that were not significant in the within-breed association studies. Two missense mutations in the ABCG2 (p.Y581S, rs43702337, P = 4.3e-34) and GHR (p.F279Y, rs385640152, P = 1.6e-74) genes were the top variants at QTL on chromosomes 6 and 20. Another known causal missense mutation in the DGAT1 gene (p.A232K, rs109326954, P = 8.4e-1436) was the second top variant at a QTL on chromosome 14 but its allelic substitution effects were inconsistent across breeds. It turned out that the conflicting allelic substitution effects resulted from flaws in the imputed genotypes due to the use of a multi-breed reference population for genotype imputation. Conclusions Many QTL for milk production traits segregate across breeds and across-breed meta-analysis has greater power to detect such QTL than within-breed association testing. Association testing between imputed sequence variant genotypes and phenotypes of interest facilitates identifying causal mutations provided the accuracy of imputation is high. However, true causal mutations may remain undetected when the imputed sequence variant genotypes contain flaws. It is highly recommended to validate the effect of known causal variants in order to assess the ability to detect true causal mutations in association studies with imputed sequence variants., BMC Genomics, 18

Detecting Copy Number Variation (CNV) in cattle provides the opportunity to study their association with quantitative traits (Winchester et al., 2009; Zhang et al., 2009; Hou et al., 2011; Clop et al., 2012; de Almeida et al., 2016;). The aim of this study was to map CNVs in 1,410 Brown Swiss males and females using Illumina BovineHD Genotyping BeadChip data and to perform a genome-wide association analysis for production functional and health traits. After quality control, CNVs were called with the GoldenHelix SVS 8.3.1 and PennCNV software and were summarized to CNV regions (CNVRs) at a population level, i.e. overlapping CNVs, using BEDTools. Additionally common CNVRs between the two software were set as consensus. CNV-association studies were executed with the CNVRuler software using a linear regression model. Genes within significant associated CNVRs for each trait were annotated with a GO analysis using the DAVID Bioinformatics Resources 6.7. The quality control filtered out 294 samples. The GoldenHelix SVS 8.3.1 software identified 25,030 CNVs summarized to 398 CNVRs while PennCNV identified 62,341 CNVs summarized to 5,578 CNVRs. A total of 311 CNVRs were identified to be significantly associated with one or more of the evaluated traits. The result of this study is a comprehensive genomic analysis of the Brown Swiss breed, which enriches the bovine CNV map in its genome. Finally, the results of the association studies deliver new information for quantitative traits considered in selection programs of the Brown Swiss breed., International Journal of Health, Animal Science and Food Safety, Vol 3, No 1s (2016)

Swiss Fleckvieh was established in 1970 as a composite of Simmental (SI) and Red Holstein Friesian (RHF) cattle. Breed composition is currently reported based on pedigree information. Information on a large number of molecular markers potentially provides more accurate information. For the analysis, we used Illumina BovineSNP50 Genotyping Beadchip data for 90 pure SI, 100 pure RHF and 305 admixed bulls. The scope of the study was to compare the performance of hidden Markov models, as implemented in structure software, with methods conventionally used in genomic selection [BayesB, partial least squares regression (PLSR), least absolute shrinkage and selection operator (LASSO) variable selection)] for predicting breed composition. We checked the performance of algorithms for a set of 40 492 single nucleotide polymorphisms (SNPs), subsets of evenly distributed SNPs and subsets with different allele frequencies in the pure populations, using F(ST) as an indicator. Key results are correlations of admixture levels estimated with the various algorithms with admixture based on pedigree information. For the full set, PLSR, BayesB and structure performed in a very similar manner (correlations of 0.97), whereas the correlation of LASSO and pedigree admixture was lower (0.93). With decreasing number of SNPs, correlations decreased substantially only for 5% or 1% of all SNPs. With SNPs chosen according to F(ST) , results were similar to results obtained with the full set. Only when using 96 and 48 SNPs with the highest F(ST) , correlations dropped to 0.92 and 0.90 respectively. Reducing the number of pure animals in training sets to 50, 20 and 10 each did not cause a drop in the correlation with pedigree admixture.

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.

Supernumerary teats (SNT) are any teats present on a cow`s udder other than the regular four. In Swiss Brown cows, 19.9% carry SNT. Different stages of development of SNT are observed from rudimentary appendices to functional and possibly lactating teats. SNT may promote mastatis, impeach good placement of the milking machine and lower market price of the animals. No genetic analysis of the trais has been done in this cattle population, althought SNT have been routinely recorded with other conformation traits since 1995 in Switzerland. This study aimed to investigate the genetic acrchitecture of this trait through genome-wide association studies (GWAS) performed with imputed whole-genome sequence. Two trait definitions were used: Udder Clearance (UCT) considering whether a cow is carrier of any SNT or has a clear udder and Presence of Supernumerary Mammary Gland (PMG) opposing animal carrier of completely developed and possibly functional SNT with animals with a clear udder or carrier of a rudimentary SNT. Breeding values were estimated for Brown Swiss sires of at least 20 daughters with SNT records using an animal model including the random effects expert-by-year, farm-by-year and animal. The animal's dam life stage—heifer or cow—during its parity was fitted as a fixed effect in the same model. Single SNP regression using deregressed proofs of 1519 bulls with genotypes imputed to the variant list of the 5thRun of the 1000 Bulls Genome Project permitted discovery of three important regions on BTA5, BTA17 and BTA20 associated with the presence or the development of SNT. Regions on BTA17 and BTA20 reached clearly lower p-values (10−7 vs. 10−4) when associated to PMG than UCT, while variants on BTA5 were significantly linked to both trait definitions. No candidate genes on BTA 17 and BTA20 were found after functional analysis. On BTA5, however, the leucine-rich repeat-containing G protein coupled receptor 5 (LGR5) can be considered as the major gene for our trait. Encoding a protein known as an up-regulator of the Wnt pathway and having an expression level impacting the mammary gland development, LGR5 also carries a synonymous mutation that is highly associated to SNT. To conclude, GWAS in the Swiss Brown Swiss population revealed LGR5 on BTA5 as a candidate gene influencing the presence and development stage of supernumerary teats.

Contents Using pedigree data, the inbreeding coefficients of 715 Austrian dual-purpose Simmental (Fleckvieh) bulls stationed in two artificial insemination (AI) centres in Upper and Lower Austria were calculated and incorporated in statistical models for the analysis of semen quality. Five semen quality parameters (volume, concentration, motility, number of spermatozoa per ejaculate and percentage of viable spermatozoa) of approximately 30 000 ejaculates, used in two separate data sets, were investigated. The mixed model included the fixed effects age class of the bull, bull handler, semen collector, month and year of collection, number of collection per bull and day, time interval since last collection, the linear continuous effect of the inbreeding coefficient of the bull, interactions between age class and month, and age class and interval since last collection, respectively, as well as the random effect of the bull and the random residual effect. Non-linear effects of inbreeding were significant for motility only. Despite the quite low inbreeding coefficients (mean 1.3%), all semen quality traits showed inbreeding depression, in four of the five traits significantly in at least one of the data sets. The magnitude of inbreeding depression was small, which might partly be caused by the low inbreeding levels and a potential pre-selection of the bulls in the AI centres. However, monitoring of inbreeding depression on fertility traits is recommended to avoid unrecognized deterioration of such traits.

Background Advances in human genomics have allowed unprecedented productivity in terms of algorithms, software, and literature available for translating raw next-generation sequence data into high-quality information. The challenges of variant identification in organisms with lower quality reference genomes are less well documented. We explored the consequences of commonly recommended preparatory steps and the effects of single and multi sample variant identification methods using four publicly available software applications (Platypus, HaplotypeCaller, Samtools and UnifiedGenotyper) on whole genome sequence data of 65 key ancestors of Swiss dairy cattle populations. Accuracy of calling next-generation sequence variants was assessed by comparison to the same loci from medium and high-density single nucleotide variant (SNV) arrays. Results The total number of SNVs identified varied by software and method, with single (multi) sample results ranging from 17.7 to 22.0 (16.9 to 22.0) million variants. Computing time varied considerably between software. Preparatory realignment of insertions and deletions and subsequent base quality score recalibration had only minor effects on the number and quality of SNVs identified by different software, but increased computing time considerably. Average concordance for single (multi) sample results with high-density chip data was 58.3% (87.0%) and average genotype concordance in correctly identified SNVs was 99.2% (99.2%) across software. The average quality of SNVs identified, measured as the ratio of transitions to transversions, was higher using single sample methods than multi sample methods. A consensus approach using results of different software generally provided the highest variant quality in terms of transition/transversion ratio. Conclusions Our findings serve as a reference for variant identification pipeline development in non-human organisms and help assess the implication of preparatory steps in next-generation sequencing pipelines for organisms with incomplete reference genomes (pipeline code is included). Benchmarking this information should prove particularly useful in processing next-generation sequencing data for use in genome-wide association studies and genomic selection. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-948) contains supplementary material, which is available to authorized users.

Translational research program is intended to provide scientists with an incentive to examine research findings from the perspective of concrete applications or other uses, and to give outstanding researchers an opportunity to develop these findings into specific applications and/or economic, societal or social benefits. This program is jointly funded by the Austrian Science Fund (FWF) and the Austrian Ministry for Transport, Innovation and Technology (BMVIT). Therefore, the acknowledgments section of the paper “Evaluation of the lasso and the elastic net in genome-wide association studies” should state: “The financial support of the Austrian Ministry for Transport, Innovation and Technology (BMVIT) and the Austrian Science Fund (FWF) via the project TRP46-B19 is greatly acknowledged.”

The main goal of this study was to develop, apply, and validate a new method to predict an indicator for CH 4 eructed by dairy cows using milk mid-infrared (MIR) spectra. A novel feature of this model was the consideration of lactation stage to reflect changes in the metabolic status of the cow. A total of 446 daily CH 4 measurements were obtained using the SF 6 method on 142 Jersey, Holstein, and Holstein-Jersey cows. The corresponding milk samples were collected during these CH 4 measurements and were analyzed using MIR spectroscopy. A first derivative was applied to the milk MIR spectra. To validate the novel calibration equation incorporating days in milk (DIM), 2 calibration processes were developed: the first was based only on CH 4 measurements and milk MIR spectra (independent of lactation stage; ILS); the second included milk MIR spectra and DIM information (dependent on lactation stage; DLS) by using linear and quadratic modified Legendre polynomials. The coefficients of determination of ILS and DLS equations were 0.77 and 0.75, respectively, with standard error of calibration of 63g/d of CH 4 for both calibration equations. These equations were applied to 1,674,763 milk MIR spectra from Holstein cows in the first 3 parities and between 5 and 365 DIM. The average CH 4 indicators were 428, 444, and 448g/d by ILS and 444, 467, and 471g/d by DLS for cows in first, second, and third lactation, respectively. Behavior of the DLS indicator throughout the lactations was in agreement with the literature with values increasing between 0 and 100 DIM and decreasing thereafter. Conversely, the ILS indicator of CH 4 emission decreased at the beginning of the lactation and increased until the end of the lactation, which differs from the literature. Therefore, the DLS indicator seems to better reflect biological processes that drive CH 4 emissions than the ILS indicator. The ILS and DLS equations were applied to an independent data set, which included 59 respiration chamber measurements of CH 4 obtained from animals of a different breed across a different production system. Results indicated that the DLS equation was much more robust than the ILS equation allowing development of indicators of CH 4 emissions by dairy cows. Integration of DIM information into the prediction equation was found to be a good strategy to obtain biologically meaningful CH 4 values from lactating cows by accounting for biological changes that occur throughout the lactation.

In this study, direct genomic values for the functional traits general temperament, milking temperament, aggressiveness, rank order in herd, milking speed, udder depth, position of labia, and days to first heat in Brown Swiss dairy cattle were estimated based on ~777,000 (777 K) single nucleotide polymorphism (SNP) information from 1,126 animals. Accuracy of direct genomic values was assessed by a 5-fold cross-validation with 10 replicates. Correlations between deregressed proofs and direct genomic values were 0.63 for general temperament, 0.73 for milking temperament, 0.69 for aggressiveness, 0.65 for rank order in herd, 0.69 for milking speed, 0.71 for udder depth, 0.66 for position of labia, and 0.74 for days to first heat. Using the information of ~54,000 (54K) SNP led to only marginal deviations in the observed accuracy. Trying to predict the 20% youngest bulls led to correlations of 0.55, 0.77, 0.73, 0.55, 0.64, 0.59, 0.67, and 0.77, respectively, for the traits listed above. Using a novel method to estimate the accuracy of a direct genomic value (defined as correlation between direct genomic value and true breeding value and accounting for the correlation between direct genomic values and conventional breeding values) revealed accuracies of 0.37, 0.20, 0.19, 0.27, 0.48, 0.45, 0.36, and 0.12, respectively, for the traits listed above. These values are much smaller but probably also more realistic than accuracies based on correlations, given the heritabilities and samples sizes in this study. Annotation of the largest estimated SNP effects revealed 2 candidate genes affecting the traits general temperament and days to first heat.

Genotype imputation is a cost-effective way to increase the power of genomic selection or genome-wide association studies. While several genotype imputation algorithms are available, this chapter focuses on a heuristic algorithm, as implemented in the AlphaImpute software. This algorithm combines long-range phasing, haplotype library imputation, and segregation analysis and it is specifically designed to work with pedigreed populations.The chapter is organized in different sections. First the challenges related to genotype imputation in pedigreed populations are described, along with the specifics of the imputation algorithm used in AlphaImpute. In the second section, factors affecting the accuracy of genotype imputation using this algorithm are discussed. The different parameters that control AlphaImpute are detailed and examples of how to apply AlphaImpute are given.

Genotype imputation is a cost-effective way to increase the power of genomic selection or genome-wide association studies. While several genotype imputation algorithms are available, this chapter focuses on a heuristic algorithm, as implemented in the AlphaImpute software. This algorithm combines long-range phasing, haplotype library imputation, and segregation analysis and it is specifically designed to work with pedigreed populations.The chapter is organized in different sections. First the challenges related to genotype imputation in pedigreed populations are described, along with the specifics of the imputation algorithm used in AlphaImpute. In the second section, factors affecting the accuracy of genotype imputation using this algorithm are discussed. The different parameters that control AlphaImpute are detailed and examples of how to apply AlphaImpute are given.

Runs of homozygosity (ROH) are recognized as potential inbreeding measure in studies on humans. Inbreeding coefficients derived from ROH (FROH) measure proportion of the genome arranged in long homozygous segments and highly correlate with those derived from pedigree (Fped). From that we assumed that ROH represent an alternative to pedigree inbreeding levels in studies on animals too, because pedigree can be incorrect, incomplete and can not fully explain what happened in meiosis. To confirm our premise we used pedigree and genotype data from 500 Austrian dual purpose Simmental bulls to determine correlation between FROH and Fped. ROH were obtainedusing Fortran 90 soft ware created by the authors. Proportions of genome in ROH were calculated for lengths of ROH of >1, >2, >4, >8 and >16 Mb. Pedigree data were analyzed and inbreeding coefficients for complete pedigree (FpedT) and five generations (Fped5) were calculated using ENDOG soft ware. We found low FpedT and Fped5 (means of 1.5% and 0.9%) while FROH for segments >1Mb suggested much higher values (9.0%) indicating old inbreeding that can not be traced using pedigree. The highest correlations were found between FROH calculated from ROH of length >4Mb and FpedT (0.68) that is consistent with studies on humans. We conclude that inbreeding coefficients derived from ROH are useful for measuring levels of inbreeding in cattle, because ROH are not subject to mistakes as pedigrees and calculations made from those.

Contents Genetic parameters were estimated for semen production traits collected in an Austrian AI centre in the years 2000–2004. In total, 12 746 ejaculates from 301 Austrian dual-purpose Simmental (Fleckvieh) AI bulls were examined considering different effects on ejaculate volume, sperm concentration, percentage of viable spermatozoa in the ejaculate, total spermatozoa per ejaculate and motility. The model for genetic parameter estimation included the fixed effects age of bull, collection interval, number of collections on collection day, bull handler, semen collector, year and month of collection, a random additive genetic component and a permanent environmental effect. Correlations between estimated breeding values for semen traits and male fertility from the routine evaluation were calculated. The fertility trait considered in the routine evaluation is non-return rate 90 for the first insemination. All semen production traits were moderately heritable. Heritabilities for volume, concentration, percentage of viable spermatozoa, total number of spermatozoa and motility were 0.18, 0.14, 0.10, 0.22 and 0.04, respectively. Correlations between breeding values for semen quality traits and routinely estimated breeding values for male fertility were low and ranged from 0.08 to 0.17 indicating that semen production traits are rather poor predictors of male fertility.

Estimated breeding values for the selection of more profitable sheep for the sheep meat and wool industries are currently based on pedigree and phenotypic records. With the advent of a medium-density DNA marker array, which genotypes ~50 000 ovine single nucleotide polymorphisms, a third source of information has become available. The aim of this paper was to determine whether this genomic information can be used to predict estimated breeding values for wool and meat traits. The effects of all single nucleotide polymorphism markers in a multi-breed sheep reference population of 7180 individuals with phenotypic records were estimated to derive prediction equations for genomic estimated breeding values (GEBV) for greasy fleece weight, fibre diameter, staple strength, breech wrinkle score, weight at ultrasound scanning, scanned eye muscle depth and scanned fat depth. Five hundred and forty industry sires with very accurate Australian sheep breeding values were used as a validation population and the accuracies of GEBV were assessed according to correlations between GEBV and Australian sheep breeding values . The accuracies of GEBV ranged from 0.15 to 0.79 for wool traits in Merino sheep and from –0.07 to 0.57 for meat traits in all breeds studied. Merino industry sires tended to have more accurate GEBV than terminal and maternal breeds because the reference population consisted mainly of Merino haplotypes. The lower accuracy for terminal and maternal breeds suggests that the density of genetic markers used was not high enough for accurate across-breed prediction of marker effects. Our results indicate that an increase in the size of the reference population will increase the accuracy of GEBV.

Summary Identification of selection signatures is one of the current endeavors of evolutionary genetics. Admixed populations may be used to infer post-admixture selection. We calculated local ancestry for Swiss Fleckvieh, a composite of Simmental (SI) and Red Holstein Friesian (RHF), to infer such signals. Illumina Bovine SNP50 BeadChip data for 300 admixed, 88 SI and 97 RHF bulls were used. The average RHF ancestry across the whole genome was 0.70. To identify regions with high deviation from average, we considered two significance thresholds, based on a permutation test and extreme deviation from normal distribution. Regions on chromosomes 13 (46.3–47.3 Mb) and 18 (18.7–25.9 Mb) passed both thresholds in the direction of increased SI. Extended haplotype homozygosity within (iHS) and between (Rsb) populations was calculated to explore additional patterns of pre- and post-admixture selection signals. The Rsb score of admixed and SI was significant in a wide region of chromosome 18 (6.6–24.6 Mb) overlapped with one area of strong local ancestry deviation. FTO, with pleiotropic effect on milk and fertility, NOD2 on dairy and NKD1 and SALL1 on fertility traits are located there. Genetic differentiation of RHF and SI (Fst), an alternative indicator of pre-admixture selection in pure populations, was calculated. No considerable overlap of peaks of local ancestry deviations and Fst was observed. We found two regions with significant signatures of post-admixture selection in this very young composite, applying comparatively stringent significance thresholds. The signals cover relatively large genomic areas and did not allow pinpointing of the gene(s) responsible for the apparent shift in ancestry proportions.

We evaluated the accuracy of biology informed genomic prediction for dry matter intake in 2,162 Dutch Holstein cows. Sequence variants were selected from meta-analyses including GWAS summary statistics for QTL and metabolomic QTL in several dairy and crossbred beef populations. Selected variants were prioritized in GBLUP models in a five-fold crossvalidation. The accuracies were compared to genomic prediction based on routine 50k genotype data. The average accuracy for the 50k scenario was 0.683. Adding selected sequence variants in the GBLUP model did not improve the accuracies for dry matter intake. Next steps willinclude testing Bayesian variable selection methods to prioritize variants in genomic prediction for dry matter intake.