Publications by authors named "Mogens S Lund"

47 Publications

Genetic parameters of semen quality traits and genetic correlations with service sire nonreturn rate in Nordic Holstein bulls.

J Dairy Sci 2021 Sep 5;104(9):10010-10019. Epub 2021 Jun 5.

Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, PO Box 50, DK-8830 Tjele, Denmark.

Despite the importance of the quality of semen used in artificial insemination to the reproductive success of dairy herds, few studies have estimated the extent of genetic variability in semen quality traits. Even fewer studies have quantified the correlation between semen quality traits and male fertility. In this study, records of 100,058 ejaculates collected from 2,885 Nordic Holstein bulls were used to estimate genetic parameters for semen quality traits, including pre- and postcryopreservation semen concentration, sperm motility and viability, ejaculate volume, and number of doses per ejaculate. Additionally, summary data on nonreturn rate (NRR) obtained from insemination of some of the bulls (n = 2,142) to cows in different parities (heifers and parities 1-3 or more) were used to estimate correlations between the semen quality traits and service sire NRR. In the study, low to moderate heritability (0.06-0.45) was estimated for semen quality traits, indicating the possibility of improving these traits through selective breeding. The study also showed moderate to high genetic and phenotypic correlations between service sire NRR and some of the semen quality traits, including sperm viability pre- and postcryopreservation, motility postcryopreservation, and sperm concentration precryopreservation, indicating the predictive values of these traits for service sire NRR. The positive moderate to high genetic correlations between semen quality and service sire NRR traits also indicated that selection for semen quality traits might be favorable for improving service sire NRR.
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http://dx.doi.org/10.3168/jds.2021-20403DOI Listing
September 2021

Genomic prediction using a reference population of multiple pure breeds and admixed individuals.

Genet Sel Evol 2021 May 31;53(1):46. Epub 2021 May 31.

Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.

Background: In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question. In this study, we aimed at providing methodologies for the use of data from crossbred individuals with an admixed genetic background together with data from multiple pure breeds, for the purpose of genomic evaluations for both purebred and crossbred animals. A three-breed rotational crossbreeding system was mimicked using simulations based on animals genotyped with the 50 K single nucleotide polymorphism (SNP) chip.

Results: For purebred populations, within-breed genomic predictions generally led to higher accuracies than those from multi-breed predictions using combined data of pure breeds. Adding admixed population's (MIX) data to the combined pure breed data considering MIX as a different breed led to higher accuracies. When prediction models were able to account for breed origin of alleles, accuracies were generally higher than those from combining all available data, depending on the correlation of quantitative trait loci (QTL) effects between the breeds. Accuracies varied when using SNP effects from any of the pure breeds to predict the breeding values of MIX. Using those breed-specific SNP effects that were estimated separately in each pure breed, while accounting for breed origin of alleles for the selection candidates of MIX, generally improved the accuracies. Models that are able to accommodate MIX data with the breed origin of alleles approach generally led to higher accuracies than models without breed origin of alleles, depending on the correlation of QTL effects between the breeds.

Conclusions: Combining all available data, pure breeds' and admixed population's data, in a multi-breed reference population is beneficial for the estimation of breeding values for pure breeds with a small reference population. For MIX, such an approach can lead to higher accuracies than considering breed origin of alleles for the selection candidates, and using breed-specific SNP effects estimated separately in each pure breed. Including MIX data in the reference population of multiple breeds by considering the breed origin of alleles, accuracies can be further improved. Our findings are relevant for breeding programs in which crossbreeding is systematically applied, and also for populations that involve different subpopulations and between which exchange of genetic material is routine practice.
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http://dx.doi.org/10.1186/s12711-021-00637-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8168010PMC
May 2021

Genomic selection in American mink (Neovison vison) using a single-step genomic best linear unbiased prediction model for size and quality traits graded on live mink.

J Anim Sci 2021 01;99(1)

Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark.

Genomic selection relies on single-nucleotide polymorphisms (SNPs), which are often collected using medium-density SNP arrays. In mink, no such array is available; instead, genotyping by sequencing (GBS) can be used to generate marker information. Here, we evaluated the effect of genomic selection for mink using GBS. We compared the estimated breeding values (EBVs) from single-step genomic best linear unbiased prediction (SSGBLUP) models to the EBV from ordinary pedigree-based BLUP models. We analyzed seven size and quality traits from the live grading of brown mink. The phenotype data consisted of ~20,600 records for the seven traits from the mink born between 2013 and 2016. Genotype data included 2,103 mink born between 2010 and 2014, mostly breeding animals. In total, 28,336 SNP markers from 391 scaffolds were available for genomic prediction. The pedigree file included 29,212 mink. The predictive ability was assessed by the correlation (r) between progeny trait deviation (PTD) and EBV, and the regression of PTD on EBV, using 5-fold cross-validation. For each fold, one-fifth of animals born in 2014 formed the validation set. For all traits, the SSGBLUP model resulted in higher accuracies than the BLUP model. The average increase in accuracy was 15% (between 3% for fur clarity and 28% for body weight). For three traits (body weight, silky appearance of the under wool, and guard hair thickness), the difference in r between the two models was significant (P < 0.05). For all traits, the regression slopes of PTD on EBV from SSGBLUP models were closer to 1 than regression slopes from BLUP models, indicating SSGBLUP models resulted in less bias of EBV for selection candidates than the BLUP models. However, the regression coefficients did not differ significantly. In conclusion, the SSGBLUP model is superior to conventional BLUP model in the accurate selection of superior animals, and, thus, it would increase genetic gain in a selective breeding program. In addition, this study shows that GBS data work well in genomic prediction in mink, demonstrating the potential of GBS for genomic selection in livestock species.
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http://dx.doi.org/10.1093/jas/skab003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846095PMC
January 2021

Novel approach to incorporate information about recessive lethal genes increases the accuracy of genomic prediction for mortality traits.

Heredity (Edinb) 2020 09 12;125(3):155-166. Epub 2020 Jun 12.

Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle, 8830, Tjele, Denmark.

The genetic underpinnings of calf mortality can be partly polygenic and partly due to deleterious effects of recessive lethal alleles. Prediction of the genetic merits of selection candidates should thus take into account both genetic components contributing to calf mortality. However, simultaneously modeling polygenic risk and recessive lethal allele effects in genomic prediction is challenging due to effects that behave differently. In this study, we present a novel approach where mortality risk probabilities from polygenic and lethal allele components are predicted separately to compute the total risk probability of an individual for its future offspring as a basis for selection. We present methods for transforming genomic estimated breeding values of polygenic effect into risk probabilities using normal density and cumulative distribution functions and show computations of risk probability from recessive lethal alleles given sire genotypes and population recessive allele frequencies. Simulated data were used to test the novel approach as implemented in probit, logit, and linear models. In the simulation study, the accuracy of predicted risk probabilities was computed as the correlation between predicted mortality probabilities and observed calf mortality for validation sires. The results indicate that our novel approach can greatly increase the accuracy of selection for mortality traits compared with the accuracy of predictions obtained without distinguishing polygenic and lethal gene effects.
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http://dx.doi.org/10.1038/s41437-020-0329-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426854PMC
September 2020

Novel haplotypes responsible for prenatal death in Nordic Red and Danish Jersey cattle.

J Dairy Sci 2020 May 18;103(5):4570-4578. Epub 2020 Mar 18.

Center for Quantitative Genetics and Genomics, Faculty of Technical Sciences, Aarhus University, 8830 Tjele, Denmark. Electronic address:

Haplotypes that are common in a population but not observed as homotypes in living animals may harbor lethal alleles that compromise embryo survival. In this study, we searched for homozygous-deficient haplotypes in the genomes of 19,309 Nordic Red Dairy (RDC) and 4,291 Danish Jersey (JER) cattle genotyped using the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For statistically significant deficient haplotypes, we evaluated the effect on nonreturn rate in at-risk matings (mating between carrier bull and daughter of carrier sire) versus not-at-risk matings (mating between noncarrier bull and daughter of noncarrier sire). Next, we analyzed whole-genome sequence variants from the 1000 Bull Genomes Project to identify putative causal variants underlying these haplotypes. In RDC, we identified 3 homozygous-deficient regions (HDR) that overlapped with known recessive lethal mutations: a 662-kb deletion on chromosome 12 in RDC [Online Mendelian Inheritance in Animals (OMIA) 001901-9913), a missense mutation in TUBD1, g.11063520T>C, in Braunvieh cattle (OMIA 001939-9913), and a 525-kb deletion on chromosome 23 in RDC (OMIA 001991-9913)]. In addition, we identified 15 novel HDR and their tag haplotypes for the underlying causative variants. The tag haplotype located between 39.2 and 40.3 Mbp on chromosome 18 had a negative effect on nonreturn rate in at-risk mating, confirming embryonic lethality. In Danish Jersey, we identified 12 novel HDR and their tag haplotypes for underlying causative variants. For 3 of these 12 tag haplotypes, insemination records of at-risk mating showed a negative effect on nonreturn rate, confirming the association with early embryonic mortality. Cattle that had both genotype and whole-genome sequence data were analyzed to detect the causative variants underlying each tag haplotype. However, none of the functional variants or deletions showed concordance with carrier status of the novel tag haplotypes. Carrier status of these detected haplotypes can be used to select bulls to reduce the frequencies of lethal alleles in the population and to avoid at-risk matings.
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http://dx.doi.org/10.3168/jds.2019-17831DOI Listing
May 2020

Correction: Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome.

Heredity (Edinb) 2020 Apr;124(4):618

Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41437-020-0299-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265487PMC
April 2020

Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome.

Heredity (Edinb) 2020 02 22;124(2):274-287. Epub 2019 Oct 22.

Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.

Widely used genomic prediction models may not properly account for heterogeneous (co)variance structure across the genome. Models such as BayesA and BayesB assume locus-specific variance, which are highly influenced by the prior for (co)variance of single nucleotide polymorphism (SNP) effect, regardless of the size of data. Models such as BayesC or GBLUP assume a common (co)variance for a proportion (BayesC) or all (GBLUP) of the SNP effects. In this study, we propose a multi-trait Bayesian whole genome regression method (BayesN0), which is based on grouping a number of predefined SNPs to account for heterogeneous (co)variance structure across the genome. This model was also implemented in single-step Bayesian regression (ssBayesN0). For practical implementation, we considered multi-trait single-step SNPBLUP models, using (co)variance estimates from BayesN0 or ssBayesN0. Genotype data were simulated using haplotypes on first five chromosomes of 2200 Danish Holstein cattle, and phenotypes were simulated for two traits with heritabilities 0.1 or 0.4, assuming 200 quantitative trait loci (QTL). We compared prediction accuracy from different prediction models and different region sizes (one SNP, 100 SNPs, one chromosome or whole genome). In general, highest accuracies were obtained when 100 adjacent SNPs were grouped together. The ssBayesN0 improved accuracies over BayesN0, and using (co)variance estimates from ssBayesN0 generally yielded higher accuracies than using (co)variance estimates from BayesN0, for the 100 SNPs region size. Our results suggest that it could be a good strategy to estimate (co)variance components from ssBayesN0, and then to use those estimates in genomic prediction using multi-trait single-step SNPBLUP, in routine genomic evaluations.
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http://dx.doi.org/10.1038/s41437-019-0273-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6972913PMC
February 2020

Haplotypes responsible for early embryonic lethality detected in Nordic Holsteins.

J Dairy Sci 2019 Dec 20;102(12):11116-11123. Epub 2019 Sep 20.

Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark.

Widespread use of a limited number of elite sires in dairy cattle breeding increases the risk of some deleterious allelic variants spreading in the population. Genomic data are being used to detect relatively common (frequency >1%) haplotypes that never occur in the homozygous state in live animals. Such haplotypes likely include recessive lethal or semilethal alleles. The aim of this study was to detect such haplotypes in the Nordic Holstein population and to identify causal genetic factors underlying these haplotypes. Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA) genotypes for 26,312 Nordic Holstein animals were phased to construct haplotypes. Haplotypes that are common in the population but never observed as homozygous were identified. Two such haplotypes overlapped with previously identified recessive lethal mutations in Holsteins-namely, structural maintenance of chromosomes 2 (HH3) and brachyspina. In addition, we identified 9 novel putative recessive lethal-carrying haplotypes, with 26 to 36 homozygous individuals expected among the genotyped animals but only 0 to 3 homozygotes observed. For 2 out of 9 homozygous-deficient haplotypes, insemination records of at-risk mating (carrier bull with daughter of carrier sire) showed reduced insemination success compared with not-at-risk mating (noncarrier bull with daughter of noncarrier sire), supporting early embryonic mortality. To detect the causative variant underlying each homozygous-deficient haplotype, data from the 1000 Bull Genome Project were used. However, no variants or deletions identified in the chromosome regions covered by the haplotypes showed concordance with haplotype carrier status. The carrier status of detected haplotypes could be used to select bulls to reduce the frequency of the latent lethal mutations in the population. If desired, at-risk matings could be avoided.
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http://dx.doi.org/10.3168/jds.2019-16651DOI Listing
December 2019

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits.

Proc Natl Acad Sci U S A 2019 09 9;116(39):19398-19408. Epub 2019 Sep 9.

Faculty of Veterinary & Agricultural Science, The University of Melbourne, Parkville, VIC 3052, Australia.

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants, and for each set, we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent ( > 0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification marks, and selection signatures. From these results, we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In additional 7,551 cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution, and trait heritability to rank variants, and the publicly available FAETH data provide a set of biological priors for cattle genomic selection worldwide.
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http://dx.doi.org/10.1073/pnas.1904159116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6765237PMC
September 2019

Use of a Bayesian model including QTL markers increases prediction reliability when test animals are distant from the reference population.

J Dairy Sci 2019 Aug 31;102(8):7237-7247. Epub 2019 May 31.

Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Aarhus, Denmark. Electronic address:

Relatedness between reference and test animals has an important effect on the reliability of genomic prediction for test animals. Because genomic prediction has been widely applied in practical cattle breeding and bulls have been selected according to genomic breeding value without progeny testing, the sires or grandsires of candidates might not have phenotypic information and might not be in the reference population when the candidates are selected. The objective of this study was to investigate the decreasing trend of the reliability of genomic prediction given distant reference populations, using genomic best linear unbiased prediction (GBLUP) and Bayesian variable selection models with or without including the quantitative trait locus (QTL) markers detected from sequencing data. The data used in this study consisted of 22,242 bulls genotyped using the 54K SNP array from EuroGenomics. Among them, 1,444 Danish bulls born from 2006 to 2010 were selected as test animals. Different reference populations with varying relationships to test animals were created according to pedigree-based relationships. The reference individuals having a relationship with one or more test animals higher than 0.4 (scenario ρ < 0.4), 0.2 (ρ < 0.2), or 0.1 (ρ < 0.1, where ρ = relationship coefficient) were removed from reference sets; these represented the distance between reference and test animals being 2 generations, 3 generations, and 4 generations, respectively. Imputed whole-genome sequencing data of bulls from Denmark were used to conduct a genome-wide association study (GWAS). A small number of significant variants (QTL markers) from the GWAS were added to the array data. To compare the effects of different models, the basic GBLUP model, a Bayesian selection variable model, a GBLUP model with 2 components of genetic effects, and a Bayesian model with pooled array data and QTL markers were used for estimating genomic estimated breeding values (GEBV) of test animals. The reliability of genomic prediction decreased when the test animals were more generations away from the reference population. The reliability of genomic prediction was 0.461 for 1 generation away and 0.396 for 3 generations away, with the same number of individuals in the reference set, using a GBLUP model with chip markers only. The results showed that using the Bayesian method and QTL markers improved the reliability of genomic prediction in all scenarios of relationship between test and reference animals, in a range of 1.3% and 65.1% (4 generations away with only 841 individuals in the reference set). However, most gains were for predictions of milk yield and fat yield. There was little improvement for predictions of protein yield and mastitis, and no improvement for prediction of fertility, except for scenario ρ < 0.1, in which there was a large improvement for predictions of all traits. On the other hand, models including more than 10% polygenic effect decreased prediction reliability when the relationship between test and reference animals was distant.
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http://dx.doi.org/10.3168/jds.2018-15815DOI Listing
August 2019

Reliability of genomic prediction for milk fatty acid composition by using a multi-population reference and incorporating GWAS results.

Genet Sel Evol 2019 Apr 27;51(1):16. Epub 2019 Apr 27.

Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, P.O. Box 50, 8830, Tjele, Denmark.

Background: Large-scale phenotyping for detailed milk fatty acid (FA) composition is difficult due to expensive and time-consuming analytical techniques. Reliability of genomic prediction is often low for traits that are expensive/difficult to measure and for breeds with a small reference population size. An effective method to increase reference population size could be to combine datasets from different populations. Prediction models might also benefit from incorporation of information on the biological underpinnings of quantitative traits. Genome-wide association studies (GWAS) show that genomic regions on Bos taurus chromosomes (BTA) 14, 19 and 26 underlie substantial proportions of the genetic variation in milk FA traits. Genomic prediction models that incorporate such results could enable improved prediction accuracy in spite of limited reference population sizes. In this study, we combine gas chromatography quantified FA samples from the Chinese, Danish and Dutch Holstein populations and implement a genomic feature best linear unbiased prediction (GFBLUP) model that incorporates variants on BTA14, 19 and 26 as genomic features for which random genetic effects are estimated separately. Prediction reliabilities were compared to those estimated with traditional GBLUP models.

Results: Predictions using a multi-population reference and a traditional GBLUP model resulted in average gains in prediction reliability of 10% points in the Dutch, 8% points in the Danish and 1% point in the Chinese populations compared to predictions based on population-specific references. Compared to the traditional GBLUP, implementation of the GFBLUP model with a multi-population reference led to further increases in prediction reliability of up to 38% points in the Dutch, 23% points in the Danish and 13% points in the Chinese populations. Prediction reliabilities from the GFBLUP model were moderate to high across the FA traits analyzed.

Conclusions: Our study shows that it is possible to predict genetic merits for milk FA traits with reasonable accuracy by combining related populations of a breed and using models that incorporate GWAS results. Our findings indicate that international collaborations that facilitate access to multi-population datasets could be highly beneficial to the implementation of genomic selection for detailed milk composition traits.
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http://dx.doi.org/10.1186/s12711-019-0460-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6487064PMC
April 2019

Selection for temperament has no negative consequences on important production traits in farmed mink1.

J Anim Sci 2019 Apr;97(5):1987-1995

Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark.

Danish and European legislation recommend mink breeding programs that include selection for "confidence," defined as exploratory activity in a standardized behavioral test. Although this recommendation may improve mink welfare, farmers may consider this criterion risky due to possible negative consequences on other traits. The overall objectives of this study were to estimate the heritability of exploratory/fearful behavior and to identify genetic correlations with other traits of major economic importance in mink fur production. Various aspects of social influence on exploratory/fearful behavior, such as effects of the mother and litter siblings before weaning, the mother's age, and cage mates after weaning, were analyzed. In total, 26,371 1-yr-old Brown mink (Neovison vison) individuals born during the period of 2013 to2016 were included in the study. Exploratory/fearful behavior was the main trait analyzed. The production traits analyzed were live pelt quality and body weight. Both of these traits were assessed during live grading in November. Pelt length and quality were determined using the dried pelts of nonbreeders. Fertility data were obtained from the Fur Farm database. Linear mixed models were run using the restricted maximum-likelihood method. The genetic correlation between female and male behavior was 0.95 (SE = 0.06), indicating similar genetic backgrounds for both sexes (P = 0.40). For both sexes, the estimated heritability of behavior was 0.19 (SE = 0.03). We found no significant genetic correlation between behavior and production/fertility traits (P > 0.05). Common litter variance indicated a preweaning effect of litter mates and/or dam on postweaning temperament. There was a tendency for offspring from older mothers to explore more than offspring from 1-yr-old mothers. This trend was especially pronounced for males of 2-yr-old mothers (P = 0.05) and females of 4-yr-old mothers (P = 0.06). We conclude that confidence may be selected for among farm mink without detrimental effects on economically important production traits, such as pelt quality and fertility.
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http://dx.doi.org/10.1093/jas/skz089DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488327PMC
April 2019

Introgression of Chinese haplotypes contributed to the improvement of Danish Duroc pigs.

Evol Appl 2019 Feb 13;12(2):292-300. Epub 2018 Dec 13.

Department of Molecular Biology and Genetics, Centre for Quantitative Genetics and Genomics Aarhus University Tjele Denmark.

The distribution of Asian ancestry in the genome of Danish Duroc pigs was investigated using whole-genome sequencing data from European wild boars, Danish Duroc, Chinese Meishan and Bamaxiang pigs. Asian haplotypes deriving from Meishan and Bamaxiang occur widely across the genome. Signatures of selection on Asian haplotypes are common in the genome, but few of these haplotypes have been fixed. By defining 50-kb windows with more than 50% Chinese ancestry, which did not exhibit extreme genetic differentiation between Meishan and Bamaxiang as candidate regions, the enrichment of quantitative trait loci in candidate regions supports that Asian haplotypes under selection play an important role in contributing genetic variation underlying production, reproduction, meat and carcass, and exterior traits. Gene annotation of regions with the highest proportion of Chinese ancestry revealed genes of biological interest, such as . Further haplotype clustering analysis suggested that a haplotype of Chinese origin around the gene was introduced to Europe and then underwent a selective sweep in European pigs. Besides, functional genes in candidate regions, such as and , associated with fertility, and , associated with meat quality, were identified. Our results demonstrate the contribution of Asian haplotypes to the genomes of European pigs. Findings herein facilitate further genomic studies such as genomewide association study and genomic prediction by providing ancestry information of variants.
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http://dx.doi.org/10.1111/eva.12716DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6346729PMC
February 2019

A Meta-Analysis Including Pre-selected Sequence Variants Associated With Seven Traits in Three French Dairy Cattle Populations.

Front Genet 2018 6;9:522. Epub 2018 Nov 6.

UMR GABI, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy en Josas, France.

A within-breed genome-wide association study (GWAS) is useful when identifying the QTL that segregates in a breed. However, an across-breed meta-analysis can be used to increase the power of identification and precise localization of QTL that segregate in multiple breeds. Precise localization will allow including QTL information from other breeds in genomic prediction due to the persistence of the linkage phase between the causal variant and the marker. This study aimed to identify and confirm QTL detected in within-breed GWAS through a meta-analysis in three French dairy cattle breeds. A set of sequence variants selected based on their functional annotations were imputed into 50 k genotypes for 46,732 Holstein, 20,096 Montbeliarde, and 11,944 Normande cows to identify QTL for milk production, the success rate at insemination of cows (fertility) and stature. We conducted within-breed GWAS followed by across-breed meta-analysis using a weighted Z-scores model on the GWAS summary data (i.e., -values, effect direction, and sample size). After Bonferroni correction, the GWAS result identified 21,956 significantly associated SNP ( < 0.05), while meta-analysis result identified 9,604 significant SNP ( < 0.05) associated with the phenotypes. The meta-analysis identified 36 QTL for milk yield, 48 QTL for fat yield and percentage, 29 QTL for protein yield and percentage, 13 QTL for fertility, and 16 QTL for stature. Some of these QTL were not significant in the within-breed GWAS. Some previously identified causal variants were confirmed, e.g., BTA14:1802265 (fat percentage, = 1.5 × 10; protein percentage, = 7.61 × 10) both mapping the DGAT1-K232A mutation and BTA14:25006125 ( = 8.58 × 10) mapping gene was confirmed for stature in Montbeliarde. New QTL lead SNP shared between breeds included the intronic variant rs109205829 ( gene), and the intergenic variant rs41592357 (1.38 Mb upstream of the gene and 0.65 Mb downstream of the gene). Rs110425867 ( gene) was the top variant associated with fertility, and new QTL lead SNP included rs109483390 (0.1 Mb upstream of the gene and 0.07 Mb downstream of gene), and rs42412333 (0.45 Mb downstream of the gene). An across-breed meta-analysis had greater power to detect QTL as opposed to a within breed GWAS. The QTL detected here can be incorporated in routine genomic predictions.
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http://dx.doi.org/10.3389/fgene.2018.00522DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6232291PMC
November 2018

Genomic Prediction Using Multi-trait Weighted GBLUP Accounting for Heterogeneous Variances and Covariances Across the Genome.

G3 (Bethesda) 2018 11 6;8(11):3549-3558. Epub 2018 Nov 6.

Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.

Implicit assumption of common (co)variance for all loci in multi-trait Genomic Best Linear Unbiased Prediction (GBLUP) results in a genomic relationship matrix () that is common to all traits. When this assumption is violated, Bayesian whole genome regression methods may be superior to GBLUP by accounting for unequal (co)variance for all loci or genome regions. This study aimed to develop a strategy to improve the accuracy of GBLUP for multi-trait genomic prediction, using (co)variance estimates of SNP effects from Bayesian whole genome regression methods. Five generations (G1-G5, test populations) of genotype data were available by simulations based on data of 2,200 Danish Holstein cows (G0, reference population). Two correlated traits with heritabilities of 0.1 or 0.4, and a genetic correlation of 0.45 were generated. First, SNP effects and breeding values were estimated using BayesAS method, assuming (co)variance was the same for SNPs within a genome region, and different between regions. Region size was set as one SNP, 100 SNPs, a whole chromosome or whole genome. Second, posterior (co)variances of SNP effects were used to weight SNPs in construction of matrices. In general, region size of 100 SNPs led to highest prediction accuracies using BayesAS, and wGBLUP outperformed GBLUP at this region size. Our results suggest that when genetic architectures of traits favor Bayesian methods, the accuracy of multi-trait GBLUP can be as high as the Bayesian method if SNPs are weighted by the Bayesian posterior (co)variances.
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http://dx.doi.org/10.1534/g3.118.200673DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6222589PMC
November 2018

Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals.

Nat Genet 2018 03 19;50(3):362-367. Epub 2018 Feb 19.

Qualitas AG, Zug, Switzerland.

Stature is affected by many polymorphisms of small effect in humans . In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes. 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) 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.
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http://dx.doi.org/10.1038/s41588-018-0056-5DOI Listing
March 2018

Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits.

Genet Sel Evol 2017 Dec 5;49(1):89. Epub 2017 Dec 5.

Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, P.O. Box 50, 8830, Tjele, Denmark.

Background: Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls. Single-nucleotide polymorphisms (SNPs), from 50K SNP arrays, were grouped into non-overlapping genome segments. A segment was defined as one SNP, or a group of 50, 100, or 200 adjacent SNPs, or one chromosome, or the whole genome. Traditional univariate and bivariate genomic best linear unbiased prediction (GBLUP) models were also run for comparison. Reliabilities were calculated through a resampling strategy and using deterministic formula.

Results: BayesAS models improved prediction reliability for most of the traits compared to GBLUP models and this gain depended on segment size and genetic architecture of the traits. The gain in prediction reliability was especially marked for the protein composition traits β-CN, κ-CN and β-LG, for which prediction reliabilities were improved by 49 percentage points on average using the MT-BayesAS model with a 100-SNP segment size compared to the bivariate GBLUP. Prediction reliabilities were highest with the BayesAS model that uses a 100-SNP segment size. The bivariate versions of our BayesAS models resulted in extra gains of up to 6% in prediction reliability compared to the univariate versions.

Conclusions: Substantial improvement in prediction reliability was possible for most of the traits related to milk protein composition using our novel BayesAS models. Grouping adjacent SNPs into segments provided enhanced information to estimate parameters and allowing the segments to have different (co)variances helped disentangle heterogeneous (co)variances across the genome.
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http://dx.doi.org/10.1186/s12711-017-0364-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718071PMC
December 2017

Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle.

Genet Sel Evol 2016 11 4;48(1):83. Epub 2016 Nov 4.

Department of Molecular Biology and Genetics, Faculty of Science and Technology, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.

Background: Sequence data can potentially increase the reliability of genomic predictions, because such data include causative mutations instead of relying on linkage disequilibrium (LD) between causative mutations and prediction variants. However, the location of the causative mutations is not known, and the presence of many variants that are in low LD with the causative mutations may reduce prediction reliability. Our objective was to investigate whether the use of variants at quantitative trait loci (QTL) that are identified in a multi-breed genome-wide association study (GWAS) for milk, fat and protein yield would increase the reliability of within- and multi-breed genomic predictions in Holstein, Jersey and Danish Red cattle. A wide range of scenarios that test different strategies to select prediction markers, for both within-breed and multi-breed prediction, were compared.

Results: For all breeds and traits, the use of variants selected from a multi-breed GWAS resulted in substantial increases in prediction reliabilities compared to within-breed prediction using a 50 K SNP array. Reliabilities depended highly on the choice of the prediction markers, and the scenario that led to the highest reliability varied between breeds and traits. While genomic correlations across breeds were low for genome-wide sequence variants, the effects of the QTL variants that yielded the highest reliabilities were highly correlated across breeds.

Conclusions: Our results show that the use of sequence variants, which are located near peaks of QTL that are detected in a multi-breed GWAS, can increase reliability of genomic predictions.
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http://dx.doi.org/10.1186/s12711-016-0259-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5095991PMC
November 2016

Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle: A Simulation Study.

G3 (Bethesda) 2016 08 9;6(8):2553-61. Epub 2016 Aug 9.

Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, DK-8830 Tjele, Denmark.

Sequence data are expected to increase the reliability of genomic prediction by containing causative mutations directly, especially in cases where low linkage disequilibrium between markers and causative mutations limits prediction reliability, such as across-breed prediction in dairy cattle. In practice, the causative mutations are unknown, and prediction with only variants in perfect linkage disequilibrium with the causative mutations is not realistic, leading to a reduced reliability compared to knowing the causative variants. Our objective was to use sequence data to investigate the potential benefits of sequence data for the prediction of genomic relationships, and consequently reliability of genomic breeding values. We used sequence data from five dairy cattle breeds, and a larger number of imputed sequences for two of the five breeds. We focused on the influence of linkage disequilibrium between markers and causative mutations, and assumed that a fraction of the causative mutations was shared across breeds and had the same effect across breeds. By comparing the loss in reliability of different scenarios, varying the distance between markers and causative mutations, using either all genome wide markers from commercial SNP chips, or only the markers closest to the causative mutations, we demonstrate the importance of using only variants very close to the causative mutations, especially for across-breed prediction. Rare variants improved prediction only if they were very close to rare causative mutations, and all causative mutations were rare. Our results show that sequence data can potentially improve genomic prediction, but careful selection of markers is essential.
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http://dx.doi.org/10.1534/g3.116.027730DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978908PMC
August 2016

Genetic evaluation for three-way crossbreeding.

Genet Sel Evol 2015 Dec 22;47:98. Epub 2015 Dec 22.

Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, P.O. BOX 50, 8830, Tjele, Denmark.

Background: Commercial pig producers generally use a terminal crossbreeding system with three breeds. Many pig breeding organisations have started to use genomic selection for which genetic evaluation is often done by applying single-step methods for which the pedigree-based additive genetic relationship matrix is replaced by a combined relationship matrix based on both marker genotypes and pedigree. Genomic selection is implemented for purebreds, but it also offers opportunities for incorporating information from crossbreds and selecting for crossbred performance. However, models for genetic evaluation for the three-way crossbreeding system have not been developed.

Results: Four-variate models for three-way terminal crossbreeding are presented in which the first three variables contain the records for the three pure breeds and the fourth variable contains the records for the three-way crossbreds. For purebred animals, the models provide breeding values for both purebred and crossbred performances. Heterogeneity of genetic architecture between breeds and genotype by environment interactions are modelled through genetic correlations between these breeding values. Specification of the additive genetic relationships is essential for these models and can be defined either within populations or across populations. Based on these two types of additive genetic relationships, both pedigree-based, marker-based and combined relationships based on both pedigree and marker information are presented. All these models for three-way crossbreeding can be formulated using Kronecker matrix products and therefore fitted using Henderson's mixed model equations and standard animal breeding software.

Conclusions: Models for genetic evaluation in the three-way crossbreeding system are presented. They provide estimated breeding values for both purebred and crossbred performances, and can use pedigree-based or marker-based relationships, or combined relationships based on both pedigree and marker information. This provides a framework that allows information from three-way crossbred animals to be incorporated into a genetic evaluation system.
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http://dx.doi.org/10.1186/s12711-015-0177-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4689093PMC
December 2015

Genetic dissection of milk yield traits and mastitis resistance quantitative trait loci on chromosome 20 in dairy cattle.

J Dairy Sci 2015 Dec 26;98(12):9015-25. Epub 2015 Sep 26.

Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark. Electronic address:

Intense selection to increase milk yield has had negative consequences for mastitis incidence in dairy cattle. Due to low heritability of mastitis resistance and an unfavorable genetic correlation with milk yield, a reduction in mastitis through traditional breeding has been difficult to achieve. Here, we examined quantitative trait loci (QTL) that segregate for clinical mastitis and milk yield on Bos taurus autosome 20 (BTA20) to determine whether both traits are affected by a single polymorphism (pleiotropy) or by multiple closely linked polymorphisms. In the latter but not the former situation, undesirable genetic correlation could potentially be broken by selecting animals that have favorable variants for both traits. First, we performed a within-breed association study using a haplotype-based method in Danish Holstein cattle (HOL). Next, we analyzed Nordic Red dairy cattle (RDC) and Danish Jersey cattle (JER) with the goal of determining whether these QTL identified in Holsteins were segregating across breeds. Genotypes for 12,566 animals (5,966 HOL, 5,458 RDC, and 1,142 JER) were determined by using the Illumina Bovine SNP50 BeadChip (50K; Illumina, San Diego, CA), which identifies 1,568 single nucleotide polymorphisms on BTA20. Data were combined, phased, and clustered into haplotype states, followed by within- and across-breed haplotype-based association analyses using a linear mixed model. Association signals for both clinical mastitis and milk yield peaked in the 26- to 40-Mb region on BTA20 in HOL. Single-variant association analyses were carried out in the QTL region using whole sequence level variants imputed from references of 2,036 HD genotypes (BovineHD BeadChip; Illumina) and 242 whole-genome sequences. The milk QTL were also segregating in RDC and JER on the BTA20-targeted region; however, an indication of differences in the causal factor(s) was observed across breeds. A previously reported F279Y mutation (rs385640152) within the growth hormone receptor gene showed strong association with milk, fat, and protein yields. In HOL, the highest peaks for milk yield and susceptibility to mastitis were separated by over 3.5 Mb (3.8 Mb by haplotype analysis, 3.6 Mb by single nucleotide polymorphism analysis), suggesting separate genetic variants for the traits. Further analysis yielded 2 candidate mutations for the mastitis QTL, at 33,642,072 bp (rs378947583) in an intronic region of the caspase recruitment domain protein 6 gene and 35,969,994 bp (rs133596506) in an intronic region of the leukemia-inhibitory factor receptor gene. These findings suggest that it may be possible to separate these beneficial and detrimental genetic factors through targeted selective breeding.
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http://dx.doi.org/10.3168/jds.2015-9599DOI Listing
December 2015

Genome-wide association study for female fertility in Nordic Red cattle.

BMC Genet 2015 Sep 15;16:110. Epub 2015 Sep 15.

Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics Aarhus University, P.O. Box 50, DK 8830, Tjele, Denmark.

Background: The Nordic Red Cattle (NRC) consists of animls belonging to the Danish Red, Finnish Ayrshire, and Swedish Red breeds. Compared to the Holstein breed, NRC animals are smaller, have a shorter calving interval, lower mastitis incidence and lower rates of stillborn calves, however they produce less milk, fat and protein. Female fertility is an important trait for the dairy cattle farmer. Selection decisions in female fertilty in NRC are based on the female fertility index (FTI). FTI is a composite index including a number of sub-indices describing aspects of female fertility in dairy cattle. The sub-traits of FTI are: number of inseminations per conception (AIS) in cows (C) and heifers (H), the length in days of the interval from calving to first insemination (ICF) in cows, days from first to last insemination (IFL) in cows and heifers, and 56-day non-return rate (NRR) in cows and heifers. The aim of this study was first to identify QTL for FTI by conducting a genome scan for variants associated with fertility index using imputed whole genome sequence data based on 4207 Nordic Red sires, and subsequently analyzing which of the sub-traits were affected by each FTI QTL by associating them with the sub-traits.

Results: A total 17,388 significant SNP markers (-log10(P) > 8.25) were detected for FTI distributed over 25 chromosomes. The chromosomes with the most significant markers were tested for associations with the underlying sub-traits: BTA1 (822 SNP), BTA2 (220 SNP), BTA3 (83 SNP), BTA5 (195 SNP), two regions on BTA6 (503 SNP), BTA13 (980 SNP), BTA15 (23 SNP), BTA20 (345 SNP), and BTA24 (104 SNP). The fertility traits underlying the FTI peak area were: BTA1 (IFLC, IFLH), BTA2 (AISH, IFLH, NRRH), BTA3 (AISH, NRRH), BTA5 (AISC, AISH, IFLH), BTA6 (region 1: AISH, NRRH; region 2: AISH, IFLH), BTA13 (IFLH, IFLC), BTA15 (IFLC, NRRH), and BTA24 (AISH, IFLH). For BTA20 all sub-traits had SNP markers with a -log10(P) > 10. Furthermore the genes assigned to the most significant SNP for FTI were located on BTA6 (GPR125), BTA13 (ANKRD60), BTA15 (GRAMD1B), and BTA24 (ZNF521).

Conclusion: This study 1) shows that many markers within FTI QTL regions were significantly associated with both AISH and IFLH, and 2) identified candidate genes for FTI located on BTA6 (GPR125), BTA13 (ANKRD60), BTA15 (GRAMD1B), and BTA24 (ZNF521). It is not known how the genes/variants identified in this study regulate female fertility, however the majority of these genes were involved in protein binding, 3) a SNP in a QTL region for FTI on BTA20 was previously validated in three cattle breeds.
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http://dx.doi.org/10.1186/s12863-015-0269-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4570259PMC
September 2015

Selection of haplotype variables from a high-density marker map for genomic prediction.

Genet Sel Evol 2015 Aug 1;47:61. Epub 2015 Aug 1.

Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.

Background: Using haplotype blocks as predictors rather than individual single nucleotide polymorphisms (SNPs) may improve genomic predictions, since haplotypes are in stronger linkage disequilibrium with the quantitative trait loci than are individual SNPs. It has also been hypothesized that an appropriate selection of a subset of haplotype blocks can result in similar or better predictive ability than when using the whole set of haplotype blocks. This study investigated genomic prediction using a set of haplotype blocks that contained the SNPs with large effects estimated from an individual SNP prediction model. We analyzed protein yield, fertility and mastitis of Nordic Holstein cattle, and used high-density markers (about 770k SNPs). To reach an optimum number of haplotype variables for genomic prediction, predictions were performed using subsets of haplotype blocks that contained a range of 1000 to 50 000 main SNPs.

Results: The use of haplotype blocks improved the prediction reliabilities, even when selection focused on only a group of haplotype blocks. In this case, the use of haplotype blocks that contained the 20 000 to 50 000 SNPs with the highest effect was sufficient to outperform the model that used all individual SNPs as predictors (up to 1.3 % improvement in prediction reliability for mastitis, compared to individual SNP approach), and the achieved reliabilities were similar to those using all haplotype blocks available in the genome data (from 0.6 % lower to 0.8 % higher reliability).

Conclusions: Haplotype blocks used as predictors can improve the reliability of genomic prediction compared to the individual SNP model. Furthermore, the use of a subset of haplotype blocks that contains the main SNP effects from genomic data could be a feasible approach to genomic prediction in dairy cattle, given an increase in density of genotype data available. The predictive ability of the models that use a subset of haplotype blocks was similar to that obtained using either all haplotype blocks or all individual SNPs, with the benefit of having a much lower computational demand.
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http://dx.doi.org/10.1186/s12711-015-0143-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4522081PMC
August 2015

Runs of homozygosity and distribution of functional variants in the cattle genome.

BMC Genomics 2015 Jul 22;16:542. Epub 2015 Jul 22.

Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, DK-8830, Denmark.

Background: Recent developments in sequencing technology have facilitated widespread investigations of genomic variants, including continuous stretches of homozygous genomic regions. For cattle, a large proportion of these runs of homozygosity (ROH) are likely the result of inbreeding due to the accumulation of elite alleles from long-term selective breeding programs. In the present study, ROH were characterized in four cattle breeds with whole genome sequence data and the distribution of predicted functional variants was detected in ROH regions and across different ROH length classes.

Results: On average, 19.5% of the genome was located in ROH across four cattle breeds. There were an average of 715.5 ROH per genome with an average size of ~750 kbp, ranging from 10 (minimum size considered) to 49,290 kbp. There was a significant correlation between shared short ROH regions and regions putatively under selection (p < 0.001). By investigating the relationship between ROH and the predicted deleterious and non-deleterious variants, we gained insight into the distribution of functional variation in inbred (ROH) regions. Predicted deleterious variants were more enriched in ROH regions than predicted non-deleterious variants, which is consistent with observations in the human genome. We also found that increased enrichment of deleterious variants was significantly higher in short (<100 kbp) and medium (0.1 to 3 Mbp) ROH regions compared with long (>3 Mbp) ROH regions (P < 0.001), which is different than what has been observed in the human genome.

Conclusions: This study illustrates the distribution of ROH and functional variants within ROH in cattle populations. These patterns are different from those in the human genome but consistent with the natural history of cattle populations, which is confirmed by the significant correlation between shared short ROH regions and regions putatively under selection. These findings contribute to understanding the effects of inbreeding and probably selection in shaping the distribution of functional variants in the cattle genome.
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http://dx.doi.org/10.1186/s12864-015-1715-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4508970PMC
July 2015

Estimation of inbreeding using pedigree, 50k SNP chip genotypes and full sequence data in three cattle breeds.

BMC Genet 2015 Jul 22;16:88. Epub 2015 Jul 22.

Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, DK-8830, Denmark.

Background: Levels of inbreeding in cattle populations have increased in the past due to the use of a limited number of bulls for artificial insemination. High levels of inbreeding lead to reduced genetic diversity and inbreeding depression. Various estimators based on different sources, e.g., pedigree or genomic data, have been used to estimate inbreeding coefficients in cattle populations. However, the comparative advantage of using full sequence data to assess inbreeding is unknown. We used pedigree and genomic data at different densities from 50k to full sequence variants to compare how different methods performed for the estimation of inbreeding levels in three different cattle breeds.

Results: Five different estimates for inbreeding were calculated and compared in this study: pedigree based inbreeding coefficient (F(PED)); run of homozygosity (ROH)-based inbreeding coefficients (F(ROH)); genomic relationship matrix (GRM)-based inbreeding coefficients (F(GRM)); inbreeding coefficients based on excess of homozygosity (F(HOM)) and correlation of uniting gametes (F(UNI)). Estimates using ROH provided the direct estimated levels of autozygosity in the current populations and are free effects of allele frequencies and incomplete pedigrees which may increase in inaccuracy in estimation of inbreeding. The highest correlations were observed between F(ROH) estimated from the full sequence variants and the F(ROH) estimated from 50k SNP (single nucleotide polymorphism) genotypes. The estimator based on the correlation between uniting gametes (F(UNI)) using full genome sequences was also strongly correlated with F(ROH) detected from sequence data.

Conclusions: Estimates based on ROH directly reflected levels of homozygosity and were not influenced by allele frequencies, unlike the three other estimates evaluated (F(GRM), F(HOM) and FU(NI)), which depended on estimated allele frequencies. F(PED) suffered from limited pedigree depth. Marker density affects ROH estimation. Detecting ROH based on 50k chip data was observed to give estimates similar to ROH from sequence data. In the absence of full sequence data ROH based on 50k can be used to access homozygosity levels in individuals. However, genotypes denser than 50k are required to accurately detect short ROH that are most likely identical by descent (IBD).
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http://dx.doi.org/10.1186/s12863-015-0227-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4509611PMC
July 2015

Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins.

Genet Sel Evol 2015 Jun 19;47:50. Epub 2015 Jun 19.

Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark.

Background: The sensitivity of genome-wide association studies for the detection of quantitative trait loci (QTL) depends on the density of markers examined and the statistical models used. This study compares the performance of three marker densities to refine six previously detected QTL regions for mastitis traits: 54 k markers of a medium-density SNP (single nucleotide polymorphism) chip (MD), imputed 777 k markers of a high-density SNP chip (HD), and imputed whole-genome sequencing data (SEQ). Each dataset contained data for 4496 Danish Holstein cattle. Comparisons were performed using a linear mixed model (LM) and a Bayesian variable selection model (BVS).

Results: After quality control, 587, 7825, and 78 856 SNPs in the six targeted regions remained for MD, HD, and SEQ data, respectively. In general, the association patterns between SNPs and traits were similar for the three marker densities when tested using the same statistical model. With the LM model, 120 (MD), 967 (HD), and 7209 (SEQ) SNPs were significantly associated with mastitis, whereas with the BVS model, 43 (MD), 131 (HD), and 1052 (SEQ) significant SNPs (Bayes factor > 3.2) were observed. A total of 26 (MD), 75 (HD), and 465 (SEQ) significant SNPs were identified by both models. In addition, one, 16, and 33 QTL peaks for MD, HD, and SEQ data were detected according to the QTL intensity profile of SNP bins by post-analysis of the BVS model.

Conclusions: The power to detect significant associations increased with increasing marker density. The BVS model resulted in clearer boundaries between linked QTL than the LM model. Using SEQ data, the six targeted regions were refined to 33 candidate QTL regions for udder health. The comparison between these candidate QTL regions and known genes suggested that NPFFR2, SLC4A4, DCK, LIFR, and EDN3 may be considered as candidate genes for mastitis susceptibility.
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http://dx.doi.org/10.1186/s12711-015-0129-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4472403PMC
June 2015

Identification of genomic regions associated with female fertility in Danish Jersey using whole genome sequence data.

BMC Genet 2015 Jun 3;16:60. Epub 2015 Jun 3.

Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, P.O. Box 50, 8830, Tjele, Denmark.

Background: Female fertility is an important trait in cattle breeding programs. In the Nordic countries selection is based on a fertility index (FTI). The fertility index is a weighted combination of four female fertility traits estimated breeding values for number of inseminations per conception (AIS), 56-day non-return rate (NRR), number of days from first to last insemination (IFL), and number of days between calving and first insemination (ICF). The objective of this study was to identify associations between sequence variants and fertility traits in Jersey cattle based on 1,225 Jersey sires from Denmark with official breeding values for female fertility traits. The association analyses were carried out in two steps: first the cattle genome was scanned for quantitative trait loci using a sire model for FTI using imputed whole genome sequence variants; second the significant quantitative trait locus regions were re-analyzed using a linear mixed model (animal model) for both FTI and its component traits AIS, NRR, IFL and ICF. The underlying traits were analyzed separately for heifers (first parity cows) and cows (later parity cows) for AIS, NRR, and IFL.

Results: In the first step 6 QTL were detected for FTI: one QTL on each of BTA7, BTA20, BTA23, BTA25, and two QTL on BTA9 (QTL9-1 and QTL9-2). In the second step, ICF showed association with the QTL regions on BTA7, QTL9-2 QTL2 on BTA9, and BTA25, AIS for cows on BTA20 and BTA23, AIS for heifers on QTL9-2 on BTA9, IFL for cows on BTA20, BTA23 and BTA25, IFL for heifers on BTA7 and QTL9-2 on BTA9, NRR for heifers on BTA7 and BTA23, and NRR for cows on BTA23.

Conclusion: The genome wide association study presented here revealed 6 genomic regions associated with FTI. Screening these 6 QTL regions for the underlying female fertility traits revealed that different female fertility traits showed associations with different subsets of the individual FTI QTL peaks. The result of this study contributed to a better insight into the genetic control of FTI in the Danish Jersey.
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http://dx.doi.org/10.1186/s12863-015-0210-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4453229PMC
June 2015

Loci associated with adult stature also affect calf birth survival in cattle.

BMC Genet 2015 May 3;16:47. Epub 2015 May 3.

Center for Quantitative Genetics and Genomics, Aarhus University, P.O. Box 50, DK-8830, Tjele, Denmark.

Background: Understanding the underlying pleiotropic relationships among quantitative traits is necessary in order to predict correlated responses to artificial selection. The availability of large-scale next-generation sequence data in cattle has provided an opportunity to examine whether pleiotropy is responsible for overlapping QTL in multiple economic traits. In the present study, we examined QTL affecting cattle stillbirth, calf size, and adult stature located in the same genomic region.

Results: A genome scan using imputed whole genome sequence variants revealed one QTL with large effects on the service sire calving index (SCI), and body conformation index (BCI) at the same location (~39 Mb) on chromosome 6 in Nordic Red cattle. The targeted region was analyzed for SCI and BCI component traits. The QTL peak included LCORL and NCAPG genes, which had been reported to influence fetal growth and adult stature in several species. The QTL exhibited large effects on calf size and stature in Nordic Red cattle. Two deviant haplotypes (HAP1 and HAP2) were resolved which increased calf size at birth, and affected adult body conformation. However, the haplotypes also resulted in increased calving difficulties and calf mortality due to increased calf size at birth. Haplotype locations overlapped, however linkage disequilibrium (LD) between the sites was low, suggesting that two independent mutations were responsible for similar effects. The difference in prevalence between the two haplotypes in Nordic Red subpopulations suggested independent origins in different populations.

Conclusions: Results of our study identified QTL with large effects on body conformation and service sire calving traits on chromosome 6 in cattle. We present robust evidence that variation at the LCORL and NCAPG locus affects calf size at birth and adult stature. We suggest the two deviant haplotypes within the QTL were due to two independent mutations.
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http://dx.doi.org/10.1186/s12863-015-0202-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426170PMC
May 2015

Genomic prediction of genetic merit using LD-based haplotypes in the Nordic Holstein population.

BMC Genomics 2014 Dec 23;15:1171. Epub 2014 Dec 23.

Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Denmark.

Background: A haplotype approach to genomic prediction using high density data in dairy cattle as an alternative to single-marker methods is presented. With the assumption that haplotypes are in stronger linkage disequilibrium (LD) with quantitative trait loci (QTL) than single markers, this study focuses on the use of haplotype blocks (haploblocks) as explanatory variables for genomic prediction. Haploblocks were built based on the LD between markers, which allowed variable reduction. The haploblocks were then used to predict three economically important traits (milk protein, fertility and mastitis) in the Nordic Holstein population.

Results: The haploblock approach improved prediction accuracy compared with the commonly used individual single nucleotide polymorphism (SNP) approach. Furthermore, using an average LD threshold to define the haploblocks (LD≥0.45 between any two markers) increased the prediction accuracies for all three traits, although the improvement was most significant for milk protein (up to 3.1% improvement in prediction accuracy, compared with the individual SNP approach). Hotelling's t-tests were performed, confirming the improvement in prediction accuracy for milk protein. Because the phenotypic values were in the form of de-regressed proofs, the improved accuracy for milk protein may be due to higher reliability of the data for this trait compared with the reliability of the mastitis and fertility data. Comparisons between best linear unbiased prediction (BLUP) and Bayesian mixture models also indicated that the Bayesian model produced the most accurate predictions in every scenario for the milk protein trait, and in some scenarios for fertility.

Conclusions: The haploblock approach to genomic prediction is a promising method for genomic selection in animal breeding. Building haploblocks based on LD reduced the number of variables without the loss of information. This method may play an important role in the future genomic prediction involving while genome sequences.
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http://dx.doi.org/10.1186/1471-2164-15-1171DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367958PMC
December 2014

Fine mapping QTL for female fertility on BTA04 and BTA13 in dairy cattle using HD SNP and sequence data.

BMC Genomics 2014 Sep 13;15:790. Epub 2014 Sep 13.

Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, P,O, Box 50, DK-8830 Tjele, Denmark.

Background: Female fertility is important for the maintenance of the production in a dairy cattle herd. Two QTL regions on BTA04 and on BTA13 previously detected in Nordic Holstein (NH) and validated in the Danish Jersey (DJ) and Nordic Red (NR) were investigated further in the present study to further refine the QTL locations. Refined QTL regions were imputed to the full sequence data. The genes in the regions were then studied to ascertain their possible effect on fertility traits.

Results: BTA04 was screened for number of inseminations (AIS), 56-day non-return rate (NRR), days from first to last insemination (IFL), and the interval from calving to first insemination (ICF) in the range of 38,257,758 to 40,890,784 bp, whereas BTA13 was screened for ICF only in the range from 21,236,959 to 46,150,079 with the HD bovine SNP array for NH, DJ and NR. No markers in the DJ and NR breeds reached significance. By analyzing imputed sequence data the QTL position on BTA04 was narrowed down to two regions in the NH. In these two regions a total of 9 genes were identified. BTA13 was analyzed using sequence data for the NH breed. The highest -log10(P-value) was 19.41 at 33,903,159 bp. Two regions were identified: Region 1: 33,900,143-33,908,994 bp and Region 2: 34,051,815-34,056,728 bp. SNPs within and between these two regions were annotated as intergenic.

Conclusion: Screening BTA04 and BTA13 for female fertility traits in NH, NR and DJ suggested that the QTL for female fertility were specific for NH. A missense mutation in CD36 showed the strongest association with fertility traits on BTA04. The annotated SNPs on BTA13 were all intergenic variants. It is possible that BTA13 at this stage is poorly annotated such that the associated polymorphisms are located in as-yet undiscovered genes. Fertility traits are complex traits as many different biological and physiological factors determine whether a cow is fertile. Therefore it is not expected that there is a simple explanation with an obvious candidate gene but it is more likely a network of genes and intragenic variants that explain the variation of these traits.
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http://dx.doi.org/10.1186/1471-2164-15-790DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4169824PMC
September 2014
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