Publications by authors named "Paul Vanraden"

Most genotypes in the National Cooperator Database now originate from cows, but most previous studies validating genomic predictions have primarily focused on bulls. This study paired official within-breed genomic PTA (GPTA) and parent average (PA) for genotyped heifer calves born between 2019 and 2021 using the August 2021 database with their corresponding performance deviations (PDEV) for 17 different traits. The PDEV data became available when the heifers completed their first lactation and were extracted from the August 2023 database in which at least one PDEV value for those 17 traits existed for each genotyped heifer record.

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  • The productive life of a cow is the time it spends in the milking herd, and researchers studied how genetics affects it using data from a lot of cows.
  • They analyzed over a million Holstein cows with genetic information to find out what genes are linked to how long they can produce milk.
  • The study found many important gene areas that influence not only milk production but also fertility and health, helping to identify which cows should be kept or removed from the herd.
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Although genomic selection has led to considerable improvements in genetic gain, it has also seemingly led to increased rates of inbreeding and homozygosity, which can negatively affect genetic diversity and the long-term sustainability of dairy populations. Using genotypes from US Holstein animals from 3 distinct stud populations, we performed a simulation study consisting of 10 rounds of selection, with each breeding population composed of 200 males and 2,000 females. The investigated selection strategies consisted of selection using true breeding values, EBV, EBV penalized for the average future genomic inbreeding of progeny (PEN-EBV), or random selection (RAND).

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A genome-wide association study of resistance to retained placenta (RETP) using 632,212 Holstein cows and 74,747 SNPs identified 200 additive effects with -values < 10 on thirteen chromosomes but no dominance effect was statistically significant. The regions of 87.61-88.

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Large datasets allow estimation of feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits.

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  • A genome-wide association study (GWAS) analyzed fat percentage in over 1.2 million first lactation cows and confirmed significant inter-chromosome effects in a specific region on Chromosome 14.
  • The study identified two sub-regions within this area, Chr14a and Chr14b, with Chr14a showing the majority of additive × additive interactions.
  • Notably, the research highlights interactions between specific SNPs (genetic markers) linked to traits like milk production and fertility, enhancing the understanding of genetic influences on fat percentage in Holstein cows.
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This study leveraged a growing dataset of producer-recorded phenotypes for mastitis, reproductive diseases (metritis and retained placenta), and metabolic diseases (ketosis, milk fever, and displaced abomasum) to investigate the potential presence of inbreeding depression for these disease traits. Phenotypic, pedigree, and genomic information were obtained for 354,043 and 68,292 US Holstein and Jersey cows, respectively. Total inbreeding coefficients were calculated using both pedigree and genomic information; the latter included inbreeding estimates obtained using a genomic relationship matrix and runs of homozygosity.

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This study aimed at evaluating the quality of imputation accuracy (IA) by marker (IA) and by individual (IA) in US crossbred dairy cattle. Holstein × Jersey crossbreds were used to evaluate IA from a low- (7K) to a medium-density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K SNP chip.

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A genome-wide association study (GWAS) of the daughter pregnancy rate (DPR), cow conception rate (CCR), and heifer conception rate (HCR) using 1,001,374-1,194,736 first-lactation Holstein cows and 75,140-75,295 SNPs identified 7567, 3798, and 726 additive effects, as well as 22, 27, and 25 dominance effects for DPR, CCR, and HCR, respectively, with log(1/p) > 8. Most of these effects were new effects, and some new effects were in or near genes known to affect reproduction including , , and , and a gene cluster of pregnancy-associated glycoproteins. The confirmed effects included those in or near the and regions of Chr06 and the region of Chr01.

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  • The study investigates lethal recessive alleles in Nellore cattle that negatively impact reproduction and survival rates by analyzing genomic information and genotyping 62,022 animals.
  • Researchers used genomic breeding values and a genome-wide association study (GWAS) to identify key genes and SNP markers related to heifer rebreeding, post-natal mortality, and stayability.
  • Functional analyses highlighted 26 significant genes involved in tissue development and immune functions, suggesting future research directions to further understand and manage these genetic issues in breeding.
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Dairy producers have improved fertility of their herds by selecting bulls with higher conception rate evaluations. This research was motivated by the rapid increase in embryo transfer (ET) use to 11% of recent births and >1 million total births, with >5 times as many ET calves born in the United States in 2021 compared with just 5 yr earlier. Historical data used in genetic evaluations are stored in the National Cooperator Database.

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Motivation: The amount of genomic data is increasing exponentially. Using many genotyped and phenotyped individuals for genomic prediction is appealing yet challenging.

Results: We present SLEMM (short for Stochastic-Lanczos-Expedited Mixed Models), a new software tool, to address the computational challenge.

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The calculation of exact reliabilities involving the inversion of mixed model equations poses a heavy computational challenge when the system of equations is large. This has prompted the development of different approximation methods. We give an overview of the various methods and computational approaches in calculating reliability from the era before the animal model to the era of single-step genomic models.

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Maintaining a genetically diverse dairy cattle population is critical to preserving adaptability to future breeding goals and avoiding declines in fitness. This study characterized the genomic landscape of autozygosity and assessed trends in genetic diversity in 5 breeds of US dairy cattle. We analyzed a sizable genomic data set containing 4,173,679 pedigreed and genotyped animals of the Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey breeds.

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  • * The authors present the Cattle Genotype-Tissue Expression atlas (CattleGTEx), which utilizes data from over 7,000 RNA-sequencing samples to explore gene expression in more than 100 tissues.
  • * They analyze the genetic associations related to gene expression and alternative splicing, linking these findings to 43 important traits in cattle to understand the molecular mechanisms involved in livestock genetics.
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Single-step genomic BLUP (ssGBLUP) is a method for genomic prediction that integrates matrices of pedigree (A) and genomic (G) relationships into a single unified additive relationship matrix whose inverse is incorporated into a set of mixed model equations (MME) to compute genomic predictions. Pedigree information in dairy cattle is often incomplete. Missing pedigree potentially causes biases and inflation in genomic estimated breeding values (GEBV) obtained with ssGBLUP.

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  • Analyzed 723 RNA-seq datasets from 91 tissues and cell types to create a detailed gene atlas and explore tissue-specific gene functions in cattle.
  • Identified distinct patterns in gene evolution and promoter methylation, revealing that brain-specific genes evolve slowly while testis-specific genes evolve rapidly.
  • Linked tissue-specific genes to important cattle traits through genome-wide association studies, offering insights for improving livestock genetics and biology.
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Background: Traditional selection in livestock and crops focuses on additive genetic values or breeding values of the individuals. While traditional selection utilizes variation between individuals, differences between gametes within individuals have been less frequently exploited in selection programs. With the successful implementation of genomic selection in livestock and crops, estimation and selection for gametic variation is becoming possible.

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Background: Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data.

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Current USDA selection indices such as lifetime net merit (NM$) estimate lifetime profit differences, which are accurately approximated by a linear combination of 13 traits. In these indices, every animal gets credit for 2.78 lactations of the traits expressed per lactation, such as fat and protein, independent of its productive life (PL).

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A hundred years of data collection in dairy cattle can facilitate powerful studies of complex traits. Cattle GWAS have identified many associated genomic regions. With increasing numbers of cattle sequenced, fine-mapping of causal variants is becoming possible.

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Genome-wide association study (GWAS) is a powerful approach to identify genomic regions and genetic variants associated with phenotypes. However, only limited mutual confirmation from different studies is available. We conducted a large-scale GWAS using 294,079 first-lactation Holstein cows and identified new additive and dominance effects on five production traits, three fertility traits, and somatic cell score.

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The length of gestation can affect offspring health and performance. Both maternal and fetal effects contribute to gestation length; however, paternal contributions to gestation length remain elusive. Using genome-wide association study (GWAS) in 27,214 Holstein bulls with millions of gestation records, here we identify nine paternal genomic loci associated with cattle gestation length.

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Background: Although genome-wide association and genomic selection studies have primarily focused on additive effects, dominance and imprinting effects play an important role in mammalian biology and development. The degree to which these non-additive genetic effects contribute to phenotypic variation and whether QTL acting in a non-additive manner can be detected in genetic association studies remain controversial.

Results: To empirically answer these questions, we analyzed a large cattle dataset that consisted of 42,701 genotyped Holstein cows with genotyped parents and phenotypic records for eight production and reproduction traits.

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Background: Millions of genetic variants have been identified by population-scale sequencing projects, but subsets of these variants are needed for routine genomic predictions or genotyping arrays. Methods for selecting sequence variants were compared using simulated sequence genotypes and real July 2015 data from the 1000 Bull Genomes Project.

Methods: Candidate sequence variants for 444 Holstein animals were combined with high-density (HD) imputed genotypes for 26,970 progeny-tested Holstein bulls.

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