Genome-wide association study and fine-mapping using imputed sequences to prioritize candidate genes for 30 complex traits in 50,309 Holstein bulls.

Identifying causal genetic variants underlying economically important traits in dairy cattle is essential for understanding their genetic basis and optimizing breeding programs. The growing availability of sequenced reference genomes and individuals with both phenotypic and genotypic data notably enhances our ability to detect genetic associations and further pinpoint causal effects. This comprehensive GWAS of dairy cattle used deregressed breeding values as phenotypes and analyzed 11,292,243 quality-controlled, imputed sequence variants from 50,309 Holstein bulls.

Genomic partition of inbreeding depression in production traits of US Jersey cattle using functional annotations.

Inbreeding depression (InD) refers to the mean reduction in trait values due to inbreeding, with detrimental effects on survival, production, and reproduction traits that have been observed in many natural and domesticated populations. Despite efforts to measure how much reduction in the traits of interest was caused by InD, the genetic and molecular basis of these declines remains unclear, particularly in dairy cattle. In this research, we used a linear mixed model to partition the InD of 3 production traits in 245,517 genotyped Jersey cows from the Council on Dairy Cattle Breeding (Bowie, MD) database.

Improving dairy calf health through inclusion of diarrhea and respiratory health data into a US national genetic evaluation.

Calf diarrhea (DIAR) and respiratory illnesses (RESP) are leading causes of calf mortality. This study aimed to develop a comprehensive US national genomic evaluation for these important calf health traits using producer-recorded data from the National Cooperator Database. Analyses included 207,602 calf records for DIAR (age 3 to 60 d) and 681,741 records for RESP (age 3 to 365 d) from all breeds (97.

F-Based Marker Prioritization Within Quantitative Trait Loci Regions and Its Impact on Genomic Selection Accuracy: Insights from a Simulation Study with High-Density Marker Panels for Bovines.

Background/objectives: Genomic selection (GS) has improved accuracy compared to traditional methods. However, accuracy tends to plateau beyond a certain marker density. Prioritizing influential SNPs could further enhance the accuracy of GS.

Validating genomic predictions for economic traits in purebred US dairy heifers.

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.

Dry matter intake in US Holstein cows: Exploring the genomic and phenotypic impact of milk components and body weight composite.

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.

Inbreeding depression for producer-recorded udder, metabolic, and reproductive diseases in US dairy cattle.

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.

Imputation accuracy from low- to medium-density SNP chips for US crossbred dairy cattle.

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.

Implementation of Feed Efficiency in Iranian Holstein Breeding Program.

This study aimed to evaluate the economic impact of improving feed efficiency on breeding objectives for Iranian Holsteins. Production and economic data from seven dairy herds were used to estimate the economic values of different traits, and a meta-analysis was conducted to analyze the genetic relationships between feed efficiency and other traits. Economic weights were calculated for various traits, with mean values per cow and per year across herds estimated at USD 0.

Genetic architecture of a composite beef cattle population.

Composite breeds are widely used in the beef industry. Composites allow producers to combine desirable traits from the progenitor breeds and simplify herd management, without repeated crossbreeding and maintenance of purebreds. In this study, genomic information was used to evaluate the genetic composition and characteristics of a three-breed beef cattle composite.

Genetic analysis of retained placenta and its association with reproductive disorder, production, and fertility traits of Iranian Holstein dairy cows.

To facilitate breeding for improved resistance to the reproductive disorder of retained placenta (RP), genetic parameters were estimated for RP and its genetic correlation with other reproductive disorders as well as with production and fertility traits of Iranian Holstein dairy cows. Data were 154,048 lactation records collected between 2011 and 2018 from 59,610 Holstein dairy cows in 9 Iranian herds. Other reproductive disorders included dystocia, stillbirth, and twinning.

Grid search approach to discriminate between old and recent inbreeding using phenotypic, pedigree and genomic information.

Background: Although inbreeding caused by the mating of animals related through a recent common ancestor is expected to have more harmful effects on phenotypes than ancient inbreeding (old inbreeding), estimating these effects requires a clear definition of recent (new) and ancient (old) inbreeding. Several methods have been proposed to classify inbreeding using pedigree and genomic data. Unfortunately, these methods are largely based on heuristic criteria such as the number of generations from a common ancestor or length of runs of homozygosity (ROH) segments.

Runs of homozygosity and analysis of inbreeding depression.

Pedigree information was traditionally used to assess inbreeding. The availability of high-density marker panels provides an alternative to assess inbreeding, particularly in the presence of incomplete and error-prone pedigrees. Assessment of autozygosity across chromosomal segments using runs of homozygosity (ROH) has emerged as a valuable tool to estimate inbreeding due to its general flexibility and ability to quantify the chromosomal contribution to genome-wide inbreeding.

A Weighted Genomic Relationship Matrix Based on Fixation Index (F) Prioritized SNPs for Genomic Selection.

A dramatic increase in the density of marker panels has been expected to increase the accuracy of genomic selection (GS), unfortunately, little to no improvement has been observed. By including all variants in the association model, the dimensionality of the problem should be dramatically increased, and it could undoubtedly reduce the statistical power. Using all Single nucleotide polymorphisms (SNPs) to compute the genomic relationship matrix () does not necessarily increase accuracy as the additive relationships can be accurately estimated using a much smaller number of markers.

Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms.

Background: It becomes clear that the increase in the density of marker panels and even the use of sequence data didn't result in any meaningful increase in the accuracy of genomic selection (GS) using either regression (RM) or variance component (VC) approaches. This is in part due to the limitations of current methods. Association model are well over-parameterized and suffer from severe co-linearity and lack of statistical power.

Inbreeding depression in line 1 Hereford cattle population using pedigree and genomic information.

This study aimed at assessing inbreeding and its effect on growth and fertility traits using the longtime closed line 1 Hereford cattle population. Inbreeding was estimated based on pedigree (FPED) and genomic information. For the latter, three estimates were derived based on the diagonal elements of the genomic relationship matrix using estimated (FGRM) or fixed (FGRM0.

Correction to: High density marker panels, SNPs prioritizing and accuracy of genomic selection.

BMC GENETICS (2018) 19:4 DOI: 10.1186/S12863-017-0595-2: The original version of this article [1], published on 5 January 2018, contained 3 formatting errors. In this Correction the affected parts of the article are shown.

High density marker panels, SNPs prioritizing and accuracy of genomic selection.

Background: The availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in the number of parameters in the genetic association model compared to the number of phenotypes has led to further deterioration in statistical power and an increase in co-linearity and false positive rates. At best, HD panels do not significantly improve GS accuracy and, at worst, reduce accuracy.