Biology subjects

Schnabel, R. D.

Publications and source records attributed to Schnabel, R. D..

6 recordsLinked to original sources

bioRxiv2022

Genomic loci involved in sensing environmental cues and metabolism affect seasonal coat shedding in Bos taurus and Bos indicus cattle

Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results. These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations on chromosomes 5 and 23. Genotype-by- environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multi-breed datasets. Loci related to metabolism and light- sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight for both agriculture production and basic science.

genomics↗

bioRxiv2021

Uncovering the architecture of selection in two Bos taurus cattle breeds

Selection alters the genome via hard sweeps, soft sweeps, and polygenic selection. However, mapping polygenic selection is difficult because it does not leave clear signatures on the genome like a selective sweep. In populations with temporally-stratified genotypes, the Generation Proxy Selection Mapping (GPSM) method identifies variants associated with generation number (or appropriate proxy) and thus variants undergoing directional allele frequency changes. Here, we use GPSM on two large datasets of beef cattle to detect associations between an animals generation and 11 million imputed SNPs. Using these datasets with high power and dense mapping resolution, GPSM detected a total of 294 unique loci actively under selection in two cattle breeds. We observed that GPSM has a high power to detect selection in the very recent past (< 10 years), even when allele frequency changes are small. Variants identified by GPSM reside in genomic regions associated with known breed characteristics, such as fertility and maternal ability in Red Angus and carcass merit and coat color in Simmental. Over 60% of the selected loci reside in or near (<50 kb) annotated genes. Additionally, 36% of selected loci overlap known epigenetic marks or putative functional genomic regions. Using RAiSD and nSL, we identify hundreds of putative selective sweeps; however, these sweeps have little overlap with polygenic selected loci. This makes GPSM a complementary approach to sweep detection methods when temporal genotype data are available. The selected loci that we identify across methods demonstrate the complex architecture of selection in domesticated cattle.

evolutionary biology↗

bioRxiv2020

Powerful detection of polygenic selection and environmental adaptation in US beef cattle populations

Selection on complex traits can rapidly drive evolution, especially in stressful environments. This polygenic selection does not leave intense sweep signatures on the genome, rather many loci experience small allele frequency shifts, resulting in large cumulative phenotypic changes. Directional selection and local adaptation are actively changing populations; but, identifying loci underlying polygenic or environmental selection has been difficult. We use genomic data on tens of thousands of cattle from three populations, distributed over time and landscapes, in linear mixed models with novel dependent variables to map signatures of selection on complex traits and local adaptation. We identify 207 genomic loci associated with an animals birth date, representing ongoing selection for monogenic and polygenic traits. Additionally, hundreds of additional loci are associated with continuous and discrete environments, providing evidence for local adaptation. These candidate loci highlight the nervous systems possible role in local adaptation. While advanced technologies have increased the rate of directional selection in cattle, it has likely been at the expense of historically generated local adaptation, which is especially problematic in changing climates. When applied to large, diverse cattle datasets, these selection mapping methods provide an insight into how selection on complex traits continually shapes the genome. Further, by understanding the genomic loci implicated in adaptation, may help us breed more adapted and efficient cattle and begin understanding the basis for mammalian adaptation, especially in changing climates. These selection mapping approaches help clarify selective forces and loci in evolutionary, model, and agricultural contexts. Author SummaryInterest in mapping the impacts of selection and local adaptation on the genome is increasing due to the novel stressors presented by climate change. Until now, approaches have largely focused on mapping "sweeps" on large-effect loci. Highly powered datasets that are both temporally and geographically distributed have not existed. Recently, large numbers of beef cattle have been genotyped across the United States, including influential individuals with cryopreserved semen. This has created multiple powerful datasets distributed over time and landscapes. Here, we map the recent effects of selection and local adaptation in three cattle populations. The results provide insight into the biology of mammalian adaptation and generate useful tools for selecting and breeding better-adapted cattle for a changing environment.

genomics↗

bioRxiv2020

Extensive genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle

BackgroundUnderstanding the genetic basis of genotype-by-environment interactions (GxE) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. In addition, GxE information could also be useful to predict the vulnerability of populations to climate change. ResultsHere, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that GxE contribute 10%, 4%, and 3% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. GxE genome-wide association analysis (GWAA) detected a large number of GxE loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects between genotype classes regardless of differences in genotypic means. We also showed that variance-heterogeneity GWAA can detect loci enriched with GxE effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of GxE genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neural signaling, metabolic, hypoxia-induced, and immune system pathways. Knowledge of these pathways will be important as climate change becomes a burden on animal health and productivity. In addition, ecoregion-specific GxE SNPs detected in this study may play a crucial role in identifying resilient and adapted beef cattle across divergent environments. ConclusionsWe detected novel trait associations with large GxE effects for birth weight, weaning weight, and yearling weight. Functional annotation and pathway analysis uncovered genomic regions involved in response to environmental stimuli. We unraveled the relevance and complexity of the genetic basis of GxE underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals

genomics↗

bioRxiv2019

A Multi-Breed Reference Panel and Additional Rare Variation Maximizes Imputation Accuracy in Cattle

BackgroundThe use of array-based SNP genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data in the last decade. While low-density assays work exceptionally well in the context of genomic prediction, they are less useful in mapping and causal variant discovery. This project focuses on maximizing imputation accuracies to the marker set of two high-density research assays, the Illumina Bovine HD, and the GGP-F250 which contains a large proportion of rare and potentially functional variants (~850,000 total SNPs). This 850K SNP set is well-suited for both imputation to sequence-level genotypes and direct downstream analysis.\n\nResultsWe find that a large multi-breed composite imputation reference comprised of 36,131 samples with either HD and/or F250 genotypes significantly increases imputation accuracy compared to a standard within-breed reference panel, particularly at low minor allele frequencies. Imputation accuracies were maximized when an individuals ancestry was adequately represented in the composite reference, particularly with complete 850K genotypes. The addition of rare content from the F250 to our composite reference panel significantly increased the imputation accuracy of rare variants found exclusively on the HD. Additionally, we identify 50,000 variants as an ideal starting density for 850K imputation.\n\nConclusionUsing high-density genotypes on all available individuals in a multi-breed reference panel maximizes imputation accuracy for all cattle populations. Admixed breeds or those sparsely represented in the composite reference are still imputed at high accuracy which will increase further as the reference panel grows. We expect that the addition of rare variation from the F250 will increase the accuracy of imputation at the sequence level.

genomics↗

bioRxiv2018

CRUMBLER: A tool for the Prediction of Ancestry in Cattle

BackgroundIn many beef and some dairy production systems, crossbreeding is used to take advantage of breed complementarity and heterosis. Admixed animals are frequently identified by their coat color and body conformation phenotypes, however, without pedigree information it is not possible to identify the expected breed composition of an admixed animal and in the presence of selection, the actual composition may differ from expectation.\n\nResultsWe tested an approach to estimate the global ancestry of individuals using ADMIXTURE and SNPweights. ADMIXTURE estimates ancestry using a model-based approach applied to large single nucleotide polymorphism (SNP) genotype datasets. Individuals are assumed to be unrelated and a supervised analysis can be performed using reference animals sampled to represent ancestral populations. SNPweights infers ancestry using weights estimated by principal component analysis for genome-wide SNP panels that have been genotyped in the reference panel animals. We constructed analysis pipelines to determine the ancestry of individuals with potentially complex ancestries using both methods and a specified reference population SNP dataset. The reference population was constructed using breed association pedigree information and an iterative analysis to identify sets of purebred individuals representative of each breed.\n\nConclusionThe finally adopted CRUMBLER pipeline extracts a subset of genotypes that are common to all current commercially available genotyping platforms and processes these into the file formats required for the analysis software and predicts admixture proportions using the reference population dataset.

genomics↗