Genome-wide Association Study of Birth and Weaning Weights in Brangus Beef Cattle

The objective of this study was to map quantitative trait loci (QTL) associated with birth weight and weaning weight in Brangus beef cattle. A total of 6 significant QTL over 4 chromosomes were identified. Two QTL were common to both traits. The genome-wide association study (GWAS) results could help us understand the biological process of growth in Brangus. Further analyses are needed to find and validate the casual mutations responsible for these QTL

Characterization and Associations of Haplotypes Containing PLAG1 in Cattle

Genomic regions tend to be inherited in blocks known as haplotypes. The general region spanning PLAG1 shows association with various traits in beef cattle. There are few haplotypes spanning PLAG1, and these are common across breeds but do not capture the true effect. Higher density 770K genotyping demonstrates greater diversity of haplotypes in this region. The tag SNP in Holstein and Jersey dairy cattle segregates in Hereford cattle who show no effect in this region. Collectively, these results show the 50K SNP chip has inadequate coverage in this region of the genome

Characterization of the F94L Double Muscling Mutation in Pure- and Crossbred Limousin Animals

The objective of this study was to investigate the nature of the effect of the F94L variant in the myostatin (MSTN) gene on economic traits in Limousin and Limousin-Angus crossbred animals in the context of genomic analyses using Illumina BovineSNP50 Bead chip genotypes

Genomic Prediction using Single or Multi-Breed Reference Populations in US Maine-Anjou Beef Cattle

The objective of this study was to estimate accuracies of genomic predictions based on 50K SNP genotypes for 8 nationally evaluated traits in US Maine-Anjou beef cattle using single or multi-breed reference populations. The accuracies of direct genomic values (DGV) ranged from 0.22 to 0.45 for 8 studied traits when the reference populations comprised only 573 Maine-Anjou animals. Accuracies were slightly reduced and ranged from 0.21 to 0.38 when the reference population included over 9,000 animals from many other breeds as well as Maine-Anjou. These results demonstrate that including data from other populations does not generally increase accuracy of prediction in one particular population. This means every breed association must develop its own reference population if it intends to offer genomic prediction

Characterizing Haplotype Diversity in Ten US Beef Cattle Breeds

The distributions of haplotype diversity across the whole genome among 10 US beef cattle breeds were constructed. In most chromosomes for all the breeds, consistent low haplotype diversity were observed in some specific regions, 55% of which was found to match the positions of reported gene duplications. Further work is required to determine whether the low haplotype diversity is real, or a result of problems in sequencing which have limited our ability to identify informative markers in those regions. Haplotype diversity will be the subject of ongoing work to identify haplotypes that are under-represented as homozygotes, to fine-map regions with major gene effects, and to fit haplotype rather than SNP models for genomic prediction

Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U.S. Holsteins

SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.2 and 98.2%. Genomic prediction accuracies obtained using imputed 80K genotypes were between 0.817 and 0.821 for daughter pregnancy rate, between 0.838 and 0.844 for fat yield, and between 0.850 and 0.863 for milk yield. The two SNP panels optimized on the three major factors had the highest genomic prediction accuracy (0.821–0.863), and these accuracies were very close to those obtained using observed 80K genotypes (0.825–0.868). Further exploration of the underlying relationships showed that genomic prediction accuracies did not respond linearly to imputation accuracies, but were significantly affected by genotype (imputation) errors of SNPs in association with the traits to be predicted. SNPs optimal for map coverage and MAF were favorable for obtaining accurate imputation of genotypes whereas trait-associated SNPs improved genomic prediction accuracies. Thus, optimal LD SNP panels were the ones that combined both strengths. The present results have practical implications on the design of LD SNP chips for imputation-enabled genomic prediction

DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data

Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system