Michael Edward Goddard

This paper evaluates milk mid-infrared spectroscopy combined with on-farm variables to predict serum biomarkers in dairy cows, finding that blood urea nitrogen (BUN) is most accurately predicted, while other biomarkers show limited external validation performance.

The paper identifies biases in estimating heritability from ultra-rare variants and shows that accounting for these biases reveals significant heritability estimates for several traits.

This paper improves causal variant identification by genome-wide fine-mapping, showing it outperforms existing methods and identifies new causal variants for complex traits.

BayesR3AD extends Bayesian mixture models to jointly estimate additive and dominance genetic effects, improving prediction accuracy for fertility and longevity in Holstein cattle.

This study identifies age-varying genetic contributions to BMI using longitudinal data from the ALSPAC cohort, showing distinct genetic profiles in infancy versus later childhood and adolescence.

The paper reassesses selection on height using GWAS and allele frequency data, finding positive selection at height-associated loci in Europe.

The paper estimates direct effects of genetic ancestry on complex traits in a Mexican population using family-based analyses and finds significant associations for height and type 2 diabetes but not for educational attainment.

GSOR and MTAO detect widespread cis-regulatory effects on bovine complex traits by linking gene expression with genetic scores and multi-trait associations.

The paper tests three methods to identify causal genes for milk lipid traits in cattle using GWAS, transcriptomics, and gene ontology analysis, finding significant overlaps but limited power.

The paper evaluates the contribution of expression, splicing, and protein QTL in blood to SNP-based heritability and polygenic prediction across 27 traits, finding they capture significant portions of genetic variation.

This paper introduces SBayesRC, a method that improves polygenic prediction of complex traits by integrating GWAS summary statistics with functional genomic annotations, showing significant accuracy gains across ancestries.

The paper quantifies how gene expression and splicing QTL explain heritability in cattle traits using Bayesian methods across 16 tissues.

The paper shows that marker-based heritability estimators are biased under assortative mating and proposes corrected estimators for traits under equilibrium assortative mating.

The paper investigates the effects of classical mutant alleles on cattle complex traits and fitness, finding differential impacts on fertility, stature, milk production, and mastitis risk.

The paper uses Bayesian models to analyze cattle traits by incorporating functionally significant variants, showing improved genomic prediction accuracy when combining these variants with HD SNPs.

This study estimates non-additive genetic variance in human complex traits using a large sample of unrelated individuals and finds that additive variance is predominant while dominance and epistatic variances are negligible.

The paper quantifies genetic heterogeneity between European and African populations for height and BMI, finding high shared genetic variation suggesting GWAS findings in Europeans are applicable to non-Europeans.

This paper introduces SBayesR, a Bayesian method for polygenic prediction using GWAS summary statistics, achieving higher accuracy and efficiency than existing methods.

The paper identifies sequence variants with regulatory and evolutionary significance in cattle and ranks them using a Functional-And-Evolutionary Trait Heritability score to improve genomic prediction.

The paper identifies genetic variants associated with phenotypic variability and shows they exhibit genotype-by-environment interactions for obesity-related traits using UK Biobank data.

This study uses conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders, finding some variants with reduced pleiotropy effects.

This study identifies microRNA expression and target sites in 17 bovine tissues, revealing tissue-specific regulation and genomic features of microRNA-target interactions.

The paper quantifies assortative mating on complex traits by analyzing SNP data from 400,000 individuals, finding significant evidence for height and educational attainment.

The paper identifies bovine sQTLs associated with RNA splicing variation and shows they are shared across tissues and linked to complex traits.

The paper maps bovine TADs and CTCF binding motifs using genomic synteny and shows they reduce the search space for regulatory variants associated with complex traits.

The paper compares methods for estimating heritability and genetic architecture using whole genome data and finds that larger imputation panels improve accuracy of causal variant frequency estimates.

This study evaluates imputation accuracy in cattle and identifies QTL for milk fat percentage using imputed variants.