What is he best known for?
The fields of study he is best known for:
Dorian J. Garrick mainly focuses on Genetics, Quantitative trait locus, Genetic variation, Beef cattle and Linkage disequilibrium.
Genetics is frequently linked to Statistics in his study.
His Bayes' theorem, Bayesian probability, Regression analysis and Linear regression study in the realm of Statistics connects with subjects such as Breeding program.
Dorian J. Garrick combines subjects such as Pleiotropy, Genome, Genotype, Heritability and Residual feed intake with his study of Quantitative trait locus.
His Genetic variation research is multidisciplinary, relying on both Polymorphism and Candidate gene.
His Beef cattle research integrates issues from Biotechnology, Genetic correlation and Genomics.
His most cited work include:
- Extension of the bayesian alphabet for genomic selection (708 citations)
- Extension of the bayesian alphabet for genomic selection (708 citations)
- Deregressing estimated breeding values and weighting information for genomic regression analyses (422 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Genetics, Animal science, Beef cattle, Quantitative trait locus and Heritability.
His Genetics study is mostly concerned with Single-nucleotide polymorphism, Genome-wide association study, Genetic variation, Haplotype and Gene.
The study incorporates disciplines such as Biotechnology and Weaning in addition to Beef cattle.
His biological study spans a wide range of topics, including SNP, Allele, Genotype and Genetic marker.
His Heritability study integrates concerns from other disciplines, such as Statistics, Genetic correlation and Maternal effect.
His Statistics research incorporates elements of Best linear unbiased prediction and Sire.
He most often published in these fields:
- Genetics (48.09%)
- Animal science (38.65%)
- Beef cattle (25.84%)
What were the highlights of his more recent work (between 2015-2021)?
- Genetics (48.09%)
- Quantitative trait locus (25.84%)
- Single-nucleotide polymorphism (17.30%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Genetics, Quantitative trait locus, Single-nucleotide polymorphism, Animal science and Beef cattle.
His study in Genome-wide association study, Genome, Gene, Genetic variation and Brown Swiss is carried out as part of his studies in Genetics.
His work deals with themes such as Family-based QTL mapping, Genetic association and Genetic architecture, which intersect with Genome-wide association study.
Dorian J. Garrick has researched Quantitative trait locus in several fields, including Selection, Genomics, Statistics, Allele and Genotype.
His Single-nucleotide polymorphism research focuses on Heritability and how it connects with Bayes' theorem.
His work in Animal science addresses issues such as Lactation, which are connected to fields such as Herd.
Between 2015 and 2021, his most popular works were:
- Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. (118 citations)
- Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle (68 citations)
- Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle (68 citations)
In his most recent research, the most cited papers focused on:
His main research concerns Genetics, Quantitative trait locus, Single-nucleotide polymorphism, Statistics and Genome-wide association study.
Dorian J. Garrick interconnects Bayesian linear regression and Bayes' theorem in the investigation of issues within Genetics.
His studies deal with areas such as Genome, Genomics and Computational biology as well as Quantitative trait locus.
His Single-nucleotide polymorphism research includes themes of Intramuscular fat and Haplotype.
His Statistics research is multidisciplinary, incorporating perspectives in Selection and Genomic selection.
His study in Genome-wide association study is interdisciplinary in nature, drawing from both Candidate gene, Beef cattle, Genetic association, Family-based QTL mapping and Genetic architecture.
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