What is he best known for?
The fields of study he is best known for:
His main research concerns Genetics, Immunology, Allele, Genotype and Human leukocyte antigen.
Much of his study explores Genetics relationship to Type 1 diabetes.
In his study, which falls under the umbrella issue of Immunology, MHC class I, Gene product and Cell is strongly linked to Cell biology.
His research in Allele focuses on subjects like Gene mapping, which are connected to Allele frequency and Chromosome 4.
His Genotype research includes elements of Inbred strain, Inbreeding, Human genetics and Reference population.
His Human leukocyte antigen study incorporates themes from Hla genotyping, Genetic linkage and Genome.
His most cited work include:
- Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes (1358 citations)
- The Collaborative Cross, a community resource for the genetic analysis of complex traits (852 citations)
- Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. (386 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Genetics, Immunology, Gene, Internal medicine and Type 1 diabetes.
His study in Genetics concentrates on Single-nucleotide polymorphism, Allele, Quantitative trait locus, Genotype and Human leukocyte antigen.
The Single-nucleotide polymorphism study combines topics in areas such as Locus and Haplotype.
His research combines Nod and Immunology.
His work on Type 1 diabetes is being expanded to include thematically relevant topics such as Disease.
His Antigen study combines topics in areas such as Molecular biology and Transgene.
He most often published in these fields:
- Genetics (38.19%)
- Immunology (24.02%)
- Gene (17.32%)
What were the highlights of his more recent work (between 2013-2021)?
- Genetics (38.19%)
- Internal medicine (17.32%)
- Endocrinology (14.17%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Genetics, Internal medicine, Endocrinology, Gene and Type 1 diabetes.
In his works, Grant Morahan undertakes multidisciplinary study on Genetics and Trait.
His studies in Type 1 diabetes integrate themes in fields like Pregnancy, Kidney disease, Immunology, Function and Blood proteins.
Autoimmune disease and Innate immune system is closely connected to Cell biology in his research, which is encompassed under the umbrella topic of Immunology.
His research investigates the link between Single-nucleotide polymorphism and topics such as Locus that cross with problems in Human genome.
His work deals with themes such as Skeletal muscle, Recombinant DNA, Inbred strain and Candidate gene, which intersect with Quantitative trait locus.
Between 2013 and 2021, his most popular works were:
- Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. (386 citations)
- Gut microbial metabolites limit the frequency of autoimmune T cells and protect against type 1 diabetes (291 citations)
- The Affymetrix DMET Plus platform reveals unique distribution of ADME-related variants in ethnic Arabs. (141 citations)
In his most recent research, the most cited papers focused on:
The scientist’s investigation covers issues in Genetics, Internal medicine, Gene, Genome-wide association study and Single-nucleotide polymorphism.
The study incorporates disciplines such as Endocrinology and Type 1 diabetes in addition to Internal medicine.
Grant Morahan works on Gene which deals in particular with Genotype.
He has researched Genotype in several fields, including Personalized medicine, ADME and Allele.
Grant Morahan interconnects Expression quantitative trait loci, Genotyping, Genetic association, Locus and Myocardial infarction in the investigation of issues within Genome-wide association study.
Grant Morahan focuses mostly in the field of Locus, narrowing it down to topics relating to Nonsynonymous substitution and, in certain cases, Candidate gene.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
