What is he best known for?
The fields of study he is best known for:
His primary scientific interests are in Genetics, Genome-wide association study, Single-nucleotide polymorphism, Psychiatry and Bipolar disorder.
His research on Genetics frequently connects to adjacent areas such as Alcohol dependence.
His Genome-wide association study research integrates issues from Copy-number variation, Meta-analysis, Genotyping, ANK3 and Genetic architecture.
His Single-nucleotide polymorphism research is multidisciplinary, incorporating elements of Nicotine and Candidate gene.
His Psychiatry study combines topics in areas such as Psychometrics, Family history and Medical diagnosis.
The study incorporates disciplines such as Genetic heterogeneity, Schizophrenia and Genome in addition to Bipolar disorder.
His most cited work include:
- The International HapMap Project (5051 citations)
- Functional impact of global rare copy number variation in autism spectrum disorders (1592 citations)
- Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs (1570 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Genetics, Genome-wide association study, Single-nucleotide polymorphism, Psychiatry and Bipolar disorder.
His Genetics study frequently involves adjacent topics like Alcohol dependence.
John Rice has researched Alcohol dependence in several fields, including Demography and Allele.
His studies deal with areas such as Meta-analysis, Case-control study, Genetic variation and Copy-number variation as well as Genome-wide association study.
His Single-nucleotide polymorphism study integrates concerns from other disciplines, such as Haplotype and Candidate gene.
His work deals with themes such as Genome, Computational biology and Pedigree chart, which intersect with Genetic linkage.
He most often published in these fields:
- Genetics (39.66%)
- Genome-wide association study (16.06%)
- Single-nucleotide polymorphism (14.36%)
What were the highlights of his more recent work (between 2014-2021)?
- Genome-wide association study (16.06%)
- Genetics (39.66%)
- Bipolar disorder (11.68%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Genome-wide association study, Genetics, Bipolar disorder, Genetic association and Meta-analysis.
His Genome-wide association study study which covers Major depressive disorder that intersects with Genetic heterogeneity.
Much of his study explores Genetics relationship to Schizophrenia.
His research links Computational biology with Bipolar disorder.
His Meta-analysis research incorporates elements of Exome, Genetic variation and Genetic architecture.
His research integrates issues of Allele and Locus in his study of Single-nucleotide polymorphism.
Between 2014 and 2021, his most popular works were:
- Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression (1131 citations)
- Analysis of shared heritability in common disorders of the brain (726 citations)
- Genome-wide association study identifies 30 loci associated with bipolar disorder (518 citations)
In his most recent research, the most cited papers focused on:
John Rice focuses on Genome-wide association study, Genetics, Single-nucleotide polymorphism, Bipolar disorder and Genetic association.
He has included themes like SNP, Demography, Heritability, Major depressive disorder and Haplotype in his Genome-wide association study study.
The study incorporates disciplines such as Meta-analysis and Schizophrenia in addition to Genetics.
As part of the same scientific family, John Rice usually focuses on Schizophrenia, concentrating on Psychosis and intersecting with Copy-number variation, Multifactorial Inheritance and Age of onset.
His Single-nucleotide polymorphism research includes themes of Allele and Locus.
His study looks at the intersection of Bipolar disorder and topics like Genetic heterogeneity with Clinical psychology, Risk factor, Comorbidity, Bipolar I disorder and Bipolar II disorder.
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.
