What is he best known for?
The fields of study he is best known for:
His primary areas of investigation include Genetics, Genome-wide association study, Genetic association, Quantitative trait locus and Single-nucleotide polymorphism.
His study in Evolutionary biology extends to Genetics with its themes.
His Evolutionary biology study combines topics in areas such as Population structure, Genetic variants, Genetic genealogy, Family structure and Disease risk.
His Genome-wide association study research includes elements of Genetic predisposition, Population stratification, T cell, Immune system and Genetic variation.
His work carried out in the field of Genetic association brings together such families of science as False positive paradox, Principal component analysis, Imputation, Computational biology and Cohort.
His research in Quantitative trait locus intersects with topics in Body mass index, Statistics, Cohort study, Founder effect and Linkage disequilibrium.
His most cited work include:
- Variance component model to account for sample structure in genome-wide association studies (1646 citations)
- Efficient Control of Population Structure in Model Organism Association Mapping (1369 citations)
- New approaches to population stratification in genome-wide association studies (816 citations)
What are the main themes of his work throughout his whole career to date?
Noah Zaitlen focuses on Genetics, Computational biology, Genome-wide association study, Genetic association and Single-nucleotide polymorphism.
His works in Genetic variation, Heritability, SNP, Allele and Missing heritability problem are all subjects of inquiry into Genetics.
His research integrates issues of RNA, Gene, Genomics and Imputation, Genotyping in his study of Computational biology.
His biological study spans a wide range of topics, including Evolutionary biology, Population stratification, Heredity, Quantitative trait locus and Disease.
His Evolutionary biology research incorporates elements of Selection and Genetic genealogy.
His Genetic association research focuses on Genetic architecture and how it relates to Genetic correlation and Epistasis.
He most often published in these fields:
- Genetics (48.13%)
- Computational biology (46.52%)
- Genome-wide association study (30.48%)
What were the highlights of his more recent work (between 2019-2021)?
- Computational biology (46.52%)
- Genetic architecture (13.37%)
- Genetic genealogy (8.02%)
In recent papers he was focusing on the following fields of study:
Noah Zaitlen spends much of his time researching Computational biology, Genetic architecture, Genetic genealogy, Epistasis and Genetics.
His study in Computational biology is interdisciplinary in nature, drawing from both Precision medicine, RNA, Amyotrophic lateral sclerosis, Expression quantitative trait loci and Mixed model.
His Expression quantitative trait loci research includes themes of Gene expression and Genetic association.
As a part of the same scientific study, Noah Zaitlen usually deals with the Genetic association, concentrating on Phenome and frequently concerns with Genome-wide association study.
His work is dedicated to discovering how Mixed model, Sample size determination are connected with Evolutionary biology and other disciplines.
Noah Zaitlen studies Genetics, namely Allelic heterogeneity.
Between 2019 and 2021, his most popular works were:
- Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex. (23 citations)
- A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits (19 citations)
- A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits (19 citations)
In his most recent research, the most cited papers focused on:
His primary scientific interests are in Computational biology, Genetic correlation, Mixed model, Genetics and Negative selection.
Noah Zaitlen has included themes like Population genetics, Replicate, Serial analysis of gene expression, Heritability and Range in his Computational biology study.
The study incorporates disciplines such as Imputation, Genotyping, Causal inference and Best linear unbiased prediction in addition to Genetic correlation.
His Mixed model study integrates concerns from other disciplines, such as Precision medicine, False positive paradox, Sample size determination, Heteroscedasticity and Functional genomics.
His study in Allelic heterogeneity, Genome-wide association study, Gene expression, RNA and Transcriptome falls within the category of Genetics.
He has researched Negative selection in several fields, including Evolutionary biology, Pleiotropy, Robustness, Polygene and Directional selection.
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