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.
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.
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.
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.
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.
Variance component model to account for sample structure in genome-wide association studies
Hyun Min Kang;Jae Hoon Sul;Noah A. Zaitlen.
Nature Genetics (2010)
Efficient Control of Population Structure in Model Organism Association Mapping
Hyun Min Kang;Noah A. Zaitlen;Claire M. Wade;Claire M. Wade;Andrew Kirby;Andrew Kirby.
Genetics (2008)
New approaches to population stratification in genome-wide association studies
Alkes L. Price;Noah A. Zaitlen;Noah A. Zaitlen;David Reich;Nick Patterson.
Nature Reviews Genetics (2010)
Genome-wide association analysis of metabolic traits in a birth cohort from a founder population
Chiara Sabatti;Anna-Liisa Hartikainen;Anneli Pouta.
Nature Genetics (2009)
Advantages and pitfalls in the application of mixed-model association methods
Jian Yang;Noah A Zaitlen;Michael E Goddard;Peter M Visscher;Peter M Visscher.
Nature Genetics (2014)
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
Bjarni J. Vilhjálmsson;Jian Yang;Hilary K. Finucane;Alexander Gusev.
American Journal of Human Genetics (2015)
Genome-wide association study of bipolar disorder in European American and African American individuals.
E. N. Smith;E. N. Smith;C. S. Bloss;J. A. Badner;T. Barrett.
Molecular Psychiatry (2009)
Using Extended Genealogy to Estimate Components of Heritability for 23 Quantitative and Dichotomous Traits
Noah Zaitlen;Phillip L. Kraft;Phillip L. Kraft;Nick Patterson;Bogdan Pasaniuc.
PLOS Genetics (2013)
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
Hyun Min Kang;Meena Subramaniam;Sasha Targ;Michelle Nguyen.
Nature Biotechnology (2018)
Extremely low-coverage sequencing and imputation increases power for genome-wide association studies
Bogdan Pasaniuc;Bogdan Pasaniuc;Nadin Rohland;Nadin Rohland;Paul J. McLaren;Paul J. McLaren;Kiran Garimella.
Nature Genetics (2012)
Profile was last updated on December 6th, 2021.
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