Daniel G. MacArthur spends much of his time researching Genetics, Gene, Computational biology, Genome and Exome. His study in Genetic variation, Genomics, Exome sequencing, Allele and Human genome are all subfields of Genetics. His Genetic variation study combines topics from a wide range of disciplines, such as Human genetic variation, Autism spectrum disorder, Quantitative trait locus and DNA sequencing.
His Genomics study integrates concerns from other disciplines, such as Whole genome sequencing, Mutation and Bioinformatics. His studies in Gene integrate themes in fields like Actinin, Consanguinity and Skeletal muscle. The various areas that Daniel G. MacArthur examines in his Computational biology study include Indel, Transcriptome, 1000 Genomes Project, Genetic variants and Sequence.
His primary scientific interests are in Genetics, Gene, Computational biology, Exome sequencing and Exome. His Genetics study focuses mostly on Allele, Human genome, Mutation, Genome-wide association study and Allele frequency. Genetic variation, Phenotype, Transcriptome, Loss function and Exon are among the areas of Gene where the researcher is concentrating his efforts.
The Computational biology study combines topics in areas such as Human genetic variation, Genome, Genomics, Human genetics and DNA sequencing. His Exome sequencing research also works with subjects such as
His primary areas of study are Computational biology, Gene, Genetics, Exome sequencing and Genome. Daniel G. MacArthur has included themes like Genomics, RNA splicing, Allele, Genetic variation and DNA sequencing in his Computational biology study. His studies deal with areas such as Genome-wide association study and Gene knockdown as well as Genetic variation.
In his study, which falls under the umbrella issue of DNA sequencing, Annotation is strongly linked to Human genetic variation. Daniel G. MacArthur combines subjects such as Proband, FOXP1, Nonsense mutation, Genetic heterogeneity and Haplotype with his study of Exome sequencing. His work in Genome addresses subjects such as Mutation rate, which are connected to disciplines such as Function.
Daniel G. MacArthur focuses on Computational biology, Gene, Genome, Loss function and Genetic variation. His Computational biology research incorporates elements of Exome sequencing, Human genome, Allele, Exon and Human genetics. As part of the same scientific family, he usually focuses on Exome sequencing, concentrating on Mutation rate and intersecting with Function and DNA Mutational Analysis.
His Gene study frequently draws connections between adjacent fields such as Secretion. The study incorporates disciplines such as Haplotype and DNA sequencing in addition to Genome. Genomics is a subfield of Genetics that Daniel G. MacArthur investigates.
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A global reference for human genetic variation.
Adam Auton;Gonçalo R. Abecasis;David M. Altshuler;Richard M. Durbin.
Analysis of protein-coding genetic variation in 60,706 humans
Monkol Lek;Konrad J. Karczewski;Konrad J. Karczewski;Eric V. Minikel;Eric V. Minikel;Kaitlin E. Samocha.
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
Kristin G. Ardlie;David S. Deluca;Ayellet V. Segrè.
The mutational constraint spectrum quantified from variation in 141,456 humans
Konrad J. Karczewski;Laurent C. Francioli;Grace Tiao;Beryl B. Cummings.
Origins and functional impact of copy number variation in the human genome
Donald F. Conrad;Dalila Pinto;Richard Redon;Richard Redon;Lars Feuk;Lars Feuk.
Transcriptome and genome sequencing uncovers functional variation in humans
Tuuli Lappalainen;Michael Sammeth;Marc R. Friedländer;Peter A. C. ‘t Hoen.
The GTEx Consortium atlas of genetic regulatory effects across human tissues
F Aguet;AN Barbeira;R Bonazzola;A Brown.
Guidelines for investigating causality of sequence variants in human disease
D G MacArthur;T A Manolio;D P Dimmock;H L Rehm.
A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes
Daniel G. MacArthur;Daniel G. MacArthur;Suganthi Balasubramanian;Adam Frankish;Ni Huang.
Mapping copy number variation by population-scale genome sequencing
Ryan E. Mills;Klaudia Walter;Chip Stewart;Robert E. Handsaker.
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