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Chapter 11: Genome-Wide Association Studies
by
Bush, William S.
, Moore, Jason H.
in
Bankruptcy
/ Bioinformatics
/ Biology
/ Blood clots
/ Deoxyribonucleic acid
/ Diabetes
/ Disease
/ DNA
/ Drug dosages
/ Education
/ Electronic Health Records
/ Genes
/ Genetic diversity
/ Genetic Linkage
/ Genetic testing
/ Genetic Variation
/ Genetics
/ Genome-Wide Association Study
/ Genomes
/ Genomics
/ Haplotypes
/ Health risk assessment
/ Humans
/ Hypotheses
/ Hypothesis testing
/ Macular degeneration
/ Medical genetics
/ Phenotype
/ Population
/ Risk factors
/ Statistical methods
2012
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Chapter 11: Genome-Wide Association Studies
by
Bush, William S.
, Moore, Jason H.
in
Bankruptcy
/ Bioinformatics
/ Biology
/ Blood clots
/ Deoxyribonucleic acid
/ Diabetes
/ Disease
/ DNA
/ Drug dosages
/ Education
/ Electronic Health Records
/ Genes
/ Genetic diversity
/ Genetic Linkage
/ Genetic testing
/ Genetic Variation
/ Genetics
/ Genome-Wide Association Study
/ Genomes
/ Genomics
/ Haplotypes
/ Health risk assessment
/ Humans
/ Hypotheses
/ Hypothesis testing
/ Macular degeneration
/ Medical genetics
/ Phenotype
/ Population
/ Risk factors
/ Statistical methods
2012
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Do you wish to request the book?
Chapter 11: Genome-Wide Association Studies
by
Bush, William S.
, Moore, Jason H.
in
Bankruptcy
/ Bioinformatics
/ Biology
/ Blood clots
/ Deoxyribonucleic acid
/ Diabetes
/ Disease
/ DNA
/ Drug dosages
/ Education
/ Electronic Health Records
/ Genes
/ Genetic diversity
/ Genetic Linkage
/ Genetic testing
/ Genetic Variation
/ Genetics
/ Genome-Wide Association Study
/ Genomes
/ Genomics
/ Haplotypes
/ Health risk assessment
/ Humans
/ Hypotheses
/ Hypothesis testing
/ Macular degeneration
/ Medical genetics
/ Phenotype
/ Population
/ Risk factors
/ Statistical methods
2012
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Journal Article
Chapter 11: Genome-Wide Association Studies
2012
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Overview
Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.
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