Phenotypic complexity, measurement bias, and poor phenotypic resolution contribute to the missing heritability problem in genetic association studies

The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this 'missing heritability' have been mainly genetic, such as genetic heterogeneity and complex (epi-)ge...

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Bibliographic Details
Published in:PloS one 2010-11, Vol.5 (11), p.e13929-e13929
Main Authors: van der Sluis, Sophie, Verhage, Matthijs, Posthuma, Danielle, Dolan, Conor V
Format: Article
Language:English
Subjects:
Animals
Asthma
Attention deficit hyperactivity disorder
Bias
Bipolar disorder
Cognitive ability
Comparative analysis
Complexity
Computer simulation
Data processing
Female
Genetic Association Studies - methods
Genetic diversity
Genetic Predisposition to Disease - genetics
Genetic research
Genetic variance
Genetic Variation
Genetics
Genetics and Genomics
Genetics and Genomics/Complex Traits
Genetics and Genomics/Disease Models
Genome-Wide Association Study - methods
Genomes
Genomics
Genotype
Genotype & phenotype
Heritability
Heterogeneity
Humans
Intelligence
Male
Measurement
Models, Genetic
Mutualism
Ontology
Phenotype
Quantitative psychology
Simulation
Studies
Theory
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Summary:The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this 'missing heritability' have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms. We used comprehensive simulation studies to show that three phenotypic measurement issues also provide viable explanations of the missing heritability: phenotypic complexity, measurement bias, and phenotypic resolution. We identify the circumstances in which the use of phenotypic sum-scores and the presence of measurement bias lower the power to detect genetic variants. In addition, we show how the differential resolution of psychometric instruments (i.e., whether the instrument includes items that resolve individual differences in the normal range or in the clinical range of a phenotype) affects the power to detect genetic variants. We conclude that careful phenotypic data modelling can improve the genetic signal, and thus the statistical power to identify genetic variants by 20-99%.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0013929