Journal
G3-GENES GENOMES GENETICS
Volume 8, Issue 5, Pages 1659-1668Publisher
OXFORD UNIV PRESS INC
DOI: 10.1534/g3.118.200082
Keywords
Drosophila melanogaster; DGRP; genomic prediction; set test; locomotor activity
Categories
Funding
- Lundbeck Foundation [R155-2014-1724]
- Centre for Integrative Sequencing at Aarhus University
- Danish Strategic Research Council (GenSAP: Centre for Genomic Selection in Animals and Plants) [12-132452]
- Danish Natural Science Research Council (Sapere Aude grant)
- National Institutes of Health [R01-AA016560, R01-AG043490]
- European Union Seventh Framework Program (FP7) [311794]
- Lundbeck Foundation [R248-2017-2003, R5-2006-523, R67-2010-6531] Funding Source: researchfish
- NATIONAL INSTITUTE ON AGING [R01AG043490] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM [R01AA016560] Funding Source: NIH RePORTER
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Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach.
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