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The exhaustive genomic scan approach, with an application to rare-variant association analysis
- Source :
- European Journal of Human Genetics
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- Region-based genome-wide scans are usually performed by use of a priori chosen analysis regions. Such an approach will likely miss the region comprising the strongest signal and, thus, may result in increased type II error rates and decreased power. Here, we propose a genomic exhaustive scan approach that analyzes all possible subsequences and does not rely on a prior definition of the analysis regions. As a prime instance, we present a computationally ultraefficient implementation using the rare-variant collapsing test for phenotypic association, the genomic exhaustive collapsing scan (GECS). Our implementation allows for the identification of regions comprising the strongest signals in large, genome-wide rare-variant association studies while controlling the family-wise error rate via permutation. Application of GECS to two genomic data sets revealed several novel significantly associated regions for age-related macular degeneration and for schizophrenia. Our approach also offers a high potential to improve genome-wide scans for selection, methylation, and other analyses.
- Subjects :
- 0301 basic medicine
Computer science
Word error rate
Computational biology
Prime (order theory)
Article
Macular Degeneration
03 medical and health sciences
Permutation
0302 clinical medicine
Gene Frequency
Genetics
Humans
Genetic Testing
Immunological disorders
Genetics (clinical)
Selection (genetic algorithm)
Genetic association
Whole Genome Sequencing
DNA Methylation
Data processing
Identification (information)
030104 developmental biology
Mutation
Mutation (genetic algorithm)
Schizophrenia
030221 ophthalmology & optometry
A priori and a posteriori
Psychiatric disorders
Genome-Wide Association Study
Type I and type II errors
Subjects
Details
- ISSN :
- 14765438 and 10184813
- Volume :
- 28
- Database :
- OpenAIRE
- Journal :
- European Journal of Human Genetics
- Accession number :
- edsair.doi.dedup.....1112561d942b2229d6fbb00fcd13d5f5