Association mapping for compound heterozygous traits using phenotypic distance and integer programming
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
For many important complex traits, Genome Wide Association Studies (GWAS) have only recovered a small proportion of the variance in disease prevalence known to be caused by genetics. The most common explanation for this is the presence of multiple rare mutations that cannot be identified in GWAS due to a lack of statistical power. Such rare mutations may be concentrated in relatively few genes, as is the case for many known Mendelian diseases, where the mutations are often compound heterozygous (CH), defined below. Due to the multiple mutations, each of which contributes little by itself to the prevalence of the disease, GWAS also lacks power to identify genes contributing to a CH-trait. In this paper, we address the problem of finding genes that are causal for CH-traits, by introducing a discrete optimization problem, called the Phenotypic Distance Problem. We show that it can be efficiently solved on realistic-size simulated CH-data by using integer linear programming (ILP). The empirical results strongly validate this approach.
Original language | English |
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Title of host publication | Algorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings |
Editors | Mihai Pop, Hélène Touzet |
Number of pages | 12 |
Publisher | Springer Verlag, |
Publication date | 1 Jan 2015 |
Pages | 136-147 |
Article number | A1 |
ISBN (Print) | 9783662482209 |
DOIs | |
Publication status | Published - 1 Jan 2015 |
Externally published | Yes |
Event | 15th International Workshop on Algorithms in Bioinformatics, WABI 2015 - Atlanta, United States Duration: 10 Sep 2015 → 12 Sep 2015 |
Conference
Conference | 15th International Workshop on Algorithms in Bioinformatics, WABI 2015 |
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Land | United States |
By | Atlanta |
Periode | 10/09/2015 → 12/09/2015 |
Sponsor | ACM Special Interest Group in Bioinformatics (ACM SIGBio), European Association for Theoretical Computer Science (EATCS), International Society for Computational Biology (ISCB) |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9289 |
ISSN | 0302-9743 |
ID: 222641839