Association mapping for compound heterozygous traits using phenotypic distance and integer programming
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Association mapping for compound heterozygous traits using phenotypic distance and integer programming. / Gusfield, Dan; Nielsen, Rasmus.
Algorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings. ed. / Mihai Pop; Hélène Touzet. Springer Verlag, 2015. p. 136-147 A1 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9289).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Association mapping for compound heterozygous traits using phenotypic distance and integer programming
AU - Gusfield, Dan
AU - Nielsen, Rasmus
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84947730726&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-48221-6_10
DO - 10.1007/978-3-662-48221-6_10
M3 - Article in proceedings
AN - SCOPUS:84947730726
SN - 9783662482209
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 136
EP - 147
BT - Algorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings
A2 - Pop, Mihai
A2 - Touzet, Hélène
PB - Springer Verlag,
T2 - 15th International Workshop on Algorithms in Bioinformatics, WABI 2015
Y2 - 10 September 2015 through 12 September 2015
ER -
ID: 222641839