Association mapping for compound heterozygous traits using phenotypic distance and integer programming

Dan Gusfield; Rasmus Nielsen

doi:10.1007/978-3-662-48221-6_10

Association mapping for compound heterozygous traits using phenotypic distance and integer programming

^*Corresponding author af dette arbejde

Abstract

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.

Originalsprog	Engelsk
Titel	Algorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings
Redaktører	Mihai Pop, Hélène Touzet
Antal sider	12
Forlag	Springer Verlag,
Publikationsdato	1 jan. 2015
Sider	136-147
Artikelnummer	A1
ISBN (Trykt)	9783662482209
DOI	https://doi.org/10.1007/978-3-662-48221-6_10
Status	Udgivet - 1 jan. 2015
Begivenhed	15th International Workshop on Algorithms in Bioinformatics, WABI 2015 - Atlanta, USA Varighed: 10 sep. 2015 → 12 sep. 2015

Konference

Konference	15th International Workshop on Algorithms in Bioinformatics, WABI 2015
Land/Område	USA
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)

Navn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind	9289
ISSN	0302-9743

Adgang til dokumentet

10.1007/978-3-662-48221-6_10

Andre filer og links

Link to publication in Scopus

Citationsformater

Gusfield, D., & Nielsen, R. (2015). Association mapping for compound heterozygous traits using phenotypic distance and integer programming. I M. Pop, & H. Touzet (red.), Algorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings (s. 136-147). [A1] Springer Verlag,. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 9289 https://doi.org/10.1007/978-3-662-48221-6_10

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. red. / Mihai Pop; Hélène Touzet. Springer Verlag, 2015. s. 136-147 A1 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 9289).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Gusfield, D & Nielsen, R 2015, Association mapping for compound heterozygous traits using phenotypic distance and integer programming. i M Pop & H Touzet (red), Algorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings., A1, Springer Verlag, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 9289, s. 136-147, 15th International Workshop on Algorithms in Bioinformatics, WABI 2015, Atlanta, USA, 10/09/2015. https://doi.org/10.1007/978-3-662-48221-6_10

Gusfield D, Nielsen R. Association mapping for compound heterozygous traits using phenotypic distance and integer programming. I Pop M, Touzet H, red., Algorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings. Springer Verlag,. 2015. s. 136-147. A1. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 9289). doi: 10.1007/978-3-662-48221-6_10

Gusfield, Dan ; Nielsen, Rasmus. / Association mapping for compound heterozygous traits using phenotypic distance and integer programming. Algorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings. red. / Mihai Pop ; Hélène Touzet. Springer Verlag, 2015. s. 136-147 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 9289).

@inproceedings{f95349f338e449ec90556a13b05c8f42,

title = "Association mapping for compound heterozygous traits using phenotypic distance and integer programming",

abstract = "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.",

author = "Dan Gusfield and Rasmus Nielsen",

year = "2015",

month = jan,

day = "1",

doi = "10.1007/978-3-662-48221-6_10",

language = "English",

isbn = "9783662482209",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag,",

pages = "136--147",

editor = "Mihai Pop and H{\'e}l{\`e}ne Touzet",

booktitle = "Algorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings",

note = "15th International Workshop on Algorithms in Bioinformatics, WABI 2015 ; Conference date: 10-09-2015 Through 12-09-2015",

}

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 -

Association mapping for compound heterozygous traits using phenotypic distance and integer programming

Abstract

Konference

Adgang til dokumentet

Andre filer og links

Fingeraftryk

Citationsformater