A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis

Ani Manichaikul, Jee Young Moon, Śaunak Sen, Brian S. Yandell, Karl W. Broman

Research output: Contribution to journalArticle

98 Citations (Scopus)

Abstract

The identification of quantitative trait loci (QTL) and their interactions is a crucial step toward the discovery of genes responsible for variation in experimental crosses. The problem is best viewed as one of model selection, and the most important aspect of the problem is the comparison of models of different sizes. We present a penalized likelihood approach, with penalties on QTL and pairwise interactions chosen to control false positive rates. This extends the work of'Broman and Speed to allow for pairwise interactions among QTL. A conservative version of our penalized LOD score provides strict control over the rate of extraneous QTL and interactions; a more liberal criterion is more lenient on interactions but seeks to maintain control over the rate of inclusion of false loci. The key advance is that one needs only to specify a target false positive rate rather than a prior on the number of QTL and interactions. We illustrate the use of our model selection criteria as exploratory tools; simulation studies demonstrate reasonable power to detect QTL. Our liberal criterion is comparable in power to two Bayesian approaches.

Original languageEnglish (US)
Pages (from-to)1077-1086
Number of pages10
JournalGenetics
Volume181
Issue number3
DOIs
StatePublished - Mar 2009
Externally publishedYes

Fingerprint

Quantitative Trait Loci
Bayes Theorem
Genetic Association Studies
Patient Selection

ASJC Scopus subject areas

  • Genetics

Cite this

A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis. / Manichaikul, Ani; Moon, Jee Young; Sen, Śaunak; Yandell, Brian S.; Broman, Karl W.

In: Genetics, Vol. 181, No. 3, 03.2009, p. 1077-1086.

Research output: Contribution to journalArticle

Manichaikul, Ani ; Moon, Jee Young ; Sen, Śaunak ; Yandell, Brian S. ; Broman, Karl W. / A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis. In: Genetics. 2009 ; Vol. 181, No. 3. pp. 1077-1086.
@article{fe56c40da65345d9994b44ee6921f2a6,
title = "A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis",
abstract = "The identification of quantitative trait loci (QTL) and their interactions is a crucial step toward the discovery of genes responsible for variation in experimental crosses. The problem is best viewed as one of model selection, and the most important aspect of the problem is the comparison of models of different sizes. We present a penalized likelihood approach, with penalties on QTL and pairwise interactions chosen to control false positive rates. This extends the work of'Broman and Speed to allow for pairwise interactions among QTL. A conservative version of our penalized LOD score provides strict control over the rate of extraneous QTL and interactions; a more liberal criterion is more lenient on interactions but seeks to maintain control over the rate of inclusion of false loci. The key advance is that one needs only to specify a target false positive rate rather than a prior on the number of QTL and interactions. We illustrate the use of our model selection criteria as exploratory tools; simulation studies demonstrate reasonable power to detect QTL. Our liberal criterion is comparable in power to two Bayesian approaches.",
author = "Ani Manichaikul and Moon, {Jee Young} and Śaunak Sen and Yandell, {Brian S.} and Broman, {Karl W.}",
year = "2009",
month = "3",
doi = "10.1534/genetics.108.094565",
language = "English (US)",
volume = "181",
pages = "1077--1086",
journal = "Genetics",
issn = "0016-6731",
publisher = "Genetics Society of America",
number = "3",

}

TY - JOUR

T1 - A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis

AU - Manichaikul, Ani

AU - Moon, Jee Young

AU - Sen, Śaunak

AU - Yandell, Brian S.

AU - Broman, Karl W.

PY - 2009/3

Y1 - 2009/3

N2 - The identification of quantitative trait loci (QTL) and their interactions is a crucial step toward the discovery of genes responsible for variation in experimental crosses. The problem is best viewed as one of model selection, and the most important aspect of the problem is the comparison of models of different sizes. We present a penalized likelihood approach, with penalties on QTL and pairwise interactions chosen to control false positive rates. This extends the work of'Broman and Speed to allow for pairwise interactions among QTL. A conservative version of our penalized LOD score provides strict control over the rate of extraneous QTL and interactions; a more liberal criterion is more lenient on interactions but seeks to maintain control over the rate of inclusion of false loci. The key advance is that one needs only to specify a target false positive rate rather than a prior on the number of QTL and interactions. We illustrate the use of our model selection criteria as exploratory tools; simulation studies demonstrate reasonable power to detect QTL. Our liberal criterion is comparable in power to two Bayesian approaches.

AB - The identification of quantitative trait loci (QTL) and their interactions is a crucial step toward the discovery of genes responsible for variation in experimental crosses. The problem is best viewed as one of model selection, and the most important aspect of the problem is the comparison of models of different sizes. We present a penalized likelihood approach, with penalties on QTL and pairwise interactions chosen to control false positive rates. This extends the work of'Broman and Speed to allow for pairwise interactions among QTL. A conservative version of our penalized LOD score provides strict control over the rate of extraneous QTL and interactions; a more liberal criterion is more lenient on interactions but seeks to maintain control over the rate of inclusion of false loci. The key advance is that one needs only to specify a target false positive rate rather than a prior on the number of QTL and interactions. We illustrate the use of our model selection criteria as exploratory tools; simulation studies demonstrate reasonable power to detect QTL. Our liberal criterion is comparable in power to two Bayesian approaches.

UR - http://www.scopus.com/inward/record.url?scp=62549088774&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=62549088774&partnerID=8YFLogxK

U2 - 10.1534/genetics.108.094565

DO - 10.1534/genetics.108.094565

M3 - Article

C2 - 19104078

AN - SCOPUS:62549088774

VL - 181

SP - 1077

EP - 1086

JO - Genetics

JF - Genetics

SN - 0016-6731

IS - 3

ER -