A unified framework for detecting rare variant quantitative trait associations in pedigree and unrelated individuals via sequence data

Dajiang Liu, Suzanne M. Leal

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Objectives: There is great interest to sequence unrelated or pedigree samples for detecting rare variant quantitative trait associations. In order to reduce the cost of sequencing and improve power, many studies sequence selected samples with extreme traits. Existing methods for detecting rare variant associations were developed for unrelated samples. Methods are needed to analyze (selected or randomly ascertained) pedigree samples. Methods: We propose a unified framework of modeling extreme trait genetic associations (MEGA) with rare variants. Using MEGA and appropriate permutation algorithms, many rare variant tests can be extended to family data. As an application, we compared study designs using both sib-pairs and unrelated individuals. Extensive simulations were carried out using realistic population genetic and complex trait models. Results: It is demonstrated that when extreme sampling is implemented within equal-sized cohorts of unrelated individuals or sib-pairs, analyzing unrelated individuals is consistently more powerful than studying sib-pairs. A higher portion of rare variants can be identified through sequencing unrelated samples compared to sibs. Alternatively, if samples are ascertained using fixed thresholds from an infinite-sized population, sequencing one sib with the most extreme trait from each extreme concordant sib-pair is consistently the most powerful design. Conclusions: MEGA will play an important role in the analysis of sequence-based genetic association studies.

Original languageEnglish (US)
Pages (from-to)105-122
Number of pages18
JournalHuman heredity
Volume73
Issue number2
DOIs
StatePublished - May 1 2012

Fingerprint

Pedigree
Population Genetics
Genetic Association Studies
Sequence Analysis
Costs and Cost Analysis
Population

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

Cite this

@article{024ee669a5004f7398bea4739256f418,
title = "A unified framework for detecting rare variant quantitative trait associations in pedigree and unrelated individuals via sequence data",
abstract = "Objectives: There is great interest to sequence unrelated or pedigree samples for detecting rare variant quantitative trait associations. In order to reduce the cost of sequencing and improve power, many studies sequence selected samples with extreme traits. Existing methods for detecting rare variant associations were developed for unrelated samples. Methods are needed to analyze (selected or randomly ascertained) pedigree samples. Methods: We propose a unified framework of modeling extreme trait genetic associations (MEGA) with rare variants. Using MEGA and appropriate permutation algorithms, many rare variant tests can be extended to family data. As an application, we compared study designs using both sib-pairs and unrelated individuals. Extensive simulations were carried out using realistic population genetic and complex trait models. Results: It is demonstrated that when extreme sampling is implemented within equal-sized cohorts of unrelated individuals or sib-pairs, analyzing unrelated individuals is consistently more powerful than studying sib-pairs. A higher portion of rare variants can be identified through sequencing unrelated samples compared to sibs. Alternatively, if samples are ascertained using fixed thresholds from an infinite-sized population, sequencing one sib with the most extreme trait from each extreme concordant sib-pair is consistently the most powerful design. Conclusions: MEGA will play an important role in the analysis of sequence-based genetic association studies.",
author = "Dajiang Liu and Leal, {Suzanne M.}",
year = "2012",
month = "5",
day = "1",
doi = "10.1159/000336293",
language = "English (US)",
volume = "73",
pages = "105--122",
journal = "Human Heredity",
issn = "0001-5652",
publisher = "S. Karger AG",
number = "2",

}

A unified framework for detecting rare variant quantitative trait associations in pedigree and unrelated individuals via sequence data. / Liu, Dajiang; Leal, Suzanne M.

In: Human heredity, Vol. 73, No. 2, 01.05.2012, p. 105-122.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A unified framework for detecting rare variant quantitative trait associations in pedigree and unrelated individuals via sequence data

AU - Liu, Dajiang

AU - Leal, Suzanne M.

PY - 2012/5/1

Y1 - 2012/5/1

N2 - Objectives: There is great interest to sequence unrelated or pedigree samples for detecting rare variant quantitative trait associations. In order to reduce the cost of sequencing and improve power, many studies sequence selected samples with extreme traits. Existing methods for detecting rare variant associations were developed for unrelated samples. Methods are needed to analyze (selected or randomly ascertained) pedigree samples. Methods: We propose a unified framework of modeling extreme trait genetic associations (MEGA) with rare variants. Using MEGA and appropriate permutation algorithms, many rare variant tests can be extended to family data. As an application, we compared study designs using both sib-pairs and unrelated individuals. Extensive simulations were carried out using realistic population genetic and complex trait models. Results: It is demonstrated that when extreme sampling is implemented within equal-sized cohorts of unrelated individuals or sib-pairs, analyzing unrelated individuals is consistently more powerful than studying sib-pairs. A higher portion of rare variants can be identified through sequencing unrelated samples compared to sibs. Alternatively, if samples are ascertained using fixed thresholds from an infinite-sized population, sequencing one sib with the most extreme trait from each extreme concordant sib-pair is consistently the most powerful design. Conclusions: MEGA will play an important role in the analysis of sequence-based genetic association studies.

AB - Objectives: There is great interest to sequence unrelated or pedigree samples for detecting rare variant quantitative trait associations. In order to reduce the cost of sequencing and improve power, many studies sequence selected samples with extreme traits. Existing methods for detecting rare variant associations were developed for unrelated samples. Methods are needed to analyze (selected or randomly ascertained) pedigree samples. Methods: We propose a unified framework of modeling extreme trait genetic associations (MEGA) with rare variants. Using MEGA and appropriate permutation algorithms, many rare variant tests can be extended to family data. As an application, we compared study designs using both sib-pairs and unrelated individuals. Extensive simulations were carried out using realistic population genetic and complex trait models. Results: It is demonstrated that when extreme sampling is implemented within equal-sized cohorts of unrelated individuals or sib-pairs, analyzing unrelated individuals is consistently more powerful than studying sib-pairs. A higher portion of rare variants can be identified through sequencing unrelated samples compared to sibs. Alternatively, if samples are ascertained using fixed thresholds from an infinite-sized population, sequencing one sib with the most extreme trait from each extreme concordant sib-pair is consistently the most powerful design. Conclusions: MEGA will play an important role in the analysis of sequence-based genetic association studies.

UR - http://www.scopus.com/inward/record.url?scp=84860581891&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860581891&partnerID=8YFLogxK

U2 - 10.1159/000336293

DO - 10.1159/000336293

M3 - Article

VL - 73

SP - 105

EP - 122

JO - Human Heredity

JF - Human Heredity

SN - 0001-5652

IS - 2

ER -