A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples

Application to sequence data

Dajiang Liu, Suzanne M. Leal

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

For most complex trait association studies using next-generation sequencing, in addition to the primary phenotype of interest, many clinically important secondary traits are also available, which can be analyzed to map susceptibility genes. Owing to high sequencing costs, most studies use selected samples, and the sampling mechanisms of these studies can be complicated. When the primary and secondary traits are correlated, analyses of secondary phenotypes can cause spurious associations in selected samples and existing methods are inadequate to adjust for them. To address this problem, a likelihood-based method, MULTI-TRAIT-ASSOCIATION (MTA) was developed. MTA is flexible and can be applied to any study with known sampling mechanisms. It also allows efficient inferences of genetic parameters. To investigate the power of MTA and different study designs, extensive simulations were performed under rigorous population genetic and phenotypic models. It is demonstrated that there are great benefits for analyzing secondary phenotypes in selected samples. In particular, using case-control samples and samples with extreme primary phenotypes can be more powerful than analyzing random samples of equivalent size. One major challenge for sequence-based association studies is that most data sets are not of sufficient size to be adequately powered. By applying MTA, data sets ascertained under distinct mechanisms or targeted at different primary traits can be jointly analyzed to map common phenotypes and greatly increase power. The combined analysis can be performed using freely available data sets from public repositories, for example, dbGaP. In conclusion, MTA will have an important role in dissecting the etiology of complex traits.

Original languageEnglish (US)
Pages (from-to)449-456
Number of pages8
JournalEuropean Journal of Human Genetics
Volume20
Issue number4
DOIs
StatePublished - Apr 1 2012

Fingerprint

Phenotype
Sampling Studies
Genetic Models
Population Genetics
Sample Size
Costs and Cost Analysis
Genes
Datasets

All Science Journal Classification (ASJC) codes

  • Genetics(clinical)
  • Genetics

Cite this

@article{69900ee93ed74e0393272bfb9a5c3d31,
title = "A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: Application to sequence data",
abstract = "For most complex trait association studies using next-generation sequencing, in addition to the primary phenotype of interest, many clinically important secondary traits are also available, which can be analyzed to map susceptibility genes. Owing to high sequencing costs, most studies use selected samples, and the sampling mechanisms of these studies can be complicated. When the primary and secondary traits are correlated, analyses of secondary phenotypes can cause spurious associations in selected samples and existing methods are inadequate to adjust for them. To address this problem, a likelihood-based method, MULTI-TRAIT-ASSOCIATION (MTA) was developed. MTA is flexible and can be applied to any study with known sampling mechanisms. It also allows efficient inferences of genetic parameters. To investigate the power of MTA and different study designs, extensive simulations were performed under rigorous population genetic and phenotypic models. It is demonstrated that there are great benefits for analyzing secondary phenotypes in selected samples. In particular, using case-control samples and samples with extreme primary phenotypes can be more powerful than analyzing random samples of equivalent size. One major challenge for sequence-based association studies is that most data sets are not of sufficient size to be adequately powered. By applying MTA, data sets ascertained under distinct mechanisms or targeted at different primary traits can be jointly analyzed to map common phenotypes and greatly increase power. The combined analysis can be performed using freely available data sets from public repositories, for example, dbGaP. In conclusion, MTA will have an important role in dissecting the etiology of complex traits.",
author = "Dajiang Liu and Leal, {Suzanne M.}",
year = "2012",
month = "4",
day = "1",
doi = "10.1038/ejhg.2011.211",
language = "English (US)",
volume = "20",
pages = "449--456",
journal = "European Journal of Human Genetics",
issn = "1018-4813",
publisher = "Nature Publishing Group",
number = "4",

}

TY - JOUR

T1 - A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples

T2 - Application to sequence data

AU - Liu, Dajiang

AU - Leal, Suzanne M.

PY - 2012/4/1

Y1 - 2012/4/1

N2 - For most complex trait association studies using next-generation sequencing, in addition to the primary phenotype of interest, many clinically important secondary traits are also available, which can be analyzed to map susceptibility genes. Owing to high sequencing costs, most studies use selected samples, and the sampling mechanisms of these studies can be complicated. When the primary and secondary traits are correlated, analyses of secondary phenotypes can cause spurious associations in selected samples and existing methods are inadequate to adjust for them. To address this problem, a likelihood-based method, MULTI-TRAIT-ASSOCIATION (MTA) was developed. MTA is flexible and can be applied to any study with known sampling mechanisms. It also allows efficient inferences of genetic parameters. To investigate the power of MTA and different study designs, extensive simulations were performed under rigorous population genetic and phenotypic models. It is demonstrated that there are great benefits for analyzing secondary phenotypes in selected samples. In particular, using case-control samples and samples with extreme primary phenotypes can be more powerful than analyzing random samples of equivalent size. One major challenge for sequence-based association studies is that most data sets are not of sufficient size to be adequately powered. By applying MTA, data sets ascertained under distinct mechanisms or targeted at different primary traits can be jointly analyzed to map common phenotypes and greatly increase power. The combined analysis can be performed using freely available data sets from public repositories, for example, dbGaP. In conclusion, MTA will have an important role in dissecting the etiology of complex traits.

AB - For most complex trait association studies using next-generation sequencing, in addition to the primary phenotype of interest, many clinically important secondary traits are also available, which can be analyzed to map susceptibility genes. Owing to high sequencing costs, most studies use selected samples, and the sampling mechanisms of these studies can be complicated. When the primary and secondary traits are correlated, analyses of secondary phenotypes can cause spurious associations in selected samples and existing methods are inadequate to adjust for them. To address this problem, a likelihood-based method, MULTI-TRAIT-ASSOCIATION (MTA) was developed. MTA is flexible and can be applied to any study with known sampling mechanisms. It also allows efficient inferences of genetic parameters. To investigate the power of MTA and different study designs, extensive simulations were performed under rigorous population genetic and phenotypic models. It is demonstrated that there are great benefits for analyzing secondary phenotypes in selected samples. In particular, using case-control samples and samples with extreme primary phenotypes can be more powerful than analyzing random samples of equivalent size. One major challenge for sequence-based association studies is that most data sets are not of sufficient size to be adequately powered. By applying MTA, data sets ascertained under distinct mechanisms or targeted at different primary traits can be jointly analyzed to map common phenotypes and greatly increase power. The combined analysis can be performed using freely available data sets from public repositories, for example, dbGaP. In conclusion, MTA will have an important role in dissecting the etiology of complex traits.

UR - http://www.scopus.com/inward/record.url?scp=84858337563&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84858337563&partnerID=8YFLogxK

U2 - 10.1038/ejhg.2011.211

DO - 10.1038/ejhg.2011.211

M3 - Article

VL - 20

SP - 449

EP - 456

JO - European Journal of Human Genetics

JF - European Journal of Human Genetics

SN - 1018-4813

IS - 4

ER -