Next generation modeling in GWAS: comparing different genetic architectures.

Evangelina López de Maturana, Noelia Ibáñez-Escriche, Óscar González-Recio, Gaëlle Marenne, Hossein Mehrban, Stephen J. Chanock, Michael E. Goddard, Núria Malats

Research output: Contribution to journalArticle

Abstract

The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data. Both methods were compared to the single marker regression (SMR). Simulated data were generated in the context of six scenarios differing on effect size, minor allele frequency (MAF) and linkage disequilibrium (LD) between QTLs. These were based on real SNP genotypes in chromosome 21 from the Spanish Bladder Cancer Study. We show how the genetic architecture dramatically affects the behavior of the methods in terms of power, type I error and accuracy of estimates. Markers with high MAF are easier to detect by all methods, especially if they have a large effect on the phenotypic trait. A high LD between QTLs with either large or small effects differently affects the power of the methods: it impairs QTL detection with BA, irrespectively of the effect size, although boosts that of small effects with BL and SMR. We demonstrate the convenience of applying MMM rather than SMR because of their larger power and smaller type I error. Results from real data when applying MMM suggest novel associations not detected by SMR.

Original languageEnglish (US)
Pages (from-to)1235-1253
Number of pages19
JournalHuman Genetics
Volume133
Issue number10
DOIs
StatePublished - 2014
Externally publishedYes

Fingerprint

Genome-Wide Association Study
Quantitative Trait Loci
Linkage Disequilibrium
Gene Frequency
Chromosomes, Human, Pair 21
Urinary Bladder Neoplasms
Single Nucleotide Polymorphism
Genotype
Technology

ASJC Scopus subject areas

  • Genetics(clinical)
  • Genetics

Cite this

López de Maturana, E., Ibáñez-Escriche, N., González-Recio, Ó., Marenne, G., Mehrban, H., Chanock, S. J., ... Malats, N. (2014). Next generation modeling in GWAS: comparing different genetic architectures. Human Genetics, 133(10), 1235-1253. https://doi.org/10.1007/s00439-014-1461-1

Next generation modeling in GWAS : comparing different genetic architectures. / López de Maturana, Evangelina; Ibáñez-Escriche, Noelia; González-Recio, Óscar; Marenne, Gaëlle; Mehrban, Hossein; Chanock, Stephen J.; Goddard, Michael E.; Malats, Núria.

In: Human Genetics, Vol. 133, No. 10, 2014, p. 1235-1253.

Research output: Contribution to journalArticle

López de Maturana, E, Ibáñez-Escriche, N, González-Recio, Ó, Marenne, G, Mehrban, H, Chanock, SJ, Goddard, ME & Malats, N 2014, 'Next generation modeling in GWAS: comparing different genetic architectures.', Human Genetics, vol. 133, no. 10, pp. 1235-1253. https://doi.org/10.1007/s00439-014-1461-1
López de Maturana E, Ibáñez-Escriche N, González-Recio Ó, Marenne G, Mehrban H, Chanock SJ et al. Next generation modeling in GWAS: comparing different genetic architectures. Human Genetics. 2014;133(10):1235-1253. https://doi.org/10.1007/s00439-014-1461-1
López de Maturana, Evangelina ; Ibáñez-Escriche, Noelia ; González-Recio, Óscar ; Marenne, Gaëlle ; Mehrban, Hossein ; Chanock, Stephen J. ; Goddard, Michael E. ; Malats, Núria. / Next generation modeling in GWAS : comparing different genetic architectures. In: Human Genetics. 2014 ; Vol. 133, No. 10. pp. 1235-1253.
@article{eee45fc0219042eeb5a5639b52f6d34d,
title = "Next generation modeling in GWAS: comparing different genetic architectures.",
abstract = "The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data. Both methods were compared to the single marker regression (SMR). Simulated data were generated in the context of six scenarios differing on effect size, minor allele frequency (MAF) and linkage disequilibrium (LD) between QTLs. These were based on real SNP genotypes in chromosome 21 from the Spanish Bladder Cancer Study. We show how the genetic architecture dramatically affects the behavior of the methods in terms of power, type I error and accuracy of estimates. Markers with high MAF are easier to detect by all methods, especially if they have a large effect on the phenotypic trait. A high LD between QTLs with either large or small effects differently affects the power of the methods: it impairs QTL detection with BA, irrespectively of the effect size, although boosts that of small effects with BL and SMR. We demonstrate the convenience of applying MMM rather than SMR because of their larger power and smaller type I error. Results from real data when applying MMM suggest novel associations not detected by SMR.",
author = "{L{\'o}pez de Maturana}, Evangelina and Noelia Ib{\'a}{\~n}ez-Escriche and {\'O}scar Gonz{\'a}lez-Recio and Ga{\"e}lle Marenne and Hossein Mehrban and Chanock, {Stephen J.} and Goddard, {Michael E.} and N{\'u}ria Malats",
year = "2014",
doi = "10.1007/s00439-014-1461-1",
language = "English (US)",
volume = "133",
pages = "1235--1253",
journal = "Human Genetics",
issn = "0340-6717",
publisher = "Springer Verlag",
number = "10",

}

TY - JOUR

T1 - Next generation modeling in GWAS

T2 - comparing different genetic architectures.

AU - López de Maturana, Evangelina

AU - Ibáñez-Escriche, Noelia

AU - González-Recio, Óscar

AU - Marenne, Gaëlle

AU - Mehrban, Hossein

AU - Chanock, Stephen J.

AU - Goddard, Michael E.

AU - Malats, Núria

PY - 2014

Y1 - 2014

N2 - The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data. Both methods were compared to the single marker regression (SMR). Simulated data were generated in the context of six scenarios differing on effect size, minor allele frequency (MAF) and linkage disequilibrium (LD) between QTLs. These were based on real SNP genotypes in chromosome 21 from the Spanish Bladder Cancer Study. We show how the genetic architecture dramatically affects the behavior of the methods in terms of power, type I error and accuracy of estimates. Markers with high MAF are easier to detect by all methods, especially if they have a large effect on the phenotypic trait. A high LD between QTLs with either large or small effects differently affects the power of the methods: it impairs QTL detection with BA, irrespectively of the effect size, although boosts that of small effects with BL and SMR. We demonstrate the convenience of applying MMM rather than SMR because of their larger power and smaller type I error. Results from real data when applying MMM suggest novel associations not detected by SMR.

AB - The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data. Both methods were compared to the single marker regression (SMR). Simulated data were generated in the context of six scenarios differing on effect size, minor allele frequency (MAF) and linkage disequilibrium (LD) between QTLs. These were based on real SNP genotypes in chromosome 21 from the Spanish Bladder Cancer Study. We show how the genetic architecture dramatically affects the behavior of the methods in terms of power, type I error and accuracy of estimates. Markers with high MAF are easier to detect by all methods, especially if they have a large effect on the phenotypic trait. A high LD between QTLs with either large or small effects differently affects the power of the methods: it impairs QTL detection with BA, irrespectively of the effect size, although boosts that of small effects with BL and SMR. We demonstrate the convenience of applying MMM rather than SMR because of their larger power and smaller type I error. Results from real data when applying MMM suggest novel associations not detected by SMR.

UR - http://www.scopus.com/inward/record.url?scp=84910119577&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84910119577&partnerID=8YFLogxK

U2 - 10.1007/s00439-014-1461-1

DO - 10.1007/s00439-014-1461-1

M3 - Article

C2 - 24934831

AN - SCOPUS:84910119577

VL - 133

SP - 1235

EP - 1253

JO - Human Genetics

JF - Human Genetics

SN - 0340-6717

IS - 10

ER -