FrancaisEnglish (United Kingdom)
 

Responsable de publication :
Olivier Martin

Responsable éditoriale :
Rozenn Le Guyader

Administrateur de l'infoservice :
Thierry Balliau

Séminaire à l’UMR

Génétique Quantitative et Évolution – Le Moulon

Ferme du Moulon – Salle de conférences

Mercredi 12 décembre 2018

14h00

Pr Indranil Mukhopadhyay

Indian Statistical Institute
actuellement en visite en France au laboratoire de statistique d’AgroParisTech

invité par Cette adresse email est protégée contre les robots des spammeurs, vous devez activer Javascript pour la voir.

"Multi-loci association test in genetic association study
using similarity between individuals"

Abstract

The common paradigm in genome wide association studies (GWAS) is to test for association using only one SNP at a time that gives rise to multiple comparison problems ignoring their genomic and environmental context. Gene-based association tests are gaining importance for the analysis of genome-wide association studies (GWAS) because it reduces multiple testing burdens and also provides directions for future functional studies. Moreover, with whole-genome sequencing on the horizon, there is increasing recognition that agnostic biostatistical approaches will get us no far, development of comprehensive and fully informative analyses of GWAS using newer approaches is required that combine information from multiple markers at a time. So, within a gene or any genomic region of interest, testing for joint association of genetic variants would be desirable to determine their synergistic effects. Based on this idea we have proposed kernel based association test (KBAT) for binary trait as well as for quantitative phenotype (QT-KBAT) including information for both common and rare variants. These tests are shown to be powerful to detect such association. We have evaluated the power of the proposed test statistics for case-control samples and quantitative traits using the extensive simulated data sets. We have also extended our multi-loci approach to family data and also to study gene-gene interaction. In each case, we have developed asymptotic distribution of the test statistic under null hypothesis of no association. This enables us to calculate p-value vary fast without using any time consuming computational procedure.

References

- Mukhopadhyay I. et al. (2010) Association tests using kernel-based measures of multi-locus genotype similarity between individuals. Genet Epidemiol, 34(3):213-21. doi: 10.1002/gepi.20451.

- Thalamuthu A. et al. (2011) Association tests for rare and common variants based on genotypic and phenotypic measures of similarity between individuals. BMC Proc5(Suppl 9) S89. doi:  [10.1186/1753-6561-5-S9-S89].