1 / 17

Manuel Ferreira Peter Visscher Nick Martin David Duffy

A simple method to localise pleiotropic QTL using univariate linkage analyses of correlated traits. Manuel Ferreira Peter Visscher Nick Martin David Duffy. A simple method to identify pleiotropic QTL using univariate association analyses of correlated traits. Manuel Ferreira

fiona
Télécharger la présentation

Manuel Ferreira Peter Visscher Nick Martin David Duffy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A simple method to localise pleiotropic QTL using univariate linkage analyses of correlated traits Manuel Ferreira Peter Visscher Nick Martin David Duffy

  2. A simple method to identify pleiotropic QTL using univariate association analyses of correlated traits Manuel Ferreira Peter Visscher Nick Martin David Duffy

  3. Background

  4. Localize regions of the genome that may harbour susceptibility loci for a complex trait (e.g. asthma, obesity, schizophrenia) Multiple intermediate (related) phenotypes (e.g. IgE, lung function) Linkage analysis of multiple traits

  5. Traits Marker T1 T4 T5 T6 T2 T3 T7 Observed LOD scores Q [ 0.8 , 2.2 , 2.8 , 3.7 , 2.1 , 0.7 , 0.0 ] 1. Significance of linkage between one marker and one trait Ferreira et al. 2005 Am J Hum Genet, in press 2. Significance of linkage between one marker and multiple traits Ferreira et al. Eur J Hum Genet, submitted Is the observed linkage between multiple traits and Q stochastic or a result of an underlying pleiotropic QTL or multiple clustered QTL? Multivariate VC/HE, PC

  6. 1. Linkage between a marker and multiple traits Combined-sum

  7. Combined-sum approach Traits Marker T1 T4 T5 T6 T2 T3 T7 1. Vector of LOD scores Observed Q [ 0.8 , 2.2 , 2.8 , 3.7 , 2.1 , 0.7 , 0.0 ] Simulated R1 [ 2.4 , 0.1 , 3.2 , 0.0 , 1.2 , 0.4 , 0.6 ] R2 [ 1.8 , 0.3 , 0.1 , 2.7 , 1.6 , 0.0 , 0.0 ] … R10,000 [ 4.1 , 0.0 , 0.0 , 1.4 , 0.2 , 1.6 , 0.4 ] 2. Vector of empirical pointwise P-values (-log10P) Observed Q [ 1.0 , 2.6 , 3.2 , 4.0 , 1.3 , 0.4 , 0.3 ] Simulated R1 [ 2.0 , 0.4 , 3.6 , 0.3 , 0.8 , 0.3 , 0.9 ] R2 [ 1.6 , 0.5 , 0.4 , 3.1 , 1.0 , 0.3 , 0.3 ] … R10,000 [ 4.4 , 0.3 , 0.3 , 2.4 , 0.3 , 1.2 , 0.6 ]

  8. Combined-sum approach 3. Vector of ordered empirical pointwise P-values (-log10P) Observed Q [ 4.0 , 3.2 , 2.6 , 1.3 , 1.0 , 0.4 , 0.3 ] Simulated R1 [ 3.6 , 2.0 , 0.9 , 0.8 , 0.4 , 0.3 , 0.3 ] R2 [ 3.1 , 1.6 , 1.0 , 0.5 , 0.4 , 0.3 , 0.3 ] … R10,000 [ 4.4 , 2.4 , 1.2 , 0.6 , 0.3 , 0.3 , 0.3 ] Sum statistics S1 S4 S5 S6 S2 S3 S7 4. Compute m sum statistics, Sk Observed Q [ 4.0 ] [ 7.2 ] [ 9.8 ] [ 11.1 ] [ 12.1 ] [ 12.5 ] [ 12.8 ] Simulated R1 [ 3.6 ] [ 5.6 ] [ 6.5 ] [ 7.3 ] [ 7.7 ] [ 8.0 ] [ 8.3 ] R2 [ 3.1 ] [ 4.7 ] [ 5.7 ] [ 6.2 ] [ 6.6 ] [ 6.9 ] [ 7.2 ] … R10,000 [ 4.4 ] [ 6.8 ] [ 8.0 ] [ 8.6 ] [ 8.9 ] [ 9.2 ] [ 9.5 ]

  9. Combined-sum approach Sum statistics S1 S4 S5 S6 S2 S3 S7 5. Determine significance of each Sk Observed Q [ .045 ] [ .023 ] [ .015 ] [ .012 ] [ .007 ] [ .007 ] [ .007 ] Simulated R1 [ .065 ] [ .055 ] [ .050 ] [ .045 ] [ .045 ] [ .045 ] [ .047 ] R2 [ .150 ] [ .120 ] [ .101 ] [ .101 ] [ .102 ] [ .102 ] [ .104 ] … R10,000 [ .035 ] [ .030 ] [ .025 ] [ .020 ] [ .020 ] [ .021 ] [ .025 ] Hoh et al. 2001 Genome Res 11: 2115-2119. Dudbridge & Koeleman 2004 Am J Hum Genet 75: 424-435. Observed Q [ .045 ] [ .023 ] [ .015 ] [ .012 ] [ .007 ] [ .007 ] [ .007 ] 6. Identify the Skwith smallest P-value and assess its significance Simulated R1 [ .065 ] [ .055 ] [ .050 ] [ .045 ] [ .045 ] [ .045 ] [ .047 ] R2 [ .150 ] [ .120 ] [ .101 ] [ .101 ] [ .102 ] [ .102 ] [ .104 ] … R10,000 [ .035 ] [ .030 ] [ .025 ] [ .020 ] [ .020 ] [ .021 ] [ .025 ] Global empirical pointwise P = .011 e.g. S5(Q) = .007

  10. Simulations Power Combined-sum 3 traits & 1 QTL for 250 sib-pairs Eight models: varied QTL variance & trait correlation 1,000 datasets

  11. Simulations If multiple correlated traits are analysed, power to detect a QTL can be improved by considering all traits simultaneously Combined-sum approach is an efficient alternative to formal multivariate methods, applicable to any number of traits & not affected residual correlation

  12. 2. Application asthma dataset

  13. Application to asthma 215 sib-pairs (201 families) Measured for 7 asthma traits: Asthma, BHR, Atopy, Dpter, FEV1, FEV1/FVC, IgE 6% 201-300 markers, 48% 301-500, 25% 501-1000 and 21% 1001-1544 Information content 0.57 (range 0.15-0.85) Significance estimated 1,796,000 (univariate) marker replicates

  14. Application to asthma II Mixture continuous & affection traits Dpter Atopy BHR FEV1 AsthmaFEV1/FVCIgE

  15. Application to asthma II Global genome-wide P = .023

  16. Conclusion 1. Developed an efficient approach to test whether the simultaneous linkage of multiple traits to the same marker is spurious All traits must be analysed with the same marker replicates generated under the null hypothesis of no linkage 2. This approach is more powerful than univariate VC to detect a pleiotropic QTL 3. When traits are moderately correlated and the QTL influences all traits it outperforms multivariate VC It is applicable to any number of traits and it is not affected by the residual correlation between traits 4. Further testing required to assess performance under specific situations Longitudinal data, many traits, different linkage statistics 5. Applicable to association analysis, including genome-wide

  17. Acknowledgments David Smyth Allan McRae Carl Anderson

More Related