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 Peter Visscher Nick Martin David Duffy

Background

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

T1T1 Traits [ 0.8, 2.2, 2.8, 3.7, 2.1, 0.7, 0.0 ] Q Observed LOD scores 1. Significance of linkage between one marker and one trait 2. Significance of linkage between one marker and multiple traits Ferreira et al Am J Hum Genet, in press Ferreira et al. Eur J Hum Genet, submitted T2T2 T3T3 T4T4 T5T5 T6T6 T7T7 Marker 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

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

Combined-sum approach T1T1 Traits [ 0.8, 2.2, 2.8, 3.7, 2.1, 0.7, 0.0 ] Q T2T2 T3T3 T4T4 T5T5 T6T6 T7T7 Marker R1R1 R2R2 … R 10,000 [ 2.4, 0.1, 3.2, 0.0, 1.2, 0.4, 0.6 ] [ 1.8, 0.3, 0.1, 2.7, 1.6, 0.0, 0.0 ] [ 4.1, 0.0, 0.0, 1.4, 0.2, 1.6, 0.4 ] Q R1R1 R2R2 … R 10,000 [ 2.0, 0.4, 3.6, 0.3, 0.8, 0.3, 0.9 ] [ 1.6, 0.5, 0.4, 3.1, 1.0, 0.3, 0.3 ] [ 4.4, 0.3, 0.3, 2.4, 0.3, 1.2, 0.6 ] [ 1.0, 2.6, 3.2, 4.0, 1.3, 0.4, 0.3 ] Observed Simulated Observed Simulated Vector of LOD scores Vector of empirical pointwise P-values (-log 10 P)

Q R1R1 R2R2 … R 10,000 [ 3.6, 2.0, 0.9, 0.8, 0.4, 0.3, 0.3 ] [ 3.1, 1.6, 1.0, 0.5, 0.4, 0.3, 0.3 ] [ 4.4, 2.4, 1.2, 0.6, 0.3, 0.3, 0.3 ] [ 4.0, 3.2, 2.6, 1.3, 1.0, 0.4, 0.3 ] [ 4.0 ] [ 7.2 ] [ 9.8 ] [ 11.1 ] [ 12.1 ] [ 12.5 ] [ 12.8 ] [ 3.6 ] [ 5.6 ] [ 6.5 ] [ 7.3 ] [ 7.7 ] [ 8.0 ] [ 8.3 ] [ 3.1 ] [ 4.7 ] [ 5.7 ] [ 6.2 ] [ 6.6 ] [ 6.9 ] [ 7.2 ] [ 4.4 ] [ 6.8 ] [ 8.0 ] [ 8.6 ] [ 8.9 ] [ 9.2 ] [ 9.5 ] S1S1 Sum statistics S2S2 S3S3 S4S4 S5S5 S6S6 S7S7 Q R1R1 R2R2 … R 10,000 Observed Simulated Observed Simulated 3. Vector of ordered empirical pointwise P-values (-log 10 P) 4. Compute m sum statistics, S k Combined-sum approach

[.045 ] [.023 ] [.015 ] [.012 ] [.007 ] [.007 ] [.007 ] [.065 ] [.055 ] [.050 ] [.045 ] [.045 ] [.045 ] [.047 ] S1S1 Sum statistics S2S2 S3S3 S4S4 S5S5 S6S6 S7S7 Q R1R1 R2R2 … R 10,000 Observed Simulated 5. Determine significance of each S k Combined-sum approach [.150 ] [.120 ] [.101 ] [.101 ] [.102 ] [.102 ] [.104 ] [.035 ] [.030 ] [.025 ] [.020 ] [.020 ] [.021 ] [.025 ] Hoh et al Genome Res 11: Dudbridge & Koeleman 2004 Am J Hum Genet 75: [.045 ] [.023 ] [.015 ] [.012 ] [.007 ] [.007 ] [.007 ] [.065 ] [.055 ] [.050 ] [.045 ] [.045 ] [.045 ] [.047 ] Q R1R1 R2R2 … R 10,000 [.150 ] [.120 ] [.101 ] [.101 ] [.102 ] [.102 ] [.104 ] [.035 ] [.030 ] [.025 ] [.020 ] [.020 ] [.021 ] [.025 ] 6. Identify the S k with smallest P-value and assess its significance Observed Simulated e.g. S 5 (Q) =.007 Global empirical pointwise P =.011

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

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 Simulations

2. Application asthma dataset

Application to asthma 215 sib-pairs (201 families) Measured for 7 asthma traits: Asthma, BHR, Atopy, Dpter, FEV 1, FEV 1 /FVC, IgE 6% markers, 48% , 25% and 21% Information content 0.57 (range ) Significance estimated 1,796,000 (univariate) marker replicates

Application to asthma II Mixture continuous & affection traits Dpter Atopy BHR FEV1 Asthma FEV1/FVC IgE

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

All traits must be analysed with the same marker replicates generated under the null hypothesis of no linkage 1. Developed an efficient approach to test whether the simultaneous linkage of multiple traits to the same marker is spurious Conclusion 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

Acknowledgments Allan McRae Carl Anderson David Smyth