Alexander Lasiuk, Cassandra Dale; University of Wisconsin-Eau Claire

Advisor: Abra Brisbin

Collaborators: N. Sydney Moïse, Jenifer Cruickshank and Teresa Gunn

Background• Many traits with a genetic basis are correlated (e.g. height and weight).• It is not clear which method is the best way in analyzing for correlated traits. • Cardiac ventricular fibrillation in dogs is a trait composed of four correlated

phenotypes.• We are trying to best analyze a pedigree of dogs with a history of ventricular

fibrillations. Through linkage analysis we hope to identify if these SNP's have any correlation to the disease. We tested two methods, SOLAR, and MultiPhen.

Figure 3: MultiPhen Results

Method 1: SOLAR• SOLAR (Sequential Oligogenic Linkage Analysis Routines) is a software package

that uses linkage analysis, quantitative trait analysis, heritability calculation, and more to perform several kinds of statistical genetic analyses for family-based data. We used it for its ability to calculate based on multiple traits. [4]

Method 2: MultiPhen in R• MultiPhen is a package built into R that performs genetic association testing of SNPs

(one-at-a-time) and multiple phenotypes (separately or in a joint model). It does this testing by ordinal regression where SNPs are treated as the outcome and the multiple phenotypes as predictors. This can have large statistical power increases to detect genotype-phenotype associations over the univariate approach [2]

• Since dogs are dependent, we used the kinship coefficient matrix produced from the package ‘kinship2’’ [3] to account for this dependence.

• We also used singular value decomposition of the kinship coefficient matrix to find V transpose, which we used as the covariates in our analysis.

• We analyzed locations that were previously found be significant [1]

Results: Simulations• From Figure 2, we see that that MultiPhen performed with higher power than SOLAR.

Results: Dog data and MultiPhen• None of the locations tested had a significant association with ventricular fibrillation

(level of significance 0.05), although one location had a p-value of 0.166 (Figure 3).

References• [1] Brisbin A, Cruickshank J, Moïse NS, Gunn T, Bustamante CD, and Mezey JG. (2011) Fast, Exact

Linkage Analysis for Categorical Traits on Arbitrary Pedigree Designs. Genetic Epidemiology, 35(5), 1-10.

• [2] O’Reilly PF, Hoggart CJ, Pomyen Y, Calboli FCF, Elliott P, et al. (2012) MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS. PLoS ONE 7(5): e34861. doi:10.1371/journal.pone.0034861

• [3] Therneau T, Atkinson E, Sinnwell J, Schaid D, McDonnell S. (2014) “Package ‘kinship2’”, CRAN.• [4] Broeckel U, Maresso K, and Martin LJ. (2006) Linkage Analysis for Complex Diseases Using

Variance Component Analysis. Methods in Molecular Medicine, 128, 91-100.

AcknowledgementsThis work was supported by grants from the UW-Eau Claire Office of Research and Sponsored Programs.

Comparison of pedigree-based methods of dogs with cardiac ventricular fibrillation

Figure 1

Figure 1

Figure 1: Pedigree Diagram of German Shepherd dogs affected by ventricular arrhythmias, where shading represents effect level and shape represents parent (squares and triangles). Colored lines connect different representations of the same individual [1].

Conclusion• Based on our simulations, MultiPhen is the preferred method compared to SOLAR in

analyzing genetic data with correlated traits.• Future research:

• Analyze the rest of the locations for the rest of the chromosomes.• Test whether MultiPhen outperforms SOLAR under other simulated pedigree

formats.

ROC Curves from SOLAR (LEFT) and MultiPhen (RIGHT). ROC Curves use information from simulated data and show the false and true positives at different significance levels.

This figure shows the magnitude of significance of the joint model p-values given from MultiPhen of the dog pedigree data at the locations on chromosomes that we analyzed.

Figure 2: ROC Curves