TDT-HET: A new transmission disequilibrium test that incorporates locus heterogeneity into the analysis of family-based association data
1 Department of Genetics and Human Genetics Institute, Rutgers, The State University of New Jersey, 145 Bevier Road, Piscataway, NJ, 08854 USA
2 Department of Statistics & Biostatistics, Hill Center, Rutgers, The State University of New Jersey, 110 Frelinghuysen Road Piscataway, NJ 08854-8019 USA
3 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794-3600 USA
4 Texas Scottish Rite Hospital for Children, 2222 Welborn Street, Dallas, TX 72519 USA
5 Department of Orthopedic Surgery and McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390 USA
BMC Bioinformatics 2012, 13:13 doi:10.1186/1471-2105-13-13Published: 20 January 2012
Locus heterogeneity is one of the most documented phenomena in genetics. To date, relatively little work had been done on the development of methods to address locus heterogeneity in genetic association analysis. Motivated by Zhou and Pan's work, we present a mixture model of linked and unlinked trios and develop a statistical method to estimate the probability that a heterozygous parent transmits the disease allele at a di-allelic locus, and the probability that any trio is in the linked group. The purpose here is the development of a test that extends the classic transmission disequilibrium test (TDT) to one that accounts for locus heterogeneity.
Our simulations suggest that, for sufficiently large sample size (1000 trios) our method has good power to detect association even the proportion of unlinked trios is high (75%). While the median difference (TDT-HET empirical power - TDT empirical power) is approximately 0 for all MOI, there are parameter settings for which the power difference can be substantial. Our multi-locus simulations suggest that our method has good power to detect association as long as the markers are reasonably well-correlated and the genotype relative risk are larger. Results of both single-locus and multi-locus simulations suggest our method maintains the correct type I error rate.
Finally, the TDT-HET statistic shows highly significant p-values for most of the idiopathic scoliosis candidate loci, and for some loci, the estimated proportion of unlinked trios approaches or exceeds 50%, suggesting the presence of locus heterogeneity.
We have developed an extension of the TDT statistic (TDT-HET) that allows for locus heterogeneity among coded trios. Benefits of our method include: estimates of parameters in the presence of heterogeneity, and reasonable power even when the proportion of linked trios is small. Also, we have extended multi-locus methods to TDT-HET and have demonstrated that the empirical power may be high to detect linkage. Last, given that we obtain PPBs, we conjecture that the TDT-HET may be a useful method for correctly identifying linked trios. We anticipate that researchers will find this property increasingly useful as they apply next-generation sequencing data in family based studies.