Generalized disequilibrium test for association in qualitative traits incorporating imprinting effects based on extended pedigrees

Abstract

Background: For dichotomous traits, the generalized disequilibrium test with the moment estimate of the variance (GDT-ME) is a powerful family-based association method. Genomic imprinting is an important epigenetic phenomenon and currently, there has been increasing interest of incorporating imprinting to improve the test power of association analysis. However, GDT-ME does not take imprinting effects into account, and it has not been investigated whether it can be used for association analysis when the effects indeed exist.

Results: In this article, based on a novel decomposition of the genotype score according to the paternal or maternal source of the allele, we propose the generalized disequilibrium test with imprinting (GDTI) for complete pedigrees without any missing genotypes. Then, we extend GDTI and GDT-ME to accommodate incomplete pedigrees with some pedigrees having missing genotypes, by using a Monte Carlo (MC) sampling and estimation scheme to infer missing genotypes given available genotypes in each pedigree, denoted by MCGDTI and MCGDT-ME, respectively. The proposed GDTI and MCGDTI methods evaluate the differences of the paternal as well as maternal allele scores for all discordant relative pairs in a pedigree, including beyond first-degree relative pairs. Advantages of the proposed GDTI and MCGDTI test statistics over existing methods are demonstrated by simulation studies under various simulation settings and by application to the rheumatoid arthritis dataset. Simulation results show that the proposed tests control the size well under the null hypothesis of no association, and outperform the existing methods under various imprinting effect models. The existing GDT-ME and the proposed MCGDT-ME can be used to test for association even when imprinting effects exist. For the application to the rheumatoid arthritis data, compared to the existing methods, MCGDTI identifies more loci statistically significantly associated with the disease.

Conclusions: Under complete and incomplete imprinting effect models, our proposed GDTI and MCGDTI methods, by considering the information on imprinting effects and all discordant relative pairs within each pedigree, outperform all the existing test statistics and MCGDTI can recapture much of the missing information. Therefore, MCGDTI is recommended in practice.

Keywords: Generalized disequilibrium test; Genomic imprinting; Monte Carlo sampling; Qualitative trait.

Fig. 1

Pedigree structures for the simulation studies. a Two-generation family. b Three-generation pedigree. c Four-generation pedigree. Genotypes of individual 1 in two-generation family, individuals 1, 4 and 5 in three-generation pedigree and individuals 1 and 3 in four-generation pedigree are assumed to be missing for the analysis based on incomplete data

Fig. 2

Simulated powers of all the test statistics. The test statistics are T1: GDTI, T2: MCGDTI_T, T3: MCGDTI_E, T4: MCGDT-ME_T, T5: MCGDT-ME_E, T6: GDT-ME, T7: GDT, T8: MCPDTI_T and T9: MCPDTI_E. The simulations are conducted under complete imprinting effect model at 1% significance level based on 10,000 replicates for 150 pedigrees when LD = 0.092,5, 0.142,5, and 0.157,5, and RR = 1.500, 1.833 and 2.182, respectively. The first 5 statistics are proposed tests, while the remaining 4 are existing tests. a LD = 0.092,5 and RR = 1.500; b LD = 0.142,5 and RR = 1.500; c LD = 0.157,5 and RR = 1.500; d LD = 0.092,5 and RR = 1.833; e LD = 0.142,5 and RR = 1.833; f LD = 0.157,5 and RR = 1.833; g LD= 0.092,5 and RR = 2.182; h LD = 0.142,5 and RR = 2.182; i LD = 0.157,5 and RR = 2.182

Fig. 3

Simulated powers of all the test statistics. The test statistics are T1: GDTI, T2: MCGDTI_T, T3: MCGDTI_E, T4: MCGDT-ME_T, T5: MCGDT-ME_E, T6: GDT-ME, T7: GDT, T8: MCPDTI_T and T9: MCPDTI_E. The simulations are conducted under incomplete imprinting effect model at 1% significance level based on 10,000 replicates for 150 pedigrees when LD = 0.092,5, 0.142,5, and 0.157,5, and RR = 1.500, 1.833 and 2.182, respectively. The first 5 statistics are proposed tests, while the remaining 4 are existing tests. a LD = 0.092,5 and RR = 1.500; b LD = 0.142,5 and RR = 1.500; c LD = 0.157,5 and RR = 1.500; d LD = 0.092,5 and RR = 1.833; e LD = 0.142,5 and RR = 1.833; f LD = 0.157,5 and RR = 1.833; g LD = 0.092,5 and RR = 2.182; h LD = 0.142,5 and RR = 2.182; i LD = 0.157,5 and RR = 2.182

Fig. 4

Simulated powers of all the test statistics. The test statistics are T1: GDTI, T2: MCGDTI_T, T3: MCGDTI_E, T4: MCGDT-ME_T, T5: MCGDT-ME_E, T6: GDT-ME, T7: GDT, T8: MCPDTI_T and T9: MCPDTI_E. The simulations are conducted under no imprinting effect model at 1% significance level based on 10,000 replicates for 150 pedigrees when LD = 0.092,5, 0.142,5, and 0.157,5, and RR = 1.500, 1.833 and 2.182, respectively. The first 5 statistics are proposed tests, while the remaining 4 are existing tests. a LD = 0.092,5 and RR = 1.500; b LD = 0.142,5 and RR = 1.500; c LD = 0.157,5 and RR = 1.500; d LD = 0.092,5 and RR = 1.833; e LD = 0.142,5 and RR = 1.833; f LD = 0.157,5 and RR = 1.833; g LD = 0.092,5 and RR = 2.182; h LD = 0.142,5 and RR = 2.182; i LD = 0.157,5 and RR = 2.182