- Open Access
Detection of imprinting effects for hypertension based on general pedigrees utilizing all affected and unaffected individuals
© Zhang and Lin; licensee BioMed Central Ltd. 2014
- Published: 17 June 2014
Imprinting effects can lead to parent-of-origin patterns in many complex human diseases. For hypertension, previous studies revealed the possible involvement of imprinted genes. Genetic Analysis Workshop 18 real data, with hypertensive phenotype and genotype of more than 1000 individuals from 20 pedigrees, provided us an opportunity to further substantiate such findings. To test for imprinting effects, we developed a pedigree-parental-asymmetry test taking both affected and unaffected offspring into consideration (PPATu). We carried out a simulation study based on the Genetic Analysis Workshop 18 pedigrees to show that PPATu has well-controlled type I error and is indeed more powerful than the pedigree-parental-asymmetry test (PPAT), an existing method that does not utilize information from unaffected offspring. We then applied PPATu to Genetic Analysis Workshop 18 genome-wide association study data from 20 pedigrees. We identified a number of single-nucleotide polymorphisms showing significant imprinting effects that are within genomic regions that have been previously implicated to be associated with hypertension.
- Imprint Gene
- Angelman Syndrome
- Unaffected Individual
- Genetic Analysis Workshop
- Affected Offspring
Genomic imprinting refers to the phenomenon of unequal expression of a heterozygous genotype depending on which parent (father or mother) the imprinted variant is inherited from. It is estimated that approximately 1% of all mammalian genes are imprinted (http://igc.otago.ac.nz). Among these imprinted genes, Beckwith-Wiedemann syndrome, Silver-Russell syndrome, Angelman syndrome, and Prader-Willi syndrome are the best known.
Numerous methods have been proposed to detect imprinting effects. For a diallelic genetic marker locus, the parental-asymmetry test (PAT) that considers imbalance of parental origins of the variant allele is simple and powerful. A series of generalizations of PAT, such as the pedigree-parental-asymmetry test (PPAT) for general pedigree data, widen its practical range . However, these tests use only information on affected offspring and their parents. Recently, PATu  was proposed to take unaffected offspring in a nuclear family into consideration, making fuller use of data to improve power. In this study, we propose a novel parent-of-origin effects test, PPATu, that uses both affected and unaffected offspring in general pedigrees, and apply the method to the Genetic Analysis Workshop 18 (GAW18) data, consisting of 20 large pedigrees, to study the hypertensive phenotype. Previous studies revealed the possible involvement of imprinted genes in hypertension [3, 4]. The GAW18 data thus provide us the opportunity to further substantiate such findings.
Suppose that the marker of interest has 2 alleles, M1 and M2, and the disease allele is more likely to be associated with marker allele M1. Let 0, 1, and 2 represent the marker genotypes M2M2, M1M2, and M1M1, respectively. For a child-parents trio, let F, M, and C denote the marker genotypes of the father, mother, and child, respectively. Throughout this article, mating symmetry is assumed; that is, P(F=f, M=m)=P(F=m, M=f) for all f, m = 0,1,2. We also assume that there is no maternal effect; that is, the maternal genotype does not confer additional risk on the child's phenotype.
Suppose we have N independent pedigrees, and for the ith pedigree, we have nu i unaffected and na i affected offspring. Define
The standardized test statistic follows the N(0, 1) distribution asymptotically. When there is maternal imprinting effect, PPATu will be positive; when there is paternal imprinting effect, it will be negative. Note that the contributions from trios in a pedigree are not independent, and their correlations are accounted for in the variance. In our simulation study and application below, we compare the performance of PPATu with PPAT, whose statistic is defined without the negative terms in the S statistic; that is, without utilizing information on trios with unaffected offspring. More specifically,
Combinations of 9 imprinting settings and 9 no-imprinting settings
GAW18 data analysis
We consider GAW18 real genome-wide association studies pedigree data that contain a total of 472,049 single-nucleotide polymorphism (SNP) genotypes on odd chromosomes and phenotype data, including systolic and diastolic blood pressure (SBP and DBP). In our study, we use a hypertensive binary phenotype; an individual is classified as affected if SBP > 140 mm Hg, or DBP > 90 mm Hg, or on antihypertensive medication at the first examination. There are 20 pedigrees; the sizes range from 27 to 107 individuals. In total, there are only 157 affected offspring, while there are 709 unaffected ones. Hence, based on the experience gained in our simulation, we expect a substantial gain in information for PPATu that makes use of information from both affected and unaffected individuals when compared to PPAT. To reduce the effect of multiple testing, we first used pedigree disequilibrium test (PDT ) to identify SNPs that are associated with hypertension at the 0.05 level, and then performed imprinting effect tests, focusing only on those SNPs. In our analysis, all trios with complete data within each pedigree were included in computing the test statistic. Furthermore, although many tests were performed, we did not attempt to correct for multiple testing given the small sample size (a total of only 20 pedigrees).
Cross-classification of results based on p values fromassociation(PDT) andimprinting (PPAT and PPATu) tests
SNPs having p values <0.05 for both association and imprinting tests
In this article, we propose an imprinting test that utilizes both affected and unaffected individuals from general pedigrees, the type of data provided by GAW18. We expect PPATu to be more powerful than the existing test PPAT  because the former makes full use of information by taking unaffected offspring into consideration. Indeed, our simulation study shows that PPATu has higher power than PPAT without an elevated type I error rate based on the GAW18 pedigrees. Our results from analysis of the GAW18 data using PPATu leads to the identification of a number of SNPs that are within genomic regions previously implicated for the hypertensive phenotype. Nevertheless, further investigation is warranted especially to evaluate the performance of the methods under different study designs and ascertainment criteria.
The authors would like to acknowledge the NIH grant that supports GAWs and the GAW18 data providers. This work was supported in part by NSF grant DMS 1208928.
The GAW18 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. The Genetic Analysis Workshop is supported by NIH grant R01 GM031575.
This article has been published as part of BMC Proceedings Volume 8 Supplement 1, 2014: Genetic Analysis Workshop 18. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcproc/supplements/8/S1. Publication charges for this supplement were funded by the Texas Biomedical Research Institute.
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