The transmission disequilibrium test and imprinting effects test based on case-parent pairs

Innovative approach to identify multigenomic and environmental interactions associated with birth defects in family-based hybrid designs

Genes, including those with transgenerational effects, work in concert with behavioral, environmental, and social factors via complex biological networks to determine human health. Understanding complex relationships between causal factors underlying human health is an essential step towards deciphering biological mechanisms. We propose a new analytical framework to investigate the interactions between maternal and offspring genetic variants or their surrogate single nucleotide polymorphisms (SNPs) and environmental factors using family-based hybrid study design. The proposed approach can analyze diverse genetic and environmental factors and accommodate samples from a variety of family units, including case/control-parental triads, and case/control-parental dyads, while minimizing potential bias introduced by population admixture. Comprehensive simulations demonstrated that our innovative approach outperformed the log-linear approach, the best available method for case-control family data. The proposed approach had greater statistical power and was capable to unbiasedly estimate the maternal and child genetic effects and the effects of environmental factors, while controlling the Type I error rate against population stratification. Using our newly developed approach, we analyzed the associations between maternal and fetal SNPs and obstructive and conotruncal heart defects, with adjustment for demographic and lifestyle factors and dietary supplements. Fourteen and 11 fetal SNPs were associated with obstructive and conotruncal heart defects, respectively. Twenty-seven and 17 maternal SNPs were associated with obstructive and conotruncal heart defects, respectively. In addition, maternal body mass index was a significant risk factor for obstructive defects. The proposed approach is a powerful tool for interrogating the etiological mechanism underlying complex traits.

Detection of parent-of-origin effects for quantitative traits using general pedigree data

Genomic imprinting is a genetic phenomenon in which certain alleles are differentially expressed in a parent-of-origin-specific manner, and plays an important role in the study of complex traits. For a diallelic marker locus in human, the parentalasymmetry tests Q-PAT(c) with any constant c were developed to detect parent-of-origin effects for quantitative traits. However, these methods can only be applied to deal with nuclear families and thus are not suitable for extended pedigrees. In this study, by making no assumption about the distribution of the quantitative trait, we first propose the pedigree parentalasymmetry tests Q-PPAT(c) with any constant c for quantitative traits to test for parent-of-origin effects based on nuclear families with complete information from general pedigree data, in the presence of association between marker alleles under study and quantitative traits. When there are any genotypes missing in pedigrees, we utilize Monte Carlo (MC) sampling and estimation and develop the Q-MCPPAT(c) statistics to test for parent-of-origin effects. Various simulation studies are conducted to assess the performance of the proposed methods, for different sample sizes, genotype missing rates, degrees of imprinting effects and population models. Simulation results show that the proposed methods control the size well under the null hypothesis of no parent-of-origin effects and Q-PPAT(c) are robust to population stratification. In addition, the power comparison demonstrates that Q-PPAT(c) and Q-MCPPAT(c) for pedigree data are much more powerful than Q-PAT(c) only using two-generation nuclear families selected from extended pedigrees.

A powerful approach for association analysis incorporating imprinting effects

MOTIVATION

For a diallelic marker locus, the transmission disequilibrium test (TDT) is a simple and powerful design for genetic studies. The TDT was originally proposed for use in families with both parents available (complete nuclear families) and has further been extended to 1-TDT for use in families with only one of the parents available (incomplete nuclear families). Currently, the increasing interest of the influence of parental imprinting on heritability indicates the importance of incorporating imprinting effects into the mapping of association variants.

RESULTS

In this article, we extend the TDT-type statistics to incorporate imprinting effects and develop a series of new test statistics in a general two-stage framework for association studies. Our test statistics enjoy the nature of family-based designs that need no assumption of Hardy-Weinberg equilibrium. Also, the proposed methods accommodate complete and incomplete nuclear families with one or more affected children. In the simulation study, we verify the validity of the proposed test statistics under various scenarios, and compare the powers of the proposed statistics with some existing test statistics. It is shown that our methods greatly improve the power for detecting association in the presence of imprinting effects. We further demonstrate the advantage of our methods by the application of the proposed test statistics to a rheumatoid arthritis dataset.

CONTACT

wingfung@hku.hk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

VEGF polymorphisms are associated with endocardial cushion defects: a family-based case-control study

Endocardial cushion defects (ECDs) of the cardiac outflow tract are among the most common congenital heart disease phenotypes. VEGF is essential for endocardial cushion formation and derangements in VEGF synthesis lead to ECD. Three functional single nucleotide polymorphisms (SNPs) in the VEGF gene -2578 C>A, -1154 G>A, and -634 G>C play a role in cardiogenesis. In a Dutch case-control family study of triads, 190 case and 317 control children with both parents, we investigated linkage and association between these VEGF SNPs and ECD. Allele frequencies for the three VEGF SNPs were comparable between ECD children and controls. However, VEGF alleles -2578 C and -1154 G were transmitted more frequently to children with ECD (p = 0.003 and p = 0.002), in particular perimembranous ventricular septal defects (p = 0.012 and p = 0.006). The -2578A/-1154A/-634G haplotype was associated with a reduced risk of ECD (OR 0.7; 95% CI, 0.6-1.0) and was significantly less transmitted to children with ECD (p = 0.002). In a Dutch population, we show that the VEGF 2578 C, -1154 G alleles, and the AAG haplotype are associated with ECD. Possible VEGF gene-environment interactions exposures are discussed.

Designs for linkage analysis and association studies of complex diseases

Genetic linkage analysis has been a traditional means for identifying regions of the genome with large genetic effects that contribute to a disease. Following linkage analysis, association studies are widely pursued to fine-tune regions with significant linkage signals. For complex diseases which often involve function of multi-genetic variants each with small or moderate effect, linkage analysis has little power compared to association studies. In this chapter, we give a brief review of design issues related to linkage analysis and association studies with human genetic data. We introduce methods commonly used for linkage and association studies and compared the relative merits of the family-based and population-based association studies. Compared to candidate gene studies, a genomewide blind searching of disease variant is proving to be a more powerful approach. We briefly review the commonly used two-stage designs in genome-wide association studies. As more and more biological evidences indicate the role of genomic imprinting in disease, identifying imprinted genes becomes critically important. Design and analysis in genetic mapping imprinted genes are introduced in this chapter. Recent efforts in integrating gene expression analysis and genetic mapping, termed expression quantitative trait loci (eQTLs) mapping or genetical genomics analysis, offer new prospect in elucidating the genetic architecture of gene expression. Designs in genetical genomics analysis are also covered in this chapter.

Detection of parent-of-origin effects in complete and incomplete nuclear families with multiple affected children using multiple tightly linked markers

For a diallelic marker locus, the parental-asymmetry test (PAT) based on case-parents trios and its extensions to accommodate incomplete unclear families (1-PAT and C-PAT) are simple and powerful approaches to test for parent-of-origin effects. However, haplotype analysis is generally regarded as advantageous over single-marker analysis in genetic study of common complex diseases. This is mainly due to the fact that complex diseases are often associated with multiple markers. As such, HAP-PAT was constructed to test for parent-of-origin effects in the framework of haplotype analysis. However, its applicability is limited due to the need for complete parental information. In this paper, for nuclear families with only one parent and multiple affected children, we develop HAP-1-PAT to test for parent-of-origin effects using multiple tightly linked markers. We further propose HAP-C-PAT to combine data from families with both parents and those with only one parent. We carry out a simulation study to evaluate the validity and power of the test statistics in various settings, including incomplete family rates, marker/disease-locus linkage disequilibrium patterns, and population models. We perform analysis for all possible combinations of the markers being considered. A permutation-based Monte Carlo procedure is devised to determine the significance of the tests; the corrected global p values taking into account of multiple testing are used for inferences. The results show that HAP-1-PAT and HAP-C-PAT would work well even under the population stratification demographic model and assortative mating demographic model. Furthermore, for the disease models considered, there are significant gains in power from haplotype analysis compared to single-marker analysis, and from combined analysis using HAP-C-PAT compared to analysis using HAP-PAT for the complete family data only.

Detection of parent-of-origin effects based on complete and incomplete nuclear families with multiple affected children

Parent-of-origin effects are important in studying genetic traits. More than 1% of all mammalian genes are believed to show parent-of-origin effects. Some statistical methods may be ineffective or fail to detect linkage or association for a gene with parent-of-origin effects. Based on case-parents trios, the parental-asymmetry test (PAT) is simple and powerful in detecting parent-of-origin effects. However, it is common in practice to collect nuclear families with both parents as well as nuclear families with only one parent. In this paper, when only one parent is available for each family with an arbitrary number of affected children, we firstly develop a new test statistic 1-PAT to test for parent-of-origin effects in the presence of association between an allele at the marker locus under study and a disease gene. Then we extend the PAT to accommodate complete nuclear families each with one or more affected children. Combining families with both parents and families with only one parent, the C-PAT is proposed to detect parent-of-origin effects. The validity of the test statistics is verified by simulation in various scenarios of parameter values. A power study shows that using the additional information from incomplete nuclear families in the analysis greatly improves the power of the tests, compared to that based on only complete nuclear families. Also, utilizing all affected children in each family, the proposed tests have a higher power than when only one affected child from each family is selected. Additional power comparison also demonstrates that the C-PAT is more powerful than a number of other tests for detecting parent-of-origin effects.

Review and evaluation of methods correcting for population stratification with a focus on underlying statistical principles

When two or more populations have been separated by geographic or cultural boundaries for many generations, drift, spontaneous mutations, differential selection pressures and other factors may lead to allele frequency differences among populations. If these 'parental' populations subsequently come together and begin inter-mating, disequilibrium among linked markers may span a greater genetic distance than it typically does among populations under panmixia [see glossary]. This extended disequilibrium can make association studies highly effective and more economical than disequilibrium mapping in panmictic populations since less marker loci are needed to detect regions of the genome that harbor phenotype-influencing loci. However, under some circumstances, this process of intermating (as well as other processes) can produce disequilibrium between pairs of unlinked loci and thus create the possibility of confounding or spurious associations due to this population stratification. Accordingly, researchers are advised to employ valid statistical tests for linkage disequilibrium mapping allowing conduct of genetic association studies that control for such confounding. Many recent papers have addressed this need. We provide a comprehensive review of advances made in recent years in correcting for population stratification and then evaluate and synthesize these methods based on statistical principles such as (1) randomization, (2) conditioning on sufficient statistics, and (3) identifying whether the method is based on testing the genotype-phenotype covariance (conditional upon familial information) and/or testing departures of the marginal distribution from the expected genotypic frequencies.