Detection of gene-environment interactions in joint segregation and linkage analysis

Bayesian Semiparametric Estimation of Cancer-specific Age-at-onset Penetrance with Application to Li-Fraumeni Syndrome

Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration until individuals in a high-risk group develop different cancers, and accommodate family data using family-wise likelihoods. We tackle the ascertainment bias arising when family data are collected through probands in a high-risk population in which disease cases are more likely to be observed. We apply the proposed method to a cohort of 186 families with Li-Fraumeni syndrome identified through probands with sarcoma treated at MD Anderson Cancer Center from 1944 to 1982.

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

Joint effects of germ-line TP53 mutation, MDM2 SNP309, and gender on cancer risk in family studies of Li-Fraumeni syndrome

Li-Fraumeni syndrome (LFS) is a rare familial cancer syndrome characterized by early cancer onset, diverse tumor types, and multiple primary tumors. Germ-line TP53 mutations have been identified in most LFS families. A high-frequency single-nucleotide polymorphism, SNP309 (rs2279744), in MDM2 was recently confirmed to be a modifier of cancer risk in several case-series studies: substantially earlier cancer onset was observed in SNP309 G-allele carriers than in wild-type individuals by 7-16 years. However, cancer risk analyses that jointly account for measured hereditary TP53 mutations and MDM2 SNP309 have not been systematically investigated in familial cases. Here, we determined the combined effects of measured TP53 mutations, MDM2 SNP309, and gender and their interactions simultaneously in LFS families. We used the method that is designed for extended pedigrees and structured for age-specific risk models based on Cox proportional hazards regression. We analyzed the cancer incidence in 19 extended pedigrees with germ-line TP53 mutations ascertained through the clinical LFS phenotype. The dataset consisted of 463 individuals with 129 TP53 mutation carriers. Our analyses showed that the TP53 germ-line mutation and its interaction with gender were strongly associated with familial cancer incidence and that the association between MDM2 SNP309 and increased cancer risk was modest. In contrast with several case-series studies, the interaction between MDM2 SNP309 and TP53 mutation was not statistically significant in our LFS family cohort. Our results showed that SNP309 G-alleles were associated with accelerated tumor formation in both carriers and non-carriers of germ-line TP53 mutations.

Gene--environment-wide association studies: emerging approaches

Despite the yield of recent genome-wide association (GWA) studies, the identified variants explain only a small proportion of the heritability of most complex diseases. This unexplained heritability could be partly due to gene--environment (G×E) interactions or more complex pathways involving multiple genes and exposures. This Review provides a tutorial on the available epidemiological designs and statistical analysis approaches for studying specific G×E interactions and choosing the most appropriate methods. I discuss the approaches that are being developed for studying entire pathways and available techniques for mining interactions in GWA data. I also explore methods for marrying hypothesis-driven pathway-based approaches with 'agnostic' GWA studies.

Effects of measured susceptibility genes on cancer risk in family studies

Numerous family studies have been performed to assess the associations between cancer incidence and genetic and non-genetic risk factors and to quantitatively evaluate the cancer risk attributable to these factors. However, mathematical models that account for a measured hereditary susceptibility gene have not been fully explored in family studies. In this report, we proposed statistical approaches to precisely model a measured susceptibility gene fitted to family data and simultaneously determine the combined effects of individual risk factors and their interactions. Our approaches are structured for age-specific risk models based on Cox proportional hazards regression methods. They are useful for analyses of families and extended pedigrees in which measured risk genotypes are segregated within the family and are robust even when the genotypes are available only in some members of a family. We exemplified these methods by analyzing six extended pedigrees ascertained through soft-tissue sarcoma patients with p53 germ-line mutations. Our analyses showed that germ-line p53 mutations and sex had significant interaction effects on cancer risk. Our proposed methods in family studies are accurate and robust for assessing age-specific cancer risk attributable to a measured hereditary susceptibility gene, providing valuable inferences for genetic counseling and clinical management.

Complex segregation analysis accounting for GxE of bone mineral density in European pedigrees selected through a male proband with low BMD

Osteoporosis is a common multifactorial disorder characterized by low bone mass (BMD) and high susceptibility to low-trauma fractures. Family and twin studies have found a strong genetic component in the determination of BMD, but the mode of inheritance of this trait is not yet fully understood. BMD is a complex trait whose expression is confounded by environmental influences and polygenic inheritance. Detection of potential gene-environment interactions is of great interest in the determination of bone health status. Here we have conducted segregation analyses, using the regressive class D models, in a sample of 100 European pedigrees (NEMO) with 713 subjects (524 measured for phenotypes) identified via a male with low BMD values at either the Lumbar Spine or the Femoral Neck. Segregation analyses were conducted on the residuals of LS-BMD and FN-BMD adjusted for gender, age and BMI. We tested for gene-covariate (GxE) interactions, and investigated the impact of significant GxE interactions on segregation results. Without GxE a major effect was found to be marginally significant in LS-BMD and highly significant in FN-BMD. For both traits the Mendelian hypothesis was rejected. Significant Age x gene and BMI x gene interactions were revealed. Accounting for GxE increased statistical evidence for a major factor in LS-BMD, and improved the fit of the data to the Mendelian transmission model for both traits. The best fitting models suggested a codominant major gene accounting for 45% (LS-BMD) and 44% (FN-BMD) of the adjusted BMDs. However, substantial residual correlations were also found, and these remained highly significant after accounting for the major gene.

Diplotype trend regression analysis of the ADH gene cluster and the ALDH2 gene: multiple significant associations with alcohol dependence

The set of alcohol-metabolizing enzymes has considerable genetic and functional complexity. The relationships between some alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) genes and alcohol dependence (AD) have long been studied in many populations, but not comprehensively. In the present study, we genotyped 16 markers within the ADH gene cluster (including the ADH1A, ADH1B, ADH1C, ADH5, ADH6, and ADH7 genes), 4 markers within the ALDH2 gene, and 38 unlinked ancestry-informative markers in a case-control sample of 801 individuals. Associations between markers and disease were analyzed by a Hardy-Weinberg equilibrium (HWE) test, a conventional case-control comparison, a structured association analysis, and a novel diplotype trend regression (DTR) analysis. Finally, the disease alleles were fine mapped by a Hardy-Weinberg disequilibrium (HWD) measure (J). All markers were found to be in HWE in controls, but some markers showed HWD in cases. Genotypes of many markers were associated with AD. DTR analysis showed that ADH5 genotypes and diplotypes of ADH1A, ADH1B, ADH7, and ALDH2 were associated with AD in European Americans and/or African Americans. The risk-influencing alleles were fine mapped from among the markers studied and were found to coincide with some well-known functional variants. We demonstrated that DTR was more powerful than many other conventional association methods. We also found that several ADH genes and the ALDH2 gene were susceptibility loci for AD, and the associations were best explained by several independent risk genes.

CHRM2 gene predisposes to alcohol dependence, drug dependence and affective disorders: results from an extended case–control structured association study

Cholinergic muscarinic 2 receptor (CHRM2) is implicated in memory and cognition, functions impaired in many neuropsychiatric disorders. Wang et al. [Wang, J.C., Hinrichs, A.L., Stock, H., Budde, J., Allen, R., Bertelsen, S., Kwon, J.M., Wu, W., Dick, D.M., Rice, J. et al. (2004) Evidence of common and specific genetic effects: association of the muscarinic acetylcholine receptor M2 (CHRM2) gene with alcohol dependence and major depressive syndrome. Hum. Mol. Genet., 13, 1903-1911] reported that variation in CHRM2 gene predisposed to alcohol dependence (AD) and major depressive syndrome. We examined the relationships between variation in CHRM2 and AD, drug dependence (DD) and affective disorders, using a novel extended case-control structured association (SA) method. Six markers at CHRM2 and 38 ancestry-informative markers (AIMs) were genotyped in a sample of 871 subjects, including 333 healthy controls [287 European-Americans (EAs) and 46 African-Americans (AAs)] and 538 AD and/or DD subjects (415 with AD and 346 with DD and 382 EAs and 156 AAs). The same CHRM2 markers were genotyped in a sample of 137 EA subjects with affective disorders. All of the six markers were in Hardy-Weinberg equilibrium in controls, but SNP3 (rs1824024) was in Hardy-Weinberg disequilibrium in the AD and DD groups. Using conventional case-control comparisons, some markers were nominally significantly or suggestively associated with phenotypes before or after controlling for population stratification and admixture effects, but these associations were not significant after multiple test correction. However, regression analysis identified specific alleles, genotypes, haplotypes and diplotypes that were significantly associated with risk for each disorder. We conclude that variation in CHRM2 predisposes to AD, DD and affective disorders. One haplotype block within the 5'-UTR of CHRM2 may be more important for the development of these disorders than other regions. Interaction between two specific alleles within this block and interaction between two specific diplotypes covering this block multiplicatively increased risk for AD and DD. Although interaction between these two diplotypes also increased risk for affective disorders, the magnitude of the increased risk was less than the sum of the individual risks. In addition, a specific diplotype might inversely affect risk for AD and DD and risk for affective disorders.

Gene-environment interaction and affected sib pair linkage analysis

OBJECTIVES

Gene-environment (GxE) interaction influences risk for many complex disease traits. However, genome screens using affected sib pair linkage techniques are typically conducted without regard for GxE interaction. We propose a simple extension of the commonly used mean test and evaluate its power for several forms of GxE interaction.

METHODS

We compute expected IBD sharing by sibling exposure profile, that is by whether two sibs are exposed (EE), unexposed (UU), or are discordant for exposure (EU). We describe a simple extension of the mean test, the "mean-interaction" test that utilizes heterogeneity in IBD sharing across EE, EU, and UU sib pairs in a test for linkage.

RESULTS

The mean-interaction test provides greater power than the mean test for detecting linkage in the presence of moderate or strong GxE interaction, typically when the interaction relative risk (R(ge)) exceeds 3 or is less than 1/3. In the presence of strong interaction (R(ge) = 10), the required number of affected sib pairs to achieve 80% power for detecting linkage is approximately 30% higher when the environmental factor is ignored in the mean test, than when it is utilized in the mean-interaction test.

CONCLUSION

Linkage methods that incorporate environmental data and allow for interaction can lead to increased power for localizing a disease gene involved in a GxE interaction.

The role of interacting determinants in the localization of genes

We describe the potential gains in power for localizing disease genes that can be obtained by allowing for interactions with environmental agents or other genes. The focus is on linkage and association methods in nuclear families with dichotomous phenotypes. A logistic model incorporating various main effects and interactions is used for penetrance, but similar methods apply to censored age-at-onset or continuous phenotypes. We begin by discussing the influence of gene-environment interactions in segregation analysis, illustrated with analysis of smoking as a modifying factor for lung cancer. We then discuss a number of approaches to linkage analysis-model-free and model-based(including generalized estimating equations) incorporating interactions with environmental factors and other genes, either candidate genes or linked loci. We find that a test of heterogeneity in IBD sharing probabilities across strata defined by sharing of environmental factors can offer greater power for detecting linkage than the simple mean test, provided the interaction effect is sufficiently strong; we explore the conditions under which this gain in power occurs. Finally, we describe approaches for testing association and disequilibrium involving interactions, utilizing case-control, case-parent, and pedigree-based approaches. A technical problem that must be addressed in many analyses is the effect of missing data on environmental covariates; we use multiple imputation in an analysis of lung cancer segregation to illustrate an approach to this problem.

Detection of interaction involving identified genes: available study designs

Advances in molecular genetic techniques have led to an increased ability to examine gene-environment interactions. Studies to detect gene-environment interactions are motivated by different situations, including 1) most identified cancer genes having associated lifetime risks less than 100% (i.e., incomplete penetrance), 2) hereditary factors that control the metabolism of carcinogens that may modulate risk of disease as hypothesized in pharmacogenetics, and 3) inconsistent associations across studies between a cancer and a suspected risk factor. The above situations and others have led to increased study of interaction between genetic and environmental factors. Less studied so far, but with increased potential for the future, is interaction between identified genes. Gene-gene interaction studies would also be motivated by the situations described above. Approaches to detect gene-environment and gene-gene interactions are reviewed. Available risk estimates, required types of subjects, and feasibility of the proposed study designs are discussed; efficiency and power for interaction assessment are summarized where available. In general, most designs allow for estimating risk associated with a genetic factor, environmental factor, and interaction effect. Although power and efficiency for detecting interactions have been assessed for specific situations in some of the methods, further investigations are needed to define the efficiency spectra of each design.

Bias and efficiency in family-based gene-characterization studies: conditional, prospective, retrospective, and joint likelihoods

We revisit the usual conditional likelihood for stratum-matched case-control studies and consider three alternatives that may be more appropriate for family-based gene-characterization studies: First, the prospective likelihood, that is, Pr(D/G,A second, the retrospective likelihood, Pr(G/D); and third, the ascertainment-corrected joint likelihood, Pr(D,G/A). These likelihoods provide unbiased estimators of genetic relative risk parameters, as well as population allele frequencies and baseline risks. The parameter estimates based on the retrospective likelihood remain unbiased even when the ascertainment scheme cannot be modeled, as long as ascertainment only depends on families' phenotypes. Despite the need to estimate additional parameters, the prospective, retrospective, and joint likelihoods can lead to considerable gains in efficiency, relative to the conditional likelihood, when estimating genetic relative risk. This is true if baseline risks and allele frequencies can be assumed to be homogeneous. In the presence of heterogeneity, however, the parameter estimates assuming homogeneity can be seriously biased. We discuss the extent of this problem and present a mixed models approach for providing consistent parameter estimates when baseline risks and allele frequencies are heterogeneous. The efficiency gains of the mixed-model prospective, retrospective, and joint likelihoods relative to the efficiency of conditional likelihood are small in the situations presented here.

Detecting interactions between gene, site, and environmental variables using GAP

Regressive models that incorporate measured variables and assumed genetic parameters were used to detect interactions between gene, research site, and environmental variables in GAW11 Problem 2. Replicates 1 to 5 were used in the analyses. Significant three-way gene x environment x site interactions were seen for all models, regardless of what assumptions were made about genetic transmission. Therefore, regressive models within each of the four sites were examined for significant gene x environment interactions. At one site, there was a pattern of gene x environment interaction that was consistent in most of the genetic models assumed. Joint and separate segregation and linkage analyses were compared in this site. No patterns of gene x environment interaction were seen in the other sites. Results from this analysis show that regressive modeling can identify complex interactions in data from heterogeneous populations even when ascertainment assumptions are violated.

The Emerging Importance of Genetics in Epidemiologic Research III. Bioinformatics and statistical genetic methods

PURPOSE

To outline potential benefits of integrating recent developments in bioinformatics and statistical genetics with traditional epidemiologic studies to localize genes influencing complex phenotypes and examine genetic effects on disease susceptibility.

METHODS

An overview of bioinformatic and statistical approaches for localizing disease-susceptibility genes as well as challenges associated with identifying functional DNA variants and context-dependent genetic effects concludes this three-part series on the importance of genetics in epidemiologic research.

RESULTS

Rapidly evolving bioinformatic and statistical methods are providing invaluable information on newly-discovered genes and molecular variation influencing human diseases that is readily available to epidemiologic researchers.

CONCLUSIONS

Integrating bioinformatics and molecular biotechnology with epidemiologic methods of assessing disease risk is rapidly expanding our ability to identify genetic influences on complex human diseases. These technological advances are likely to have a profound impact on current knowledge of complex disease etiology, and may reveal novel approaches to disease treatment and prevention.