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Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1-1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene-environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a "Gene-Environment Think Tank" on January 10-11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors.

For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single-nucleotide polymorphisms (SNPs) associated with clinical outcomes. The primary clinical endpoint for many of these studies are time-to-event outcomes such as time of death or disease progression which are subject to censoring mechanisms. The Cox score test can be readily employed to test the association between a SNP and the outcome of interest. In addition to the effect and sample size, and censoring distribution, the power of the test will depend on the underlying genetic risk model and the distribution of the risk allele. We propose a rigorous account for power and sample size calculations under a variety of genetic risk models without resorting to the commonly used contiguous alternative assumption. Practical advice along with an open-source software package to design SNP association studies with survival outcomes are provided.

Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.

Numerous studies have shown recessive major gene inheritance of body mass index (BMI) in white populations; few have examined the inheritance of BMI in the African-American population where obesity is more prevalent, nor in African populations where obesity is comparatively rare. To evaluate the inheritance of obesity in two different populations of African origin, we used segregation analysis to determine the transmission of BMI in 95 African-American families and 400 Nigerian families. Probands were selected from participants in the population-based International Collaborative Study on Hypertension in Blacks. Using class D regressive models, results from the segregation analysis of the African-American data showed evidence of a major gene effect on BMI. The Nigerian results were strikingly similar, with comparable estimates for the genotype frequencies and means and strong evidence for a major effect in the transmission of BMI. The high BMI allele frequency estimate of 24% is consistent with estimates in other studies, but the mode of transmission appeared codominant, which differs from studies involving predominantly white populations. In the Nigerian analysis, however, the probability of a high BMI homozygote parent transmitting a low BMI allele to his/her offspring was significantly different from the Mendelian expectation of zero (estimated τBB = 0.45), suggesting that additional complexities exist in the major gene inheritance of BMI in this population. The strong similarity of the genotype frequencies and means obtained from the African-American and Nigerian samples suggests that a common codominant major gene effect may contribute to the variation in BMI in both populations. Genet. Epidemiol. 18:360–376, 2000. © 2000 Wiley-Liss, Inc.

Advances in high throughput technology have enabled the generation of unprecedented amounts of genomic data (e.g., next generation sequence data, transcriptomics, metabolomics, and proteomics), which promises to unravel the genetic architecture of complex traits. These discoveries may lead to novel therapeutic targets, guide disease prevention, and enable personalized medicine. However, the pace of data generation surpasses the ability to process and analyze the vast amounts of data. For example, in a typical study of transcription regulation, the relationship between more than 1 million genetic variants and ten thousand transcript levels are explored, requiring tens of billions of tests. In order to address this problem, we propose a fast, accurate, and robust method that can assess the significance of associations between quantitative phenotypes and genotypes. The method is an extension of the allelic test commonly used in case-control studies for the analysis of quantitative traits. We show the asymptotic equivalence of the proposed test to linear regression results. We also reduce a generalized linear regression problem to the comparison of two groups, which can handle non-normal and survival time phenotypes.

When planning resequencing studies for complex diseases, previous association and linkage studies can constrain the range of plausible genetic models for a given locus. Here, we explore the combinations of causal risk allele frequency (RAFC) and genotype relative risk (GRRC) consistent with no or limited evidence for affected sibling pair (ASP) linkage and strong evidence for case-control association. We find that significant evidence for case-control association combined with no or moderate evidence for ASP linkage can define a lower bound for the plausible RAFC. Using data from large type 2 diabetes (T2D) linkage and genome-wide association study meta-analyses, we find that under reasonable model assumptions, 23 of 36 autosomal T2D risk loci are unlikely to be due to causal variants with combined RAFC < 0.005, and four of the 23 are unlikely to be due to causal variants with combined RAFC < 0.05. Genet. Epidemiol. 00:1‐9, 2012. C � 2012 Wiley Periodicals, Inc.

When planning resequencing studies for complex diseases, previous association and linkage studies can constrain the range of plausible genetic models for a given locus. Here, we explore the combinations of causal risk allele frequency (RAFC ) and genotype relative risk (GRRC ) consistent with no or limited evidence for affected sibling pair (ASP) linkage and strong evidence for case-control association. We find that significant evidence for case-control association combined with no or moderate evidence for ASP linkage can define a lower bound for the plausible RAFC . Using data from large type 2 diabetes (T2D) linkage and genome-wide association study meta-analyses, we find that under reasonable model assumptions, 23 of 36 autosomal T2D risk loci are unlikely to be due to causal variants with combined RAFC0.005, and four of the 23 are unlikely to be due to causal variants with combined RAFC0.05.

A novel Monte Carlo method for linkage analyses involving large pedigrees and many polymorphic loci is introduced. Issues related to the efficiency of the method are discussed

A genome-wide correlation analysis and cluster analysis were utilized to determine chromosomal regions that had similar nonparametric linkage scores across families in order to locate interacting susceptibility loci for asthma. Conditional analysis was performed to detect any increase in lod score over baseline. Eight of the strongest 5% of the correlations in the German and CSGA asthma data sets occurred in both data sets. The strongest positive correlations found in both data sets were between the 200 cM region on chromosome 2 with chromosome 12 at 90-120 cM (r = 0.26) and also with chromosome 6 at 40-70 cM (r = 0.24). While the cluster analysis did not find any regions that clustered across data sets, this method did detect clustering in regions that have been previously linked to asthma.

Variance components models were used to analyze total IgE levels in families ascertained though the Collaborative Study of the Genetics of Asthma (CSGA) using a genome-wide array of polymorphic markers. While IgE levels are known to be associated with clinical asthma and recognized to be under strong genetic control (here the heritability was estimated at 44-60% in the three racial groups), specific genes influencing this trait are still largely unknown. Multipoint analysis of 323 markers yielded little indication of specific regions containing a trait locus controlling total serum IgE levels (adjusted for age and gender). Although a number of regions showed LOD statistics above 1.5 in Caucasian families (chromosome 4) and in African-American families (chromosomes 2 and 4), none yielded consistent evidence in all three racial groups. Analysis of total IgE adjusted for gender, age and Allergy Index (a quantitative score of skin test sensitivity to 14 common aeroallergens) was conducted on these data. In this analysis, a much stronger signal for a trait locus controlling adjusted log[total IgE] was seen on the telomeric end of chromosome 18, but only in Caucasian families. This region accounted for most of the genetic variation in log[total IgE], and may represent a quantitative trait locus for IgE levels independent of atopic response. Oligogenic analysis accounting simultaneously for the contribution of this locus on chromosome 18 and other chromosomal regions showing some evidence of linkage in these Caucasian families (on chromosomes 2, 4 and 20) failed to yield significant evidence for interaction.

Genetic Analysis Workshop II Problems 2 and 3 were analyzed using the segregation analysis program, POINTER and the linkage analysis program LIPED. Results of the segregation analyses were acceptable with respect to both parameter estimation and hypothesis testing. Results of the linkage analyses were also good. Although it was noted that the linkage and population association data were sometimes compatible with more than one hypothesis, the correct relationships among the trait and marker loci were generally among those found compatible with the data.

A study was designed to address the relative merits of different sampling strategies for detecting linkage. Genotypes of pedigree members were generated by the use of a single genetic model, and the pedigrees were subdivided into dominant-appearing, recessive-appearing, and "interesting" subsets. An investigator blind to how the data had been generated applied two different selection rules to determine which individuals in each pedigree would be "typed" for linkage analysis. Linkage analyses were then conducted on these pedigree subsets, as well as on the combined data, by the use of three autosomal dominant models, three autosomal recessive models, and the generating (i.e., "true") model. Results suggest (1) that linkage is likely to be detected even in the absence of knowledge of the mode of transmission, if a range of models can be examined; (2) that false evidence for heterogeneity will not necessarily result when pedigrees are selected according to apparent mode of transmission for analysis; (3) that recessive-appearing pedigrees (i.e., those with multiplex sibships) may be particularly useful for detecting linkage; and (4) that including information on unaffected second-degree relatives adds little to linkage studies of affected individuals and their first-degree relatives.

Simulated multigenerational pedigrees were analyzed using the program GENPED and POINTER to examine the 1) limits of segregation analysis for detecting single locus, two-allele transmission of a dichotomous trait and 2) accuracy of the parameter estimates. Ten data sets of 30 pedigrees each (approximately 25 persons per pedigree) were simulated. The genotypic penetrance values were varied but the population prevalence of the trait was kept constant at 2%. For some data sets a linked marker locus was also simulated. Previous results had shown that a single major locus could be easily detected when the heterozygote penetrance (f1) was high or midway between the two homozygote penetrances. In this study, we found a single major locus could not be consistently detected by either method of segregation analysis when f1 was "low" to "intermediate." Accuracy of the parameter estimates depended on assumptions about the population prevalence. In those cases where the major locus could not be detected by segregation analysis, linkage to a marker locus could be detected as long as the marker was closely linked and there were not phenocopies in the population. Owing to the limited number of simulations in this study, we cannot generalize these findings. However, they provide a basis for further testing of methods of segregation analysis when factors such as the parameter values, family structure, and ascertainment scheme are varied.

Sulfate conjugation is an important pathway in the biotransformation of drugs and neurotransmitters. The thermolabile (TL) form of the enzyme phenol sulfotransferase (PST) catalyzes the sulfation of catecholamine neurotransmitters and drugs such as methyldopa and acetaminophen. Platelet TL PST activity was measured in blood samples from 232 individuals in 49 nuclear families. Correlations ranged from 0.43 to 0.45 for parent-offspring pairs and from 0.44 to 0.47 for siblings. Mother-father correlations were not significantly different from zero. Although evidence was not unequivocal, both segregation and commingling analyses provided some support for a major gene influence on TL PST activity, with other variation due to polygenic background. In both sets of analyses, however, support for a major gene hypothesis depended upon skewness in the TL PST activity distribution. A polygenic model with high heritability (0.77) was most strongly supported with the log transformed data. These results confirm and extend a previous report of high heritability of TL PST based on a study of twins. In addition, our results raise the possibility of a major gene effect on this important catecholamine- and drug-metabolizing enzyme--a possibility that can now be evaluated using biochemical techniques.

Although stuttering is known to be a familial disorder, no clear evidence regarding precise mode of transmission has arisen from previous research. In this report segregation analysis is applied to data on 386 stuttering probands and their first-degree relatives in an effort to discriminate among possible genetic models for the transmission of stuttering. Two different segregation analysis programs, PAP and POINTER, gave comparable results with respect to both hypothesis testing and parameter estimation. Specifically, the transmission of stuttering observed in these families cannot be adequately explained by a Mendelian major locus. The hypothesis of no polygenic component in the transmission of stuttering can, however, be rejected. Existence in these data of potential heterogeneity and possible violations of assumptions concerning ascertainment are considered in interpreting the results.

The association between insulin-dependent diabetes mellitus (IDDM) and an allele of a restriction fragment length polymorphism (RFLP) 5' to the coding region of the insulin gene has raised the possibility that variation in the vicinity of the insulin gene confers susceptibility to IDDM. To test this hypothesis, the distribution of insulin gene sharing in affected sib pairs (ASPs) from the Genetic Analysis Workshop 5 (GAW5) families has been compared with that expected on the basis of random assortment. There is no deviation from random expectation in insulin gene sharing among 95 ASPs from families fully informative for the insulin gene. This is also true when insulin gene sharing is conditioned on HLA sharing, on the particular HLA DR types in ASPs, or on the parents' insulin allele classes. These results thus provide no evidence that variation at or near the insulin gene confers susceptibility to IDDM. However, we also used computer simulation to investigate how the insulin gene region could contribute susceptibility to IDDM without yielding evidence for distortion in insulin gene sharing in a sample comparable to that of GAW5. We found that various levels of insulin gene involvement in IDDM could generate a population association between the insulin gene RFLP and IDDM comparable to that reported in the literature, without producing significant distortion in insulin gene sharing of ASPs.

Two-point linkage analyses yielded correct identification of linkage groups and good estimates of recombination frequencies in both males and females. Additional analyses compared various family structures for the total number of typed individuals necessary to detect linkage. Results suggest that for codominant markers with moderate recombination frequency, pedigrees may not provide substantial savings over nuclear families.