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Using a recently developed semiparametric method for combined linkage/linkage-disequilibrium analysis, we analyzed the Collaborative Study on the Genetics of Alcoholism data subset developed for Genetic Analysis Workshop 11 (GAW11). This semiparametric approach estimates recombination fractions for linkage, marker log odds ratios for linkage-disequilibrium, their product for combined linkage/linkage-disequilibrium, and corresponding z-scores. We used two outcomes: alcohol dependence and "alcoholism-free" and a genome-wide significance level of 4.1 (which corresponds to a genome-wide lod score of 3.6). For the alcohol dependence outcome, we observed significant linkage signals at D1S1588-D1S1631, D1S547, D2S399, D2S425, D4S2361, D7S1796, and D7S1824. We also found significant linkage-disequilibrium signals at D1S547 and D7S1795. For the "alcoholism-free" outcome, we found significant linkage signals at D4S2457, D41651 (both flank ADH3), D11S2359, and D16S47 and significant linkage-disequilibrium signals at D4S2361, FABP2, D11S2359, D19S431 and D19S47-D19S198-D19S601.

A genome-wide scan of a simulated data set for fictitious disease genes was conducted using both semiparametric and nonparametric methods. The semiparametric model-based method, which tests for linkage/linkage disequilibrium separately and together, correctly identified all three underlying disease loci along with two false positives through the linkage analysis. However, the nonparametric model-free method which tests combined linkage/linkage disequilibrium, failed to yield any results due to the lack of linkage disequilibrium information in the data.

Decreased age at onset in successive generations has been observed for a number of diseases. Two nonparametric matched and unmatched test statistics are proposed, taking into account not only current age or age at death for unaffected individuals and age at disease onset for affected individuals, but also possible correlations among family members. Both are asymptotically normal with readily estimated variances from the data. A simulation study is conducted to compare the proposed tests with the commonly used paired t-test and log-rank test. It has been shown that the proposed test statistics yield valid conclusions in assessing genetic anticipation under all situations considered. However, the paired t-test is valid only when the censoring distributions are comparable between two generations, whereas the log-rank test is valid when the correlation among family members is weak. As expected, the matched test is most powerful when the data are heterogeneous, and the unmatched and the log-rank tests are most powerful when the data are homogeneous and the correlation is weak. Lastly, a population-based family study of breast cancer conducted at the Fred Hutchinson Cancer Research Center is used for illustration of the proposed and the log-rank tests. The preliminary analysis suggests that there appears a decreased age at onset over the successive generations in breast cancer.

Most complex traits such as cancer and coronary heart diseases are attributed either to heritable factors or to environmental factors or to both. Dissecting the genetic and environmental etiology of complex traits thus requires an interdisciplinary research strategy. Genetic studies generally involve families and investigate familial aggregations of traits, segregation of major disease genes, and locations of disease genes on the human genome, the latter of which can be identified via linkage analysis. Epidemiologic studies often use population-based case-control studies to establish the role of specific environmental factors. Integrating both objectives, genetic epidemiology is to assess the associations of environmental factors with disease status, to quantify the aggregation of cases within families, to characterize putative disease genes via segregation analysis, and to localize disease genes via linkage analysis with genetic markers. To accomplish these objectives through designed studies, we propose a class of population-based family study designs, which are formed by choosing among sampling designs at three stages. The objectives of sampling at these three stages are 1) combined aggregation and association analysis, 2) combined segregation, aggregation, and association analysis, and 3) combined linkage, segregation, aggregation, and association analysis. These designs form an interdisciplinary research framework for genetic epidemiology. Our preliminary exploration of this framework and related analytic methods indicates that population-based family study designs retain the efficiency of linkage analysis for localizing disease genes without losing the property of being population-based, and they will therefore allow an assessment of a joint contribution of genetic and environmental factors to complex traits.

The genetic epidemiologic study of coronary heart disease conducted by a group in the Donner Laboratory used survey and ad hoc sampling plans, resulting in a random and an ad hoc sample of families, respectively. These two samples were used to evaluate the ad hoc sampling plan used in that study. Following the description of data collection, we adjusted for ascertainment, but still found that the aggregation patterns of many lipoproteins in the ad hoc sample were different from those in the random sample.

This paper describes an analytical method that is used to assess patterns of disease aggregation within family based on family history information collected in case-control studies. In such a study, cases and controls are thought of as probands whose relatives are identified, and relatives' phenotypes and other covariates such as age, sex, and genealogical relationship with the probands are recorded. By modeling the dependence of relatives' phenotypes on case-control status and other covariates, this method yields adjusted odds ratios that quantify familial aggregation. The estimated standard errors are obtained for statistical inference since the method acknowledges the potential correlations between relatives' phenotypes by using the estimating equations technique. In population-based case-control studies, the estimates and statistical inferences are generalizable to the general population. To illustrate this method, we analyzed a case-control study of colorectal cancer involving 5,190 relatives of 792 cases and 4,478 relatives of 680 population-based controls conducted in Hawaii. Although detailed results will be presented elsewhere, the colorectal cancer was found to aggregate within family with an odds ratio of 2.74 (95% confidence interval (CI): 1.78-4.21). Among parents, the odds ratio for familial aggregation was 2.38 (95% CI: 1.25-4.54). The corresponding value for siblings was 3.09 (95% CI: 1.87-5.11). It was also found that the odds ratio increases from about 2.00 for relatives of the probands who were 50 years or older to 7.66 and 12.84 for relatives of the probands who were between 40 and 50 years and under 40 years, respectively, suggesting that the familial aggregation of colorectal cancer decreases as probands' age increases.