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Many variants with low frequencies or with low to modest effects likely remain unidentified in genome-wide association studies (GWAS) because of stringent genome-wide thresholds for detection. To improve the power of detection, variant prioritization based on their functional annotations and epigenetic landmarks has been used successfully. Here, we propose a novel method of prioritization of a GWAS by exploiting gene-level knowledge (e.g., annotations to pathways and ontologies) and show that it further improves power. Often, disease associated variants are found near genes that are coinvolved in specific biological pathways relevant to disease process. Utilization of this knowledge to conduct a prioritized scan increases the power to detect loci that map to genes clustered in a few specific pathways. We have developed a computationally scalable framework based on penalized logistic regression (termed GKnowMTest-Genomic Knowledge-guided Multiplte Testing) to enable a prioritized pathway-guided GWAS scan with a very large number of gene-level annotations. We demonstrate that the proposed strategy improves overall power and maintains the Type 1 error globally. Our method works on genome-wide summary level data and a user-specified list of pathways (e.g., those extracted from large pathway databases without reference to biology of a specific disease). It automatically reweights the input p values by incorporating the pathway enrichments as "adaptively learned" from the data using a cross-validation technique to avoid overfitting. We used whole-genome simulations and some publicly available GWAS data sets to illustrate the application of our method. The GKnowMTest framework has been implemented as a user-friendly open-source R package.

Mapping a locus controlling a quantitative genetic trait (e.g., blood pressure) to a specific genomic region is of considerable interest. Data on the quantitative trait under consideration and several codominant genetic markers with known genomic locations are collected from members of families and statistically analyzed to draw inferences on the genomic position of the trait locus. The vector of parameters of interest comprises the pairwise recombination fractions, θ, between the putative quantitative trait locus and the marker loci. One of the major complications in estimating θ for a quantitative trait in humans is the lack of haplotype information on members of families. The purpose of this study was to devise a computationally simple and efficient method of estimation of θ in the absence of haplotype information. We have proposed a two-stage estimation procedure using the expectation-maximization (EM) algorithm. In the first stage, parameters of the QTL are estimated based on data of a sample of unrelated individuals. From estimates thus obtained, we have used a Bayes' rule to infer QTL genotypes of parents in families. Finally, in the second stage of the procedure, we have proposed an EM algorithm for obtaining the maximum likelihood estimate of θ based on data of informative families (which are identified upon inferring parental QTL genotypes performed in the first stage). We have shown, using simulated data, that the proposed procedure is cost-effective, computationally simple, and statistically efficient. As expected, analysis of data on multiple markers jointly is more efficient than the analysis based on single markers. Genet. Epidemiol. 19:97–126, 2000. © 2000 Wiley-Liss, Inc.

The International Genetic Epidemiology Society (IGES) has examined the charges against James V. Neel and his colleagues contained in the recently published book by Patrick Tierney entitled Darkness in El Dorado: How Scientists and Journalists Devastated the Amazon (W.W. Norton, 2000). The book implicates Neel in causing or promoting an epidemic of measles among the Yanomamo Indians of Venezuela in 1968 leading to "hundreds if not thousands" of deaths by using a "dinosaur" vaccine (Edmonston B) as a deliberate "experiment" to test his "eugenic" theories. Tierney also attempts to link this research, funded by the Atomic Energy Commission (AEC), with a broader tapestry of human radiation experiments. To investigate these serious charges, the IGES undertook a thorough examination of most source documents referenced in Tierney's book, Neel's field logs, notes, first-hand reports, contemporary writings, film sound tracks, etc., and conducted interviews with many relevant persons. The IGES finds that these allegations are false. Neel was not a eugenicist and was in fact highly critical of both the scientific basis of eugenics and its coercive social policies. In this regard, Tierney has grossly misrepresented Neel's views on a wide range of social implications of modern civilization for the long-term health of the gene pool. Far from causing an epidemic of measles, Neel did his utmost to protect the Yanomamo from the ravages of the impending epidemic by a vaccination program using a vaccine that was widely used at the time and administered in an appropriate manner. There was nothing experimental about the vaccination program, which in fact severely hindered the primary scientific objectives of the expedition. Although the research was funded in large part by the AEC, there was no element of radiation research and the work had no connection with the ethical abuses that have been reported from AEC-sponsored radiation research, such as studies of heavy isotopes. Neel's seminal contributions to a broad range of topics in human genetics have been extensively chronicled elsewhere. His research on the Yanomamo in particular has provided unique insights into the evolutionary biology of our species, the role of sociocultural practices, such as kinship relationships and selective pressures in shaping the genetic diversity of primitive population isolates, as well as the general picture of health in such populations. The IGES decries the damage done to the reputation of one of its founders and its first President and the misperception this book may have caused about the conduct of research in genetic epidemiology. Ethical issues about scientific research in primitive populations deserve serious and wide discussion, but the IGES condemns the gross misrepresentation of the facts and demonization of the principal characters in this book.

When nonrandom segregation of marker haplotypes from parents to offspring is detected, leading to an increased parental haplotype sharing by affected offspring, an association between the disease and the marker loci is often inferred. In this paper, we provide the uniformly most powerful test for testing nonrandom segregation, and compare the power of this test with another test that is available in the literature. Other statistical properties of the two tests are also discussed. Further, since nonrandom segregation can result from linkage of the disease and marker loci, when the hypothesis of random segregation is rejected, it is of interest to estimate the underlying parameter assuming linkage. We provide an estimation procedure.

An epidemiological profile of vitiligo in Calcutta is presented. Prevalence data were gathered from 15,685 individuals drawn from the general population; pedigree data were collected through 293 vitiligo patients. The overall prevalence of vitiligo is about 5 per 1,000 individuals. There are no significant sex or age differences in prevalence rates. About a 4.5-fold increase in prevalence is observed among close biological relatives of affected individuals. There is, however, no clearcut correspondence between relative risks and kinship coefficients. There are no significant differences in the frequencies of various types of vitiligo between probands with and without positive family history. The overall mean and modal ages of onset are about 22 years and 15 years, respectively. The mean ages among males (24.8 years) and females (19.3 years) are significantly different.

It is often of interest to know whether there is increased occurrence of a trait in a pedigree or other structured set of epidemiological data. In answering such questions most current methods use aggregate measures, such as relative risk, that may not relate the outcome for each individual to that individual's risk. In this paper we present a simple method, and its computational algorithm, to overcome this limitation. This new method also permits one to identify high-risk families or subsets of a collection of data, which is not always possible using other approaches. In a study of cancer risk among relatives of retinoblastoma patients, by applying this new method it was found that 11 of 33 families each obtained through a unilateral retinoblastoma patient are at statistically high risk of cancer at all sites combined, while there are 15 of 47 such families obtained through a bilaterally affected proband. These results are unlikely to have occured by chance, indicating an overall excess risk in the ancestors of these retinoblastoma cases. The proposed test procedure does not specify the cause of elevated risk; however, a method is proposed that provides some indication regarding possible causal mechanisms under some circumstances.

For an autosomal recessive disease, a statistical procedure is developed for detecting nonrandom segregation of marker haplotypes from an unaffected parent to affected children, specifically for the case when the alternative hypothesis of linkage between the disease and marker loci is postulated. The test procedure is locally most powerful and, depending on family size and number of families sampled, any one of the three test statistics proposed can be used. An application of this procedure provides evidence of linkage between tuberculoid leprosy and HLA.

Data on 298 pedigrees, each collected through an affected proband, have been analyzed to study familial aggregation and genetics of vitiligo. The extent of familial aggregation is statistically significant at the 5% level. The disease does not appear to be inherited in a simple dominant or recessive fashion. The herita-bility of liability to the disease is 46% ± 4.82%. Neither common family environment nor a major locus with additional sources (environmental and/or polygenic) can be excluded as a cause of familial aggregation. Association of the disease with six polymorphic genetic marker loci have been studied. Significant associations with ACP1 and RH loci have been found. This and earlier studies indicate that the disease is associated with genetic loci on different chromosomes, which points to a polygenic nature of the disease.

Several studies have reported association between noninsulin-dependent diabetes mellitus and GC, the vitamin D binding protein of human plasma, with the GC 1 allele in significant excess among diabetics. Additionally, there is a considerable body of animal data suggesting that vitamin D has a significant impact on insulin secretion. Examination of the insulin levels in Dogrib Indians showed that the lowest levels of fasting insulin were associated with the GC IF-IF genotype. The present study examined levels of glucose, C-peptide, and insulin at fasting and 1 hr and 2 hr following a 75 g oral glucose challenge, in a population of Hispanic-Americans and Anglos in the San Luis Valley of southern Colorado. The sample comprised a total of 468 individuals with normal glucose tolerance. Of these, 289 were Anglos and 179 were Hispanic-Americans. An analysis of covariance was performed to determine the effect of the GC genotypes on mean levels of the primary variables--glucose, C-peptide, and insulin--and a secondary variable--insulinogenic index adjusting for the covariates age, body mass index (BMI), gender, and ethnicity. The analyses revealed that there is a significant difference in mean levels of glucose at fasting (F value = 2.46; P = 0.033) among the GC genotypes in the sample. Additionally, the differences in mean levels of 1 hr postprandial glucose among the GC genotypes although not significant at a 5% level, were significant at the 10% level. No other significant phenotypic effects were observed. These analyses are not in concordance with the results of an earlier study, where lower fasting insulin was associated with the GC 1F-1F genotype.