Osteoporosis is a complex disease that affects >10 million people in the United States and results in 1.5 million fractures annually. In addition, the high prevalence of osteopenia (low bone mass) in the general population places a large number of people at risk for developing the disease. In an effort to identify genetic factors influencing bone density, we characterized a family that includes individuals who possess exceptionally dense bones but are otherwise phenotypically normal. This high-bone-mass trait (HBM) was originally localized by linkage analysis to chromosome 11q12-13. We refined the interval by extending the pedigree and genotyping additional markers. A systematic search for mutations that segregated with the HBM phenotype uncovered an amino acid change, in a predicted beta-propeller module of the low-density lipoprotein receptor-related protein 5 (LRP5), that results in the HBM phenotype. During analysis of >1,000 individuals, this mutation was observed only in affected individuals from the HBM kindred. By use of in situ hybridization to rat tibia, expression of LRP5 was detected in areas of bone involved in remodeling. Our findings suggest that the HBM mutation confers a unique osteogenic activity in bone remodeling, and this understanding may facilitate the development of novel therapies for the treatment of osteoporosis.

Asthma is a common respiratory disorder characterized by recurrent episodes of coughing, wheezing and breathlessness. Although environmental factors such as allergen exposure are risk factors in the development of asthma, both twin and family studies point to a strong genetic component. To date, linkage studies have identified more than a dozen genomic regions linked to asthma. In this study, we performed a genome-wide scan on 460 Caucasian families and identified a locus on chromosome 20p13 that was linked to asthma (log(10) of the likelihood ratio (LOD), 2.94) and bronchial hyperresponsiveness (LOD, 3.93). A survey of 135 polymorphisms in 23 genes identified the ADAM33 gene as being significantly associated with asthma using case-control, transmission disequilibrium and haplotype analyses (P = 0.04 0.000003). ADAM proteins are membrane-anchored metalloproteases with diverse functions, which include the shedding of cell-surface proteins such as cytokines and cytokine receptors. The identification and characterization of ADAM33, a putative asthma susceptibility gene identified by positional cloning in an outbred population, should provide insights into the pathogenesis and natural history of this common disease.

Alcohol dependence is a leading cause of morbidity and premature death. Several lines of evidence suggest a substantial genetic component to the risk for alcoholism: sibs of alcoholic probands have a 3-8 fold increased risk of also developing alcoholism, and twin heritability estimates of 50-60% are reported by contemporary studies of twins. We report on the results of a six-center collaborative study to identify susceptibility loci for alcohol dependence. A genome-wide screen examined 291 markers in 987 individuals from 105 families. Two-point and multipoint nonparametric linkage analyses were performed to detect susceptibility loci for alcohol dependence. Multipoint methods provided the strongest suggestions of linkage with susceptibility loci for alcohol dependence on chromosomes 1 and 7, and more modest evidence for a locus on chromosome 2. In addition, there was suggestive evidence for a protective locus on chromosome 4 near the alcohol dehydrogenase genes, for which protective effects have been reported in Asian populations.

Alcohol dependence is a leading cause of morbidity and premature death. Several lines of evidence suggest a substantial genetic component to the risk for alcoholism: sibs of alcoholic probands have a 3-8 fold increased risk of also developing alcoholism, and twin heritability estimates of 50-60% are reported by contemporary studies of twins. We report on the results of a six-center collaborative study to identify susceptibility loci for alcohol dependence. A genome-wide screen examined 291 markers in 987 individuals from 105 families. Two-point and multipoint nonparametric linkage analyses were performed to detect susceptibility loci for alcohol dependence. Multipoint methods provided the strongest suggestions of linkage with susceptibility loci for alcohol dependence on chromosomes 1 and 7, and more modest evidence for a locus on chromosome 2. In addition, there was suggestive evidence for a protective locus on chromosome 4 near the alcohol dehydrogenase genes, for which protective effects have been reported in Asian populations.

There has been a great interest and a few successes in the identification of complex disease susceptibility genes in recent years. Association studies, where a large number of single-nucleotide polymorphisms (SNPs) are typed in a sample of cases and controls to determine which genes are associated with a specific disease, provide a powerful approach for complex disease gene mapping. Genes of interest in those studies may contain large numbers of SNPs that classical statistical methods cannot handle simultaneously without requiring prohibitively large sample sizes. By contrast, high-dimensional nonparametric methods thrive on large numbers of predictors. This work explores the application of one such method, random forests, to the problem of identifying SNPs predictive of the phenotype in the case-control study design. A random forest is a collection of classification trees grown on bootstrap samples of observations, using a random subset of predictors to define the best split at each node. The observations left out of the bootstrap samples are used to estimate prediction error. The importance of a predictor is quantified by the increase in misclassification occurring when the values of the predictor are randomly permuted. We extend the concept of importance to pairs of predictors, to capture joint effects, and we explore the behavior of importance measures over a range of two-locus disease models in the presence of a varying number of SNPs unassociated with the phenotype. We illustrate the application of random forests with a data set of asthma cases and unaffected controls genotyped at 42 SNPs in ADAM33, a previously identified asthma susceptibility gene. SNPs and SNP pairs highly associated with asthma tend to have the highest importance index value, but predictive importance and association do not always coincide.