Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.

A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.In the past few years, gene-expression microarrays and other general molecular profiling technologies have been applied to a wide range of biological problems and have contributed to discoveries about the complex network of biochemical processes underlying living Correspondence should be addressed to E.E.S. (eric_schadt@merck.com). Note: Supplementary information is available on the Nature Genetics website. COMPETING INTERESTS STATEMENTThe authors declare that they have no competing financial interests. NIH Public Access Author ManuscriptNat Genet. Author manuscript; available in PMC 2010 March 18. Published in final edited form as:Nat Genet. 2005 July ; 37(7): 710-717. doi:10.1038/ng1589. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript systems 1 , common human diseases 2,3 and gene discovery and structure determination [4][5][6] . Microarrays have also helped to identify biomarkers 7 , disease subtypes 3,8,9 and mechanisms of toxicity 10 and, more recently, to elucidate the genetics of gene expression in human populations 11,12 and to reconstruct gene networks by integrating gene-expression and genetic data 13 . The use of molecular profiling technologies as tools to identify genes underlying common, polygenic diseases has been less successful. Hundreds or even thousands of genes whose expression changes are associated with disease traits have been identified, but determining which of the genes cause disease rather than respond to the disease state has proven difficult.Microarray data have recently been combined with other experimental approaches to facilitate identification of key mechanistic drivers of complex traits 3,[13][14][15][16][17] . One such technique involves treating relative transcript abundances as quantitative traits in segregating populations. In this method, chromosomal regions that control the level of expression of a particular gene are mapped as expression quantitative trait loci (eQTLs). Gene-expression QTLs that contain the gene encoding t...

Family-based tests of linkage disequilibrium typically are based on nuclear-family data including affected individuals and their parents or their unaffected siblings. A limitation of such tests is that they generally are not valid tests of association when data from related nuclear families from larger pedigrees are used. Standard methods require selection of a single nuclear family from any extended pedigrees when testing for linkage disequilibrium. Often data are available for larger pedigrees, and it would be desirable to have a valid test of linkage disequilibrium that can use all potentially informative data. In this study, we present the pedigree disequilibrium test (PDT) for analysis of linkage disequilibrium in general pedigrees. The PDT can use data from related nuclear families from extended pedigrees and is valid even when there is population substructure. Using computer simulations, we demonstrated validity of the test when the asymptotic distribution is used to assess the significance, and examined statistical power. Power simulations demonstrate that, when extended pedigree data are available, substantial gains in power can be attained by use of the PDT rather than existing methods that use only a subset of the data. Furthermore, the PDT remains more powerful even when there is misclassification of unaffected individuals. Our simulations suggest that there may be advantages to using the PDT even if the data consist of independent families without extended family information. Thus, the PDT provides a general test of linkage disequilibrium that can be widely applied to different data structures.

Combining genetic inheritance information, for both molecular profiles and complex traits, is a promising strategy not only for detecting quantitative trait loci (QTLs) for complex traits but for understanding which genes, pathways, and biological processes are also under the influence of a given QTL. As a primary step in determining the feasibility of such an approach in humans, we present the largest survey to date, to our knowledge, of the heritability of gene-expression traits in segregating human populations. In particular, we measured expression for 23,499 genes in lymphoblastoid cell lines for members of 15 Centre d'Etude du Polymorphisme Humain (CEPH) families. Of the total set of genes, 2,340 were found to be expressed, of which 31% had significant heritability when a false-discovery rate of 0.05 was used. QTLs were detected for 33 genes on the basis of at least one P value <.000005. Of these, 13 genes possessed a QTL within 5 Mb of their physical location. Hierarchical clustering was performed on the basis of both Pearson correlation of gene expression and genetic correlation. Both reflected biologically relevant activity taking place in the lymphoblastoid cell lines, with greater coherency represented in Kyoto Encyclopedia of Genes and Genomes database (KEGG) pathways than in Gene Ontology database pathways. However, more pathway coherence was observed in KEGG pathways when clustering was based on genetic correlation than when clustering was based on Pearson correlation. As more expression data in segregating populations are generated, viewing clusters or networks based on genetic correlation measures and shared QTLs will offer potentially novel insights into the relationship among genes that may underlie complex traits.

One strategy for localization of a quantitative-trait locus (QTL) is to test whether the distribution of a quantitative trait depends on the number of copies of a specific genetic-marker allele that an individual possesses. This approach tests for association between alleles at the marker and the QTL, and it assumes that association is a consequence of the marker being physically close to the QTL. However, problems can occur when data are not from a homogeneous population, since associations can arise irrespective of a genetic marker being in physical proximity to the QTL-that is, no information is gained regarding localization. Methods to address this problem have recently been proposed. These proposed methods use family data for indirect stratification of a population, thereby removing the effect of associations that are due to unknown population substructure. They are, however, restricted in terms of the number of children per family that can be used in the analysis. Here we introduce tests that can be used on family data with parent and child genotypes, with child genotypes only, or with a combination of these types of families, without size restrictions. Furthermore, equations that allow one to determine the sample size needed to achieve desired power are derived. By means of simulation, we demonstrate that the existing tests have an elevated false-positive rate when the size restrictions are not followed and that a good deal of information is lost as a result of adherence to the size restrictions. Finally, we introduce permutation procedures that are recommended for small samples but that can also be used for extensions of the tests to multiallelic markers and to the simultaneous use of more than one marker.

OBJECTIVE -Almost 90% of type 1 diabetes appears in individuals without a close family history. We sought to evaluate the best current predictive strategy, multiple defined autoantibodies, in a long-term prospective study in the general population. RESEARCH DESIGN AND METHODS -Autoantibodies to pancreatic islets (islet cellantibodies [ICAs]) and defined autoantibodies (d-aab) to human GAD, IA2/ICA512, and insulin were tested in 4,505 Washington schoolchildren. Eight years later, 3,000 (67%) subjects were recontacted, including 97% of subjects with any test Ͼ99th percentile.RESULTS -Six subjects developed diabetes (median interval 2.8 years), all from among the 12 individuals with multiple d-aab, representing 50% positive predictive value (95% CI 25-75%) and 100% sensitivity (58 -100%). Among the others, diabetes occurred in 0 of 6 with one d-aab plus ICA, 0 of 26 with ICA only, 0 of 7 with one d-aab equaling the 99th percentile and another d-aab equaling the 97.5th percentile, 0 of 86 with one d-aab, and 0 of 2,863 with no d-aab or ICA. Adjusted for verification bias, multiple d-aab were 99.9% specific (99.86 -99.93%). At this age, new d-aab seldom appeared. Once present, d-aab usually persisted regardless of disease progression, although less so for insulin autoantibodies. Insulin secretion by sequential glucose tolerance testing remained normal in four multiple d-aab subjects not developing diabetes. Of children developing diabetes, five of six (83%) would be included if HLA-DQ genotyping preceded antibody testing, but HLA-DQ did not explain outcomes among high-risk subjects, even when considered along with other genetic markers.CONCLUSIONS -Multiple d-aab were established by age 14 years and prospectively identified all schoolchildren who developed type 1 diabetes within 8 years. Diabetes Care 25:505-511, 2002T ype 1 diabetes is an organ-specific autoimmune disease with aberrant immune responses to specific ␤-cell autoantigens. Worldwide incidence appears to be increasing (1). Prevention is important because diabetes interrupts normal development in children and carries the threat of severe complications in the most active period of life (2).Both environmental and genetic factors play etiological roles. The most informative genetic locus, HLA class II, confers about half of the total genetic risk (3) but has low positive predictive value (PPV) when used alone in the general population (4,5). Autoantibodies provide a practical readout of ␤-cell autoimmunity, are easily sampled in venous blood, and have become a mainstay of type 1 diabetes prediction efforts. Initially described in terms of the islet cell antibody (ICA) immunofluorescence assay on pancreatic sections, autoantibodies are now often described in terms of defined ICA target antigens, such as insulin (6,7), GAD (8), and the tyrosine phosphatase homologue IA2/ICA512 (9 -11). Autoantibodies are useful to detect developing type 1 diabetes in close relatives of diabetic patients, whose risk is ϳ3%. However, most cases are sporadic rather than familial (...

Niemann-Pick C1-like 1 (NPC1L1) is an intestinal cholesterol transporter and the molecular target of ezetimibe, a cholesterol absorption inhibitor demonstrated to reduce LDL-cholesterol (LDL-C) both as monotherapy and when co-administered with 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins). Interestingly, significant interindividual variability has been observed for rates of intestinal cholesterol absorption and LDL-C reductions at both baseline and post ezetimibe treatment. To test the hypothesis that genetic variation in NPC1L1 could influence the LDL-C response to ezetimibe, we performed extensive resequencing of the gene in 375 apparently healthy individuals and genotyped hypercholesterolemic patients from clinical trial cohorts. No association was observed between NPC1L1 single-nucleotide polymorphism and baseline cholesterol. However, significant associations to LDL-C response to treatment with ezetimibe were observed in patients treated with ezetimibe in two large clinical trials. Our data demonstrate that DNA sequence variants in NPC1L1 are associated with an improvement in response to ezetimibe pharmacotherapy and suggest that detailed analysis of genetic variability in clinical trial cohorts can lead to improved understanding of factors contributing to variable drug response.

Pyrimethamine (PM) plus sulfadoxine (SD) is the last remaining affordable drug for treating uncomplicated malaria in Africa. The selective pressure exerted by the slowly eliminated combination PM/SD was compared with that exerted by the more rapidly eliminated combination chlorproguanil/dapsone (CPG/Dap) on Kenyan Plasmodium falciparum. Point mutations were analyzed in dihydrofolate reductase and dihydropteroate synthase and in the genetic diversity of 3 genes in isolates collected before and after CPG/Dap and PM/SD treatments. PM/SD was associated strongly with the disappearance of fully drug-sensitive parasites and with a significant increase in the prevalence of resistant parasites in subsequent parasitemias. However, this was not a characteristic of treatment with CPG/Dap. Moreover, most of the patients who returned with recrudescent infections were in the PM/SD-treated group. The data predict a longer useful therapeutic life for CPG/Dap than for PM/SD, and, thus, CPG/Dap is a preferable alternative for treatment of chloroquine-resistant falciparum malaria in sub-Saharan Africa.