SUMMARY The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer’s disease (LOAD), we constructed gene regulatory networks in 1647 post-mortem brain tissues from LOAD patients and non-demented subjects, and demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune and microglia-specific module dominated by genes involved in pathogen phagocytosis, containing TYROBP as a key regulator and up-regulated in LOAD. Mouse microglia cells over-expressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a novel framework to test models of disease mechanisms underlying LOAD.

Natural variation in gene expression is extensive in humans and other organisms, and variation in the baseline expression level of many genes has a heritable component. To localize the genetic determinants of these quantitative traits (expression phenotypes) in humans, we used microarrays to measure gene expression levels and performed genome-wide linkage analysis for expression levels of 3,554 genes in 14 large families. For approximately 1,000 expression phenotypes, there was significant evidence of linkage to specific chromosomal regions. Both cis- and trans-acting loci regulate variation in the expression levels of genes, although most act in trans. Many gene expression phenotypes are influenced by several genetic determinants. Furthermore, we found hotspots of transcriptional regulation where significant evidence of linkage for several expression phenotypes (up to 31) coincides, and expression levels of many genes that share the same regulatory region are significantly correlated. The combination of microarray techniques for phenotyping and linkage analysis for quantitative traits allows the genetic mapping of determinants that contribute to variation in human gene expression.

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body-mass-index (up to 77,167 participants), following up 16 loci in an additional 29 studies (up to 113,636 subjects). We identified 13 novel loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1, and CPEB4 (P 1.9 × 10−9 to 1.8 × 10−40), and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex-difference 1.9 × 10−3 to 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution, independent of overall adiposity, and reveal powerful gene-by-sex interactions.

We screened DNA sequence variants on an exome-focused genotyping array in >300,000 participants with replication in >280,000 participants and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice revealed lipid changes consistent with the human data. We utilized mapped variants to address four clinically relevant questions and found the following: (1) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease; (2) outside of the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (3) only some mechanisms of lowering LDL-C seemed to increase risk for type 2 diabetes; and (4) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (e.g., TM6SF2, PNPLA3) tracked with higher liver fat, higher risk for type 2 diabetes, and lower risk for coronary artery disease whereas TG-lowering alleles involved in peripheral lipolysis (e.g., LPL, ANGPTL4) had no effect on liver fat but lowered risks for both type 2 diabetes and coronary artery disease.