Identification of novel susceptibility genes associated with seven autoimmune disorders using whole genome molecular interaction networks.

Convergent evidence from multiple and independent genetics studies implicate a small number of genes that predispose individuals to multiple autoimmune disorders (AuD). These intersecting loci reinforced the hypothesis that disorders with overlapping etiology group into a cluster of closely related genes within a whole genome molecular interaction network. We tested the hypothesis that "biological network proximity" within a whole genome molecular interaction network can be used to inform the search for multigene inheritance. Using a set of nine previously published genome wide association studies (GWAS) of AuD genes, we generated AuD-specific molecular interaction networks to identify networks of associated genes. We show that all nine "seed genes" can be connected within a 35-member network via interactions with 26 connecting genes. We show that this network is more connected than expected by chance, and 13 of the connecting genes showed association with multiple AuD upon GWAS reanalysis. Furthermore, we report association of SNPs in five new genes (IL10RA, DGKA, GRB2, STAT5A, and NFATC2) which were not previously considered as AuD candidates, and show significant association in novel disease samples of Crohn's disease and systemic lupus erythematosus. Furthermore, we show that the connecting genes show no association in four non-AuD GWAS. Finally, we test the connecting genes in psoriasis GWAS, and show association to previously identified loci and report new loci. These findings support the hypothesis that molecular interaction networks can be used to inform the search for multigene disease etiology, especially for disorders with overlapping etiology.

Fine mapping of candidate regions for bipolar disorder provides strong evidence for susceptibility loci on chromosomes 7q.

Genomewide scans of bipolar disorder (BP) have not produced consistent linkage findings. Follow-up studies using enlarged samples and enhanced marker density can bolster or refute claims of linkage and pave the way to gene discovery. We conducted linkage and association analyses, using a ~3-cM density map of 10 candidate regions, in a large BP pedigree sample (865 individuals from 56 pedigrees). The candidate regions were identified in a previous 10-cM genome-wide scan using a subset of this sample (373 individuals from 40 pedigrees). The present sample consists of the expanded original pedigrees ("core" pedigrees) and 16 additional pedigrees. We obtained experiment-wide significant linkage on chromosome 7q34 (LOD score 3.53, P < 0.001), substantially stronger than that observed in the genome-wide scan. Support for linkage was sustained on chromosomes 2p13, 4q31, 8q13, 13q32, 14q21, and 17q11, though at a more modest level. Family-based association analysis was consistent with the linkage results at all regions with linkage evidence, except 4q an 8q, but the results fell short of statistical significance. Three of the previously implicated regions-9q31, 10q21 and 10q24-showed substantial reduction in evidence of linkage. Our results strongly support 7q34 as a region harboring susceptibility locus for BP. Somewhat lesser, yet notable support was obtained for 2p13, 4q31, 8q13, 13q32, 14q21, and 17q11. These regions could be considered prime candidates for future gene finding efforts.

Molecular Interaction Network Approach (MINA) identifies association of novel candidate disease genes.

Molecular Interaction Network Approach (MINA) was used to elucidate candidate disease genes. The approach was implemented to identify novel gene association with commonly known autoimmune diseases [1]. In MINA, we evaluated the hypothesis that "network proximity" within a whole genome molecular interaction network can be used to inform the search for multigene inheritance. There are now numerous examples of gene discoveries based upon network proximity between novel and previously identified disease genes (Yin et al., 2017 [2], Wang et al., 2011 [3], and Barrenas et al., 2009 [4]). This study extends the application of interaction networks to the interrogation of Genome Wide Association studies: first, by showing that a group of nine autoimmune diseases (AuD) genes "seed genes", are connected in a highly non-random manner within a whole genome network; and second, by showing that the minimal number of connecting genes required to connect a maximal number of AuD candidate genes are highly enriched as candidate genes for AuD predisposing mutations. The findings imply that a threshold number of candidate genes for any heritable disorder can be used to "seed" a molecular interaction network that •Serves to validate the disease status of closely associated seed genes•Identifies genes that are highly enriched as novel candidate disease genes•Provides a strategy for elucidation of epistatic gene x gene interactions The method could provide a critical toll for understanding the genetic architecture of common traits and disorders.

Consequences of copper accumulation in the livers of the Atp7b-/- (Wilson disease gene) knockout mice.

Wilson disease is a severe genetic disorder associated with intracellular copper overload. The affected gene, ATP7B, has been identified, but the molecular events leading to Wilson disease remain poorly understood. Here, we demonstrate that genetically engineered Atp7b-/- mice represent a valuable model for dissecting the disease mechanisms. These mice, like Wilson disease patients, have intracellular copper accumulation, low-serum oxidase activity, and increased copper excretion in urine. Their liver pathology developed in stages and was determined by the time of exposure to elevated copper rather than copper concentration per se. The disease progressed from mild necrosis and inflammation to extreme hepatocellular injury, nodular regeneration, and bile duct proliferation. Remarkably, all animals older than 9 months showed regeneration of large portions of the liver accompanied by the localized occurrence of cholangiocarcinoma arising from the proliferating bile ducts. The biochemical characterization of Atp7b-/- livers revealed copper accumulation in several cell compartments, particularly in the cytosol and nuclei. The increase in nuclear copper is accompanied by marked enlargement of the nuclei and enhanced DNA synthesis, with these changes occurring before pathology development. Our results suggest that the early effects of copper on cell genetic material contribute significantly to pathology associated with Atp7b inactivation.

Genome scan meta-analysis of schizophrenia and bipolar disorder, part III: Bipolar disorder.

Genome scans of bipolar disorder (BPD) have not produced consistent evidence for linkage. The rank-based genome scan meta-analysis (GSMA) method was applied to 18 BPD genome scan data sets in an effort to identify regions with significant support for linkage in the combined data. The two primary analyses considered available linkage data for "very narrow" (i.e., BP-I and schizoaffective disorder-BP) and "narrow" (i.e., adding BP-II disorder) disease models, with the ranks weighted for sample size. A "broad" model (i.e., adding recurrent major depression) and unweighted analyses were also performed. No region achieved genomewide statistical significance by several simulation-based criteria. The most significant P values (<.01) were observed on chromosomes 9p22.3-21.1 (very narrow), 10q11.21-22.1 (very narrow), and 14q24.1-32.12 (narrow). Nominally significant P values were observed in adjacent bins on chromosomes 9p and 18p-q, across all three disease models on chromosomes 14q and 18p-q, and across two models on chromosome 8q. Relatively few BPD pedigrees have been studied under narrow disease models relative to the schizophrenia GSMA data set, which produced more significant results. There was no overlap of the highest-ranked regions for the two disorders. The present results for the very narrow model are promising but suggest that more and larger data sets are needed. Alternatively, linkage might be detected in certain populations or subsets of pedigrees. The narrow and broad data sets had considerable power, according to simulation studies, but did not produce more highly significant evidence for linkage. We note that meta-analysis can sometimes provide support for linkage but cannot disprove linkage in any candidate region.