Pattern recognition of gene expression data on signalling networks of cancer

Krebs ist ein Ergebnis abweichender zellulärer Signalübertragungen. Das Verständnis der Eigenschaften dieser komplexen Netzwerke wird es ermöglichen, effiziente therapeutische Strategien zu entwickeln. Oft werden bei der Analyse von Tumoren nur einzelne Signalpfade berücksichtigt. Diese Art Analyse vernachlässigt das Prinzip zusammenhängender Signalproteine in einem Netzwerk. Die Analyse, die in dieser Dissertation beschrieben wird, verwendet einen auf Netzwerken basierenden Ansatz, um ein Verständnis der komplexen zellulären Signaltransduktionspfade (sog. Signalwege) zu ermöglichen. In dieser Dissertation wurden menschliche Tumor-Genexpressionsdaten in das menschliche Protein-Protein-Interaktionsnetzwerk eingebettet und Signalwege mittels eines auf der Graphentheorie basierenden Ansatzes vorausberechnet. Mehrere Eigenschaften von normalen und Tumorsignalnetzwerken wurden aus diesen berechneten Signalwege unter Verwendung von 10 Tumordatensätzen abgeleitet. Es wird gezeigt, dass die Signalwege der betrachteten Tumore verglichen mit denen in normalen Gewebe kürzere Kaskaden und stärker differenzierte Signalwege verwenden. Das Signalnetzwerk im Tumor ist allgemein differenzierter und stärker vernetzt als in normalen Zellen. Eine netzwerkbasierende Analyse wurde ausgeführt, um die verschiedenen Netzwerkeigenschaften zwischen normalen und Tumorzellen mittels mehrerer Tumorgenexpressions-Datensätzen zu vergleichen. Die Ergebnisse bestätigen ein Model weniger geordneter Signalwege in Tumoren, was in einer größeren Robustheit der Signalwege des Tumors resultiert. Mit den Erkenntnissen dieser Studie wird ein neues Signalübertragungsmotiv vorgeschlagen, das sich in hoher Anzahl in den analysierten Datensätzen findet

Regulation patterns in signaling networks of cancer

<p>Abstract</p> <p>Background</p> <p>Formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. We wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample.</p> <p>Results</p> <p>We investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. Proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network (high correlation = small distance). In cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. Proteins of high signaling frequency were enriched with cancer mutations. These proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells.</p> <p>Conclusion</p> <p>Our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. From these cancer-specific regulation patterns novel signaling motifs are proposed.</p

A 2-step penalized regression method for family-based next-generation sequencing association studies

Large-scale genetic studies are often composed of related participants, and utilizing familial relationships can be cumbersome and computationally challenging. We present an approach to efficiently handle sequencing data from complex pedigrees that incorporates information from rare variants as well as common variants. Our method employs a 2-step procedure that sequentially regresses out correlation from familial relatedness and then uses the resulting phenotypic residuals in a penalized regression framework to test for associations with variants within genetic units. The operating characteristics of this approach are detailed using simulation data based on a large, multigenerational cohort

On Family-Based Genome-Wide Association Studies with Large Pedigrees: Observations and Recommendations

Family based association studies are employed less often than case-control designs in the search for disease-predisposing genes. The optimal statistical genetic approach for complex pedigrees is unclear when evaluating both common and rare variants. We examined the empirical power and type I error rates of 2 common approaches, the measured genotype approach and family-based association testing, through simulations from a set of multigenerational pedigrees. Overall, these results suggest that much larger sample sizes will be required for family-based studies and that power was better using MGA compared to FBAT. Taking into account computational time and potential bias, a 2-step strategy is recommended with FBAT followed by MGA

\u3cem\u3eABCC9\u3c/em\u3e Gene Polymorphism Is Associated with Hippocampal Sclerosis of Aging Pathology

Hippocampal sclerosis of aging (HS-Aging) is a high-morbidity brain disease in the elderly but risk factors are largely unknown. We report the first genome-wide association study (GWAS) with HS-Aging pathology as an endophenotype. In collaboration with the Alzheimer\u27s Disease Genetics Consortium, data were analyzed from large autopsy cohorts: (#1) National Alzheimer\u27s Coordinating Center (NACC); (#2) Rush University Religious Orders Study and Memory and Aging Project; (#3) Group Health Research Institute Adult Changes in Thought study; (#4) University of California at Irvine 90+ Study; and (#5) University of Kentucky Alzheimer\u27s Disease Center. Altogether, 363 HS-Aging cases and 2,303 controls, all pathologically confirmed, provided statistical power to test for risk alleles with large effect size. A two-tier study design included GWAS from cohorts #1-3 (Stage I) to identify promising SNP candidates, followed by focused evaluation of particular SNPs in cohorts #4-5 (Stage II). Polymorphism in the ATP-binding cassette, sub-family C member 9 (ABCC9) gene, also known as sulfonylurea receptor 2, was associated with HS-Aging pathology. In the meta-analyzed Stage I GWAS, ABCC9 polymorphisms yielded the lowest p values, and factoring in the Stage II results, the meta-analyzed risk SNP (rs704178:G) attained genome-wide statistical significance (p = 1.4 × 10-9), with odds ratio (OR) of 2.13 (recessive mode of inheritance). For SNPs previously linked to hippocampal sclerosis, meta-analyses of Stage I results show OR = 1.16 for rs5848 (GRN) and OR = 1.22 rs1990622 (TMEM106B), with the risk alleles as previously described. Sulfonylureas, a widely prescribed drug class used to treat diabetes, also modify human ABCC9 protein function. A subsample of patients from the NACC database (n = 624) were identified who were older than age 85 at death with known drug history. Controlling for important confounders such as diabetes itself, exposure to a sulfonylurea drug was associated with risk for HS-Aging pathology (p = 0.03). Thus, we describe a novel and targetable dementia risk factor

Genetic Sharing with Cardiovascular Disease Risk Factors and Diabetes Reveals Novel Bone Mineral Density Loci.

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Targeted sequencing of genome wide significant loci associated with bone mineral density (BMD) reveals significant novel and rare variants: The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) targeted sequencing study

Background: Bone mineral density (BMD) is a heritable phenotype that predicts fracture risk. We performed fine-mapping by targeted sequencing at WLS, MEF2C, ARHGAP1/F2 and JAG1 loci prioritized by eQTL and bioinformatic approaches among 56 BMD loci from our previous GWAS meta-analysis. Methods and Results: Targeted sequencing was conducted in 1,291 Caucasians from the Framingham Heart Study (n=925) and Cardiovascular Health Study (n=366), including 206 women and men with extreme low femoral neck (FN) BMD. A total of 4,964 sequence variants (SNVs) were observed and 80% were rare with MAF < 1%. The associations between previously identified SNPs in these loci and BMD, while nominally significant in sequenced participants, were no longer significant after multiple testing corrections. Conditional analyses did not find proteincoding variants that may be responsible for GWAS signals. On the other hand, in the sequenced subjects, we identified novel associations in WLS, ARHGAP1, and 5' of MEF2C (P-values < 8x10-5; false discovery rate (FDR) q-values < 0.01) that were much more strongly associated with BMD compared to the GWAS SNPs. These associated SNVs are less-common; independent from previous GWAS signals in the same loci; and located in gene regulatory elements. Conclusions: Our findings suggest that protein-coding variants in selected GWAS loci did not contribute to GWAS signals. By performing targeted sequencing in GWAS loci, we identified less-common and rare non-coding SNVs associated with BMD independently from GWAS common SNPs, suggesting both common and less-common variants may associate with disease risks and phenotypes in the same loci

Targeted sequencing of genome wide significant loci associated with bone mineral density (BMD) reveals significant novel and rare variants: the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) targeted sequencing study.

BackgroundBone mineral density (BMD) is a heritable phenotype that predicts fracture risk. We performed fine-mapping by targeted sequencing at WLS, MEF2C, ARHGAP1/F2 and JAG1 loci prioritized by eQTL and bioinformatic approaches among 56 BMD loci from our previous GWAS meta-analysis. Targeted sequencing was conducted in 1,291 Caucasians from the Framingham Heart Study ( n =  925) and Cardiovascular Health Study ( n =  366), including 206 women and men with extreme low femoral neck (FN) BMD. A total of 4,964 sequence variants (SNVs) were observed and 80% were rare with MAF &lt;1%. The associations between previously identified SNPs in these loci and BMD, while nominally significant in sequenced participants, were no longer significant after multiple testing corrections. Conditional analyses did not find protein-coding variants that may be responsible for GWAS signals. On the other hand, in the sequenced subjects, we identified novel associations in WLS , ARHGAP1 , and 5' of MEF2C ( P- values &lt; 8x10  -   5 ; false discovery rate (FDR) q-values &lt; 0.01) that were much more strongly associated with BMD compared to the GWAS SNPs. These associated SNVs are less-common; independent from previous GWAS signals in the same loci; and located in gene regulatory elements. Our findings suggest that protein-coding variants in selected GWAS loci did not contribute to GWAS signals. By performing targeted sequencing in GWAS loci, we identified less-common and rare non-coding SNVs associated with BMD independently from GWAS common SNPs, suggesting both common and less-common variants may associate with disease risks and phenotypes in the same loci