Software and methods for oligonucleotide and cDNA array data analysis.

Two HTML-based programs were developed to analyze and filter gene-expression data: 'Bullfrog' for Affymetrix oligonucleotide arrays and 'Spot' for custom cDNA arrays. The programs provide intuitive data-filtering tools through an easy-to-use interface. A background subtraction and normalization program for cDNA arrays was also built that provides an informative summary report with data-quality assessments. These programs are freeware to aid in the analysis of gene-expression results and facilitate the search for genes responsible for interesting biological processes and phenotypes

Genetic dissection of the pluripotent proteome through multi-omics data integration.

Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies

Genomic Copy Number Analysis in Alzheimer's Disease and Mild Cognitive Impairment: An ADNI Study

Copy number variants (CNVs) are DNA sequence alterations, resulting in gains (duplications) and losses (deletions) of genomic segments. They often overlap genes and may play important roles in disease. Only one published study has examined CNVs in late-onset Alzheimer's disease (AD), and none have examined mild cognitive impairment (MCI). CNV calls were generated in 288 AD, 183 MCI, and 184 healthy control (HC) non-Hispanic Caucasian Alzheimer's Disease Neuroimaging Initiative participants. After quality control, 222 AD, 136 MCI, and 143 HC participants were entered into case/control association analyses, including candidate gene and whole genome approaches. Although no excess CNV burden was observed in cases (AD and/or MCI) relative to controls (HC), gene-based analyses revealed CNVs overlapping the candidate gene CHRFAM7A, as well as CSMD1, SLC35F2, HNRNPCL1, NRXN1, and ERBB4 regions, only in cases. Replication in larger samples is important, after which regions detected here may be promising targets for resequencing

Dependence of Hippocampal Function on ERRγ-Regulated Mitochondrial Metabolism

SummaryNeurons utilize mitochondrial oxidative phosphorylation (OxPhos) to generate energy essential for survival, function, and behavioral output. Unlike most cells that burn both fat and sugar, neurons only burn sugar. Despite its importance, how neurons meet the increased energy demands of complex behaviors such as learning and memory is poorly understood. Here we show that the estrogen-related receptor gamma (ERRγ) orchestrates the expression of a distinct neural gene network promoting mitochondrial oxidative metabolism that reflects the extraordinary neuronal dependence on glucose. ERRγ−/− neurons exhibit decreased metabolic capacity. Impairment of long-term potentiation (LTP) in ERRγ−/− hippocampal slices can be fully rescued by the mitochondrial OxPhos substrate pyruvate, functionally linking the ERRγ knockout metabolic phenotype and memory formation. Consistent with this notion, mice lacking neuronal ERRγ in cerebral cortex and hippocampus exhibit defects in spatial learning and memory. These findings implicate neuronal ERRγ in the metabolic adaptations required for memory formation

Drosophila Brakeless Interacts with Atrophin and Is Required for Tailless-Mediated Transcriptional Repression in Early Embryos

Complex gene expression patterns in animal development are generated by the interplay of transcriptional activators and repressors at cis-regulatory DNA modules (CRMs). How repressors work is not well understood, but often involves interactions with co-repressors. We isolated mutations in the brakeless gene in a screen for maternal factors affecting segmentation of the Drosophila embryo. Brakeless, also known as Scribbler, or Master of thickveins, is a nuclear protein of unknown function. In brakeless embryos, we noted an expanded expression pattern of the Krüppel (Kr) and knirps (kni) genes. We found that Tailless-mediated repression of kni expression is impaired in brakeless mutants. Tailless and Brakeless bind each other in vitro and interact genetically. Brakeless is recruited to the Kr and kni CRMs, and represses transcription when tethered to DNA. This suggests that Brakeless is a novel co-repressor. Orphan nuclear receptors of the Tailless type also interact with Atrophin co-repressors. We show that both Drosophila and human Brakeless and Atrophin interact in vitro, and propose that they act together as a co-repressor complex in many developmental contexts. We discuss the possibility that human Brakeless homologs may influence the toxicity of polyglutamine-expanded Atrophin-1, which causes the human neurodegenerative disease dentatorubral-pallidoluysian atrophy (DRPLA)

Replication of Newly Identified Genetic Associations Between Abdominal Aortic Aneurysm and SMYD2, LINC00540, PCIF1/MMP9/ZNF335, and ERG

OBJECTIVE: A recently published genome wide association study of abdominal aortic aneurysms (AAA), based on pooled case control data of European ancestry, identified four new loci for AAA: SMYD2 (top single nucleotide polymorphism [SNP] rs1795061), LINC00540 (rs9316871), PCIF1/MMP9/ZNF335 (rs3827066), and ERG (rs2836411). Of the four, rs1795061 and rs2836411 showed significant heterogeneity across studies and the p value for rs9316871 did not reach the genome wide significance threshold until discovery and replication data were pooled together in that study. The objective of this study was to replicate these newly identified genetic associations for AAA in a US based prospective cohort study, the Atherosclerosis Risk in Communities (ARIC) Study, and a Greece based case control study. METHODS: ARIC identified 408 clinically diagnosed AAAs among 8 962 individuals of European ancestry during a median of 22 years of follow up. The Greek case control study included 341 AAAs of European ancestry recruited in a tertiary referral centre and 292 geographically and ethnically matched controls recruited from the same institution. A Cox proportional hazards model was used to analyse the ARIC data and logistic regression to analyse the Greek data. RESULTS: In ARIC, rs9316871 and rs3827066 were significantly associated with AAA risk (HR [p] was 0.77 [.004] and 1.22 [.03], respectively), rs2836411 was associated at borderline significance (1.13 [.08]), whereas rs1795061 was not associated (p = .55). In the Greek case control study, rs1795061 and rs2836411 were significantly associated with AAA (OR [p] was 1.66 [< .001] and 1.29 [.04], respectively), whereas rs9316871 was not (p = .81). Genotyping of rs3827066 did not succeed. In the meta-analysis of the two studies, the association for rs9316871and rs2836411 was statistically significant and consistent between the two studies: p = .02 and .007, respectively. CONCLUSIONS: Associations between rs9316871and rs2836411 and AAA risk were replicated in the meta-analysis of the two independent cohorts, providing further support for the importance of these loci in the aetiology of AAA

Combining quantitative and qualitative breast density measures to assess breast cancer risk

Abstract Background Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk. Methods We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume. Results Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84–4.47, and OR 2.56, 95% CI 1.87–3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75–3.09, and OR 1.50, 95% CI 0.82–2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623–0.654, vs. C-statistic 0.614, 95% CI 0.598–0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%. Conclusions Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone

Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's Disease Genetics: The PDGene Database

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P<5×10−8) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10−8). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies

Mammographic density and risk of breast cancer by age and tumor characteristics

Introduction: Understanding whether mammographic density (MD) is associated with all breast tumor subtypes and whether the strength of association varies by age is important for utilizing MD in risk models. Methods: Data were pooled from six studies including 3414 women with breast cancer and 7199 without who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous logistic regression to calculate breast cancer odds according to tumor type, histopathological characteristics, and receptor (estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2)) status by age (51%) versus average density (11-25%). Women ages 2.1 cm) versus small tumors and positive versus negative lymph node status (P’s < 0.01). For women ages <55 years, there was a stronger association of MD with ER-negative breast cancer than ER-positive tumors compared to women ages 55–64 and ≥65 years (Page-interaction = 0.04). MD was positively associated with both HER2-negative and HER2-positive tumors within each age group. Conclusion: MD is strongly associated with all breast cancer subtypes, but particularly tumors of large size and positive lymph nodes across all ages, and ER-negative status among women ages <55 years, suggesting high MD may play an important role in tumor aggressiveness, especially in younger women