Abstract

The Statistics Core will: 1) Provide advice on research design and statistical and genetic analyses, including sample size and power calculations. 2) Create and manage databases for human population, family, and murine data sets. 3) Conduct statistical analyses of human population, family, and murine data, including univariate and rnultivariate analyses and data transformations and adjustments. 4) Conduct genetic analyses of human population and family data, including: a) two-point and multi-point linkage analyses of quantitative traits and qualitative disease status with genetic markers, and b) family and population based tests of association of disease status and quantitative traits with genetic markers, including SNPs. 5) Conduct analyses of expression array data to quantify gene expression, identify differentially expressed genes, detect global expression signatures, and construct phenotype classifiers based on gene expression profiles. 6) Aid in the interpretation of the results of analyses to identify the next steps in a research plan. 7) Provide advice on experimental design and computer analyses of data generated by murine crosses.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Program Projects (P01)
Project #
5P01HL028481-25
Application #
7758813
Study Section
Heart, Lung, and Blood Initial Review Group (HLBP)
Project Start
Project End
Budget Start
2009-02-01
Budget End
2010-01-31
Support Year
25
Fiscal Year
2009
Total Cost
$191,247
Indirect Cost

  Institution

Name
University of California Los Angeles
Department
Type
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095

  Publications

Miao, Zong; Alvarez, Marcus; Pajukanta, Päivi et al. (2018) ASElux: an ultra-fast and accurate allelic reads counter. Bioinformatics 34:1313-1320
Kurt, Zeyneb; Barrere-Cain, Rio; LaGuardia, Jonnby et al. (2018) Tissue-specific pathways and networks underlying sexual dimorphism in non-alcoholic fatty liver disease. Biol Sex Differ 9:46
Orozco, Luz D; Farrell, Colin; Hale, Christopher et al. (2018) Epigenome-wide association in adipose tissue from the METSIM cohort. Hum Mol Genet 27:1830-1846
Chella Krishnan, Karthickeyan; Kurt, Zeyneb; Barrere-Cain, Rio et al. (2018) Integration of Multi-omics Data from Mouse Diversity Panel Highlights Mitochondrial Dysfunction in Non-alcoholic Fatty Liver Disease. Cell Syst 6:103-115.e7
Freund, Malika Kumar; Burch, Kathryn S; Shi, Huwenbo et al. (2018) Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits. Am J Hum Genet 103:535-552
Pan, David Z; Garske, Kristina M; Alvarez, Marcus et al. (2018) Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS. Nat Commun 9:1512
Small, Kerrin S; Todor?evi?, Marijana; Civelek, Mete et al. (2018) Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition. Nat Genet 50:572-580
Mangul, Serghei; Yang, Harry Taegyun; Strauli, Nicolas et al. (2018) ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues. Genome Biol 19:36
Cantor, Rita; Navarro, Linda; Pan, Calvin (2018) Identifying fenofibrate responsive CpG sites. BMC Proc 12:43
Rahmani, Elior; Schweiger, Regev; Shenhav, Liat et al. (2018) BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference. Genome Biol 19:141

Showing the most recent 10 out of 518 publications