summary statistics presentation課件

1、Summary statistics analysisSummary statistics analysisRuifeng Wang12/11/2015Outlines: Why analyzing summary statistics is important Summary statistics database Approaches of analyzing summary statistics List extra latest papers using summary statisticsWhy analyzing summary statistics is importantWhy

2、 analyzing summary statistics is importantSummary data downloadSummary data downloadImportant ConsortiumTraitwebsiteGenetic Factors for Osteoporosis Consortium (GEFOS) Femoral Neck BMDLumbar Spine BMD/?q=content/data-release-2015 Genetic Investigation of Anthropometric Traits cons

3、ortium (GIANT) BMI, Height, traits related to waist circumference/collaboration/giant/index.php/Data_Release Global Lipids Genetics Consortium Results (GLGC)Lipid levels/abecasis/public/lipids2013/ International Inflammatory Bowel Disease Genetics C

4、onsortium (IIBDGC)IBD/downloads.html Meta-analysis of Glucose and Insulin-Related Traits Consortium (MAGIC)Glycemic, metabolic traits./downloads/ The Genetics of Personality Consortium (GPC)Neuroticism, Extraversion, Openness to Experience, Ag

5、reeableness/GPC/ Summary data downloadSummary data downloadImportant ConsortiumTraitwebsiteDIAbetes Genetics Replication and Meta-analysis Consortium (DIAGRAM)Type 2 diabetes/downloads.html Coronary ARtery Disease Genome wide Replication an

6、d Meta-analysis consortium (CARDIoGRAMplusC4D)Coronary artery disease and myocardial infarction/downloads/ Tobacco and Genetics Consortium (TAG)Several smoking phenotypes/pgc/downloads International Consortium for Blood Pressure GWAS (BP)Blood pres

7、sure/icbp_088023401234-9812599.html International Genomics of Alzheimers Project (IGAP)Alzheimers disease http:/www.pasteur-lille.fr/en/recherche/u744/igap/igap_download.php Other GWAS summary statistics downloads you may find here:/Approaches of analy

8、zing summary statisticsApproaches of analyzing summary statistics1. cFDR2. Genetic analysis incorporating Pleiotropy and Annotation (GPA)3. MetaCCA4. Meta-analysis of correlated traits5. Fine mappingMetaCCA1. Stephens, M. (2013). A unified framework for association analysis with multiple related phe

9、notypes. PLoS One 8(7): e65245.2. Yang, J., et al. (2012). Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44(4): 369-375, S361-363.3. Cichonska, A., et al. (2015). metaCCA: Summary statistics-based multivari

10、ate meta-analysis of genome-wide association studies using canonical correlation analysis. Genetic Epidemiology 39(7): 540-540.Before: 1. one SNP against multiple traits 1. 2. multiple SNPs against one trait 2. metaCCA: it unifies both of the existing approaches by allowing canonical correlation ana

11、lysis (CCA) of multiple SNPs against multiple traits based on univariate summary statistics 3.Matlab package: MetaCCAOverview of metaCCA frameworkX and Y denote genotype and phenotype matrices of dimensions N*G and N*P, respectively.N is the number of samples, G and P are the number of genotypic and

12、 phenotypic variables.Core equations of metaCCAMeta-analysis of correlated Traits via summary Meta-analysis of correlated Traits via summary statistics from GWASs statistics from GWASs goal: Studying through summary statistics from the GWAS of multiple correlated phenotypes can identify more loci.Ex

13、ample: the author applied this method to the Continental Origins and Genetic Epidemiology Network African ancestry samples for three blood pressure traits (systolic blood pressure, diastolic blood pressure, hepertension) and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p 5.0*10-

14、8) associated with hypertension-related traits that were missed by a single-trait analysis.Meta-analysis of correlated Traits via summary Meta-analysis of correlated Traits via summary statistics from GWASs statistics from GWASs Zhu, X., et al. (2015). Meta-analysis of correlated traits via summary

15、statistics from GWASs with an application in hypertension. Am J Hum Genet 96(1): 21-36.CPASSOC softwareEstimation of the correlation matrix R among test statisticsEstimation of the correlation matrix R among test statisticsOther related papers Pare, G., et al. (2015). A method to estimate the contri

16、bution of regional genetic associations to complex traits from summary association statistics. bioRxiv: 024067. Kichaev, G., et al. (2014). Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet 10(10): e1004722. Kichaev, G. and B. Pasaniuc (2015).

17、Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies. Am J Hum Genet 97(2): 260-271. Gusev, A., et al. (2015). Integrative approaches for large-scale transcriptome-wide association studies. bioRxiv: 024083. Finucane, H. K., et al. (2015). Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature genetics 47(11): 1228-1235. Zhu, X. and M. Stephens Bayesian variant-based pathway enrichment analysis using GWAS summary statistics.“ Pasaniuc, B., et al. (201