Meta-analysis and combining information in genetics and by Rudy Guerra, Darlene R. Goldstein

By Rudy Guerra, Darlene R. Goldstein

content material: Pt. zero. Introductory fabric --
1. short creation to meta-analysis, genetics, and genomics / Darlene R. Goldstein and Rudy Guerra --
Pt I. related info kinds I: Genotype information --
2. Combining info throughout genome-wide linkage scans / Carol J. Etzel and Tracy J. Costello --
three. Genome seek meta-analysis (GSMA): a nonparametric approach for meta-analysis of genome-wide linkage reviews / Cathryn M. Lewis --
four. Heterogeneity in meta-analysis of quantitative trait linkage reviews / Hans C. van Houwelingen and Jeremie J. P. Lebrec --
five. empirical Bayesian framework for QTL genome-wide scans / Kui Zhang ... [et al.] --
Pt. II. related info kinds II: Gene Expression information --
6. Composite speculation trying out: an technique equipped on intersection-union checks and Bayesian posterior possibilities / Stephen Erickson, Kyoungmi Kim and David B. Allison --
7. Frequentist and Bayesian mistakes pooling tools for boosting statistical energy in small pattern microarray facts research / Jae ok. Lee, Hyung Jun Cho and Michael O'Connell --
eight. importance trying out for small microarray experiments / Charles Kooperberg ... [et al.] --
nine. comparability of meta-analysis to mixed research of a replicated microarray examine / Darlene R. Goldstein ... [et al.] --
10. replacement probe set definitions for combining microarray facts throughout experiences utilizing diverse types of Affymetrix oligonucleotide arrays / Jeffrey S. Morris ... [et al.] --
eleven. Gene ontology-based meta-analysis of genome-scale experiments / Chad A. Shaw --
Pt. III. Combining diversified information kinds --
12. Combining genomic facts in human experiences / Debashis Ghosh, Daniel Rhodes and Arul Chinnaiyan --
thirteen. evaluate of statistical techniques for expression trait loci mapping / Christina Kendziorski and Meng Chen --
14. Incorporating pass annotation info in expression trait loci mapping / J. Blair Christian and Rudy Guerra --
15. misclassification version for inferring transcriptional regulatory networks / Ning solar and Hongyu Zhao --
sixteen. facts integration for the learn of protein interactions / Fengzhu solar ... [et al.] --
17. Gene bushes, species bushes, and species networks / Luay Nakhleh, Derek Ruths and Hideki Innan.

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2006). Sensitivity analyses are also recommended as a strategy to assess and correct for publication bias (Copas and Shi, 2000). 1 Linkage analysis A central problem in genetic analysis is to determine which gene(s), if any, affect particular phenotypes (observable trait characteristics), as well as the chromosomal locations of these genes, their different alleles (variant forms of the gene) and, ultimately, their biochemical modes of action. Linkage analysis is an initial step in elucidating the genetic mechanisms affecting a trait of interest.

3. As in the case for the FE model, confidence intervals and p-values from the RE model are obtained assuming normality of the effect distribution. 4 Combining p-values In FE and RE meta-analysis, combined estimates of effect size provide the basis for analysis. Other methods of meta-analysis, dating back to at least the 1930s, are based on combining (transformed) p-values. COMBINING INFORMATION 7 It is assumed that each of k independent studies tests a common null hypothesis and that each yields a one-tailed p-value in the same direction.

The variance of nsq β˜q is 1/ ks=1 t=1 wst . 3. Calculate the test statistic at Lq : tq′ = β˜q / Var β˜q . 4. The analysis point Lq′ that has the smallest (large negative value) significant tq′ value over the entire chromosomal segment is the point estimate of location of the QTL. An estimate of the genetic variance at location Lq′ is given by σ ˆg2 = β˜q′ /(−2). Etzel and Guerra (2002) further describe a bootstrapping procedure to obtain critical values or construct confidence intervals for location of the putative QTL and the genetic variance.

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