Darlene R. Goldstein

Household smoking behavior and ETS exposure among children with asthma in low-income, minority households

Environmental tobacco smoke (ETS) exposure was measured among 242 children with asthma who live in homes where at least one person smokes. Subjects were identified through clinics, schools, community agencies, and hospitals serving low-income, medically underserved communities in Los Angeles. Parents were surveyed about smoking behaviors in the household, childrens ETS exposure, and attitudes towards smoking and smoking behavior change. Validation measures included urine cotinine for the child with asthma and passive air nicotine monitors placed in the subjects homes. Overall reported levels of household smoking and ETS exposure were low, with a significant amount of household smoking taking place outside rather than inside the home. Over 47% of the respondents reported absolute restrictions against smoking in the home, and these restrictions were associated with lower reported levels of smoking, ETS exposure, and air nicotine and urine cotinine concentrations.

The Effects of Genotyping Errors and Interference on Estimation of Genetic Distance

Analysis of linkage data has typically been carried out assuming genotyping errors are absent. Recent studies have shown, however, that the impact of ignoring genotyping errors can be great, especially in dense marker maps [Buetow, Am J Hum Genet 1991; 49:985-994; Lincoln and Lander, Genomics 1992; 14:604-610]. Because most organisms exhibit positive chiasma interference, we use the chi 2 model [Foss et al., Genetics 1993; 144:681-691] to examine the role interference plays in the estimation of genetic distance in the presence of genotyping errors. For simplicity, we confine our analyses to samples of 1,000 fully informative gametes. Our results support previous findings that ignoring errors inflates distance estimates. The larger the error rate, the greater the inflation. For a given error rate, the relative error in estimated genetic distance is greatest when interference is known to be weak or absent. An approximation to relative error which quantifies the relation to distance, error rate, and interference is provided. Robustness of estimation to error misspecification is also investigated. When the assumed error rate is too low, distance is overestimated while interference is underestimated. The situation is reversed when too large an error rate is assumed (interference is overestimated, and distance underestimated). Unfortunately, the joint estimation of distance and interference is not very robust to error misspecification.

Meta-analysis and combining information in genetics and genomics

Introductory Material A brief introduction to meta-analysis, genetics, and genomics Darlene R. Goldstein and Rudy Guerra Similar Data Types I: Genotype Data Combining information across genome-wide linkage scans Carol J. Etzel and Tracy J. Costello Genome search meta-analysis (GSMA): a nonparametric method for meta-analysis of genome-wide linkage studies Cathryn M. Lewis Heterogeneity in meta-analysis of quantitative trait linkage studies Hans C. van Houwelingen and Jeremie J.P. Lebrec An empirical Bayesian framework for QTL genome-wide scans Kui Zhang, Howard Wiener, T. Mark Beasley, Christopher I. Amos, and David B. Allison Similar Data Types II: Gene Expression Data Composite hypothesis testing: an approach built on intersection-union tests and Bayesian posterior probabilities Stephen Erickson, Kyoungmi Kim, and David B. Allison Frequentist and Bayesian error pooling methods for enhancing statistical power in small sample microarray data analysis Jae K. Lee, Hyung Jun Cho, and Michael OConnell Significance testing for small microarray experiments Charles Kooperberg, Aaron Aragaki, Charles C. Carey, and Suzannah Rutherford Comparison of meta-analysis to combined analysis of a replicated microarray study Darlene R. Goldstein, Mauro Delorenzi, Ruth Luthi-Carter, and Thierry Sengstag Alternative probe set definitions for combining microarray data across studies using different versions of Affymetrix oligonucleotide arrays Jeffrey S. Morris, Chunlei Wu, Kevin R. Coombes, Keith A. Baggerly, Jing Wang, and Li Zhang Gene ontology-based meta-analysis of genome-scale experiments Chad A. Shaw Combining Different Data Types Combining genomic data in human studies Debashis Ghosh, Daniel Rhodes, and Arul Chinnaiyan An overview of statistical approaches for expression trait loci mapping Christina Kendziorski and Meng Chen Incorporating GO annotation information in expression trait loci mapping J. Blair Christian and Rudy Guerra A misclassification model for inferring transcriptional regulatory networks Ning Sun and Hongyu Zhao Data integration for the study of protein interactions Fengzhu Sun, Ting Chen, Minghua Deng, Hyunju Lee, and Zhidong Tu Gene trees, species trees, and species networks Luay Nakhleh, Derek Ruths, and Hideki Innan References Index

Pedigree selection and tests of linkage in a Hutterite asthma pedigree.

We explored methods for kinship and linkage analysis in a Hutterite pedigree comprising 1,544 individuals, 72 of whom were diagnosed with asthma. Subpedigrees were selected by (a) identifying nuclear families containing asthmatics, (b) identifying couples with many asthmatic descendants in an ad hoc manner, and (c) finding the most recent common ancestors of all asthmatics. Markov chain Monte Carlo (MCMC) methods were used to estimate allele sharing in the larger subpedigrees and transmission/disequilibrium tests were performed on nuclear families. On chromosome 5q near the cytokine cluster, modest evidence for linkage to asthma was obtained. Using MCMC, we were able to evaluate the evidence for linkage in complex subpedigrees of several hundred individuals, and hence, incorporate some of the co‐ancestry of this founder population.

Power of a score test for quantitative trait linkage analysis of relative pairs

The score test of Dudoit and Speed [(2000) Biostatistics 1:1–26] to detect linkage between a trait locus and a marker locus, using identity by descent data on sib pairs, is extended to other types of relative pairs (grandparent/grandchild, avuncular, and half‐sib relationships). The test is based on the likelihood of the recombination fraction θ between trait and marker loci, conditional on phenotypes of the relatives. We present results of simulation studies characterizing power and robustness properties of this linkage score test, and compare the power of the score test to that of the classical and modified Haseman‐Elston tests. The score test has considerable power, particularly under sampling schemes where selection is on double probands. Use of a generic additive model [Goldstein et al., submitted] with allele frequency p = 0.2, heritability H = 0.3, and a moderate residual correlation of ρ = 0.2 resulted in a very good overall performance across a wide range of trait‐generating models. Genet. Epidemiol. 19(Suppl 1):S85–S91, 2000.

Factors influencing the identification of major genes in a complex disease genome scan

A two‐stage linkage strategy was employed to identify major genes for a simulated complex disease via a genome scan. The importance of several approaches for improving the ability to locate major genes has been illustrated. These approaches are: adjusting for covariates, ascertaining through multiple affected family members, increasing the sample size, and using multipoint linkage analysis.