Andy Liu

Publications (2)11.08 Total impact

• Article: The impact of self-identified race on epidemiologic studies of gene expression.

  Sunita Sharma, Amy Murphy, Judie Howrylak, Blanca Himes, Michael H Cho, Jen-Hwa Chu, Gary M Hunninghake, Anne Fuhlbrigge, Barbara Klanderman, John Ziniti, [......], Andy Liu, Stanley J Szefler, Robert Strunk, Mario Castro, Nadia N Hansel, Gregory B Diette, Becky M Vonakis, N Franklin Adkinson, Vincent J Carey, Benjamin A Raby
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  ABSTRACT: Although population differences in gene expression have been established, the impact on differential gene expression studies in large populations is not well understood. We describe the effect of self-reported race on a gene expression study of lung function in asthma. We generated gene expression profiles for 254 young adults (205 non-Hispanic whites and 49 African Americans) with asthma on whom concurrent total RNA derived from peripheral blood CD4(+) lymphocytes and lung function measurements were obtained. We identified four principal components that explained 62% of the variance in gene expression. The dominant principal component, which explained 29% of the total variance in gene expression, was strongly associated with self-identified race (P<10(-16)). The impact of these racial differences was observed when we performed differential gene expression analysis of lung function. Using multivariate linear models, we tested whether gene expression was associated with a quantitative measure of lung function: pre-bronchodilator forced expiratory volume in one second (FEV(1)). Though unadjusted linear models of FEV(1) identified several genes strongly correlated with lung function, these correlations were due to racial differences in the distribution of both FEV(1) and gene expression, and were no longer statistically significant following adjustment for self-identified race. These results suggest that self-identified race is a critical confounding covariate in epidemiologic studies of gene expression and that, similar to genetic studies, careful consideration of self-identified race in gene expression profiling studies is needed to avoid spurious association.
  Genetic Epidemiology 02/2011; 35(2):93-101. · 3.44 Impact Factor
• Article: Mapping of numerous disease-associated expression polymorphisms in primary peripheral blood CD4+ lymphocytes.

  Amy Murphy, Jen-Hwa Chu, Mousheng Xu, Vincent J Carey, Ross Lazarus, Andy Liu, Stanley J Szefler, Robert Strunk, Karen Demuth, Mario Castro, Nadia N Hansel, Gregory B Diette, Becky M Vonakis, N Franklin Adkinson, Barbara J Klanderman, Jody Senter-Sylvia, John Ziniti, Christoph Lange, Tomi Pastinen, Benjamin A Raby
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  ABSTRACT: Genome-wide association studies of human gene expression promise to identify functional regulatory genetic variation that contributes to phenotypic diversity. However, it is unclear how useful this approach will be for the identification of disease-susceptibility variants. We generated gene expression profiles for 22 184 mRNA transcripts using RNA derived from peripheral blood CD4+ lymphocytes, and genome-wide genotype data for 516 512 autosomal markers in 200 subjects. We screened for cis-acting variants by testing variants mapping within 50 kb of expressed transcripts for association with transcript abundance using generalized linear models. Significant associations were identified for 1585 genes at a false discovery rate of 0.05 (corresponding to P-values ranging from 1 × 10(-91) to 7 × 10(-4)). Importantly, we identified evidence of regulatory variation for 119 previously mapped disease genes, including 24 examples where the variant with the strongest evidence of disease-association demonstrates strong association with specific transcript abundance. The prevalence of cis-acting variants among disease-associated genes was 63% higher than the genome-wide rate in our data set (P = 6.41 × 10(-6)), and although many of the implicated loci were associated with immune-related diseases (including asthma, connective tissue disorders and inflammatory bowel disease), associations with genes implicated in non-immune-related diseases including lipid profiles, anthropomorphic measurements, cancer and neurologic disease were also observed. Genetic variants that confer inter-individual differences in gene expression represent an important subset of variants that contribute to disease susceptibility. Population-based integrative genetic approaches can help identify such variation and enhance our understanding of the genetic basis of complex traits.
  Human Molecular Genetics 12/2010; 19(23):4745-57. · 7.64 Impact Factor