A comparison of genomic copy number calls by Partek Genomics Suite, Genotyping Console and Birdsuite algorithms to quantitative PCR.

BACKGROUND: Copy number variants are >1 kb genomic amplifications or deletions that can be identified using array platforms. However, arrays produce substantial background noise that contributes to high false discovery rates of variants. We hypothesized that quantitative PCR could finitely determine copy number and assess the validity of calling algorithms. RESULTS: Using data from 29 Affymetrix SNP 6.0 arrays, we determined copy numbers using three programs: Partek Genomics Suite, Affymetrix Genotyping Console 2.0 and Birdsuite. We compared array calls at 25 chromosomal regions to those determined by qPCR and found nearly identical calls in regions of copy number 2. Conversely, agreement differed in regions called variant by at least one method. The highest overall agreement in calls, 91%, was between Birdsuite and quantitative PCR. Partek Genomics Suite calls agreed with quantitative PCR 76% of the time while the agreement of Affymetrix Genotyping Console 2.0 with quantitative PCR was 79%. CONCLUSIONS: In 38 independent samples, 96% of Birdsuite calls agreed with quantitative PCR. Analysis of three copy number calling programs and quantitative PCR showed Birdsuite to have the greatest agreement with quantitative PCR.

An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studies.

MOTIVATION: In genome-wide association studies (GWAS) of complex diseases, genetic variants having real but weak associations often fail to be detected at the stringent genome-wide significance level. Pathway analysis, which tests disease association with combined association signals from a group of variants in the same pathway, has become increasingly popular. However, because of the complexities in genetic data and the large sample sizes in typical GWAS, pathway analysis remains to be challenging. We propose a new statistical model for pathway analysis of GWAS. This model includes a fixed effects component that models mean disease association for a group of genes, and a random effects component that models how each gene's association with disease varies about the gene group mean, thus belongs to the class of mixed effects models. RESULTS: The proposed model is computationally efficient and uses only summary statistics. In addition, it corrects for the presence of overlapping genes and linkage disequilibrium (LD). Via simulated and real GWAS data, we showed our model improved power over currently available pathway analysis methods while preserving type I error rate. Furthermore, using the WTCCC Type 1 Diabetes (T1D) dataset, we demonstrated mixed model analysis identified meaningful biological processes that agreed well with previous reports on T1D. Therefore, the proposed methodology provides an efficient statistical modeling framework for systems analysis of GWAS. AVAILABILITY: The software code for mixed models analysis is freely available at http://biostat.mc.vanderbilt.edu/LilyWang.

Genome-wide analysis of copy number variation in type 1 diabetes.

Type 1 diabetes (T1D) tends to cluster in families, suggesting there may be a genetic component predisposing to disease. However, a recent large-scale genome-wide association study concluded that identified genetic factors, single nucleotide polymorphisms, do not account for overall familiality. Another class of genetic variation is the amplification or deletion of >1 kilobase segments of the genome, also termed copy number variations (CNVs). We performed genome-wide CNV analysis on a cohort of 20 unrelated adults with T1D and a control (Ctrl) cohort of 20 subjects using the Affymetrix SNP Array 6.0 in combination with the Birdsuite copy number calling software. We identified 39 CNVs as enriched or depleted in T1D versus Ctrl. Additionally, we performed CNV analysis in a group of 10 monozygotic twin pairs discordant for T1D. Eleven of these 39 CNVs were also respectively enriched or depleted in the Twin cohort, suggesting that these variants may be involved in the development of islet autoimmunity, as the presently unaffected twin is at high risk for developing islet autoimmunity and T1D in his or her lifetime. These CNVs include a deletion on chromosome 6p21, near an HLA-DQ allele. CNVs were found that were both enriched or depleted in patients with or at high risk for developing T1D. These regions may represent genetic variants contributing to development of islet autoimmunity in T1D.

Change in mRNA Expression after Atenolol, a Beta-adrenergic Receptor Antagonist and Association with Pharmacological Response.

AIMS: Genetic determinants of variability in response to beta-blockers are poorly characterized. We defined changes in mRNA expression after a beta-blocker to identify novel genes that could affect response and correlated these with inhibition of exercise-induced tachycardia, a measure of beta-blocker sensitivity. METHODS: Nine subjects exercised before and after a single oral dose of 25mg atenolol and mRNA gene expression was measured using an Affymetrix GeneChip Human Gene 1.0 ST Array. The area under the heart rate-exercise intensity curve (AUC) was calculated for each subject; the difference between post- and pre-atenolol AUCs (Delta AUC), a measure of beta-blocker response, was correlated with the fold-change in mRNA expression of the genes that changed more than 1.3-fold. RESULTS: Fifty genes showed more than 1.3-fold increase in expression; 9 of these reached statistical significance (P < 0.05). Thirty-six genes had more than 1.3-fold decrease in expression after atenolol; 6 of these reached statistical significance (P < 0.05). Change in mRNA expression of FGFBP2 and Probeset ID 8118979 was significantly correlated with atenolol response (P = 0.03 and 0.02, respectively). CONCLUSION: The expression of several genes not previously identified as part of the adrenergic signaling pathway changed in response to a single oral dose of atenolol. Variation in these genes could contribute to unexplained differences in response to beta-blockers.