Power (%) of the <i>ADA</i> method with two sets of candidate <i>P</i>-value truncation thresholds.

<p>Power (%) of the <i>ADA</i> method with two sets of candidate <i>P</i>-value truncation thresholds.</p

Comparison of power by <i>r_isk</i> (the percentage of deleterious variants among the <i>d</i> causal variants), PAR, and <i>d</i> (the number of causal variants).

<p>The figure shows the power comparison by <i>r_isk</i> (left column, given PAR = 0.3% and <i>d</i> = 20), PAR (middle column, given <i>d</i> = 20 and <i>r_isk</i> = 80%), and <i>d</i> (right column, given <i>r_isk</i> = 80% and PAR = 0.3%). The nominal significance level was set at 0.05 (top row) and 0.01 (bottom row), respectively.</p

Analysis of the Dallas Heart Study data.

<p>^a<i>P</i>-values were estimated based on 10⁴ permutations.</p

Type-I error rates.

<p>Type-I error rates.</p

Trans-ethnic predicted expression genome-wide association analysis identifies a gene for estrogen receptor-negative breast cancer

<div><p>Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identification of individual genetic variants with weak effects using traditional GWAS methods. To address this challenge, we utilized a gene-level expression-based method, implemented in the MetaXcan software, to predict gene expression levels for 11,536 genes using expression quantitative trait loci and examine the genetically-predicted expression of specific genes for association with overall breast cancer risk and estrogen receptor (ER)-negative breast cancer risk. Using GWAS datasets from a Challenge launched by National Cancer Institute, we identified <i>TP53INP2</i> (tumor protein p53-inducible nuclear protein 2) at 20q11.22 to be significantly associated with ER-negative breast cancer (Z = -5.013, p = 5.35×10⁻⁷, Bonferroni threshold = 4.33×10⁻⁶). The association was consistent across four GWAS datasets, representing European, African and Asian ancestry populations. There are 6 single nucleotide polymorphisms (SNPs) included in the prediction of <i>TP53INP2</i> expression and five of them were associated with estrogen-receptor negative breast cancer, although none of the SNP-level associations reached genome-wide significance. We conducted a replication study using a dataset outside of the Challenge, and found the association between <i>TP53INP2</i> and ER-negative breast cancer was significant (p = 5.07x10^-3). Expression of <i>HP</i> (16q22.2) showed a suggestive association with ER-negative breast cancer in the discovery phase (Z = 4.30, p = 1.70x10^-5) although the association was not significant after Bonferroni adjustment. Of the 249 genes that are 250 kb within known breast cancer susceptibility loci identified from previous GWAS, 20 genes (8.0%) were statistically significant associated with ER-negative breast cancer (p<0.05), compared to 582 (5.2%) of 11,287 genes that are not close to previous GWAS loci. This study demonstrated that expression-based gene mapping is a promising approach for identifying cancer susceptibility genes.</p></div

The left (right) graph is the histogram of lifespan for group with genotype ' / ' ('-/-') in the data from Redmann & Argyropoulos

<p><b>Copyright information:</b></p><p>Taken from "Testing for differences in distribution tails to test for differences in 'maximum' lifespan"</p><p>http://www.biomedcentral.com/1471-2288/8/49</p><p>BMC Medical Research Methodology 2008;8():49-49.</p><p>Published online 25 Jul 2008</p><p>PMCID:PMC2529340.</p><p></p

The 20q11.22 locus spanned for expression quantitative trait loci of <i>TN53INT2</i>, and analysis of regulation enhancer with data from ENCODE through UCSC Genome Browser, including transcription factor binding sites and human mammary epithelial cells (HMEC) histone modification marks.

<p>Chromosomal coordinates are in NCBI build 37.</p

Regulatory element annotation of variants that predicted expression of <i>TP53INP2</i> using HaploReg [33].

<p>Regulatory element annotation of variants that predicted expression of <i>TP53INP2</i> using HaploReg [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006727#pgen.1006727.ref033" target="_blank">33</a>].</p

The left (right) graph is the histogram of lifespan for WL-HF (EO-HF) group in the data from Vasselli et al

[].<p><b>Copyright information:</b></p><p>Taken from "Testing for differences in distribution tails to test for differences in 'maximum' lifespan"</p><p>http://www.biomedcentral.com/1471-2288/8/49</p><p>BMC Medical Research Methodology 2008;8():49-49.</p><p>Published online 25 Jul 2008</p><p>PMCID:PMC2529340.</p><p></p

dbGaP datasets used in the our gene level expression-based GWAS analysis.

<p>dbGaP datasets used in the our gene level expression-based GWAS analysis.</p