6 research outputs found
Additional file 1: Table S1a. of Characterization of BRCA1 and BRCA2 variants in multi-ethnic Asian cohort from a Malaysian case-control study
BRCA1 variants included in genotyping assay design. A total of 23 BRCA1 variants were included in the genotyping assay. Of these, two variants were excluded due to genotyping call rate <95%. Table S1b. BRCA2 variants included in genotyping assay design. A total of 44 BRCA2 variants were included in the genotyping assay. Of these, two variants were excluded due to genotyping call rate <95%. Table S2. Characteristics of Malaysian breast cancer cases and healthy controls in ethnicity subgroups: (a) Chinese, (b) Malay and (c) Indian. There was no difference in age for cases and controls for Chinese and Indian women, but healthy women were on average 2Â years older than the cases for Malay women. Table S3a. Frequency of BRCA1 variants detected in ethnicity subgroups. The table describes the frequency of BRCA1 variants detected in Chinese, Malay and Indian women. Table S3b. Frequency of BRCA2 variants detected in ethnicity subgroups. The table describes the frequency of BRCA2 variants detected in Chinese, Malay and Indian women. (DOCX 195 kb
Additional file 2: Figure S1. of Characterization of BRCA1 and BRCA2 variants in multi-ethnic Asian cohort from a Malaysian case-control study
Association of BRCA1 and BRCA2 variants with breast cancer risk in all breast cancer cases and healthy controls. The forest plot illustrates the association of BRCA1 and BRCA2 variants with breast cancer risk in all breast cancer cases and healthy controls. Figure S2. Association of variants with breast cancer risk in ethnicity subgroups: (a) BRCA1 and (b) BRCA2. The forest plot illustrates the association of BRCA1 and BRCA2 variants with breast cancer risk in certain ethnicity subgroups that can be analyzed. (DOCX 145 kb
Comparison of the marginal phenotype-SNP associations provided by GUESS, SNPTEST and piMASS in the single trait analysis of TG.
<p>(To increase readability, the log<sub>10</sub>(BFs) are truncated at 20). (A) Genome-wide log<sub>10</sub>(BF) obtained from GUESS. Significant SNPs found associated at an FDR of 5% are depicted by black dots (with the SNP's name) whereas significant SNPs that are also in the top Best Model Visited are represented by red dots (also with the SNP's name). (B) Genome-wide log<sub>10</sub>(BF) obtained from SNPTEST. The horizontal dashed line indicates the level of log<sub>10</sub>(BF) that provides strong evidence of a phenotype-SNP association with Marginal Posterior Probability of inclusion close to 1. For comparison purposes, SNPs detected by GUESS are highlighted (their name is printed). SNPs found by SNPTEST with log<sub>10</sub>(BF)>5 are coloured coded according to the level of pairwise Pearson correlation with the closest significant GUESS SNP (see colour bar for correlation scale). (C) Genome-wide log<sub>10</sub>(BF) obtained from piMASS. The horizontal dashed line indicates the level of log<sub>10</sub>(BF) that provides strong evidence for a phenotype-SNP association. (D) log<sub>10</sub>(BF) signals obtained from SNPTEST in a region of chromosome 11 spanning nearly 500 Kb (116,519,739–116,845,104 bp). The horizontal dashed line and colour code used to identify relevant SNPs are the same as defined in (B). Top bars indicate the position of genes in the region retrieved from Ensembl R66. (E) Scatterplot of genome-wide log<sub>10</sub>(BF) of TG obtained from GUESS and SNPTEST. Colour code used to identify relevant SNPs and the horizontal dashed line are as defined in (A) and (B). (F) Scatterplot of genome-wide log<sub>10</sub>(BF) of TG obtained from GUESS and piMASS. The colour code used to identify relevant SNPs and the horizontal dashed line are as defined in (A) and (B).</p
Schematic representation of the analysis of single and multiple phenotypes using GUESS.
<p>(A–B) Given a group of single traits (APOA1, APOB, HDL, LDL and TG), we constructed two top-down trees (green and blue colour coded) made by biologically driven combinations of phenotypes and centred on the pathways of LDL (A) and HDL (B). Each branch of the trees was regressed on the whole set of tagged SNPs (∼273K SNPs) using GUESS and adjusting for sex, age and body mass index. (C) Output from GUESS is used to derive the Best Models Visited (BMV), i.e. the most supported multivariate models, and their Model Posterior Probability (MPP), i.e. the fraction of the model space explained by the BMV (MPP of the top BMV and the cumulative MPP of the top five BMV are indicated in the first two columns, respectively). Based on an empirical FDR procedure, we selected a parsimonious set of significant SNPs (indicated on the top of the table with the associated locus) that explains the variation of each branch of the two trees. Merging this information with the list of SNPs in the top BMV allowed us to highlight a robust subset of significant SNPs that repeatedly contribute to the top supported model (significant SNPs are depicted in black whereas significant SNPs that are also in the top BMV are indicated in red). For each SNPs, comparison of the marginal strength of association across different combinations of traits is possible by a new rescaled measure of marginal phenotype-SNP association, Ratio of Bayes Factors (RBF) (phenotype-SNP log<sub>10</sub>(RBF) is truncated at 20 to increase readability). Based on Ensembl R66 annotation, each locus is classified as: (1) intronic, (2) 3′UTR, (3) downstream, (4) previously associated and (5) a tagSNP of a previously associated SNP. The name of the locus is also reported on the right of each branch of the two trees with the same colour code used in the table: black if the locus is associated with the phenotypes with FDR<5%, red if the locus is also in the top BMV with FDR<5%.</p
Comparison of the marginal phenotype-SNP associations provided by GUESS and SNPTEST in the multiple traits analysis of TG-LDL-APOB.
<p>(To increase readability, the log<sub>10</sub>(BFs) are truncated at 20). (A) Genome-wide log<sub>10</sub>(BF) obtained from GUESS. Significant SNPs found associated at 5% FDR are depicted by black dots (with the SNP's name) whereas significant SNPs that are also in the top Best Model Visited are represented by red dots (with the SNP's name). (B) Genome-wide log<sub>10</sub>(BF) obtained from SNPTEST. The horizontal dashed line indicates the level of log<sub>10</sub>(BF) that provides strong evidence of a phenotype-SNP association with Marginal Posterior Probability of inclusion close to 1. For comparison purposes, SNPs found by GUESS are highlighted (their name is printed). SNPs with log<sub>10</sub>(BF)>5 are coloured coded according to the level of pairwise Pearson correlation with the closest significant GUESS SNP (see colour bar for correlation scale). (C) log<sub>10</sub>(BF) signal obtained from SNPTEST in a region of chromosome 11 spanning nearly 500 Kb (116,519,739–116,845,104 bp). The horizontal dashed line and colour code used to identify relevant SNPs are as defined in (B). Top bars indicate the position of genes in the region retrieved from Ensembl R66. (D) Scatterplot of genome-wide log<sub>10</sub>(BF) of TG-LDL-APOB obtained from GUESS and SNPTEST. The colour code used to identify relevant SNPs and the horizontal dashed line are as defined in (A) and (B).</p
Receiver Operating Characteristic (ROC) curves of SNPTEST (black), SPLS (blue), MLASSO (dark green), (M)ANOVA (purple), piMASS (green) and GUESS (red) for multiple traits and single trait simulated datasets.
<p>For GUESS, ROC curves are obtained using the top Best Model Visited (BMV) (red star) and the Marginal Posterior Probability of Inclusion (MPPI) (solid red line). For SNPTEST, the ROC curve is calculated using log<sub>10</sub>(BF) while for piMASS ROC curves are obtained using MPPI. (Average) number of SNPs retained by SPLS and MLASSO under different levels of penalization are indicated (A–B). For MANOVA Wilks (A–B) and ANOVA Kruskal (C–D), the ROC curve is derived using the SNPs declared significant over a range of FDR levels. Number of false positives (<i>x</i>-axis) is indicated at the top of the figure while proportion of false positives is presented at the bottom. Given the large number of predictors (273,294), false positives are truncated at 10<sup>−4</sup> at which level a large number already occurs (27.5).</p