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Meta-Analysis
. 2015 Nov;36(11):1314-26.
doi: 10.1093/carcin/bgv128. Epub 2015 Sep 10.

Identification of Lung Cancer Histology-Specific Variants Applying Bayesian Framework Variant Prioritization Approaches Within the TRICL and ILCCO Consortia

Darren R Brenner  1 Christopher I Amos  2 Yonathan Brhane  3 Maria N Timofeeva  4 Neil Caporaso  5 Yufei Wang  6 David C Christiani  7 Heike Bickeböller  8 Ping Yang  9 Demetrius Albanes  5 Victoria L Stevens  10 Susan Gapstur  10 James McKay  11 Paolo Boffetta  12 David Zaridze  13 Neonilia Szeszenia-Dabrowska  14 Jolanta Lissowska  15 Peter Rudnai  16 Eleonora Fabianova  17 Dana Mates  18 Vladimir Bencko  19 Lenka Foretova  20 Vladimir Janout  21 Hans E Krokan  22 Frank Skorpen  23 Maiken E Gabrielsen  23 Lars Vatten  24 Inger Njølstad  25 Chu Chen  26 Gary Goodman  26 Mark Lathrop  27 Tõnu Vooder  28 Kristjan Välk  29 Mari Nelis  30 Andres Metspalu  30 Peter Broderick  6 Timothy Eisen  31 Xifeng Wu  32 Di Zhang  32 Wei Chen  33 Margaret R Spitz  34 Yongyue Wei  7 Li Su  7 Dong Xie  9 Jun She  9 Keitaro Matsuo  35 Fumihiko Matsuda  36 Hidemi Ito  37 Angela Risch  38 Joachim Heinrich  39 Albert Rosenberger  8 Thomas Muley  40 Hendrik Dienemann  41 John K Field  42 Olaide Raji  42 Ying Chen  42 John Gosney  42 Triantafillos Liloglou  42 Michael P A Davies  42 Michael Marcus  42 John McLaughlin  3 Irene Orlow  43 Younghun Han  2 Yafang Li  2 Xuchen Zong  3 Mattias Johansson  11 EPIC InvestigatorsGeoffrey Liu  44 Shelley S Tworoger  45 Loic Le Marchand  46 Brian E Henderson  47 Lynne R Wilkens  46 Juncheng Dai  48 Hongbing Shen  48 Richard S Houlston  6 Maria T Landi  5 Paul Brennan  11 Rayjean J Hung  49
Affiliations
Free PMC article
Meta-Analysis

Identification of Lung Cancer Histology-Specific Variants Applying Bayesian Framework Variant Prioritization Approaches Within the TRICL and ILCCO Consortia

Darren R Brenner et al. Carcinogenesis. .
Free PMC article

Abstract

Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P > 5×10(-8)) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P = 4.6×10(-7)) and MTMR2 at 11q21 (rs10501831, P = 3.1×10(-6)) with SCC, as well as GAREM at 18q12.1 (rs11662168, P = 3.4×10(-7)) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P = 1.05×10(-4) for KCNIP4, represented by rs9799795) and AC (P = 2.16×10(-4) for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.

Figures

Figure 1.
Figure 1.
Study-specific results for top variants of interest in the discovery set.
Figure 2.
Figure 2.
Results of fixed-effects analysis and prioritization techniques for SCC. Panel A. P-values from random effects models across studies. P-value: red line = 10−7, black line = 10−5 and green line = 10−4. Panel B. P-values from hierarchical modeling. HM P-value: red line = 10−7, black line = 10−5 and green line = 10−4. Panel C. Approximate Bayes Factor Values from BFDP modeling. ABF: red line = 10−4, black line = 10−2 and green line = 10−1. Panel D. Q-values from stratified false discovery rate modeling. SFDR: black line = 10−2 and green line = 10−1.
Figure 3.
Figure 3.
Results of fixed-effects analysis and prioritization techniques for AC. Panel A. P-values from random effects models across studies. P-value: red line = 10−7, black line = 10−5 and green line = 10−4. Panel B. P-values from hierarchical modeling. HM P-value: red line = 10−7, black line = 10−5 and green line = 10−4. Panel C. Approximate Bayes Factor Values from BFDP modeling. ABF: red line = 10−4, black line = 10−2 and green line = 10−1. Panel D. Q-values from stratified false discovery rate modeling. SFDR: black line = 10−2 and green line = 10−1.

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