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1.  Factors associated with oxidative stress and cancer risk in the Breast and Prostate Cancer Cohort Consortium (BPC3) 
Free radical research  2014;48(3):380-386.
Both endogenous factors (genomic variations) and exogenous factors (environmental exposures, lifestyle) impact the balance of reactive oxygen species (ROS). Variants of the ND3 (rs2853826; G10398A) gene of the mitochondrial genome, manganese superoxide dismutase (MnSOD; rs4880 Val16Ala) and glutathione peroxidase (GPX-1; rs1050450 Pro198Leu) are purported to have functional effects on regulation of ROS balance. In this study, we examined associations of breast and prostate cancer risk and survival with these variants, and interactions between rs4880 - rs1050450 and alcohol consumption - rs2853826. Nested case-control studies were conducted in the Breast and Prostate Cancer Cohort Consortium (BPC3), consisting of nine cohorts. The analyses included over 10726 post-menopausal breast and 7532 prostate cancer cases with matched controls. Logistic regression models were used to evaluate associations with risk, and proportional hazard models were used for survival outcomes. We did not observe significant interactions between polymorphisms in MnSOD and GPX-1, or between mitochondrial polymorphisms and alcohol intake and risk of either breast (p-interaction of 0.34 and 0.98 respectively) or prostate cancer (p-interaction of 0.49 and 0.50 respectively). We observed a weak inverse association between prostate cancer risk and GPX-1 Leu198Leu carriers (OR 0.87, 95% CI 0.79 – 0.97, p = 0.01). Overall survival among women with breast cancer was inversely associated with G10398 carriers who consumed alcohol (HR 0.66 95% CI 0.49 – 0.88). Given the high power in our study, it is unlikely that interactions tested have more than moderate effects on breast or prostate cancer risk. Observed associations need both further epidemiological and biological confirmation.
PMCID: PMC4591275  PMID: 24437375
MnSOD; GPX-1; mitochondria; alcohol; breast; prostate
2.  Post-GWAS gene–environment interplay in breast cancer: results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79 000 women 
Human Molecular Genetics  2014;23(19):5260-5270.
We studied the interplay between 39 breast cancer (BC) risk SNPs and established BC risk (body mass index, height, age at menarche, parity, age at menopause, smoking, alcohol and family history of BC) and prognostic factors (TNM stage, tumor grade, tumor size, age at diagnosis, estrogen receptor status and progesterone receptor status) as joint determinants of BC risk. We used a nested case–control design within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), with 16 285 BC cases and 19 376 controls. We performed stratified analyses for both the risk and prognostic factors, testing for heterogeneity for the risk factors, and case–case comparisons for differential associations of polymorphisms by subgroups of the prognostic factors. We analyzed multiplicative interactions between the SNPs and the risk factors. Finally, we also performed a meta-analysis of the interaction ORs from BPC3 and the Breast Cancer Association Consortium. After correction for multiple testing, no significant interaction between the SNPs and the established risk factors in the BPC3 study was found. The meta-analysis showed a suggestive interaction between smoking status and SLC4A7-rs4973768 (Pinteraction = 8.84 × 10−4) which, although not significant after considering multiple comparison, has a plausible biological explanation. In conclusion, in this study of up to almost 79 000 women we can conclusively exclude any novel major interactions between genome-wide association studies hits and the epidemiologic risk factors taken into consideration, but we propose a suggestive interaction between smoking status and SLC4A7-rs4973768 that if further replicated could help our understanding in the etiology of BC.
PMCID: PMC4159150  PMID: 24895409
3.  Limited evidence that cancer susceptibility regions are preferential targets for somatic mutation 
Genome Biology  2015;16(1):193.
Genome wide-association studies have successfully identified several hundred independent loci harboring common cancer susceptibility alleles that are distinct from the more than 110 cancer predisposition genes. The latter are generally characterized by disruptive mutations in coding genes that have been established as ‘drivers’ of cancer in large somatic sequencing studies. We set out to determine whether, similarly, common cancer susceptibility loci map to genes that have altered frequencies of mutation.
In our analysis of the intervals defined by the cancer susceptibility markers, we observed that cancer susceptibility regions have gene mutation frequencies comparable to background mutation frequencies. Restricting analyses to genes that have been determined to be pleiotropic across cancer types, genes affected by expression quantitative trait loci, or functional genes indicates that most cancer susceptibility genes classified into these subgroups do not display mutation frequencies that deviate from those expected. We observed limited evidence that cancer susceptibility regions that harbor common alleles with small estimated effect sizes are preferential targets for altered somatic mutation frequencies.
Our findings suggest a complex interplay between germline susceptibility and somatic mutation, underscoring the cumulative effect of common variants on redundant pathways as opposed to driver genes. Complex biological pathways and networks likely link these genetic features of carcinogenesis, particularly as they relate to distinct polygenic models for each cancer type.
PMCID: PMC4571124  PMID: 26374197
4.  The new sequencer on the block: comparison of Life Technology’s Proton sequencer to an Illumina HiSeq for whole-exome sequencing 
Human genetics  2013;132(10):1153-1163.
We assessed the performance of the new Life Technologies Proton sequencer by comparing whole-exome sequence data in a Centre d’Etude du Polymorphisme Humain trio (family 1463) to the Illumina HiSeq instrument. To simulate a typical user’s results, we utilized the standard capture, alignment and variant calling methods specific to each platform. We restricted data analysis to include the capture region common to both methods. The Proton produced high quality data at a comparable average depth and read length, and the Ion Reporter variant caller identified 96 % of single nucleotide polymorphisms (SNPs) detected by the HiSeq and GATK pipeline. However, only 40 % of small insertion and deletion variants (indels) were identified by both methods. Usage of the trio structure and segregation of platform-specific alleles supported this result. Further comparison of the trio data with Complete Genomics sequence data and Illumina SNP microarray genotypes documented high concordance and accurate SNP genotyping of both Proton and Illumina platforms. However, our study underscored the problem of accurate detection of indels for both the Proton and HiSeq platforms.
PMCID: PMC4564298  PMID: 23757002
5.  Cross-cancer pleiotropic analysis of endometrial cancer: PAGE and E2C2 consortia 
Carcinogenesis  2014;35(9):2068-2073.
In the large-scale analysis of GWAS-identified risk variants for other cancers on endometrial cancer risk, a SNP near TET2 gene, rs7679673, previously associated with prostate and breast cancer risk demonstrated a robust association with endometrial cancer (P = 7.37 × 10− 5).
Genome-wide association studies (GWAS) have identified a large number of cancer-associated single nucleotide polymorphisms (SNPs), several of which have been associated with multiple cancer sites suggesting pleiotropic effects and shared biological mechanisms across some cancers. We hypothesized that SNPs associated with other cancers may be additionally associated with endometrial cancer. We examined 213 SNPs previously associated with 14 other cancers for their associations with endometrial cancer in 3758 endometrial cancer cases and 5966 controls of European ancestry from two consortia: Population Architecture Using Genomics and Epidemiology and the Epidemiology of Endometrial Cancer Consortium. Study-specific logistic regression estimates adjusted for age, body mass index and the most significant principal components of genetic ancestry were combined using fixed-effect meta-analysis to evaluate the association between each SNP and endometrial cancer risk. A Bonferroni-corrected P value of 2.35×10−4 was used to determine statistical significance of the associations. SNP rs7679673, ~6.3kb upstream of TET2 and previously reported to be associated with prostate cancer risk, was associated with endometrial cancer risk in the direction opposite to that for prostate cancer [meta-analysis odds ratio = 0.87 (per copy of the C allele), 95% confidence interval = 0.81, 0.93; P = 7.37×10−5] with no evidence of heterogeneity across studies (P heterogeneity = 0.66). This pleiotropic analysis is the first to suggest TET2 as a susceptibility locus for endometrial cancer.
PMCID: PMC4146418  PMID: 24832084
6.  Gene-environment interaction involving recently identified colorectal cancer susceptibility loci 
Genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) that are associated with risk of colorectal cancer (CRC). Prior research has evaluated the presence of gene-environment interaction involving the first 10 identified susceptibility loci, but little work has been conducted on interaction involving SNPs at recently identified susceptibility loci, including: rs10911251, rs6691170, rs6687758, rs11903757, rs10936599, rs647161, rs1321311, rs719725, rs1665650, rs3824999, rs7136702, rs11169552, rs59336, rs3217810, rs4925386, and rs2423279.
Data on 9160 cases and 9280 controls from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and Colon Cancer Family Registry (CCFR) were used to evaluate the presence of interaction involving the above-listed SNPs and sex, body mass index (BMI), alcohol consumption, smoking, aspirin use, post-menopausal hormone (PMH) use, as well as intake of dietary calcium, dietary fiber, dietary folate, red meat, processed meat, fruit, and vegetables. Interaction was evaluated using a fixed-effects meta-analysis of an efficient Empirical Bayes estimator, and permutation was used to account for multiple comparisons.
None of the permutation-adjusted p-values reached statistical significance.
The associations between recently identified genetic susceptibility loci and CRC are not strongly modified by sex, BMI, alcohol, smoking, aspirin, PMH use, and various dietary factors.
Results suggest no evidence of strong gene-environment interactions involving the recently identified 16 susceptibility loci for CRC taken one at a time.
PMCID: PMC4209726  PMID: 24994789
Colorectal Cancer; Gene-Environment Interaction; Polymorphism; Single Nucleotide; Genetic Predisposition to Disease; Diet
7.  Telomere length in white blood cell DNA and lung cancer: a pooled analysis of three prospective cohorts 
Cancer research  2014;74(15):4090-4098.
We investigated the relationship between telomere length and lung cancer in a pooled analysis from three prospective cohort studies: the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, conducted among men and women in the United States, and previously published data from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) trial conducted among male smokers in Finland, and the Shanghai Women's Health Study (SWHS), which is comprised primarily of never-smokers. The pooled population included 847 cases and 847 controls matched by study, age, and sex. Leukocyte telomere length was measured by a monochrome multiplex quantitative PCR assay. We used conditional logistic regression models to calculate odds ratios (OR) and their 95% confidence intervals (CI) for the association between telomere length and lung cancer risk, adjusted for age and pack-years of smoking. Longer telomere length was associated with increased lung cancer risk in the pooled analysis (OR(95% CI) by quartile: 1.00; 1.24(0.90–1.71); 1.27(0.91–1.78); and 1.86(1.33–2.62); P-trend=0.000022). Findings were consistent across the three cohorts and strongest for subjects with very long telomere length, i.e., lung cancer risks for telomere length (OR(95% CI)) in the upper half of the fourth quartile were 2.41(1.28–4.52), 2.16(1.11–4.23) and 3.02(1.39–6.58) for the PLCO trial, the ATBC trial, and the SWHS, respectively. In addition, the association persisted among cases diagnosed more than six years after blood collection and was particularly evident for female adenocarcinoma cases. Telomere length in white blood cell DNA may be a biomarker of future increased risk of lung cancer in diverse populations.
PMCID: PMC4119534  PMID: 24853549
Leukocytes; Lung cancer; Prospective; Telomeres
8.  Genome-wide interaction study of smoking and bladder cancer risk 
Carcinogenesis  2014;35(8):1737-1744.
Our GWAS of smoking and bladder cancer risk based on data from 5,424 cases and 10,162 controls suggest that exploring additive and multiplicative gene–environment interactions can identify novel susceptibility loci that are associated with risk for different subgroups.
Bladder cancer is a complex disease with known environmental and genetic risk factors. We performed a genome-wide interaction study (GWAS) of smoking and bladder cancer risk based on primary scan data from 3002 cases and 4411 controls from the National Cancer Institute Bladder Cancer GWAS. Alternative methods were used to evaluate both additive and multiplicative interactions between individual single nucleotide polymorphisms (SNPs) and smoking exposure. SNPs with interaction P values < 5 × 10− 5 were evaluated further in an independent dataset of 2422 bladder cancer cases and 5751 controls. We identified 10 SNPs that showed association in a consistent manner with the initial dataset and in the combined dataset, providing evidence of interaction with tobacco use. Further, two of these novel SNPs showed strong evidence of association with bladder cancer in tobacco use subgroups that approached genome-wide significance. Specifically, rs1711973 (FOXF2) on 6p25.3 was a susceptibility SNP for never smokers [combined odds ratio (OR) = 1.34, 95% confidence interval (CI) = 1.20–1.50, P value = 5.18 × 10− 7]; and rs12216499 (RSPH3-TAGAP-EZR) on 6q25.3 was a susceptibility SNP for ever smokers (combined OR = 0.75, 95% CI = 0.67–0.84, P value = 6.35 × 10− 7). In our analysis of smoking and bladder cancer, the tests for multiplicative interaction seemed to more commonly identify susceptibility loci with associations in never smokers, whereas the additive interaction analysis identified more loci with associations among smokers—including the known smoking and NAT2 acetylation interaction. Our findings provide additional evidence of gene–environment interactions for tobacco and bladder cancer.
PMCID: PMC4123644  PMID: 24662972
9.  Estimating the heritability of colorectal cancer 
Human Molecular Genetics  2014;23(14):3898-3905.
A sizable fraction of colorectal cancer (CRC) is expected to be explained by heritable factors, with heritability estimates ranging from 12 to 35% twin and family studies. Genome-wide association studies (GWAS) have successfully identified a number of common single-nucleotide polymorphisms (SNPs) associated with CRC risk. Although it has been shown that these CRC susceptibility SNPs only explain a small proportion of the genetic risk, it is not clear how much of the heritability these SNPs explain and how much is left to be detected by other, yet to be identified, common SNPs. Therefore, we estimated the heritability of CRC under different scenarios using Genome-Wide Complex Trait Analysis in the Genetics and Epidemiology of Colorectal Cancer Consortium including 8025 cases and 10 814 controls. We estimated that the heritability explained by known common CRC SNPs identified in GWAS was 0.65% (95% CI:0.3–1%; P = 1.11 × 10−16), whereas the heritability explained by all common SNPs was at least 7.42% (95% CI: 4.71–10.12%; P = 8.13 × 10−8), suggesting that many common variants associated with CRC risk remain to be detected. Comparing the heritability explained by the common variants with that from twin and family studies, a fraction of the heritability may be explained by other genetic variants, such as rare variants. In addition, our analysis showed that the gene × smoking interaction explained a significant proportion of the CRC variance (P = 1.26 × 10−2). In summary, our results suggest that known CRC SNPs only explain a small proportion of the heritability and more common SNPs have yet to be identified.
PMCID: PMC4065150  PMID: 24562164
10.  A genome-wide association study of marginal zone lymphoma shows association to the HLA region 
Vijai, Joseph | Wang, Zhaoming | Berndt, Sonja I | Skibola, Christine F | Slager, Susan L | de Sanjose, Silvia | Melbye, Mads | Glimelius, Bengt | Bracci, Paige M | Conde, Lucia | Birmann, Brenda M | Wang, Sophia S | Brooks-Wilson, Angela R | Lan, Qing | de Bakker, Paul I W | Vermeulen, Roel C H | Portlock, Carol | Ansell, Stephen M | Link, Brian K | Riby, Jacques | North, Kari E | Gu, Jian | Hjalgrim, Henrik | Cozen, Wendy | Becker, Nikolaus | Teras, Lauren R | Spinelli, John J | Turner, Jenny | Zhang, Yawei | Purdue, Mark P | Giles, Graham G | Kelly, Rachel S | Zeleniuch-Jacquotte, Anne | Ennas, Maria Grazia | Monnereau, Alain | Bertrand, Kimberly A | Albanes, Demetrius | Lightfoot, Tracy | Yeager, Meredith | Chung, Charles C | Burdett, Laurie | Hutchinson, Amy | Lawrence, Charles | Montalvan, Rebecca | Liang, Liming | Huang, Jinyan | Ma, Baoshan | Villano, Danylo J | Maria, Ann | Corines, Marina | Thomas, Tinu | Novak, Anne J | Dogan, Ahmet | Liebow, Mark | Thompson, Carrie A | Witzig, Thomas E | Habermann, Thomas M | Weiner, George J | Smith, Martyn T | Holly, Elizabeth A | Jackson, Rebecca D | Tinker, Lesley F | Ye, Yuanqing | Adami, Hans-Olov | Smedby, Karin E | De Roos, Anneclaire J | Hartge, Patricia | Morton, Lindsay M | Severson, Richard K | Benavente, Yolanda | Boffetta, Paolo | Brennan, Paul | Foretova, Lenka | Maynadie, Marc | McKay, James | Staines, Anthony | Diver, W Ryan | Vajdic, Claire M | Armstrong, Bruce K | Kricker, Anne | Zheng, Tongzhang | Holford, Theodore R | Severi, Gianluca | Vineis, Paolo | Ferri, Giovanni M | Ricco, Rosalia | Miligi, Lucia | Clavel, Jacqueline | Giovannucci, Edward | Kraft, Peter | Virtamo, Jarmo | Smith, Alex | Kane, Eleanor | Roman, Eve | Chiu, Brian C H | Fraumeni, Joseph F | Wu, Xifeng | Cerhan, James R | Offit, Kenneth | Chanock, Stephen J | Rothman, Nathaniel | Nieters, Alexandra
Nature communications  2015;6:5751.
Marginal zone lymphoma (MZL) is the third most common subtype of B-cell non-Hodgkin lymphoma. Here we perform a two-stage GWAS of 1,281 MZL cases and 7,127 controls of European ancestry and identify two independent loci near BTNL2 (rs9461741, P=3.95×10−15) and HLA-B (rs2922994, P=2.43×10−9) in the HLA region significantly associated with MZL risk. This is the first evidence that genetic variation in the major histocompatibility complex influences MZL susceptibility.
PMCID: PMC4287989  PMID: 25569183
11.  Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study 
Human Molecular Genetics  2015;24(18):5356-5366.
Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10−15), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10−6). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk.
PMCID: PMC4550826  PMID: 26138067
12.  Methodological Considerations in Estimation of Phenotype Heritability Using Genome-Wide SNP Data, Illustrated by an Analysis of the Heritability of Height in a Large Sample of African Ancestry Adults 
PLoS ONE  2015;10(6):e0131106.
Height has an extremely polygenic pattern of inheritance. Genome-wide association studies (GWAS) have revealed hundreds of common variants that are associated with human height at genome-wide levels of significance. However, only a small fraction of phenotypic variation can be explained by the aggregate of these common variants. In a large study of African-American men and women (n = 14,419), we genotyped and analyzed 966,578 autosomal SNPs across the entire genome using a linear mixed model variance components approach implemented in the program GCTA (Yang et al Nat Genet 2010), and estimated an additive heritability of 44.7% (se: 3.7%) for this phenotype in a sample of evidently unrelated individuals. While this estimated value is similar to that given by Yang et al in their analyses, we remain concerned about two related issues: (1) whether in the complete absence of hidden relatedness, variance components methods have adequate power to estimate heritability when a very large number of SNPs are used in the analysis; and (2) whether estimation of heritability may be biased, in real studies, by low levels of residual hidden relatedness. We addressed the first question in a semi-analytic fashion by directly simulating the distribution of the score statistic for a test of zero heritability with and without low levels of relatedness. The second question was addressed by a very careful comparison of the behavior of estimated heritability for both observed (self-reported) height and simulated phenotypes compared to imputation R2 as a function of the number of SNPs used in the analysis. These simulations help to address the important question about whether today's GWAS SNPs will remain useful for imputing causal variants that are discovered using very large sample sizes in future studies of height, or whether the causal variants themselves will need to be genotyped de novo in order to build a prediction model that ultimately captures a large fraction of the variability of height, and by implication other complex phenotypes. Our overall conclusions are that when study sizes are quite large (5,000 or so) the additive heritability estimate for height is not apparently biased upwards using the linear mixed model; however there is evidence in our simulation that a very large number of causal variants (many thousands) each with very small effect on phenotypic variance will need to be discovered to fill the gap between the heritability explained by known versus unknown causal variants. We conclude that today's GWAS data will remain useful in the future for causal variant prediction, but that finding the causal variants that need to be predicted may be extremely laborious.
PMCID: PMC4488332  PMID: 26125186
13.  Genetic risk variants associated with in situ breast cancer 
Breast cancer in situ (BCIS) diagnoses, a precursor lesion for invasive breast cancer, comprise about 20 % of all breast cancers (BC) in countries with screening programs. Family history of BC is considered one of the strongest risk factors for BCIS.
To evaluate the association of BC susceptibility loci with BCIS risk, we genotyped 39 single nucleotide polymorphisms (SNPs), associated with risk of invasive BC, in 1317 BCIS cases, 10,645 invasive BC cases, and 14,006 healthy controls in the National Cancer Institute’s Breast and Prostate Cancer Cohort Consortium (BPC3). Using unconditional logistic regression models adjusted for age and study, we estimated the association of SNPs with BCIS using two different comparison groups: healthy controls and invasive BC subjects to investigate whether BCIS and BC share a common genetic profile.
We found that five SNPs (CDKN2BAS-rs1011970, FGFR2-rs3750817, FGFR2-rs2981582, TNRC9-rs3803662, 5p12-rs10941679) were significantly associated with BCIS risk (P value adjusted for multiple comparisons <0.0016). Comparing invasive BC and BCIS, the largest difference was for CDKN2BAS-rs1011970, which showed a positive association with BCIS (OR = 1.24, 95 % CI: 1.11–1.38, P = 1.27 x 10−4) and no association with invasive BC (OR = 1.03, 95 % CI: 0.99–1.07, P = 0.06), with a P value for case-case comparison of 0.006. Subgroup analyses investigating associations with ductal carcinoma in situ (DCIS) found similar associations, albeit less significant (OR = 1.25, 95 % CI: 1.09–1.42, P = 1.07 x 10−3). Additional risk analyses showed significant associations with invasive disease at the 0.05 level for 28 of the alleles and the OR estimates were consistent with those reported by other studies.
Our study adds to the knowledge that several of the known BC susceptibility loci are risk factors for both BCIS and invasive BC, with the possible exception of rs1011970, a putatively functional SNP situated in the CDKN2BAS gene that may be a specific BCIS susceptibility locus.
Electronic supplementary material
The online version of this article (doi:10.1186/s13058-015-0596-x) contains supplementary material, which is available to authorized users.
PMCID: PMC4487950  PMID: 26070784
15.  Prostate Cancer (PCa) Risk Variants and Risk of Fatal PCa in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium 
European urology  2014;65(6):1069-1075.
Screening and diagnosis of prostate cancer (PCa) is hampered by an inability to predict who has the potential to develop fatal disease and who has indolent cancer. Studies have identified multiple genetic risk loci for PCa incidence, but it is unknown whether they could be used as biomarkers for PCa-specific mortality (PCSM).
To examine the association of 47 established PCa risk single-nucleotide polymorphisms (SNPs) with PCSM.
Design, setting, and participants
We included 10 487 men who had PCa and 11 024 controls, with a median follow-up of 8.3 yr, during which 1053 PCa deaths occurred.
Outcome measurements and statistical analysis
The main outcome was PCSM. The risk allele was defined as the allele associated with an increased risk for PCa in the literature. We used Cox proportional hazards regression to calculate the hazard ratios of each SNP with time to progression to PCSM after diagnosis. We also used logistic regression to calculate odds ratios for each risk SNP, comparing fatal PCa cases to controls.
Results and limitations
Among the cases, we found that 8 of the 47 SNPs were significantly associated (p < 0.05) with time to PCSM. The risk allele of rs11672691 (intergenic) was associated with an increased risk for PCSM, while 7 SNPs had risk alleles inversely associated (rs13385191 [C2orf43], rs17021918 [PDLIM5], rs10486567 [JAZF1], rs6465657 [LMTK2], rs7127900 (intergenic), rs2735839 [KLK3], rs10993994 [MSMB], rs13385191 [C2orf43]). In the case-control analysis, 22 SNPs were associated (p < 0.05) with the risk of fatal PCa, but most did not differentiate between fatal and nonfatal PCa. Rs11672691 and rs10993994 were associated with both fatal and nonfatal PCa, while rs6465657, rs7127900, rs2735839, and rs13385191 were associated with nonfatal PCa only.
Eight established risk loci were associated with progression to PCSM after diagnosis. Twenty-two SNPs were associated with fatal PCa incidence, but most did not differentiate between fatal and nonfatal PCa. The relatively small magnitudes of the associations do not translate well into risk prediction, but these findings merit further follow-up, because they may yield important clues about the complex biology of fatal PCa. Patient summary: In this report, we assessed whether established PCa risk variants could predict PCSM. We found eight risk variants associated with PCSM: One predicted an increased risk of PCSM, while seven were associated with decreased risk. Larger studies that focus on fatal PCa are needed to identify more markers that could aid prediction.
PMCID: PMC4006298  PMID: 24411283
Prostate cancer; Risk single nucleotide polymorphisms; Prostate cancer mortality; Genetic epidemiology
16.  Insulin-like Growth Factor Pathway Genetic Polymorphisms, Circulating IGF1 and IGFBP3, and Prostate Cancer Survival 
The insulin-like growth factor (IGF) signaling pathway has been implicated in prostate cancer (PCa) initiation, but its role in progression remains unknown.
Among 5887 PCa patients (704 PCa deaths) of European ancestry from seven cohorts in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium, we conducted Cox kernel machine pathway analysis to evaluate whether 530 tagging single nucleotide polymorphisms (SNPs) in 26 IGF pathway-related genes were collectively associated with PCa mortality. We also conducted SNP-specific analysis using stratified Cox models adjusting for multiple testing. In 2424 patients (313 PCa deaths), we evaluated the association of prediagnostic circulating IGF1 and IGFBP3 levels and PCa mortality. All statistical tests were two-sided.
The IGF signaling pathway was associated with PCa mortality (P = .03), and IGF2-AS and SSTR2 were the main contributors (both P = .04). In SNP-specific analysis, 36 SNPs were associated with PCa mortality with P trend less than .05, but only three SNPs in the IGF2-AS remained statistically significant after gene-based corrections. Two were in linkage disequilibrium (r 2 = 1 for rs1004446 and rs3741211), whereas the third, rs4366464, was independent (r 2 = 0.03). The hazard ratios (HRs) per each additional risk allele were 1.19 (95% confidence interval [CI] = 1.06 to 1.34; P trend = .003) for rs3741211 and 1.44 (95% CI = 1.20 to 1.73; P trend < .001) for rs4366464. rs4366464 remained statistically significant after correction for all SNPs (P trend.corr = .04). Prediagnostic IGF1 (HRhighest vs lowest quartile = 0.71; 95% CI = 0.48 to 1.04) and IGFBP3 (HR = 0.93; 95% CI = 0.65 to 1.34) levels were not associated with PCa mortality.
The IGF signaling pathway, primarily IGF2-AS and SSTR2 genes, may be important in PCa survival.
PMCID: PMC4081624  PMID: 24824313
17.  Leveraging population admixture to explain missing heritability of complex traits 
Nature genetics  2014;46(12):1356-1362.
Despite recent progress on estimating the heritability explained by genotyped SNPs (hg2), a large gap between hg2 and estimates of total narrow-sense heritability (h2) remains. Explanations for this gap include rare variants, or upward bias in family-based estimates of h2 due to shared environment or epistasis. We estimate h2 from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (hγ2). We show that hγ2 = 2FSTCθ(1−θ)h2, where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We examined 21,497 African Americans from three cohorts, analyzing 13 phenotypes. For height and BMI, we obtained h2 estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of hg2 in these and other data, but smaller than family-based estimates of h2.
PMCID: PMC4244251  PMID: 25383972
18.  The UBC-40 Urothelial Bladder Cancer cell line index: a genomic resource for functional studies 
BMC Genomics  2015;16(1):403.
Urothelial bladder cancer is a highly heterogeneous disease. Cancer cell lines are useful tools for its study. This is a comprehensive genomic characterization of 40 urothelial bladder carcinoma (UBC) cell lines including information on origin, mutation status of genes implicated in bladder cancer (FGFR3, PIK3CA, TP53, and RAS), copy number alterations assessed using high density SNP arrays, uniparental disomy (UPD) events, and gene expression.
Based on gene mutation patterns and genomic changes we identify lines representative of the FGFR3-driven tumor pathway and of the TP53/RB tumor suppressor-driven pathway. High-density array copy number analysis identified significant focal gains (1q32, 5p13.1-12, 7q11, and 7q33) and losses (i.e. 6p22.1) in regions altered in tumors but not previously described as affected in bladder cell lines. We also identify new evidence for frequent regions of UPD, often coinciding with regions reported to be lost in tumors. Previously undescribed chromosome X losses found in UBC lines also point to potential tumor suppressor genes. Cell lines representative of the FGFR3-driven pathway showed a lower number of UPD events.
Overall, there is a predominance of more aggressive tumor subtypes among the cell lines. We provide a cell line classification that establishes their relatedness to the major molecularly-defined bladder tumor subtypes. The compiled information should serve as a useful reference to the bladder cancer research community and should help to select cell lines appropriate for the functional analysis of bladder cancer genes, for example those being identified through massive parallel sequencing.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-1450-3) contains supplementary material, which is available to authorized users.
PMCID: PMC4470036  PMID: 25997541
Urothelial bladder cancer; Cell line; Genomics; Mutation; Oncogene; Tumor suppressor
19.  Genome-wide association study of susceptibility loci for breast cancer in Sardinian population 
BMC Cancer  2015;15:383.
Despite progress in identifying genes associated with breast cancer, many more risk loci exist. Genome-wide association analyses in genetically-homogeneous populations, such as that of Sardinia (Italy), could represent an additional approach to detect low penetrance alleles.
We performed a genome-wide association study comparing 1431 Sardinian patients with non-familial, BRCA1/2-mutation-negative breast cancer to 2171 healthy Sardinian blood donors. DNA was genotyped using GeneChip Human Mapping 500 K Arrays or Genome-Wide Human SNP Arrays 6.0. To increase genomic coverage, genotypes of additional SNPs were imputed using data from HapMap Phase II. After quality control filtering of genotype data, 1367 cases (9 men) and 1658 controls (1156 men) were analyzed on a total of 2,067,645 SNPs.
Overall, 33 genomic regions (67 candidate SNPs) were associated with breast cancer risk at the p < 10−6 level. Twenty of these regions contained defined genes, including one already associated with breast cancer risk: TOX3. With a lower threshold for preliminary significance to p < 10−5, we identified 11 additional SNPs in FGFR2, a well-established breast cancer-associated gene. Ten candidate SNPs were selected, excluding those already associated with breast cancer, for technical validation as well as replication in 1668 samples from the same population. Only SNP rs345299, located in intron 1 of VAV3, remained suggestively associated (p-value, 1.16x10−5), but it did not associate with breast cancer risk in pooled data from two large, mixed-population cohorts.
This study indicated the role of TOX3 and FGFR2 as breast cancer susceptibility genes in BRCA1/2-wild-type breast cancer patients from Sardinian population.
Electronic supplementary material
The online version of this article (doi:10.1186/s12885-015-1392-9) contains supplementary material, which is available to authorized users.
PMCID: PMC4434540  PMID: 25956309
Breast cancer risk; BRCA1/2 mutation analysis; Genome-wide association study; Sardinian population
20.  Pleiotropic effects of genetic risk variants for other cancers on colorectal cancer risk: PAGE, GECCO, and CCFR Consortia 
Gut  2013;63(5):800-807.
Genome-wide association studies (GWAS) have identified a large number of single nucleotide polymorphisms (SNPs) associated with a wide array of cancer sites. Several of these variants demonstrate associations with multiple cancers, suggesting pleiotropic effects and shared biological mechanisms across some cancers. We hypothesized that SNPs previously associated with other cancers may additionally be associated with colorectal cancer. In a large-scale study, we examined 171 SNPs previously associated with 18 different cancers for their associations with colorectal cancer.
We examined 13,338 colorectal cancer cases and 40,967 controls from three consortia: Population Architecture using Genetics and Epidemiology (PAGE), Genetic Epidemiology of Colorectal Cancer (GECCO), and the Colon Cancer Family Registry (CCFR). Study-specific logistic regression results, adjusted for age, sex, principal components of genetic ancestry, and/or study specific factors (as relevant) were combined using fixed-effect meta-analyses to evaluate the association between each SNP and colorectal cancer risk. A Bonferroni-corrected p-value of 2.92×10−4 was used to determine statistical significance of the associations.
Two correlated SNPs— rs10090154 and rs4242382—in Region 1 of chromosome 8q24, a prostate cancer susceptibility region, demonstrated statistically significant associations with colorectal cancer risk. The most significant association was observed with rs4242382 (meta-analysis OR=1.12; 95% CI: 1.07–1.18; P=1.74×10−5), which also demonstrated similar associations across racial/ethnic populations and anatomical sub-sites.
This is the first study to clearly demonstrate Region 1 of chromosome 8q24 as a susceptibility locus for colorectal cancer, thus adding colorectal cancer to the list of cancer sites linked to this particular multi-cancer risk region at 8q24.
PMCID: PMC3918490  PMID: 23935004
colorectal cancer; pleiotropy; genome-wide association study; single nucleotide polymorphism
21.  A genome-wide association study of prostate cancer in West African men 
Human genetics  2013;133(5):509-521.
Age-adjusted mortality rates for prostate cancer are higher for African American men compared with those of European ancestry. Recent data suggest that West African men also have elevated risk for prostate cancer relative to European men. Genetic susceptibility to prostate cancer could account for part of this difference.
We conducted a genome-wide association study (GWAS) of prostate cancer in West African men in the Ghana Prostate Study. Association testing was performed using multivariable logistic regression adjusted for age and genetic ancestry for 474 prostate cancer cases and 458 population-based controls on the Illumina HumanOmni-5 Quad BeadChip.
The most promising association was at 10p14 within an intron of a long non-coding RNA (lncRNA RP11-543F8.2) 360 kb centromeric of GATA3 (p=1.29E−7). In sub-analyses, SNPs at 5q31.3 were associated with high Gleason score (≥7) cancers, the strongest of which was a missense SNP in PCDHA1 (rs34575154, p=3.66E−8), and SNPs at Xq28 (rs985081, p=8.66E−9) and 6q21 (rs2185710, p=5.95E−8) were associated with low Gleason score (<7) cancers. We sought to validate our findings in silico in the African Ancestry Prostate Cancer GWAS Consortium, but only one SNP, at 10p14, replicated at p<0.05. Of the 90 prostate cancer loci reported from studies of men of European, Asian or African American ancestry, we were able to test 81 in the Ghana Prostate Study, and 10 of these replicated at p<0.05.
Further genetic studies of prostate cancer in West African men are needed to confirm our promising susceptibility loci.
PMCID: PMC3988225  PMID: 24185611
prostate cancer; Africa; GWAS; case-control
22.  Insulin-like Growth Factor Pathway Genetic Polymorphisms, Circulating IGF1 and IGFBP3, and Prostate Cancer Survival 
The insulin-like growth factor (IGF) signaling pathway has been implicated in prostate cancer (PCa) initiation, but its role in progression remains unknown.
Among 5887 PCa patients (704 PCa deaths) of European ancestry from seven cohorts in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium, we conducted Cox kernel machine pathway analysis to evaluate whether 530 tagging single nucleotide polymorphisms (SNPs) in 26 IGF pathway-related genes were collectively associated with PCa mortality. We also conducted SNP-specific analysis using stratified Cox models adjusting for multiple testing. In 2424 patients (313 PCa deaths), we evaluated the association of prediagnostic circulating IGF1 and IGFBP3 levels and PCa mortality. All statistical tests were two-sided.
The IGF signaling pathway was associated with PCa mortality (P = .03), and IGF2-AS and SSTR2 were the main contributors (both P = .04). In SNP-specific analysis, 36 SNPs were associated with PCa mortality with P trend less than .05, but only three SNPs in the IGF2-AS remained statistically significant after gene-based corrections. Two were in linkage disequilibrium (r 2 = 1 for rs1004446 and rs3741211), whereas the third, rs4366464, was independent (r 2 = 0.03). The hazard ratios (HRs) per each additional risk allele were 1.19 (95% confidence interval [CI] = 1.06 to 1.34; P trend = .003) for rs3741211 and 1.44 (95% CI = 1.20 to 1.73; P trend < .001) for rs4366464. rs4366464 remained statistically significant after correction for all SNPs (P trend.corr = .04). Prediagnostic IGF1 (HRhighest vs lowest quartile = 0.71; 95% CI = 0.48 to 1.04) and IGFBP3 (HR = 0.93; 95% CI = 0.65 to 1.34) levels were not associated with PCa mortality.
The IGF signaling pathway, primarily IGF2-AS and SSTR2 genes, may be important in PCa survival.
PMCID: PMC4111284
23.  A Comprehensive Resequence-Analysis of 250kb Region of 8q24.21 in Men of African Ancestry 
The Prostate  2014;74(6):579-589.
Genome-wide association studies (GWAS) have identified that a ∼1M region centromeric to the MYC oncogene on chromosome 8q24.21 harbors at least 5 independent loci associated with prostate cancer risk and additional loci associated with cancers of breast, colon, bladder, and chronic lymphocytic leukemia (CLL). Because GWAS identify genetic markers that may be indirectly associated with disease, fine-mapping based on sequence analysis provides important insights into patterns of linkage disequilibrium (LD) and is critical in defining the optimal variants to nominate for biological follow-up.
To catalog variation in individuals of African ancestry, we resequenced a region (250kb; chr8:128,050,768-128,300,801, hg19) containing several prostate cancer susceptibility loci as well as a locus associated with CLL. Our samples set included 78 individuals from Ghana and 47 of African-Americans from Johns Hopkins University.
After quality control metrics were applied to next-generation sequence data, 1,838 SNPs were identified. Of these, 285 were novel and not yet reported in any public database. Using genotypes derived from sequencing, we refined the LD and recombination hotspots within the region and determined a set of tag SNPs to be used in future fine-mapping studies. Based on LD, we annotated putative risk loci and their surrogates using ENCODE data, which should help guide laboratory studies.
In comparison to the 1000 Genome Project data, we have identified additional variants that could be important in establishing priorities for future functional work designed to explain the biological basis of associations between SNPs and both prostate cancer and chronic lymphocytic leukemia.
PMCID: PMC4199861  PMID: 24783269
24.  Genome-wide association study identifies multiple susceptibility loci for diffuse large B-cell lymphoma 
Cerhan, James R | Berndt, Sonja I | Vijai, Joseph | Ghesquières, Hervé | McKay, James | Wang, Sophia S | Wang, Zhaoming | Yeager, Meredith | Conde, Lucia | de Bakker, Paul I W | Nieters, Alexandra | Cox, David | Burdett, Laurie | Monnereau, Alain | Flowers, Christopher R | De Roos, Anneclaire J | Brooks-Wilson, Angela R | Lan, Qing | Severi, Gianluca | Melbye, Mads | Gu, Jian | Jackson, Rebecca D | Kane, Eleanor | Teras, Lauren R | Purdue, Mark P | Vajdic, Claire M | Spinelli, John J | Giles, Graham G | Albanes, Demetrius | Kelly, Rachel S | Zucca, Mariagrazia | Bertrand, Kimberly A | Zeleniuch-Jacquotte, Anne | Lawrence, Charles | Hutchinson, Amy | Zhi, Degui | Habermann, Thomas M | Link, Brian K | Novak, Anne J | Dogan, Ahmet | Asmann, Yan W | Liebow, Mark | Thompson, Carrie A | Ansell, Stephen M | Witzig, Thomas E | Weiner, George J | Veron, Amelie S | Zelenika, Diana | Tilly, Hervé | Haioun, Corinne | Molina, Thierry Jo | Hjalgrim, Henrik | Glimelius, Bengt | Adami, Hans-Olov | Bracci, Paige M | Riby, Jacques | Smith, Martyn T | Holly, Elizabeth A | Cozen, Wendy | Hartge, Patricia | Morton, Lindsay M | Severson, Richard K | Tinker, Lesley F | North, Kari E | Becker, Nikolaus | Benavente, Yolanda | Boffetta, Paolo | Brennan, Paul | Foretova, Lenka | Maynadie, Marc | Staines, Anthony | Lightfoot, Tracy | Crouch, Simon | Smith, Alex | Roman, Eve | Diver, W Ryan | Offit, Kenneth | Zelenetz, Andrew | Klein, Robert J | Villano, Danylo J | Zheng, Tongzhang | Zhang, Yawei | Holford, Theodore R | Kricker, Anne | Turner, Jenny | Southey, Melissa C | Clavel, Jacqueline | Virtamo, Jarmo | Weinstein, Stephanie | Riboli, Elio | Vineis, Paolo | Kaaks, Rudolph | Trichopoulos, Dimitrios | Vermeulen, Roel C H | Boeing, Heiner | Tjonneland, Anne | Angelucci, Emanuele | Di Lollo, Simonetta | Rais, Marco | Birmann, Brenda M | Laden, Francine | Giovannucci, Edward | Kraft, Peter | Huang, Jinyan | Ma, Baoshan | Ye, Yuanqing | Chiu, Brian C H | Sampson, Joshua | Liang, Liming | Park, Ju-Hyun | Chung, Charles C | Weisenburger, Dennis D | Chatterjee, Nilanjan | Fraumeni, Joseph F | Slager, Susan L | Wu, Xifeng | de Sanjose, Silvia | Smedby, Karin E | Salles, Gilles | Skibola, Christine F | Rothman, Nathaniel | Chanock, Stephen J
Nature genetics  2014;46(11):1233-1238.
PMCID: PMC4213349  PMID: 25261932
25.  Defining the role of common variation in the genomic and biological architecture of adult human height 
Wood, Andrew R | Esko, Tonu | Yang, Jian | Vedantam, Sailaja | Pers, Tune H | Gustafsson, Stefan | Chu, Audrey Y | Estrada, Karol | Luan, Jian’an | Kutalik, Zoltán | Amin, Najaf | Buchkovich, Martin L | Croteau-Chonka, Damien C | Day, Felix R | Duan, Yanan | Fall, Tove | Fehrmann, Rudolf | Ferreira, Teresa | Jackson, Anne U | Karjalainen, Juha | Lo, Ken Sin | Locke, Adam E | Mägi, Reedik | Mihailov, Evelin | Porcu, Eleonora | Randall, Joshua C | Scherag, André | Vinkhuyzen, Anna AE | Westra, Harm-Jan | Winkler, Thomas W | Workalemahu, Tsegaselassie | Zhao, Jing Hua | Absher, Devin | Albrecht, Eva | Anderson, Denise | Baron, Jeffrey | Beekman, Marian | Demirkan, Ayse | Ehret, Georg B | Feenstra, Bjarke | Feitosa, Mary F | Fischer, Krista | Fraser, Ross M | Goel, Anuj | Gong, Jian | Justice, Anne E | Kanoni, Stavroula | Kleber, Marcus E | Kristiansson, Kati | Lim, Unhee | Lotay, Vaneet | Lui, Julian C | Mangino, Massimo | Leach, Irene Mateo | Medina-Gomez, Carolina | Nalls, Michael A | Nyholt, Dale R | Palmer, Cameron D | Pasko, Dorota | Pechlivanis, Sonali | Prokopenko, Inga | Ried, Janina S | Ripke, Stephan | Shungin, Dmitry | Stancáková, Alena | Strawbridge, Rona J | Sung, Yun Ju | Tanaka, Toshiko | Teumer, Alexander | Trompet, Stella | van der Laan, Sander W | van Setten, Jessica | Van Vliet-Ostaptchouk, Jana V | Wang, Zhaoming | Yengo, Loïc | Zhang, Weihua | Afzal, Uzma | Ärnlöv, Johan | Arscott, Gillian M | Bandinelli, Stefania | Barrett, Amy | Bellis, Claire | Bennett, Amanda J | Berne, Christian | Blüher, Matthias | Bolton, Jennifer L | Böttcher, Yvonne | Boyd, Heather A | Bruinenberg, Marcel | Buckley, Brendan M | Buyske, Steven | Caspersen, Ida H | Chines, Peter S | Clarke, Robert | Claudi-Boehm, Simone | Cooper, Matthew | Daw, E Warwick | De Jong, Pim A | Deelen, Joris | Delgado, Graciela | Denny, Josh C | Dhonukshe-Rutten, Rosalie | Dimitriou, Maria | Doney, Alex SF | Dörr, Marcus | Eklund, Niina | Eury, Elodie | Folkersen, Lasse | Garcia, Melissa E | Geller, Frank | Giedraitis, Vilmantas | Go, Alan S | Grallert, Harald | Grammer, Tanja B | Gräßler, Jürgen | Grönberg, Henrik | de Groot, Lisette C.P.G.M. | Groves, Christopher J | Haessler, Jeffrey | Hall, Per | Haller, Toomas | Hallmans, Goran | Hannemann, Anke | Hartman, Catharina A | Hassinen, Maija | Hayward, Caroline | Heard-Costa, Nancy L | Helmer, Quinta | Hemani, Gibran | Henders, Anjali K | Hillege, Hans L | Hlatky, Mark A | Hoffmann, Wolfgang | Hoffmann, Per | Holmen, Oddgeir | Houwing-Duistermaat, Jeanine J | Illig, Thomas | Isaacs, Aaron | James, Alan L | Jeff, Janina | Johansen, Berit | Johansson, Åsa | Jolley, Jennifer | Juliusdottir, Thorhildur | Junttila, Juhani | Kho, Abel N | Kinnunen, Leena | Klopp, Norman | Kocher, Thomas | Kratzer, Wolfgang | Lichtner, Peter | Lind, Lars | Lindström, Jaana | Lobbens, Stéphane | Lorentzon, Mattias | Lu, Yingchang | Lyssenko, Valeriya | Magnusson, Patrik KE | Mahajan, Anubha | Maillard, Marc | McArdle, Wendy L | McKenzie, Colin A | McLachlan, Stela | McLaren, Paul J | Menni, Cristina | Merger, Sigrun | Milani, Lili | Moayyeri, Alireza | Monda, Keri L | Morken, Mario A | Müller, Gabriele | Müller-Nurasyid, Martina | Musk, Arthur W | Narisu, Narisu | Nauck, Matthias | Nolte, Ilja M | Nöthen, Markus M | Oozageer, Laticia | Pilz, Stefan | Rayner, Nigel W | Renstrom, Frida | Robertson, Neil R | Rose, Lynda M | Roussel, Ronan | Sanna, Serena | Scharnagl, Hubert | Scholtens, Salome | Schumacher, Fredrick R | Schunkert, Heribert | Scott, Robert A | Sehmi, Joban | Seufferlein, Thomas | Shi, Jianxin | Silventoinen, Karri | Smit, Johannes H | Smith, Albert Vernon | Smolonska, Joanna | Stanton, Alice V | Stirrups, Kathleen | Stott, David J | Stringham, Heather M | Sundström, Johan | Swertz, Morris A | Syvänen, Ann-Christine | Tayo, Bamidele O | Thorleifsson, Gudmar | Tyrer, Jonathan P | van Dijk, Suzanne | van Schoor, Natasja M | van der Velde, Nathalie | van Heemst, Diana | van Oort, Floor VA | Vermeulen, Sita H | Verweij, Niek | Vonk, Judith M | Waite, Lindsay L | Waldenberger, Melanie | Wennauer, Roman | Wilkens, Lynne R | Willenborg, Christina | Wilsgaard, Tom | Wojczynski, Mary K | Wong, Andrew | Wright, Alan F | Zhang, Qunyuan | Arveiler, Dominique | Bakker, Stephan JL | Beilby, John | Bergman, Richard N | Bergmann, Sven | Biffar, Reiner | Blangero, John | Boomsma, Dorret I | Bornstein, Stefan R | Bovet, Pascal | Brambilla, Paolo | Brown, Morris J | Campbell, Harry | Caulfield, Mark J | Chakravarti, Aravinda | Collins, Rory | Collins, Francis S | Crawford, Dana C | Cupples, L Adrienne | Danesh, John | de Faire, Ulf | den Ruijter, Hester M | Erbel, Raimund | Erdmann, Jeanette | Eriksson, Johan G | Farrall, Martin | Ferrannini, Ele | Ferrières, Jean | Ford, Ian | Forouhi, Nita G | Forrester, Terrence | Gansevoort, Ron T | Gejman, Pablo V | Gieger, Christian | Golay, Alain | Gottesman, Omri | Gudnason, Vilmundur | Gyllensten, Ulf | Haas, David W | Hall, Alistair S | Harris, Tamara B | Hattersley, Andrew T | Heath, Andrew C | Hengstenberg, Christian | Hicks, Andrew A | Hindorff, Lucia A | Hingorani, Aroon D | Hofman, Albert | Hovingh, G Kees | Humphries, Steve E | Hunt, Steven C | Hypponen, Elina | Jacobs, Kevin B | Jarvelin, Marjo-Riitta | Jousilahti, Pekka | Jula, Antti M | Kaprio, Jaakko | Kastelein, John JP | Kayser, Manfred | Kee, Frank | Keinanen-Kiukaanniemi, Sirkka M | Kiemeney, Lambertus A | Kooner, Jaspal S | Kooperberg, Charles | Koskinen, Seppo | Kovacs, Peter | Kraja, Aldi T | Kumari, Meena | Kuusisto, Johanna | Lakka, Timo A | Langenberg, Claudia | Le Marchand, Loic | Lehtimäki, Terho | Lupoli, Sara | Madden, Pamela AF | Männistö, Satu | Manunta, Paolo | Marette, André | Matise, Tara C | McKnight, Barbara | Meitinger, Thomas | Moll, Frans L | Montgomery, Grant W | Morris, Andrew D | Morris, Andrew P | Murray, Jeffrey C | Nelis, Mari | Ohlsson, Claes | Oldehinkel, Albertine J | Ong, Ken K | Ouwehand, Willem H | Pasterkamp, Gerard | Peters, Annette | Pramstaller, Peter P | Price, Jackie F | Qi, Lu | Raitakari, Olli T | Rankinen, Tuomo | Rao, DC | Rice, Treva K | Ritchie, Marylyn | Rudan, Igor | Salomaa, Veikko | Samani, Nilesh J | Saramies, Jouko | Sarzynski, Mark A | Schwarz, Peter EH | Sebert, Sylvain | Sever, Peter | Shuldiner, Alan R | Sinisalo, Juha | Steinthorsdottir, Valgerdur | Stolk, Ronald P | Tardif, Jean-Claude | Tönjes, Anke | Tremblay, Angelo | Tremoli, Elena | Virtamo, Jarmo | Vohl, Marie-Claude | Amouyel, Philippe | Asselbergs, Folkert W | Assimes, Themistocles L | Bochud, Murielle | Boehm, Bernhard O | Boerwinkle, Eric | Bottinger, Erwin P | Bouchard, Claude | Cauchi, Stéphane | Chambers, John C | Chanock, Stephen J | Cooper, Richard S | de Bakker, Paul IW | Dedoussis, George | Ferrucci, Luigi | Franks, Paul W | Froguel, Philippe | Groop, Leif C | Haiman, Christopher A | Hamsten, Anders | Hayes, M Geoffrey | Hui, Jennie | Hunter, David J. | Hveem, Kristian | Jukema, J Wouter | Kaplan, Robert C | Kivimaki, Mika | Kuh, Diana | Laakso, Markku | Liu, Yongmei | Martin, Nicholas G | März, Winfried | Melbye, Mads | Moebus, Susanne | Munroe, Patricia B | Njølstad, Inger | Oostra, Ben A | Palmer, Colin NA | Pedersen, Nancy L | Perola, Markus | Pérusse, Louis | Peters, Ulrike | Powell, Joseph E | Power, Chris | Quertermous, Thomas | Rauramaa, Rainer | Reinmaa, Eva | Ridker, Paul M | Rivadeneira, Fernando | Rotter, Jerome I | Saaristo, Timo E | Saleheen, Danish | Schlessinger, David | Slagboom, P Eline | Snieder, Harold | Spector, Tim D | Strauch, Konstantin | Stumvoll, Michael | Tuomilehto, Jaakko | Uusitupa, Matti | van der Harst, Pim | Völzke, Henry | Walker, Mark | Wareham, Nicholas J | Watkins, Hugh | Wichmann, H-Erich | Wilson, James F | Zanen, Pieter | Deloukas, Panos | Heid, Iris M | Lindgren, Cecilia M | Mohlke, Karen L | Speliotes, Elizabeth K | Thorsteinsdottir, Unnur | Barroso, Inês | Fox, Caroline S | North, Kari E | Strachan, David P | Beckmann, Jacques S. | Berndt, Sonja I | Boehnke, Michael | Borecki, Ingrid B | McCarthy, Mark I | Metspalu, Andres | Stefansson, Kari | Uitterlinden, André G | van Duijn, Cornelia M | Franke, Lude | Willer, Cristen J | Price, Alkes L. | Lettre, Guillaume | Loos, Ruth JF | Weedon, Michael N | Ingelsson, Erik | O’Connell, Jeffrey R | Abecasis, Goncalo R | Chasman, Daniel I | Goddard, Michael E | Visscher, Peter M | Hirschhorn, Joel N | Frayling, Timothy M
Nature genetics  2014;46(11):1173-1186.
Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explain one-fifth of heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ~2,000, ~3,700 and ~9,500 SNPs explained ~21%, ~24% and ~29% of phenotypic variance. Furthermore, all common variants together captured the majority (60%) of heritability. The 697 variants clustered in 423 loci enriched for genes, pathways, and tissue-types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/beta-catenin, and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.
PMCID: PMC4250049  PMID: 25282103

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