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Background: Project Healthy Bones (PHB) is a 24-week, peer-led exercise and education program for older adults at risk of osteoporosis. Method: Residents from an assisted living and senior community program were enrolled after medical clearance. Participant demographics, geriatric fitness assessments, exercise logs, quizzes, and surveys were collected at baseline and 24 weeks. Data were analyzed using paired t tests and ANOVA of change scores for the pooled data within the R statistical environment. Results: Forty of the 53 enrolled participants completed the program. Participants improved their strength, balance, posture, and flexibility, resulting in a reduced risk of falls and fractures. In addition, their knowledge of bone health, nutrition, and fall prevention increased. Conclusion: Offering low-cost disease-specific programs such as PHB helps minimize the complications of osteoporosis and improve the overall health of participants. Implementing disease-specific public health programs in assisted living centers can increase access to programs.

Background: QT interval (QT) genome-wide association studies (GWAS) have identified upwards of 35 common variant loci, including SNPs adjacent to putative transcription elongation factor TCEA3. Transcription elongation control has broad effects on gene expression, the misregulation of which is known to influence cardiac conduction system morphogenesis as well as activation or repression of key regulatory genes. Thus, we hypothesized that a genome-wide gene-gene interaction study of TCEA3 lead SNP rs2298632 would identify novel loci that influence QT. Methods: Using 1000 Genomes imputed data (>20 million SNPs) in n=67,445 participants (69% Caucasian; 18% Hispanic/Latino; 11% African American) from 10 studies, we conducted genome-wide meta-analyses to test for the presence of: interaction effect loci by examining rs2298632xSNP interactions on QT; and joint effect loci by simultaneously examining SNP main effects and rs2298632xSNP interactions on QT. Inverse-variance weighted meta-analysis of genomically controlled ancestry- and study-specific summary effects estimated using multivariable adjusted linear models or generalized estimating equations that incorporated robust standard errors was performed using METAL. SNPs demonstrating evidence of heterogeneity (Cochran’s Q P < 0.05) and SNPs that were infrequent or rare (minor allele frequency [MAF] Results: We identified one genome-wide significant interaction effect locus ( P INT -8 ) at PVT1 (lead SNP: rs4733591; mean MAF = 32%), a long non-coding RNA gene for which previous GWAS identified suggestive associations with left ventricular systolic dysfunction. We also identified four genome-wide significant joint effect loci ( P JOINT -8 ) that mapped within or nearby NUCKS1 (lead SNP: rs823094; MAF = 0.33) , CASR (lead SNP = rs17251221; MAF = 0.13) , ACTBL2 (lead SNP = rs7737409; MAF = 0.22) , and KDM1B (lead SNP = rs34969716; MAF = 0.26) , loci with roles in calcium sensing, regulation of gene expression through histone modification, and tumor suppression. Conclusion: Extension of traditional main effects GWAS to interrogate gene-gene interactions for biologically motivated loci like TCEA3 may help inform the structure and function of genetic pathways underlying complex traits like QT.

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution., A full list of acknowledgments appears in the Supplementary Note 4. Co-author A.J.M.d.C. recently passed away while this work was in process. This work was performed under the auspices of the Genetic Investigation of ANthropometric Traits (GIANT) consortium. We acknowledge the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium for encouraging CHARGE studies to participate in this effort and for the contributions of CHARGE members to the analyses conducted for this research. Funding for this study was provided by the Aase and Ejner Danielsens Foundation; Academy of Finland (41071, 77299, 102318, 110413, 117787, 121584, 123885, 124243, 124282, 126925, 129378, 134309, 286284); Accare Center for Child and Adolescent Psychiatry; Action on Hearing Loss (G51); Agence Nationale de la 359 Recherche; Agency for Health Care Policy Research (HS06516); ALF/LUA research grant in Gothenburg; ALFEDIAM; ALK-Abelló A/S; Althingi; American Heart Association (13POST16500011); Amgen; Andrea and Charles Bronfman Philanthropies; Ardix Medical; Arthritis Research UK; Association Diabète Risque Vasculaire; Australian National Health and Medical Research Council (241944, 339462, 389875, 389891, 389892, 389927, 389938, 442915, 442981, 496739, 552485, 552498); Avera Institute; Bayer Diagnostics; Becton Dickinson; BHF (RG/14/5/30893); Boston Obesity Nutrition Research Center (DK46200), Bristol-Myers Squibb; British Heart Foundation (RG/10/12/28456, RG2008/08, RG2008/014, SP/04/002); Medical Research Council of Canada; Canadian Institutes for Health Research (FRCN-CCT-83028); Cancer Research UK; Cardionics; Cavadis B.V., Center for Medical Systems Biology; Center of Excellence in Genomics; CFI; CIHR; City of Kuopio; CNAMTS; Cohortes Santé TGIR; Contrat de Projets État-Région; Croatian Science Foundation (8875); Danish Agency for Science, Technology and Innovation; Danish Council for Independent Research (DFF-1333-00124, DFF-1331-00730B); County Council of Dalarna; Dalarna University; Danish Council for Strategic Research; Danish Diabetes Academy; Danish Medical Research Council; Department of Health, UK; Development Fund from the University of Tartu (SP1GVARENG); Diabetes Hilfs- und Forschungsfonds Deutschland; Diabetes UK; Diabetes Research and Wellness Foundation Fellowship; Donald W. Reynolds Foundation; Dr Robert Pfleger-Stiftung; Dutch Brain Foundation; Dutch Diabetes Research Foundation; Dutch Inter University Cardiology Institute; Dutch Kidney Foundation (E033); Dutch Ministry of Justice; the DynaHEALTH action No. 633595, Economic Structure Enhancing Fund of the Dutch Government; Else Kröner-Fresenius-Stiftung (2012_A147, P48/08//A11/08); Emil Aaltonen Foundation; Erasmus University Medical Center Rotterdam; Erasmus MC and Erasmus University Rotterdam; the Municipality of Rotterdam; Estonian Government (IUT20-60, IUT24-6); Estonian Research Roadmap through the Estonian Ministry of Education and Research (3.2.0304.11-0312); European Research Council (ERC Starting Grant and 323195:SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC); European Regional Development Fund; European Science Foundation (EU/QLRT-2001-01254); European Commission (018947, 018996, 201668, 223004, 230374, 279143, 284167, 305739, BBMRI-LPC-313010, HEALTH-2011.2.4.2-2-EU-MASCARA, HEALTH-2011-278913, HEALTH-2011-294713-EPLORE, HEALTH-F2-2008-201865-GEFOS, HEALTH-F2-2013-601456, HEALTH-F4-2007-201413, HEALTH-F4-2007-201550-HYPERGENES, HEALTH-F7-305507 HOMAGE, IMI/115006, LSHG-CT-2006-018947, LSHG-CT-2006-01947, LSHM-CT-2004-005272, LSHM-CT-2006-037697, LSHM-CT-2007-037273, QLG1-CT-2002-00896, QLG2-CT-2002-01254); Faculty of Biology and Medicine of Lausanne; Federal Ministry of Education and Research (01ZZ0103, 01ZZ0403, 01ZZ9603, 03IS2061A, 03ZIK012); Federal State of Mecklenburg-West Pomerania; Fédération Française de Cardiologie; Finnish Cultural Foundation; Finnish Diabetes Association; Finnish Foundation of Cardiovascular Research; Finnish Heart Association; Fondation Leducq; Food Standards Agency; Foundation for Strategic Research; French Ministry of Research; FRSQ; Genetic Association Information Network (GAIN) of the Foundation for the NIH; German Federal Ministry of Education and Research (BMBF, 01ER1206, 01ER1507); GlaxoSmithKline; Greek General Secretary of Research and Technology; Göteborg Medical Society; Health and Safety Executive; Healthcare NHS Trust; Healthway; Western Australia; Heart Foundation of Northern Sweden; Helmholtz Zentrum München—German Research Center for Environmental Health; Hjartavernd; Ingrid Thurings Foundation; INSERM; InterOmics (PB05 MIUR-CNR); INTERREG IV Oberrhein Program (A28); Interuniversity Cardiology Institute of the Netherlands (ICIN, 09.001); Italian Ministry of Health (ICS110.1/RF97.71); Italian Ministry of Economy and Finance (FaReBio di Qualità); Marianne and Marcus Wallenberg Foundation; the Ministry of Health, Welfare and Sports, the Netherlands; J.D.E. and Catherine T, MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health; Juho Vainio Foundation; Juvenile Diabetes Research Foundation International; KfH Stiftung Präventivmedizin e.V.; King's College London; Knut and Alice Wallenberg Foundation; Kuopio University Hospital; Kuopio, Tampere and Turku University Hospital Medical Funds (X51001); La Fondation de France; Leenaards Foundation; Lilly; LMUinnovativ; Lundberg Foundation; Magnus Bergvall Foundation; MDEIE; Medical Research Council UK (G0000934, G0601966, G0700931, MC_U106179471, MC_UU_12019/1); MEKOS Laboratories; Merck Santé; Ministry for Health, Welfare and Sports, The Netherlands; Ministry of Cultural Affairs of Mecklenburg-West Pomerania; Ministry of Economic Affairs, The Netherlands; Ministry of Education and Culture of Finland (627;2004-2011); Ministry of Education, Culture and Science, The Netherlands; Ministry of Science, Education and Sport in the Republic of Croatia (108-1080315-0302); MRC centre for Causal Analyses in Translational Epidemiology; MRC Human Genetics Unit; MRC-GlaxoSmithKline pilot programme (G0701863); MSD Stipend Diabetes; National Institute for Health Research; Netherlands Brain Foundation (F2013(1)-28); Netherlands CardioVascular Research Initiative (CVON2011-19); Netherlands Genomics Initiative (050-060-810); Netherlands Heart Foundation (2001 D 032, NHS2010B280); Netherlands Organization for Scientific Research (NWO) and Netherlands Organisation for Health Research and Development (ZonMW) (56-464-14192, 60-60600-97-118, 100-001-004, 261-98-710, 400-05-717, 480-04-004, 480-05-003, 481-08-013, 904-61-090, 904-61-193, 911-11-025, 985-10-002, Addiction-31160008, BBMRI–NL 184.021.007, GB-MaGW 452-04-314, GB-MaGW 452-06-004, GB-MaGW 480-01-006, GB-MaGW 480-07-001, GB-MW 940-38-011, Middelgroot-911-09-032, NBIC/BioAssist/RK 2008.024, Spinozapremie 175.010.2003.005, 175.010.2007.006); Neuroscience Campus Amsterdam; NHS Foundation Trust; National Institutes of Health (1RC2MH089951, 1Z01HG000024, 24152, 263MD9164, 263MD821336, 2R01LM010098, 32100-2, 32122, 32108, 5K99HL130580-02, AA07535, AA10248, AA11998, AA13320, AA13321, AA13326, AA14041, AA17688, AG13196, CA047988, DA12854, DK56350, DK063491, DK078150, DK091718, DK100383, DK078616, ES10126, HG004790, HHSN268200625226C, HHSN268200800007C, HHSN268201200036C, HHSN268201500001I, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSN271201100004C, HL043851, HL45670, HL080467, HL085144, HL087660, HL054457, HL119443, HL118305, HL071981, HL034594, HL126024, HL130114, KL2TR001109, MH66206, MH081802, N01AG12100, N01HC55015, N01HC55016, N01C55018, N01HC55019, N01HC55020, N01HC55021, N01HC55022, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC95159, N01HC95160, N01HC95161, N01HC95162, N01HC95163, N01HC95164, N01HC95165, N01HC95166, N01HC95167, N01HC95168, N01HC95169, N01HG65403, N01WH22110, N02HL6‐4278, N01-HC-25195, P01CA33619, R01HD057194, R01HD057194, R01AG023629, R01CA63, R01D004215701A, R01DK075787, R01DK062370, R01DK072193, R01DK075787, R01DK089256, R01HL53353, R01HL59367, R01HL086694, R01HL087641, R01HL087652, R01HL103612, R01HL105756, R01HL117078, R01HL120393, R03 AG046389, R37CA54281, RC2AG036495, RC4AG039029, RPPG040710371, RR20649, TW008288, TW05596, U01AG009740, U01CA98758, U01CA136792, U01DK062418, U01HG004402, U01HG004802, U01HG007376, U01HL080295, UL1RR025005, UL1TR000040, UL1TR000124, UL1TR001079, 2T32HL007055-36, T32GM074905, HG002651, HL084729, N01-HC-25195, UM1CA182913); NIH, National Institute on Aging (Intramural funding, NO1-AG-1-2109); Northern Netherlands Collaboration of Provinces; Novartis Pharma; Novo Nordisk; Novo Nordisk Foundation; Nutricia Research Foundation (2016-T1); ONIVINS; Parnassia Bavo group; Pierre Fabre; Province of Groningen; Päivikki and Sakari Sohlberg Foundation; Påhlssons Foundation; Paavo Nurmi Foundation; Radboud Medical Center Nijmegen; Research Centre for Prevention and Health, the Capital Region of Denmark; the Research Institute for Diseases in the Elderly; Research into Ageing; Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center; Roche; Royal Society; Russian Foundation for Basic Research (NWO-RFBR 047.017.043); Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06); Sanofi-Aventis; Scottish Government Health Directorates, Chief Scientist Office (CZD/16/6); Siemens Healthcare; Social Insurance Institution of Finland (4/26/2010); Social Ministry of the Federal State of Mecklenburg-West Pomerania; Société Francophone du 358 Diabète; State of Bavaria; Stiftelsen för Gamla Tjänarinnor; Stockholm County Council (560183, 592229); Strategic Cardiovascular and Diabetes Programmes of Karolinska Institutet and Stockholm County Council; Stroke Association; Swedish Diabetes Association; Swedish Diabetes Foundation (2013-024); Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation (20120197, 20150711); Swedish Research Council (0593, 8691, 2012-1397, 2012-1727, and 2012-2215); Swedish Society for Medical Research; Swiss Institute of Bioinformatics; Swiss National Science Foundation (3100AO-116323/1, 31003A-143914, 33CSCO-122661, 33CS30-139468, 33CS30-148401, 51RTP0_151019); Tampere Tuberculosis Foundation; Technology Foundation STW (11679); The Fonds voor Wetenschappelijk Onderzoek Vlaanderen, Ministry of the Flemish Community (G.0880.13, G.0881.13); The Great Wine Estates of the Margaret River Region of Western Australia; Timber Merchant Vilhelm Bangs Foundation; Topcon; Tore Nilsson Foundation; Torsten and Ragnar Söderberg's Foundation; United States – Israel Binational Science Foundation (Grant 2011036), Umeå University; University Hospital of Regensburg; University of Groningen; University Medical Center Groningen; University of Michigan; University of Utrecht; Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) (b2011036); Velux Foundation; VU University’s Institute for Health and Care Research; Västra Götaland Foundation; Wellcome Trust (068545, 076113, 079895, 084723, 088869, WT064890, WT086596, WT098017, WT090532, WT098051, 098381); Wissenschaftsoffensive TMO; Yrjö Jahnsson Foundation; and Åke Wiberg Foundation. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute (NHLBI); the National Institutes of Health (NIH); or the U.S. Department of Health and Human Services.

Objective: Innate immune abnormalities have been frequently reported in children with autism spectrum disorders (ASD), but a role of innate immunity in ASD is not well understood. This study explored a possible role of innate immunity in ASD clinical features and co-morbidities.Methods: Purified peripheral blood monocytes (PBMo) from ASD (N=125) and non-ASD (N=36) subjects were cultured overnight with or without stimulants of innate immunity, and production of pro-inflammatory and counter-regulator cytokines were assessed. Behavioral symptoms were assessed by aberrant behavioral checklist (ABC) at the time of PBMo sampling.Results: ASD PBMo revealed highly variable IL-1β/IL-10 ratios, in contrast to a tight range of IL-1β/IL-10 ratios in non-ASD control cells. There was no association between cytokine levels or IL-1β/IL-10 ratios and ABC subscale scores when ASD data was analyzed, as a whole. However, when ASD data was separated into high, low, or normal (equivalent to controls) IL-1β/IL-1 ratio groups, IL-1β levels were positively associated with stereotypy in the high ratio group. In contrast, IL-1β and IL-10 levels were negatively associated with irritability, lethargy, and hyperactivity in the normal ratio group. The low ratio group revealed a negative association between IL-1β levels and lethargy. When longitudinal changes in cytokine production from PBMo were studied in selected ASD subjects, fluctuating ratios were found in ASD subjects with deviated (high or low) IL-1β/IL-10 ratios, but ratios remained stable in ASD subjects with normal ratios. ASD subjects with deviated ratios were found to have higher frequencies of non-IgE mediated food allergy (NFA) (p

PR interval (PR) prolongation is a predictor of atrial fibrillation, a common cardiac arrhythmia, and is an established risk factor for pacemaker implantation, heart failure, stroke, and all-cause mortality. Previous genome-wide association studies in European (EU) and African (AA) descent populations have identified multiple loci associated with PR. However, it is unclear whether these loci are relevant in other racial groups, including Hispanic/Latinos (HL). Extending studies to include admixed populations such as HL also provides the potential to narrow regions associated with PR and identify population specific variants. Therefore, we evaluated 1,135 SNPs from four previously identified PR loci ( SCN5A, SCN10A, CAV1-CAV2, and TBX5-TBX3 ), fine-mapped on the Illumina Metabochip for association with PR. Associations were examined using linear regression assuming an additive genetic model and adjusting for global ancestry and clinical covariates (age, sex, RR interval, body mass index, height, systolic blood pressure) in 11,800 HL participants from participating studies of the Population Architecture using Genomics and Epidemiology (PAGE) consortia. Three of the four loci ( SCN5A, SCN10A, and CAV1-CAV2 ) generalized to HL populations ( P 2 >0.2 with EU-identified index SNPs). At each of these loci, we identified SNPs with stronger evidence of association with PR than the EU-identified index SNP when examined in the HL population ( SCN5A : rs7374540; SCN10A : rs6801957; CAV-CAV2 : rs717957). Conditional analyses also identified two additional independent SNPs at each of the three generalized loci ( SCN5A : rs3796387, rs9861242; SCN10A : rs10428132, rs7433723; CAV1-CAV2 : rs3801995, rs3807994), representing potential secondary signals. Furthermore, linkage disequilibrium (LD) patterns in Hispanics enabled the narrowing of the regions flanking the EU-identified index SNPs. For example, at the CAV1-CAV2 locus, HL LD patterns indicated that seven SNPs were in LD with rs3807989, the EU-identified index SNP, compared to 17 SNPs that were in LD with rs3807989 when applying EU LD patterns, reducing the size of the region by 71 kilobases. Finally, we examined evidence for population specific signals outside of the specific regions identified in EU populations which were associated with PR in Hispanics by focusing on SNPs uncorrelated with the EU index signals. At all four loci, multiple SNPs had evidence of association independent of the signals examined based on EU results. In conclusion, the same genetic loci that influence PR in European descent populations are also important in Hispanic populations, although we detected evidence for population specific SNPs. These results emphasize the importance of examining genetic associations in diverse populations in order to narrow the genomic interval for future functional interrogation.

Introduction: Waist-to-hip ratio (WHR) is associated with cardiometabolic disease and its risk factors independent of body-mass-index (BMI). Previous GWAS on WHR adjusting for BMI (WHR’) have identified several SNPs but mainly among European descent populations (EA SNPs). While these SNPs point to general loci of interest, they are not likely the causal variants. Expanding analyses of these loci to other race and ethnic populations can dually assess whether these loci apply to more diverse populations and utilize differences in genetic linkage disequilibrium (LD) patterns to narrow the region containing the causal variant. Objective: To better capture genetic variants of WHR’ in self-identified African Americans (AA) within 14 loci containing previously reported WHR’ EA SNPs. Methods: Up to 14,322 AA participants (aged 20-93 years, 61% female) were included from seven studies. Individuals were genotyped using the Illumina iSelect Metabochip array consisting of nearly 200,000 SNPs enriched to fine map GWAS signals of cardiometabolic traits, including the 14 WHR’ loci. We modeled WHR’ adjusting for age, sex, study site, and the first three ancestry principle components for each study and conducted an inverse variance weighted meta-analysis. Variants reaching a Bonferroni adjusted threshold of P Results: Within the 14 regions, seven loci (in or near TBX15-WARS2, DNM3-PIGC, GRB14, NISCH-STAB1, ADAMTS9, VEGFA, and RSPO3 ) contained a significant AA SNP. In the GRB14 locus, the AA SNP (rs10195252, P =1.93x10 -06 ) was the same as the EA SNP. Among four of the seven loci, the AA SNPs (rs12096179 near TBX15-WARS2, rs2371767 in ADAMTS9 , rs1358990 near VEGFA, and rs9385487 in RSPO3 ) were not independent of the EA SNPs (LD r 2 ≥0.3), but were more significant in AA, possibly narrowing the region containing the functional variant. Two AA SNPs (rs11715796 in NISCH-STAB1 and rs4916251 near DNM3-PIGC ) suggesting independence from the EA SNP (r 2 NISCH-STAB1 had a stronger association after conditioning on the EA SNP, signifying independence. The AA SNP near DNM3-PIGC was less significant after conditioning, indicating it points to the same signal as the EA SNP. We have identified participants from additional cohorts for inclusion in future analyses and will conduct a sex-stratified analysis to further explore WHR’ loci among AA. Conclusions: Examination of 14 previously identified loci for WHR’ in AA individuals revealed one novel signal in NISCH-STAB1 . Five additional non-independent AA SNPs suggested a narrower region containing the WHR’ altering variant based on the LD structure differences between EA and AA. These findings highlight the value in expanding genetic association studies to other race and ethnic populations.

Clinical response to immunomodulatory therapies in multiple sclerosis (MS) is variable among patients. Currently, there are no validated biomarkers of clinical response to any of the approved treatments for MS. The objective of this study was to determine if HLA-class II alleles predict the clinical response to glatiramer acetate (GA).This was a prospective study of 64 MS patients with relapsing-remitting disease. Patients were HLA-typed and classified as GA-responders or non-responders after 2 years of treatment based on a clinical criterion. Statistical models were used to determine whether HLA-DR and DQ alleles and haplotypes predict the clinical response to GA.Tests of association of response singled out four alleles and two haplotypes with nominal p0.01. The presence of alleles DR15 or DQ6 or the absence of DR17 and DQ2 alleles was associated with favorable clinical response. The presence of the DR15-DQ6 haplotype and the absence of the DR17-DQ2 haplotype were also associated with favorable treatment response. A best fitting two-haplotype model resulted in the identification of three prognostic categories (good, neutral, and poor). A DR15-DQ6 positive but DR17-DQ2 negative combination was strongly predictive of a favorable clinical response (71%). Conversely, a DR15-DQ6 negative but DR17-DQ2 positive combination was strongly predictive of poor clinical response to GA (17%).HLA-DR and DQ typing may prove to be useful biomarkers of predicting response to GA in MS and may help select patients appropriate for this treatment.

Background: Genome-wide association studies have identified hundreds of susceptibility loci for various cancer sites including prostate cancer. Several of the risk loci have demonstrated pleiotropic effects with multiple cancers, suggesting shared biological pathways in carcinogenesis. In the present study, we systematically investigated the effects of other cancer-associated variants on prostate cancer risk. Methods: We examined 196 genetic risk variants identified for 18 cancer and cancer-related traits in a total of 28,135 prostate cancer cases and 37,218 controls. This large meta-analysis of multiethnic samples was assembled from three consortia: Population Architecture using Genetics and Epidemiology (PAGE), Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) and Breast and Prostate Cancer Cohort Consortium (BPC3). Study- and ethnic-specific logistic regressions results were combined through fixed-effect meta-analysis to evaluate the association between each variant and prostate cancer risk, applying a Bonferroni-corrected significance threshold of 2.6×10-4. We also assessed the cumulative effect of other cancer risk variants on prostate cancer susceptibility via risk score analysis. Results: Three variants, RTEL rs6010620, TERT rs2853676 and HLA rs6457327, demonstrated statistically significant associations with prostate cancer risk after correcting for multiple comparisons. Originally reported as a follicular lymphoma risk variant, rs6457327 was associated with prostate cancer at the genome-wide significant level (meta-analysis OR=0.93, 95% CI: 0.91-0.96; p=4.5×10-8). The cumulative genetic risk score for risk variants of other cancers demonstrated a significant effect on prostate cancer risk (p=0.021). Conclusions: This systematic pleiotropy analysis identified HLA as a novel susceptibility locus for prostate cancer in a large multiethnic population. The genetic variant with pleiotropic effects on both follicular lymphoma and prostate cancer provides evidence for shared etiologic mechanisms between prostate cancer and other cancer sites. Citation Format: Ying Han, Shelly-Ann Love, William S. Bush, Daniele Campa, Anne M. Butler, Logan Dumitrescu, Konstantinos Tsilidis, Iona Cheng, Lynne R. Wilkens, Jay H. Fowke, Jose Luis Ambite, Steve Buyske, S. Lani Park, The PRACTICAL Consortium, The BPC3 Consortium, Christopher A. Haiman, Loic Le Marchand, Lucia A. Hindorff, Federico Canzian, Fredrick R. Schumacher. Pleiotropy analysis identifies a novel prostate cancer variant at 6p21.33: The PAGE, PRACTICAL, and BPC3 Consortia. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5064. doi:10.1158/1538-7445.AM2014-5064