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AbstractSelf-rated health questions have been proven to be a highly reliable and valid measure of overall health as measured by other indicators in many population groups. It also has been shown to be a very good predictor of mortality, chronic or severe diseases, and the need for services, and is positively correlated with clinical assessments. Genetic factors have been estimated to account for 25–64% of the variance in the liability of self-rated health. The aim of the present study was to identify Single Nucleotide Polymorphisms (SNPs) underlying the heritability of self-rated health by conducting a genome-wide association analysis in a large sample of 6,706 Australian individuals aged 18–92. No genome wide significant SNPs associated with self-rated health could be identified, indicating that self-rated health may be influenced by a large number of SNPs with very small effect size. A very large sample will be needed to identify these SNPs.

International Parkinson's Disease Genomics Consortium (IPDGC). ; [Background] Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome‐wide association studies. The most recent large‐scale PD genome‐wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome‐wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus. ; [Methods] We included all significant genome‐wide signals from multiple recent PD genome‐wide association studies including themost recent PD risk genome‐wide association study, age‐at‐onset genome‐wide association study, progression genome‐wide association study, and Asian population PD risk genome‐wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self‐ranked criteria. Multiple databases were queried for each gene to collect additional causal data. ; [Results] We created a PD genome‐wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow‐up functional studies to identify potential therapeutic targets. ; [Conclusions] Our PD genome‐wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large‐scale PD genome‐wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA. ; This work was supported in part by the Intramural Research Programs of the National Institute of Neurological Disorders and Stroke (NINDS), the National Institute on Aging (NIA), and the National Institute of Environmental Health Sciences, both part of the National Institutes of Health, Department of Health and Human Services (project numbers 1ZIA‐NS003154, Z01‐AG000949‐02, and Z01‐ES101986). We thank the research participants and employees of 23andMe for making this work possible. C.W. is supported by the UK Dementia Research Institute funded by the Medical Research Council (MRC), Alzheimer's Society and Alzheimer's Research UK. C.S. is supported by the Ser Cymru II program, which is partly funded by Cardiff University and the European Regional Development Fund through the Welsh Government. Data were generated as part of the PsychENCODE Consortium supported by: U01MH103339, U01MH103365, U01MH103392, U01MH103340, U01MH103346, R01MH105472, R01MH094714, R01MH105898, R21MH102791, R21MH105881, R21MH103877, and P50MH106934 awarded to Schahram Akbarian (Icahn School of Medicine at Mount Sinai), Gregory Crawford (Duke), Stella Dracheva (Icahn School of Medicine at Mount Sinai), Peggy Farnham (USC), Mark Gerstein (Yale), Daniel Geschwind (UCLA), Thomas M. Hyde (LIBD), Andrew Jaffe (LIBD), James A. Knowles (USC), Chunyu Liu (UIC), Dalila Pinto (Icahn School of Medicine at Mount Sinai), Nenad Sestan (Yale), Pamela Sklar (Icahn School of Medicine at Mount Sinai), Matthew State (UCSF), Patrick Sullivan (UNC), Flora Vaccarino (Yale), Sherman Weissman (Yale), Kevin White (UChicago), and Peter Zandi (JHU). The Genotype‐Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this article were obtained from the GTEx Portal on February 12, 2020. Molecular data for the Trans‐Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Genome sequencing for "NHLBI TOPMed: Atherosclerosis Risk in Communities (ARIC)" (phs001211.v2.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1)and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for the "NHLBI TOPMed: Cleveland Clinic Atrial Fibrillation (CCAF) Study" (phs001189.v1.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: Trans‐Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Project: Cardiovascular Health Study (phs001368.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for "NHLBI TOPMed: Partners HealthCare Biobank" (phs001024.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: Whole Genome Sequencing of Venous Thromboembolism (WGS of VTE)" (phs001402.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for "NHLBI TOPMed: Novel Risk Factors for the Development of Atrial Fibrillation in Women" (phs001040.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: The Genetics and Epidemiology of Asthma in Barbados" (phs001143.v2.p1) was performed by Illumina Genomic Services (3R01HL104608‐04S1). Genome sequencing for "NHLBI TOPMed: The Vanderbilt Genetic Basis of Atrial Fibrillation" (phs001032.v4.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: Heart and Vascular Health Study (HVH)" (phs000993.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1) and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for "NHLBI TOPMed: Genetic Epidemiology of COPD (COPDGene)" (phs000951.v3.p3) was performed at the University of Washington Northwest Genomics Center (3R01HL089856‐08S1) and at the Broad Institute of MIT and Harvard (HHSN268201500014C). Genome sequencing for "NHLBI TOPMed: The Vanderbilt Atrial Fibrillation Ablation Registry" (phs000997.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2, 3U54HG003067‐13S1). Genome sequencing for "NHLBI TOPMed: The Jackson Heart Study" (phs000964.v3.p1) was performed at the University of Washington Northwest Genomics Center (HHSN268201100037C). Genome sequencing for "NHLBI TOPMed: Genetics of Cardiometabolic Health in the Amish" (phs000956.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL121007‐01S1). Genome sequencing for "NHLBI TOPMed: Massachusetts General Hospital Atrial Fibrillation (MGH AF) Study" (phs001062.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1, 3U54HG003067‐12S2, 3U54HG003067‐13S1, 3UM1HG008895‐01S2). Genome sequencing for "NHLBI TOPMed: The Framingham Heart Study" (phs000974.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample‐identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL‐120393; U01HL‐120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. The Atherosclerosis Risk in Communities study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institute of Health, Department of Health and Human Services, under contract numbers (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I). The authors thank the staff and participants of the ARIC study for their important contributions. The research reported in this article was supported by grants from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute grants R01 HL090620 and R01 HL111314, the NIH National Center for Research Resources for Case Western Reserve University and Cleveland Clinic Clinical and Translational Science Award (CTSA) UL1‐RR024989, the Department of Cardiovascular Medicine philanthropic research fund, Heart and Vascular Institute, Cleveland Clinic, the Fondation Leducq grant 07‐CVD 03, and the Atrial Fibrillation Innovation Center, state of Ohio. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01‐HC85079, N01‐HC‐85080, N01‐HC‐85081, N01‐HC‐85082, N01‐HC‐85083, N01‐HC‐85084, N01‐HC‐85085, N01‐HC‐85086, N01‐HC‐35129, N01‐HC‐15103, N01‐HC‐55222, N01‐HC‐75150, N01‐HC‐45133, and N01‐ HC‐85239; grant numbers U01 HL080295 and U01 HL130014 from the National Heart, Lung, and Blood Institute, and R01 AG023629 from the National Institute on Aging, with additional contribution from the National Institute of Neurological Disorders and Stroke. A full list of principal CHS investigators and institutions can be found at https://chs-nhlbi.org/pi. This article was not prepared in collaboration with CHS investigators and does not necessarily reflect the opinions or views of CHS or the NHLBI. We thank the Broad Institute for generating high‐quality sequence data supported by NHLBI grant 3R01HL092577‐06S1 to Dr. Patrick Ellinor. Funded in part by grants from the National Institutes of Health, National Heart, Lung, and Blood Institute (HL66216 and HL83141), and the National Human Genome Research Institute (HG04735). The Women's Genome Health Study (WGHS) is supported by HL 043851 and HL099355 from the National Heart, Lung, and Blood Institute and CA 047988 from the National Cancer Institute, the Donald W. Reynolds Foundation with collaborative scientific support and funding for genotyping provided by Amgen. AF end‐point confirmation was supported by HL‐093613 and a grant from the Harris Family Foundation and Watkin's Foundation. The Genetics and Epidemiology of Asthma in Barbados is supported by National Institutes of Health (NIH) National Heart, Lung, and Blood Institute TOPMed (R01 HL104608‐S1), and R01 AI20059, K23 HL076322, and RC2 HL101651. The research reported in this article was supported by grants from the American Heart Association to Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (HL092217), and Dr. Roden (U19 HL65962, and UL1 RR024975). This project was also supported by a CTSA award (UL1TR000445) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences of the NIH. The research reported in this article was supported by grants HL068986, HL085251, HL095080, and HL073410 from the National Heart, Lung, and Blood Institute. This article was not prepared in collaboration with Heart and Vascular Health (HVH) Study investigators and does not necessarily reflect the opinions or views of the HVH Study or the NHLBI. This research used data generated by the COPDGene study, which was supported by NIH grants U01 HL089856 and U01 HL089897. The COPDGene project is also supported by the COPD Foundation through contributions made by an Industry Advisory Board composed of Pfizer, AstraZeneca, Boehringer Ingelheim, Novartis, and Sunovion. Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample‐identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL‐120393‐02S1; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. This study is part of the Centers for Common Disease Genomics (CCDG) program, a large‐scale genome sequencing effort to identify rare risk and protective alleles that contribute to a range of common disease phenotypes. The CCDG program is funded by the National Human Genome Research Institute (NHGRI) and the National Heart, Lung, and Blood Institute (NHLBI). Sequencing was completed at the Human Genome Sequencing Center at Baylor College of Medicine under NHGRI grant UM1 HG008898. The research reported in this article was supported by grants from the American Heart Association to Dr. Shoemaker (11CRP742009) and Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (R01 HL092217) and Dr. Roden (U19 HL65962 and UL1 RR024975). The project was also supported by a CTSA award (UL1 TR00045) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the NIH. The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I/HHSN26800001), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). The authors also thank the staffs and participants of the JHS. The Amish studies on which these data are based were supported by NIH grants R01 AG18728, U01 HL072515, R01 HL088119, R01 HL121007, and P30 DK072488. See publication PMID: 18440328. The research reported in this article was supported by NIH grants K23HL071632, K23HL114724, R21DA027021, R01HL092577, R01HL092577S1, R01HL104156, K24HL105780, and U01HL65962. The research has also been supported by an Established Investigator Award from the American Heart Association (13EIA14220013) and by support from the Fondation Leducq (14CVD01). This article was not prepared in collaboration with MGH AF Study investigators and does not necessarily reflect the opinions or views of the MGH AF Study investigators or the NHLBI. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (contract nos. N01‐HC‐25195, HHSN268201500001I, and 75N92019D00031). This article was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.

Genetic technologies used in breeding of small ruminants requires searching for new molecular markers of productive traits. The most effective for this is genome-wide association study (GWAS) of single nucleotide polymorphisms (SNP) with economically valuable traits. The paper presents results of study of associations of the frequency of single nucleotide polymorphisms with a rank assessment according to complex of productive traits (super-elite) in Romanov sheep using DNA biochips Ovine Infinium HD BeadChip 600K. Eleven SNPs have been found having significant correlation with the animals belonging to the "super-elite" group. Five substitutions are located in the genes introns, six are related to intergenic polymorphisms. The highest reliability of association with productivity was observed in substitution rs410516628 (р = 3,14 · 10-9) located on the 3rd chromosome. Substitution rs422028000 on 2nd chromosome differs with the fact that in the "super-elite" group it was found in 90 % of haplotypes. Polymorphisms rs411162754 (1st chromosome) and rs417281100 (10th chromosome) in our study turned out to be the rarest – only in "super-elite" group and only in a quarter of haplotypes. The genes located near the identified SNPs are mainly associated with metabolic and regulatory processes. Our study has identified several new candidate genes with polymorphism probably associated with the ranking in terms of productivity in Romanov sheep: LTBP1, KCNH8, LMX1B, ZBTB43, MSRA, CHPF, PID1 and DNER. The results obtained create a theoretical basis for further study of candidate genes affecting implementation of phenotypic traits in Romanov sheep. The revealed polymorphisms associated with the productive traits of sheep can be used in practical breeding as molecular and genetic markers for selection of parental pairs.

Improvements in immunosuppression have modified short- term survival of deceased- donor allografts, but not their rate of long- term failure. Mismatches between donor and recipient HLA play an important role in the acute and chronic allogeneic immune response against the graft. Perfect matching at clinically relevant HLA loci does not obviate the need for immunosuppression, suggesting that additional genetic variation plays a critical role in both short- and long- term graft outcomes. By combining patient data and samples from supranational cohorts across the United Kingdom and European Union, we performed the first large- scale genome- wide association study analyzing both donor and recipient DNA in 2094 complete renal transplant-pairs with replication in 5866 complete pairs. We studied deceased- donor grafts allocated on the basis of preferential HLA matching, which provided some control for HLA genetic effects. No strong donor or recipient genetic effects contributing to long- or short- term allograft survival were found outside the HLA region. We discuss the implications for future research and clinical application.

Publisher's version (útgefin grein) ; Rationale: Idiopathic pulmonary fibrosis (IPF) is a complex lung disease characterized by scarring of the lung that is believed to result from an atypical response to injury of the epithelium. Genome-wide association studies have reported signals of association implicating multiple pathways including host defense, telomere maintenance, signaling, and cell-cell adhesion. Objectives: To improve our understanding of factors that increase IPF susceptibility by identifying previously unreported genetic associations. Methods: We conducted genome-wide analyses across three independent studies and meta-analyzed these results to generate the largest genome-wide association study of IPF to date (2,668 IPF cases and 8,591 controls). We performed replication in two independent studies (1,456 IPF cases and 11,874 controls) and functional analyses (including statistical fine-mapping, investigations into gene expression, and testing for enrichment of IPF susceptibility signals in regulatory regions) to determine putatively causal genes. Polygenic risk scores were used to assess the collective effect of variants not reported as associated with IPF. Measurements and Main Results: We identified and replicated threenewgenome-wide significant (P<5×10-8) signals of association with IPF susceptibility (associated with altered gene expression of KIF15, MAD1L1, and DEPTOR) and confirmed associations at 11 previously reported loci. Polygenic risk score analyses showed that the combined effect of many thousands of as yet unreported IPF susceptibility variants contribute to IPF susceptibility. Conclusions: The observation that decreased DEPTOR expression associates with increased susceptibility to IPF supports recent studies demonstrating the importance of mTOR signaling in lung fibrosis. New signals of association implicating KIF15 and MAD1L1 suggest a possible role of mitotic spindle-assembly genes in IPF susceptibility. ; R.J.A. is an Action for Pulmonary Fibrosis Research Fellow. L.V.W. holds a GSK/British Lung Foundation Chair in Respiratory Research. R.G.J. is supported by a National Institute for Health Research (NIHR) Research Professorship (NIHR reference RP-2017-08-ST2-014). I.N. is supported by the NHLBI (R01HL130796). B.G.-G. is funded by Agencia Canaria de Investigación, Innovación y Sociedad de la Información (TESIS2015010057) cofunded by European Social Fund. J.M.O. is supported by the NHLBI (K23HL138190). C.F. is supported by the Spanish Ministry of Science, Innovation and Universities (grant RTC-2017-6471-1; Ministerio de Ciencia e Innovacion/Agencia Estatal de Investigación/Fondo Europeo de Desarrollo Regional, Unión Europea) cofinanced by the European Regional Development Funds "A way of making Europe" from the European Union and by agreement OA17/008 with Instituto Tecnológico y de Energías Renovables to strengthen scientific and technological education, training, research, development and innovation in Genomics, Personalized Medicine and Biotechnology. The Spain Biobank array genotyping service was performed at CEGEN-PRB3-ISCIII, which is supported by PT17/0019, of the PE I+D+i 2013–2016, funded by Instituto de Salud Carlos III, and cofinanced by the European Regional Development Funds. P.L.M. is an Action for Pulmonary Fibrosis Research Fellow. M.O. is a fellow of the Parker B. Francis Foundation and a Scholar of the Michael Smith Foundation for Health Research. B.D.H. is supported by NIH K08 HL136928, Parker B. Francis Research Opportunity Award. M.H.C. and G.M.H. are supported by NHLBI grants R01HL113264 (M.H.C.), R01HL137927 (M.H.C.), R01HL135142 (M.H.C. and G.M.H.), R01111024 (G.M.H.), and R01130974 (G.M.H.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funding body has no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. T.M.M. is supported by an NIHR Clinician Scientist Fellowship (NIHR Ref: CS-2013-13-017) and a British Lung Foundation Chair in Respiratory Research (C17-3). M.D.T. is supported by a Wellcome Trust Investigator Award (WT202849/Z/16/Z). The research was partially supported by the NIHR Leicester Biomedical Research Centre; the views expressed are those of the author(s) and not necessarily those of the National Health Service (NHS), the NIHR, or the Department of Health. I.P.H. was partially supported by the NIHR Nottingham Biomedical Research Centre; the views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. I.S. is supported by Medical Research Council (G1000861) and Asthma UK (AUK-PG-2013-188). D.F. was supported by an Intermediate Fellowship from the Wellcome Trust (097152/Z/11/Z). This work was partially supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. V.N. is funded by an NIHR Clinical Lectureship. G.G. is supported by project grant 141513-051 from the Icelandic Research Fund and Landspitali Scientific Fund A-2016-023, A-2017-029, and A-2018-025. D.J.L. and A.M. are supported by Multi-Ethnic Study of Atherosclerosis (MESA) and the MESA SNP Health Association Resource (SHARe) project are conducted and supported by the NHLBI in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR-001881, and DK063491. Funding for SHARe genotyping was provided by NHLBI Contract N02-HL-64278. Genotyping was performed at Affymetrix (Santa Clara, California) and the Broad Institute of Harvard and Massachusetts Institute of Technology (Boston, Massachusetts) using the Affymetrix Genome-Wide Human SNP Array 6.0. This work was supported by NIH grants R01 HL131565 (A.M.), R01 HL103676 (D.J.L.), and R01 HL137234 (D.J.L.). ; Peer Reviewed

The results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Any views expressed are those of the author(s) and not necessarily those of the funders.

Recent Genome-Wide Association Studies (GWAS) have identified four low-penetrance ovarian cancer susceptibility loci. We hypothesized that further moderate- or low-penetrance variants exist among the subset of single-nucleotide polymorphisms (SNPs) not well tagged by the genotyping arrays used in the previous studies, which would account for some of the remaining risk. We therefore conducted a time- and cost-effective stage 1 GWAS on 342 invasive serous cases and 643 controls genotyped on pooled DNA using the high-density Illumina 1M-Duo array. We followed up 20 of the most significantly associated SNPs, which are not well tagged by the lower density arrays used by the published GWAS, and genotyping them on individual DNA. Most of the top 20 SNPs were clearly validated by individually genotyping the samples used in the pools. However, none of the 20 SNPs replicated when tested for association in a much larger stage 2 set of 4,651 cases and 6,966 controls from the Ovarian Cancer Association Consortium. Given that most of the top 20 SNPs from pooling were validated in the same samples by individual genotyping, the lack of replication is likely to be due to the relatively small sample size in our stage 1 GWAS rather than due to problems with the pooling approach. We conclude that there are unlikely to be any moderate or large effects on ovarian cancer risk untagged by less dense arrays. However, our study lacked power to make clear statements on the existence of hitherto untagged small-effect variants.

BACKGROUND: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry. METHODS: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP). RESULTS: We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10-8. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10-7, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84-0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10-7, HR = 1.27, 95% CI = 1.16-1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster. CONCLUSIONS: We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients. ; BCAC is funded by Cancer Research UK [C1287/A16563 and C1287/A10118], the European Union's Horizon 2020 Research and Innovation Programme (Grant numbers 634935 and 633784 for BRIDGES and B-CAST, respectively), and by the European Community's Seventh Framework Programme under grant agreement number 223175 (Grant number HEALTH-F2-2009-223175) (COGS).

WOS: 000373197500020 ; PubMed ID: 27016271 ; BACKGROUND AND OBJECTIVE: Developmental language disorder (DLD) is a highly prevalent neurodevelopmental disorder associated with negative outcomes in different domains; the etiology of DLD is unknown. To investigate the genetic underpinnings of DLD, we performed genome-wide association and whole exome sequencing studies in a geographically isolated population with a substantially elevated prevalence of the disorder (ie, the AZ sample). METHODS: DNA samples were collected from 359 individuals for the genome-wide association study and from 12 severely affected individuals for whole exome sequencing. Multifaceted phenotypes, representing major domains of expressive language functioning, were derived from collected speech samples. RESULTS: Gene-based analyses revealed a significant association between SETBP1 and complexity of linguistic output (P = 5.47 x 10(-7)). The analysis of exome variants revealed coding sequence variants in 14 genes, most of which play a role in neural development. Targeted enrichment analysis implicated myocyte enhancer factor-2 (MEF2)-regulated genes in DLD in the AZ population. The main findings were successfully replicated in an independent cohort of children at risk for related disorders (n = 37). CONCLUSIONS: MEF2-regulated pathways were identified as potential candidate pathways in the etiology of DLD. Several genes (including the candidate SETBP1 and other MEF2-related genes) seem to jointly influence certain, but not all, facets of the DLD phenotype. Even when genetic and environmental diversity is reduced, DLD is best conceptualized as etiologically complex. Future research should establish whether the signals detected in the AZ population can be replicated in other samples and languages and provide further characterization of the identified pathway. ; National Institute of Health [R01 DC007665, P50 HD052120]; NIH Centers for Mendelian Genomics [5U54HG006504]; National Science Foundation Integrative Graduate Education and Research Traineeship grant [114399]; Government of the Russian Federation [14.Z50.31.0027]; National Institutes of Health (NIH) ; Supported by National Institute of Health grants R01 DC007665 (Dr Grigorenko, Principal Investigator) and P50 HD052120 (Richard Wagner, Principal Investigator), NIH Centers for Mendelian Genomics (5U54HG006504), National Science Foundation Integrative Graduate Education and Research Traineeship grant 114399 (Dr Magnuson, Principal Investigator), and grant 14.Z50.31.0027 from the Government of the Russian Federation (Dr Grigorenko, Principal Investigator). Funded by the National Institutes of Health (NIH).

WOS: 000373197500020 ; PubMed ID: 27016271 ; BACKGROUND AND OBJECTIVE: Developmental language disorder (DLD) is a highly prevalent neurodevelopmental disorder associated with negative outcomes in different domains; the etiology of DLD is unknown. To investigate the genetic underpinnings of DLD, we performed genome-wide association and whole exome sequencing studies in a geographically isolated population with a substantially elevated prevalence of the disorder (ie, the AZ sample). METHODS: DNA samples were collected from 359 individuals for the genome-wide association study and from 12 severely affected individuals for whole exome sequencing. Multifaceted phenotypes, representing major domains of expressive language functioning, were derived from collected speech samples. RESULTS: Gene-based analyses revealed a significant association between SETBP1 and complexity of linguistic output (P = 5.47 x 10(-7)). The analysis of exome variants revealed coding sequence variants in 14 genes, most of which play a role in neural development. Targeted enrichment analysis implicated myocyte enhancer factor-2 (MEF2)-regulated genes in DLD in the AZ population. The main findings were successfully replicated in an independent cohort of children at risk for related disorders (n = 37). CONCLUSIONS: MEF2-regulated pathways were identified as potential candidate pathways in the etiology of DLD. Several genes (including the candidate SETBP1 and other MEF2-related genes) seem to jointly influence certain, but not all, facets of the DLD phenotype. Even when genetic and environmental diversity is reduced, DLD is best conceptualized as etiologically complex. Future research should establish whether the signals detected in the AZ population can be replicated in other samples and languages and provide further characterization of the identified pathway. ; National Institute of Health [R01 DC007665, P50 HD052120]; NIH Centers for Mendelian Genomics [5U54HG006504]; National Science Foundation Integrative Graduate Education and Research Traineeship grant [114399]; Government of the Russian Federation [14.Z50.31.0027]; National Institutes of Health (NIH) ; Supported by National Institute of Health grants R01 DC007665 (Dr Grigorenko, Principal Investigator) and P50 HD052120 (Richard Wagner, Principal Investigator), NIH Centers for Mendelian Genomics (5U54HG006504), National Science Foundation Integrative Graduate Education and Research Traineeship grant 114399 (Dr Magnuson, Principal Investigator), and grant 14.Z50.31.0027 from the Government of the Russian Federation (Dr Grigorenko, Principal Investigator). Funded by the National Institutes of Health (NIH).

As part of the Genes, Environment and Development Initiative, the Minnesota Center for Twin and Family Research (MCTFR) undertook a genome-wide association study, which we describe here. A total of 8,405 research participants, clustered in four-member families, have been successfully genotyped on 527,829 single nucleotide polymorphism (SNP) markers using Illumina's Human660W-Quad array. Quality control screening of samples and markers as well as SNP imputation procedures are described. We also describe methods for ancestry control and how the familial clustering of the MCTFR sample can be accounted for in the analysis using a Rapid Feasible Generalized Least Squares algorithm. The rich longitudinal MCTFR assessments provide numerous opportunities for collaboration.

Breastfeeding has been an important survival trait during human history, though it has long been recognized that individuals differ in their exact breastfeeding behavior. Here our aims were, first, to explore to what extent genetic and environmental influences contributed to the individual differences in breastfeeding behavior; second, to detect possible genetic variants related to breastfeeding; and lastly, to test if the genetic variants associated with breastfeeding have been previously found to be related with breast size. Data were collected from a large community-based cohort of Australian twins, with 3,364 women participating in the twin modelling analyses and 1,521 of them included in the genome-wide association study (GWAS). Monozygotic (MZ) twin correlations (rMZ= 0.52, 95% CI 0.46–0.57) were larger than dizygotic (DZ) twin correlations (rDZ= 0.35, 95% CI 0.25–0.43) and the best-fitting model was the one composed by additive genetics and unique environmental factors, explaining 53% and 47% of the variance in breastfeeding behavior, respectively. No breastfeeding-related genetic variants reached genome-wide significance. The polygenic risk score analyses showed no significant results, suggesting breast size does not influence breastfeeding. This study confers a replication of a previous one exploring the sources of variance of breastfeeding and, to our knowledge, is the first one to conduct a GWAS on breastfeeding and look at the overlap with variants for breast size.

[Motivation] Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS. ; [Results] We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P-values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. This rules out spurious correlations between phenotypes and SNPs that can arise from marginal methods because the 'spuriously correlated' SNP merely happens to be correlated with the 'truly causal' SNP. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype. We demonstrate the value of our method by applying it to the seven diseases analyzed by the Wellcome Trust Case Control Consortium (WTCCC). We show, in particular, that our method is also capable of finding significant SNPs that were not identified in the original WTCCC study, but were replicated in other independent studies. ; E.F. and L.B. gratefully acknowledge financial support from the European Research Council (grant 295642, The Foundations of Economic Preferences, FEP). D.S. gratefully acknowledges financial support from the German National Science Foundation (DFG, grant SCHU 2828/2-1, Inference statistical methods for behavioral genetics and neuroeconomics). A.N. gratefully acknowledges support from the Instituto de Salud Carlos III (grants RD12/0032/0011 and PT13/0001/0026) and the Spanish Government Grant (BFU2012-38236) and from FEDER. ; Peer reviewed

1 página-. Resumen del póster presentado en el Seminario celebrado en Tullm, Austria, entre el 8 y el 11 de abril de 2018. ; In current study, GWAS has been performed for the first time for detecting high-resolution QTL for resistance to fumonisin accumulation in maize kernel. A subset of 270 inbred lines from a maize diversity panel (composed of 302 inbred lines) that represents much of the diversity available in public breeding sector around the world was evaluated under inoculation with Fusarium verticillioides. Significant associations between polymorphisms at SNPs in bins 1.07, 1.09, 2.08, 3.02, 3.04, 3.05, 3.06, 3.08, 3.09, 4.02, 4.05, 5.02, 6.07, 7.05, 8.07, 9.03 and fumonisin accumulation (determined by ELISA tests) in maize kernel were detected. At least, four associations should be behind novel QTL because QTL were not found in that bin or in adjacent bins in previous studies (Robertson-Hoyt et al. 2006; Maschietto et al. 2017). These novel QTLs for fumonisin content would be at bins 3.02, 5.02, 7.05, and 8.03. Genes likely involved in ROS production, ROS-scavenging and ROS-detoxification, in signaling and/or regulation of plant hormone-mediated responses and lipid metabolism were proposed as candidate genes behind significant associations. ; This research was funded by the "Plan Estatal de Ciencia y Tecnologia de España" (project AGL2015-67313-C2-1-R, co-financed with European Union funds under the FEDER program) and by Autonomous Government of Galicia, Spain (projects IN607A/013 and ED431F 2016/014). ; Peer reviewed

Motivation: Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS. Results: We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P -values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. This rules out spurious correlations between phenotypes and SNPs that can arise from marginal methods because the 'spuriously correlated' SNP merely happens to be correlated with the 'truly causal' SNP. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype. We demonstrate the value of our method by applying it to the seven diseases analyzed by the Wellcome Trust Case Control Consortium (WTCCC). We show, in particular, that our method is also capable of finding significant SNPs that were not identified in the original WTCCC study, but were replicated in other independent studies. Availability and implementation: Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS. Contact:peter.buehlmann@stat.math.ethz.ch Supplementary information:Supplementary data are available at Bioinformatics online. ; E.F. and L.B. gratefully acknowledge financial support from the European Research Council (grant 295642, The Foundations of Economic Preferences, FEP). D.S. gratefully acknowledges financial support from the German National Science Foundation (DFG, grant SCHU 2828/2-1, Inference statistical methods for behavioral genetics and neuroeconomics). A.N. gratefully acknowledges support from the Instituto de Salud Carlos III (grants RD12/0032/0011 and PT13/0001/0026) and the Spanish Government Grant (BFU2012-38236) and from FEDER.