Matrix metalloproteinase-2 is a consistent prognostic factor in gastric cancer

In a pioneer study, we showed 10 years ago that enhanced tissue levels of the matrix metalloproteinases (MMPs) MMP-2 and MMP-9 in gastric cancers, as determined by zymography, were related with worse overall survival of the patients. To corroborate these observations, we now assessed MMP-2 and MMP-9 with new techniques in an expanded group of gastric cancer patients (n=81) and included for comparison MMP-7, MMP-8 and the tissue inhibitors of MMPs, TIMP-1 and -2. All MMPs and TIMP-1 were significantly increased in tumour tissue compared to normal gastric mucosa. Matrix metalloproteinase-7, -8 and -9, and the TIMPs showed some correlations with the clinicopathologic parameters TNM, WHO and Laurén classification, but their levels were not related with survival. Regardless of the determination method used, that is, enzyme-linked immunosorbent assay or bioactivity assay, an enhanced tumour MMP-2 level did not show a significant correlation with any of the clinicopathological parameters, but was confirmed to be an independent prognostic factor in gastric cancer

Tissue level, activation and cellular localisation of TGF-β1 and association with survival in gastric cancer patients

Transforming growth factor-β1 (TGF-β1), a tumour suppressing as well as tumour-promoting cytokine, is stored as an extracellular matrix-bound latent complex. We examined TGF-β1 activation and localisation of TGF-β1 activity in gastric cancer. Gastric tumours showed increased stromal and epithelial total TGF-β1 staining by immunohistochemistry. Active TGF-β1 was present in malignant epithelial cells, but most strongly in smooth muscle actin expressing fibroblasts. Normal gastric mucosa from the same patient showed some staining for total, and little for active TGF-β1. Active TGF-β1 levels were determined by ELISA on tissue homogenates, confirming a strong increase in active TGF-β1 in tumours compared to corresponding normal mucosa. Moreover, high tumour TGF-β1 activity levels were significantly associated with clinical parameters, including worse survival of the patients. Total and active TGF-β1 levels were not correlated, suggesting a specific activation process. Of the different proteases tested, active TGF-β1 levels were only correlated with urokinase activity levels. The correlation with urokinase activity suggests a role for plasmin in TGF-β1 activation in the tumour microenvironment, resulting in transformation of resident fibroblasts to tumour promoting myofibroblasts. In conclusion we have shown localisation and clinical relevance of TGF-β1 activity levels in gastric cancer

Multiple Loci Are Associated with White Blood Cell Phenotypes

White blood cell (WBC) count is a common clinical measure from complete blood count assays, and it varies widely among healthy individuals. Total WBC count and its constituent subtypes have been shown to be moderately heritable, with the heritability estimates varying across cell types. We studied 19,509 subjects from seven cohorts in a discovery analysis, and 11,823 subjects from ten cohorts for replication analyses, to determine genetic factors influencing variability within the normal hematological range for total WBC count and five WBC subtype measures. Cohort specific data was supplied by the CHARGE, HeamGen, and INGI consortia, as well as independent collaborative studies. We identified and replicated ten associations with total WBC count and five WBC subtypes at seven different genomic loci (total WBC count—6p21 in the HLA region, 17q21 near ORMDL3, and CSF3; neutrophil count—17q21; basophil count- 3p21 near RPN1 and C3orf27; lymphocyte count—6p21, 19p13 at EPS15L1; monocyte count—2q31 at ITGA4, 3q21, 8q24 an intergenic region, 9q31 near EDG2), including three previously reported associations and seven novel associations. To investigate functional relationships among variants contributing to variability in the six WBC traits, we utilized gene expression- and pathways-based analyses. We implemented gene-clustering algorithms to evaluate functional connectivity among implicated loci and showed functional relationships across cell types. Gene expression data from whole blood was utilized to show that significant biological consequences can be extracted from our genome-wide analyses, with effect estimates for significant loci from the meta-analyses being highly corellated with the proximal gene expression. In addition, collaborative efforts between the groups contributing to this study and related studies conducted by the COGENT and RIKEN groups allowed for the examination of effect homogeneity for genome-wide significant associations across populations of diverse ancestral backgrounds

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence

Intelligence is associated with important economic and health-related life outcomes1. Despite intelligence having substantial heritability2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered3,4,5. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10−8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10−6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10−6). Despite the well-known difference in twin-based heritability2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10−29). These findings provide new insight into the genetic architecture of intelligence

Genetic variants linked to education predict longevity

Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members’ polygenic profile score for education to predict their parents’ longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total ndeaths = 79,702) and ∼2.4% lower risk for fathers (total ndeaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity.</p

Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility.

Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.CHARGE: Funding support for ‘Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium’ was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419). Sequence data for ‘Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium’ was provided by Eric Boerwinkle on behalf of the Atherosclerosis Risk in Communities (ARIC) Study, L. Adrienne Cupples, principal investigator for the Framingham Heart Study, and Bruce Psaty, principal investigator for the Cardiovascular Health Study. Sequencing was carried out at the Baylor Genome Center (U54 HG003273). Further support came from HL120393, ‘Rare variants and NHLBI traits in deeply phenotyped cohorts’ (Bruce Psaty, principal investigator). Supporting funding was also provided by NHLBI with the CHARGE infrastructure grant HL105756. In addition, M.J.P. was supported through the 2014 CHARGE Visiting Fellow grant—HL105756, Dr Bruce Psaty, PI. ENCODE: ENCODE collaborators Ben Brown and Marcus Stoiber were supported by the LDRD# 14-200 (B.B. and M.S.) and 4R00HG006698-03 (B.B.) grants. AGES: This study has been funded by NIA contract N01-AG-12100 with contributions from NEI, NIDCD and NHLBI, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association) and the Althingi (the Icelandic Parliament). ARIC: The Atherosclerosis Risk in Communities (ARIC) Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute (NHLBI) contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. We thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. CARDIA: The CARDIA Study is conducted and supported by the National Heart, Lung, and Blood Institute in collaboration with the University of Alabama at Birmingham (HHSN268201300025C & HHSN268201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C), and Johns Hopkins University School of Medicine (HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging. Exome chip genotyping and data analyses were funded in part by grants U01-HG004729, R01-HL093029 and R01-HL084099 from the National Institutes of Health to Dr Myriam Fornage. This manuscript has been reviewed by CARDIA for scientific content. CHES: This work was supported in part by The Chinese-American Eye Study (CHES) grant EY017337, an unrestricted departmental grant from Research to Prevent Blindness, and the Genetics of Latinos Diabetic Retinopathy (GOLDR) Study grant EY14684. CHS: This CHS research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants HL080295, HL087652, HL103612, HL068986 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG023629 from the National Institute on Aging (NIA). A full list of CHS investigators and institutions can be found at http://www.chs-nhlbi.org/pi.htm. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The CoLaus Study: We thank the co-primary investigators of the CoLaus study, Gerard Waeber and Peter Vollenweider, and the PI of the PsyColaus Study Martin Preisig. We gratefully acknowledge Yolande Barreau, Anne-Lise Bastian, Binasa Ramic, Martine Moranville, Martine Baumer, Marcy Sagette, Jeanne Ecoffey and Sylvie Mermoud for their role in the CoLaus data collection. The CoLaus study was supported by research grants from GlaxoSmithKline and from the Faculty of Biology and Medicine of Lausanne, Switzerland. The PsyCoLaus study was supported by grants from the Swiss National Science Foundation (#3200B0–105993) and from GlaxoSmithKline (Drug Discovery—Verona, R&D). CROATIA-Korcula: The CROATIA-Korcula study would like to acknowledge the invaluable contributions of the recruitment team in Korcula, the administrative teams in Croatia and Edinburgh and the people of Korcula. Exome array genotyping was performed at the Wellcome Trust Clinical Research Facility Genetics Core at Western General Hospital, Edinburgh, UK. The CROATIA-Korcula study on the Croatian island of Korucla was supported through grants from the Medical Research Council UK and the Ministry of Science, Education and Sport in the Republic of Croatia (number 108-1080315-0302). EFSOCH: We are extremely grateful to the EFSOCH study participants and the EFSOCH study team. The opinions given in this paper do not necessarily represent those of NIHR, the NHS or the Department of Health. The EFSOCH study was supported by South West NHS Research and Development, Exeter NHS Research and Development, the Darlington Trust, and the Peninsula NIHR Clinical Research Facility at the University of Exeter. Timothy Frayling, PI, is supported by the European Research Council grant: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. EPIC-Potsdam: We thank all EPIC-Potsdam participants for their invaluable contribution to the study. The study was supported in part by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e.V.). The recruitment phase of the EPIC-Potsdam study was supported by the Federal Ministry of Science, Germany (01 EA 9401) and the European Union (SOC 95201408 05 F02). The follow-up of the EPIC-Potsdam study was supported by German Cancer Aid (70-2488-Ha I) and the European Community (SOC 98200769 05 F02). Furthermore, we thank Ellen Kohlsdorf for data management as well as the follow-up team headed by Dr Manuala Bergmann for case ascertainment. ERF: The ERF study was supported by grants from the Netherlands Organization for Scientific Research (NWO) and a joint grant from NWO and the Russian Foundation for Basic research (Pionier, 047.016.009, 047.017.043), Erasmus MC, and the Centre for Medical Systems Biology (CMSB; National Genomics Initiative). Exome sequencing analysis in ERF was supported by the ZonMw grant (91111025). For the ERF Study, we are grateful to all participants and their relatives, to general practitioners and neurologists for their contributions, to P. Veraart for her help in genealogy and to P. Snijders for his help in data collection. FamHS: The Family Heart Study (FamHS) was supported by NIH grants R01-HL-087700 and R01-HL-088215 (Michael A. Province, PI) from NHLBI; and R01-DK-8925601 and R01-DK-075681 (Ingrid B. Borecki, PI) from NIDDK. FENLAND: The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust. We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust. FHS: Genotyping, quality control and calling of the Illumina HumanExome BeadChip in the Framingham Heart Study was supported by funding from the National Heart, Lung and Blood Institute Division of Intramural Research (Daniel Levy and Christopher J. O’Donnell, Principle Investigators). A portion of this research was conducted using the Linux Clusters for Genetic Analysis (LinGA) computing resources at Boston University Medical Campus. Also supported by National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) R01 DK078616, NIDDK K24 DK080140 and American Diabetes Association Mentor-Based Postdoctoral Fellowship Award #7-09-MN-32, all to Dr Meigs, a Canadian Diabetes Association Research Fellowship Award to Dr Leong, a research grant from the University of Verona, Italy to Dr Dauriz, and NIDDK Research Career Award K23 DK65978, a Massachusetts General Hospital Physician Scientist Development Award and a Doris Duke Charitable Foundation Clinical Scientist Development Award to Dr Florez. FIA3: We are indebted to the study participants who dedicated their time and samples to these studies. We thank Åsa Ågren (Umeå Medical Biobank) for data organization and Kerstin Enquist and Thore Johansson (Västerbottens County Council) for technical assistance with DNA extraction. This particular project was supported by project grants from the Swedish Heart-Lung Foundation, Umeå Medical Research Foundation and Västerbotten County Council. The Genetics Epidemiology of Metabolic Syndrome (GEMS) Study: We thank Metabolic Syndrome GEMs investigators: Scott Grundy, Jonathan Cohen, Ruth McPherson, Antero Kesaniemi, Robert Mahley, Tom Bersot, Philip Barter and Gerard Waeber. We gratefully acknowledge the contributions of the study personnel at each of the collaborating sites: John Farrell, Nicholas Nikolopoulos and Maureen Sutton (Boston); Judy Walshe, Monica Prentice, Anne Whitehouse, Julie Butters and Tori Nicholls (Australia); Heather Doelle, Lynn Lewis and Anna Toma (Canada); Kari Kervinen, Seppo Poykko, Liisa Mannermaa and Sari Paavola (Finland); Claire Hurrel, Diane Morin, Alice Mermod, Myriam Genoud and Roger Darioli (Switzerland); Guy Pepin, Sibel Tanir, Erhan Palaoglu, Kerem Ozer, Linda Mahley and Aysen Agacdiken (Turkey); and Deborah A. Widmer, Rhonda Harris and Selena Dixon (United States). Funding for the GEMS study was provided by GlaxoSmithKline. GeneSTAR: The Johns Hopkins Genetic Study of Atherosclerosis Risk (GeneSTAR) Study was supported by NIH grants through the National Heart, Lung, and Blood Institute (HL58625-01A1, HL59684, HL071025-01A1, U01HL72518, HL112064, and HL087698) and the National Institute of Nursing Research (NR0224103) and by M01-RR000052 to the Johns Hopkins General Clinical Research Center. Genotyping services were provided through the RS&G Service by the Northwest Genomics Center at the University of Washington, Department of Genome Sciences, under U.S. Federal Government contract number HHSN268201100037C from the National Heart, Lung, and Blood Institute. GLACIER: We are indebted to the study participants who dedicated their time, data and samples to the GLACIER Study as part of the Västerbottens hälsoundersökningar (Västerbottens Health Survey). We thank John Hutiainen and Åsa Ågren (Northern Sweden Biobank) for data organization and Kerstin Enquist and Thore Johansson (Västerbottens County Council) for extracting DNA. We also thank M. Sterner, M. Juhas and P. Storm (Lund University Diabetes Center) for their expert technical assistance with genotyping and genotype data preparation. The GLACIER Study was supported by grants from Novo Nordisk, the Swedish Research Council, Påhlssons Foundation, The Heart Foundation of Northern Sweden, the Swedish Heart Lung Foundation, the Skåne Regional Health Authority, Umeå Medical Research Foundation and the Wellcome Trust. This particular project was supported by project grants from the Swedish Heart-Lung Foundation, the Swedish Research Council, the Swedish Diabetes Association, Påhlssons Foundation and Novo nordisk (all grants to P. W. Franks). GOMAP (Genetic Overlap between Metabolic and Psychiatric Disease): This work was funded by the Wellcome Trust (098051). We thank all participants for their important contribution. We are grateful to Georgia Markou, Laiko General Hospital Diabetes Centre, Maria Emetsidou and Panagiota Fotinopoulou, Hippokratio General Hospital Diabetes Centre, Athina Karabela, Dafni Psychiatric Hospital, Eirini Glezou and Marios Matzioros, Dromokaiteio Psychiatric Hospital, Angela Rentari, Harokopio University of Athens, and Danielle Walker, Wellcome Trust Sanger Institute. Generation Scotland: Scottish Family Health Study (GS:SFHS): GS:SFHS is funded by the Chief Scientist Office of the Scottish Government Health Directorates, grant number CZD/16/6 and the Scottish Funding Council. Exome array genotyping for GS:SFHS was funded by the Medical Research Council UK and performed at the Wellcome Trust Clinical Research Facility Genetics Core at Western General Hospital, Edinburgh, UK. We also acknowledge the invaluable contributions of the families who took part in the Generation Scotland: Scottish Family Health Study, the general practitioners and Scottish School of Primary Care for their help in recruiting them, and the whole Generation Scotland team, which includes academic researchers, IT staff, laboratory technicians, statisticians and research managers. The chief investigators of Generation Scotland are David J. Porteous (University of Edinburgh), Lynne Hocking (University of Aberdeen), Blair Smith (University of Dundee), and Sandosh Padmanabhan (University of Glasgow). GSK (CoLaus, GEMS, Lolipop): We thank the GEMS Study Investigators: Philip Barter, PhD; Y. Antero Kesäniemi, PhD; Robert W. Mahley, PhD; Ruth McPherson, FRCP; and Scott M. Grundy, PhD. Dr Waeber MD, the CoLaus PI’s Peter Vollenweider MD and Gerard Waeber MD, the LOLIPOP PI’s, Jaspal Kooner MD and John Chambers MD, as well as the participants in all the studies. The GEMS study was sponsored in part by GlaxoSmithKline. The CoLaus study was supported by grants from GlaxoSmithKline, the Swiss National Science Foundation (Grant 33CSCO-122661) and the Faculty of Biology and Medicine of Lausanne. Health ABC: The Health, Aging and Body Composition (HABC) Study is supported by NIA contracts N01AG62101, N01AG62103 and N01AG62106. The exome-wide association study was funded by NIA grant 1R01AG032098-01A1 to Wake Forest University Health Sciences and was supported in part by the Intramural Research Program of the NIH, National Institute on Aging (Z01 AG000949-02 and Z01 AG007390-07, Human subjects protocol UCSF IRB is H5254-12688-11). Portions of this study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD. (http:/biowulf.nih.gov). Health2008: The Health2008 cohort was supported by the Timber Merchant Vilhelm Bang’s Foundation, the Danish Heart Foundation (Grant number 07-10-R61-A1754-B838-22392F), and the Health Insurance Foundation (Helsefonden) (Grant number 2012B233). HELIC: This work was funded by the Wellcome Trust (098051) and the European Research Council (ERC-2011-StG 280559-SEPI). The MANOLIS cohort is named in honour of Manolis Giannakakis, 1978–2010. We thank the residents of Anogia and surrounding Mylopotamos villages, and of the Pomak villages, for taking part. The HELIC study has been supported by many individuals who have contributed to sample collection (including Antonis Athanasiadis, Olina Balafouti, Christina Batzaki, Georgios Daskalakis, Eleni Emmanouil, Chrisoula Giannakaki, Margarita Giannakopoulou, Anastasia Kaparou, Vasiliki Kariakli, Stella Koinaki, Dimitra Kokori, Maria Konidari, Hara Koundouraki, Dimitris Koutoukidis, Vasiliki Mamakou, Eirini Mamalaki, Eirini Mpamiaki, Maria Tsoukana, Dimitra Tzakou, Katerina Vosdogianni, Niovi Xenaki, Eleni Zengini), data entry (Thanos Antonos, Dimitra Papagrigoriou, Betty Spiliopoulou), sample logistics (Sarah Edkins, Emma Gray), genotyping (Robert Andrews, Hannah Blackburn, Doug Simpkin, Siobhan Whitehead), research administration (Anja Kolb-Kokocinski, Carol Smee, Danielle Walker) and informatics (Martin Pollard, Josh Randall). INCIPE: NIcole Soranzo’s research is supported by the Wellcome Trust (Grant Codes WT098051 and WT091310), the EU FP7 (EPIGENESYS Grant Code 257082 and BLUEPRINT Grant Code HEALTH-F5-2011-282510). Inter99: The Inter99 was initiated by Torben Jørgensen (PI), Knut Borch-Johnsen (co-PI), Hans Ibsen and Troels F. Thomsen. The steering committee comprises the former two and Charlotta Pisinger. The study was financially supported by research grants from the Danish Research Council, the Danish Centre for Health Technology Assessment, Novo Nordisk Inc., Research Foundation of Copenhagen County, Ministry of Internal Affairs and Health, the Danish Heart Foundation, the Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, the Becket Foundation and the Danish Diabetes Association. Genetic studies of both Inter99 and Health 2008 cohorts were funded by the Lundbeck Foundation and produced by The Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention and Care (LuCamp, www.lucamp.org ). The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). InterAct Consortium: Funding for the InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197). We thank all EPIC participants and staff for their contribution to the study. We thank the lab team at the MRC Epidemiology Unit for sample management and Nicola Kerrison for data management. IPM BioMe Biobank: The Mount Sinai IPM BioMe Program is supported by The Andrea and Charles Bronfman Philanthropies. Analyses of BioMe data was supported in part through the computational resources and staff expertise provided by the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai. The Insulin Resistance Atherosclerosis Family Study (IRASFS): The IRASFS was conducted and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (HL060944, HL061019, and HL060919). Exome chip genotyping and data analyses were funded in part by grants DK081350 and HG007112. A subset of the IRASFS exome chips were contributed with funds from the Department of Internal Medicine at the University of Michigan. Computing resources were provided, in part, by the Wake Forest School of Medicine Center for Public Health Genomics. The Insulin Resistance Atherosclerosis Study (IRAS): The IRAS was conducted and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (HL047887, HL047889, HL047890 and HL47902). Exome chip genotyping and data analyses were funded in part by grants DK081350 and HG007112). Computing resources were provided, in part, by the Wake Forest School of Medicine Center for Public Health Genomics. JHS: The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung and Blood Institute and the National Institute on Minority Health and Health Disparities. ExomeChip genotyping was supported by the NHLBI of the National Institutes of Health under award number R01HL107816 to S. Kathiresan. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The London Life Sciences Prospective Population (LOLIPOP) Study: We thank the co-primary investigators of the LOLIPOP study: Jaspal Kooner, John Chambers and Paul Elliott. The LOLIPOP study is supported by the National Institute for Health Research Comprehensive Biomedical Research Centre Imperial College Healthcare NHS Trust, the British Heart Foundation (SP/04/002), the Medical Research Council (G0700931), the Wellcome Trust (084723/Z/08/Z) and the National Institute for Health Research (RP-PG-0407-10371). MAGIC: Data on glycaemic traits were contributed by MAGIC investigators and were downloaded from www.magicinvestigators.org. MESA: The Multi-Ethnic Study of Atherosclerosis (MESA) and MESA SHARe project are conducted and supported by contracts N01-HC-95159 through N01-HC-95169 and RR-024156 from the National Heart, Lung, and Blood Institute (NHLBI). Funding for MESA SHARe genotyping was provided by NHLBI Contract N02-HL-6-4278. MESA Family is conducted and supported in collaboration with MESA investigators; support is provided by grants and co

Heritabilities, apolipoprotein E, and effects of inbreeding on plasma lipids in a genetically isolated population: The Erasmus Rucphen family study

Despite considerable progress in unravelling the genetic basis of dyslipidemias, most findings are based on families with extreme phenotypes. We studied lipid levels in an extended pedigree ascertained irrespective of phenotype from the population of a recent genetic isolate in the Netherlands. Heritabilities of plasma lipid measures were examined; this analysis also included estimates of the proportion of variance attributable to ApoE genotype. The association between inbreeding and lipids was also considered, as a substantial fraction of the population had known inbreeding. A total of 868 individuals from this pedigree, containing more than 60,000 people over 15 generations, were investigated in this study. Laboratory analysis of these subjects included the determination of fasting plasma lipids. ApoE ε2/3/4 status was ascertained using TaqMan assays. Heritabilities for plasma lipids were estimated with adjustments for multiple covariates using SOLAR. Heritabilities for total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglycerides (TG), TC/HDL ratio, and TG/HDL ratio were found to be 0.35, 0.56, 0.30, 0.24, 0.49, and 0.39, respectively. The addition of ApoE genotype in the model significantly decreased these estimates (Δh2= -0.030, -0.004, -0.054, and -0.006 for TC, HDL, LDL, and TG). In a further analysis, TC and LDL were positively associated with the extent of inbreeding (ptrend= 0.02 and ptrend= 0.05, respectively). These data provide estimates of lipid heritability unbiased due to selection and suggest that this population represents a good opportunity to localize novel genes influencing plasma lipid levels