Identification and biochemical analysis of a novel APOB mutation that causes autosomal dominant hypercholesterolemia

Patients with autosomal dominant hypercholesterolemia (ADH) have a high risk of developing cardiovascular disease that can be effectively treated using statin drugs. Molecular diagnosis and family cascade screening is recommended for early identification of individuals at risk, but up to 40% of families have no mutation detected in known genes. This study combined linkage analysis and exome sequencing to identify a novel variant in exon 3 of APOB (Arg50Trp). Mass spectrometry established that low-density lipoprotein (LDL) containing Arg50Trp APOB accumulates in the circulation of affected individuals, suggesting defective hepatic uptake. Previously reported mutations in APOB causing ADH have been located in exon 26. This is the first report of a mutation outside this region causing this phenotype, therefore, more extensive screening of this large and highly polymorphic gene may be necessary in ADH families. This is now feasible due to the high capacity of recently available sequencing platforms

Prediagnostic concentrations of plasma genistein and prostate cancer risk in 1,605 men with prostate cancer and 1,697 matched control participants in EPIC

PURPOSE: Data from prospective epidemiological studies in Asian populations and from experimental studies in animals and cell lines suggest a possible protective association between dietary isoflavones and the development of prostate cancer. We examined the association between circulating concentrations of genistein and prostate cancer risk in a case-control study nested in the European Prospective Investigation into Cancer and Nutrition. METHODS: Concentrations of the isoflavone genistein were measured in prediagnostic plasma samples for 1,605 prostate cancer cases and 1,697 matched control participants. Relative risks (RRs) for prostate cancer in relation to plasma concentrations of genistein were estimated by conditional logistic regression. RESULTS: Plasma genistein concentrations were not associated with prostate cancer risk; the multivariate relative risk for men in the highest fifth of genistein compared with men in the lowest fifth was 1.00 (95 % confidence interval: 0.79, 1.27; p linear trend = 0.82). There was no evidence of heterogeneity in this association by age at blood collection, country of recruitment, or cancer stage or histological grade. CONCLUSION: Plasma genistein concentration was not associated with prostate cancer risk in this large cohort of European men

Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independent of obesity.

We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterize their association with intermediate phenotypes, and to investigate their role in type 2 diabetes (T2D) risk among normal-weight, overweight, and obese individuals. We investigated the association of genetic scores with euglycemic-hyperinsulinemic clamp- and oral glucose tolerance test-based measures of insulin resistance and secretion and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight, and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs per allele [95% CI], -0.03 [-0.04, -0.01]; P = 0.004). This score was associated with lower BMI (-0.01 [-0.01, -0.0]; P = 0.02) and gluteofemoral fat mass (-0.03 [-0.05, -0.02; P = 1.4 × 10(-6)) and with higher alanine transaminase (0.02 [0.01, 0.03]; P = 0.002) and γ-glutamyl transferase (0.02 [0.01, 0.03]; P = 0.001). While the secretion score had a stronger association with T2D in leaner individuals (Pinteraction = 0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI or waist strata (Pinteraction > 0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.The MRC-Ely Study was funded by the Medical Research Council (MC_U106179471) and Diabetes UK. We are grateful to all the volunteers, and to the staff of St. Mary’s Street Surgery, Ely and the study team. 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. DBS and RKS are funded by the Wellcome Trust, the U.K. NIHR Cambridge Biomedical Research Centre and the MRC Centre for Obesity and Related Metabolic Disease. Genotyping in ULSAM was performed by the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se), which is supported by Uppsala University, Uppsala University Hospital, Science for Life Laboratory - Uppsala and the Swedish Research Council (Contracts 80576801 and 70374401). The RISC Study was supported by European Union grant QLG1-CT-2001-01252 and AstraZeneca. The RISC Study Project Management Board: B Balkau, F Bonnet, SW Coppack, JM Dekker, E Ferrannini, A Golay, A Mari, A Natali, J Petrie, M Walker. 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 of the MRC Epidemiology Unit for data management. Funding for the EPIC-InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197).In addition, EPIC-InterAct investigators acknowledge funding from the following agencies: PWF: Swedish Research Council, Novo Nordisk, Swedish Diabetes Association, Swedish Heart-Lung Foundation; LCG: Swedish Research Council; NS: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (Nº 6236); LA: We thank the participants of the Spanish EPIC cohort for their contribution to the study as well as to the team of trained nurses who participated in the recruitment; RK: German Cancer Aid, German Ministry of Research (BMBF); TJK: Cancer Research UK; PMN: Swedish Research Council; KO: Danish Cancer Society; SP: Compagnia di San Paolo; JRQ: Asturias Regional Government; OR: The Västerboten County Council; AMWS and DLvdA: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands; RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; IS: Verification of diabetes cases was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht; IB: Wellcome Trust grant 098051 and United Kingdom NIHR Cambridge Biomedical Research Centre; MIM: InterAct, Wellcome Trust (083270/Z/07/Z), MRC (G0601261); ER: Imperial College Biomedical Research.This is the author accepted manuscript. The final version is available from the American Diabetes Association via http://dx.doi.org/10.2337/db14-031

Circulating Fatty Acids and Prostate Cancer Risk: Individual Participant Meta-Analysis of Prospective Studies

Background: Individual studies have suggested that some circulating fatty acids are associated with prostate cancer risk, but have not been large enough to provide precise estimates of associations, particularly by stage and grade of disease. Methods: Principal investigators of prospective studies on circulating fatty acids and prostate cancer were invited to collaborate. Investigators provided individual participant data on circulating fatty acids (weight percent) and other characteristics of prostate cancer cases and controls. Prostate cancer risk by study-specific fifths of 14 fatty acids was estimated using multivariable-adjusted conditional logistic regression. All statistical tests were two-sided. Results: Five thousand and ninety-eight case patients and 6649 control patients from seven studies with an average follow-up of 5.1 (SD = 3.3) years were included. Stearic acid (18:0) was inversely associated with total prostate cancer (odds ratio [OR] Q5 vs Q1 = 0.88, 95% confidence interval [CI] = 0.78 to 1.00, P trend = .043). Prostate cancer risk was, respectively, 14% and 16% greater in the highest fifth of eicosapentaenoic acid (20:5n-3) (OR = 1.14, 95% CI = 1.01 to 1.29, P trend = .001) and docosapentaenoic acid (22:5n-3) (OR = 1.16, 95% CI = 1.02 to 1.33, P trend = .003), but in each case there was heterogeneity between studies (P = .022 and P < .001, respectively). There was heterogeneity in the association between docosapentaenoic acid and prostate cancer by grade of disease (P = .006); the association was statistically significant for low-grade disease but not high-grade disease. The remaining 11 fatty acids were not statistically associated with total prostate cancer risk. Conclusion: There was no strong evidence that circulating fatty acids are important predictors of prostate cancer risk. It is not clear whether the modest associations of stearic, eicosapentaenoic, and docosapentaenoic acid are causal

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical Covid-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalisation2-4 following SARS-CoV-2 infection. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from critically-ill cases with population controls in order to find underlying disease mechanisms. Here, we use whole genome sequencing in 7,491 critically-ill cases compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical Covid-19. We identify 16 new independent associations, including variants within genes involved in interferon signalling (IL10RB, PLSCR1), leucocyte differentiation (BCL11A), and blood type antigen secretor status (FUT2). Using transcriptome-wide association and colocalisation to infer the effect of gene expression on disease severity, we find evidence implicating multiple genes, including reduced expression of a membrane flippase (ATP11A), and increased mucin expression (MUC1), in critical disease. Mendelian randomisation provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5, CD209) and coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of Covid-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication, or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between critically-ill cases and population controls is highly efficient for detection of therapeutically-relevant mechanisms of disease

Adiposity, hormone replacement therapy use and breast cancer risk by age and hormone receptor status: a large prospective cohort study

INTRODUCTION: Associations of hormone-receptor positive breast cancer with excess adiposity are reasonably well characterized; however, uncertainty remains regarding the association of body mass index (BMI) with hormone-receptor negative malignancies, and possible interactions by hormone replacement therapy (HRT) use. METHODS: Within the European EPIC cohort, Cox proportional hazards models were used to describe the relationship of BMI, waist and hip circumferences with risk of estrogen-receptor (ER) negative and progesterone-receptor (PR) negative (n = 1,021) and ER+PR+ (n = 3,586) breast tumors within five-year age bands. Among postmenopausal women, the joint effects of BMI and HRT use were analyzed. RESULTS: For risk of ER-PR- tumors, there was no association of BMI across the age bands. However, when analyses were restricted to postmenopausal HRT never users, a positive risk association with BMI (third versus first tertile HR = 1.47 (1.01 to 2.15)) was observed. BMI was inversely associated with ER+PR+ tumors among women aged ≤49 years (per 5 kg/m2 increase, HR = 0.79 (95%CI 0.68 to 0.91)), and positively associated with risk among women ≥65 years (HR = 1.25 (1.16 to 1.34)). Adjusting for BMI, waist and hip circumferences showed no further associations with risks of breast cancer subtypes. Current use of HRT was significantly associated with an increased risk of receptor-negative (HRT current use compared to HRT never use HR: 1.30 (1.05 to 1.62)) and positive tumors (HR: 1.74 (1.56 to 1.95)), although this risk increase was weaker for ER-PR- disease (Phet = 0.035). The association of HRT was significantly stronger in the leaner women (BMI ≤22.5 kg/m2) than for more overweight women (BMI ≥25.9 kg/m2) for, both, ER-PR- (HR: 1.74 (1.15 to 2.63)) and ER+PR+ (HR: 2.33 (1.84 to 2.92)) breast cancer and was not restricted to any particular HRT regime. CONCLUSIONS: An elevated BMI may be positively associated with risk of ER-PR- tumors among postmenopausal women who never used HRT. Furthermore, postmenopausal HRT users were at an increased risk of ER-PR- as well as ER+PR+ tumors, especially among leaner women. For hormone-receptor positive tumors, but not for hormone-receptor negative tumors, our study confirms an inverse association of risk with BMI among young women of premenopausal age. Our data provide evidence for a possible role of sex hormones in the etiology of hormone-receptor negative tumors

Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation.

Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.This work was supported by NIH fellowship F32 GM106584 (AG), NIH grants R01 MH101244(A.G.), R01 CA188392 (B.P.), U01 CA194393(B.P.), R01 GM107427 (M.L.F.), R01 CA193910 (M.L.F./M.P.) and Prostate Cancer Foundation Challenge Award (M.L.F./M.P.). This study makes use of data generated by the Wellcome Trust Case Control Consortium and the Wellcome Trust Sanger Institute. A full list of the investigators who contributed to the generation of the Wellcome Trust Case Control Consortium data is available on www.wtccc.org.uk. Funding for the Wellcome Trust Case Control Consortium project was provided by the Wellcome Trust under award 076113. This study makes use of data generated by the UK10K Consortium. A full list of the investigators who contributed to the generation of the data is available online (http://www.UK10K.org). The PRACTICAL consortium was supported by the following grants: European Commission's Seventh Framework Programme grant agreement n° 223175 (HEALTH-F2-2009-223175), Cancer Research UK Grants C5047/A7357, C1287/A10118, C5047/A3354, C5047/A10692, C16913/A6135 and The National Institute of Health (NIH) Cancer Post-Cancer GWAS initiative Grant: no. 1 U19 CA 148537-01 (the GAME-ON initiative); Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007 and C5047/A10692), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112—the GAME-ON initiative), the Department of Defense (W81XWH-10-1-0341), A Linneus Centre (Contract ID 70867902), Swedish Research Council (grant no K2010-70X-20430-04-3), the Swedish Cancer Foundation (grant no 09-0677), grants RO1CA056678, RO1CA082664 and RO1CA092579 from the US National Cancer Institute, National Institutes of Health; US National Cancer Institute (R01CA72818); support from The National Health and Medical Research Council, Australia (126402, 209057, 251533, 396414, 450104, 504700, 504702, 504715, 623204, 940394 and 614296); NIH grants CA63464, CA54281 and CA098758; US National Cancer Institute (R01CA128813, PI: J.Y. Park); Bulgarian National Science Fund, Ministry of Education and Science (contract DOO-119/2009; DUNK01/2–2009; DFNI-B01/28/2012); Cancer Research UK grants [C8197/A10123] and [C8197/A10865]; grant code G0500966/75466; NIHR Health Technology Assessment Programme (projects 96/20/06 and 96/20/99); Cancer Research UK grant number C522/A8649, Medical Research Council of England grant number G0500966, ID 75466 and The NCRI, UK; The US Dept of Defense award W81XWH-04-1-0280; Australia Project Grant [390130, 1009458] and Enabling Grant [614296 to APCB]; the Prostate Cancer Foundation of Australia (Project Grant [PG7] and Research infrastructure grant [to APCB]); NIH grant R01 CA092447; Vanderbilt-Ingram Cancer Center (P30 CA68485); Cancer Research UK [C490/A10124] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge; Competitive Research Funding of the Tampere University Hospital (9N069 and X51003); Award Number P30CA042014 from the National Cancer Institute.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/0.1038/ncomms1097

Characterizing Associations and SNP-Environment Interactions for GWAS-Identified Prostate Cancer Risk Markers—Results from BPC3

Genome-wide association studies (GWAS) have identified multiple single nucleotide polymorphisms (SNPs) associated with prostate cancer risk. However, whether these associations can be consistently replicated, vary with disease aggressiveness (tumor stage and grade) and/or interact with non-genetic potential risk factors or other SNPs is unknown. We therefore genotyped 39 SNPs from regions identified by several prostate cancer GWAS in 10,501 prostate cancer cases and 10,831 controls from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). We replicated 36 out of 39 SNPs (P-values ranging from 0.01 to 10−28). Two SNPs located near KLK3 associated with PSA levels showed differential association with Gleason grade (rs2735839, P = 0.0001 and rs266849, P = 0.0004; case-only test), where the alleles associated with decreasing PSA levels were inversely associated with low-grade (as defined by Gleason grade <8) tumors but positively associated with high-grade tumors. No other SNP showed differential associations according to disease stage or grade. We observed no effect modification by SNP for association with age at diagnosis, family history of prostate cancer, diabetes, BMI, height, smoking or alcohol intake. Moreover, we found no evidence of pair-wise SNP-SNP interactions. While these SNPs represent new independent risk factors for prostate cancer, we saw little evidence for effect modification by other SNPs or by the environmental factors examined

Genetic Variability of the mTOR Pathway and Prostate Cancer Risk in the European Prospective Investigation on Cancer (EPIC)

The mTOR (mammalian target of rapamycin) signal transduction pathway integrates various signals, regulating ribosome biogenesis and protein synthesis as a function of available energy and amino acids, and assuring an appropriate coupling of cellular proliferation with increases in cell size. In addition, recent evidence has pointed to an interplay between the mTOR and p53 pathways. We investigated the genetic variability of 67 key genes in the mTOR pathway and in genes of the p53 pathway which interact with mTOR. We tested the association of 1,084 tagging SNPs with prostate cancer risk in a study of 815 prostate cancer cases and 1,266 controls nested within the European Prospective Investigation into Cancer and Nutrition (EPIC). We chose the SNPs (n = 11) with the strongest association with risk (p<0.01) and sought to replicate their association in an additional series of 838 prostate cancer cases and 943 controls from EPIC. In the joint analysis of first and second phase two SNPs of the PRKCI gene showed an association with risk of prostate cancer (ORallele = 0.85, 95% CI 0.78–0.94, p = 1.3×10−3 for rs546950 and ORallele = 0.84, 95% CI 0.76–0.93, p = 5.6×10−4 for rs4955720). We confirmed this in a meta-analysis using as replication set the data from the second phase of our study jointly with the first phase of the Cancer Genetic Markers of Susceptibility (CGEMS) project. In conclusion, we found an association with prostate cancer risk for two SNPs belonging to PRKCI, a gene which is frequently overexpressed in various neoplasms, including prostate cancer