Sub-sampling of NMR Correlation and Exchange Experiments

Sub-sampling is applied to simulated $T_1$-$D$ NMR signals and its influence on inversion performance is evaluated. For this different levels of sub-sampling were employed ranging from the fully sampled signal down to only less than two percent of the original data points. This was combined with multiple sample schemes including fully random sampling, truncation and a combination of both. To compare the performance of different inversion algorithms, the so-generated sub-sampled signals were inverted using Tikhonov regularization, modified total generalized variation (MTGV) regularization, deep learning and a combination of deep learning and Tikhonov regularization. Further, the influence of the chosen cost function on the relative inversion performance was investigated. Overall, it could be shown that for a vast majority of instances, deep learning clearly outperforms regularization based inversion methods, if the signal is fully or close to fully sampled. However, in the case of significantly sub-sampled signals regularization yields better inversion performance than its deep learning counterpart with MTGV clearly prevailing over Tikhonov. Additionally, fully random sampling could be identified as the best overall sampling scheme independent of the inversion method. Finally, it could also be shown that the choice of cost function does vastly influence the relative rankings of the tested inversion algorithms highlighting the importance of choosing the cost function accordingly to experimental intentions

Generalized Cross-Validation as a Method of Hyperparameter Search for MTGV Regularization

The concept of generalized cross-validation (GCV) is applied to modified total generalized variation (MTGV) regularization. Current implementations of the MTGV regularization rely on manual (or semi-manual) hyperparameter optimization, which is both time-consuming and subject to bias. The combination of MTGV-regularization and GCV allows for a straightforward hyperparameter search during regularization. This significantly increases the efficiency of the MTGV-method, because it limits the number of hyperparameters, which have to be tested and, improves the practicality of MTGV regularization as a standard technique for inversion of NMR signals. The combined method is applied to simulated and experimental NMR data and the resulting reconstructed distributions are presented. It is shown that for all data sets studied the proposed combination of MTGV and GCV minimizes the GCV score allowing an optimal hyperparameter choice

Developing and enhancing biodiversity monitoring programmes: a collaborative assessment of priorities

1.Biodiversity is changing at unprecedented rates, and it is increasingly important that these changes are quantified through monitoring programmes. Previous recommendations for developing or enhancing these programmes focus either on the end goals, that is the intended use of the data, or on how these goals are achieved, for example through volunteer involvement in citizen science, but not both. These recommendations are rarely prioritized. 2.We used a collaborative approach, involving 52 experts in biodiversity monitoring in the UK, to develop a list of attributes of relevance to any biodiversity monitoring programme and to order these attributes by their priority. We also ranked the attributes according to their importance in monitoring biodiversity in the UK. Experts involved included data users, funders, programme organizers and participants in data collection. They covered expertise in a wide range of taxa. 3.We developed a final list of 25 attributes of biodiversity monitoring schemes, ordered from the most elemental (those essential for monitoring schemes; e.g. articulate the objectives and gain sufficient participants) to the most aspirational (e.g. electronic data capture in the field, reporting change annually). This ordered list is a practical framework which can be used to support the development of monitoring programmes. 4.People's ranking of attributes revealed a difference between those who considered attributes with benefits to end users to be most important (e.g. people from governmental organizations) and those who considered attributes with greatest benefit to participants to be most important (e.g. people involved with volunteer biological recording schemes). This reveals a distinction between focussing on aims and the pragmatism in achieving those aims. 5.Synthesis and applications. The ordered list of attributes developed in this study will assist in prioritizing resources to develop biodiversity monitoring programmes (including citizen science). The potential conflict between end users of data and participants in data collection that we discovered should be addressed by involving the diversity of stakeholders at all stages of programme development. This will maximize the chance of successfully achieving the goals of biodiversity monitoring programmes

The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer.

Breast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes BRCA1, BRCA2, PALB2, ATM, and CHEK2 are associated with breast cancer risk. FANCM, which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants FANCM:p.Arg658*, p.Gln1701*, and p.Arg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of BRCA1 or BRCA2. These three variants were also studied functionally by measuring survival and chromosome fragility in FANCM -/- patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that FANCM:p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44, P = 0.034 and OR = 3.79; P = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for FANCM:p.Arg658* and found that also FANCM:p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96; P = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with FANCM:p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare FANCM deleterious variants p.Arg658* and p.Arg1931* are risk factors for ER-negative and TNBC subtypes. Overall our data suggest that the effect of truncating variants on breast cancer risk may depend on their position in the gene. Cell sensitivity to olaparib exposure, identifies a possible therapeutic option to treat FANCM-associated tumors

Fine-mapping of the HNF1B multicancer locus identifies candidate variants that mediate endometrial cancer risk.

Common variants in the hepatocyte nuclear factor 1 homeobox B (HNF1B) gene are associated with the risk of Type II diabetes and multiple cancers. Evidence to date indicates that cancer risk may be mediated via genetic or epigenetic effects on HNF1B gene expression. We previously found single-nucleotide polymorphisms (SNPs) at the HNF1B locus to be associated with endometrial cancer, and now report extensive fine-mapping and in silico and laboratory analyses of this locus. Analysis of 1184 genotyped and imputed SNPs in 6608 Caucasian cases and 37 925 controls, and 895 Asian cases and 1968 controls, revealed the best signal of association for SNP rs11263763 (P = 8.4 × 10(-14), odds ratio = 0.86, 95% confidence interval = 0.82-0.89), located within HNF1B intron 1. Haplotype analysis and conditional analyses provide no evidence of further independent endometrial cancer risk variants at this locus. SNP rs11263763 genotype was associated with HNF1B mRNA expression but not with HNF1B methylation in endometrial tumor samples from The Cancer Genome Atlas. Genetic analyses prioritized rs11263763 and four other SNPs in high-to-moderate linkage disequilibrium as the most likely causal SNPs. Three of these SNPs map to the extended HNF1B promoter based on chromatin marks extending from the minimal promoter region. Reporter assays demonstrated that this extended region reduces activity in combination with the minimal HNF1B promoter, and that the minor alleles of rs11263763 or rs8064454 are associated with decreased HNF1B promoter activity. Our findings provide evidence for a single signal associated with endometrial cancer risk at the HNF1B locus, and that risk is likely mediated via altered HNF1B gene expression

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition

SecM-Stalled Ribosomes Adopt an Altered Geometry at the Peptidyl Transferase Center

A structure of a ribosome stalled during translation of the SecM peptide provides insight into the mechanism by which the large subunit active site is inactivated

Comprehensive genetic assessment of the ESR1 locus identifies a risk region for endometrial cancer.

Excessive exposure to estrogen is a well-established risk factor for endometrial cancer (EC), particularly for cancers of endometrioid histology. The physiological function of estrogen is primarily mediated by estrogen receptor alpha, encoded by ESR1. Consequently, several studies have investigated whether variation at the ESR1 locus is associated with risk of EC, with conflicting results. We performed comprehensive fine-mapping analyses of 3633 genotyped and imputed single nucleotide polymorphisms (SNPs) in 6607 EC cases and 37 925 controls. There was evidence of an EC risk signal located at a potential alternative promoter of the ESR1 gene (lead SNP rs79575945, P=1.86×10(-5)), which was stronger for cancers of endometrioid subtype (P=3.76×10(-6)). Bioinformatic analysis suggests that this risk signal is in a functionally important region targeting ESR1, and eQTL analysis found that rs79575945 was associated with expression of SYNE1, a neighbouring gene. In summary, we have identified a single EC risk signal located at ESR1, at study-wide significance. Given SNPs located at this locus have been associated with risk for breast cancer, also a hormonally driven cancer, this study adds weight to the rationale for performing informed candidate fine-scale genetic studies across cancer types.This work was supported by the National Health and Medical Research Council of Australia (ID#1031333 to A B Spurdle, DF, A M Dunning, ID#39435 to ANECS, ID#552402, QIMR Controls); National Health and Medical Research Council of Australia Fellowship Scheme (to A B Spurdle); Principal Research Fellow of Cancer Research UK (to D F Easton); Joseph Mitchell Trust (to A M Dunning); Oxford Comprehensive Biomedical Research Centre (to I Tomlinson); The European Community's Seventh Framework Programme (grant agreement number 22175 (HEALTH-F2-2009-223175) (COGS); Cancer Research UK (C1287/A10118 to COGS and BCAC, C1287/A10710, C12292/A11174, C1281/A12014 to COGS and BCAC, C5047/A15007, C5047/A10692, C8197/A16565, C490/A10124 to SEARCH, CORGI - NSECG, to I Tomlinson); National Institutes of Health (CA128978, R01 CA122443 to MECS and MAY, P30 CA15083 to MECS, P50 CA136393 to MECS and MAY, CAHRES); Post-Cancer GWAS Initiative (1U19 CA148537, 1U19 CA148065, 1U19 CA148112 – the GAME-ON initiative); Department of Defence (W81XWH-10-1-0341); Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer; Komen Foundation for the Cure; The Breast Cancer Research Foundation; Ovarian Cancer Research Fund (to COGS); Cancer Council Queensland (ID#4196615 to ANECS); Council Cancer Tasmania (ID#403031, #ID457636 to ANECS); Medical Research Council (G0000934 to the British 1958 Birth Cohort); Wellcome Trust (068545/Z/02, 085475 to the British 1958 Birth Cohort); Wellcome Trust Human Genetics Grant (090532/Z/09/Z to NSECG); European Union (EU FP7 CHIBCHA to NSECG); The University of Newcastle (to QIMR Controls, to NECS); Gladys M Brawn Senior Research Fellowship (QIMR Controls); The Vincent Fairfax Family Foundation (QIMR Controls); Hunter Medical Research Institute (HCS, NECS); Hunter Area Pathology Service (HCS); ELAN fund of the University of Erlangen (BECS); Verelst Foundation for endometrial cancer (LES); Fred C and Katherine B Anderson Foundation (to MECS, to MAY); Mayo Foundation (to MECS, to MAY); Ovarian Cancer Research Fund with support of the Smith family, in memory of Kathryn Sladek Smith (MECS, PPD/RPCI.07 to OCAC); Helse Vest Grant (MoMaTEC); University of Bergen (MoMaTEC); Melzer Foundation (MoMaTEC); The Norwegian Cancer Society – Harald Andersens legat (MoMaTEC); The Research Council of Norway (MoMaTEC); Haukeland University of Hospital (MoMaTEC); NBN Children's Cancer Research Group (NECS); Ms Jennie Thomas (NECS); regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet (20110222, 20110483, 20110141 and DF 07015 all to RENDOCAS, to KARBAC); The Swedish Labor Market Insurance (100069 to RENDOCAS); The Swedish Cancer Society (11 0439 to RENDOCAS); Agency for Science, Technology and Research of Singapore (CAHRES); Susan G Komen Breast Cancer Foundation (CAHRES); UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge (OCAC); Baden-Württemberg state Ministry of Science, Research and Arts (ESTHER); Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (ESTHER); Federal Ministry of Education and Research (BMBF) Germany (01KW9975/5 to GENICA, 01KW9976/8 to GENICA, 01KW9977/0 to GENICA, 01KW0114 to GENICA, to ESTHER); Robert Bosch Foundation (GENICA); Deutsches Krebsforschungszentrum – DKFZ (GENICA); Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum, IPA (GENICA); Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus (GENICA); Deutsche Krebshilfe e.V. (70-2892-BR I to MARIE); Hamburg Cancer Society (MARIE); German Cancer Research Center (MARIE); Breast Cancer Research Foundation (MCBCS); David F. and Margaret T. Grohne Family Foundation (MCBCS); Ting Tsung and Wei Fong Chao Foundation (MCBCS); VicHealth (MCCS); Cancer Council Victoria (MCCS); Breakthrough Breast Cancer (UKBGS); Institute of Cancer Research (UKBGS); and NHS funding to the NIHR Biomedical Research Centre (UKBGS/ICR).This is the final version of the article. It first appeared from the Society for Endocrinology via http://dx.doi.org/10.1530/ERC-15-031