Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Loss-of-function mutations in SLC30A8 protect against type 2 diabetes.

Neðst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn View/OpenLoss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets, but none have yet been described for type 2 diabetes (T2D). Through sequencing or genotyping of ~150,000 individuals across 5 ancestry groups, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8) and harbors a common variant (p.Trp325Arg) associated with T2D risk and glucose and proinsulin levels. Collectively, carriers of protein-truncating variants had 65% reduced T2D risk (P = 1.7 × 10(-6)), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34Serfs*50) demonstrated reduced glucose levels (-0.17 s.d., P = 4.6 × 10(-4)). The two most common protein-truncating variants (p.Arg138* and p.Lys34Serfs*50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested that reduced zinc transport increases T2D risk, and phenotypic heterogeneity was observed in mouse Slc30a8 knockouts. In contrast, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, suggesting ZnT8 inhibition as a therapeutic strategy in T2D prevention.US National Institutes of Health (NIH) Training 5-T32-GM007748-33 Doris Duke Charitable Foundation 2006087 Fulbright Diabetes UK Fellowship BDA 11/0004348 Broad Institute from Pfizer, Inc. NIH U01 DK085501 U01 DK085524 U01 DK085545 U01 DK085584 Swedish Research Council Dnr 521-2010-3490 Dnr 349-2006-237 European Research Council (ERC) GENETARGET T2D GA269045 ENGAGE 2007-201413 CEED3 2008-223211 Sigrid Juselius Foundation Folkh lsan Research Foundation ERC AdG 293574 Research Council of Norway 197064/V50 KG Jebsen Foundation University of Bergen Western Norway Health Authority Lundbeck Foundation Novo Nordisk Foundation Wellcome Trust WT098017 WT064890 WT090532 WT090367 WT098381 Uppsala University Swedish Research Council and the Swedish Heart- Lung Foundation Academy of Finland 124243 102318 123885 139635 Finnish Heart Foundation Finnish Diabetes Foundation, Tekes 1510/31/06 Commission of the European Community HEALTH-F2-2007-201681 Ministry of Education and Culture of Finland European Commission Framework Programme 6 Integrated Project LSHM-CT-2004-005272 City of Kuopio and Social Insurance Institution of Finland Finnish Foundation for Cardiovascular Disease NIH/NIDDK U01-DK085545 National Heart, Lung, and Blood Institute (NHLBI) National Institute on Minority Health and Health Disparities N01 HC-95170 N01 HC-95171 N01 HC-95172 European Union Seventh Framework Programme, DIAPREPP Swedish Child Diabetes Foundation (Barndiabetesfonden) 5U01DK085526 DK088389 U54HG003067 R01DK072193 R01DK062370 Z01HG000024info:eu-repo/grantAgreement/EC/FP7/20201

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

dentification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 x 10(-8)) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.Peer reviewe

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.

We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P &lt; 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.</p

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

A Low-Frequency Inactivating Akt2 Variant Enriched in the Finnish Population is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting insulin, a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in fasting plasma insulin (FI) levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-hour insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio=1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.Academy of Finland (129293, 128315, 129330, 131593, 139635, 139635, 121584, 126925, 124282, 129378, 258753); Action on Hearing Loss (G51); Ahokas Foundation; American Diabetes Association (#7-12-MN-02); Atlantic Canada Opportunities Agency; Augustinus foundation; Becket foundation; Benzon Foundation; Biomedical Research Council; British Heart Foundation (SP/04/002); Canada Foundation for Innovation; Commission of the European Communities, Directorate C-Public Health (2004310); Copenhagen County; Danish Centre for Evaluation and Health Technology Assessment; Danish Council for Independent Research; Danish Heart Foundation (07-10-R61-A1754-B838-22392F); Danish Medical Research Council; Danish Pharmaceutical Association; Emil Aaltonen Foundation; European Research Council Advanced Research Grant; European Union FP7 (EpiMigrant, 279143; FP7/2007-2013; 259749); Finland's Slottery Machine Association; Finnish Cultural Foundation; Finnish Diabetes Research Foundation; Finnish Foundation for Cardiovascular Research; Finnish Foundation of Cardiovascular Research; Finnish Medical Society; Finnish National Public Health Institute; Finska Läkaresällskapet; Folkhälsan Research Foundation; Foundation for Life and Health in Finland; German Center for Diabetes Research (DZD) ; German Federal Ministry of Education and Research; Health Care Centers in Vasa, Närpes and Korsholm; Health Insurance Foundation (2012B233) ; Helsinki University Central Hospital Research Foundation; Hospital districts of Pirkanmaa, Southern Ostrobothnia, North Ostrobothnia, Central Finland, and Northern Savo; Ib Henriksen foundation; Juho Vainio Foundation; Korea Centers for Disease Control and Prevention (4845–301); Korea National Institute of Health (2012-N73002-00); Li Ka Shing Foundation; Liv och Hälsa; Lundbeck Foundation; Marie-Curie Fellowship (PIEF-GA-2012-329156); Medical Research Council (G0601261, G0900747-91070, G0601966, G0700931); Ministry of Education in Finland; Ministry of Social Affairs and Health in Finland; MRC-PHE Centre for Environment and Health;Municipal Heath Care Center and Hospital in Jakobstad; Närpes Health Care Foundation; National Institute for Health Research (RP-PG-0407-10371); National Institutes of Health (U01 DK085526, U01 DK085501, U01 DK085524, U01 DK085545, U01 DK085584, U01 DK088389, RC2-DK088389, DK085545, DK098032, HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN, R01MH107666 and K12CA139160268201300050C, U01 DK062370, R01 DK066358, U01DK085501, R01HL102830, R01DK073541, PO1AG027734, R01AG046949, 1R01AG042188, P30AG038072, R01 MH101820, R01MH090937, P30DK020595, R01 DK078616, NIDDK K24 DK080140, 1RC2DK088389, T32GM007753); National Medical Research Council; National Research Foundation of Korea (NRF-2012R1A2A1A03006155); Nordic Center of Excellence in Disease Genetics; Novo Nordisk; Ollqvist Foundation; OrionFarmos Research Foundation; Paavo Nurmi Foundation; Perklén Foundation; Samfundet Folkhälsan; Signe and Ane Gyllenberg Foundation; Sigrid Juselius Foundation; Social Insurance Institution of Finland; South East Norway Health Authority (2011060); Swedish Cultural Foundation in Finland; Swedish Heart-Lung Foundation; Swedish Research Council; Swedish Research Council (Linné and Strategic Research Grant); The American Federation for Aging Research; The Einstein Glenn Center; The European Commission (HEALTH-F4-2007-201413); The Finnish Diabetes Association; The Folkhälsan Research Foundation; The Påhlssons Foundation; The provinces of Newfoundland and Labrador, Nova Scotia, and New Brunswick; The Sigrid Juselius Foundation; The Skåne Regional Health Authority; The Swedish Heart-Lung Foundation; Timber Merchant Vilhelm Bang’s Foundation; Turku University Foundation; Uppsala University; Wellcome Trust (064890, 083948, 085475, 086596, 090367, 090532, 092447, 095101/Z/10/Z, 200837/Z/16/Z, 095552, 098017, 098381, 098051, 084723, 072960/2/ 03/2, 086113/Z/08/Z, WT098017, WT064890, WT090532, WT098017, 098051, WT086596/Z/08/A and 086596/Z/08/Z). Detailed acknowledgment of funding sources is provided in the Additional Acknowledgements section of the Supplementary Materials