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1.  Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems 
BMC Genomics  2016;17:874.
Background
Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies.
Results
We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package.
Conclusion
Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-016-3198-9) contains supplementary material, which is available to authorized users.
doi:10.1186/s12864-016-3198-9
PMCID: PMC5097440  PMID: 27814671
Mergeomics; Integrative genomics; Multidimensional data integration; Functional genomics; Gene networks; Key drivers; Cholesterol; Blood glucose
2.  Genome-Wide Meta-Analysis of Sciatica in Finnish Population 
PLoS ONE  2016;11(10):e0163877.
Sciatica or the sciatic syndrome is a common and often disabling low back disorder in the working-age population. It has a relatively high heritability but poorly understood molecular mechanisms. The Finnish population is a genetic isolate where small founder population and bottleneck events have led to enrichment of certain rare and low frequency variants. We performed here the first genome-wide association (GWAS) and meta-analysis of sciatica. The meta-analysis was conducted across two GWAS covering 291 Finnish sciatica cases and 3671 controls genotyped and imputed at 7.7 million autosomal variants. The most promising loci (p<1x10-6) were replicated in 776 Finnish sciatica patients and 18,489 controls. We identified five intragenic variants, with relatively low frequencies, at two novel loci associated with sciatica at genome-wide significance. These included chr9:14344410:I (rs71321981) at 9p22.3 (NFIB gene; p = 1.30x10-8, MAF = 0.08) and four variants at 15q21.2: rs145901849, rs80035109, rs190200374 and rs117458827 (MYO5A; p = 1.34x10-8, MAF = 0.06; p = 2.32x10-8, MAF = 0.07; p = 3.85x10-8, MAF = 0.06; p = 4.78x10-8, MAF = 0.07, respectively). The most significant association in the meta-analysis, a single base insertion rs71321981 within the regulatory region of the transcription factor NFIB, replicated in an independent Finnish population sample (p = 0.04). Despite identifying 15q21.2 as a promising locus, we were not able to replicate it. It was differentiated; the lead variants within 15q21.2 were more frequent in Finland (6–7%) than in other European populations (1–2%). Imputation accuracies of the three significantly associated variants (chr9:14344410:I, rs190200374, and rs80035109) were validated by genotyping. In summary, our results suggest a novel locus, 9p22.3 (NFIB), which may be involved in susceptibility to sciatica. In addition, another locus, 15q21.2, emerged as a promising one, but failed to replicate.
doi:10.1371/journal.pone.0163877
PMCID: PMC5072673  PMID: 27764105
3.  No Association of Coronary Artery Disease with X-Chromosomal Variants in Comprehensive International Meta-Analysis 
Loley, Christina | Alver, Maris | Assimes, Themistocles L. | Bjonnes, Andrew | Goel, Anuj | Gustafsson, Stefan | Hernesniemi, Jussi | Hopewell, Jemma C. | Kanoni, Stavroula | Kleber, Marcus E. | Lau, King Wai | Lu, Yingchang | Lyytikäinen, Leo-Pekka | Nelson, Christopher P. | Nikpay, Majid | Qu, Liming | Salfati, Elias | Scholz, Markus | Tukiainen, Taru | Willenborg, Christina | Won, Hong-Hee | Zeng, Lingyao | Zhang, Weihua | Anand, Sonia S. | Beutner, Frank | Bottinger, Erwin P. | Clarke, Robert | Dedoussis, George | Do, Ron | Esko, Tõnu | Eskola, Markku | Farrall, Martin | Gauguier, Dominique | Giedraitis, Vilmantas | Granger, Christopher B. | Hall, Alistair S. | Hamsten, Anders | Hazen, Stanley L. | Huang, Jie | Kähönen, Mika | Kyriakou, Theodosios | Laaksonen, Reijo | Lind, Lars | Lindgren, Cecilia | Magnusson, Patrik K. E. | Marouli, Eirini | Mihailov, Evelin | Morris, Andrew P. | Nikus, Kjell | Pedersen, Nancy | Rallidis, Loukianos | Salomaa, Veikko | Shah, Svati H. | Stewart, Alexandre F. R. | Thompson, John R. | Zalloua, Pierre A. | Chambers, John C. | Collins, Rory | Ingelsson, Erik | Iribarren, Carlos | Karhunen, Pekka J. | Kooner, Jaspal S. | Lehtimäki, Terho | Loos, Ruth J. F. | März, Winfried | McPherson, Ruth | Metspalu, Andres | Reilly, Muredach P. | Ripatti, Samuli | Sanghera, Dharambir K. | Thiery, Joachim | Watkins, Hugh | Deloukas, Panos | Kathiresan, Sekar | Samani, Nilesh J. | Schunkert, Heribert | Erdmann, Jeanette | König, Inke R.
Scientific Reports  2016;6:35278.
In recent years, genome-wide association studies have identified 58 independent risk loci for coronary artery disease (CAD) on the autosome. However, due to the sex-specific data structure of the X chromosome, it has been excluded from most of these analyses. While females have 2 copies of chromosome X, males have only one. Also, one of the female X chromosomes may be inactivated. Therefore, special test statistics and quality control procedures are required. Thus, little is known about the role of X-chromosomal variants in CAD. To fill this gap, we conducted a comprehensive X-chromosome-wide meta-analysis including more than 43,000 CAD cases and 58,000 controls from 35 international study cohorts. For quality control, sex-specific filters were used to adequately take the special structure of X-chromosomal data into account. For single study analyses, several logistic regression models were calculated allowing for inactivation of one female X-chromosome, adjusting for sex and investigating interactions between sex and genetic variants. Then, meta-analyses including all 35 studies were conducted using random effects models. None of the investigated models revealed genome-wide significant associations for any variant. Although we analyzed the largest-to-date sample, currently available methods were not able to detect any associations of X-chromosomal variants with CAD.
doi:10.1038/srep35278
PMCID: PMC5059659  PMID: 27731410
4.  Effect of Insulin Resistance on Monounsaturated Fatty Acid Levels: A Multi-cohort Non-targeted Metabolomics and Mendelian Randomization Study 
PLoS Genetics  2016;12(10):e1006379.
Insulin resistance (IR) and impaired insulin secretion contribute to type 2 diabetes and cardiovascular disease. Both are associated with changes in the circulating metabolome, but causal directions have been difficult to disentangle. We combined untargeted plasma metabolomics by liquid chromatography/mass spectrometry in three non-diabetic cohorts with Mendelian Randomization (MR) analysis to obtain new insights into early metabolic alterations in IR and impaired insulin secretion. In up to 910 elderly men we found associations of 52 metabolites with hyperinsulinemic-euglycemic clamp-measured IR and/or β-cell responsiveness (disposition index) during an oral glucose tolerance test. These implicated bile acid, glycerophospholipid and caffeine metabolism for IR and fatty acid biosynthesis for impaired insulin secretion. In MR analysis in two separate cohorts (n = 2,613) followed by replication in three independent studies profiled on different metabolomics platforms (n = 7,824 / 8,961 / 8,330), we discovered and replicated causal effects of IR on lower levels of palmitoleic acid and oleic acid. A trend for a causal effect of IR on higher levels of tyrosine reached significance only in meta-analysis. In one of the largest studies combining “gold standard” measures for insulin responsiveness with non-targeted metabolomics, we found distinct metabolic profiles related to IR or impaired insulin secretion. We speculate that the causal effects on monounsaturated fatty acid levels could explain parts of the raised cardiovascular disease risk in IR that is independent of diabetes development.
Author Summary
Impaired glucose homeostasis leads to diabetes and cardiovascular disease and has two main components: failure to secrete enough insulin from pancreatic β-cells and reduced insulin-stimulated cellular uptake of glucose and other nutrients in target tissues (insulin resistance, IR). We used metabolomics analysis in non-diabetic persons to measure a non-selective range of small molecules including amino acids, lipids, and sugars. Pathway analysis highlighted distinct metabolic pathways linked to IR (e.g. bile acid production) and impaired insulin secretion (fatty acid biosynthesis), but causal directions remained unclear. Mendelian Randomization (MR) analysis can test for causal effects in observational studies in the absence of randomized controlled trials. Using MR analysis in up to four large independent studies, we found evidence that IR causes a decrease in levels of the main endogenous monounsaturated fatty acids palmitoleic acid and oleic acid, as well as suggestive evidence for higher levels of the amino acid tyrosine. We provide a possible explanation for parts of the diabetes-independent risk of cardiovascular disease in persons with IR.
doi:10.1371/journal.pgen.1006379
PMCID: PMC5074591  PMID: 27768686
5.  Rare Functional Variant in TM2D3 is Associated with Late-Onset Alzheimer's Disease 
PLoS Genetics  2016;12(10):e1006327.
We performed an exome-wide association analysis in 1393 late-onset Alzheimer’s disease (LOAD) cases and 8141 controls from the CHARGE consortium. We found that a rare variant (P155L) in TM2D3 was enriched in Icelanders (~0.5% versus <0.05% in other European populations). In 433 LOAD cases and 3903 controls from the Icelandic AGES sub-study, P155L was associated with increased risk and earlier onset of LOAD [odds ratio (95% CI) = 7.5 (3.5–15.9), p = 6.6x10-9]. Mutation in the Drosophila TM2D3 homolog, almondex, causes a phenotype similar to loss of Notch/Presenilin signaling. Human TM2D3 is capable of rescuing these phenotypes, but this activity is abolished by P155L, establishing it as a functionally damaging allele. Our results establish a rare TM2D3 variant in association with LOAD susceptibility, and together with prior work suggests possible links to the β-amyloid cascade.
Author Summary
Alzheimer’s disease (AD) is the most common cause of dementia in the older adult population. There is substantial evidence for an important genetic contribution to AD risk. While prior work has comprehensively evaluated the contribution of common genetic variants in large population-based cohorts, the role of rare variants remains to be defined. Here, we have used a newer genotyping array to characterize less common variants, including those likely to impact the function of encoded proteins, in a combined cohort of 1393 AD cases and 8141 control subjects without AD. Our results implicate a novel, amino acid-changing variant, P155L, in the TM2D3 gene. This variant was discovered to be more common in the Icelandic population, where it was significantly associated with both increased risk and earlier age of onset of AD. Lastly, in order to examine the potential functional impact of the implicated variant, we performed additional studies in the fruit fly. Our results suggest that P155L causes a loss-of-function in TM2D3, in the context of Notch-Presenilin signal transduction. In sum, we identify a novel, rare TM2D3 variant in association with AD risk and highlight functional connections with AD-relevant biology.
doi:10.1371/journal.pgen.1006327
PMCID: PMC5072721  PMID: 27764101
6.  Genomic prediction of coronary heart disease 
European Heart Journal  2016;37(43):3267-3278.
Aims
Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores.
Methods and results
We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61–1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18–1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5–1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6–5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12–18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking.
Conclusions
A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.
doi:10.1093/eurheartj/ehw450
PMCID: PMC5146693  PMID: 27655226
Genomic risk score; Coronary heart disease; Myocardial infarction; Framingham risk score; Primary prevention
7.  Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels 
van Leeuwen, Elisabeth M | Sabo, Aniko | Bis, Joshua C | Huffman, Jennifer E | Manichaikul, Ani | Smith, Albert V | Feitosa, Mary F | Demissie, Serkalem | Joshi, Peter K | Duan, Qing | Marten, Jonathan | van Klinken, Jan B | Surakka, Ida | Nolte, Ilja M | Zhang, Weihua | Mbarek, Hamdi | Li-Gao, Ruifang | Trompet, Stella | Verweij, Niek | Evangelou, Evangelos | Lyytikäinen, Leo-Pekka | Tayo, Bamidele O | Deelen, Joris | van der Most, Peter J | van der Laan, Sander W | Arking, Dan E | Morrison, Alanna | Dehghan, Abbas | Franco, Oscar H | Hofman, Albert | Rivadeneira, Fernando | Sijbrands, Eric J | Uitterlinden, Andre G | Mychaleckyj, Josyf C | Campbell, Archie | Hocking, Lynne J | Padmanabhan, Sandosh | Brody, Jennifer A | Rice, Kenneth M | White, Charles C | Harris, Tamara | Isaacs, Aaron | Campbell, Harry | Lange, Leslie A | Rudan, Igor | Kolcic, Ivana | Navarro, Pau | Zemunik, Tatijana | Salomaa, Veikko | Kooner, Angad S | Kooner, Jaspal S | Lehne, Benjamin | Scott, William R | Tan, Sian-Tsung | de Geus, Eco J | Milaneschi, Yuri | Penninx, Brenda W J H | Willemsen, Gonneke | de Mutsert, Renée | Ford, Ian | Gansevoort, Ron T | Segura-Lepe, Marcelo P | Raitakari, Olli T | Viikari, Jorma S | Nikus, Kjell | Forrester, Terrence | McKenzie, Colin A | de Craen, Anton J M | de Ruijter, Hester M | Pasterkamp, Gerard | Snieder, Harold | Oldehinkel, Albertine J | Slagboom, P Eline | Cooper, Richard S | Kähönen, Mika | Lehtimäki, Terho | Elliott, Paul | van der Harst, Pim | Jukema, J Wouter | Mook-Kanamori, Dennis O | Boomsma, Dorret I | Chambers, John C | Swertz, Morris | Ripatti, Samuli | Willems van Dijk, Ko | Vitart, Veronique | Polasek, Ozren | Hayward, Caroline | Wilson, James G | Wilson, James F | Gudnason, Vilmundur | Rich, Stephen S | Psaty, Bruce M | Borecki, Ingrid B | Boerwinkle, Eric | Rotter, Jerome I | Cupples, L Adrienne | van Duijn, Cornelia M
Journal of Medical Genetics  2016;53(7):441-449.
Background
So far, more than 170 loci have been associated with circulating lipid levels through genome-wide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels.
Methods
We used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from ∼60 000 individuals in the discovery stage and ∼90 000 samples in the replication stage.
Results
Our study resulted in the identification of five new associations with circulating lipid levels at four loci. All four loci are within genes that can be linked biologically to lipid metabolism. One of the variants, rs116843064, is a damaging missense variant within the ANGPTL4 gene.
Conclusions
This study illustrates that GWAS with high-scale imputation may still help us unravel the biological mechanism behind circulating lipid levels.
doi:10.1136/jmedgenet-2015-103439
PMCID: PMC4941146  PMID: 27036123
Complex traits; Epidemiology; Genetics; Genome-wide; circulating lipid levels
8.  Mendelian randomisation analysis strongly implicates adiposity with risk of developing colorectal cancer 
British Journal of Cancer  2016;115(2):266-272.
Background:
Observational studies have associated adiposity with an increased risk of colorectal cancer (CRC). However, such studies do not establish a causal relationship. To minimise bias from confounding we performed a Mendelian randomisation (MR) analysis to examine the relationship between adiposity and CRC.
Methods:
We used SNPs associated with adult body mass index (BMI), waist-hip ratio (WHR), childhood obesity and birth weight as instrumental variables in a MR analysis of 9254 CRC cases and 18 386 controls.
Results:
In the MR analysis, the odds ratios (ORs) of CRC risk per unit increase in BMI, WHR and childhood obesity were 1.23 (95% CI: 1.02–1.49, P=0.033), 1.59 (95% CI: 1.08–2.34, P=0.019) and 1.07 (95% CI: 1.03–1.13, P=0.018), respectively. There was no evidence for association between birth weight and CRC (OR=1.22, 95% CI: 0.89–1.67, P=0.22). Combining these data with a concurrent MR-based analysis for BMI and WHR with CRC risk (totalling to 18 190 cases, 27 617 controls) provided increased support, ORs for BMI and WHR were 1.26 (95% CI: 1.10–1.44, P=7.7 × 10−4) and 1.40 (95% CI: 1.14–1.72, P=1.2 × 10−3), respectively.
Conclusions:
These data provide further evidence for a strong causal relationship between adiposity and the risk of developing CRC highlighting the urgent need for prevention and treatment of adiposity.
doi:10.1038/bjc.2016.188
PMCID: PMC4947703  PMID: 27336604
Mendelian randomisation; adiposity; colorectal cancer
9.  The Contribution of GWAS Loci in Familial Dyslipidemias 
PLoS Genetics  2016;12(5):e1006078.
Familial combined hyperlipidemia (FCH) is a complex and common familial dyslipidemia characterized by elevated total cholesterol and/or triglyceride levels with over five-fold risk of coronary heart disease. The genetic architecture and contribution of rare Mendelian and common variants to FCH susceptibility is unknown. In 53 Finnish FCH families, we genotyped and imputed nine million variants in 715 family members with DNA available. We studied the enrichment of variants previously implicated with monogenic dyslipidemias and/or lipid levels in the general population by comparing allele frequencies between the FCH families and population samples. We also constructed weighted polygenic scores using 212 lipid-associated SNPs and estimated the relative contributions of Mendelian variants and polygenic scores to the risk of FCH in the families. We identified, across the whole allele frequency spectrum, an enrichment of variants known to elevate, and a deficiency of variants known to lower LDL-C and/or TG levels among both probands and affected FCH individuals. The score based on TG associated SNPs was particularly high among affected individuals compared to non-affected family members. Out of 234 affected FCH individuals across the families, seven (3%) carried Mendelian variants and 83 (35%) showed high accumulation of either known LDL-C or TG elevating variants by having either polygenic score over the 90th percentile in the population. The positive predictive value of high score was much higher for affected FCH individuals than for similar sporadic cases in the population. FCH is highly polygenic, supporting the hypothesis that variants across the whole allele frequency spectrum contribute to this complex familial trait. Polygenic SNP panels improve identification of individuals affected with FCH, but their clinical utility remains to be defined.
Author Summary
Familial combined hyperlipidemia (FCH) is a familial dyslipidemia and the most common familial risk factor for premature coronary heart disease. Its genetic architecture is poorly understood. Rare high-impact variants have been identified in some patients, but have not explained a substantial portion of the trait. FCH has previously been speculated to be a polygenic disorder, but genetic data supporting this hypothesis have so far been incomplete. We provide experimental evidence for the polygenicity and heterogeneity of FCH in a large set of affected families using comprehensive genome-wide variant data. Approximately a third of the affected FCH individuals in our sample had high polygenic burden, and only a minority carried high-impact variants identifiable by genotyping. We show that the polygenic burden of affected FCH family members is comparable to that observed in individuals with similar lipid phenotypes in the general population. Genetic variants identified in large-scale population studies can also underlie the typical phenotypes observed in complex familial diseases such as FCH. Advances in genetic diagnosis based on population samples may thus also benefit FCH families. Families without high polygenic burden are good candidates for sequencing studies to identify rare variants not observable with genotyping.
doi:10.1371/journal.pgen.1006078
PMCID: PMC4882070  PMID: 27227539
10.  Genetic fine-mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci 
Gaulton, Kyle J | Ferreira, Teresa | Lee, Yeji | Raimondo, Anne | Mägi, Reedik | Reschen, Michael E | Mahajan, Anubha | Locke, Adam | Rayner, N William | Robertson, Neil | Scott, Robert A | Prokopenko, Inga | Scott, Laura J | Green, Todd | Sparso, Thomas | Thuillier, Dorothee | Yengo, Loic | Grallert, Harald | Wahl, Simone | Frånberg, Mattias | Strawbridge, Rona J | Kestler, Hans | Chheda, Himanshu | Eisele, Lewin | Gustafsson, Stefan | Steinthorsdottir, Valgerdur | Thorleifsson, Gudmar | Qi, Lu | Karssen, Lennart C | van Leeuwen, Elisabeth M | Willems, Sara M | Li, Man | Chen, Han | Fuchsberger, Christian | Kwan, Phoenix | Ma, Clement | Linderman, Michael | Lu, Yingchang | Thomsen, Soren K | Rundle, Jana K | Beer, Nicola L | van de Bunt, Martijn | Chalisey, Anil | Kang, Hyun Min | Voight, Benjamin F | Abecasis, Goncalo R | Almgren, Peter | Baldassarre, Damiano | Balkau, Beverley | Benediktsson, Rafn | Blüher, Matthias | Boeing, Heiner | Bonnycastle, Lori L | Borringer, Erwin P | Burtt, Noël P | Carey, Jason | Charpentier, Guillaume | Chines, Peter S | Cornelis, Marilyn C | Couper, David J | Crenshaw, Andrew T | van Dam, Rob M | Doney, Alex SF | Dorkhan, Mozhgan | Edkins, Sarah | Eriksson, Johan G | Esko, Tonu | Eury, Elodie | Fadista, João | Flannick, Jason | Fontanillas, Pierre | Fox, Caroline | Franks, Paul W | Gertow, Karl | Gieger, Christian | Gigante, Bruna | Gottesman, Omri | Grant, George B | Grarup, Niels | Groves, Christopher J | Hassinen, Maija | Have, Christian T | Herder, Christian | Holmen, Oddgeir L | Hreidarsson, Astradur B | Humphries, Steve E | Hunter, David J | Jackson, Anne U | Jonsson, Anna | Jørgensen, Marit E | Jørgensen, Torben | Kao, Wen-Hong L | Kerrison, Nicola D | Kinnunen, Leena | Klopp, Norman | Kong, Augustine | Kovacs, Peter | Kraft, Peter | Kravic, Jasmina | Langford, Cordelia | Leander, Karin | Liang, Liming | Lichtner, Peter | Lindgren, Cecilia M | Lindholm, Eero | Linneberg, Allan | Liu, Ching-Ti | Lobbens, Stéphane | Luan, Jian’an | Lyssenko, Valeriya | Mӓnnistö, Satu | McLeod, Olga | Meyer, Julia | Mihailov, Evelin | Mirza, Ghazala | Mühleisen, Thomas W | Müller-Nurasyid, Martina | Navarro, Carmen | Nöthen, Markus M | Oskolkov, Nikolay N | Owen, Katharine R | Palli, Domenico | Pechlivanis, Sonali | Peltonen, Leena | Perry, John RB | Platou, Carl GP | Roden, Michael | Ruderfer, Douglas | Rybin, Denis | van der Schouw, Yvonne T | Sennblad, Bengt | Sigurđsson, Gunnar | Stančáková, Alena | Steinbach, Gerald | Storm, Petter | Strauch, Konstantin | Stringham, Heather M | Sun, Qi | Thorand, Barbara | Tikkanen, Emmi | Tonjes, Anke | Trakalo, Joseph | Tremoli, Elena | Tuomi, Tiinamaija | Wennauer, Roman | Wiltshire, Steven | Wood, Andrew R | Zeggini, Eleftheria | Dunham, Ian | Birney, Ewan | Pasquali, Lorenzo | Ferrer, Jorge | Loos, Ruth JF | Dupuis, Josée | Florez, Jose C | Boerwinkle, Eric | Pankow, James S | van Duijn, Cornelia | Sijbrands, Eric | Meigs, James B | Hu, Frank B | Thorsteinsdottir, Unnur | Stefansson, Kari | Lakka, Timo A | Rauramaa, Rainer | Stumvoll, Michael | Pedersen, Nancy L | Lind, Lars | Keinanen-Kiukaanniemi, Sirkka M | Korpi-Hyövӓlti, Eeva | Saaristo, Timo E | Saltevo, Juha | Kuusisto, Johanna | Laakso, Markku | Metspalu, Andres | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Ripatti, Samuli | Salomaa, Veikko | Ingelsson, Erik | Boehm, Bernhard O | Bergman, Richard N | Collins, Francis S | Mohlke, Karen L | Koistinen, Heikki | Tuomilehto, Jaakko | Hveem, Kristian | Njølstad, Inger | Deloukas, Panagiotis | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | de Faire, Ulf | Hamsten, Anders | Illig, Thomas | Peters, Annette | Cauchi, Stephane | Sladek, Rob | Froguel, Philippe | Hansen, Torben | Pedersen, Oluf | Morris, Andrew D | Palmer, Collin NA | Kathiresan, Sekar | Melander, Olle | Nilsson, Peter M | Groop, Leif C | Barroso, Inês | Langenberg, Claudia | Wareham, Nicholas J | O’Callaghan, Christopher A | Gloyn, Anna L | Altshuler, David | Boehnke, Michael | Teslovich, Tanya M | McCarthy, Mark I | Morris, Andrew P
Nature genetics  2015;47(12):1415-1425.
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/near KCNQ1. “Credible sets” of variants most likely to drive each distinct signal mapped predominantly to non-coding sequence, implying that T2D association 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 this T2D-risk allele 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.
doi:10.1038/ng.3437
PMCID: PMC4666734  PMID: 26551672
11.  Genetic fine-mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci 
Gaulton, Kyle J | Ferreira, Teresa | Lee, Yeji | Raimondo, Anne | Mägi, Reedik | Reschen, Michael E | Mahajan, Anubha | Locke, Adam | Rayner, N William | Robertson, Neil | Scott, Robert A | Prokopenko, Inga | Scott, Laura J | Green, Todd | Sparso, Thomas | Thuillier, Dorothee | Yengo, Loic | Grallert, Harald | Wahl, Simone | Frånberg, Mattias | Strawbridge, Rona J | Kestler, Hans | Chheda, Himanshu | Eisele, Lewin | Gustafsson, Stefan | Steinthorsdottir, Valgerdur | Thorleifsson, Gudmar | Qi, Lu | Karssen, Lennart C | van Leeuwen, Elisabeth M | Willems, Sara M | Li, Man | Chen, Han | Fuchsberger, Christian | Kwan, Phoenix | Ma, Clement | Linderman, Michael | Lu, Yingchang | Thomsen, Soren K | Rundle, Jana K | Beer, Nicola L | van de Bunt, Martijn | Chalisey, Anil | Kang, Hyun Min | Voight, Benjamin F | Abecasis, Goncalo R | Almgren, Peter | Baldassarre, Damiano | Balkau, Beverley | Benediktsson, Rafn | Blüher, Matthias | Boeing, Heiner | Bonnycastle, Lori L | Borringer, Erwin P | Burtt, Noël P | Carey, Jason | Charpentier, Guillaume | Chines, Peter S | Cornelis, Marilyn C | Couper, David J | Crenshaw, Andrew T | van Dam, Rob M | Doney, Alex SF | Dorkhan, Mozhgan | Edkins, Sarah | Eriksson, Johan G | Esko, Tonu | Eury, Elodie | Fadista, João | Flannick, Jason | Fontanillas, Pierre | Fox, Caroline | Franks, Paul W | Gertow, Karl | Gieger, Christian | Gigante, Bruna | Gottesman, Omri | Grant, George B | Grarup, Niels | Groves, Christopher J | Hassinen, Maija | Have, Christian T | Herder, Christian | Holmen, Oddgeir L | Hreidarsson, Astradur B | Humphries, Steve E | Hunter, David J | Jackson, Anne U | Jonsson, Anna | Jørgensen, Marit E | Jørgensen, Torben | Kao, Wen-Hong L | Kerrison, Nicola D | Kinnunen, Leena | Klopp, Norman | Kong, Augustine | Kovacs, Peter | Kraft, Peter | Kravic, Jasmina | Langford, Cordelia | Leander, Karin | Liang, Liming | Lichtner, Peter | Lindgren, Cecilia M | Lindholm, Eero | Linneberg, Allan | Liu, Ching-Ti | Lobbens, Stéphane | Luan, Jian’an | Lyssenko, Valeriya | Männistö, Satu | McLeod, Olga | Meyer, Julia | Mihailov, Evelin | Mirza, Ghazala | Mühleisen, Thomas W | Müller-Nurasyid, Martina | Navarro, Carmen | Nöthen, Markus M | Oskolkov, Nikolay N | Owen, Katharine R | Palli, Domenico | Pechlivanis, Sonali | Peltonen, Leena | Perry, John RB | Platou, Carl GP | Roden, Michael | Ruderfer, Douglas | Rybin, Denis | van der Schouw, Yvonne T | Sennblad, Bengt | Sigurðsson, Gunnar | Stančáková, Alena | Steinbach, Gerald | Storm, Petter | Strauch, Konstantin | Stringham, Heather M | Sun, Qi | Thorand, Barbara | Tikkanen, Emmi | Tonjes, Anke | Trakalo, Joseph | Tremoli, Elena | Tuomi, Tiinamaija | Wennauer, Roman | Wiltshire, Steven | Wood, Andrew R | Zeggini, Eleftheria | Dunham, Ian | Birney, Ewan | Pasquali, Lorenzo | Ferrer, Jorge | Loos, Ruth JF | Dupuis, Josée | Florez, Jose C | Boerwinkle, Eric | Pankow, James S | van Duijn, Cornelia | Sijbrands, Eric | Meigs, James B | Hu, Frank B | Thorsteinsdottir, Unnur | Stefansson, Kari | Lakka, Timo A | Rauramaa, Rainer | Stumvoll, Michael | Pedersen, Nancy L | Lind, Lars | Keinanen-Kiukaanniemi, Sirkka M | Korpi-Hyövälti, Eeva | Saaristo, Timo E | Saltevo, Juha | Kuusisto, Johanna | Laakso, Markku | Metspalu, Andres | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Ripatti, Samuli | Salomaa, Veikko | Ingelsson, Erik | Boehm, Bernhard O | Bergman, Richard N | Collins, Francis S | Mohlke, Karen L | Koistinen, Heikki | Tuomilehto, Jaakko | Hveem, Kristian | Njølstad, Inger | Deloukas, Panagiotis | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | de Faire, Ulf | Hamsten, Anders | Illig, Thomas | Peters, Annette | Cauchi, Stephane | Sladek, Rob | Froguel, Philippe | Hansen, Torben | Pedersen, Oluf | Morris, Andrew D | Palmer, Collin NA | Kathiresan, Sekar | Melander, Olle | Nilsson, Peter M | Groop, Leif C | Barroso, Inês | Langenberg, Claudia | Wareham, Nicholas J | O’Callaghan, Christopher A | Gloyn, Anna L | Altshuler, David | Boehnke, Michael | Teslovich, Tanya M | McCarthy, Mark I | Morris, Andrew P
Nature genetics  2015;47(12):1415-1425.
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/near KCNQ1. “Credible sets” of variants most likely to drive each distinct signal mapped predominantly to non-coding sequence, implying that T2D association 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 this T2D-risk allele 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.
doi:10.1038/ng.3437
PMCID: PMC4666734  PMID: 26551672
12.  Effect of Catechol-O-methyltransferase-gene (COMT) Variants on Experimental and Acute Postoperative Pain in 1,000 Women undergoing Surgery for Breast Cancer 
Anesthesiology  2013;119(6):1422-1433.
Background
Catechol-O-methyltransferase (COMT) metabolizes catecholamines in different tissues. Polymorphisms in COMT gene can attenuate COMT activity and increase sensitivity to pain. Human studies exploring the effect of COMT polymorphisms on pain sensitivity have mostly included small, heterogeneous samples and have ignored several important single nucleotide polymorphisms (SNPs). This study examines the effect of COMT polymorphisms on experimental and postoperative pain phenotypes in a large ethnically homogeneous female patient cohort.
Methods
Intensity of cold (+2–4°C) and heat (+48°C) pain and tolerance to cold pain were assessed in 1,000 patients scheduled for breast cancer surgery. Acute postoperative pain and oxycodone requirements were recorded. Twenty-two COMT SNPs were genotyped and their association with six pain phenotypes analyzed with linear regression.
Results
There was no association between any of the tested pain phenotypes and SNP rs4680. The strongest association signals were seen between rs165774 and heat pain intensity as well as rs887200 and cold pain intensity. In both cases, minor allele carriers reported less pain. Neither of these results remained significant after strict multiple testing corrections. When analyzed further, the effect of rs887200 was, however, shown to be significant and consistent throughout the cold pressure test. No evidence of association between the SNPs and postoperative oxycodone consumption was found.
Conclusions
SNPs rs887200 and rs165774 located in the untranslated regions of the gene had the strongest effects on pain sensitivity. Their effect on pain is described here for the first time. These results should be confirmed in further studies and the potential functional mechanisms of the variants studied.
doi:10.1097/ALN.0000000000000013
PMCID: PMC4869072  PMID: 24343288
13.  Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors 
Fall, Tove | Hägg, Sara | Ploner, Alexander | Mägi, Reedik | Fischer, Krista | Draisma, Harmen H.M. | Sarin, Antti-Pekka | Benyamin, Beben | Ladenvall, Claes | Åkerlund, Mikael | Kals, Mart | Esko, Tõnu | Nelson, Christopher P. | Kaakinen, Marika | Huikari, Ville | Mangino, Massimo | Meirhaeghe, Aline | Kristiansson, Kati | Nuotio, Marja-Liisa | Kobl, Michael | Grallert, Harald | Dehghan, Abbas | Kuningas, Maris | de Vries, Paul S. | de Bruijn, Renée F.A.G. | Willems, Sara M. | Heikkilä, Kauko | Silventoinen, Karri | Pietiläinen, Kirsi H. | Legry, Vanessa | Giedraitis, Vilmantas | Goumidi, Louisa | Syvänen, Ann-Christine | Strauch, Konstantin | Koenig, Wolfgang | Lichtner, Peter | Herder, Christian | Palotie, Aarno | Menni, Cristina | Uitterlinden, André G. | Kuulasmaa, Kari | Havulinna, Aki S. | Moreno, Luis A. | Gonzalez-Gross, Marcela | Evans, Alun | Tregouet, David-Alexandre | Yarnell, John W.G. | Virtamo, Jarmo | Ferrières, Jean | Veronesi, Giovanni | Perola, Markus | Arveiler, Dominique | Brambilla, Paolo | Lind, Lars | Kaprio, Jaakko | Hofman, Albert | Stricker, Bruno H. | van Duijn, Cornelia M. | Ikram, M. Arfan | Franco, Oscar H. | Cottel, Dominique | Dallongeville, Jean | Hall, Alistair S. | Jula, Antti | Tobin, Martin D. | Penninx, Brenda W. | Peters, Annette | Gieger, Christian | Samani, Nilesh J. | Montgomery, Grant W. | Whitfield, John B. | Martin, Nicholas G. | Groop, Leif | Spector, Tim D. | Magnusson, Patrik K. | Amouyel, Philippe | Boomsma, Dorret I. | Nilsson, Peter M. | Järvelin, Marjo-Riitta | Lyssenko, Valeriya | Metspalu, Andres | Strachan, David P. | Salomaa, Veikko | Ripatti, Samuli | Pedersen, Nancy L. | Prokopenko, Inga | McCarthy, Mark I. | Ingelsson, Erik
Diabetes  2015;64(5):1841-1852.
Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10−107) and stratified analyses (all P < 3.3 × 10−30). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.
doi:10.2337/db14-0988
PMCID: PMC4407863  PMID: 25712996
14.  Genome‐wide time‐to‐event analysis on smoking progression stages in a family‐based study 
Brain and Behavior  2016;e00462.
Abstract
Background
Various pivotal stages in smoking behavior can be identified, including initiation, conversion from experimenting to established use, development of tolerance, and cessation. Previous studies have shown high heritability for age of smoking initiation and cessation; however, time‐to‐event genome‐wide association studies aiming to identify underpinning genes that accelerate or delay these transitions are missing to date.
Methods
We investigated which single nucleotide polymorphisms (SNPs) across the whole genome contribute to the hazard ratio of transition between different stages of smoking behavior by performing time‐to‐event analyses within a large Finnish twin family cohort (N = 1962), and further conducted mediation analyses of plausible intermediate traits for significant SNPs.
Results
Genome‐wide significant signals were detected for three of the four transitions: (1) for smoking cessation on 10p14 (P = 4.47e‐08 for rs72779075 flanked by RP11‐575N15 and GATA3), (2) for tolerance on 11p13 (P = 1.29e‐08 for rs11031684 in RP1‐65P5.1), mediated by smoking quantity, and on 9q34.12 (P = 3.81e‐08 for rs2304808 in FUBP3), independent of smoking quantity, and (3) for smoking initiation on 19q13.33 (P = 3.37e‐08 for rs73050610 flanked by TRPM4 and SLC6A16) in analysis adjusted for first time sensations. Although our top SNPs did not replicate, another SNP in the TRPM4‐SLC6A16 gene region showed statistically significant association after region‐based multiple testing correction in an independent Australian twin family sample.
Conclusion
Our results suggest that the functional effect of the TRPM4‐SLC6A16 gene region deserves further investigation, and that complex neurotransmitter networks including dopamine and glutamate may play a critical role in smoking initiation. Moreover, comparison of these results implies that genetic contributions to the complex smoking behavioral phenotypes vary among the transitions.
doi:10.1002/brb3.462
PMCID: PMC4842934  PMID: 27134767
Cessation; genome‐wide association study; initiation; smoking behavior; time‐to‐event analysis
15.  Genome‐wide time‐to‐event analysis on smoking progression stages in a family‐based study 
Brain and Behavior  2016;6(5):e00462.
Abstract
Background
Various pivotal stages in smoking behavior can be identified, including initiation, conversion from experimenting to established use, development of tolerance, and cessation. Previous studies have shown high heritability for age of smoking initiation and cessation; however, time‐to‐event genome‐wide association studies aiming to identify underpinning genes that accelerate or delay these transitions are missing to date.
Methods
We investigated which single nucleotide polymorphisms (SNPs) across the whole genome contribute to the hazard ratio of transition between different stages of smoking behavior by performing time‐to‐event analyses within a large Finnish twin family cohort (N = 1962), and further conducted mediation analyses of plausible intermediate traits for significant SNPs.
Results
Genome‐wide significant signals were detected for three of the four transitions: (1) for smoking cessation on 10p14 (P = 4.47e‐08 for rs72779075 flanked by RP11‐575N15 and GATA3), (2) for tolerance on 11p13 (P = 1.29e‐08 for rs11031684 in RP1‐65P5.1), mediated by smoking quantity, and on 9q34.12 (P = 3.81e‐08 for rs2304808 in FUBP3), independent of smoking quantity, and (3) for smoking initiation on 19q13.33 (P = 3.37e‐08 for rs73050610 flanked by TRPM4 and SLC6A16) in analysis adjusted for first time sensations. Although our top SNPs did not replicate, another SNP in the TRPM4‐SLC6A16 gene region showed statistically significant association after region‐based multiple testing correction in an independent Australian twin family sample.
Conclusion
Our results suggest that the functional effect of the TRPM4‐SLC6A16 gene region deserves further investigation, and that complex neurotransmitter networks including dopamine and glutamate may play a critical role in smoking initiation. Moreover, comparison of these results implies that genetic contributions to the complex smoking behavioral phenotypes vary among the transitions.
doi:10.1002/brb3.462
PMCID: PMC4842934  PMID: 27134767
Cessation; genome‐wide association study; initiation; smoking behavior; time‐to‐event analysis
16.  Adiposity as a cause of cardiovascular disease: a Mendelian randomization study 
Background: Adiposity, as indicated by body mass index (BMI), has been associated with risk of cardiovascular diseases in epidemiological studies. We aimed to investigate if these associations are causal, using Mendelian randomization (MR) methods.
Methods: The associations of BMI with cardiovascular outcomes [coronary heart disease (CHD), heart failure and ischaemic stroke], and associations of a genetic score (32 BMI single nucleotide polymorphisms) with BMI and cardiovascular outcomes were examined in up to 22 193 individuals with 3062 incident cardiovascular events from nine prospective follow-up studies within the ENGAGE consortium. We used random-effects meta-analysis in an MR framework to provide causal estimates of the effect of adiposity on cardiovascular outcomes.
Results: There was a strong association between BMI and incident CHD (HR = 1.20 per SD-increase of BMI, 95% CI, 1.12–1.28, P = 1.9·10−7), heart failure (HR = 1.47, 95% CI, 1.35–1.60, P = 9·10−19) and ischaemic stroke (HR = 1.15, 95% CI, 1.06–1.24, P = 0.0008) in observational analyses. The genetic score was robustly associated with BMI (β = 0.030 SD-increase of BMI per additional allele, 95% CI, 0.028–0.033, P = 3·10−107). Analyses indicated a causal effect of adiposity on development of heart failure (HR = 1.93 per SD-increase of BMI, 95% CI, 1.12–3.30, P = 0.017) and ischaemic stroke (HR = 1.83, 95% CI, 1.05–3.20, P = 0.034). Additional cross-sectional analyses using both ENGAGE and CARDIoGRAMplusC4D data showed a causal effect of adiposity on CHD.
Conclusions: Using MR methods, we provide support for the hypothesis that adiposity causes CHD, heart failure and, previously not demonstrated, ischaemic stroke.
doi:10.1093/ije/dyv094
PMCID: PMC4553708  PMID: 26016847
Cardiovascular disease; epidemiology; body mass index; Mendelian randomization
17.  Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research 
A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase.
doi:10.1038/ejhg.2015.165
PMCID: PMC4929882  PMID: 26306643
18.  A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease 
Nikpay, Majid | Goel, Anuj | Won, Hong-Hee | Hall, Leanne M | Willenborg, Christina | Kanoni, Stavroula | Saleheen, Danish | Kyriakou, Theodosios | Nelson, Christopher P | Hopewell, Jemma C | Webb, Thomas R | Zeng, Lingyao | Dehghan, Abbas | Alver, Maris | Armasu, Sebastian M | Auro, Kirsi | Bjonnes, Andrew | Chasman, Daniel I | Chen, Shufeng | Ford, Ian | Franceschini, Nora | Gieger, Christian | Grace, Christopher | Gustafsson, Stefan | Huang, Jie | Hwang, Shih-Jen | Kim, Yun Kyoung | Kleber, Marcus E | Lau, King Wai | Lu, Xiangfeng | Lu, Yingchang | Lyytikäinen, Leo-Pekka | Mihailov, Evelin | Morrison, Alanna C | Pervjakova, Natalia | Qu, Liming | Rose, Lynda M | Salfati, Elias | Saxena, Richa | Scholz, Markus | Smith, Albert V | Tikkanen, Emmi | Uitterlinden, Andre | Yang, Xueli | Zhang, Weihua | Zhao, Wei | de Andrade, Mariza | de Vries, Paul S | van Zuydam, Natalie R | Anand, Sonia S | Bertram, Lars | Beutner, Frank | Dedoussis, George | Frossard, Philippe | Gauguier, Dominique | Goodall, Alison H | Gottesman, Omri | Haber, Marc | Han, Bok-Ghee | Huang, Jianfeng | Jalilzadeh, Shapour | Kessler, Thorsten | König, Inke R | Lannfelt, Lars | Lieb, Wolfgang | Lind, Lars | Lindgren, Cecilia M | Lokki, Marja-Liisa | Magnusson, Patrik K | Mallick, Nadeem H | Mehra, Narinder | Meitinger, Thomas | Memon, Fazal-ur-Rehman | Morris, Andrew P | Nieminen, Markku S | Pedersen, Nancy L | Peters, Annette | Rallidis, Loukianos S | Rasheed, Asif | Samuel, Maria | Shah, Svati H | Sinisalo, Juha | Stirrups, Kathleen E | Trompet, Stella | Wang, Laiyuan | Zaman, Khan S | Ardissino, Diego | Boerwinkle, Eric | Borecki, Ingrid B | Bottinger, Erwin P | Buring, Julie E | Chambers, John C | Collins, Rory | Cupples, L Adrienne | Danesh, John | Demuth, Ilja | Elosua, Roberto | Epstein, Stephen E | Esko, Tõnu | Feitosa, Mary F | Franco, Oscar H | Franzosi, Maria Grazia | Granger, Christopher B | Gu, Dongfeng | Gudnason, Vilmundur | Hall, Alistair S | Hamsten, Anders | Harris, Tamara B | Hazen, Stanley L | Hengstenberg, Christian | Hofman, Albert | Ingelsson, Erik | Iribarren, Carlos | Jukema, J Wouter | Karhunen, Pekka J | Kim, Bong-Jo | Kooner, Jaspal S | Kullo, Iftikhar J | Lehtimäki, Terho | Loos, Ruth J F | Melander, Olle | Metspalu, Andres | März, Winfried | Palmer, Colin N | Perola, Markus | Quertermous, Thomas | Rader, Daniel J | Ridker, Paul M | Ripatti, Samuli | Roberts, Robert | Salomaa, Veikko | Sanghera, Dharambir K | Schwartz, Stephen M | Seedorf, Udo | Stewart, Alexandre F | Stott, David J | Thiery, Joachim | Zalloua, Pierre A | O’Donnell, Christopher J | Reilly, Muredach P | Assimes, Themistocles L | Thompson, John R | Erdmann, Jeanette | Clarke, Robert | Watkins, Hugh | Kathiresan, Sekar | McPherson, Ruth | Deloukas, Panos | Schunkert, Heribert | Samani, Nilesh J | Farrall, Martin
Nature genetics  2015;47(10):1121-1130.
Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185 thousand CAD cases and controls, interrogating 6.7 million common (MAF>0.05) as well as 2.7 million low frequency (0.005
doi:10.1038/ng.3396
PMCID: PMC4589895  PMID: 26343387
Nature Communications  2016;7:11122.
Genome-wide association studies have identified numerous loci linked with complex diseases, for which the molecular mechanisms remain largely unclear. Comprehensive molecular profiling of circulating metabolites captures highly heritable traits, which can help to uncover metabolic pathophysiology underlying established disease variants. We conduct an extended genome-wide association study of genetic influences on 123 circulating metabolic traits quantified by nuclear magnetic resonance metabolomics from up to 24,925 individuals and identify eight novel loci for amino acids, pyruvate and fatty acids. The LPA locus link with cardiovascular risk exemplifies how detailed metabolic profiling may inform underlying aetiology via extensive associations with very-low-density lipoprotein and triglyceride metabolism. Genetic fine mapping and Mendelian randomization uncover wide-spread causal effects of lipoprotein(a) on overall lipoprotein metabolism and we assess potential pleiotropic consequences of genetically elevated lipoprotein(a) on diverse morbidities via electronic health-care records. Our findings strengthen the argument for safe LPA-targeted intervention to reduce cardiovascular risk.
Circulating metabolites reflect human health and disease. Here, Kettunen et al. perform a genome-wide association study on 123 circulating metabolic traits and identify novel genetic loci influencing systemic metabolism. They also link new molecular pathways with a known cardiovascular risk factor Lp(a).
doi:10.1038/ncomms11122
PMCID: PMC4814583  PMID: 27005778
Human Molecular Genetics  2016;25(11):2349-2359.
To identify new risk loci for colorectal cancer (CRC), we conducted a meta-analysis of seven genome-wide association studies (GWAS) with independent replication, totalling 13 656 CRC cases and 21 667 controls of European ancestry. The combined analysis identified a new risk association for CRC at 2q35 marked by rs992157 (P = 3.15 × 10−8, odds ratio = 1.10, 95% confidence interval = 1.06–1.13), which is intronic to PNKD (paroxysmal non-kinesigenic dyskinesia) and TMBIM1 (transmembrane BAX inhibitor motif containing 1). Intriguingly this susceptibility single-nucleotide polymorphism (SNP) is in strong linkage disequilibrium (r2 = 0.90, D′ = 0.96) with the previously discovered GWAS SNP rs2382817 for inflammatory bowel disease (IBD). Following on from this observation we examined for pleiotropy, or shared genetic susceptibility, between CRC and the 200 established IBD risk loci, identifying an additional 11 significant associations (false discovery rate [FDR]) < 0.05). Our findings provide further insight into the biological basis of inherited genetic susceptibility to CRC, and identify risk factors that may influence the development of both CRC and IBD.
doi:10.1093/hmg/ddw087
PMCID: PMC5081051  PMID: 27005424
Circulation  2015;131(9):774-785.
Background
High-throughput profiling of circulating metabolites may improve cardiovascular risk prediction over established risk factors.
Methods and Results
We applied quantitative NMR metabolomics to identify biomarkers for incident cardiovascular disease during long-term follow-up. Biomarker discovery was conducted in the FINRISK study (n=7256; 800 events). Replication and incremental risk prediction was assessed in the SABRE study (n=2622; 573 events) and British Women’s Health and Heart Study (n=3563; 368 events). In targeted analyses of 68 lipids and metabolites, 33 measures were associated with incident cardiovascular events at P<0.0007 after adjusting for age, sex, blood pressure, smoking, diabetes and medication. When further adjusting for routine lipids, four metabolites were associated with future cardiovascular events in meta-analyses: higher serum phenylalanine (hazard ratio per standard deviation: 1.18 [95%CI 1.12–1.24]; P=4×10−10) and monounsaturated fatty acid levels (1.17 [1.11–1.24]; P=1×10−8) were associated with increased cardiovascular risk, while higher omega-6 fatty acids (0.89 [0.84–0.94]; P=6×10−5) and docosahexaenoic acid levels (0.90 [0.86–0.95]; P=5×10−5) were associated with lower risk. A risk score incorporating these four biomarkers was derived in FINRISK. Risk prediction estimates were more accurate in the two validation cohorts (relative integrated discrimination improvement 8.8% and 4.3%), albeit discrimination was not enhanced. Risk classification was particularly improved for persons in the 5–10% risk range (net reclassification 27.1% and 15.5%). Biomarker associations were further corroborated with mass spectrometry in FINRISK (n=671) and the Framingham Offspring Study (n=2289).
Conclusions
Metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated and polyunsaturated fatty acids as biomarkers for cardiovascular risk. This study substantiates the value of high-throughput metabolomics for biomarker discovery and improved risk assessment.
doi:10.1161/CIRCULATIONAHA.114.013116
PMCID: PMC4351161  PMID: 25573147
biomarkers; metabolomics; risk prediction; amino acids; fatty acids
Statistics in medicine  2008;27(27):5786-5798.
SUMMARY
Some cognitive functions undergo transitions in old age, which motivates the use of a change point model for the individual trajectory. The age when the change occurs varies between individuals and is treated as random. We illustrate the properties of a random change point model and use it for data from a Swedish study of change in cognitive function in old age. Variance estimates are obtained from Markov chain Monte Carlo simulation using Gibbs sampling. The random change point model is compared with models within the family of linear random effects models. The focus is on the ability to capture variability in measures of cognitive function. The models make different assumptions about the variance over the age span, and we demonstrate that the random change point model has the most reasonable structure.
doi:10.1002/sim.3380
PMCID: PMC4761443  PMID: 18680123
change point model; cognitive function; variance estimation; Markov chain Monte Carlo; Gibbs sampling
Bioinformatics  2016;32(13):1981-1989.
Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests.
Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.
Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies.
Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco
Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btw052
PMCID: PMC4920109  PMID: 27153689
Surakka, Ida | Horikoshi, Momoko | Mägi, Reedik | Sarin, Antti-Pekka | Mahajan, Anubha | Lagou, Vasiliki | Marullo, Letizia | Ferreira, Teresa | Miraglio, Benjamin | Timonen, Sanna | Kettunen, Johannes | Pirinen, Matti | Karjalainen, Juha | Thorleifsson, Gudmar | Hägg, Sara | Hottenga, Jouke-Jan | Isaacs, Aaron | Ladenvall, Claes | Beekman, Marian | Esko, Tõnu | Ried, Janina S | Nelson, Christopher P | Willenborg, Christina | Gustafsson, Stefan | Westra, Harm-Jan | Blades, Matthew | de Craen, Anton JM | de Geus, Eco J | Deelen, Joris | Grallert, Harald | Hamsten, Anders | Havulinna, Aki S. | Hengstenberg, Christian | Houwing-Duistermaat, Jeanine J | Hyppönen, Elina | Karssen, Lennart C | Lehtimäki, Terho | Lyssenko, Valeriya | Magnusson, Patrik KE | Mihailov, Evelin | Müller-Nurasyid, Martina | Mpindi, John-Patrick | Pedersen, Nancy L | Penninx, Brenda WJH | Perola, Markus | Pers, Tune H | Peters, Annette | Rung, Johan | Smit, Johannes H | Steinthorsdottir, Valgerdur | Tobin, Martin D | Tsernikova, Natalia | van Leeuwen, Elisabeth M | Viikari, Jorma S | Willems, Sara M | Willemsen, Gonneke | Schunkert, Heribert | Erdmann, Jeanette | Samani, Nilesh J | Kaprio, Jaakko | Lind, Lars | Gieger, Christian | Metspalu, Andres | Slagboom, P Eline | Groop, Leif | van Duijn, Cornelia M | Eriksson, Johan G | Jula, Antti | Salomaa, Veikko | Boomsma, Dorret I | Power, Christine | Raitakari, Olli T | Ingelsson, Erik | Järvelin, Marjo-Riitta | Stefansson, Kari | Franke, Lude | Ikonen, Elina | Kallioniemi, Olli | Pietiäinen, Vilja | Lindgren, Cecilia M | Thorsteinsdottir, Unnur | Palotie, Aarno | McCarthy, Mark I | Morris, Andrew P | Prokopenko, Inga | Ripatti, Samuli
Nature genetics  2015;47(6):589-597.
Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes imputation in 62,166 samples, we identify association to lipids in 93 loci including 79 previously identified loci with new lead-SNPs, 10 new loci, 15 loci with a low-frequency and 10 loci with missense lead-SNPs, and, 2 loci with an accumulation of rare variants. In six loci, SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC, and APOE), or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2), explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for LDL-C and TC. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to re-sequencing.
doi:10.1038/ng.3300
PMCID: PMC4757735  PMID: 25961943
Lu, Yingchang | Day, Felix R. | Gustafsson, Stefan | Buchkovich, Martin L. | Na, Jianbo | Bataille, Veronique | Cousminer, Diana L. | Dastani, Zari | Drong, Alexander W. | Esko, Tõnu | Evans, David M. | Falchi, Mario | Feitosa, Mary F. | Ferreira, Teresa | Hedman, Åsa K. | Haring, Robin | Hysi, Pirro G. | Iles, Mark M. | Justice, Anne E. | Kanoni, Stavroula | Lagou, Vasiliki | Li, Rui | Li, Xin | Locke, Adam | Lu, Chen | Mägi, Reedik | Perry, John R. B. | Pers, Tune H. | Qi, Qibin | Sanna, Marianna | Schmidt, Ellen M. | Scott, William R. | Shungin, Dmitry | Teumer, Alexander | Vinkhuyzen, Anna A. E. | Walker, Ryan W. | Westra, Harm-Jan | Zhang, Mingfeng | Zhang, Weihua | Zhao, Jing Hua | Zhu, Zhihong | Afzal, Uzma | Ahluwalia, Tarunveer Singh | Bakker, Stephan J. L. | Bellis, Claire | Bonnefond, Amélie | Borodulin, Katja | Buchman, Aron S. | Cederholm, Tommy | Choh, Audrey C. | Choi, Hyung Jin | Curran, Joanne E. | de Groot, Lisette C. P. G. M. | De Jager, Philip L. | Dhonukshe-Rutten, Rosalie A. M. | Enneman, Anke W. | Eury, Elodie | Evans, Daniel S. | Forsen, Tom | Friedrich, Nele | Fumeron, Frédéric | Garcia, Melissa E. | Gärtner, Simone | Han, Bok-Ghee | Havulinna, Aki S. | Hayward, Caroline | Hernandez, Dena | Hillege, Hans | Ittermann, Till | Kent, Jack W. | Kolcic, Ivana | Laatikainen, Tiina | Lahti, Jari | Leach, Irene Mateo | Lee, Christine G. | Lee, Jong-Young | Liu, Tian | Liu, Youfang | Lobbens, Stéphane | Loh, Marie | Lyytikäinen, Leo-Pekka | Medina-Gomez, Carolina | Michaëlsson, Karl | Nalls, Mike A. | Nielson, Carrie M. | Oozageer, Laticia | Pascoe, Laura | Paternoster, Lavinia | Polašek, Ozren | Ripatti, Samuli | Sarzynski, Mark A. | Shin, Chan Soo | Narančić, Nina Smolej | Spira, Dominik | Srikanth, Priya | Steinhagen-Thiessen, Elisabeth | Sung, Yun Ju | Swart, Karin M. A. | Taittonen, Leena | Tanaka, Toshiko | Tikkanen, Emmi | van der Velde, Nathalie | van Schoor, Natasja M. | Verweij, Niek | Wright, Alan F. | Yu, Lei | Zmuda, Joseph M. | Eklund, Niina | Forrester, Terrence | Grarup, Niels | Jackson, Anne U. | Kristiansson, Kati | Kuulasmaa, Teemu | Kuusisto, Johanna | Lichtner, Peter | Luan, Jian'an | Mahajan, Anubha | Männistö, Satu | Palmer, Cameron D. | Ried, Janina S. | Scott, Robert A. | Stancáková, Alena | Wagner, Peter J. | Demirkan, Ayse | Döring, Angela | Gudnason, Vilmundur | Kiel, Douglas P. | Kühnel, Brigitte | Mangino, Massimo | Mcknight, Barbara | Menni, Cristina | O'Connell, Jeffrey R. | Oostra, Ben A. | Shuldiner, Alan R. | Song, Kijoung | Vandenput, Liesbeth | van Duijn, Cornelia M. | Vollenweider, Peter | White, Charles C. | Boehnke, Michael | Boettcher, Yvonne | Cooper, Richard S. | Forouhi, Nita G. | Gieger, Christian | Grallert, Harald | Hingorani, Aroon | Jørgensen, Torben | Jousilahti, Pekka | Kivimaki, Mika | Kumari, Meena | Laakso, Markku | Langenberg, Claudia | Linneberg, Allan | Luke, Amy | Mckenzie, Colin A. | Palotie, Aarno | Pedersen, Oluf | Peters, Annette | Strauch, Konstantin | Tayo, Bamidele O. | Wareham, Nicholas J. | Bennett, David A. | Bertram, Lars | Blangero, John | Blüher, Matthias | Bouchard, Claude | Campbell, Harry | Cho, Nam H. | Cummings, Steven R. | Czerwinski, Stefan A. | Demuth, Ilja | Eckardt, Rahel | Eriksson, Johan G. | Ferrucci, Luigi | Franco, Oscar H. | Froguel, Philippe | Gansevoort, Ron T. | Hansen, Torben | Harris, Tamara B. | Hastie, Nicholas | Heliövaara, Markku | Hofman, Albert | Jordan, Joanne M. | Jula, Antti | Kähönen, Mika | Kajantie, Eero | Knekt, Paul B. | Koskinen, Seppo | Kovacs, Peter | Lehtimäki, Terho | Lind, Lars | Liu, Yongmei | Orwoll, Eric S. | Osmond, Clive | Perola, Markus | Pérusse, Louis | Raitakari, Olli T. | Rankinen, Tuomo | Rao, D. C. | Rice, Treva K. | Rivadeneira, Fernando | Rudan, Igor | Salomaa, Veikko | Sørensen, Thorkild I. A. | Stumvoll, Michael | Tönjes, Anke | Towne, Bradford | Tranah, Gregory J. | Tremblay, Angelo | Uitterlinden, André G. | van der Harst, Pim | Vartiainen, Erkki | Viikari, Jorma S. | Vitart, Veronique | Vohl, Marie-Claude | Völzke, Henry | Walker, Mark | Wallaschofski, Henri | Wild, Sarah | Wilson, James F. | Yengo, Loïc | Bishop, D. Timothy | Borecki, Ingrid B. | Chambers, John C. | Cupples, L. Adrienne | Dehghan, Abbas | Deloukas, Panos | Fatemifar, Ghazaleh | Fox, Caroline | Furey, Terrence S. | Franke, Lude | Han, Jiali | Hunter, David J. | Karjalainen, Juha | Karpe, Fredrik | Kaplan, Robert C. | Kooner, Jaspal S. | McCarthy, Mark I. | Murabito, Joanne M. | Morris, Andrew P. | Bishop, Julia A. N. | North, Kari E. | Ohlsson, Claes | Ong, Ken K. | Prokopenko, Inga | Richards, J. Brent | Schadt, Eric E. | Spector, Tim D. | Widén, Elisabeth | Willer, Cristen J. | Yang, Jian | Ingelsson, Erik | Mohlke, Karen L. | Hirschhorn, Joel N. | Pospisilik, John Andrew | Zillikens, M. Carola | Lindgren, Cecilia | Kilpeläinen, Tuomas Oskari | Loos, Ruth J. F.
Nature Communications  2016;7:10495.
To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P<5 × 10−8), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk.
A genome-wide association meta-analysis study here shows novel genetic loci to be associated to body fat percentage, and describes cross-phenotype association that further demonstrate a close relationship between adiposity and cardiovascular disease risk.
doi:10.1038/ncomms10495
PMCID: PMC4740398  PMID: 26833246

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