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author:("nikkanen, emm")
1.  Genetic Risk Prediction and a Two-Stage Risk Screening Strategy for Coronary Heart Disease 
Objective
Genome-wide association studies have identified several genetic variants associated with coronary heart disease (CHD). The aim of this study was to evaluate the genetic risk discrimination and reclassification and apply the results for a two-stage population risk screening strategy for CHD.
Approach and Results
We genotyped 28 genetic variants in 24 124 participants in four Finnish population-based, prospective cohorts (recruitment years 1992-2002). We constructed a multi-locus genetic risk score and evaluated its association with incident cardiovascular disease events. During the median follow-up time of 12 years (IQR 8.75–15.25 years), we observed 1093 CHD, 1552 cardiovascular disease (CVD) and 731 acute coronary syndrome (ACS) events. Adding genetic information to conventional risk factors and family history improved risk discrimination of CHD (C-index 0.856 vs. 0.851, P=0.0002) and other end points (CVD: C-index 0.840 vs. 0.837, P=0.0004; ACS: C-index 0.859 vs. 0.855, P=0.001). In a standard population of 100 000 individuals, additional genetic screening of subjects at intermediate risk for CHD would reclassify 2144 (12%) subjects into high risk category. Statin allocation for these subjects is estimated to prevent 135 CHD cases over 14 years. Similar results were obtained by external validation, where the effects were estimated from a training dataset and applied for a test dataset.
Conclusions
Genetic risk score improves risk prediction of CHD and helps to identify individuals at high risk for the first CHD event. Genetic screening for individuals at intermediate cardiovascular risk could help to prevent future cases through better targeting of statins.
doi:10.1161/ATVBAHA.112.301120
PMCID: PMC4210840  PMID: 23599444
Cardiovascular genomics; Genetic association; Genetic epidemiology; Risk factor; Risk prediction
2.  The Molecular Genetic Architecture of Self-Employment 
van der Loos, Matthijs J. H. M. | Rietveld, Cornelius A. | Eklund, Niina | Koellinger, Philipp D. | Rivadeneira, Fernando | Abecasis, Gonçalo R. | Ankra-Badu, Georgina A. | Baumeister, Sebastian E. | Benjamin, Daniel J. | Biffar, Reiner | Blankenberg, Stefan | Boomsma, Dorret I. | Cesarini, David | Cucca, Francesco | de Geus, Eco J. C. | Dedoussis, George | Deloukas, Panos | Dimitriou, Maria | Eiriksdottir, Guðny | Eriksson, Johan | Gieger, Christian | Gudnason, Vilmundur | Höhne, Birgit | Holle, Rolf | Hottenga, Jouke-Jan | Isaacs, Aaron | Järvelin, Marjo-Riitta | Johannesson, Magnus | Kaakinen, Marika | Kähönen, Mika | Kanoni, Stavroula | Laaksonen, Maarit A. | Lahti, Jari | Launer, Lenore J. | Lehtimäki, Terho | Loitfelder, Marisa | Magnusson, Patrik K. E. | Naitza, Silvia | Oostra, Ben A. | Perola, Markus | Petrovic, Katja | Quaye, Lydia | Raitakari, Olli | Ripatti, Samuli | Scheet, Paul | Schlessinger, David | Schmidt, Carsten O. | Schmidt, Helena | Schmidt, Reinhold | Senft, Andrea | Smith, Albert V. | Spector, Timothy D. | Surakka, Ida | Svento, Rauli | Terracciano, Antonio | Tikkanen, Emmi | van Duijn, Cornelia M. | Viikari, Jorma | Völzke, Henry | Wichmann, H. -Erich | Wild, Philipp S. | Willems, Sara M. | Willemsen, Gonneke | van Rooij, Frank J. A. | Groenen, Patrick J. F. | Uitterlinden, André G. | Hofman, Albert | Thurik, A. Roy
PLoS ONE  2013;8(4):e60542.
Economic variables such as income, education, and occupation are known to affect mortality and morbidity, such as cardiovascular disease, and have also been shown to be partly heritable. However, very little is known about which genes influence economic variables, although these genes may have both a direct and an indirect effect on health. We report results from the first large-scale collaboration that studies the molecular genetic architecture of an economic variable–entrepreneurship–that was operationalized using self-employment, a widely-available proxy. Our results suggest that common SNPs when considered jointly explain about half of the narrow-sense heritability of self-employment estimated in twin data (σg2/σP2 = 25%, h2 = 55%). However, a meta-analysis of genome-wide association studies across sixteen studies comprising 50,627 participants did not identify genome-wide significant SNPs. 58 SNPs with p<10−5 were tested in a replication sample (n = 3,271), but none replicated. Furthermore, a gene-based test shows that none of the genes that were previously suggested in the literature to influence entrepreneurship reveal significant associations. Finally, SNP-based genetic scores that use results from the meta-analysis capture less than 0.2% of the variance in self-employment in an independent sample (p≥0.039). Our results are consistent with a highly polygenic molecular genetic architecture of self-employment, with many genetic variants of small effect. Although self-employment is a multi-faceted, heavily environmentally influenced, and biologically distal trait, our results are similar to those for other genetically complex and biologically more proximate outcomes, such as height, intelligence, personality, and several diseases.
doi:10.1371/journal.pone.0060542
PMCID: PMC3617140  PMID: 23593239
3.  Genome-wide association study identifies multiple loci influencing human serum metabolite levels 
Nature genetics  2012;44(3):269-276.
Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations (P < 2.31 × 10−10) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders.
doi:10.1038/ng.1073
PMCID: PMC3605033  PMID: 22286219
4.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segrè, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian'an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John RB | Platou, Carl GP | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden89-, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
5.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segré, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian’an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John R B | Platou, Carl G P | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
6.  Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts 
PLoS Medicine  2013;10(2):e1001383.
A mendelian randomization study based on data from multiple cohorts conducted by Karani Santhanakrishnan Vimaleswaran and colleagues re-examines the causal nature of the relationship between vitamin D levels and obesity.
Background
Obesity is associated with vitamin D deficiency, and both are areas of active public health concern. We explored the causality and direction of the relationship between body mass index (BMI) and 25-hydroxyvitamin D [25(OH)D] using genetic markers as instrumental variables (IVs) in bi-directional Mendelian randomization (MR) analysis.
Methods and Findings
We used information from 21 adult cohorts (up to 42,024 participants) with 12 BMI-related SNPs (combined in an allelic score) to produce an instrument for BMI and four SNPs associated with 25(OH)D (combined in two allelic scores, separately for genes encoding its synthesis or metabolism) as an instrument for vitamin D. Regression estimates for the IVs (allele scores) were generated within-study and pooled by meta-analysis to generate summary effects.
Associations between vitamin D scores and BMI were confirmed in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (n = 123,864). Each 1 kg/m2 higher BMI was associated with 1.15% lower 25(OH)D (p = 6.52×10−27). The BMI allele score was associated both with BMI (p = 6.30×10−62) and 25(OH)D (−0.06% [95% CI −0.10 to −0.02], p = 0.004) in the cohorts that underwent meta-analysis. The two vitamin D allele scores were strongly associated with 25(OH)D (p≤8.07×10−57 for both scores) but not with BMI (synthesis score, p = 0.88; metabolism score, p = 0.08) in the meta-analysis. A 10% higher genetically instrumented BMI was associated with 4.2% lower 25(OH)D concentrations (IV ratio: −4.2 [95% CI −7.1 to −1.3], p = 0.005). No association was seen for genetically instrumented 25(OH)D with BMI, a finding that was confirmed using data from the GIANT consortium (p≥0.57 for both vitamin D scores).
Conclusions
On the basis of a bi-directional genetic approach that limits confounding, our study suggests that a higher BMI leads to lower 25(OH)D, while any effects of lower 25(OH)D increasing BMI are likely to be small. Population level interventions to reduce BMI are expected to decrease the prevalence of vitamin D deficiency.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Obesity—having an unhealthy amount of body fat—is increasing worldwide. In the US, for example, a third of the adult population is now obese. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30.0 kg/m2. Although there is a genetic contribution to obesity, people generally become obese by consuming food and drink that contains more energy than they need for their daily activities. Thus, obesity can be prevented by having a healthy diet and exercising regularly. Compared to people with a healthy weight, obese individuals have an increased risk of developing diabetes, heart disease and stroke, and tend to die younger. They also have a higher risk of vitamin D deficiency, another increasingly common public health concern. Vitamin D, which is essential for healthy bones as well as other functions, is made in the skin after exposure to sunlight but can also be obtained through the diet and through supplements.
Why Was This Study Done?
Observational studies cannot prove that obesity causes vitamin D deficiency because obese individuals may share other characteristics that reduce their circulating 25-hydroxy vitamin D [25(OH)D] levels (referred to as confounding). Moreover, observational studies cannot indicate whether the larger vitamin D storage capacity of obese individuals (vitamin D is stored in fatty tissues) lowers their 25(OH)D levels or whether 25(OH)D levels influence fat accumulation (reverse causation). If obesity causes vitamin D deficiency, monitoring and treating vitamin D deficiency might alleviate some of the adverse health effects of obesity. Conversely, if low vitamin D levels cause obesity, encouraging people to take vitamin D supplements might help to control the obesity epidemic. Here, the researchers use bi-directional “Mendelian randomization” to examine the direction and causality of the relationship between BMI and 25(OH)D. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the influence of a modifiable environmental exposure and the outcome of interest. Because gene variants do not change over time and are inherited randomly, they are not prone to confounding and are free from reverse causation. Thus, if a lower vitamin D status leads to obesity, genetic variants associated with lower 25(OH)D concentrations should be associated with higher BMI, and if obesity leads to a lower vitamin D status, then genetic variants associated with higher BMI should be associated with lower 25(OH)D concentrations.
What Did the Researchers Do and Find?
The researchers created a “BMI allele score” based on 12 BMI-related gene variants and two “25(OH)D allele scores,” which are based on gene variants that affect either 25(OH)D synthesis or breakdown. Using information on up to 42,024 participants from 21 studies, the researchers showed that the BMI allele score was associated with both BMI and with 25(OH)D levels among the study participants. Based on this information, they calculated that each 10% increase in BMI will lead to a 4.2% decrease in 25(OH)D concentrations. By contrast, although both 25(OH)D allele scores were strongly associated with 25(OH)D levels, neither score was associated with BMI. This lack of an association between 25(OH)D allele scores and obesity was confirmed using data from more than 100,000 individuals involved in 46 studies that has been collected by the GIANT (Genetic Investigation of Anthropometric Traits) consortium.
What Do These Findings Mean?
These findings suggest that a higher BMI leads to a lower vitamin D status whereas any effects of low vitamin D status on BMI are likely to be small. That is, these findings provide evidence for obesity as a causal factor in the development of vitamin D deficiency but not for vitamin D deficiency as a causal factor in the development of obesity. These findings suggest that population-level interventions to reduce obesity should lead to a reduction in the prevalence of vitamin D deficiency and highlight the importance of monitoring and treating vitamin D deficiency as a means of alleviating the adverse influences of obesity on health.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001383.
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish); a data brief provides information about the vitamin D status of the US population
The World Health Organization provides information on obesity (in several languages)
The UK National Health Service Choices website provides detailed information about obesity and a link to a personal story about losing weight; it also provides information about vitamin D
The International Obesity Taskforce provides information about the global obesity epidemic
The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)
The US Office of Dietary Supplements provides information about vitamin D (in English and Spanish)
MedlinePlus has links to further information about obesity and about vitamin D (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
Overview and details of the collaborative large-scale genetic association study (D-CarDia) provide information about vitamin D and the risk of cardiovascular disease, diabetes and related traits
doi:10.1371/journal.pmed.1001383
PMCID: PMC3564800  PMID: 23393431
7.  GENETIC VARIANTS AND BLOOD PRESSURE IN A POPULATION-BASED COHORT: THE CARDIOVASCULAR RISK IN YOUNG FINNS STUDY 
Hypertension  2011;58(6):1079-1085.
Clinical relevance of a genetic predisposition to elevated blood pressure was quantified during the transition from childhood to adulthood in a population-based Finnish cohort (N=2,357). Blood pressure was measured at baseline in 1980 (age 3–18 years) and in follow-ups in 1983, 1986, 2001 and 2007. Thirteen single nucleotide polymorphisms associated with blood pressure were genotyped and three genetic risk scores associated with systolic and diastolic blood pressure and their combination were derived for all participants. Effects of the genetic risk score were 0.47 mmHg for systolic and 0.53 mmHg for diastolic blood pressure (both p<0.01). The combination genetic risk score was associated with diastolic blood pressure from age 9 onwards (β=0.68 mmHg, p=0.015). Replications in 1194 participants of the Bogalusa Heart Study showed essentially similar results. The participants in the highest quintile of the combination genetic risk score had a 1.82-fold risk of hypertension in adulthood (p<0.0001) compared with the lowest quintile, independent of a family history of premature hypertension. These findings show that genetic variants are associated with preclinical blood pressure traits in childhood, individuals with several susceptibility alleles have on average a 0.5 mmHg higher blood pressure and this trajectory continues from childhood to adulthood.
doi:10.1161/HYPERTENSIONAHA.111.179291
PMCID: PMC3247907  PMID: 22025373
Epidemiological study; Genetic risk score; Blood Pressure; Cardiovascular disease
8.  Genome-Wide Screen for Metabolic Syndrome Susceptibility Loci Reveals Strong Lipid Gene Contribution but No Evidence for Common Genetic Basis for Clustering of Metabolic Syndrome Traits 
Background
Genome-wide association (GWA) studies have identified several susceptibility loci for metabolic syndrome (MetS) component traits, but have had variable success in identifying susceptibility loci to the syndrome as an entity. We conducted a GWA study on MetS and its component traits in four Finnish cohorts consisting of 2637 MetS cases and 7927 controls, both free of diabetes, and followed the top loci in an independent sample with transcriptome and NMR-based metabonomics data. Furthermore, we tested for loci associated with multiple MetS component traits using factor analysis and built a genetic risk score for MetS.
Methods and Results
A previously known lipid locus, APOA1/C3/A4/A5 gene cluster region (SNP rs964184), was associated with MetS in all four study samples (P=7.23×10−9 in meta-analysis). The association was further supported by serum metabolite analysis, where rs964184 associated with various VLDL, TG, and HDL metabolites (P=0.024-1.88×10−5). Twenty-two previously identified susceptibility loci for individual MetS component traits were replicated in our GWA and factor analysis. Most of these associated with lipid phenotypes and none with two or more uncorrelated MetS components. A genetic risk score, calculated as the number of alleles in loci associated with individual MetS traits, was strongly associated with MetS status.
Conclusions
Our findings suggest that genes from lipid metabolism pathways have the key role in the genetic background of MetS. We found little evidence for pleiotropy linking dyslipidemia and obesity to the other MetS component traits such as hypertension and glucose intolerance.
doi:10.1161/CIRCGENETICS.111.961482
PMCID: PMC3378651  PMID: 22399527
metabolic syndrome; risk factors; genome-wide association study; meta-analysis; lipids
9.  Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile 
Kilpeläinen, Tuomas O | Zillikens, M Carola | Stančáková, Alena | Finucane, Francis M | Ried, Janina S | Langenberg, Claudia | Zhang, Weihua | Beckmann, Jacques S | Luan, Jian’an | Vandenput, Liesbeth | Styrkarsdottir, Unnur | Zhou, Yanhua | Smith, Albert Vernon | Zhao, Jing-Hua | Amin, Najaf | Vedantam, Sailaja | Shin, So Youn | Haritunians, Talin | Fu, Mao | Feitosa, Mary F | Kumari, Meena | Halldorsson, Bjarni V | Tikkanen, Emmi | Mangino, Massimo | Hayward, Caroline | Song, Ci | Arnold, Alice M | Aulchenko, Yurii S | Oostra, Ben A | Campbell, Harry | Cupples, L Adrienne | Davis, Kathryn E | Döring, Angela | Eiriksdottir, Gudny | Estrada, Karol | Fernández-Real, José Manuel | Garcia, Melissa | Gieger, Christian | Glazer, Nicole L | Guiducci, Candace | Hofman, Albert | Humphries, Steve E | Isomaa, Bo | Jacobs, Leonie C | Jula, Antti | Karasik, David | Karlsson, Magnus K | Khaw, Kay-Tee | Kim, Lauren J | Kivimäki, Mika | Klopp, Norman | Kühnel, Brigitte | Kuusisto, Johanna | Liu, Yongmei | Ljunggren, Östen | Lorentzon, Mattias | Luben, Robert N | McKnight, Barbara | Mellström, Dan | Mitchell, Braxton D | Mooser, Vincent | Moreno, José Maria | Männistö, Satu | O’Connell, Jeffery R | Pascoe, Laura | Peltonen, Leena | Peral, Belén | Perola, Markus | Psaty, Bruce M | Salomaa, Veikko | Savage, David B | Semple, Robert K | Skaric-Juric, Tatjana | Sigurdsson, Gunnar | Song, Kijoung S | Spector, Timothy D | Syvänen, Ann-Christine | Talmud, Philippa J | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Uitterlinden, André G | van Duijn, Cornelia M | Vidal-Puig, Antonio | Wild, Sarah H | Wright, Alan F | Clegg, Deborah J | Schadt, Eric | Wilson, James F | Rudan, Igor | Ripatti, Samuli | Borecki, Ingrid B | Shuldiner, Alan R | Ingelsson, Erik | Jansson, John-Olov | Kaplan, Robert C | Gudnason, Vilmundur | Harris, Tamara B | Groop, Leif | Kiel, Douglas P | Rivadeneira, Fernando | Walker, Mark | Barroso, Inês | Vollenweider, Peter | Waeber, Gérard | Chambers, John C | Kooner, Jaspal S | Soranzo, Nicole | Hirschhorn, Joel N | Stefansson, Kari | Wichmann, H-Erich | Ohlsson, Claes | O’Rahilly, Stephen | Wareham, Nicholas J | Speliotes, Elizabeth K | Fox, Caroline S | Laakso, Markku | Loos, Ruth J F
Nature Genetics  2011;43(8):753-760.
Genome-wide association studies have identified 32 loci associated with body mass index (BMI), a measure that does not allow distinguishing lean from fat mass. To identify adiposity loci, we meta-analyzed associations between ~2.5 million SNPs and body fat percentage from 36,626 individuals, and followed up the 14 most significant (P<10−6) independent loci in 39,576 individuals. We confirmed the previously established adiposity locus in FTO (P=3×10−26), and identified two new loci associated with body fat percentage, one near IRS1 (P=4×10−11) and one near SPRY2 (P=3×10−8). Both loci harbour genes with a potential link to adipocyte physiology, of which the locus near IRS1 shows an intriguing association pattern. The body-fat-decreasing allele associates with decreased IRS1 expression and with an impaired metabolic profile, including decreased subcutaneous-to-visceral fat ratio, increased insulin resistance, dyslipidemia, risk of diabetes and coronary artery disease, and decreased adiponectin levels. Our findings provide new insights into adiposity and insulin resistance.
doi:10.1038/ng.866
PMCID: PMC3262230  PMID: 21706003
10.  Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies 
Elks, Cathy E. | Perry, John R.B. | Sulem, Patrick | Chasman, Daniel I. | Franceschini, Nora | He, Chunyan | Lunetta, Kathryn L. | Visser, Jenny A. | Byrne, Enda M. | Cousminer, Diana L. | Gudbjartsson, Daniel F. | Esko, Tõnu | Feenstra, Bjarke | Hottenga, Jouke-Jan | Koller, Daniel L. | Kutalik, Zoltán | Lin, Peng | Mangino, Massimo | Marongiu, Mara | McArdle, Patrick F. | Smith, Albert V. | Stolk, Lisette | van Wingerden, Sophie W. | Zhao, Jing Hua | Albrecht, Eva | Corre, Tanguy | Ingelsson, Erik | Hayward, Caroline | Magnusson, Patrik K.E. | Smith, Erin N. | Ulivi, Shelia | Warrington, Nicole M. | Zgaga, Lina | Alavere, Helen | Amin, Najaf | Aspelund, Thor | Bandinelli, Stefania | Barroso, Ines | Berenson, Gerald S. | Bergmann, Sven | Blackburn, Hannah | Boerwinkle, Eric | Buring, Julie E. | Busonero, Fabio | Campbell, Harry | Chanock, Stephen J. | Chen, Wei | Cornelis, Marilyn C. | Couper, David | Coviello, Andrea D. | d’Adamo, Pio | de Faire, Ulf | de Geus, Eco J.C. | Deloukas, Panos | Döring, Angela | Smith, George Davey | Easton, Douglas F. | Eiriksdottir, Gudny | Emilsson, Valur | Eriksson, Johan | Ferrucci, Luigi | Folsom, Aaron R. | Foroud, Tatiana | Garcia, Melissa | Gasparini, Paolo | Geller, Frank | Gieger, Christian | Gudnason, Vilmundur | Hall, Per | Hankinson, Susan E. | Ferreli, Liana | Heath, Andrew C. | Hernandez, Dena G. | Hofman, Albert | Hu, Frank B. | Illig, Thomas | Järvelin, Marjo-Riitta | Johnson, Andrew D. | Karasik, David | Khaw, Kay-Tee | Kiel, Douglas P. | Kilpeläinen, Tuomas O. | Kolcic, Ivana | Kraft, Peter | Launer, Lenore J. | Laven, Joop S.E. | Li, Shengxu | Liu, Jianjun | Levy, Daniel | Martin, Nicholas G. | McArdle, Wendy L. | Melbye, Mads | Mooser, Vincent | Murray, Jeffrey C. | Murray, Sarah S. | Nalls, Michael A. | Navarro, Pau | Nelis, Mari | Ness, Andrew R. | Northstone, Kate | Oostra, Ben A. | Peacock, Munro | Palmer, Lyle J. | Palotie, Aarno | Paré, Guillaume | Parker, Alex N. | Pedersen, Nancy L. | Peltonen, Leena | Pennell, Craig E. | Pharoah, Paul | Polasek, Ozren | Plump, Andrew S. | Pouta, Anneli | Porcu, Eleonora | Rafnar, Thorunn | Rice, John P. | Ring, Susan M. | Rivadeneira, Fernando | Rudan, Igor | Sala, Cinzia | Salomaa, Veikko | Sanna, Serena | Schlessinger, David | Schork, Nicholas J. | Scuteri, Angelo | Segrè, Ayellet V. | Shuldiner, Alan R. | Soranzo, Nicole | Sovio, Ulla | Srinivasan, Sathanur R. | Strachan, David P. | Tammesoo, Mar-Liis | Tikkanen, Emmi | Toniolo, Daniela | Tsui, Kim | Tryggvadottir, Laufey | Tyrer, Jonathon | Uda, Manuela | van Dam, Rob M. | van Meurs, Joyve B.J. | Vollenweider, Peter | Waeber, Gerard | Wareham, Nicholas J. | Waterworth, Dawn M. | Weedon, Michael N. | Wichmann, H. Erich | Willemsen, Gonneke | Wilson, James F. | Wright, Alan F. | Young, Lauren | Zhai, Guangju | Zhuang, Wei Vivian | Bierut, Laura J. | Boomsma, Dorret I. | Boyd, Heather A. | Crisponi, Laura | Demerath, Ellen W. | van Duijn, Cornelia M. | Econs, Michael J. | Harris, Tamara B. | Hunter, David J. | Loos, Ruth J.F. | Metspalu, Andres | Montgomery, Grant W. | Ridker, Paul M. | Spector, Tim D. | Streeten, Elizabeth A. | Stefansson, Kari | Thorsteinsdottir, Unnur | Uitterlinden, André G. | Widen, Elisabeth | Murabito, Joanne M. | Ong, Ken K. | Murray, Anna
Nature genetics  2010;42(12):1077-1085.
To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P=5.4×10−60) and 9q31.2 (P=2.2×10−33), we identified 30 novel menarche loci (all P<5×10−8) and found suggestive evidence for a further 10 loci (P<1.9×10−6). New loci included four previously associated with BMI (in/near FTO, SEC16B, TRA2B and TMEM18), three in/near other genes implicated in energy homeostasis (BSX, CRTC1, and MCHR2), and three in/near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and MAGENTA pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing.
doi:10.1038/ng.714
PMCID: PMC3140055  PMID: 21102462
11.  A Genome-Wide Screen for Interactions Reveals a New Locus on 4p15 Modifying the Effect of Waist-to-Hip Ratio on Total Cholesterol 
Surakka, Ida | Isaacs, Aaron | Karssen, Lennart C. | Laurila, Pirkka-Pekka P. | Middelberg, Rita P. S. | Tikkanen, Emmi | Ried, Janina S. | Lamina, Claudia | Mangino, Massimo | Igl, Wilmar | Hottenga, Jouke-Jan | Lagou, Vasiliki | van der Harst, Pim | Mateo Leach, Irene | Esko, Tõnu | Kutalik, Zoltán | Wainwright, Nicholas W. | Struchalin, Maksim V. | Sarin, Antti-Pekka | Kangas, Antti J. | Viikari, Jorma S. | Perola, Markus | Rantanen, Taina | Petersen, Ann-Kristin | Soininen, Pasi | Johansson, Åsa | Soranzo, Nicole | Heath, Andrew C. | Papamarkou, Theodore | Prokopenko, Inga | Tönjes, Anke | Kronenberg, Florian | Döring, Angela | Rivadeneira, Fernando | Montgomery, Grant W. | Whitfield, John B. | Kähönen, Mika | Lehtimäki, Terho | Freimer, Nelson B. | Willemsen, Gonneke | de Geus, Eco J. C. | Palotie, Aarno | Sandhu, Manj S. | Waterworth, Dawn M. | Metspalu, Andres | Stumvoll, Michael | Uitterlinden, André G. | Jula, Antti | Navis, Gerjan | Wijmenga, Cisca | Wolffenbuttel, Bruce H. R. | Taskinen, Marja-Riitta | Ala-Korpela, Mika | Kaprio, Jaakko | Kyvik, Kirsten O. | Boomsma, Dorret I. | Pedersen, Nancy L. | Gyllensten, Ulf | Wilson, James F. | Rudan, Igor | Campbell, Harry | Pramstaller, Peter P. | Spector, Tim D. | Witteman, Jacqueline C. M. | Eriksson, Johan G. | Salomaa, Veikko | Oostra, Ben A. | Raitakari, Olli T. | Wichmann, H.-Erich | Gieger, Christian | Järvelin, Marjo-Riitta | Martin, Nicholas G. | Hofman, Albert | McCarthy, Mark I. | Peltonen, Leena | van Duijn, Cornelia M. | Aulchenko, Yurii S. | Ripatti, Samuli
PLoS Genetics  2011;7(10):e1002333.
Recent genome-wide association (GWA) studies described 95 loci controlling serum lipid levels. These common variants explain ∼25% of the heritability of the phenotypes. To date, no unbiased screen for gene–environment interactions for circulating lipids has been reported. We screened for variants that modify the relationship between known epidemiological risk factors and circulating lipid levels in a meta-analysis of genome-wide association (GWA) data from 18 population-based cohorts with European ancestry (maximum N = 32,225). We collected 8 further cohorts (N = 17,102) for replication, and rs6448771 on 4p15 demonstrated genome-wide significant interaction with waist-to-hip-ratio (WHR) on total cholesterol (TC) with a combined P-value of 4.79×10−9. There were two potential candidate genes in the region, PCDH7 and CCKAR, with differential expression levels for rs6448771 genotypes in adipose tissue. The effect of WHR on TC was strongest for individuals carrying two copies of G allele, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference in TC concentration, while for A allele homozygous the difference was 0.12 sd. Our findings may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles. However, more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus.
Author Summary
Circulating serum lipids contribute greatly to the global health by affecting the risk for cardiovascular diseases. Serum lipid levels are partly inherited, and already 95 loci affecting high- and low-density lipoprotein cholesterol, total cholesterol, and triglycerides have been found. Serum lipids are also known to be affected by multiple epidemiological risk factors like body composition, lifestyle, and sex. It has been hypothesized that there are loci modifying the effects between risk factors and serum lipids, but to date only candidate gene studies for interactions have been reported. We conducted a genome-wide screen with meta-analysis approach to identify loci having interactions with epidemiological risk factors on serum lipids with over 30,000 population-based samples. When combining results from our initial datasets and 8 additional replication cohorts (maximum N = 17,102), we found a genome-wide significant locus in chromosome 4p15 with a joint P-value of 4.79×10−9 modifying the effect of waist-to-hip ratio on total cholesterol. In the area surrounding this genetic variant, there were two genes having association between the genotypes and the gene expression in adipose tissue, and we also found enrichment of association in genes belonging to lipid metabolism related functions.
doi:10.1371/journal.pgen.1002333
PMCID: PMC3197672  PMID: 22028671
12.  A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses 
Lancet  2010;376(9750):1393-1400.
Summary
Background
Comparison of patients with coronary heart disease and controls in genome-wide association studies has revealed several single nucleotide polymorphisms (SNPs) associated with coronary heart disease. We aimed to establish the external validity of these findings and to obtain more precise risk estimates using a prospective cohort design.
Methods
We tested 13 recently discovered SNPs for association with coronary heart disease in a case-control design including participants differing from those in the discovery samples (3829 participants with prevalent coronary heart disease and 48 897 controls free of the disease) and a prospective cohort design including 30 725 participants free of cardiovascular disease from Finland and Sweden. We modelled the 13 SNPs as a multilocus genetic risk score and used Cox proportional hazards models to estimate the association of genetic risk score with incident coronary heart disease. For case-control analyses we analysed associations between individual SNPs and quintiles of genetic risk score using logistic regression.
Findings
In prospective cohort analyses, 1264 participants had a first coronary heart disease event during a median 10·7 years' follow-up (IQR 6·7–13·6). Genetic risk score was associated with a first coronary heart disease event. When compared with the bottom quintile of genetic risk score, participants in the top quintile were at 1·66-times increased risk of coronary heart disease in a model adjusting for traditional risk factors (95% CI 1·35–2·04, p value for linear trend=7·3×10−10). Adjustment for family history did not change these estimates. Genetic risk score did not improve C index over traditional risk factors and family history (p=0·19), nor did it have a significant effect on net reclassification improvement (2·2%, p=0·18); however, it did have a small effect on integrated discrimination index (0·004, p=0·0006). Results of the case-control analyses were similar to those of the prospective cohort analyses.
Interpretation
Using a genetic risk score based on 13 SNPs associated with coronary heart disease, we can identify the 20% of individuals of European ancestry who are at roughly 70% increased risk of a first coronary heart disease event. The potential clinical use of this panel of SNPs remains to be defined.
Funding
The Wellcome Trust; Academy of Finland Center of Excellence for Complex Disease Genetics; US National Institutes of Health; the Donovan Family Foundation.
doi:10.1016/S0140-6736(10)61267-6
PMCID: PMC2965351  PMID: 20971364

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