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1.  Genetic variants influencing circulating lipid levels and risk of coronary artery disease 
Objectives
Genetic studies might provide new insights into the biological mechanisms underlying lipid metabolism and risk of CAD. We therefore conducted a genome-wide association study to identify novel genetic determinants of LDL-c, HDL-c and triglycerides.
Methods and results
We combined genome-wide association data from eight studies, comprising up to 17,723 participants with information on circulating lipid concentrations. We did independent replication studies in up to 37,774 participants from eight populations and also in a population of Indian Asian descent. We also assessed the association between SNPs at lipid loci and risk of CAD in up to 9,633 cases and 38,684 controls.
We identified four novel genetic loci that showed reproducible associations with lipids (P values 1.6 × 10−8 to 3.1 × 10−10). These include a potentially functional SNP in the SLC39A8 gene for HDL-c, a SNP near the MYLIP/GMPR and PPP1R3B genes for LDL-c and at the AFF1 gene for triglycerides. SNPs showing strong statistical association with one or more lipid traits at the CELSR2, APOB, APOE-C1-C4-C2 cluster, LPL, ZNF259-APOA5-A4-C3-A1 cluster and TRIB1 loci were also associated with CAD risk (P values 1.1 × 10−3 to 1.2 × 10−9).
Conclusions
We have identified four novel loci associated with circulating lipids. We also show that in addition to those that are largely associated with LDL-c, genetic loci mainly associated with circulating triglycerides and HDL-c are also associated with risk of CAD. These findings potentially provide new insights into the biological mechanisms underlying lipid metabolism and CAD risk.
doi:10.1161/ATVBAHA.109.201020
PMCID: PMC3891568  PMID: 20864672
lipids; lipoproteins; genetics; epidemiology
2.  Candidate genes for obesity-susceptibility show enriched association within a large genome-wide association study for BMI 
Human Molecular Genetics  2012;21(20):4537-4542.
Before the advent of genome-wide association studies (GWASs), hundreds of candidate genes for obesity-susceptibility had been identified through a variety of approaches. We examined whether those obesity candidate genes are enriched for associations with body mass index (BMI) compared with non-candidate genes by using data from a large-scale GWAS. A thorough literature search identified 547 candidate genes for obesity-susceptibility based on evidence from animal studies, Mendelian syndromes, linkage studies, genetic association studies and expression studies. Genomic regions were defined to include the genes ±10 kb of flanking sequence around candidate and non-candidate genes. We used summary statistics publicly available from the discovery stage of the genome-wide meta-analysis for BMI performed by the genetic investigation of anthropometric traits consortium in 123 564 individuals. Hypergeometric, rank tail-strength and gene-set enrichment analysis tests were used to test for the enrichment of association in candidate compared with non-candidate genes. The hypergeometric test of enrichment was not significant at the 5% P-value quantile (P = 0.35), but was nominally significant at the 25% quantile (P = 0.015). The rank tail-strength and gene-set enrichment tests were nominally significant for the full set of genes and borderline significant for the subset without SNPs at P < 10−7. Taken together, the observed evidence for enrichment suggests that the candidate gene approach retains some value. However, the degree of enrichment is small despite the extensive number of candidate genes and the large sample size. Studies that focus on candidate genes have only slightly increased chances of detecting associations, and are likely to miss many true effects in non-candidate genes, at least for obesity-related traits.
doi:10.1093/hmg/dds283
PMCID: PMC3607467  PMID: 22791748
3.  Genome-Wide Association Studies Identify CHRNA5/3 and HTR4 in the Development of Airflow Obstruction 
Wilk, Jemma B. | Shrine, Nick R. G. | Loehr, Laura R. | Zhao, Jing Hua | Manichaikul, Ani | Lopez, Lorna M. | Smith, Albert Vernon | Heckbert, Susan R. | Smolonska, Joanna | Tang, Wenbo | Loth, Daan W. | Curjuric, Ivan | Hui, Jennie | Cho, Michael H. | Latourelle, Jeanne C. | Henry, Amanda P. | Aldrich, Melinda | Bakke, Per | Beaty, Terri H. | Bentley, Amy R. | Borecki, Ingrid B. | Brusselle, Guy G. | Burkart, Kristin M. | Chen, Ting-hsu | Couper, David | Crapo, James D. | Davies, Gail | Dupuis, Josée | Franceschini, Nora | Gulsvik, Amund | Hancock, Dana B. | Harris, Tamara B. | Hofman, Albert | Imboden, Medea | James, Alan L. | Khaw, Kay-Tee | Lahousse, Lies | Launer, Lenore J. | Litonjua, Augusto | Liu, Yongmei | Lohman, Kurt K. | Lomas, David A. | Lumley, Thomas | Marciante, Kristin D. | McArdle, Wendy L. | Meibohm, Bernd | Morrison, Alanna C. | Musk, Arthur W. | Myers, Richard H. | North, Kari E. | Postma, Dirkje S. | Psaty, Bruce M. | Rich, Stephen S. | Rivadeneira, Fernando | Rochat, Thierry | Rotter, Jerome I. | Artigas, María Soler | Starr, John M. | Uitterlinden, André G. | Wareham, Nicholas J. | Wijmenga, Cisca | Zanen, Pieter | Province, Michael A. | Silverman, Edwin K. | Deary, Ian J. | Palmer, Lyle J. | Cassano, Patricia A. | Gudnason, Vilmundur | Barr, R. Graham | Loos, Ruth J. F. | Strachan, David P. | London, Stephanie J. | Boezen, H. Marike | Probst-Hensch, Nicole | Gharib, Sina A. | Hall, Ian P. | O’Connor, George T. | Tobin, Martin D. | Stricker, Bruno H.
Rationale: Genome-wide association studies (GWAS) have identified loci influencing lung function, but fewer genes influencing chronic obstructive pulmonary disease (COPD) are known.
Objectives: Perform meta-analyses of GWAS for airflow obstruction, a key pathophysiologic characteristic of COPD assessed by spirometry, in population-based cohorts examining all participants, ever smokers, never smokers, asthma-free participants, and more severe cases.
Methods: Fifteen cohorts were studied for discovery (3,368 affected; 29,507 unaffected), and a population-based family study and a meta-analysis of case-control studies were used for replication and regional follow-up (3,837 cases; 4,479 control subjects). Airflow obstruction was defined as FEV1 and its ratio to FVC (FEV1/FVC) both less than their respective lower limits of normal as determined by published reference equations.
Measurements and Main Results: The discovery meta-analyses identified one region on chromosome 15q25.1 meeting genome-wide significance in ever smokers that includes AGPHD1, IREB2, and CHRNA5/CHRNA3 genes. The region was also modestly associated among never smokers. Gene expression studies confirmed the presence of CHRNA5/3 in lung, airway smooth muscle, and bronchial epithelial cells. A single-nucleotide polymorphism in HTR4, a gene previously related to FEV1/FVC, achieved genome-wide statistical significance in combined meta-analysis. Top single-nucleotide polymorphisms in ADAM19, RARB, PPAP2B, and ADAMTS19 were nominally replicated in the COPD meta-analysis.
Conclusions: These results suggest an important role for the CHRNA5/3 region as a genetic risk factor for airflow obstruction that may be independent of smoking and implicate the HTR4 gene in the etiology of airflow obstruction.
doi:10.1164/rccm.201202-0366OC
PMCID: PMC3480517  PMID: 22837378
chronic obstructive pulmonary disease; single-nucleotide polymorphism; genes
4.  Meta-analysis and imputation refines the association of 15q25 with smoking quantity 
Liu, Jason Z. | Tozzi, Federica | Waterworth, Dawn M. | Pillai, Sreekumar G. | Muglia, Pierandrea | Middleton, Lefkos | Berrettini, Wade | Knouff, Christopher W. | Yuan, Xin | Waeber, Gérard | Vollenweider, Peter | Preisig, Martin | Wareham, Nicholas J | Zhao, Jing Hua | Loos, Ruth J.F. | Barroso, Inês | Khaw, Kay-Tee | Grundy, Scott | Barter, Philip | Mahley, Robert | Kesaniemi, Antero | McPherson, Ruth | Vincent, John B. | Strauss, John | Kennedy, James L. | Farmer, Anne | McGuffin, Peter | Day, Richard | Matthews, Keith | Bakke, Per | Gulsvik, Amund | Lucae, Susanne | Ising, Marcus | Brueckl, Tanja | Horstmann, Sonja | Wichmann, H.-Erich | Rawal, Rajesh | Dahmen, Norbert | Lamina, Claudia | Polasek, Ozren | Zgaga, Lina | Huffman, Jennifer | Campbell, Susan | Kooner, Jaspal | Chambers, John C | Burnett, Mary Susan | Devaney, Joseph M. | Pichard, Augusto D. | Kent, Kenneth M. | Satler, Lowell | Lindsay, Joseph M. | Waksman, Ron | Epstein, Stephen | Wilson, James F. | Wild, Sarah H. | Campbell, Harry | Vitart, Veronique | Reilly, Muredach P. | Li, Mingyao | Qu, Liming | Wilensky, Robert | Matthai, William | Hakonarson, Hakon H. | Rader, Daniel J. | Franke, Andre | Wittig, Michael | Schäfer, Arne | Uda, Manuela | Terracciano, Antonio | Xiao, Xiangjun | Busonero, Fabio | Scheet, Paul | Schlessinger, David | St Clair, David | Rujescu, Dan | Abecasis, Gonçalo R. | Grabe, Hans Jörgen | Teumer, Alexander | Völzke, Henry | Petersmann, Astrid | John, Ulrich | Rudan, Igor | Hayward, Caroline | Wright, Alan F. | Kolcic, Ivana | Wright, Benjamin J | Thompson, John R | Balmforth, Anthony J. | Hall, Alistair S. | Samani, Nilesh J. | Anderson, Carl A. | Ahmad, Tariq | Mathew, Christopher G. | Parkes, Miles | Satsangi, Jack | Caulfield, Mark | Munroe, Patricia B. | Farrall, Martin | Dominiczak, Anna | Worthington, Jane | Thomson, Wendy | Eyre, Steve | Barton, Anne | Mooser, Vincent | Francks, Clyde | Marchini, Jonathan
Nature genetics  2010;42(5):436-440.
Smoking is a leading global cause of disease and mortality1. We performed a genomewide meta-analytic association study of smoking-related behavioral traits in a total sample of 41,150 individuals drawn from 20 disease, population, and control cohorts. Our analysis confirmed an effect on smoking quantity (SQ) at a locus on 15q25 (P=9.45e-19) that includes three genes encoding neuronal nicotinic acetylcholine receptor subunits (CHRNA5, CHRNA3, CHRNB4). We used data from the 1000 Genomes project to investigate the region using imputation, which allowed analysis of virtually all common variants in the region and offered a five-fold increase in coverage over the HapMap. This increased the spectrum of potentially causal single nucleotide polymorphisms (SNPs), which included a novel SNP that showed the highest significance, rs55853698, located within the promoter region of CHRNA5. Conditional analysis also identified a secondary locus (rs6495308) in CHRNA3.
doi:10.1038/ng.572
PMCID: PMC3612983  PMID: 20418889
5.  Genome-wide association and large scale follow-up identifies 16 new loci influencing lung function 
Artigas, María Soler | Loth, Daan W | Wain, Louise V | Gharib, Sina A | Obeidat, Ma’en | Tang, Wenbo | Zhai, Guangju | Zhao, Jing Hua | Smith, Albert Vernon | Huffman, Jennifer E | Albrecht, Eva | Jackson, Catherine M | Evans, David M | Cadby, Gemma | Fornage, Myriam | Manichaikul, Ani | Lopez, Lorna M | Johnson, Toby | Aldrich, Melinda C | Aspelund, Thor | Barroso, Inês | Campbell, Harry | Cassano, Patricia A | Couper, David J | Eiriksdottir, Gudny | Franceschini, Nora | Garcia, Melissa | Gieger, Christian | Gislason, Gauti Kjartan | Grkovic, Ivica | Hammond, Christopher J | Hancock, Dana B | Harris, Tamara B | Ramasamy, Adaikalavan | Heckbert, Susan R | Heliövaara, Markku | Homuth, Georg | Hysi, Pirro G | James, Alan L | Jankovic, Stipan | Joubert, Bonnie R | Karrasch, Stefan | Klopp, Norman | Koch, Beate | Kritchevsky, Stephen B | Launer, Lenore J | Liu, Yongmei | Loehr, Laura R | Lohman, Kurt | Loos, Ruth JF | Lumley, Thomas | Al Balushi, Khalid A | Ang, Wei Q | Barr, R Graham | Beilby, John | Blakey, John D | Boban, Mladen | Boraska, Vesna | Brisman, Jonas | Britton, John R | Brusselle, Guy G | Cooper, Cyrus | Curjuric, Ivan | Dahgam, Santosh | Deary, Ian J | Ebrahim, Shah | Eijgelsheim, Mark | Francks, Clyde | Gaysina, Darya | Granell, Raquel | Gu, Xiangjun | Hankinson, John L | Hardy, Rebecca | Harris, Sarah E | Henderson, John | Henry, Amanda | Hingorani, Aroon D | Hofman, Albert | Holt, Patrick G | Hui, Jennie | Hunter, Michael L | Imboden, Medea | Jameson, Karen A | Kerr, Shona M | Kolcic, Ivana | Kronenberg, Florian | Liu, Jason Z | Marchini, Jonathan | McKeever, Tricia | Morris, Andrew D | Olin, Anna-Carin | Porteous, David J | Postma, Dirkje S | Rich, Stephen S | Ring, Susan M | Rivadeneira, Fernando | Rochat, Thierry | Sayer, Avan Aihie | Sayers, Ian | Sly, Peter D | Smith, George Davey | Sood, Akshay | Starr, John M | Uitterlinden, André G | Vonk, Judith M | Wannamethee, S Goya | Whincup, Peter H | Wijmenga, Cisca | Williams, O Dale | Wong, Andrew | Mangino, Massimo | Marciante, Kristin D | McArdle, Wendy L | Meibohm, Bernd | Morrison, Alanna C | North, Kari E | Omenaas, Ernst | Palmer, Lyle J | Pietiläinen, Kirsi H | Pin, Isabelle | Polašek, Ozren | Pouta, Anneli | Psaty, Bruce M | Hartikainen, Anna-Liisa | Rantanen, Taina | Ripatti, Samuli | Rotter, Jerome I | Rudan, Igor | Rudnicka, Alicja R | Schulz, Holger | Shin, So-Youn | Spector, Tim D | Surakka, Ida | Vitart, Veronique | Völzke, Henry | Wareham, Nicholas J | Warrington, Nicole M | Wichmann, H-Erich | Wild, Sarah H | Wilk, Jemma B | Wjst, Matthias | Wright, Alan F | Zgaga, Lina | Zemunik, Tatijana | Pennell, Craig E | Nyberg, Fredrik | Kuh, Diana | Holloway, John W | Boezen, H Marike | Lawlor, Debbie A | Morris, Richard W | Probst-Hensch, Nicole | Kaprio, Jaakko | Wilson, James F | Hayward, Caroline | Kähönen, Mika | Heinrich, Joachim | Musk, Arthur W | Jarvis, Deborah L | Gläser, Sven | Järvelin, Marjo-Riitta | Stricker, Bruno H Ch | Elliott, Paul | O’Connor, George T | Strachan, David P | London, Stephanie J | Hall, Ian P | Gudnason, Vilmundur | Tobin, Martin D
Nature Genetics  2011;43(11):1082-1090.
Pulmonary function measures reflect respiratory health and predict mortality, and are used in the diagnosis of chronic obstructive pulmonary disease (COPD). We tested genome-wide association with the forced expiratory volume in 1 second (FEV1) and the ratio of FEV1 to forced vital capacity (FVC) in 48,201 individuals of European ancestry, with follow-up of top associations in up to an additional 46,411 individuals. We identified new regions showing association (combined P<5×10−8) with pulmonary function, in or near MFAP2, TGFB2, HDAC4, RARB, MECOM (EVI1), SPATA9, ARMC2, NCR3, ZKSCAN3, CDC123, C10orf11, LRP1, CCDC38, MMP15, CFDP1, and KCNE2. Identification of these 16 new loci may provide insight into the molecular mechanisms regulating pulmonary function and into molecular targets for future therapy to alleviate reduced lung function.
doi:10.1038/ng.941
PMCID: PMC3267376  PMID: 21946350
6.  Effect of Five Genetic Variants Associated with Lung Function on the Risk of Chronic Obstructive Lung Disease, and Their Joint Effects on Lung Function 
Rationale: Genomic loci are associated with FEV1 or the ratio of FEV1 to FVC in population samples, but their association with chronic obstructive pulmonary disease (COPD) has not yet been proven, nor have their combined effects on lung function and COPD been studied.
Objectives: To test association with COPD of variants at five loci (TNS1, GSTCD, HTR4, AGER, and THSD4) and to evaluate joint effects on lung function and COPD of these single-nucleotide polymorphisms (SNPs), and variants at the previously reported locus near HHIP.
Methods: By sampling from 12 population-based studies (n = 31,422), we obtained genotype data on 3,284 COPD case subjects and 17,538 control subjects for sentinel SNPs in TNS1, GSTCD, HTR4, AGER, and THSD4. In 24,648 individuals (including 2,890 COPD case subjects and 13,862 control subjects), we additionally obtained genotypes for rs12504628 near HHIP. Each allele associated with lung function decline at these six SNPs contributed to a risk score. We studied the association of the risk score to lung function and COPD.
Measurements and Main Results: Association with COPD was significant for three loci (TNS1, GSTCD, and HTR4) and the previously reported HHIP locus, and suggestive and directionally consistent for AGER and TSHD4. Compared with the baseline group (7 risk alleles), carrying 10–12 risk alleles was associated with a reduction in FEV1 (β = –72.21 ml, P = 3.90 × 10−4) and FEV1/FVC (β = –1.53%, P = 6.35 × 10−6), and with COPD (odds ratio = 1.63, P = 1.46 × 10−5).
Conclusions: Variants in TNS1, GSTCD, and HTR4 are associated with COPD. Our highest risk score category was associated with a 1.6-fold higher COPD risk than the population average score.
doi:10.1164/rccm.201102-0192OC
PMCID: PMC3398416  PMID: 21965014
FEV1; FVC; genome-wide association study; modeling risk
7.  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
8.  Variability in the Heritability of Body Mass Index: A Systematic Review and Meta-Regression 
Evidence for a major role of genetic factors in the determination of body mass index (BMI) comes from studies of related individuals. Despite consistent evidence for a heritable component of BMI, estimates of BMI heritability vary widely between studies and the reasons for this remain unclear. While some variation is natural due to differences between populations and settings, study design factors may also explain some of the heterogeneity. We performed a systematic review that identified 88 independent estimates of BMI heritability from twin studies (total 140,525 twins) and 27 estimates from family studies (42,968 family members). BMI heritability estimates from twin studies ranged from 0.47 to 0.90 (5th/50th/95th centiles: 0.58/0.75/0.87) and were generally higher than those from family studies (range: 0.24–0.81; 5th/50th/95th centiles: 0.25/0.46/0.68). Meta-regression of the results from twin studies showed that BMI heritability estimates were 0.07 (P = 0.001) higher in children than in adults; estimates increased with mean age among childhood studies (+0.012/year, P = 0.002), but decreased with mean age in adult studies (−0.002/year, P = 0.002). Heritability estimates derived from AE twin models (which assume no contribution of shared environment) were 0.12 higher than those from ACE models (P < 0.001), whilst lower estimates were associated with self reported versus DNA-based determination of zygosity (−0.04, P = 0.02), and with self reported versus measured BMI (−0.05, P = 0.03). Although the observed differences in heritability according to aspects of study design are relatively small, together, the above factors explained 47% of the heterogeneity in estimates of BMI heritability from twin studies. In summary, while some variation in BMI heritability is expected due to population-level differences, study design factors explained nearly half the heterogeneity reported in twin studies. The genetic contribution to BMI appears to vary with age and may have a greater influence during childhood than adult life.
doi:10.3389/fendo.2012.00029
PMCID: PMC3355836  PMID: 22645519
body mass index; twin study; family study; heritability
9.  Genetic Susceptibility to Obesity and Related Traits in Childhood and Adolescence 
Diabetes  2010;59(11):2980-2988.
OBJECTIVE
Large-scale genome-wide association (GWA) studies have thus far identified 16 loci incontrovertibly associated with obesity-related traits in adults. We examined associations of variants in these loci with anthropometric traits in children and adolescents.
RESEARCH DESIGN AND METHODS
Seventeen variants representing 16 obesity susceptibility loci were genotyped in 1,252 children (mean ± SD age 9.7 ± 0.4 years) and 790 adolescents (15.5 ± 0.5 years) from the European Youth Heart Study (EYHS). We tested for association of individual variants and a genetic predisposition score (GPS-17), calculated by summing the number of effect alleles, with anthropometric traits. For 13 variants, summary statistics for associations with BMI were meta-analyzed with previously reported data (Ntotal = 13,071 children and adolescents).
RESULTS
In EYHS, 15 variants showed associations or trends with anthropometric traits that were directionally consistent with earlier reports in adults. The meta-analysis showed directionally consistent associations with BMI for all 13 variants, of which 9 were significant (0.033–0.098 SD/allele; P < 0.05). The near-TMEM18 variant had the strongest effect (0.098 SD/allele P = 8.5 × 10−11). Effect sizes for BMI tended to be more pronounced in children and adolescents than reported earlier in adults for variants in or near SEC16B, TMEM18, and KCTD15, (0.028–0.035 SD/allele higher) and less pronounced for rs925946 in BDNF (0.028 SD/allele lower). Each additional effect allele in the GPS-17 was associated with an increase of 0.034 SD in BMI (P = 3.6 × 10−5), 0.039 SD, in sum of skinfolds (P = 1.7 × 10−7), and 0.022 SD in waist circumference (P = 1.7 × 10−4), which is comparable with reported results in adults (0.039 SD/allele for BMI and 0.033 SD/allele for waist circumference).
CONCLUSIONS
Most obesity susceptibility loci identified by GWA studies in adults are already associated with anthropometric traits in children/adolescents. Whereas the association of some variants may differ with age, the cumulative effect size is similar.
doi:10.2337/db10-0370
PMCID: PMC2963559  PMID: 20724581
10.  Genetic variation in LIN28B is associated with the timing of puberty 
Nature genetics  2009;41(6):729-733.
The timing of puberty is highly variable1. We carried out a genome-wide association study for age at menarche in 4,714 women and report an association in LIN28B on chromosome 6 (rs314276, minor allele frequency (MAF) = 0.33, P = 1.5 × 10−8). In independent replication studies in 16,373 women, each major allele was associated with 0.12 years earlier menarche (95% CI = 0.08–0.16; P = 2.8 × 10−10; combined P = 3.6 × 10−16). This allele was also associated with earlier breast development in girls (P = 0.001; N = 4,271); earlier voice breaking (P = 0.006, N = 1,026) and more advanced pubic hair development in boys (P = 0.01; N = 4,588); a faster tempo of height growth in girls (P = 0.00008; N = 4,271) and boys (P = 0.03; N = 4,588); and shorter adult height in women (P = 3.6 × 10−7; N = 17,274) and men (P = 0.006; N = 9,840) in keeping with earlier growth cessation. These studies identify variation in LIN28B, a potent and specific regulator of microRNA processing2, as the first genetic determinant regulating the timing of human pubertal growth and development.
doi:10.1038/ng.382
PMCID: PMC3000552  PMID: 19448623
11.  Power assessment for genetic association study of human longevity using offspring of long-lived subjects 
European journal of epidemiology  2010;25(7):501-506.
Recently, an indirect genetic association approach that compares genotype frequencies in offspring of long-lived subjects and offspring from random families has been introduced to study gene-longevity associations. Although the indirect genetic association has certain advantages over the direct association approach that compares genotype frequency between centenarians and young controls, the power has been of concern. This paper reports a power study performed on the indirect approach using computer simulation. We perform our simulation study by introducing the current Danish population life table and the proportional hazard model for generating individual lifespan. Family genotype data is generated using a genetic linkage program for given SNP allele frequency. Power is estimated by setting the type I error rate at 0.05 and by calculating the Armitage’s chi-squared test statistic for 200 replicate samples for each setting of the specified allele risk and frequency parameters under different modes of inheritance and for different sample sizes. The indirect genetic association analysis is a valid approach for studying gene-longevity association, but the sample size requirement is about 3–4 time larger than the direct approach. It also has low power in detecting non-additive effect genes. Indirect genetic association using offspring from families with both parents as nonagenarians is nearly as powerful as using offspring from families with one centenarian parent. In conclusion, the indirect design can be a good choice for studying longevity in comparison with other alternatives, when relatively large sample size is available.
doi:10.1007/s10654-010-9465-1
PMCID: PMC2988164  PMID: 20512403
Longevity; Offspring; Indirect genetic association; Power
12.  Eight blood pressure loci identified by genome-wide association study of 34,433 people of European ancestry 
Newton-Cheh, Christopher | Johnson, Toby | Gateva, Vesela | Tobin, Martin D | Bochud, Murielle | Coin, Lachlan | Najjar, Samer S | Zhao, Jing Hua | Heath, Simon C | Eyheramendy, Susana | Papadakis, Konstantinos | Voight, Benjamin F | Scott, Laura J | Zhang, Feng | Farrall, Martin | Tanaka, Toshiko | Wallace, Chris | Chambers, John C | Khaw, Kay-Tee | Nilsson, Peter | van der Harst, Pim | Polidoro, Silvia | Grobbee, Diederick E | Onland-Moret, N Charlotte | Bots, Michiel L | Wain, Louise V | Elliott, Katherine S | Teumer, Alexander | Luan, Jian’an | Lucas, Gavin | Kuusisto, Johanna | Burton, Paul R | Hadley, David | McArdle, Wendy L | Brown, Morris | Dominiczak, Anna | Newhouse, Stephen J | Samani, Nilesh J | Webster, John | Zeggini, Eleftheria | Beckmann, Jacques S | Bergmann, Sven | Lim, Noha | Song, Kijoung | Vollenweider, Peter | Waeber, Gerard | Waterworth, Dawn M | Yuan, Xin | Groop, Leif | Orho-Melander, Marju | Allione, Alessandra | Di Gregorio, Alessandra | Guarrera, Simonetta | Panico, Salvatore | Ricceri, Fulvio | Romanazzi, Valeria | Sacerdote, Carlotta | Vineis, Paolo | Barroso, Inês | Sandhu, Manjinder S | Luben, Robert N | Crawford, Gabriel J. | Jousilahti, Pekka | Perola, Markus | Boehnke, Michael | Bonnycastle, Lori L | Collins, Francis S | Jackson, Anne U | Mohlke, Karen L | Stringham, Heather M | Valle, Timo T | Willer, Cristen J | Bergman, Richard N | Morken, Mario A | Döring, Angela | Gieger, Christian | Illig, Thomas | Meitinger, Thomas | Org, Elin | Pfeufer, Arne | Wichmann, H Erich | Kathiresan, Sekar | Marrugat, Jaume | O’Donnell, Christopher J | Schwartz, Stephen M | Siscovick, David S | Subirana, Isaac | Freimer, Nelson B | Hartikainen, Anna-Liisa | McCarthy, Mark I | O’Reilly, Paul F | Peltonen, Leena | Pouta, Anneli | de Jong, Paul E | Snieder, Harold | van Gilst, Wiek H | Clarke, Robert | Goel, Anuj | Hamsten, Anders | Peden, John F | Seedorf, Udo | Syvänen, Ann-Christine | Tognoni, Giovanni | Lakatta, Edward G | Sanna, Serena | Scheet, Paul | Schlessinger, David | Scuteri, Angelo | Dörr, Marcus | Ernst, Florian | Felix, Stephan B | Homuth, Georg | Lorbeer, Roberto | Reffelmann, Thorsten | Rettig, Rainer | Völker, Uwe | Galan, Pilar | Gut, Ivo G | Hercberg, Serge | Lathrop, G Mark | Zeleneka, Diana | Deloukas, Panos | Soranzo, Nicole | Williams, Frances M | Zhai, Guangju | Salomaa, Veikko | Laakso, Markku | Elosua, Roberto | Forouhi, Nita G | Völzke, Henry | Uiterwaal, Cuno S | van der Schouw, Yvonne T | Numans, Mattijs E | Matullo, Giuseppe | Navis, Gerjan | Berglund, Göran | Bingham, Sheila A | Kooner, Jaspal S | Paterson, Andrew D | Connell, John M | Bandinelli, Stefania | Ferrucci, Luigi | Watkins, Hugh | Spector, Tim D | Tuomilehto, Jaakko | Altshuler, David | Strachan, David P | Laan, Maris | Meneton, Pierre | Wareham, Nicholas J | Uda, Manuela | Jarvelin, Marjo-Riitta | Mooser, Vincent | Melander, Olle | Loos, Ruth JF | Elliott, Paul | Abecasis, Goncalo R | Caulfield, Mark | Munroe, Patricia B
Nature genetics  2009;41(6):666-676.
Elevated blood pressure is a common, heritable cause of cardiovascular disease worldwide. To date, identification of common genetic variants influencing blood pressure has proven challenging. We tested 2.5m genotyped and imputed SNPs for association with systolic and diastolic blood pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed up findings with direct genotyping (N≤71,225 European ancestry, N=12,889 Indian Asian ancestry) and in silico comparison (CHARGE consortium, N=29,136). We identified association between systolic or diastolic blood pressure and common variants in 8 regions near the CYP17A1 (P=7×10−24), CYP1A2 (P=1×10−23), FGF5 (P=1×10−21), SH2B3 (P=3×10−18), MTHFR (P=2×10−13), c10orf107 (P=1×10−9), ZNF652 (P=5×10−9) and PLCD3 (P=1×10−8) genes. All variants associated with continuous blood pressure were associated with dichotomous hypertension. These associations between common variants and blood pressure and hypertension offer mechanistic insights into the regulation of blood pressure and may point to novel targets for interventions to prevent cardiovascular disease.
doi:10.1038/ng.361
PMCID: PMC2891673  PMID: 19430483
13.  Physical Activity Attenuates the Genetic Predisposition to Obesity in 20,000 Men and Women from EPIC-Norfolk Prospective Population Study 
PLoS Medicine  2010;7(8):e1000332.
Shengxu Li and colleagues use data from a large prospective observational cohort to examine the extent to which a genetic predisposition toward obesity may be modified by living a physically active lifestyle.
Background
We have previously shown that multiple genetic loci identified by genome-wide association studies (GWAS) increase the susceptibility to obesity in a cumulative manner. It is, however, not known whether and to what extent this genetic susceptibility may be attenuated by a physically active lifestyle. We aimed to assess the influence of a physically active lifestyle on the genetic predisposition to obesity in a large population-based study.
Methods and Findings
We genotyped 12 SNPs in obesity-susceptibility loci in a population-based sample of 20,430 individuals (aged 39–79 y) from the European Prospective Investigation of Cancer (EPIC)-Norfolk cohort with an average follow-up period of 3.6 y. A genetic predisposition score was calculated for each individual by adding the body mass index (BMI)-increasing alleles across the 12 SNPs. Physical activity was assessed using a self-administered questionnaire. Linear and logistic regression models were used to examine main effects of the genetic predisposition score and its interaction with physical activity on BMI/obesity risk and BMI change over time, assuming an additive effect for each additional BMI-increasing allele carried. Each additional BMI-increasing allele was associated with 0.154 (standard error [SE] 0.012) kg/m2 (p = 6.73×10−37) increase in BMI (equivalent to 445 g in body weight for a person 1.70 m tall). This association was significantly (pinteraction = 0.005) more pronounced in inactive people (0.205 [SE 0.024] kg/m2 [p = 3.62×10−18; 592 g in weight]) than in active people (0.131 [SE 0.014] kg/m2 [p = 7.97×10−21; 379 g in weight]). Similarly, each additional BMI-increasing allele increased the risk of obesity 1.116-fold (95% confidence interval [CI] 1.093–1.139, p = 3.37×10−26) in the whole population, but significantly (pinteraction = 0.015) more in inactive individuals (odds ratio [OR] = 1.158 [95% CI 1.118–1.199; p = 1.93×10−16]) than in active individuals (OR = 1.095 (95% CI 1.068–1.123; p = 1.15×10−12]). Consistent with the cross-sectional observations, physical activity modified the association between the genetic predisposition score and change in BMI during follow-up (pinteraction = 0.028).
Conclusions
Our study shows that living a physically active lifestyle is associated with a 40% reduction in the genetic predisposition to common obesity, as estimated by the number of risk alleles carried for any of the 12 recently GWAS-identified loci.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
In the past few decades, the global incidence of obesity—defined as a body mass index (BMI, a simple index of weight-for-height that uses the weight in kilograms divided by the square of the height in meters) of 30 and over, has increased so much that this growing public health concern is now commonly referred to as the “obesity epidemic.” Once considered prevalent only in high-income countries, obesity is an increasing health problem in low- and middle-income countries, particularly in urban settings. In 2005, at least 400 million adults world-wide were obese, and the projected figure for 2015 is a substantial increase of 300 million to around 700 million. Childhood obesity is also a growing concern. Contributing factors to the obesity epidemic are a shift in diet to an increased intake of energy-dense foods that are high in fat and sugars and a trend towards decreased physical activity due to increasingly sedentary lifestyles.
However, genetics are also thought to play a critical role as genetically predisposed individuals may be more prone to obesity if they live in an environment that has abundant access to energy-dense food and labor-saving devices.
Why Was This Study Done?
Although recent genetic studies (genome-wide association studies) have identified 12 alleles (a DNA variant that is located at a specific position on a specific chromosome) associated with increased BMI, there has been no convincing evidence of the interaction between genetics and lifestyle. In this study the researchers examined the possibility of such an interaction by assessing whether individuals with a genetic predisposition to increased obesity risk could modify this risk by increasing their daily physical activity.
What Did the Researchers Do and Find?
The researchers used a population-based cohort study of 25,631 people living in Norwich, UK (The EPIC-Norfolk study) and identified individuals who were 39 to 79 years old during a health check between 1993 and 1997. The researchers invited these people to a second health examination. In total, 20,430 individuals had baseline data available, of which 11,936 had BMI data at the second health check. The researchers used genotyping methods and then calculated a genetic predisposition score for each individual and their occupational and leisure-time physical activities were assessed by using a validated self-administered questionnaire. Then, the researchers used modeling techniques to examine the main effects of the genetic predisposition score and its interaction with physical activity on BMI/obesity risk and BMI change over time. The researchers found that each additional BMI-increasing allele was associated with an increase in BMI equivalent to 445 g in body weight for a person 1.70 m tall and that the size of this effect was greater in inactive people than in active people. In individuals who have a physically active lifestyle, this increase was only 379 g/allele, or 36% lower than in physically inactive individuals in whom the increase was 592 g/allele. Furthermore, in the total sample each additional obesity-susceptibility allele increased the odds of obesity by 1.116-fold. However, the increased odds per allele for obesity risk were 40% lower in physically active individuals (1.095 odds/allele) compared to physically inactive individuals (1.158 odds/allele).
What Do These Findings Mean?
The findings of this study indicate that the genetic predisposition to obesity can be reduced by approximately 40% by having a physically active lifestyle. The findings of this study suggest that, while the whole population benefits from increased physical activity levels, individuals who are genetically predisposed to obesity would benefit more than genetically protected individuals. Furthermore, these findings challenge the deterministic view of the genetic predisposition to obesity that is often held by the public, as they show that even the most genetically predisposed individuals will benefit from adopting a healthy lifestyle. The results are limited by participants self-reporting their physical activity levels, which is less accurate than objective measures of physical activity.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000332.
This study relies on the results of previous genome-wide association studies The National Human Genome Research Institute provides an easy-to-follow guide to understanding such studies
The International Association for the Study of Obesity aims to improve global health by promoting the understanding of obesity and weight-related diseases through scientific research and dialogue
The International Obesity Taskforce is the research-led think tank and advocacy arm of the International Association for the Study of Obesity
The Global Alliance for the Prevention of Obesity and Related Chronic Disease is a global action program that addresses the issues surrounding the prevention of obesity
The National Institutes of Health has its own obesity task force, which includes 26 institutes
doi:10.1371/journal.pmed.1000332
PMCID: PMC2930873  PMID: 20824172
14.  Genome-wide association study identifies five loci associated with lung function 
Repapi, Emmanouela | Sayers, Ian | Wain, Louise V | Burton, Paul R | Johnson, Toby | Obeidat, Ma’en | Zhao, Jing Hua | Ramasamy, Adaikalavan | Zhai, Guangju | Vitart, Veronique | Huffman, Jennifer E | Igl, Wilmar | Albrecht, Eva | Deloukas, Panos | Henderson, John | Granell, Raquel | McArdle, Wendy L | Rudnicka, Alicja R | Barroso, Inês | Loos, Ruth J F | Wareham, Nicholas J | Mustelin, Linda | Rantanen, Taina | Surakka, Ida | Imboden, Medea | Wichmann, H Erich | Grkovic, Ivica | Jankovic, Stipan | Zgaga, Lina | Hartikainen, Anna-Liisa | Peltonen, Leena | Gyllensten, Ulf | Johansson, Åsa | Zaboli, Ghazal | Campbell, Harry | Wild, Sarah H | Wilson, James F | Gläser, Sven | Homuth, Georg | Völzke, Henry | Mangino, Massimo | Soranzo, Nicole | Spector, Tim D | Polašek, Ozren | Rudan, Igor | Wright, Alan F | Heliövaara, Markku | Ripatti, Samuli | Pouta, Anneli | Naluai, Åsa Torinsson | Olin, Anna-Carin | Torén, Kjell | Cooper, Matthew N | James, Alan L | Palmer, Lyle J | Hingorani, Aroon D | Wannamethee, S Goya | Whincup, Peter H | Smith, George Davey | Ebrahim, Shah | McKeever, Tricia M | Pavord, Ian D | MacLeod, Andrew K | Morris, Andrew D | Porteous, David J | Cooper, Cyrus | Dennison, Elaine | Shaheen, Seif | Karrasch, Stefan | Schnabel, Eva | Schulz, Holger | Grallert, Harald | Bouatia-Naji, Nabila | Delplanque, Jérôme | Froguel, Philippe | Blakey, John D | Britton, John R | Morris, Richard W | Holloway, John W | Lawlor, Debbie A | Hui, Jennie | Nyberg, Fredrik | Jarvelin, Marjo-Riitta | Jackson, Cathy | Kähönen, Mika | Kaprio, Jaakko | Probst-Hensch, Nicole M | Koch, Beate | Hayward, Caroline | Evans, David M | Elliott, Paul | Strachan, David P | Hall, Ian P | Tobin, Martin D
Nature genetics  2009;42(1):36-44.
Pulmonary function measures are heritable traits that predict morbidity and mortality and define chronic obstructive pulmonary disease (COPD). We tested genome-wide association with forced expiratory volume in 1 s (FEV1) and the ratio of FEV1 to forced vital capacity (FVC) in the SpiroMeta consortium (n = 20,288 individuals of European ancestry). We conducted a meta-analysis of top signals with data from direct genotyping (n ≤ 32,184 additional individuals) and in silico summary association data from the CHARGE Consortium (n = 21,209) and the Health 2000 survey (n ≤ 883). We confirmed the reported locus at 4q31 and identified associations with FEV1 or FEV1/FVC and common variants at five additional loci: 2q35 in TNS1 (P = 1.11 × 10−12), 4q24 in GSTCD (2.18 × 10−23), 5q33 in HTR4 (P = 4.29 × 10−9), 6p21 in AGER (P = 3.07 × 10−15) and 15q23 in THSD4 (P = 7.24 × 10−15). mRNA analyses showed expression of TNS1, GSTCD, AGER, HTR4 and THSD4 in human lung tissue. These associations offer mechanistic insight into pulmonary function regulation and indicate potential targets for interventions to alleviate respiratory disease.
doi:10.1038/ng.501
PMCID: PMC2862965  PMID: 20010834
15.  LRRK2 gene G2019S mutation and SNPs [haplotypes] in subtypes of Parkinson’s disease 
Parkinsonism & related disorders  2008;15(3):175-180.
Mutation within the Leucine-rich repeat kinase 2 (LRRK2) gene has been identified as a cause of autosomal dominant Parkinson’s disease (PD). The purpose of this study was to determine the frequency of G2019S mutation and whether the differences in the allele and genotype distribution of six SNPs within LRRK2 gene are associated with PD in an American non-Hispanic white population. The sample included 350 sporadic PD (SPD), 225 familial PD (FPD) patients and 186 controls of the same race and ethnicity. The frequency of LRRK2 G2019S mutation in our total sample of PD (FPD and SPD) was 1.56%. The frequency of this mutation was 3.5% in the FPD and 0.3% in the SPD groups, respectively. Allele and genotype frequencies of six SNPs were compared between PD and control samples. In addition, PD groups were categorized by sporadic PD (no family history), familial PD (first degree relative with PD) and age of onset (AON, ≤50 or ≥51 years). The haplotypes of the six SNPs were also constructed for association analysis. After correction for multiple comparisons, there was no association between any SNPs (allele or genotype) and PD groups. One of the haplotypes was modestly associated with the combined PD (SPD & FPD) sample. There was also no association with age at onset of PD. Our study suggests that the LRRK2 gene may be a risk factor or the cause for a very small fraction of PD in American white population.
doi:10.1016/j.parkreldis.2008.05.004
PMCID: PMC2761091  PMID: 18752982
LRRK2; SNPs; Parkinson’s disease; haplotype
16.  A multilevel linear mixed model of the association between candidate genes and weight and body mass index using the Framingham longitudinal family data 
BMC Proceedings  2009;3(Suppl 7):S115.
Obesity has become an epidemic in many countries and is one of the major risk conditions for disease including type 2 diabetes, coronary heart disease, stroke, dyslipidemia, and hypertension. Recent genome-wide association studies have identified two genes (FTO and near MC4R) that were unequivocally associated with body mass index (BMI) and obesity. For the Genetic Analysis Workshop 16, data from the Framingham Heart Study were made available, including longitudinal anthropometric and metabolic traits for 7130 Caucasian individuals over three generations, each with follow-up data at up to four time points. We explored the associations between four single-nucleotide polymorphisms (SNPs) on FTO (rs1121980, rs9939609) or near MC4R (rs17782313, rs17700633) with weight and BMI under an additive model. We applied multilevel linear mixed model for continuous outcomes, using the Affymetrix 500k genome-wide genotype data for the four SNPs. The results of the multilevel modeling in the entire sample indicated that the minor alleles of the four SNPs were associated with higher weight and higher BMI. The most significant associations were between rs9939609 and weight (p = 4.7 × 10-6) and BMI (p = 8.9 × 10-8). The results also showed that, for SNPs at FTO, the homozygotes for the minor allele had the most pronounced increase in weight and BMI, while the common allele homozygotes gained less weight and BMI during the follow-up period. Linkage disequilibrium (LD) between the two FTO SNPs was strong (D' = 0.997, r2 = 0.875) but their haplotype was not significantly associated with either weight or BMI. The two SNPs near MC4R were in weak LD (D' = 0.487, r2 = 0.166).
PMCID: PMC2795887  PMID: 20017980
17.  Power for Genetic Association Study of Human Longevity Using the Case-Control Design 
American Journal of Epidemiology  2008;168(8):890-896.
The efficiency of the popular case-control design in gene-longevity association studies needs to be verified because, different from a binary trait, longevity represents only the extreme end of the continuous life span distribution without a clear cutoff for defining the phenotype. In this paper, the authors use the current Danish life tables to simulate individual life span by using a variety of scenarios and assess the empirical power for different sample sizes when cases are defined as centenarians or as nonagenarians. Results show that, although using small samples of centenarians (several hundred) provides power to detect only common alleles with large effects (a >20% reduction in hazard rate), large samples of centenarians (>1,000) achieve power to capture genes responsible for minor effects (5%–10% hazard reduction depending on the mode of inheritance). Although the method provides good power for rare alleles with multiplicative or dominant effects, it performs poorly for rare recessive alleles. Power is drastically reduced when nonagenarians are considered cases, with a more than 5-fold difference in the size of the case sample required to achieve comparable power as that found with centenarians.
doi:10.1093/aje/kwn205
PMCID: PMC2732955  PMID: 18756013
association; case-control studies; computer simulation; genetics; longevity
18.  Common variants near MC4R are associated with fat mass, weight and risk of obesity 
Loos, Ruth J F | Lindgren, Cecilia M | Li, Shengxu | Wheeler, Eleanor | Zhao, Jing Hua | Prokopenko, Inga | Inouye, Michael | Freathy, Rachel M | Attwood, Antony P | Beckmann, Jacques S | Berndt, Sonja I | Bergmann, Sven | Bennett, Amanda J | Bingham, Sheila A | Bochud, Murielle | Brown, Morris | Cauchi, Stéphane | Connell, John M | Cooper, Cyrus | Smith, George Davey | Day, Ian | Dina, Christian | De, Subhajyoti | Dermitzakis, Emmanouil T | Doney, Alex S F | Elliott, Katherine S | Elliott, Paul | Evans, David M | Farooqi, I Sadaf | Froguel, Philippe | Ghori, Jilur | Groves, Christopher J | Gwilliam, Rhian | Hadley, David | Hall, Alistair S | Hattersley, Andrew T | Hebebrand, Johannes | Heid, Iris M | Herrera, Blanca | Hinney, Anke | Hunt, Sarah E | Jarvelin, Marjo-Riitta | Johnson, Toby | Jolley, Jennifer D M | Karpe, Fredrik | Keniry, Andrew | Khaw, Kay-Tee | Luben, Robert N | Mangino, Massimo | Marchini, Jonathan | McArdle, Wendy L | McGinnis, Ralph | Meyre, David | Munroe, Patricia B | Morris, Andrew D | Ness, Andrew R | Neville, Matthew J | Nica, Alexandra C | Ong, Ken K | O'Rahilly, Stephen | Owen, Katharine R | Palmer, Colin N A | Papadakis, Konstantinos | Potter, Simon | Pouta, Anneli | Qi, Lu | Randall, Joshua C | Rayner, Nigel W | Ring, Susan M | Sandhu, Manjinder S | Scherag, André | Sims, Matthew A | Song, Kijoung | Soranzo, Nicole | Speliotes, Elizabeth K | Syddall, Holly E | Teichmann, Sarah A | Timpson, Nicholas J | Tobias, Jonathan H | Uda, Manuela | Vogel, Carla I Ganz | Wallace, Chris | Waterworth, Dawn M | Weedon, Michael N | Willer, Cristen J | Wraight, Vicki L | Yuan, Xin | Zeggini, Eleftheria | Hirschhorn, Joel N | Strachan, David P | Ouwehand, Willem H | Caulfield, Mark J | Samani, Nilesh J | Frayling, Timothy M | Vollenweider, Peter | Waeber, Gerard | Mooser, Vincent | Deloukas, Panos | McCarthy, Mark I | Wareham, Nicholas J | Barroso, Inês | Jacobs, Kevin B | Chanock, Stephen J | Hayes, Richard B | Lamina, Claudia | Gieger, Christian | Illig, Thomas | Meitinger, Thomas | Wichmann, H-Erich | Kraft, Peter | Hankinson, Susan E | Hunter, David J | Hu, Frank B | Lyon, Helen N | Voight, Benjamin F | Ridderstrale, Martin | Groop, Leif | Scheet, Paul | Sanna, Serena | Abecasis, Goncalo R | Albai, Giuseppe | Nagaraja, Ramaiah | Schlessinger, David | Jackson, Anne U | Tuomilehto, Jaakko | Collins, Francis S | Boehnke, Michael | Mohlke, Karen L
Nature genetics  2008;40(6):768-775.
To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 × 10−6) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 × 10−15) and 5,988 children aged 7–11 (0.13 Z-score units; P = 1.5 × 10−8). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 × 10−11). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 × 10−4). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits.
doi:10.1038/ng.140
PMCID: PMC2669167  PMID: 18454148
19.  LDL-cholesterol concentrations: a genome-wide association study 
Lancet  2008;371(9611):483-491.
Summary
Background
LDL cholesterol has a causal role in the development of cardiovascular disease. Improved understanding of the biological mechanisms that underlie the metabolism and regulation of LDL cholesterol might help to identify novel therapeutic targets. We therefore did a genome-wide association study of LDL-cholesterol concentrations.
Methods
We used genome-wide association data from up to 11 685 participants with measures of circulating LDL-cholesterol concentrations across five studies, including data for 293 461 autosomal single nucleotide polymorphisms (SNPs) with a minor allele frequency of 5% or more that passed our quality control criteria. We also used data from a second genome-wide array in up to 4337 participants from three of these five studies, with data for 290 140 SNPs. We did replication studies in two independent populations consisting of up to 4979 participants. Statistical approaches, including meta-analysis and linkage disequilibrium plots, were used to refine association signals; we analysed pooled data from all seven populations to determine the effect of each SNP on variations in circulating LDL-cholesterol concentrations.
Findings
In our initial scan, we found two SNPs (rs599839 [p=1·7×10−15] and rs4970834 [p=3·0×10−11]) that showed genome-wide statistical association with LDL cholesterol at chromosomal locus 1p13.3. The second genome screen found a third statistically associated SNP at the same locus (rs646776 [p=4·3×10−9]). Meta-analysis of data from all studies showed an association of SNPs rs599839 (combined p=1·2×10−33) and rs646776 (p=4·8×10−20) with LDL-cholesterol concentrations. SNPs rs599839 and rs646776 both explained around 1% of the variation in circulating LDL-cholesterol concentrations and were associated with about 15% of an SD change in LDL cholesterol per allele, assuming an SD of 1 mmol/L.
Interpretation
We found evidence for a novel locus for LDL cholesterol on chromosome 1p13.3. These results potentially provide insight into the biological mechanisms that underlie the regulation of LDL cholesterol and might help in the discovery of novel therapeutic targets for cardiovascular disease.
doi:10.1016/S0140-6736(08)60208-1
PMCID: PMC2292820  PMID: 18262040
20.  Mining gene networks with application to GAW15 Problem 1 
BMC Proceedings  2007;1(Suppl 1):S52.
The Genetic Analysis Workshop 15 (GAW15) Problem 1 contained baseline expression levels of 8793 genes in immortalized B cells from 194 individuals in 14 Centre d'Etude du Polymorphisme Humain (CEPH) Utah pedigrees. Previous analysis of the data showed linkage and association and evidence of substantial individual variations. In particular, correlation was examined on expression levels of 31 genes and 25 target genes corresponding to two master regulatory regions. In this analysis, we apply Bayesian network analysis to gain further insight into these findings. We identify strong dependences and therefore provide additional insight into the underlying relationships between the genes involved. More generally, the approach is expected to be applicable for integrated analysis of genes on biological pathways.
PMCID: PMC2367510  PMID: 18466552
21.  Retrospective analysis of main and interaction effects in genetic association studies of human complex traits 
BMC Genetics  2007;8:70.
Background
The etiology of multifactorial human diseases involves complex interactions between numerous environmental factors and alleles of many genes. Efficient statistical tools are demanded in identifying the genetic and environmental variants that affect the risk of disease development. This paper introduces a retrospective polytomous logistic regression model to measure both the main and interaction effects in genetic association studies of human discrete and continuous complex traits. In this model, combinations of genotypes at two interacting loci or of environmental exposure and genotypes at one locus are treated as nominal outcomes of which the proportions are modeled as a function of the disease trait assigning both main and interaction effects and with no assumption of normality in the trait distribution. Performance of our method in detecting interaction effect is compared with that of the case-only model.
Results
Results from our simulation study indicate that our retrospective model exhibits high power in capturing even relatively small effect with reasonable sample sizes. Application of our method to data from an association study on the catalase -262C/T promoter polymorphism and aging phenotypes detected significant main and interaction effects for age-group and allele T on individual's cognitive functioning and produced consistent results in estimating the interaction effect as compared with the popular case-only model.
Conclusion
The retrospective polytomous logistic regression model can be used as a convenient tool for assessing both main and interaction effects in genetic association studies of human multifactorial diseases involving genetic and non-genetic factors as well as categorical or continuous traits.
doi:10.1186/1471-2156-8-70
PMCID: PMC2099440  PMID: 17937824
22.  Integrated analysis of genetic data with R 
Human Genomics  2006;2(4):258-265.
Genetic data are now widely available. There is, however, an apparent lack of concerted effort to produce software systems for statistical analysis of genetic data compared with other fields of statistics. It is often a tremendous task for end-users to tailor them for particular data, especially when genetic data are analysed in conjunction with a large number of covariates. Here, R http://www.r-project.org, a free, flexible and platform-independent environment for statistical modelling and graphics is explored as an integrated system for genetic data analysis. An overview of some packages currently available for analysis of genetic data is given. This is followed by examples of package development and practical applications. With clear advantages in data management, graphics, statistical analysis, programming, internet capability and use of available codes, it is a feasible, although challenging, task to develop it into an integrated platform for genetic analysis; this will require the joint efforts of many researchers.
doi:10.1186/1479-7364-2-4-258
PMCID: PMC3525150  PMID: 16460651
linkage and association analysis; complex traits; software
23.  Selecting cases from nuclear families for case-control association analysis 
BMC Genetics  2005;6(Suppl 1):S105.
We examine the efficiency of a number of schemes to select cases from nuclear families for case-control association analysis using the Genetic Analysis Workshop 14 simulated dataset. We show that with this simulated dataset comparing all affected siblings with unrelated controls is considerably more powerful than all of the other approaches considered. We find that the test statistic is increased by almost 3-fold compared to the next best sampling schemes of selecting all affected sibs only from families with affected parents (AFaff), one affected sib with most evidence of allele-sharing from each family (SF), and all affected sibs from families with evidence for linkage (AFL). We consider accounting for biological relatedness of samples in the association analysis to maintain the correct type I error. We also discuss the relative efficiencies of increasing the ratio of unrelated cases to controls, methods to confirm associations and issues to consider when applying our conclusions to other complex disease datasets.
doi:10.1186/1471-2156-6-S1-S105
PMCID: PMC1866834  PMID: 16451561
24.  Mixed-effects Cox models of alcohol dependence in extended families 
BMC Genetics  2005;6(Suppl 1):S127.
The presence of disease is commonly used in genetic studies; however, the time to onset often provides additional information. To apply the popular Cox model for such data, it is desirable to consider the familial correlation, which involves kinship or identity by descent (IBD) information between family members. Recently, such a framework has been developed and implemented in a UNIX-based S-PLUS package called kinship, extending the Cox model with mixed effects and familial relationship. The model is of great potential in joint analysis of family data with genetic and environmental factors. We apply this framework to data from the Collaborative Study on the Genetics of Alcoholism data as part of Genetic Analysis Workshop 14. We use the S-PLUS package, ported into the R environment , for the analysis of microsatellite data on chromosomes 4 and 7. In these analyses, IBD information at those markers is used in addition to the basic Cox model with mixed effects, which provides estimates of the relative contribution of specific genetic markers. D4S1645 had the largest variance and contribution to the log-likelihood on chromosome 4, but the significance of this finding requires further investigation.
doi:10.1186/1471-2156-6-S1-S127
PMCID: PMC1866810  PMID: 16451585
25.  Genome-wide association analyses identify 18 new loci associated with serum urate concentrations 
Köttgen, Anna | Albrecht, Eva | Teumer, Alexander | Vitart, Veronique | Krumsiek, Jan | Hundertmark, Claudia | Pistis, Giorgio | Ruggiero, Daniela | O’Seaghdha, Conall M | Haller, Toomas | Yang, Qiong | Tanaka, Toshiko | Johnson, Andrew D | Kutalik, Zoltán | Smith, Albert V | Shi, Julia | Struchalin, Maksim | Middelberg, Rita P S | Brown, Morris J | Gaffo, Angelo L | Pirastu, Nicola | Li, Guo | Hayward, Caroline | Zemunik, Tatijana | Huffman, Jennifer | Yengo, Loic | Zhao, Jing Hua | Demirkan, Ayse | Feitosa, Mary F | Liu, Xuan | Malerba, Giovanni | Lopez, Lorna M | van der Harst, Pim | Li, Xinzhong | Kleber, Marcus E | Hicks, Andrew A | Nolte, Ilja M | Johansson, Asa | Murgia, Federico | Wild, Sarah H | Bakker, Stephan J L | Peden, John F | Dehghan, Abbas | Steri, Maristella | Tenesa, Albert | Lagou, Vasiliki | Salo, Perttu | Mangino, Massimo | Rose, Lynda M | Lehtimäki, Terho | Woodward, Owen M | Okada, Yukinori | Tin, Adrienne | Müller, Christian | Oldmeadow, Christopher | Putku, Margus | Czamara, Darina | Kraft, Peter | Frogheri, Laura | Thun, Gian Andri | Grotevendt, Anne | Gislason, Gauti Kjartan | Harris, Tamara B | Launer, Lenore J | McArdle, Patrick | Shuldiner, Alan R | Boerwinkle, Eric | Coresh, Josef | Schmidt, Helena | Schallert, Michael | Martin, Nicholas G | Montgomery, Grant W | Kubo, Michiaki | Nakamura, Yusuke | Tanaka, Toshihiro | Munroe, Patricia B | Samani, Nilesh J | Jacobs, David R | Liu, Kiang | D’Adamo, Pio | Ulivi, Sheila | Rotter, Jerome I | Psaty, Bruce M | Vollenweider, Peter | Waeber, Gerard | Campbell, Susan | Devuyst, Olivier | Navarro, Pau | Kolcic, Ivana | Hastie, Nicholas | Balkau, Beverley | Froguel, Philippe | Esko, Tõnu | Salumets, Andres | Khaw, Kay Tee | Langenberg, Claudia | Wareham, Nicholas J | Isaacs, Aaron | Kraja, Aldi | Zhang, Qunyuan | Wild, Philipp S | Scott, Rodney J | Holliday, Elizabeth G | Org, Elin | Viigimaa, Margus | Bandinelli, Stefania | Metter, Jeffrey E | Lupo, Antonio | Trabetti, Elisabetta | Sorice, Rossella | Döring, Angela | Lattka, Eva | Strauch, Konstantin | Theis, Fabian | Waldenberger, Melanie | Wichmann, H-Erich | Davies, Gail | Gow, Alan J | Bruinenberg, Marcel | Study, LifeLines Cohort | Stolk, Ronald P | Kooner, Jaspal S | Zhang, Weihua | Winkelmann, Bernhard R | Boehm, Bernhard O | Lucae, Susanne | Penninx, Brenda W | Smit, Johannes H | Curhan, Gary | Mudgal, Poorva | Plenge, Robert M | Portas, Laura | Persico, Ivana | Kirin, Mirna | Wilson, James F | Leach, Irene Mateo | van Gilst, Wiek H | Goel, Anuj | Ongen, Halit | Hofman, Albert | Rivadeneira, Fernando | Uitterlinden, Andre G | Imboden, Medea | von Eckardstein, Arnold | Cucca, Francesco | Nagaraja, Ramaiah | Piras, Maria Grazia | Nauck, Matthias | Schurmann, Claudia | Budde, Kathrin | Ernst, Florian | Farrington, Susan M | Theodoratou, Evropi | Prokopenko, Inga | Stumvoll, Michael | Jula, Antti | Perola, Markus | Salomaa, Veikko | Shin, So-Youn | Spector, Tim D | Sala, Cinzia | Ridker, Paul M | Kähönen, Mika | Viikari, Jorma | Hengstenberg, Christian | Nelson, Christopher P | Consortium, CARDIoGRAM | Consortium, DIAGRAM | Consortium, ICBP | Consortium, MAGIC | Meschia, James F | Nalls, Michael A | Sharma, Pankaj | Singleton, Andrew B | Kamatani, Naoyuki | Zeller, Tanja | Burnier, Michel | Attia, John | Laan, Maris | Klopp, Norman | Hillege, Hans L | Kloiber, Stefan | Choi, Hyon | Pirastu, Mario | Tore, Silvia | Probst-Hensch, Nicole M | Völzke, Henry | Gudnason, Vilmundur | Parsa, Afshin | Schmidt, Reinhold | Whitfield, John B | Fornage, Myriam | Gasparini, Paolo | Siscovick, David S | Polašek, Ozren | Campbell, Harry | Rudan, Igor | Bouatia-Naji, Nabila | Metspalu, Andres | Loos, Ruth J F | van Duijn, Cornelia M | Borecki, Ingrid B | Ferrucci, Luigi | Gambaro, Giovanni | Deary, Ian J | Wolffenbuttel, Bruce H R | Chambers, John C | März, Winfried | Pramstaller, Peter P | Snieder, Harold | Gyllensten, Ulf | Wright, Alan F | Navis, Gerjan | Watkins, Hugh | Witteman, Jacqueline C M | Sanna, Serena | Schipf, Sabine | Dunlop, Malcolm G | Tönjes, Anke | Ripatti, Samuli | Soranzo, Nicole | Toniolo, Daniela | Chasman, Daniel I | Raitakari, Olli | Kao, W H Linda | Ciullo, Marina | Fox, Caroline S | Caulfield, Mark | Bochud, Murielle | Gieger, Christian
Nature genetics  2012;45(2):145-154.
Elevated serum urate concentrations can cause gout, a prevalent and painful inflammatory arthritis. By combining data from >140,000 individuals of European ancestry within the Global Urate Genetics Consortium (GUGC), we identified and replicated 28 genome-wide significant loci in association with serum urate concentrations (18 new regions in or near TRIM46, INHBB, SFMBT1, TMEM171, VEGFA, BAZ1B, PRKAG2, STC1, HNF4G, A1CF, ATXN2, UBE2Q2, IGF1R, NFAT5, MAF, HLF, ACVR1B-ACVRL1 and B3GNT4). Associations for many of the loci were of similar magnitude in individuals of non-European ancestry. We further characterized these loci for associations with gout, transcript expression and the fractional excretion of urate. Network analyses implicate the inhibins-activins signaling pathways and glucose metabolism in systemic urate control. New candidate genes for serum urate concentration highlight the importance of metabolic control of urate production and excretion, which may have implications for the treatment and prevention of gout.
doi:10.1038/ng.2500
PMCID: PMC3663712  PMID: 23263486

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