Peer reviewer: Huixiao Hong, PhD, Division of Systems Biology, National Center for Toxicological Research, USFDA, 3900 NCTR Road, Jefferson, AR 72079, United States
S- Editor Wang JL L- Editor Roemmele A E- Editor Zheng XM
We have witnessed tremendous success in genome-wide association studies (GWAS) in recent years. Since the identification of variants in the complement factor H gene on the risk of age-related macular degeneration, GWAS have become ubiquitous in genetic studies and have led to the identification of genetic variants that are associated with a variety of complex human diseases and traits. These discoveries have changed our understanding of the biological architecture of common, complex diseases and have also provided new hypotheses to test. New tools, such as next-generation sequencing, will be an important part of the future of genetics research; however, GWAS studies will continue to play an important role in disease gene discovery. Many traits have yet to be explored by GWAS, especially in minority populations, and large collaborative studies are currently being conducted to maximize the return from existing GWAS data. In addition, GWAS technology continues to improve, increasing genomic coverage for major global populations and decreasing the cost of experiments. Although much of the variance attributable to genetic factors for many important traits is still unexplained, GWAS technology has been instrumental in mapping over a thousand genes to hundreds of traits. More discoveries are made each month and the scale, quality and quantity of current work has a steady trend upward. We briefly review the current key trends in GWAS, which can be summarized with three goals: increase power, increase collaborations and increase populations.
Genome-wide association studies (GWAS) were motivated by new thinking about approaches for mapping traits to genomic regions and several developments in large scientific projects, such as the completion of the homo sapiens reference sequence by the Human Genome Project and the cataloging of common genetic variants by the International HapMap Project[2-5]. GWAS are based on the premise that densely genotyped common, or high frequency, alleles will have statistical power to detect causal associations with traits at nearby, ungenotyped common polymorphisms through short-range linkage disequilibrium (LD). LD is the nonrandom association between pairs of alleles. The basis for this strategy is the common disease common variant (CDCV) hypothesis, in which it is proposed that high-prevalence traits are most likely determined by high-frequency genetic variants. This approach has been proven effective in many scenarios for mapping small genomic regions to traits (see the National Human Genome Research Institute Catalog of Published Genome-Wide Association Studies, http://www.genome.gov/GWAStudies/ )[8,9]. Many of these newly associated regions would not have been considered good candidates for targeted genotyping studies based on biological knowledge or previous linkage evidence, illustrating the difficulty of improvising a hypothesis based on the molecular biology of a gene and its products.
Since the identification of variants in the complement factor H (CFH) gene associating with the risk of age-related macular degeneration (AMD), GWAS have become ubiquitous in genetic epidemiology and have led to the identification of genetic variants that are associated with a variety of human diseases and traits, such as type 1[11,12] and type 2 diabetes[13-15], inflammatory bowel disease, Crohn’s disease[17,18], breast cancer, human height and body mass index, to name a few. It has revolutionized the search for genetic contributions to complex traits[22,23].
In GWAS, the tests of association with traits are conducted at between hundreds of thousands to millions of densely spaced single nucleotide polymorphisms (SNPs). GWAS require no a priori biological knowledge and are therefore an agnostic method for localizing the genetic effects of complex human diseases. These study designs rely on genotyping platforms which are designed by assay manufacturers and genotyping in cases and controls, families that contain multiple affected individuals or random subjects from the population if a quantitative trait is the focus of the investigation. These platforms come primarily from two manufacturers, Affymetrix (http://www.affymetrix.com/ ) and Illumina (http://www.illumina.com/ ), and the rationale for the SNPs assayed differs between these companies. The Illumina approach to GWAS design employs haplotype tagging to select SNPs based on local correlation with other nearby SNPs, such that redundant genetic variation containing very similar statistical information is not assayed. The Affymetrix platforms use a different design, where the human genome is saturated with SNPs that are selected based on their location between two restriction enzyme sites. Regardless of platform, the goal of GWAS is to evaluate the majority of common alleles for association with traits through pairwise correlation with assayed SNPs.
Despite the large size of GWAS data, computational tools make GWAS feasible to analyze on standard desktop computer hardware. However, the large number of hypothesis tests in GWAS creates a challenge for statistical testing. An often-cited genome-wide significance level is 5 × 10-8, based on the assumption of one million independent pieces of genetic information in the human genome[25,26], and less stringent thresholds were also verified[27,28]. Few studies have adequate sample size to maintain the power needed to detect small to moderate effect sizes that predominate in GWAS. The current approach for elucidating genes that influence complex disease is to increase the power in GWAS through increased sample sizes assembled by collaboration among research groups[29,30].
As of June 01 2011, 906 publications have been documented and 4514 SNPs have been associated with human disease and traits at a significance level of 10-5 in the Catalog of the Published GWAS (http://www.genome.gov/GWAStudies ). Given the flood of GWAS publications in recent years, this review is not all-inclusive but highlights the key trends in current approaches to GWAS.
The often-cited first success in GWAS (defined as at least 100K SNPs), the discovery of CFH in AMD, used a small data set (by current standards) of only 96 cases and 50 controls genotyped using the Affymetrix GeneChip Mapping 100K set of microarrays. This study proved the concept of a “brute force” approach to scan the entire human genome for human diseases. Soon after, researchers started using larger sample sizes to augment power in GWAS. In 2007, the Wellcome Trust Case Control Consortium carried out GWAS of seven common diseases using 14 000 cases and 3000 shared controls. The need for statistical power (through the incorporation of larger sample sizes) and the requirement for independent replication of association signals also motivated researchers to employ meta-analysis, often with the aid of genotype imputation, to overcome the limitations associated with each individual GWAS analysis.
Early meta-analyses in GWAS reported success in Parkinson’s disease and Type 2 diabetes[33,34]. A meta-analysis combines results from multiple independent studies with similar data to address related research hypotheses. It is a more powerful approach to estimate the true effect size than analysis of data from a single study. In recent genetic studies, meta-analysis has led to many successful discoveries of genetic variants with different phenotypes, including type 1 diabetes, type 2 diabetes, chronic kidney disease, retinal microcirculation, serum lipid concentrations, glucose and insulin response, fasting glucose homeostasis, blood pressure and hypertension, atrial fibrillation, Crohn’s disease, metabolic syndrome, human height[20,45], body mass index and blood pressure[41,46]. Meta-analyses of several thousands of samples for human diseases[36,47], and even a quarter-million individuals for common human traits, are becoming more common. In addition to increasing sample size, meta-analysis allows researchers to bypass the potential Institutional Review Board (IRB) issues of individual-level data sharing, as meta-data do not increase the risk of study subjects being re-identified and their personal information made public.
The density and number of assayed SNPs in GWAS products have improved rapidly, from the Affymetrix 100K array used in the AMD GWAS to the currently often used, the Affymetrix 6.0 (> 1 million markers) and the Illumina Human 1M (> 1 million markers). Leveraging the advances in the HapMap project[2,4,5] and the 1000 Genomes Project (1KGP), the Illumina HumanOmni 2.5 (about 2.5 million markers) is also available and the Illumina HumanOmni 5M (about 5 million markers) will soon become a reality (http://www.illumina.com/ ). For estimates of genomic coverage for various platforms, see Barrett et al and Li et al.
The recent invention of genotype imputation has become a cost-effective approach to increase genomic coverage in large genomic scans. It not only enables the pooling of GWAS results from different genotyping chips with different SNPs, which meta-analyses have benefited significantly from, but also increases the power of genome scans. Genotype imputation methods utilize haplotypes inferred from a densely genotyped reference panel of subjects with known ethnicity to infer the conditional probabilities of missing genotypes in a study sample genotyped at a subset of SNPs[50,51]. Imputation of genotypes also leverages publicly available resources such as the International HapMap Project data[2,4,5] and resequencing data from the 1KGP.
Most of the meta-analyses to date have used the HapMap Phase II reference panels (about 3 million markers). The 1KGP reference panel, with about 16 million variant sites, will most likely become the reference panel of choice for future GWAS. This allows researchers to evaluate many more SNPs than are provided by GWAS manufacturers, or to fill-in SNPs that are only in one study in a meta-analysis without increasing the genotyping cost of the study.
The dilemma, that significant GWAS hits so far only explain a small proportion of heritability, has shifted researchers’ attention from GWAS genotyping chips to sequencing, with the belief that rare variants might be the culprit for the missing heritability. It was also predicted that DNA sequencing would become a routine tool in genetic research.
The cost of data generation, storage and processing and bioinformatics analysis add another level of difficulty to whole-genome sequencing experiments in large samples. The per-subject cost for generating individual-level genotype data from GWAS is still much less than the cost of resequencing at a depth that is sufficient for making genotype calls throughout the genome. As a result, especially for traits for which GWAS have not yet been conducted on a large-scale, we believe that array-based GWAS assays will continue to be important, especially with the aid of genotype imputation and new design of high-density GWAS chips.
Some recent research has shown that association testing from sequence data may provide slightly more statistical power than variant-based genotyping on a per-subject basis using two recently developed tests of association[54,55]. However, we note that due to the large difference in the cost of resequencing to the cost of variant-based genotyping, on a per-unit of resources basis, many more subjects could be genotyped with variant-based methods than could be resequenced. As a result, the statistical power to detect an association might be better in a large sample of variant-based genotypes than in a small sample of sequence-based genotypes, utilizing the same resources.
Furthermore, once GWAS have elucidated novel regions, targeted resequencing for direct association of alleles in implicated regions can be performed at a fraction of the cost of whole-genome or whole-exome resequencing. Therefore, the use of GWAS can offer benefits at subsequent stages of an investigation and reduce the overall costs of novel locus discovery compared to an approach that relied exclusively on resequencing. The marriage between GWAS arrays and sequencing is likely to be the future, e.g. GWAS arrays followed by targeted sequencing or whole genome sequencing followed by GWAS custom arrays. Regardless of the strategy taken, good coverage of important loci and sufficient sample size to detect associations with rare alleles are indispensable.
The requirement for large sample sizes and replications have motivated massive scientific collaborations. Many new genetic consortia have arisen due to the challenges of conducting successful investigations with GWAS, e.g. the Diabetes Genetics Replication And Meta-analysis Consortium, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the Meta-Analyses of Glucose and Insulin-related traits Consortium, the Genetic Investigation of ANthropometric Traits consortium, the Genetics of Obesity-related Liver Disease consortium, the Chronic Kidney Disease consortium, the Global Blood Pressure Genetics consortium, the Candidate-gene Association Resource consortium and the Coronary Artery Disease (C4D) Genetics Consortium. Genetic consortia targeting Asian populations have also been formed, e.g. the Asian Genetic Epidemiology Network consortium, which includes 12 GWAS studies of Asian participants (http://agenconsortium.org/ ). By using prospective cohort studies, the CHARGE consortium has been very successful in producing numerous high-impact publications on a variety of phenotypes. Publications from these consortia sometimes are co-authored by over a hundred researchers, illustrating the collaborative nature of modern genetic epidemiology. This trend is unprecedented in the field and is likely to continue as technology matures and the cost of experiments using the latest tools increases beyond the ability of any single research group to afford highly-powered studies.
A rare trait allele may not be annotated in the databases of common variants maintained by the HapMap project or the National Center for Biotechnology website dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/ ), thereby excluding the possibility of detecting that SNP through imputation and subsequent association analysis. The genetic determinants for a trait may also be unique for each population of human subjects, where sensitive functional gene or regulatory regions are perturbed by independent sets of rare mutations that occurred after geographic or cultural barriers led to increased genetic distance. Thus, the same associated allele from GWAS across multiple ethnic groups does not necessarily imply the same underlying architecture of causal alleles in LD and it should not be expected that a causal allele in one population will have the same association in another population with a distinct demographic history.
Recent studies show that multi-ethnic GWAS can improve the power for novel locus discovery. A recent example of the association of the variants in KCNQ1 with type 2 diabetes in East-Asian population samples[62,63] were not identified in earlier GWAS in European samples. The associated SNP, rs2283228, has a minor allele frequency (MAF) of about 40% in East-Asian samples. However, the MAF in European samples is only about 5%. At this level of MAF, there is simply not enough power at the GWAS significance level of 5 × 10-8 to detect association in European samples conducted earlier than the two East-Asian samples[13-15,33,65]. Moreover, some risk alleles may be population-specific, which also highlights the importance of conducting GWAS in samples of non-European ancestry.
Early GWAS conducted in Parkinson disease’s (PD) did not yield results that reached genome-wide significance[66-68]. Associations with PD have been replicated in the candidate gene and GWAS contexts, including those described early in PD association studies, such as α-synuclein (SNCA)[69-75] and the microtubule-associated protein tau (MAPT) inversion region on chromosome 17 in European-ancestry subjects[76-89], as well as ubiquitin-specific protease 24[90-92], ELAV-like 4[90,93,94], monoamine oxidase B, Apolipoprotein E and the mitochondrial haplogroups[97-104]. The consistency of results, particularly for SNCA and MAPT, suggest that the failure to reach genome-wide significance in previous studies is due to the relatively small GWAS datasets. More recently, GWAS-based investigations into the genetic determinants of PD have been more fruitful, definitively identifying several associated regions in the genes MAPT, SNCA, HLA-DRB5, BST1, GAK and LRRK2, ACMSD, STK39, MCCC1/LAMP3, SYT11, and CCDC62/HIP1R in both Caucasian and Asian patients, although the MAPT association seems to be the result of a chromosomal inversion only present in Europeans[105-110].
The public health impact and economic burden of obesity is substantial as obesity is associated with increased risks for type 2 diabetes mellitus, cardiovascular disease, dyslipidemia, hypertension, sleep apnea and several forms of cancer[111,112]. In the US, the obesity epidemic disproportionately affects certain ethnic minorities, including Mexican and African-Americans. Mexican Americans are the fastest growing minority group in the US and are expected to represent 18% of the US population by 2025 (http://www.census.gov/ ). Obesity and comorbid conditions such as diabetic retinopathy have higher prevalence in Mexican Americans than in European Americans[114-116], which will introduce significant social and economic costs if the corresponding genetic research is left far behind.
The PAGE network (Population Architecture using Genomics and Epidemiology) is a National Human Genome Research Institute funded initiative designed to characterize GWAS-identified variants in cohorts, including individuals of ancestral groups other than European-decent, to determine if the variants identified are globally associated with various complex traits. Investigators in PAGE are exploring traits that have undergone extensive evaluation in GWAS including lipids, obesity, type II diabetes, stroke, and various cancers. More information about the PAGE network can be found at http://www.pagestudy.org/ .
The study of epidemics of heritable diseases and knowledge about the genetic architecture of complex human traits has developed rapidly in the last two decades. These advances have been primarily due to improvements in genotyping technology and a commensurate increase in the amount and availability of data with which to describe and understand the nature of genetic variation in human populations. During this period, genetic studies of human traits have moved away from a focus on assaying a small number of loci to identify regions of linkage to traits in family studies to samples of hundreds of thousands of study subjects assaying millions of SNPs for statistical association with traits using a variety of study designs. There is perhaps no better example of this than GWAS, a fundamental tool that has reshaped the way that studies are designed, collaborations are forged and thinking about the architecture of complex human traits.
Because of the rapid pace of discoveries resulting from GWAS and the promise of many more from newer technologies, it seems reasonable to look forward to a time when patients have their genomes genotyped or sequenced and analyzed to provide a personal profile of disease susceptibilities, drug compatibilities and other heritable traits. Approaches continue to rapidly evolve for employing GWAS but it is likely that the approach will be a viable way to discover the connections between inter-individual genetic variation and phenotypes for the foreseeable future.
We would like to thank Digna R Velez Edwards for helpful discussions on these topics.
Peer reviewer: Huixiao Hong, PhD, Division of Systems Biology, National Center for Toxicological Research, USFDA, 3900 NCTR Road, Jefferson, AR 72079, United States
S- Editor Wang JL L- Editor Roemmele A E- Editor Zheng XM
|1.||Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W. Initial sequencing and analysis of the human genome. Nature. 2001;409:860-921. [PubMed] [DOI]|
|2.||International HapMap Consortium. A haplotype map of the human genome. Nature. 2005;437:1299-1320. [PubMed] [DOI]|
|3.||Eichler EE, Nickerson DA, Altshuler D, Bowcock AM, Brooks LD, Carter NP, Church DM, Felsenfeld A, Guyer M, Lee C. Completing the map of human genetic variation. Nature. 2007;447:161-165. [PubMed] [DOI]|
|4.||Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449:851-861. [PubMed] [DOI]|
|5.||Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs RA, Peltonen L, Dermitzakis E, Schaffner SF, Yu F, Peltonen L. Integrating common and rare genetic variation in diverse human populations. Nature. 2010;467:52-58. [PubMed] [DOI]|
|6.||Weir BS. Genetic data analysis II: methods for discrete population genetic data. 2nd ed. Sunderland, MA: Sinauer Associates Inc 1996; 91-140.|
|7.||International HapMap Consortium. The International HapMap Project. Nature. 2003;426:789-796. [PubMed] [DOI]|
|8.||Barrett JC, Cardon LR. Evaluating coverage of genome-wide association studies. Nat Genet. 2006;38:659-662. [PubMed] [DOI]|
|9.||Cardon LR, Bell JI. Association study designs for complex diseases. Nat Rev Genet. 2001;2:91-99. [PubMed] [DOI]|
|10.||Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308:385-389. [PubMed] [DOI]|
|11.||Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V, Bailey R, Nejentsev S, Field SF, Payne F. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat Genet. 2007;39:857-864. [PubMed] [DOI]|
|12.||Smyth DJ, Cooper JD, Bailey R, Field S, Burren O, Smink LJ, Guja C, Ionescu-Tirgoviste C, Widmer B, Dunger DB. A genome-wide association study of nonsynonymous SNPs identifies a type 1 diabetes locus in the interferon-induced helicase (IFIH1) region. Nat Genet. 2006;38:617-619. [PubMed] [DOI]|
|13.||Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, Styrkarsdottir U, Gretarsdottir S, Emilsson V, Ghosh S. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007;39:770-775. [PubMed] [DOI]|
|14.||Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JR, Rayner NW, Freathy RM. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007;316:1336-1341. [PubMed] [DOI]|
|15.||Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316:1331-1336. [PubMed] [DOI]|
|16.||Duerr RH, Taylor KD, Brant SR, Rioux JD, Silverberg MS, Daly MJ, Steinhart AH, Abraham C, Regueiro M, Griffiths A. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science. 2006;314:1461-1463. [PubMed] [DOI]|
|17.||Rioux JD, Xavier RJ, Taylor KD, Silverberg MS, Goyette P, Huett A, Green T, Kuballa P, Barmada MM, Datta LW. Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease pathogenesis. Nat Genet. 2007;39:596-604. [PubMed] [DOI]|
|18.||Barrett JC, Hansoul S, Nicolae DL, Cho JH, Duerr RH, Rioux JD, Brant SR, Silverberg MS, Taylor KD, Barmada MM. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nat Genet. 2008;40:955-962. [PubMed] [DOI]|
|19.||Antoniou AC, Wang X, Fredericksen ZS, McGuffog L, Tarrell R, Sinilnikova OM, Healey S, Morrison J, Kartsonaki C, Lesnick T. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet. 2010;42:885-892. [PubMed] [DOI]|
|20.||Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, Willer CJ, Jackson AU, Vedantam S, Raychaudhuri S. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010;467:832-838. [PubMed] [DOI]|
|21.||Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Allen HL, Lindgren CM, Luan J, Mägi R. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42:937-948. [PubMed] [DOI]|
|22.||Hardy J, Singleton A. Genomewide association studies and human disease. N Engl J Med. 2009;360:1759-1768. [PubMed] [DOI]|
|23.||Manolio TA, Brooks LD, Collins FS. A HapMap harvest of insights into the genetics of common disease. J Clin Invest. 2008;118:1590-1605. [PubMed] [DOI]|
|24.||Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559-575. [PubMed] [DOI]|
|25.||Pe'er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol. 2008;32:381-385. [PubMed] [DOI]|
|26.||Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273:1516-1517. [PubMed] [DOI]|
|27.||Gao X, Becker LC, Becker DM, Starmer JD, Province MA. Avoiding the high Bonferroni penalty in genome-wide association studies. Genet Epidemiol. 2010;34:100-105. [PubMed] [DOI]|
|28.||Gao X. Multiple testing corrections for imputed SNPs. Genet Epidemiol. 2011;35:154-158. [PubMed] [DOI]|
|29.||Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661-678. [PubMed] [DOI]|
|30.||Psaty BM, O'Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI, Uitterlinden AG, Harris TB, Witteman JC, Boerwinkle E. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2:73-80. [PubMed] [DOI]|
|31.||Hindorff LA, MacArthur J, Wise A, Junkins HA, Hall PN, Klemm AK, Manolio TA. A Catalog of Published Genome-Wide Association Studies. Accessed June 01, 2011. Available from: http://www.genome.gov/gwastudies.|
|32.||Evangelou E, Maraganore DM, Ioannidis JP. Meta-analysis in genome-wide association datasets: strategies and application in Parkinson disease. PLoS One. 2007;2:e196. [PubMed] [DOI]|
|33.||Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316:1341-1345. [PubMed] [DOI]|
|34.||Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, de Bakker PI, Abecasis GR, Almgren P, Andersen G. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40:638-645. [PubMed] [DOI]|
|35.||Barrett JC, Clayton DG, Concannon P, Akolkar B, Cooper JD, Erlich HA, Julier C, Morahan G, Nerup J, Nierras C. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet. 2009;41:703-707. [PubMed] [DOI]|
|36.||Köttgen A, Pattaro C, Böger CA, Fuchsberger C, Olden M, Glazer NL, Parsa A, Gao X, Yang Q, Smith AV. New loci associated with kidney function and chronic kidney disease. Nat Genet. 2010;42:376-384. [PubMed] [DOI]|
|37.||Ikram MK, Sim X, Jensen RA, Cotch MF, Hewitt AW, Ikram MA, Wang JJ, Klein R, Klein BE, Breteler MM. Four novel Loci (19q13, 6q24, 12q24, and 5q14) influence the microcirculation in vivo. PLoS Genet. 2010;6:e1001184. [PubMed] [DOI]|
|38.||Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466:707-713. [PubMed] [DOI]|
|39.||Saxena R, Hivert MF, Langenberg C, Tanaka T, Pankow JS, Vollenweider P, Lyssenko V, Bouatia-Naji N, Dupuis J, Jackson AU. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet. 2010;42:142-148. [PubMed] [DOI]|
|40.||Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42:105-116. [PubMed] [DOI]|
|41.||Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, Glazer NL, Morrison AC, Johnson AD, Aspelund T. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009;41:677-687. [PubMed] [DOI]|
|42.||Benjamin EJ, Rice KM, Arking DE, Pfeufer A, van Noord C, Smith AV, Schnabel RB, Bis JC, Boerwinkle E, Sinner MF. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat Genet. 2009;41:879-881. [PubMed] [DOI]|
|43.||Franke A, McGovern DP, Barrett JC, Wang K, Radford-Smith GL, Ahmad T, Lees CW, Balschun T, Lee J, Roberts R. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat Genet. 2010;42:1118-1125. [PubMed] [DOI]|
|44.||Kraja AT. Metabolic syndrome - modern pharmacological, genetic, clinical and environmental contributions. Endocr Metab Immune Disord Drug Targets. 2010;10:84-85. [PubMed] [DOI]|
|45.||Lanktree MB, Guo Y, Murtaza M, Glessner JT, Bailey SD, Onland-Moret NC, Lettre G, Ongen H, Rajagopalan R, Johnson T. Meta-analysis of Dense Genecentric Association Studies Reveals Common and Uncommon Variants Associated with Height. Am J Hum Genet. 2011;88:6-18. [PubMed] [DOI]|
|46.||Kato N, Takeuchi F, Tabara Y, Kelly TN, Go MJ, Sim X, Tay WT, Chen CH, Zhang Y, Yamamoto K. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat Genet. 2011;43:531-538. [PubMed] [DOI]|
|47.||Hancock DB, Eijgelsheim M, Wilk JB, Gharib SA, Loehr LR, Marciante KD, Franceschini N, van Durme YM, Chen TH, Barr RG. Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function. Nat Genet. 2010;42:45-52. [PubMed] [DOI]|
|48.||1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061-1073. [PubMed] [DOI]|
|49.||Li C, Li M, Long JR, Cai Q, Zheng W. Evaluating cost efficiency of SNP chips in genome-wide association studies. Genet Epidemiol. 2008;32:387-395. [PubMed] [DOI]|
|50.||Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34:816-834. [PubMed] [DOI]|
|51.||Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39:906-913. [PubMed] [DOI]|
|53.||Liu DJ, Leal SM. Replication strategies for rare variant complex trait association studies via next-generation sequencing. Am J Hum Genet. 2010;87:790-801. [PubMed] [DOI]|
|54.||Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008;83:311-321. [PubMed] [DOI]|
|55.||Madsen BE, Browning SR. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 2009;5:e1000384. [PubMed] [DOI]|
|56.||Speliotes EK, Yerges-Armstrong LM, Wu J, Hernaez R, Kim LJ, Palmer CD, Gudnason V, Eiriksdottir G, Garcia ME, Launer LJ. Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits. PLoS Genet. 2011;7:e1001324. [PubMed] [DOI]|
|57.||Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, Najjar SS, Zhao JH, Heath SC, Eyheramendy S. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009;41:666-676. [PubMed] [DOI]|
|58.||Musunuru K, Lettre G, Young T, Farlow DN, Pirruccello JP, Ejebe KG, Keating BJ, Yang Q, Chen MH, Lapchyk N. Candidate gene association resource (CARe): design, methods, and proof of concept. Circ Cardiovasc Genet. 2010;3:267-275. [PubMed] [DOI]|
|59.||Coronary Artery Disease (C4D) Genetics Consortium. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat Genet. 2011;43:339-344. [PubMed] [DOI]|
|60.||Tishkoff SA, Reed FA, Friedlaender FR, Ehret C, Ranciaro A, Froment A, Hirbo JB, Awomoyi AA, Bodo JM, Doumbo O. The genetic structure and history of Africans and African Americans. Science. 2009;324:1035-1044. [PubMed] [DOI]|
|61.||Pulit SL, Voight BF, de Bakker PI. Multiethnic genetic association studies improve power for locus discovery. PLoS One. 2010;5:e12600. [PubMed] [DOI]|
|62.||Unoki H, Takahashi A, Kawaguchi T, Hara K, Horikoshi M, Andersen G, Ng DP, Holmkvist J, Borch-Johnsen K, Jørgensen T. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet. 2008;40:1098-1102. [PubMed] [DOI]|
|63.||Yasuda K, Miyake K, Horikawa Y, Hara K, Osawa H, Furuta H, Hirota Y, Mori H, Jonsson A, Sato Y. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat Genet. 2008;40:1092-1097. [PubMed] [DOI]|
|64.||McCarthy MI. Casting a wider net for diabetes susceptibility genes. Nat Genet. 2008;40:1039-1040. [PubMed] [DOI]|
|65.||Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445:881-885. [PubMed] [DOI]|
|66.||Fung HC, Scholz S, Matarin M, Simón-Sánchez J, Hernandez D, Britton A, Gibbs JR, Langefeld C, Stiegert ML, Schymick J. Genome-wide genotyping in Parkinson's disease and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol. 2006;5:911-916. [PubMed] [DOI]|
|67.||Maraganore DM, de Andrade M, Lesnick TG, Strain KJ, Farrer MJ, Rocca WA, Pant PV, Frazer KA, Cox DR, Ballinger DG. High-resolution whole-genome association study of Parkinson disease. Am J Hum Genet. 2005;77:685-693. [PubMed] [DOI]|
|68.||Pankratz N, Wilk JB, Latourelle JC, DeStefano AL, Halter C, Pugh EW, Doheny KF, Gusella JF, Nichols WC, Foroud T. Genomewide association study for susceptibility genes contributing to familial Parkinson disease. Hum Genet. 2009;124:593-605. [PubMed] [DOI]|
|69.||Farrer M, Maraganore DM, Lockhart P, Singleton A, Lesnick TG, de Andrade M, West A, de Silva R, Hardy J, Hernandez D. alpha-Synuclein gene haplotypes are associated with Parkinson's disease. Hum Mol Genet. 2001;10:1847-1851. [PubMed] [DOI]|
|70.||Krüger R, Vieira-Saecker AM, Kuhn W, Berg D, Müller T, Kühnl N, Fuchs GA, Storch A, Hungs M, Woitalla D. Increased susceptibility to sporadic Parkinson's disease by a certain combined alpha-synuclein/apolipoprotein E genotype. Ann Neurol. 1999;45:611-617. [PubMed]|
|71.||Maraganore DM, de Andrade M, Elbaz A, Farrer MJ, Ioannidis JP, Krüger R, Rocca WA, Schneider NK, Lesnick TG, Lincoln SJ. Collaborative analysis of alpha-synuclein gene promoter variability and Parkinson disease. JAMA. 2006;296:661-670. [PubMed] [DOI]|
|72.||McCulloch CC, Kay DM, Factor SA, Samii A, Nutt JG, Higgins DS, Griffith A, Roberts JW, Leis BC, Montimurro JS. Exploring gene-environment interactions in Parkinson's disease. Hum Genet. 2008;123:257-265. [PubMed] [DOI]|
|73.||Mueller JC, Fuchs J, Hofer A, Zimprich A, Lichtner P, Illig T, Berg D, Wüllner U, Meitinger T, Gasser T. Multiple regions of alpha-synuclein are associated with Parkinson's disease. Ann Neurol. 2005;57:535-541. [PubMed] [DOI]|
|74.||Myhre R, Toft M, Kachergus J, Hulihan MM, Aasly JO, Klungland H, Farrer MJ. Multiple alpha-synuclein gene polymorphisms are associated with Parkinson's disease in a Norwegian population. Acta Neurol Scand. 2008;118:320-327. [PubMed] [DOI]|
|75.||Sutherland GT, Halliday GM, Silburn PA, Mastaglia FL, Rowe DB, Boyle RS, O'Sullivan JD, Ly T, Wilton SD, Mellick GD. Do polymorphisms in the familial Parkinsonism genes contribute to risk for sporadic Parkinson's disease? Mov Disord. 2009;24:833-838. [PubMed] [DOI]|
|76.||Fidani L, Kalinderi K, Bostantjopoulou S, Clarimon J, Goulas A, Katsarou Z, Hardy J, Kotsis A. Association of the Tau haplotype with Parkinson's disease in the Greek population. Mov Disord. 2006;21:1036-1039. [PubMed] [DOI]|
|77.||Fung HC, Xiromerisiou G, Gibbs JR, Wu YR, Eerola J, Gourbali V, Hellström O, Chen CM, Duckworth J, Papadimitriou A. Association of tau haplotype-tagging polymorphisms with Parkinson's disease in diverse ethnic Parkinson's disease cohorts. Neurodegener Dis. 2006;3:327-333. [PubMed] [DOI]|
|78.||Goris A, Williams-Gray CH, Clark GR, Foltynie T, Lewis SJ, Brown J, Ban M, Spillantini MG, Compston A, Burn DJ. Tau and alpha-synuclein in susceptibility to, and dementia in, Parkinson's disease. Ann Neurol. 2007;62:145-153. [PubMed] [DOI]|
|79.||Healy DG, Abou-Sleiman PM, Lees AJ, Casas JP, Quinn N, Bhatia K, Hingorani AD, Wood NW. Tau gene and Parkinson's disease: a case-control study and meta-analysis. J Neurol Neurosurg Psychiatry. 2004;75:962-965. [PubMed] [DOI]|
|80.||Kwok JB, Teber ET, Loy C, Hallupp M, Nicholson G, Mellick GD, Buchanan DD, Silburn PA, Schofield PR. Tau haplotypes regulate transcription and are associated with Parkinson's disease. Ann Neurol. 2004;55:329-334. [PubMed] [DOI]|
|81.||Levecque C, Elbaz A, Clavel J, Vidal JS, Amouyel P, Alpérovitch A, Tzourio C, Chartier-Harlin MC. Association of polymorphisms in the Tau and Saitohin genes with Parkinson's disease. J Neurol Neurosurg Psychiatry. 2004;75:478-480. [PubMed] [DOI]|
|82.||Mamah CE, Lesnick TG, Lincoln SJ, Strain KJ, de Andrade M, Bower JH, Ahlskog JE, Rocca WA, Farrer MJ, Maraganore DM. Interaction of alpha-synuclein and tau genotypes in Parkinson's disease. Ann Neurol. 2005;57:439-443. [PubMed] [DOI]|
|83.||Martin ER, Scott WK, Nance MA, Watts RL, Hubble JP, Koller WC, Lyons K, Pahwa R, Stern MB, Colcher A. Association of single-nucleotide polymorphisms of the tau gene with late-onset Parkinson disease. JAMA. 2001;286:2245-2250. [PubMed] [DOI]|
|84.||Scott WK, Nance MA, Watts RL, Hubble JP, Koller WC, Lyons K, Pahwa R, Stern MB, Colcher A, Hiner BC. Complete genomic screen in Parkinson disease: evidence for multiple genes. JAMA. 2001;286:2239-2244. [PubMed] [DOI]|
|85.||Skipper L, Wilkes K, Toft M, Baker M, Lincoln S, Hulihan M, Ross OA, Hutton M, Aasly J, Farrer M. Linkage disequilibrium and association of MAPT H1 in Parkinson disease. Am J Hum Genet. 2004;75:669-677. [PubMed] [DOI]|
|86.||Vandrovcova J, Pittman AM, Malzer E, Abou-Sleiman PM, Lees AJ, Wood NW, de Silva R. Association of MAPT haplotype-tagging SNPs with sporadic Parkinson's disease. Neurobiol Aging. 2009;30:1477-1482. [PubMed] [DOI]|
|87.||Winkler S, König IR, Lohmann-Hedrich K, Vieregge P, Kostic V, Klein C. Role of ethnicity on the association of MAPT H1 haplotypes and subhaplotypes in Parkinson's disease. Eur J Hum Genet. 2007;15:1163-1168. [PubMed] [DOI]|
|88.||Zabetian CP, Hutter CM, Factor SA, Nutt JG, Higgins DS, Griffith A, Roberts JW, Leis BC, Kay DM, Yearout D. Association analysis of MAPT H1 haplotype and subhaplotypes in Parkinson's disease. Ann Neurol. 2007;62:137-144. [PubMed] [DOI]|
|89.||Zappia M, Annesi G, Nicoletti G, Serra P, Arabia G, Pugliese P, Messina D, Caracciolo M, Romeo N, Annesi F. Association of tau gene polymorphism with Parkinson's disease. Neurol Sci. 2003;24:223-224. [PubMed] [DOI]|
|90.||Haugarvoll K, Toft M, Ross OA, Stone JT, Heckman MG, White LR, Lynch T, Gibson JM, Wszolek ZK, Uitti RJ. ELAVL4, PARK10, and the Celts. Mov Disord. 2007;22:585-587. [PubMed] [DOI]|
|91.||Li Y, Schrodi S, Rowland C, Tacey K, Catanese J, Grupe A. Genetic evidence for ubiquitin-specific proteases USP24 and USP40 as candidate genes for late-onset Parkinson disease. Hum Mutat. 2006;27:1017-1023. [PubMed] [DOI]|
|92.||Oliveira SA, Li YJ, Noureddine MA, Zuchner S, Qin X, Pericak-Vance MA, Vance JM. Identification of risk and age-at-onset genes on chromosome 1p in Parkinson disease. Am J Hum Genet. 2005;77:252-264. [PubMed] [DOI]|
|93.||Dehghan A, Yang Q, Peters A, Basu S, Bis JC, Rudnicka AR, Kavousi M, Chen MH, Baumert J, Lowe GD. Association of novel genetic Loci with circulating fibrinogen levels: a genome-wide association study in 6 population-based cohorts. Circ Cardiovasc Genet. 2009;2:125-133. [PubMed] [DOI]|
|94.||Noureddine MA, Qin XJ, Oliveira SA, Skelly TJ, van der Walt J, Hauser MA, Pericak-Vance MA, Vance JM, Li YJ. Association between the neuron-specific RNA-binding protein ELAVL4 and Parkinson disease. Hum Genet. 2005;117:27-33. [PubMed] [DOI]|
|95.||Kurth JH, Kurth MC, Poduslo SE, Schwankhaus JD. Association of a monoamine oxidase B allele with Parkinson's disease. Ann Neurol. 1993;33:368-372. [PubMed] [DOI]|
|96.||Rubinsztein DC, Hanlon CS, Irving RM, Goodburn S, Evans DG, Kellar-Wood H, Xuereb JH, Bandmann O, Harding AE. Apo E genotypes in multiple sclerosis, Parkinson's disease, schwannomas and late-onset Alzheimer's disease. Mol Cell Probes. 1994;8:519-525. [PubMed] [DOI]|
|97.||Autere J, Moilanen JS, Finnilä S, Soininen H, Mannermaa A, Hartikainen P, Hallikainen M, Majamaa K. Mitochondrial DNA polymorphisms as risk factors for Parkinson's disease and Parkinson's disease dementia. Hum Genet. 2004;115:29-35. [PubMed] [DOI]|
|98.||Gaweda-Walerych K, Maruszak A, Safranow K, Bialecka M, Klodowska-Duda G, Czyzewski K, Slawek J, Rudzinska M, Styczynska M, Opala G. Mitochondrial DNA haplogroups and subhaplogroups are associated with Parkinson's disease risk in a Polish PD cohort. J Neural Transm. 2008;115:1521-1526. [PubMed] [DOI]|
|99.||Ghezzi D, Marelli C, Achilli A, Goldwurm S, Pezzoli G, Barone P, Pellecchia MT, Stanzione P, Brusa L, Bentivoglio AR. Mitochondrial DNA haplogroup K is associated with a lower risk of Parkinson's disease in Italians. Eur J Hum Genet. 2005;13:748-752. [PubMed] [DOI]|
|100.||Huerta C, Castro MG, Coto E, Blázquez M, Ribacoba R, Guisasola LM, Salvador C, Martínez C, Lahoz CH, Alvarez V. Mitochondrial DNA polymorphisms and risk of Parkinson's disease in Spanish population. J Neurol Sci. 2005;236:49-54. [PubMed] [DOI]|
|101.||Kösel S, Grasbon-Frodl EM, Mautsch U, Egensperger R, von Eitzen U, Frishman D, Hofmann S, Gerbitz KD, Mehraein P, Graeber MB. Novel mutations of mitochondrial complex I in pathologically proven Parkinson disease. Neurogenetics. 1998;1:197-204. [PubMed]|
|102.||Pyle A, Foltynie T, Tiangyou W, Lambert C, Keers SM, Allcock LM, Davison J, Lewis SJ, Perry RH, Barker R. Mitochondrial DNA haplogroup cluster UKJT reduces the risk of PD. Ann Neurol. 2005;57:564-567. [PubMed] [DOI]|
|103.||Ross OA, McCormack R, Maxwell LD, Duguid RA, Quinn DJ, Barnett YA, Rea IM, El-Agnaf OM, Gibson JM, Wallace A. mt4216C variant in linkage with the mtDNA TJ cluster may confer a susceptibility to mitochondrial dysfunction resulting in an increased risk of Parkinson's disease in the Irish. Exp Gerontol. 2003;38:397-405. [PubMed] [DOI]|
|104.||van der Walt JM, Nicodemus KK, Martin ER, Scott WK, Nance MA, Watts RL, Hubble JP, Haines JL, Koller WC, Lyons K. Mitochondrial polymorphisms significantly reduce the risk of Parkinson disease. Am J Hum Genet. 2003;72:804-811. [PubMed] [DOI]|
|105.||Edwards TL, Scott WK, Almonte C, Burt A, Powell EH, Beecham GW, Wang L, Züchner S, Konidari I, Wang G. Genome-wide association study confirms SNPs in SNCA and the MAPT region as common risk factors for Parkinson disease. Ann Hum Genet. 2010;74:97-109. [PubMed] [DOI]|
|106.||Saad M, Lesage S, Saint-Pierre A, Corvol JC, Zelenika D, Lambert JC, Vidailhet M, Mellick GD, Lohmann E, Durif F. Genome-wide association study confirms BST1 and suggests a locus on 12q24 as the risk loci for Parkinson's disease in the European population. Hum Mol Genet. 2011;20:615-627. [PubMed] [DOI]|
|107.||Satake W, Nakabayashi Y, Mizuta I, Hirota Y, Ito C, Kubo M, Kawaguchi T, Tsunoda T, Watanabe M, Takeda A. Genome-wide association study identifies common variants at four loci as genetic risk factors for Parkinson's disease. Nat Genet. 2009;41:1303-1307. [PubMed] [DOI]|
|108.||Simón-Sánchez J, Schulte C, Bras JM, Sharma M, Gibbs JR, Berg D, Paisan-Ruiz C, Lichtner P, Scholz SW, Hernandez DG. Genome-wide association study reveals genetic risk underlying Parkinson's disease. Nat Genet. 2009;41:1308-1312. [PubMed] [DOI]|
|109.||Simón-Sánchez J, van Hilten JJ, van de Warrenburg B, Post B, Berendse HW, Arepalli S, Hernandez DG, de Bie RM, Velseboer D, Scheffer H. Genome-wide association study confirms extant PD risk loci among the Dutch. Eur J Hum Genet. 2011;19:655-661. [PubMed] [DOI]|
|110.||Spencer CC, Plagnol V, Strange A, Gardner M, Paisan-Ruiz C, Band G, Barker RA, Bellenguez C, Bhatia K, Blackburn H. Dissection of the genetics of Parkinson's disease identifies an additional association 5' of SNCA and multiple associated haplotypes at 17q21. Hum Mol Genet. 2011;20:345-353. [PubMed] [DOI]|
|111.||Field AE, Coakley EH, Must A, Spadano JL, Laird N, Dietz WH, Rimm E, Colditz GA. Impact of overweight on the risk of developing common chronic diseases during a 10-year period. Arch Intern Med. 2001;161:1581-1586. [PubMed] [DOI]|
|112.||Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity. JAMA. 1999;282:1523-1529. [PubMed] [DOI]|
|113.||Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999-2004. JAMA. 2006;295:1549-1555. [PubMed] [DOI]|
|114.||Flegal KM, Ezzati TM, Harris MI, Haynes SG, Juarez RZ, Knowler WC, Perez-Stable EJ, Stern MP. Prevalence of diabetes in Mexican Americans, Cubans, and Puerto Ricans from the Hispanic Health and Nutrition Examination Survey, 1982-1984. Diabetes Care. 1991;14:628-638. [PubMed] [DOI]|
|115.||Haffner SM, Fong D, Stern MP, Pugh JA, Hazuda HP, Patterson JK, van Heuven WA, Klein R. Diabetic retinopathy in Mexican Americans and non-Hispanic whites. Diabetes. 1988;37:878-884. [PubMed] [DOI]|
|116.||Hazuda HP, Mitchell BD, Haffner SM, Stern MP. Obesity in Mexican American subgroups: findings from the San Antonio Heart Study. Am J Clin Nutr. 1991;53:1529S-1534S. [PubMed]|
|117.||Pendergrass SA, Brown-Gentry K, Dudek SM, Torstenson ES, Ambite JL, Avery CL, Buyske S, Cai C, Fesinmeyer MD, Haiman C. The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. Genet Epidemiol. 2011;35:410-422. [PubMed] [DOI]|