HomeList of TitleSearchContact Us
Open Journal of Hematology

ISSN: 2075-907X
Volume 8, 2017

Genomic markers associated with insulin resistance and Type 2 Diabetes in African Americans: A review.

Crystal Heath Dodson*, Lisa Maness
School of Nursing, College of Health and Human Services, University of North Carolina, USA

DOI: 10.13055/ojhmt_8_1_2.170302



Corresponding Address:
* School of Nursing, College of Health and Human Services, University of North Carolina at Wilmington, McNeill Hall 3030, USA; Phone: (336) 962-7877; Email: dodsonc@uncw.edu

Abstract

Purpose:

Personalized medicine is exponentially advancing. In order to optimize an individual's healthcare, their individual genetic makeup, as well as environmental profiles, need to be quantified. The prevalence of type 2 diabetes among African Ancestry is alarmingly high within northwester North Carolina, with preliminary studies indicating that levels are even higher than the general U.S. population. Our overall goal is to identify genetic markers that are linked to type 2 diabetes and insulin resistance within this population.

Materials and Methods:

A literature review was conducted to determine which genomic markers have been associated with type 2 diabetes and insulin resistance with the African population.

Results and Conclusions:

Thirteen studies reported genomic markers that have been associated with type 2 diabetes within the African Ancestry. A compilation of these genomic markers are presented within this review.

Keywords: Genomics, biomarkers, diabetes, African ancestry, genomic markers.

© Dodson et al.; licensee Ross Science Publishers
open-access license: ROSS Open Access articles will be distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided that the original work will always be cited properly.

Article Processing History:
Receiving date: 10-1-2017
Acceptance date: 2-3-2017
Electronic publication date: 2-3-2017
Year of Publication: 2017




Background

Type 2 diabetes mellitus is the seventh leading cause of death in the United States [1]. Patients with type 2 diabetes are insulin resistant and suffer the effects of hyperglycemia along with secondary effects that include high blood pressure, cardiovascular disease, stroke, blindness, and increased risk of cancer, placing these individuals at greater risk for all of the leading causes of death. It is estimated that 29.1 million people are affected by this disease and the rate of diabetes is estimated to 53.1 million people by 2025 [2]. The total estimated cost of diabetes in the United States in 2012 was 245 billion dollars [3]. Although type 2 diabetes affects all ethnicities, Hispanics, Native Americans, and African Ancestry (AA) are most vulnerable. Thirteen point two million non-Hispanic blacks are affected by diabetes in the United States. The risk for diabetes in AAs is 77 percent higher than non-Hispanic white Americans [4]. This indicates an overwhelming need for research that can ease the health and financial burden that this disease places on the AA population.

On January 20, 2015, President Obama announced the Precision Medicine Initiative® [5]. Precision medicine is an emerging method for disease prevention and treatment that considers individual variations in genes, environment, and lifestyle. This initiative proposes to generate evidence-based practice needed to translate the notion of precision medicine into clinical practice. Studies have identified a genetic predisposition among AA for insulin resistance, but further studies are necessary to substantiate this claim. The aim of this project is to identify common genetic markers found in AA populations with type 2 diabetes.


Methods

To identify eligible studies, a search was conducted within the PUBMED database for English language articles published at any point in time. Key words included genome-wide association study, single nucleotide polymorphism(s), African American, African Ancestry, non-Hispanic blacks, type 2 diabetes, diabetes, adult-onset diabetes, and non-insulin dependent diabetes. Reference lists from articles retrieved from search engines were searched by hand.

Initially, the titles and abstracts of all the articles were reviewed to determine eligibility, then the full text of the articles that met eligibility were assessed. Studies included in this review met the following criteria: quantitative study, assessments of type 2 diabetes as opposed to type 1 diabetes, and inclusion of African American population. Studies were included regardless of study type or quality. However, methodological issues were addressed throughout the review in order for the reader to weigh the outcomes of each study accurately.

A manual data extraction tool was used to pull out pertinent data from the articles. The data extracted included the purpose statement, study design, sample characteristics and size, country of origin, journal, findings, and limitations. Then a matrix of all the articles included in the review was developed.


Findings

The total number of articles resulting from the electronic and manual search was 30. After reviewing each article based on duplication and inclusion criteria, the final number of research reports identified was 13 studies, as referenced in Table 1. The earliest study was published in 2007 and the latest in 2014. Six of the articles reported only genes associated with type 2 diabetes within the AA population. Four of the articles reported on single nucleotide polymorphisms associated with type 2 diabetes in AAs. Finally, three of the twelve studies reported on both genomic markers. Additionally, almost all of the studies (n=12) were conducted in the United States, with one conducted in Canada.

Table 1.

Human genome studies have identified association among AA and type 2 diabetes. In a genome-wide association study involving African Americans, Elbein et al. found evidence that chromosome 2 is linked to the development of type 2 diabetes [6]. Alternatively, Jeff et al. identified 13 single nucleotide polymorphism correlated with genes TCIRG1, CHKA, and ALDH3B1 on chromosome 11 and type 2 diabetes in AA populations [7]. Landberg et al. noted that three of the 32 genome-wide associated search (GWAS)-derived single nucleotide polymorphisms (SNPs) showed nominally significant association with type 2 diabetes in the AA cohort [8]. Two of the replicated SNPs were rs864745 in JAZF1 gene and rs10490072 in BCL11A gene. Additionally, Leak et al. reported that type 2 diabetes was observed among 4 SNPs: rs2021785, rs1609659, rs4814597, and rs2269023, while Long revealed that seven of 29 SNPs examined were associated with type 2 diabetes risk, including rs6769511 of the IGF2BP2 gene, 2 SNPs in the WFS1 gene (rs4689388 and rs1801214), rs7903146 of the TCF7L2 gene, and 3 SNPs in the KCNQ1 gene (rs231362, rs2237892, and rs2237897) [9, 10]. Notably, the association for rs7903146 reached significance in the GWAS. Among SNPs determined by other researchers to be genome-wide significant, Palmer et al. found a single SNP, rs7560163, reaching genome-wide significance with 4 other SNPs associated with type 2 diabetes- rs7542900, rs4659485, rs2722769, rs7107217; these may represent novel loci that contribute to Type 2 Diabetes [11].

Although type 2 diabetes affects all ages, genders, and ethnicities, some groups are more vulnerable than others, such as Hispanics, Native Americans, and African Ancestry (AA). More than 13 million non-Hispanic blacks are affected by diabetes in the United States. The risk for diabetes in AA is 77 percent higher than non-Hispanic white Americans [12]. Studies have identified genetic association among AA and type 2 diabetes. For example, Keaton et al. suggested that there are ethnic-specific differences in genetic architecture underlying type 2 diabetes, and that these differences complicate the understanding of how risk allele load impacts disease susceptibility [13]. Cheng et al. noted that there were two potential loci for type 2 diabetes, but concluded that no single gene is sufficient to explain a large portion of the observed population difference in risk of diabetes since there undoubtedly is a complex interplay [14]. Research has shown that AA populations with diabetes mellitus have specific genes that those without the disease do not contain (Table 1). For example, Bressler et al. found rs1421085C to be protective against diabetes in AAs while Grant et al. found rs3751812 to be the only SNP conferring risks to the AA population [15, 16]. Long et al. found rs7903146 to have genome-wide significance for diabetes in AAs [10]. Other studies involving type 2 diabetes and African Americans have been performed, as well, that indicate that other genes may be important to the disease or issues associated with the disease [17, 18, 19] (Table 1).


Discussion

AA have nearly twice the prevalence of type 2 diabetes than European-Americans (EA) [7]. Multiple genome-wide studies have shown that there are SNPs present in AA with the disease when compared to EA as well as SNPs present more often in AA with type 2 diabetes than those without the disease. There are also SNPs that correlate with protecting AA from type 2 diabetes. SNPs that has been shown in more than one study to correlate with a genome-wide significance for type 2 diabetes in AA is rs7903146 [10, 18]. Another SNP found to have genome-wide significance for AA with type 2 diabetes is rs7560163. These and other SNPs found more often in populations with type 2 diabetes should be a focus in personalizing treatments for the disease and serving patients on a case by case basis. For example, patients who carry a gene that correlates with higher risk can be made aware of that risk and learn to improve their lifestyles and dietary habits with the goal of preventing the diagnosis altogether. Alternatively, for individuals who have already developed the disease, those with a particular SNP associated with type 2 diabetes may respond better to a specific treatment regimen over another.

However, there are challenges to personalized medicine that include processing large amounts of data, understanding the overall impact of the various SNPs, correlating genotypes with phenotypes, and understanding how treatments can be optimized based on this information [20]. It is clear that more research needs to be carried out, with consideration of these challenges, to determine which treatment approaches would better serve type 2 diabetes patients with each of the at-risk SNPs. It may be that patients with one SNP are better approached using lifestyle changes while another may require medications and more extensive interventions. There is little to no research published that delves into optimal treatments for each SNP found to correlate significantly with the disease.

There are also ethical concerns over the use of individual genomes being used in personalized medicine [21, 22]. Many people are not comfortable with sharing this private information and have fears of losing confidentiality. Just as subjects participating in genome research need to be assured that they will have complete privacy after consenting, so do patients whose SNPs are used in personalized medicine [22]. The benefits of personalized medicine could be extensive as long as patient confidentiality is not placed at risk. Benefits should not put a patient at-risk for being turned away by insurance companies, for example. Fears such as these were addressed in the Genetic Information Nondiscrimination Act of 2008 [23]. This act protects Americans against discrimination from both insurance companies and employers. Furthermore, consent must be obtained when research involving the genome is used. Researchers must explain how information will be used and indicate how confidentiality will be maintained. Insurance companies may not use genomic information to set premiums or determine eligibility just as employers may not use it to hire, fire, determine salaries, or decide on promotions.


Future perspective

Multiple SNPs have been shown to be correlated with AA at risk for type 2 diabetes. This information may be useful to individuals for understanding risk factors for the disease as well as medical practitioners who are treating these patients. However, most patients with type 2 diabetes or with the hereditary risk, do not know which SNPs they carry that put them at risk. This information could allow people to improve their lifestyles before the disease is in full swing. Another key missing piece is understanding what treatments or preventions may best serve those with a particular SNP. There have been little to no advances made in science toward reaching this goal. Thus, more research is necessary so that links can be made between high-risk genes and receiving the necessary treatment to avoid the condition altogether or reduce the symptoms.


References

[1] American Diabetes AssociationStatistics about Diabetes. http://www.diabetes.org/diabetes-basics/statistics/
[2] Rowley W, Bezold C. Creating public awareness: state 2025 diabetes forecasts. Popul Health Manag. 2012; 15: 194–200. [PMID: 22283662] [DOI: 10.1089/pop.2011.0053]
[3] Center for Disease Control and PreventionNational Diabetes Statistic Report 2014. http://www.cdc.gov/diabetes/data/statistics/2014statisticsreport.html
[4] Chow E, Foster H, Gonzalez V, McIver L. The disparate impact of diabetes on racial/ethnic minority populations. Clin Diabetes. 2012; 30: 130–3. [DOI: 10.2337/diaclin.30.3.130]
[5] National Institutes of HealthPrecision Medicine Initiative. http://www.nih.gov/precision-medicine-initiative-cohort-program
[6] Elbein S, Das S, Hallman D, Hanis CL, Hasstedt SJ. Genome-wide linkage and admixture mapping of type 2 diabetes in African American families from the American Diabetes Association GENNID (Genetics of NIDDM) study cohort. Diabetes. 2009; 58: 268–74. [PMID: 18840782] [PMCID: PMC2606884] [DOI: 10.2337/db08-0931]
[7] Jeff J, Armstrong L, Ritchie M, Denny JC, Kho AN, Basford MA, Wolf WA, Pacheco JA, Li R, Chisholm RL, Roden DM, Hayes MG, Crawford DC. Admixture mapping and subsequent fine-mapping suggests a biologically relevant and novel association on chromosome 11 for type 2 diabetes in Africa. PLoS One. 2014; 9: e86931. [PMID: 24595071] [PMCID: PMC3940426] [DOI: 10.1371/journal.pone.0086931]
[8] Langberg K, Ma L, Sharma N, Hanis CL, Elbein SC, Hasstedt SJ, Das SK. Single nucleotide polymorphisms in JAZF1 and BCL11A gene are nominally associated with type 2 diabetes in African-American families from the GENNID study. J Hum Genet. 2012; 57: 57–61. [PMID: 22113416] [PMCID: PMC3266455] [DOI: 10.1038/jhg.2011.133]
[9] Leak T, Keene K, Langefeld C, Gallagher CJ, Mychaleckyj JC, Freedman BI, Bowden DW, Rich SS, Sale MM. Association of the proprotein convertase subtilisin/kexin-type 2 (PCSK2) gene with type 2 diabetes in an African American population. Mol Genet Metab. 2007; 92: 145–50. [PMID: 17618154] [PMCID: PMC2752824] [DOI: 10.1016/j.ymgme.2007.05.014]
[10] Long J, Edwards T, Signorello L, Cai Q, Zheng W, Shu XO, Blot WJ. Evaluation of genome-wide association study-identified type 2 diabetes loci in African Americans. Am J Epidemiol. 2012; 176: 995–1001. [PMID: 23144361] [PMCID: PMC3571243] [DOI: 10.1093/aje/kws176]
[11] Palmer ND, McDonough CW, Hicks PJ, Roh BH, Wing MR, An SS, Hester JM, Cooke JN, Bostrom MA, Rudock ME, Talbert ME, Lewis JP, DIAGRAM Consortium, MAGIC Investigators, Ferrara A, Lu L, Ziegler JT, Sale MM, Divers J, Shriner D, Adeyemo A, Rotimi CN, Ng MC, Langefeld CD, Freedman BI, Bowden DW, Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Boström KB, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Couper DJ, Crawford G, Doney AS, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PR, Jørgensen T, Kao WH, Klopp N, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Perry JR, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Mohlke KL, Morris AD, Palmer CN, Pramstaller PP, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Wareham NJ, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Hu FB, Meigs JB, Pankow JS, Pedersen O, Wichmann HE, Barroso I, Florez JC, Frayling TM, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI, Soranzo N, Wheeler E, Glazer NL, Bouatia-Naji N, Mägi R, Randall J, Johnson T, Elliott P, Rybin D, Henneman P, Dehghan A, Hottenga JJ, Song K, Goel A, Egan JM, Lajunen T, Doney A, Kanoni S, Cavalcanti-Proença C, Kumari M, Timpson NJ, Zabena C, Ingelsson E, An P, O'Connell J, Luan J, Elliott A, McCarroll SA, Roccasecca RM, Pattou F, Sethupathy P, Ariyurek Y, Barter P, Beilby JP, Ben-Shlomo Y, Bergmann S, Bochud M, Bonnefond A, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead SJ, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Crisponi L, Day IN, de Geus EJ, Delplanque J, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Grundy S, Gwilliam R, Hallmans G, Hammond N, Han X, Hartikainen AL, Hayward C, Heath SC, Hercberg S, Hicks AA, Hillman DR, Hingorani AD, Hui J, Hung J, Jula A, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Mahley R, Mangino M, Manning AK, Martínez-Larrad MT, McAteer JB, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Mukherjee S, Naitza S, Neville MJ, Oostra BA, Orrù M, Pakyz R, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Seedorf U, Sharp SJ, Shields B, Sijbrands EJ, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvänen AC, Tanaka T, Tönjes A, Uitterlinden AG, van Dijk KW, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Ward KL, Watkins H, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, Borecki IB, Loos RJ, Meneton P, Magnusson PK, Nathan DM, Williams GH, Silander K, Salomaa V, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Ríos M, Lind L, Palmer LJ, Franks PW, Ebrahim S, Marmot M, Kao WH, Pramstaller PP, Wright AF, Stumvoll M, Hamsten A, Buchanan TA, Valle TT, Rotter JI, Siscovick DS, Penninx BW, Boomsma DI, Deloukas P, Spector TD, Ferrucci L, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Sladek R. A genome-wide association search for type 2 diabetes genes in African Americans. PLoS One. 2012; 7: e29202. [PMID: 22238593] [PMCID: PMC3251563] [DOI: 10.1371/journal.pone.0029202]
[12] Centers for Disease ControlAge-adjusted Rates of Diagnosed Diabetes per 100 Civilian, Non-institutionalized Population, by Race and Sex, United States, 1980-2014. http://www.cdc.gov/diabetes/statistics/prev/national/figraceethsex.htm
[13] Keaton J, Cooke-Bailey J, Palmer N, Freedman BI, Langfield CD, Ng MC, Bowden DW. A comparison of type 2 diabetes risk allele load between African Americans and European Americans. Hum Genet. 2014; 133: 1487–95. [PMID: 25273842] [PMCID: PMC4225163] [DOI: 10.1007/s00439-014-1486-5]
[14] Cheng CY, Reich D, Haiman CA, Tandon A, Patterson N, Selvin E, Akylbekova EL, Brancati FL, Coresh J, Boerwinkle E, Altshuler D, Taylor HA, Henderson BE, Wilson JG, Kao WH. African ancestry and its correlation to type 2 diabetes in African Americans: a genetic admixture analysis in three U. S. population cohorts. PLoS One. 2012; 7: e32840. [PMID: 22438884] [PMCID: PMC3306373] [DOI: 10.1371/journal.pone.0032840]
[15] Bressler J, Kao W, Pankow J, Boerwinkle E. Risk of type 2 diabetes and obesity is differentially associated with variation in FTO in whites and African-Americans in the ARIC study. PLoS One. 2010; 5: e10521. [PMID: 20502638] [PMCID: PMC2873943] [DOI: 10.1371/journal.pone.0010521]
[16] Grant SF, Li M, Bradfield JP, Kim CE, Annaiah K, Santa E, Glessner JT, Casalunovo T, Frackelton EC, Otieno FG, Shaner JL, Smith RM, Imielinski M, Eckert AW, Chiavacci RM, Berkowitz RI, Hakonarson H. Association analysis of the FTO gene with obesity in children of Caucasian and African ancestry reveals a common tagging SNP. PLoS One. 2008; 3: e1746. [PMID: 18335027] [PMCID: PMC2262153] [DOI: 10.1371/journal.pone.0001746]
[17] Dastani Z, Hivert MF, Timpson N, Perry JR, Yuan X, Scott RA, Henneman P, Heid IM, Kizer JR, Lyytikäinen LP, Fuchsberger C, Tanaka T, Morris AP, Small K, Isaacs A, Beekman M, Coassin S, Lohman K, Qi L, Kanoni S, Pankow JS, Uh HW, Wu Y, Bidulescu A, Rasmussen-Torvik LJ, Greenwood CM, Ladouceur M, Grimsby J, Manning AK, Liu CT, Kooner J, Mooser VE, Vollenweider P, Kapur KA, Chambers J, Wareham NJ, Langenberg C, Frants R, Willems-Vandijk K, Oostra BA, Willems SM, Lamina C, Winkler TW, Psaty BM, Tracy RP, Brody J, Chen I, Viikari J, Kähönen M, Pramstaller PP, Evans DM, St Pourcain B, Sattar N, Wood AR, Bandinelli S, Carlson OD, Egan JM, Böhringer S, van Heemst D, Kedenko L, Kristiansson K, Nuotio ML, Loo BM, Harris T, Garcia M, Kanaya A, Haun M, Klopp N, Wichmann HE, Deloukas P, Katsareli E, Couper DJ, Duncan BB, Kloppenburg M, Adair LS, Borja JB, DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, Wilson JG, Musani S, Guo X, Johnson T, Semple R, Teslovich TM, Allison MA, Redline S, Buxbaum SG, Mohlke KL, Meulenbelt I, Ballantyne CM, Dedoussis GV, Hu FB, Liu Y, Paulweber B, Spector TD, Slagboom PE, Ferrucci L, Jula A, Perola M, Raitakari O, Florez JC, Salomaa V, Eriksson JG, Frayling TM, Hicks AA, Lehtimäki T, Smith GD, Siscovick DS, Kronenberg F, van Duijn C, Loos RJ, Waterworth DM, Meigs JB, Dupuis J, Richards JB, Voight BF, Scott LJ, Steinthorsdottir V, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Hofmann OM, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Boström KB, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Crawford G, Doney AS, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PR, Jørgensen T, Kao WH, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Morris AD, Palmer CN, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Pedersen O, Barroso I, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI, Soranzo N, Wheeler E, Glazer NL, Bouatia-Naji N, Mägi R, Randall J, Elliott P, Rybin D, Dehghan A, Hottenga JJ, Song K, Goel A, Lajunen T, Doney A, Cavalcanti-Proença C, Kumari M, Timpson NJ, Zabena C, Ingelsson E, An P, O'Connell J, Luan J, Elliott A, McCarroll SA, Roccasecca RM, Pattou F, Sethupathy P, Ariyurek Y, Barter P, Beilby JP, Ben-Shlomo Y, Bergmann S, Bochud M, Bonnefond A, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead SJ, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Crisponi L, Day IN, de Geus EJ, Delplanque J, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Grundy S, Gwilliam R, Hallmans G, Hammond N, Han X, Hartikainen AL, Hayward C, Heath SC, Hercberg S, Hillman DR, Hingorani AD, Hui J, Hung J, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Mahley R, Mangino M, Martínez-Larrad MT, McAteer JB, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Mukherjee S, Naitza S, Neville MJ, Orrù M, Pakyz R, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Seedorf U, Sharp SJ, Shields B, Sigurðsson G, Sijbrands EJ, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvänen AC, Tönjes A, Uitterlinden AG, van Dijk KW, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Ward KL, Watkins H, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, DIAGRAM Consortium, GIANT Consortium, Global B Pgen Consortium, Borecki IB, Meneton P, Magnusson PK, Nathan DM, Williams GH, Silander K, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Serrano-Ríos M, Lind L, Palmer LJ, Hu FB 1st, Franks PW, Ebrahim S, Marmot M, Kao WH, Pramstaller PP, Wright AF, Stumvoll M, Hamsten A, Procardis Consortium, Buchanan TA, Valle TT, Rotter JI, Penninx BW, Boomsma DI, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Peltonen L, Mooser V, Sladek R, MAGIC investigators, GLGC Consortium, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Chasman DI, Johansen CT, Fouchier SW, Peloso GM, Barbalic M, Ricketts SL, Bis JC, Feitosa MF, Orho-Melander M, Melander O, Li X, Li M, Cho YS, Go MJ, Kim YJ, Lee JY, Park T, Kim K, Sim X, Ong RT, Croteau-Chonka DC, Lange LA, Smith JD, Ziegler A, Zhang W, Zee RY, Whitfield JB, Thompson JR, Surakka I, Spector TD, Smit JH, Sinisalo J, Scott J, Saharinen J, Sabatti C, Rose LM, Roberts R, Rieder M, Parker AN, Pare G, O'Donnell CJ, Nieminen MS, Nickerson DA, Montgomery GW, McArdle W, Masson D, Martin NG, Marroni F, Lucas G, Luben R, Lokki ML, Lettre G, Launer LJ, Lakatta EG, Laaksonen R, Kyvik KO, König IR, Khaw KT, Kaplan LM, Johansson Å, Janssens AC, Igl W, Hovingh GK, Hengstenberg C, Havulinna AS, Hastie ND, Harris TB, Haritunians T, Hall AS, Groop LC, Gonzalez E, Freimer NB, Erdmann J, Ejebe KG, Döring A, Dominiczak AF, Demissie S, Deloukas P, de Faire U, Crawford G, Chen YD, Caulfield MJ, Boekholdt SM, Assimes TL, Quertermous T, Seielstad M, Wong TY, Tai ES, Feranil AB, Kuzawa CW, Taylor HA Jr, Gabriel SB, Holm H, Gudnason V, Krauss RM, Ordovas JM, Munroe PB, Kooner JS, Tall AR, Hegele RA, Kastelein JJ, Schadt EE, Strachan DP, Reilly MP, Samani NJ, Schunkert H, Cupples LA, Sandhu MS, Ridker PM, Rader DJ, Kathiresan S. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet. 2012; 8: e1002607. [PMID: 22479202] [PMCID: PMC3315470] [DOI: 10.1371/journal.pgen.1002607]
[18] McCormack S, Grant S. Genetics of obesity and type 2 diabetes in African Americans. J Obes. 2013; 2013: 396416. [DOI: 10.1155/2013/396416]
[19] Sale MM, Lu L, Spruill IJ, Fernandes JK, Lok KH, Divers J, Langefeld CD, Garvey WT. Genome-wide linkage scan in Gullah-speaking African American families with type 2 diabetes: the Sea Islands Genetic African American Registry (Project SUGAR). Diabetes. 2009; 58: 260–7. [PMID: 18835935] [PMCID: PMC2606883] [DOI: 10.2337/db08-0198]
[20] Fernald G, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB. Bioinformatics challenges for personalized medicine. Bioinformatics. 2011; 27: 1741–8. [PMID: 21596790] [PMCID: PMC3117361] [DOI: 10.1093/bioinformatics/btr295]
[21] Tabor H, Berkman B, Hull S, Bamshad MJ. Genomics really gets personal: how exome and whole genome sequencing challenge the ethical framework of human genetics research. Am J Med Genet. 2011; 155: 2916–24. [PMID: 22038764] [PMCID: PMC4819320] [DOI: 10.1002/ajmg.a.34357]
[22] Kaye J, Boddington P, de Vries J, Hawkins N, Melham K. Ethical implications of the use of whole genome methods in medical research. Eur J Human Genet. 2010; 18: 398–403. [PMID: 19888293] [PMCID: PMC2879322] [DOI: 10.1038/ejhg.2009.191]
[23] National Human Genome Research InstituteGenetic Discrimination. https://www.genome.gov/10002077/genetic-discrimination/
2017 Ross Science Publishers