A Genome-Wide Association Study of BMI in American Indians

Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona, USA.
Obesity (Impact Factor: 3.73). 06/2011; 19(10):2102-6. DOI: 10.1038/oby.2011.178
Source: PubMed


Numerous studies have been done to understand genetic contributors to BMI, but only a limited number of studies have been done in nonwhite groups such as American Indians. A genome-wide association study (GWAS) for BMI was therefore performed in Pima Indians. BMI measurements from a longitudinal study of 1,120 Pima Indians and 454,194 single-nucleotide polymorphisms (SNPs) from the 1 million Affymetrix SNP panel were used (35% of SNPs were excluded due to minor allele frequency <0.05). Data included BMI measured at multiple examinations collected from 1965 to 2004, as well as the maximum BMI at one of these visits. General and within-family tests were performed using a maximum-likelihood based mixed model procedure. No SNP reached a genome-wide significance level (estimated at P < 4.94 × 10(-7)). For repeated measures analyses, the strongest associations for general and within-family tests mapped to two different regions on chromosome 6 (rs9342220 (P = 1.39 × 10(-6)) and rs7758764 (P = 2.51 × 10(-6)), respectively). For maximum BMI, the strongest association for the general tests mapped to chromosome 4 (rs17612333; P = 1.98 × 10(-6)) and to chromosome 3 (rs11127958; P = 1.53 × 10(-6)) for the within-family tests. Further analysis is important because only a few of these regions have been previously implicated in a GWAS and genetic susceptibility may differ by ethnicity.

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Available from: Clifton Bogardus, Feb 01, 2016
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    • "A previous GWAS in Pima Indians (N = 1,266) provided genotype information on 454,194 SNPs [11]. The GWAS SNPs passed the following quality control criteria: Hardy-Weinberg Equilibrium p-value>0.001, "
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    ABSTRACT: Background Genotype imputation is commonly used in genetic association studies to test untyped variants using information on linkage disequilibrium (LD) with typed markers. Imputing genotypes requires a suitable reference population in which the LD pattern is known, most often one selected from HapMap. However, some populations, such as American Indians, are not represented in HapMap. In the present study, we assessed accuracy of imputation using HapMap reference populations in a genome-wide association study in Pima Indians. Results Data from six randomly selected chromosomes were used. Genotypes in the study population were masked (either 1% or 20% of SNPs available for a given chromosome). The masked genotypes were then imputed using the software Markov Chain Haplotyping Algorithm. Using four HapMap reference populations, average genotype error rates ranged from 7.86% for Mexican Americans to 22.30% for Yoruba. In contrast, use of the original Pima Indian data as a reference resulted in an average error rate of 1.73%. Conclusions Our results suggest that the use of HapMap reference populations results in substantial inaccuracy in the imputation of genotypes in American Indians. A possible solution would be to densely genotype or sequence a reference American Indian population.
    Full-text · Article · Jul 2014 · PLoS ONE
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    • "Garte et al. [14] have reported a similar degree of differing PAFs among two metabolic gene SNPs in Japan versus Korea and Singapore. An extensive genome-wide association study [15] of body mass index (BMI) among 1,120 Pima Indian participants, ascertained largely through familial relationships, used an over 900,000 SNP microarray but found a high rate (35%) of PAFs < 5%. In addition, one SNP with strong genome-wide association in the European population [16] and a PAF of 21% was monomorphic in this Pima population. "
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    ABSTRACT: The prevalence of variant alleles among single nucleotide polymorphisms (SNPs) is not well known for many minority populations. These population allele frequencies (PAFs) are necessary to guide genetic epidemiology studies and to understand the population specific contribution of these variants to disease risk. Large differences in PAF among certain functional groups of genes could also indicate possible selection pressure or founder effects of interest. The 50K SNP, custom genotyping microarray (CARe) was developed, focusing on about 2,000 candidate genes and pathways with demonstrated pathophysiologic influence on cardiovascular disease (CVD). The CARe microarray was used to genotype 216 unaffected controls in a study of pre-eclampsia among a Northern Plains, American Indian tribe. The allelic prevalences of 34,240 SNPs suitable for analysis, were determined and compared with corresponding HapMap prevalences for the Caucasian population. Further analysis was conducted to compare the frequency of statistically different prevalences among functionally related SNPs, as determined by the DAVID Bioinformatics Resource. Of the SNPs with PAFs in both datasets, 9.8%,37.2% and 47.1% showed allele frequencies among the American Indian population greater than, less than and either greater or less than (respectively) the HapMap Caucasian population. The 2,547 genes were divided into 53 functional groups using the highest stringency criteria. While none of these groups reached the Bonferroni corrected p value of 0.00094, there were 7 of these 53 groups with significantly more or less differing PAFs, each with a probability of less than 0.05 and an overall probability of 0.0046. In comparison to the HapMap Caucasian population, there are substantial differences in the prevalence among an American Indian community of SNPs related to CVD. Certain functional groups of genes and related SNPs show possible evidence of selection pressure or founder effects.
    Full-text · Article · Sep 2013 · PLoS ONE
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    • "The Pima Indians of Arizona have an extremely high prevalence of obesity, and body mass index (BMI) is highly heritable (2). We previously conducted a genome-wide association study (GWAS) to identify variation associated with BMI in 1120 Pima Indians (3). This report included replication data on 9 SNPs in 2133 subjects; among the 9 SNPs, 5 had nominal evidence for replication, rs17612333, rs9381282, rs11652094, rs1418029 and rs4811346, but none of the associations achieved genome-wide statistical significance (3). "
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    ABSTRACT: To identify genes that affect BMI in American Indians who are predominately of Pima Indian heritage, we previously completed a genome-wide association study (GWAS) in 1120 American Indians. That study also included follow-up genotyping for 9 SNPs in 2133 additional subjects. A comprehensive follow-up study has subsequently been completed where 292 SNPs were genotyped in 3562 subjects, of which 128 SNPs were assessed for replication in 3238 additional subjects. In the combined subjects (n=6800), BMI associations for two SNPs, rs12882548 and rs11652094, approached genome-wide significance (P=6.7×10(-7) and 8.1×10(-7), respectively). Rs12882548 is located in a gene desert on chromosome 14 and rs11652094 maps near MAP2K3. Several SNPs in the MAP2K3 region including rs11652094 were also associated with BMI in Caucasians from the GIANT consortium (P=10(-2)-10(-5)), and the combined P-values across both American Indians and Caucasian were P=10(-4)-10(-9). Follow-up sequencing across MAP2K3 identified several paralogous sequence variants (PSVs) indicating that the region may have been duplicated. MAP2K3 expression levels in adipose tissue biopsies were positively correlated with BMI, although it is unclear if this correlation is a cause or effect. In vitro studies with cloned MAP2K3 promoters suggest that MAP2K3 expression may be up-regulated during adipogenesis. Microarray analyses of mouse hypothalamus cells expressing constitutively active MAP2K3 identified several up-regulated genes involved in immune/inflammatory pathways and a gene, Hap1, thought to play a role in appetite regulation. We conclude that MAP2K3 is a reproducible obesity locus that may affect body weight via complex mechanisms involving appetite regulation and hypothalamic inflammation.
    Full-text · Article · Jul 2013 · Human Molecular Genetics
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