Autosomal Genomic Scan for Loci Linked to Obesity and Energy Metabolism in Pima Indians

Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States
The American Journal of Human Genetics (Impact Factor: 10.93). 03/1998; 62(3):659-68. DOI: 10.1086/301758
Source: PubMed


An autosomal genomic scan to search for linkage to obesity and energy metabolism was completed in Pima Indians, a population prone to obesity. Obesity was assessed by percent body fat (by hydrodensitometry) and fat distribution (the ratio of waist circumference to thigh circumference). Energy metabolism was measured in a respiratory chamber as 24-h metabolic rate, sleeping metabolic rate, and 24-h respiratory quotient (24RQ), an indicator of the ratio of carbohydrate oxidation to fat oxidation. Five hundred sixteen microsatellite markers with a median spacing of 6.4 cM were analyzed, in 362 siblings who had measurements of body composition and in 220 siblings who had measurements of energy metabolism. These comprised 451 sib pairs in 127 nuclear families, for linkage analysis to obesity, and 236 sib pairs in 82 nuclear families, for linkage analysis to energy metabolism. Pointwise and multipoint methods for regression of sib-pair differences in identity by descent, as well as a sibling-based variance-components method, were used to detect linkage. LOD scores >=2 were found at 11q21-q22, for percent body fat (LOD=2.1; P=.001), at 11q23-q24, for 24-h energy expenditure (LOD=2.0; P=.001), and at 1p31-p21 (LOD=2.0) and 20q11.2 (LOD=3.0; P=.0001), for 24RQ, by pointwise and multipoint analyses. With the variance-components method, the highest LOD score (LOD=2.3 P=.0006) was found at 18q21, for percent body fat, and at 1p31-p21 (LOD=2.8; P=.0003), for 24RQ. Possible candidate genes include LEPR (leptin receptor), at 1p31, and ASIP (agouti-signaling protein), at 20q11.2.

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Available from: Clifton Bogardus, Jul 07, 2014
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    • "PPAR activation in adipose tissue also increases the capacity of fatty acids synthesis (Tontonoz and Spiegelman, 2008). The FASN gene is located on chromosome 17 in a region associated with body fat in Pima Indians (Norman et al., 1998). Furthermore, FASN encodes fatty acid synthase, which forms a complex homodimeric enzyme and plays an important role in biosynthesis of long chain fatty acids from "
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    ABSTRACT: The objective of this study was to investigate the correlation between cattle breeds and deposit of adipose tissues in different positions and the gene expressions of peroxisome proliferator-activated receptor gamma (), fatty acid synthase (FASN), and Acyl-CoA dehydrogenase (ACADM), which are associated with lipid metabolism and are valuable for understanding the physiology in fat depot and meat quality. Yanbian yellow cattle and Yan yellow cattle reared under the same conditions display different fat proportions in the carcass. To understand this difference, the expression of , FASN, and ACADM in different adipose tissues and longissimus dorsi muscle (LD) in these two breeds were analyzed using the Real-time quantitative polymerase chain reaction method (qRT-PCR). The result showed that gene expression was significantly higher in adipose tissue than in LD in both breeds. expression was also higher in abdominal fat, in perirenal fat than in the subcutaneous fat (p
    Asian Australasian Journal of Animal Sciences 01/2014; 27(1). DOI:10.5713/ajas.2013.13422 · 0.54 Impact Factor
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    • "It is well documented that MAP kinases including MAP2K3 mediate inflammatory responses (35,36), and MAP2K3 can be activated by proinflammatory cytokines such as interleukin-1 and tumor necrosis factor (37). Our microarray results are consistent with MAP2K3 playing a role in inflammatory pathways in both adipose and hypothalamus tissues; and, there are reports suggesting that hypothalamic inflammation can lead to leptin and insulin resistance which in turn affects appetite regulation (38,39). One of the inflammatory response genes, Myd88, modestly up-regulated in response to constitutively active MAP2K3-A in the mouse hypothalamus cells has been shown to be component of a TLR4 hypothalamic inflammatory signaling pathway that may affect food intake (9). "
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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.
    Human Molecular Genetics 07/2013; 22(21). DOI:10.1093/hmg/ddt291 · 6.39 Impact Factor
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    • "Therefore, it can be assumed that Pltp also modulates the level of adiposity in WOKW rats. Additional studies in humans and mice have shown that there is an association between PLTP genetic variants and obesity-related phenotypes [22]–[25]. Therefore, Pltp could be a candidate gene for obesity in WOKW rats. "
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    ABSTRACT: Because inbred rat strains are widely used as laboratory models, knowledge of phenotypic and genetic variations between strains will be useful to obtain insight into the relationship between different strains. We studied phenotypic traits: of each strain--BN/K, DA/K and WOKW--10 male rats were studied for body weight and serum constituents at an age of 10 and 30 weeks. In addition, a total of 95 rats were studied for life expectancy. At an age of 30 weeks, these male rats were killed by an overdose of anesthetic (Sevofluran, Abbott), and the subcutaneous and visceral adipose tissue as well as bone tissue were removed to study the expression of 20 genes. There were significant differences in body weight, serum lipids and leptin at an age of 30 weeks between strains. Regarding life expectancy, BN rats lived longest (1072±228d). The highest gene expression was found in bone of BN rats. In adipose tissues, Nfkb1 is only expressed in subcutaneous adipocytes, and 5 genes, Col2a1, Mmp9, Tnfa, Ins1 and Cyp24a1, are not expressed in adipocytes. The ranking BN = DA>WOKW was observed in only one gene in subcutaneous (Fto) and visceral adipocytes (Col6a1). There were no significant differences in gene expression of one gene in subcutaneous adipocytes and of 3 genes in visceral adipocytes. Comparing the gene expression in visceral and subcutaneous adipocytes, only one gene showed a comparable behavior (Bmp1). From these results, it can be concluded that obvious phenotypic differences are caused by genetic differences between three rat strains, BN, DA and WOKW, as supported by gene expression studies in bone and adipose tissues. Especially BN rats can be used to study the genetic basis of long life.
    PLoS ONE 06/2012; 7(6):e38981. DOI:10.1371/journal.pone.0038981 · 3.23 Impact Factor
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