A genome-wide scan for obesity in African-Americans

The Relevance of Genomic Signatures at Adhesion GPCR Loci in Humans

Adhesion G protein-coupled receptors (aGPCRs) have a long evolutionary history dating back to very basal unicellular eukaryotes. Almost every vertebrate is equipped with a set of different aGPCRs. Genomic sequence data of several hundred extinct and extant species allows for reconstruction of aGPCR phylogeny in vertebrates and non-vertebrates in general but also provides a detailed view into the recent evolutionary history of human aGPCRs. Mining these sequence sources with bioinformatic tools can unveil many facets of formerly unappreciated aGPCR functions. In this review, we extracted such information from the literature and open public sources and provide insights into the history of aGPCR in humans. This includes comprehensive analyses of signatures of selection, variability of human aGPCR genes, and quantitative traits at human aGPCR loci. As indicated by a large number of genome-wide genotype-phenotype association studies, variations in aGPCR contribute to specific human phenotypes. Our survey demonstrates that aGPCRs are significantly involved in adaptation processes, phenotype variations, and diseases in humans.

Obesity genetics: a monopoly game of genes

Obesity is a complex disease that affects all ethnic populations worldwide. The etiology of this disease is based on the interaction of genetic factors, environment and lifestyles indicators. Genetic contribution to the epidemic has gained attention from 2 sources: monogenic syndromes that display severe obesity, and the polygenic model of common obesity. Single mutations can render a syndrome with severe obesity resulting from alteration in central o peripheral appetite control mechanisms. The interaction of several polymorphisms and epigenetic modifications constitute the basic plot for common obesity, molecular ingredients that should not confuse the investigator-they make this riddle even harder to decipher.

The Human Obesity Gene Map: The 2003 Update

This is the tenth update of the human obesity gene map, incorporating published results up to the end of October 2003 and continuing the previous format. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, quantitative trait loci (QTLs) from human genome-wide scans and animal crossbreeding experiments, and association and linkage studies with candidate genes and other markers is reviewed. Transgenic and knockout murine models relevant to obesity are also incorporated (N = 55). As of October 2003, 41 Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. QTLs reported from animal models currently number 183. There are 208 human QTLs for obesity phenotypes from genome-wide scans and candidate regions in targeted studies. A total of 35 genomic regions harbor QTLs replicated among two to five studies. Attempts to relate DNA sequence variation in specific genes to obesity phenotypes continue to grow, with 272 studies reporting positive associations with 90 candidate genes. Fifteen such candidate genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. Overall, more than 430 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful sites can be found at http://obesitygene.pbrc.edu.

The Human Obesity Gene Map: The 2004 Update

This paper presents the eleventh update of the human obesity gene map, which incorporates published results up to the end of October 2004. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTLs) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2004, 173 human obesity cases due to single-gene mutations in 10 different genes have been reported, and 49 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 166 genes which, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 221. The number of human obesity QTLs derived from genome scans continues to grow, and we have now 204 QTLs for obesity-related phenotypes from 50 genome-wide scans. A total of 38 genomic regions harbor QTLs replicated among two to four studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably with 358 findings of positive associations with 113 candidate genes. Among them, 18 genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. Overall, >600 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful publications and genomic and other relevant sites can be found at http://obesitygene.pbrc.edu.

A mathematical model of weight loss under total starvation: evidence against the thrifty-gene hypothesis

The thrifty-gene hypothesis (TGH) posits that the modern genetic predisposition to obesity stems from a historical past where famine selected for genes that promote efficient fat deposition. It has been previously argued that such a scenario is unfeasible because under such strong selection any gene favouring fat deposition would rapidly move to fixation. Hence, we should all be predisposed to obesity: which we are not. The genetic architecture of obesity that has been revealed by genome-wide association studies (GWAS), however, calls into question such an argument. Obesity is caused by mutations in many hundreds (maybe thousands) of genes, each with a very minor, independent and additive impact. Selection on such genes would probably be very weak because the individual advantages they would confer would be very small. Hence, the genetic architecture of the epidemic may indeed be compatible with, and hence support, the TGH. To evaluate whether this is correct, it is necessary to know the likely effects of the identified GWAS alleles on survival during starvation. This would allow definition of their advantage in famine conditions, and hence the likely selection pressure for such alleles to have spread over the time course of human evolution. We constructed a mathematical model of weight loss under total starvation using the established principles of energy balance. Using the model, we found that fatter individuals would indeed survive longer and, at a given body weight, females would survive longer than males, when totally starved. An allele causing deposition of an extra 80 g of fat would result in an extension of life under total starvation by about 1.1-1.6% in an individual with 10 kg of fat and by 0.25-0.27% in an individual carrying 32 kg of fat. A mutation causing a per allele effect of 0.25% would become completely fixed in a population with an effective size of 5 million individuals in 6000 selection events. Because there have probably been about 24,000 famine events since the evolution of hominins 4 million years ago, there has been ample time even for genes with only very minor impacts on adiposity to move to fixation. The observed polymorphic variation in the genes causing the predisposition to obesity is incompatible with the TGH, unless all these single nucleotide polymorphisms (SNPs) arose in the last 900,000 years, a requirement we know is incorrect. The TGH is further weakened by the observation of no link between the effect size of these SNPs and their prevalence, which would be anticipated under the TGH model of selection if all the SNPs had arisen in the last 900,000 years.

Set points, settling points and some alternative models: theoretical options to understand how genes and environments combine to regulate body adiposity

The close correspondence between energy intake and expenditure over prolonged time periods, coupled with an apparent protection of the level of body adiposity in the face of perturbations of energy balance, has led to the idea that body fatness is regulated via mechanisms that control intake and energy expenditure. Two models have dominated the discussion of how this regulation might take place. The set point model is rooted in physiology, genetics and molecular biology, and suggests that there is an active feedback mechanism linking adipose tissue (stored energy) to intake and expenditure via a set point, presumably encoded in the brain. This model is consistent with many of the biological aspects of energy balance, but struggles to explain the many significant environmental and social influences on obesity, food intake and physical activity. More importantly, the set point model does not effectively explain the 'obesity epidemic'--the large increase in body weight and adiposity of a large proportion of individuals in many countries since the 1980s. An alternative model, called the settling point model, is based on the idea that there is passive feedback between the size of the body stores and aspects of expenditure. This model accommodates many of the social and environmental characteristics of energy balance, but struggles to explain some of the biological and genetic aspects. The shortcomings of these two models reflect their failure to address the gene-by-environment interactions that dominate the regulation of body weight. We discuss two additional models--the general intake model and the dual intervention point model--that address this issue and might offer better ways to understand how body fatness is controlled.

Do genetic variations alter the effects of exercise training on cardiovascular disease and can we identify the candidate variants now or in the future?

Cardiovascular disease (CVD) and CVD risk factors are highly heritable, and numerous lines of evidence indicate they have a strong genetic basis. While there is nothing known about the interactive effects of genetics and exercise training on CVD itself, there is at least some literature addressing their interactive effect on CVD risk factors. There is some evidence indicating that CVD risk factor responses to exercise training are also heritable and, thus, may have a genetic basis. While roughly 100 studies have reported significant effects of genetic variants on CVD risk factor responses to exercise training, no definitive conclusions can be generated at the present time, because of the lack of consistent and replicated results and the small sample sizes evident in most studies. There is some evidence supporting “possible” candidate genes that may affect these responses to exercise training: APO E and CETP for plasma lipoprotein-lipid profiles; eNOS, ACE, EDN1, and GNB3 for blood pressure; PPARG for type 2 diabetes phenotypes; and FTO and BAR genes for obesity-related phenotypes. However, while genotyping technologies and statistical methods are advancing rapidly, the primary limitation in this field is the need to generate what in terms of exercise intervention studies would be almost incomprehensible sample sizes. Most recent diabetes, obesity, and blood pressure genetic studies have utilized populations of 10,000–250,000 subjects, which result in the necessary statistical power to detect the magnitude of effects that would probably be expected for the impact of an individual gene on CVD risk factor responses to exercise training. Thus at this time it is difficult to see how this field will advance in the future to the point where robust, consistent, and replicated data are available to address these issues. However, the results of recent large-scale genomewide association studies for baseline CVD risk factors may drive future hypothesis-driven exercise training intervention studies in smaller populations addressing the impact of specific genetic variants on well-defined physiological phenotypes.

A unique genetic defect on chromosome 3 is responsible for juvenile obesity in the Berlin Fat Mouse

OBJECTIVE

This study aimed at the mapping and estimation of genetic and sex effects contributing to the obese phenotype of the Berlin Fat Mouse Inbred line 860 (BFMI860). This mouse line is predisposed for juvenile obesity. BFMI860 mice accumulate 24% total fat mass at 10 weeks of age under a standard maintenance diet.

DESIGN

A total of 471 mice of a (BFMI860 x C57BL/6NCrl) F₂ intercross population were fed a standard maintenance diet and were analysed for body composition at 10 weeks when they finished their rapid growth phase.

RESULTS

The most striking result was the identification of a novel obesity locus on chromosome 3 (Chr 3) at 40 Mb, explaining 39% of the variance of total fat mass in the F₂ population under a standard diet. This locus was named jObes1 (juvenile obesity 1). The BFMI860 allele effect was recessive. Males and females homozygous at jObes1 had on average 3.0 and 3.3 g more total fat mass at 10 weeks than the other two genotype classes, respectively. The effect was evident in all white adipose tissues, brown adipose tissue and also in liver. The position of the Chr 3 effect is syntenic to an obesity locus in humans. Additional loci for total fat mass and different white adipose tissue weights with minor effects were detected on mouse Chr 5 and 6. Another locus on Chr 4 had influence especially on liver weight. Many loci including jObes1 affected males and females to a different extent.

CONCLUSION

The major locus on Chr 3 for juvenile obesity and its interaction with sex is unique and makes the BFMI860 mice an interesting resource for the discovery of novel genetic factors predisposing obesity, which might also contribute to obesity in humans. The results suggested that metabolic and regulatory pathways differed between the sexes.

Detecting rare variants for complex traits using family and unrelated data

Large genome-wide association studies (GWAS) have been performed to detect common genetic variants involved in common diseases, but most of the variants found this way account for only a small portion of the trait variance. Furthermore, candidate gene-based resequencing suggests that many rare genetic variants contribute to the trait variance of common diseases. Here we propose two designs, sibpair and unrelated-case designs, to detect rare genetic variants in either a candidate gene-based or genome-wide association analysis. First we show that we can detect and classify together rare risk haplotypes using a relatively small sample with either of these designs, and then have increased power to test association in a larger case-control sample. This method can also be applied to resequencing data. Next we apply the method to the Wellcome Trust Case Control Consortium (WTCCC) coronary artery disease (CAD) and hypertension (HT) data, the latter being the only trait for which no genome-wide association evidence was reported in the original WTCCC study, and identify one interesting gene associated with HT and four associated with CAD at a genome-wide significance level of 5%. These results suggest that searching for rare genetic variants is feasible and can be fruitful in current GWAS, candidate gene studies or resequencing studies.

Admixture mapping of quantitative trait loci for BMI in African Americans: evidence for loci on chromosomes 3q, 5q, and 15q

Obesity is a heritable trait and a major risk factor for highly prevalent common diseases such as hypertension and type 2 diabetes. Previously we showed that BMI was positively correlated with African ancestry among the African Americans (AAs) in the US National Heart, Lung, and Blood Institute's Family Blood Pressure Program (FBPP). In a set of 1,344 unrelated AAs, using Individual Ancestry (IA) estimates at 284 marker locations across the genome, we now present a quantitative admixture mapping analysis of BMI. We used a set of unrelated individuals from Nigeria to represent the African ancestral population and the European American (EA) in the FBPP as the European ancestral population. The analysis was based on a common set of 284 microsatellite markers genotyped in all three groups. We considered the quantitative trait, BMI, as the response variable in a regression analysis with the marker location specific excess European ancestry as the explanatory variable. After suitably adjusting for different covariates such as sex, age, and network, we found strong evidence for a positive association with European ancestry at chromosome locations 3q29 and 5q14 and a negative association on chromosome 15q26. To our knowledge, this is the largest quantitative admixture mapping effort in terms of sample size and marker locus involvement for the trait. These results suggest that these regions may harbor genes influencing BMI in the AA population.

Latent common genetic components of obesity traits

BACKGROUND

Obesity is rapidly becoming a global epidemic. Unlike many complex human diseases, obesity is defined not just by a single trait or phenotype, but jointly by measures of anthropometry and metabolic status.

METHODS

We applied maximum likelihood factor analysis to identify common latent factors underlying observed covariance in multiple obesity-related measures. Both the genetic components and the mode of inheritance of the common factors were evaluated. A total of 1775 participants from 590 families for whom measures on obesity-related traits were available were included in this study.

RESULTS

The average age of participants was 37 years, 39% of the participants were obese (body mass index >or= 30.0 kg/m(2)) and 26% were overweight (body mass index 25.0-29.9 kg/m(2)). Two latent common factors jointly accounting for over 99% of the correlations among obesity-related traits were identified. Complex segregation analysis of the age- and sex-adjusted latent factors provide evidence for a Mendelian mode of inheritance of major genetic effect with heritability estimates of 40.4 and 47.5% for the first and second factors, respectively.

CONCLUSIONS

These findings provide a support for multivariate-based approach for investigating pleiotropic effects on obesity-related traits, which can be applied in both genetic linkage and association mapping.

Fat infiltration in muscle: new evidence for familial clustering and associations with diabetes

OBJECTIVE

Increased fat infiltration in skeletal muscle has been associated with diabetes. Quantitative computed tomography (QCT) can be used to measure muscle density, which reflects the lipid content of skeletal muscle such that greater fat infiltration in skeletal muscle is associated with lower muscle density. The relative contribution of genetic and environmental factors to fat infiltration in skeletal muscle has not been assessed. Therefore, our aim is to determine genetic and environmental contributions to measures of skeletal muscle composition, and describe their associations with type 2 diabetes in multigenerational families of African ancestry.

METHODS AND PROCEDURES

Peripheral QCT (pQCT) measures of skeletal muscle density were obtained for the calf in 471 individuals (60% women; mean 43 years) belonging to eight large, multigenerational Afro-Caribbean families (mean family size 51 individuals; 3,535 relative pairs).

RESULTS

The proportion of variance in muscle density due to additive genetic effects (residual heritability) was 35.0% (P < 0.001) and significant covariates (age, gender, BMI, and parity) explained 55.0% of the total phenotypic variation in muscle density. Muscle density was lower (P < 0.001) in 62 diabetics (69.5 mg/cm(3)) than in 339 nondiabetics (74.3 mg/cm(3)) and remained significant after adjusting for age, gender, and BMI (P = 0.005) or age, gender, and waist circumference (P = 0.01).

DISCUSSION

Our results provide new evidence that ectopic lipid deposition in skeletal muscle is a heritable trait and is associated with diabetes, independent of overall and central obesity in families of African heritage. Genome-wide screens and candidate gene studies are warranted to identify the genetic factors contributing to ectopic deposition of skeletal muscle fat.

A unified association analysis approach for family and unrelated samples correcting for stratification

There are two common designs for association mapping of complex diseases: case-control and family-based designs. A case-control sample is more powerful to detect genetic effects than a family-based sample that contains the same numbers of affected and unaffected persons, although additional markers may be required to control for spurious association. When family and unrelated samples are available, statistical analyses are often performed in the family and unrelated samples separately, conditioning on parental information for the former, thus resulting in reduced power. In this report, we propose a unified approach that can incorporate both family and case-control samples and, provided the additional markers are available, at the same time corrects for population stratification. We apply the principal components of a marker matrix to adjust for the effect of population stratification. This unified approach makes it unnecessary to perform a conditional analysis of the family data and is more powerful than the separate analyses of unrelated and family samples, or a meta-analysis performed by combining the results of the usual separate analyses. This property is demonstrated in both a variety of simulation models and empirical data. The proposed approach can be equally applied to the analysis of both qualitative and quantitative traits.

Genetic effects on obesity assessed by bivariate genome scan: the Mexican-American coronary artery disease study

OBJECTIVE

To identify the genetic determinants of obesity using univariate and bivariate models in a genome scan.

RESEARCH METHODS AND PROCEDURES

We evaluated the genetic and environmental effects and performed a genome-wide linkage analysis of obesity-related traits in 478 subjects from 105 Mexican-American nuclear families ascertained through a proband with documented coronary artery disease. The available obesity traits include BMI, body surface area (BSA), waist-to-hip ratio (WHR), and trunk fat mass as percentage of body weight. Heritability estimates and multipoint linkage analysis were performed using a variance components procedure implemented in SOLAR software.

RESULTS

The heritability estimates were 0.62 for BMI, 0.73 for BSA, 0.40 for WHR, and 0.38 for trunk fat mass as percentage of body weight. Using a bivariate genetic model, we observed significant genetic correlations between BMI and other obesity-related traits (all p < 0.01). Evidence for univariate linkage was observed at 252 to approximately 267 cM on chromosome 2 for three obesity-related traits (except for WHR) and at 163 to approximately 167 cM on chromosome 5 for BMI and BSA, with the maximum logarithm of the odds ratio score of 3.12 (empirical p value, 0.002) for BSA on chromosome 2. Use of the bivariate linkage model yielded an additional peak (logarithm of the odds ratio = 3.25, empirical p value, 0.002) at 25 cM on chromosome 7 for the pair of BMI and BSA.

DISCUSSION

The evidence for linkage on chromosomes 2q36-37 and 5q36 is supported both by univariate and bivariate analysis, and an additional linkage peak at 7p15 was identified by the bivariate model. This suggests that use of the bivariate model provides additional information to identify linkage of genes responsible for obesity-related traits.

Is There an Association Between Sweetened Beverages and Adiposity?

Four mechanisms were reviewed to explain the possible association between sweetened beverages and increased overweight or obesity: excess caloric intake, glycemic index and glycemic load, lack of effect of liquid calories on satiety, and displacement of milk. The findings were inconsistent across studies. The strongest support was for the excess caloric intake hypothesis, but the findings were not conclusive. Assigning possible links between sweetened beverage consumption and adiposity requires research that compares and contrasts specific mechanisms, especially in populations at risk for obesity, while controlling for likely confounding variables.

The human obesity gene map: the 2005 update

This paper presents the 12th update of the human obesity gene map, which incorporates published results up to the end of October 2005. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTL) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2005, 176 human obesity cases due to single-gene mutations in 11 different genes have been reported, 50 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 244 genes that, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 408. The number of human obesity QTLs derived from genome scans continues to grow, and we now have 253 QTLs for obesity-related phenotypes from 61 genome-wide scans. A total of 52 genomic regions harbor QTLs supported by two or more studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably, with 426 findings of positive associations with 127 candidate genes. A promising observation is that 22 genes are each supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. The electronic version of the map with links to useful publications and relevant sites can be found at http://obesitygene.pbrc.edu.

Sex-specific findings from a genome-wide linkage analysis of human fatness in non-Hispanic whites and African Americans: the HyperGEN study

OBJECTIVE

To conduct a full genome search for genes potentially influencing two related phenotypes: body mass index (BMI, kg/m2) and percent body fat (PBF) from bioelectric impedance in men and women.

DESIGN

A total of 3383 participants, 1348 men and 2035 women; recruitment was initiated with hypertensive sibpairs and expanded to first-degree relatives in a multicenter study of hypertension genetics.

MEASUREMENTS

Genotypes for 387 highly polymorphic markers spaced to provide a 10 cM map (CHLC-8) were generated by the NHLBI Mammalian Genotyping Service (Marshfield, WI, USA). Quantitative trait loci for obesity phenotypes, BMI and PBF, were examined with a variance components method using SOLAR, adjusting for hypertensive status, ethnicity, center, age, age2, sex, and age2 x sex. As we detected a significant genotype-by-sex interaction in initial models and because of the importance of sex effects in the expression of these phenotypes, models thereafter were stratified by sex. No genotype-by-ethnicity interactions were found.

RESULTS

A QTL influencing PBF in women was detected on chromosome12q (12q24.3-12q24.32, maximum empirical LOD score=3.8); a QTL influencing this phenotype in men was found on chromosome 15q (15q25.3, maximum empirical LOD score=3.0). These QTLs were detected in African-American and white women (12q) and men (15q). QTLs influencing both BMI and PBF were found over a broad region on chromosome 3 in men. QTLs on chromosomes 3 and 12 were found in the combined sample of men and women, but with weaker significance.

CONCLUSION

The locations with highest LOD scores have been previously reported for obesity phenotypes, indicating that at least two genomic regions influence obesity-related traits. Furthermore, our results indicate the importance of considering context-dependent effects in the search for obesity QTLs.

Loci contributing to adult height and body mass index in African American families ascertained for type 2 diabetes

Height and body mass index (BMI) have high heritability in most studies. High BMI and reduced height are well-recognized as important risk factors for a number of cardiovascular diseases. We investigated these phenotypes in African American families originally ascertained for studies of linkage with type 2 diabetes using self-reported height and weight. We conducted a genome wide scan in 221 families containing 580 individuals and 672 relative pairs of African American descent. Estimates of heritability and support for linkage were assessed by genetic variance component analyses using SOLAR software. The estimated heritabilities for height and BMI were 0.43 and 0.64, respectively. We have identified major loci contributing to variation in height on chromosomes 15 (LOD = 2.61 at 35 cM, p = 0.0004), 3 (LOD = 1.82 at 84 cM, p = 0.0029), 8 (LOD = 1.92 at 135 cM, p = 0.0024) and 17 (LOD = 1.70 at 110 cM, p = 0.0044). A broad region on chromosome 4 supported evidence of linkage to variation in BMI, with the highest LOD = 2.66 at 168 cM (p = 0.0005). Two height loci and two BMI loci appear to confirm the existence of quantitative trait loci previously identified by other studies, providing important replicative data to allow further resolution of linkage regions suitable for positional cloning of these cardiovascular disease risk loci.

Meta-analysis of five genome-wide linkage studies for body mass index reveals significant evidence for linkage to chromosome 8p

OBJECTIVE

To perform a meta-analysis of genome-wide linkage scans using body mass index (BMI) to identify genetic loci predisposing to obesity.

DATA

A total of 13 published genome scans on obesity have used BMI as their primary end point. Five of these 13 groups agreed to provide detailed results from their scans that were required for a meta-analysis. Collectively, these five studies included a total of 2814 individuals from 505 families.

METHODS

The results of the five studies were analysed using the GSMA (genome scans meta-analysis) method.

RESULTS

The analysis revealed significant evidence for linkage of the quantitative phenotype BMI to 8p (P<0.0005).

Genetic analysis of adiponectin and obesity in Hispanic families: the IRAS Family Study

Adiponectin, coded for by the APM1 gene, is a novel adipocyte-derived hormone implicated in energy homeostasis and obesity. Several genetic studies have observed evidence of association between APM1 gene polymorphisms and features of the metabolic syndrome, such as insulin resistance and obesity. As part of a comprehensive genetic analysis of the APM1 gene, we have screened 96 unrelated individuals for polymorphisms in the promoter, coding regions, and 3'untranslated region (UTR). Three promoter single-nucleotide polymorphisms (SNPs), two rare coding SNPs (G113A and T1233C), and 13 SNPs in the 3'UTR were identified. Eighteen SNPs were genotyped in 811 Hispanic individuals from 45 families in the IRAS Family Study (IRASFS). SNPs were tested for association with six obesity quantitative traits (body mass index, waist, waist:hip ratio, subcutaneous adipose tissue, visceral adipose tissue, and visceral:subcutaneous ratio). Significant evidence of association to at least one of the obesity traits was identified in seven of the 18 SNPs (<0.001-0.05). The promoter SNP INS CA-11156 was the most consistently associated SNP and was associated significantly with all measures of obesity, except the visceral:subcutaneous ratio (P-values 0.009-0.03). Haplotype analysis supported this evidence of association, with haplotypes containing an insertion of one CA repeat at position -11156 consistently being associated with lower obesity values (P-value <0.001-0.05). The adiponectin polymorphisms, in particular those in the promoter region, thus show significant association with obesity measures in the Hispanic population. Additional studies are needed to confirm our findings and determine which polymorphism causes the functional effect.

The mitochondrial rhomboid protease PSARL is a new candidate gene for type 2 diabetes

AIMS/HYPOTHESIS

This study aimed to identify genes that are expressed in skeletal muscle, encode proteins with functional significance in mitochondria, and are associated with type 2 diabetes.

METHODS

We screened for differentially expressed genes in skeletal muscle of Psammomys obesus (Israeli sand rats), and prioritised these on the basis of genomic localisation and bioinformatics analysis for proteins with likely mitochondrial functions.

RESULTS

We identified a mitochondrial intramembrane protease, known as presenilins-associated rhomboid-like protein (PSARL) that is associated with insulin resistance and type 2 diabetes. Expression of PSARL was reduced in skeletal muscle of diabetic Psammomys obesus, and restored after exercise training to successfully treat the diabetes. PSARL gene expression in human skeletal muscle was correlated with insulin sensitivity as assessed by glucose disposal during a hyperinsulinaemic-euglycaemic clamp. In 1,031 human subjects, an amino acid substitution (Leu262Val) in PSARL was associated with increased plasma insulin concentration, a key risk factor for diabetes. Furthermore, this variant interacted strongly with age to affect insulin levels, accounting for 5% of the variation in plasma insulin in elderly subjects.

CONCLUSIONS/INTERPRETATION

Variation in PSARL sequence and/or expression may be an important new risk factor for type 2 diabetes and other components of the metabolic syndrome.

Genetic linkage and association of the growth hormone secretagogue receptor (ghrelin receptor) gene in human obesity

The growth hormone secretagogue receptor (GHSR) (ghrelin receptor) plays an important role in the regulation of food intake and energy homeostasis. The GHSR gene lies on human chromosome 3q26 within a quantitative trait locus strongly linked to multiple phenotypes related to obesity and the metabolic syndrome. Because the biological function and location of the GHSR gene make it an excellent candidate gene, we tested the relation between common single nucleotide polymorphisms (SNPs) in the GHSR gene and human obesity. We performed a comprehensive analysis of SNPs, linkage disequilibrium (LD), and haplotype structure across the entire GHSR gene region (99.3 kb) in 178 pedigrees with multiple obese members (DNA of 1,095 Caucasians) and in an independent sample of the general population (MONICA Augsburg left ventricular hypertrophy substudy; DNA of 1,418 Caucasians). The LD analysis revealed a disequilibrium block consisting of five SNPs, consistent in both study cohorts. We found linkage among all five SNPs, their haplotypes, and BMI. Further, we found suggestive evidence for transmission disequilibrium for the minor SNP alleles (P < 0.05) and the two most common haplotypes with the obesity affection status ("susceptible" P = 0.025, "nonsusceptible" P = 0.045) in the family cohort using the family-based association test program. Replication of these findings in the general population resulted in stronger evidence for an association of the SNPs (best P = 0.00001) and haplotypes with the disease ("susceptible" P = 0.002, "nonsusceptible" P = 0.002). To our knowledge, these data are the first to demonstrate linkage and association of SNPs and haplotypes within the GHSR gene region and human obesity. This linkage, together with significant transmission disequilibrium in families and replication of this association in an independent population, provides evidence that common SNPs and haplotypes within the GHSR region are involved in the pathogenesis of human obesity.

Whole genome scan for obstructive sleep apnea and obesity in African-American families

Obstructive sleep apnea (OSA) is a common, chronic disease associated with obesity. OSA and obesity are both prevalent in African Americans, who are also at increased risk for secondary complications. To identify susceptibility loci for OSA, we undertook a 9-centimorgans genome scan in 59 African-American pedigrees ascertained on the basis of either an affected individual with laboratory-confirmed disease or a proband who was a neighborhood control subject. Variance component linkage analysis was performed for the quantitative phenotypes apnea-hypopnea index (AHI) and body mass index. A candidate region on chromosome 8q (logarithm of odds [LOD] = 1.29, p = 0.006) gave the only evidence for linkage to the AHI. Body mass index was linked to multiple regions, most significantly to markers on chromosome 4q (LOD = 2.63, p = 0.0006) and 8q (LOD = 2.56, p = 0.0007). Evidence of linkage to the AHI was only slightly reduced after adjustment for body mass index. After adjustment for the AHI, some of the primary linkages to body mass index were greatly reduced whereas others remained suggestive. Our results suggest that there are both shared and unshared genetic factors underlying susceptibility to OSA and obesity, and that the genetic determinants of obesity in this population may be modulated by apnea severity.

A genome-wide scan for type 2 diabetes in African-American families reveals evidence for a locus on chromosome 6q

African Americans are at increased risk of type 2 diabetes and many diabetes complications. We have carried out a genome-wide scan for African American type 2 diabetes using 638 affected sibling pairs (ASPs) from 247 families ascertained through impaired renal function to identify type 2 diabetes loci in this high-risk population. Of the 638 ASPs, 210 were concordant for diabetes with impaired renal function. A total of 390 markers, at an average spacing of 9 cM, were genotyped by the Center for Inherited Disease Research (CIDR) as part of the International Type 2 Diabetes Linkage Analysis Consortium. Nonparametric linkage (NPL) analyses conducted using the exponential model implemented in Genehunter Plus provided suggestive evidence for linkage at 6q24-q27 (163.5 cM, logarithm of odds [LOD] 2.26). Multilocus NPL regression analysis identified the 6q locus (D6S1035, LOD 2.67) and two additional regions: 7p (LOD 1.06) and 18q (LOD 0.87) as important in this model. NPL regression-based interaction analyses and ordered subset analyses (OSAs) supported the presence of a locus at chromosome 7p (29-34 cM) in the pedigrees with the earliest mean age of diagnosis of type 2 diabetes (P = 0.009 for interaction, DeltaP = 0.0034 for OSA) and lower mean BMI (P = 0.009 for interaction, DeltaP = 0.070 for OSA). These results provide evidence that genes predisposing African-American individuals to type 2 diabetes are located in the 6q and 7p regions of the genome.

A genome-wide linkage scan for body mass index on Framingham Heart Study families

BACKGROUND

Genome-wide scan data from a community-based sample was used to identify the genetic factors that affect body mass index (BMI). BMI was defined as weight (kg) over the square of height (m), where weight and height were obtained from the first measurement available between the ages of 40 and 50 years.

RESULTS

Significant familial correlations were observed in mother:father (spouse) relative pairs and in all relative pairs examined except parent:daughter pairs. Single-point sib-pair regression analysis provided nominal evidence for linkage (p < 0.05) of loci to BMI at 23 markers. Multi-point sib-pair regression analysis provided nominal evidence for linkage to BMI at 42 loci on 12 chromosomes. Empirical p-values showed results consistent with the multi-point results; all but three of the loci identified by multi-point analysis were also significant.

CONCLUSION

The largest regions of nominally significant linkage were found on chromosomes 2, 3, and 11. The most significant evidence for linkage was obtained with markers D2S1788, D2S1356, D2S1352, D3S1744, and D11S912 from multi-point sib-pair single-trait regression analysis. Our results are in agreement with some of the recently published reports on BMI using various data sets including the Framingham Heart Study data.

The inherited basis of diabetes mellitus: implications for the genetic analysis of complex traits

Diabetes encompasses a heterogeneous group of diseases, each with a substantial genetic component. We review the division of diabetes into different subtypes based on clinical phenotype, the fruitful pursuit of genes underlying monogenic forms of the disease, the successes and drawbacks of whole-genome linkage scans in type 1 and type 2 diabetes, and the recent identification of several diabetes genes by large association studies. We use the lessons learned from this extensive body of evidence to illustrate general implications for the genetic analysis of complex traits.

The genetics of obesity

Obesity prevalence has increased markedly over the past few decades. The obesity pandemic has huge implications for public health and our society. Although multiple studies show that the genetic contribution to obesity is significant, our genes have not changed appreciably over this time period. It was hypothesized that natural selection favors genotypes that result in a thrifty metabolism because individuals who carry these genotypes would be more likely to survive times of nutrient scarcity and to pass these genotypes to successive generations. Now that most of the world has adopted an increasingly "obesigenic" lifestyle of excess caloric intake and decreased physical activity, these same genes contribute to obesity and poor health. With the exception of the rare mutations that cause severe morbid obesity, it seems that numerous genes, each with modest effect, contribute to an individual's predisposition toward the more common forms of obesity. Variants in several candidate genes have been identified: association analyses and functional studies show that they contribute to modest obesity and related phenotypes. More recently, insights regarding gene-gene interactions have begun to emerge. Genome-wide scans for obesity phenotypes have led to the identification of several chromosome regions that are likely to harbor obesity susceptibility genes. Because of the increasing number of genome scans, several regions of replication have emerged. Positional cloning of these genes will undoubtedly unveil new insights into the molecular and pathophysiologic mechanisms of energy homeostasis and obesity.

Obesity--is it a genetic disorder?

Obesity is one of the most pressing problems in the industrialized world. Twin, adoption and family studies have shown that genetic factors play a significant role in the pathogenesis of obesity. Rare mutations in humans and model organisms have provided insights into the pathways involved in body weight regulation. Studies of candidate genes indicate that some of the genes involved in pathways regulating energy expenditure and food intake may play a role in the predisposition to obesity. Amongst these genes, sequence variations in the adrenergic receptors, uncoupling proteins, peroxisome proliferator-activated receptor, and the leptin receptor genes are of particular relevance. Results that have been replicated in at least three genome-wide scans suggest that key genes are located on chromosomes 2p, 3q, 5p, 6p, 7q, 10p, 11q, 17p and 20q. We conclude that the currently available evidence suggests four levels of genetic determination of obesity: genetic obesity, strong genetic predisposition, slight genetic predisposition, and genetically resistant. This growing body of research may help in the development of anti-obesity agents and perhaps genetic tests to predict the risk for obesity.

Perspectives: molecular genetic research in human obesity

Within the past decade the molecular basis of single forms of monogenic obesity has been elucidated. With the exception of functionally relevant mutations in the melanocortin-4 receptor gene, which occur in approximately 2-4% of extremely obese individuals, all other currently known monogenic forms are rare and additionally associated with distinct endocrinological abnormalities. A large number of association studies have been performed in 'normal' obesity. Whereas many associations have been reported, it is largely unclear which of these represent true positive findings. Over 20 genome scans pertaining to obesity and related phenotypes have been performed; specific chromosomal peak regions have been identified in different scans. We review the current status and discuss relevant issues related to phenotyping, association and linkage studies. We recommend that the procedure via which a consensus is reached as to what constitutes a true positive association finding requires formalization.

Linkage for BMI at 3q27 region confirmed in an African-American population

Significant and suggestive linkage for BMI on 3q27 has been reported by several groups, including our own study in African Americans. To further establish the linkage evidence on 3q27, we recruited an independent African-American sample comprising 545 individuals in 128 families. We genotyped 15 short tandem-repeat markers evenly spaced in the 112 cM region around the peak on 3q27 identified in our earlier study. Multipoint linkage analysis by GENEHUNTER2 gave the maximum logarithm of odds (LOD) score 2.4 at map position 188 cM in this sample. When we combined the two samples, linkage evidence was increased to a maximum LOD score (MLS) of 4.3 (point-wise P = 4.34 x 10(-6)) at 188 cM, with a 7 cM 1-LOD-drop interval around the peak. The multiple replications of linkage evidence in the region on 3q27 strongly confirm its potential importance as a candidate region in the search for obesity-related genes.

The human obesity gene map: the 2002 update

This is the ninth update of the human obesity gene map, incorporating published results through October 2002 and continuing the previous format. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, quantitative trait loci (QTLs) from human genome-wide scans and various animal crossbreeding experiments, and association and linkage studies with candidate genes and other markers is reviewed. For the first time, transgenic and knockout murine models exhibiting obesity as a phenotype are incorporated (N = 38). As of October 2002, 33 Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and the causal genes or strong candidates have been identified for 23 of these syndromes. QTLs reported from animal models currently number 168; there are 68 human QTLs for obesity phenotypes from genome-wide scans. Additionally, significant linkage peaks with candidate genes have been identified in targeted studies. Seven genomic regions harbor QTLs replicated among two to five studies. Attempts to relate DNA sequence variation in specific genes to obesity phenotypes continue to grow, with 222 studies reporting positive associations with 71 candidate genes. Fifteen such candidate genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. More than 300 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful sites can be found at http://obesitygene.pbrc.edu.

Genome scan for childhood and adolescent obesity in German families

OBJECTIVE

Several genome scans have been performed for adult obesity. Because single formal genetic studies suggest a higher heritability of body weight in adolescence and because genes that influence body weight in adulthood might not be the same as those that are relevant in childhood and adolescence, we performed a whole genome scan.

METHODS

The genome scan was based on 89 families with 2 or more obese children (sample 1). The mean age of the index patients was 13.63 +/- 2.75 years. A total of 369 individuals were initially genotyped for 437 microsatellite markers. A second sample of 76 families was genotyped using microsatellite markers that localize to regions for which maximum likelihood binomial logarithm of the odd (MLB LOD) scores on use of the concordant sibling pair approach exceeded 0.7 in sample 1.

RESULTS

The regions with MLB LOD scores >0.7 were on chromosomes 1p32.3-p33, 2q37.1-q37.3, 4q21, 8p22, 9p21.3, 10p11.23, 11q11-q13.1, 14q24-ter, and 19p13-q12 in sample 1; MLB LOD scores on chromosomes 8p and 19q exceeded 1.5. In sample 2, MLB LOD scores of 0.68 and 0.71 were observed for chromosomes 10p11.23 and 11q13, respectively.

CONCLUSION

We consider that several of the peaks identified in other scans also gave a signal in this scan as promising for ongoing pursuits to identify relevant genes. The genetic basis of childhood and adolescent obesity might not differ that much from adult obesity.

A genome-wide scan for body mass index among Nigerian families

OBJECTIVE

Interest in mapping genetic variants that are associated with obesity remains high because of the increasing prevalence of obesity and its complications worldwide. Data on genetic determinants of obesity in African populations are rare.

RESEARCH METHODS AND PROCEDURES

We have undertaken a genome-wide scan for body mass index (BMI) in 182 Nigerian families that included 769 individuals.

RESULTS

The prevalence of obesity was only 5%, yet polygenic heritability for BMI was in the expected range (0.46 +/- 0.07). Tandem repeat markers (402) were typed across the genome with an average map density of 9 cM. Pedigree-based analysis using a variance components linkage model demonstrated evidence for linkage on chromosome 7 (near marker D7S817 at 7p14) with a logarithm of odds (LOD) score of 3.8 and on chromosome 11 (marker D11S2000 at 11q22) with an LOD score of 3.3. Weaker evidence for linkage was found on chromosomes 1 (1q21, LOD = 2.2) and 8 (8p22, LOD = 2.3). Several candidate genes, including neuropeptide Y, DRD2, APOA4, lamin A/C, and lipoprotein lipase, lie in or close to the chromosomal regions where strong linkage signals were found.

DISCUSSION

The findings of this study suggest that, as in other populations with higher prevalences of obesity, positive linkage signals can be found on genome scans for obesity-related traits. Follow-up studies may be warranted to investigate these linkages, especially the one on chromosome 11, which has been reported in a population at the opposite end of the BMI distribution.

The emerging pattern of the genetic contribution to human obesity

It has been a little more than 5 years since the publication of the first genome scans focused on obesity-related phenotypes in humans. While the number of scans reported has grown steadily during this time, the results from many of these studies have been modest at best. However, there are a handful of studies that have now reported highly significant findings, and even more important perhaps is the fact that several of these findings have now been replicated as well. Currently there is strong statistical support for approximately half a dozen quantitative trait loci (QTLs) influencing obesity-related phenotypes across a number of populations and ethnic groups. While some of these signals localize near genes that might have been considered a priori as candidate genes for obesity, several others offer evidence for previously unsuspected genes. As a result, there is an intriguing pattern of genetic contribution to obesity that has begun to emerge and which promises to greatly increase our understanding of the relationship between obesity and other chronic diseases such as coronary heart disease and type 2 diabetes.

Identification of hypertension-related QTLs in African American sib pairs

To link hypertension-related phenotypes with chromosomal loci, genome scans were performed in 150 African American sib pairs concordant for essential hypertension. Phenotypes included blood pressure, anthropomorphic measurements, and estimates of body fluid compartments as determined by impedance plethysmography. These phenotypes were also measured in 335 normotensive African Americans. Phenotypes with LOD scores >3.3 were further evaluated for significance by use of permutation procedures. Significant linkage was detected for body mass index (BMI) on chromosomes 1 and 8 and for the ratio of extracellular water to total body water (ECF/TBW) on chromosomes 3, 5, 6, and 7. Both BMI and ECF/TBW were greater in hypertensive sibs than in normotensive subjects (P<0.001). In a subset of hypertensive sibs and normotensive subjects, average 24-hour blood pressures were correlated with ECF/TBW (P<0.01). A region linked to BMI in the hypertensive sibs corresponds to a region of conserved synteny containing blood pressure-related QTLs in an F2 cross of Brown NorwayxDahl salt-sensitive rats. Focusing on hypertension-related phenotypes is a promising approach for identifying the genetic determinants of hypertension.

Genome scan among Nigerians linking blood pressure to chromosomes 2, 3, and 19

An understanding of the genetic influences on hypertension would help unravel the pathophysiology of this complex disorder and improve our understanding of causal mechanisms. Contemporary technology makes it possible to examine enough genetic markers to support a generalized search across the entire genome for candidate regions. In the present study, a family set was recruited from southwest Nigeria, and 378 microsatellite markers were typed on 792 individuals in 196 families. Multipoint variance component analysis identified linkage signals (logarithm of the odds [LOD] 1.74, P<0.0023) for systolic blood pressure on 19p (D19S714) and 19q (D19S246), whereas for diastolic blood pressure, linkage was observed on 2p (D2S1790), 3p (D3S1304), 5q (D5S1462), 7p (D7S3046), 7q (D7S821), and 10q (D10S1221). Other regions of interest (1.18<LOD<1.74, 0.0023<P<0.01) were found on chromosomes 1, 6, 8, 9, and 11. These results provide additional evidence of linkage between blood pressure and several genomic regions reported in previous studies. Some of these regions additionally harbor hypertension candidate genes. Although evidence of linkage for blood pressure has been very slow to accumulate, even in comparison to other complex traits, the sum of current evidence appears to implicate, in particular, 2p, 3p, and 19p. Study designs that make it possible to confirm these results with association analysis and narrow the genomic interval are needed in order to make progress in this field.

Genomewide linkage analysis of body mass index across 28 years of the Framingham Heart Study

We performed a genomewide linkage analysis of six separate measurements of body mass index (BMI) taken over a span of 28 years, from 1971 to 1998, in the Framingham Heart Study. Variance-components linkage analysis was performed on 330 families, using 401 polymorphic markers. The number of individuals with data at each exam ranged from 1,930, in 1971, to 1,401, in 1998. Sex, age, and age squared were included as covariates in the model. There was substantial evidence for linkage on chromosome 6q23-25, in the area of D6S1009, GATA184A08, D6S2436, and D6S305. The six measurements had maximum LOD scores of 4.64, 2.29, 2.41, 1.40, 0.99, and 3.08, respectively, all in the chromosome 6q23-25 region. There was also evidence for linkage of multiple measures on chromosome 11q14 in the area of D11S1998, D11S4464, and D11S912. The six measurements had maximum LOD scores of 0.61, 3.27, 1.30, 0.68, 1.30, and 2.29, respectively, all in the chromosome 11q14 region. Both of these regions have been reported in previous studies. Evidence in the same regions from multiple measurements does not constitute replication; however, it does indicate that linkage studies of BMI are robust with respect to measurement error. It is unclear whether the variation in LOD scores in these regions is due to age effects, varying sample size, or other confounding factors.

Using mouse models to dissect the genetics of obesity

Mice have proved to be powerful models for understanding obesity in humans and farm animals. Single-gene mutants and genetically modified mice have been used successfully to discover genes and pathways that can regulate body weight. For polygenic obesity, the most common pattern of inheritance, many quantitative trait loci (QTLs) have been mapped in crosses between selected and inbred mouse lines. Most QTL effects are additive, and diet, age and gender modify the genetic effects. Congenic, recombinant inbred, advanced intercross, and chromosome substitution strains are needed to map QTLs finely, to identify the genes underlying the traits, and to examine interactions between them.

A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program

A combined analysis of genome scans for obesity was undertaken using the interim results from the National Heart, Lung, and Blood Institute Family Blood Pressure Program. In this research project, four multicenter networks of investigators conducted eight individual studies. Data were available on 6,849 individuals from four ethnic groups (white, black, Mexican American, and Asian). The sample represents the largest single collection of genomewide scan data that has been analyzed for obesity and provides a test of the reproducibility of linkage analysis for a complex phenotype. Body mass index (BMI) was used as the measure of adiposity. Genomewide linkage analyses were first performed separately in each of the eight ethnic groups in the four networks, through use of the variance-component method. Only one region in the analyses of the individual studies showed significant linkage with BMI: 3q22.1 (LOD 3.45, for the GENOA network black sample). Six additional regions were found with an associated LOD >2, including 3p24.1, 7p15.2, 7q22.3, 14q24.3, 16q12.2, and 17p11.2. Among these findings, the linkage at 7p15.2, 7q22.3, and 17p11.2 has been reported elsewhere. A modified Fisher's omnibus procedure was then used to combine the P values from each of the eight genome scans. A complimentary approach to the meta-analysis was undertaken, combining the average allele-sharing identity by descent (pi) for whites, blacks, and Mexican Americans. Using this approach, we found strong linkage evidence for a quantitative-trait locus at 3q27 (marker D3S2427; LOD 3.40, P=.03). The same location has been shown to be linked with obesity-related traits and diabetes in at least two other studies. These results (1) confirm the previously reported obesity-susceptibility locus on chromosomes 3, 7, and 17 and (2) demonstrate that combining samples from different studies can increase the power to detect common genes with a small-to-moderate effect, so long as the same gene has an effect in all samples considered.