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

Recent advances of myotubularin-related (MTMR) protein family in cardiovascular diseases

Belonging to a lipid phosphatase family containing 16 members, myotubularin-related proteins (MTMRs) are widely expressed in a variety of tissues and organs. MTMRs preferentially hydrolyzes phosphatidylinositol 3-monophosphate and phosphatidylinositol (3,5) bis-phosphate to generate phosphatidylinositol and phosphatidylinositol 5-monophosphate, respectively. These phosphoinositides (PIPs) promote membrane degradation during autophagosome-lysosomal fusion and are also involved in various regulatory signal transduction. Based on the ability of modulating the levels of these PIPs, MTMRs exert physiological functions such as vesicle trafficking, cell proliferation, differentiation, necrosis, cytoskeleton, and cell migration. It has recently been found that MTMRs are also involved in the occurrence and development of several cardiovascular diseases, including cardiomyocyte hypertrophy, proliferation of vascular smooth muscle cell, LQT1, aortic aneurysm, etc. This review summarizes the functions of MTMRs and highlights their pathophysiological roles in cardiovascular diseases.

Genetics of Obesity in East Asians

Obesity has become a public health problem worldwide. Compared with Europe, people in Asia tend to suffer from type 2 diabetes with a lower body mass index (BMI). Genome-wide association studies (GWASs) have identified over 750 loci associated with obesity. Although the majority of GWAS results were conducted in individuals of European ancestry, a recent GWAS in individuals of Asian ancestry has made a significant contribution to the identification of obesity susceptibility loci. Indeed, owing to the multifactorial character of obesity with a strong environmental component, the revealed loci may have distinct contributions in different ancestral genetic backgrounds and in different environments as presented through diet and exercise among other factors. Uncovering novel, yet unrevealed genes in non-European ancestries may further contribute to explaining the missing heritability for BMI. In this review, we aimed to summarize recent advances in obesity genetics in individuals of Asian ancestry. We therefore compared proposed mechanisms underlying susceptibility loci for obesity associated with individuals of European and Asian ancestries and discussed whether known genetic variants might explain ethnic differences in obesity risk. We further acknowledged that GWAS implemented in individuals of Asian ancestries have not only validated the potential role of previously specified obesity susceptibility loci but also exposed novel ones, which have been missed in the initial genetic studies in individuals of European ancestries. Thus, multi-ethnic studies have a great potential not only to contribute to a better understanding of the complex etiology of human obesity but also potentially of ethnic differences in the prevalence of obesity, which may ultimately pave new avenues in more targeted and personalized obesity treatments.

The MTMR9 rs2293855 polymorphism is associated with glucose tolerance, insulin secretion, insulin sensitivity and increased risk of prediabetes

BACKGROUND

Polymorphism of rs2293855 in gene MTMR9 has been associated with obesity and metabolic syndrome. We aim to study the association of rs2293855 with type 2 diabetes mellitus (T2DM) intermediate phenotypes in a Han Chinese population.

METHODS

The polymorphism was genotyped in 838 Han Chinese individuals using Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS); all participants underwent a 75 g oral glucose tolerance test (OGTT); associations between the polymorphism and glucose tolerance, indices of insulin secretion and indices of insulin sensitivity were analyzed.

RESULTS

The frequency of genotypes and alleles differed significantly between normal glucose tolerance and prediabetes (P=0.043 and P=0.009, respectively). The GG homozygous presented higher fasting plasma glucose (P=0.009), higher 2-hour plasma glucose (P=0.024) and higher glucose area under the curve (AUC, P=0.01). Moreover, the G allele of rs2293855 was associated with glucose intolerance (fasting glucose, P=0.012; glucose AUC, P=0.006; 2-h glucose, P=0.024); it is also associated with decreased indices of insulin sensitivity (fasting insulin, P=0.043; insulin sensitivity index composite, P=0.009; homeostasis model assessment of insulin resistance, HOMA-IR, P=0.008) and decreased indices of insulin secretion (HOMA of beta cell function, HOMA-B, P=0.028; insulinogenic index, P=0.003). In addition, the minor allele G was also associated with increased risk of prediabetes (OR=1.463, 95%CI: 1.066-2.009, P=0.018).

CONCLUSIONS

Polymorphism of rs2293855 in MTMR9 is associated with measures of glucose tolerance, indices of insulin secretion and indices of insulin sensitivity. We also suggest that allele G is likely to increase the risk of prediabetes by influencing both insulin secretion and insulin sensitivity.

Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs

Evidence that complex traits are highly polygenic has been presented by population-based genome-wide association studies (GWASs) through the identification of many significant variants, as well as by family-based de novo sequencing studies indicating that several traits have a large mutational target size. Here, using a third study design, we show results consistent with extreme polygenicity for body mass index (BMI) and height. On a sample of 20,240 siblings (from 9,570 nuclear families), we used a within-family method to obtain narrow-sense heritability estimates of 0.42 (SE = 0.17, p = 0.01) and 0.69 (SE = 0.14, p = 6 × 10(-)(7)) for BMI and height, respectively, after adjusting for covariates. The genomic inflation factors from locus-specific linkage analysis were 1.69 (SE = 0.21, p = 0.04) for BMI and 2.18 (SE = 0.21, p = 2 × 10(-10)) for height. This inflation is free of confounding and congruent with polygenicity, consistent with observations of ever-increasing genomic-inflation factors from GWASs with large sample sizes, implying that those signals are due to true genetic signals across the genome rather than population stratification. We also demonstrate that the distribution of the observed test statistics is consistent with both rare and common variants underlying a polygenic architecture and that previous reports of linkage signals in complex traits are probably a consequence of polygenic architecture rather than the segregation of variants with large effects. The convergent empirical evidence from GWASs, de novo studies, and within-family segregation implies that family-based sequencing studies for complex traits require very large sample sizes because the effects of causal variants are small on average.

[Genetic scanning on chromosome 8 loci for coronary heart disease]

At present, genome-wide association study on coronary heart disease (CHD) has been carried out in several major medical research centers worldwide. Most studies of CHD susceptibility loci or regions focused on chromosome 1, 3, 9, 11 and 16, while studies on chromosome 8 are rare. To the best of our knowledge, the genome study on chromosome 8 about CHD in Chinese Han population has never been reported before. We aimed to identify CHD susceptibility loci or regions in the Chinese Han population. First, two separated DNA pooling samples were prepared from 156 CHD cases and 1000 normal controls. Then, a total of 13 microsatellite markers at an interval of 10 cM on chromosome 8 were selected for genetic scanning. Finally, the difference of allele frequency at each locus between two pooled samples was analyzed by Chi-square test. Significant differences were found between cases and controls at D8S264(8p23.3-p23.2) and D8S285(8q12.1) (both P<0.05). Therefore, 8p23.3-p23.2 and 8q12.1 are possible to be associated with CHD and further study is needed to screen susceptible genes around these regions.

Ancestral susceptibility to colorectal cancer

Every year, approximately 1 million new colorectal cancer (CRC) cases are diagnosed and about half a million people worldwide die due to this cancer. Known differences in CRC incidence rates are mainly attributed to differences in diet and other environmental factors represented, among others, by nutrition-related complex diseases (e.g. obesity and diabetes mellitus type II). Within the last years, it has become evident that environmental risk factors can be complemented by a genetic component when considering the risk of CRC. For example, a number of polymorphisms are known to be associated with an increased risk of obesity and obesity is a risk factor for CRC. Several studies have shown that the 'ancestral-susceptibility model' can be reasonably applied to nutrition-related complex diseases such as obesity. The work in hand shortly discusses whether the ancestral-susceptibility model can also be applied to CRC as a nutrition-related complex disease.

Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study

Adiponectin has a variety of metabolic effects on obesity, insulin sensitivity, and atherosclerosis. To identify genes influencing variation in plasma adiponectin levels, we performed genome-wide linkage and association scans of adiponectin in two cohorts of subjects recruited in the Genetic Epidemiology of Metabolic Syndrome Study. The genome-wide linkage scan was conducted in families of Turkish and southern European (TSE, n = 789) and Northern and Western European (NWE, N = 2,280) origin. A whole genome association (WGA) analysis (500K Affymetrix platform) was carried out in a set of unrelated NWE subjects consisting of approximately 1,000 subjects with dyslipidemia and 1,000 overweight subjects with normal lipids. Peak evidence for linkage occurred at chromosome 8p23 in NWE subjects (lod = 3.10) and at chromosome 3q28 near ADIPOQ, the adiponectin structural gene, in TSE subjects (lod = 1.70). In the WGA analysis, the single-nucleotide polymorphisms (SNPs) most strongly associated with adiponectin were rs3774261 and rs6773957 (P < 10(-7)). These two SNPs were in high linkage disequilibrium (r(2) = 0.98) and located within ADIPOQ. Interestingly, our fourth strongest region of association (P < 2 x 10(-5)) was to an SNP within CDH13, whose protein product is a newly identified receptor for high-molecular-weight species of adiponectin. Through WGA analysis, we confirmed previous studies showing SNPs within ADIPOQ to be strongly associated with variation in adiponectin levels and further observed these to have the strongest effects on adiponectin levels throughout the genome. We additionally identified a second gene (CDH13) possibly influencing variation in adiponectin levels. The impact of these SNPs on health and disease has yet to be determined.

Association of single-nucleotide polymorphisms in MTMR9 gene with obesity

Genetic factors are clearly involved in the development of obesity, but the genetic background of obesity remains largely unclear. Starting from 62 663 gene-based single-nucleotide polymorphisms (SNPs) in three sequential case-control association studies, we identified a replicated association between the obesity phenotype (BMI > or =30 kg/m(2)) and a SNP (rs2293855) located in the myotublarin-related protein 9 (MTMR9) gene in the chromosomal segment 8p23-p22. P-values (minor allele dominant model) of the first set (93 cases versus 649 controls) and the second set (564 cases versus 562 controls) were 0.008 and 0.0002, respectively. The association was replicated in the third set [394 cases versus 958 controls, P = 0.005, odds ratio (95% CI) =1.40 (1.11-1.78)]. The global P-value was 0.0000005. A multiple regression analysis revealed that gender, age BMI and rs2293855 genotype (minor allele dominant model) were significantly associated with both systolic and diastolic blood pressures. MTMR9 was shown to be the only gene within the haplotype block that contained SNPs associated with obesity. Both the transcript and protein of MTMR9 were detected in the rodent lateral hypothalamic area as well as in the arcuate nucleus, and the protein co-existed with orexin, melanin concentrating hormone, neuropeptide Y and proopiomelanocortin. The levels of MTMR9 transcript in the murine hypothalamic region increased after fasting and were decreased by a high-fat diet. Our data suggested that genetic variations in MTMR9 may confer a predisposition towards obesity and hypertension through regulation of hypothalamic neuropeptides.

Meta-analysis of genome-wide linkage studies in BMI and obesity

OBJECTIVE

The objective was to provide an overall assessment of genetic linkage data of BMI and BMI-defined obesity using a nonparametric genome scan meta-analysis.

RESEARCH METHODS AND PROCEDURES

We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome-wide logarithm of the odds (LOD) scores, non-parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI-defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry.

RESULTS

Bins at chromosome 13q13.2- q33.1, 12q23-q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3-22.3 were also observed for BMI-defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values <0.05 is not different from that expected by chance. The strongest evidence was obtained in the families ascertained for hypertension at 9q31.1-qter and 12p11.21-q23 (p < 0.01).

CONCLUSION

Despite having substantial statistical power, we did not unequivocally implicate specific loci for BMI or obesity. This may be because genes influencing adiposity are of very small effect, with substantial genetic heterogeneity and variable dependence on environmental factors. However, the observation that the FTO gene maps to one of the highest ranking bins for obesity is interesting and, while not a validation of this approach, indicates that other potential loci identified in this study should be investigated further.

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.