A common genetic variant is associated with adult and childhood obesity

Genome-wide association study of electrocardiographic and heart rate variability traits: the Framingham Heart Study

BACKGROUND

Heritable electrocardiographic (ECG) and heart rate variability (HRV) measures, reflecting pacemaking, conduction, repolarization and autonomic function in the heart have been associated with risks for cardiac arrhythmias. Whereas several rare monogenic conditions with extreme phenotypes have been noted, few common genetic factors contributing to interindividual variability in ECG and HRV measures have been identified. We report the results of a community-based genomewide association study of six ECG and HRV intermediate traits.

METHODS

Genotyping using Affymetrix 100K GeneChip was conducted on 1345 related Framingham Heart Study Original and Offspring cohort participants. We analyzed 1175 Original and Offspring participants with ECG data (mean age 52 years, 52% women) and 548 Offspring participants with HRV data (mean age 48 years, 51% women), in relation to 70,987 SNPs with minor allele frequency > or = 0.10, call rate > or = 80%, Hardy-Weinberg p-value > or = 0.001. We used generalized estimating equations to test association of SNP alleles with multivariable-adjusted residuals for QT, RR, and PR intervals, the ratio of low frequency to high frequency power (LF/HFP), total power (TP) and the standard deviation of normal RR intervals (SDNN).

RESULTS

Associations at p < 10(-3) were found for 117 (QT), 105 (RR), 111 (PR), 102 (LF/HF), 121 (TP), and 102 (SDNN) SNPs. Several common variants in NOS1AP (4 SNPs with p-values < 10(-3); lowest p-value, rs6683968, p = 1 x 10(-4)) were associated with adjusted QT residuals, consistent with our previously reported finding for NOS1AP in an unrelated sample of FHS Offspring and other cohorts. All results are publicly available at NCBI's dbGaP at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.

CONCLUSION

In the community-based Framingham Heart Study none of the ECG and HRV results individually attained genomewide significance. However, the presence of bona fide QT-associated SNPs among the top 117 results for QT duration supports the importance of efforts to validate top results from the reported scans. Finding genetic variants associated with ECG and HRV quantitative traits may identify novel genes and pathways implicated in arrhythmogenesis and allow for improved recognition of individuals at high risk for arrhythmias in the general population.

A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study

BACKGROUND

Blood lipid levels including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) are highly heritable. Genome-wide association is a promising approach to map genetic loci related to these heritable phenotypes.

METHODS

In 1087 Framingham Heart Study Offspring cohort participants (mean age 47 years, 52% women), we conducted genome-wide analyses (Affymetrix 100K GeneChip) for fasting blood lipid traits. Total cholesterol, HDL-C, and TG were measured by standard enzymatic methods and LDL-C was calculated using the Friedewald formula. The long-term averages of up to seven measurements of LDL-C, HDL-C, and TG over a approximately 30 year span were the primary phenotypes. We used generalized estimating equations (GEE), family-based association tests (FBAT) and variance components linkage to investigate the relationships between SNPs (on autosomes, with minor allele frequency > or =10%, genotypic call rate > or =80%, and Hardy-Weinberg equilibrium p > or = 0.001) and multivariable-adjusted residuals. We pursued a three-stage replication strategy of the GEE association results with 287 SNPs (P < 0.001 in Stage I) tested in Stage II (n approximately 1450 individuals) and 40 SNPs (P < 0.001 in joint analysis of Stages I and II) tested in Stage III (n approximately 6650 individuals).

RESULTS

Long-term averages of LDL-C, HDL-C, and TG were highly heritable (h2 = 0.66, 0.69, 0.58, respectively; each P < 0.0001). Of 70,987 tests for each of the phenotypes, two SNPs had p < 10(-5) in GEE results for LDL-C, four for HDL-C, and one for TG. For each multivariable-adjusted phenotype, the number of SNPs with association p < 10(-4) ranged from 13 to 18 and with p < 10(-3), from 94 to 149. Some results confirmed previously reported associations with candidate genes including variation in the lipoprotein lipase gene (LPL) and HDL-C and TG (rs7007797; P = 0.0005 for HDL-C and 0.002 for TG). The full set of GEE, FBAT and linkage results are posted at the database of Genotype and Phenotype (dbGaP). After three stages of replication, there was no convincing statistical evidence for association (i.e., combined P < 10(-5) across all three stages) between any of the tested SNPs and lipid phenotypes.

CONCLUSION

Using a 100K genome-wide scan, we have generated a set of putative associations for common sequence variants and lipid phenotypes. Validation of selected hypotheses in additional samples did not identify any new loci underlying variability in blood lipids. Lack of replication may be due to inadequate statistical power to detect modest quantitative trait locus effects (i.e., <1% of trait variance explained) or reduced genomic coverage of the 100K array. GWAS in FHS using a denser genome-wide genotyping platform and a better-powered replication strategy may identify novel loci underlying blood lipids.

Genome-wide association with select biomarker traits in the Framingham Heart Study

BACKGROUND

Systemic biomarkers provide insights into disease pathogenesis, diagnosis, and risk stratification. Many systemic biomarker concentrations are heritable phenotypes. Genome-wide association studies (GWAS) provide mechanisms to investigate the genetic contributions to biomarker variability unconstrained by current knowledge of physiological relations.

METHODS

We examined the association of Affymetrix 100K GeneChip single nucleotide polymorphisms (SNPs) to 22 systemic biomarker concentrations in 4 biological domains: inflammation/oxidative stress; natriuretic peptides; liver function; and vitamins. Related members of the Framingham Offspring cohort (n = 1012; mean age 59 +/- 10 years, 51% women) had both phenotype and genotype data (minimum-maximum per phenotype n = 507-1008). We used Generalized Estimating Equations (GEE), Family Based Association Tests (FBAT) and variance components linkage to relate SNPs to multivariable-adjusted biomarker residuals. Autosomal SNPs (n = 70,987) meeting the following criteria were studied: minor allele frequency > or = 10%, call rate > or = 80% and Hardy-Weinberg equilibrium p > or = 0.001.

RESULTS

With GEE, 58 SNPs had p < 10(-6): the top SNPs were rs2494250 (p = 1.00*10(-14)) and rs4128725 (p = 3.68*10(-12)) for monocyte chemoattractant protein-1 (MCP1), and rs2794520 (p = 2.83*10(-8)) and rs2808629 (p = 3.19*10(-8)) for C-reactive protein (CRP) averaged from 3 examinations (over about 20 years). With FBAT, 11 SNPs had p < 10(-6): the top SNPs were the same for MCP1 (rs4128725, p = 3.28*10(-8), and rs2494250, p = 3.55*10(-8)), and also included B-type natriuretic peptide (rs437021, p = 1.01*10(-6)) and Vitamin K percent undercarboxylated osteocalcin (rs2052028, p = 1.07*10(-6)). The peak LOD (logarithm of the odds) scores were for MCP1 (4.38, chromosome 1) and CRP (3.28, chromosome 1; previously described) concentrations; of note the 1.5 support interval included the MCP1 and CRP SNPs reported above (GEE model). Previous candidate SNP associations with circulating CRP concentrations were replicated at p < 0.05; the SNPs rs2794520 and rs2808629 are in linkage disequilibrium with previously reported SNPs. GEE, FBAT and linkage results are posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.

CONCLUSION

The Framingham GWAS represents a resource to describe potentially novel genetic influences on systemic biomarker variability. The newly described associations will need to be replicated in other studies.

Interpreting analyses of continuous covariates in affected sibling pair linkage studies

Datasets collected for linkage analyses of complex human diseases often include a number of clinical or environmental covariates. In this study, we evaluated the performance of three linkage analysis methods when the relationship between continuous covariates and disease risk or linkage heterogeneity was modeled in three different ways: (1) The covariate distribution is determined by a quantitative trait locus (QTL), which contributes indirectly to the disease risk; (2) the covariate is not genetically determined, but influences the disease risk through statistical interaction with a disease susceptibility locus; (3) the covariate distribution differs in families linked or unlinked to a particular disease susceptibility locus. We analyzed simulated datasets with a regression-based QTL analysis, a nonparametric analysis of the binary affection status, and the ordered subset analysis (OSA). We found that a significant OSA result may be due to a gene that influences variability in the population distribution of a continuous disease risk factor. Conversely, a regression-based QTL analysis may detect the presence of gene-environment (GxE) interaction in a sample of primarily affected individuals. The contribution of unaffected siblings and the size of baseline lod scores may help distinguish between QTL and GxE models. As illustrated by a linkage study of multiplex families with age-related macular degeneration, our findings assist in the interpretation of analysis results in real datasets. They suggest that the side-by-side evaluation of OSA and QTL results may provide important information about the relationship of measured covariates with either disease risk or linkage heterogeneity.

Multistage Sampling for Genetic Studies

In the past, to study Mendelian diseases, segregating families have been carefully ascertained for segregation analysis, followed by collecting extended multiplex families for linkage analysis. This would then be followed by association studies, using independent case-control samples and/or additional family data. Recently, for complex diseases, the initial sampling has been for a genome-wide linkage analysis, often using independent sib-pairs or nuclear families, to identify candidate regions for follow-up with association studies, again using case-control samples and/or additional family data. We now have the ability to conduct genome-wide association studies using 100,000-500,000 diallelic genetic markers. For such studies we focus especially on efficient two-stage association sampling designs, which can retain nearly optimal statistical power at about half the genotyping cost. Similarly, beginning an association study by genotyping pooled samples may also be a viable option if the cost of accurately pooling DNA samples outweighs genotyping costs. Finally, we note that the sampling of family data for linkage analysis is not a practice that should be automatically discontinued.

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.

Genomewide weighted hypothesis testing in family-based association studies, with an application to a 100K scan

For genomewide association (GWA) studies in family-based designs, we propose a novel two-stage strategy that weighs the association P values with the use of independently estimated weights. The association information contained in the family sample is partitioned into two orthogonal components--namely, the between-family information and the within-family information. The between-family component is used in the first (i.e., screening) stage to obtain a relative ranking of all the markers. The within-family component is used in the second (i.e., testing) stage in the framework of the standard family-based association test, and the resulting P values are weighted using the estimated marker ranking from the screening step. The approach is appealing, in that it ensures that all the markers are tested in the testing step and, at the same time, also uses information from the screening step. Through simulation studies, we show that testing all the markers is more powerful than testing only the most promising ones from the screening step, which was the method suggested by Van Steen et al. A comparison with a population-based approach shows that the approach achieves comparable power. In the presence of a reasonable level of population stratification, our approach is only slightly affected in terms of power and, since it is a family-based method, is completely robust to spurious effects. An application to a 100K scan in the Framingham Heart Study illustrates the practical advantages of our approach. The proposed method is of general applicability; it extends to any setting in which prior, independent ranking of hypotheses is available.

Bivariate linkage study of proximal hip geometry and body size indices: the Framingham study

Femoral geometry and body size are both characterized by substantial heritability. The purpose of this study was to discern whether hip geometry and body size (height and body mass index, BMI) share quantitative trait loci (QTL). Dual-energy X-ray absorptiometric scans of the proximal femur from 1,473 members in 323 pedigrees (ages 31-96 years) from the Framingham Osteoporosis Study were studied. We measured femoral neck length, neck-shaft angle, subperiosteal width (outer diameter), cross-sectional bone area, and section modulus, at the narrowest section of the femoral neck (NN), intertrochanteric (IT), and femoral shaft (S) regions. In variance component analyses, genetic correlations (rho ( G )) between hip geometry traits and height ranged 0.30-0.59 and between hip geometry and BMI ranged 0.11-0.47. In a genomewide linkage scan with 636 markers, we obtained nominally suggestive linkages (bivariate LOD scores > or =1.9) for geometric traits and either height or BMI at several chromosomes (4, 6, 9, 15, and 21). Two loci, on chr. 2 (80 cM, BMI/shaft section modulus) and chr. X (height/shaft outer diameter), yielded bivariate LOD scores > or =3.0; although these loci were linked in univariate analyses with a geometric trait, neither was linked with either height or BMI. In conclusion, substantial genetic correlations were found between the femoral geometric traits, height and BMI. Linkage signals from bivariate linkage analyses of bone geometric indices and body size were similar to those obtained in univariate linkage analyses of femoral geometric traits, suggesting that most of the detected QTL primarily influence geometry of the hip.

Power analysis for genome-wide association studies

Background

Genome-wide association studies are a promising new tool for deciphering the genetics of complex diseases. To choose the proper sample size and genotyping platform for such studies, power calculations that take into account genetic model, tag SNP selection, and the population of interest are required.

Results

The power of genome-wide association studies can be computed using a set of tag SNPs and a large number of genotyped SNPs in a representative population, such as available through the HapMap project. As expected, power increases with increasing sample size and effect size. Power also depends on the tag SNPs selected. In some cases, more power is obtained by genotyping more individuals at fewer SNPs than fewer individuals at more SNPs.

Conclusion

Genome-wide association studies should be designed thoughtfully, with the choice of genotyping platform and sample size being determined from careful power calculations.

Association of variants of the interleukin-23 receptor gene with susceptibility to pediatric Crohn's disease

BACKGROUND & AIMS

Recently an association was shown between the single nucleotide polymorphism (SNP), rs11209026, within the interleukin-23 receptor (IL23R) locus and Crohn's disease (CD) as a consequence of a genome-wide association study of this disease in adults. We examined the effects of this and other previously reported SNPs at this locus with respect to CD in children.

METHODS

By using data from our ongoing genome-wide association study in our cohort of 142 pediatric CD patients and 281 matched controls, we investigated the association of the previously reported SNPs at the IL23R locus with the childhood form of this disease.

RESULTS

By using the Fisher exact test, the minor allele frequency of rs11209026 in the patients was 1.75%, whereas it was 6.61% in the controls, yielding a protective odds ratio (OR) of 0.25 (95% confidence interval, 0.10-0.65; 1-sided P = 9.2 x 10(-4)). Furthermore, of all the SNPs previously reported, rs11209026 was associated the most strongly. A subsequent family based association test (which is more resistant to population stratification) with 65 sets of trios derived from our initial patient cohort yielded significant association with rs11209026 in a transmission disequilibrium test (1-sided P = .0017). In contrast, no association was detected to the caspase-recruitment domain 15 gene for the inflammatory bowel disease phenotype.

CONCLUSIONS

The OR of the IL23R variant in our pediatric study is highly comparable with that reported previously in a non-Jewish adult inflammatory bowel disease case-control cohort (OR, 0.26). As such, variants in the IL23R gene confer a similar magnitude of risk of CD to children as for their adult counterparts.

Genetic susceptibility to peripheral arterial disease: a dark corner in vascular biology

Peripheral arterial disease (PAD) is characterized by reduced blood flow to the limbs, usually as a consequence of atherosclerosis, and affects approximately 12 million Americans. It is a common cause of cardiovascular morbidity and an independent predictor of cardiovascular mortality. Similar to other atherosclerotic diseases, such as coronary artery disease, PAD is the result of the complex interplay between injurious environmental stimuli and genetic predisposing factors of the host. Genetic susceptibility to PAD is likely contributed by sequence variants in multiple genes, each with modest effects. Although many of these variants probably alter susceptibility both to PAD and to coronary artery disease, it is likely that there exists a set of variants specifically to alter susceptibility to PAD. Despite the prevalence of PAD and its high societal burden, relatively little is known about such genetic variants. This review summarizes our limited present knowledge and gives an overview of recent, more powerful approaches to elucidating the genetic basis of PAD. We discuss the advantages and limitations of genetic studies and highlight the need for collaborative networks of PAD investigators for shedding light on this dark corner of vascular biology.

Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone.

The common genetic variant upstream of INSIG2 gene is not associated with obesity in Indian population

The high incidence of obesity has resulted in increased morbidity and mortality worldwide. Obesity, a common lifestyle disorder, is caused by multiple factors with heredity playing a strong causal role. Recently, a genetic variation upstream of insulin-induced gene 2 (INSIG2) (rs7566605) has been reported to be associated with obesity in four separate cohorts. Because the lifestyle and food preferences of a large proportion of Indian population differ from the rest of the world, we studied the impact of this polymorphism with body mass index (BMI). The study consisted of two cohorts--1577 healthy individuals from three major linguistic lineages in India and 610 coronary artery disease cases and controls. In the two cohorts studied, no significant association was observed between the polymorphism and BMI. However, frequency of homozygous variant genotype was higher in non-obese individuals as compared with obese individuals in both cohorts although the difference was marginally significant only in the case-control cohort under the assumption of a recessive model. Furthermore, regardless of age and sex, mean BMI did not vary with genotype under the assumptions of recessive model. Thus, in contrast to earlier reports, the variant upstream of INSIG2 is not a determinant of BMI in Indian population.

Linkage disequilibrium in wild mice

Crosses between laboratory strains of mice provide a powerful way of detecting quantitative trait loci for complex traits related to human disease. Hundreds of these loci have been detected, but only a small number of the underlying causative genes have been identified. The main difficulty is the extensive linkage disequilibrium (LD) in intercross progeny and the slow process of fine-scale mapping by traditional methods. Recently, new approaches have been introduced, such as association studies with inbred lines and multigenerational crosses. These approaches are very useful for interval reduction, but generally do not provide single-gene resolution because of strong LD extending over one to several megabases. Here, we investigate the genetic structure of a natural population of mice in Arizona to determine its suitability for fine-scale LD mapping and association studies. There are three main findings: (1) Arizona mice have a high level of genetic variation, which includes a large fraction of the sequence variation present in classical strains of laboratory mice; (2) they show clear evidence of local inbreeding but appear to lack stable population structure across the study area; and (3) LD decays with distance at a rate similar to human populations, which is considerably more rapid than in laboratory populations of mice. Strong associations in Arizona mice are limited primarily to markers less than 100 kb apart, which provides the possibility of fine-scale association mapping at the level of one or a few genes. Although other considerations, such as sample size requirements and marker discovery, are serious issues in the implementation of association studies, the genetic variation and LD results indicate that wild mice could provide a useful tool for identifying genes that cause variation in complex traits.

Candidate genes and growth curves for adiposity in African- and European-American youth

OBJECTIVE

Obesity is associated with multiple health problems and often originates in childhood. This study investigated the association of genes with the development of general and central obesity from childhood into adulthood.

DESIGN

Individual growth curves for measures of general adiposity were examined in an 11-year (1987-1998) cohort study. Single-nucleotide polymorphisms (SNPs) in 11 candidate genes were genotyped.

SUBJECTS

Five hundred and twenty-six subjects classified by race (49% African American (AA)), sex (47% male) and socio-economic status (SES).

RESULTS

AA female carriers of the 27Glu allele in the ADRB2 gene had a larger waist circumference (P<0.05). Subjects of high SES with the ApoB 4145Lys allele had a larger mean waist circumference than those without this allele (P<0.05). Only in the presence of an adverse environment (low SES) did carriers of the NOS3 298Asp allele have a larger mean body mass index, waist circumference and sum of skinfolds (P<0.05).

CONCLUSION

These results suggest that several polymorphisms are associated with the mean level of adiposity, with the effects depending on other factors such as race, sex and/or SES.

Changes in body composition after childhood cancer treatment: Impact on future health status—A review

PURPOSE

To describe data on changes in body composition in childhood cancer survivors. Underlying mechanisms in development of obesity are addressed, in order to discuss intervention strategies.

METHODS

A systematic literature search was undertaken with a number of search terms.

RESULTS

Female survivors of ALL and brain tumours, especially if treated with cranial irradiation, showed a higher prevalence of obesity compared with the general population, while survivors of other malignancies had a higher prevalence of underweight. Influences of corticosteroid treatment and cytostatics on body composition are uncertain. Diminished physical activity, early adiposity rebound (<5 years of age) and/or hypothalamic involvement of tumour or treatment, and subsequent growth hormone deficiency, may play a role in the development of obesity in childhood cancer survivors.

CONCLUSION

Longitudinal prospective studies in more extensive cohorts are necessary to estimate actual prevalence and facilitate the unravelling of the underlying mechanisms in change of body composition.

Genetic susceptibility and body mass in childhood cancer survivors

Childhood cancer survivors are at increased risk of many long-term treatment-related sequel such as second cancers, cardiovascular disease, and pulmonary complications. Certain treatments seem to influence the risk of becoming overweight, obese, or underweight, and abnormal body mass index (BMI) is associated with increased morbidity and mortality. Because BMI is modifiable, it is important to identify treatment and patient-related factors contributing to altered BMI. New research areas include exploring how genetic susceptibility through population polymorphism may contribute to BMI. Illuminating potential gene-environment interactions that influence obesity and underweight might be more readily accomplished in a study of high-risk individuals (i.e., childhood cancer survivors) with well-characterized exposures. The new Childhood Cancer Research Network in the Children's Oncology Group, when fully implemented, should make it less difficult in the future to recruit the large numbers of patients needed for such studies.

Obesity and polymorphisms in genes regulating human adipose tissue

Obesity is the result of an imbalance between food intake and energy expenditure resulting in the storing of energy as fat. Adipose tissue contains the largest store of energy in the body and plays important roles in regulating energy partitioning. Developments in genomics, in particular microarray-based expression profiling, have provided scientists with a number of new candidate genes whose expression in adipose tissue is regulated by obesity. Integrating expression profiles with genome-wide linkage and/or association analyses is a promising strategy to identify new genes underlying susceptibility to obesity. This article provides a comprehensive review of adipose-tissue-expressed genes implicated in predisposition to human obesity. The authors consider the following genes of particular interest: peroxisome proliferator-activated receptor gamma and, potentially, INSIG2 acting in adipogenesis; the adrenoreceptors beta 2 and 3, as well as hormone-sensitive lipase acting on lipolysis; uncoupling protein 2 acting in mitochondria energy expenditure; and among secreted molecules the cytokine tumor necrosis factor alpha and the hormone leptin. With the rapid development in genome research, we predict that additional alleles in genes regulating adipose tissue function will be established as risk factors for common obesity in the coming years. This has important implications for the prevention of obesity and may also offer new therapeutic targets.

Non-Replication and Inconsistency in the Genome-Wide Association Setting

Non-replication and inconsistency had been common features in the search for common variants of candidate genes affecting the risk of complex diseases. They may continue to require attention in the current era, when massive hypothesis-free testing of genetic variants is feasible. An empirical evaluation of the early experience with genome-wide association (GWA) studies suggests several examples where proposed associations have failed to be replicated by subsequent investigations. Non-replication and inconsistency is defined here in the framework of cumulative meta-analysis. Ideally, associations exist, GWA finds them, and subsequent investigations should replicate them. However, a number of other possibilities need to be considered. No common genetic variants may associate with the phenotype of interest and GWA may find nothing; or associations may exist, but GWA may miss them. Associations that do not exist may be falsely selected by the GWA and subsequent studies may appropriately refute them or falsely replicate them. Finally, GWA may find true associations that are nevertheless falsely non-replicated in the subsequent studies; or associations may be genuinely inconsistent across study populations. A list of options is presented for consideration in each of these scenarios.

Relevance of Genetics and Genomics for Prevention and Treatment of Cardiovascular Disease

Atherosclerotic cardiovascular disease (CVD) is a major health problem in the United States and around the world. Evidence accumulated over decades convincingly demonstrates that family history in a parent or a sibling is associated with atherosclerotic CVD, manifested as coronary heart disease, stroke, and/or peripheral arterial disease. Although there are several mendelian disorders that contribute to CVD, most common forms of CVD are believed to be multifactorial and to result from many genes, each with a relatively small effect working alone or in combination with modifier genes and/or environmental factors. The identification and the characterization of these genes and their modifiers would enhance prediction of CVD risk and improve prevention, treatment, and quality of care. This scientific statement describes the approaches researchers are using to advance understanding of the genetic basis of CVD and details the current state of knowledge regarding the genetics of myocardial infarction, atherosclerotic CVD, hypercholesterolemia, and hypertension. Current areas of interest and investigation--including gene-environment interaction, pharmacogenetics, and genetic counseling--are also discussed. The statement concludes with a list of specific recommendations intended to help incorporate usable knowledge into current clinical and public health practice, foster and guide future research, and prepare both researchers and practitioners for the changes likely to occur as molecular genetics moves from the laboratory to clinic.

Converging evidence for a pseudoautosomal cytokine receptor gene locus in schizophrenia

Schizophrenia is a strongly heritable disorder, and identification of potential candidate genes has accelerated in recent years. Genomewide scans have identified multiple large linkage regions across the genome, with fine-mapping studies and other investigations of biologically plausible targets demonstrating several promising candidate genes of modest effect. The recent introduction of technological platforms for whole-genome association (WGA) studies can provide an opportunity to rapidly identify novel targets, although no WGA studies have been reported in the psychiatric literature to date. We report results of a case-control WGA study in schizophrenia, examining approximately 500 000 markers, which revealed a strong effect (P=3.7 x 10(-7)) of a novel locus (rs4129148) near the CSF2RA (colony stimulating factor, receptor 2 alpha) gene in the pseudoautosomal region. Sequencing of CSF2RA and its neighbor, IL3RA (interleukin 3 receptor alpha) in an independent case-control cohort revealed both common intronic haplotypes and several novel, rare missense variants associated with schizophrenia. The presence of cytokine receptor abnormalities in schizophrenia may help explain prior epidemiologic data relating the risk for this illness to altered rates of autoimmune disorders, prenatal infection and familial leukemia.

Effect of an estrogen receptor-alpha intron 4 polymorphism on fat mass in 11-year-old children

CONTEXT

Polymorphisms in the ESR1 gene encoding estrogen receptor (ER)-alpha may be associated with fat mass in adults.

OBJECTIVES

The objective of the study was to establish whether ESR1 polymorphisms influence fat mass in childhood.

DESIGN

This was a cross-sectional analysis after genotyping of rs9340799, rs2234693, and rs7757956 ESR1 polymorphisms.

SETTING

The Avon Longitudinal Study of Parents and Children (ALSPAC) was a population-based prospective study.

PARTICIPANTS

Participants included 3097 11-yr-old children with results for ESR1 genotyping, puberty measures, and dual-energy x-ray absorptiometry results.

OUTCOMES

Relationships between ESR1 polymorphisms and indices of body composition were measured.

RESULTS

The rs7757956 polymorphism was associated with fat mass (P = 0.002). Total body fat mass (adjusted for height) was reduced by 6% in children with TA/AA genotypes, and risk of being overweight (> or =85th centile of fat mass) was decreased by 20%. This genetic effect appeared to interact with puberty in girls (P = 0.05 for interaction): in those with the TT genotype, total body fat mass (adjusted for height) was 18% higher in Tanner stages 3-5 vs. stages 1-2; the equivalent difference was 7% in those with TA/AA genotypes. Furthermore, the risk of being overweight was 36% lower in girls with TA/AA genotypes in Tanner stages 3-5, but no reduction was seen in those in stages 1-2. Neither rs9340799 nor rs2234693 polymorphisms were associated with body composition measures.

CONCLUSIONS

Fat mass in 11-yr-old children was related to the rs7757956 ESR1 polymorphism. This association was strongest in girls in more advanced puberty, in whom the risk of being overweight was reduced by 36% in those with the TA/AA genotype.

A common haplotype in the G-protein-coupled receptor gene GPR74 is associated with leanness and increased lipolysis

The G-protein-coupled receptor GPR74 is a novel candidate gene for body weight regulation. In humans, it is predominantly expressed in brain, heart, and adipose tissue. We report a haplotype in the GPR74 gene, ATAG, with allele frequency ~4% in Scandinavian cohorts, which was associated with protection against obesity in two samples selected for obese and lean phenotypes (odds ratio for obesity 0.48 and 0.62; nominal P=.0014 and .014; n=1,013 and 1,423, respectively). In a population-based sample, it was associated with lower waist (P=.02) among 3,937 men and with obesity protection (odds ratio 0.36; P=.036) among those selected for obese or lean phenotypes. The ATAG haplotype was associated with increased adipocyte lipid mobilization (lipolysis) in vivo and in vitro. In human fat cells, GPR74 receptor stimulation and inhibition caused a significant and marked decrease and increase, respectively, of lipolysis, which could be linked to catecholamine stimulation of adipocytes through beta -adrenergic receptors. These findings suggest that a common haplotype in the GPR74 gene protects against obesity, which, at least in part, is caused by a relief of inhibition of lipid mobilization from adipose tissue. The latter involves a cross-talk between GPR74 and beta -adrenoceptor signaling to lipolysis in fat cells.

Possession of ATM sequence variants as predictor for late normal tissue responses in breast cancer patients treated with radiotherapy

PURPOSE

The ATM gene product is a central component of cell cycle regulation and genomic surveillance. We hypothesized that DNA sequence alterations in ATM predict for adverse effects after external beam radiotherapy for early breast cancer.

METHODS AND MATERIALS

A total of 131 patients with a minimum of 2 years follow-up who had undergone breast-conserving surgery and adjuvant radiotherapy were screened for sequence alterations in ATM using DNA from blood lymphocytes. Genetic variants were identified using denaturing high performance liquid chromatography. The Radiation Therapy Oncology Group late morbidity scoring schemes for skin and subcutaneous tissues were applied to quantify the radiation-induced effects.

RESULTS

Of the 131 patients, 51 possessed ATM sequence alterations located within exons or in short intron regions flanking each exon that encompass putative splice site regions. Of these 51 patients, 21 (41%) exhibited a minimum of a Grade 2 late radiation response. In contrast, of the 80 patients without an ATM sequence variation, only 18 (23%) had radiation-induced adverse responses, for an odds ratio of 2.4 (95% confidence interval, 1.1-5.2). Fifteen patients were heterozygous for the G-->A polymorphism at nucleotide 5557, which causes substitution of asparagine for aspartic acid at position 1853 of the ATM protein. Of these 15 patients, 8 (53%) exhibited a Grade 2-4 late response compared with 31 (27%) of the 116 patients without this alteration, for an odds ratio of 3.1 (95% confidence interval, 1.1-9.4).

CONCLUSION

Sequence variants located in the ATM gene, in particular the 5557 G-->A polymorphism, may predict for late adverse radiation responses in breast cancer patients.

A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity

Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type 2 diabetes-susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass.

[The National Genome Research Network. Genome research in Germany]

In 2001 Germany's Federal Ministry of Education and Research (BMFM) initiated the National Genome Research Network (NGFN). The goals of the NGFN are the investigation of the molecular basis of common diseases to improve new methods for prevention, diagnosis and therapy. The disease-oriented genome networks investigate cardiovascular diseases, cancer, diseases of the nervous system, diseases due to environmental factors and infection, and inflammation. They are supported by technological platforms and a component for technology transfer. The explicit aims include better integration of public health research and economy in order to gain an efficient economical and technological utilisation and application in community health. This article describes the creation of the NGFN in the context of international and national genome research, shows the structure and content of the NGFN and gives examples for NGFN research in networks on a highly, internationally recognised level.

Gene-diet interactions in childhood obesity: paucity of evidence as the epidemic of childhood obesity continues to rise

Childhood obesity is growing rapidly worldwide. Although there have been enormous advances in the genetic underpinnings of obesity in recent years, the pathways that lead to obesity are still not completely understood. One of the ongoing challenges is the lack of a comprehensive definition of the obese phenotype that encompasses intermediary phenotypic expressions of biological and behavioral nature. Interactions between genetic and environmental factors, including nutrient exposures and dietary behaviors, can influence the development of the obese phenotype. Specifically, genes play a decisive role in the etiology of childhood obesity under the permissive circumstances of an obesogenic environment (increase in energy intake with a decrease in physical activity). Like many diseases, the causes of obesity are complex and their investigation requires novel approaches. Given the many contributors to obesity (weight gain, weight loss, weight maintenance, variability in body composition), as well as the dynamic nature of this issue, genomic tools must continue to be employed to evaluate all dimensions of the obesity phenotype, such as biochemical characteristics, susceptibility markers, nutrient intake, feeding practices and gene-environment interactions. Fundamental knowledge of the types of genes involved and available gene-diet interaction studies in children's obesity are reviewed. Although there is a paucity of existing literature in this specific domain of childhood obesity, ongoing investigations utilizing large cohorts have potential for providing the knowledge needed for targeted interventions in the future.

Association studies of BMI and type 2 diabetes in the neuropeptide Y pathway: a possible role for NPY2R as a candidate gene for type 2 diabetes in men

The neuropeptide Y (NPY) family of peptides and receptors regulate food intake. Inherited variation in this pathway could influence susceptibility to obesity and its complications, including type 2 diabetes. We genotyped a set of 71 single nucleotide polymorphisms (SNPs) that capture the most common variation in NPY, PPY, PYY, NPY1R, NPY2R, and NPY5R in 2,800 individuals of recent European ancestry drawn from the near extremes of BMI distribution. Five SNPs located upstream of NPY2R were nominally associated with BMI in men (P values = 0.001-0.009, odds ratios [ORs] 1.27-1.34). No association with BMI was observed in women, and no consistent associations were observed for other genes in this pathway. We attempted to replicate the association with BMI in 2,500 men and tested these SNPs for association with type 2 diabetes in 8,000 samples. We observed association with BMI in men in only one replication sample and saw no association in the combined replication samples (P = 0.154, OR = 1.09). Finally, a 9% haplotype was associated with type 2 diabetes in men (P = 1.73 x 10(-4), OR = 1.36) and not in women. Variation in this pathway likely does not have a major influence on BMI, although small effects cannot be ruled out; NPY2R should be considered a candidate gene for type 2 diabetes in men.

Identifying the genotype behind the phenotype: a role model found in VKORC1 and its association with warfarin dosing

Genotype-phenotype studies in pharmacogenomics promise to identify the genetic factors that contribute substantially to variation in individual drug response. While most genetic association studies have failed to deliver this promise, several recent examples serve as a reminder that these associations do exist and can be identified when investigated using well-designed studies. Here, we describe the path taken to identify the association between common vitamin K epoxide reductase complex subunit 1 genetic variation and warfarin dosing in patients. We also describe the key elements that led the way, such as definition of the phenotype, confirmation of a genetic component, determination of biological plausibility and selection of genetic polymorphisms. We also describe several avenues that are yet to be explored for the specific vitamin K epoxide reductase complex subunit 1 warfarin example that can also be generalized as future directions for many genetic association studies in pharmacogenomics. These future avenues will be best explored using diverse approaches encompassing clinical, statistical and genomic methods currently being developed for genotype-phenotype studies in human populations.

Genotyping the risk of anthracycline-induced cardiotoxicity

Anthracyclines belong to the most successful antineoplastic drugs, but they are cardiotoxic, which may result in congestive heart failure (CHF). The CHF risk increases with the cumulative anthracycline dose, but it seems also to be modified by individual factors. A role of the individual genetic background is consistent with the altered sensitivity to anthracyclines observed in many transgenic and knockout mouse strains. First clinical data obtained in humans suggest the existence of predisposing variants in genes involved in the oxidative stress, and in the metabolism and transport of anthracyclines. These data will have to be verified in further clinical trials before any attempts of their application in the individual cardiotoxicity prediction can be undertaken. In the meantime, anthracycline-induced cardiotoxicity can be best reduced by application of liposomal anthracycline formulations or by a co-medication with the cardioprotective iron chelator dexrazoxane.

Atherosclerosis: the path from genomics to therapeutics

Recent rapid advances in genomic tools and techniques hold great promise for transforming the practice of cardiovascular medicine. Resources including the Human Genome Project and the International HapMap project, major technological advances in high-throughput genotyping and methods of statistical analysis, and methods for high-throughput gene expression and small molecule profiling allow researchers to confront issues that will fundamentally change the practice of cardiovascular medicine during the 21st century. Genomic, proteomic, and metabolomic studies of complex cardiovascular diseases such as atherosclerosis will bridge epidemiology and basic biology, and promise increased understanding of cardiovascular disease processes. Genetic approaches applied to atherosclerosis will continue to identify genes and pathways involved in the predisposition to and pathophysiology of atherosclerosis. Gene expression profiling refines our understanding of the dynamic nature of the atherosclerotic vascular wall and promises discovery and validation of targets for therapeutic intervention. Opportunities to translate genetic, genomic, proteomic, and metabolomic information into cardiovascular clinical practice have never been greater, but their fruition requires validation in large independent cohorts, achieved only through collaborative effort. Their continued success will depend on ongoing cooperation within the cardiovascular research community.

Medical sequencing at the extremes of human body mass

Body weight is a quantitative trait with significant heritability in humans. To identify potential genetic contributors to this phenotype, we resequenced the coding exons and splice junctions of 58 genes in 379 obese and 378 lean individuals. Our 96-Mb survey included 21 genes associated with monogenic forms of obesity in humans or mice, as well as 37 genes that function in body weight-related pathways. We found that the monogenic obesity-associated gene group was enriched for rare nonsynonymous variants unique to the obese population compared with the lean population. In addition, computational analysis predicted a greater fraction of deleterious variants within the obese cohort. Together, these data suggest that multiple rare alleles contribute to obesity in the population and provide a medical sequencing-based approach to detect them.

Overcoming the winner's curse: estimating penetrance parameters from case-control data

Genomewide association studies are now a widely used approach in the search for loci that affect complex traits. After detection of significant association, estimates of penetrance and allele-frequency parameters for the associated variant indicate the importance of that variant and facilitate the planning of replication studies. However, when these estimates are based on the original data used to detect the variant, the results are affected by an ascertainment bias known as the "winner's curse." The actual genetic effect is typically smaller than its estimate. This overestimation of the genetic effect may cause replication studies to fail because the necessary sample size is underestimated. Here, we present an approach that corrects for the ascertainment bias and generates an estimate of the frequency of a variant and its penetrance parameters. The method produces a point estimate and confidence region for the parameter estimates. We study the performance of this method using simulated data sets and show that it is possible to greatly reduce the bias in the parameter estimates, even when the original association study had low power. The uncertainty of the estimate decreases with increasing sample size, independent of the power of the original test for association. Finally, we show that application of the method to case-control data can improve the design of replication studies considerably.

The association of a SNP upstream of INSIG2 with body mass index is reproduced in several but not all cohorts

A SNP upstream of the INSIG2 gene, rs7566605, was recently found to be associated with obesity as measured by body mass index (BMI) by Herbert and colleagues. The association between increased BMI and homozygosity for the minor allele was first observed in data from a genome-wide association scan of 86,604 SNPs in 923 related individuals from the Framingham Heart Study offspring cohort. The association was reproduced in four additional cohorts, but was not seen in a fifth cohort. To further assess the general reproducibility of this association, we genotyped rs7566605 in nine large cohorts from eight populations across multiple ethnicities (total n = 16,969). We tested this variant for association with BMI in each sample under a recessive model using family-based, population-based, and case-control designs. We observed a significant (p < 0.05) association in five cohorts but saw no association in three other cohorts. There was variability in the strength of association evidence across examination cycles in longitudinal data from unrelated individuals in the Framingham Heart Study Offspring cohort. A combined analysis revealed significant independent validation of this association in both unrelated (p = 0.046) and family-based (p = 0.004) samples. The estimated risk conferred by this allele is small, and could easily be masked by small sample size, population stratification, or other confounders. These validation studies suggest that the original association is less likely to be spurious, but the failure to observe an association in every data set suggests that the effect of SNP rs7566605 on BMI may be heterogeneous across population samples.

Obesity is an epidemic in the United States of America and developing world, portending an epidemic of related diseases such as diabetes and heart disease. While diet and lifestyle contribute to obesity, half of the population variation in body mass index, a common measure of obesity, is determined by inherited factors. Many studies have reported that common sequence variants in genes are associated with an increased risk for obesity, yet most of these are not reproducible in other study cohorts, suggesting that some are false. Recently, Herbert et al. reported a slightly increased risk of obesity for people carrying two copies of the minor allele at a common variant near INSIG2. We present our attempts to further evaluate this potential association with obesity in additional populations. We find evidence of increased risk of obesity for people carrying two copies of the minor allele in five out of nine cohorts tested, using both family- and population-based testing. We indicate possible reasons for the varied results, with the hope of encouraging a combined analysis across study cohorts to more precisely define the effect of this INSIG2 gene variant.

Efficient association mapping of quantitative trait loci with selective genotyping

Selective genotyping (i.e., genotyping only those individuals with extreme phenotypes) can greatly improve the power to detect and map quantitative trait loci in genetic association studies. Because selection depends on the phenotype, the resulting data cannot be properly analyzed by standard statistical methods. We provide appropriate likelihoods for assessing the effects of genotypes and haplotypes on quantitative traits under selective-genotyping designs. We demonstrate that the likelihood-based methods are highly effective in identifying causal variants and are substantially more powerful than existing methods.

Interpretation of simultaneous linkage and family-based association tests in genome screens

Linkage and association analyses have played important roles in identifying susceptibility genes for complex diseases. Linkage tests and family-based tests of association are often applied in the same data to help fine-map disease loci or validate results. This paradigm increases efficiency by making maximal use of family data sets. However, it is not intuitively clear under what conditions association and linkage tests performed in the same data set may be correlated. Understanding this relationship is important for interpreting the combined results of both tests. We used computer simulations and theoretical statements to estimate the correlation between linkage statistics (affected sib pair maximum LOD scores) and family-based association statistics (pedigree disequilibrium test (PDT) and association in the pressure of linkage (APL)) under various hypotheses. Different types of pedigrees were studied: nuclear families with affected sib pairs, extended pedigrees and incomplete pedigrees. Both simulation and theoretical results showed that when there is no linkage or no association, the linkage and association tests are not correlated. When there is linkage and association in the data, the two tests have a positive correlation. We concluded that when linkage and association tests are applied in the same data, the type I error rate of neither test will be affected and that power can be increased by applying tests conditionally.

Adaptive two-stage analysis of genetic association in case-control designs

We study a two-stage analysis of genetic association for case-control studies. In the first stage, we compare Hardy-Weinberg disequilibrium coefficients between cases and controls and, in the second stage, we apply the Cochran- Armitage trend test. The two analyses are statistically independent when Hardy-Weinberg equilibrium holds in the population, so all the samples are used in both stages. The significance level in the first stage is adaptively determined based on its conditional power. Given the level in the first stage, the level for the second stage analysis is determined with the overall Type I error being asymptotically controlled. For finite sample sizes, a parametric bootstrap method is used to control the overall Type I error rate. This two-stage analysis is often more powerful than the Cochran-Armitage trend test alone for a large association study. The new approach is applied to SNPs from a real study.

Meta-analysis in genome-wide association datasets: strategies and application in Parkinson disease

BACKGROUND

Genome-wide association studies hold substantial promise for identifying common genetic variants that regulate susceptibility to complex diseases. However, for the detection of small genetic effects, single studies may be underpowered. Power may be improved by combining genome-wide datasets with meta-analytic techniques.

METHODOLOGY/PRINCIPAL FINDINGS

Both single and two-stage genome-wide data may be combined and there are several possible strategies. In the two-stage framework, we considered the options of (1) enhancement of replication data and (2) enhancement of first-stage data, and then, we also considered (3) joint meta-analyses including all first-stage and second-stage data. These strategies were examined empirically using data from two genome-wide association studies (three datasets) on Parkinson disease. In the three strategies, we derived 12, 5, and 49 single nucleotide polymorphisms that show significant associations at conventional levels of statistical significance. None of these remained significant after conservative adjustment for the number of performed analyses in each strategy. However, some may warrant further consideration: 6 SNPs were identified with at least 2 of the 3 strategies and 3 SNPs [rs1000291 on chromosome 3, rs2241743 on chromosome 4 and rs3018626 on chromosome 11] were identified with all 3 strategies and had no or minimal between-dataset heterogeneity (I(2) = 0, 0 and 15%, respectively). Analyses were primarily limited by the suboptimal overlap of tested polymorphisms across different datasets (e.g., only 31,192 shared polymorphisms between the two tier 1 datasets).

CONCLUSIONS/SIGNIFICANCE

Meta-analysis may be used to improve the power and examine the between-dataset heterogeneity of genome-wide association studies. Prospective designs may be most efficient, if they try to maximize the overlap of genotyping platforms and anticipate the combination of data across many genome-wide association studies.

Unraveling the genetics of human obesity

The use of modern molecular biology tools in deciphering the perturbed biochemistry and physiology underlying the obese state has proven invaluable. Identifying the hypothalamic leptin/melanocortin pathway as critical in many cases of monogenic obesity has permitted targeted, hypothesis-driven experiments to be performed, and has implicated new candidates as causative for previously uncharacterized clinical cases of obesity. Meanwhile, the effects of mutations in the melanocortin-4 receptor gene, for which the obese phenotype varies in the degree of severity among individuals, are now thought to be influenced by one's environmental surroundings. Molecular approaches have revealed that syndromes (Prader-Willi and Bardet-Biedl) previously assumed to be controlled by a single gene are, conversely, regulated by multiple elements. Finally, the application of comprehensive profiling technologies coupled with creative statistical analyses has revealed that interactions between genetic and environmental factors are responsible for the common obesity currently challenging many Westernized societies. As such, an improved understanding of the different “types” of obesity not only permits the development of potential therapies, but also proposes novel and often unexpected directions in deciphering the dysfunctional state of obesity.

Biobanken

Biobanks are an important tool of genetic epidemiology, which investigates the role of genetic factors and their interaction with environmental factors (in a broad meaning) for the occurrence of diseases in human populations. Its aim is to understand the influence of genetics on the development of diseases, their course and the clinical implications, with the final goal to improve prevention, diagnostics and therapy. The extraordinary development of genetics in the last decades - with respect to the understanding of the meaning of genes for human health, as well as the availability of cost-effective high throughput methods in the lab, has opened enormous opportunities to study genetic factors. In addition, access to large samples of patients or from the population is needed. This can be realized via biobanks. Large biobanks with 500,000 or more participants are being established or planned in the UK, Japan, Australia, Sweden and the US. However, in Germany only two smaller activities are ongoing, KORA-gen in the south and POPGEN in the north. Possibilities to reach larger numbers for Germany, based on existing cohorts or disease networks, are discussed. For the implementation and use of biobanks, stringent ethical boundary conditions have to be taken into account. The opinion of the German National Ethics Council on Biobanks for Research as well as the new recommendations of the Telematic Platform (TMF), which has been developed in close collaboration with the Data Protection Officers, improve transparency and legal security.

Comment on "A common genetic variant is associated with adult and childhood obesity"

Herbert et al. (Reports, 14 April 2006, p. 279) reported an association between the INSIG2 gene variant rs7566605 and obesity in four sample populations, under a recessive model. We attempted to replicate this result in 10,265 Caucasian individuals, combining family-based, case-control, and general population studies, but found no support for a major role of this variant in obesity.