Showing papers in "Human Heredity in 2013"

Impact of Informing Overweight Individuals about the Role of Genetics in Obesity: An Online Experimental Study

Abstract: Background/Aims: Increasing public awareness of obesity genetics could have beneficial or harmful effects on overweight individuals. This study examined the impact of genetic information on weight-related cognitions as well as interest in personalized genetic information about obesity among overweight individuals. Methods: Online survey respondents (n = 655) were randomly assigned to read either genetic, gene-environment, or nongenetic obesity causal information. Fifty-two percent of the participants were female, 82.4% were White, 45% had an annual income of USD Results: Participants in the genetic and gene-environment conditions were more likely to believe genetics increase obesity risk than participants in the nongenetic condition (both p Conclusion: Dissemination of information about obesity genetics may have neither a beneficial nor a harmful impact on how overweight individuals perceive themselves. Some overweight individuals may be interested in receiving personalized genetic information. The actual effects of obesity genetic information being incorporated into public health messages and of personalized genetic information on obesity prevention and treatment interventions remain to be seen.

Genetics of Food Intake Self-Regulation in Childhood: Literature Review and Research Opportunities

Abstract: Pediatric obesity results from a daily energy imbalance between intake and expenditure, an imbalance potentially as slight as ~30-50 kcal/day (e.g., a few extra sips of cola or bites of a cookie). That an 'energy gap' so small may be so powerful suggests the importance of understanding mechanisms of food intake self-regulation (FISR). This review focuses on 4 behavioral indices of FISR in childhood: (1) eating in the absence of hunger; (2) eating rate; (3) caloric compensation and satiety responsiveness, and (4) food responsiveness. Evidence from pediatric samples around the world indicates that these traits are associated with body mass index, are heritable, and are linked to polymorphisms in the FTO gene. We review these data, also discussing their relevance to practical issues of parental feeding styles, portion sizes, and health literacy and numeracy. Research gaps and opportunities for future investigation are discussed. Multidisciplinary approaches and study designs that can address gene-environment interactions are needed to advance the science of FISR and stimulate new avenues for childhood obesity prevention.

Genetic variation at NCAN locus is associated with inflammation and fibrosis in non-alcoholic fatty liver disease in morbid obesity.

Abstract: Objective: Obesity-associated non-alcoholic fatty liver disease (NAFLD) may cause liver dysfunction and failure. In a previously reported genome-wide association meta-analysis, single nucleotide polymorphisms (SNPs) near PNPLA3 , NCAN , GCKR , LYPLAL1 and PPP1R3B were associated with NAFLD and with distinctive serum lipid profiles. The present study examined the relevance of these variants to NAFLD in extreme obesity. Methods: In 1,092 bariatric surgery patients, the candidate SNPs were genotyped and association analyses with liver histology and serum lipids were performed. Results: We replicated the association of hepatosteatosis with PNPLA3 rs738409[G] and with NCAN rs2228603[T]. We also replicated the association of rs2228603[T] with hepatic inflammation and fibrosis. rs2228603[T] was associated

Functional Analysis of Seven Genes Linked to Body Mass Index and Adiposity by Genome-Wide Association Studies: A Review

Abstract: Genome-wide association studies (GWAS) have identified a total of about 40 single nucleotide polymorphisms (SNPs) that show significant linkage to body mass index, a widely utilised surrogate measure of adiposity. However, only 8 of these associations have been confirmed by follow-up GWAS using more sophisticated measures of adiposity (computed tomography). Among these 8, there is a SNP close to the gene FTO which has been the subject of considerable work to diagnose its function. The remaining 7 SNPs are adjacent to, or within, the genes NEGR1, TMEM18, ETV5, FLJ35779, LINGO2, SH2B1 and GIPR, most of which are less well studied than FTO, particularly in the context of obesity. This article reviews the available data on the functions of these genes, including information gleaned from studies in humans and animal models. At present, we have virtually no information on the putative mechanism associating the genes FLJ35779 and LINGO2 to obesity. All of these genes are expressed in the brain, and for 2 of them (SH2B1 and GIPR), a direct link to the appetite regulation system is known. SH2B1 is an enhancer of intracellular signalling in the JAK-STAT pathway, and GIPR is the receptor for an appetite-linked hormone (GIP) produced by the alimentary tract. NEGR1, ETV5 and SH2B1 all have suggested roles in neurite outgrowth, and hence SNPs adjacent to these genes may affect development of the energy balance circuitry. Although the genes have central patterns of gene expression, implying a central neuronal connection to energy balance, for at least 4 of them (NEGR1, TMEM18, SH2B1 and GIPR), there are also significant peripheral functions related to adipose tissue biology. These functions may contribute to their effects on the obese phenotype.

The positive association of obesity variants with adulthood adiposity strengthens over an 80-year period: A gene-by-birth year interaction

Abstract: Objective: To test the hypothesis that the statistical effect of obesity-related genetic variants on adulthood adiposity traits depends on birth year. Methods: The study sample included 907 related, non-Hispanic White participants in the Fels Longitudinal Study, born between 1901 and 1986, and aged 25–64.99 years (474 females; 433 males) at the time of measurement. All had both genotype data from which a genetic risk score (GRS) composed of 32 well-replicated obesity-related common single nucleotide polymorphisms was created, and phenotype data [including body mass index (BMI), waist circumference, and the sum of four subcutaneous skinfolds]. Maximum likelihood-based variance components analysis was used to estimate trait heritabilities, main effects of GRS and birth year, GRS-by-birth year interaction, sex, and age. Results: Positive GRS-by-birth year interaction effects were found for BMI (p < 0.001), waist circumference

A new HLA map of Europe: Regional genetic variation and its implication for peopling history, disease-association studies and tissue transplantation.

Abstract: Objectives: HLA genes are highly polymorphic in human populations as a result of diversifying selection related to their immune function. However, HLA geographic variation worldwide suggests that demographic factors also shaped their evolution. We here analyzed in detail HLA genetic variation in Europe in order to identify signatures of migration history and/or natural selection. Methods: Relationships between HLA diversity and geography were analyzed at 7 loci through several approaches including linear regression on gene diversity and haplotype frequencies. Regional variation was also assessed on HLA multi-locus phenotypes through structure analysis. Deviation from neutrality was tested by resampling. Results: Geographic distance was a strong predictor of HLA variation at 5 loci (A, B, C, DRB1 and DPB1) in Europe, and latitude significantly shaped HLA gene diversity and haplotype frequencies. Whereas the main level of genetic diversity was found within populations, both HLA gene frequencies and phenotypic profiles revealed regional variation, Southeast Europe, Great Britain and Finland being the most distinctive. Effects of natural selection were suggested at the DQ loci. Conclusions: HLA regional variation was observed in Europe and can be related to population history, locus HLA-A providing by far the strongest signals. This new HLA map of Europe represents an invaluable reference for disease-association studies and tissue transplantation.

Assessing the Impact of Population Stratification on Association Studies of Rare Variation

Abstract: Aims: The study of rare variants, which can potentially explain a great proportion of heritability, has emerged as an important topic in human gene mapping of complex diseases. Although several statistical methods have been developed to increase the power to detect disease-related rare variants, none of these methods address an important issue that often arises in genetic studies: false positives due to population stratification. Using simulations, we investigated the impact of population stratification on false-positive rates of rare-variant association tests. Methods: We simulated a series of case-control studies assuming various sample sizes and levels of population structure. Using such data, we examined the impact of population stratification on rare-variant collapsing and burden tests of rare variation. We further evaluated the ability of 2 existing methods (principal component analysis and genomic control) to correct for stratification in such rare-variant studies. Results: We found that population stratification can have a significant influence on studies of rare variants especially when the sample size is large and the population is severely stratified. Our results showed that principal component analysis performed quite well in most situations, while genomic control often yielded conservative results. Conclusions: Our results imply that researchers need to carefully match cases and controls on ancestry in order to avoid false positives caused by population structure in studies of rare variants, particularly if genome-wide data are not available.

Gene × environment interactions in obesity: the state of the evidence.

Abstract: Background/Aims: Obesity is a pervasive and highly prevalent disease that poses substantial health risks to those it affects. The rapid emergence of obesity as a global epidemic and the patterns an ...

Impact of HLA Diversity on Donor Selection in Organ and Stem Cell Transplantation

Abstract: The human major histocompatibility complex is a multigene system encoding polymorphic human leucocyte antigens (HLA) that present peptides derived from pathogens to the immune system. The high diversity of HLA alleles and haplotypes in the worldwide populations represents a major barrier to organ and allogeneic hematopoietic stem cell transplantation, because HLA incompatibilities are efficiently recognized by T and B lymphocytes. In organ transplantation, pre-transplant anti-HLA antibodies need to be taken into account for organ allocation. Although HLA-incompatible transplants can be performed thanks to immunosuppressive drugs, the de novo production of anti-HLA antibodies still represents a major cause of graft failure. The HLAMatchmaker computer algorithm determines the immunogenicity of HLA mismatches and allows to define HLA antigens that will not induce an antibody response. Because of the much higher stringency of HLA compatibility criteria in stem cell transplantation, the best donor is a HLA genotypically identical sibling. However, more than 50% of the transplants are now performed with hematopoietic stem cells from volunteer donors selected from the international registry. The development of European national registries covering populations with different HLA haplotype frequencies is essential for optimizing donor search algorithms and providing the best chance for European patients to find a fully compatible donor.

TFAP2B -dietary protein and glycemic index interactions and weight maintenance after weight loss in the DiOGenes trial

Abstract: Background: TFAP2B rs987237 is associated with obesity and has shown interaction with the dietary fat-to-carbohydrate ratio, which has an effect on weight loss. We investigated interactions between ...

The Onset of Lactase Persistence in Europe

Abstract: The genomic region containing the lactase (LCT) gene shows one of the strongest signals of positive selection in Europeans, detectable using a range of approaches including haplotype length, linked microsatellite variation and population-differentiation-based tests. Lactase is the enzyme that carries out the digestion of the milk sugar lactose. Its expression decreases at some point after the weaning period is over in most mammals and in around 68% of all living adult humans. However, in some humans, particularly those from populations with a history of dairying, lactase is expressed throughout adulthood. This trait is called lactase persistence (LP), and in people of European ancestry, it is associated with a single mutation (-13910*T). Evidence from the detection of dairy fat residues in potsherds, and allele frequencies in ancient DNA samples suggest that LP arose after dairying practices had developed. However, the reasons why LP may have been advantageous are still debated, and the respective contribution of demography and natural selection remains to be disentangled. This paper discusses various studies, from archaeology to population genetics, that have shed some light on the subject by investigating the evolution of LP in Europe.

Epigenetics and obesity: a relationship waiting to be explained.

Abstract: Obesity can have multifactorial causes that may change with development and are not simply attributable to one's genetic constitution. To date, expensive and laborious genome-wide association studies have only ascribed a small contribution of genetic variants to obesity. The emergence of the field of epigenetics now offers a new paradigm with which to study excess fat mass. Currently, however, there are no compelling epigenetic studies to explain the role of epigenetics in obesity, especially from a developmental perspective. It is clear that until there are advances in the understanding of the main mechanisms by which different fat types, i.e. brown, beige, and white, are established and how these differ between depots and species, population-based studies designed to determine specific aspects of epigenetics will be potentially limited. Obesity is a slowly evolving condition that is not simply explained by changes in the intake of one macronutrient. The latest advances in epigenetics, coupled with the establishment of relevant longitudinal models of obesity, which incorporate functionally relevant end points, may now permit the precise contribution of epigenetic modifications to excess fat mass to be effectively studied.

Human Cardiovascular Disease IBC Chip- Wide Association with Weight Loss and Weight Regain in the Look AHEAD Trial

Abstract: Background/Aims: The present study identified genetic predictors of weight change during behavioral weight loss treatment. Methods: Participants were 3,899 overweight/obese individuals with type 2 diabetes from Look AHEAD, a randomized controlled trial to determine the effects of intensive lifestyle intervention (ILI), including weight loss and physical activity, relative to diabetes support and education, on cardiovascular outcomes. Analyses focused on associations of single nucleotide polymorphisms (SNPs) on the Illumina CARe iSelect (IBC) chip (minor allele frequency >5%; n = 31,959) with weight change at year 1 and year 4, and weight regain at year 4, among individuals who lost ≥3% at year 1. Results: Two novel regions of significant chip-wide association with year-1 weight loss in ILI were identified (p ABCB11 rs484066 was associated with 1.16 kg higher weight per minor allele at year 1, whereas TNFRSF11A, or RANK, rs17069904 was associated with 1.70 kg lower weight per allele at year 1. Conclusions: This study, the largest to date on genetic predictors of weight loss and regain, indicates that SNPs within ABCB11, related to bile salt transfer, and TNFRSF11A, implicated in adipose tissue physiology, predict the magnitude of weight loss during behavioral intervention. These results provide new insights into potential biological mechanisms and may ultimately inform weight loss treatment.

Next-Generation Sequence Analysis of Genes Associated with Obesity and Nonalcoholic Fatty Liver Disease-Related Cirrhosis in Extreme Obesity

Abstract: Objectives: Genome-wide association studies (GWAS) have led to the identification of single nucleotide polymorphisms in or near several loci that are associated with the risk of obesity and nonalcoholic fatty liver disease (NAFLD). We hypothesized that missense variants in GWAS and related candidate genes may underlie cases of extreme obesity and NAFLD-related cirrhosis, an extreme manifestation of NAFLD. Methods: We performed whole-exome sequencing on 6 Caucasian patients with extreme obesity [mean body mass index (BMI) 84.4] and 4 obese Caucasian patients (mean BMI 57.0) with NAFLD-related cirrhosis. Results: Sequence analysis was performed on 24 replicated GWAS and selected candidate obesity genes and 5 loci associated with NAFLD. No missense variants were identified in 19 of the 29 genes analyzed, although all patients carried at least 2 missense variants in the remaining genes without excess homozygosity. One patient with extreme obesity carried 2 novel damaging mutations in BBS1 and was homozygous for benign and damaging MC3R variants. In addition, 1 patient with NAFLD-related cirrhosis was compound heterozygous for rare damaging mutations in PNPLA3. Conclusions: These results indicate that analyzing candidate loci previously identified by GWAS analyses using whole-exome sequencing is an effective strategy to identify potentially causative missense variants underlying extreme obesity and NAFLD-related cirrhosis.

A network-based kernel machine test for the identification of risk pathways in genome-wide association studies.

Abstract: Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.

An analytical comparison of the principal component method and the mixed effects model for association studies in the presence of cryptic relatedness and population stratification.

Abstract: The principal component method and the mixed effects model represent two popular approaches to controlling for population structure and cryptic relatedness in genetic association studies. There are only a handful of studies comparing their performance. These studies are typically based on simulation studies and the results are therefore limited in their applicability. In this paper, we conduct an analytical comparison of these two approaches in the presence of cryptic relatedness and population structure in terms of their validity and efficiency. In the presence of cryptic relatedness, we show that both methods are valid, but the mixed effects model is more powerful for detecting association. In the presence of population structure, however, we show that both methods can be invalid. The biases and variances of the estimates from the two methods are compared. Examples and simulation studies are provided to demonstrate the conclusions.

Identification of pleiotropic genetic effects on obesity and brain anatomy

Abstract: Background/Aims: Obesity is a major contributor to the global burden of chronic disease and disability, though current knowledge of causal biologic underpinnings is lacking. Through the regulation of energy homeostasis and interactions with adiposity and gut signals, the brain is thought to play a significant role in the development of this disorder. While neuroanatomical variation has been associated with obesity, it is unclear if this relationship is influenced by common genetic mechanisms. In this study, we sought genetic components that influence both brain anatomy and body mass index (BMI) to provide further insight into the role of the brain in energy homeostasis and obesity. Methods: MRI images of brain anatomy were acquired in 839 Mexican American individuals from large extended pedigrees. Bivariate linkage and quantitative analyses were performed in SOLAR. Results: Genetic factors associated with an increased BMI were also associated with a reduced cortical surface area

A note on exact conditional and unconditional tests for Hardy-Weinberg equilibrium.

Abstract: The exact conditional approach is frequently used for testing Hardy-Weinberg equilibrium in population genetics. This approach respects the test size as compared to the traditionally used asymptotic approaches. It is a full-enumeration method and very computational. Many efficient algorithms have been successfully developed to implement this exact approach. An alternative to the conditional approach is the unconditional approach, which relaxes the restriction of the fixed number of allelic counts as in the conditional approach. The first unconditional test considered in this study is the one based on maximization, which has been shown to be more powerful than the conditional test to loci with two alleles for small sample sizes. By using the p value of the conditional approach as a test statistic in the following maximization step, the second unconditional test is uniformly more powerful than the conditional approach. We compared these exact tests based on three commonly used test statistics with regards to type I error rate and power. It is recommended to use the second unconditional approach in practice due to the power gain in the case with two alleles.

A rapid gene-based genome-wide association test with multivariate traits

Abstract: Objectives: A gene-based genome-wide association study (GWAS) provides a powerful alternative to the traditional single single nucleotide polymorphism (SNP) association analysis due to its substantial reduction in the multiple testing burden and possible gain in power due to modeling multiple SNPs within a gene. A gene-based association analysis on multivariate traits is often of interest, but it imposes substantial analytical as well as computational challenges to implement it at a genome-wide level. Methods: We propose a rapid implementation of the multivariate multiple linear regression (RMMLR) approach in unrelated individuals as well as in families. Our approach allows for covariates. Moreover, the asymptotic distribution of the test statistic is not heavily influenced by the linkage disequilibrium (LD) among the SNPs and hence can be used efficiently to perform a gene-based GWAS. We have developed a corresponding R package to implement such multivariate gene-based GWAS with this RMMLR approach. Results: Through extensive simulation, we compared several approaches for both single and multivariate traits. Our RMMLR approach maintained a correct type I error level even for sets of SNPs in strong LD. It also demonstrated a substantial gain in power to detect a gene when it is associated with a subset of the traits. We also studied performances of the approaches on the Minnesota Center for Twin Family Research dataset. Conclusions: In our overall comparison, our RMMLR approach provides an efficient and powerful tool to perform a gene-based GWAS with single or multivariate traits and maintains the type I error appropriately.

Genetic Admixture and Obesity: Recent Perspectives and Future Applications

Abstract: The process of the colonization of the New World that occurred centuries ago served as a natural experiment, creating unique combinations of genetic material in newly formed admixed populations. Through a genetic admixture approach, the identification and genotyping of ancestry informative markers have allowed for the estimation of proportions of ancestral parental populations among individuals in a sample. These admixture estimates have been used in different ways to understand the genetic contributions to individual variation in obesity and body composition parameters, particularly among diverse admixed groups known to differ in obesity prevalence within the United States. Although progress has been made through the use of genetic admixture approaches, further investigations are needed in order to explore the interaction of environmental factors with the degree of genetic ancestry in individuals. A challenge to confront at this time would be to further stratify and define environments in progressively more granular terms, including nutrients, muscle biology, stress responses at the cellular level, and the social and built environments.

Propagation of obesity across generations: the roles of differential realized fertility and assortative mating by body mass index.

Abstract: Background/Aims: To quantify the extent to which the increase in obesity observed across recent generations of the American population is associated with the individual or combined effects of assortative mating (AM) for body mass index (BMI) and differential realized fertility by BMI. Methods: A Monte Carlo framework is formed and informed using data collected from the National Longitudinal Survey of Youth (NLSY). The model has 2 portions: one that generates childbirth events on an annual basis and another that produces a BMI for each child. Once the model is informed using the data, a reference distribution of offspring BMIs is simulated. We quantify the effects of our factors of interest by removing them from the model and comparing the resulting offspring BMI distributions with that of the baseline scenario. Results: An association between maternal BMI and number of offspring is evidenced in the NLSY data as well as the presence of AM. These 2 factors combined are associated with an increased mean BMI (+0.067, 95% CI: 0.056; 0.078), an increased BMI variance (+0.578, 95% CI: 0.418; 0.736) and an increased prevalence of obesity (RR 1.032, 95% CI: 1.023; 1.041) and BMIs >40 (RR 1.083, 95% CI: 1.053; 1.118) among offspring. Conclusion: Our investigation suggests that both differential realized fertility and AM by BMI appear to play a role in the increasing prevalence of obesity in America.

Implications of population history of European Romani on genetic susceptibility to disease.

Abstract: Objectives: The population history of European Romani is characterized by extensive bottleneck and admixture events, but the impact of this unique demographic history on the genetic risk for disease remains unresolved. Methods: Genome-wide SNP data on Romani, non-Romani Europeans and Indians were analyzed. The excess of homozygous variants in Romani genomes was assessed according to their potential functional effect. We also explored the frequencies of risk variants associated with five common diseases which are present at an increased prevalence in Romani compared to other Europeans. Results: Slightly deleterious variants are present at increased frequencies in European Romani, likely a result of relaxed purifying selection due to bottlenecks in their population history. The frequencies of SNPs associated with common metabolic and cardiovascular diseases are also increased compared to their European hosts. Conclusions: As observed in other founder populations, we confirm the impact of bottlenecks on the abundance of slightly deleterious variants in Romani groups, probably including metabolic and cardiovascular risk variants.

Ancient DNA: A window to the past of Europe.

Abstract: Objectives: The history of European populations is characterised by numerous migrations or demographic events that are likely to have had major impacts on the European gene pool patterns. This paper will focus on how ancient DNA (aDNA) data contribute to our understanding of past population dynamics in Europe. Methods: Technological challenges of the palaeogenetic approach will be discussed. With these limitations in mind, it will be shown that the acquisition of aDNA now permits a glimpse of how human genetic diversity has changed, spatially and temporally, in Europe, from the Palaeolithic through to the present day. Results: Although early modern human DNA sequences come only from rare exceptionally well-preserved specimens, genetic samples of a reasonable size are becoming available for the Mesolithic and the Neolithic periods, permitting a discussion of regional variation in the inferred mode of the spread of farming. Palaeogenetic data collected for ancient and more recent periods regularly demonstrate genetic discontinuity between past and present populations. Conclusions: The results indicate that only large diachronic aDNA datasets from throughout Europe will permit researchers to reliably identify all demographic and evolutionary events that shaped the modern European gene pool.

Individual genetic variations related to satiety and appetite control increase risk of obesity in preschool-age children in the strong kids program

Abstract: Background/Aims: The burden of the childhood obesity epidemic is well recognized; nevertheless, the genetic markers and gene-environment interactions associated with the development of common obesity are still unknown. In this study, candidate genes associated to satiety and appetite control pathways with obesity-related traits were tested in Caucasian preschoolers from the STRONG Kids project. Methods: Eight genetic variants in genes related to obesity (BDNF, LEPR, FTO, PCSK1, POMC, TUB, LEP, and MC4R) were genotyped in 128 children from the STRONG Kids project (mean age 39.7 months). Data were analyzed for individual associations and to test for genetic predisposition scores (GPSs) with body mass index (BMI) and anthropometric traits (Z-scores, e.g. height-for-age Z-score, HAZ). Covariates included age, sex, and breastfeeding (BF) duration. Results: Obesity and overweight prevalence was 6.3 and 19.5%, respectively, according to age- and sex-specific BMI percentiles. Individual genetic associations of MC4R and LEPR markers with HAZ were strengthened when BF duration was included as a covariate. Our GPSs show that, as the number of risk alleles increased, the risk of higher BMI and HAZ also increased. Overall, the GPSs assembled were able to explain 2-3% of the variability in BMI and HAZ phenotypes. Conclusion: Genetic associations with common obesity-related phenotypes were found in the STRONG Kids project. GPSs assembled for specific candidate genes were associated with BMI and HAZ phenotypes.

Estimating the range of obesity treatment response variability in humans: methods and illustrations.

Abstract: Background/Aims: The rising prevalence of human obesity worldwide has focused research on a variety of interventions that result in highly varied degrees of weight loss (WL). The advent of genomic testing has quantified estimates of both the contribution of genetic factors to the development of obesity as well as racial/ethnic variation of risk alleles across subpopulations. More recent studies have examined genetic associations with effectiveness of WL interventions, but to date are unable to explain a large proportion of the variance observed. Methods: We describe and provide two illustrations of statistical methods to estimate upper and lower bounds of WL treatment response heterogeneity (TRH) in the absence of genotypic data, using published summary statistics and a raw data set from WL studies. Results: Thirty-two studies had some evidence of a positive mean treatment effect with respect to the control intervention. Twelve of these 32 studies reported WL TRH. Of these 12, 3 demonstrated an estimated proportion of >5% of the sampled population having an outcome opposite the mean effect. In the raw data set, bounds estimations for change in waist circumference revealed tighter ranges in men than women. Conclusion: Future studies may be able to take advantage of multiple approaches, including the method we describe, to identify and quantify the presence of TRH in studies of WL or related outcomes.

Interface between pharmacotherapy and genes in human obesity.

Abstract: Obesity is a polygenic chronic condition, and dysregulation in multiple underlying energy balance processes drives the obese phenotype. Lifestyle changes can be difficult to sustain long term, and anti-obesity drugs can be an advantageous component of a successful weight loss plan. However, due to lack of efficacy or adverse safety profiles, there is currently a limited selection of anti-obesity drugs on the market. This, coupled with the notable interindividual variability in efficacy of approved treatments, represents a significant unmet medical need. In this review, we will highlight this variability in weight loss response to these existing anti-obesity compounds and discuss how underpinning genetic variation is associated with weight loss outcomes. Existing research in the field of pharmacogenomics and obesity drugs will be highlighted, as will possibilities for future focus. We will conclude by exploring examples of successful pharmacogenomics studies, and also by asking how pharmacogenomics can be built into the drug development pipeline for the benefit of patients and pharmaceutical companies alike.

Genetic Evidence for Prehistoric Demographic Changes in Europe

Abstract: Objectives: Two main models have been proposed to explain the origins of the patterns of genetic variation in Europe, one emphasizing Paleolithic and the other Neolithic immigration from the Southeast. In this paper, I summarize how the models developed and how they can help address some open questions. Methods: The rationale of the methods traditionally supporting the Neolithic and the Paleolithic models is discussed, and the evidence supporting either of them is reviewed. Results: Ancient DNA evidence proves for good that the studies traditionally supporting the Paleolithic model had serious methodological flaws. This does not imply that the alternative model is right, but rather calls for further analyses explicitly testing the two models against the genomic information now available. Conclusions: Questions that need to be addressed include whether the two main models differ enough to be discriminated by analyses of modern DNA diversity, and to what extent inferences from ancient mitochondrial DNA can be trusted in the absence of sufficient datasets of ancient nuclear DNA. The time seems ripe for the construction of a more complex (and hence more realistic) model, incorporating the possibility of different processes affecting different geographic locations at different times.

Optimal methods for using posterior probabilities in association testing.

Abstract: Objective: The use of haplotypes to impute the genotypes of unmeasured single nucleotide variants continues to rise in popularity. Simulation results suggest that the use of the dosage as a one-dimensional summary statistic of imputation posterior probabilities may be optimal both in terms of statistical power and computational efficiency; however, little theoretical understanding is available to explain and unify these simulation results. In our analysis, we provide a theoretical foundation for the use of the dosage as a one-dimensional summary statistic of genotype posterior probabilities from any technology. Methods: We analytically evaluate the dosage, mode and the more general set of all one-dimensional summary statistics of two-dimensional (three posterior probabilities that must sum to 1) genotype posterior probability vectors. Results: We prove that the dosage is an optimal one-dimensional summary statistic under a typical linear disease model and is robust to violations of this model. Simulation results confirm our theoretical findings. Conclusions: Our analysis provides a strong theoretical basis for the use of the dosage as a one-dimensional summary statistic of genotype posterior probability vectors in related tests of genetic association across a wide variety of genetic disease models.

Investigating European genetic history through computer simulations.

Abstract: Background/Aims: The genetic diversity of Europeans has been shaped by various evolutionary forces including their demographic history. Genetic data can thus be used to draw inferences on the population history of Europe using appropriate statistical methods such as computer simulation, which constitutes a powerful tool to study complex models. Methods: Here, we focus on spatially explicit simulation, a method which takes population movements over space and time into account. We present its main principles and then describe a series of studies using this approach that we consider as particularly significant in the context of European prehistory. Results and Conclusion: All simulation studies agree that ancient demographic events played a significant role in the establishment of the European gene pool; but while earlier works support a major genetic input from the Near East during the Neolithic transition, the most recent ones revalue positively the contribution of pre-Neolithic hunter-gatherers and suggest a possible impact of very ancient demographic events. This result of a substantial genetic continuity from pre-Neolithic times to the present challenges some recent studies analyzing ancient DNA. We discuss the possible reasons for this discrepancy and identify future lines of investigation in order to get a better understanding of European evolution.