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      Variability in the Heritability of Body Mass Index: A Systematic Review and Meta-Regression

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          Abstract

          Evidence for a major role of genetic factors in the determination of body mass index (BMI) comes from studies of related individuals. Despite consistent evidence for a heritable component of BMI, estimates of BMI heritability vary widely between studies and the reasons for this remain unclear. While some variation is natural due to differences between populations and settings, study design factors may also explain some of the heterogeneity. We performed a systematic review that identified 88 independent estimates of BMI heritability from twin studies (total 140,525 twins) and 27 estimates from family studies (42,968 family members). BMI heritability estimates from twin studies ranged from 0.47 to 0.90 (5th/50th/95th centiles: 0.58/0.75/0.87) and were generally higher than those from family studies (range: 0.24–0.81; 5th/50th/95th centiles: 0.25/0.46/0.68). Meta-regression of the results from twin studies showed that BMI heritability estimates were 0.07 ( P = 0.001) higher in children than in adults; estimates increased with mean age among childhood studies (+0.012/year, P = 0.002), but decreased with mean age in adult studies (−0.002/year, P = 0.002). Heritability estimates derived from AE twin models (which assume no contribution of shared environment) were 0.12 higher than those from ACE models ( P < 0.001), whilst lower estimates were associated with self reported versus DNA-based determination of zygosity (−0.04, P = 0.02), and with self reported versus measured BMI (−0.05, P = 0.03). Although the observed differences in heritability according to aspects of study design are relatively small, together, the above factors explained 47% of the heterogeneity in estimates of BMI heritability from twin studies. In summary, while some variation in BMI heritability is expected due to population-level differences, study design factors explained nearly half the heterogeneity reported in twin studies. The genetic contribution to BMI appears to vary with age and may have a greater influence during childhood than adult life.

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          Most cited references61

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          Six new loci associated with body mass index highlight a neuronal influence on body weight regulation.

          Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 x 10(-8)): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.
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            Genetic and environmental factors in relative body weight and human adiposity.

            We review the literature on the familial resemblance of body mass index (BMI) and other adiposity measures and find strikingly convergent results for a variety of relationships. Results from twin studies suggest that genetic factors explain 50 to 90% of the variance in BMI. Family studies generally report estimates of parent-offspring and sibling correlations in agreement with heritabilities of 20 to 80%. Data from adoption studies are consistent with genetic factors accounting for 20 to 60% of the variation in BMI. Based on data from more than 25,000 twin pairs and 50,000 biological and adoptive family members, the weighted mean correlations are .74 for MZ twins, .32 for DZ twins, .25 for siblings, .19 for parent-offspring pairs, .06 for adoptive relatives, and .12 for spouses. Advantages and disadvantages of twin, family, and adoption studies are reviewed. Data from the Virginia 30,000, including twins and their parents, siblings, spouses, and children, were analyzed using a structural equation model (Stealth) which estimates additive and dominance genetic variance, cultural transmission, assortative mating, nonparental shared environment, and special twin and MZ twin environmental variance. Genetic factors explained 67% of the variance in males and females, of which half is due to dominance. A small proportion of the genetic variance was attributed to the consequences of assortative mating. The remainder of the variance is accounted for by unique environmental factors, of which 7% is correlated across twins. No evidence was found for a special MZ twin environment, thereby supporting the equal environment assumption. These results are consistent with other studies in suggesting that genetic factors play a significant role in the causes of individual differences in relative body weight and human adiposity.
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              Explaining heterogeneity in meta-analysis: a comparison of methods.

              Exploring the possible reasons for heterogeneity between studies is an important aspect of conducting a meta-analysis. This paper compares a number of methods which can be used to investigate whether a particular covariate, with a value defined for each study in the meta-analysis, explains any heterogeneity. The main example is from a meta-analysis of randomized trials of serum cholesterol reduction, in which the log-odds ratio for coronary events is related to the average extent of cholesterol reduction achieved in each trial. Different forms of weighted normal errors regression and random effects logistic regression are compared. These analyses quantify the extent to which heterogeneity is explained, as well as the effect of cholesterol reduction on the risk of coronary events. In a second example, the relationship between treatment effect estimates and their precision is examined, in order to assess the evidence for publication bias. We conclude that methods which allow for an additive component of residual heterogeneity should be used. In weighted regression, a restricted maximum likelihood estimator is appropriate, although a number of other estimators are also available. Methods which use the original form of the data explicitly, for example the binomial model for observed proportions rather than assuming normality of the log-odds ratios, are now computationally feasible. Although such methods are preferable in principle, they often give similar results in practice. Copyright 1999 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Front Endocrinol (Lausanne)
                Front Endocrinol (Lausanne)
                Front. Endocrin.
                Frontiers in Endocrinology
                Frontiers Research Foundation
                1664-2392
                28 February 2012
                2012
                : 3
                : 29
                Affiliations
                [1] 1simpleMedical Research Council Epidemiology Unit, Institute of Metabolic Science Cambridge, UK
                [2] 2simpleDepartment of Paediatrics, University of Cambridge Cambridge, UK
                Author notes

                Edited by: David Meyre, McMaster University, Canada

                Reviewed by: Claire M. A. Haworth, King’s College London, UK; Christian Dina, CNRS, France; Jane Wardle, University College London, UK

                *Correspondence: Ken K. Ong, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital Box 285, Cambridge CB2 0QQ, UK. e-mail: ken.ong@ 123456mrc-epid.cam.ac.uk

                This article was submitted to Frontiers in Genomic Endocrinology, a specialty of Frontiers in Endocrinology.

                Article
                10.3389/fendo.2012.00029
                3355836
                22645519
                eb31759e-8894-46de-bfe6-ef33a9788f4c
                Copyright © 2012 Elks, den Hoed, Zhao, Sharp, Wareham, Loos and Ong.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

                History
                : 20 October 2011
                : 07 February 2012
                Page count
                Figures: 6, Tables: 5, Equations: 0, References: 86, Pages: 16, Words: 11673
                Categories
                Endocrinology
                Original Research

                Endocrinology & Diabetes
                family study,heritability,body mass index,twin study
                Endocrinology & Diabetes
                family study, heritability, body mass index, twin study

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