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      Modeling Genetic and Environmental Factors to Increase Heritability and Ease the Identification of Candidate Genes for Birth Weight: A Twin Study

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          Abstract

          Heritability estimates of birth weight have been inconsistent. Possible explanations are heritability changes during gestational age or the influence of covariates (e.g. chorionicity). The aim of this study was to model birth weights of twins across gestational age and to quantify the genetic and environmental components. We intended to reduce the common environmental variance to increase heritability and thereby the chance of identifying candidate genes influencing the genetic variance of birth weight. Perinatal data were obtained from 4232 live-born twin pairs from the East Flanders Prospective Twin Survey, Belgium. Heritability of birth weights across gestational ages was estimated using a non-linear multivariate Gaussian regression with covariates in the means model and in covariance structure. Maternal, twin-specific, and placental factors were considered as covariates. Heritability of birth weight decreased during gestation from 25 to 42 weeks. However, adjusting for covariates increased the heritability over this time period, with the highest heritability for first-born twins of multipara with separate placentas, who were staying alive (from 52% at 25 weeks to 30% at 42 weeks). Twin-specific factors revealed latent genetic components, whereas placental factors explained common and unique environmental factors. The number of placentas and site of the insertion of the umbilical cord masked the effect of chorionicity. Modeling genetic and environmental factors leads to a better estimate of their role in growth during gestation. For birth weight, mainly environmental factors were explained, resulting in an increase of the heritability and thereby the chance of finding genes influencing birth weight in linkage and association studies.

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          Most cited references38

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          Genetic influence on birthweight and gestational length determined by studies in offspring of twins.

          To determine the relative importance of genetic effects on birthweight, gestational length and small for gestational age. A cohort study, using individual record linkage between the population-based Swedish Twin and Birth Registers to estimate twin similarities in twins with known zygosity. Included were 868 monozygotic and 1141 dizygotic female twin pairs, born in Sweden before 1959, who both delivered single births from 1973-1993. Quantitative genetic methods, offspring birthweight, gestational length and small for gestational age birth in twin sisters. Twin similarities measured as probandwise concordance rates and intra-class correlations for birthweight, gestational length and small for gestational age births. Concordance rates and intra-class correlations for birthweight, gestational length and small for gestational age were consistently higher in monozygotic compared with dizygotic twins. Model fitting suggested heritability estimates in the range from 25% to 40%. This study suggests genetic effects not only for birthweight and fetal growth, but also for gestational length. The mediation of these genetic effects may partly be due to similarities in maternal antropometric measures, lifestyle and medical complications during pregnancy. The study does not distinguish between fetal and maternal genetic effects.
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            Combined linkage and association sib-pair analysis for quantitative traits.

            An extension to current maximum-likelihood variance-components procedures for mapping quantitative-trait loci in sib pairs that allows a simultaneous test of allelic association is proposed. The method involves modeling of the allelic means for a test of association, with simultaneous modeling of the sib-pair covariance structure for a test of linkage. By partitioning of the mean effect of a locus into between- and within-sibship components, the method controls for spurious associations due to population stratification and admixture. The power and efficacy of the method are illustrated through simulation of various models of both real and spurious association.
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              Choosing among generalized linear models applied to medical data.

              When testing for a treatment effect or a difference among groups, the distributional assumptions made about the response variable can have a critical impact on the conclusions drawn. For example, controversy has arisen over transformations of the response (Keene). An alternative approach is to use some member of the family of generalized linear models. However, this raises the issue of selecting the appropriate member, a problem of testing non-nested hypotheses. Standard model selection criteria, such as the Akaike information criterion (AIC), can be used to resolve problems. These procedures for comparing generalized linear models are applied to checking for difference in T4 cell counts between two disease groups. We conclude that appropriate model selection criteria should be specified in the protocol for any study, including clinical trials, in order that optimal inferences can be drawn about treatment differences.
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                Author and article information

                Contributors
                +31-43-3881012 , +31-43-3884573 , marij.gielen@gen.unimaas.nl
                Journal
                Behav Genet
                Behavior Genetics
                Springer US (Boston )
                0001-8244
                1573-3297
                22 December 2007
                January 2008
                : 38
                : 1
                : 44-54
                Affiliations
                [1 ]Nutrition and Toxicology Research Institute Maastricht (NUTRIM), Maastricht University, Maastricht, The Netherlands
                [2 ]Department of Population Genetics, Genomics and Bioinformatics, Maastricht University, Universiteitssingel 50, P.O. Box 616, 6200 MD Maastricht, The Netherlands
                [3 ]Department for Human Genetics, Faculty of Medicine, Catholic University of Leuven, Leuven, Belgium
                [4 ]Department of Clinical Genetics, University Hospital Maastricht, Maastricht, The Netherlands
                [5 ]Association for Scientific Research in Multiple Births, Destelbergen, Belgium
                [6 ]Department of Obstetrics and Gynecology, University Hospital Maastricht, Maastricht, The Netherlands
                Article
                9170
                10.1007/s10519-007-9170-3
                2226023
                18157630
                aac9694a-4103-439c-9ca4-f1b0046fc558
                © The Author(s) 2007
                History
                : 22 March 2007
                : 17 September 2007
                Categories
                Original Paper
                Custom metadata
                © Springer Science+Business Media, LLC 2008

                Genetics
                gestational age,heritability,twins,variance.,growth curves,birth weight
                Genetics
                gestational age, heritability, twins, variance., growth curves, birth weight

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