Volume 3 Supplement 7
Genetic Analysis Workshop 16
Genome-wide discovery of maternal effect variants
© Kent et al; licensee BioMed Central Ltd. 2009
Published: 15 December 2009
Many phenotypes may be influenced by the prenatal environment of the mother and/or maternal care, and these maternal effects may have a heritable component. We have implemented in the computer program SOLAR a variance components-based method for detecting indirect effects of maternal genotype on offspring phenotype. Of six phenotypes measured in three generations of the Framingham Heart Study, height showed the strongest evidence (P = 0.02) of maternal effect. We conducted a genome-wide association analysis for height, testing both the direct effect of the focal individual's genotype and the indirect effect of the maternal genotype. Offspring height showed suggestive evidence of association with maternal genotype for two single-nucleotide polymorphisms in the trafficking protein particle complex 9 gene TRAPPC9 (NIBP), which plays a role in neuronal NF-κB signalling. This work establishes a methodological framework for identifying genetic variants that may influence the contribution of the maternal environment to offspring phenotypes.
Many phenotypes may be influenced by the prenatal environment of the mother and/or maternal care, and these maternal effects may have a heritable component. Much research has focused on the impact of measurable properties of the mother (e.g., adiposity, diabetes, alcohol, or tobacco use) on subsequent phenotypes in their children (e.g., birthweight , insulin resistance , cognitive function ). A more general question is: does the mother's measured genotype influence offspring phenotypes, whether or not the intermediate maternal phenotypes are known or measurable? This 'agnostic' (with respect to maternal phenotype) approach has the potential both to identify novel genetic variants of maternal effect and, via 'reverse epidemiology,' to identify novel maternal phenotypes for such effects.
For the purposes of this study, we accept the strict definition of a genetic maternal effect as the indirect effect of maternal genotype on offspring phenotype , as distinct from asymmetric transmission of parental alleles (e.g., mitochondrial inheritance ) or asymmetric expression of alleles in the offspring depending on parent of origin (e.g., imprinting ). Here we develop mixed variance-components models in the computer program SOLAR  to estimate maternal random effects on quantitative phenotypes, and use the best such models as null hypotheses for measured-genotype genome-wide association tests of single-nucleotide polymorphism (SNP) genotypes of individuals and their mothers.
Data include adult quantitative phenotypes and Affymetrix SNP genotypes provided in the Genetic Analysis Workshop (GAW) 16 Framingham Heart Study (FHS) data release (Problem 2). All authors of this study are 'approved users' of these data per the NHLBI Data Use Certification of April 2008. Analysis of these data was approved by the Institutional Review Board of the University of Texas Health Science Center, San Antonio.
Outlying phenotype measurements (more than four standard deviations from the mean) were removed from the lipid measures (10 for total cholesterol, 9 for high-density lipoprotein (HDL), 43 for triglyceride (TG)) on the assumption that these represented assay errors. The data were normal-quantile-transformed before analysis using the SOLAR "inormal" option to meet the distributional assumptions of the variance components and regression methods. The normal quantile ("inverse normal") transformation is robust to a range of departures from normality and also removed scale effects by standardizing the data. Transformations of this type are convenient for batch processing of multiple phenotypes (e.g., Peng et al. ).
Individuals with incomplete genotype data were given imputed genotype scores for the missing markers using the -- infer option in the computer program Merlin [9, 10] Merlin imputes an expected genotype score based on the probability of each possible genotype at a locus given information on marker allele frequency, adjacent markers, and pedigree relationships. We chose not to exclude any SNPs or individuals on the basis of number of incomplete genotypes (unless no genotypes were available at all), given the robustness of imputation from family data . Genotypes were similarly imputed for all SNPs for the non-genotyped implicit mothers of genotyped and phenotyped founders. These maternal genotypes entered the association analysis as properties of their offspring (see "Measured genotype analysis," below); the 'virtual' mothers did not enter the analysis otherwise.
Variance components estimation
with R = 1 if i = j (equivalent to the identity matrix in Eq. 1), ρ sib ∈ [0,1] if i, j are siblings or half-siblings, ρmo ∈ [-1,1] if i, j are mother and offspring, or 0 otherwise. Our modification from Bijma  was that twins were not treated differently than other siblings because dizygotic twins could not be distinguished in the de-identified FHS data. Our full mixed model also included the fixed effects of relevant covariates and the random effect of mitochondrial inheritance, σ2 mito ; the mitochondrial variance component is structured by a matrix whose elements are 1 if i, j belong to the same matriline or 0 otherwise, as described by Czerwinski et al. .
Measured genotype analysis
Measured genotype analysis was conducted for each polymorphic SNP by including its genotype score (the number of copies of the minor allele, range [0,2] with non-integral values for imputed genotypes) as a covariate in the mixed model . Unlike standard association analysis, we included the indirect effect of the mother's genotype in addition to the direct effect of the focal subject's genotype. These effects were tested separately, with an additional test of the mother's genotype conditional on that of the focal subject. The latter test was intended to account for the non-independence of maternal and offspring genotypes: reduction of evidence of maternal association in the conditional test would suggest that the unconditioned maternal effect represented a 'bleed-through' of the direct effect, while an increase in evidence would suggest that the locus affects the trait both directly and indirectly.
Screening for evidence of maternal effects
FHS cohorts/examination periods used in this study
FHS Cohort/Exam Period
Age in years [mean (SE)]
Original/Exam 4 (1954-1958)
Offspring/Exam 3 (1983-1987)
Generation 3/Exam 1 (2002-2005)
Parameter estimates [mean (SE)] for saturated random-effects model
Comparative evidence (log likelihoods), variance-components models
Polygenic + mitochondrial
Measured genotype analysis
Suggestive associations with height
Sorted by OMG
Sorted by MMG
Sorted by CMMG
Several recent studies have undertaken genome-wide association analysis of human height [20–22]. These studies have typically examined very large numbers of individuals (~10,000-25,000, with multi-stage designs), larger than the 6,775 individuals in the FHS cohort available for this study. These studies agree in finding numerous loci associated with height, as may be expected for a trait long assumed to be polygenic. Under these circumstances, it is not surprising that we did not replicate specific SNPs or locations identified in these larger studies. It should be noted, however, that we did find suggestive evidence of association (OMG) with a broad genomic region identified by Gudbjartsson et al. : 1q24-25 (Table 4). Our candidate genes in this region is MPZL1 (OMIM #604376), a protein tyrosine phosphatase involved in cell proliferation and differentiation. Our next four highest 'hits' were in the mucolipin2 gene MCOLN2 on 1p22.
Because the published genome-wide association study on height used unrelated individuals, none reported maternal effects. Interestingly, among our strongest maternal associations are repeated hits in two regions: the TRAPPC9 gene on chromosome 8 and an intergenic region on chromosome 18. The trafficking protein particle complex 9 gene TRAPPC9 (NIBP) plays a role in neuronal NF-κB signalling  but has not, to our knowledge, been associated with stature in any published study. The existence of repeated (albeit suggestive) associations in this gene makes it a candidate for further investigation of the effects of maternal genotype on height.
We have implemented combined random-effects, measured-genotype fixed effects approach for discovery of genetic variants contributing to the indirect effect of maternal genotype on offspring phenotype. We have identified two regions on chromosomes 2 and 8 - with suggestive association at two SNPs in each region - that may contribute to maternal effects on human height. The tools developed here should be of use for a variety of phenotypes and diseases for which an effect of maternal environment is known or suspected, including height, hypertension, birthweight, and the metabolic syndrome.
List of abbreviations used
Body mass index
Conditional maternal measured genotype
Diastolic blood pressure
Framingham Heart Study
Genetic Analysis Workshop 16
Maternal measured genotype
Own measured genotype
Polygenic SBP: Systolic blood pressure
The Genetic Analysis Workshops are supported by NIH grant R01 GM031575 from the National Institute of General Medical Sciences. We thank the NHLBI and the Framingham Heart Study participants and investigators for making these data available. Our work in this study was supported by NIH grant R01 MH59490 (John Blangero, PI) and was performed in computing facilities funded in part by the AT&T Foundation.
This article has been published as part of BMC Proceedings Volume 3 Supplement 7, 2009: Genetic Analysis Workshop 16. The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/3?issue=S7.
- Kazumi T, Kawaguchi A, Yoshino G: Associations of middle-aged mother's but not father's body mass index with 18-year-old son's waist circumferences, birth weight, and serum hepatic enzyme levels. Metabolism. 2005, 54: 466-470. 10.1016/j.metabol.2004.10.014.View ArticlePubMedGoogle Scholar
- Gonzalez-Ortiz M, Martinez-Abundis E: Maternal effect of type 2 diabetes mellitus on insulin sensitivity and metabolic profile in healthy young Mexicans. Diabetes Nutr Metab. 1999, 12: 32-36.PubMedGoogle Scholar
- Julvez J, Ribas-Fitó N, Torrent M, Forns M, Garcia-Esteban R, Sunyer J: Maternal smoking habits and cognitive development of children at age 4 years in a population-based birth cohort. Int J Epidemiol. 2007, 36: 825-832. 10.1093/ije/dym107.View ArticlePubMedGoogle Scholar
- Hager R, Cheverud JM, Wolf JB: Maternal effects as the cause of parent-of-origin effects that mimic genomic imprinting. Genetics. 2008, 178: 1755-1762. 10.1534/genetics.107.080697.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang Q, Kim SK, Sun F, Cui J, Larson MG, Vasan RS, Levy D, Schwartz F: Maternal influence on blood pressure suggests involvement of mitochondrial DNA in the pathogenesis of hypertension: the Framingham Heart Study. J Hypertens. 2007, 25: 2067-2073. 10.1097/HJH.0b013e328285a36e.View ArticlePubMedGoogle Scholar
- Xiong DH, Wang JT, Wang W, Guo YF, Xiao P, Shen H, Jiang H, Chen Y, Deng H, Drees B, Recker RR, Deng HW: Genetic determinants of osteoporosis: lessons learned from a large genome-wide linkage study. Hum Biol. 2007, 79: 593-608.PubMedGoogle Scholar
- Almasy L, Blangero J: Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998, 62: 1198-1211. 10.1086/301844.PubMed CentralView ArticlePubMedGoogle Scholar
- Peng B, Yu RK, DeHoff KL, Amos CI: Normalizing a large number of traits using empirical normal quantile transformation. BMC Proc. 2007, 1 (Suppl 1): S156-10.1186/1753-6561-1-s1-s156.PubMed CentralView ArticlePubMedGoogle Scholar
- Abecasis G, Cherny SS, Cookson WO, Cardon LR: Merlin - rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002, 30: 97-101. 10.1038/ng786.View ArticlePubMedGoogle Scholar
- Burdick JT, Chen W-M, Abecasis GR, Cheung VG: In silico method for inferring genotypes in pedigrees. Nat Genet. 2006, 38: 1002-1004. 10.1038/ng1863.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen W-M, Abecasis GR: Family-based association tests for genomewide association scans. Am J Hum Genet. 2007, 81: 913-926. 10.1086/521580.PubMed CentralView ArticlePubMedGoogle Scholar
- Willham RL: The covariance between relatives for characters composed of components contributed by related individuals. Biometrics. 1963, 19: 18-27. 10.2307/2527570.View ArticleGoogle Scholar
- Lynch M, Walsh B: Maternal effects. Genetics and Analysis of Quantitative Traits. 1998, Sunderland, MA; Sinauer Associates, IncGoogle Scholar
- Bijma P: Estimating maternal genetic effects in livestock. J Anim Sci. 2006, 84: 800-806.PubMedGoogle Scholar
- Czerwinski SA, Williams JT, Demerath EW, Towne B, Siervogel RM, Blangero J: Does accounting for mitochondrial genetic variation improve the fit of genetic models?. Genet Epidemiol. 2001, 21 (Suppl 1): S779-S782.PubMedGoogle Scholar
- Blangero J, Göring HHH, Kent JW Jr, Williams JT, Peterson CP, Almasy L, Dyer TD: Quantitative trait nucleotide analysis using Bayesian model selection. Hum Biol. 2005, 77: 541-559. 10.1353/hub.2006.0003.View ArticlePubMedGoogle Scholar
- Cui JS, Hopper JL, Harrap SB: Antihypertensive treatments obscure familial contributions to blood pressure variation. Hypertension. 2003, 41: 207-210. 10.1161/01.HYP.0000044938.94050.E3.View ArticlePubMedGoogle Scholar
- Tobin MD, Sheehan NA, Scurrah KJ, Burton PR: Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Statist Med. 2005, 24: 2911-2935. 10.1002/sim.2165.View ArticleGoogle Scholar
- Amos C, de Andrade M, Zhu D: Comparison of multivariate tests for genetic linkage. Hum Hered. 2001, 51: 133-144. 10.1159/000053334.View ArticlePubMedGoogle Scholar
- Lettre G, Jackson AU, Gieger C, Schumacher FR, Berndt SI, Sanna S, Eyheramendy S, Voight BF, Butler JL, Guiducci C, Illig T, Hackett R, Heid IM, Jacobs KB, Lyssenko V, Uda M, Diabetes Genetics Initiative; FUSION; KORA; Prostate, Lung Colorectal and Ovarian Cancer Screening Trial; Nurses' Health Study; SardiNIA, Boehnke M, Chanock SJ, Groop LC, Hu FB, Isomaa B, Kraft P, Peltonen L, Salomaa V, Schlessinger D, Hunter DJ, Hayes RB, Abecasis GR, Wichmann HE, Mohlke KL, Hirschhorn JN: Identification of ten loci associated with height highlights new biological pathways in human growth. Nat Genet. 2008, 40: 584-591. 10.1038/ng.125.PubMed CentralView ArticlePubMedGoogle Scholar
- Gudbjartsson DF, Walters GB, Thorleifsson G, Stefansson H, Halldorsson BV, Zusmanovich P, Sulem P, Thorlacius S, Gylfason A, Steinberg S, Helgadottir A, Ingason A, Steinthorsdottir V, Olafsdottir EJ, Olafsdottir GH, Jonsson T, Borch-Johnsen K, Hansen T, Andersen G, Jorgensen T, Pedersen O, Aben KK, Witjes JA, Swinkels DW, den Heijer M, Franke B, Verbeek AL, Becker DM, Yanek LR, Becker LC, Tryggvadottir L, Rafnar T, Gulcher J, Kiemeney LA, Kong A, Thorsteinsdottir U, Stefansson K: Many sequence variants affecting diversity of adult human height. Nat Genet. 2008, 40: 609-615. 10.1038/ng.122.View ArticlePubMedGoogle Scholar
- Weedon MN, Lango H, Lindgren CM, Wallace C, Evans DM, Mangino M, Freathy RM, Perry JR, Stevens S, Hall AS, Samani NJ, Shields B, Prokopenko I, Farrall M, Dominiczak A, Diabetes Genetics Initiative; Wellcome Trust Case Control Consortium, Johnson T, Bergmann S, Beckmann JS, Vollenweider P, Waterworth DM, Mooser V, Palmer CN, Morris AD, Ouwehand WH, Cambridge GEM Consortium, Zhao JH, Li S, Loos RJ, Barroso I, Deloukas P, Sandhu MS, Wheeler E, Soranzo N, Inouye M, Wareham NJ, Caulfield M, Munroe PB, Hattersley AT, McCarthy MI, Frayling TM: Genome-wide association analysis identifies 20 loci that influence adult height. Nat Genet. 2008, 40: 575-583. 10.1038/ng.121.PubMed CentralView ArticlePubMedGoogle Scholar
- Hu W-H, Pendergast JS, Mo X-M, Brambilla R, Bracchi-Ricard V, Li F, Walters WM, Blits B, He L, Schaal S, Bethea JR: NIBP, a novel NIK and IKK-beta-binding protein that enhances NF-kappa-B activation. J Biol Chem. 2005, 280: 29233-29241. 10.1074/jbc.M501670200.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.