Racial Differences in Genetic and Environmental Risk to Preterm Birth

Abstract

Preterm birth is more prevalent in African Americans than European Americans and contributes to 3.4 times more African American infant deaths. Models of social inequity do not appreciably account for this marked disparity and molecular genetic studies have yet to characterize whether allelic differences that exist between races contribute to this gap. In this study, biometrical genetic models are applied to a large mixed-race sample consisting of 733,339 births to measure the extent that heritable factors and environmental exposures predict the timing of birth and explain differences between racial groups. Although we expected significant differences in mean gestational age between racial groups, we did not anticipate the variance of gestational age in African Americans (σ2 = 7.097) to be nearly twice that of European Americans (σ2 = 3.764). Our results show that this difference in the variance of gestational age can largely be attributed to environmental sources; which were 3.1 times greater in African Americans. Specifically, environmental factors that change between pregnancies, versus exposures that influence all pregnancies within a family, are largely responsible for the increased reproductive heterogeneity observed in African American mothers. Although the contribution of both fetal and maternal genetic factors differed between race categories, genetic studies may best be directed to understanding the differences in the socio-cultural sources of this heterogeneity, and their possible interaction with genetic differences within and between races. This study provides a comprehensive description of the relative genetic and environmental contributions to racial differences in gestational age.

About the Authors:

Timothy P. York

* E-mail: [email protected]

Affiliations Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America, Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America

Jerome F. Strauss III

Affiliations Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America, Department of Obstetrics and Gynecology, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America

Michael C. Neale

Affiliations Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America, Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America, Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America

Lindon J. Eaves

Affiliations Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America, Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America, Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America

Introduction

Preterm birth, defined as delivery before 37 weeks of complete gestation, is a major cause of perinatal mortality and morbidity. Prematurity is also associated with long term complications including developmental delay, and central nervous system disorders [1]. The difference in prevalence of preterm birth observed between self-reported African American and European American race (17.8% and 11.5% respectively) remains largely unexplained and contributes to 3.4 times more African American infant deaths [2]. The challenge of identifying the factors contributing to this difference is hampered by the lack of knowledge about the etiology of preterm birth. It is thought to be heterogeneous and multifactorial involving both genetic and environmental contributions [3], [4], [5], [6].

While salient risk factors for preterm birth have been indentified, ambiguity about the overall contribution of genetic and environmental sources remains. Previous preterm birth associated with an increased odds of 5.9 (95% C.I. = 4.1, 8.6) for a subsequent preterm birth [7], may involve not only fetal and maternal genetic sources that are shared in successive births, but also environmental exposures common to all pregnancies of individual mothers. Similarly, the increased risk associated with self-identified African American race (odds ratio = 1.4 (95% C.I. = 1.1, 1.8), referent = white; reported in Lang, et al. [7]) may be attributed to allelic differences between racial groups [8], [9] or to environmental exposures more prevalent in one group compared with the other, or both.

A further complication in understanding whether the genetic and environmental risks that track with these variables contribute to prematurity per se, is how these factors differ between racial groups. Although many environmental risk factors for preterm birth, such as low socioeconomic status [10], increased stressful life events [11], and poorer prenatal care [12], are more commonly observed in African Americans versus European Americans, models of social, psychosocial and economic disparities have failed to account for the racial difference in preterm birth rates to an appreciable extent [10], [11], [13]. Several studies have demonstrated that culturally-defined categories such as race correlate closely with genetic clusters implying that allelic differences may partly explain between group phenotypic differences [14], [15]. Yet, the limited understanding of the contribution of genes to phenotypic variation precludes the testing of assumptions regarding genetic contributions to racial differences [16], [17].

Although there is no obligatory connection between the causes of differences in preterm birth rates between racial groups and those responsible for variation within groups, knowledge of the latter may inform the former, especially if there are known racial disparities in environmental covariates within groups. Quantitative genetic methods can be used separate phenotypic variation into fetal genetic, maternal genetic, familial environmental and pregnancy-specific environmental components [3]. In this study we show how the pattern of covariances in a large mixed race sample of Virginia siblings, half-siblings and the children of twins provide sufficient information to describe a comprehensive picture of genetic and environmental racial heterogeneity and offers direction for future research.

Results

The prevalence of gestational ages less than or equal to 37 weeks was 14.7% in European Americans and 20.6% in African Americans. At a threshold of less than 37 weeks the percentages were 7.8% and 11.9% for European American and African Americans respectively. The average adjusted gestational age of African Americans of 38.91 weeks was significantly less than the average value for European Americans of 39.39 weeks (p-value <0.001; refer to model 3 in Table 1). The maximum likelihood estimate of variance in gestational age for African Americans was 7.097, almost twice as large as that observed for European Americans (σ2 = 3.764). Although the difference in mean values of gestational age between racial groups was expected the difference in variance was not.

[Figure omitted. See PDF.]

Table 1. Indices of model fit to assess within-group genetic and environmental contributions and between-group racial heterogeneity.

https://doi.org/10.1371/journal.pone.0012391.t001

Table 1 summarizes model-fitting statistics for several models for the source and magnitude of factors contributing to variation in gestational age and their heterogeneity between races. The full model (Model 1 in Table 1) allowed for the effects of fetal genetic (f2), maternal genetic (m2), shared environment (c2) and unique environment (e2) to take unique values in each race. This model also included a parameter, h, to allow for differences in the contribution of the shared (familial) environment between full and half-siblings. Compared to model 1, a nested model (model 2) with h removed resulted in a non-significant degradation in model fit and indicated that this parameter could be omitted. All subsequent nested models (with fewer parameters) were compared to model 2. Models 4 to 8 indicated that the variance components could not be equated across racial groups and provided evidence for both genetic and environmental heterogeneity. The sequential omission of variance components in models 9 to 14 showed that dropping the f2 contribution for African Americans provided the most parsimonious fit to the data. In summary, tests indicated the presence of race-specific effects of the fetal genotype, f2, the maternal genotype, m2, non-genetic effects shared by successive pregnancies of the same mother, c2, and random environmental effects specific to individual pregnancies, e2. Table 2 shows estimates and confidence intervals of variance components and proportions of variance for the best fitting and full genetic model. Fetal genetic factors explain up to 35.2% of variability of gestational age in European American (EA) and a negligible amount in African American (AA) births. The timing of gestation in AAs was more sensitive to: i) the effects of the maternal genome (m2AA = 1.040 versus m2EA = 0.503); ii) environment factors shared across siblings (c2AA = 1.281 versus c2EA = 0.264) and; iii) environmental factors unique to each pregnancy (e2AA = 4.777 versus e2EA = 1.674).

[Figure omitted. See PDF.]

Table 2. Estimated variance components from model 2 with empirically derived 95% bootstrap confidence intervals adjusted for covariates (birth order, maternal age, fetal sex, source of care, smoking, maternal education).

https://doi.org/10.1371/journal.pone.0012391.t002

Discussion

Our results show that the significant racial difference in the variance of gestational age can largely be attributed to non-genetic sources that contribute to differences between successive pregnancies of the same mother and between sibships. Taken together, these contributions were 3.1 times greater in African Americans versus European Americans. For both racial groups the magnitude of unique environmental influences was approximately twice as large as the combined effect of maternal genetic and shared environmental factors, which operate to create stability in the uterine environment within sibships. This suggests that the observed racial difference in variance of gestational age was due in large part to the effect of greater environmental heterogeneity in African Americans. This greater environmental variance generates larger differences among successive births to the same mother, as opposed to stable differences which would affect all pregnancies of the same mother.

Substantial heterogeneity in the effect of environmental exposures were detected between racial groups even after accounting for differences that can be ascribed to fetal and maternal genetic influences. These results also persist over and above the effects of multiple covariates known to correlate with prematurity. With the exception of maternal education, which changes to a small degree over successive pregnancies, the remaining covariates were pregnancy-specific and are expected to diminish the large effect we observe for the unique environment. The non-overlapping 95% confidence intervals (Table 2) for both environmental parameters along with the highly significant deterioration in model fit (models 7 and 8, Table 1) suggests that a large remainder of race-specific environmental variance is not accounted for by these covariates. Omitting the covariates from the model had a negligible effect on the race differences in means, variances and genetic and environmental parameter estimates.

We corroborated the contribution of fetal and maternal genetic factors to variation in gestational age [3], [18], [19] and showed in our study fetal genetic effects were exclusively present in the European American sample. The null contribution of fetal genetic factors to variation in African American gestational age may reflect the consequences of a large contribution of unique environmental sources. Estimates of these components are negatively correlated because they both contribute to estimates of differences between individual pregnancies of the same mother. Attempts to equate either the fetal or maternal genetic contribution across races resulted in significant degradation in model fit statistics and suggests a differential contribution of fetal and maternal genes. Yet differences in genetic contributions between races were modest even in this very large sample compared to the large differences reported for environmental factors. This gives considerable weight to further identifying environmental exposures that contribute to the increase heterogeneity observed in African Americans. We note that the differences in point estimates of genetic parameters could reflect either true differences in genetic variance between races or gene by environment interaction (GxE).

The rate of births before 37 weeks gestation has increased by 21% from the period of 1989 to 2006 with consistent differences observed between races over this period [12]. Yet, despite the clear documentation of this public health problem and racial disparity, little progress has been made in identifying the antecedents of preterm birth. The results of this study suggest four avenues for further research. First, the largest contribution to differences in gestational age both within and between groups was pregnancy-specific environmental sources. Future studies could profitably focus on identification of exposures that change between pregnancies and characterize the observed increased reproductive heterogeneity in African American mothers. Second, the environment common over all pregnancies of the same mother has a sizable effect in explaining between family differences between African Americans versus European Americans. Although the overall effect of the shared environment was smaller than that of unique environmental sources, its significant effect suggests that there is a pervasive contribution of familial characteristics which may include social and economic factors. Third, interaction between these two environmental sources may be an additional source of increased gestational age heterogeneity in African Americans. For example, access to prenatal health care may not only be less available to African Americans due to higher poverty levels but also, when available, unpredictably so over successive pregnancies. Fourth, in the present study the contribution of fetal and maternal genetic factors was significant but explained less variability in gestational age both within and between racial groups than either environmental source. Further modeling of genetic effects could incorporate possible interactions between genetic variation and sources of the large difference in environmental heterogeneity observed between races. For instance, the increased preterm birth risk associated with bacterial vaginosis, which is more prevalent in African Americans, is modified by a rare variant in the promoter region of TNF [20]. Examination of the genetic sensitivity to environments would not be restricted to loci that differ in allele frequencies between racial groups. Overall, these additional research directions are consistent with both investigation of exposures during pregnancy and models of exposures over the life-course that influence reproductive potential [21].

In summary, we report quantitative genetic analyses in a large sample of Virginia families to describe how genetic and environmental factors contribute to differences in variability of gestational age between Americans of European and African ancestry. Environmental factors, particularly environmental exposures that differ across pregnancies, were largely responsible for the increased variability in the timing of African American births compared with European Americans. This greater environmental variation of African American births could be, for instance, a reflection of the greater unpredictability in accessing prenatal care or to their greater vulnerability to the effects of random non-genetic influences via genetic and/or social mechanisms. Future genetic studies may best be directed to understanding the racial differences in the socio-cultural sources of this heterogeneity, and their possible interaction with genetic differences within and between races. Otherwise, in order to further our understanding of the observed racial disparity in preterm birth, these results argue for greater resources to be invested in the identification and measurement of environmental influences that are less stable over successive pregnancies in African Americans versus European Americans.

Methods

Study Sample

Pregnancy histories were obtained by combining the results of two separate requests for birth records from the Virginia Department of Health Office of Vital Records. A data-merge identified full and half-sibships by combining birth records that shared parental social security numbers (SSN) from Virginia births between 1989 and 2008. Individuals in full-sibships were required to share both the maternal and paternal SSN, while individuals in maternal half-sibships shared only the maternal SSN and those sharing only the paternal SSN were identified as paternal half-sibships. Records with either parental SSN missing were excluded. The result of this match was combined with a second set of birth records obtained from a previous study [3] comprising the offspring of twin parents identified through the Mid-Atlantic Twin Registry [22]. Records were obtained by matching the SSNs of registered twins against parental SSNs on birth records held by the Office of Vital Records. The Virginia Commonwealth University IRB approved the study design, sample collection and waiver of informed consent (VCU IRB# HM11443). Informed consent was not required since personally identifiable information was not sent to the authors from either the MATR or VDH. Birth outcome exclusion criteria included multiple birth, any congenital anomalies, hydramnios/oligohydramnios, pregnancies complicated by pregnancy induced hypertension and eclampsia, Rh sensitization, abruptio placenta and placenta previa, or any medically necessitated preterm delivery. Gestational age was recorded as completed weeks as estimated by the physician. For each birth record race was classified as African American if the child's race and the race of both parents was listed as non-Hispanic Black and European American if the child's race and the race of both parents was listed as non-Hispanic White. After screening, the sample used in this study consisted of 733,339 births of which 17.8% were classified as African American (Table 3).

[Figure omitted. See PDF.]

Table 3. Sample frequencies by parental relationship and race.

https://doi.org/10.1371/journal.pone.0012391.t003

Model for Maternal and Fetal Effects

Expectations for genetic and environmental contributions to variances and covariances of relatives are derived from biometrical genetic theory [23], [24], [25]. The decomposition of within group phenotypic variation for birth outcomes can be described as a weighted combination of fetal (f2) and maternal (m2) genetic and shared (c2) and unique (e2) environmental latent variables. The proportion of genetic influences shared between related individuals (biologically or otherwise) is inferred by the laws of segregation assuming random mating. By this model, the covariance between sibling births can be explained by the one-half of genes they share (½f2), the maternal genetic factors from a common mother (m2) and aspects of the familial environment that they share (c2). Unique environmental factors (e2) are not shared and account for pregnancy specific environmental exposures in addition to measurement error. The covariance between maternal half-siblings would differ in that they share one-fourth of their genes in common (¼f2+m2+c2). Using these expectations a rough estimate of fetal genetic influences (f2) on phenotypic variance can be derived by subtracting four times the difference of the full-sibling and maternal half-sibling correlation (f2 = 4((½f2+m2+c2) − (¼f2+m2+c2))).

All individuals who share maternal genetic influences also share the contribution of fetal genes, yet the converse is not true if one considers paternal half-siblings. Thus, an estimate of maternal genetic influences (m2) can be derived by subtracting the correlation of paternal half-siblings from maternal half-siblings since both relationships share one-fourth of their fetal genes but only maternal half-siblings share mothers in common (m2 = (¼f2+m2+c2) − (¼f2+c2)).

Additional relationships beyond full and half-sibships need to be considered to distinguish the influence of shared and unique environmental from genetic sources (Table 4). The offspring of monozygotic (MZ) twins, like other biological half-siblings, share one-fourth of their genes (f2) in common, while the offspring of dizygotic (DZ) twins, like other first cousins, share one-eighth their genetic load. Cousins related through MZ female twins would also share all of their maternal genetic influence but not the effects of the shared environment since they are members of different sibships. Accordingly, an estimate of the shared environment can be calculated by subtracting the correlation of the offspring of MZ female twins from the maternal half-sibship correlation (c2 = (¼f2+m2+c2) − (¼f2+m2)). An estimate of unique environmental sources can be obtained by subtracting both the genetic and common environment from the total phenotypic variance (vt2), e2 = vt2 − f2 − m2 − c2. The importance of a factor accounting for within group variance can be calculated as the proportion of variance explained relative to total variance; thus the proportion of fetal genetic variance is calculated as f2/(f2 + m2 + c2 + e2).

[Figure omitted. See PDF.]

Table 4. Expected covariance of gestational age expressed as variance components between pregnancy outcomes as a function of relationship between offspring.

https://doi.org/10.1371/journal.pone.0012391.t004

Although it is instructive to derive estimates of genetic and environmental effects using correlations between relatives, in practice, structural equation modeling is preferred to make a simultaneous decomposition of the covariance matrix using widely available software implementing maximum likelihood [26], [27], [28] or Bayesian [29], [30] approaches. These methods yield confidence intervals of parameter estimates and goodness-of-fit indices quantifying how well the model accounts for the empirical variances and covariances and enabling the testing of hypotheses regarding the causes of variation within groups and their heterogeneity between groups.

Parameter Estimation and Hypothesis-Testing

A convenient feature of structural equation methods is the ease in which families composed of different relationships and sizes can be incorporated [31]. Expectations for covariance matrices were specified for each sibship and children of twins family type based on the equations in Table 4. We followed the model specification as described in York et al. [3] for continuous outcomes in which multiple births are treated as repeated measures within the same family. In contrast to methods that pool births from the same mother, this treatment maintains the information content of each family and allows for the inclusion of measured covariates that may differ across births. Model assumptions included: (1) random mating; (2) genetic effects were additive and constant over pregnancies; (3) the influence of fetal and maternal genetic differences are the same for male and female fetuses (i.e., genetic effects are autosomal and neither X-linked nor sex-limited); (4) genetic and environmental variables do not interact and; (5) environmental effects were pregnancy specific apart from the effects of maternal genotype, shared environmental effects, measured covariates and other aspects of the parental phenotype (e.g., cultural inheritance).

To balance computation time with gains in information sibships were limited to the first four reported births, which corresponded to 96.7% of available births. Measured covariates were included based on prior evidence of association with preterm birth risk or mean levels differed between race, namely: birth order, maternal age, maternal education, source of care (private physician or other), fetal sex and number of reported cigarettes smoked daily while pregnant. Maximum likelihood estimates of the means and expected covariance matrices were obtained using the structural equation modeling program Mx [26]. A test of heterogeneity was performed by equating the genetic and environmental parameters across racial groups and assessing the decline in model fit. The contribution of individual parameters were examined by dropping each in turn from the model and observing the decline in fit of the submodel by the likelihood ratio chi-square test and change in the Akaike Information Criterion (AIC) in an attempt to arrive at a model yielding the optimal balance of parsimony and goodness-of-fit. Confidence intervals for the genetic and environmental parameters were obtained from 1,000 iteration bootstrap estimates by randomly sampling the families with replacement to generate samples with the same number of families.

Author Contributions

Conceived and designed the experiments: TPY JfSI LJE. Performed the experiments: TPY. Analyzed the data: TPY MCN LJE. Contributed reagents/materials/analysis tools: TPY MCN LJE. Wrote the paper: TPY JfSI LJE.

Citation: York TP, Strauss JF III, Neale MC, Eaves LJ (2010) Racial Differences in Genetic and Environmental Risk to Preterm Birth. PLoS ONE5(8): e12391. https://doi.org/10.1371/journal.pone.0012391

References

1. Behrman RE, Butler AS (2007) Preterm birth: Causes, consequences, and prevention. Washington D. C.: Academy Press. RE BehrmanAS Butler2007Preterm birth: Causes, consequences, and prevention.Washington D. C.Academy Press

2. Mathews MS, MacDorman MF (2008) Infant mortality statistics from the 2005 period linked birth/infant death data set. National Vital Statistics Reports 57: 1–32.MS MathewsMF MacDorman2008Infant mortality statistics from the 2005 period linked birth/infant death data set.National Vital Statistics Reports57132

3. York TP, Strauss JF 3rd, Neale MC, Eaves LJ (2009) Estimating fetal and maternal genetic contributions to premature birth from multiparous pregnancy histories of twins using MCMC and maximum-likelihood approaches. Twin Res Hum Genet 12: 333–342.TP YorkJF Strauss 3rdMC NealeLJ Eaves2009Estimating fetal and maternal genetic contributions to premature birth from multiparous pregnancy histories of twins using MCMC and maximum-likelihood approaches.Twin Res Hum Genet12333342

4. Treloar SA, Macones GA, Mitchell LE, Martin NG (2000) Genetic influences on premature parturition in an Australian twin sample. TwinRes 3: 80–82.SA TreloarGA MaconesLE MitchellNG Martin2000Genetic influences on premature parturition in an Australian twin sample.TwinRes38082

5. Romero R, Espinoza J, Kusanovic JP, Gotsch F, Hassan S, et al. (2006) The preterm parturition syndrome. BJOG 113: Suppl 317–42.R. RomeroJ. EspinozaJP KusanovicF. GotschS. Hassan2006The preterm parturition syndrome.BJOG113Suppl 31742

6. Muglia LJ, Katz M (2010) The enigma of spontaneous preterm birth. N Engl J Med 362: 529–535.LJ MugliaM. Katz2010The enigma of spontaneous preterm birth.N Engl J Med362529535

7. Lang JM, Lieberman E, Cohen A (1996) A comparison of risk factors for preterm labor and term small-for-gestational-age birth. Epidemiology 7: 369–376.JM LangE. LiebermanA. Cohen1996A comparison of risk factors for preterm labor and term small-for-gestational-age birth.Epidemiology7369376

8. Anum EA, Springel EH, Shriver MD, Strauss JF 3rd (2009) Genetic Contributions to Disparities in Preterm Birth. Pediatr Res 65: 1–9.EA AnumEH SpringelMD ShriverJF Strauss 3rd2009Genetic Contributions to Disparities in Preterm Birth.Pediatr Res6519

9. Menon R, Velez DR, Thorsen P, Vogel I, Jacobsson B, et al. (2006) Ethnic differences in key candidate genes for spontaneous preterm birth: TNF-alpha and its receptors. Hum Hered 62: 107–118.R. MenonDR VelezP. ThorsenI. VogelB. Jacobsson2006Ethnic differences in key candidate genes for spontaneous preterm birth: TNF-alpha and its receptors.Hum Hered62107118

10. Kaufman JS, Cooper RS, McGee DL (1997) Socioeconomic status and health in blacks and whites: the problem of residual confounding and the resiliency of race. Epidemiology 8: 621–628.JS KaufmanRS CooperDL McGee1997Socioeconomic status and health in blacks and whites: the problem of residual confounding and the resiliency of race.Epidemiology8621628

11. Lu MC, Chen B (2004) Racial and ethnic disparities in preterm birth: the role of stressful life events. AmJObstetGynecol 191: 691–699.MC LuB. Chen2004Racial and ethnic disparities in preterm birth: the role of stressful life events.AmJObstetGynecol191691699

12. Martin JA, Hamilton BE, Sutton PD, Ventura SJ, Menacker F, et al. (2009) Births: Final Data for 2006. National Center for Health Statistics 57: 1–104.JA MartinBE HamiltonPD SuttonSJ VenturaF. Menacker2009Births: Final Data for 2006.National Center for Health Statistics571104

13. Goldenberg RL, Cliver SP, Mulvihill FX, Hickey CA, Hoffman HJ, et al. (1996) Medical, psychosocial, and behavioral risk factors do not explain the increased risk for low birth weight among black women. Am J Obstet Gynecol 175: 1317–1324.RL GoldenbergSP CliverFX MulvihillCA HickeyHJ Hoffman1996Medical, psychosocial, and behavioral risk factors do not explain the increased risk for low birth weight among black women.Am J Obstet Gynecol17513171324

14. Risch N, Burchard E, Ziv E, Tang H (2002) Categorization of humans in biomedical research: genes, race and disease. Genome Biol 3: comment2007.N. RischE. BurchardE. ZivH. Tang2002Categorization of humans in biomedical research: genes, race and disease.Genome Biol3comment2007

15. Bamshad M, Wooding S, Salisbury BA, Stephens JC (2004) Deconstructing the relationship between genetics and race. Nat Rev Genet 5: 598–609.M. BamshadS. WoodingBA SalisburyJC Stephens2004Deconstructing the relationship between genetics and race.Nat Rev Genet5598609

16. Mountain JL, Risch N (2004) Assessing genetic contributions to phenotypic differences among ‘racial’ and ‘ethnic’ groups. Nat Genet 36: S48–53.JL MountainN. Risch2004Assessing genetic contributions to phenotypic differences among ‘racial’ and ‘ethnic’ groups.Nat Genet36S4853

17. Fiscella K (2005) Race, genes and preterm delivery. J Natl Med Assoc 97: 1516–1526.K. Fiscella2005Race, genes and preterm delivery.J Natl Med Assoc9715161526

18. Lunde A, Melve KK, Gjessing HK, Skjaerven R, Irgens LM (2007) Genetic and environmental influences on birth weight, birth length, head circumference, and gestational age by use of population-based parent-offspring data. Am J Epidemiol 165: 734–741.A. LundeKK MelveHK GjessingR. SkjaervenLM Irgens2007Genetic and environmental influences on birth weight, birth length, head circumference, and gestational age by use of population-based parent-offspring data.Am J Epidemiol165734741

19. van den Oord EJ, Rowe DC (2000) Racial differences in birth health risk: a quantitative genetic approach. Demography 37: 285–298.EJ van den OordDC Rowe2000Racial differences in birth health risk: a quantitative genetic approach.Demography37285298

20. Macones GA, Parry S, Elkousy M, Clothier B, Ural SH, et al. (2004) A polymorphism in the promoter region of TNF and bacterial vaginosis: preliminary evidence of gene-environment interaction in the etiology of spontaneous preterm birth. AmJObstetGynecol 190: 1504–1508.GA MaconesS. ParryM. ElkousyB. ClothierSH Ural2004A polymorphism in the promoter region of TNF and bacterial vaginosis: preliminary evidence of gene-environment interaction in the etiology of spontaneous preterm birth.AmJObstetGynecol19015041508

21. Lu MC, Halfon N (2003) Racial and ethnic disparities in birth outcomes: a life-course perspective. Matern Child Health J 7: 13–30.MC LuN. Halfon2003Racial and ethnic disparities in birth outcomes: a life-course perspective.Matern Child Health J71330

22. Anderson LS, Beverly WT, Corey LA, Murrelle L (2002) The Mid-Atlantic Twin Registry. TwinRes 5: 449–455.LS AndersonWT BeverlyLA CoreyL. Murrelle2002The Mid-Atlantic Twin Registry.TwinRes5449455

23. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. New York.DS FalconerTFC Mackay1996Introduction to quantitative genetics.New York

24. Eaves LJ, Last KA, Young PA, Martin NG (1978) Model-fitting approaches to the analysis of human behaviour. Heredity 41: 249–320.LJ EavesKA LastPA YoungNG Martin1978Model-fitting approaches to the analysis of human behaviour.Heredity41249320

25. Martin NG, Eaves LJ (1977) The genetical analysis of covariance structure. Heredity 38: 79–95.NG MartinLJ Eaves1977The genetical analysis of covariance structure.Heredity387995

26. Neale MC, Boker SM, Xie G, Maes HM (1999) Mx: Statistical Modeling. Richmond: Department of Psychiatry, Virginia Commonwealth University. MC NealeSM BokerG. XieHM Maes1999Mx: Statistical Modeling.Richmond: Department of Psychiatry, Virginia Commonwealth University

27. Joreskog KG, Sorbom D (1996) LISREL 8: User's reference guide. Chicago: Scientific Software International. KG JoreskogD. Sorbom1996LISREL 8: User's reference guide.ChicagoScientific Software International

28. Neale MC, Cardon LR (1992) Methodology for Genetic Studies of Twins and Families. Dordrecht: Kluwer Academic Publishers. MC NealeLR Cardon1992Methodology for Genetic Studies of Twins and Families.DordrechtKluwer Academic Publishers

29. Spiegelhalter D, Thomas A, Best N, Lunn D (2003) WinBUGS User Manual. MRC Biostatistics Unit, Institute of Public Health and Department of Epidemiology and Public Health, Imperial College School of Medicine, UK. D. SpiegelhalterA. ThomasN. BestD. Lunn2003WinBUGS User Manual.MRC Biostatistics Unit, Institute of Public Health and Department of Epidemiology and Public Health, Imperial College School of Medicine, UK

30. Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS - a Baysian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10: 325–337.DJ LunnA. ThomasN. BestD. Spiegelhalter2000WinBUGS - a Baysian modelling framework: concepts, structure, and extensibility.Statistics and Computing10325337

31. Posthuma D, Beem AL, de Geus EJ, Van Baal GC, von Hjelmborg JB, et al. (2003) Theory and practice in quantitative genetics. TwinRes 6: 361–376.D. PosthumaAL BeemEJ de GeusGC Van BaalJB von Hjelmborg2003Theory and practice in quantitative genetics.TwinRes6361376