Timothy P. York

Genes Determine Stability and the Environment Determines Change in Cognitive Ability During 35 Years of Adulthood

Previous research has demonstrated stability of cognitive ability and marked heritability during adulthood, but questions remain about the extent to which genetic factors account for this stability. We conducted a 35-year longitudinal assessment of general cognitive ability using the Armed Forces Qualification Test administered to 7,232 male twins in early adulthood and readministered to a subset of 1,237 twins during late middle age. The proportion of variance in cognitive functioning explained by genetic factors was .49 in young adulthood and .57 in late middle age. The correlation between the two administrations was .74 with a genetic correlation of 1.0, indicating that the same genetic influences operated at both times. Genetic factors were primarily responsible for stability, and nonshared environmental factors were primarily responsible for change. The genetic factors influencing cognition may change across other eras, but the same genetic influences are operating from early adulthood to late middle age.

Genetic Dissection of Acute Ethanol Responsive Gene Networks in Prefrontal Cortex: Functional and Mechanistic Implications

Background Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain) across a highly diverse family of 27 isogenic mouse strains (BXD panel) before and after treatment with ethanol. Results Acute ethanol altered the expression of ∼2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanols effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b, Gria1, Sncb and Nell2. Conclusions The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol response of gene networks could have important implications for future studies regarding the mechanisms and treatment of alcohol use disorders.

Racial Differences in Genetic and Environmental Risk to Preterm Birth

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.

Epistasis between COMT and MTHFR in Maternal-Fetal Dyads Increases Risk for Preeclampsia

Preeclampsia is a leading cause of perinatal morbidity and mortality. This disorder is thought to be multifactorial in origin, with multiple genes, environmental and social factors, contributing to disease. One proposed mechanism is placental hypoxia-driven imbalances in angiogenic and anti-angiogenic factors, causing endothelial cell dysfunction. Catechol-O-methyltransferase (Comt)-deficient pregnant mice have a preeclampsia phenotype that is reversed by exogenous 2-methoxyestradiol (2-ME), an estrogen metabolite generated by COMT. 2-ME inhibits Hypoxia Inducible Factor 1α, a transcription factor mediating hypoxic responses. COMT has been shown to interact with methylenetetrahydrofolate reductase (MTHFR), which modulates the availability of S-adenosylmethionine (SAM), a COMT cofactor. Variations in MTHFR have been associated with preeclampsia. By accounting for allelic variation in both genes, the role of COMT has been clarified. COMT allelic variation is linked to enzyme activity and four single nucleotide polymorphisms (SNPs) (rs6269, rs4633, rs4680, and rs4818) form haplotypes that characterize COMT activity. We tested for association between COMT haplotypes and the MTHFR 677 C→T polymorphism and preeclampsia risk in 1103 Chilean maternal-fetal dyads. The maternal ACCG COMT haplotype was associated with reduced risk for preeclampsia (P = 0.004), and that risk increased linearly from low to high activity haplotypes (P = 0.003). In fetal samples, we found that the fetal ATCA COMT haplotype and the fetal MTHFR minor “T” allele interact to increase preeclampsia risk (p = 0.022). We found a higher than expected number of patients with preeclampsia with both the fetal risk alleles alone (P = 0.052) and the fetal risk alleles in combination with a maternal balancing allele (P<0.001). This non-random distribution was not observed in controls (P = 0.341 and P = 0.219, respectively). Our findings demonstrate a role for both maternal and fetal COMT in preeclampsia and highlight the importance of including allelic variation in MTHFR.

Fetal and Maternal Genes’ Influence on Gestational Age in a Quantitative Genetic Analysis of 244,000 Swedish Births

Although there is increasing evidence that genetic factors influence gestational age, it is unclear to what extent this is due to fetal and/or maternal genes. In this study, we apply a novel analytical model to estimate genetic and environmental contributions to pregnancy history records obtained from 165,952 Swedish families consisting of offspring of twins, full siblings, and half-siblings (1987-2008). Results indicated that fetal genetic factors explained 13.1% (95% confidence interval (CI): 6.8, 19.4) of the variation in gestational age at delivery, while maternal genetic factors accounted for 20.6% (95% CI: 18.1, 23.2). The largest contribution to differences in the timing of birth were environmental factors, of which 10.1% (95% CI: 7.0, 13.2) was due to factors shared by births of the same mother, and 56.2% (95% CI: 53.0, 59.4) was pregnancy specific. Similar models fit to the same data dichotomized at clinically meaningful thresholds (e.g., preterm birth) resulted in less stable parameter estimates, but the collective results supported a model of homogeneous genetic and environmental effects across the range of gestational age. Since environmental factors explained most differences in the timing of birth, genetic studies may benefit from understanding the specific effect of fetal and maternal genes in the context of these yet-unidentified factors.

The contribution of genetic and environmental factors to the duration of pregnancy

This review describes how improvements in biometric-genetic studies of twin kinships, half-sibships, and cousinships have now demonstrated a sizeable fetal genetic and maternal genetic contribution to the spontaneous onset of labor. This is an important development because previous literature for the most part reports only an influence of the maternal genome. Current estimates of the percent of variation that is attributable to fetal genetic factors range from 11-35%; the range for the maternal genetic contribution is 13-20%. These same studies demonstrate an even larger influence of environmental sources over and above the influence of genetic sources and previously identified environmental risk factors. With these estimates in hand, a major goal for research on pregnancy duration is to identify specific allelic variation and environmental risk to account for this estimated genetic and environmental variation. A review of the current literature can serve as a guide for future research efforts.

Fetal ERAP2 variation is associated with preeclampsia in African Americans in a case-control study

BackgroundPreeclampsia affects 3-8% of pregnancies and is a major cause of maternal and perinatal morbidity and mortality worldwide. This complex disorder is characterized by alterations in the immune and vascular systems and involves multiple organs. There is strong evidence for a genetic contribution to preeclampsia. Two different single nucleotide polymorphisms (SNPs) in the endoplasmic reticulum aminopeptidase 2 (ERAP2) gene were recently reported to be associated with increased risk for preeclampsia in two different populations. ERAP2 is expressed in placental tissue and it is involved in immune responses, inflammation, and blood pressure regulation; making it is an attractive preeclampsia candidate gene. Furthermore, ERAP2 expression is altered in first trimester placentas of women destined to develop preeclampsia.MethodsA case-control design was used to test for associations between two SNPs in ERAP2, rs2549782 and rs17408150, and preeclampsia status in 1103 Chilean maternal-fetal dyads and 1637 unpaired African American samples (836 maternal, 837 fetal).ResultsWe found that the fetal minor allele (G) of rs2549782 was associated with an increased risk for preeclampsia in the African American population (P = 0.009), but not in the Chilean population. We found no association between rs17408150 and risk for preeclampsia in the Chilean population. Association between rs17408150 and risk for preeclampsia was not tested in the African American population due to the absence of the minor allele in this population.ConclusionsWe report an association between fetal ERAP2 and preeclampsia in an African American population. In conjunction with previous studies, which have found maternal associations with this gene in an Australian/New Zealand population and a Norwegian population, ERAP2 has now been associated with preeclampsia in three populations. This provides strong evidence that ERAP2 plays a role in the development of preeclampsia.

Comparison of multivariate adaptive regression splines and logistic regression in detecting SNP-SNP interactions and their application in prostate cancer

AbstractSingle nucleotide polymorphism (SNP) interaction plays a critical role for complex diseases. The primary limitation of logistic regressions (LR) in testing SNP-SNP interactions is that coefficient estimates may not be valid because of numerous terms in a model. Multivariate adaptive regression splines (MARS) have useful features to effectively reduce the number of terms in a model. To study how MARS can address these drawbacks possibly better than LR, the power of MARS and LR with SNPs using the reference-coding and additive-mode scheme was compared using simulated data of ten SNPs for 400 subjects based on 1,000 replications for five interaction models. In overall scenarios, MARS performed better than LR. In the model with a dominant two-way interaction, the power range was 76-96% for MARS and 1-8% for LR in both coding schemes. In the dominant three-way interaction model, the power was 57-85% for MARS and less than 4% for LR. In the prostate cancer example, we evaluated the association between ten SNPs and prostate cancer risk in 649 Caucasians. The best model with one two-way and one three-way interaction was selected using MARS. The findings supported that MARS may provide a useful tool for exploring SNP-SNP interactions.

Common disease analysis using Multivariate Adaptive Regression Splines (MARS): Genetic Analysis Workshop 12 simulated sequence data.

A newly developed modern analytic approach, Multivariate Adaptive Regression Splines (MARS), was used to identify both genetic and non‐genetic factors involved in the etiology of a common disease. We tested this method on the simulated data provided by the Genetic Analysis Workshop (GAW) 12 in problem 2 for the isolated population. MARS simultaneously analyzes all inputs, in this case DNA sequence variants and non‐genetic data, and selectively prunes away variables contributing insignificantly to fit by internal cross‐validation to arrive at a generalizable predictive model of the response. The relevant factors identified, by means of an importance value computed by MARS, were assumed to be associated with risk to the disease. The application of a series of subsequent models identified the quantitative traits and a single major gene contributing directly to risk liability using five sets of 7,000 individuals.

Estimating Fetal and Maternal Genetic Contributions to Premature Birth From Multiparous Pregnancy Histories of Twins Using MCMC and Maximum-Likelihood Approaches

The analysis of genetic and environmental contributions to preterm birth is not straightforward in family studies, as etiology could involve both maternal and fetal genes. Markov Chain Monte Carlo (MCMC) methods are presented as a flexible approach for defining user-specified covariance structures to handle multiple random effects and hierarchical dependencies inherent in children of twin (COT) studies of pregnancy outcomes. The proposed method is easily modified to allow for the study of gestational age as a continuous trait and as a binary outcome reflecting the presence or absence of preterm birth. Estimation of fetal and maternal genetic factors and the effect of the environment are demonstrated using MCMC methods implemented in WinBUGS and maximum likelihood methods in a Virginia COT sample comprising 7,061 births. In summary, although the contribution of maternal and fetal genetic factors was supported using both outcomes, additional births and/or extended relationships are required to precisely estimate both genetic effects simultaneously. We anticipate the flexibility of MCMC methods to handle increasingly complex models to be of particular relevance for the study of birth outcomes.