The association between childhood maltreatment, psychopathology, and adult sexual victimization in men and women: results from three independent samples.

BACKGROUND: Childhood maltreatment (CM) has consistently been linked with adverse outcomes including substance use disorders and adult sexual revictimization. Adult sexual victimization itself has been linked with psychopathology but has predominately been studied in women. The current investigation examines the impact of CM and co-occurring psychopathology on adult sexual victimization in men and women, replicating findings in three distinct samples. METHOD: We investigated the association between continuous CM factor scores and adult sexual victimization in the Childhood Trauma Study (CTS) sample (N = 2564). We also examined the unique relationship between childhood sexual abuse (CSA) and adult sexual victimization while adjusting for co-occurring substance dependence and psychopathology. We replicated these analyses in two additional samples: the Comorbidity and Trauma Study (CATS; N = 1981) and the Australian Twin-Family Study of Alcohol Use Disorders (OZ-ALC; N = 1537). RESULTS: Analyses revealed a significant association with CM factor scores and adult sexual victimization for both men and women across all three samples. The CSA factor score was strongly associated with adult sexual victimization after adjusting for substance dependence and psychopathology; higher odds ratios were observed in men (than women) consistently across the three samples. CONCLUSIONS: A continuous measure of CSA is independently associated with adult sexual trauma risk across samples in models that included commonly associated substance dependence and psychopathology as covariates. The strength of the association between this CSA measure and adult sexual victimization is higher in magnitude for men than women, pointing to the need for further investigation of sexual victimization in male community samples.

Evidence of CNIH3 involvement in opioid dependence.

Opioid dependence, a severe addictive disorder and major societal problem, has been demonstrated to be moderately heritable. We conducted a genome-wide association study in Comorbidity and Trauma Study data comparing opioid-dependent daily injectors (N=1167) with opioid misusers who never progressed to daily injection (N=161). The strongest associations, observed for CNIH3 single-nucleotide polymorphisms (SNPs), were confirmed in two independent samples, the Yale-Penn genetic studies of opioid, cocaine and alcohol dependence and the Study of Addiction: Genetics and Environment, which both contain non-dependent opioid misusers and opioid-dependent individuals. Meta-analyses found five genome-wide significant CNIH3 SNPs. The A allele of rs10799590, the most highly associated SNP, was robustly protective (P=4.30E-9; odds ratio 0.64 (95% confidence interval 0.55-0.74)). Epigenetic annotation predicts that this SNP is functional in fetal brain. Neuroimaging data from the Duke Neurogenetics Study (N=312) provide evidence of this SNP's in vivo functionality; rs10799590 A allele carriers displayed significantly greater right amygdala habituation to threat-related facial expressions, a phenotype associated with resilience to psychopathology. Computational genetic analyses of physical dependence on morphine across 23 mouse strains yielded significant correlations for haplotypes in CNIH3 and functionally related genes. These convergent findings support CNIH3 involvement in the pathophysiology of opioid dependence, complementing prior studies implicating the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate system.

Genetic analysis of the age at menopause by using estimating equations and Bayesian random effects models.

Multi-wave self-report data on age at menopause in 2182 female twin pairs (1355 monozygotic and 827 dizygotic pairs), were analysed to estimate the genetic, common and unique environmental contribution to variation in age at menopause. Two complementary approaches for analysing correlated time-to-onset twin data are considered: the generalized estimating equations (GEE) method in which one can estimate zygosity-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modelled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the freeware package BUGS.

Linkage analysis for diseases with variable age of onset.

We present a method for the multivariate linkage analysis of the age of onset of a disease. The approach allows the incorporation of covariates for the study of gene by environment interactions. It is applicable to general pedigrees. The likelihood of the data is expressed as a function of the number of alleles identical by descent at a marker, the censored ages of onset and disease status, and environmental exposures. In a simulation study, we compare the power to detect linkage under different sampling schemes for either a dominant or recessive trait when approximately 10% of individuals are gene carriers. The majority of the linkage information from a sample of randomly selected sib pairs was retained when the analyses were limited to sibships with one sibling having early-onset disease (<59 years old). Incorporating parental phenotypes could improve the power to detect the gene. When the sample consists of affected sib pairs (ASPs) having variable age of onset, the likelihood ratio (LR) test had higher power than the means (t(2)) test for detecting a locus with a large genetic relative risk (R(g) = 20). However, the power of the two tests was similar when ASPs are selected so that the proband has an early onset of disease. Lastly, the LR test had more power than the t(2) test to detect linkage in the presence of gene by environment interactions.

Brain event-related potentials, dopamine D2 receptor gene polymorphism, and smoking.

This paper explores the relationship between the DRD2 gene polymorphism, P300, and smoking. Both smoking and DRD2 have significant reducing effects on P300 amplitude. The effect of smoking is apparent only in the presence of the A1 allele of the DRD2 locus. Transmission/disequilibrium analyses show a negative association between the A2 allele and smoking initiation, suggesting a protective effect of this allele. When the sample is stratified into lower- and higher-P300 categories, we find a significant association between A1 and current smoking only in individuals with lower P300. Both concordance for smoking and DRD2 genotype are significant predictors of sib-pair similarity in P300 amplitude. These results suggest a synergistic effect of different neurogenetic risk factors contributing to nicotine dependence. Neurocognitive variation (P300) may moderate the association between DRD2 and smoking. Alternatively, DRD2 genotype may modulate the long-term impact of nicotine on neurocognitive functioning.

Resiliency factors protecting against teenage alcohol use and smoking: influences of religion, religious involvement and values, and ethnicity in the Missouri Adolescent Female Twin Study.

The objective of this study was to investigate the contribution of ethnicity (African American vs European/other ancestry), family religious affiliation, religious involvement, and religious values, to risk of alcohol and cigarette use in adolescent girls; and to estimate genetic and shared environmental effects on religious involvement and values. Telephone interviews were conducted with a sample of female like-sex twin pairs, aged 13-20 (n = 1687 pairs, including 220 minority pairs), as well as with one or both parents of twins aged 11-20 (n = 2111 families). These data, together with one-year follow-up twin questionnaire data, and two-year follow-up parent interview data, were used to compare ethnic differences. Proportional hazards regression models and genetic variance component models were fitted to the data. Despite higher levels of exposure to family, school and neighborhood environmental adversities, African American adolescents were less likely to become teenage drinkers or smokers. They showed greater religious involvement (frequency of attendance at religious services) and stronger religious values (eg belief in relying upon their religious beliefs to guide day-to-day living). Controlling for religious affiliation, involvement and values removed the ethnic difference in alcohol use, but had no effect on the difference in rates of smoking. Religious involvement and values exhibited high heritability in African Americans, but only modest heritability in EOAs. The strong protective effect of adolescent religious involvement and values, and its contribution to lower rates of African American alcohol use, was confirmed. We speculate about the possible association between high heritability of African American religious behavior and an accelerated maturation of religious values during adolescence.

A frailty approach for modelling diseases with variable age of onset in families: the NHLBI Family Heart Study.

We use frailty models to analyse the effect of latent genetic and environmental risk factors on hazard functions in nuclear families. The approach expresses latent risk factors (frailties) as functions of the effects of a single major gene and shared familial risk. The latter may result from shared polygenes and/or a common environment. Genetic frailties are modelled using a two-point distribution, and residual frailties (shared environment, polygenes) using a gamma distribution. The two-point distribution follows the laws of Mendelian transmission, under either dominant or recessive gene action. We describe a robust EM approach for the joint estimation of the magnitude of genetic, covariate, gene by covariate interaction effects while allowing residual familial correlation. We illustrate the method on coronary heart disease data from the National Heart, Lung, and Blood Institute Family Heart Study. In addition, a simulation study shows that ignoring possible residual correlation in disease status due to a shared familial environment leads to an overestimate of the relative risk associated with a latent genotype.

Modeling disease incidence rates in families.

We apply an extended Cox model to study latent genes and measured environmental exposures simultaneously as risk factors for disease. Using this method, we assume Mendelian transmission of the genes and either dominant or recessive gene action. We compared the results from this model with those obtained under a model that includes the environmental variables and a family risk score. We demonstrate the method in samples of 1,433 Caucasian families (N = 6,791) and 206 African-American families (N = 771) from the National Heart, Lung, and Blood Institute Family Heart Study. In Caucasians, we found evidence suggesting that having ever smoked increased the risk of coronary heart disease only in individuals who carry a genetic susceptibility. We also noted that in both Caucasian and African-American families, the relative risk of coronary heart disease for ever-treated vs never-treated for high serum total cholesterol increased after including an unobserved susceptibility genotype in the model. This finding implied that there may be genes influencing coronary heart disease independent of those that influence total cholesterol. Such findings were not evident when genetic risk was summarized by the family history score. We also discuss the extension of the model to address the etiology of complex diseases.

Testing causal hypotheses in multivariate linkage analysis of quantitative traits: general formulation and application to sibpair data.

We provide a general framework for the development of model-free methods for the linkage analysis of multivariate phenotypic data. It is possible within this framework to test both for linkage of a set of phenotypes to one or more markers and for the presence of structural relations among the phenotypes themselves. This report presents the general model, paying special attention to the assumptions that enter its formulation, and outlines the estimation procedures that may be used.

Optimization of genome search strategies for homozygosity mapping: influence of marker spacing on power and threshold criteria for identification of candidate regions.

Mapping of genes involved in rare recessive diseases is usually difficult because of the lack of families with more than one affected progeny. The problem may be avoided by using inbred affected individuals and the strategy of homozygosity mapping. In practice, the use of homozygosity mapping in a genome-wide scan requires that a set of markers regularly spaced and spanning the whole genome are tested. Investigators are then faced to the problem of choosing the spacing of markers. To help solve this problem, we give some useful clues by computing (1) the expected length of the region of identity by descent around the disease locus, (2) the distribution, given the spacing of markers, of the number of affected individuals expected not to be homozygous at the marker closest to the disease locus and, (3) the expected type-one error. We show that even if the markers are very closely spaced, it is not unlikely that some affected individuals in the sample will not be homozygous at the marker closest to the disease locus. Excluding a region by the criterion that all affected individuals in the sample are not homozygous may then dramatically increase the rate of false negatives. We thus propose to relax the criterion to declare a region candidate, based on the sample size and the spacing of markers.

Explained and unexplained medical symptoms in generalized anxiety and panic disorder: relationship to the somatoform disorders.

We have examined the numbers and types of symptoms in a sample of 90 patients with generalized anxiety disorder (GAD) and 77 patients with panic disorder (PD) collected from six different sites during the conduct of a multicenter clinical trial. This information was obtained utilizing the Health Questionnaire, a 47-item self-report list of medical symptoms, patterned after the Somatization Disorder section of the Diagnostic Interview Schedule. Although the patients in this sample had a wide variety of medically explained and unexplained physical symptoms, none of them qualified for a diagnosis of somatization disorder by DSM-III-R criteria. GAD and PD patients reported remarkably similar numbers of explained and unexplained medical symptoms. The panoply of somatic symptoms presented by these patients presents a formidable diagnostic challenge for clinicians. These findings suggest that the pattern of overutilization of medical services that is well documented for PD patients may also be found for GAD patients.

Trade-off between sibship size and sampling scheme for detecting quantitative trait loci.

There is general consensus that linkage with a quantitative trait locus cannot be detected with a reasonable number of sibpairs unless that trait has a very high heritability or the pairs are highly selected. However, the latter may require the screening of a very large number of pairs before a relatively small number of highly informative pairs is attained. At the same time, it is known that, for the same number of typed individuals, larger sibships tend to provide more linkage information than do independent sibpairs. We thus compared the efficiency of two sampling schemes, (1) random and (2) single ascertainment via an individual with an extreme phenotype, for sibship sizes ranging from two to five. The results demonstrate the clear trade-off between sibship size and the stringency of the ascertainment scheme.

Linkage analysis of complex traits using affected sibpairs: effects of single-locus approximations on estimates of the required sample size.

We investigated the power of the affected sibpair method for detecting a disease locus when the disease is inherited through two bi-allelic loci. The power was computed for all possible values of the gene frequencies and penetrances that lead to a given population prevalence and a given sibling relative risk. A method to generate rapidly all possible models that give a specific population prevalence and relative risk is provided. We applied it to the case of a two-locus disease with a prevalence of 10% and a low sibling relative risk of 1.5. For this particular example, regardless of the true underlying model, a sample size (N = 450 for alpha = 0.05, N = 1,500 for alpha = 0.0001) may be determined such that one would expect enough power (0.80) to detect at least one of the two disease genes. In addition to the general case, we examined a special class of models in which the marginal penetrances at each locus are either recessive or dominant. In this instance, the gene frequencies were excellent predictors of the power afforded by a particular sample size. These methods have been implemented in a C program called SIBPOWER which is freely available from the first author. With this program, investigators can perform their own power calculations for any two-locus model of their choice thus avoiding the need to use single-locus approximations that may grossly underestimate the necessary sample size.

Trade-off between false positives and false negatives in the linkage analysis of complex traits.

This study examines the issue of false positives in genomic scans for detecting complex trait loci using subpair linkage methods and investigates the trade-off between the rate of false positives and the rate of false negatives. It highlights the tremendous cost in terms of power brought about by an excessive control of type I error and, at the same time, confirms that a larger number of false positives can occur otherwise in the course of a genomic scan. Finally, it compares the power and rate of false positives obtained in preplanned replicated studies conducted using a liberal significance level to those for single-step studies that use the same total sample size but stricter levels of significance. For the models considered here, replicate studies were found more attractive as long as one is willing to accept a trade-off, exchanging a much lower rate of false negatives for a slight increase in the rate of false positives.

Genome screening using extremely discordant and extremely concordant sib pairs.

We applied extreme sib-pair methods in two ways to the GAW10 Problem 2A data sets to detect susceptible quantitative trait loci using extremely discordant sib pairs only, and combining them with the available extremely concordant sib pairs as suggested by the authors elsewhere. Ten successive original replicates were combined into one sampling pool so as to get the necessary number of extreme sib pairs. A total of 100 replicates were used to produce 10 such data sets for both initial detection and confirmations. Strong signals were found with markers D5G15 for Q1, D8G27-28 for Q4, and D9G7-9 for Q5.

Probabilities of identity-by-descent patterns in sibships when the parents are not genotyped.

An assumption-free algorithm has been applied to compute the probabilities of all possible identity-by-descent (IBD) configurations when the parents have not been genotyped. These probabilities can be used to determine the amount of information that a particular sibship will bring to a nonparametric linkage analysis. It is shown that the number of possible configurations can be extremely large even when the markers are closely spaced and are rather polymorphic. Further, it is not always possible to reduce this number to a manageable one simply by consideration of the relative probabilities of the configurations.

The role of smoothing techniques in the interpretation of results from genomic scans using sib-pair data.

We compare the results of genomic scans conducted with the Haseman-Elston sib-pair method using either (1) the average marker information from several adjacent loci or (2) each marker individually. Under smoothing, the squared sib-pair trait difference is regressed on the average number of alleles shared identical by descent averaged at several adjacent loci. This results in a significant decrease in the number of false-positives when compared to the individual marker approach. Linkage of Q4 to MG4 was found only with smoothing but not the individual marker approach. Overall, smoothing resulted in the loss of two true linkages.

A chromosome-based method to infer IBD scores for missing and ambiguous markers.

We propose a probability model to impute missing identical-by-descent (IBD) vectors for linkage analysis, when adjacent marker loci are typed and interference is estimable. A chromosome-based IBD distribution, conditioned on available marker data, is computed using a fast algorithm to estimate the joint probability of genes IBD at several equally spaced linked loci. Weighted IBD vectors are then used in various test statistics for linkage analysis. As an example, we analyzed the 18 affected sib pairs in the GAW9 Problem 1 data set using Risch's lod-score test.

Distribution in time of seizures during presurgical EEG monitoring.

We studied the length of stay needed to record a certain number of seizures in a highly selected group of patients with intractable epilepsy in the final stages of presurgical monitoring. The mean length of stay needed to record one seizure was 2.9 to 3.7 days, depending on the recording technique, 4.5 to 5.5 monitoring days to record three seizures, and 6.1 to 7.6 days to record five seizures. It took 5 days to record at least one seizure in 90% of patients, 7 to 10 days to record three seizures, and 8 to 12 days to record five seizures. One-third of all patients had fewer than five seizures during their stay. Extrapolating from the results in our unit, we computed a need for 91 to 227 presurgical epilepsy monitoring beds to evaluate 2,000 to 5,000 similar epilepsy surgery candidates requiring invasive monitoring in the United States each year. Additional beds would be needed to monitor patients admitted for other purposes.