A Novel Mixture Model to Estimate the Time to Drug Effect Onset and Its Association with Covariates.

OBJECTIVE: Drugs take effect at different times in different individuals. Consequently, researchers seek to examine how the timing of the biological response to drugs may be affected by factors such as gender, genotypes, age, or baseline symptom scores. METHODS: Typically, studies measure symptoms immediately after the initiation of drug treatment and then at a sequence of later time points. In this study, we develop a statistical mixture model for analyzing such longitudinal data. Our method estimates the onset of drug effect and assesses the association between the probability distribution of the onset times and possible contributing factors. Our mixture model treats the timing of onset as missing for each individual but restricts it, for simplicity, to two possible onset points, early or late. To estimate the model, we use an expectation-maximization-based approach and provide the general formulas of the variance and covariance matrix for the estimated parameters. RESULTS: We evaluate the model's overall utility and performance via simulation studies. In addition, we illustrate its use by application to longitudinal data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. The algorithm identified age and anxiety status as significant factors in affecting the onset distribution of citalopram (Celexa).

Comprehensive family-based association study of the glutamate transporter gene SLC1A1 in obsessive-compulsive disorder.

SLC1A1 encodes a neuronal glutamate transporter and is a promising candidate gene for obsessive-compulsive disorder (OCD). Several independent research groups have reported significant associations between OCD and single nucleotide polymorphisms (SNPs) in this gene. Previously, we evaluated 13 SNPs in, or near, SLC1A1 and reported a strong association signal with rs301443, a SNP 7.5 kb downstream of the gene [Shugart et al. (2009); Am J Med Genet Part B 150B:886­892]. The aims of the current study were first, to further investigate this finding by saturating the region around rs301443; and second, to explore the entire gene more thoroughly with a dense panel of SNP markers. We genotyped an additional 111 SNPs in or near SLC1A1, covering from 9 kb upstream to 84 kb downstream of the gene at average spacing of 1.7 kb per SNP, and conducted family-based association analyses in 1,576 participants in 377 families.We found that none of the surrounding markers were in linkage disequilibrium with rs301443, nor were any associated with OCD. We also found that SNP rs4740788, located about 8.8 kb upstream of the gene, was associated with OCD in all families (P = 0.003) and in families with male affecteds (P = 0.002). A three-SNP haplotype (rs4740788­rs10491734­rs10491733) was associated with OCD in the total sample (P = 0.00015) and in families with male affecteds (P = 0.0007). Although of nominal statistical significance considering the number of comparisons, these findings provide further support for the involvement of SLC1A1 in the pathogenesis of OCD.

The association between parental bonding and obsessive compulsive disorder in offspring at high familial risk.

BACKGROUND: The aim of the current study is to estimate the association between parenting factors derived from the Parental Bonding Instrument (PBI) and a lifetime DSM-IV diagnosis of OCD. METHOD: Data were from approximately 1200 adults from 465 families assessed as part of a large family and genetic study of OCD. The association of three parenting factors, for fathers and mothers, with offspring OCD status were examined; analyses were stratified by parental OCD status and family loading for OCD (multiplex versus sporadic). RESULTS: Three factors were derived by principal components factor analysis of the PBI (maternal and paternal care, overprotection and control). Maternal overprotection was associated with OCD in offspring with familial OCD (familial cases) but only if neither parent was affected with OCD, which suggests independent but additive environmental and genetic risk (OR = 5.9, 95% CI 1.2, 29.9, p = 0.031). Paternal care was a protective factor in those not at high genetic risk (sporadic cases) (OR = 0.2, 95% CI 0.0, 0.8, p = 0.027). Maternal overprotection was also associated with offspring OCD in sporadic families (OR = 2.9, 95% CI 1.3, 6.6, p = 0.012). The finding that parental overprotection and care were not associated with offspring OCD when at least one parent had OCD addressed directly the hypothesis of maternal or paternal OCD adversely impacting parenting. CONCLUSIONS: This study provides evidence that aspects of parenting may contribute to the development of OCD among offspring. Prospective studies of children at risk for OCD are needed to explore the direction of causality.

Further development of YBOCS dimensions in the OCD Collaborative Genetics study: symptoms vs. categories.

Despite progress in identifying homogeneous subphenotypes of obsessive-compulsive disorder (OCD) through factor analysis of the Yale Brown Obsessive-Compulsive Scale Symptom Checklist (YBOCS-SC), prior solutions have been limited by a reliance on presupposed symptom categories rather than discrete symptoms. Furthermore, there have been few attempts to evaluate the familiality of OCD symptom dimensions. The purpose of this study was to extend prior work by this collaborative group in category-based dimensions by conducting the first-ever exploratory dichotomous factor analysis using individual OCD symptoms, comparing these results to a refined category-level solution, and testing the familiality of derived factors. Participants were 485 adults in the six-site OCD Collaborative Genetics Study, diagnosed with lifetime OCD using semi-structured interviews. YBOCS-SC data were factor analyzed at both the individual item and symptom category levels. Factor score intraclass correlations were calculated using a subsample of 145 independent affected sib pairs. The item- and category-level factor analyses yielded nearly identical 5-factor solutions. While significant sib-sib associations were found for four of the five factors, Hoarding and Taboo Thoughts were the most robustly familial (r ICC>or=0.2). This report presents considerable converging evidence for a five-factor structural model of OCD symptoms, including separate factor analyses employing individual symptoms and symptom categories, as well as sibling concordance. The results support investigation of this multidimensional model in OCD genetic linkage studies.

Genetic association study of BDNF in depression: finding from two cohort studies and a meta-analysis.

Depression is common and a major cause of morbidity and mortality and is also known to have serious effects on quality of life. Both clinical and pharmacologic studies have implicated the role of brain-derived neurotrophic factor (BDNF) as a susceptibility locus for the development of mental illness, including depression, bipolar disorder, and schizophrenia. Population-based genetic studies have examined the association between BDNF and a variety of depression outcomes, but the results have not clearly established the role of BDNF in the development of this complex disorder. The aim of this study was to test for associations between two genetic variants in BDNF, Val66Met (rs6265) and -270 C > T, and depression measured in two independent samples. In this analysis we included 3,548 participants from British Women's Heart and Health Study (BWHHS) and 6,836 mothers from Avon Longitudinal Study of Parents and Children (ALSPAC) who had complete data on genotype and depression outcomes. We did not detect any strong evidence of associations between any of the two polymorphisms and indicators of depression in either BWHHS or ALSPAC samples. Further, we carried out a systematic review and meta-analysis of all association studies of these two BDNF polymorphisms and depression. The meta-analysis of Val66Met in depression obtained an overall summary OR of 1.06 (95% CI: 0.89-1.26, P = 0.537) comparing MM with VV genotypes and an OR of 0.97 (95% CI: 0.89-1.05, P = 0.403) comparing MV with VV genotypes. Our findings suggest that BDNF genotype does not exert a major influence on the development of depression.

Hoarding in obsessive-compulsive disorder: results from the OCD Collaborative Genetics Study.

Hoarding behavior occurs frequently in obsessive-compulsive disorder (OCD). Results from previous studies suggest that individuals with OCD who have hoarding symptoms are clinically different than non-hoarders and may represent a distinct clinical group. In the present study, we compared 235 hoarding to 389 non-hoarding participants, all of whom had OCD, collected in the course of the OCD Collaborative Genetics Study. We found that, compared to non-hoarding individuals, hoarders were more likely to have symmetry obsessions and repeating, counting, and ordering compulsions; poorer insight; more severe illness; difficulty initiating or completing tasks; and indecision. Hoarders had a greater prevalence of social phobia and generalized anxiety disorder. Hoarders also had a greater prevalence of obsessive-compulsive and dependent personality disorders. Five personality traits were independently associated with hoarding: miserliness, preoccupation with details, difficulty making decisions, odd behavior or appearance, and magical thinking. Hoarding and indecision were more prevalent in the relatives of hoarding than of non-hoarding probands. Hoarding in relatives was associated with indecision in probands, independently of proband hoarding status. The findings suggest that hoarding behavior may help differentiate a distinct clinical subgroup of people with OCD and may aggregate in some OCD families. Indecision may be a risk factor for hoarding in these families.

Covariate-based linkage analysis: application of a propensity score as the single covariate consistently improves power to detect linkage.

Successful identification of genetic risk loci for complex diseases has relied on the ability to minimize disease and genetic heterogeneity to increase the power to detect linkage. One means to account for disease heterogeneity is by incorporating covariate data. However, the inclusion of each covariate will add one degree of freedom to the allele sharing based linkage test, which may in fact decrease power. We explore the application of a propensity score, which is typically used in causal inference to combine multiple covariates into a single variable, as a means of allowing for multiple covariates with the addition of only one degree of freedom. In this study, binary trait data, simulated under various models involving genetic and environmental effects, were analyzed using a nonparametric linkage statistic implemented in LODPAL. Power and type I error rates were evaluated. Results suggest that the use of the propensity score to combine multiple covariates as a single covariate consistently improves the power compared to an analysis including no covariates, each covariate individually, or all covariates simultaneously. Type I error rates were inflated for analyses with covariates and increased with increasing number of covariates, but reduced to nominal rates with sample sizes of 1000 families. Therefore, we recommend using the propensity score as a single covariate in the linkage analysis of a trait suspected to be influenced by multiple covariates because of its potential to increase the power to detect linkage, while controlling for the increase in the type I error.

Application of family-based association testing to assess the genotype-phenotype association involved in complex traits using single-nucleotide polymorphisms.

BACKGROUND: We used the FBAT (family-based association test) software to test for association between 300 individual single-nucleotide polymorphisms and P1 (a latent trait of Kofendred Personality Disorder) in 100 simulated replicates of the Aipotu population. Using the Genetic Analysis Workshop 14 dataset, we calculated the power of FBAT to detect linkage disequilibrium on chromosome 3 (D2). Also, we calculated the false-positive rate on chromosome 1, which contains a true locus (D1) but no linkage disequilibrium was simulated between the trait and all the surrounding single-nucleotide polymorphisms. RESULTS: We were able to detect the associations between phenotype P1 and three adjacent markers B03T3056 (average p-value = 0.0002), B03T3057 (average p-value = 0.00072), and B03T3058 (average p-value = 0.0038) with power of 98%, 87%, 71% on chromosome 3, respectively. The overall false positive rate to detect association was 0.06 on chromosome 1. CONCLUSION: The power to detect a significant association in 100 nuclear families affected with the latent trait of Kofendred Personality Disorder by using FBAT was reasonable (based on 100 replicates). In the future, we will compare the performance of FBAT with alternative approaches, such as using FBAT-generalized estimating equations methods to test for association in families affected with complex traits.

Application of the propensity score in a covariate-based linkage analysis of the Collaborative Study on the Genetics of Alcoholism.

BACKGROUND: Covariate-based linkage analyses using a conditional logistic model as implemented in LODPAL can increase the power to detect linkage by minimizing disease heterogeneity. However, each additional covariate analyzed will increase the degrees of freedom for the linkage test, and therefore can also increase the type I error rate. Use of a propensity score (PS) has been shown to improve consistently the statistical power to detect linkage in simulation studies. Defined as the conditional probability of being affected given the observed covariate data, the PS collapses multiple covariates into a single variable. This study evaluates the performance of the PS to detect linkage evidence in a genome-wide linkage analysis of microsatellite marker data from the Collaborative Study on the Genetics of Alcoholism. Analytical methods included nonparametric linkage analysis without covariates, with one covariate at a time including multiple PS definitions, and with multiple covariates simultaneously that corresponded to the PS definitions. Several definitions of the PS were calculated, each with increasing number of covariates up to a maximum of five. To account for the potential inflation in the type I error rates, permutation based p-values were calculated. RESULTS: Results suggest that the use of individual covariates may not necessarily increase the power to detect linkage. However the use of a PS can lead to an increase when compared to using all covariates simultaneously. Specifically, PS3, which combines age at interview, sex, and smoking status, resulted in the greatest number of significant markers identified. All methods consistently identified several chromosomal regions as significant, including loci on chromosome 2, 6, 7, and 12. CONCLUSION: These results suggest that the use of a propensity score can increase the power to detect linkage for a complex disease such as alcoholism, especially when multiple important covariates can be used to predict risk and thereby minimize linkage heterogeneity. However, because the PS is calculated as a conditional probability of being affected, it does require the presence of observed covariate data on both affected and unaffected individuals, which may not always be available in real data sets.

Suggestive linkage to chromosomal regions 13q31 and 22q12 in families with psychotic bipolar disorder.

OBJECTIVE: Linkage studies of bipolar disorder and schizophrenia have found overlapping evidence for susceptibility genes in four chromosomal regions-10p12-14, 13q32, 18p11.2, and 22q12-13. The authors previously demonstrated familial clustering of psychotic symptoms-defined as hallucinations and/or delusions-in some bipolar disorder pedigrees. In this study they used stratified linkage analysis to test the hypothesis that those bipolar disorder pedigrees most enriched for psychotic symptoms would show greater evidence of linkage to the regions of previous bipolar disorder/schizophrenia linkage overlap. METHOD: Nonparametric linkage analyses using GENEHUNTER and ASPEX were performed on 65 bipolar disorder families. Family subsets were defined by the number of family members with psychotic mood disorder. RESULTS: The 10 families in which three or more members had psychotic mood disorder showed suggestive evidence of linkage to 13q31 (nonparametric linkage score=3.56; LOD score=2.52) and 22q12 (nonparametric linkage score=3.32; LOD score=3.06). These results differed significantly from those for the entire study group of 65 families, which showed little or no linkage evidence in the two regions. The 10 families with three or more psychotic members did not show evidence of linkage to 10p12-14 or 18p11.2. The 95% confidence interval on 22q12 spanned 4.3 centimorgans (2.6 megabases) and was congruent with previous findings. CONCLUSIONS: Bipolar disorder families in which psychotic symptoms cluster may carry susceptibility genes on chromosomal regions 13q31 and 22q12. Replication should be attempted in similar families and perhaps in schizophrenia families in which mood symptoms cluster because these overlapping phenotypes may correlate most closely with the putative susceptibility genes. The localization of the 22q12 finding particularly encourages further study of this region.

Etiological heterogeneity of familial periventricular heterotopia and hydrocephalus.

Periventricular heterotopia (PH) represents a neuronal migration disorder that results in gray matter nodules along the lateral ventricles beneath an otherwise normal appearing cortex. While prior reports have shown that mutations in the filamin A (FLNA) gene can cause X-linked dominant PH, an increasing number of studies suggest the existence of additional PH syndromes. Further classification of these cortical malformation syndromes associated with PH allows for determination of the causal genes. Here we report three familial cases of PH with hydrocephalus. One pedigree has a known FLNA mutation with hydrocephalus occurring in the setting of valproic acid exposure. Another pedigree demonstrated possible linkage to the Xq28 locus including FLNA, although uncharacteristically a male was affected and sequencing of the FLNA gene in this individual revealed no mutation. However, in the third family with an autosomal mode of inheritance, microsatellite analysis ruled out linkage with the FLNA gene. Routine karyotyping and fluorescent in situ hybridization using BAC probes localized to FLNA also showed no evidence of genomic rearrangement. Western blot analysis of one of the affected individuals demonstrated normal expression of the FLNA protein. Lastly, sequencing of greater than 95% of the FLNA gene in an affected member failed to demonstrate a mutation. In conclusion, these findings demonstrate the etiological heterogeneity of PH with hydrocephalus. Furthermore, there likely exists an autosomal PH gene, distinct from the previously described X-linked and autosomal recessive forms. Affected individuals have severe developmental delay and may have radiographic findings of hydrocephalus.

The power and statistical behaviour of allele-sharing statistics when applied to models with two disease loci.

We have evaluated the power for detecting a common trait determined by two loci, using seven statistics, of which five are implemented in the computer program SimWalk2, and two are implemented in GENEHUNTER. Unlike most previous reports which involve evaluations of the power of allele-sharing statistics for a single disease locus, we have used a simulated data set of general pedigrees in which a two-locus disease is segregating and evaluated several nonparametric linkage statistics implemented in the two programs. We found that the power for detecting linkage using the S(all) statistic in GENEHUNTER (GH, version 2.1), implemented as statistic E in SimWalk2 (version 2.82), is different in the two. The P values associated with statistic E output by SimWalk2 are consistently more conservative than those from GENEHUNTER except when the underlying model includes heterogeneity at a level of 50% where the P values output are very comparable. On the other hand, when the thresholds are determined empirically under the null hypothesis, S(all) in GENEHUNTER and statistic E have similar power.

Family-based tests applied to extended pedigrees identify rare variants related to hypertension.

The application of family-based tests to whole-genome sequenced data provides a new window on the role of rare variant alleles in the etiology of disease. By applying family-based tests to these data, we can now identify rare variants associated with disease. Approaches for common variants, by contrast, require large sample sizes for power, and are powerless when faced with rare variants. When we tested Yip et al's 2011 family-based association tests for rare variants on pedigrees from the Genetic Analysis Workshop 18, we found that weighted collapsing methods generally have more power than unweighted methods, but are more prone to type I errors. We then evaluated a sliding window modification of the weighted family-based association tests for rare variants method. Although this modification inflates the rate of false positives, it significantly increases the power of family-based association tests for rare variants to identify causal rare variants.

Identification of genotype errors.

It has been documented that there exist some errors in most large genotype datasets and that an error rate of 1-2% is adequate to lead to the distortion of map distance as well as a false conclusion of linkage (Abecasis et al. Eur J Hum Genet 9(2):130-134, 2001), therefore one needs to ensure that the data are as clean as possible. On the other hand, the process of data cleaning is tedious and demands efforts and experience. O'Connell and Weeks implemented four error-checking algorithms in computer software called PedCheck. In this chapter, the four algorithms implemented in PedCheck are discussed with a focus on the genotype-elimination method. Furthermore, an example for four levels of error checking permitted by PedCheck is provided with the required input files. In addition, alternative algorithms implemented in other statistical computing programs are also briefly discussed.

Demographic and clinical characteristics associated with treatment status in family members with obsessive-compulsive disorder.

This study investigated the demographic and clinical factors that influence treatment status in family members with obsessive compulsive disorder (OCD). Six hundred and two subjects from the OCD Collaborative Genetics Study were interviewed using the Structured Clinical Interview for DSM-IV (SCID) to diagnose Axis I disorders, and the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) for assessment of OCD symptoms. The demographic and clinical data were compared between subjects who had received treatment and those who had not. A precipitous onset of symptoms, severe illness, multiple obsessions and compulsions, and co-morbid affective disorders were all positively associated with receiving treatment. Older age and the presence of obsessive compulsive personality disorder (OCPD) or OCPD traits were negatively associated with treatment. Gender and age at onset of symptoms did not predict treatment history. The mean duration from onset of symptoms to receiving treatment was 13.8+/-SD 11.9 years, but there was a direct relationship between current age and time to treatment, with younger subjects receiving treatment sooner. Clinical factors are predominant in predicting treatment status in family members with OCD. Although the mean duration from onset of symptoms to treatment was long, younger family members appear to receive treatment sooner.

Dopaminergic polymorphisms in Tourette syndrome: association with the DAT gene (SLC6A3).

Tourette syndrome (TS) is a chronic neuropsychiatric disorder characterized by involuntary motor and phonic tics. The pattern of inheritance and associated genetic abnormality has yet to be fully characterized. A dopaminergic abnormality in this disorder is supported by response to specific therapies, nuclear imaging, and postmortem studies. In this protocol, dopaminergic polymorphisms were examined for associations with TS and attention-deficit hyperactivity disorder (ADHD). Polymorphisms investigated included the dopamine transporter (DAT1 DdeI and DAT1 VNTR), dopamine receptor (D4 Upstream Repeat and D4 VNTR), dopamine converting enzyme (dopamine beta-hydroxylase), and the acid phosphatase locus 1 (ACP1) gene. DNA was obtained from 266 TS individuals +/- ADHD and 236 controls that were ethnicity-matched. A significant association, using a genotype-based association analysis, was identified for the TS-total and TS-only versus control groups for the DAT1 DdeI polymorphism (AG vs. AA, P = 0.004 and P = 0.01, respectively). Population structure, estimated by the genotyping of 27 informative SNP markers, identified 3 subgroups. A statistical re-evaluation of the DAT1 DdeI polymorphism following population stratification confirmed the association for the TS-total and TS-only groups, but the degree of significance was reduced (P = 0.017 and P = 0.016, respectively). This study has identified a significant association between the presence of TS and a DAT polymorphism. Since abnormalities of the dopamine transporter have been hypothesized in the pathophysiology of TS, it is possible that this could be a functional allele associated with clinical expression.

The OCD collaborative genetics study: methods and sample description.

Results from twin and family studies suggest that obsessive-compulsive disorder (OCD) may be transmitted in families but, to date, genes for the disorder have not been identified. The OCD Collaborative Genetics Study (OCGS) is a six-site collaborative genetic linkage study of OCD. Specimens and blinded clinical data will be made available through the National Institute of Mental Health (NIMH) cell repository. In this initial report, we describe the methods of the study and present clinical characteristics of affected individuals for researchers interested in this valuable resource for genetic studies of OCD. The project clinically evaluated and collected blood specimens from 238 families containing 299 OCD-affected sibling pairs and their parents, and additional affected relative pairs, for a genome-wide linkage study. Of the 999 individuals interviewed to date, 624 were diagnosed with "definite" OCD. The mean age of subjects was 36 years (range 7-95). The majority of affected individuals (66%) were female. The mean age at onset of obsessive-compulsive symptoms was 9.5 years. Specific mood disorders, anxiety disorders, eating disorders, and skin picking were more prevalent in female cases, whereas tics, Tourette disorder, and alcohol dependence were more prevalent in male cases. Compared to "definite" cases of OCD, "probable" cases (n = 82) had, on average, later age at onset of obsessive-compulsive symptoms, lower severity score, and fewer numbers of different categories of obsessions and compulsions, and they were less likely to have received treatment for their symptoms.

CGH-targeted linkage analysis reveals a possible BRCA1 modifier locus on chromosome 5q.

Women with germline mutations in BRCA1 have a greatly elevated risk of breast and ovarian cancer. However, considerable variation in the degree of breast cancer risk associated with a BRCA1 mutation has been observed, suggesting that modifiers of BRCA1 penetrance may exist. We hypothesized that the modifier genes might be located in regions of allelic imbalance in the tumors of BRCA1 mutation carriers, as have been reported on chromosomes 4p, 4q and 5q. In order to determine whether novel genetic modifiers of BRCA1-associated breast cancer penetrance in these regions exist, we used non-parametric linkage analysis methods to determine whether allele sharing of chromosomes 4p, 4q and 5q was observed preferentially within BRCA1 mutation families in women with BRCA1 mutations and breast cancer. No significant linkage on chromosome 4p or 4q was observed associated with breast cancer risk in BRCA1 mutation carriers. However, we observed a significant linkage signal at D5S1471 on chromosome 5q (P = 0.009) in all the families analyzed together. The significance of this observation increased in the subset of families with an average of breast cancer diagnosis less than 45 years (P = 0.003). These results suggest the presence of one or more genes on chromosome 5q33-34 that modify breast cancer risk in BRCA1 mutation carriers. The approach described here may be utilized to identify penetrance modifiers in other autosomal dominant syndromes.

Mutations in the RPGR gene cause X-linked cone dystrophy.

X-linked cone dystrophy is a type of hereditary retinal degeneration characterized by a progressive dysfunction of the day vision or photopic (cone) system with preservation of night vision or scotopic (rod) function. The disease presents with a triad of photophobia, loss of color vision and reduced central vision. This phenotype is distinct from retinitis pigmentosa (RP) in which there are prominent night and peripheral vision disturbances. X-linked cone dystrophy is a genetically heterogeneous disorder, with linkage to loci on Xp11.4--Xp21.1 (COD1, OMIM 304020) and Xq27 (COD2, OMIM 303800). COD1 maps to a region that harbors the RPGR gene, mutations in which account for >70% of patients with X-linked RP. The majority of these mutations reside in one purine-rich exon, ORF15, encoding 567 amino acids with a repetitive domain rich in glutamic acid residues. We mapped two families with X-linked cone dystrophy to the COD1 locus and identified two distinct mutations in ORF15 in the RPGR gene (ORF15+1343_1344delGG and ORF15+694_708del15) leading to a frame-shift and premature termination of translation in one case and a deletion of five amino acids in another. Consistent with expression of RPGR in rods and cones, our results show that mutations in RPGR, in addition to X-linked RP, can also cause cone-specific degeneration.