Objective

To investigate the underlying phenotypic constructs in autism spectrum disorders (ASD) and to identify genetic loci that are linked to these empirically derived factors.

Method

Exploratory factor analysis was applied to two datasets with 28 selected Autism Diagnostic Interview-Revised (ADI-R) algorithm items. The first dataset was from the Autism Genome Project (AGP) phase I (1,236 ASD subjects from 618 families); the second was from the AGP phase II (804 unrelated ASD subjects). Variables derived from the factor analysis were then used as quantitative traits in genome-wide variance components linkage analyses.

Results

Six factors, joint attention, social interaction and communication, non-verbal communication, repetitive sensory-motor behaviour, peer interaction, and compulsion/restricted interests, were retained for both datasets. There was good agreement between the factor loading patterns from the two datasets. All factors showed familial aggregation. Suggestive evidence for linkage was obtained for the joint attention factor on 11q23. Genome-wide significant evidence for linkage was obtained for the repetitive sensory-motor behaviour factor on 19q13.3.

Conclusions

This study demonstrates that the underlying phenotypic constructs based on the ADI-R algorithm items are replicable in independent datasets; and the empirically derived factors are suitable and informative in genetic studies of ASD.

Background

Studies of copy number variation (CNV) have successfully characterized loci and molecular pathways involved in a range of neuropsychiatric conditions. We conducted an analysis of rare CNVs in Tourette Syndrome (TS) to identify novel risk regions and relevant molecular pathways, evaluate the burden of structural variation in cases versus controls, and to assess the overlap of identified variations with those implicated in other neuropsychiatric syndromes.

Methods

We conducted a case-control study of 460 individuals with TS, including 148 parent-child trios and 1131 controls. CNV analysis was undertaken using 370K to 1M probe arrays, and genome-wide genotyping data was used to match cases and controls for ancestry. Transmitted and de novo CNVs present in < 1% of the population were evaluated.

Results

While there was no significant increase in the number of de novo or transmitted rare CNVs in cases versus controls, pathway analysis using multiple algorithms showed enrichment of genes within histamine receptor (H1R and H2R) signaling pathways (p=5.8×10-4-1.6×10-2) as well as “axon guidance”, “cell adhesion”, “nervous system development” and “synaptic structure and function” processes. Genes mapping within rare CNVs in TS showed significant overlap with those previously identified in autism spectrum disorders (ASD), but not intellectual disability or schizophrenia. Three large, likely-pathogenic, de novo events were identified, including one disrupting multiple gamma-Aminobutyric acid (GABA) receptor genes.

Conclusions

We identify further evidence supporting recent findings regarding the involvement of histaminergic and GABAergic mechanisms in the etiology of TS and show an overlap of rare CNVs in TS and ASD.

Background

We report on copy number variants (CNVs) found in Palauan subjects ascertained for schizophrenia and related psychotic disorders in extended pedigrees in Palau. We compare CNVs found in this Oceanic population to those seen in other samples, typically of European ancestry. Assessing CNVs in Palauan extended pedigrees yields insight into the evolution of risk CNVs, such as how they arise, are transmitted, and are lost from populations by stochastic or selective processes, none of which is easily measured from case-control samples.

Methods

DNA samples from 197 subjects affected with schizophrenia and related psychotic disorders, 185 of their relatives, and 159 controls were successfully characterized for CNVs using Affymetrix Genomewide Human SNP Array 5.0.

Results

CNVs thought to be associated with risk for schizophrenia and related disorders also occur in affected individuals in Palau, specifically 15q11.2 and 1q21.1 deletions, partial duplication of IL1RAPL1 (Xp21.3), and chromosome X duplications (Klinefleter’s syndrome). Partial duplication within A2BP1 appears to convey an 8-fold increased risk in males (95% CI, 0.8–84.4) but not females (OR=0.4, 95% CI, 0.03–4.9). Affected-only linkage analysis using this variant yields a LOD score of 3.5.

Conclusions

This study reveals CNVs that confer risk to schizophrenia and related psychotic disorders in Palau, most of which have been previously observed in samples of European ancestry. Only a few of these CNVs show evidence that they have existed for many generations, consistent with risk variants diminishing reproductive success.

Apolipoprotein E (APOE) ε4 alleles increase the risk for late-onset Alzheimer disease (LOAD) and decrease the age of onset. Recently, sequencing the APOE region in a small sample of LOAD subjects identified a variable length poly-T repeat sequence in the nearby gene, TOMM40, which may affect age of onset. We genotyped the TOMM40 poly-T repeat using a novel statistical approach to refine the identification of allele length in 892 LOAD subjects and evaluated its effects on age of onset. Because psychosis in LOAD is a heritable phenotype which has shown conflicting associations with APOE genotype, we also evaluated the association of poly-T repeat length with psychosis. Poly-T repeat lengths had a trimodal distribution which differed between APOE genotype groups. After accounting for APOE ε4 there was no association of poly-T repeat length with age of onset. Neither APOE ε4 nor poly-T repeat length was associated with psychosis. Our findings do not support the association of poly-T repeat length with age of onset in LOAD. The clinical implications of this repeat length polymorphism remain to be elucidated.

Psychotic symptoms occur in approximately 40% of subjects with Alzheimer’s disease (AD) and are associated with more rapid cognitive decline and increased functional deficits. They show heritability up to 61% and have been proposed as a marker for a disease subtype suitable for gene mapping efforts. We undertook a combined analysis of three genome-wide association studies (GWAS) to identify loci that a) increase susceptibility to an AD and subsequent psychotic symptoms; or b) modify risk of psychotic symptoms in the presence of neurodegeneration caused by AD. 1299 AD cases with psychosis (AD+P), 735 AD cases without psychosis (AD−P) and 5659 controls were drawn from GERAD1, the NIA-LOAD family study and the University of Pittsburgh ADRC GWAS. Unobserved genotypes were imputed to provide data on > 1.8 million SNPs. Analyses in each dataset were completed comparing a) AD+P to AD−P cases, and b) AD+P cases with controls (GERAD1, ADRC only). Aside from the APOE locus, the strongest evidence for association was observed in an intergenic region on chromosome 4 (rs753129; ‘AD+PvAD−P’ P=2.85 × 10−7; ‘AD+PvControls’ P=1.11 × 10−4). SNPs upstream of SLC2A9 (rs6834555, P=3.0×10−7) and within VSNL1 (rs4038131, P=5.9×10−7) showed strongest evidence for association with AD+P when compared to controls. These findings warrant further investigation in larger, appropriately powered samples in which the presence of psychotic symptoms in AD has been well characterised.

Background

Autism spectrum disorders (ASD) are early onset neurodevelopmental syndromes typified by impairments in reciprocal social interaction and communication, accompanied by restricted and repetitive behaviors. While rare and especially de novo genetic variation are known to affect liability, whether common genetic polymorphism plays a substantial role is an open question and the relative contribution of genes and environment is contentious. It is probable that the relative contributions of rare and common variation, as well as environment, differs between ASD families having only a single affected individual (simplex) versus multiplex families who have two or more affected individuals.

Methods

By using quantitative genetics techniques and the contrast of ASD subjects to controls, we estimate what portion of liability can be explained by additive genetic effects, known as narrow-sense heritability. We evaluate relatives of ASD subjects using the same methods to evaluate the assumptions of the additive model and partition families by simplex/multiplex status to determine how heritability changes with status.

Results

By analyzing common variation throughout the genome, we show that common genetic polymorphism exerts substantial additive genetic effects on ASD liability and that simplex/multiplex family status has an impact on the identified composition of that risk. As a fraction of the total variation in liability, the estimated narrow-sense heritability exceeds 60% for ASD individuals from multiplex families and is approximately 40% for simplex families. By analyzing parents, unaffected siblings and alleles not transmitted from parents to their affected children, we conclude that the data for simplex ASD families follow the expectation for additive models closely. The data from multiplex families deviate somewhat from an additive model, possibly due to parental assortative mating.

Conclusions

Our results, when viewed in the context of results from genome-wide association studies, demonstrate that a myriad of common variants of very small effect impacts ASD liability.

The major histocompatibility complex (MHC) on chromosome 6p is an established risk locus for ulcerative colitis (UC) and Crohn’s disease (CD). We aimed to better define MHC association signals in UC and CD by combining data from dense single nucleotide polymorphism (SNP) genotyping and from imputation of classical HLA types, their constituent SNPs and corresponding amino acids in 562 UC, 611 CD, and 1,428 control subjects. Univariate and multivariate association analyses were performed, controlling for ancestry. In univariate analyses, absence of the rs9269955 C allele was strongly associated with risk for UC (P = 2.67×10−13). rs9269955 is a SNP in the codon for amino acid position 11 of HLA-DRβ1, located in the P6 pocket of the HLA-DR antigen binding cleft. This amino acid position was also the most significantly UC-associated amino acid in omnibus tests (P = 2.68×10−13). Multivariate modeling identified rs9269955-C and 13 other variants in best predicting UC versus control status. In contrast, there was only suggestive association evidence between the MHC and CD. Taken together, these data demonstrate that variation at HLA-DRβ1, amino acid 11 in the P6 pocket of the HLA-DR complex antigen binding cleft is a major determinant of chromosome 6p association with ulcerative colitis.

Objective

Published studies suggest associations between circadian gene polymorphisms and bipolar I disorder (BPI), as well as schizoaffective disorder (SZA) and schizophrenia (SZ). The results are plausible, based on prior studies of circadian abnormalities. As replications have not been attempted uniformly, we evaluated representative, common polymorphisms in all three disorders.

Methods

We assayed 276 publicly available ‘tag’ single nucleotide polymorphisms (SNPs) at 21 circadian genes among 523 patients with BPI, 527 patients with SZ/SZA, and 477 screened adult controls. Detected associations were evaluated in relation to two published genome-wide association studies (GWAS).

Results

Using gene-based tests, suggestive associations were noted between EGR3 and BPI (p = 0.017), and between NPAS2 and SZ/SZA (p = 0.034). Three SNPs were associated with both sets of disorders (NPAS2: rs13025524 and rs11123857; RORB: rs10491929; p < 0.05). None of the associations remained significant following corrections for multiple comparisons. Approximately 15% of the analyzed SNPs overlapped with an independent study that conducted GWAS for BPI; suggestive overlap between the GWAS analyses and ours was noted at ARNTL.

Conclusions

Several suggestive, novel associations were detected with circadian genes and BPI and SZ/SZA, but the present analyses do not support associations with common polymorphisms that confer risk with odds ratios greater than 1.5. Additional analyses using adequately powered samples are warranted to further evaluate these results.

Psychotic symptoms occur in approximately 40% of subjects with Alzheimer disease (AD with Psychosis, AD+P) and identify a subgroup with more rapid cognitive decline. We evaluated in 867 AD subjects the association of AD+P with genes which may modify the pathologic process via effects on the accumulation of amyloid beta (Aβ) protein and/or hyperphosphorylated microtubule-associated protein tau (MAPT): amyloid precursor protein (APP), beta-site amyloid precursor protein cleaving enzyme (BACE1), sortilin-related receptor (SORL1), and MAPT. Each gene was thoroughly interrogated with tag SNPs, and gene-based tests were used to enhance power. We found no association of these genes with AD+P.

Objective: Various measures of neurocognitive function show mean differences among individuals with schizophrenia (SZ), their relatives, and population controls. We use eigenvector transformations that maximize heritability of multiple neurocognitive measures, namely principal components of heritability (PCH), and evaluate how they distribute in SZ families and controls. Methods: African-Americans with SZ or schizoaffective disorder (SZA) (n = 514), their relatives (n = 1092), and adult controls (n = 300) completed diagnostic interviews and computerized neurocognitive tests. PCH were estimated from 9 neurocognitive domains. Three PCH, PCH1–PCH3, were modeled to determine if status (SZ, relative, and control), other psychiatric covariates, and education were significant predictors of mean values. A small-scale linkage analysis was also conducted in a subset of the sample. Results: PCH1, PCH2, and PCH3 account for 72% of the genetic variance. PCH1 represents 8 of 9 neurocognitive domains, is most highly correlated with spatial processing and emotion recognition, and has unadjusted heritability of 68%. The means for PCH1 differ significantly among SZ, their relatives, and controls. PCH2, orthogonal to PCH1, is most closely correlated with working memory and has an unadjusted heritability of 45%. Mean PCH2 is different only between SZ families and controls. PCH3 apparently represents a heritable component of neurocognition similar across the 3 diagnostic groups. No significant linkage evidence to PCH1–PCH3 or individual neurocognitive measures was discovered. Conclusions: PCH1 is highly heritable and genetically correlated with SZ. It should prove useful in future genetic analyses. Mean PCH2 differentiates SZ families and controls but not SZ and unaffected family members.

Progressive supranuclear palsy (PSP) is a movement disorder with prominent tau neuropathology. Brain diseases with abnormal tau deposits are called tauopathies, the most common being Alzheimer’s disease. Environmental causes of tauopathies include repetitive head trauma associated with some sports. To identify common genetic variation contributing to risk for tauopathies, we carried out a genome-wide association study of 1,114 PSP cases and 3,247 controls (Stage 1) followed up by a second stage where 1,051 cases and 3,560 controls were genotyped for Stage 1 SNPs that yielded P ≤ 10−3. We found significant novel signals (P < 5 × 10−8) associated with PSP risk at STX6, EIF2AK3, and MOBP. We confirmed two independent variants in MAPT affecting risk for PSP, one of which influences MAPT brain expression. The genes implicated encode proteins for vesicle-membrane fusion at the Golgi-endosomal interface, for the endoplasmic reticulum unfolded protein response, and for a myelin structural component.

Discoveries from genome-wide association studies have contributed to our knowledge of the genetic etiology of many complex diseases. However, these account for only a small fraction of each disease's heritability. Here, we comment on approaches currently available to uncover more of the genetic 'dark matter,' including an approach introduced recently by Naukkarinen and colleagues. These authors propose a method for distinguishing between gene expression driven by genetic variation and that driven by non-genetic factors. This dichotomy allows investigators to focus statistical tests and further molecular analyses on a smaller set of genes, thereby discovering new genetic variation affecting risk for disease. We need more methods like this one if we are to shed a powerful light on dark matter. By enhancing our understanding of molecular genetic etiology, such methods will help us to understand disease processes better and will advance the promise of personalized medicine.

Background

Associations between schizophrenia (SCZ) and polymorphisms at the regulator of G-protein signaling 4 (RGS4) gene have been reported (single nucleotide polymorphisms [SNPs] 1, 4, 7, and 18). Yet, similar to other SCZ candidate genes, studies have been inconsistent with respect to the associated alleles.

Methods

In an effort to resolve the role for RGS4 in SCZ susceptibility, we undertook a genotype-based meta-analysis using both published and unpublished family-based and case-control samples (total n = 13,807).

Results

The family-based dataset consisted of 10 samples (2160 families). Significant associations with individual SNPs/haplotypes were not observed. In contrast, global analysis revealed significant transmission distortion (p = .0009). Specifically, analyses suggested overtransmission of two common haplotypes that account for the vast majority of all haplotypes. Separate analyses of 3486 cases and 3755 control samples (eight samples) detected a significant association with SNP 4 (p = .01). Individual haplotype analyses were not significant, but evaluation of test statistics from individual samples suggested significant associations.

Conclusions

Our collaborative meta-analysis represents one of the largest SCZ association studies to date. No individual risk factor arose from our analyses, but interpretation of these results is not straightforward. Our analyses suggest risk due to at least two common haplotypes in the presence of heterogeneity. Similar analysis for other putative susceptibility genes is warranted.

Background

Consanguinity has been suggested as a risk factor for psychsoses in some Middle Eastern countries, but adequate control data are unavailable. Our recent studies in Egypt have shown elevated parental consanguinity rates among patients with bipolar I disorder (BP1), compared with controls. We have now extended our analyses to Schizophrenia (SZ) in the same population.

Methods

A case-control study was conducted at Mansoura University Hospital, Mansoura, Egypt (SZ, n = 75; controls, n = 126, and their available parents). The prevalence of consanguinity was estimated from family history data (‘self report’), followed by DNA analysis using short tandem repeat polymorphisms (STRPs, n = 63) (‘DNA-based’ rates).

Results

Self reported consanguinity was significantly elevated among the patients (SZ: 46.6%, controls: 19.8%, OR 3.53, 95% CI 1.88, 6.64; p = 0.000058, 1 d.f.). These differences were confirmed using DNA based estimates for coefficients of inbreeding (inbreeding coefficients as means ± standard error, cases: 0.058 ± 0.007, controls: 0.022 ± 0.003).

Conclusions

Consanguinity rates are signifcantly elevated among Egyptian SZ patients in the Nile delta region. The associations are similar to those observed with BP1 in our earlier study. If replicated, the substantial risk associated with consanguinity raises public health concerns. They may also pave the way for gene mapping studies.

Epistasis could be an important source of risk for disease. How interacting loci might be discovered is an open question for genome-wide association studies (GWAS). Most researchers limit their statistical analyses to testing individual pairwise interactions (i.e., marginal tests for association). A more effective means of identifying important predictors is to fit models that include many predictors simultaneously (i.e., higher dimensional models).

We explore a procedure called screen and clean (SC) for identifying liability loci, including interactions, by using the lasso procedure, which is a model selection tool for high dimensional regression. We approach the problem by using a varying dictionary consisting of terms to include in the model. In the first step the lasso dictionary includes only main effects. The most promising SNPs are identified using a screening procedure. Next the lasso dictionary is adjusted to include these main effects and the corresponding interaction terms. Again, promising terms are identified using lasso screening. Then significant terms are identified through the cleaning process. Implementation of SC for GWAS requires algorithms to explore the complex model space induced by the many SNPs genotyped and their interactions. We propose and explore a set of algorithms and find that SC successfully controls Type I error while yielding good power to identify risk loci and their interactions. When the method is applied to data obtained from the Wellcome Trust Case Control Consortium study of Type 1 Diabetes it uncovers evidence supporting interaction within the HLA class II region as well as within Chromosome 12q24.

Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a group. A further complication is that any given rare variant could have no effect, could increase risk, or could be protective. We propose here the C-alpha test statistic as a novel approach for testing for the presence of this mixture of effects across a set of rare variants. Unlike existing burden tests, C-alpha, by testing the variance rather than the mean, maintains consistent power when the target set contains both risk and protective variants. Through simulations and analysis of case/control data, we demonstrate good power relative to existing methods that assess the burden of rare variants in individuals.

Author Summary

Developments in sequencing technology now enable us to assay all genetic variation, much of which is extremely rare. We propose to test the distribution of rare variants we observe in cases versus controls. To do so, we present a novel application of the C-alpha statistic to test these rare variants. C-alpha aims to determine whether the set of variants observed in cases and controls is a mixture, such that some of the variants confer risk or protection or are phenotypically neutral. Risk variants are expected to be more common in cases; protective variants more common in controls. C-alpha is sensitive to this imbalance, regardless of its origin—risk, protective, or both—but is ideally suited for a mixture of protective and risk variants. Variation in APOB nicely illustrates a mixture, in that certain rare variants increase triglyceride levels while others decrease it. The hallmark feature of C-alpha is that it uses the distribution of variation observed in cases and controls to detect the presence of a mixture, thus implicating genes or pathways as risk factors for disease.

Objective

The authors sought to assess the relationship between candidate genes and two clinical outcomes, namely, symptomatic improvement and the occurrence of suicidal events, in a sample of treatment-resistant depressed adolescents.

Method

A subsample of depressed adolescents participating in the Treatment of SSRI-Resistant Depression in Adolescents (TORDIA) trial, 155 of whom were of European origin, were genotyped with respect to 21 polymorphisms on 12 genes that have a reported association with depression, treatment response, or suicidal events. Participants had not responded to a previous adequate trial with an antidepressant and were randomized to receive either another selective serotonin reuptake inhibitor or venlafaxine, with or without cognitive-behavioral therapy (CBT). Single-nucleotide polymorphism (SNP) analyses were conducted using PLINK with permutation procedures.

Results

No relationship was observed between any polymorphism and response to treatment. The FKBP5 (which codes for a protein causing subsensitivity of the glucocorticoid receptor) rs1360780TT and rs3800373GG genotypes were associated with suicidal events (N=18), even after controlling for treatment effects and relevant covariates. These two SNPs were in significant linkage disequilibrium (r=0.91).

Conclusions

The FKBP5 genotypes associated with suicidal events in this study have been reported by others to cause the greatest degree of glucocorticoid receptor subsensitivity. These results are consistent with those of other studies linking alterations in the hypothalamic-pituitary-adrenal axis with suicidal behavior. The small number of events and lack of a placebo condition make these results preliminary. Replication with a larger sample and a placebo condition is needed to assess whether these events are related to treatment.

Recessive mutations in the Phenylalanine hydroxylase (PAH) gene predispose to phenylketonuria (PKU) in conjunction with dietary exposure to phenylalanine. Previous studies have suggested PAH variations could confer risk for schizophrenia, but comprehensive follow-up has not been reported. We analyzed 15 common PAH “tag” SNPs and 3 exonic variations that are rare in Caucasians but common in African-Americans among four independent samples (total n = 5,414). The samples included two US Caucasian cohorts (260 trios, 230 independent cases, 474 controls), Bulgarian families (659 trios), and an African-American sample (464 families, 401 controls). Analyses of both US Caucasian samples revealed associations with five SNPs; most notably the common allele (G) of rs1522305 from case-control analyses (z = 2.99, p = 0.006). This SNP was independently replicated in the Bulgarian cohort (z = 2.39, p = 0.015). A non-significant trend was also observed among African-American families (z = 1.39, p = 0.165), and combined analyses of all four samples were significant (rs1522305: χ2 = 23.28, 8 d.f., p = 0.003). These results for this SNP met our a priori criteria for statistical significance, namely an association that was robust to multiple testing correction in one sample, a replicated risk allele in multiple samples, and combined analyses that were nominally significant. Case-control results in African-Americans detected an association with L321L (p = 0.047, OR = 1.46). Our analyses suggest several associations at PAH, with consistent evidence for rs1522305. Further analyses, including additional variations and environmental influences such as phenylalanine exposure are warranted.

Many psychiatric conditions and traits are associated with significant heritability. Genetic risk for psychiatric conditions encompass rare variants, identified due to major effect, as well as common variants, the latter analyzed by association analyses. We review guidelines for common variant association analyses, undertaking after assessing evidence of heritability. We highlight the importance of: suitably large sample sizes; an experimental design that controls for ancestry; careful data cleaning; correction for multiple testing; small P values for positive findings; assessment of effect size for positive findings; and, inclusion of an independent replication sample. We also note the importance of a critical discussion of any prior findings, biological follow-up where possible, and a means of accessing the raw data.

To increase the likelihood of finding genetic variation conferring liability to eating disorders, we measured over 100 attributes thought to be related to liability to eating disorders on affected individuals from multiplex families and two cohorts: one recruited through a proband with anorexia nervosa (AN; AN cohort); the other recruited through a proband with bulimia nervosa (BN; BN cohort). By a multilayer decision process based on expert evaluation and statistical analysis, six traits were selected for linkage analysis (1): obsessionality (OBS), age at menarche (MENAR) and anxiety (ANX) for quantitative trait locus (QTL) linkage analysis; and lifetime minimum Body Mass Index (BMI), concern over mistakes (CM) and food-related obsessions (OBF) for covariate-based linkage analysis. The BN cohort produced the largest linkage signals: for QTL linkage analysis, four suggestive signals: (for MENAR, at 10p13; for ANX, at 1q31.1, 4q35.2, and 8q13.1); for covariate-based linkage analyses, both significant and suggestive linkages (for BMI, one significant [4q21.1] and three suggestive [3p23, 10p13, 5p15.3]; for CM, two significant [16p13.3, 14q21.1] and three suggestive [4p15.33, 8q11.23, 10p11.21]; and for OBF, one significant [14q21.1] and five suggestive [4p16.1, 10p13.1, 8q11.23, 16p13.3, 18p11.31]). Results from the AN cohort were far less compelling: for QTL linkage analysis, two suggestive signals (for OBS at 6q21 and for ANX at 9p21.3); for covariate-based linkage analysis, five suggestive signals (for BMI at 4q13.1, for CM at 11p11.2 and 17q25.1, and for OBF at 17q25.1 and 15q26.2). Overlap between the two cohorts was minimal for substantial linkage signals.

Incomplete response in the treatmen tof late-life depression is a large public health challenge: at least 50% of older people fail to respond adequately to first-line antidepressant pharmacotherapy, even under optimal treatment conditions. Treatment-resistant late-life depression (TRLLD) increases risk for early relapse, undermines adherence to treatment for coexisting medical disorders, amplifies disability and cognitive impairment, imposes greater burden on family caregivers, and increases the risk for early mortality, including suicide, Gettinq to and sustaininq remission is the primary goal of treatment yet there is a paucity of empirical data on how best to manage TRLLD. A pilot study by our group on aripiprazole augmentation in 24 incomplete responders to sequential SSRI and SRNI pharmacotherapy found that 50% remitted over 12 weeks with the addition of aripiprazole, and that remission was sustained in all participants during 6 months of continuation treatment In addition to controlled assessment, evidence is needed to support personalized treatment by testing the moderating role of clinical (eg, comorbid anxiety, medical burden, and executive impairment) and genetic (eg, selected polymorphisms in serotonin, norepinephrine, and dopamine genes) variables, while also controlling for variability in drug exposure. Such studies may advance us toward the goal of personalized treatment in late-life depression.

Vulnerability to anorexia nervosa (AN) and bulimia nervosa (BN) arise from the interplay of genetic and environmental factors. To explore the genetic contribution, we measured over 100 psychiatric, personality and temperament phenotypes of individuals with eating disorders from 154 multiplex families accessed through an AN proband (AN cohort) and 244 multiplex families accessed through a BN proband (BN cohort). To select a parsimonious subset of these attributes for linkage analysis, we subjected the variables to a multilayer decision process based on expert evaluation and statistical analysis. Criteria for trait choice included relevance to eating disorders pathology, published evidence for heritability, and results from our data. Based on these criteria, we chose six traits to analyze for linkage. Obsessionality, Age-at-Menarche, and a composite Anxiety measure displayed features of heritable quantitative traits, such as normal distribution and familial correlation, and thus appeared ideal for quantitative trait locus (QTL) linkage analysis. By contrast, some families showed highly concordant and extreme values for three variables — lifetime minimum Body Mass Index (lowest BMI attained during the course of illness), concern over mistakes, and food-related obsessions — whereas others did not. These distributions are consistent with a mixture of populations, and thus the variables were matched with covariate linkage analysis. Linkage results appear in a subsequent report. Our report lays out a systematic roadmap for utilizing a rich set of phenotypes for genetic analyses, including the selection of linkage methods paired to those phenotypes.

The ε4 allele of APOE confers a two- to four-fold increased risk for late-onset Alzheimer’s disease (LOAD), but LOAD pathology does not all fit neatly around APOE. It is conceivable that genetic variation proximate to APOE contributes to LOAD risk. Therefore, we investigated the degree of linkage disequilibrium (LD) for a comprehensive set of 50 SNPs in and surrounding the APOE using a substantial Caucasian sample of 1100 chromosomes. SNPs in APOE were further molecular haplotyped to determine their phases. One set of SNPs in TOMM40, roughly 15 Kb upstream of APOE, showed intriguing LD with the ε4 allele, and were strongly associated with the risk for developing AD. However, when all the SNPs were entered into a logit model, only the effect of APOE ε4 remained significant. These observations diminish the possibility that loci in the TOMM40 may have a major effect on the risk of LOAD in Caucasians.

In the search for genetic determinants of complex disease, two approaches to association analysis are most often employed, testing single loci or testing a small group of loci jointly via haplotypes for their relationship to disease status. It is still debatable which of these approaches is more favourable, and under what conditions. The former has the advantage of simplicity but suffers severely when alleles at the tested loci are not in linkage disequilibrium (LD) with liability alleles; the latter should capture more of the signal encoded in LD, but is far from simple. The complexity of haplotype analysis could be especially troublesome for association scans over large genomic regions, which, in fact, is becoming the standard design. For these reasons, the authors have been evaluating statistical methods that bridge the gap between single-locus and haplotype-based tests. In this article, they present one such method, which uses non-parametric regression techniques embodied by Bayesian adaptive regression splines (BARS). For a set of markers falling within a common genomic region and a corresponding set of single-locus association statistics, the BARS procedure integrates these results into a single test by examining the class of smooth curves consistent with the data. The non-parametric BARS procedure generally finds no signal when no liability allele exists in the tested region (ie it achieves the specified size of the test) and it is sensitive enough to pick up signals when a liability allele is present. The BARS procedure provides a robust and potentially powerful alternative to classical tests of association, diminishes the multiple testing problem inherent in those tests and can be applied to a wide range of data types, including genotype frequencies estimated from pooled samples.