Brion S. Maher

A Genome-Wide Analysis of Liberal and Conservative Political Attitudes

The assumption that the transmission of social behaviors and political preferences is purely cultural has been challenged repeatedly over the last 40 years by the combined evidence of large studies of adult twins and their relatives, adoption studies, and twins reared apart. Variance components and path modeling analyses using data from extended families quantified the overall genetic influence on political attitudes, but few studies have attempted to localize the parts of the genome which accounted for the heritability estimates found for political preferences. Here, we present the first genome-wide analysis of Conservative-Liberal attitudes from a sample of 13,000 respondents whose DNA was collected in conjunction with a 50-item sociopolitical attitude questionnaire. Several significant linkage peaks were identified and potential candidate genes discussed.

Identification and Replication of a Combined Epigenetic and Genetic Biomarker Predicting Suicide and Suicidal Behaviors

Considerable research suggests that suicide involves effects of genes, the environment, and their interaction. Analysis of three independent data sets of post-mortem brains revealed signs of increased methylation in one particular gene, SKA2, a finding that was extended to peripheral blood samples from other cohorts of prospectively followed individuals.

Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis

Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to this ranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.

Meta-analysis of genome-wide association studies of anxiety disorders

Anxiety disorders (ADs), namely generalized AD, panic disorder and phobias, are common, etiologically complex conditions with a partially genetic basis. Despite differing on diagnostic definitions based on clinical presentation, ADs likely represent various expressions of an underlying common diathesis of abnormal regulation of basic threat–response systems. We conducted genome-wide association analyses in nine samples of European ancestry from seven large, independent studies. To identify genetic variants contributing to genetic susceptibility shared across interview-generated DSM-based ADs, we applied two phenotypic approaches: (1) comparisons between categorical AD cases and supernormal controls, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. We used logistic and linear regression, respectively, to analyze the association between these phenotypes and genome-wide single nucleotide polymorphisms. Meta-analysis for each phenotype combined results across the nine samples for over 18 000 unrelated individuals. Each meta-analysis identified a different genome-wide significant region, with the following markers showing the strongest association: for case–control contrasts, rs1709393 located in an uncharacterized non-coding RNA locus on chromosomal band 3q12.3 (P=1.65 × 10−8); for FS, rs1067327 within CAMKMT encoding the calmodulin-lysine N-methyltransferase on chromosomal band 2p21 (P=2.86 × 10−9). Independent replication and further exploration of these findings are needed to more fully understand the role of these variants in risk and expression of ADs.

Prioritization and association analysis of murine-derived candidate genes in anxiety-spectrum disorders.

BACKGROUND Anxiety disorders are common psychiatric conditions that are highly comorbid with each other and related phenotypes such as depression, likely due to a shared genetic basis. Fear-related behaviors in mice have long been investigated as potential models of anxiety disorders, making integration of information from both murine and human genetic data a powerful strategy for identifying potential susceptibility genes for these conditions. METHODS We combined genome-wide association analysis of fear-related behaviors with strain distribution pattern analysis in heterogeneous stock mice to identify a preliminary list of 52 novel candidate genes. We ranked these according to three complementary sources of prior anxiety-related genetic data: 1) extant linkage and knockout studies in mice, 2) a meta-analysis of human linkage scans, and 3) a preliminary human genome-wide association study. We genotyped tagging single nucleotide polymorphisms covering the nine top-ranked regions in a two-stage association study of 1316 subjects from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders chosen for high or low genetic loading for anxiety-spectrum phenotypes (anxiety disorders, neuroticism, and major depression). RESULTS Multiple single nucleotide polymorphisms in the PPARGC1A gene demonstrated association in both stages that survived gene-wise correction for multiple testing. CONCLUSIONS Integration of genetic data across human and murine studies suggests PPARGC1A as a potential susceptibility gene for anxiety-related disorders.

Meta-analyses of genome-wide linkage scans of anxiety-related phenotypes

Genetic factors underlying trait neuroticism, reflecting a tendency towards negative affective states, may overlap genetic susceptibility for anxiety disorders and help explain the extensive comorbidity amongst internalizing disorders. Genome-wide linkage (GWL) data from several studies of neuroticism and anxiety disorders have been published, providing an opportunity to test such hypotheses and identify genomic regions that harbor genes common to these phenotypes. In all, 11 independent GWL studies of either neuroticism (n=8) or anxiety disorders (n=3) were collected, which comprised of 5341 families with 15 529 individuals. The rank-based genome scan meta-analysis (GSMA) approach was used to analyze each trait separately and combined, and global correlations between results were examined. False discovery rate (FDR) analysis was performed to test for enrichment of significant effects. Using 10 cM intervals, bins nominally significant for both GSMA statistics, PSR and POR, were found on chromosomes 9, 11, 12, and 14 for neuroticism and on chromosomes 1, 5, 15, and 16 for anxiety disorders. Genome-wide, the results for the two phenotypes were significantly correlated, and a combined analysis identified additional nominally significant bins. Although none reached genome-wide significance, an excess of significant PSRP-values were observed, with 12 bins falling under a FDR threshold of 0.50. As demonstrated by our identification of multiple, consistent signals across the genome, meta-analytically combining existing GWL data is a valuable approach to narrowing down regions relevant for anxiety-related phenotypes. This may prove useful for prioritizing emerging genome-wide association data for anxiety disorders.

Genetic Association of MPPED2 and ACTN2 with Dental Caries

The first genome-wide association study of dental caries focused on primary teeth in children aged 3 to 12 yr and nominated several novel genes: ACTN2, EDARADD, EPHA7, LPO, MPPED2, MTR, and ZMPSTE24. Here we interrogated 156 single-nucleotide polymorphisms (SNPs) within these candidate genes for evidence of association with dental caries experience in 13 race- and age-stratified samples from 6 independent studies (n = 3600). Analysis was performed separately for each sample, and results were combined across samples via meta-analysis. MPPED2 was significantly associated with caries via meta-analysis across the 5 childhood samples, with 4 SNPs showing significant associations after gene-wise adjustment for multiple comparisons (p < .0026). These results corroborate the previous genome-wide association study, although the functional role of MPPED2 in caries etiology remains unknown. ACTN2 also showed significant association via meta-analysis across childhood samples (p = .0014). Moreover, in adults, genetic association was observed for ACTN2 SNPs in individual samples (p < .0025), but no single SNP was significant via meta-analysis across all 8 adult samples. Given its compelling biological role in organizing ameloblasts during amelogenesis, this study strengthens the hypothesis that ACTN2 influences caries risk. Results for the other candidate genes neither proved nor precluded their associations with dental caries.

Homeobox Genes in Obsessive-Compulsive Disorder

Background: Despite evidence that obsessive‐compulsive disorder (OCD) is a familial neuropsychiatric condition, progress aimed at identifying genetic determinants of the disorder has been slow. The OCD Collaborative Genetics Study (OCGS) has identified several OCD susceptibility loci through linkage analysis. Methods: In this study we investigate two regions on chromosomes 15q and 1q by first refining the linkage region using additional short tandem repeat polymorphic (STRP) markers. We then performed association analysis on single nucleotide polymorphisms (SNP) genotyped (markers placed every 2–4 kb) in the linkage regions in the OCGS sample of 376 rigorously phenotyped affected families. Results: Three SNPs are most strongly associated with OCD: rs11854486 (P = 0.00005 [0.046 after adjustment for multiple tests]; genetic relative risk (GRR) = 11.1 homozygous and 1.6 heterozygous) and rs4625687 [P = 0.00007 (after adjustment = 0.06); GRR = 2.4] on 15q; and rs4387163 (P = 0.0002 (after adjustment = 0.08); GRR = 1.97) on 1q. The first SNP is adjacent to NANOGP8, the second SNP is in MEIS2, and the third is 150 kb between PBX1 and LMX1A. Conclusions: All the genes implicated by association signals are homeobox genes and are intimately involved in neurodevelopment. PBX1 and MEIS2 exert their effects by the formation of a heterodimeric complex, which is involved in development of the striatum, a brain region involved in the pathophysiology of OCD. NANOGP8 is a retrogene of NANOG, a homeobox transcription factor known to be involved in regulation of neuronal development. These findings need replication; but support the hypothesis that genes involved in striatal development are implicated in the pathogenesis of OCD.

Polygenic Scores in Epidemiology: Risk Prediction, Etiology, and Clinical Utility

Genes account for a significant proportion of the risk for most common diseases. The genome-wide association scan (GWAS) era of genetic epidemiology has generated a massive amount of data, revolutionized our thinking on the genetic architecture of common diseases and positioned the field to realistically consider risk prediction for common polygenic diseases, such as non-familial cancers, and autoimmune, cardiovascular, and psychiatric diseases. Polygenic scoring is an approach that shows promise for understanding the polygenic contribution to common human diseases. This is an approach typically relying on genome-wide SNP data, where a set of SNPs identified in a discovery GWAS are used to construct composite polygenic scores. These scores are then used in additional samples for association testing or risk prediction. This review summarizes the extant literature on the use, power, and accuracy of polygenic scores in studies of the etiology of disease and the promise for disease risk prediction.

Genome-wide and gene-based association studies of anxiety disorders in european and african american samples

Anxiety disorders (ADs) are common mental disorders caused by a combination of genetic and environmental factors. Since ADs are highly comorbid with each other, partially due to shared genetic basis, studying AD phenotypes in a coordinated manner may be a powerful strategy for identifying potential genetic loci for ADs. To detect these loci, we performed genome-wide association studies (GWAS) of ADs. In addition, as a complementary approach to single-locus analysis, we also conducted gene- and pathway-based analyses. GWAS data were derived from the control sample of the Molecular Genetics of Schizophrenia (MGS) project (2,540 European American and 849 African American subjects) genotyped on the Affymetrix GeneChip 6.0 array. We applied two phenotypic approaches: (1) categorical case-control comparisons (CC) based upon psychiatric diagnoses, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. Linear and logistic models were used to analyse the association with ADs using FS and CC traits, respectively. At the single locus level, no genome-wide significant association was found. A trans-population gene-based meta-analysis across both ethnic subsamples using FS identified three genes (MFAP3L on 4q32.3, NDUFAB1 and PALB2 on 16p12) with genome-wide significance (false discovery rate (FDR] <5%). At the pathway level, several terms such as transcription regulation, cytokine binding, and developmental process were significantly enriched in ADs (FDR <5%). Our approaches studying ADs as quantitative traits and utilizing the full GWAS data may be useful in identifying susceptibility genes and pathways for ADs.