Biology subjects

Docherty, A.

Publications and source records attributed to Docherty, A..

4 recordsLinked to original sources

bioRxiv2018

JEPEGMIX2-P: Novel pathway transcriptomic method greatly increases detection of molecular pathways in cosmopolitan cohorts

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), e.g. across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred as transcriptomic wide association analysis (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effect of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular bases of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to it quadratic (in the number of SNPs) computational burden for computing LD across large regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that i) has a linear computational burden, ii) uses a large and diverse reference panel (33K subjects), iii) is competitive (adjusts for background enrichment in gene TWAS statistics) and iv) is applicable as-is to ethnically mixed cohorts. To underline its potential for increasing the power to uncover genetic signals over the state-of-the-art and commonly used non-transcriptomics methods, e.g. MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

bioinformatics↗

bioRxiv2017

Genome-wide association study of suicide death:Results from the first wave of Utah completed suicide data

ObjectiveSuicide death is a highly preventable, yet growing, worldwide health crisis. To date, there has been a lack of adequately powered genomic studies of suicide, with no sizeable suicide death cohorts available for study. To address this limitation, we conducted the first comprehensive genomic analysis of suicide death, using a previously unpublished suicide cohort. MethodsThe analysis sample consisted of 3,413 population-ascertained cases of European ancestry and 14,810 ancestrally matched controls. Analytical methods included principle components analysis for ancestral matching and adjusting for population stratification, linear mixed model genome-wide association testing (conditional on genetic relatedness matrix), gene and gene set enrichment testing, polygenic score analyses, as well as SNP heritability and genetic correlation estimation using LD score regression. ResultsGWAS identified two genome-wide significant loci (6 SNPs, p<5x10-8). Gene-based analyses implicated 19 genes on chromosomes 13, 15, 16, 17, and 19 (q<0.05). Suicide heritability was estimated h2 =0.2463, SE = 0.0356 using summary statistics from a multivariate logistic GWAS adjusting for ancestry. Notably, suicide polygenic scores were robustly predictive of out of sample suicide death, as were polygenic scores for several other psychiatric disorders and psychological traits, particularly behavioral disinhibition and major depressive disorder. ConclusionsIn this report, we identify multiple genome-wide significant loci/genes, and demonstrate robust polygenic score prediction of suicide death case-control status, adjusting for ancestry, in independent training and test sets. Additionally, we report that suicide death cases have increased genetic risk for behavioral disinhibition, major depression, autism spectrum disorder, psychosis, and alcohol use disorder relative to controls. Results demonstrate the ability of polygenic scores to robustly, and multidimensionally, predict suicide death case-control status.

genomics↗

bioRxiv2017

Pathway-based polygenic risk implicates GO: 17144 drug metabolism in recurrent depressive disorder

The Psychiatric Genomics Consortium (PGC) has made major advances in the molecular etiology of MDD, confirming that MDD is highly polygenic, with any top risk loci conferring a very small proportion of variance in case-control status (1). Pathway enrichment results from PGC meta-analyses can also be used to help inform molecular drug targets. Prior to any knowledge of molecular biomarkers for MDD, drugs targeting molecular pathways have proved successful in treating MDD. However, it is possible that with information from PGC analyses, examining specific molecular pathway(s) implicated in MDD can further inform our study of molecular drug targets. Using a large case-control GWAS based on low-coverage whole genome sequencing (N = 10,640), we derived polygenic risk scores for MDD and for MDD specific to each of over 300 molecular pathways. We then used these data to identify sets of scores significantly predictive of case status, accounting for critical covariates. Over and above global polygenic risk for MDD, polygenic risk within the GO: 17144 drug metabolism pathway significantly predicted recurrent depression. In transcriptomic analyses, two pathway genes yielded suggestive signals at FDR q-values = .054: CYP2C19 (family of Cytochrome P450) and CBR1 (Carbonyl Reductase 1). Because the neuroleptic carbamazepine is a known inducer of CYP2C19, future research might examine whether drug metabolism PRS has any influence on clinical presentation and treatment response. Overall, results indicate that pathway-based risk might inform treatment of severe depression. We discuss limitations to the generalizability of these preliminary findings, and urge replication in future research.

genetics↗

bioRxiv2017

Identification of genome-wide significant shared genomic segments in large extended Utah families at high risk for completed suicide

Suicide is the 10th leading cause of death in the US. While environment has undeniable impact, evidence suggests genetic factors play a significant role in completed suicide. We linked a resource of >4,500 DNA samples from completed suicides obtained from the Utah Medical Examiner to genealogical records and medical records data available on over 8 million individuals. This linking has resulted in the identification of high-risk extended families (7-9 generations) with significant familial risk of completed suicide. Familial aggregation across distant relatives minimizes effects of shared environment, provides more genetically homogeneous risk groups, and magnifies genetic risks through familial repetition. We analyzed Illumina PsychArray genotypes from suicide cases in 43 high-risk families, identifying 30 distinct shared genomic segments with genome-wide evidence (p=2.02E-07 to 1.30E-18) of segregation with completed suicide. The 207 genes implicated by the shared regions provide a focused set of genes for further study; 18 have been previously associated with suicide risk. While PsychArray variants do not represent exhaustive variation within the 207 genes, we investigated these for specific segregation within the high-risk families, and for association of variants with predicted functional impact in ~1300 additional Utah suicides unrelated to the discovery families. None of the limited PsychArray variants explained the high-risk family segregation; sequencing of these regions will be needed to discover segregating risk variants, which may be rarer or regulatory. However, additional association tests yielded four significant PsychArray variants (SP110, rs181058279; AGBL2, rs76215382; SUCLA2, rs121908538; APH1B, rs745918508), raising the likelihood that these genes confer risk of completed suicide.

genetics↗