Biology subjects

Bigdeli, T. B.

Publications and source records attributed to Bigdeli, T. B..

4 recordsLinked to original sources

bioRxiv2022

Modeling the longitudinal changes of ancestry diversity in the Million Veteran Program

The Million Veteran Program (MVP) participants represent 100 years of US history, including significant social and demographic change over time. Our study assessed two aspects of the MVP: (i) longitudinal changes in population diversity and (ii) how these changes can be accounted for in genome-wide association studies (GWAS). The MVP was divided into five birth cohorts (N-range=123,888 [born from 1943-1947] to 136,699 [born from 1948-1953]). Groups of participants were defined by (i) HARE (harmonized ancestry and race/ethnicity) and (ii) a random-forest clustering approach using the 1000 Genomes Project and the Human Genome Diversity Project (1kGP+HGDP) reference panels (77 world populations representing six continental groups). In these groups, we performed GWASs of height, a trait potentially affected by population stratification. Birth cohorts demonstrate important trends in ancestry diversity over time. More recent HARE-assigned Europeans, Africans, and Hispanics had lower European ancestry proportions than older birth cohorts (0.010<Cohens d<0.259, p<7.80x10-4). Conversely, HARE-assigned East Asians showed an increase in European ancestry proportion over time. In GWAS of height using HARE assignments, genomic inflation due to population stratification was prevalent across all birth cohorts (linkage disequilibrium score regression intercept=1.08{+/-}0.042). The 1kGP+HGDP-based ancestry assignment significantly reduced the population stratification (mean intercept reduction=0.045{+/-}0.007, p<0.05) confounding in the GWAS statistics. This study provides a comprehensive characterization of ancestry diversity of the MVP cohort over time and highlights that more refined modeling of genetic diversity (e.g., the 1kGP+HGDP-based ancestry assignment) can more accurately capture the polygenic architecture of traits and diseases that could be affected by population stratification.

genomics↗

bioRxiv2018

Genome-wide association study of suicide attempt in psychiatric disorders identifies association with major depression polygenic risk scores

ObjectiveOver 90% of suicide attempters have a psychiatric diagnosis, however twin and family studies suggest that the genetic etiology of suicide attempt (SA) is partially distinct from that of the psychiatric disorders themselves. Here, we present the largest genome-wide association study (GWAS) on suicide attempt using major depressive disorder (MDD), bipolar disorder (BIP) and schizophrenia (SCZ) cohorts from the Psychiatric Genomics Consortium.\n\nMethodSamples comprise 1622 suicide attempters and 8786 non-attempters with MDD, 3264 attempters and 5500 non-attempters with BIP and 1683 attempters and 2946 non-attempters with SCZ. SA GWAS were performed comparing attempters to non-attempters in each disorder followed by meta-analysis across disorders. Polygenic risk scoring investigated the genetic relationship between SA and the psychiatric disorders.\n\nResultsThree genome-wide significant loci for SA were found: one associated with SA in MDD, one in BIP, and one in the meta-analysis of SA in mood disorders. These associations were not replicated in independent mood disorder cohorts from the UK Biobank and iPSYCH. Polygenic risk scores for major depression were significantly associated with SA in MDD (P=0.0002), BIP (P=0.0006) and SCZ (P=0.0006).\n\nConclusionsThis study provides new information on genetic associations and the genetic etiology of SA across psychiatric disorders. The finding that polygenic risk scores for major depression predict suicide attempt across disorders provides a possible starting point for predictive modelling and preventative strategies. Further collaborative efforts to increase sample size hold potential to robustly identify genetic associations and gain biological insights into the etiology of suicide attempt.

genetics↗

bioRxiv2018

Trans-ancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest GWAS to date of DSM - IV diagnosed AD. Genome - wide data on 14,904 individuals with AD and 37,944 controls from 28 case / control and family - based studies were meta - analyzed, stratified by genetic ancestry (European, N = 46,568; African; N = 6,280). Independent, genome - wide significant effects of different ADH1B variants were identified in European (rs1229984; p = 9.8E - 13) and African ancestries (rs2066702; p = 2.2E - 9). Significant genetic correlations were observed with schizophrenia, ADHD, depression, and use of cigarettes and cannabis. There was only modest genetic correlation with alcohol consumption and inconsistent associations with problem drinking. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and non - pathological drinking behaviors.

genetics↗

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↗