James A. Knowles - US grants

This project is designed to identify and locate genetic risk factors for sustained cigarette smoking and nicotine addiction. Cigarette smoking remains the largest preventable cause of death in the United States accounting for an estimated 480,000 deaths each year. Unfortunately, one quarter of all Americans continue to smoke, despite the public health warnings and social pressure. Twin studies indicate approximately 50 percent of the variance in smoking initiation and 70 percent of smoking persistence are accounted for by genetic factors. We propose to collect 400 same-sex sibling pairs for use in a genetic linkage study. The probands will be ascertained based on their presentation to a smoking cessation clinic. Both members of the sibling pair will be required to be persistent smokers with at least a 10 year history of smoking and also have a high level of nicotine dependence, as measured by the Fagerstrom Tolerance Questionnaire (FTQ greater than or equal to 7). The siblings will also be evaluated for phenotypes that are known to be associated with smoking. These include a history of major depression and/or alcohol abuse, and certain personality factors. In year 3, 200 sib-pairs and their available parents will be genotyped with 377 microsatellite markers evenly spaced across the human genome. From simulations we have done, we expect to detect one or two of the nicotine susceptibility loci (assuming a three-locus model), but also 3 false-positive signals using a mean test statistic (at p less than 0.001). In year 5, an additional 200 sib-pairs and their available parents will be genotyped with the same markers and we expect that most of the false-positive linkage signals will not be replicated. Simulations indicate that we expect to detect (at p less than 0.0001), on average, 2 genes if there are 2 or 3 loci accounting for the genetic variance and one gene if there are 4 loci. To follow-up these areas of linkage all sibs and available parents will be genotyped with markers spaced at 1-2 cM in the chromosome regions that continue to have the best support for linkage. We also propose to examine the genetic variation in 21 candidate genes from dopaminergic, nicotinic and P450 systems in our sample. Detecting these genes would be a significant first step in our understanding of the genetic variation of addiction to nicotine and possibly other drugs of abuse.

DESCRIPTION (provided by applicant): The OCD Collaborative Genetics Group proposes to conduct a genome-wide association study of early-onset obsessive-compulsive disorder (OCD). This group of six academic centers has collaborated over the past five years on an on-going genetic linkage study of OCD and has demonstrated ability to recruit and diagnose individuals with this disorder. In this proposal, the Collaboration will conduct psychiatric evaluations on 2,000 individuals with obsessive-compulsive disorder (OCD) and collect DNA from these individuals and both their parents. The genotyping and analyses will be performed in two stages. In the first stage 1,000 triads will be genotyped with a 550,000 single nucleotide polymorphisms (SNPs) panel at the Illumina laboratory. We will estimate the genetic effect sizes for all 550,000 SNPs, and then rank all SNPs based on their conditional power estimates. The 1,534 SNPs with the highest power rankings will be genotyped in the second stage (1,000 triads). The combined p-values from the two stages (which will have to be adjusted for only 1,534 comparisons, but not for 550,000) that are less than 5%/1534 (Bonferroni correction) will be considered genome-wide significant. The indirect association approach proposed will be followed up using more direct association techniques (gene based), innovative gene-gene interaction analyses (gene cluster based), and additional molecular and functional approaches. The results of these analyses will guide future molecular strategies to identify genes involved in the pathogenesis of OCD. The clinical and genotype data from the sample will be publicly available for OCD genetics research.

DESCRIPTION (provided by applicant): We will create a transcriptional atlas of the developing human brain, including novel transcripts and regional expression patterns that can be followed up subsequently with histochemical mapping for cellular specificity. These data will be made available to the research community at-large via an easy-to-use, web-based informatics framework. To accomplish these goals, we will purify RNA and DNA from 3 male and 3 female post-mortem brains at the approximate ages of 25 weeks, 2 months, 1year, 2.5 years, 7 years, 13 -19 years and 20-30 years. For the 25 week brains, we will sample Dorsolateral Prefrontal Cortex (DLPFC), Orbital Frontal Cortex (OFC), Inferior Temporal Cortex (ITC) and Hippocampus (HIP). The rest of the time points will add Occipital Cortex (OC), cerebellum (CB) and striatum (STR), for a total of 276 brain samples. We will profile DNA from these 276 samples with 1 million SNPs (Human1MDuo array) and will determine methylation patterns with an Illumina Infinium array. RNA from these 276 samples will be used to determine regional expression pattern, identify novel genes and alternative transcripts using RNA-Seq. Each poly-A RNA sample will be sequenced with 45 million reads to provide detection at 1 copy/cell (15%). 30 million of these reads will be 72 bp X 2 paired-end and 15 million reads will be 150+125 bp paired-end to provide the maximal information on alternative splicing. We will also sequence small RNA molecules from each sample using 15 million 36 bp strand specific single-end reads. The RNA-Seq data will be analyzed with GenomeStudio (Illumina, Inc.), ERANGE3, NextGENe" (Softgenetics Inc.), TransSeq (USC) and novel programs we will develop, to identify novel genes and determine the levels of transcription, allelic expression and alternative splicing of all genes. We will then compare these measures of transcription across brain regions, developmental ages and gender. We will also perform RNA-Seq with 5,000 bp read lengths using a third generation DNA sequencer (G3), in collaboration with Life Technologies, Inc, to confirm or refine our alternative splicing models. An automated RNA-Seq analysis workflow that is portable to other laboratories will be developed and an easy-to-use, web-based informatics framework for communication of these data to other scientists will be designed and implemented. PUBLIC HEALTH RELEVANCE: Mental disorders are increasingly recognized as brain disorders that have their origins during development. While these developmental brain disorders occur in people at genetic risk, relatively little is known about how specific risk genes or gene variants affect brain development. We will determine the expression patterns of genes in particular brain regions at particular points in development. This information is essential for understanding how genetic variation affects normal and abnormal brain development, potentially giving rise to mental disorders.

DESCRIPTION (provided by applicant): Schizophrenia is a severe mental illness that afflicts approximately one percent of the world's population. Although it is clear that approximately a half of the liability to develop the disorder is genetic, few genes have been unequivocally implicated in the etiology of the disorder. Recent data from a large (~3,000 cases and 3,000 controls) study of copy number variations in schizophrenia found evidence for three rare deletions on chromosomes 1q, 15q and 22p that may cause schizophrenia. The evidence from these deletions is insufficient to conclusively determine if they cause schizophrenia and, if they do, which of the deleted genes are responsible. We propose to determine the DNA sequence of the entire 5.3 Mb delineated by these three deletions in 1,000 individuals with schizophrenia and 1,000 controls to look for both common and rare variants in the deleted regions. The individuals to be sequenced will be chosen from the Portuguese Island Cohort (PIC). The DNA sequence data will be analyzed using logistic regression to determine regions of increased genetic variation in cases as compared to controls. These regions will then be genotyped in a larger sample of up to 20,000 cases and controls to determine which genes/regions in the deleted regions play a causative role in the development of schizophrenia. PUBLIC HEALTH RELEVANCE: Schizophrenia is a severe mental illness that afflicts approximately one percent of the world's population. Although it is clear that approximately a half of the liability to develop the disorder is genetic, few genes have been unequivocally implicated in the etiology of the disorder. Recent data from a large (~3,000 cases and 3,000 controls) study of copy number variations in schizophrenia found evidence for three rare deletions on chromosomes 1q, 15q and 22p that may cause schizophrenia.

DESCRIPTION (provided by applicant): Our overall aim is to assess the technical and biological noise in measured RNA levels in single cells in a number of human tissue types, and to develop analytical tools to address the complexity observed at the single-cell level. Understanding the sources and relative sizes of technical and biological noise has become essential, as the lower detection limit of RNA-Seq is now in the range of 10 picograms of total RNA -- i.e. the amount of RNA in single cells. Technical noise can come from several different sources that we will attempt to evaluate separately. These include: 1) sample procurement and RNA retrieval, 2) sequencing library preparation, 3) sequencing methodology, 4) batch effects in sequencing experiments, 5) bioinformatics approaches for data analysis, 6) gene-gene variability. Assessing the relative magnitude of technical noise from different sources will infor how to reduce that noise in future experiments, and thereby reduce interference with studies of meaningful biological variations or noise. Biological noise, or inter-cell differences arise from differences in cellular history or fate, stages of cell cycle, connections to neighboring cells, an true functional differences of ostensibly identical cells (e.g., different olfactory receptors amon olfactory neurons). We propose to study three different cellular systems that we expect to have different levels of inter-cell variation (biological noise): first, syncytiotrophoblast cells from placenta, which are expected to have relatively low inter-cell variation; second, olfactory neurons from nasal neuroepithelium, each of which is expected to express a different olfactory receptor, providing a positive control for differences in the RNA-Seq data; and third, individual Purkinje neurons from the cerebellum, which may have larger inter-cell variation. The method to extract cytoplasm from individual cells -- patch clamp pipette extraction -- does not require fully disrupting the tissue or dispersing the cells. We have already used patch clamp to determine the transcriptomes of multiple individual neurons in the mouse brain, using the cytoplasm extracted from single cells on which we had already performed patch-clamp electrophysiology recordings, followed by RNA-Seq. For each of the cell types chosen - syncytiotrophoblasts, olfactory neurons, Purkinje neurons, cortical neurons we will generate single-cell transcriptome datasets to evaluate heterogeneity among ostensibly similar cells, using patch clamp to extract cell contents and RNA-Seq; investigate sources of technical noise and apply a systematic approach to reduce technical noise. We will test whether neuronal plasticity is reflected as a change in the transcriptome. All analytical tools and the transcriptome database developed here will be shared openly on our website and all project data will be deposited into dbGAP and the Short Read Archive (or its replacement) 6 months after data QC.

DESCRIPTION (provided by applicant): Sequencing of DNA and cDNA libraries on next-generation sequencing (NGS) platforms has become the method of choice for genomic and transcriptional analyses. One obstacle that inhibits wider adoption of NGS techniques is the lack of comprehensive, yet easy to use software packages with which to conduct data analysis. To meet this need, we have developed RseqFlow, a set of common analytic modules for the analysis of RNA-seq data which is formalized into an easy to use workflow. The workflow is managed by the Pegasus Workflow Management System (WMS), which maps the modules to available computational resources and automatically executes the steps in the appropriate order. A Virtual Machine (VM) was created for the software package which eliminates complex configuration and installation steps. In this proposal, we plan to extend RseqFlow to include more analytic functions and also to generalize it to work for multiple model organisms including the Mouse, Worm, Fruit fly, Plant and Yeast. We also propose the development of a similar workflow for the analysis of genome re-sequencing data. Both of the workflows will take advantage of several analytic tools we have developed, including PerM (short read alignment), ComB (SNP Calling), Clippers (Indel/Junction detection), and WeaV (de novo assembly). One of the unique features of our workflow is an iterative alignment strategy where sequence variants are used to update the sequence and improve alignment accuracy which in turn affords us the ability to accurately determine not only SNPs and indels but also structural and copy-number variations. A final effort will include combining the workflows for RNA-seq data and genome re-sequencing data to perform RNA editing analysis. All programs developed under this proposal will be rigorously tested on a number of different data sets and on multiple computational platfonns, and use sound software engineering practices. All software released under this proposal will be open source and greatly benefit many biological projects which incorporate DNA and RNA sequencing approaches. PUBLIC HEALTH RELEVANCE: High throughput sequencing (HTS) has been used to study of human genetics and diseases, human microbial communities, and is also a growing analytical tool for clinical trials. The goal of this research is to develop computational software workflows o aid in the analysis of DNA and RNA sequencing data sets. Our open-source software tools will benefit researchers worldwide who use HTS to perform various biological and medical studies.

DESCRIPTION (provided by applicant): This project funds the expansion the Genomic Psychiatry Cohort (GPC) by ascertaining and enrolling 5,000 patients suffering from Obsessive-Compulsive Disorder (OCD) and performing a genome-wide association study (GWAS) on 5,000 OCD patients and 5,000 already ascertained and genotyped OCD-free matched controls. This study is a critical step to achieving the necessary statistical power for discovery. We will more than double the total number of available world-wide participants for GWAS of OCD, and create the only large re-contactable cohort for OCD. The GPC is a large (n=33,000), USC-based, clinical cohort designed to be a major resource for large-scale genomic studies, studies focusing on RDoC and/or other alternate phenotype constructs, nested case- control/clinical studies, long-term disease course studies, and genomic variant-to-phenotype studies (Pato et al, 2013). Additionally, the GPC is Open Source in that the cohort can be accessed for additional collaborative studies by approved non-USC based investigators. The GPC is currently composed of patients with schizophrenia (n=10,000), patients with bipolar disorder (n=5,000), family members of these patients (n=3,000) and control participants with no history or family history of OCD, schizophrenia or bipolar disorder (n=15,000). We are able to re-contact more than 88% of the participants. The proposed GPC-OCD cohort and our proven track record of meeting, or exceeding, the sample collection goals for large-scale genetic studies positions us very well to make further progress in understanding the molecular basis of OCD by (i) the discovery of additional genetic risk factors (rare and common); and (ii) identifying a large enough group of specific genetic variations to study how they relate to neuropsychiatric phenotypes at all levels, including, but not limited to, the illnesses themselves.

DESCRIPTION (provided by applicant): Schizophrenia and bipolar disorder are neuropsychiatric brain disorders that affect more than 2% of the population worldwide and cause enormous human suffering. Both disorders are highly heritable (>60-80%), but the 100+ loci that have been identified to date collectively do not account for a significant percentage of overall disease causation. A better understanding of gene expression patterns and the role of environmental factors in forming these patterns is crucial. Using cultured neuronal cells derived from olfactory neuroepithelium (CNON) from 50 patients with schizophrenia and 50 healthy controls, we will determine epigenetic chromatin marks in a sample with sufficient statistical power to discover mQTL and ChIP-QTL in developing neurons. Available long RNA-seq (strand-specific ncRNA and mRNA >100bp), small RNA-seq (piRNA and miRNA), and >30x whole genome sequence will be combined with data from NOMe-seq at >18X and ChIP-seq of H3K4me1, H3K4me3, and H3K27Ac. The in- vitro data will be complemented by data from analyses of high-quality, post-mortem adult brains derived from Caucasian males with schizophrenia (SCZ; n=8) or bipolar disorder (BPD; n=8) and normal controls (CTL; n=8). Analyses will include long RNA-seq (strand-specific ncRNA and mRNA >100bp), small RNA-seq (piRNA and miRNA), NOMe-seq at >18X and ChIP-seq of H3K4me1, H3K4me3, and H3K27Ac in dissected sections from the dorsal lateral prefrontal cortex (DLPFC), hippocampus (HIP), amygdala (AMY), dorsal caudate (DC), and the nucleus accumbens (NAc). We will map genomic, transcriptomic and epigenomic changes specific to either brain region or disease and develop an easy-to-use, web-based informatics framework for communication of the raw and computed data of this PsychENCODE project to other neuroscientists.

Schizophrenia affects about 1 percent of the world population. We do not currently understand the biological underpinnings of schizophrenia well enough to design effective treatment strategies. Twin and adoption studies supported a genetic etiology. It is becoming clear that its inheritance is partitioned between the rare and common variants in hundreds of genes, each one having a small effect. The sample size required to elucidate the genetic etiology of schizophrenia can be achieved through large scale collaborative efforts, which are currently ongoing. A study published by Psychiatric Genomics Consortium in 2014 identified 108 Genome- Wide significant loci associated for the disorder in samples with European ancestry. Although it was an important milestone in schizophrenia genetics these loci only explain a small proportion of the genetic variance. Expansion of this approach in size, and to other population groups, is required to discovery of additional genetic variants associated with schizophrenia. We propose to ascertain and collect 10,000 cases and 10,000 controls from Pakistan. We have already ascertained and collected 2,000 cases and 1,000 controls. We have formed a consortium of Pakistani psychiatrists at 12 centers. These samples will be genotyped at Stanley Center for Psychiatric Research with Illumina Global Screening Array (GSA), which will contain a backbone of ~660,000 SNPs, which provides LD coverage and imputation accuracy of >0.8, for over 87% of the South Asian genome. There is a strong tradition of consanguineous marriages in Pakistan which has an advantage for genetic studies, especially of recessively inherited traits. We will analyze these data for common SNPs, haplotypes, and copy number variations (CNVs). In association analysis we will examine for recessive inheritance, in addition to additive models of common variants to disease. We will perform a homozygosity mapping to identify regions that are enriched for Runs of Homozygosity (ROH) in cases, as compared to controls. This population will have a different linkage disequilibrium structure which will help to narrow down the genomic intervals containing the potential causative variants at the 108 loci identified in the Caucasian Genome Wide Association Study. We will share these data with broader scientific community via the Psychiatric Genomics Consortium and dbGaP.

ABSTRACT Bipolar Disorder affects about 1 percent of the world population. We do not currently understand the biological underpinnings of Bipolar Disorder well enough to design effective treatment strategies. Twin and adoption studies support a genetic etiology and we now know this is partitioned between both rare and common variants in hundreds of genes, each variant having a small effect. The sample size required to more fully elucidate the genetic etiology of Bipolar Disorder can only be achieved through large scale collaborative efforts. For instance, the 2018 Psychiatric Genomics Consortium (PGC) Bipolar Disorder Genome-Wide Association Study (GWAS) discovered 30 significant loci using samples with European ancestry. Although this is an important milestone in understanding the genetics of Bipolar Disorder, collectively these loci only explain a small proportion of the genetic variance. Expansion of this approach to other population groups, with adequate sample size, is required to discovery of additional genetic variants associated with Bipolar Disorder. We propose to ascertain and collect 10,000 cases and 2,000 controls from Pakistan. We currently have an ongoing study in Pakistan (GEN-SCRIP) that will collect an additional 10,000 controls. We have formed a consortium of Pakistani psychiatrists at 11 centers. The 12,000 samples will be genotyped at Stanley Center for Psychiatric Research with Illumina Global Screening Array (GSA), which will contain a backbone of ~660,000 SNPs, which provides LD coverage and imputation accuracy of >0.8, for over 87% of the South Asian genome. There is a strong tradition of consanguineous marriages in Pakistan which has an advantage for genetic studies, especially of recessively inherited traits. We will analyze these data for common SNPs, haplotypes, and copy number variations (CNVs). In association analysis we will examine for recessive inheritance, in addition to additive models of common variants to disease. We will perform a homozygosity mapping to identify regions that are enriched for Runs of Homozygosity (ROH) in cases, as compared to controls. This population will have a different linkage disequilibrium structure which will help to narrow down the genomic intervals containing the potential causative variants at the 30 loci identified in the PGC2 BP GWAS. We will share these data with broader scientific community via the NIMH Genetics Repository and Genomic Research, the Psychiatric Genomics Consortium and dbGaP.