Biostatistics & Bioinformatics Unit (research summary)

1. Biostatistics & Bioinformatics Unit (research summary)

2. BBU members • Peter Holmans • Valentina Moskvina • Andrew Pocklington • Marian Hamshere • Dobril Ivanov • Giancarlo Russo • Alex Richards • Alexey Vedernikov

3. Research Areas • Genome-wide association analyses • Polygenic analyses to investigate genetic architecture and relationship between traits • Sub-phenotype analysis: can refining the phenotype refine the association signal? • Gene-wide analysis to summarise association evidence per gene • Genome-wide interaction analysis: statistically desirable? • Integrating gene expression data and association data: are eQTLs useful for predicting disease? • Pathway analysis: are sets of biologically-related genes enriched for association (or CNV) signal? • Next generation sequencing

4. Data • Samples • Bipolar, Schizophrenia, Alzheimer’s Disease, Parkinson’s, ADHD, VCFS • 3k cases, 5k controls (on average) • 9k cases, 12k controls through collaboration • SNP data • 500k -1.2M genotyped (genome-wide on chip) • 8.5M imputed • currently 1kG+hapmap3 CEU+TSI samples as reference panel • 256 node cluster (PBS script) • 200k custom chip • QC • Remove poor samples, poor SNPs, minimise systematic bias, cluster plots • Merging data • Strand alignment, overlapping samples • Analyse & store results • Plink, snptest, mach2dat, custom scripts etc Marian Hamshere

5. Data SNPs Genes (exons) Genome

6. Phenotype data • Plenty! • Psychological measures, e.g. grandiose delusions, depression • fMRi brain volume • Neurocognitive measures, e.g. IQ, speed tasks • Define phenotypes across • Collections • Diseases • Targeted/guided hypotheses • Data reduction techniques, e.g. PCA • Cross disorder polygenic analysis (with care!) • Phenotype analysis -> guide GWAS Marian Hamshere

7. Databases • 37 interrelated MySQL databases on 3 servers, approximately: - 730 tables - 9,600,000,000 records - 345 GB of disk space • 2 Web Applications using the databases WGA Results: - Genome-wide association analysis results (AD, BD, SZ, PD) - SNP-to-Gene and Gene-to-SNP annotations - Upload SNP lists for analysis and annotation - Download query results, top hits, entire resultsets WTCCC mirror: - analysis, SNP, genotype, phenotype and sample data - selection and graphical representation of data according to user filters Alexey Vedernikov

8. eQTLs and polygenic score analysis • Analysis - Polygenic score analysis is a method of aggregating genotype data across many SNPs to predict affected status - eQTL analysis is a way of linking genotype data with gene expression levels - eQTL analysis is used to define groups of SNPs with a greater or lesser effect on global brain gene expression • Data - eQTLs are defined in the datasets of Myers et al (8361 transcripts and 380157 SNPs, for 163 adult control brains) and Gibbs et al (2532685 SNPs and around 14000 transcripts in 125 adult control brain samples, across 4 brain areas) - ISC and MGS data: SNPs with a greater effect on global gene expression generally predict schizophrenia affected status significantly better than those with a lesser effect Alex Richards

9. Gene-wide analysis • Analysis - GWA studies are focused on SNPs as the unit of analysis - Complex patterns of association might not be reflected by association to the same SNPs in different samples - Power to detect association might be enhanced by exploiting information from multiple (quasi) independent signals within genes - Risk likely reflects the co-action of several loci but the approximate numbers of loci involved at the individual or the population levels are unknown • Methods - SNP – Gene annotations - Permutations - Use of summary statistics only Valentina Moskvina

10. Biological models of disease • Molecular systems biology: models of neuronal signalling and diversity Text-mining/data curation (interactions, functional annotations) Network analysis Data integration Andrew Pocklington

11. Biological models of disease • Molecular systems biology: models of neuronal signalling and diversity • Neurobiology of disease: use these models to understand disease genetics (e.g. by identifying biologically-relevant sets of genes for pathway analysis). Andrew Pocklington

12. Biological models of disease 21,000 genes 179 neuro-anatomical structures http://www.brain-map.org/

13. Biological models of disease 21,000 genes 179 neuro-anatomical structures http://www.brain-map.org/

14. Pathway analysis Testing whether biologically-related genes are enriched for association signal • GWAS data: do pathways contain a larger number of significantly-associated genes than expected? (allowing for varying gene sizes, numbers of SNPs, genetic linkage,...) Peter Holmans

17. Pathway analysis Testing whether biologically-related genes are enriched for association signal • GWAS data: do pathways contain a larger number of significantly-associated genes than expected? (allowing for varying gene sizes, numbers of SNPs, genetic linkage,...) • CNV data: do CNVs in cases hit more genes in a pathway than CNVs in controls? (allowing for varying gene and CNV sizes) Peter Holmans

19. Pathway analysis Testing whether biologically-related genes are enriched for association signal • GWAS data: do pathways contain a larger number of significantly-associated genes than expected ? (allowing for varying gene sizes, numbers of SNPs) • CNV data: do CNVs in cases hit more genes in a pathway than CNVs in controls ? (allowing for varying gene and CNV sizes) • Future: • Continue to develop better-annotated pathways, using genomic data from multiple sources (e.g. expression, proteomics) • Extend methods to use next-gen sequencing data Peter Holmans

20. Next Generation Sequencing • Targeted exome capture: - Digest DNA to ~300bp - Clean and anneal adapters- Perform Pre-capture PCR with indexed primers- Hybridise exome capture probes- Clean and extract captured regions- Perform Post-capture PCR- Quantify and pool DNA samples • Data processing - Indexing, aligning and sorting the output reads - Removing PCR duplicate reads - Analysis of target capturing and coverage - Local realignment around indels - Recalibration of phred scores - QC using depth information and phred scores - Variants calling • Analysis... Giancarlo Russo