Materials & Methods

Collection and dissection of post-mortem human brain tissue

Central nervous system (CNS) tissues originating from 134 control individuals was collected by the Medical Research Council (MRC) Sudden Death Brain and Tissue Bank, Edinburgh, UK (Millar et al. 2007), and the Sun Health Research Institute (SHRI) an affiliate of Sun Health Corporation, USA (Beach et al. 2008). Samples originating from the MRC Sudden Death Brain and Tissue Bank were removed from whole brains as fresh tissue and anatomical regions of interest were sampled from brain coronal slices at autopsy and immediately flash frozen. In the case of samples originating from the SHRI, whole brains were removed as fresh tissue at autopsy and brain coronal slices were frozen. Anatomical regions of interest were sampled from brain coronal slices on dry ice.

From each individual we analyzed up to 10 brain regions: cerebellar cortex (CRBL), frontal cortex (FCTX), hippocampus (HIPP), medulla (specifically inferior olivary nucleus, MEDU), occipital cortex (specifically primary visual cortex, OCTX), putamen (PUTM), substantia nigra (SNIG), thalamus (THAL), temporal cortex (TCTX) and intralobular white matter (WHMT).

All individuals were confirmed to be neuropathologically normal by a consultant neuropathologist using histology performed on sections prepared from paraffin-embedded brain tissue blocks. A detailed description of the samples used in the study, tissue processing and dissection is provided in Trabzuni et al (2011). All samples had fully informed consent for retrieval and were authorized for ethically approved scientific investigation (Research Ethics Committee number 10/H0716/3).

RNA isolation and processing of brain samples analysed using Affymetrix Exon 1.0 ST Arrays

Total RNA was isolated from human post-mortem brain tissues based on the single-step method of RNA isolation (Chomczynski & Sacchi 1987) using the miRNeasy 96 kit (Qiagen). The quality of total RNA was evaluated by the 2100 Bioanalyzer (Agilent) and RNA 6000 Nano Kit (Agilent) before processing with the Ambion® WT Expression Kit and Affymetrix GeneChip Whole Transcript Sense Target Labeling Assay, and hybridization to the Affymetrix Exon 1.0 ST Arrays following the manufacturers’ protocols. Hybridized arrays were scanned on an Affymetrix GeneChip® Scanner 3000 7G and visually inspected for hybridization artifacts. Further details regarding RNA isolation, quality control and processing are reported in Trabzuni et al (2011).

Analysis of Affymetrix Exon 1.0 ST Arrays data

All arrays were pre-processed using Robust Multi-array Average normalisation (RMA; Irizarry et al., 2003) and log2 transformation in Affymetrix Power Tools version 1.14-3. In each case, we also calculated the “detection above background” (DABG) metric. After re-mapping the Affymetrix probe sets onto human genome build 19 (GRCh37) and using Netaffx annotation file Release 31(HuEx-1_0-st-v2 Probeset Annotations), we restricted analysis to 292 thousand probe sets which were annotated to have gene names according to NCBI Reference Sequence build 36 and contained at least 3 uniquely hybrizing probes that were free of common European (frequency > 1%) SNPs or indels (according to the 1000 Genomes Interim Phase v3, March 2012). Gene-level expression was estimated for 26 thousand genes by calculating the Winzorised mean (below 10% and above 90%) signal of all probe sets corresponding to each gene. The resulting expression data was adjusted for brain bank, gender and batch effects in Partek’s Genomics Suite v6.6 (Partek Incorporated, USA).

DNA extraction, genotyping and imputation

Genomic DNA was extracted from sub-dissected samples (100–200 mg) of human post-mortem brain tissue using Qiagen’s DNeasy Blood & Tissue Kit (Qiagen,UK). All samples were genotyped on the Illumina Infinium Omni1-Quad BeadChip and on the Immunochip, a custom genotyping array designed for the fine-mapping of auto-immune disorders (International Parkinson Disease Genomics Consortium, 2011; Plagnol et al., 2011). The BeadChips were scanned using an iScan (Illumina, USA) with an AutoLoader (Illumina, USA). GenomeStudio v.1.8.X (Illumina, USA) was used for analysing the data and generating SNP calls.

After standard quality controls (removal of suspected non-European descent individuals, samples with call rate < 95% and checks on reported sex status, cryptic relatedness, autosomal heterozygosity rate check, monomorphic SNPs or call rate < 95%, no genomic position info or redundant SNPs, p-value for deviation from HWE < 0.0001, genotyping call rate < 95%, less than 2 heterozygotes present, mismatching alleles 1000G even after allowing for  strand), imputation was performed using MaCH (Li et al., 2009; Li et al., 2010) and minimac ( using the European panel of the 1000 Genomes Project (March 2012: Integrated Phase I haplotype release version 3, based on the 2010-11 data freeze and 2012-03-14 haplotypes). We used the resulting ~5.88 million SNPs and ~577 thousand indels with good post-imputation quality (Rsq > 0.50) and minor allele frequency of at least 5%.

Expression QTL (eQTL) analysis

The QTL analysis was run for each expression profile (either exon-level or gene-level) against every genetic marker (either SNP or indel) in MatrixEQTL (Shabalin, 2012). Subsequent analyses were conducted in R (R Development Core Team, 2011). 


This work was supported by the MRC through the MRC Sudden Death Brain Bank (C.S.), a Project Grant (G0901254 to J.H. and M.W.) and Training Fellowship (G0802462 to M.R.). D.T. was supported by the King Faisal Specialist Hospital and Research Centre, Saudi Arabia.

Computing facilities used at King’s College London were supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London.

We would like to thank AROS Applied Biotechnology AS company laboratories and Affymetrix for their valuable input.


Chomczynski P, Sacchi N. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem. 1987 Apr;162(1):156-9.

International Parkinson Disease Genomics Consortium, Nalls MA, Plagnol V, Hernandez DG, Sharma M, Sheerin UM, Saad M, Simón-Sánchez J, Schulte C, Lesage S, Sveinbjörnsdóttir S, Stefánsson K, Martinez M, Hardy J, Heutink P, Brice A, Gasser T, Singleton AB, Wood NW. Imputation of sequence variants for identification of genetic risks for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet. 2011 Feb 19;377(9766):641-9.

Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003 Apr;4(2):249-64.

Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010 Dec;34(8):816-34.

Li Y, Willer C, Sanna S, Abecasis G. Genotype imputation. Annu Rev Genomics Hum Genet. 2009;10:387-406.

Millar T, Walker R, Arango JC, Ironside JW, Harrison DJ, MacIntyre DJ, Blackwood D, Smith C, Bell JE. Tissue and organ donation for research in forensic pathology: the MRC Sudden Death Brain and Tissue Bank. J Pathol. 2007 Dec;213(4):369-75.

Plagnol V, Nalls MA, Bras JM, Hernandez DG, Sharma M, et al. (2011) A two-stage meta-analysis identifies several new loci for Parkinson’s disease. PLoS Genet 7: e1002142.

R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL

Shabalin AA. (2012) Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012 May 15;28(10):1353-8. Epub 2012 Apr 6.

Trabzuni D, Ryten M, Walker R, Smith C, Imran S, Ramasamy A, Weale ME, Hardy J. Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. J Neurochem. 2011 Oct;119(2):275-82.