It is widely assumed that genetic differences in gene expression underpin much of the difference among individuals and many of the quantitative traits of interest to geneticists. Despite this, there has been little work on genetic variability in human gene expression and almost none in the human brain, because tools for assessing this genetic variability have not been available. Now, with whole-genome SNP genotyping arrays and whole-transcriptome expression arrays, such experiments have become feasible. We have carried out whole-genome genotyping and expression analysis on a series of 193 neuropathologically normal human brain samples using the Affymetrix GeneChip Human Mapping 500K Array Set and Illumina HumanRefseq-8 Expression BeadChip platforms. Here we present data showing that 58% of the transcriptome is cortically expressed in at least 5% of our samples and that of these cortically expressed transcripts, 21% have expression profiles that correlate with their genotype. These genetic-expression effects should be useful in determining the underlying biology of associations with common diseases of the human brain and in guiding the analysis of the genomic regions involved in the control of normal gene expression.

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

Mutation of LRRK2, encoding dardarin, is the most common known genetic cause of Parkinson's disease (PD). The large size of this gene and the relative ease with which the most common mutations can be screened means that although more than 50 LRRK2 screening papers have been published, few have analyzed the entire coding sequence. Furthermore, no comprehensive sequence-based analysis has been performed on control samples. Here, we present sequencing of all coding exons in a series of 275 PD cases and 275 neurologically normal controls and analysis of the LRRK2 locus for whole gene multiplications or deletions. We also present case-control SNP association results using 74 SNPs genotyped across LRRK2. We identified six novel disease-associated missense mutations, including two that altered the same residue of the protein. These data and analysis of previously reported disease-segregating mutations shows that the majority of disease-causing mutations lie in the C-terminal half of the protein.