Spatio-temporal transcriptome of the human brain.

Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.

Linear score tests for variance components in linear mixed models and applications to genetic association studies.

Following the rapid development of genome-scale genotyping technologies, genetic association mapping has become a popular tool to detect genomic regions responsible for certain (disease) phenotypes, especially in early-phase pharmacogenomic studies with limited sample size. In response to such applications, a good association test needs to be (1) applicable to a wide range of possible genetic models, including, but not limited to, the presence of gene-by-environment or gene-by-gene interactions and non-linearity of a group of marker effects, (2) accurate in small samples, fast to compute on the genomic scale, and amenable to large scale multiple testing corrections, and (3) reasonably powerful to locate causal genomic regions. The kernel machine method represented in linear mixed models provides a viable solution by transforming the problem into testing the nullity of variance components. In this study, we consider score-based tests by choosing a statistic linear in the score function. When the model under the null hypothesis has only one error variance parameter, our test is exact in finite samples. When the null model has more than one variance parameter, we develop a new moment-based approximation that performs well in simulations. Through simulations and analysis of real data, we demonstrate that the new test possesses most of the aforementioned characteristics, especially when compared to existing quadratic score tests or restricted likelihood ratio tests.

An application for assessing quality of RNA hybridized to Affymetrix GeneChips.

UNLABELLED: This paper describes a stand-alone application for estimating the 3' to 5' ratio by fitting a mixed effects model to the interior pixel intensities of perfect match probes for selected control probe sets from an Affymetrix *.DAT file. The effectiveness of this method was demonstrated previously by an application of the method to two microarray datasets for which external verification of RNA quality was known. This application provides a more objective assessment of sample quality in that both a point estimate and 95% confidence interval about the 3' to 5' ratio are provided. AVAILABILITY: The software and installation instructions are freely available for download at http://www.people.vcu.edu/~kjarcher/Research/Data.htm

Analytical strategies for discovery and replication of genetic effects in pharmacogenomic studies.

In the past decade, the pharmaceutical industry and biomedical research sector have devoted considerable resources to pharmacogenomics (PGx) with the hope that understanding genetic variation in patients would deliver on the promise of personalized medicine. With the advent of new technologies and the improved collection of DNA samples, the roadblock to advancements in PGx discovery is no longer the lack of high-density genetic information captured on patient populations, but rather the development, adaptation, and tailoring of analytical strategies to effectively harness this wealth of information. The current analytical paradigm in PGx considers the single-nucleotide polymorphism (SNP) as the genomic feature of interest and performs single SNP association tests to discover PGx effects - ie, genetic effects impacting drug response. While it can be straightforward to process single SNP results and to consider how this information may be extended for use in downstream patient stratification, the rate of replication for single SNP associations has been low and the desired success of producing clinically and commercially viable biomarkers has not been realized. This may be due to the fact that single SNP association testing is suboptimal given the complexities of PGx discovery in the clinical trial setting, including: 1) relatively small sample sizes; 2) diverse clinical cohorts within and across trials due to genetic ancestry (potentially impacting the ability to replicate findings); and 3) the potential polygenic nature of a drug response. Subsequently, a shift in the current paradigm is proposed: to consider the gene as the genomic feature of interest in PGx discovery. The proof-of-concept study presented in this manuscript demonstrates that genomic region-based association testing has the potential to improve the power of detecting single SNP or complex PGx effects in the discovery stage (by leveraging the underlying genetic architecture and reducing the multiplicity burden), and it can also improve power in the replication stage.

Estimating heritability in pharmacogenetic studies.

AIM: This article aims to evaluate the performance of a recent method to estimate heritability of continuous and binary traits, specifically in the context of pharmacogenetic studies. MATERIALS & METHODS: The approach to be evaluated was designed to estimate heritability in large-scale disease studies. Extensive simulation studies designed to emulate common scenarios seen in pharmacogenetic studies were performed to elucidate the potential utility of this approach outside of disease genetics. The simulations cover continuous and binary traits with small-to-moderate heritability values across a variety of samples sizes in genome-wide, as well as candidate gene, settings. RESULTS: On a genome-wide scale, a combination of relatively large sample sizes (i.e., n ≥ 1000) and at least moderate underlying heritability (i.e., ≥ 0.25) are needed in order to attain reasonable statistical power. However, in candidate gene studies, reasonable power can be attained across a more broad range of scenarios. CONCLUSION: Our simulation studies show that the proposed approach has clear utility in the context of pharmacogenetic studies, especially in candidate gene settings, and provides novel supplementary information that can be used to inform decision-making in the pharmaceutical industry.

Identifying genes progressively silenced in preneoplastic and neoplastic liver tissues.

High-throughput genomic technologies are increasingly being used to identify therapeutic targets and risk factors for specific diseases. Using 116 independent liver samples, we identified 793 probe sets that demonstrated a significant association in the frequency of absent calls as tissues progressed from normal to pre-neoplastic to neoplastic, followed by a bioinformatic approach which identified that 78.9% of the significant probe sets contained at least one CpG island in the gene promoter region compared with 58.9% of the remaining genes examined. Our results indicate that further high-throughput methylation studies to more fully characterize molecular events involved in hepatocarcinogenesis are warranted.