Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression

We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimer's disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene-gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 464 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathways database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24 months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing chemokine, Jak-stat and insulin signalling pathways, and tight junction interactions. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection, and identify a number of previously validated AD genes including CR1, APOE and TOMM40

Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome.

In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS

Methodological Issues in Multistage Genome-Wide Association Studies

Because of the high cost of commercial genotyping chip technologies, many investigations have used a two-stage design for genome-wide association studies, using part of the sample for an initial discovery of ``promising'' SNPs at a less stringent significance level and the remainder in a joint analysis of just these SNPs using custom genotyping. Typical cost savings of about 50% are possible with this design to obtain comparable levels of overall type I error and power by using about half the sample for stage I and carrying about 0.1% of SNPs forward to the second stage, the optimal design depending primarily upon the ratio of costs per genotype for stages I and II. However, with the rapidly declining costs of the commercial panels, the generally low observed ORs of current studies, and many studies aiming to test multiple hypotheses and multiple endpoints, many investigators are abandoning the two-stage design in favor of simply genotyping all available subjects using a standard high-density panel. Concern is sometimes raised about the absence of a ``replication'' panel in this approach, as required by some high-profile journals, but it must be appreciated that the two-stage design is not a discovery/replication design but simply a more efficient design for discovery using a joint analysis of the data from both stages. Once a subset of highly-significant associations has been discovered, a truly independent ``exact replication'' study is needed in a similar population of the same promising SNPs using similar methods.Comment: Published in at http://dx.doi.org/10.1214/09-STS288 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org