Combining observational and experimental datasets using shrinkage estimators.

We consider the problem of combining data from observational and experimental sources to draw causal conclusions. To derive combined estimators with desirable properties, we extend results from the Stein shrinkage literature. Our contributions are threefold.

DETECTING MULTIPLE REPLICATING SIGNALS USING ADAPTIVE FILTERING PROCEDURES.

Replicability is a fundamental quality of scientific discoveries: we are interested in those signals that are detectable in different laboratories, different populations, across time etc. Unlike meta-analysis which accounts for experimental variability but does not guarantee replicability, testing a partial conjunction (PC) null aims specifically to identify the signals that are discovered in multiple studies. In many contemporary applications, for example, comparing multiple high-throughput genetic experiments, a large number of PC nulls need to be tested simultaneously, calling for a multiple comparisons correction.

Propensity score methods for merging observational and experimental datasets.

We consider how to merge a limited amount of data from a randomized controlled trial (RCT) into a much larger set of data from an observational data base (ODB), to estimate an average causal treatment effect. Our methods are based on stratification. The strata are defined in terms of effect moderators as well as propensity scores estimated in the ODB.

CONFOUNDER ADJUSTMENT IN MULTIPLE HYPOTHESIS TESTING.

We consider large-scale studies in which thousands of significance tests are performed simultaneously. In some of these studies, the multiple testing procedure can be severely biased by latent confounding factors such as batch effects and unmeasured covariates that correlate with both primary variable(s) of interest (e.g.

Optimal multiple testing under a Gaussian prior on the effect sizes.

We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal [Formula: see text]-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests.

Genome-Wide Scan Informed by Age-Related Disease Identifies Loci for Exceptional Human Longevity.

We developed a new statistical framework to find genetic variants associated with extreme longevity. The method, informed GWAS (iGWAS), takes advantage of knowledge from large studies of age-related disease in order to narrow the search for SNPs associated with longevity. To gain support for our approach, we first show there is an overlap between loci involved in disease and loci associated with extreme longevity.

Moment based gene set tests.

Background: Permutation-based gene set tests are standard approaches for testing relationships between collections of related genes and an outcome of interest in high throughput expression analyses. Using M random permutations, one can attain p-values as small as 1/(M+1). When many gene sets are tested, we need smaller p-values, hence larger M, to achieve significance while accounting for the number of simultaneous tests being made.

A sparse transmission disequilibrium test for haplotypes based on Bradley-Terry graphs.

Background: Linkage and association analysis based on haplotype transmission disequilibrium can be more informative than single marker analysis. Several works have been proposed in recent years to extend the transmission disequilibrium test (TDT) to haplotypes. Among them, a powerful approach called the evolutionary tree TDT (ET-TDT) incorporates information about the evolutionary relationship among haplotypes using the cladogram of the locus.

Aging mice show a decreasing correlation of gene expression within genetic modules.

In this work we present a method for the differential analysis of gene co-expression networks and apply this method to look for large-scale transcriptional changes in aging. We derived synonymous gene co-expression networks from AGEMAP expression data for 16-month-old and 24-month-old mice. We identified a number of functional gene groups that change co-expression with age.

Properties of balanced permutations.

This paper takes a close look at balanced permutations, a recently developed sample reuse method with applications in bioinformatics. It turns out that balanced permutation reference distributions do not have the correct null behavior, which can be traced to their lack of a group structure. We find that they can give p-values that are too permissive to varying degrees.

AGEMAP: a gene expression database for aging in mice.

We present the AGEMAP (Atlas of Gene Expression in Mouse Aging Project) gene expression database, which is a resource that catalogs changes in gene expression as a function of age in mice. The AGEMAP database includes expression changes for 8,932 genes in 16 tissues as a function of age. We found great heterogeneity in the amount of transcriptional changes with age in different tissues.

Transcriptional profiling of aging in human muscle reveals a common aging signature.

We analyzed expression of 81 normal muscle samples from humans of varying ages, and have identified a molecular profile for aging consisting of 250 age-regulated genes. This molecular profile correlates not only with chronological age but also with a measure of physiological age. We compared the transcriptional profile of muscle aging to previous transcriptional profiles of aging in the kidney and the brain, and found a common signature for aging in these diverse human tissues.

A quasi-Monte Carlo Metropolis algorithm.

This work presents a version of the Metropolis-Hastings algorithm using quasi-Monte Carlo inputs. We prove that the method yields consistent estimates in some problems with finite state spaces and completely uniformly distributed inputs. In some numerical examples, the proposed method is much more accurate than ordinary Metropolis-Hastings sampling.

A transcriptional profile of aging in the human kidney.

In this study, we found 985 genes that change expression in the cortex and the medulla of the kidney with age. Some of the genes whose transcripts increase in abundance with age are known to be specifically expressed in immune cells, suggesting that immune surveillance or inflammation increases with age. The age-regulated genes show a similar aging profile in the cortex and the medulla, suggesting a common underlying mechanism for aging.

Nomogram for predicting the likelihood of delayed graft function in adult cadaveric renal transplant recipients.

Delayed graft function (DGF) is the need for dialysis in the first week after transplantation. Studied were risk factors for DGF in adult (age >/=16 yr) cadaveric renal transplant recipients by means of a multivariable modeling procedure. Only donor and recipient factors known before transplantation were chosen so that the probabilities of DGF could be calculated before transplantation and appropriate preventative measures taken.

A gene recommender algorithm to identify coexpressed genes in C. elegans.

One of the most important uses of whole-genome expression data is for the discovery of new genes with similar function to a given list of genes (the query) already known to have closely related function. We have developed an algorithm, called the gene recommender, that ranks genes according to how strongly they correlate with a set of query genes in those experiments for which the query genes are most strongly coregulated. We used the gene recommender to find other genes coexpressed with several sets of query genes, including genes known to function in the retinoblastoma complex.

A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae).

Genomic sequencing is no longer a novelty, but gene function annotation remains a key challenge in modern biology. A variety of functional genomics experimental techniques are available, from classic methods such as affinity precipitation to advanced high-throughput techniques such as gene expression microarrays. In the future, more disparate methods will be developed, further increasing the need for integrated computational analysis of data generated by these studies.