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Fan, C.

Publications and source records attributed to Fan, C..

3 recordsLinked to original sources

An Integrative Boosting Approach for Predicting Survival Time With Multiple Genomics Platforms

Recent technological advances have made it possible to collect multiple types of genomics data on the same set of patients. It is of great interest to integrate multiple genomics data types together for predicting disease outcomes. We propose a variable selection method, termed Integrative Boosting (I-Boost), that makes proper use of all available clinical and genomics data in predicting individual patient survival time. Through simulation studies and applications to data sets from The Cancer Genome Atlas, we demonstrate that I-Boost provides substantially higher prediction accuracy than existing variable selection methods. Using I-Boost, we show that (1) the integration of multiple genomics platforms with clinical variables significantly improves the prediction accuracy for survival time over the use of clinical variables alone; (2) gene expression values are typically more prognostic of survival time than other genomics data types; and (3) gene modules/signatures are at least as prognostic as the collection of individual gene expression data.

bioinformatics

Structure of the human lipid-sensitive cation channel TRPC3

The TRPC channels are crucially involved in store-operated calcium entry and calcium homeostasis, and they are thus implicated in human diseases such as neurodegenerative disease, cardiac hypertrophy, and spinocerebellar ataxia. We present structure of the full-length human TRPC3, a lipid-gated TRPC member, in a lipid-occupied, closed state at 3.3 Angstrom. TRPC3 has an acorn-like shape with four elbow-like membrane reentrant helices prior to the first transmembrane helix. The TRP helix is perpendicular to, and thus disengaged from, the pore-lining S6, suggesting a different gating mechanism. The third transmembrane helix S3 is remarkably long, resulting in a windmill-like transmembrane domain, and constituting an extracellular domain that may serve as a sensor of external stimuli. We identified two lipid binding sites, one being sandwiched between the pre-S1 elbow and the S4-S5 linker, and the other being close to the ion-conducting pore, where the conserved LWF motif of the TRPC family is located.

biophysics

Semi-Parametric Covariate-Modulated Local False Discovery Rate For Genome-Wide Association Studies

While genome-wide association studies (GWAS) have discovered thousands of risk loci for heritable disorders, so far even very large meta-analyses have recovered only a fraction of the heritability of most complex traits. Recent work utilizing variance components models has demonstrated that a larger fraction of the heritability of complex phenotypes is captured by the additive effects of SNPs than is evident only in loci surpassing genome-wide significance thresholds, typically set at a Bonferroni-inspired p [≤] 5 x 10-8. Procedures that control false discovery rate can be more powerful, yet these are still under-powered to detect the majority of non-null effects from GWAS. The current work proposes a novel Bayesian semi-parametric two-group mixture model and develops a Markov Chain Monte Carlo (MCMC) algorithm for a covariate-modulated local false discovery rate (cmfdr). The probability of being non-null depends on a set of covariates via a logistic function, and the non-null distribution is approximated as a linear combination of B-spline densities, where the weight of each B-spline density depends on a multinomial function of the covariates. The proposed methods were motivated by work on a large meta-analysis of schizophrenia GWAS performed by the Psychiatric Genetics Consortium (PGC). We show that the new cmfdr model fits the PGC schizophrenia GWAS test statistics well, performing better than our previously proposed parametric gamma model for estimating the non-null density and substantially improving power over usual fdr. Using loci declared significant at cmfdr [≤] 0.20, we perform follow-up pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) homo sapiens pathways database. We demonstrate that the increased yield from the cmfdr model results in an improved ability to test for pathways associated with schizophrenia compared to using those SNPs selected according to usual fdr.

bioinformatics