Project description:BACKGROUND:Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Among them, the most successful one is Random Forests (RF). Despite of performing well in terms of prediction accuracy in some data sets with moderate size, RF still suffers from working in GWAS for selecting informative SNPs and building accurate prediction models. In this paper, we propose to use a new two-stage quality-based sampling method in random forests, named ts-RF, for SNP subspace selection for GWAS. The method first applies p-value assessment to find a cut-off point that separates informative and irrelevant SNPs in two groups. The informative SNPs group is further divided into two sub-groups: highly informative and weak informative SNPs. When sampling the SNP subspace for building trees for the forest, only those SNPs from the two sub-groups are taken into account. The feature subspaces always contain highly informative SNPs when used to split a node at a tree. RESULTS:This approach enables one to generate more accurate trees with a lower prediction error, meanwhile possibly avoiding overfitting. It allows one to detect interactions of multiple SNPs with the diseases, and to reduce the dimensionality and the amount of Genome-wide association data needed for learning the RF model. Extensive experiments on two genome-wide SNP data sets (Parkinson case-control data comprised of 408,803 SNPs and Alzheimer case-control data comprised of 380,157 SNPs) and 10 gene data sets have demonstrated that the proposed model significantly reduced prediction errors and outperformed most existing the-state-of-the-art random forests. The top 25 SNPs in Parkinson data set were identified by the proposed model including four interesting genes associated with neurological disorders. CONCLUSION:The presented approach has shown to be effective in selecting informative sub-groups of SNPs potentially associated with diseases that traditional statistical approaches might fail. The new RF works well for the data where the number of case-control objects is much smaller than the number of SNPs, which is a typical problem in gene data and GWAS. Experiment results demonstrated the effectiveness of the proposed RF model that outperformed the state-of-the-art RFs, including Breiman's RF, GRRF and wsRF methods.

Project description:Banking of high-quality placental tissue specimens will enable biomarker discovery and molecular studies on diseases involving placental dysfunction. Systematic studies aimed at developing feasible standardized methodology for placental collection for genomic analyses are lacking. To determine the acceptable timeframe for placental collection, we collected multiple samples from first and third trimester placentas at serial time points 0-120 minutes after delivery, simultaneously comparing the traditional snap-freeze technique to collection in commercial solutions designed to preserve RNA (RNAlaterTM, Ambion), and DNA (DNAgard®, Biomatrica). The performance of RNAlater for preserving DNA was also tested. Nucleic acid quality was assessed by determining the RNA integrity number (RIN) and genome-wide expression and DNA methylation microarray profiling. We found that samples collected in RNAlater had higher and more consistent RINs compared to snap frozen tissue, with similar RINs obtained for tissue collected in RNAlater as large (1 cm3) and small (~0.1 cm3) tissue pieces. RNAlater appeared to better stabilize the time zero gene expression pattern compared to snap freezing for first trimester placenta. Microarray DNA methylation analysis showed that overall the DNA methylation profiles remained quite stable over a two hour time period after removal of the placenta from the uterus, with the DNAgard condition being superior to both snap freezing and RNAlater. The collection of placental samples in RNAlater and DNAgard is simple, and eliminates the need for liquid nitrogen or a freezer on-site. Moreover, the quality of the nucleic acids and the resulting data from samples collected in these preservation solutions is actually higher than that from samples collected using the traditional snap-freeze method. Thus, this new approach to placental sample collection is both easier to implement in busy clinical environments and yields higher quality data. 48 samples

Project description:Banking of high-quality placental tissue specimens will enable biomarker discovery and molecular studies on diseases involving placental dysfunction. Systematic studies aimed at developing feasible standardized methodology for placental collection for genomic analyses are lacking. To determine the acceptable timeframe for placental collection, we collected multiple samples from first and third trimester placentas at serial time points 0-120 minutes after delivery, simultaneously comparing the traditional snap-freeze technique to collection in commercial solutions designed to preserve RNA (RNAlaterTM, Ambion), and DNA (DNAgard®, Biomatrica). The performance of RNAlater for preserving DNA was also tested. Nucleic acid quality was assessed by determining the RNA integrity number (RIN) and genome-wide expression and DNA methylation microarray profiling. We found that samples collected in RNAlater had higher and more consistent RINs compared to snap frozen tissue, with similar RINs obtained for tissue collected in RNAlater as large (1 cm3) and small (~0.1 cm3) tissue pieces. RNAlater appeared to better stabilize the time zero gene expression pattern compared to snap freezing for first trimester placenta. Microarray DNA methylation analysis showed that overall the DNA methylation profiles remained quite stable over a two hour time period after removal of the placenta from the uterus, with the DNAgard condition being superior to both snap freezing and RNAlater. The collection of placental samples in RNAlater and DNAgard is simple, and eliminates the need for liquid nitrogen or a freezer on-site. Moreover, the quality of the nucleic acids and the resulting data from samples collected in these preservation solutions is actually higher than that from samples collected using the traditional snap-freeze method. Thus, this new approach to placental sample collection is both easier to implement in busy clinical environments and yields higher quality data. 48 samples In this study, our objective was to identify the optimal timing and mode of collection for nucleic acids of sufficient quality to perform genome-wide RNA gene expression and DNA methylation studies for downstream molecular and functional enrichment analysis. To do this, we evaluated three different placenta collection methods: snap freezing in liquid nitrogen, RNAlaterTM, and DNAgard, over a two-hour window upon removal from the uterus, to determine: 1) the optimal collection method(s) for evaluation of mRNA expression and DNA methylation; and 2) the time period after delivery during which such optimal samples should be collected.

Dataset Information

Optimized between-group classification: a new jackknife-based gene selection procedure for genome-wide expression data.

Publications

Optimized between-group classification: a new jackknife-based gene selection procedure for genome-wide expression data.

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