MicroPredict: predicting species-level taxonomic abundance of whole-shotgun metagenomic data using only 16S amplicon sequencing data.

BACKGROUND: The importance of the human microbiome in the analysis of various diseases is emerging. The two main methods used to profile the human microbiome are 16S rRNA gene sequencing (16S sequencing) and whole-genome shotgun sequencing (WGS). Owing to the full coverage of the genome in sequencing, WGS has multiple advantages over 16S sequencing, including higher taxonomic profiling resolution at the species-level and functional profiling analysis. However, 16S sequencing remains widely used because of its relatively low cost. Although WGS is the standard method for obtaining accurate species-level data, we found that 16S sequencing data contained rich information to predict high-resolution species-level abundances with reasonable accuracy. OBJECTIVE: In this study, we proposed MicroPredict, a method for accurately predicting WGS-comparable species-level abundance data using 16S taxonomic profile data. METHODS: We employed a mixed model using two key strategies: (1) modeling both sample- and species-specific information for predicting WGS abundances, and (2) accounting for the possible correlations among different species. RESULTS: We found that MicroPredict outperformed the other machine learning methods. CONCLUSION: We expect that our approach will help researchers accurately approximate the species-level abundances of microbiome profiles in datasets for which only cost-effective 16S sequencing has been applied.

PASTRY: achieving balanced power for detecting risk and protective minor alleles in meta-analysis of association studies with overlapping subjects.

BACKGROUND: Meta-analysis is a statistical method that combines the results of multiple studies to increase statistical power. When multiple studies participating in a meta-analysis utilize the same public dataset as controls, the summary statistics from these studies become correlated. To solve this challenge, Lin and Sullivan proposed a method to provide an optimal test statistic adjusted for the correlation. This method quickly became the standard practice. However, we identified an unexpected power asymmetry phenomenon in this standard framework. This can lead to unbalanced power for detecting protective minor alleles and risk minor alleles. RESULTS: We found that the power asymmetry of the current framework is mainly due to the errors in approximating the correlation term. We then developed a meta-analysis method based on an accurate correlation estimator, called PASTRY (A method to avoid Power ASymmeTRY). PASTRY outperformed the standard method on both simulated and real datasets in terms of the power symmetry. CONCLUSIONS: Our findings suggest that PASTRY can help to alleviate the power asymmetry problem. PASTRY is available at https://github.com/hanlab-SNU/PASTRY .

Expression of neurofibromin 1 in colorectal cancer and cetuximab resistance.

Neurofibromin 1 (NF1) is a tumor suppressor that has been previously reported to regulate RAS­MAPK signaling. The present study investigated the possible relationship between NF1 expression and anti­EGFR antibody (cetuximab) sensitivity in colorectal cancer cell lines. In addition, primary or metastatic colorectal cancer samples from patients treated with cetuximab were assessed for the association of cetuximab sensitivity. The quantities of the NF1 transcript, NF1­related pathway enrichment and NF1 mutation profile were measured and investigated using RNA sequencing and targeted DNA sequencing. Based on growth inhibition and colony formation assay results, cell lines were designated to be cetuximab­sensitive (NCI­H508 and Caco2) or cetuximab­resistant (KM12C and SM480). Western blotting revealed NF1 was highly expressed in cetuximab­sensitive cell lines whilst there was little expression in their cetuximab­resistant counterparts. Knocking down NF1 expression using small interfering RNA in the cetuximab­sensitive cell lines enhanced the phosphorylation of MEK and ERK according to western blotting. NF1 knockdown also reduced apoptosis, as observed by the decreased number of apoptotic bodies by DAPI nuclear staining and reduced cleavage of caspase and poly­(ADP ribose) polymerase. NF1 overexpression by transfection with GTPase­activating protein­related domain subunit rendered the cetuximab­resistant cell lines, KM12C and SW480, more susceptible to cetuximab­induced apoptosis. RNA sequencing of 111 RAS and BRAFV600 wild­type tumor samples collected from cetuximab­treated patients with metastatic colorectal cancer revealed that the pre­treatment NF1 expression levels were not associated with the cetuximab response. However, tumor samples obtained after cetuximab treatment displayed slightly lower NF1 transcript levels compared with those in the pre­treatment samples, suggesting that exposure to the anti­EGFR antibody may be associated with reduced NF1 expression levels. Next­generation sequencing revealed that the frequency of inactivating mutations in NF1 were rare (1.8%) in patients with colorectal cancer and were not associated with the protein expression levels of NF1 except for in a small number of cases (0.5%), where the biallelic inactivation of NF1 was observed. To conclude, the present study showed that modification of NF1 expression can affect sensitivity to cetuximab in colorectal cancer cell lines, though a limitation exists in terms of its potential application as a biomarker for RAS and BRAFV600 wild­type tumors.

Analysis of differences in human leukocyte antigen between the two Wellcome Trust Case Control Consortium control datasets.

The Wellcome Trust Case Control Consortium (WTCCC) study was a large genome-wide association study that aimed to identify common variants associated with seven diseases. That study combined two control datasets (58C and UK Blood Services) as shared controls. Prior to using the combined controls, the WTCCC performed analyses to show that the genomic content of the control datasets was not significantly different. Recently, the analysis of human leukocyte antigen (HLA) genes has become prevalent due to the development of HLA imputation technology. In this project, we extended the between-control homogeneity analysis of the WTCCC to HLA. We imputed HLA information in the WTCCC control dataset and showed that the HLA content was not significantly different between the two control datasets, suggesting that the combined controls can be used as controls for HLA fine-mapping analysis based on HLA imputation.

Genomic GPS: using genetic distance from individuals to public data for genomic analysis without disclosing personal genomes.

Genomic global positioning system (GPS) applies the multilateration technique commonly used in the GPS to genomic data. In the framework we present here, investigators calculate genetic distances from their samples to reference samples, which are from data held in the public domain, and share this information with others. This sharing enables certain types of genomic analysis, such as identifying sample overlaps and close relatives, decomposing ancestry, and mapping of geographical origin without disclosing personal genomes. Thus, our method can be seen as a balance between open data sharing and privacy protection.