Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets.

Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (M(e)) for the adjustment of multiple testing, but current methods of calculation for M(e) are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M(e). Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M(e), and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of ~10(-7) as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds ~5 × 10(-8) for current or merged commercial genotyping arrays, ~10(-8) for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10(-8) for the common SNPs only within genes.

Automated identification of medically important bacteria by 16S rRNA gene sequencing using a novel comprehensive database, 16SpathDB.

Despite the increasing use of 16S rRNA gene sequencing, interpretation of 16S rRNA gene sequence results is one of the most difficult problems faced by clinical microbiologists and technicians. To overcome the problems we encountered in the existing databases during 16S rRNA gene sequence interpretation, we built a comprehensive database, 16SpathDB (http://147.8.74.24/16SpathDB) based on the 16S rRNA gene sequences of all medically important bacteria listed in the Manual of Clinical Microbiology and evaluated its use for automated identification of these bacteria. Among 91 nonduplicated bacterial isolates collected in our clinical microbiology laboratory, 71 (78%) were reported by 16SpathDB as a single bacterial species having >98.0% nucleotide identity with the query sequence, 19 (20.9%) were reported as more than one bacterial species having >98.0% nucleotide identity with the query sequence, and 1 (1.1%) was reported as no match. For the 71 bacterial isolates reported as a single bacterial species, all results were identical to their true identities as determined by a polyphasic approach. For the 19 bacterial isolates reported as more than one bacterial species, all results contained their true identities as determined by a polyphasic approach and all of them had their true identities as the "best match in 16SpathDB." For the isolate (Gordonibacter pamelaeae) reported as no match, the bacterium has never been reported to be associated with human disease and was not included in the Manual of Clinical Microbiology. 16SpathDB is an automated, user-friendly, efficient, accurate, and regularly updated database for 16S rRNA gene sequence interpretation in clinical microbiology laboratories.