Novel genotyping algorithms for rare variants significantly improve the accuracy of Applied Biosystems™ Axiom™ array genotyping calls: Retrospective evaluation of UK Biobank array data.

The UK Biobank genotyped about 500k participants using Applied Biosystems Axiom microarrays. Participants were subsequently sequenced by the UK Biobank Exome Sequencing Consortium. Axiom genotyping was highly accurate in comparison to sequencing results, for almost 100,000 variants both directly genotyped on the UK Biobank Axiom array and via whole exome sequencing. However, in a study using the exome sequencing results of the first 50k individuals as reference (truth), it was observed that the positive predictive value (PPV) decreased along with the number of heterozygous array calls per variant. We developed a novel addition to the genotyping algorithm, Rare Heterozygous Adjusted (RHA), to significantly improve PPV in variants with minor allele frequency below 0.01%. The improvement in PPV was roughly equal when comparing to the exome sequencing of 50k individuals, or to the more recent ~200k individuals. Sensitivity was higher in the 200k data. The improved calling algorithm, along with enhanced quality control of array probesets, significantly improved the positive predictive value and the sensitivity of array data, making it suitable for the detection of ultra-rare variants.

A Custom Genotyping Array Reveals Population-Level Heterogeneity for the Genetic Risks of Prostate Cancer and Other Cancers in Africa.

Although prostate cancer is the leading cause of cancer mortality for African men, the vast majority of known disease associations have been detected in European study cohorts. Furthermore, most genome-wide association studies have used genotyping arrays that are hindered by SNP ascertainment bias. To overcome these disparities in genomic medicine, the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network has developed a genotyping array that is optimized for African populations. The MADCaP Array contains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of common genetic variants in African populations. This array also has a high density of markers in genomic regions associated with cancer susceptibility, including 8q24. We assessed the effectiveness of the MADCaP Array by genotyping 399 prostate cancer cases and 403 controls from seven urban study sites in sub-Saharan Africa. Samples from Ghana and Nigeria clustered together, whereas samples from Senegal and South Africa yielded distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores for prostate cancer were higher in Nigeria than in Senegal. In summary, individual and population-level differences in prostate cancer risk were revealed using a novel genotyping array. SIGNIFICANCE: This study presents an Africa-specific genotyping array, which enables investigators to identify novel disease associations and to fine-map genetic loci that are associated with prostate and other cancers.

Large-scale cytological profiling for functional analysis of bioactive compounds.

Cytological profiling (CP) is an unbiased image-based screening technique that uses automated microscopy and image analysis to profile compounds based on numerous quantifiable phenotypic features. We used CP to evaluate a library of nearly 500 compounds with documented mechanisms of action (MOAs) spanning a wide range of biological pathways. We developed informatics techniques for generating dosage-independent phenotypic "fingerprints" for each compound, and for quantifying the likelihood that a compound's CP fingerprint corresponds to its annotated MOA. We identified groups of features that distinguish classes with closely related phenotypes, such as microtubule poisons vs. HSP90 inhibitors, and DNA synthesis vs. proteasome inhibitors. We tested several cases in which cytological profiles indicated novel mechanisms, including a tyrphostin kinase inhibitor involved in mitochondrial uncoupling, novel microtubule poisons, and a nominal PPAR-gamma ligand that acts as a proteasome inhibitor, using independent biochemical assays to confirm the MOAs predicted by the CP signatures. We also applied maximal-information statistics to identify correlations between cytological features and kinase inhibitory activities by combining the CP fingerprints of 24 kinase inhibitors with published data on their specificities against a diverse panel of kinases. The resulting analysis suggests a strategy for probing the biological functions of specific kinases by compiling cytological data from inhibitors of varying specificities.

HALO384: a halo-based potency prediction algorithm for high-throughput detection of antimicrobial agents.

A high-throughput (HT) agar-based halo assay is described, which allows for rapid screening of chemical libraries for bioactivity in microorganisms such as yeast and bacteria. A pattern recognition algorithm was developed to identify halo-like shapes in plate reader optical density (OD) measurements. The authors find that the total growth inhibition within a detected halo provides an accurate estimate of a compound's potency measured in terms of its EC(50). The new halo recognition method performs significantly better than an earlier method based on single-point OD readings. An assay based on the halo algorithm was used to screen a 21,120-member library of drug-like compounds in Saccharomyces cerevisiae, leading to the identification of novel bioactive scaffolds containing derivatives of varying potencies. The authors also show that the HT halo assay can be performed with the pathogenic bacterium Vibrio cholerae and that liquid culture EC(50) values and halo scores show a good correlation in this organism. These results suggest that the HT halo assay provides a rapid and inexpensive way to screen for bioactivity in multiple microorganisms.

Recommending pathway genes using a compendium of clustering solutions.

A common approach for identifying pathways from gene expression data is to cluster the genes without using prior information about a pathway, which often identifies only the dominant coexpression groups. Recommender systems are well-suited for using the known genes of a pathway to identify the appropriate experiments for predicting new members. However, existing systems, such as the GeneRecommender, ignore how genes naturally group together within specific experiments. We present a collaborative filtering approach which uses the pattern of how genes cluster together in different experiments to recommend new genes in a pathway. Clusters are first identified within a single experiment series. Informative clusters, in which the user-supplied query genes appear together, are identified. New genes that cluster with the known genes, in a significant fraction of the informative clusters, are recommended. We implemented a prototype of our system and measured its performance on hundreds of pathways. We find that our method performs as well as an established approach while significantly increasing the speed and scalability of searching large datasets. [Supplemental material is available online at sysbio.soe.ucsc.edu/cluegene/psb07.]