Adaptive sampling with the Oxford Nanopore MinION sequencer to identify new genes involved in male infertility
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Male infertility is a widespread problem that is often hard to diagnose as multiple genes can impact this diverse phenotype. The advent of long-read sequencing using the MinION or PromethION platforms provides us a methodology to cost efficiently assess mutations across genes having an impact in male infertility at speed. In the present work we evaluated and implemented both platforms across control and patient samples. For this we utilized adaptive sampling on the MinION platform to target 21.1 megabase pairs (Mbp) of search space including 101 known male infertility genes, large regions of the Y chromosome known to contain microduplications and microdeletions (e.g. AZFb/AZFc), and 13 protein-coding genes that we hypothesize underly the cause of infertility. Over a four-month period we were able to sequence and analyze 48 men with male infertility and fertile control samples including reference DNA samples (HG001,HG002,HG003,HG004) together with replicates of certain samples amounting to 64 flow cells so far. All samples were reported with SNV and phased SV based on Princess. Thus, providing most comprehensive information across these genes. All our samples show consistently high data quality with 97% of mapped reads and high F-scores (precision and sensitivity) across the control samples for SNV and SV. This is possible due to the effectiveness of the selected sequencing averaging a 5-6 fold increase of coverage across the 113 genes of interest over non-regions-of-interest. Currently we are in the process of using phase data and deleteriousness scores (CADD, DANN) to filter and rank variants in each individual for compound heterozygosity and loss-of-function. To further optimize the speed of this approach we have now implemented the P2-solo in our pipeline showing a significant increase in data generation. Overall, in our presentation we will show how we our implementation of the MinION system yielded meaningful data that can be used to identify new mutations involved in male infertility.