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Poster presentations: Genetics 2020

P820 An easy and rapid targeted next generation sequencing-based genotyping assay for the validated IBD risk loci

S. Verstockt1, L. Hannes2, S. Deman2, W.J. Wollants1, E. Souche2, B. Verstockt1, I. van der Werf3, A. Hoischen4, M. Ferrante1, S. Vermeire1, I. Cleynen2

1Department of Chronic Diseases- Metabolism and Ageing, KU Leuven, Leuven, Belgium, 2Department of Human Genetics, KU Leuven, Leuven, Belgium, 3University of Antwerp, Department of Biomedical sciences, Antwerp, Belgium, 4Radboud University Medical Center, Department of Human Genetics, Nijmegen, The Netherlands

Background

Inflammatory bowel diseases (IBD) are complex genetic diseases for which 242 susceptibility loci have been identified thus far. For translational or functional follow-up studies it can be of interest to know the genotype of specific variants. For other studies a composite genetic risk score–the polygenic risk score–is of value. There currently is a gap in technology to genotype a few hundred variants in a flexible and cost-effective way. We therefore developed a genotyping assay for the 242 validated IBD susceptibility loci.

Methods

Using MIPgen v.1.1, we designed molecular inversion probes (MIPs) covering 269 independent variants from the 242 IBD loci. MIP libraries were prepared according to Neveling et al. (Clin Chem. 2017), followed by paired-end sequencing using a MiSeq® System (Illumina). In the pilot studies, 16 IBD patients were genotyped, and results were compared with available immunochip (ichip) data. Genotypes for the covered variants were obtained using an in-house developed pipeline, and performance metrics were assessed (incl. genotyping call rate, percentage off-target reads and concordance with ichip-based genotypes). After optimisation, we genotyped 279 individuals (168 IBD patients and 111 non-IBD controls). We also calculated a weighted IBD polygenic risk score (PRSice 2.0) for these.

Results

Despite a genotyping call rate of 94.3%, the first pilot run suffered from a high rate of off-target reads (52.5%). After redesigning poorly-performing MIPs, off-target reads dropped to 9.4%, and the genotyping call rate increased to 97.5%. Concordance with genotypes previously obtained from ichip was 99.3%. When applying the optimised design on a larger scale (i.e. on the 279 individuals), we obtained similar performance metrics, with 8.0% off-target reads and a genotyping call rate of 97.3%. Moreover, upscaling resulted in a turnaround time of 2.5 working days/96 samples and a cost of €14/sample. The calculated IBD polygenic risk scores showed higher scores in patients as compared with controls (5.5E−03 vs. 4.0E−03, p = 8.80E−10; R² IBD polygenic risk score = 0.15, p = 1.28E−07), however with a large overlap between both groups. Quartile analysis showed that individuals within the highest quartile had an 8.1-fold (95% CI: 3.7–17.5) increase in risk towards IBD compared with individuals in the first quartile.

Conclusion

We developed a cost-effective genotyping assay for currently known IBD risk loci, with an integrated bioinformatics pipeline from raw sequencing data to individual genotypes and calculation of a polygenic risk score. Furthermore, this assay enables genotyping of individuals on a large scale while remaining flexible to implement newly identified genetic variants.