Assisting the analysis of insertions and deletions using regional allele frequencies.

Accurate estimation of population allele frequency (AF) is crucial for gene discovery and genetic diagnostics. However, determining AF for frameshift-inducing small insertions and deletions (indels) faces challenges due to discrepancies in mapping and variant calling methods. Here, we propose an innovative approach to assess indel AF. We developed CRAFTS-indels (Calculating Regional Allele Frequency Targeting Small indels), an algorithm that combines AF of distinct indels within a given region and provides "regional AF" (rAF). We tested and validated CRAFTS-indels using three independent datasets: gnomAD v2 (n=125,748 samples), an internal dataset (IGM; n=39,367), and the UK BioBank (UKBB; n=469,835). By comparing rAF against standard AF, we identified rare indels with rAF exceeding standard AF (sAF≤10-4 and rAF>10-4) as "rAF-hi" indels. Notably, a high percentage of rare indels were "rAF-hi", with a higher proportion in gnomAD v2 (11-20%) and IGM (11-22%) compared to the UKBB (5-9% depending on the CRAFTS-indels' parameters). Analysis of the overlap of regions based on their rAF with low complexity regions and with ClinVar classification supported the pertinence of rAF. Using the internal dataset, we illustrated the utility of CRAFTS-indel in the analysis of de novo variants and the potential negative impact of rAF-hi indels in gene discovery. In summary, annotation of indels with cohort specific rAF can be used to handle some of the limitations of current annotation pipelines and facilitate detection of novel gene disease associations. CRAFTS-indels offers a user-friendly approach to providing rAF annotation. It can be integrated into public databases such as gnomAD, UKBB and used by ClinVar to revise indel classifications.

Developing a genetic testing panel for evaluation of morbidities in kidney transplant recipients.

Cardiovascular disease, infection, malignancy, and thromboembolism are major causes of morbidity and mortality in kidney transplant recipients (KTR). Prospectively identifying monogenic conditions associated with post-transplant complications may enable personalized management. Therefore, we developed a transplant morbidity panel (355 genes) associated with major post-transplant complications including cardiometabolic disorders, immunodeficiency, malignancy, and thrombophilia. This gene panel was then evaluated using exome sequencing data from 1590 KTR. Additionally, genes associated with monogenic kidney and genitourinary disorders along with American College of Medical Genetics (ACMG) secondary findings v3.2 were annotated. Altogether, diagnostic variants in 37 genes associated with Mendelian kidney and genitourinary disorders were detected in 9.9% (158/1590) of KTR; 25.9% (41/158) had not been clinically diagnosed. Moreover, the transplant morbidity gene panel detected diagnostic variants for 56 monogenic disorders in 9.1% KTRs (144/1590). Cardiovascular disease, malignancy, immunodeficiency, and thrombophilia variants were detected in 5.1% (81), 2.1% (34), 1.8% (29) and 0.2% (3) among 1590 KTRs, respectively. Concordant phenotypes were present in half of these cases. Reviewing implications for transplant care, these genetic findings would have allowed physicians to set specific risk factor targets in 6.3% (9/144), arrange intensive surveillance in 97.2% (140/144), utilize preventive measures in 13.2% (19/144), guide disease-specific therapy in 63.9% (92/144), initiate specialty referral in 90.3% (130/144) and alter immunosuppression in 56.9% (82/144). Thus, beyond diagnostic testing for kidney disorders, sequence annotation identified monogenic disorders associated with common post-transplant complications in 9.1% of KTR, with important clinical implications. Incorporating genetic diagnostics for transplant morbidities would enable personalized management in pre- and post-transplant care.