The DRAGEN KIV-2 copy number caller accurately estimated KIV-2 copy numbers from short-read sequencing, demonstrating a 0.982 correlation with optical mapping and outperforming SNVs in predicting Lp(a) levels.
Observational (n=6,208)
Yes
Does the DRAGEN KIV-2 CN caller accurately estimate KIV-2 copy number and correlate with Lp(a) levels compared to optical mapping and SNV predictors?
The DRAGEN KIV-2 CN caller provides an accurate, short-read sequencing-based method to estimate KIV-2 copy numbers, which correlate significantly with Lp(a) levels and CVD risk, outperforming traditional SNV predictors.
Effect estimate: Correlation 0.982
p-value: p=1.88e-109
The abundance of Lp(a) protein holds significant implications for the risk of cardiovascular disease (CVD), which is directly impacted by the copy number (CN) of KIV-2, a 5.5 kbp sub-region. KIV-2 is highly polymorphic in the population and accurate analysis is challenging. In this study, we present the DRAGEN KIV-2 CN caller, which utilizes short reads. Data across 166 WGS show that the caller has high accuracy, compared to optical mapping and can further phase approximately 50% of the samples. We compared KIV-2 CN numbers to 24 previously postulated KIV-2 relevant SNVs, revealing that many are ineffective predictors of KIV-2 copy number. Population studies, including USA-based cohorts, showed distinct KIV-2 CN, distributions for European-, African-, and Hispanic-American populations and further underscored the limitations of SNV predictors. We demonstrate that the CN estimates correlate significantly with the available Lp(a) protein levels and that phasing is highly important.
Behera et al. (Thu,) conducted a observational in Cardiovascular disease risk (n=6,208). DRAGEN KIV-2 CN caller vs. Bionano optical mapping was evaluated on Correlation of total KIV-2 copy number between DRAGEN LPA caller and Bionano optical mapping (Correlation 0.982, p=1.88e-109). The DRAGEN KIV-2 copy number caller accurately estimated KIV-2 copy numbers from short-read sequencing, demonstrating a 0.982 correlation with optical mapping and outperforming SNVs in predicting Lp(a) levels.