Cannon, S. | Search bioRxiv

bioRxiv2026

A New Disease Gene for Hypokalemic Periodic Paralysis, KCNA7, Established in a Multigenerational Family

Hypokalemic periodic paralysis (HypoPP) is an inherited skeletal muscle ion channelopathy of CACNA1S or SCN4A characterized by recurrent episodes of weakness, often triggered by rest after exercise or by reduced K+ (carbohydrate ingestion, stress). Here, we describe a multigenerational family in whom a KCNA7 missense variant [c.834A>C (p.Arg278Ser)] co-segregated with susceptibility to recurrent attacks of weakness in association with hypokalemia as low as 1.3 mEq/L and a 40% decrement of the compound muscle action potential after exercise, suggesting an additional HypoPP gene. Arg278 is the outermost positively charged residue (R1) within the S4 transmembrane segment of the voltage-sensor domain of KV1.7, orthologous to the canonical patten of arginine missense mutations at R1 or R2 in S4 segments for HypoPP-mutant CaV1.1 and NaV1.4 channels. To determine the functional consequences of Arg278Ser, we expressed wild-type and mutant KV1.7 channels in Xenopus oocytes and HEK293 cells. Arg278Ser, but not wild-type KV1.7, generated an anomalous inwardly rectifying current at hyperpolarized membrane potentials that was nonselective Na+ or K+, consistent with the anomalous gating pore conductance that causes susceptibility to HypoPP. Other pathogenic variants of KCNA7 have previously been implicated only in inherited cardiac arrhythmias. Collectively, the genetic, structural and electrophysiological findings support KCNA7 Arg278Ser as a pathogenic variant underlying HypoPP and implicate KCNA7 as a disease gene for periodic paralysis, extending the established gating-pore mechanism of HypoPP to KV1.7.

genetics↗

bioRxiv2020

Algorithmic and data modeling: will algorithmic modeling improve predictions of traits evaluated on ordinal scales?

Selection of markers linked to alleles at quantitative trait loci (QTL) for tolerance to Iron Deficiency Chlorosis (IDC) has not been successful. Genomic selection has been advocated for continuous numeric traits such as yield and plant height. For ordinal data types such as IDC, genomic prediction models have not been systematically compared. The objectives of research reported in this manuscript were to evaluate the most commonly used genomic prediction method, ridge regression and its equivalent logistic ridge regression method, with algorithmic modeling methods including random forest, gradient boosting, support vector machine, K-nearest neighbors, Naive Bayes, and artificial neural network using the usual comparator metric of prediction accuracy. In addition we compared the methods using metrics of greater importance for decisions about selecting and culling lines for use in variety development and genetic improvement projects. These metrics include specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. We found that Support Vector Machine provided the best specificity for culling IDC susceptible lines, while Random Forest GP models provided the best combined set of decision metrics for retaining IDC tolerant and culling IDC susceptible lines.

genomics↗