ABSTRACT
Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high.
ABSTRACT
(1) Background: Sympathetic overactivity is a major contributor to resistant hypertension (RH). According to animal studies, sympathetic overactivity increases immune responses, thereby aggravating hypertension and cardiovascular outcomes. Renal denervation (RDN) reduces sympathetic nerve activity in RH. Here, we investigate the effect of RDN on T-cell signatures in RH. (2) Methods: Systemic inflammation and T-cell subsets were analyzed in 17 healthy individuals and 30 patients with RH at baseline and 6 months after RDN. (3) Results: The patients with RH demonstrated higher levels of pro-inflammatory cytokines and higher frequencies of CD4+ effector memory (TEM), CD4+ effector memory residential (TEMRA) and CD8+ central memory (TCM) cells than the controls. After RDN, systolic automated office blood pressure (BP) decreased by -17.6 ± 18.9 mmHg. Greater BP reductions were associated with higher CD4+ TEM (r -0.421, p = 0.02) and CD8+ TCM (r -0.424, p = 0.02) frequencies at baseline. The RDN responders, that is, the patients with ≥10mmHg systolic BP reduction, showed reduced pro-inflammatory cytokine levels, whereas the non-responders had unchanged inflammatory activity and higher CD8+ TEMRA frequencies with increased cellular cytokine production. (4) Conclusions: The pro-inflammatory state of patients with RH is characterized by altered T-cell signatures, especially in non-responders. A detailed analysis of T cells might be useful in selecting patients for RDN.
