Ridge-penalized adaptive Mantel test and its application in imaging genetics

The impact of non-environmental factors on the chemical variation of Radix Scrophulariae

Radix Scrophulariae is a commonly used Chinese herb derived from the dried root of Scrophularia ningpoesis Hemsl. (S. ningpoensis). It is difficult to accurately estimate the dosage of Chinese medicinal materials used in the prescription because of the chemical variation caused by various factors. To analyze the non-environmental factors affecting the chemical variation of Radix Scrophulariae, we planted nine different cultivated varieties of S. ningpoensis in the same plantation. Based on sequence-related amplified polymorphism (SRAP), simple sequence repeats (SSR) markers and high-performance liquid chromatography (HPLC) analysis, we found that the materials from the cultivated varieties could be divided into two groups, the Zhejiang group, and the southwest China group. The genetic distance based on molecular data between the two groups was above 0.3882, and the Euclidean distance based on chemical data between the two groups was above 5.312. The correlation analysis between the genetic distance matrix based on SRAP and the Euclidean distance matrix based on 18 HPLC peaks of the whole underground part revealed that the genetic differentiation and chemical variation were positively related, r = 0.7196 (p < 0.05). The genetic background, different part of the roots and the different development of the roots are the three non-environmental factors causing the chemical variation. The coefficient of variation (C.V) of chemical composition of Radix Scrophulariae with different genetic background reached to 93.62 %, the C.V of the chemical composition of Radix Scrophulariae derived from the same variety reached to 64.21 %, the C.V of the chemical composition of Radix Scrophulariae derived from the middle part of the roots of S. ningpoensis from the same variety reached to 45.55 %. The C.V of chemical composition of Radix Scrophulairae produced in the same plantation could be controlled to 38.43 % by using the same variety of roots with the approximate mass derived from the middle part of the roots under 20 g. Our findings provided insights to decrease the chemical variation of Chinese medicinal materials by controlling non-environmental factors.

Ridge Penalization in High-Dimensional Testing With Applications to Imaging Genetics

High-dimensionality is ubiquitous in various scientific fields such as imaging genetics, where a deluge of functional and structural data on brain-relevant genetic polymorphisms are investigated. It is crucial to identify which genetic variations are consequential in identifying neurological features of brain connectivity compared to merely random noise. Statistical inference in high-dimensional settings poses multiple challenges involving analytical and computational complexity. A widely implemented strategy in addressing inference goals is penalized inference. In particular, the role of the ridge penalty in high-dimensional prediction and estimation has been actively studied in the past several years. This study focuses on ridge-penalized tests in high-dimensional hypothesis testing problems by proposing and examining a class of methods for choosing the optimal ridge penalty. We present our findings on strategies to improve the statistical power of ridge-penalized tests and what determines the optimal ridge penalty for hypothesis testing. The application of our work to an imaging genetics study and biological research will be presented.