San-Guo Zhang's research while affiliated with Chinese Academy of Sciences and other places

Joint analysis of multiple phenotypes can have better interpretation of complex diseases and increase statistical power to detect more significant single nucleotide polymorphisms (SNPs) compare to traditional single phenotype analysis in genome-wide association analysis. Principle component analysis (PCA), as a popular dimension reduction method, has been broadly used in the analysis of multiple phenotypes. Since PCA transforms the original phenotypes into principal components (PCs), it is natural to think that by analyzing these PCs, we can combine information across phenotypes. Existing PCA-based methods can be divided into two categories, either selecting one particular PC manually or combining information from all PCs. In this paper, we propose an adaptive principle component test (APCT) which selects and combines the PCs adaptively by using Cauchy combination method. Our proposed method can be seen as a generalization of traditional PCA based method since it contains two existing methods as special situation. Extensive simulation shows that our method is robust and can generate powerful result in various situations. The real data analysis of stock mice data also demonstrate that our proposed APCT can identify significant SNPs that are missed by traditional methods.

Introduction: To investigate the risk factors for myopia progression in primary school children and build prediction models by applying machine learning to longitudinal, cycloplegic autorefraction data. Methods: A total of 2740 children from grade 1 to grade 6 were examined annually over a period of 5 years. Myopia progression was determined as change in cycloplegic autorefraction. Questionnaires were administered to gauge environmental factors. Each year, risk factors were evaluated and prediction models were built in a training group and then tested in an independent hold-out group using the random forest algorithm. Results: Six variables appeared in prediction models on myopia progression for all 5 years, with combined weight of 77% and prediction accuracy over 80%. Uncorrected distance visual acuity (UDVA) had the greatest weight (mean 28%, range 22-39%), followed by spherical equivalent (20%, 7-28%), axial length (13%, 10-14%), flat keratometry reading (K1) (7%, 4-11%), gender (6%, 2-9%), and parental myopia (3%, 1-10%). UDVA and spherical equivalent had peak weight at the second and third study years, respectively. The weight of myopic parents decreased steadily over the 5 years (9.5%, 1.9%, 1.8%, 1%, and 1.3%). Weekly time spent reading, reading distance, reading in bed, and frequency of eating meat were included as variables in different study years. Conclusions: Myopia progression in children was predicted well by machine learning models. UDVA and spherical equivalents were good predictive factors for myopia progression in children through primary school. Parental myopia was found to play a substantial role in the early stage of myopia progression but waned as children grew older.