Xiang Zhou

TL;DR: It is reported that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1,BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes.

TL;DR: The Cancer Genome Atlas project has analyzed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours as mentioned in this paper.

TL;DR: This method is approximately n times faster than the widely used exact method known as efficient mixed-model association (EMMA), where n is the sample size, making exact genome-wide association analysis computationally practical for large numbers of individuals.

TL;DR: This work applies Bayesian sparse linear mixed model (BSLMM) and compares it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction, and demonstrates that BSLMM considerably outperforms either of the other two methods.

TL;DR: Efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests are presented, which offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.