False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.

Big datasets, accumulated from biomedical and agronomic studies, provide the potential to identify genes that control complex human diseases and agriculturally important traits through genome-wide association studies (GWAS). However, big datasets also lead to extreme computational challenges, especially when sophisticated statistical models are employed to simultaneously reduce false positives and false negatives. The newly developed fixed and random model circulating probability unification (FarmCPU) method uses a bin method under the assumption that quantitative trait nucleotides (QTNs) are evenly distributed throughout the genome. The estimated QTNs are used to separate a mixed linear model into a computationally efficient fixed effect model (FEM) and a computationally expensive random effect model (REM), which are then used iteratively. To completely eliminate the computationally expensive REM, we replaced REM with FEM by using Bayesian information criteria. To eliminate the requirement that QTNs be evenly distributed throughout the genome, we replaced the bin method with linkage disequilibrium information. The new method is called Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Both real and simulated data analyses demonstrated that BLINK improves statistical power compared to FarmCPU, in addition to remarkably reducing computing time. Now, a dataset with one million individuals and one-half million markers can be analyzed within three hours, instead of one week using FarmCPU.

Calorie restriction (CR) extends lifespan and elicits numerous effects beneficial to health and metabolism in various model organisms, but the underlying mechanisms are not completely understood. Gut microbiota has been reported to be associated with the beneficial effects of CR; however, it is unknown whether these effects of CR are causally mediated by gut microbiota. In this study, we employed an antibiotic-induced microbiota-depleted mouse model to investigate the functional role of gut microbiota in CR. Depletion of gut microbiota rendered mice resistant to CR-induced loss of body weight, accompanied by the increase in fat mass, the reduction in lean mass and the decline in metabolic rate. Depletion of gut microbiota led to increases in fasting blood glucose and cholesterol levels independent of CR. A few metabolism-modulating hormones including leptin and insulin were altered by CR and/or gut microbiota depletion. In addition, CR altered the composition of gut microbiota with significant increases in major probiotic genera such as Lactobacillus and Bifidobacterium, together with the decrease of Helicobacter. In addition, we performed fecal microbiota transplantation in mice fed with high-fat diet. Mice with transferred microbiota from calorie-restricted mice resisted high fat diet-induced obesity and exhibited metabolic improvement such as alleviated hepatic lipid accumulation. Collectively, these data indicate that CR-induced metabolic improvement especially in body weight reduction is mediated by intestinal microbiota to a certain extent.

No abstract