Statistical power for detecting epistasis QTL effects under the F-2 design

Epistasis refers to gene interaction effect involving two or more genes. Statistical methods for mapping quantitative trait loci (QTL) with epistasis effects have become available recently. However, little is known about the statistical power and sample size requirements for mapping epistatic QTL using genetic markers. In this study, we developed analytical formulae to calculate the statistical power and sample requirement for detecting each epistasis effect under the F-2 design based on crossing inbred lines. Assuming two unlinked interactive QTL and the same absolute value for all epistasis effects, the heritability of additive × additive (a × a) effect is twice as large as that of additive × dominance (a × d) or dominance × additive (d × a) effect, and is four times as large as that of dominance × dominance (d × d) effect. Consequently, among the four types of epistasis effects involving two loci, 'a × a' effect is the easiest to detect whereas 'd × d' effect is the most difficult to detect. The statistical power for detecting 'a × a' effect is similar to that for detecting dominance effect of a single QTL. The sample size requirements for detecting 'a × d', 'd × a' and 'd × d' are highly sensitive to increased distance between the markers and the interacting QTLs. Therefore, using dense marker coverage is critical to detecting those effects

Statistical power for detecting epistasis QTL effects under the F-2 design

Epistasis refers to gene interaction effect involving two or more genes. Statistical methods for mapping quantitative trait loci (QTL) with epistasis effects have become available recently. However, little is known about the statistical power and sample size requirements for mapping epistatic QTL using genetic markers. In this study, we developed analytical formulae to calculate the statistical power and sample requirement for detecting each epistasis effect under the F-2 design based on crossing inbred lines. Assuming two unlinked interactive QTL and the same absolute value for all epistasis effects, the heritability of additive $\times$ additive (a $\times$ a) effect is twice as large as that of additive $\times$ dominance (a $\times$ d) or dominance $\times$ additive (d $\times$ a) effect, and is four times as large as that of dominance $\times$ dominance (d $\times$ d) effect. Consequently, among the four types of epistasis effects involving two loci, `a $\times$ a' effect is the easiest to detect whereas `d $\times$ d' effect is the most difficult to detect. The statistical power for detecting `a $\times$ a' effect is similar to that for detecting dominance effect of a single QTL. The sample size requirements for detecting `a $\times$ d', `d $\times$ a' and `d $\times$ d' are highly sensitive to increased distance between the markers and the interacting QTLs. Therefore, using dense marker coverage is critical to detecting those effects

Lurasidone and risk for metabolic syndrome: results from short- and long-term clinical studies in patients with schizophrenia

OBJECTIVE: To assess the effects of treatment with lurasidone on risk for metabolic syndrome (MetS) in patients with schizophrenia. METHODS: Rates of metabolic syndrome during treatment with lurasidone (40-160 mg/d) were analyzed using pooled, short-term data from three randomized, double-blind, placebo-controlled studies (vs olanzapine and quetiapine XR); long-term data from two active-comparator-controlled studies (vs risperidone and quetiapine XR); and data from two open-label studies in which patients were switched from olanzapine or risperidone to lurasidone. RESULTS: MetS was defined based on the National Cholesterol Education Program criteria. In short-term studies, the odds of meeting criteria for MetS at week 6 LOCF (adjusted for baseline metabolic syndrome status) was similar for the lurasidone and placebo groups (OR = 1.18; [95% CI, 0.81-1.71]; P = .39), but the odds (vs placebo) were significantly greater for olanzapine (OR = 2.81; [95% CI, 1.53-5.15]; P \u3c .001) and quetiapine (OR = 3.49; [95% CI, 1.93-6.29]; P \u3c .0001). No dose effect was observed for lurasidone across the dose range of 40-160 mg/d. In long-term studies, the odds of MetS after 12 months of treatment was significantly higher for risperidone compared with lurasidone (OR = 2.12; 95% CI, 1.15-3.90; P = .016) and for quetiapine XR compared with lurasidone (OR = 3.92; 95% CI, 1.15-13.40; P = .029). In open-label extension studies, the rate of MetS decreased in patients switched to lurasidone after 6 weeks of treatment with olanzapine or 12 months of treatment with risperidone. CONCLUSION: In this analysis of lurasidone clinical trials, the odds of developing metabolic syndrome were minimal during short- and long-term treatment with lurasidone (40-160 mg/d)

Mapping Quantitative Trait Loci Using Naturally Occurring Genetic Variance Among Commercial Inbred Lines of Maize (Zea mays L.)

Many commercial inbred lines are available in crops. A large amount of genetic variation is preserved among these lines. The genealogical history of the inbred lines is usually well documented. However, quantitative trait loci (QTL) responsible for the genetic variances among the lines are largely unexplored due to lack of statistical methods. In this study, we show that the pedigree information of the lines along with the trait values and marker information can be used to map QTL without the need of further crossing experiments. We develop a Monte Carlo method to estimate locus-specific identity-by-descent (IBD) matrices. These IBD matrices are further incorporated into a mixed-model equation for variance component analysis. QTL variance is estimated and tested at every putative position of the genome. The actual QTL are detected by scanning the entire genome. Applying this new method to a well-documented pedigree of maize (Zea mays L.) that consists of 404 inbred lines, we mapped eight QTL for the maize male flowering trait, growing degree day heat units to pollen shedding (GDUSHD). These detected QTL contributed >80% of the variance observed among the inbred lines. The QTL were then used to evaluate all the inbred lines using the best linear unbiased prediction (BLUP) technique. Superior lines were selected according to the estimated QTL allelic values, a technique called marker-assisted selection (MAS). The MAS procedure implemented via BLUP may be routinely used by breeders to select superior lines and line combinations for development of new cultivars