The Statistics of Bulk Segregant Analysis Using Next Generation Sequencing

We describe a statistical framework for QTL mapping using bulk segregant analysis (BSA) based on high throughput, short-read sequencing. Our proposed approach is based on a smoothed version of the standard statistic, and takes into account variation in allele frequency estimates due to sampling of segregants to form bulks as well as variation introduced during the sequencing of bulks. Using simulation, we explore the impact of key experimental variables such as bulk size and sequencing coverage on the ability to detect QTLs. Counterintuitively, we find that relatively large bulks maximize the power to detect QTLs even though this implies weaker selection and less extreme allele frequency differences. Our simulation studies suggest that with large bulks and sufficient sequencing depth, the methods we propose can be used to detect even weak effect QTLs and we demonstrate the utility of this framework by application to a BSA experiment in the budding yeast Saccharomyces cerevisiae

Estimates of technical inefficiency in stochastic frontier models with panel data: generalized panel jackknife estimation

Technical inefficiency, Stochastic frontier, Panel data, Jackknife, Bootstrap, C10, C15,

A bias reducing technique in kernel distribution function estimation

Bandwidth, Bias, Convex combination, Kernel,

Visualising concordance

Concordance coefficients, Graphical methods, Ranks, Parallel coordinates, Polar coordinates, Bubble plot, Pin-cushion plot,