Optimizing novel methods for dissecting complex traits

Evolution acts on variation within species where qualitative variation, like eye color, and quantitative variation, like body size, can be distinguished. It is a major aim in biology to identify the molecular basis of this variation, that is to identify the genetic mutations that are responsible for differences between individuals. Such an enhanced understanding of variation will, for example, help to improve the yield of crop plants and allow to custom tailor medical treatment to the unique genetic make-up of a patient. While the genetic basis of most qualitative traits could be readily identified, revealing the genetic basis of quantitative traits remains a challenge, some even argue the major challenge for biology in the 21th century. Novel approaches for mapping quantitative traits will help to meet this challenge. Recently, due to novel sequencing technologies, two new approaches for identifying the molecular basis of quantitative traits became feasible. In Evolve and Resequencing (E&R) studies molecular changes in experimentally evolving populations are monitored and with Pool-GWAS the genetic make-up of two groups of individuals, showing the most pronounced differences for a trait of interest (e.g. short versus tall specimens), is compared. However, it is not known if these novel methods are actually any better than previously used methods (e.g. GWAS) and how the design of these methods can be improved. Using massive computer simulations I propose to thoroughly evaluate the strength and weaknesses of E&R and Pool-GWAS relative to previous approaches and to provide guidelines for an improved design of these two methods. The results of this work will allow researchers to choose the most suitable approach for identifying the molecular basis of a quantitative trait of interest and thus to meet the major challenge for biology in the 21th century.

Evolution acts on variation within species where qualitative variation, like eye color, and quantitative variation, like body size, can be distinguished. It is a major aim in biology to identify the molecular basis of this variation, that is to identify the genetic mutations that are responsible for differences between individuals. Such an enhanced understanding of variation will, for example, help to improve the yield of crop plants and allow us to custom tailor medical treatment to the unique genetic make-up of a patient. While the genetic basis of most qualitative traits could be readily identified, revealing the genetic basis of quantitative traits remains a challenge, some even argue the major challenge for biology in the 21th century. Novel approaches for mapping quantitative traits will help to meet this challenge. Recently, due to the advent of novel sequencing technologies, a new approaches for identifying the molecular basis of quantitative traits became feasible. In Evolve and Resequencing (E&R) studies molecular changes in experimentally evolving populations are monitored. However, it is not known if this novel method is actually any better than available methods (e.g. GWAS) and how the design of this method can be improved. Using massive computer simulations we show that E&R is a powerful novel approach for finding the genetic basis of quantitative traits. Especially when an optimized selection regime is used, where for example 90% of the individuals are selected at the beginning of the experiment and 20% at the end, E&R studies may have a better performance than GWAS. Additionally, E&R studies avoid some of the limitations of GWAS. For example, E&R identifies more weak effect loci and more loci segregating at low frequency than GWAS. However, E&R also has some limitations such as a low power to identify loci segregating at high frequency. Finally, we identified test statistics that maximize the performance of E&R studies and developed a novel software for performing genome-wide forward simulations of evolving populations, i.e. MimicrEE2.