forqs: Forward-in-time Simulation of Recombination, Quantitative Traits, and Selection

forqs is a forward-in-time simulation of recombination, quantitative traits, and selection. It was designed to investigate haplotype patterns resulting from scenarios where substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on polygenic quantitative traits. forqs is implemented as a command- line C++ program. Source code and binary executables for Linux, OSX, and Windows are freely available under a permissive BSD license.Comment: preprint include Supplementary Information. https://bitbucket.org/dkessner/forq

Analysis and Simulation Methods for Artificial Selection Experiments in the Investigation of the Genetic Basis of Complex Traits

One of the fundamental goals of research in modern genetics is to determine the genetic basis for complex traits. One experimental approach to this problem is artificial selection, where individual organisms are selected each generation for extreme values of the trait under study. In these experiments, the investigator identifies putative trait loci based on genetic differentiation in evolved populations. Recently, researchers have combined artificial selection with genome- wide pooled massively parallel sequencing to identify quantitative trait loci. In this dissertation, I present analysis and simulation methods applicable to pooled sequencing and artificial selection experiments.In Chapter 1, I provide some background on artificial selection, massively parallel sequencing, and the use of simulations in population genetics. In Chapter 2, I present an expectation-maximization (EM) algorithm for estimating haplotype frequencies in a pooled sample directly from mapped sequence reads, in the case where the possible haplotypes are known. This method is relevant to the analysis of pooled sequencing data from selection experiments, in addition to the calculation of proportions of different specieswithin a metagenomics sample. The method outperforms existing methods based on single-site allele frequencies, as well as simple approaches using sequence read data. I have implemented the method in a freely available open-source software tool called harp (Haplotype Analysis of Reads in Pools). In Appendix A, I present additional analyses to show that the method improves estimates of relative abundances and community diversity at higher taxon levels.In Chapter 3, I present a new forward-in-time simulator forqs (Forward-in-time simulation of Recombination, Quantitative traits, and Selection). forqs was designed to investigate haplotype patterns resulting from scenarios where substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on polygenic quantitative traits. The simulator uses a memory-efficient representation of chromosomes that allows the simulation of whole genomes. In addition, forqs explicitly models quantitative traits, and its modular design gives the user great flexibility in specifying trait architectures, selection and demography.In Chapter 4, I present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci (QTLs), using the forqs simulator from Chapter 3. I show that modeling loci with constant selection coefficients does not fully capture the dynamics of QTLs under artificial selection. I also show that a substantial portion of the genetic variance of the trait (50-100%) can be explained by detected QTLs in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, I show that the power to detect and localize QTLs depends crucially on the opportunity for recombination during the experiment. Finally, I show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates (using the harp method from Chapter 2)

Analysis and Simulation Methods for Artificial Selection Experiments in the Investigation of the Genetic Basis of Complex Traits

One of the fundamental goals of research in modern genetics is to determine the genetic basis for complex traits. One experimental approach to this problem is artificial selection, where individual organisms are selected each generation for extreme values of the trait under study. In these experiments, the investigator identifies putative trait loci based on genetic differentiation in evolved populations. Recently, researchers have combined artificial selection with genome- wide pooled massively parallel sequencing to identify quantitative trait loci. In this dissertation, I present analysis and simulation methods applicable to pooled sequencing and artificial selection experiments.In Chapter 1, I provide some background on artificial selection, massively parallel sequencing, and the use of simulations in population genetics. In Chapter 2, I present an expectation-maximization (EM) algorithm for estimating haplotype frequencies in a pooled sample directly from mapped sequence reads, in the case where the possible haplotypes are known. This method is relevant to the analysis of pooled sequencing data from selection experiments, in addition to the calculation of proportions of different specieswithin a metagenomics sample. The method outperforms existing methods based on single-site allele frequencies, as well as simple approaches using sequence read data. I have implemented the method in a freely available open-source software tool called harp (Haplotype Analysis of Reads in Pools). In Appendix A, I present additional analyses to show that the method improves estimates of relative abundances and community diversity at higher taxon levels.In Chapter 3, I present a new forward-in-time simulator forqs (Forward-in-time simulation of Recombination, Quantitative traits, and Selection). forqs was designed to investigate haplotype patterns resulting from scenarios where substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on polygenic quantitative traits. The simulator uses a memory-efficient representation of chromosomes that allows the simulation of whole genomes. In addition, forqs explicitly models quantitative traits, and its modular design gives the user great flexibility in specifying trait architectures, selection and demography.In Chapter 4, I present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci (QTLs), using the forqs simulator from Chapter 3. I show that modeling loci with constant selection coefficients does not fully capture the dynamics of QTLs under artificial selection. I also show that a substantial portion of the genetic variance of the trait (50-100%) can be explained by detected QTLs in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, I show that the power to detect and localize QTLs depends crucially on the opportunity for recombination during the experiment. Finally, I show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates (using the harp method from Chapter 2)

An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people.

Rare genetic variants contribute to complex disease risk; however, the abundance of rare variants in human populations remains unknown. We explored this spectrum of variation by sequencing 202 genes encoding drug targets in 14,002 individuals. We find rare variants are abundant (1 every 17 bases) and geographically localized, so that even with large sample sizes, rare variant catalogs will be largely incomplete. We used the observed patterns of variation to estimate population growth parameters, the proportion of variants in a given frequency class that are putatively deleterious, and mutation rates for each gene. We conclude that because of rapid population growth and weak purifying selection, human populations harbor an abundance of rare variants, many of which are deleterious and have relevance to understanding disease risk

A cross-platform toolkit for mass spectrometry and proteomics

To the Editor: Mass spectrometry–based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2 and characterize protein complexes3. Despite successes, the interpretation of vast proteomics data