Local ancestry corrects for population structure in Saccharomyces cerevisiae genome-wide association studies

High-throughput approaches to functional characterization of genetic variation in yeast

Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.

Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations

With the advent of high throughput sequencing technologies, genome-wide association studies (GWAS) have become a powerful paradigm for dissecting the genetic origins of the observed phenotypic variation. We recently completely sequenced the genome of 1011 Saccharomyces cerevisiae isolates, laying a strong foundation for GWAS. To assess the feasibility and the limits of this approach, we performed extensive simulations using five selected subpopulations as well as the total set of 1011 genomes. We measured the ability to detect the causal genetic variants involved in Mendelian and more complex traits using a linear mixed model approach. The results showed that population structure is well accounted for and is not the main problem when the sample size is high enough. While the genetic determinant of a Mendelian trait is easily mapped in all studied subpopulations, discrepancies are seen between datasets when performing GWAS on a complex trait in terms of detection, false positive and false negative rate. Finally, we performed GWAS on the different defined subpopulations using a real quantitative trait (resistance to copper sulfate) and showed the feasibility of this approach. The performance of each dataset depends simultaneously on several factors such as sample size, relatedness and population evolutionary history.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Genomic, Transcriptomic and Epigenomic Tools to Study the Domestication of Plants and Animals: A Field Guide for Beginners

In the last decade, genomics and the related fields of transcriptomics and epigenomics have revolutionized the study of the domestication process in plants and animals, leading to new discoveries and new unresolved questions. Given that some domesticated taxa have been more studied than others, the extent of genomic data can range from vast to nonexistent, depending on the domesticated taxon of interest. This review is meant as a rough guide for students and academics that want to start a domestication research project using modern genomic tools, as well as for researchers already conducting domestication studies that are interested in following a genomic approach and looking for alternate strategies (cheaper or more efficient) and future directions. We summarize the theoretical and technical background needed to carry out domestication genomics, starting from the acquisition of a reference genome and genome assembly, to the sampling design for population genomics, paleogenomics, transcriptomics, epigenomics and experimental validation of domestication-related genes. We also describe some examples of the aforementioned approaches and the relevant discoveries they made to understand the domestication of the studied taxa.

Fine-Scale Inference of Ancestry Segments Without Prior Knowledge of Admixing Groups

We present an algorithm for inferring ancestry segments and characterizing admixture events, which involve an arbitrary number of genetically differentiated groups coming together. This allows inference of the demographic history of the species, properties of admixing groups, identification of signatures of natural selection, and may aid disease gene mapping. The algorithm employs nested hidden Markov models to obtain local ancestry estimation along the genome for each admixed individual. In a range of simulations, the accuracy of these estimates equals or exceeds leading existing methods. Moreover, and unlike these approaches, we do not require any prior knowledge of the relationship between subgroups of donor reference haplotypes and the unseen mixing ancestral populations. Our approach infers these in terms of conditional "copying probabilities." In application to the Human Genome Diversity Project, we corroborate many previously inferred admixture events (e.g., an ancient admixture event in the Kalash). We further identify novel events such as complex four-way admixture in San-Khomani individuals, and show that Eastern European populations possess [Formula: see text] ancestry from a group resembling modern-day central Asians. We also identify evidence of recent natural selection favoring sub-Saharan ancestry at the human leukocyte antigen (HLA) region, across North African individuals. We make available an R and C++ software library, which we term MOSAIC (which stands for MOSAIC Organizes Segments of Ancestry In Chromosomes).

Fine Scale Genomic Signals of Admixture and Alien Introgression among Asian Rice Landraces

Modern rice cultivars are adapted to a range of environmental conditions and human preferences. At the root of this diversity is a marked genetic structure, owing to multiple foundation events. Admixture and recurrent introgression from wild sources have played upon this base to produce the myriad adaptations existing today. Genome-wide studies bring support to this idea, but understanding the history and nature of particular genetic adaptations requires the identification of specific patterns of genetic exchange. In this study, we explore the patterns of haplotype similarity along the genomes of a subset of rice cultivars available in the 3,000 Rice Genomes data set. We begin by establishing a custom method of classification based on a combination of dimensionality reduction and kernel density estimation. Through simulations, the behavior of this classifier is studied under scenarios of varying genetic divergence, admixture, and alien introgression. Finally, the method is applied to local haplotypes along the genome of a Core set of Asian Landraces. Taking the Japonica, Indica, and cAus groups as references, we find evidence of reciprocal introgressions covering 2.6% of reference genomes on average. Structured signals of introgression among reference accessions are discussed. We extend the analysis to elucidate the genetic structure of the group circum-Basmati: we delimit regions of Japonica, cAus, and Indica origin, as well as regions outlier to these groups (13% on average). Finally, the approach used highlights regions of partial to complete loss of structure that can be attributed to selective pressures during domestication.

Genome-wide association across Saccharomyces cerevisiae strains reveals substantial variation in underlying gene requirements for toxin tolerance

Cellulosic plant biomass is a promising sustainable resource for generating alternative biofuels and biochemicals with microbial factories. But a remaining bottleneck is engineering microbes that are tolerant of toxins generated during biomass processing, because mechanisms of toxin defense are only beginning to emerge. Here, we exploited natural diversity in 165 Saccharomyces cerevisiae strains isolated from diverse geographical and ecological niches, to identify mechanisms of hydrolysate-toxin tolerance. We performed genome-wide association (GWA) analysis to identify genetic variants underlying toxin tolerance, and gene knockouts and allele-swap experiments to validate the involvement of implicated genes. In the process of this work, we uncovered a surprising difference in genetic architecture depending on strain background: in all but one case, knockout of implicated genes had a significant effect on toxin tolerance in one strain, but no significant effect in another strain. In fact, whether or not the gene was involved in tolerance in each strain background had a bigger contribution to strain-specific variation than allelic differences. Our results suggest a major difference in the underlying network of causal genes in different strains, suggesting that mechanisms of hydrolysate tolerance are very dependent on the genetic background. These results could have significant implications for interpreting GWA results and raise important considerations for engineering strategies for industrial strain improvement.

Understanding the genetic architecture of complex traits is important for elucidating the genotype-phenotype relationship. Many studies have sought genetic variants that underlie phenotypic variation across individuals, both to implicate causal variants and to inform on architecture. Here we used genome-wide association analysis to identify genes and processes involved in tolerance of toxins found in plant-biomass hydrolysate, an important substrate for sustainable biofuel production. We found substantial variation in whether or not individual genes were important for tolerance across genetic backgrounds. Whether or not a gene was important in a given strain background explained more variation than the alleleic differences in the gene. These results suggest substantial variation in gene contributions, and perhaps underlying mechanisms, of toxin tolerance.

Gene-gene and gene-environment interactions in complex traits in yeast

One of the fundamental questions in biology is how the genotype regulates the phenotype. An increasing number of studies indicate that, in most cases, the effect of a genetic locus on the phenotype is context-dependent, i.e. it is influenced by the genetic background and the environment in which the phenotype is measured. Still, the majority of the studies, in both model organisms and humans, that map the genetic regulation of phenotypic variation in complex traits primarily identify additive loci with independent effects. This does not reflect an absence of the contribution of genetic interactions to phenotypic variation, but instead is a consequence of the technical limitations in mapping gene-gene interactions (GGI) and gene-environment interactions (GEI). Yeast, with its detailed molecular understanding, diverse population genomics and ease of genetic manipulation, is a unique and powerful resource to study the contributions of GGI and GEI in the regulation of phenotypic variation. Here we review studies in yeast that have identified GGI and GEI that regulate phenotypic variation, and discuss the contribution of these findings in explaining missing heritability of complex traits, and how observations from these GGI and GEI studies enhance our understanding of the mechanisms underlying genetic robustness and adaptability that shape the architecture of the genotype-phenotype map.

Deciphering the Genic Basis of Yeast Fitness Variation by Simultaneous Forward and Reverse Genetics

The budding yeast Saccharomyces cerevisiae is the best studied eukaryote in molecular and cell biology, but its utility for understanding the genetic basis of phenotypic variation in natural populations is limited by inefficient association mapping due to strong and complex population structure. To overcome this challenge, we generated genome sequences for 85 strains and performed a comprehensive population genomic survey of a total of 190 diverse strains. We identified considerable variation in population structure among chromosomes and identified 181 genes that are absent from the reference genome. Many of these nonreference genes are expressed and we functionally confirmed that two of these genes confer increased resistance to antifungals. Next, we simultaneously measured the growth rates of over 4,500 laboratory strains, each of which lacks a nonessential gene, and 81 natural strains across multiple environments using unique DNA barcode present in each strain. By combining the genome-wide reverse genetic information gained from the gene deletion strains with a genome-wide association analysis from the natural strains, we identified genomic regions associated with fitness variation in natural populations. To experimentally validate a subset of these associations, we used reciprocal hemizygosity tests, finding that while the combined forward and reverse genetic approaches can identify a single causal gene, the phenotypic consequences of natural genetic variation often follow a complicated pattern. The resources and approach provided outline an efficient and reliable route to association mapping in yeast and significantly enhance its value as a model for understanding the genetic mechanisms underlying phenotypic variation and evolution in natural populations.

Budding Yeast Strains and Genotype-Phenotype Mapping

A small number of well-studied laboratory strains of Saccharomyces cerevisiae, mostly derived from S288C, are used in yeast research. Although powerful, studies for understanding S288C do not always capture the phenotypic essence or the genetic complexity of S. cerevisiae biology. This is particularly problematic for multilocus phenotypes identified in laboratory strains because these loci have never been jointly exposed to natural selection and the corresponding phenotypes do not represent optimization for any particular purpose or environment. Isolation and sequencing of new natural yeast strains also reveal that the total sequence diversity of the S. cerevisiae global population is poorly sampled in common laboratory strains. Here we discuss methodologies required for using the natural genetic variation in yeast to complete a genotype-phenotype map.

Genetic Analysis of Complex Traits in Saccharomyces cerevisiae

Defining the relationship between genotype and phenotype is a central challenge in biology. A powerful approach to this problem is to determine the genetic architecture and molecular basis of phenotypic differences among genetically diverse individuals. Saccharomyces cerevisiae is an important model system for such work. Current genetic mapping approaches for this species exploit high-throughput phenotyping and sequencing to facilitate the detection of a large fraction of the genomic loci that underlie trait variation among isolates. Once identified, several methods exist to determine the specific genes and genetic variants that underlie these loci and cause phenotypic variations. In this introduction, we provide a brief overview of research on complex traits in yeast and discuss different genetic mapping approaches applied to yeast studies.

Population genomics of yeasts: towards a comprehensive view across a broad evolutionary scale

With the advent of high-throughput technologies for sequencing, the complete description of the genetic variation that occurs in populations, also known as population genomics, is foreseeable but far from being reached. Explaining the forces that govern patterns of genetic variation is essential to elucidate the evolutionary history of species. Genetic variation results from a wide assortment of evolutionary forces, among which mutation, selection, recombination and drift play major roles in shaping genomes. In addition, exploring the genetic variation within a population also corresponds to the first step towards dissecting the genotype-phenotype relationship. In this context, yeast species are of particular interest because they represent a unique resource for studying the evolution of intraspecific genetic diversity in a phylum spanning a broad evolutionary scale. Here, we briefly review recent progress in yeast population genomics and provide some perspective on this rapidly evolving field. In fact, we truly believe that it is of interest to supplement comparative and early population genomic studies with the deep sequencing of more extensive sets of individuals from the same species. In parallel, it would be more than valuable to uncover the intraspecific variation of a large number of unexplored species, including those that are closely and more distantly related. Altogether, these data would enable substantially more powerful genomic scans for functional dissection.

The 100-genomes strains, an S. cerevisiae resource that illuminates its natural phenotypic and genotypic variation and emergence as an opportunistic pathogen

Saccharomyces cerevisiae, a well-established model for species as diverse as humans and pathogenic fungi, is more recently a model for population and quantitative genetics. S. cerevisiae is found in multiple environments—one of which is the human body—as an opportunistic pathogen. To aid in the understanding of the S. cerevisiae population and quantitative genetics, as well as its emergence as an opportunistic pathogen, we sequenced, de novo assembled, and extensively manually edited and annotated the genomes of 93 S. cerevisiae strains from multiple geographic and environmental origins, including many clinical origin strains. These 93 S. cerevisiae strains, the genomes of which are near-reference quality, together with seven previously sequenced strains, constitute a novel genetic resource, the “100-genomes” strains. Our sequencing coverage, high-quality assemblies, and annotation provide unprecedented opportunities for detailed interrogation of complex genomic loci, examples of which we demonstrate. We found most phenotypic variation to be quantitative and identified population, genotype, and phenotype associations. Importantly, we identified clinical origin associations. For example, we found that an introgressed PDR5 was present exclusively in clinical origin mosaic group strains; that the mosaic group was significantly enriched for clinical origin strains; and that clinical origin strains were much more copper resistant, suggesting that copper resistance contributes to fitness in the human host. The 100-genomes strains are a novel, multipurpose resource to advance the study of S. cerevisiae population genetics, quantitative genetics, and the emergence of an opportunistic pathogen.

A population genetic signal of polygenic adaptation

Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.

The process of adaptation is of fundamental importance in evolutionary biology. Within the last few decades, genotyping technologies and new statistical methods have given evolutionary biologists the ability to identify individual regions of the genome that are likely to have been important in this process. When adaptation occurs in traits that are underwritten by many genes, however, the genetic signals left behind are more diffuse, and no individual region of the genome is likely to show strong signatures of selection. Identifying this signature therefore requires a detailed annotation of sites associated with a particular phenotype. Here we develop and implement a suite of statistical methods to integrate this sort of annotation from genome wide association studies with allele frequency data from many populations, providing a powerful way to identify the signal of adaptation in polygenic traits. We apply our methods to test for the impact of selection on human height, skin pigmentation, body mass index, type 2 diabetes risk, and inflammatory bowel disease risk. We find relatively strong signals for height and skin pigmentation, moderate signals for inflammatory bowel disease, and comparatively little evidence for body mass index and type 2 diabetes risk.

Insights into molecular evolution from yeast genomics

Enabled by comparative genomics, yeasts have increasingly developed into a powerful model system for molecular evolution. Here we survey several areas in which yeast studies have made important contributions, including regulatory evolution, gene duplication and divergence, evolution of gene order and evolution of complexity. In each area we highlight key studies and findings based on techniques ranging from statistical analysis of large datasets to direct laboratory measurements of fitness. Future work will combine traditional evolutionary genetics analysis and experimental evolution with tools from systems biology to yield mechanistic insight into complex phenotypes.

Genome-wide mapping of cellular traits using yeast

Yeast has long enjoyed superiority as a genetic model because of its short generation time and ease of generating alleles for genetic analysis. However, recent developments of guided nucleases for genome editing in higher eukaryotes, and funding pressures for translational findings, force all model organism communities to reaffirm and rearticulate the advantages of their chosen creature. Here I examine the utility of budding yeast for understanding the genetic basis of cellular traits, using natural variation as well as classical genetic perturbations, and its future prospects compared to undertaking the work in human cell lines. Will yeast remain central, or will it join the likes of phage as an early model that is no longer widely used to answer the pressing questions?

Sporulation genes associated with sporulation efficiency in natural isolates of yeast

Yeast sporulation efficiency is a quantitative trait and is known to vary among experimental populations and natural isolates. Some studies have uncovered the genetic basis of this variation and have identified the role of sporulation genes (IME1, RME1) and sporulation-associated genes (FKH2, PMS1, RAS2, RSF1, SWS2), as well as non-sporulation pathway genes (MKT1, TAO3) in maintaining this variation. However, these studies have been done mostly in experimental populations. Sporulation is a response to nutrient deprivation. Unlike laboratory strains, natural isolates have likely undergone multiple selections for quick adaptation to varying nutrient conditions. As a result, sporulation efficiency in natural isolates may have different genetic factors contributing to phenotypic variation. Using Saccharomyces cerevisiae strains in the genetically and environmentally diverse SGRP collection, we have identified genetic loci associated with sporulation efficiency variation in a set of sporulation and sporulation-associated genes. Using two independent methods for association mapping and correcting for population structure biases, our analysis identified two linked clusters containing 4 non-synonymous mutations in genes - HOS4, MCK1, SET3, and SPO74. Five regulatory polymorphisms in five genes such as MLS1 and CDC10 were also identified as putative candidates. Our results provide candidate genes contributing to phenotypic variation in the sporulation efficiency of natural isolates of yeast.