1
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Boocock J, Alexander N, Alamo Tapia L, Walter-McNeill L, Patel SP, Munugala C, Bloom JS, Kruglyak L. Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction. eLife 2025; 13:RP95566. [PMID: 40073070 PMCID: PMC11903034 DOI: 10.7554/elife.95566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2025] Open
Abstract
Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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Affiliation(s)
- James Boocock
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
| | - Noah Alexander
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
| | - Leslie Alamo Tapia
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
| | - Laura Walter-McNeill
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
| | - Shivani Prashant Patel
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
| | - Chetan Munugala
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Department of Biological Chemistry, University of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
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2
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Gervais O, Nagamine Y. Comparing genomic studies in animal breeding and human genetics: focus on disease-related traits in livestock - A review. Anim Biosci 2025; 38:189-197. [PMID: 39483033 PMCID: PMC11725742 DOI: 10.5713/ab.24.0487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Revised: 08/08/2024] [Accepted: 09/05/2024] [Indexed: 11/03/2024] Open
Abstract
Genomic studies of diseases can be divided into two types: i) analyses that reveal causal genes by focusing on linkage disequilibrium between observed and causal variants and ii) those that simultaneously assess numerous genetic markers to estimate the polygenic effects of a particular genomic region or entire genome. The field of human genetics has emphasized the discovery of causal genes, but these represent only a fraction of the total genetic variance. Therefore, alternative approaches, such as the polygenic risk score, which estimates the genetic risk for a given trait or disease based on all genetic markers (rather than on known causal variants only), have begun to garner attention. In many respects, these human genetic methods are similar to those originally developed for the estimation of breeding values (i.e., total additive genetic effects) in livestock. However, despite these similarities in methods, the fields of human and animal genetics still differ markedly in terms of research objectives, target populations, and other characteristics. For example, livestock populations have continually been selected and inbred throughout their history; consequently, their effective population size has shrunk and preferred genes (such as those influencing disease resistance and production traits) have accumulated in the modern breeding populations. By examining the characteristics of these two fields, particularly from the perspectives of disease and disease resistance, this review aims to improve understanding of the intrinsic differences between genomic studies using human compared with livestock populations.
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Affiliation(s)
- Olivier Gervais
- College of International Relations, Nihon University, Mishima, Shizuoka 411-8555,
Japan
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Sakyoku, Kyoto 606-8507,
Japan
| | - Yoshitaka Nagamine
- College of Bioresource Sciences, Nihon University, Fujisawa, Kanagawa 252-0880,
Japan
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3
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Avery RR, Collins MA, Albert FW. Genotype-by-environment interactions shape ubiquitin-proteasome system activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.21.624644. [PMID: 39605480 PMCID: PMC11601593 DOI: 10.1101/2024.11.21.624644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
In genotype-by-environment interactions (GxE), the effect of a genetic variant on a trait depends on the environment. GxE influences numerous organismal traits across eukaryotic life. However, we have a limited understanding of how GxE shapes the molecular processes that give rise to organismal traits. Here, we characterized how GxE shapes protein degradation, an essential molecular process that influences numerous aspects of cellular and organismal physiology. Using the yeast Saccharomyces cerevisiae, we characterized GxE in the activity of the ubiquitin-proteasome system (UPS), the primary protein degradation system in eukaryotes. By mapping genetic influences on the degradation of six substrates that engage multiple distinct UPS pathways across eight diverse environments, we discovered extensive GxE in the genetics of UPS activity. Hundreds of locus effects on UPS activity varied depending on the substrate, the environment, or both. Most of these cases corresponded to loci that were present in one environment but not another ("presence / absence" GxE), while a smaller number of loci had opposing effects in different environments ("sign change" GxE). The number of loci exhibiting GxE, their genomic location, and the type of GxE (presence / absence or sign change) varied across UPS substrates. Loci exhibiting GxE were clustered at genomic regions that contain core UPS genes and especially at regions containing variation that affects the expression of thousands of genes, suggesting indirect contributions to UPS activity. Our results reveal highly complex interactions at the level of substrates and environments in the genetics of protein degradation.
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Affiliation(s)
- Randi R Avery
- Department of Genetics, Cell Biology, & Genetics, University of Minnesota, Minneapolis, MN, USA
| | - Mahlon A Collins
- Department of Genetics, Cell Biology, & Genetics, University of Minnesota, Minneapolis, MN, USA
| | - Frank W Albert
- Department of Genetics, Cell Biology, & Genetics, University of Minnesota, Minneapolis, MN, USA
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4
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Boocock J, Alexander N, Tapia LA, Walter-McNeill L, Patel SP, Munugala C, Bloom JS, Kruglyak L. Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.07.570640. [PMID: 38106186 PMCID: PMC10723400 DOI: 10.1101/2023.12.07.570640] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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Affiliation(s)
- James Boocock
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Noah Alexander
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leslie Alamo Tapia
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Laura Walter-McNeill
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Shivani Prashant Patel
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Chetan Munugala
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
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Aguilar-Rodríguez J, Vila J, Chen SAA, Razo-Mejia M, Ghosh O, Fraser HB, Jarosz DF, Petrov DA. Massively parallel experimental interrogation of natural variants in ancient signaling pathways reveals both purifying selection and local adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.30.621178. [PMID: 39553990 PMCID: PMC11565963 DOI: 10.1101/2024.10.30.621178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2024]
Abstract
The nature of standing genetic variation remains a central debate in population genetics, with differing perspectives on whether common variants are mostly neutral or have functional effects. We address this question by directly mapping the fitness effects of over 9,000 natural variants in the Ras/PKA and TOR/Sch9 pathways-key regulators of cell proliferation in eukaryotes-across four conditions in Saccharomyces cerevisiae. While many variants are neutral in our assay, on the order of 3,500 exhibited significant fitness effects. These non-neutral variants tend to be missense and affect conserved, more densely packed, and less solvent-exposed protein regions. They are also typically younger, occur at lower frequencies, and more often found in heterozygous states, suggesting they are subject to purifying selection. A substantial fraction of non-neutral variants showing strong fitness effects in our experiments, however, is present at high frequencies in the population. These variants show signs of local adaptation as they tend to be found specifically in domesticated strains adapted to human-made environments. Our findings support the view that while common variants are often neutral, a significant proportion have adaptive functional consequences and are driven into the population by local positive selection. This study highlights the potential to explore the functional effects of natural genetic variation on a genome scale with quantitative fitness measurements in the laboratory, bridging the gap between population genetics and functional genomics to understand evolutionary dynamics in the wild.
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Affiliation(s)
- José Aguilar-Rodríguez
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jean Vila
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Shi-An A. Chen
- Department of Biology, Stanford University, Stanford, CA, USA
- Present address: Altos Labs, Bay Area Institute of Science, Redwood City, CA, USA
| | | | - Olivia Ghosh
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Physics, Stanford University, Stanford, CA
| | | | - Dan F. Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA
| | - Dmitri A. Petrov
- Department of Biology, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
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Chen J, Jia Y, Zhong J, Zhang K, Dai H, He G, Li F, Zeng L, Fan C, Xu H. Novel mutation leading to splice donor loss in a conserved site of DMD gene causes Duchenne muscular dystrophy with cryptorchidism. J Med Genet 2024; 61:741-749. [PMID: 38621993 PMCID: PMC11287555 DOI: 10.1136/jmg-2024-109896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/04/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND As one of the most common congenital abnormalities in male births, cryptorchidism has been found to have a polygenic aetiology according to previous studies of common variants. However, little is known about genetic predisposition of rare variants for cryptorchidism, since rare variants have larger effective size on diseases than common variants. METHODS In this study, a cohort of 115 Chinese probands with cryptorchidism was analysed using whole-genome sequencing, alongside 19 parental controls and 2136 unaffected men. Additionally, CRISPR-Cas9 editing of a conserved variant was performed in a mouse model, with MRI screening used to observe the phenotype. RESULTS In 30 of 115 patients (26.1%), we identified four novel genes (ARSH, DMD, MAGEA4 and SHROOM2) affecting at least five unrelated patients and four known genes (USP9Y, UBA1, BCORL1 and KDM6A) with the candidate rare pathogenic variants affecting at least two cases. Burden tests of rare variants revealed the genome-wide significances for newly identified genes (p<2.5×10-6) under the Bonferroni correction. Surprisingly, novel and known genes were mainly found on X chromosome (seven on X and one on Y) and all rare X-chromosomal segregating variants exhibited a maternal inheritance rather than de novo origin. CRISPR-Cas9 mouse modelling of a splice donor loss variant in DMD (NC_000023.11:g.32454661C>G), which resides in a conserved site across vertebrates, replicated bilateral cryptorchidism phenotypes, confirmed by MRI at 4 and 10 weeks. The movement tests further revealed symptoms of Duchenne muscular dystrophy (DMD) in transgenic mice. CONCLUSION Our results revealed the role of the DMD gene mutation in causing cryptorchidism. The results also suggest that maternal-X inheritance of pathogenic defects could have a predominant role in the development of cryptorchidism.
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Affiliation(s)
- Jianhai Chen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
- Department of Ecology and Evolution, The University of Chicago, Chicago, Illinois, USA
| | - Yangying Jia
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
- Department of Chemistry, The University of Chicago, Chicago, Illinois, USA
| | - Jie Zhong
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Kun Zhang
- Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hongzheng Dai
- Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Guanglin He
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Fuping Li
- Laboratory of Molecular Translational Medicine, Center for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li Zeng
- Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chuanzhu Fan
- Department of Biological Sciences, Wayne State University, Detroit, Michigan, USA
| | - Huayan Xu
- Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
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7
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. Mol Syst Biol 2024; 20:362-373. [PMID: 38355920 PMCID: PMC10987670 DOI: 10.1038/s44320-024-00021-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
- Institut Universitaire de France (IUF), Paris, France.
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8
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Roy KR, Smith JD, Li S, Vonesch SC, Nguyen M, Burnett WT, Orsley KM, Lee CS, Haber JE, St Onge RP, Steinmetz LM. Dissecting quantitative trait nucleotides by saturation genome editing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.577784. [PMID: 38352467 PMCID: PMC10862795 DOI: 10.1101/2024.02.02.577784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Genome editing technologies have the potential to transform our understanding of how genetic variation gives rise to complex traits through the systematic engineering and phenotypic characterization of genetic variants. However, there has yet to be a system with sufficient efficiency, fidelity, and throughput to comprehensively identify causal variants at the genome scale. Here we explored the ability of templated CRISPR editing systems to install natural variants genome-wide in budding yeast. We optimized several approaches to enhance homology-directed repair (HDR) with donor DNA templates, including donor recruitment to target sites, single-stranded donor production by bacterial retrons, and in vivo plasmid assembly. We uncovered unique advantages of each system that we integrated into a single superior system named MAGESTIC 3.0. We used MAGESTIC 3.0 to dissect causal variants residing in 112 quantitative trait loci across 32 environmental conditions, revealing an enrichment for missense variants and loci with multiple causal variants. MAGESTIC 3.0 will facilitate the functional analysis of the genome at single-nucleotide resolution and provides a roadmap for improving template-based genome editing systems in other organisms.
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Affiliation(s)
- Kevin R Roy
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Justin D Smith
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Shengdi Li
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Sibylle C Vonesch
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
- Laboratory for Genome Editing and Systems Genetics, VIB-KU Leuven Center for Microbiology, Leuven, Belgium
- KU Leuven Center for Microbial and Plant Genetics, Department M2S, Leuven, Belgium
| | - Michelle Nguyen
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Wallace T Burnett
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Kevin M Orsley
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Cheng-Sheng Lee
- Brandeis University, Rosenstiel Basic Medical Sciences Research Center and Department of Biology, Waltham, MA
| | - James E Haber
- Brandeis University, Rosenstiel Basic Medical Sciences Research Center and Department of Biology, Waltham, MA
| | - Robert P St Onge
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, California, USA
| | - Lars M Steinmetz
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
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9
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Tsouris A, Fournier T, Friedrich A, Hou J, Dunham MJ, Schacherer J. Species-wide survey of the expressivity and complexity spectrum of traits in yeast. PLoS Genet 2024; 20:e1011119. [PMID: 38236897 PMCID: PMC10826966 DOI: 10.1371/journal.pgen.1011119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/30/2024] [Accepted: 01/02/2024] [Indexed: 01/31/2024] Open
Abstract
Assessing the complexity and expressivity of traits at the species level is an essential first step to better dissect the genotype-phenotype relationship. As trait complexity behaves dynamically, the classic dichotomy between monogenic and complex traits is too simplistic. However, no systematic assessment of this complexity spectrum has been carried out on a population scale to date. In this context, we generated a large diallel hybrid panel composed of 190 unique hybrids coming from 20 natural isolates representative of the S. cerevisiae genetic diversity. For each of these hybrids, a large progeny of 160 individuals was obtained, leading to a total of 30,400 offspring individuals. Their mitotic growth was evaluated on 38 conditions inducing various cellular stresses. We developed a classification algorithm to analyze the phenotypic distributions of offspring and assess the trait complexity. We clearly found that traits are mainly complex at the population level. On average, we found that 91.2% of cross/trait combinations exhibit high complexity, while monogenic and oligogenic cases accounted for only 4.1% and 4.7%, respectively. However, the complexity spectrum is very dynamic, trait specific and tightly related to genetic backgrounds. Overall, our study provided greater insight into trait complexity as well as the underlying genetic basis of its spectrum in a natural population.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Téo Fournier
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Maitreya J. Dunham
- Genome Sciences Department, University of Washington, Seattle, Washington, United States of America
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
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10
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Meyer CT, Lynch GK, Stamo DF, Miller EJ, Chatterjee A, Kralj JM. A high-throughput and low-waste viability assay for microbes. Nat Microbiol 2023; 8:2304-2314. [PMID: 37919425 PMCID: PMC10686820 DOI: 10.1038/s41564-023-01513-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 10/03/2023] [Indexed: 11/04/2023]
Abstract
Counting viable cells is a universal practice in microbiology. The colony-forming unit (CFU) assay has remained the gold standard to measure viability across disciplines, but it is time-intensive and resource-consuming. Here we describe the geometric viability assay (GVA) that replicates CFU measurements over 6 orders of magnitude while reducing over 10-fold the time and consumables required. GVA computes a sample's viable cell count on the basis of the distribution of embedded colonies growing inside a pipette tip. GVA is compatible with Gram-positive and Gram-negative planktonic bacteria (Escherichia coli, Pseudomonas aeruginosa and Bacillus subtilis), biofilms and fungi (Saccharomyces cerevisiae). Laborious CFU experiments such as checkerboard assays, treatment time-courses and drug screens against slow-growing cells are simplified by GVA. The ease and low cost of GVA evinces that it can replace existing viability assays and enable viability measurements at previously impractical scales.
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Affiliation(s)
- Christian T Meyer
- Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA.
- Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
- Antimicrobial Regeneration Consortium (ARC) Labs, Louisville, CO, USA.
- Duet Biosystems, Nashville, CO, USA.
| | - Grace K Lynch
- Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Dana F Stamo
- Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
- Antimicrobial Regeneration Consortium (ARC) Labs, Louisville, CO, USA
| | - Eugene J Miller
- Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Anushree Chatterjee
- Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
- Antimicrobial Regeneration Consortium (ARC) Labs, Louisville, CO, USA.
- Sachi Bio, Louisville, CO, USA.
| | - Joel M Kralj
- Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA.
- Think Bioscience, Boulder, CO, USA.
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11
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Kosik KS. Search Strategies for Alzheimer Protector Genes. Ann Neurol 2023; 94:613-617. [PMID: 37574772 DOI: 10.1002/ana.26764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/31/2023] [Accepted: 08/05/2023] [Indexed: 08/15/2023]
Abstract
Large families with autosomal dominant mutations leading to Alzheimer's disease and related conditions can show putative protective gene variants; however, no systematic search strategy exists to find these genes. Described here are the unique demographic circumstances and genetic setting in which the discovery of protective variants is likely. The identification of these genes may reveal pathways with therapeutic implications. ANN NEUROL 2023;94:613-617.
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Affiliation(s)
- Kenneth S Kosik
- The Neuroscience Research Institute and The Department of Molecular Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, California, USA
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12
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Fox E. Polygenic scores, and the genome-wide association studies they derive from, will have difficulty identifying genes that predispose one to develop a social behavioral trait. Behav Brain Sci 2023; 46:e214. [PMID: 37694986 DOI: 10.1017/s0140525x22002370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Polygenic scores (PGSs) have several limitations. They are confounded with environmental effects on behavior and cannot be used to study how mutations affect brain function and behavior. For this, mutations with large effects, which often arise in only one geographical population are needed. Genome-wide association studies (GWASs), commonly used for identifying mutations, have difficulty detecting these mutations. A strategy that overcomes this challenge is discussed.
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Affiliation(s)
- Edward Fox
- Department of Psychological Science, Purdue University, West Lafayette, IN, USA https://www.purdue.edu/hhs/psy/directory/faculty/Fox_Edward.html
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13
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Weber SE, Frisch M, Snowdon RJ, Voss-Fels KP. Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets. FRONTIERS IN PLANT SCIENCE 2023; 14:1217589. [PMID: 37731980 PMCID: PMC10507710 DOI: 10.3389/fpls.2023.1217589] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/21/2023] [Indexed: 09/22/2023]
Abstract
In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.
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Affiliation(s)
- Sven E. Weber
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Matthias Frisch
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Kai P. Voss-Fels
- Institute for Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
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14
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Kovuri P, Yadav A, Sinha H. Role of genetic architecture in phenotypic plasticity. Trends Genet 2023; 39:703-714. [PMID: 37173192 DOI: 10.1016/j.tig.2023.04.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 05/15/2023]
Abstract
Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.
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Affiliation(s)
- Purnima Kovuri
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India; Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai, India; Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
| | - Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Himanshu Sinha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India; Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai, India; Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.
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15
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550015. [PMID: 37503053 PMCID: PMC10370210 DOI: 10.1101/2023.07.21.550015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
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16
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Weller CA, Andreev I, Chambers MJ, Park M, Bloom JS, Sadhu MJ. Highly complete long-read genomes reveal pangenomic variation underlying yeast phenotypic diversity. Genome Res 2023; 33:729-740. [PMID: 37127330 PMCID: PMC10317115 DOI: 10.1101/gr.277515.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 04/26/2023] [Indexed: 05/03/2023]
Abstract
Understanding the genetic causes of trait variation is a primary goal of genetic research. One way that individuals can vary genetically is through variable pangenomic genes: genes that are only present in some individuals in a population. The presence or absence of entire genes could have large effects on trait variation. However, variable pangenomic genes can be missed in standard genotyping workflows, owing to reliance on aligning short-read sequencing to reference genomes. A popular method for studying the genetic basis of trait variation is linkage mapping, which identifies quantitative trait loci (QTLs), regions of the genome that harbor causative genetic variants. Large-scale linkage mapping in the budding yeast Saccharomyces cerevisiae has found thousands of QTLs affecting myriad yeast phenotypes. To enable the resolution of QTLs caused by variable pangenomic genes, we used long-read sequencing to generate highly complete de novo genome assemblies of 16 diverse yeast isolates. With these assemblies, we resolved QTLs for growth on maltose, sucrose, raffinose, and oxidative stress to specific genes that are absent from the reference genome but present in the broader yeast population at appreciable frequency. Copies of genes also duplicate onto chromosomes where they are absent in the reference genome, and we found that these copies generate additional QTLs whose resolution requires pangenome characterization. Our findings show the need for highly complete genome assemblies to identify the genetic basis of trait variation.
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Affiliation(s)
- Cory A Weller
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Ilya Andreev
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Michael J Chambers
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Morgan Park
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, California 90095, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, California 90095, USA
- Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, California 90095, USA
- Institute for Quantitative and Computational Biology, University of California, Los Angeles, Los Angeles, California 90095, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, California 90095, USA
| | - Meru J Sadhu
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
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17
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Chen SAA, Kern AF, Ang RML, Xie Y, Fraser HB. Gene-by-environment interactions are pervasive among natural genetic variants. CELL GENOMICS 2023; 3:100273. [PMID: 37082145 PMCID: PMC10112290 DOI: 10.1016/j.xgen.2023.100273] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/09/2022] [Accepted: 01/31/2023] [Indexed: 04/22/2023]
Abstract
Gene-by-environment (GxE) interactions, in which a genetic variant's phenotypic effect is condition specific, are fundamental for understanding fitness landscapes and evolution but have been difficult to identify at the single-nucleotide level. Although many condition-specific quantitative trait loci (QTLs) have been mapped, these typically contain numerous inconsequential variants in linkage, precluding understanding of the causal GxE variants. Here, we introduce BARcoded Cas9 retron precise parallel editing via homology (CRISPEY-BAR), a high-throughput precision genome editing strategy, and use it to map GxE interactions of naturally occurring genetic polymorphisms impacting yeast growth. We identified hundreds of GxE variants within condition-specific QTLs, revealing unexpected genetic complexity. Moreover, we found that 93.7% of non-neutral natural variants within ergosterol biosynthesis pathway genes showed GxE interactions, including many impacting antifungal drug resistance through diverse molecular mechanisms. In sum, our results suggest an extremely complex, context-dependent fitness landscape characterized by pervasive GxE interactions while also demonstrating massively parallel genome editing as an effective means for investigating this complexity.
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Affiliation(s)
- Shi-An A. Chen
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Alexander F. Kern
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Roy Moh Lik Ang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yihua Xie
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Hunter B. Fraser
- Department of Biology, Stanford University, Stanford, CA 94305, USA
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18
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Andreev I, Laidlaw KME, Giovanetti SM, Urtecho G, Shriner D, Bloom JS, MacDonald C, Sadhu MJ. Discovery of a rapidly evolving yeast defense factor, KTD1, against the secreted killer toxin K28. Proc Natl Acad Sci U S A 2023; 120:e2217194120. [PMID: 36800387 PMCID: PMC9974470 DOI: 10.1073/pnas.2217194120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 12/09/2022] [Indexed: 02/18/2023] Open
Abstract
Secreted protein toxins are widely used weapons in conflicts between organisms. Elucidating how organisms genetically adapt to defend themselves against these toxins is fundamental to understanding the coevolutionary dynamics of competing organisms. Within yeast communities, "killer" toxins are secreted to kill nearby sensitive yeast, providing a fitness advantage in competitive growth environments. Natural yeast isolates vary in their sensitivity to these toxins, but to date, no polymorphic genetic factors contributing to defense have been identified. We investigated the variation in resistance to the killer toxin K28 across diverse natural isolates of the Saccharomyces cerevisiae population. Using large-scale linkage mapping, we discovered a novel defense factor, which we named KTD1. We identified many KTD1 alleles, which provided different levels of K28 resistance. KTD1 is a member of the DUP240 gene family of unknown function, which is rapidly evolving in a region spanning its two encoded transmembrane helices. We found that this domain is critical to KTD1's protective ability. Our findings implicate KTD1 as a key polymorphic factor in the defense against K28 toxin.
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Affiliation(s)
- Ilya Andreev
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Kamilla M. E. Laidlaw
- Biology Department, University of York, YorkYO10 5DD, UK
- York Biomedical Research Institute, University of York, YorkYO10 5NG, UK
| | - Simone M. Giovanetti
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Guillaume Urtecho
- Molecular Biology Interdepartmental Doctoral Program, University of California, Los Angeles, CA90095
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD20892
| | - Joshua S. Bloom
- Department of Human Genetics, University of California, Los Angeles, CA90095
- Department of Biological Chemistry, University of California, Los Angeles, CA90095
- HHMI, University of California, Los Angeles, CA90095
- Institute for Quantitative and Computational Biology, University of California, Los Angeles, CA90095
- Department of Computational Medicine, University of California, Los Angeles, CA90095
| | - Chris MacDonald
- Biology Department, University of York, YorkYO10 5DD, UK
- York Biomedical Research Institute, University of York, YorkYO10 5NG, UK
| | - Meru J. Sadhu
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD20892
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19
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Hu X, Carver BF, El-Kassaby YA, Zhu L, Chen C. Weighted kernels improve multi-environment genomic prediction. Heredity (Edinb) 2023; 130:82-91. [PMID: 36522412 PMCID: PMC9905581 DOI: 10.1038/s41437-022-00582-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/27/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Crucial to variety improvement programs is the reliable and accurate prediction of genotype's performance across environments. However, due to the impactful presence of genotype by environment (G×E) interaction that dictates how changes in expression and function of genes influence target traits in different environments, prediction performance of genomic selection (GS) using single-environment models often falls short. Furthermore, despite the successes of genome-wide association studies (GWAS), the genetic insights derived from genome-to-phenome mapping have not yet been incorporated in predictive analytics, making GS models that use Gaussian kernel primarily an estimator of genomic similarity, instead of the underlying genetics characteristics of the populations. Here, we developed a GS framework that, in addition to capturing the overall genomic relationship, can capitalize on the signal of genetic associations of the phenotypic variation as well as the genetic characteristics of the populations. The capacity of predicting the performance of populations across environments was demonstrated by an overall gain in predictability up to 31% for the winter wheat DH population. Compared to Gaussian kernels, we showed that our multi-environment weighted kernels could better leverage the significance of genetic associations and yielded a marked improvement of 4-33% in prediction accuracy for half-sib families. Furthermore, the flexibility incorporated in our Bayesian implementation provides the generalizable capacity required for predicting multiple highly genetic heterogeneous populations across environments, allowing reliable GS for genetic improvement programs that have no access to genetically uniform material.
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Affiliation(s)
- Xiaowei Hu
- grid.65519.3e0000 0001 0721 7331Department of Statistics, Oklahoma State University, Stillwater, OK USA ,grid.27755.320000 0000 9136 933XPresent Address: Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
| | - Brett F. Carver
- grid.65519.3e0000 0001 0721 7331Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK USA
| | - Yousry A. El-Kassaby
- grid.17091.3e0000 0001 2288 9830Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, BC Canada
| | - Lan Zhu
- grid.65519.3e0000 0001 0721 7331Department of Statistics, Oklahoma State University, Stillwater, OK USA
| | - Charles Chen
- Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK, USA.
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20
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Meyer CT, Lynch GK, Stamo DF, Miller EJ, Chatterjee A, Kralj JM. High Throughput Viability Assay for Microbiology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.04.522767. [PMID: 36712102 PMCID: PMC9881960 DOI: 10.1101/2023.01.04.522767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Counting viable cells is a universal practice in microbiology. The colony forming unit (CFU) assay has remained the gold standard to measure viability across disciplines; however, it is time-intensive and resource-consuming. Herein, we describe the Geometric Viability Assay (GVA) that replicates CFU measurements over 6-orders of magnitude while reducing over 10-fold the time and consumables. GVA computes a sample's viable cell count based on the distribution of embedded colonies growing inside a pipette tip. GVA is compatible with gram-positive and -negative planktonic bacteria, biofilms, and yeast. Laborious CFU experiments such as checkerboard assays, treatment time-courses, and drug screens against slow-growing cells are simplified by GVA. We therefore screened a drug library against exponential and stationary phase E. coli leading to the discovery of the ROS-mediated, bactericidal mechanism of diphenyliodonium. The ease and low cost of GVA evinces it can accelerate existing viability assays and enable measurements at previously impractical scales.
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Affiliation(s)
- Christian T. Meyer
- BioFrontiers and MCDB Department, University of Colorado Boulder, Boulder, CO, USA
- Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Grace K. Lynch
- BioFrontiers and MCDB Department, University of Colorado Boulder, Boulder, CO, USA
| | - Dana F. Stamo
- Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Eugene J. Miller
- BioFrontiers and MCDB Department, University of Colorado Boulder, Boulder, CO, USA
| | - Anushree Chatterjee
- Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
- Antimicrobial Regeneration Consortium (ARC) Labs, Louisville, CO, USA
- Sachi Bioworks, Louisville, CO, USA
| | - Joel M. Kralj
- BioFrontiers and MCDB Department, University of Colorado Boulder, Boulder, CO, USA
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21
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Dhindsa RS, Wang Q, Vitsios D, Burren OS, Hu F, DiCarlo JE, Kruglyak L, MacArthur DG, Hurles ME, Petrovski S. A minimal role for synonymous variation in human disease. Am J Hum Genet 2022; 109:2105-2109. [PMID: 36459978 PMCID: PMC9808499 DOI: 10.1016/j.ajhg.2022.10.016] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Synonymous mutations change the DNA sequence of a gene without affecting the amino acid sequence of the encoded protein. Although some synonymous mutations can affect RNA splicing, translational efficiency, and mRNA stability, studies in human genetics, mutagenesis screens, and other experiments and evolutionary analyses have repeatedly shown that most synonymous variants are neutral or only weakly deleterious, with some notable exceptions. Based on a recent study in yeast, there have been claims that synonymous mutations could be as important as nonsynonymous mutations in causing disease, assuming the yeast findings hold up and translate to humans. Here, we argue that there is insufficient evidence to overturn the large, coherent body of knowledge establishing the predominant neutrality of synonymous variants in the human genome.
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Affiliation(s)
- Ryan S. Dhindsa
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA,Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, USA,Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA,Corresponding author
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA
| | - Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Oliver S. Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Fengyuan Hu
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - James E. DiCarlo
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Leonid Kruglyak
- Department of Human Genetics and Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA,Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Daniel G. MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Centre for Population Genomics, Garvan Institute of Medical Research, and UNSW Sydney, Sydney, NSW, Australia,Centre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, VIC, Australia
| | | | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK,Department of Medicine, University of Melbourne, Austin Health, Melbourne, VIC, Australia,Corresponding author
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22
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High-throughput approaches to functional characterization of genetic variation in yeast. Curr Opin Genet Dev 2022; 76:101979. [PMID: 36075138 DOI: 10.1016/j.gde.2022.101979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/29/2022] [Accepted: 08/02/2022] [Indexed: 11/20/2022]
Abstract
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.
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23
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Mullis MN, Ghione C, Lough-Stevens M, Goldstein I, Matsui T, Levy SF, Dean MD, Ehrenreich IM. Complex genetics cause and constrain fungal persistence in different parts of the mammalian body. Genetics 2022; 222:6698696. [PMID: 36103708 PMCID: PMC9630980 DOI: 10.1093/genetics/iyac138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/26/2022] [Indexed: 12/05/2022] Open
Abstract
Determining how genetic polymorphisms enable certain fungi to persist in mammalian hosts can improve understanding of opportunistic fungal pathogenesis, a source of substantial human morbidity and mortality. We examined the genetic basis of fungal persistence in mice using a cross between a clinical isolate and the lab reference strain of the budding yeast Saccharomyces cerevisiae. Employing chromosomally encoded DNA barcodes, we tracked the relative abundances of 822 genotyped, haploid segregants in multiple organs over time and performed linkage mapping of their persistence in hosts. Detected loci showed a mix of general and antagonistically pleiotropic effects across organs. General loci showed similar effects across all organs, while antagonistically pleiotropic loci showed contrasting effects in the brain vs the kidneys, liver, and spleen. Persistence in an organ required both generally beneficial alleles and organ-appropriate pleiotropic alleles. This genetic architecture resulted in many segregants persisting in the brain or in nonbrain organs, but few segregants persisting in all organs. These results show complex combinations of genetic polymorphisms collectively cause and constrain fungal persistence in different parts of the mammalian body.
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Affiliation(s)
- Martin N Mullis
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Caleb Ghione
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Michael Lough-Stevens
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Ilan Goldstein
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Takeshi Matsui
- Stanford University Joint Initiative for Metrology in Biology, , CA 94305, USA
- SLAC National Accelerator Laboratory , Menlo Park, CA, 94025, USA
- Stanford University Department of Genetics, , Stanford, CA 94305, USA
| | - Sasha F Levy
- Stanford University Joint Initiative for Metrology in Biology, , CA 94305, USA
- SLAC National Accelerator Laboratory , Menlo Park, CA, 94025, USA
- Stanford University Department of Genetics, , Stanford, CA 94305, USA
| | - Matthew D Dean
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Ian M Ehrenreich
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
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24
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Lye Z, Choi JY, Purugganan MD. Deleterious mutations and the rare allele burden on rice gene expression. Mol Biol Evol 2022; 39:6693943. [PMID: 36073358 PMCID: PMC9512150 DOI: 10.1093/molbev/msac193] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Deleterious genetic variation is maintained in populations at low frequencies. Under a model of stabilizing selection, rare (and presumably deleterious) genetic variants are associated with increase or decrease in gene expression from some intermediate optimum. We investigate this phenomenon in a population of largely Oryza sativa ssp. indica rice landraces under normal unstressed wet and stressful drought field conditions. We include single nucleotide polymorphisms, insertion/deletion mutations, and structural variants in our analysis and find a stronger association between rare variants and gene expression outliers under the stress condition. We also show an association of the strength of this rare variant effect with linkage, gene expression levels, network connectivity, local recombination rate, and fitness consequence scores, consistent with the stabilizing selection model of gene expression.
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Affiliation(s)
- Zoe Lye
- Center for Genomics and Systems Biology, New York University, New York, NY 10003
| | - Jae Young Choi
- Center for Genomics and Systems Biology, New York University, New York, NY 10003
| | - Michael D Purugganan
- Center for Genomics and Systems Biology, New York University, New York, NY 10003.,Center for Genomics and Systems Biology, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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25
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Hine E, Runcie DE, Allen SL, Wang Y, Chenoweth SF, Blows MW, McGuigan K. Maintenance of quantitative genetic variance in complex, multi-trait phenotypes: The contribution of rare, large effect variants in two Drosophila species. Genetics 2022; 222:6663993. [PMID: 35961029 PMCID: PMC9526065 DOI: 10.1093/genetics/iyac122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/02/2022] [Indexed: 11/29/2022] Open
Abstract
The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.
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Affiliation(s)
- Emma Hine
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA 95616, USA
| | - Scott L Allen
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
| | - Yiguan Wang
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia.,Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 3FL, UK
| | - Stephen F Chenoweth
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
| | - Mark W Blows
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
| | - Katrina McGuigan
- School of Biological Sciences, The University of Queensland, Brisbane 4072 Australia
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26
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Olguín V, Durán A, Las Heras M, Rubilar JC, Cubillos FA, Olguín P, Klein AD. Genetic Background Matters: Population-Based Studies in Model Organisms for Translational Research. Int J Mol Sci 2022; 23:7570. [PMID: 35886916 PMCID: PMC9316598 DOI: 10.3390/ijms23147570] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 02/01/2023] Open
Abstract
We are all similar but a bit different. These differences are partially due to variations in our genomes and are related to the heterogeneity of symptoms and responses to treatments that patients exhibit. Most animal studies are performed in one single strain with one manipulation. However, due to the lack of variability, therapies are not always reproducible when treatments are translated to humans. Panels of already sequenced organisms are valuable tools for mimicking human phenotypic heterogeneities and gene mapping. This review summarizes the current knowledge of mouse, fly, and yeast panels with insightful applications for translational research.
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Affiliation(s)
- Valeria Olguín
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
| | - Anyelo Durán
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
| | - Macarena Las Heras
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
| | - Juan Carlos Rubilar
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
| | - Francisco A. Cubillos
- Departamento de Biología, Santiago, Facultad de Química y Biología, Universidad de Santiago de Chile, Santiago 9170022, Chile;
- Millennium Institute for Integrative Biology (iBio), Santiago 7500565, Chile
| | - Patricio Olguín
- Program in Human Genetics, Institute of Biomedical Sciences, Biomedical Neurosciences Institute, Department of Neuroscience, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile;
| | - Andrés D. Klein
- Centro de Genética y Genómica, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago 7610658, Chile; (V.O.); (A.D.); (M.L.H.); (J.C.R.)
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27
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Nguyen Ba AN, Lawrence KR, Rego-Costa A, Gopalakrishnan S, Temko D, Michor F, Desai MM. Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast. eLife 2022; 11:73983. [PMID: 35147078 PMCID: PMC8979589 DOI: 10.7554/elife.73983] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 02/11/2022] [Indexed: 11/25/2022] Open
Abstract
Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.
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Affiliation(s)
- Alex N Nguyen Ba
- Department of Organismic and Evolutionary Biology, Harvard University
| | | | - Artur Rego-Costa
- Department of Organismic and Evolutionary Biology, Harvard University
| | | | | | | | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University
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28
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Andersen EC, Rockman MV. Natural genetic variation as a tool for discovery in Caenorhabditis nematodes. Genetics 2022; 220:iyab156. [PMID: 35134197 PMCID: PMC8733454 DOI: 10.1093/genetics/iyab156] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 09/11/2021] [Indexed: 11/12/2022] Open
Abstract
Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.
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Affiliation(s)
- Erik C Andersen
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60201, USA
| | - Matthew V Rockman
- Department of Biology and Center for Genomics & Systems Biology, New York University, New York, NY 10003, USA
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29
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Karlsson IK, Escott-Price V, Gatz M, Hardy J, Pedersen NL, Shoai M, Reynolds CA. Measuring heritable contributions to Alzheimer's disease: polygenic risk score analysis with twins. Brain Commun 2022; 4:fcab308. [PMID: 35169705 PMCID: PMC8833403 DOI: 10.1093/braincomms/fcab308] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 09/17/2021] [Accepted: 10/18/2021] [Indexed: 11/23/2022] Open
Abstract
The heritability of Alzheimer's disease estimated from twin studies is greater than the heritability derived from genome-based studies, for reasons that remain unclear. We apply both approaches to the same twin sample, considering both Alzheimer's disease polygenic risk scores and heritability from twin models, to provide insight into the role of measured genetic variants and to quantify uncaptured genetic risk. A population-based heritability and polygenic association study of Alzheimer's disease was conducted between 1986 and 2016 and is the first study to incorporate polygenic risk scores into biometrical twin models of Alzheimer's disease. The sample included 1586 twins drawn from the Swedish Twin Registry which were nested within 1137 twin pairs (449 complete pairs and 688 incomplete pairs) with clinically based diagnoses and registry follow-up (M age = 85.28, SD = 7.02; 44% male; 431 cases and 1155 controls). We report contributions of polygenic risk scores at P < 1 × 10-5, considering a full polygenic risk score (PRS), PRS without the APOE region (PRS.no.APOE) and PRS.no.APOE plus directly measured APOE alleles. Biometric twin models estimated the contribution of environmental influences and measured (PRS) and unmeasured genes to Alzheimer's disease risk. The full PRS and PRS.no.APOE contributed 10.1 and 2.4% to Alzheimer's disease risk, respectively. When APOE ɛ4 alleles were added to the model with the PRS.no.APOE, the total contribution was 11.4% to Alzheimer's disease risk, where APOE ɛ4 explained 9.3% and PRS.no.APOE dropped from 2.4 to 2.1%. The total genetic contribution to Alzheimer's disease risk, measured and unmeasured, was 71% while environmental influences unique to each twin accounted for 29% of the risk. The APOE region accounts for much of the measurable genetic contribution to Alzheimer's disease, with a smaller contribution from other measured polygenic influences. Importantly, substantial background genetic influences remain to be understood.
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Affiliation(s)
- Ida K. Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Aging Research Network—Jönköping (ARN-J), School of Health and 6 Welfare, Jönköping University, Jönköping, Sweden
| | - Valentina Escott-Price
- UK Dementia Research Institute at Cardiff, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Margaret Gatz
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - John Hardy
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- UK Dementia Research Institute at UCL and Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
- Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, 1 Wakefield Street, London WC1N 1PJ, UK
- UCL Movement Disorders Centre, University College London, London, UK
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Nancy L. Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Maryam Shoai
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- UK Dementia Research Institute at UCL and Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | - Chandra A. Reynolds
- Department of Psychology, University of California—Riverside, Riverside, CA, USA
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30
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Zeng Z, Aptekmann AA, Bromberg Y. Decoding the effects of synonymous variants. Nucleic Acids Res 2021; 49:12673-12691. [PMID: 34850938 PMCID: PMC8682775 DOI: 10.1093/nar/gkab1159] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 11/02/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects.
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Affiliation(s)
- Zishuo Zeng
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08873, USA
| | - Ariel A Aptekmann
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08873, USA
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08873, USA
- Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA
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31
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Kondratyev NV, Alfimova MV, Golov AK, Golimbet VE. Bench Research Informed by GWAS Results. Cells 2021; 10:3184. [PMID: 34831407 PMCID: PMC8623533 DOI: 10.3390/cells10113184] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/11/2021] [Accepted: 11/11/2021] [Indexed: 12/15/2022] Open
Abstract
Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.
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Affiliation(s)
| | | | - Arkadiy K. Golov
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
- Institute of Gene Biology, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vera E. Golimbet
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
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32
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Peltier E, Vion C, Abou Saada O, Friedrich A, Schacherer J, Marullo P. Flor Yeasts Rewire the Central Carbon Metabolism During Wine Alcoholic Fermentation. FRONTIERS IN FUNGAL BIOLOGY 2021; 2:733513. [PMID: 37744152 PMCID: PMC10512321 DOI: 10.3389/ffunb.2021.733513] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/31/2021] [Indexed: 09/26/2023]
Abstract
The identification of natural allelic variations controlling quantitative traits could contribute to decipher metabolic adaptation mechanisms within different populations of the same species. Such variations could result from human-mediated selection pressures and participate to the domestication. In this study, the genetic causes of the phenotypic variability of the central carbon metabolism of Saccharomyces cerevisiae were investigated in the context of the enological fermentation. The genetic determinism of this trait was found out by a quantitative trait loci (QTL) mapping approach using the offspring of two strains belonging to the wine genetic group of the species. A total of 14 QTL were identified from which 8 were validated down to the gene level by genetic engineering. The allelic frequencies of the validated genes within 403 enological strains showed that most of the validated QTL had allelic variations involving flor yeast specific alleles. Those alleles were brought in the offspring by one parental strain that contains introgressions from the flor yeast genetic group. The causative genes identified are functionally linked to quantitative proteomic variations that would explain divergent metabolic features of wine and flor yeasts involving the tricarboxylic acid cycle (TCA), the glyoxylate shunt and the homeostasis of proton and redox cofactors. Overall, this work led to the identification of genetic factors that are hallmarks of adaptive divergence between flor yeast and wine yeast in the wine biotope. These results also reveal that introgressions originated from intraspecific hybridization events promoted phenotypic variability of carbon metabolism observed in wine strains.
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Affiliation(s)
- Emilien Peltier
- Unité de Recherche Œnologie EA 4577, USC 1366 INRA, Bordeaux INP, ISVV, Université de Bordeaux, Bordeaux, France
- Biolaffort, Bordeaux, France
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Charlotte Vion
- Unité de Recherche Œnologie EA 4577, USC 1366 INRA, Bordeaux INP, ISVV, Université de Bordeaux, Bordeaux, France
- Biolaffort, Bordeaux, France
| | - Omar Abou Saada
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | | | - Philippe Marullo
- Unité de Recherche Œnologie EA 4577, USC 1366 INRA, Bordeaux INP, ISVV, Université de Bordeaux, Bordeaux, France
- Biolaffort, Bordeaux, France
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33
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Phillips MA, Kutch IC, McHugh KM, Taggard SK, Burke MK. Crossing design shapes patterns of genetic variation in synthetic recombinant populations of Saccharomyces cerevisiae. Sci Rep 2021; 11:19551. [PMID: 34599243 PMCID: PMC8486856 DOI: 10.1038/s41598-021-99026-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 09/14/2021] [Indexed: 11/20/2022] Open
Abstract
"Synthetic recombinant" populations have emerged as a useful tool for dissecting the genetics of complex traits. They can be used to derive inbred lines for fine QTL mapping, or the populations themselves can be sampled for experimental evolution. In the latter application, investigators generally value maximizing genetic variation in constructed populations. This is because in evolution experiments initiated from such populations, adaptation is primarily fueled by standing genetic variation. Despite this reality, little has been done to systematically evaluate how different methods of constructing synthetic populations shape initial patterns of variation. Here we seek to address this issue by comparing outcomes in synthetic recombinant Saccharomyces cerevisiae populations created using one of two strategies: pairwise crossing of isogenic strains or simple mixing of strains in equal proportion. We also explore the impact of the varying the number of parental strains. We find that more genetic variation is initially present and maintained when population construction includes a round of pairwise crossing. As perhaps expected, we also observe that increasing the number of parental strains typically increases genetic diversity. In summary, we suggest that when constructing populations for use in evolution experiments, simply mixing founder strains in equal proportion may limit the adaptive potential.
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Affiliation(s)
- Mark A Phillips
- Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA.
| | - Ian C Kutch
- Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA
| | - Kaitlin M McHugh
- Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA
| | - Savannah K Taggard
- Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA
| | - Molly K Burke
- Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA.
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34
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Naik B, Ahmed SMQ, Laha S, Das SP. Genetic Susceptibility to Fungal Infections and Links to Human Ancestry. Front Genet 2021; 12:709315. [PMID: 34490039 PMCID: PMC8417537 DOI: 10.3389/fgene.2021.709315] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/13/2021] [Indexed: 12/25/2022] Open
Abstract
Over the ages, fungi have associated with different parts of the human body and established symbiotic associations with their host. They are mostly commensal unless there are certain not so well-defined factors that trigger the conversion to a pathogenic state. Some of the factors that induce such transition can be dependent on the fungal species, environment, immunological status of the individual, and most importantly host genetics. In this review, we discuss the different aspects of how host genetics play a role in fungal infection since mutations in several genes make hosts susceptible to such infections. We evaluate how mutations modulate the key recognition between the pathogen associated molecular patterns (PAMP) and the host pattern recognition receptor (PRR) molecules. We discuss the polymorphisms in the genes of the immune system, the way it contributes toward some common fungal infections, and highlight how the immunological status of the host determines fungal recognition and cross-reactivity of some fungal antigens against human proteins that mimic them. We highlight the importance of single nucleotide polymorphisms (SNPs) that are associated with several of the receptor coding genes and discuss how it affects the signaling cascade post-infection, immune evasion, and autoimmune disorders. As part of personalized medicine, we need the application of next-generation techniques as a feasible option to incorporate an individual’s susceptibility toward invasive fungal infections based on predisposing factors. Finally, we discuss the importance of studying genomic ancestry and reveal how genetic differences between the human race are linked to variation in fungal disease susceptibility.
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Affiliation(s)
- Bharati Naik
- Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Sumayyah M Q Ahmed
- Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Suparna Laha
- Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Shankar Prasad Das
- Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
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35
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Peltier E, Bibi-Triki S, Dutreux F, Caradec C, Friedrich A, Llorente B, Schacherer J. Dissection of quantitative trait loci in the Lachancea waltii yeast species highlights major hotspots. G3 (BETHESDA, MD.) 2021; 11:jkab242. [PMID: 34544138 PMCID: PMC8496267 DOI: 10.1093/g3journal/jkab242] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/06/2021] [Indexed: 11/30/2022]
Abstract
Dissecting the genetic basis of complex trait remains a real challenge. The budding yeast Saccharomyces cerevisiae has become a model organism for studying quantitative traits, successfully increasing our knowledge in many aspects. However, the exploration of the genotype-phenotype relationship in non-model yeast species could provide a deeper insight into the genetic basis of complex traits. Here, we have studied this relationship in the Lachancea waltii species which diverged from the S. cerevisiae lineage prior to the whole-genome duplication. By performing linkage mapping analyses in this species, we identified 86 quantitative trait loci (QTL) impacting the growth in a large number of conditions. The distribution of these loci across the genome has revealed two major QTL hotspots. A first hotspot corresponds to a general growth QTL, impacting a wide range of conditions. By contrast, the second hotspot highlighted a trade-off with a disadvantageous allele for drug-free conditions which proved to be advantageous in the presence of several drugs. Finally, a comparison of the detected QTL in L. waltii with those which had been previously identified for the same trait in a closely related species, Lachancea kluyveri was performed. This analysis clearly showed the absence of shared QTL across these species. Altogether, our results represent a first step toward the exploration of the genetic architecture of quantitative trait across different yeast species.
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Affiliation(s)
- Emilien Peltier
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | | | - Fabien Dutreux
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Claudia Caradec
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Bertrand Llorente
- CNRS UMR7258, INSERM U1068, Aix Marseille Université UM105, Institut Paoli-Calmettes, CRCM, Marseille, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
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36
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Guirao‐Rico S, González J. Benchmarking the performance of Pool-seq SNP callers using simulated and real sequencing data. Mol Ecol Resour 2021; 21:1216-1229. [PMID: 33534960 PMCID: PMC8251607 DOI: 10.1111/1755-0998.13343] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 12/21/2020] [Accepted: 01/27/2021] [Indexed: 12/13/2022]
Abstract
Population genomics is a fast-developing discipline with promising applications in a growing number of life sciences fields. Advances in sequencing technologies and bioinformatics tools allow population genomics to exploit genome-wide information to identify the molecular variants underlying traits of interest and the evolutionary forces that modulate these variants through space and time. However, the cost of genomic analyses of multiple populations is still too high to address them through individual genome sequencing. Pooling individuals for sequencing can be a more effective strategy in Single Nucleotide Polymorphism (SNP) detection and allele frequency estimation because of a higher total coverage. However, compared to individual sequencing, SNP calling from pools has the additional difficulty of distinguishing rare variants from sequencing errors, which is often avoided by establishing a minimum threshold allele frequency for the analysis. Finding an optimal balance between minimizing information loss and reducing sequencing costs is essential to ensure the success of population genomics studies. Here, we have benchmarked the performance of SNP callers for Pool-seq data, based on different approaches, under different conditions, and using computer simulations and real data. We found that SNP callers performance varied for allele frequencies up to 0.35. We also found that SNP callers based on Bayesian (SNAPE-pooled) or maximum likelihood (MAPGD) approaches outperform the two heuristic callers tested (VarScan and PoolSNP), in terms of the balance between sensitivity and FDR both in simulated and sequencing data. Our results will help inform the selection of the most appropriate SNP caller not only for large-scale population studies but also in cases where the Pool-seq strategy is the only option, such as in metagenomic or polyploid studies.
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Affiliation(s)
- Sara Guirao‐Rico
- Institute of Evolutionary BiologyCSIC‐Universitat Pompeu FabraBarcelonaSpain
| | - Josefa González
- Institute of Evolutionary BiologyCSIC‐Universitat Pompeu FabraBarcelonaSpain
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Parts L, Batté A, Lopes M, Yuen MW, Laver M, San Luis B, Yue J, Pons C, Eray E, Aloy P, Liti G, van Leeuwen J. Natural variants suppress mutations in hundreds of essential genes. Mol Syst Biol 2021; 17:e10138. [PMID: 34042294 PMCID: PMC8156963 DOI: 10.15252/msb.202010138] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 01/04/2023] Open
Abstract
The consequence of a mutation can be influenced by the context in which it operates. For example, loss of gene function may be tolerated in one genetic background, and lethal in another. The extent to which mutant phenotypes are malleable, the architecture of modifiers and the identities of causal genes remain largely unknown. Here, we measure the fitness effects of ~ 1,100 temperature-sensitive alleles of yeast essential genes in the context of variation from ten different natural genetic backgrounds and map the modifiers for 19 combinations. Altogether, fitness defects for 149 of the 580 tested genes (26%) could be suppressed by genetic variation in at least one yeast strain. Suppression was generally driven by gain-of-function of a single, strong modifier gene, and involved both genes encoding complex or pathway partners suppressing specific temperature-sensitive alleles, as well as general modifiers altering the effect of many alleles. The emerging frequency of suppression and range of possible mechanisms suggest that a substantial fraction of monogenic diseases could be managed by modulating other gene products.
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Affiliation(s)
- Leopold Parts
- Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
- Wellcome Sanger InstituteWellcome Genome CampusHinxtonUK
- Department of Computer ScienceUniversity of TartuTartuEstonia
| | - Amandine Batté
- Center for Integrative GenomicsUniversity of LausanneLausanneSwitzerland
| | - Maykel Lopes
- Center for Integrative GenomicsUniversity of LausanneLausanneSwitzerland
| | - Michael W Yuen
- Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Meredith Laver
- Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Bryan‐Joseph San Luis
- Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoONCanada
| | - Jia‐Xing Yue
- University of Côte d’AzurCNRSINSERMIRCANNiceFrance
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute for Science and TechnologyBarcelonaSpain
| | - Elise Eray
- Center for Integrative GenomicsUniversity of LausanneLausanneSwitzerland
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute for Science and TechnologyBarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
| | - Gianni Liti
- University of Côte d’AzurCNRSINSERMIRCANNiceFrance
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Brion C, Caradec C, Pflieger D, Friedrich A, Schacherer J. Pervasive Phenotypic Impact of a Large Nonrecombining Introgressed Region in Yeast. Mol Biol Evol 2021; 37:2520-2530. [PMID: 32359150 PMCID: PMC7475044 DOI: 10.1093/molbev/msaa101] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
To explore the origin of the diversity observed in natural populations, many studies have investigated the relationship between genotype and phenotype. In yeast species, especially in Saccharomyces cerevisiae, these studies are mainly conducted using recombinant offspring derived from two genetically diverse isolates, allowing to define the phenotypic effect of genetic variants. However, large genomic variants such as interspecies introgressions are usually overlooked even if they are known to modify the genotype–phenotype relationship. To have a better insight into the overall phenotypic impact of introgressions, we took advantage of the presence of a 1-Mb introgressed region, which lacks recombination and contains the mating-type determinant in the Lachancea kluyveri budding yeast. By performing linkage mapping analyses in this species, we identified a total of 89 loci affecting growth fitness in a large number of conditions and 2,187 loci affecting gene expression mostly grouped into two major hotspots, one being the introgressed region carrying the mating-type locus. Because of the absence of recombination, our results highlight the presence of a sexual dimorphism in a budding yeast for the first time. Overall, by describing the phenotype–genotype relationship in the Lachancea kluyveri species, we expanded our knowledge on how genetic characteristics of large introgression events can affect the phenotypic landscape.
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Affiliation(s)
- Christian Brion
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Claudia Caradec
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - David Pflieger
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.,Institut Universitaire de France (IUF), Paris, France
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Swanzey E, O'Connor C, Reinholdt LG. Mouse Genetic Reference Populations: Cellular Platforms for Integrative Systems Genetics. Trends Genet 2021; 37:251-265. [PMID: 33010949 PMCID: PMC7889615 DOI: 10.1016/j.tig.2020.09.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/28/2020] [Accepted: 09/02/2020] [Indexed: 02/07/2023]
Abstract
Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation.
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Affiliation(s)
| | - Callan O'Connor
- The Jackson Laboratory, Bar Harbor, ME, USA; Tufts Graduate School of Biomedical Sciences, Boston, MA, USA
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Boocock J, Sadhu MJ, Durvasula A, Bloom JS, Kruglyak L. Ancient balancing selection maintains incompatible versions of the galactose pathway in yeast. Science 2021; 371:415-419. [PMID: 33479156 PMCID: PMC8384573 DOI: 10.1126/science.aba0542] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 05/08/2020] [Accepted: 12/17/2020] [Indexed: 02/02/2023]
Abstract
Metabolic pathways differ across species but are expected to be similar within a species. We discovered two functional, incompatible versions of the galactose pathway in Saccharomyces cerevisiae We identified a three-locus genetic interaction for growth in galactose, and used precisely engineered alleles to show that it arises from variation in the galactose utilization genes GAL2, GAL1/10/7, and phosphoglucomutase (PGM1), and that the reference allele of PGM1 is incompatible with the alternative alleles of the other genes. Multiloci balancing selection has maintained the two incompatible versions of the pathway for millions of years. Strains with alternative alleles are found primarily in galactose-rich dairy environments, and they grow faster in galactose but slower in glucose, revealing a trade-off on which balancing selection may have acted.
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Affiliation(s)
- James Boocock
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Meru J Sadhu
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Arun Durvasula
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biology, University of California, Los Angeles, Los Angeles, CA, USA
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Roth C, Murray D, Scott A, Fu C, Averette AF, Sun S, Heitman J, Magwene PM. Pleiotropy and epistasis within and between signaling pathways defines the genetic architecture of fungal virulence. PLoS Genet 2021; 17:e1009313. [PMID: 33493169 PMCID: PMC7861560 DOI: 10.1371/journal.pgen.1009313] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 02/04/2021] [Accepted: 12/17/2020] [Indexed: 01/11/2023] Open
Abstract
Cryptococcal disease is estimated to affect nearly a quarter of a million people annually. Environmental isolates of Cryptococcus deneoformans, which make up 15 to 30% of clinical infections in temperate climates such as Europe, vary in their pathogenicity, ranging from benign to hyper-virulent. Key traits that contribute to virulence, such as the production of the pigment melanin, an extracellular polysaccharide capsule, and the ability to grow at human body temperature have been identified, yet little is known about the genetic basis of variation in such traits. Here we investigate the genetic basis of melanization, capsule size, thermal tolerance, oxidative stress resistance, and antifungal drug sensitivity using quantitative trait locus (QTL) mapping in progeny derived from a cross between two divergent C. deneoformans strains. Using a "function-valued" QTL analysis framework that exploits both time-series information and growth differences across multiple environments, we identified QTL for each of these virulence traits and drug susceptibility. For three QTL we identified the underlying genes and nucleotide differences that govern variation in virulence traits. One of these genes, RIC8, which encodes a regulator of cAMP-PKA signaling, contributes to variation in four virulence traits: melanization, capsule size, thermal tolerance, and resistance to oxidative stress. Two major effect QTL for amphotericin B resistance map to the genes SSK1 and SSK2, which encode key components of the HOG pathway, a fungal-specific signal transduction network that orchestrates cellular responses to osmotic and other stresses. We also discovered complex epistatic interactions within and between genes in the HOG and cAMP-PKA pathways that regulate antifungal drug resistance and resistance to oxidative stress. Our findings advance the understanding of virulence traits among diverse lineages of Cryptococcus, and highlight the role of genetic variation in key stress-responsive signaling pathways as a major contributor to phenotypic variation.
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Affiliation(s)
- Cullen Roth
- Department of Biology, Duke University, Durham, North Carolina, United States of America
- University Program in Genetics and Genomics, Duke University, Durham, North Carolina, United States of America
| | - Debra Murray
- Department of Biology, Duke University, Durham, North Carolina, United States of America
| | - Alexandria Scott
- Department of Biology, Duke University, Durham, North Carolina, United States of America
| | - Ci Fu
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Anna F. Averette
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Sheng Sun
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Joseph Heitman
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Paul M. Magwene
- Department of Biology, Duke University, Durham, North Carolina, United States of America
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Brion C, Lutz SM, Albert FW. Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation. eLife 2020; 9:60645. [PMID: 33191917 PMCID: PMC7707838 DOI: 10.7554/elife.60645] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 11/14/2020] [Indexed: 01/27/2023] Open
Abstract
Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.
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Affiliation(s)
- Christian Brion
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Sheila M Lutz
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Frank Wolfgang Albert
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
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Lahue C, Madden AA, Dunn RR, Smukowski Heil C. History and Domestication of Saccharomyces cerevisiae in Bread Baking. Front Genet 2020; 11:584718. [PMID: 33262788 PMCID: PMC7686800 DOI: 10.3389/fgene.2020.584718] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/13/2020] [Indexed: 11/30/2022] Open
Abstract
The yeast Saccharomyces cerevisiae has been instrumental in the fermentation of foods and beverages for millennia. In addition to fermentations like wine, beer, cider, sake, and bread, S. cerevisiae has been isolated from environments ranging from soil and trees, to human clinical isolates. Each of these environments has unique selection pressures that S. cerevisiae must adapt to. Bread dough, for example, requires S. cerevisiae to efficiently utilize the complex sugar maltose; tolerate osmotic stress due to the semi-solid state of dough, high salt, and high sugar content of some doughs; withstand various processing conditions, including freezing and drying; and produce desirable aromas and flavors. In this review, we explore the history of bread that gave rise to modern commercial baking yeast, and the genetic and genomic changes that accompanied this. We illustrate the genetic and phenotypic variation that has been documented in baking strains and wild strains, and how this variation might be used for baking strain improvement. While we continue to improve our understanding of how baking strains have adapted to bread dough, we conclude by highlighting some of the remaining open questions in the field.
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Affiliation(s)
- Caitlin Lahue
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States
- Department of Applied Ecology, North Carolina State University, Raleigh, NC, United States
| | - Anne A. Madden
- Department of Applied Ecology, North Carolina State University, Raleigh, NC, United States
| | - Robert R. Dunn
- Department of Applied Ecology, North Carolina State University, Raleigh, NC, United States
- Center for Evolutionary Hologenomics, The GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Caiti Smukowski Heil
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States
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Rau CD, Gonzales NM, Bloom JS, Park D, Ayroles J, Palmer AA, Lusis AJ, Zaitlen N. Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for "polygenic epistasis". PLoS Genet 2020; 16:e1009165. [PMID: 33104702 PMCID: PMC7644088 DOI: 10.1371/journal.pgen.1009165] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 11/05/2020] [Accepted: 10/02/2020] [Indexed: 12/22/2022] Open
Abstract
Background The majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors. Results We applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection. Conclusions Unlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast. Many statistical tests which link genetic markers in the genome to differences in traits rely on the assumption that the same polymorphism will have identical effects in different individuals. However, there is substantial evidence indicating that this is not the case. Epistasis is the phenomenon in which multiple polymorphisms interact with one another to amplify or negate each other’s effects on a trait. We hypothesized that individual SNP effects could be changed in a polygenic manner, such that the proportion of as genetic ancestry, rather than specific markers, might be used to capture epistatic interactions. Motivated by this possibility, we develop a new statistical test that allowed us to examine the genome to identify polymorphisms which have different effects depending on the ancestral makeup of each individual. We use our test in two different populations of inbred mice and a yeast panel and demonstrate that these sorts of variable effect polymorphisms exist in 14 different physical traits in mice and 38 phenotypes in yeast as well as in murine gene expression. We use the term “polygenic epistasis” to distinguish these interactions from the more conventional two- or multi-locus interactions.
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Affiliation(s)
- Christoph D. Rau
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Natalia M. Gonzales
- Department of Human Genetics, University of Chicago, Chicago, IL, United States of America
| | - Joshua S. Bloom
- Department of Human Genetics, UCLA, Los Angeles, CA, United States of America
| | - Danny Park
- Department of Medicine, UCSF, San Francisco, CA, United States of America
| | - Julien Ayroles
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States of America
| | - Abraham A. Palmer
- Department of Psychiatry, and Institute for Genomic Medicine, UCSD, San Diego, CA, United States of America
| | - Aldons J. Lusis
- Department of Human Genetics, UCLA, Los Angeles, CA, United States of America
| | - Noah Zaitlen
- Department of Neurology, UCLA, Los Angeles, CA, United States of America
- * E-mail:
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Jakobson CM, Jarosz DF. What Has a Century of Quantitative Genetics Taught Us About Nature's Genetic Tool Kit? Annu Rev Genet 2020; 54:439-464. [PMID: 32897739 DOI: 10.1146/annurev-genet-021920-102037] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms.
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Affiliation(s)
- Christopher M Jakobson
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California 94305, USA;
| | - Daniel F Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California 94305, USA; .,Department of Developmental Biology, Stanford University School of Medicine, Stanford, California 94305, USA
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Affiliation(s)
- Mark A Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA.
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA.
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Corso M, Perreau F, Mouille G, Lepiniec L. Specialized phenolic compounds in seeds: structures, functions, and regulations. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2020; 296:110471. [PMID: 32540001 DOI: 10.1016/j.plantsci.2020.110471] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 03/11/2020] [Accepted: 03/13/2020] [Indexed: 05/24/2023]
Abstract
Plants produce a huge diversity of specialized metabolites (SM) throughout their life cycle that play important physiological and ecological functions. SM can protect plants and seeds against diseases, predators, and abiotic stresses, or support their interactions with beneficial or symbiotic organisms. They also have strong impacts on human nutrition and health. Despite this importance, the biosynthesis and biological functions of most of the SM remain elusive and their diversity and/or quantity have been reduced in most crops during domestication. Seeds present a large number of SM that are important for their physiological, agronomic, nutritional or industrial qualities and hence, provide interesting models for both studying biosynthesis and producing large amounts of specialized metabolites. For instance, phenolics are abundant and widely distributed in seeds. More specifically, flavonoid pathway has been instrumental for understanding environmental or developmental regulations of specialized metabolic pathways, at the molecular and cellular levels. Here, we summarize current knowledge on seed phenolics as model, and discuss how recent progresses in omics approaches could help to further characterize their diversity, regulations, and the underlying molecular mechanisms involved.
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Affiliation(s)
- Massimiliano Corso
- Institut Jean-Pierre Bourgin, Université Paris-Saclay, INRAE, AgroParisTech, 78000, Versailles, France.
| | - François Perreau
- Institut Jean-Pierre Bourgin, Université Paris-Saclay, INRAE, AgroParisTech, 78000, Versailles, France
| | - Grégory Mouille
- Institut Jean-Pierre Bourgin, Université Paris-Saclay, INRAE, AgroParisTech, 78000, Versailles, France
| | - Loïc Lepiniec
- Institut Jean-Pierre Bourgin, Université Paris-Saclay, INRAE, AgroParisTech, 78000, Versailles, France
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48
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Linder RA, Majumder A, Chakraborty M, Long A. Two Synthetic 18-Way Outcrossed Populations of Diploid Budding Yeast with Utility for Complex Trait Dissection. Genetics 2020; 215:323-342. [PMID: 32241804 PMCID: PMC7268983 DOI: 10.1534/genetics.120.303202] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 03/31/2020] [Indexed: 02/07/2023] Open
Abstract
Advanced-generation multiparent populations (MPPs) are a valuable tool for dissecting complex traits, having more power than genome-wide association studies to detect rare variants and higher resolution than F2 linkage mapping. To extend the advantages of MPPs in budding yeast, we describe the creation and characterization of two outbred MPPs derived from 18 genetically diverse founding strains. We carried out de novo assemblies of the genomes of the 18 founder strains, such that virtually all variation segregating between these strains is known, and represented those assemblies as Santa Cruz Genome Browser tracks. We discovered complex patterns of structural variation segregating among the founders, including a large deletion within the vacuolar ATPase VMA1, several different deletions within the osmosensor MSB2, a series of deletions and insertions at PRM7 and the adjacent BSC1, as well as copy number variation at the dehydrogenase ALD2 Resequenced haploid recombinant clones from the two MPPs have a median unrecombined block size of 66 kb, demonstrating that the population is highly recombined. We pool-sequenced the two MPPs to 3270× and 2226× coverage and demonstrated that we can accurately estimate local haplotype frequencies using pooled data. We further downsampled the pool-sequenced data to ∼20-40× and showed that local haplotype frequency estimates remained accurate, with median error rates 0.8 and 0.6% at 20× and 40×, respectively. Haplotypes frequencies are estimated much more accurately than SNP frequencies obtained directly from the same data. Deep sequencing of the two populations revealed that 10 or more founders are present at a detectable frequency for > 98% of the genome, validating the utility of this resource for the exploration of the role of standing variation in the architecture of complex traits.
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Affiliation(s)
- Robert A Linder
- Department of Ecology and Evolutionary Biology, School of Biological Sciences, University of California, Irvine, California 92697-2525
| | - Arundhati Majumder
- Department of Ecology and Evolutionary Biology, School of Biological Sciences, University of California, Irvine, California 92697-2525
| | - Mahul Chakraborty
- Department of Ecology and Evolutionary Biology, School of Biological Sciences, University of California, Irvine, California 92697-2525
| | - Anthony Long
- Department of Ecology and Evolutionary Biology, School of Biological Sciences, University of California, Irvine, California 92697-2525
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Pallares LF. Searching for solutions to the missing heritability problem. eLife 2019; 8:53018. [PMID: 31799931 PMCID: PMC6892610 DOI: 10.7554/elife.53018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/02/2019] [Indexed: 01/04/2023] Open
Abstract
Rare genetic variants in yeast explain a large amount of phenotypic variation in a complex trait like growth.
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Affiliation(s)
- Luisa F Pallares
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States.,Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States
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Fournier T, Abou Saada O, Hou J, Peter J, Caudal E, Schacherer J. Extensive impact of low-frequency variants on the phenotypic landscape at population-scale. eLife 2019; 8:49258. [PMID: 31647416 PMCID: PMC6892612 DOI: 10.7554/elife.49258] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 10/23/2019] [Indexed: 12/14/2022] Open
Abstract
Genome-wide association studies (GWAS) allow to dissect complex traits and map genetic variants, which often explain relatively little of the heritability. One potential reason is the preponderance of undetected low-frequency variants. To increase their allele frequency and assess their phenotypic impact in a population, we generated a diallel panel of 3025 yeast hybrids, derived from pairwise crosses between natural isolates and examined a large number of traits. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a third is governed by non-additive effects, with complete dominance having a key role. By performing GWAS on the diallel panel, we found that associated variants with low frequency in the initial population are overrepresented and explain a fraction of the phenotypic variance as well as an effect size similar to common variants. Overall, we highlighted the relevance of low-frequency variants on the phenotypic variation.
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Affiliation(s)
- Téo Fournier
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Omar Abou Saada
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jackson Peter
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Elodie Caudal
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
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