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Ren S, Li J, Dorado J, Sierra A, González-Díaz H, Duardo A, Shen B. From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine. Health Inf Sci Syst 2024; 12:6. [PMID: 38125666 PMCID: PMC10728428 DOI: 10.1007/s13755-023-00264-5] [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: 08/24/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.
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Affiliation(s)
- Shumin Ren
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Jiakun Li
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
| | - Julián Dorado
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Alejandro Sierra
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Humbert González-Díaz
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Aliuska Duardo
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Bairong Shen
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
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2
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Ribeiro TDS, Lollar MJ, Sprengelmeyer QD, Huang Y, Benson DM, Orr MS, Johnson ZC, Corbett-Detig RB, Pool JE. Recombinant inbred line panels inform the genetic architecture and interactions of adaptive traits in Drosophila melanogaster. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594228. [PMID: 38798433 PMCID: PMC11118405 DOI: 10.1101/2024.05.14.594228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The distribution of allelic effects on traits, along with their gene-by-gene and gene-by-environment interactions, contributes to the phenotypes available for selection and the trajectories of adaptive variants. Nonetheless, uncertainty persists regarding the effect sizes underlying adaptations and the importance of genetic interactions. Herein, we aimed to investigate the genetic architecture and the epistatic and environmental interactions involving loci that contribute to multiple adaptive traits using two new panels of Drosophila melanogaster recombinant inbred lines (RILs). To better fit our data, we re-implemented functions from R/qtl (Broman et al . 2003) using additive genetic models. We found 14 quantitative trait loci (QTL) underlying melanism, wing size, song pattern, and ethanol resistance. By combining our mapping results with population genetic statistics, we identified potential new genes related to these traits. None of the detected QTLs showed clear evidence of epistasis, and our power analysis indicated that we should have seen at least one significant interaction if sign epistasis or strong positive epistasis played a pervasive role in trait evolution. In contrast, we did find roles for gene-by-environment interactions involving pigmentation traits. Overall, our data suggest that the genetic architecture of adaptive traits often involves alleles of detectable effect, that strong epistasis does not always play a role in adaptation, and that environmental interactions can modulate the effect size of adaptive alleles.
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3
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Bylino OV, Ogienko AA, Batin MA, Georgiev PG, Omelina ES. Genetic, Environmental, and Stochastic Components of Lifespan Variability: The Drosophila Paradigm. Int J Mol Sci 2024; 25:4482. [PMID: 38674068 PMCID: PMC11050664 DOI: 10.3390/ijms25084482] [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: 01/04/2024] [Revised: 03/25/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024] Open
Abstract
Lifespan is a complex quantitative trait involving genetic and non-genetic factors as well as the peculiarities of ontogenesis. As with all quantitative traits, lifespan shows considerable variation within populations and between individuals. Drosophila, a favourite object of geneticists, has greatly advanced our understanding of how different forms of variability affect lifespan. This review considers the role of heritable genetic variability, phenotypic plasticity and stochastic variability in controlling lifespan in Drosophila melanogaster. We discuss the major historical milestones in the development of the genetic approach to study lifespan, the breeding of long-lived lines, advances in lifespan QTL mapping, the environmental factors that have the greatest influence on lifespan in laboratory maintained flies, and the mechanisms, by which individual development affects longevity. The interplay between approaches to study ageing and lifespan limitation will also be discussed. Particular attention will be paid to the interaction of different types of variability in the control of lifespan.
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Affiliation(s)
- Oleg V. Bylino
- Department of Regulation of Genetic Processes, Laboratory of Molecular Organization of the Genome, Institute of Gene Biology RAS, 119334 Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Institute of Gene Biology, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Anna A. Ogienko
- Department of Regulation of Genetic Processes, Institute of Molecular and Cellular Biology SB RAS, 630090 Novosibirsk, Russia
| | - Mikhail A. Batin
- Open Longevity, 15260 Ventura Blvd., Sherman Oaks, Los Angeles, CA 91403, USA
| | - Pavel G. Georgiev
- Department of Regulation of Genetic Processes, Laboratory of Molecular Organization of the Genome, Institute of Gene Biology RAS, 119334 Moscow, Russia
| | - Evgeniya S. Omelina
- Department of Regulation of Genetic Processes, Institute of Molecular and Cellular Biology SB RAS, 630090 Novosibirsk, Russia
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4
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Mackay TFC, Anholt RRH. Pleiotropy, epistasis and the genetic architecture of quantitative traits. Nat Rev Genet 2024:10.1038/s41576-024-00711-3. [PMID: 38565962 DOI: 10.1038/s41576-024-00711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2024] [Indexed: 04/04/2024]
Abstract
Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.
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Affiliation(s)
- Trudy F C Mackay
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
| | - Robert R H Anholt
- Center for Human Genetics, Clemson University, Greenwood, SC, USA.
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
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5
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Escamilla DM, Dietz N, Bilyeu K, Hudson K, Rainey KM. Genome-wide association study reveals GmFulb as candidate gene for maturity time and reproductive length in soybeans (Glycine max). PLoS One 2024; 19:e0294123. [PMID: 38241340 PMCID: PMC10798547 DOI: 10.1371/journal.pone.0294123] [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: 04/06/2023] [Accepted: 10/25/2023] [Indexed: 01/21/2024] Open
Abstract
The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits.
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Affiliation(s)
- Diana M. Escamilla
- Department of Agronomy, Purdue University, West Lafayette, Indiana, United States of America
| | - Nicholas Dietz
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, United States of America
| | - Kristin Bilyeu
- Plant Genetics Research Unit, United States Department of Agriculture (USDA)−Agricultural Research Service (ARS), Columbia, Missouri, United States of America
| | - Karen Hudson
- USDA-ARS Crop Production and Pest Control Research Unit, West Lafayette, Indiana, United States of America
| | - Katy Martin Rainey
- Department of Agronomy, Purdue University, West Lafayette, Indiana, United States of America
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Tang D, Freudenberg J, Dahl A. Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits. Am J Hum Genet 2023; 110:1875-1887. [PMID: 37922884 PMCID: PMC10645564 DOI: 10.1016/j.ajhg.2023.10.002] [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: 06/05/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.
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Affiliation(s)
- David Tang
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
| | - Jerome Freudenberg
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
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Alves K, Brito LF, Sargolzaei M, Schenkel FS. Genome-wide association studies for epistatic genetic effects on fertility and reproduction traits in Holstein cattle. J Anim Breed Genet 2023; 140:624-637. [PMID: 37350080 DOI: 10.1111/jbg.12813] [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: 12/21/2022] [Revised: 05/29/2023] [Accepted: 06/09/2023] [Indexed: 06/24/2023]
Abstract
Non-additive genetic effects are well known to play an important role in the phenotypic expression of complex traits, such as fertility and reproduction. In this study, a genome scan was performed using 41,640 single nucleotide polymorphism (SNP) markers to identify genomic regions associated with epistatic (additive-by-additive) effects in fertility and reproduction traits in Holstein cattle. Nine fertility and reproduction traits were analysed on 5825 and 6090 Holstein heifers and cows with phenotypes and genotypes, respectively. The Marginal Epistasis Test (MAPIT) was used to identify SNPs with significant marginal epistatic effects at a chromosome-wise 5% and 10% false discovery rate (FDR) level. The -log10 (p) values were adjusted by the genomic inflation factor (λ) to correct for the potential bias on the p-values and minimize the possible effects of population stratification. After adjustments, MAPIT enabled the identification of genomic regions with significant marginal epistatic effects for heifers on BTA5 for age at first insemination, BTA3 and BTA24 for non-return rate (NRR); BTA16 and BTA28 for gestation length (GL); BTA1, BTA4 and BTA17 for stillbirth (SB). For the cow traits, MAPIT enabled the identification of regions on BTA11 for GL, BTA11 and BTA16 for SB and BTA19 for calf size (CZ). An additional approach for mapping epistasis in a genome-wide association study was also proposed, in which the genome scan was performed using estimates of epistatic values as the input pseudo-phenotypes, computed using single-trait animal models. Significant SNPs were identified at the chromosome-wise 5% and 10% FDR levels for all traits. For the heifer traits, significant regions were found on BTA7 for AFS; BTA12 for NRR; BTA14 and BTA19 for GL; BTA19 for calving ease (CE); BTA5, BTA24, BTA25 and in the X chromosome for SB; BTA23 and in the X chromosome for CZ and in the X chromosome for the number of services (NS). For the cow traits, significant regions were found on BTA29 and in the X chromosome for NRR, BTA11, BTA16 and in the X chromosome for SB, BTA2 for GL, BTA28 for CZ, BTA19 for calving to first insemination, and in the X chromosome for NS and first insemination to conception. The results suggest that the epistatic genetic effects are likely due to many loci with a small effect rather than few loci with a large effect and/or a single SNP marker alone do not capture the epistatic effects well. The genomic architecture of fertility and reproduction traits is complex, and these results should be validated in independent dairy cattle populations and using alternative statistical models.
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Affiliation(s)
- Kristen Alves
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
- Bayer CropScience Inc., Guelph, Ontario, Canada
| | - Luiz F Brito
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Mehdi Sargolzaei
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
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8
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Alves K, Brito LF, Schenkel FS. Genomic prediction of fertility and calving traits in Holstein cattle based on models including epistatic genetic effects. J Anim Breed Genet 2023; 140:568-581. [PMID: 37254293 DOI: 10.1111/jbg.12810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/21/2023] [Accepted: 05/11/2023] [Indexed: 06/01/2023]
Abstract
The goal of this study was to investigate whether the inclusion of genomic information and epistatic (additive by additive) genetic effects would increase the accuracy of predicting phenotypes adjusted for known environmental effects, reduce prediction bias and minimize the confounding between additive and additive by additive epistatic effects on fertility and calving traits in Holstein cattle. Phenotypic and genotypic records were available for 6090 cows. Eight cow traits were assessed including 56-day nonreturn rate (NRR), number of services (NS), calving to first insemination (CTFS), first insemination to conception (FSTC), gestation length (GL), calving ease (CE), stillbirth (SB) and calf size (CZ). Four scenarios were assessed for their ability to predict adjusted phenotypes, which included: (1) traditional pedigree-based Best Linear Unbiased Prediction (P-BLUP) for additive genetic effects (PA); (2) P-BLUP for additive and epistatic (additive by additive) genetic effects (PAE); (3) genomic BLUP (G-BLUP) for additive genetic effects (GA); and (4) G-BLUP for additive and epistatic genetic effects (GAEn, where n = 1-3 depending on the alternative ways to construct the epistatic genomic matrix used). Constructing epistatic relationship matrix as the Hadamard product of the additive genomic relationship matrix (GAE1), which is the usual method and implicitly assumes a model that fits all pairwise interactions between markers twice and includes the interactions of the markers with themselves (dominance). Two additional constructions of the epistatic genomic relationship matrix were compared to test whether removing the double counting of interactions and the interaction of the markers with themselves (GAE2), and removing double counting of interactions between markers, but including the interaction of the markers with themselves (GAE3) would had an impact on the prediction and estimation error correlation (i.e. confounding) between additive and epistatic genetic effects. Fitting epistatic genetic effects explained up to 5.7% of the variance for NRR (GAE3), 7.7% for NS (GAE1), 11.9% for CTFS (GAE3), 11.1% for FSTC (GAE2), 25.7% for GL (GAE1), 2.3% for CE (GAE1), 14.3% for SB (GAE3) and 15.2% for CZ (GAE1). Despite a substantial proportion of variance being explained by epistatic effects for some traits, the prediction accuracies were similar or lower for GAE models compared with pedigree models and genomic models without epistatic effects. Although the prediction accuracy of direct genomic values did not change significantly between the three variations of the epistatic genetic relationship matrix used, removing the interaction of the markers with themselves reduced the confounding between additive and additive by additive epistatic effects. These results suggest that epistatic genetic effects contribute to the variance of some fertility and calving traits in Holstein cattle. However, the inclusion of epistatic genetic effects in the genomic prediction of these traits is complex and warrant further investigation.
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Affiliation(s)
- Kristen Alves
- Center for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - Luiz F Brito
- Center for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Flavio S Schenkel
- Center for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
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Wientjes YCJ, Bijma P, van den Heuvel J, Zwaan BJ, Vitezica ZG, Calus MPL. The long-term effects of genomic selection: 2. Changes in allele frequencies of causal loci and new mutations. Genetics 2023; 225:iyad141. [PMID: 37506255 PMCID: PMC10471209 DOI: 10.1093/genetics/iyad141] [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: 05/17/2023] [Revised: 05/17/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (∼15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles.
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Affiliation(s)
- Yvonne C J Wientjes
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, The Netherlands
| | - Piter Bijma
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, The Netherlands
| | - Joost van den Heuvel
- Laboratory of Genetics, Wageningen University & Research, 6700 AH Wageningen, The Netherlands
| | - Bas J Zwaan
- Laboratory of Genetics, Wageningen University & Research, 6700 AH Wageningen, The Netherlands
| | | | - Mario P L Calus
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, The Netherlands
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Rand MD, Tennessen JM, Mackay TFC, Anholt RRH. Perspectives on the Drosophila melanogaster Model for Advances in Toxicological Science. Curr Protoc 2023; 3:e870. [PMID: 37639638 PMCID: PMC10463236 DOI: 10.1002/cpz1.870] [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] [Indexed: 08/31/2023]
Abstract
The use of Drosophila melanogaster for studies of toxicology has grown considerably in the last decade. The Drosophila model has long been appreciated as a versatile and powerful model for developmental biology and genetics because of its ease of handling, short life cycle, low cost of maintenance, molecular genetic accessibility, and availability of a wide range of publicly available strains and data resources. These features, together with recent unique developments in genomics and metabolomics, make the fly model especially relevant and timely for the development of new approach methodologies and movements toward precision toxicology. Here, we offer a perspective on how flies can be leveraged to identify risk factors relevant to environmental exposures and human health. First, we review and discuss fundamental toxicologic principles for experimental design with Drosophila. Next, we describe quantitative and systems genetics approaches to resolve the genetic architecture and candidate pathways controlling susceptibility to toxicants. Finally, we summarize the current state and future promise of the emerging field of Drosophila metabolomics for elaborating toxic mechanisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
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Affiliation(s)
- Matthew D. Rand
- Department of Environmental Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | | | - Trudy F. C. Mackay
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, 114 Gregor Mendel Circle, Greenwood, South Carolina 29646, USA
| | - Robert R. H. Anholt
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, 114 Gregor Mendel Circle, Greenwood, South Carolina 29646, USA
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11
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Ang RML, Chen SAA, Kern AF, Xie Y, Fraser HB. Widespread epistasis among beneficial genetic variants revealed by high-throughput genome editing. CELL GENOMICS 2023; 3:100260. [PMID: 37082144 PMCID: PMC10112194 DOI: 10.1016/j.xgen.2023.100260] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/27/2022] [Accepted: 01/06/2023] [Indexed: 04/22/2023]
Abstract
The phenotypic effect of any genetic variant can be altered by variation at other genomic loci. Known as epistasis, these genetic interactions shape the genotype-phenotype map of every species, yet their origins remain poorly understood. To investigate this, we employed high-throughput genome editing to measure the fitness effects of 1,826 naturally polymorphic variants in four strains of Saccharomyces cerevisiae. About 31% of variants affect fitness, of which 24% have strain-specific fitness effects indicative of epistasis. We found that beneficial variants are more likely to exhibit genetic interactions and that these interactions can be mediated by specific traits such as flocculation ability. This work suggests that adaptive evolution will often involve trade-offs where a variant is only beneficial in some genetic backgrounds, potentially explaining why many beneficial variants remain polymorphic. In sum, we provide a framework to understand the factors influencing epistasis with single-nucleotide resolution, revealing widespread epistasis among beneficial variants.
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Affiliation(s)
- Roy Moh Lik Ang
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Shi-An A. Chen
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Alexander F. Kern
- Department of Genetics, Stanford University, 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
- Corresponding author
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Singhal P, Veturi Y, Dudek SM, Lucas A, Frase A, van Steen K, Schrodi SJ, Fasel D, Weng C, Pendergrass R, Schaid DJ, Kullo IJ, Dikilitas O, Sleiman PMA, Hakonarson H, Moore JH, Williams SM, Ritchie MD, Verma SS. Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets. Am J Hum Genet 2023; 110:575-591. [PMID: 37028392 PMCID: PMC10119154 DOI: 10.1016/j.ajhg.2023.03.007] [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: 12/12/2022] [Accepted: 03/07/2023] [Indexed: 04/09/2023] Open
Abstract
Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.
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Affiliation(s)
- Pankhuri Singhal
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yogasudha Veturi
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Scott M Dudek
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anastasia Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alex Frase
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kristel van Steen
- Department of Human Genetics, Katholieke Universiteit Leuven, ON4 Herestraat 49, 3000 Leuven, Belgium
| | - Steven J Schrodi
- Laboratory of Genetics, School of Medicine and Public Health, University of Wisconsin, Madison, WI 53706, USA
| | - David Fasel
- Columbia University, New York, NY 10027, USA
| | | | | | | | | | | | | | - Hakon Hakonarson
- Children's Hospital of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Scott M Williams
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Shefali S Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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13
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Zhang X, Zhu T, Wang L, Lv X, Yang W, Qu C, Li H, Wang H, Ning Z, Qu L. Genome-Wide Association Study Reveals the Genetic Basis of Duck Plumage Colors. Genes (Basel) 2023; 14:genes14040856. [PMID: 37107611 PMCID: PMC10137861 DOI: 10.3390/genes14040856] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 03/17/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
Plumage color is an artificially and naturally selected trait in domestic ducks. Black, white, and spotty are the main feather colors in domestic ducks. Previous studies have shown that black plumage color is caused by MC1R, and white plumage color is caused by MITF. We performed a genome-wide association study (GWAS) to identify candidate genes associated with white, black, and spotty plumage in ducks. Two non-synonymous SNPs in MC1R (c.52G>A and c.376G>A) were significantly related to duck black plumage, and three SNPs in MITF (chr13:15411658A>G, chr13:15412570T>C and chr13:15412592C>G) were associated with white plumage. Additionally, we also identified the epistatic interactions between causing loci. Some ducks with white plumage carry the c.52G>A and c.376G>A in MC1R, which also compensated for black and spotty plumage color phenotypes, suggesting that MC1R and MITF have an epistatic effect. The MITF locus was supposed to be an upstream gene to MC1R underlying the white, black, and spotty colors. Although the specific mechanism remains to be further clarified, these findings support the importance of epistasis in plumage color variation in ducks.
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Affiliation(s)
- Xinye Zhang
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Yuanmingyuan West Road 2, Beijing 100193, China
| | - Tao Zhu
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Yuanmingyuan West Road 2, Beijing 100193, China
| | - Liang Wang
- Beijing Municipal General Station of Animal Science, Beijing 100107, China
| | - Xueze Lv
- Beijing Municipal General Station of Animal Science, Beijing 100107, China
| | - Weifang Yang
- Beijing Municipal General Station of Animal Science, Beijing 100107, China
| | - Changqing Qu
- Engineering Technology Research Center of Anti-Aging Chinese Herbal Medicine of Anhui Province, Fuyang Normal University, Fuyang 236037, China
| | - Haiying Li
- College of Animal Science, Xinjiang Agricultural University, Urumchi 830052, China
| | - Huie Wang
- College of Animal Science, Tarim University, Alar 843300, China
| | - Zhonghua Ning
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Yuanmingyuan West Road 2, Beijing 100193, China
| | - Lujiang Qu
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Yuanmingyuan West Road 2, Beijing 100193, China
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14
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Anholt RRH, Mackay TFC. The genetic architecture of behavioral canalization. Trends Genet 2023:S0168-9525(23)00033-1. [PMID: 36878820 DOI: 10.1016/j.tig.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 03/07/2023]
Abstract
Behaviors are components of fitness and contribute to adaptive evolution. Behaviors represent the interactions of an organism with its environment, yet innate behaviors display robustness in the face of environmental change, which we refer to as 'behavioral canalization'. We hypothesize that positive selection of hub genes of genetic networks stabilizes the genetic architecture for innate behaviors by reducing variation in the expression of interconnected network genes. Robustness of these stabilized networks would be protected from deleterious mutations by purifying selection or suppressing epistasis. We propose that, together with newly emerging favorable mutations, epistatically suppressed mutations can generate a reservoir of cryptic genetic variation that could give rise to decanalization when genetic backgrounds or environmental conditions change to allow behavioral adaptation.
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Affiliation(s)
- Robert R H Anholt
- Department of Genetics and Biochemistry and Center for Human Genetics, Clemson University, 114 Gregor Mendel Circle, Greenwood, SC 29646, USA.
| | - Trudy F C Mackay
- Department of Genetics and Biochemistry and Center for Human Genetics, Clemson University, 114 Gregor Mendel Circle, Greenwood, SC 29646, USA
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15
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Palmer W, Jacygrad E, Sagayaradj S, Cavanaugh K, Han R, Bertier L, Beede B, Kafkas S, Golino D, Preece J, Michelmore R. Genome assembly and association tests identify interacting loci associated with vigor, precocity, and sex in interspecific pistachio rootstocks. G3 (BETHESDA, MD.) 2022; 13:6861913. [PMID: 36454230 PMCID: PMC9911073 DOI: 10.1093/g3journal/jkac317] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 12/03/2022]
Abstract
Understanding the basis of hybrid vigor remains a key question in crop breeding and improvement, especially for rootstock development where F1 hybrids are extensively utilized. Full-sibling UCB-1 F1 seedling rootstocks are widely planted in commercial pistachio orchards that are generated by crossing 2 highly heterozygous outbreeding parental trees of Pistacia atlantica (female) and P. integerrima (male). This results in extensive phenotypic variability, prompting costly removal of low-yielding small trees. To identify the genetic basis of this variability, we assembled chromosome-scale genome assemblies of the parental trees of UCB-1. We genotyped 960 UCB-1 trees in an experimental orchard for which we also collected multiyear phenotypes. We genotyped an additional 1,358 rootstocks in 6 commercial pistachio orchards and collected single-year tree-size data. Genome-wide single marker association tests identified loci associated with tree size and shape, sex, and precocity. In the experimental orchard, we identified multiple trait-associated loci and a strong candidate for ZZ/ZW sex chromosomes. We found significant marker associations unique to different traits and to early vs late phenotypic measures of the same trait. We detected 2 loci strongly associated with rootstock size in commercial orchards. Pseudo-testcross classification of markers demonstrated that the trait-associated alleles for each locus were segregating in the gametes of opposite parents. These 2 loci interact epistatically to generate the bimodal distribution of tree size with undesirable small trees observed by growers. We identified candidate genes within these regions. These findings provide a foundational resource for marker development and genetic selection of vigorous pistachio UCB-1 rootstock.
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Affiliation(s)
- William Palmer
- Genome Center, University of California, Davis, One Shields Ave, Davis, CA 95616, USA,Present address: Gencove, 30-02 48th Avenue, Suite 370, Long Island City, NY 11101, USA
| | - Ewelina Jacygrad
- Genome Center, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
| | - Sagayamary Sagayaradj
- Genome Center, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
| | - Keri Cavanaugh
- Genome Center, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
| | - Rongkui Han
- Genome Center, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
| | - Lien Bertier
- Genome Center, University of California, Davis, One Shields Ave, Davis, CA 95616, USA,Present address: Ohalo Genetics, 9565 Soquel Dr. Suite 101, Aptos, CA 95003, USA
| | - Bob Beede
- UC Cooperative Extension, 680 North Campus Dr., Hanford, CA 93230, USA
| | - Salih Kafkas
- Department of Horticulture, University of Çukurova, 01330 Adana, Turkey
| | - Deborah Golino
- Foundation Plant Services, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
| | - John Preece
- National Clonal Germplasm Repository, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
| | - Richard Michelmore
- Corresponding author: Departments of Plant Sciences, Molecular & Cellular Biology, Medical Microbiology and Immunology, University of California, Davis, One Shields Ave, Davis, CA, 95616, USA.
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16
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Saha S, Spinelli L, Castro Mondragon JA, Kervadec A, Lynott M, Kremmer L, Roder L, Krifa S, Torres M, Brun C, Vogler G, Bodmer R, Colas AR, Ocorr K, Perrin L. Genetic architecture of natural variation of cardiac performance from flies to humans. eLife 2022; 11:82459. [DOI: 10.7554/elife.82459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 10/25/2022] [Indexed: 11/17/2022] Open
Abstract
Deciphering the genetic architecture of human cardiac disorders is of fundamental importance but their underlying complexity is a major hurdle. We investigated the natural variation of cardiac performance in the sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP). Genome-wide associations studies (GWAS) identified genetic networks associated with natural variation of cardiac traits which were used to gain insights as to the molecular and cellular processes affected. Non-coding variants that we identified were used to map potential regulatory non-coding regions, which in turn were employed to predict transcription factors (TFs) binding sites. Cognate TFs, many of which themselves bear polymorphisms associated with variations of cardiac performance, were also validated by heart-specific knockdown. Additionally, we showed that the natural variations associated with variability in cardiac performance affect a set of genes overlapping those associated with average traits but through different variants in the same genes. Furthermore, we showed that phenotypic variability was also associated with natural variation of gene regulatory networks. More importantly, we documented correlations between genes associated with cardiac phenotypes in both flies and humans, which supports a conserved genetic architecture regulating adult cardiac function from arthropods to mammals. Specifically, roles for PAX9 and EGR2 in the regulation of the cardiac rhythm were established in both models, illustrating that the characteristics of natural variations in cardiac function identified in Drosophila can accelerate discovery in humans.
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Affiliation(s)
- Saswati Saha
- Aix-Marseille University, INSERM, TAGC, Turing Center for Living systems
| | - Lionel Spinelli
- Aix-Marseille University, INSERM, TAGC, Turing Center for Living systems
| | | | - Anaïs Kervadec
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute
| | - Michaela Lynott
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute
| | - Laurent Kremmer
- Aix-Marseille University, INSERM, TAGC, Turing Center for Living systems
| | - Laurence Roder
- Aix-Marseille University, INSERM, TAGC, Turing Center for Living systems
| | - Sallouha Krifa
- Aix-Marseille University, INSERM, TAGC, Turing Center for Living systems
| | - Magali Torres
- Aix-Marseille University, INSERM, TAGC, Turing Center for Living systems
| | - Christine Brun
- Aix-Marseille University, INSERM, TAGC, Turing Center for Living systems
- CNRS
| | - Georg Vogler
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute
| | - Rolf Bodmer
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute
| | - Alexandre R Colas
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute
| | - Karen Ocorr
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute
| | - Laurent Perrin
- Aix-Marseille University, INSERM, TAGC, Turing Center for Living systems
- CNRS
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17
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Li P, Li G, Zhang YW, Zuo JF, Liu JY, Zhang YM. A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an F 2 population. PLANT COMMUNICATIONS 2022; 3:100319. [PMID: 35576159 PMCID: PMC9251438 DOI: 10.1016/j.xplc.2022.100319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 03/07/2022] [Accepted: 03/22/2022] [Indexed: 06/09/2023]
Abstract
Theoretical and applied studies demonstrate the difficulty of detecting extremely over-dominant and small-effect genes for quantitative traits via bulked segregant analysis (BSA) in an F2 population. To address this issue, we proposed an integrated strategy for mapping various types of quantitative trait loci (QTLs) for quantitative traits via a combination of BSA and whole-genome sequencing. In this strategy, the numbers of read counts of marker alleles in two extreme pools were used to predict the numbers of read counts of marker genotypes. These observed and predicted numbers were used to construct a new statistic, Gw, for detecting quantitative trait genes (QTGs), and the method was named dQTG-seq1. This method was significantly better than existing BSA methods. If the goal was to identify extremely over-dominant and small-effect genes, another reserved DNA/RNA sample from each extreme phenotype F2 plant was sequenced, and the observed numbers of marker alleles and genotypes were used to calculate Gw to detect QTGs; this method was named dQTG-seq2. In simulated and real rice dataset analyses, dQTG-seq2 could identify many more extremely over-dominant and small-effect genes than BSA and QTL mapping methods. dQTG-seq2 may be extended to other heterogeneous mapping populations. The significance threshold of Gw in this study was determined by permutation experiments. In addition, a handbook for the R software dQTG.seq, which is available at https://cran.r-project.org/web/packages/dQTG.seq/index.html, has been provided in the supplemental materials for the users' convenience. This study provides a new strategy for identifying all types of QTLs for quantitative traits in an F2 population.
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Affiliation(s)
- Pei Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Guo Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Wen Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jian-Fang Zuo
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jin-Yang Liu
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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18
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Matsui T, Mullis MN, Roy KR, Hale JJ, Schell R, Levy SF, Ehrenreich IM. The interplay of additivity, dominance, and epistasis on fitness in a diploid yeast cross. Nat Commun 2022; 13:1463. [PMID: 35304450 PMCID: PMC8933436 DOI: 10.1038/s41467-022-29111-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 02/22/2022] [Indexed: 12/27/2022] Open
Abstract
In diploid species, genetic loci can show additive, dominance, and epistatic effects. To characterize the contributions of these different types of genetic effects to heritable traits, we use a double barcoding system to generate and phenotype a panel of ~200,000 diploid yeast strains that can be partitioned into hundreds of interrelated families. This experiment enables the detection of thousands of epistatic loci, many whose effects vary across families. Here, we show traits are largely specified by a small number of hub loci with major additive and dominance effects, and pervasive epistasis. Genetic background commonly influences both the additive and dominance effects of loci, with multiple modifiers typically involved. The most prominent dominance modifier in our data is the mating locus, which has no effect on its own. Our findings show that the interplay between additivity, dominance, and epistasis underlies a complex genotype-to-phenotype map in diploids.
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Affiliation(s)
- Takeshi Matsui
- Joint Initiative for Metrology in Biology, Stanford, CA, 94305, USA
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Martin N Mullis
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA
- Twist Bioscience, 681 Gateway Blvd, South San Francisco, CA, 94080, USA
| | - Kevin R Roy
- Joint Initiative for Metrology in Biology, Stanford, CA, 94305, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA
| | - Joseph J Hale
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA
| | - Rachel Schell
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA
| | - Sasha F Levy
- Joint Initiative for Metrology in Biology, Stanford, CA, 94305, USA.
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
| | - Ian M Ehrenreich
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA.
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19
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Wientjes YCJ, Bijma P, Calus MPL, Zwaan BJ, Vitezica ZG, van den Heuvel J. The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture. Genet Sel Evol 2022; 54:19. [PMID: 35255802 PMCID: PMC8900405 DOI: 10.1186/s12711-022-00709-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 02/10/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Genomic selection has revolutionized genetic improvement in animals and plants, but little is known about its long-term effects. Here, we investigated the long-term effects of genomic selection on response to selection, genetic variance, and the genetic architecture of traits using stochastic simulations. We defined the genetic architecture as the set of causal loci underlying each trait, their allele frequencies, and their statistical additive effects. We simulated a livestock population under 50 generations of phenotypic, pedigree, or genomic selection for a single trait, controlled by either only additive, additive and dominance, or additive, dominance, and epistatic effects. The simulated epistasis was based on yeast data.
Results
Short-term response was always greatest with genomic selection, while response after 50 generations was greater with phenotypic selection than with genomic selection when epistasis was present, and was always greater than with pedigree selection. This was mainly because loss of genetic variance and of segregating loci was much greater with genomic and pedigree selection than with phenotypic selection. Compared to pedigree selection, selection response was always greater with genomic selection. Pedigree and genomic selection lost a similar amount of genetic variance after 50 generations of selection, but genomic selection maintained more segregating loci, which on average had lower minor allele frequencies than with pedigree selection. Based on this result, genomic selection is expected to better maintain genetic gain after 50 generations than pedigree selection. The amount of change in the genetic architecture of traits was considerable across generations and was similar for genomic and pedigree selection, but slightly less for phenotypic selection. Presence of epistasis resulted in smaller changes in allele frequencies and less fixation of causal loci, but resulted in substantial changes in statistical additive effects across generations.
Conclusions
Our results show that genomic selection outperforms pedigree selection in terms of long-term genetic gain, but results in a similar reduction of genetic variance. The genetic architecture of traits changed considerably across generations, especially under selection and when non-additive effects were present. In conclusion, non-additive effects had a substantial impact on the accuracy of selection and long-term response to selection, especially when selection was accurate.
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20
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Macdonald SJ, Cloud-Richardson KM, Sims-West DJ, Long AD. Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing. Genetics 2022; 220:iyab238. [PMID: 35100395 PMCID: PMC8893256 DOI: 10.1093/genetics/iyab238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/19/2021] [Indexed: 01/22/2024] Open
Abstract
Despite the value of recombinant inbred lines for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to recombinant inbred lines for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here, we describe such an extreme quantitative trait locus, or extreme quantitative trait loci, mapping strategy that builds on an existing multiparental population, the Drosophila Synthetic Population Resource, and involves phenotyping and genotyping a population derived by mixing hundreds of Drosophila Synthetic Population Resource recombinant inbred lines. Simulations demonstrate that challenging, yet experimentally tractable extreme quantitative trait loci designs (≥4 replicates, ≥5,000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional recombinant inbred line-based quantitative trait loci mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated extreme quantitative trait loci experiment that identifies 7 quantitative trait loci for caffeine resistance. Two mapped extreme quantitative trait loci factors replicate loci previously identified in recombinant inbred lines, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping extreme quantitative trait loci design has considerable advantages.
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Affiliation(s)
- Stuart J Macdonald
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS 66047, USA
| | | | - Dylan J Sims-West
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045, USA
| | - Anthony D Long
- Department of Ecology and Evolutionary Biology, University of California at Irvine, Irvine, CA 92697, USA
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21
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Benowitz KM, Allan CW, Degain BA, Li X, Fabrick JA, Tabashnik BE, Carrière Y, Matzkin LM. Novel genetic basis of resistance to Bt toxin Cry1Ac in Helicoverpa zea. Genetics 2022; 221:6540856. [PMID: 35234875 PMCID: PMC9071530 DOI: 10.1093/genetics/iyac037] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 02/25/2022] [Indexed: 11/14/2022] Open
Abstract
Crops genetically engineered to produce insecticidal proteins from the bacterium Bacillus thuringiensis have advanced pest management, but their benefits are diminished when pests evolve resistance. Elucidating the genetic basis of pest resistance to Bacillus thuringiensis toxins can improve resistance monitoring, resistance management, and the design of new insecticides. Here, we investigated the genetic basis of resistance to Bacillus thuringiensis toxin Cry1Ac in the lepidopteran Helicoverpa zea, one of the most damaging crop pests in the United States. To facilitate this research, we built the first chromosome-level genome assembly for this species, which has 31 chromosomes containing 375 Mb and 15,482 predicted proteins. Using a genome-wide association study, fine-scale mapping, and RNA-seq, we identified a 250-kb quantitative trait locus on chromosome 13 that was strongly associated with resistance in a strain of Helicoverpa zea that had been selected for resistance in the field and lab. The mutation in this quantitative trait locus contributed to but was not sufficient for resistance, which implies alleles in more than one gene contributed to resistance. This quantitative trait locus contains no genes with a previously reported role in resistance or susceptibility to Bacillus thuringiensis toxins. However, in resistant insects, this quantitative trait locus has a premature stop codon in a kinesin gene, which is a primary candidate as a mutation contributing to resistance. We found no changes in gene sequence or expression consistently associated with resistance for 11 genes previously implicated in lepidopteran resistance to Cry1Ac. Thus, the results reveal a novel and polygenic basis of resistance.
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Affiliation(s)
- Kyle M Benowitz
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA,Department of Biology, Austin Peay State University, Clarksville, TN 37040, USA,Corresponding author: Department of Biology, Austin Peay State University, Sundquist Science Center, 681 Summer St., Clarksville, TN 37040, USA.
| | - Carson W Allan
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA
| | - Benjamin A Degain
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA
| | - Xianchun Li
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA
| | - Jeffrey A Fabrick
- U.S. Department of Agriculture, Agricultural Research Service, U.S. Arid Land Agricultural Research Center, Maricopa, AZ 85138, USA
| | - Bruce E Tabashnik
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA
| | - Yves Carrière
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA
| | - Luciano M Matzkin
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA,Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA,Bio5 Institute, University of Arizona, Tucson, AZ 85721, USA
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22
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Rand DM, Mossman JA, Spierer AN, Santiago JA. Mitochondria as environments for the nuclear genome in Drosophila: mitonuclear G×G×E. J Hered 2022; 113:37-47. [PMID: 34964900 PMCID: PMC8851671 DOI: 10.1093/jhered/esab066] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 10/21/2021] [Indexed: 12/13/2022] Open
Abstract
Mitochondria evolved from a union of microbial cells belonging to distinct lineages that were likely anaerobic. The evolution of eukaryotes required a massive reorganization of the 2 genomes and eventual adaptation to aerobic environments. The nutrients and oxygen that sustain eukaryotic metabolism today are processed in mitochondria through coordinated expression of 37 mitochondrial genes and over 1000 nuclear genes. This puts mitochondria at the nexus of gene-by-gene (G×G) and gene-by-environment (G×E) interactions that sustain life. Here we use a Drosophila model of mitonuclear genetic interactions to explore the notion that mitochondria are environments for the nuclear genome, and vice versa. We construct factorial combinations of mtDNA and nuclear chromosomes to test for epistatic interactions (G×G), and expose these mitonuclear genotypes to altered dietary environments to examine G×E interactions. We use development time and genome-wide RNAseq analyses to assess the relative contributions of mtDNA, nuclear chromosomes, and environmental effects on these traits (mitonuclear G×G×E). We show that the nuclear transcriptional response to alternative mitochondrial "environments" (G×G) has significant overlap with the transcriptional response of mitonuclear genotypes to altered dietary environments. These analyses point to specific transcription factors (e.g., giant) that mediated these interactions, and identified coexpressed modules of genes that may account for the overlap in differentially expressed genes. Roughly 20% of the transcriptome includes G×G genes that are concordant with G×E genes, suggesting that mitonuclear interactions are part of an organism's environment.
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Affiliation(s)
- David M Rand
- Department of Ecology, Evolution and Organismal Biology, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
| | - James A Mossman
- Department of Ecology, Evolution and Organismal Biology, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
| | - Adam N Spierer
- Department of Ecology, Evolution and Organismal Biology, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
| | - John A Santiago
- Department of Molecular Biology, Cellular Biology, and Biochemistry, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
- Department of Pathology and Laboratory Medicine, Brown University, 80 Waterman Street, Providence, Rhode Island 02912, USA
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23
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Raben TG, Lello L, Widen E, Hsu SDH. From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits. Methods Mol Biol 2022; 2467:421-446. [PMID: 35451785 DOI: 10.1007/978-1-0716-2205-6_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.
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Affiliation(s)
| | - Louis Lello
- Michigan State University, East Lansing, MI, USA
- Genomic Prediction, North Brunswick, NJ, USA
| | - Erik Widen
- Michigan State University, East Lansing, MI, USA
| | - Stephen D H Hsu
- Michigan State University, East Lansing, MI, USA.
- Genomic Prediction, North Brunswick, NJ, USA.
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24
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Davoodi P, Ehsani A, Vaez Torshizi R, Masoudi AA. New insights into genetics underlying of plumage color. Anim Genet 2021; 53:80-93. [PMID: 34855995 DOI: 10.1111/age.13156] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2021] [Indexed: 01/12/2023]
Abstract
Plumage color can be considered as a social signal in chickens and a breeding identification tool among breeders. The relationship between plumage color and trait groups of immunity, growth and fertility is still a controversial issue. This research aimed to determine the genome-wide additive and epistatic variants affecting plumage color variation in chickens using the chicken Illumina 60k high-density SNP array. Two scenarios of genome-wide additive association studies using all SNPs and independent SNPs were carried out. To perform epistatic association analysis, the LD pruning approach was used to reduce the complexity of the analysis. We detected seven novel significant loci using all of the SNPs in the model and 14 SNPs using the LD pruning approach associated with plumage color. Moreover, 89 significantly associated SNP-SNP interactions (P-value <10-6 ) distributed in 25 chromosomes were identified, indicating that all of the signals together putatively influence the quantitative variation of plumage color. By annotating genes relevant to top SNPs, we have distinguished 18 potential candidate genes comprising HNF4beta, CKMT1B, TBC1D22A, RPL8, CACNA2D1, FZD4, SGMS1, IRF8, OPTN, LOC420362, TRABD, OvoDA1, DAD1, USP6, RBM12B, MIR1772, MIR1709 and MIR6696 and also 89 putative gene-gene combinations responsible for plumage color variation in chickens. Furthermore, several KEGG pathways including metabolic pathway, cytokine-cytokine receptor interaction, focal adhesion, melanogenesis, glycosaminoglycan biosynthesis-keratan sulfate and sphingolipid metabolism were enriched in the gene-set analysis. The results indicated that plumage color is a highly polygenic trait which, in turn, can be affected by multiple coding genes, regulatory genes and gene-gene epistasis interactions. In addition to genes with additive effects, epistatic genes with tiny individual effect sizes but significant effects in a pair have the potential to control plumage coloration in chickens.
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Affiliation(s)
- P Davoodi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, 14115-336, Tehran, Iran
| | - A Ehsani
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, 14115-336, Tehran, Iran
| | - R Vaez Torshizi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, 14115-336, Tehran, Iran
| | - A A Masoudi
- Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, 14115-336, Tehran, Iran
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25
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Hansen TF, Pélabon C. Evolvability: A Quantitative-Genetics Perspective. ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS 2021. [DOI: 10.1146/annurev-ecolsys-011121-021241] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The concept of evolvability emerged in the early 1990s and soon became fashionable as a label for different streams of research in evolutionary biology. In evolutionary quantitative genetics, evolvability is defined as the ability of a population to respond to directional selection. This differs from other fields by treating evolvability as a property of populations rather than organisms or lineages and in being focused on quantification and short-term prediction rather than on macroevolution. While the term evolvability is new to quantitative genetics, many of the associated ideas and research questions have been with the field from its inception as biometry. Recent research on evolvability is more than a relabeling of old questions, however. New operational measures of evolvability have opened possibilities for understanding adaptation to rapid environmental change, assessing genetic constraints, and linking micro- and macroevolution.
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Affiliation(s)
- Thomas F. Hansen
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway
| | - Christophe Pélabon
- Center for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
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26
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Islam MS, McCord PH, Olatoye MO, Qin L, Sood S, Lipka AE, Todd JR. Experimental evaluation of genomic selection prediction for rust resistance in sugarcane. THE PLANT GENOME 2021; 14:e20148. [PMID: 34510803 DOI: 10.1002/tpg2.20148] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/22/2021] [Indexed: 06/13/2023]
Abstract
The total sugarcane (Saccharum L.) production has increased worldwide; however, the rate of growth is lower compared with other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. To investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 432 sugarcane clones using an augmented design with two replications. Two major diseases in sugarcane, brown and orange rust (BR and OR), were screened artificially using whorl inoculation method in the field over two crop cycles. The genotypic data were generated through target enrichment sequencing technologies. After filtering, a set of 8,825 single nucleotide polymorphic markers were used to assess the prediction accuracy of multiple GS models. Using fivefold cross-validation, we observed GS prediction accuracies for BR and OR that ranged from 0.28 to 0.43 and 0.13 to 0.29, respectively, across two crop cycles and combined cycles. The prediction ability further improved by including a known major gene for resistance to BR as a fixed effect in the GS model. It also substantially reduced the minimum number of markers and training population size required for GS. The nonparametric GS models outperformed the parametric GS suggesting that nonadditive genetic effects could contribute genomic sources underlying BR and OR. This study demonstrated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding for disease resistance.
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Affiliation(s)
- Md S Islam
- Sugarcane Production Research Unit, USDA-ARS, Canal Point, FL, USA
| | - Per H McCord
- Sugarcane Production Research Unit, USDA-ARS, Canal Point, FL, USA
- Current address: Irrigated Agriculture Research and Extension Center, WA State Univ., Prosser, WA, USA
| | - Marcus O Olatoye
- Dep. of Crop Sciences, Univ. of Illinois, Urbana-Champaign, IL, USA
| | - Lifang Qin
- Sugarcane Production Research Unit, USDA-ARS, Canal Point, FL, USA
- Current address: Guangxi Univ., Nanning, Guangxi, China
| | - Sushma Sood
- Sugarcane Production Research Unit, USDA-ARS, Canal Point, FL, USA
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27
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A systematic analysis of genetic interactions and their underlying biology in childhood cancer. Commun Biol 2021; 4:1139. [PMID: 34615983 PMCID: PMC8494736 DOI: 10.1038/s42003-021-02647-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 09/08/2021] [Indexed: 02/08/2023] Open
Abstract
Childhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.
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28
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Özsoy ED, Yılmaz M, Patlar B, Emecen G, Durmaz E, Magwire MM, Zhou S, Huang W, Anholt RRH, Mackay TFC. Epistasis for head morphology in Drosophila melanogaster. G3 (BETHESDA, MD.) 2021; 11:jkab285. [PMID: 34568933 PMCID: PMC8473977 DOI: 10.1093/g3journal/jkab285] [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] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/28/2021] [Indexed: 11/12/2022]
Abstract
Epistasis-gene-gene interaction-is common for mutations with large phenotypic effects in humans and model organisms. Epistasis impacts quantitative genetic models of speciation, response to natural and artificial selection, genetic mapping, and personalized medicine. However, the existence and magnitude of epistasis between alleles with small quantitative phenotypic effects are controversial and difficult to assess. Here, we use the Drosophila melanogaster Genetic Reference Panel of sequenced inbred lines to evaluate the magnitude of naturally occurring epistasis modifying the effects of mutations in jing and inv, two transcription factors that have subtle quantitative effects on head morphology as homozygotes. We find significant epistasis for both mutations and performed single marker genome-wide association analyses to map candidate modifier variants and loci affecting head morphology. A subset of these loci was significantly enriched for a known genetic interaction network, and mutations of the candidate epistatic modifier loci also affect head morphology.
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Affiliation(s)
- Ergi D Özsoy
- Department of Biology, Functional and Evolutionary Genetics Laboratory (FEGL), Science Faculty, Hacettepe University, 06800 Beytepe, Ankara, Turkey
| | - Murat Yılmaz
- Department of Biology, Functional and Evolutionary Genetics Laboratory (FEGL), Science Faculty, Hacettepe University, 06800 Beytepe, Ankara, Turkey
| | - Bahar Patlar
- Department of Biology, Functional and Evolutionary Genetics Laboratory (FEGL), Science Faculty, Hacettepe University, 06800 Beytepe, Ankara, Turkey
| | - Güzin Emecen
- Department of Biology, Functional and Evolutionary Genetics Laboratory (FEGL), Science Faculty, Hacettepe University, 06800 Beytepe, Ankara, Turkey
| | - Esra Durmaz
- Department of Biology, Functional and Evolutionary Genetics Laboratory (FEGL), Science Faculty, Hacettepe University, 06800 Beytepe, Ankara, Turkey
| | - Michael M Magwire
- Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA
| | - Shanshan Zhou
- Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA
| | - Wen Huang
- Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA
| | - Robert R H Anholt
- Department of Genetics, North Carolina State University, Raleigh, NC 27695-7614, USA
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7614, USA
| | - Trudy F C Mackay
- Department of Genetics, North Carolina State University, Raleigh, NC 27695-7614, USA
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7614, USA
- Department of Genetics and Biochemistry, Center for Human Genetics, Clemson University, Greenwood, SC 29646, USA
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29
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MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes. BIOLOGY 2021; 10:biology10090921. [PMID: 34571798 PMCID: PMC8469369 DOI: 10.3390/biology10090921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 11/17/2022]
Abstract
Simple Summary The interactions between SNPs, which are known as epistasis, can strongly influence the phenotype. Their detection is still a challenge, which is made even more difficult through the existence of background associations that can hide correct epistatic interactions. To address the limitations of existing methods, we present in this study our novel method MIDESP for the detection of epistatic SNP pairs. It is the first mutual information-based method that can be applied to both qualitative and quantitative phenotypes and which explicitly accounts for background associations in the dataset. Abstract The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.
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30
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Vojgani E, Pook T, Martini JWR, Hölker AC, Mayer M, Schön CC, Simianer H. Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2913-2930. [PMID: 34115154 PMCID: PMC8354961 DOI: 10.1007/s00122-021-03868-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 05/24/2021] [Indexed: 06/12/2023]
Abstract
The accuracy of genomic prediction of phenotypes can be increased by including the top-ranked pairwise SNP interactions into the prediction model. We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from two European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested at six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings for predictions within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Across all studied traits, locations and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from -0.3 to + 27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP, with an average increase of 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for "sparse testing" approaches in which only a subset of the lines/hybrids of interest is observed at each location.
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Affiliation(s)
- Elaheh Vojgani
- Center for Integrated Breeding Research, Animal Breeding and Genetics Group, University of Goettingen, Goettingen, Germany.
| | - Torsten Pook
- Center for Integrated Breeding Research, Animal Breeding and Genetics Group, University of Goettingen, Goettingen, Germany
| | - Johannes W R Martini
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, State of Mexico, Mexico
| | - Armin C Hölker
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manfred Mayer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Henner Simianer
- Center for Integrated Breeding Research, Animal Breeding and Genetics Group, University of Goettingen, Goettingen, Germany
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31
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Amorim ST, Stafuzza NB, Kluska S, Peripolli E, Pereira ASC, Muller da Silveira LF, de Albuquerque LG, Baldi F. Genome-wide interaction study reveals epistatic interactions for beef lipid-related traits in Nellore cattle. Anim Genet 2021; 53:35-48. [PMID: 34407235 DOI: 10.1111/age.13124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2021] [Indexed: 11/27/2022]
Abstract
Gene-gene interactions cause hidden genetic variation in natural populations and could be responsible for the lack of replication that is typically observed in complex traits studies. This study aimed to identify gene-gene interactions using the empirical Hilbert-Schmidt Independence Criterion method to test for epistasis in beef fatty acid profile traits of Nellore cattle. The dataset contained records from 963 bulls, genotyped using a 777 962k SNP chip. Meat samples of Longissimus muscle, were taken to measure fatty acid composition, which was quantified by gas chromatography. We chose to work with the sums of saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA), omega-3 (OM3), omega-6 (OM6), SFA:PUFA and OM3:OM6 fatty acid ratios. The SNPs in the interactions where P < 10 - 8 were mapped individually and used to search for candidate genes. Totals of 602, 3, 13, 23, 13, 215 and 169 candidate genes for SFAs, MUFAs, PUFAs, OM3s, OM6s and SFA:PUFA and OM3:OM6 ratios were identified respectively. The candidate genes found were associated with cholesterol, lipid regulation, low-density lipoprotein receptors, feed efficiency and inflammatory response. Enrichment analysis revealed 57 significant GO and 18 KEGG terms ( P < 0.05), most of them related to meat quality and complementary terms. Our results showed substantial genetic interactions associated with lipid profile, meat quality, carcass and feed efficiency traits for the first time in Nellore cattle. The knowledge of these SNP-SNP interactions could improve understanding of the genetic and physiological mechanisms that contribute to lipid-related traits and improve human health by the selection of healthier meat products.
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Affiliation(s)
- S T Amorim
- Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Via de acesso Prof. Paulo Donato Castellane, s/no, Jaboticabal, CEP 14884-900, Brazil
| | - N B Stafuzza
- Instituto de Zootecnia - Centro de Pesquisa em Bovinos de Corte, Rodovia Carlos Tonanni, Km94, Sertãozinho, 14174-000, Brazil
| | - S Kluska
- Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Via de acesso Prof. Paulo Donato Castellane, s/no, Jaboticabal, CEP 14884-900, Brazil
| | - E Peripolli
- Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Via de acesso Prof. Paulo Donato Castellane, s/no, Jaboticabal, CEP 14884-900, Brazil
| | - A S C Pereira
- Faculdade de Zootecnia e Engenharia de Alimentos, Núcleo de Apoio à Pesquisa em Melhoramento Animal, Biotecnologia e Transgenia, Universidade de São Paulo, Rua Duque de Caxias Norte, 225, Pirassununga, CEP 13635-900, Brazil
| | - L F Muller da Silveira
- Faculdade de Zootecnia e Engenharia de Alimentos, Núcleo de Apoio à Pesquisa em Melhoramento Animal, Biotecnologia e Transgenia, Universidade de São Paulo, Rua Duque de Caxias Norte, 225, Pirassununga, CEP 13635-900, Brazil
| | - L G de Albuquerque
- Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Via de acesso Prof. Paulo Donato Castellane, s/no, Jaboticabal, CEP 14884-900, Brazil
| | - F Baldi
- Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Via de acesso Prof. Paulo Donato Castellane, s/no, Jaboticabal, CEP 14884-900, Brazil
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32
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Weller CA, Tilk S, Rajpurohit S, Bergland AO. Accurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations. G3-GENES GENOMES GENETICS 2021; 11:6156828. [PMID: 33677482 PMCID: PMC8759814 DOI: 10.1093/g3journal/jkab062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 02/19/2021] [Indexed: 11/27/2022]
Abstract
Genetic association studies seek to uncover the link between genotype and phenotype, and often utilize inbred reference panels as a replicable source of genetic variation. However, inbred reference panels can differ substantially from wild populations in their genotypic distribution, patterns of linkage-disequilibrium, and nucleotide diversity. As a result, associations discovered using inbred reference panels may not reflect the genetic basis of phenotypic variation in natural populations. To address this problem, we evaluated a mapping population design where dozens to hundreds of inbred lines are outbred for few generations, which we call the Hybrid Swarm. The Hybrid Swarm approach has likely remained underutilized relative to pre-sequenced inbred lines due to the costs of genome-wide genotyping. To reduce sequencing costs and make the Hybrid Swarm approach feasible, we developed a computational pipeline that reconstructs accurate whole genomes from ultra-low-coverage (0.05X) sequence data in Hybrid Swarm populations derived from ancestors with phased haplotypes. We evaluate reconstructions using genetic variation from the Drosophila Genetic Reference Panel as well as variation from neutral simulations. We compared the power and precision of Genome-Wide Association Studies using the Hybrid Swarm, inbred lines, recombinant inbred lines (RILs), and highly outbred populations across a range of allele frequencies, effect sizes, and genetic architectures. Our simulations show that these different mapping panels vary in their power and precision, largely depending on the architecture of the trait. The Hybrid Swam and RILs outperform inbred lines for quantitative traits, but not for monogenic ones. Taken together, our results demonstrate the feasibility of the Hybrid Swarm as a cost-effective method of fine-scale genetic mapping.
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Affiliation(s)
- Cory A Weller
- Department of Biology, University of Virginia, Charlottesville, VA 22904, USA
| | - Susanne Tilk
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Subhash Rajpurohit
- Department of Biological and Life Sciences, Ahmedabad University, Ahmedabad 380009, India
| | - Alan O Bergland
- Department of Biology, University of Virginia, Charlottesville, VA 22904, USA
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33
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Hua G, Chen J, Wang J, Li J, Deng X. Genetic basis of chicken plumage color in artificial population of complex epistasis. Anim Genet 2021; 52:656-666. [PMID: 34224160 DOI: 10.1111/age.13094] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/10/2021] [Indexed: 12/18/2022]
Abstract
Chicken plumage color, the genetic basis of which is often affected by epistasis, has long interested scientists. In the current study, a population of complex epistasis was constructed by crossing dominant White Leghorn chickens with recessive white feather chickens. Through a genome-wide association study, we identified single nucleotide polymorphisms and genes significantly associated with white and colored plumage in hens at different developmental stages. Interestingly, white plumage in adulthood was associated with the recessive white feather gene (TYR), whereas white feathers at birth stage were associated with the dominant white feather gene (PMEL), indicating age-related roles for these genes. TYR was shown to exert an epistatic effect on PMEL in adult hens. Additionally, TYR had an epistatic effect on barred plumage, while barred plumage had an epistatic effect on black plumage. TYR had no epistatic effect on the yellow plumage. We confirmed that the barred plumage gene is CDKN2A, as reported in previous studies. Golgb1 and REEP3, which play important roles in the Golgi network and affect the formation of feather pigments, are important candidate genes for yellow plumage. The candidate genes for black plumage are CAMKK1 and IFT22. Further research is warranted to elucidate the molecular mechanisms underlying these traits.
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Affiliation(s)
- Guoying Hua
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding, and Reproduction of the Ministry of Agriculture, China Agricultural University, Beijing, 100193, China
| | - Jianfei Chen
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding, and Reproduction of the Ministry of Agriculture, China Agricultural University, Beijing, 100193, China
| | - Jiankui Wang
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding, and Reproduction of the Ministry of Agriculture, China Agricultural University, Beijing, 100193, China
| | - Junying Li
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding, and Reproduction of the Ministry of Agriculture, China Agricultural University, Beijing, 100193, China
| | - Xuemei Deng
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding, and Reproduction of the Ministry of Agriculture, China Agricultural University, Beijing, 100193, China
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34
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Fruciano C, Franchini P, Jones JC. Capturing the rapidly evolving study of adaptation. J Evol Biol 2021; 34:856-865. [PMID: 34145685 DOI: 10.1111/jeb.13871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/12/2021] [Accepted: 05/12/2021] [Indexed: 11/30/2022]
Abstract
Research on the genomics of adaptation is rapidly changing. In the last few decades, progress in this area has been driven by methodological advances, not only in the way increasingly large amounts of molecular data are generated (e.g. with high-throughput sequencing), but also in the way these data are analysed. This includes a growing appreciation and quantitative treatment of covariation among units within the same data type (e.g. genes) or across data types (e.g. genes and phenotypes). The development and adoption of more and more integrative tools have resulted in richer and more interesting empirical work. This special issue - comprising methodological, empirical, and review papers - aims to capture a 'snapshot' of this rapidly evolving field. We discuss in particular three important themes in the study of adaptation: the genetic architecture of adaptive variation, protein-coding and regulatory changes, and parallel evolution. We highlight how more traditional key themes in the study of genetic architecture (e.g. the number of loci underlying adaptive traits and the distribution of their effects) are now being complemented by other factors (e.g. how patterns of linkage and number of loci interact to affect the ability to adapt). Similarly, apart from addressing the relative importance of protein-coding and regulatory changes, we now have the tools to look in-depth at specific types of regulatory variation to gain a clearer picture of regulatory networks. Finally, parallel evolution has always been central to the study of adaptation, but now we are often able to address the question of whether - and to what extent - parallelism at the organismal or phenotypic level is matched by parallelism at the genetic level. Perhaps most importantly, we can now determine what mechanisms are driving parallelism (or lack thereof) across levels of biological organization. All these recent methodological developments open up new directions for future studies of adaptive changes across traits, levels of biological organization, demographic contexts and time scales.
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Affiliation(s)
- Carmelo Fruciano
- National Research Council - Institute of Marine Biological Resources and Biotechnologies, Messina, Italy.,Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, PSL Université Paris, Paris, France.,School of Biological Sciences, University of Portsmouth, Portsmouth, UK
| | - Paolo Franchini
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Julia C Jones
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
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Genetic basis of variation in cocaine and methamphetamine consumption in outbred populations of Drosophila melanogaster. Proc Natl Acad Sci U S A 2021; 118:2104131118. [PMID: 34074789 PMCID: PMC8201854 DOI: 10.1073/pnas.2104131118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
We used Drosophila melanogaster to map the genetic basis of naturally occurring variation in voluntary consumption of cocaine and methamphetamine. We derived an outbred advanced intercross population (AIP) from 37 sequenced inbred wild-derived lines of the Drosophila melanogaster Genetic Reference Panel (DGRP), which are maximally genetically divergent, have minimal residual heterozygosity, are not segregating for common inversions, and are not infected with Wolbachia pipientis We assessed consumption of sucrose, methamphetamine-supplemented sucrose, and cocaine-supplemented sucrose and found considerable phenotypic variation for consumption of both drugs, in both sexes. We performed whole-genome sequencing and extreme quantitative trait locus (QTL) mapping on the top 10% of consumers for each replicate, sex, and condition and an equal number of randomly selected flies. We evaluated changes in allele frequencies among high consumers and control flies and identified 3,033 variants significantly (P < 1.9 × 10-8) associated with increased consumption, located in or near 1,962 genes. Many of these genes are associated with nervous system development and function, and 77 belong to a known gene-gene interaction subnetwork. We assessed the effects of RNA interference (RNAi) on drug consumption for 22 candidate genes; 17 had a significant effect in at least one sex. We constructed allele-specific AIPs that were homozygous for alternative candidate alleles for 10 single-nucleotide polymorphisms (SNPs) and measured average consumption for each population; 9 SNPs had significant effects in at least one sex. The genetic basis of voluntary drug consumption in Drosophila is polygenic and implicates genes with human orthologs and associated variants with sex- and drug-specific effects.
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36
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Rice BR, Lipka AE. Diversifying maize genomic selection models. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:33. [PMID: 37309328 PMCID: PMC10236107 DOI: 10.1007/s11032-021-01221-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/07/2021] [Indexed: 06/14/2023]
Abstract
Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.
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Affiliation(s)
- Brian R. Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL USA
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37
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Sheppard B, Rappoport N, Loh PR, Sanders SJ, Zaitlen N, Dahl A. A model and test for coordinated polygenic epistasis in complex traits. Proc Natl Acad Sci U S A 2021; 118:e1922305118. [PMID: 33833052 PMCID: PMC8053945 DOI: 10.1073/pnas.1922305118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Interactions between genetic variants-epistasis-is pervasive in model systems and can profoundly impact evolutionary adaption, population disease dynamics, genetic mapping, and precision medicine efforts. In this work, we develop a model for structured polygenic epistasis, called coordinated epistasis (CE), and prove that several recent theories of genetic architecture fall under the formal umbrella of CE. Unlike standard epistasis models that assume epistasis and main effects are independent, CE captures systematic correlations between epistasis and main effects that result from pathway-level epistasis, on balance skewing the penetrance of genetic effects. To test for the existence of CE, we propose the even-odd (EO) test and prove it is calibrated in a range of realistic biological models. Applying the EO test in the UK Biobank, we find evidence of CE in 18 of 26 traits spanning disease, anthropometric, and blood categories. Finally, we extend the EO test to tissue-specific enrichment and identify several plausible tissue-trait pairs. Overall, CE is a dimension of genetic architecture that can capture structured, systemic forms of epistasis in complex human traits.
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Affiliation(s)
- Brooke Sheppard
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143
| | - Nadav Rappoport
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA 94143
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Stephan J Sanders
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143
| | - Noah Zaitlen
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143;
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
| | - Andy Dahl
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095;
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637
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38
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Reiskind MOB, Moody ML, Bolnick DI, Hanifin CT, Farrior CE. Nothing in Evolution Makes Sense Except in the Light of Biology. Bioscience 2021; 71:370-382. [PMID: 33867868 PMCID: PMC8038875 DOI: 10.1093/biosci/biaa170] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
A key question in biology is the predictability of the evolutionary process. If we can correctly predict the outcome of evolution, we may be better equipped to anticipate and manage species' adaptation to climate change, habitat loss, invasive species, or emerging infectious diseases, as well as improve our basic understanding of the history of life on Earth. In the present article, we ask the questions when, why, and if the outcome of future evolution is predictable. We first define predictable and then discuss two conflicting views: that evolution is inherently unpredictable and that evolution is predictable given the ability to collect the right data. We identify factors that generate unpredictability, the data that might be required to make predictions at some level of precision or at a specific timescale, and the intellectual and translational value of understanding when prediction is or is not possible.
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Affiliation(s)
- Martha O Burford Reiskind
- Department of Biological Sciences and the director of the Genetic and Genomic Scholars graduate program, North Carolina State University, Raleigh, North Carolina, United States
| | - Michael L Moody
- Department of Biological Sciences and director of Herbarium UTEP, University of Texas, El Paso, El Paso, Texas, United States
| | - Daniel I Bolnick
- University of Connecticut, Mansfield, Connecticut, United States, and editor-in-chief of The American Naturalist, Chicago, Illinois, United States
| | | | - Caroline E Farrior
- University of Texas at Austin, Austin, Texas, United States, The author order was determined by a random number generator
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39
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Vojgani E, Pook T, Simianer H. Phenotype Prediction Under Epistasis. Methods Mol Biol 2021; 2212:105-120. [PMID: 33733353 DOI: 10.1007/978-1-0716-0947-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Reliable methods of phenotype prediction from genomic data play an increasingly important role in many areas of plant and animal breeding. Thus, developing methods that enhance prediction accuracy is of major interest. Here, we provide three methods for this purpose: (1) Genomic Best Linear Unbiased Prediction (GBLUP) as a model just accounting for additive SNP effects; (2) Epistatic Random Regression BLUP (ERRBLUP) as a full epistatic model which incorporates all pairwise SNP interactions, and (3) selective Epistatic Random Regression BLUP (sERRBLUP) as an epistatic model which incorporates a subset of pairwise SNP interactions selected based on their absolute effect sizes or the effect variances, which is computed based on solutions from the ERRBLUP model. We compared the predictive ability obtained from GBLUP, ERRBLUP, and sERRBLUP with genotypes from a publicly available wheat dataset and respective simulated phenotypes. Results showed that sERRBLUP provides a substantial increase in prediction accuracy compared to the other methods when the optimal proportion of SNP interactions is kept in the model, especially when an optimal proportion of SNP interactions is selected based on the SNP interaction effect sizes. All methods described here are implemented in the R-package EpiGP, which is able to process large-scale genomic data in a computationally efficient way.
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Affiliation(s)
- Elaheh Vojgani
- Center for Integrated Breeding Research, Animal Breeding and Genetics Group, Department of Animal Sciences, University of Goettingen, Goettingen, Germany.
| | - Torsten Pook
- Center for Integrated Breeding Research, Animal Breeding and Genetics Group, Department of Animal Sciences, University of Goettingen, Goettingen, Germany
| | - Henner Simianer
- Center for Integrated Breeding Research, Animal Breeding and Genetics Group, Department of Animal Sciences, University of Goettingen, Goettingen, Germany
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40
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Spierer AN, Mossman JA, Smith SP, Crawford L, Ramachandran S, Rand DM. Natural variation in the regulation of neurodevelopmental genes modifies flight performance in Drosophila. PLoS Genet 2021; 17:e1008887. [PMID: 33735180 PMCID: PMC7971549 DOI: 10.1371/journal.pgen.1008887] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 01/26/2021] [Indexed: 12/28/2022] Open
Abstract
The winged insects of the order Diptera are colloquially named for their most recognizable phenotype: flight. These insects rely on flight for a number of important life history traits, such as dispersal, foraging, and courtship. Despite the importance of flight, relatively little is known about the genetic architecture of flight performance. Accordingly, we sought to uncover the genetic modifiers of flight using a measure of flies’ reaction and response to an abrupt drop in a vertical flight column. We conducted a genome wide association study (GWAS) using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, and identified a combination of additive and marginal variants, epistatic interactions, whole genes, and enrichment across interaction networks. Egfr, a highly pleiotropic developmental gene, was among the most significant additive variants identified. We functionally validated 13 of the additive candidate genes’ (Adgf-A/Adgf-A2/CG32181, bru1, CadN, flapper (CG11073), CG15236, flippy (CG9766), CREG, Dscam4, form3, fry, Lasp/CG9692, Pde6, Snoo), and introduce a novel approach to whole gene significance screens: PEGASUS_flies. Additionally, we identified ppk23, an Acid Sensing Ion Channel (ASIC) homolog, as an important hub for epistatic interactions. We propose a model that suggests genetic modifiers of wing and muscle morphology, nervous system development and function, BMP signaling, sexually dimorphic neural wiring, and gene regulation are all important for the observed differences flight performance in a natural population. Additionally, these results represent a snapshot of the genetic modifiers affecting drop-response flight performance in Drosophila, with implications for other insects. Insect flight is a widely recognizable phenotype of many winged insects, hence the name: flies. While fruit flies, or Drosophila melanogaster, are a genetically tractable model, flight performance is a highly integrative phenotype, and therefore challenging to identify comprehensively which genetic modifiers contribute to its genetic architecture. Accordingly, we screened 197 Drosophila Genetic Reference Panel lines for their ability to react and respond to an abrupt drop. Using several computational approaches, we identified additive, marginal, and epistatic variants, as well as whole genes and altered sub-networks of gene-gene and protein-protein interaction networks that contribute to variation in flight performance. More generally, we demonstrate the benefits of employing multiple methodologies to elucidate the genetic architecture of complex traits. Many variants and genes mapped to regions of the genome that affect neurodevelopment, wing and muscle development, and regulation of gene expression. We also introduce PEGASUS_flies, a Drosophila-adapted version of the PEGASUS platform first used in human studies, to infer gene-level significance of association based on the gene’s distribution of individual variant P-values. Our results contribute to the debate over the relative importance of individual, additive factors and epistatic, or higher order, interactions, in the mapping of genotype to phenotype.
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Affiliation(s)
- Adam N Spierer
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
| | - Jim A Mossman
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
| | - Samuel Pattillo Smith
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
- Microsoft Research New England, Cambridge, Massachusetts, United States of America
| | - Sohini Ramachandran
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
| | - David M Rand
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
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41
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Grainger TN, Rudman SM, Schmidt P, Levine JM. Competitive history shapes rapid evolution in a seasonal climate. Proc Natl Acad Sci U S A 2021; 118:e2015772118. [PMID: 33536336 PMCID: PMC8017725 DOI: 10.1073/pnas.2015772118] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Eco-evolutionary dynamics will play a critical role in determining species' fates as climatic conditions change. Unfortunately, we have little understanding of how rapid evolutionary responses to climate play out when species are embedded in the competitive communities that they inhabit in nature. We tested the effects of rapid evolution in response to interspecific competition on subsequent ecological and evolutionary trajectories in a seasonally changing climate using a field-based evolution experiment with Drosophila melanogaster Populations of D. melanogaster were either exposed, or not exposed, to interspecific competition with an invasive competitor, Zaprionus indianus, over the summer. We then quantified these populations' ecological trajectories (abundances) and evolutionary trajectories (heritable phenotypic change) when exposed to a cooling fall climate. We found that competition with Z. indianus in the summer affected the subsequent evolutionary trajectory of D. melanogaster populations in the fall, after all interspecific competition had ceased. Specifically, flies with a history of interspecific competition evolved under fall conditions to be larger and have lower cold fecundity and faster development than flies without a history of interspecific competition. Surprisingly, this divergent fall evolutionary trajectory occurred in the absence of any detectible effect of the summer competitive environment on phenotypic evolution over the summer or population dynamics in the fall. This study demonstrates that competitive interactions can leave a legacy that shapes evolutionary responses to climate even after competition has ceased, and more broadly, that evolution in response to one selective pressure can fundamentally alter evolution in response to subsequent agents of selection.
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Affiliation(s)
- Tess Nahanni Grainger
- Ecology and Evolutionary Biology Department, Princeton University, Princeton NJ 08544;
| | - Seth M Rudman
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104
- School of Biological Sciences, Washington State University, Vancouver, WA 98686
| | - Paul Schmidt
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104
| | - Jonathan M Levine
- Ecology and Evolutionary Biology Department, Princeton University, Princeton NJ 08544
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42
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Kryazhimskiy S. Emergence and propagation of epistasis in metabolic networks. eLife 2021; 10:e60200. [PMID: 33527897 PMCID: PMC7924954 DOI: 10.7554/elife.60200] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 02/01/2021] [Indexed: 12/11/2022] Open
Abstract
Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.
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Affiliation(s)
- Sergey Kryazhimskiy
- Division of Biological Sciences, University of California, San DiegoLa JollaUnited States
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43
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Han KL, Barreto FS. Pervasive Mitonuclear Coadaptation Underlies Fast Development in Interpopulation Hybrids of a Marine Crustacean. Genome Biol Evol 2021; 13:6121088. [PMID: 33502469 PMCID: PMC7947751 DOI: 10.1093/gbe/evab004] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/03/2021] [Indexed: 12/21/2022] Open
Abstract
Cellular energy production requires coordinated interactions between genetic components from the nuclear and mitochondrial genomes. This coordination results in coadaptation of interacting elements within populations. Interbreeding between divergent gene pools can disrupt coadapted loci and result in hybrid fitness breakdown. While specific incompatible loci have been detected in multiple eukaryotic taxa, the extent of the nuclear genome that is influenced by mitonuclear coadaptation is not clear in any species. Here, we used F2 hybrids between two divergent populations of the copepod Tigriopus californicus to examine mitonuclear coadaptation across the nuclear genome. Using developmental rate as a measure of fitness, we found that fast-developing copepods had higher ATP synthesis capacity than slow developers, suggesting variation in developmental rates is at least partly associated with mitochondrial dysfunction. Using Pool-seq, we detected strong biases for maternal alleles across 7 (of 12) chromosomes in both reciprocal crosses in high-fitness hybrids, whereas low-fitness hybrids showed shifts toward the paternal population. Comparison with previous results on a different hybrid cross revealed largely different patterns of strong mitonuclear coadaptation associated with developmental rate. Our findings suggest that functional coadaptation between interacting nuclear and mitochondrial components is reflected in strong polygenic effects on this life-history phenotype, and reveal that molecular coadaptation follows independent evolutionary trajectories among isolated populations.
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Affiliation(s)
- Kin-Lan Han
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, USA.,Department of Biology, University of Washington, Seattle, Washington, USA
| | - Felipe S Barreto
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, USA
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Marderstein AR, Davenport ER, Kulm S, Van Hout CV, Elemento O, Clark AG. Leveraging phenotypic variability to identify genetic interactions in human phenotypes. Am J Hum Genet 2021; 108:49-67. [PMID: 33326753 DOI: 10.1016/j.ajhg.2020.11.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.
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Affiliation(s)
- Andrew R Marderstein
- Tri-Institutional Program in Computational Biology & Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Emily R Davenport
- Department of Biology, Huck Institutes of the Life Sciences, Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Scott Kulm
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | | | - Olivier Elemento
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Andrew G Clark
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA.
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Goldstein I, Ehrenreich IM. The complex role of genetic background in shaping the effects of spontaneous and induced mutations. Yeast 2020; 38:187-196. [PMID: 33125810 PMCID: PMC7984271 DOI: 10.1002/yea.3530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/09/2020] [Accepted: 10/24/2020] [Indexed: 12/27/2022] Open
Abstract
Spontaneous and induced mutations frequently show different phenotypic effects across genetically distinct individuals. It is generally appreciated that these background effects mainly result from genetic interactions between the mutations and segregating loci. However, the architectures and molecular bases of these genetic interactions are not well understood. Recent work in a number of model organisms has tried to advance knowledge of background effects both by using large‐scale screens to find mutations that exhibit this phenomenon and by identifying the specific loci that are involved. Here, we review this body of research, emphasizing in particular the insights it provides into both the prevalence of background effects across different mutations and the mechanisms that cause these background effects. A large fraction of mutations show different effects in distinct individuals. These background effects are mainly caused by epistasis with segregating loci. Mapping studies show a diversity of genetic architectures can be involved. Genetically complex changes in gene expression are often, but not always, causative.
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Affiliation(s)
- Ilan Goldstein
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, California, 90089-2910, USA
| | - Ian M Ehrenreich
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, California, 90089-2910, USA
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A Novel Mapping Strategy Utilizing Mouse Chromosome Substitution Strains Identifies Multiple Epistatic Interactions That Regulate Complex Traits. G3-GENES GENOMES GENETICS 2020; 10:4553-4563. [PMID: 33023974 PMCID: PMC7718749 DOI: 10.1534/g3.120.401824] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The genetic contribution of additive vs. non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate approach for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTL) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTL regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTL for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTL; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.
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Morgante F, Huang W, Sørensen P, Maltecca C, Mackay TFC. Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits. G3 (BETHESDA, MD.) 2020; 10:4599-4613. [PMID: 33106232 PMCID: PMC7718734 DOI: 10.1534/g3.120.401847] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 10/12/2020] [Indexed: 02/07/2023]
Abstract
The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.
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Affiliation(s)
- Fabio Morgante
- Department of Biological Sciences and W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC 27695
- Program in Genetics, North Carolina State University, Raleigh, NC 27695
| | - Wen Huang
- Department of Biological Sciences and W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC 27695
- Program in Genetics, North Carolina State University, Raleigh, NC 27695
| | - Peter Sørensen
- Center of Quantitative Genetics and Genomics and Department of Molecular Biology and Genetics, Aarhus University, Tjele 8830, Denmark
| | - Christian Maltecca
- Program in Genetics, North Carolina State University, Raleigh, NC 27695
- Department of Animal Science, North Carolina State University, Raleigh, NC 27695
| | - Trudy F C Mackay
- Department of Biological Sciences and W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC 27695
- Program in Genetics, North Carolina State University, Raleigh, NC 27695
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Ye S, Li J, Zhang Z. Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction. J Anim Sci Biotechnol 2020; 11:109. [PMID: 33292577 PMCID: PMC7708144 DOI: 10.1186/s40104-020-00515-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 09/22/2020] [Indexed: 12/02/2022] Open
Abstract
Background Presently, multi-omics data (e.g., genomics, transcriptomics, proteomics, and metabolomics) are available to improve genomic predictors. Omics data not only offers new data layers for genomic prediction but also provides a bridge between organismal phenotypes and genome variation that cannot be readily captured at the genome sequence level. Therefore, using multi-omics data to select feature markers is a feasible strategy to improve the accuracy of genomic prediction. In this study, simultaneously using whole-genome sequencing (WGS) and gene expression level data, four strategies for single-nucleotide polymorphism (SNP) preselection were investigated for genomic predictions in the Drosophila Genetic Reference Panel. Results Using genomic best linear unbiased prediction (GBLUP) with complete WGS data, the prediction accuracies were 0.208 ± 0.020 (0.181 ± 0.022) for the startle response and 0.272 ± 0.017 (0.307 ± 0.015) for starvation resistance in the female (male) lines. Compared with GBLUP using complete WGS data, both GBLUP and the genomic feature BLUP (GFBLUP) did not improve the prediction accuracy using SNPs preselected from complete WGS data based on the results of genome-wide association studies (GWASs) or transcriptome-wide association studies (TWASs). Furthermore, by using SNPs preselected from the WGS data based on the results of the expression quantitative trait locus (eQTL) mapping of all genes, only the startle response had greater accuracy than GBLUP with the complete WGS data. The best accuracy values in the female and male lines were 0.243 ± 0.020 and 0.220 ± 0.022, respectively. Importantly, by using SNPs preselected based on the results of the eQTL mapping of significant genes from TWAS, both GBLUP and GFBLUP resulted in great accuracy and small bias of genomic prediction. Compared with the GBLUP using complete WGS data, the best accuracy values represented increases of 60.66% and 39.09% for the starvation resistance and 27.40% and 35.36% for startle response in the female and male lines, respectively. Conclusions Overall, multi-omics data can assist genomic feature preselection and improve the performance of genomic prediction. The new knowledge gained from this study will enrich the use of multi-omics in genomic prediction.
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Affiliation(s)
- Shaopan Ye
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China.
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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: 0.8] [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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Abstract
Canalization refers to the evolution of populations such that the number of individuals who deviate from the optimum trait, or experience disease, is minimized. In the presence of rapid cultural, environmental, or genetic change, the reverse process of decanalization may contribute to observed increases in disease prevalence. This review starts by defining relevant concepts, drawing distinctions between the canalization of populations and robustness of individuals. It then considers evidence pertaining to three continuous traits and six domains of disease. In each case, existing genetic evidence for genotype-by-environment interactions is insufficient to support a strong inference of decanalization, but we argue that the advent of genome-wide polygenic risk assessment now makes an empirical evaluation of the role of canalization in preventing disease possible. Finally, the contributions of both rare and common variants to congenital abnormality and adult onset disease are considered in light of a new kerplunk model of genetic effects.
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Affiliation(s)
- Greg Gibson
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA;
| | - Kristine A Lacek
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA;
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