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Teng J, Zhai T, Zhang X, Zhao C, Wang W, Tang H, Wang D, Shang Y, Ning C, Zhang Q. Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information. Brief Bioinform 2024; 25:bbae276. [PMID: 38856170 PMCID: PMC11163384 DOI: 10.1093/bib/bbae276] [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: 02/23/2024] [Revised: 05/05/2024] [Accepted: 05/24/2024] [Indexed: 06/11/2024] Open
Abstract
In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).
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Affiliation(s)
- Jun Teng
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
- Shandong Futeng Food Co. Ltd., Zaozhuang 277500, Shandong, China
| | - Tingting Zhai
- National Key Laboratory of Wheat Improvement, College of Life Science, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Xinyi Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Changheng Zhao
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Wenwen Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Hui Tang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Dan Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Yingli Shang
- College of Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Chao Ning
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
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Chen PB, Chen R, LaPierre N, Chen Z, Mefford J, Marcus E, Heffel MG, Soto DC, Ernst J, Luo C, Flint J. Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior. CELL GENOMICS 2024; 4:100545. [PMID: 38697120 PMCID: PMC11099346 DOI: 10.1016/j.xgen.2024.100545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/23/2024] [Accepted: 04/04/2024] [Indexed: 05/04/2024]
Abstract
Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.
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Affiliation(s)
- Patrick B Chen
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Rachel Chen
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nathan LaPierre
- Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zeyuan Chen
- Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joel Mefford
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Emilie Marcus
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Matthew G Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniela C Soto
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jason Ernst
- Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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Chen PB, Chen R, LaPierre N, Chen Z, Mefford J, Marcus E, Heffel MG, Soto DC, Ernst J, Luo C, Flint J. Complementation testing identifies causal genes at quantitative trait loci underlying fear related behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.574060. [PMID: 38260483 PMCID: PMC10802323 DOI: 10.1101/2024.01.03.574060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Knowing the genes involved in quantitative traits provides a critical entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. Here we address a key step towards that goal by deploying a test that directly queries whether a gene mediates the effect of a quantitative trait locus (QTL). To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six QTLs, and identified six genes. Four genes, Lsamp, Ptprd, Nptx2 and Sh3gl, have known roles in synapse function; the fifth gene, Psip1, is a transcriptional co-activator not previously implicated in behavior; the sixth is a long non-coding RNA 4933413L06Rik with no known function. Single nucleus transcriptomic and epigenetic analyses implicated excitatory neurons as likely mediating the genetic effects. Surprisingly, variation in transcriptome and epigenetic modalities between inbred strains occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results open a bottleneck in using genetic mapping of QTLs to find novel biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.
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Wright SN, Leger BS, Rosenthal SB, Liu SN, Jia T, Chitre AS, Polesskaya O, Holl K, Gao J, Cheng R, Garcia Martinez A, George A, Gileta AF, Han W, Netzley AH, King CP, Lamparelli A, Martin C, St Pierre CL, Wang T, Bimschleger H, Richards J, Ishiwari K, Chen H, Flagel SB, Meyer P, Robinson TE, Solberg Woods LC, Kreisberg JF, Ideker T, Palmer AA. Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks. Cell Rep 2023; 42:112873. [PMID: 37527041 PMCID: PMC10546330 DOI: 10.1016/j.celrep.2023.112873] [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: 11/08/2022] [Revised: 07/05/2023] [Accepted: 07/11/2023] [Indexed: 08/03/2023] Open
Abstract
A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.
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Affiliation(s)
- Sarah N Wright
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Program in Biomedical Sciences, University of California San Diego, La Jolla, CA 93093, USA
| | - Sara Brin Rosenthal
- Center for Computational Biology & Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sophie N Liu
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Tongqiu Jia
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Apurva S Chitre
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Katie Holl
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jianjun Gao
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Riyan Cheng
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Angel Garcia Martinez
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Anthony George
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA
| | - Alexander F Gileta
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Wenyan Han
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Alesa H Netzley
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christopher P King
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | | | - Connor Martin
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | | | - Tengfei Wang
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Hannah Bimschleger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Jerry Richards
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA
| | - Keita Ishiwari
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Pharmacology and Toxicology, University at Buffalo, Buffalo, NY 14203, USA
| | - Hao Chen
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Shelly B Flagel
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Paul Meyer
- Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | - Terry E Robinson
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Leah C Solberg Woods
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Jason F Kreisberg
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA.
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA.
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Koskinen MK, Hovatta I. Genetic insights into the neurobiology of anxiety. Trends Neurosci 2023; 46:318-331. [PMID: 36828693 DOI: 10.1016/j.tins.2023.01.007] [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: 09/15/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/25/2023]
Abstract
Anxiety and fear are evolutionarily conserved emotions that increase the likelihood of an organism surviving threatening situations. Anxiety and vigilance states are regulated by neural networks involving multiple brain regions. In anxiety disorders, this intricate regulatory system is disturbed, leading to excessive or prolonged anxiety or fear. Anxiety disorders have both genetic and environmental risk factors. Genetic research has the potential to identify specific genetic variants causally associated with specific phenotypes. In recent decades, genome-wide association studies (GWASs) have revealed variants predisposing to neuropsychiatric disorders, suggesting novel neurobiological pathways in the etiology of these disorders. Here, we review recent human GWASs of anxiety disorders, and genetic studies of anxiety-like behavior in rodent models. These studies are paving the way for a better understanding of the neurobiological mechanisms underlying anxiety disorders.
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Affiliation(s)
- Maija-Kreetta Koskinen
- SleepWell Research Program and Department of Psychology and Logopedics, Faculty of Medicine, PO Box 21, 00014, University of Helsinki, Helsinki, Finland
| | - Iiris Hovatta
- SleepWell Research Program and Department of Psychology and Logopedics, Faculty of Medicine, PO Box 21, 00014, University of Helsinki, Helsinki, Finland.
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Buhusi CV, Meyer AE, Oprisan SA, Buhusi M. Not All Mice Are Created Equal: Interval Timing Accuracy and Scalar Timing in 129, Swiss-Webster, and C57BL/6 Mice. TIMING & TIME PERCEPTION 2022; 11:242-262. [PMID: 37065684 PMCID: PMC10103834 DOI: 10.1163/22134468-bja10052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Abstract
Many species, including humans, show both accurate timing − appropriate time estimation in the seconds to minutes range − and scalar timing − time estimation error varies linearly with estimated duration. Behavioral paradigms aimed at investigating interval timing are expected to evaluate these dissociable characteristics of timing. However, when evaluating interval timing in models of neuropsychiatric disease, researchers are confronted with a lack of adequate studies about the parent (background) strains, since accuracy and scalar timing have only been demonstrated for the C57BL/6 strain of mice (Buhusi, Aziz, Winslow, Carter, Swearingen, & Buhusi (2009) Behav. Neurosci., 123, 1102–1113). We used a peak-interval (PI) procedure with three intervals − a protocol in which other species, including humans, demonstrate accurate, scalar timing − to evaluate timing accuracy and scalar timing in three strains of mice frequently used in genetic and behavioral studies: 129, Swiss-Webster (SW), and C57BL/6. C57BL/6 mice showed accurate, scalar timing, while 129 and SW mice showed departures from accuracy and/or scalar timing. Results suggest that the genetic background/strain of the mouse is a critical variable for studies investigating interval timing in genetically engineered mice. Our study validates the PI procedure with multiple intervals as a proper technique, and the C57BL/6 strain as the most suitable genetic background to date for behavioral investigations of interval timing in genetically engineered mice modeling human disorders. In contrast, studies using mice in 129, SW, or mixed-background strains should be interpreted with caution, and thorough investigations of accuracy and scalar timing should be conducted before a less studied strain of mouse is considered for use in timing studies.
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Affiliation(s)
- Catalin V. Buhusi
- Neuroscience Program, Department of Psychology, Utah State University, Logan, UT 84322, USA
| | - Abby E. Meyer
- Department of Physics and Astronomy, College of Charleston, Charleston, SC 29424, USA
| | - Sorinel A. Oprisan
- Department of Physics and Astronomy, College of Charleston, Charleston, SC 29424, USA
| | - Mona Buhusi
- Neuroscience Program, Department of Psychology, Utah State University, Logan, UT 84322, USA
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Genetic characterization of outbred Sprague Dawley rats and utility for genome-wide association studies. PLoS Genet 2022; 18:e1010234. [PMID: 35639796 PMCID: PMC9187121 DOI: 10.1371/journal.pgen.1010234] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 06/10/2022] [Accepted: 05/03/2022] [Indexed: 12/30/2022] Open
Abstract
Sprague Dawley (SD) rats are among the most widely used outbred laboratory rat populations. Despite this, the genetic characteristics of SD rats have not been clearly described, and SD rats are rarely used for experiments aimed at exploring genotype-phenotype relationships. In order to use SD rats to perform a genome-wide association study (GWAS), we collected behavioral data from 4,625 SD rats that were predominantly obtained from two commercial vendors, Charles River Laboratories and Harlan Sprague Dawley Inc. Using double-digest genotyping-by-sequencing (ddGBS), we obtained dense, high-quality genotypes at 291,438 SNPs across 4,061 rats. This genetic data allowed us to characterize the variation present in Charles River vs. Harlan SD rats. We found that the two populations are highly diverged (FST > 0.4). Furthermore, even for rats obtained from the same vendor, there was strong population structure across breeding facilities and even between rooms at the same facility. We performed multiple separate GWAS by fitting a linear mixed model that accounted for population structure and using meta-analysis to jointly analyze all cohorts. Our study examined Pavlovian conditioned approach (PavCA) behavior, which assesses the propensity for rats to attribute incentive salience to reward-associated cues. We identified 46 significant associations for the various metrics used to define PavCA. The surprising degree of population structure among SD rats from different sources has important implications for their use in both genetic and non-genetic studies. Outbred Sprague Dawley rats are among the most commonly used rats for neuroscience, physiology and pharmacological research; in the year 2020, 4,188 publications contained the keyword “Sprague Dawley”. Rats identified as “Sprague Dawley” are sold by several commercial vendors, including Charles River Laboratories and Harlan Sprague Dawley Inc. (now Envigo). Despite their widespread use, little is known about the genetic diversity of SD. We genotyped more than 4,000 SD rats, which we used for a genome-wide association study (GWAS) and to characterize genetic differences between SD rats from Charles River Laboratories and Harlan. Our analysis revealed extensive population structure both between and within vendors. The GWAS for Pavlovian conditioned approach (PavCA) identified a number of genome-wide significant loci for that complex behavioral trait. Our results demonstrate that, despite sharing an identical name, SD rats that are obtained from different vendors are very different. Future studies should carefully define the exact source of SD rats being used and may exploit their genetic diversity for genetic studies of complex traits.
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