A path integral approach for allele frequency dynamics under polygenic selection
Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence (E&R) experiments, in which laboratory populat...
Uložené v:
| Vydané v: | Genetics (Austin) Ročník 229; číslo 1; s. 1 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
08.01.2025
|
| Predmet: | |
| ISSN: | 1943-2631, 1943-2631 |
| On-line prístup: | Zistit podrobnosti o prístupe |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence (E&R) experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change (AFC). Predicting AFCs under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here, we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e. the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of AFC to test for selection, as well as explore optimal design choices for E&R experiments to uncover the genetic architecture of polygenic traits under selection. |
|---|---|
| AbstractList | Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence (E&R) experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change (AFC). Predicting AFCs under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here, we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e. the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of AFC to test for selection, as well as explore optimal design choices for E&R experiments to uncover the genetic architecture of polygenic traits under selection.Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence (E&R) experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change (AFC). Predicting AFCs under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here, we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e. the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of AFC to test for selection, as well as explore optimal design choices for E&R experiments to uncover the genetic architecture of polygenic traits under selection. Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence (E&R) experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change (AFC). Predicting AFCs under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here, we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e. the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of AFC to test for selection, as well as explore optimal design choices for E&R experiments to uncover the genetic architecture of polygenic traits under selection. |
| Author | Anderson, Nathan W Kirk, Lloyd Schraiber, Joshua G Ragsdale, Aaron P |
| Author_xml | – sequence: 1 givenname: Nathan W orcidid: 0000-0002-7729-8664 surname: Anderson fullname: Anderson, Nathan W organization: Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA – sequence: 2 givenname: Lloyd orcidid: 0009-0000-2624-8184 surname: Kirk fullname: Kirk, Lloyd organization: Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA – sequence: 3 givenname: Joshua G surname: Schraiber fullname: Schraiber, Joshua G organization: Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA – sequence: 4 givenname: Aaron P orcidid: 0000-0003-0715-3432 surname: Ragsdale fullname: Ragsdale, Aaron P organization: Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39531638$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkDtPwzAUhS1URB-wMyGPLKF-1I49VhUFpEowwBzdODdtkOOEOBny7wmiSEz3DJ_O-XSXZBaagITccvbAmZXrIwbsKxfX1QjIjbggC243MhFa8tm_PCfLGD8ZY9oqc0Xm0irJtTQL8ralLfQnWoUejx14Cm3bNeBOtGw6Ct6jR1p2-DVgcCMtxgD1NEiHUGBH28aPk0PlaJw411dNuCaXJfiIN-e7Ih_7x_fdc3J4fXrZbQ-JE4b1CRciV4JzJ0FLW6YbZVhqS2uNdFCw3ILlm1xIidwaBSWzhdMp5irXpQSlxIrc__ZOupNc7LO6ig69h4DNEDPJhUk10_oHvTujQ15jkbVdVUM3Zn9fEN9RFWLI |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. |
| Copyright_xml | – notice: The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1093/genetics/iyae182 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1943-2631 |
| ExternalDocumentID | 39531638 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NHGRI NIH HHS grantid: 5T32HG002760 – fundername: NHGRI NIH HHS grantid: T32 HG002760 |
| GroupedDBID | --- --Z -DZ -~X 0R~ 18M 29H 2KS 2WC 34G 36B 39C 5GY 5RE 5WD 7X7 85S 8C1 8FE 8FH 8G5 8R4 8R5 A8Z AABZA AACZT AAPXW AARHZ AAUAY AAVAP ABDFA ABDNZ ABEJV ABGNP ABJNI ABMNT ABNHQ ABPPZ ABPTD ABUFD ABVGC ABXVV ABXZS ACFRR ACGOD ACIHN ACIPB ACNCT ACPRK ACUTJ ADBBV ADGKP ADIPN ADQBN ADVEK AEAQA AENEX AFFZL AFGWE AFKRA AFRAH AHGBF AHMBA AHMMS AJBYB AJEEA AJNCP ALMA_UNASSIGNED_HOLDINGS ALXQX AOIJS ATCPS ATGXG AZQEC BAWUL BBNVY BCRHZ BENPR BES BEYMZ BHPHI BKNYI BKOMP BPHCQ BTFSW BVXVI CGR CJ0 CS3 CUY CVF D0L DIK DU5 E3Z EBS ECM EIF EJD EMB F5P F8P F9R FD6 FLUFQ FOEOM FRP FYUFA GUQSH GX1 H13 HCIFZ INIJC JXSIZ KOP KQ8 KSI KSN L7B LK8 M0K M0R M2O M2P M7P MV1 NOMLY NPM OBOKY OCZFY OJZSN OK1 OMK OPAEJ OWPYF PQQKQ PROAC Q2X QM4 QM9 QN7 R0Z RHI ROX RXW SJN SV3 TAE TGS TN5 TR2 TWZ U5U UHB UKR UNMZH UPT W8F WH7 WOQ XSW YHG YKV YSK YZZ ZCA ~KM 7X8 |
| ID | FETCH-LOGICAL-c280t-122b5211c3a639f7458079f9983cad0b9a914b233e1985af09dc67eb5b6f3a552 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001379162000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1943-2631 |
| IngestDate | Thu Oct 02 10:22:09 EDT 2025 Fri Nov 14 01:40:56 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | evolve and resequence polygenic selection diffusion approximation transition density |
| Language | English |
| License | The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c280t-122b5211c3a639f7458079f9983cad0b9a914b233e1985af09dc67eb5b6f3a552 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-7729-8664 0000-0003-0715-3432 0009-0000-2624-8184 |
| PMID | 39531638 |
| PQID | 3128760665 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_3128760665 pubmed_primary_39531638 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-Jan-08 |
| PublicationDateYYYYMMDD | 2025-01-08 |
| PublicationDate_xml | – month: 01 year: 2025 text: 2025-Jan-08 day: 08 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Genetics (Austin) |
| PublicationTitleAlternate | Genetics |
| PublicationYear | 2025 |
| References | 38915613 - bioRxiv. 2024 Jun 14:2024.06.14.599114. doi: 10.1101/2024.06.14.599114. |
| References_xml | – reference: 38915613 - bioRxiv. 2024 Jun 14:2024.06.14.599114. doi: 10.1101/2024.06.14.599114. |
| SSID | ssj0006958 |
| Score | 2.4742153 |
| Snippet | Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 1 |
| SubjectTerms | Alleles Gene Frequency Genetic Drift Models, Genetic Multifactorial Inheritance Phenotype Selection, Genetic |
| Title | A path integral approach for allele frequency dynamics under polygenic selection |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39531638 https://www.proquest.com/docview/3128760665 |
| Volume | 229 |
| WOSCitedRecordID | wos001379162000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LSwMxEA5qFbz4ftQXEbyGbpLNJjlJEYsXSw8KvS3ZPKBQtrVbhf33Tna3eBIEL7ktZCeTyTczyfch9GAyl2appqTILCQoPFiiBAuECiU9DUoanzZiE3I8VtOpnnQFt6q7VrmJiU2gdgsba-QDDoFURrQtHpcfJKpGxe5qJ6GxjXocoEz0ajn9YQvPdKPPCXk6JyzjtGtTQhI_gNWJjwSrwaw2nkYavt8AZnPQjA7_O8UjdNBBTDxsfeIYbfnyBO21opP1KZoMcZQhxh1RxBxvaMUx4FccpVXmHodVe8W6xq6VrK9wfG22wsvFvIa_mllcNQo6sKxn6H30_Pb0QjpdBWKZStaEMlbAqU0tN4BPgkyFSqQOkHhxa1xSaKNpWjDOPdVKmJBoZzPpC1FkgRsh2DnaKRelv0QY8InMbKT9S0zqOS9S5QCC8FR6x612fXS_MVUOfhubEab0i88q_zFWH1209s6XLcFGzjVEBggMV3_4-hrtsyjJG6si6gb1Auxaf4t27dd6Vq3uGoeAcTx5_QaHn8Dp |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+path+integral+approach+for+allele+frequency+dynamics+under+polygenic+selection&rft.jtitle=Genetics+%28Austin%29&rft.au=Anderson%2C+Nathan+W&rft.au=Kirk%2C+Lloyd&rft.au=Schraiber%2C+Joshua+G&rft.au=Ragsdale%2C+Aaron+P&rft.date=2025-01-08&rft.issn=1943-2631&rft.eissn=1943-2631&rft.volume=229&rft.issue=1&rft.spage=1&rft_id=info:doi/10.1093%2Fgenetics%2Fiyae182&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1943-2631&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1943-2631&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1943-2631&client=summon |