A New Family-based Approach for Detecting Allele-Specific Expression and for Mapping Possible eQTLs

A New Family-based Approach for Detecting Allele-Specific Expression and for Mapping Possible eQTLs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A New Family-based Approach for Detecting Allele-Specific Expression and for Mapping Possible eQTLs Maher Alnajjar, Zsófia Fekete, Tibor Nagy, Zoltán Német, Agshin Sakif, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6515696/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Sep, 2025 Read the published version in Animals → Version 2 posted You are reading this latest preprint version Show more versions Abstract Allele-specific expression (ASE) reflects the unequal expression of the parental alleles and can imply functional variants in cis-regulatory elements. The conventional ASE detection methods often depend on the presence of heterozygous variants in transcripts or sequencing a large number of individuals, which is often limited. In this study, we present a family-based strategy for detecting ASE and potential cis-regulatory elements utilizing both RNA-seq and whole-genome sequencing (WGS) from a pedigree. Using a rabbit family consisting of two divergent parents and their eight offspring, we identified 913 ASE genes by analyzing inheritance patterns of gene expression levels. Expression was classified into three levels—high, medium, and low—and used to define seven distinct expression groups across the family (e.g., H_L: high in the mother, low in the father, and intermediate in offspring). Many ASE genes lacked heterozygous exonic variants, and inference was achieved via RNA read count patterns. We also pinpointed conserved transcription factor binding sites (TFBS) with sequence variants showing similar inheritance genotypic patterns (e.g., AAxBB), suggesting their regulatory roles as eQTLs. Differential gene expression (DEG) analysis between the parents highlighted some candidate genes related to meat production and quality traits. Our findings show that the family-based method using RNA-seq and WGS data is powerful and efficient for exploring ASE and for mapping possible eQTLs. Molecular Genetics Animal Science Bioinformatics Allele-Specific Expression Cis-Regulatory Elements Transcription Factor Binding Sites Rabbit Meat eQTL Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Additionalfile1.xlsx Supplementary Information: Additional file 1: RNA-seq and WGS sample quality control and alignment. Numbers of reads from RNA-seq and WGS separately. Alignment analysis and duplication rates. Additionalfile2.docx Additional file 2: Figure S.1: DEG comparison of parental samples before mother_2 sample elimination. Variance stabilizing transformation (VST) heatmap and correlation matrix of the parents. Additionalfile3.xlsx Additional file 3: Lists of up- and downregulated genes and GO enrichment analysis. Pathway sizes are provided, as are the number of genes found in each pathway set of genes, along with the false discovery rate (FDR) and log fold change. Additionalfile4.docx Additional file 4: Figure S.1: Pipeline overview. Figure S.2: BDH2 gene with allele-specific expression analysis across the family members. Figure S.3: Novel gene (ENSOCUG00000010624) expression analysis across family members. Figure S4: SLC2A11 gene ASE analysis in the family model. Additionalfile5.xlsx Additional file 5: List of heterozygous and homozygous variant numbers in the family in the WGS. Additionalfile6.xlsx Additional file 6: Lists of predicted ASE genes in their categories along with normalized counts and Log2FC values. The table contains the predicted expression at each individual based on the normalized read counts and the Log2FC relative to the father. H_L, L_H, M_M, H_M or M_L, L_M or M_H. H: high, L: low and M: moderate. The mother’s phenotype is listed first in the abbreviation. Additionalfile7.xlsx Additional file 7: Comparison (lists) of results from our approach and the conventional methods for the variants found in the transcripts. The RNA variants found along with their calculated allele ratios are provided in the source tab. The predicted phenotype (H, L, or M) is predicted using our method on the basis of the log2FC relative to the father. Additionalfile8.xlsx Additional file 8: Lists of the numbers of complete variants and matched variants at each ASE gene with respect to their expression category. The number of variants found in the gene (exon, intron, and 10 kb upstream and downstream) and how many variants were found to match the predicted phenotype pattern in the same corresponding section of the gene is given for each gene. Additionalfile9.docx Additional file 9: phase_M.py haplotype phasing methodology. The main mechanism used to phase the parent haplotypes in the offspring uses the abundance of the variants in the gene body or the extended region. Additionalfile10.xlsx Additional file 10: Variants discovered in the TFBS in the gene’s surrounding regions. The genotype of the variant at each individual (_gt) in the 10 kb upstream and downstream of the gene, as well as the transcription factor (TF) overlapping at this variant along with its consensus sequence, the strand (+/-), and the position of the variant relative to the consensus sequence are shown. (VAR_POS_in_consensus). Variants that comply with ASE prediction cases matched the conserved region of the TFBS obtained from our SummitDB. These variants follow the same pattern of expression as predicted by the log2FC relative to the father. Information about whether these variants fall within the boundary region of a neighboring gene (exon or intron) and the distance from the corresponding gene is also provided. The distance of the TFBS from the potentially regulated gene is added for each TF, along with the consensus sequence, and the position of the TF relative to the consensus sequence is determined. Additionalfile11.xlsx Additional file 11: Variants discovered in the TFBS in the gene’s intronic region. Similarly, the genotype of the variant at each individual (_gt) found in the intronic region, as well as the transcription factor (TF) overlapping at this variant along with its consensus sequence, the strand (+/-), and the position of the variant relative to the consensus sequence is shown. (VAR_POS_in_consensus). Cite Share Download PDF Status: Published Journal Publication published 21 Sep, 2025 Read the published version in Animals → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6515696","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467148904,"identity":"b0964cf1-e893-4ec4-8568-b433b4c996fe","order_by":0,"name":"Maher Alnajjar","email":"","orcid":"","institution":"Hungarian University of Agriculture and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maher","middleName":"","lastName":"Alnajjar","suffix":""},{"id":467148905,"identity":"3acfa8fc-c392-492e-953a-0c313f08bc80","order_by":1,"name":"Zsófia Fekete","email":"","orcid":"","institution":"University of Eastern 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Numbers of reads from RNA-seq and WGS separately. Alignment analysis and duplication rates.\u003c/p\u003e","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/1e69ad5e7c73726b8cc91b2b.xlsx"},{"id":86711137,"identity":"84ab901d-5a3e-4b9c-b842-69999ce47da5","added_by":"auto","created_at":"2025-07-14 18:45:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":258252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2:\u003c/strong\u003e \u003cstrong\u003eFigure S.1\u003c/strong\u003e: DEG comparison of parental samples before mother_2 sample elimination. Variance stabilizing transformation (VST) heatmap and correlation matrix of the parents.\u003c/p\u003e","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/4d84313dd4b05f797b387abb.docx"},{"id":86711645,"identity":"d071b848-d3d1-4351-87ed-4ff1ef45124f","added_by":"auto","created_at":"2025-07-14 18:53:26","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":58722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3:\u003c/strong\u003e Lists of up- and downregulated genes and GO enrichment analysis. Pathway sizes are provided, as are the number of genes found in each pathway set of genes, along with the false discovery rate (FDR) and log fold change.\u003c/p\u003e","description":"","filename":"Additionalfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/a14c668b7997f392b4485e5b.xlsx"},{"id":86711646,"identity":"39d20429-10e7-4795-b3be-8fc7293e6154","added_by":"auto","created_at":"2025-07-14 18:53:26","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":888121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 4:\u003c/strong\u003e \u003cstrong\u003eFigure S.1:\u003c/strong\u003e Pipeline overview. \u003cstrong\u003eFigure S.2: \u003c/strong\u003eBDH2 gene with allele-specific expression analysis across the family members. \u003cstrong\u003eFigure S.3: \u003c/strong\u003eNovel gene\u003cstrong\u003e (\u003c/strong\u003eENSOCUG00000010624) expression analysis across family members. \u003cstrong\u003eFigure S4: \u003c/strong\u003eSLC2A11 gene ASE analysis in the family model.\u003c/p\u003e","description":"","filename":"Additionalfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/56eaccd06b29fb650acd12fd.docx"},{"id":86711138,"identity":"00913383-48e4-4db5-8ae8-d27d0ec449e4","added_by":"auto","created_at":"2025-07-14 18:45:26","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 5:\u003c/strong\u003e List of heterozygous and homozygous variant numbers in the family in the WGS.\u003c/p\u003e","description":"","filename":"Additionalfile5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/b163cdfe0abba592ef348394.xlsx"},{"id":86711647,"identity":"1063e507-c384-4cb3-88e6-baabf0e74c25","added_by":"auto","created_at":"2025-07-14 18:53:26","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":205229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 6:\u003c/strong\u003e Lists of predicted ASE genes in their categories along with normalized counts and Log2FC values. The table contains the predicted expression at each individual based on the normalized read counts and the Log2FC relative to the father. H_L, L_H, M_M, H_M or M_L, L_M or M_H. H: high, L: low and M: moderate. The mother’s phenotype is listed first in the abbreviation.\u003c/p\u003e","description":"","filename":"Additionalfile6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/03c5b69b038a67a283fb7ebf.xlsx"},{"id":86710835,"identity":"6cedb04a-e110-4a0f-b3dc-b538b360e19c","added_by":"auto","created_at":"2025-07-14 18:37:26","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":93777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 7:\u003c/strong\u003e Comparison (lists) of results from our approach and the conventional methods for the variants found in the transcripts. The RNA variants found along with their calculated allele ratios are provided in the source tab. The predicted phenotype (H, L, or M) is predicted using our method on the basis of the log2FC relative to the father.\u003c/p\u003e","description":"","filename":"Additionalfile7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/3f14fff36adef88fe09a6422.xlsx"},{"id":86711142,"identity":"407e7b17-8480-4fa0-891f-309930ba0a27","added_by":"auto","created_at":"2025-07-14 18:45:26","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":74250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 8\u003c/strong\u003e: Lists of the numbers of complete variants and matched variants at each ASE gene with respect to their expression category. The number of variants found in the gene (exon, intron, and 10 kb upstream and downstream) and how many variants were found to match the predicted phenotype pattern in the same corresponding section of the gene is given for each gene.\u003c/p\u003e","description":"","filename":"Additionalfile8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/8903ac7a7db21e6bf0c6771c.xlsx"},{"id":86711145,"identity":"a6ff20b9-6b3f-43c6-959a-06e722333fae","added_by":"auto","created_at":"2025-07-14 18:45:26","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":213936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 9: \u003c/strong\u003ephase_M.py\u003cstrong\u003e \u003c/strong\u003ehaplotype phasing methodology. The main mechanism used to phase the parent haplotypes in the offspring uses the abundance of the variants in the gene body or the extended region.\u003c/p\u003e","description":"","filename":"Additionalfile9.docx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/68410e0e27b5a852b7999936.docx"},{"id":86711148,"identity":"f37ce60a-cd17-4e64-bc9c-ab4054ec7f31","added_by":"auto","created_at":"2025-07-14 18:45:26","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":42235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 10: \u003c/strong\u003eVariants discovered in the TFBS in the gene’s surrounding regions. The genotype of the variant at each individual (_gt) in the 10 kb upstream and downstream of the gene, as well as the transcription factor (TF) overlapping at this variant along with its consensus sequence, the strand (+/-), and the position of the variant relative to the consensus sequence are shown. (VAR_POS_in_consensus). Variants that comply with ASE prediction cases matched the conserved region of the TFBS obtained from our SummitDB. These variants follow the same pattern of expression as predicted by the log2FC relative to the father. Information about whether these variants fall within the boundary region of a neighboring gene (exon or intron) and the distance from the corresponding gene is also provided. The distance of the TFBS from the potentially regulated gene is added for each TF, along with the consensus sequence, and the position of the TF relative to the consensus sequence is determined.\u003c/p\u003e","description":"","filename":"Additionalfile10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6515696/v2/9bcde437e3c86576a7014b8a.xlsx"},{"id":86711652,"identity":"9e1a2d90-4548-46b6-a76e-df99615caf8b","added_by":"auto","created_at":"2025-07-14 18:53:26","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":50329,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 11: \u003c/strong\u003eVariants discovered in the TFBS in the gene’s intronic region. Similarly, the genotype of the variant at each individual (_gt) found in the intronic region, as well as the transcription factor (TF) overlapping at this variant along with its consensus sequence, the strand (+/-), and the position of the variant relative to the consensus sequence is shown. 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The conventional ASE detection methods often depend on the presence of heterozygous variants in transcripts or sequencing a large number of individuals, which is often limited. In this study, we present a family-based strategy for detecting ASE and potential cis-regulatory elements utilizing both RNA-seq and whole-genome sequencing (WGS) from a pedigree. Using a rabbit family consisting of two divergent parents and their eight offspring, we identified 913 ASE genes by analyzing inheritance patterns of gene expression levels. Expression was classified into three levels—high, medium, and low—and used to define seven distinct expression groups across the family (e.g., H_L: high in the mother, low in the father, and intermediate in offspring). Many ASE genes lacked heterozygous exonic variants, and inference was achieved via RNA read count patterns. We also pinpointed conserved transcription factor binding sites (TFBS) with sequence variants showing similar inheritance genotypic patterns (e.g., AAxBB), suggesting their regulatory roles as eQTLs. Differential gene expression (DEG) analysis between the parents highlighted some candidate genes related to meat production and quality traits. Our findings show that the family-based method using RNA-seq and WGS data is powerful and efficient for exploring ASE and for mapping possible eQTLs.\u003c/p\u003e","manuscriptTitle":"A New Family-based Approach for Detecting Allele-Specific Expression and for Mapping Possible eQTLs","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-07-14 18:37:21","doi":"10.21203/rs.3.rs-6515696/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2025-06-05 16:43:53","doi":"10.21203/rs.3.rs-6515696/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9b5cdde-f956-4bc0-bb34-f51249a11224","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51135855,"name":"Molecular Genetics"},{"id":51135856,"name":"Animal Science"},{"id":51135857,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2025-09-23T14:42:19+00:00","versionOfRecord":{"articleIdentity":"rs-6515696","link":"https://doi.org/10.3390/ani15182766","journal":{"identity":"animals","isVorOnly":true,"title":"Animals"},"publishedOn":"2025-09-22 00:00:00","publishedOnDateReadable":"September 22nd, 2025"},"versionCreatedAt":"2025-07-14 18:37:21","video":"","vorDoi":"10.3390/ani15182766","vorDoiUrl":"https://doi.org/10.3390/ani15182766","workflowStages":[]},"version":"v2","identity":"rs-6515696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6515696","identity":"rs-6515696","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}