Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data
Mapping of chromosomal regions harboring genetic polymorphisms that regulate complex traits is usually followed by a search for the causative mutations underlying the observed effects. This is often a challenging task even after fine mapping, as millions of base pairs including many genes will typic...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-11-01
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| Series: | Frontiers in Genetics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00226/full |
| _version_ | 1819055958146416640 |
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| author | Muhammad eAhsan Xidan eLi Andreas E Lundberg Marcin eKierczak Paul B Siegel Örjan eCarlborg Stefan eMarklund |
| author_facet | Muhammad eAhsan Xidan eLi Andreas E Lundberg Marcin eKierczak Paul B Siegel Örjan eCarlborg Stefan eMarklund |
| author_sort | Muhammad eAhsan |
| collection | DOAJ |
| description | Mapping of chromosomal regions harboring genetic polymorphisms that regulate complex traits is usually followed by a search for the causative mutations underlying the observed effects. This is often a challenging task even after fine mapping, as millions of base pairs including many genes will typically need to be investigated. Thus to trace the causative mutation(s) there is a great need for efficient bioinformatic strategies. Here, we searched for genes and mutations regulating growth in the Virginia chicken lines – an experimental population comprising two lines that have been divergently selected for body weight at 56 days for more than 50 generations. Several QTL regions have been mapped in an F2 intercross between the lines, and the regions have subsequently been replicated and fine mapped using an Advanced Intercross Line. We have further analyzed the QTL regions where the largest genetic divergence between the High-Weight selected (HWS) and Low-Weight selected (LWS) lines was observed. Such regions, covering about 37% of the actual QTL regions, were identified by comparing the allele frequencies of the HWS and LWS lines using both individual 60K SNP chip genotyping of birds and analysis of read proportions from genome resequencing of DNA pools. Based on a combination of criteria including significance of the QTL, allele frequency difference of identified mutations between the selected lines, gene information on relevance for growth, and the predicted functional effects of identified mutations we propose here a subset of candidate mutations of highest priority for further evaluation in functional studies. The candidate mutations were identified within the GCG, IGFBP2, GRB14, CRIM1, FGF16, VEGFR-2, ALG11, EDN1, SNX6 and BIRC7 genes. We believe that the proposed method of combining different types of genomic information increases the probability that the genes underlying the observed QTL effects are represented among the candidate mutations identified. |
| first_indexed | 2024-12-21T13:15:47Z |
| format | Article |
| id | doaj.art-28f0b93f9ecb4948bad1367981003680 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-21T13:15:47Z |
| publishDate | 2013-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-28f0b93f9ecb4948bad13679810036802022-12-21T19:02:44ZengFrontiers Media S.A.Frontiers in Genetics1664-80212013-11-01410.3389/fgene.2013.0022663009Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip dataMuhammad eAhsan0Xidan eLi1Andreas E Lundberg2Marcin eKierczak3Paul B Siegel4Örjan eCarlborg5Stefan eMarklund6Swedish University of Agricultural SciencesSwedish University of Agricultural SciencesSwedish University of Agricultural SciencesSwedish University of Agricultural SciencesVirginia Polytechnic Institute and State UniversitySwedish University of Agricultural SciencesSwedish University of Agricultural SciencesMapping of chromosomal regions harboring genetic polymorphisms that regulate complex traits is usually followed by a search for the causative mutations underlying the observed effects. This is often a challenging task even after fine mapping, as millions of base pairs including many genes will typically need to be investigated. Thus to trace the causative mutation(s) there is a great need for efficient bioinformatic strategies. Here, we searched for genes and mutations regulating growth in the Virginia chicken lines – an experimental population comprising two lines that have been divergently selected for body weight at 56 days for more than 50 generations. Several QTL regions have been mapped in an F2 intercross between the lines, and the regions have subsequently been replicated and fine mapped using an Advanced Intercross Line. We have further analyzed the QTL regions where the largest genetic divergence between the High-Weight selected (HWS) and Low-Weight selected (LWS) lines was observed. Such regions, covering about 37% of the actual QTL regions, were identified by comparing the allele frequencies of the HWS and LWS lines using both individual 60K SNP chip genotyping of birds and analysis of read proportions from genome resequencing of DNA pools. Based on a combination of criteria including significance of the QTL, allele frequency difference of identified mutations between the selected lines, gene information on relevance for growth, and the predicted functional effects of identified mutations we propose here a subset of candidate mutations of highest priority for further evaluation in functional studies. The candidate mutations were identified within the GCG, IGFBP2, GRB14, CRIM1, FGF16, VEGFR-2, ALG11, EDN1, SNX6 and BIRC7 genes. We believe that the proposed method of combining different types of genomic information increases the probability that the genes underlying the observed QTL effects are represented among the candidate mutations identified.http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00226/fullGrowthSNPResequencingQTLcandidate genesfunctional prediction |
| spellingShingle | Muhammad eAhsan Xidan eLi Andreas E Lundberg Marcin eKierczak Paul B Siegel Örjan eCarlborg Stefan eMarklund Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data Frontiers in Genetics Growth SNP Resequencing QTL candidate genes functional prediction |
| title | Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data |
| title_full | Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data |
| title_fullStr | Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data |
| title_full_unstemmed | Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data |
| title_short | Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data |
| title_sort | identification of candidate genes and mutations in qtl regions for chicken growth using bioinformatic analysis of ngs and snp chip data |
| topic | Growth SNP Resequencing QTL candidate genes functional prediction |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00226/full |
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