Comparative study of population genomic approaches for mapping colony-level traits.
Social insect colonies exhibit colony-level phenotypes such as social immunity and task coordination, which are produced by the individual phenotypes. Mapping the genetic basis of such phenotypes requires associating the colony-level phenotype with the genotypes in the colony. In this paper, we exam...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-03-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007653 |
| _version_ | 1818348843038670848 |
|---|---|
| author | Shani Inbar Pnina Cohen Tal Yahav Eyal Privman |
| author_facet | Shani Inbar Pnina Cohen Tal Yahav Eyal Privman |
| author_sort | Shani Inbar |
| collection | DOAJ |
| description | Social insect colonies exhibit colony-level phenotypes such as social immunity and task coordination, which are produced by the individual phenotypes. Mapping the genetic basis of such phenotypes requires associating the colony-level phenotype with the genotypes in the colony. In this paper, we examine alternative approaches to DNA extraction, library construction, and sequencing for genome wide association studies (GWAS) of colony-level traits using a population sample of Cataglyphis niger ants. We evaluate the accuracy of allele frequency estimation from sequencing a pool of individuals (pool-seq) from each colony using either whole-genome sequencing or reduced representation genomic sequencing. Based on empirical measurement of the experimental noise in sequenced DNA pools, we show that reduced representation pool-seq is drastically less accurate than whole-genome pool-seq. Surprisingly, normalized pooling of samples did not result in greater accuracy than un-normalized pooling. Subsequently, we evaluate the power of the alternative approaches for detecting quantitative trait loci (QTL) of colony-level traits by using simulations that account for an environmental effect on the phenotype. Our results can inform experimental designs and enable optimizing the power of GWAS depending on budget, availability of samples and research goals. We conclude that for a given budget, sequencing un-normalized pools of individuals from each colony provides optimal QTL detection power. |
| first_indexed | 2024-12-13T17:56:29Z |
| format | Article |
| id | doaj.art-628047d401324f149bd99b9acc2b0e5f |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-13T17:56:29Z |
| publishDate | 2020-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-628047d401324f149bd99b9acc2b0e5f2022-12-21T23:36:22ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-03-01163e100765310.1371/journal.pcbi.1007653Comparative study of population genomic approaches for mapping colony-level traits.Shani InbarPnina CohenTal YahavEyal PrivmanSocial insect colonies exhibit colony-level phenotypes such as social immunity and task coordination, which are produced by the individual phenotypes. Mapping the genetic basis of such phenotypes requires associating the colony-level phenotype with the genotypes in the colony. In this paper, we examine alternative approaches to DNA extraction, library construction, and sequencing for genome wide association studies (GWAS) of colony-level traits using a population sample of Cataglyphis niger ants. We evaluate the accuracy of allele frequency estimation from sequencing a pool of individuals (pool-seq) from each colony using either whole-genome sequencing or reduced representation genomic sequencing. Based on empirical measurement of the experimental noise in sequenced DNA pools, we show that reduced representation pool-seq is drastically less accurate than whole-genome pool-seq. Surprisingly, normalized pooling of samples did not result in greater accuracy than un-normalized pooling. Subsequently, we evaluate the power of the alternative approaches for detecting quantitative trait loci (QTL) of colony-level traits by using simulations that account for an environmental effect on the phenotype. Our results can inform experimental designs and enable optimizing the power of GWAS depending on budget, availability of samples and research goals. We conclude that for a given budget, sequencing un-normalized pools of individuals from each colony provides optimal QTL detection power.https://doi.org/10.1371/journal.pcbi.1007653 |
| spellingShingle | Shani Inbar Pnina Cohen Tal Yahav Eyal Privman Comparative study of population genomic approaches for mapping colony-level traits. PLoS Computational Biology |
| title | Comparative study of population genomic approaches for mapping colony-level traits. |
| title_full | Comparative study of population genomic approaches for mapping colony-level traits. |
| title_fullStr | Comparative study of population genomic approaches for mapping colony-level traits. |
| title_full_unstemmed | Comparative study of population genomic approaches for mapping colony-level traits. |
| title_short | Comparative study of population genomic approaches for mapping colony-level traits. |
| title_sort | comparative study of population genomic approaches for mapping colony level traits |
| url | https://doi.org/10.1371/journal.pcbi.1007653 |
| work_keys_str_mv | AT shaniinbar comparativestudyofpopulationgenomicapproachesformappingcolonyleveltraits AT pninacohen comparativestudyofpopulationgenomicapproachesformappingcolonyleveltraits AT talyahav comparativestudyofpopulationgenomicapproachesformappingcolonyleveltraits AT eyalprivman comparativestudyofpopulationgenomicapproachesformappingcolonyleveltraits |