Species-wide genomics of kākāpō provides tools to accelerate recovery.

Humans Animals Endangered Species Parrots Genomics Genome New Zealand

Journal

Nature ecology & evolution

ISSN: 2397-334X

Titre abrégé: Nat Ecol Evol

Pays: England

ID NLM: 101698577

Informations de publication

Date de publication:
10 2023

Historique:

received: 26 11 2022

accepted: 11 07 2023

medline: 9 10 2023

pubmed: 29 8 2023

entrez: 28 8 2023

Statut: ppublish

Résumé

The kākāpō is a critically endangered, intensively managed, long-lived nocturnal parrot endemic to Aotearoa New Zealand. We generated and analysed whole-genome sequence data for nearly all individuals living in early 2018 (169 individuals) to generate a high-quality species-wide genetic variant callset. We leverage extensive long-term metadata to quantify genome-wide diversity of the species over time and present new approaches using probabilistic programming, combined with a phenotype dataset spanning five decades, to disentangle phenotypic variance into environmental and genetic effects while quantifying uncertainty in small populations. We find associations for growth, disease susceptibility, clutch size and egg fertility within genic regions previously shown to influence these traits in other species. Finally, we generate breeding values to predict phenotype and illustrate that active management over the past 45 years has maintained both genome-wide diversity and diversity in breeding values and, hence, evolutionary potential. We provide new pathways for informing future conservation management decisions for kākāpō, including prioritizing individuals for translocation and monitoring individuals with poor growth or high disease risk. Overall, by explicitly addressing the challenge of the small sample size, we provide a template for the inclusion of genomic data that will be transformational for species recovery efforts around the globe.

Identifiants

DOI: 10.1038/s41559-023-02165-y PMID: 37640765

pubmed: 37640765

doi: 10.1038/s41559-023-02165-y

pii: 10.1038/s41559-023-02165-y

doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

Pagination

1693-1705

Investigateurs

Karen Andrew (K)

Lisa Argilla (L)

Karen Arnold (K)

James Bohan (J)

Liam Bolitho (L)

Nichy Brown (N)

Jo Carpenter (J)

Jodie Crane (J)

Margie Grant (M)

Glen Greaves (G)

Brett Halkett (B)

Rory Hannan (R)

Sam Haultain (S)

Bryony Hitchcock (B)

Leigh Joyce (L)

Sara Larcombe (S)

Jo Ledington (J)

Jinty MacTavish (J)

Phil Marsh (P)

Gilbert Mingam (G)

Freya Moore (F)

Lyndsay Murray (L)

Errol Nye (E)

Jake Osborne (J)

Lou Parker (L)

Chris Phillips (C)

Roy Phillips (R)

Brodie Philp (B)

Tim Raemaekers (T)

Jenny Rickett (J)

Rachel Rouse (R)

Rachael Sagar (R)

Alisha Sherriff (A)

Theo Thompson (T)

Jason Van de Wetering (JV)

Nicki van Zyl (N)

Jen Waite (J)

Jim Watts (J)

Commentaires et corrections

Type : CommentIn

Informations de copyright

Références

Ouborg, N. J., Pertoldi, C., Loeschcke, V., Bijlsma, R. K. & Hedrick, P. W. Conservation genetics in transition to conservation genomics. Trends Genet. 26, 177–187 (2010).

pubmed: 20227782 doi: 10.1016/j.tig.2010.01.001

Formenti, G. et al. The era of reference genomes in conservation genomics. Trends Ecol. Evol. 37, 197–202 (2022).

pubmed: 35086739 doi: 10.1016/j.tree.2021.11.008

Kardos, M. et al. The crucial role of genome-wide genetic variation in conservation. Proc. Natl Acad. Sci. USA 118, e2104642118 (2021).

pubmed: 34772759 pmcid: 8640931 doi: 10.1073/pnas.2104642118

Kardos, M. & Shafer, A. B. A. The peril of gene-targeted conservation. Trends Ecol. Evol. 33, 827–839 (2018).

pubmed: 30241778 doi: 10.1016/j.tree.2018.08.011

Wright, B. R., Hogg, C. J., McLennan, E. A., Belov, K. & Grueber, C. E. Assessing evolutionary processes over time in a conservation breeding program: a combined approach using molecular data, simulations and pedigree analysis. Biodivers. Conserv. 30, 1011–1029 (2021).

doi: 10.1007/s10531-021-02128-4

Robinson, J. A. et al. Genomic flatlining in the endangered island fox. Curr. Biol. 26, 1183–1189 (2016).

pubmed: 27112291 doi: 10.1016/j.cub.2016.02.062

Sherwin, W. B., Murray, N. D., Graves, J. A. M. & Brown, P. R. Measurement of genetic variation in endangered populations: bandicoots (Marsupialia: Peramelidae) as an example. Conserv. Biol. 5, 103–108 (1991).

doi: 10.1111/j.1523-1739.1991.tb00392.x

Mitrovski, P., Hoffmann, A. A., Heinze, D. A. & Weeks, A. R. Rapid loss of genetic variation in an endangered possum. Biol. Lett. 4, 134–138 (2008).

pubmed: 17956839 doi: 10.1098/rsbl.2007.0454

Kirkpatrick, M. & Jarne, P. The effects of a bottleneck on inbreeding depression and the genetic load. Am. Nat. 155, 154–167 (2000).

pubmed: 10686158 doi: 10.1086/303312

Taylor, R. S., Jensen, E. L., Coltman, D. W., Foote, A. D. & Lamichhaney, S. Seeing the whole picture: what molecular ecology is gaining from whole genomes. Mol. Ecol. 30, 5917–5922 (2021).

pubmed: 34845797 doi: 10.1111/mec.16282

Williams, G. R. The kakapo (Strigops habrotilus, Gray): a review and reappraisal of a near-extinct species (New Zealand Wildlife Service, 1956).

Lentini, P. E., Stirnemann, I. A., Stojanovic, D., Worthy, T. H. & Stein, J. A. Using fossil records to inform reintroduction of the kakapo as a refugee species. Biol. Conserv. 217, 157–165 (2018).

doi: 10.1016/j.biocon.2017.10.027

Karl, B. J. & Best, H. A. Feral cats on Stewart Island; their foods, and their effects on kakapo. N. Z. J. Zool. 9, 287–293 (1982).

doi: 10.1080/03014223.1982.10423857

Lloyd, B. D. & Powlesland, R. G. The decline of kakapo Strigops habroptilus and attempts at conservation by translocation. Biol. Conserv. 69, 75–85 (1994).

doi: 10.1016/0006-3207(94)90330-1

Merton, D. V., Morris, R. B. & Atkinson, I. A. E. Lek behaviour in a parrot: the kakapo Strigops habroptilus of New Zealand. Ibis 126, 277–283 (1984).

doi: 10.1111/j.1474-919X.1984.tb00250.x

Clout, M. N. & Merton, D. V. Saving the kakapo: the conservation of the world’s most peculiar parrot. Bird Conserv. Int. 8, 281–296 (1998).

doi: 10.1017/S0959270900001933

Elliott, G. P., Merton, D. V. & Jansen, P. W. Intensive management of a critically endangered species: the kakapo. Biol. Conserv. 99, 121–133 (2001).

doi: 10.1016/S0006-3207(00)00191-9

Ngāi Tahu Claims Settlement Act 1998 No 97 (version as at 1 July 2022), Public Act Contents (New Zealand Legislation, 2022); https://legislation.govt.nz/act/public/1998/0097/latest/DLM429090.html

Savage, J. L., Crane, J. M. S., Team, K. R. & Hemmings, N. Low hatching success in the critically endangered kakapo is driven by early embryo mortality not infertility. Anim. Conserv. 25, 352–360 (2021).

doi: 10.1111/acv.12746

White, K. L., Eason, D. K., Jamieson, I. G. & Robertson, B. C. Evidence of inbreeding depression in the critically endangered parrot, the kakapo. Anim. Conserv. 18, 341–347 (2015).

doi: 10.1111/acv.12177

Bergner, L. M., Jamieson, I. G. & Robertson, B. C. Combining genetic data to identify relatedness among founders in a genetically depauperate parrot, the Kakapo (Strigops habroptilus). Conserv. Genet. 15, 1013–1020 (2014).

doi: 10.1007/s10592-014-0595-y

Robertson, B. C. The role of genetics in kakapo recovery. Notornis 53, 173 (2006).

Miller, H. C., Lambert, D. M., Millar, C. D., Robertson, B. C. & Minot, E. O. Minisatellite DNA profiling detects lineages and parentage in the endangered kakapo (Strigops habroptilus) despite low microsatellite DNA variation. Conserv. Genet. 4, 265–274 (2003).

doi: 10.1023/A:1024037601708

Robertson, B. C., Frauenfelder, N., Eason, D. K., Elliott, G. & Moorhouse, R. Thirty polymorphic microsatellite loci from the critically endangered kakapo (Strigops habroptilus). Mol. Ecol. Resour. 9, 664–666 (2009).

pubmed: 21564722 doi: 10.1111/j.1755-0998.2008.02506.x

Rhie, A. et al. Towards complete and error-free genome assemblies of all vertebrate species. Nature 592, 737–746 (2021).

pubmed: 33911273 pmcid: 8081667 doi: 10.1038/s41586-021-03451-0

Dussex, N. et al. Population genomics of the critically endangered kākāpō. Cell 1, 100002 (2021).

Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022).

pubmed: 35357919 pmcid: 9186530 doi: 10.1126/science.abj6987

Cai, C. et al. Brassica rapa Genome 2.0: a reference upgrade through sequence re-assembly and gene re-annotation. Mol. Plant 10, 649–651 (2017).

pubmed: 27890636 doi: 10.1016/j.molp.2016.11.008

Holt, C. et al. Improved genome assembly and annotation for the rock pigeon (Columba livia). G3 Genes Genom. Genet. 8, 1391–1398 (2018).

doi: 10.1534/g3.117.300443

Li, Y., Pi, M., Gao, Q., Liu, Z. & Kang, C. Updated annotation of the wild strawberry Fragaria vesca V4 genome. Hortic. Res. 6, 61 (2019).

pubmed: 31069085 pmcid: 6491553 doi: 10.1038/s41438-019-0142-6

Yun, T. et al. Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics 36, 5582–5589 (2020).

doi: 10.1093/bioinformatics/btaa1081

Lin, Y.-L. et al. Comparison of GATK and DeepVariant by trio sequencing. Sci. Rep. 12, 1809 (2022).

pubmed: 35110657 pmcid: 8810758 doi: 10.1038/s41598-022-05833-4

Supernat, A., Vidarsson, O. V., Steen, V. M. & Stokowy, T. Comparison of three variant callers for human whole genome sequencing. Sci. Rep. 8, 17851 (2018).

pubmed: 30552369 pmcid: 6294778 doi: 10.1038/s41598-018-36177-7

Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983–987 (2018).

pubmed: 30247488 doi: 10.1038/nbt.4235

Pei, S. et al. Benchmarking variant callers in next-generation and third-generation sequencing analysis. Brief. Bioinform. 22, bbaa148 (2021).

pubmed: 32698196 doi: 10.1093/bib/bbaa148

Galla, S. J. et al. The relevance of pedigrees in the conservation genomics era. Mol. Ecol. 31, 41–54 (2022).

pubmed: 34553796 doi: 10.1111/mec.16192

Duntsch, L. et al. Polygenic basis for adaptive morphological variation in a threatened Aotearoa | New Zealand bird, the hihi (Notiomystis cincta). Proc. R. Soc. B 287, 20200948 (2020).

pubmed: 32842928 pmcid: 7482260 doi: 10.1098/rspb.2020.0948

John, S. & Stephan, W. Important role of genetic drift in rapid polygenic adaptation. Ecol. Evol. 10, 1278–1287 (2020).

pubmed: 32076513 pmcid: 7029068 doi: 10.1002/ece3.5981

Santure, A. W. & Garant, D. Wild GWAS—association mapping in natural populations. Mol. Ecol. Resour. 18, 729–738 (2018).

pubmed: 29782705 doi: 10.1111/1755-0998.12901

Bosse, M. et al. Recent natural selection causes adaptive evolution of an avian polygenic trait. Science 358, 365–368 (2017).

pubmed: 29051380 doi: 10.1126/science.aal3298

Foster, Y. et al. Genomic signatures of inbreeding in a critically endangered parrot, the kākāpō. G3 Genes Genom. Genet. 11, jkab307 (2021).

doi: 10.1093/g3journal/jkab307

Curtin, S. J. et al. Validating genome-wide association candidates controlling quantitative variation in nodulation. Plant Physiol. 173, 921–931 (2017).

pubmed: 28057894 pmcid: 5291020 doi: 10.1104/pp.16.01923

Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).

pubmed: 2513255 pmcid: 1203831 doi: 10.1093/genetics/123.3.585

Renaud, G., Hanghøj, K., Korneliussen, T. S., Willerslev, E. & Orlando, L. Joint estimates of heterozygosity and runs of homozygosity for modern and ancient samples. Genetics 212, 587–614 (2019).

pubmed: 31088861 pmcid: 6614887 doi: 10.1534/genetics.119.302057

Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 56, 330–338 (1922).

doi: 10.1086/279872

Fisher, R. A. A fuller theory of ‘junctions’ in inbreeding. Heredity 8, 187–197 (1954).

doi: 10.1038/hdy.1954.17

Keller, M. C., Visscher, P. M. & Goddard, M. E. Quantification of inbreeding due to distant ancestors and its detection using dense single nucleotide polymorphism data. Genetics 189, 237–249 (2011).

pubmed: 21705750 pmcid: 3176119 doi: 10.1534/genetics.111.130922

Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).

pubmed: 22728672 pmcid: 3679285 doi: 10.4161/fly.19695

Liu, Z. et al. Genetic variations for egg quality of chickens at late laying period revealed by genome-wide association study. Sci. Rep. 8, 10832 (2018).

pubmed: 30018363 pmcid: 6050282 doi: 10.1038/s41598-018-29162-7

Zhang, L.-C., Ning, Z.-H., Xu, G.-Y., Hou, Z.-C. & Yang, N. Heritabilities and genetic and phenotypic correlations of egg quality traits in brown-egg dwarf layers. Poult. Sci. 84, 1209–1213 (2005).

pubmed: 16156204 doi: 10.1093/ps/84.8.1209

Elliott, G. P. Productivity of kakapo (Strigops habroptilus) on offshore island refuges. Notornis 53, 138–142 (2006).

Cottam, Y., Merton, D. V. & Hendricks, W. Nutrient composition of the diet of parent-raised kakapo nestlings. Notornis 53, 90 (2006).

Biedrzycki, R. J., Sier, A. E., Liu, D., Dreikorn, E. N. & Weeks, D. E. Spinning convincing stories for both true and false association signals. Genet. Epidemiol. 43, 356–364 (2019).

pubmed: 30657194 pmcid: 6590226 doi: 10.1002/gepi.22189

Dash, G. K. et al. in Bioinformatics in Rice Research: Theories and Techniques (eds Gupta, M. K. & Behera, L.) 413–457 https://doi.org/10.1007/978-981-16-3993-7_19 (Springer, 2021).

Haque, M. R., Ansari, S. H., Najmi, A. K. & Ahmad, M. A. Monoterpene phenolic compound thymol prevents high fat diet induced obesity in murine model. Toxicol. Mech. Methods 24, 116–123 (2014).

pubmed: 24175857 doi: 10.3109/15376516.2013.861888

Davydov, E. V. et al. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol. 6, e1001025 (2010).

pubmed: 21152010 pmcid: 2996323 doi: 10.1371/journal.pcbi.1001025

Huber, C. D., Kim, B. Y. & Lohmueller, K. E. Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution. PLoS Genet. 16, e1008827 (2020).

pubmed: 32469868 pmcid: 7286533 doi: 10.1371/journal.pgen.1008827

García-Dorado, A. & Caballero, A. Neutral genetic diversity as a useful tool for conservation biology. Conserv. Genet. 22, 541–545 (2021).

doi: 10.1007/s10592-021-01384-9

Bertorelle, G. et al. Genetic load: genomic estimates and applications in non-model animals. Nat. Rev. Genet. 23, 492–503 (2022).

pubmed: 35136196 doi: 10.1038/s41576-022-00448-x

Mathur, S. & DeWoody, J. A. Genetic load has potential in large populations but is realized in small inbred populations. Evol. Appl. 14, 1540–1557 (2021).

pubmed: 34178103 pmcid: 8210801 doi: 10.1111/eva.13216

Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

pubmed: 30423086 pmcid: 6129281 doi: 10.1093/bioinformatics/bty560

Schubert, M., Lindgreen, S. & Orlando, L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res. Notes 9, 88 (2016).

pubmed: 26868221 pmcid: 4751634 doi: 10.1186/s13104-016-1900-2

Andrews, S. et al. FastQC: A Quality Control Tool for High Throughput Sequence Data (Babraham Bioinformatics, 2010); https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

pubmed: 27312411 pmcid: 5039924 doi: 10.1093/bioinformatics/btw354

Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv https://doi.org/10.48550/arXiv.1303.3997 (2013).

Danecek, P. Twelve years of SAMtools and BCFtools. GigaScience 10, giab008 (2021).

pubmed: 33590861 pmcid: 7931819 doi: 10.1093/gigascience/giab008

Van der Auwera, G. A. & O’Connor, B. D. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (O’Reilly Media, 2020).

Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

pubmed: 21653522 pmcid: 3137218 doi: 10.1093/bioinformatics/btr330

Narasimhan, V. et al. BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data. Bioinformatics 32, 1749–1751 (2016).

pubmed: 26826718 pmcid: 4892413 doi: 10.1093/bioinformatics/btw044

Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).

pubmed: 25722852 pmcid: 4342193 doi: 10.1186/s13742-015-0047-8

Li, H. & Ralph, P. Local PCA shows how the effect of population structure differs along the genome. Genetics 211, 289–304 (2019).

pubmed: 30459280 doi: 10.1534/genetics.118.301747

Guhlin, J. & Ralph, P. jguhlin/lostruct-py: 0.0.4. Zenodo https://doi.org/10.5281/zenodo.4001195 (2020).

scikit-bio Development Team. scikit-bio: a bioinformatics library for data scientists, students, and developers. GitHub https://github.com/biocore/scikit-bio/issues/1691 (2020).

Whalen, A., Ros-Freixedes, R., Wilson, D. L., Gorjanc, G. & Hickey, J. M. Hybrid peeling for fast and accurate calling, phasing, and imputation with sequence data of any coverage in pedigrees. Genet. Sel. Evol. 50, 67 (2018).

pubmed: 30563452 pmcid: 6299538 doi: 10.1186/s12711-018-0438-2

Killick, R., Haynes, K. & Eckley, I. changepoint: an R package for changepoint analysis. J. Stat. Soft. 58, 1–19 (2014).

Killick, R., Fearnhead, P. & Eckley, I. A. Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107, 1590–1598 (2012).

doi: 10.1080/01621459.2012.737745

Hedrick, P. W. & Lacy, R. C. Measuring relatedness between inbred individuals. J. Hered. 106, 20–25 (2015).

pubmed: 25472983 doi: 10.1093/jhered/esu072

Waples, R. K., Albrechtsen, A. & Moltke, I. Allele frequency-free inference of close familial relationships from genotypes or low-depth sequencing data. Mol. Ecol. 28, 35–48 (2019).

pubmed: 30462358 pmcid: 6850436 doi: 10.1111/mec.14954

Dodds, K. G. et al. Construction of relatedness matrices using genotyping-by-sequencing data. BMC Genomics 16, 1047 (2015).

pubmed: 26654230 pmcid: 4675043 doi: 10.1186/s12864-015-2252-3

Galla, S. J. et al. A comparison of pedigree, genetic and genomic estimates of relatedness for informing pairing decisions in two critically endangered birds: implications for conservation breeding programmes worldwide. Evol. Appl. 13, 991–1008 (2020).

pubmed: 32431748 pmcid: 7232769 doi: 10.1111/eva.12916

Wang, J. Parentage and sibship exclusions: higher statistical power with more family members. Heredity 99, 205–217 (2007).

pubmed: 17487215 doi: 10.1038/sj.hdy.6800984

Hauser, S. S., Galla, S. J., Putnam, A. S., Steeves, T. E. & Latch, E. K. Comparing genome-based estimates of relatedness for use in pedigree-based conservation management. Mol. Ecol. Resour. 22, 2546–2558 (2022).

pubmed: 35510790 doi: 10.1111/1755-0998.13630

Pedregosa, F. et al. scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

Eason, D. K. & Moorhouse, R. J. Hand-rearing kakapo (Strigops habroptilus), 1997–2005. Notornis 53, 116 (2006).

von Hurst, P. R., Moorhouse, R. J. & Raubenheimer, D. Preferred natural food of breeding kakapo is a high value source of calcium and vitamin D. J. Steroid Biochem. Mol. Biol. 164, 177–179 (2016).

doi: 10.1016/j.jsbmb.2015.10.017

TensorFlow Developers. TensorFlow. Zenodo https://doi.org/10.5281/zenodo.4960227 (2021).

TensorFlow Developers. TensorFlow (v2.8.0). Zenodo https://doi.org/10.5281/zenodo.5949125 (2022).

Crispim, A. C. et al. Multi-trait GWAS and new candidate genes annotation for growth curve parameters in Brahman cattle. PLoS ONE 10, e0139906 (2015).

pubmed: 26445451 pmcid: 4622042 doi: 10.1371/journal.pone.0139906

Winsor, C. P. The Gompertz curve as a growth curve. Proc. Natl Acad. Sci. USA 18, 1–8 (1932).

pubmed: 16577417 pmcid: 1076153 doi: 10.1073/pnas.18.1.1

Kluyver, T. et al. Jupyter notebooks—a publishing format for reproducible computational workflows. Elpub 2016, 87–90 (2016).

Habier, D., Fernando, R. L., Kizilkaya, K. & Garrick, D. J. Extension of the Bayesian alphabet for genomic selection. BMC Bioinform. 12, 186 (2011).

doi: 10.1186/1471-2105-12-186

Legarra, A., Robert-Granié, C., Croiseau, P., Guillaume, F. & Fritz, S. Improved Lasso for genomic selection. Genet. Res. 93, 77–87 (2011).

doi: 10.1017/S0016672310000534

Meuwissen, T. H. E., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).

pubmed: 11290733 pmcid: 1461589 doi: 10.1093/genetics/157.4.1819

Yin, L. L., Zhang, H. H., Li, X. Y., Zhao, S. H. & Liu X.L. hibayes: an R package to fit individual-level, summary-level and single-step Bayesian regression models for genomic prediction and genome-wide association studies. Preprint at bioRxiv https://doi.org/10.1101/2022.02.12.480230 (2022).

Moser, G. et al. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet. 11, e1004969 (2015).

pubmed: 25849665 pmcid: 4388571 doi: 10.1371/journal.pgen.1004969

Bradbury, P. J. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635 (2007).

pubmed: 17586829 doi: 10.1093/bioinformatics/btm308

Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).

pubmed: 16380716 doi: 10.1038/ng1702

Endelman, J. B. & Jannink, J.-L. Shrinkage estimation of the realized relationship matrix. G3 Genes Genom. Genet. 2, 1405–1413 (2012).

doi: 10.1534/g3.112.004259

Klopfenstein, D. V. et al. GOATOOLS: a Python library for Gene Ontology analyses. Sci. Rep. 8, 10872 (2018).

pubmed: 30022098 pmcid: 6052049 doi: 10.1038/s41598-018-28948-z

Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Biol. Evol. 38, 5825–5829 (2021).

pubmed: 34597405 pmcid: 8662613 doi: 10.1093/molbev/msab293

Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).

pubmed: 30418610 doi: 10.1093/nar/gky1085

Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).

pubmed: 33828273 pmcid: 8026399 doi: 10.1038/s41592-021-01101-x

Guhlin, J., et al. GenomicsAotearoa/Kakapo: publication scripts and workflows. Zenodo https://doi.org/10.5281/zenodo.8015520 (2023).

Taylor, H. R. The use and abuse of genetic marker-based estimates of relatedness and inbreeding. Ecol. Evol. 5, 3140–3150 (2015).

pubmed: 26357542 pmcid: 4559056 doi: 10.1002/ece3.1541

Lacy, R. C., Ballou, J. D. & Pollak, J. P. PMx: software package for demographic and genetic analysis and management of pedigreed populations. Methods Ecol. Evol. 3, 433–437 (2012).

doi: 10.1111/j.2041-210X.2011.00148.x