Evidence map›Paper›PMID 35635119›Full record

ArticleMolecular ecology resources2022

Leveraging an existing whole-genome resequencing population data set to characterize toll-like receptor gene diversity in a threatened bird.

Molly Magid, Jana R Wold, Roger Moraga, Ilina Cubrinovska, Dave M Houston, Brett D Gartrell, Tammy E Steeves

Abstract read
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Article in Molecular ecology resources, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Molly MagidSchool of Biological Sciences, University of Canterbury, Christchurch, New Zealand.ORCID https://orcid.org/0000-0002-6598-2530
Jana R WoldSchool of Biological Sciences, University of Canterbury, Christchurch, New Zealand.ORCID https://orcid.org/0000-0001-8095-4282
Roger MoragaTea Break Bioinformatics, Ltd, Palmerston North, New Zealand.ORCID https://orcid.org/0000-0001-7806-1135
Ilina CubrinovskaSchool of Biological Sciences, University of Canterbury, Christchurch, New Zealand.
Dave M HoustonDepartment of Conservation, Biodiversity Group, Auckland, New Zealand.
Brett D GartrellInstitute of Veterinary, Animal, and Biomedical Sciences, Wildbase, Massey University, Palmerston North, New Zealand.ORCID https://orcid.org/0000-0002-8062-9313
Tammy E SteevesSchool of Biological Sciences, University of Canterbury, Christchurch, New Zealand.ORCID https://orcid.org/0000-0003-2112-5761

Funding

Fulbright New ZealandMinistry of Business, Innovation and EmploymentNew Zealand Department of ConservationNew Zealand Ministry of Business, Innovation and Employment Endeavour Fund UOCX1602University of Canterbury
6 · The paper itself

Abstract

Species recovery programs are increasingly using genomic data to measure neutral genetic diversity and calculate metrics like relatedness. While these measures can inform conservation management, determining the mechanisms underlying inbreeding depression requires information about functional genes associated with adaptive or maladaptive traits. Toll-like receptors (TLRs) are one family of functional genes, which play a crucial role in recognition of pathogens and activation of the immune system. Previously, these genes have been analysed using species-specific primers and PCR. Here, we leverage an existing short-read reference genome, whole-genome resequencing population data set, and bioinformatic tools to characterize TLR gene diversity in captive and wild tchūriwat'/tūturuatu/shore plover (Thinornis novaeseelandiae), a threatened bird endemic to Aotearoa New Zealand. Our results show that TLR gene diversity in tchūriwat'/tūturuatu is low, and forms two distinct captive and wild genetic clusters. The bioinformatic approach presented here has broad applicability to other threatened species with existing genomic resources in Aotearoa New Zealand and beyond.

Indexed as

Endangered SpeciesToll-Like ReceptorsAnimalsBirdsGenomeSequence Analysis, DNAToll-Like Receptorsconservation genomicsimmune genesshore plovertoll-like receptorswhole-genome sequences

Identifiers

PMID35635119
PMCPMC9543821

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.