High-quality sequencing of nearly the entire kākāpō population, funded through a Genomics Aotearoa project, is helping New Zealand to manage the health of this critically endangered species.
Not only is it already making a difference to kākāpō survival, but establishing sequencing methods to work with populations under threat is also likely to be the foundation for the future of endangered wildlife science in New Zealand and potentially the rest of the world.
The state-of-the-art methods developed by Dr. Joseph Guhlin (University of Otago ) and an international team to study kākāpō have revealed important aspects of kākāpō biology. The methods, reusable code, and pipeline is a blueprint and tool for conservation genomics in other species. This has massive implications, especially in intensively managed species.
Dr. Guhlin’s work over the last year has had two very significant outcomes:
- an in-depth understanding of kākāpō biology that simply would not be possible without genomics.
- high-quality code and reusable pipeline – allowing other researchers to rapidly integrate these methods into their own work – which has significantly advanced New Zealand’s genomic capability.
This has given researchers the tools needed to identify specific genetic characteristics that are crucial to survival.
“Using technology created by Google, we have achieved what is likely the highest quality variant dataset for any endangered species in the world. This dataset is made available, through DOC and Ngāi Tahu, for future researchers working with Kākāpō,” Dr Guhlin said.
Department of Conservation’s Science Advisor for Kākāpō Recovery, Dr. Andrew Digby, believes the genetic tools this study provides will make an immense difference to kākāpō conservation.
“Kākāpō suffer from disease and low reproductive output, so by understanding the genetic reasons for these problems, we can now help mitigate them. It gives us the ability to predict things like kākāpō chick growth and susceptibility to disease, which changes our on-the-ground management practices and will help improve survival rates.”
While the study marks the beginning of a new era of kākāpō conservation genetics, Dr. Digby acknowledges what it means for the future of all threatened species.
“The Kakapo125+ project is a great example of how genetic data can assist population growth. The novel genetic and machine learning tools developed can be applied to improve the productivity and survival of other taonga under conservation management.”
The research results have been published in the prestigious international journal Nature Ecology and Evolution.
Understanding kākāpō biology
Kākāpō are a unique, critically endangered parrot living in the wild on predator-free islands in New Zealand. Intensive conservation management recovered the population from a low of 51 individuals in 1995, to 252 in 2023.
The initial sequencing of the genomes (the complete set of genetic material present in a cell or organism) of every New Zealand kākāpō was completed in late 2018 through the kākāpō 125+ project, established by the Department of Conservation with funding organised by the Genetic Rescue Foundation.
Genomics Aotearoa-funded research subsequently produced a new high-quality variation dataset in 2022, utilising the latest genomic technology to provide a much more detailed look at kākāpō.
A range of biological features were studied to link genomic information with observable traits (phenotype). This has produced a wealth of information on genetic variations affecting growth, fertility, embryo survival, and clutch size.
- Genomics has helped to understand kākāpō egg clutch size and how genetics may play a role in both the first and second clutch.
- Egg shape was explored, which provides a baseline test for heritability.
- One important trait is early growth rate. The genomic analyses have shown genes found in chickens and ducks playing a role in similar traits. With this information, genomic data, and genomically informed parental trait values, there should be clear signals for earlier vet intervention for chicks when they are sick.
- Correlating aspergillosis and genomic data will signal disease risk for individual birds allowing the Recovery Team to prioritise translocations and vet treatment.
Understanding how genetics contributes to early growth provides an opportunity for better monitoring and earlier intervention if a chick is not thriving. Prioritising care using genomics-based information could mean selecting individuals for moving and monitoring individuals with poor growth or high disease risk, particularly birds at high risk for aspergillosis or when individuals have abnormal growth rates. Working out genetic values for parents can be used to better predict future offspring traits.
Importantly, the team has generated breeding values to predict observable phenotype traits and illustrates that active management over the past 45 years has maintained both genome-wide diversity and diversity in breeding values, hence, evolutionary potential.
A high-quality pipeline – establishing methods for working with endangered populations
Dr. Guhlin’s research is not only benefitting a highly endangered taonga species, but it is also world-leading.
Refining current population sequencing methods used by researchers globally has allowed the team to develop a high-quality and repeatable process - a method of classifying differences between sequencing reads.
It has emphasised the value of using near whole genome sequence datasets and genomic approaches to understand the biology of a species. It has also highlighted that a multidisciplinary approach leads to the best conservation outcomes.
Having the ability to sequence the genome of any threatened species is just the beginning; a high-quality variant dataset helps reveal fundamental information that presents answers to a raft of survival questions.
And that will help to address some of the harder conservation issues in more depth, to estimate population structure and genetic diversity, as well as addressing infertility and diseases. Ultimately, it will help us to save our other critically endangered species.
One of the major challenges for kākāpō research has been its small population. By using this information to account for uncertainty, we are able to better provide information for the management team to make decisions. This pipeline is also unique in that it provides a template for genomic data from very small samples, something that will be transformational for recovery efforts in other species around the globe.
Some background - what is a genome, and why is it important?
A genome helps to decipher the genes found in an organism to understand how genes work together to direct its growth, development, and maintenance. Sequencing a genome is figuring out the order of DNA nucleotides, or bases, in a genome; as an example, the human genome is made up of over 3 billion of these genetic bases.
DNA sequencing is done by a machine capable of reading a sequence of DNA bases. The technology cannot read whole genomes in one go; current methods can only handle short stretches of DNA at a time to read short pieces of bases from a genomic sequence.
Bioinformatic techniques are then used to piece together the reads like a jigsaw into a continuous genomic sequence that provides much more detail.
They started with single reference genome assembly to align and merge DNA fragments to understand gene interaction but have now graduated to a much more detailed, high-quality genome. Constructing this high-quality reference genome means improving sequence accuracy, filling assembly gaps, and fixing structural misassembles within the genetic information.
Put simply, a high-quality genome is one that has been “polished,” with fewer missing or fragmented genes and a higher number of complete genes.
Such high-quality reference genome sequences are essential for detailed research but obviously need considerably more work in assembly corrections and filling in gaps.
GENOMIC TERMS
Genomics: The study of the genome, the complete set of genetic material present in a cell or organism.
Bioinformatics: The development and usage of methods and software tools for understanding biological data derived from genomics.
Whole genome sequencing (WGS): Also known as full genome sequencing, complete genome sequencing, or entire genome sequencing, is the process of determining the entirety, or nearly the entirety, of the DNA sequence of an organism's genome at a single time.
Structural variants: Larger differences in genetic variation between individuals. Structural variants (SVs) such as deletions, insertions, duplications, inversions, and translocations are scattered through genomes and are often associated with gene expression changes.
Phenotype: Observable characteristics influenced both by an organism’s genes and by the environment.
Short-read sequencing: When DNA is broken into short fragments that are amplified (copied) and then sequenced to produce 'reads.' Bioinformatic techniques are then used to piece together the reads like a jigsaw into a continuous genomic sequence that provides much more detail.
Variant calling: a method of classifying differences between sequencing reads generated by Next Generation Sequencing (NGS) experiments and a reference genome.