A new statistical method developed by the team of Jérôme Goudet, group leader at the Swiss Institute of Bioinformatics (SIB) and associate professor at the Faculty of Biology and Medicine (FBM) of the University of Lausanne (UNIL), opens the way to more accurate detection of inbreeding depression*. It has been published in the latest edition of the Proceedings of the National Academy of Sciences (PNAS). Accurate quantification of this inbreeding, which can have serious consequences for the health of a population, is important to effectively guide biodiversity conservation efforts.
Overcoming known biases
Traditional methods of measuring inbreeding work well for large homogeneous populations, where most individuals are not closely related, as is the case for the human species. However, these approaches show limitations in populations where individuals are related to each other to varying degrees. This limitation can lead to biased estimates of inbreeding depression, and poses challenges when studying populations comprising few individuals, for example in species at risk of extinction.To overcome this bias, the authors have compared the classical statistical approach, a linear regression model, with a mixed model that takes population structure into account. By including the degree of relatedness between individuals estimated from genomic data, the scientists have developed a method that provides reliable results and can be applied to a variety of species. This innovative method opens up new prospects for assessing the harmful effects of inbreeding where it is most needed, in small populations of endangered species", says Prof. Goudet.
Using data from the ’1000 Genomes Project
To extend their methodology to smaller sample sizes and more complex populations, the authors simulated traits based on empirical data from phase 3 of the. By varying the size and homogeneity of the groups analyzed, the specialists were able to compare the effectiveness of their method for different types of samples. They then validated their method on an empirical dataset of house sparrows from an isolated archipelago in north-west Norway, and were able to show that their approach is more accurate than the traditional method.As Eléonore Lavanchy, PhD student in the ’Population Genetics and Genomics’ research group at the University of Lausanne’s Department of Ecology and Evolution and at the SIB, and first author of the study, points out: ’These results demonstrate that the method we propose also works in small and isolated populations. These are becoming increasingly common as a result of the biodiversity crisis we face today.’
*Inbreeding is the result of mating between members of the same family, which can lead to increased expression of detrimental genetic variants, impacting on survival and reproduction rates. It is often associated with compromised health conditions, a phenomenon known as inbreeding depression, which has been observed in many different species, from humans to animals to plants. Measuring inbreeding and its consequences on health is essential in many areas of biology, including the preservation of biodiversity and endangered species.
Article:
Lavanchy, E., Weir, B.S. and Goudet, J. (2024) Detecting inbreeding depression in structured populations. PNAS 12(19):e2315780121; https://doi.org/10.1073/pnas.2315780121
