Genetic load

Abstract

We tend to assume that all genetic variation is good: the higher the variation in a population, the more diverse its gene pool, the more secure its future. Unfortunately, the relationship between genetic variation and survival of a population or a species is more complex. Some genetic variants are advantageous, increasing the survival and reproductive output of an individual, while others may be neutral now (but potentially advantageous in the future), and finally, some genetic variants are disadvantageous. All organisms carry a burden of disadvantageous genetic variants, called 'genetic load'. This genetic load can lead to diseases or morphological abnormalities and reduce survival or reproduction. Although genetic load has been studied for decades, recent technological advances now enable a more detailed understanding of its characteristics and potential consequences in wild species.

Figure 1. Inbreeding load and inbreeding depression.

(A) Schematic showing how inbreeding can lead to the expression of genetic load. When two related individuals (who share some ancestors) mate, they have a higher chance of both carrying the same deleterious mutation. If an offspring inherits two copies of this mutation (one from each parent), the harmful trait or condition is unmasked and can be expressed. This happens because the offspring lacks a healthy version of the gene to counteract the mutation. (B) California condor (photo: © Joe Lewis/Flickr (CC BY-SA 2.0)) and (C) Florida panthers (photo: © Everglades National Park/Flickr) are examples of inbreeding effects. (D) Galápagos cactus finch (photo: © Jordan Fischer/Flickr (CC BY 2.0)) and (E) killer whales show inbreeding depression (in killer whales evident by lower survival for longer runs of homozygosity; reused with permission by Springer Nature, from Kardos et al. (2023) https://doi.org/10.1038/s41559-023-01995-0). (F) Mountain gorilla (photo: © Rod Waddington/Flickr (CC BY-SA 2.0)) and (G) Chinese crocodile lizard (photo: © Holger Krisp/Wikimedia Commons (CC BY 3.0)) are both species in which highly deleterious mutations have been purged through inbreeding.

Figure 2. What is a deleterious mutation?

(A) Schematic of a gene. (B) Four different variants (original, with synonymous mutation, with missense mutation, with stop-gained mutation) of the same gene region and the corresponding predicted protein conformation as evaluated using Alphafold. Upper rows per sequence show the nucleotide (DNA) sequence, lower rows the amino acid (protein) sequence. (C,D) Visualization of real missense mutation as observed in Indian tigers with pseudo melanism (reused from Sagar et al. (2021) https://doi.org/10.1073/pnas.2025273118 (CC BY 4.0)). (C) Left: Indian tiger with normal coat patterning; right: Indian tiger with pseudo melanism. (D) Illustration of the corresponding gene Transmembrane aminopeptidase QM1 Domain and alignment of amino acids in wild-type tiger, pseudomelanistic tiger (with a mutation to Y), leopard, domestic cat and dog (all with the same amino acid sequence as wild-type tigers).

Figure 3. A practical guide to estimate genetic load at the molecular level in wild populations.

(A) Workflow for estimating genetic load using molecular methods. Estimates of load require a genome assembly with gene annotations that provide the positions and extent of the associated protein sequences (or at least sequence conservation scores, e.g. GERP). Resequencing genomes of several individuals in a population allows us to then align these individual genomes to the annotated genome and identify ‘SNPs’ or single nucleotide polymorphisms (single point mutations) but also insertions or deletions of several nucleotides (structural variation) compared to a reference genome assembly. We can then list which of these mutations are expected to be neutral (do not change the amino acid sequence), missense (change the amino acid sequence, shown as dots) or loss-of-function (result in a loss of protein function, shown as crosses; see also Figure 2). (B) Different scenarios of population history. scenario 1: large connected populations; scenario 2: small and isolated populations; scenario 3: population with recent decline. (C) Population genetics provides expectations for distributions of genetic load under different scenarios. Left: expected load under scenario 1; centre: comparatively higher missense load because of genetic drift right: comparatively higher missense load (genetic drift) but lower loss-of-function load following purging due to inbreeding. Centre and right are possible under scenarios 2 and 3.