Non-Additive Quantitative Traits in Mammals

It is well known that animals that are hybrids of parents with differing genetic makeup tend to be more vigorous and productive than their parents: they are not simply the parental average. This phenomenon of hybrid vigor is variously described as non-additive, heterotic, transgressive or dominant, depending on the exact behaviour of the trait in question. Hybrid vigour is very important in animal breeding, but its causes are poorly understood at the level of the genome.

It is also accepted that physical traits are often under the control of many genes, and that the expression of these genes individually tend to behave additively. There is thus a gap in our understanding - how do we explain hybrid vigour if most genes behave additively? In this project we aim to answer this question.

In preparatory work leading up to this project we have examined aspects of of non-additive traits in populations of pigs, mice and rats. We find that non-additive effects on traits are surprisingly common. Second, when we attempt to identify the genes that are causal for these effects we noticed that often a genetic variant with non-addivive effects on both a physical trait and the expression of a gene have the remarkable property that the affected gene is often on a different chromosome. Thus long-range effects on the expression of a gene tend to be non-additive, in contrast to short-range effects which are additive, and which do not play a role in hybrid vigour. Up to now research has focused on these short-range effects, but our data suggest that it is the long-range effects that are the most relevant to non-additive effects.We have also found that sometimes the effect of a gene depends on whether it is inherited from the mother or father, adding an additional level of non-additive complexity. Yet another source of non-linearity is that genomes may be re-arranged, moving the locations of genes and sometimes silencing them.

In our proposal we will analyse genetic data in population of pigs, mice and rats on which we also have extensive data on growth and other traits, to seek patterns that are predictive of non-additive phenomena. By looking across many data sets we can be sure that our conclusions are robust and are likely to generalise to other mammals.

Our previous research on complex traits in outbred mice, rat and pigs has found a common genetic architecture for non-additive genetic effects, encompassing heterosis, under- and over dominance, and parent of origin effects. First, non-additive effects are common across all phenotype classes; they may have been under-reported in the literature because most non-additive quantitative trait loci (QTLs) are still detectable under the assumption of additivity. Second, when we attempt to find causal genes whose expression is also non-additive, we find that the causal variants are distal to the gene, that is they are trans-eQTLs rather than cis-eQTLs. Although this observation appears to be novel in mammals, the same general phenomenon has been seen in yeast, drosophila and arabidopsis, although so far its universality has escaped notice. In other work on structural variation in Arabidopsis, we have shown that often gene expression is silenced or modulated within translocations, providing another mechanism by which non-additivity can arise.

The important question is how non-additive effects arise.

In this project we will work mainly within a population of heterogeneous stock pigs bred and maintained at Xiangxi Agricultural University, China, together with outbred stocks of mice and rats. These cohorts have been extensively phenotyped and most have been sequenced at low coverage (making structural variant analysis possible), and/or have been genotyped on arrays. Parental genotypes are available for many of the stocks (to enable phasing of genotypes for parent-of-origin effect analyses), and RNAseq data (including long non-coding RNA) from multiple tissues is also available (for eQTL analysis). We will mine these data to distinguish between additive and non-additive effects, and to understand the causes and consequences of non-addtivity, with the eventual aim of predicting heterosis in mammals, a key to animal improvement.

The immediate beneficiaries of this project will be academic groups working on the genetic architecture of complex traits, and commercial groups working on pig genetic improvement and the development of new breeds for use by UK and Chinese farmers. This follows from the fact that the 8 founding breeds of the pig HS population capture much of the genetic variation segregating in and between European and Chinese breeds (over 30 million SNPs segregate in the population).

The academic impact of this project will be significant for the following reasons: (i) The pig HS contains an internal replication cohort (generation F6 vs F7) with which to confirm QTLs and other results. (ii) The pig HS population contains a unique combination of full genome sequence, parental genotypes and extensive phenotypes and gene expression and ChIP-seq data. This will make it possible to perform an integrated analysis in a single population
To evaluate the relative impacts of dominance, parent-of-origin and structural variation on quantitative traits. (iv) further studies in other mammals will establish the extent of the phenomena discovered in pigs.

Use of the pig HS population will help identify genetic loci containing variation that affect traits of UK pig breeding importance for selection within the UK genepool, as well as beneficial alleles that could be introgressed. This means that it can detect QTL segregating between these two groups, potentially identifying favourable alleles which have been "left behind." This will have a long-term beneficial impact on UK food security and the sustainable intensification of livestock production.

Commercial pig livestock breeders will benefit by:
1) Markers diagnostic for linkage between individual genes or small genetic intervals and components of the traits measured on the population.
3) Analysis pipelines and software for their own analyses.