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Biology subjects

Ferreira, L. A. F.

Publications and source records attributed to Ferreira, L. A. F..

2 recordsLinked to original sources

Phantom epistasis through the lens of genealogies

Phantom epistasis arises when, in the course of testing for gene-by-gene interactions, the omission of a causal variant with a purely additive effect on the phenotype causes the spurious inference of a significant interaction between two SNPs. This is more likely to arise when the two SNPs are in relatively close proximity, so while true epistasis between nearby variants could be commonplace, in practice there is no reliable way of telling apart true epistatic signals from false positives. By considering the causes of phantom epistasis from a genealogy-based perspective, we leverage the rich information contained within reconstructed genealogies (in the form of ancestral recombination graphs) to address this problem. We propose a novel method for explicitly quantifying the genealogical evidence that a given pairwise interaction is the result of phantom epistasis, which can be applied to pairs of SNPs regardless of the genetic distance between them. Our method uses only publicly-available data and so does not require access to the phenotypes and genotypes used for detecting interactions. Using simulations, we show that the method has excellent performance at even low genetic distances (around 0.5cM), and demonstrate its power to detect phantom epistasis using real data from previous studies. This opens up the exciting possibility of distinguishing spurious interactions in cis from those reflecting real biological effects.

evolutionary biology↗

Leveraging fine-scale population structure reveals conservation in genetic effect sizes between human populations across a range of human phenotypes

An understanding of genetic differences between populations is essential for avoiding confounding in genome-wide association studies (GWAS) and understanding the evolution of human traits. Polygenic risk scores constructed in one group perform poorly in highly genetically-differentiated populations, for reasons which remain controversial. We developed a statistical ancestry inference pipeline able to decompose ancestry both within and between countries, and applied it to the UK Biobank data. This identifies fine-scale patterns of genetic relatedness not captured by standard and widely used principal components (PCs), and allows fine-scale population stratification correction that removes both false positive and false negative associations for traits with geographic correlations. We also develop and apply ANCHOR, an approach leveraging segments of distinct ancestries within individuals to estimate similarity in underlying causal effect sizes between groups, using an existing PGS. Applying ANCHOR to >8000 people of mixed African and European ancestry, we demonstrate that estimated causal effect sizes are highly similar across these ancestries for 26 of 29 quantitative molecular and non-molecular phenotypes (mean correlation 0.98 +/-0.08), providing evidence that gene-environment and gene-gene interactions do not play major roles in the poor prediction of European-ancestry PRS scores in African populations for these traits, contradicting previous findings. Instead our results provide optimism that shared causal mutations operate similarly in different groups, focussing the challenge of improving GWAS "portability" between groups on joint fine-mapping.

genetics↗