This new association is an almost ideal poster child for the type of variants that people hope to discover via sequence-based association, with a low population allele frequency between 0.1% and 1%, and strong effect of the order of ten-fold relative risk.
The work of Holm and colleagues1
applies a novel and powerful approach for complex trait association studies (), representing state-of-the-art at least until WGS costs enable sequencing of the full study sample. For example, simulations4
suggest that sequencing 200 individuals from an outbred European-descent population at 6× depth can provide near-comprehensive coverage of variants down to MAF 0.01 and ~40% of variants with MAF between 0.001 and 0.01. Imputation approaches can then take advantage of variants discovered within the study sample subset, in conjunction with publicly available reference sets, to infer untyped variants in the full dataset using GWAS data as a scaffold. Indeed, this represents one of the key aims of large-scale investments to create repositories of whole genome sequence variation, such as the 1000 Genomes2
and UK10K (www.uk10k.org
Holm’s study also highlights the added value to be gained by focusing on well-characterised sample collections with deep phenotype data. When information on multiple quantitative or dichotomous traits is available, sequencing a representative, carefully-selected subset of the study sample (for example to maximise imputation efficiency by preferentially targeting distantly related individuals for the WGS set) can enable downstream association testing for a wide variety of phenotypes.
Low frequency and rare variants of large effect size, like the R721W mutation in MYH6
, represent low-hanging fruit amenable to easy detection by applying this powerful strategy. The analytical toolset for straightforward single-variant complex-trait association probing can be borrowed from GWAS and adapted to meet truly full genome scales. The field of statistical genetics is also actively developing more sophisticated methods that consider the aggregation of rare variants within functional units of interest5
. Incorporation of functional annotation currently represents a challenge for interpreting association at variants, for which putative consequences are not as readily inferred as for missense mutations.