Using Gene Genealogies to Detect Rare Variants Associated with Complex Traits

Abstract

Background and Objective: Standard population genetic theory says that deleterious genetic variants are likely rare and fairly recently introduced. However, can this expectation lead to more powerful tests of association between diseases and rare genetic variation? The gene genealogy describes the relationships between haplotypes sampled from the general population. Although ancestral tree-based methods, inspired by the gene genealogy concept, have been developed for finding associations with common genetic variants, here we ask whether gene genealogies can help in identifying genomic regions containing multiple rare causal variants. Methods: With data simulated under several demographic models and using known gene genealogies, we developed and compared several tree-based statistics to determine which, if any, could detect the type of clustering expected with rare causal variants and whether the genealogic tree provides additional information about disease associations. Results and Conclusions: We found that a novel statistic based on the scaled distance between the tips of a tree performed better than other tree-based statistics. When data were simulated with mild population growth, this statistic outperformed two standard non-tree-based methods, showing that an ancestral tree-based approach has potential for rare variant discovery.

Using Gene Genealogies to Localize Rare Variants Associated with Complex Traits in Diploid Populations

“…We conclude with a summary and discussion of our results. Our results confirm the earlier findings by Burkett et al [5] indicating potential gains in performance from ancestral tree-based approaches. They also highlight some important differences between haploid and diploid populations when localizing causal variants.…”

“…Our work extends an earlier comparison of methods for detecting disease association in a candidate genomic region [5] …”

“…However, clustering statistics based on true trees represent a best case for detecting or localizing association because tree uncertainty is eliminated. Burkett et al [5] used known trees to assess the effectiveness of tree-based approaches for the detection of disease-risk variants in a haploid population. They found that clustering statistics computed on the known trees outperform popular methods for detecting causal variants in a candidate genomic region.…”

“…They found that clustering statistics computed on the known trees outperform popular methods for detecting causal variants in a candidate genomic region. Following Burkett et al [5] , we use Mantel tests that associate phenotypic and genealogical distances as the clustering statistics. These statistics, which rely on known trees, serve as benchmarks against which to compare the popular association methods.…”

“…These statistics, which rely on known trees, serve as benchmarks against which to compare the popular association methods. However, unlike Burkett et al [5] , who focused on the detection of disease-risk variants, we focus on the localization of association signal in the candidate genomic region. Additionally, we use a diploid rather than a haploid disease model to mimic human populations.…”