Many recent papers have described flexible strategies for testing genetic associations that leverage individual sequence similarity information,20, 52–57 and it has been shown that such strategies can be as powerful, if not more so, than some traditional tests of association in many settings involving common variations.58 However, the performance of these methods when many rare variants and no common variants are considered is an open question. In addition, a limitation of these methods is that a specific DNA similarity or distance measure or metric must be chosen and this can be problematic (Box 3).59 For example, a number of approaches have described DNA sequence similarity metrics that consider the origins or phylogenetic relationships between sequences.60–62 In addition, other approaches, some of which have their roots in comparing pathogen sequences, consider weighting individual nucleotides by their frequency or putative functional effects.54, 63, 64
