Previous studies have also attempted to leverage information about predicted functional consequences of rare mutations to improve association analyses. One study of nicotine dependence found significant rare single-variant associations in CHRNB4, but only when variants were weighted by their predicted effect on the cellular response to nicotine and acetylcholine(26). Such positive findings could benefit from replication, which has not always been straightforward. For example, all rare variant associations in addiction are, to our knowledge, candidate gene analyses with type I error thresholds based only on the number of tests within that region. Historically, such analyses have produced overly optimistic estimates of the number of associated loci(27). Genome-wide analyses with more conservative type I error thresholds have reported null rare variant findings across an array of phenotypes relevant to addiction(28–30). Precisely because genome-wide analyses are conducted on many variants across the genome, they are in principle able to discover novel rare variant associations within new or known loci. One way to improve power in genome-wide analyses is through genetic association meta-analysis, which entails the aggregation of results across many studies to achieve large sample sizes.
