TY - JOUR T1 - A family-based method for leveraging random genetic variation to identify variance-controlling loci JF - bioRxiv DO - 10.1101/175596 SP - 175596 AU - Dalton Conley AU - Rebecca Johnson AU - Ben Domingue AU - Christopher Dawes AU - Jason Boardman AU - Mark Siegal Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/08/11/175596.abstract N2 - The propensity of a trait to vary within a population may have evolutionary, ecological or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased—if underpowered—way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTL). Previous methods for vQTL mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation. Deploying this strict approach on data from the Framingham Heart Study we found several alleles that appeared to alter within-family variation in height as well as pathways that appear to be enriched. However, significant SNPs failed to replicate in an independent sample. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using complete family data and more careful attention to the possible conflation of mean and variance effects. Because of the lower power of family-based analyses, we further advocate the creation of consortia that unite studies with family-based designs, thereby increasing the sample sizes available for such analyses. ER -