In addition, we estimated the proportion of variance in each EEG parameter accounted for by the combined additive effect of all Illumina markers (and those in linkage disequilibrium, or LD, with them) using GCTA (Yang et al., 2011). GCTA estimates the degree of phenotypic similarity among genetically unrelated individuals, which is then assumed due to the specific genetic variants they share. Genotypic similarity is represented in the form of a genetic relatedness matrix (GRM), which resembles a correlation matrix representing pairwise genetic similarity. In samples consisting of closely related individuals, Yang and colleagues (Yang, Lee, Goddard, & Visscher, 2013) have recommended filtering the sample by means of several thresholds of genetic relatedness in order to look for consistency across the resulting estimates. We used thresholds of .025, .05, and .10, which remove all but distant relatives. The same covariates were used as in all other analyses (age, gender, generation, recording system, and the 10 PCs from EIGENSTRAT). Because LD can bias SNP heritability estimates upward (Speed, Hemani, Johnson, & Balding, 2012), we repeated the three analyses after weighting SNPs by
