PT - JOURNAL ARTICLE AU - Luke M. Evans AU - Rasool Tahmasbi AU - Scott I. Vrieze AU - Gonçalo R. Abecasis AU - Sayantan Das AU - Doug W. Bjelland AU - Teresa R. deCandia AU - Haplotype Reference Consortium AU - Michael E. Goddard AU - Benjamin M. Neale AU - Jian Yang AU - Peter M. Visscher AU - Matthew C. Keller TI - Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits AID - 10.1101/115527 DP - 2017 Jan 01 TA - bioRxiv PG - 115527 4099 - http://biorxiv.org/content/early/2017/03/09/115527.short 4100 - http://biorxiv.org/content/early/2017/03/09/115527.full AB - Heritability, h2, is a foundational concept in genetics, critical to understanding the genetic basis of complex traits. Recently-developed methods that estimate heritability from genotyped SNPs, h2 SNP, explain substantially more genetic variance than genome-wide significant loci, but less than classical estimates from twins and families. However, h2SNP estimates have yet to be comprehensively compared under a range of genetic architectures, making it difficult to draw conclusions from sometimes conflicting published estimates. Here, we used thousands of real whole genome sequences to simulate realistic phenotypes under a variety of genetic architectures, including those from very rare causal variants. We compared the performance of ten methods across different types of genotypic data (commercial SNP array positions, whole genome sequence variants, and imputed variants) and under differing causal variant frequencies, levels of stratification, and relatedness thresholds. These results provide guidance in interpreting past results and choosing optimal approaches for future studies. We then chose two methods (GREML-MS and GREML-LDMS) that best estimated overall h2SNP and the causal variant frequency spectra to six phenotypes in the UK Biobank using imputed genome-wide variants. Our results suggest that as imputation reference panels become larger and more diverse, estimates of the frequency distribution of causal variants will become increasingly unbiased and the vast majority of trait narrow-sense heritability will be accounted for.