Missing heritability and strategies for finding the underlying causes of complex disease

Underestimation of heritability using a mixed model with a polygenic covariance structure in a genome-wide association study for complex traits

“…Generally, they use sparse markers across the genome, and rare variants with small minor allele frequencies were excluded in association analyses. [12][13][14][15] Furthermore, highly repetitive sequence variants intensively located in the human genome could not be analyzed in GWAS. Another factor was that some associated SNPs included in GWAS are not causal but are linked to causal SNPs.…”

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

“…19,20 More recently, GWAS have revealed the association of many common polymorphisms to ND sporadic cases, providing in about 3 years more reproducible and consistent findings than 2 decades of candidate-gene-driven research. 21 However, despite the step forward, the identification of potential causative loci associated to ND by GWAS explained only a little percentage of the cases, and the 'missing heritability' issue (ie, the contribution of epigenetic modifications on gene expression) 22 is still a limitation. As proof-of-concept, all previously cited approaches aimed to mutation discovery, do not provide any relevant clue about the contribution of such genetic alterations on ND onset.…”

Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies

“…While this method has led to the discovery of thousands of loci associated with hundreds of phenotypes (Welter et al 2014;Eicher et al 2015), it fails to capture the additional signal available when multiple SNPs representing independent genetic signals are examined simultaneously or when SNPs are imperfectly imputed (Wood et al 2011). Furthermore, the hidden heritability, the difference between the heritability due to genome-wide significant associations and heritability due to genotyped variants, remains substantial (Eichler et al 2010). In this work we investigate a local joint-testing approach to analysis of genetic data sets in which pairs of variants from the same locus are examined simultaneously for association with a phenotype.…”

“…If these SNPs are correlated in the study population, then marginal association methods will fail to detect the signal (2011) give evidence that linkage masking between regulatory and coding variation may be common due to balancing selection. While linkage-masked SNPs are difficult to uncover using standard marginal association methods, mixed-model heritability is determined by a simultaneous fit of all SNPs while accounting for LD and therefore includes signal from linkage-masked SNPs, implicating them as a source of hidden heritability that has not been widely considered (Eichler et al 2010). Pairwise (joint) testing for additive effects without a statistical interaction term may help unmask these associations and, more generally, improve power in the presence of gene interactions, multiple causal variants, or multiple variants differentially tagging an untyped causal SNP.…”

“…Lappalainen et al (2011) give evidence that linkage masking between regulatory and coding variation may be common due to balancing selection. While linkage-masked SNPs are difficult to uncover using standard marginal association methods, mixed-model heritability is determined by a simultaneous fit of all SNPs while accounting for LD and therefore includes signal from linkage-masked SNPs, implicating them as a source of hidden heritability that has not been widely considered (Eichler et al 2010).…”