1. Most of the genetic variation in intelligence is additive. This may be confusing to those infected by the epigenetics revolution meme. Yes, epigenetics is important, but fortunately for us linear effects still dominate the population variation* of quantitative traits. As any engineer or physicist can attest, linearity is our best friend :-)
Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits (PLoS Genetics)
The relative proportion of additive and non-additive variation for complex traits is important in evolutionary biology, medicine, and agriculture. We address a long-standing controversy and paradox about the contribution of non-additive genetic variation, namely that knowledge about biological pathways and gene networks imply that epistasis is important. Yet empirical data across a range of traits and species imply that most genetic variance is additive. We evaluate the evidence from empirical studies of genetic variance components and find that additive variance typically accounts for over half, and often close to 100%, of the total genetic variance. We present new theoretical results, based upon the distribution of allele frequencies under neutral and other population genetic models, that show why this is the case even if there are non-additive effects at the level of gene action. We conclude that interactions at the level of genes are not likely to generate much interaction at the level of variance. [italics mine]
* See comments for more discussion!
2. A conservative estimate is that a million or so people will get full sequencing in the next 5-10 years. This would cost $1 billion at $1k per genome (almost doable today), which is roughly what the original human genome project cost. I'd guess at least several times that number will get SNP genotyped in the next 5 years. My estimates will seem laughably conservative if recent sequencing price trends continue. The paper below suggests that (e.g., Table 2), given the appropriate phenotype data, sample sizes of a million should be enough to capture most of the genetic variance for traits like height. I expect intelligence to be similar.
Estimation of effect size distribution from genome-wide association studies and implications for future discoveries (Nature Genetics)
We report a set of tools to estimate the number of susceptibility loci and the distribution of their effect sizes for a trait on the basis of discoveries from existing genome-wide association studies (GWASs). We propose statistical power calculations for future GWASs using estimated distributions of effect sizes. Using reported GWAS findings for height, Crohn's disease and breast, prostate and colorectal (BPC) cancers, we determine that each of these traits is likely to harbor additional loci within the spectrum of low-penetrance common variants. These loci, which can be identified from sufficiently powerful GWASs, together could explain at least 15–20% of the known heritability of these traits. However, for BPC cancers, which have modest familial aggregation, our analysis suggests that risk models based on common variants alone will have modest discriminatory power (63.5% area under curve), even with new discoveries.
I'm off to BGI tomorrow ...