13 new IQ/genetic hits. One interesting aspect is that the sample size is ~half of Rietveld, which I think may be thanks to having direct cognitive tests available rather than an indirect measure of intelligence like years of education.

"Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53949)", Davies et al 2015 http://www.nature.com/mp/journal/vaop/ncurrent/pdf/mp2014188a.pdf / https://www.dropbox.com/s/42zhlbxr5t52wif/2015-davies.pdf ; supplements: http://www.nature.com/mp/journal/vaop/ncurrent/suppinfo/mp2014188s1.html (linked from http://infoproc.blogspot.com/2015/02/more-gwas-hits-for-cognitive-ability.html ); excerpts:

"General cognitive function is substantially heritable across the human life course from adolescence to old age. We investigated the genetic contribution to variation in this important, health- and well-being-related trait in middle-aged and older adults. We conducted a meta-analysis of genome-wide association studies of 31 cohorts (N=53 949) in which the participants had undertaken multiple, diverse cognitive tests. A general cognitive function phenotype was tested for, and created in each cohort by principal component analysis. We report 13 genome-wide significant single-nucleotide polymorphism (SNP) associations in three genomic regions, 6q16.1, 14q12 and 19q13.32 (best SNP and closest gene, respectively: rs10457441, P=3.93 × 10^−9, MIR2113; rs17522122, P=2.55 × 10^−8, AKAP6; rs10119, P=5.67 × 10^−9, APOE/TOMM40). We report one gene-based significant association with the HMGN1 gene located on chromosome 21 (P=1 × 10^−6). These genes have previously been associated with neuropsychiatric phenotypes. Meta-analysis results are consistent with a polygenic model of inheritance. To estimate SNP-based heritability, the genome-wide complex trait analysis procedure was applied to two large cohorts, the Atherosclerosis Risk in Communities Study (N=6617) and the Health and Retirement Study (N=5976). The proportion of phenotypic variation accounted for by all genotyped common SNPs was 29% (s.e.=5%) and 28% (s.e.=7%), respectively. Using polygenic prediction analysis, ~1.2% of the variance in general cognitive function was predicted in the Generation Scotland cohort (N=5487; P=1.5 × 10^−17). In hypothesis-driven tests, there was significant association between general cognitive function and four genes previously associated with Alzheimer’s disease: TOMM40, APOE, ABCG1 and MEF2C.

Candidate gene studies have found that variation in APOE genotype is the only reliable individual genetic associate of cognitive function in older age, but that might apply especially to cognitive change rather than cognitive level in old age. 27–29 Using the genome-wide complex trait analysis procedure (GCTA), genome-wide association studies (GWAS) found that ~ 51% (the s.e. was large, at 11%) of the variation in general fluid cognitive function in late middle age and older age could be accounted for by genetic variation that is tagged by single-nucleotide polymorphisms (SNPs) on the Illumina610-Quadv1 chip. 30 That study was conducted in a total discovery sample of 3511 individuals, with replication in 670 independent individuals. It found no genome-wide significant single SNP associations. From other GWAS studies of complex traits, we now know that this sample size is likely to be too small, by an order of magnitude, to detect genome-wide significant SNPs. 31

This report includes individuals from 31 population-based cohorts participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (Supplementary Table S1 and Supplementary Information 1 Section 1). All participants were of European ancestry and aged 45 years or older. Exclusion criteria included prevalent dementia and clinical stroke (including self-reported stroke). The total sample size was 53 949 individuals (N men = 23 030, N women = 30 919).

For each of the cohorts, a general fluid cognitive function component phenotype was constructed from a number of cognitive tasks, each testing a different cognitive domain. In order to construct this measure, each cohort was required to have tasks that tested at least three different cognitive domains. Principal component analysis was applied to the cognitive task scores to derive a measure of general cognitive function, which was the score on the first unrotated principal component...In summary, there was a clear single component accounting for between 33.7% and 62.3% (mean = 49.6%) of the total cognitive test variance in all cohorts...Here we give an example of the similarity of the scores obtained when using two different sets of tests to derive the general cognitive ability component. This can be illustrated in the Lothian Birth Cohort 1936, because it has such a large battery of cognitive tests. 34 Two general fluid-type cognitive function component phenotypes could be derived, each using a different battery of cognitive tests. Of course, only one of these was used in the GWAS study. The first comprised six non-verbal tests from the Wechsler Adult Intelligence Scale-III UK; these were Block Design, Digit Symbol, Symbol Search, Letter-Number Sequencing, Backward Digit Span and Matrix Reasoning. The second contained the Moray House Test, Logical Memory, Spatial Span, Four Choice Reaction Time and Verbal Fluency. These two general cognitive function phenotypes calculated from two non-overlapping batteries of cognitive tests in the Lothian Birth Cohort 1936 had a correlation of r = 0.79 (Po0.001).

In order to perform prediction analyses in Generation Scotland (GS), a meta-analysis was performed, which excluded this cohort. A multi-SNP prediction model was created using the profile scoring method implemented in PLINK. 45 This uses the effect sizes estimated in the meta-analysis...The estimated effect sizes from the meta-analysis for each of these SNPs were then used to calculate a prediction score for each GS individual. A series of prediction scores was created based on the inclusion of SNPs with a range of association Pvalues: all SNPs and SNPs with Po 0.5, Po0.1, P o0.05 or P o0.01. Linear or logistic regressions of the prediction score and cognitive phenotypes, and some health outcomes previously associated with cognitive function 46 were performed. We calculated the proportion of phenotypic variance that was predicted by adding the prediction score to a ‘null’ model that adjusted for age, sex and population stratification (four principal components). The cognitive phenotypes from GS that were included in the prediction analysis were general cognitive function, general fluid cognitive function, Wechsler Digit Symbol Substitution Task, 47 Wechsler Logical Memory Test, 48 Verbal Fluency 49 and the Mill Hill Vocabulary Scale (junior+senior synonyms). 50

Estimation of SNP-based heritability using GCTA analysis. The GCTA program 51 was used to estimate the proportion of variance explained by all common SNPs for general cognitive function in the Atherosclerosis Risk in Communities Study (ARIC) and Health and Retirement Study (HRS) cohorts. These cohorts were selected to be used for this analysis, because they are two of the largest cohorts in the study. The total numbers of individuals included in these analyses were 6617 for the ARIC cohort and 5976 for the HRS cohort...The same covariates were included in the GCTA analyses as for the SNP-based association analyses.

The SNP-based meta-analysis identified 13 SNPs associated with general cognitive function at a genome-wide significance level (P<5×10^−8 ) (Figure 1, Figure 2 and Supplementary Figure S3). These SNPs were located in three genomic regions, 6q16.1, 14q12 and 19q13.32. The top SNP in each region and genes contained in the region were,
- 6q16.1, rs10457441 (P = 3.93 × 10 − 9 ; MIR2113),
- 14q12, rs17522122 (P = 2.55 × 10 − 8 ; AKAP6/NPAS3)  and
- 19q13.32, rs10119 (P = 5.67 × 10 − 9 ; TOMM40/APOE) (Figure 2).
The effect size of rs10119 was significantly correlated with mean age of the cohort (r = − 0.424, P = 0.022; Figure 3 and Supplementary Figure S4). There was no significant correlation with cohort age for the other two SNPs (rs10457441 and rs17522122) (Supplementary Figures S5 and S6). All 361 SNPs from the meta-analysis with a P-value less than a suggestive significance threshold of P<1×10^−5 are detailed in Supplementary Table S3.

Supplementary Tables S6A and S7A show the top SNP-based results from published GWAS of educational attainment [35] and general cognitive function in childhood. [36] It should be noted that these are not independent studies due to sample overlap in some cohorts between the current study and both of these previously published studies (overlaps are: educational attainment N ~ 30 000; general cognitive function in childhood N ~ 1500). Of the 361 suggestively significant SNPs from the current metaanalysis (Supplementary Table S3), 188 and 192 SNPs demonstrated nominal significance (P<0.05) with the educational attainment phenotypes of years of education and college completion, respectively (Supplementary Table S7B). Sixteen SNPs achieved P<1×10^− 6 in the educational attainment analyses [35] ; six of these acheved nominal significance in the current metaanalysis (Supplementary Table S7A). Of the top 100 SNPs in the general cognitive function in childhood GWAS, [36] 11 reached a nominal level of significance in the current study (Supplementary Table S6A). Comparisons of gene-based results are shown in Supplementary Tables S6B, S7C and S7D. For the educational attainment phenotypes, 17 and 14 of the top 25 genes associated with years of education and college completion, 35 respectively, were nominally significant in the current gene-based results. For childhood general cognitive function,[36] eight of the top 20 genebased findings achieve nominal significance in the current study.

The results from the polygenic prediction analyses are shown in Supplementary Table S8. The maximum proportion of phenotypic variance explained in GS using the prediction set derived from the meta-analysis excluding GS was 1.27% (P = 1.5 × 10^−17 ) for general cognitive function when using the P<0.50 SNP set (N = 47 322). The proportion of variance explained in other cognitive domains ranged from near-zero values to about 1% (Supplementary Table S8). The polygenic score did not significantly predict cardiovascular disease, hypertension or type 2 diabetes in GS (all P>0.01). Supplementary Table S9 shows the results from the polygenic prediction using the published results for educational attainment 35 (years of education and completion of a college degree) and AD. 39 For educational attainment, the maximum proportion of phenotypic variance explained in GS for the general cognitive phenotype was 0.54% (P = 2.78 × 10^− 8 ) when using the P<0.50 SNP set (N = 40 239)

The 19q13.32 region, which includes the APOE ε2/3/4 haplotype and was associated with general cognitive function in this study, has previously been associated with cognitive phenotypes in old age, 55–58 AD 42,59–61 and non-pathological cognitive aging. 28,29 Here we find that the APOE/TOMM40 region is also associated with general cognitive function in middle and older age. The only published GWAS of general cognitive function in older age, to date, did not report any significant APOE/TOMM40 findings. 30 From the data presented here, it is not possible to identify a single SNP or gene within this region that is driving the association, as it is a gene-dense region that is known to exhibit a strong pattern of linkage disequilibrium.

Four of the 29 genes previously reported to be associated with AD 38–42,44 or neuropathological features of AD and related dementias 43 were associated with general cognitive function (at Po 0.01). These were TOMM40, APOE, MEF2C and ABCG1. These results suggest that there is overlap between the genetic contribution to ‘normal’ and ‘pathological’ cognitive variation in older age. A polygenic prediction analysis using a large published GWAS of AD 39 significantly predicted only 0.19% of the phenotypic variation in general cognitive function in GS. However, Harris et al. 80 reported no significant association of polygenic score for AD with general cognitive ability when using smaller sample sizes for both the creation of the polygenic score and the prediction analysis. All known cases of clinical dementia were removed from the contributing cohorts. Of course, some or all of the effects we found could be driven by the inadvertent inclusion of individuals in a prodromal stage of dementia, and that we have picked up the genetic effects on this. This is an important issue that is impossible to eliminate entirely. Ideally, one would wish to know, prospectively, which people in the current cohorts eventually developed dementia and then re-run the analyses after omitting them. However, some people will die or be lost to contact before such an assessment could be made, thus preventing complete ascertainment. On the other hand, it is possible to envisage a study that tracks individuals and re-analyses those who have kept in contact and those who do not, say over a 10-year period, develop dementia. This could clarify whether the effects we found here were on the normal range of age-related cognitive change. The present study includes individuals within and beyond the ninth decade. It is also important to note that the cognitive phenotype we measured is a composite of people’s stable trait levels and any age-related change that has occurred. Therefore, genetic effects might be contributing to either. The results of this study (SNP and gene-based) were compared with those of previously published large GWAS of educational attainment 35 and general cognitive function in childhood. 36 Before discussing these results further, it should be noted that there is sample overlap between both of these studies and the current study (overlaps are: educational attainment, N ~ 30 000; childhood general cognitive function, N ~ 1500). Around 50% of the suggestive SNPs from the current study are nominally significant for educational attainment and the 6q16.1 region is reported to be genome-wide significant for both general cognitive function and educational attainment. The gene-based findings also demonstrated some consistency, with more than half of the top 25 genes for years of education and college completion achieving nominal significance in the current study. Of the top 100 SNPs for childhood cognitive function, 11 achieved nominal significance in the current study along with eight of the top 20 gene-based findings.
These findings suggest that there is overlap between the genetic contribution to general cognitive function in late-middle and older age, and both educational attainment and childhood general cognitive function. This has also been explored in a study, which used education as a proxy phenotype for general cognitive function. 81 The bivariate heritability of educational attainment and general cognitive function has been previously estimated in GS using both pedigree-based and SNP-based methods (biv h 2 = 0.78, N ~ 20 500 and biv h 2 = 0.59, N ~ 6600, respectively). 82 The genetic correlation and bivariate heritability of childhood and older age general cognitive functions have also been previously estimated in a relatively small sample (N ~ 1900), (r g = 0.62; biv SNP h 2 = 0.21). 83
This study provides further evidence that general cognitive function is heritable and under polygenic control.

If general cognitive function is similar to other complex traits, the individual effects of common SNPs will be very small. From studies of other polygenic complex traits, it has been observed that the number of discovered variants is strongly correlated with experimental sample size. 31 This predicted increase in detectable associations for complex traits, when sample sizes increase, has been observed in GWASs of both height—a study of 183 727 individuals reported 180 significant associations, of which > 100 were novel loci compared with previous studies of fewer individuals (No 40 000) —and schizophrenia, in which a study of 21 246 cases and 38 072 controls reported 22 significant associations, of which 13 were novel loci compared with previous studies of fewer individuals (N o18 000 cases). 84,85 This is also demonstrated in the present study when compared with the previously published Cognitive Ageing in Genetics in England and Scotland consortium study (N = 3511), which reported no genome-wide significant SNP associations with general cognitive function in older age. 30

[SNP hits, from http://www.nature.com/mp/journal/vaop/ncurrent/extref/mp2014188x3.xlsx , just the first ones which are all smaller than 5.34E-08, which is just over the cutoff : ]

rs10457441
rs1872841
rs10119
rs12204181
rs9375195
rs1487441
rs1906252
rs12202969
rs17522122
rs9388171
rs9401634
rs12206087
rs9375225"