Supplementary Methods, Tables and Figures
This document contains supplementary material for:
Davies et al. Genome-wide association studies establish that human intelligence is highly heritable
and polygenic
Contents
Supplementary Methods
Construction of the fluid intelligence factor in NCNG
p. 3
Quality control of genotype data
p. 3
Estimating the proportion of intelligence variance explained by all SNPs
p. 5
Prediction analysis in the validation set using all the SNPs
p. 6
References
p. 7
Supplementary Tables
Supplementary Table 1
p. 9
Supplementary Table 2
p. 10
Supplementary Table 3
p.11
Supplementary Table 4
p.15
Supplementary Figures
Supplementary Figure 1
p. 18
Supplementary Figure 2
p. 19
Supplementary Figure 3
p. 20
Supplementary Figure 4
p. 21
Supplementary Figure 5
p. 22
Supplementary Figure 6
p. 23
1
Supplementary Figure 7
p. 24
Supplementary Figure 8
p. 25
2
Construction of the fluid intelligence factor in NCNG
For the NCNG sample, a hierarchy of PCA analyses was used. The unrotated first component for
three subtests of the California Verbal Learning Test II1 (learning and memory) defined a
memory factor. The first component from the four conditions of D-KEFS Color Word
interference (Stroop test)2 defined a speed factor. These factor scores, together with the raw score
of the Matrix Reasoning subscale of the Wechsler Abbreviated Scale of Intelligence3 and the
overall mean of median reaction times from a multiple choice reaction time task4, were used as
input for a further PCA, of which the un-rotated first component defined the gf factor.
Quality control of genotype data
NCNG
QC was performed with the R package GenABEL5, using the iterative check.marker function.
Cryptic relatedness was assessed by identity-by-state (IBS), removing one sample from a pair
with ibs.threshold > 0.85. Individuals with heterozygosity values greater than two standard
deviations from the sample mean or with unresolved sex discrepancies were removed. Finally,
SNPs with a call rate < 0.95, minor allele frequency < 0.01, and Hardy-Weinberg Equilibrium
(exact test) P value < 0.001 were excluded.
Quality control check between CAGES and NCNG cohorts
As a cross-cohort QC check, allele frequencies from the all of the CAGES samples were plotted
against those from the NCNG cohort (Supplementary Figure 1). This plot clearly demonstrates
the expected ‘cigar’ shaped distribution, with no obvious outliers. This indicates that there are no
sizeable deviations in allele frequencies between the two samples, and also that the genotyping
3
data are of good quality. This was conducted after the multidimensional scaling of the genetic
data described below.
Multidimensional scaling of genetic data
In the quality control procedure multidimensional scaling (MDS) was performed using an IBS
distance matrix to identify any individuals who were not of Caucasian origin. This analysis
incorporated unrelated HapMap samples, and individuals who were visibly outside the CEU
cluster were removed. This analysis showed a small subgroup of individuals who appeared to be
on the edge of the CEU cluster. This was particularly evident in the Manchester and Newcastle
cohorts and was thought to suggest population stratification. To investigate this issue further,
MDS was performed again using only the individuals and SNPs that had passed the QC criteria
in the five discovery cohorts. This analysis aimed to identify any underlying population
substructure which would need to be corrected for in the association analyses. Supplementary
Figure 2 is a plot of the first two components from this MDS analysis. In this figure a small
subgroup can be seen in the lower left corner of the plot area. This subgroup contains
predominantly samples from the Manchester cohort. Regression analyses were performed to
assess the effect of the first four dimensions on each of the phenotypes in each cohort. Only the
Manchester cohort produced any significant effects, and these were very weak. As a result of
these analyses it was decided that the first four MDS components should be fitted as covariates
in the genotype-cognitive phenotype association analyses in order to correct for any population
stratification that might be present.
4
In the NCNG sample, population structure was also assessed by multidimensional scaling (MDS)
analysis and the removing of outlying samples with possible recent non-Norwegian ancestry.
Estimating the proportion of intelligence variance explained by all SNPs
We used a recently developed method6 to estimate the proportion of phenotypic variance
explained by all SNPs for human intelligence. We firstly estimated pairwise genetic relationships
between 3511 individuals (the combined sample of the five CAGES cohorts) from 536,295
autosomal SNPs retained after QC. We removed one of any pair of individuals with a
relationship estimate > 0.025 by the rule of maximizing the remaining sample size and retained
3,291 individuals. After removal of the individuals that caused cryptic relatedness, the estimates
of genetic relationships between pairs of individuals are normally distributed with mean of
-0.0002 (SD = 0.0042) (Supplementary Figure 8). The variance of relatedness is consistent with
pairs of individuals being, on average, 8 meioses apart6. That is, if all pairs shared 1/512th of their
genome identical-by-descent relative to a recent base population, then the standard deviation of
their actual additive relationship would be approximately 0.004.
To investigate the effect of population structure on the estimate, we performed PCA using all
autosomal SNPs7 and included the first 4 and 20 eigenvectors as covariates when fitting the
mixed model. The estimates show little difference, suggesting that there is no large impact, if
any, of population stratification on the estimate of variance explained by all SNPs
(Supplementary Table 2). We also did not observe any apparent difference when we performed
the REML analyses using the entire sample (3,511 individuals) without exclusion of any
potentially related individuals (Supplementary Table 2).
5
Furthermore, we estimated the genetic relationships from the SNPs on individual chromosomes
and fitted all the chromosomes simultaneously in a mixed linear model to estimate the proportion
of variance explained by each of the chromosomes8.
Prediction analysis in the validation set using all the SNPs
We used the mixed linear model y = 1μ + g + e, where y is a vector of phenotypes, μ is the mean
term, 1 is a vector of ones, g is a vector of total genetic effects of individuals and e is a vector of
residual effects. In the analysis described previously, we estimated the variance of g from the
pairwise genetic relationships derived from all the SNPs. From the REML analysis we can obtain
the best linear unbiased prediction (BLUP) of the total genetic effects of all the individuals.
Alternatively, we can write the model as y = 1μ + Wu + e, where W is the N × m incidence
matrix (N is the number of individuals and m is the number of SNPs) and u is a vector of SNP
effects. The ij-th element of W matrix is ( xij  2 p j ) / 2 p j (1  p j ) , with xij being the number of
reference alleles for the j-th SNP of the i-th individual and pj being the frequency of reference
allele for the j-th SNP. Since these two models are mathematically equivalent 9, we can transform
the BLUP of g to the BLUP of u by uˆ  WA1gˆ / m , where A is the genetic relationship matrix
estimated from the SNPs. We estimated the SNP effects in the CAGES study using the mixed
model approach, and predicted the total genetic effects of the individuals in the NCNG study by
the scoring approach implemented in PLINK10.
An alternative prediction approach is to perform a combined analysis of the discovery
and validation sets but treating the phenotypes of the validation set as missing. This approach,
which is statistically equivalent to the prediction procedure described above, does not estimate
the SNP effects explicitly, but predicts the missing observations in the validation set relying on
6
the genetic relationships between individuals in the discovery and validation sets derived from
all the SNPs. For the prediction analysis within the CAGES study, we treated the phenotypes of
each of the five cohorts in turn as missing and predicted the total genetic effects of the missing
records from the pairwise genetic relationships between one cohort and the other four cohorts.
References
1.
Delis DC, Karmer JH, Kaplan E. California Verbal Learning Test. 2nd edn. Psychological
Corporation: San Antonio, TX, 2000.
2.
Trenerry MR. Stroop Neuropsychological Screening Test manual. Psychological
Assessment Resources, Odessa, FL, 1989.
3.
Wechsler D. Wechsler Abbreviated Scale of Intelligence. Psychological Corporation: San
Antonio, TX, 1999.
4.
Espeseth T, Greenwood PM, Reinvang I, Fjell AM, Walhovd KB, Westlye LT et al.
Interactive effects of APOE and CHRNA4 on attention and white matter volume in healthy
middle-aged and older adults. Cogn Affective Behav Neurosci 2006; 6: 31-43.
5.
Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R package for genomewide association analysis. Bioinformatics 2007; 23: 1294-1296.
6.
Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR et al. Common
SNPs explain a large proportion of the heritability for human height. Nat Genet 2010; 42:
565-569.
7.
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal
components analysis corrects for stratification in genome-wide association studies. Nat
Genet 2006; 38: 904-909.
7
8.
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait
analysis. Am J Hum Genet 2010; 88: 76-82.
9.
VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008; 91:
4414-4423.
10. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool
set for whole-genome association and population-based linkage analyses. Am J Hum Genet
2007; 81: 559-575.
8
Cohort
N gf
N gc
LBC1921
N genotyped
(N females)
517 (303)
505
515
LBC1936
1005 (496)
989
1003
ABC1936
426 (208)
348
417
Manchester
805 (572)
805
797
Newcastle
758 (536)
753
750
Total
3,511 (2115)
3,400
3,482
NCNG
670 (456)
630
643
Total
4,181
4,030
4,125
Supplementary Table 1
The total number of participants (number of females) per cohort is indicated. Participant numbers
with fluid (gf) and crystallized (gc) intelligence phenotypes are also shown.
9
Combined‡ N
h2 (se)
P value
h2 (se) 4PC
P value 4PC
h2 (se) 20PC
P value 20PC
‡
Scotland
N
h2 (se)
P value
‡
England
N
h2 (se)
P value
gc
<0.025*
3254
0.39 (0.11)
8.3×10-5
0.40 (0.11)
5.7×10-5
0.37 (0.11)
2.6×10-4
1787
0.37 (0.19)
0.022
1477
0.35 (0.23)
0.056
gc
no cut-off†
3483
0.40 (0.10)
1.2×10-5
0.41 (0.10)
7.7×10-6
0.38 (0.10)
6.0×10-5
1935
0.40 (0.17)
0.006
1548
0.36 (0.21)
0.040
gf
<0.025*
3181
0.52 (0.11)
6.6×10-8
0.51 (0.11)
1.2×10-7
0.45 (0.11)
1.1×10-5
1703
0.17 (0.20)
0.187
1488
0.99 (0.22)
1.4×10-5
gf
no cut-off†
3403
0.53 (0.10)
3.8×10-9
0.54 (0.10)
2.7×10-9
0.46 (0.10)
1.4×10-6
1844
0.26 (0.18)
0.073
1559
0.99 (0.21)
2.3×10-7
Supplementary Table 2
Shown are the estimates of the proportion of phenotypic variance explained by all SNPs (h2) for
the traits gf and gc. Estimates are shown for the total combined samples, the combined Scottish
samples and the combined English samples‡. The total combined sample is also shown here with
the first four (4PC) and 20 (20PC) eigenvectors included as covariates when estimating the
genetic variance (Vg). Both gf and gc estimates are shown here with* and without† a cut-off for
cryptic relatedness (estimated coefficient of relatedness of > 0.025). The P value is calculated
from a likelihood ratio test statistic to test the null hypothesis h2 = 0.
10
Chr.
1
4
10
8
3
7
6
13
4
10
8
3
22
12
9
16
2
SNP
rs236316
rs1277311
rs2588966
rs4871791
rs6771063
rs12374930
rs707966
rs3818477
rs4487406
rs16927981
rs4147331
rs1992984
rs738959
rs11615115
rs11794727
rs11867129
rs7603548
Effect
Allele
C
C
C
C
A
A
C
A
C
A
C
A
A
A
A
G
C
14
14
14
2
4
13
rs6572117
rs8013963
rs11846415
rs828888
rs4861096
rs6562208
C
A
G
G
C
C
12
10
9
9
9
9
6
10
2
13
13
22
4
rs2734562
rs12775092
rs10869134
rs11143141
rs10869142
rs12338453
rs10498772
rs1774234
rs1546512
rs4430638
rs9524154
rs713765
rs1390096
A
C
A
C
C
C
G
C
C
C
G
G
A
Gene
BCAR3
IGFBP7
CPB2
C10orf27
SLC17A8
ABCA3
LRFN5
BOLA3
KLRC4/KLRC3
GDA
GDA
GDA
GDA
PLA2R1
GPC6
11
Function
intronic
intronic
unknown
unknown
unknown
unknown
unknown
intronic
unknown
5' upstream
unknown
unknown
unknown
intronic
unknown
intronic
unknown
coding
synonymous
unknown
unknown
intronic
unknown
unknown
5' upstream/3'
downstream
unknown
intronic
intronic
intronic
intronic
unknown
unknown
intronic
unknown
intronic
unknown
unknown
Beta
-0.1197
0.1107
0.1077
0.1061
-0.1052
-0.1449
0.1199
-0.1103
-0.216
-0.1443
0.1084
0.1002
0.1119
0.2039
0.1905
0.1745
-0.1011
SE
0.0227
0.0225
0.0225
0.0225
0.0225
0.0312
0.0261
0.0243
0.0477
0.0321
0.0243
0.0225
0.0252
0.0459
0.0429
0.0393
0.0228
P-value
1.27×10-7
9.04×10-7
1.69×10-6
2.45×10-6
3.05×10-6
3.51×10-6
4.47×10-6
5.76×10-6
6.06×10-6
6.99×10-6
7.87×10-6
8.70×10-6
8.80×10-6
9.03×10-6
9.13×10-6
9.21×10-6
9.21×10-6
-0.1097
0.1284
0.1093
-0.0997
0.1359
0.1244
0.0248
0.0291
0.0248
0.0227
0.031
0.0284
9.67×10-6
1.02×10-5
1.04×10-5
1.08×10-5
1.17×10-5
1.19×10-5
-0.1203
0.1401
0.1369
0.1369
0.1362
0.1357
0.1775
0.1154
0.2941
0.1273
0.109
-0.1092
-0.108
0.0275
0.0321
0.0314
0.0314
0.0315
0.0314
0.0412
0.0268
0.0685
0.0297
0.0255
0.0256
0.0255
1.22×10-5
1.29×10-5
1.33×10-5
1.33×10-5
1.55×10-5
1.59×10-5
1.67×10-5
1.69×10-5
1.74×10-5
1.87×10-5
1.90×10-5
2.01×10-5
2.23×10-5
13
15
1
4
18
14
22
13
2
16
2
1
10
14
7
2
6
5
7
10
4
3
12
7
1
12
7
X
1
18
3
21
10
6
2
8
13
13
4
2
5
rs1412483
rs8037296
rs12097284
rs10518065
rs11661481
rs11158157
rs5999265
rs7982789
rs828903
rs9931578
rs13028697
rs3950119
rs11256826
rs1951882
rs2286707
rs2191581
rs7753153
rs10440778
rs10240666
rs17107098
rs3775256
rs9821867
rs1887276
rs1767721
rs12067005
rs3906005
rs2286492
rs1076177
rs2391228
rs539975
rs7615007
rs2849925
rs7902905
rs912026
rs828884
rs7833566
rs9598403
rs9598404
rs4149535
rs17604526
rs2895497
A
A
C
A
A
A
A
G
A
A
C
C
C
C
G
A
A
A
A
G
A
G
A
G
A
A
G
C
A
A
A
C
C
A
A
A
C
C
A
A
C
RAB8B
L3MBTL4
MTHFD2
LOC92017
CTNNA2
BCAR3
FAM126A
ESR1
BMPR1A
UCHL1
SLC17A8
KIAA1324L
MTF2
FAM126A
ANK3
BOLA3
SULT1E1
12
unknown
intronic
unknown
unknown
intronic
unknown
unknown
unknown
intronic
intronic
intronic
intronic
unknown
unknown
intronic
unknown
intronic
unknown
unknown
intronic
intronic
unknown
intronic
intronic
5' upstream
unknown
3' utr
unknown
unknown
unknown
unknown
unknown
intronic
unknown
intronic
unknown
unknown
unknown
intronic
unknown
unknown
0.1059
-0.0955
0.1908
0.1304
-0.145
0.1006
0.1067
-0.1044
0.0947
0.1072
0.0937
-0.1228
0.1269
-0.1023
-0.1055
-0.1243
0.1472
0.0935
-0.1022
-0.6862
0.1232
-0.0954
-0.0925
0.1038
0.1306
-0.1444
0.1625
-0.126
-0.1117
0.1665
0.0927
-0.1165
-0.1016
-0.1026
-0.0982
-0.1516
0.1102
-0.1102
0.1155
0.2188
0.0912
0.025
0.0226
0.0452
0.0309
0.0345
0.024
0.0255
0.0249
0.0226
0.0256
0.0225
0.0294
0.0305
0.0246
0.0255
0.03
0.0356
0.0226
0.0247
0.1664
0.0299
0.0232
0.0225
0.0253
0.0318
0.0351
0.0396
0.0307
0.0273
0.0407
0.0227
0.0285
0.0249
0.0252
0.0241
0.0372
0.0271
0.0271
0.0284
0.0538
0.0225
2.32×10-5
2.40×10-5
2.46×10-5
2.51×10-5
2.70×10-5
2.71×10-5
2.84×10-5
2.85×10-5
2.89×10-5
2.90×10-5
3.01×10-5
3.04×10-5
3.17×10-5
3.18×10-5
3.49×10-5
3.50×10-5
3.53×10-5
3.62×10-5
3.65×10-5
3.74×10-5
3.76×10-5
3.86×10-5
3.87×10-5
3.94×10-5
3.97×10-5
3.98×10-5
4.03×10-5
4.08×10-5
4.19×10-5
4.35×10-5
4.36×10-5
4.44×10-5
4.44×10-5
4.56×10-5
4.63×10-5
4.67×10-5
4.69×10-5
4.69×10-5
4.77×10-5
4.79×10-5
4.87×10-5
15
3
14
1
15
22
7
2
6
3
1
7
1
6
14
15
11
2
1
3
X
9
2
19
17
3
X
12
9
6
5
5
10
1
1
10
4
17
2
2
15
rs17506333
rs2596623
rs945316
rs12730489
rs552806
rs5994882
rs7788695
rs7340453
rs12206094
rs17646070
rs236285
rs7781178
rs6677770
rs2063345
rs7152201
rs16975550
rs10890568
rs2889744
rs2743979
rs17646346
rs2897164
rs11791687
rs1430282
rs12462725
rs8073225
rs6445318
rs4830761
rs10860590
rs16914315
rs2766534
rs36000196
rs1479679
rs10740354
rs236322
rs424723
rs4749233
rs4241784
rs642612
rs4849975
rs828887
rs1017546
A
C
A
C
C
A
C
C
C
G
A
A
C
C
C
A
C
C
A
A
G
C
A
C
C
A
C
A
A
G
C
C
C
G
C
A
G
C
A
C
G
THRB
MTHFD2
FOXO3
LSAMP
BCAR3
KIAA1324L
BCAR3
SLC22A3
GUCY1A2
AJAP1
CNTN4
DMD
ZFP14
HRNBP3
SLC17A8
PALM2
CDH9
KIAA1274
BCAR3
AJAP1
MASTL
ENPP6
DNAH17
BOLA3
13
unknown
intronic
unknown
unknown
unknown
unknown
unknown
intronic
intronic
intronic
intronic
intronic
intronic
intronic
unknown
unknown
intronic
unknown
intronic
intronic
intronic
unknown
unknown
intronic
intronic
unknown
unknown
3' downstream
intronic
unknown
unknown
intronic
intronic
intronic
intronic
intronic
intronic
intronic
unknown
intronic
unknown
0.1018
0.1013
-0.0998
-0.0911
0.0984
0.1105
-0.102
-0.0909
0.1012
0.1986
-0.1155
-0.1018
-0.1169
-0.1015
0.1001
-0.1008
0.28
0.1193
0.0903
0.1531
-0.1125
0.1705
-0.177
-0.1125
0.1029
0.0901
-0.1222
0.0894
0.0996
-0.1089
0.1022
-0.1351
0.0906
0.1018
-0.0895
0.1206
-0.0896
-0.0888
0.1881
0.0942
0.0889
0.0251
0.025
0.0247
0.0226
0.0244
0.0274
0.0253
0.0226
0.0252
0.0494
0.0287
0.0253
0.0291
0.0253
0.025
0.0252
0.07
0.0298
0.0226
0.0384
0.0282
0.0428
0.0444
0.0282
0.0258
0.0226
0.0307
0.0225
0.0251
0.0275
0.0258
0.0341
0.0229
0.0257
0.0226
0.0305
0.0227
0.0225
0.0476
0.0239
0.0226
4.90×10-5
4.92×10-5
5.33×10-5
5.47×10-5
5.53×10-5
5.55×10-5
5.57×10-5
5.74×10-5
5.76×10-5
5.87×10-5
5.92×10-5
5.93×10-5
5.96×10-5
6.02×10-5
6.07×10-5
6.26×10-5
6.33×10-5
6.39×10-5
6.44×10-5
6.62×10-5
6.66×10-5
6.68×10-5
6.70×10-5
6.73×10-5
6.75×10-5
6.88×10-5
6.95×10-5
6.98×10-5
7.22×10-5
7.27×10-5
7.36×10-5
7.37×10-5
7.37×10-5
7.48×10-5
7.60×10-5
7.76×10-5
7.81×10-5
7.83×10-5
7.83×10-5
7.95×10-5
8.03×10-5
14
2
13
13
8
1
6
5
4
6
22
11
2
9
19
X
11
6
2
3
18
2
7
rs10483944
rs7586823
rs10161939
rs7986967
rs13257991
rs236301
rs6911407
rs10045691
rs11737630
rs961243
rs11703579
rs7950955
rs17023156
rs1359328
rs2271842
rs4829242
rs7126049
rs2802288
rs950535
rs708233
rs1444115
rs2043074
rs17152220
A
C
A
C
A
A
A
A
C
A
C
C
C
C
A
C
C
A
C
A
C
C
A
FAM49A
BCAR3
SLC10A7
AP2A2
MEGF9
ZNF545
IL1RAPL1
FOXO3
STK39
COBL
unknown
intronic
unknown
unknown
unknown
intronic
unknown
unknown
intronic
unknown
unknown
3' utr
unknown
intronic
intronic
intronic
unknown
intronic
intronic
unknown
unknown
unknown
intronic
-0.1141
-0.0883
0.1056
-0.098
0.0951
-0.1142
-0.0896
0.2276
-0.2386
-0.4082
0.108
0.088
-0.1522
-0.1076
0.1085
-0.1097
-0.1079
-0.089
0.0873
0.0877
0.1082
-0.152
-0.1141
0.029
0.0225
0.0269
0.0249
0.0242
0.0291
0.0229
0.0581
0.0609
0.1042
0.0276
0.0225
0.0389
0.0275
0.0278
0.0281
0.0276
0.0228
0.0224
0.0225
0.0278
0.039
0.0293
8.17×10-5
8.51×10-5
8.52×10-5
8.59×10-5
8.59×10-5
8.72×10-5
8.80×10-5
8.91×10-5
8.95×10-5
8.96×10-5
9.09×10-5
9.10×10-5
9.11×10-5
9.16×10-5
9.20×10-5
9.44×10-5
9.49×10-5
9.54×10-5
9.56×10-5
9.58×10-5
9.79×10-5
9.83×10-5
9.93×10-5
Supplementary Table 3
The estimated effect (beta), standard error (SE) and p values are shown for SNPs which achieved
a significance of p < 1 x 10-4 in the meta-analysis of the CAGES cohorts for fluid intelligence.
For 5’upstream and 3’ downstream the SNP is located within 2 kb from the UTR start or end.
14
Chr.
3
3
3
1
1
X
1
12
8
8
7
7
4
12
4
X
3
3
10
15
1
9
X
12
18
12
12
3
12
2
1
3
5
5
9
3
1
SNP
rs11922502
rs7627367
rs11922715
rs58245
rs6671410
rs5967970
rs376299
rs33249
rs13265868
rs12676079
rs1534376
rs6955743
rs4608848
rs39638
rs2345049
rs12556578
rs12633186
rs713372
rs12240404
rs2088143
rs2883272
rs445990
rs12557468
rs1485348
rs1035270
rs11609312
rs2347590
rs7642724
rs12300399
rs12618801
rs460911
rs9683362
rs2034246
rs2112186
rs10960798
rs7624356
rs463128
Effect
Allele
C
G
A
A
G
C
G
A
A
C
A
G
C
A
C
A
G
G
C
C
A
G
A
C
A
A
C
A
A
A
A
A
A
A
G
A
A
Gene
ULK4
LPHN3
WWTR1
FRMD4A
RYR3
DDX31
ULK4
15
Function
unknown
intronic
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
intronic
unknown
unknown
intronic
intronic
intronic
unknown
intronic
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
intronic
unknown
Beta
0.2092
-0.1213
-0.1715
-0.1172
0.1036
0.2088
-0.1138
-0.1145
0.1003
-0.0996
0.0994
0.0988
-0.0994
0.1125
-0.0985
0.1834
-0.3143
0.1094
0.154
-0.1631
-0.1168
-0.0909
-0.1836
0.1091
-0.0957
0.1061
0.1055
-0.2933
0.1053
-0.1406
-0.1066
0.0936
0.0909
0.1117
0.0932
-0.1077
0.1056
SE
0.0402
0.0246
0.0354
0.0242
0.0219
0.0456
0.025
0.0253
0.0221
0.0221
0.0221
0.0221
0.0223
0.0253
0.0221
0.0415
0.0711
0.0252
0.0354
0.0375
0.0269
0.021
0.0424
0.0253
0.0222
0.0248
0.0248
0.0691
0.0248
0.0332
0.0252
0.0221
0.0215
0.0265
0.0221
0.0256
0.0251
P-value
1.90×10-7
8.38×10-7
1.31×10-6
1.34×10-6
2.30×10-6
4.56×10-6
5.29×10-6
5.93×10-6
5.95×10-6
6.76×10-6
6.98×10-6
7.87×10-6
7.88×10-6
8.62×10-6
8.64×10-6
9.73×10-6
9.78×10-6
1.36×10-5
1.36×10-5
1.37×10-5
1.41×10-5
1.49×10-5
1.50×10-5
1.57×10-5
1.69×10-5
1.89×10-5
2.13×10-5
2.21×10-5
2.22×10-5
2.23×10-5
2.29×10-5
2.34×10-5
2.35×10-5
2.52×10-5
2.57×10-5
2.61×10-5
2.65×10-5
X
21
5
X
X
21
1
10
X
10
5
3
5
4
X
3
7
9
X
6
4
3
2
1
7
14
7
4
1
1
X
9
1
2
2
rs5932703
rs243556
rs1875957
rs5932769
rs5932820
rs2839581
rs1519883
rs17632029
rs12559168
rs10508388
rs7704901
rs6769249
rs6876431
rs12509742
rs2213346
rs6787045
rs1038585
rs7466085
rs28492889
rs11968993
rs2345043
rs2117153
rs17399334
rs10785805
rs1990594
rs12588868
rs701332
rs7683090
rs1581762
rs411238
rs5932821
rs4394477
rs12119184
rs17269098
rs6729916
C
A
A
C
A
A
A
C
A
G
C
A
A
A
A
G
C
A
A
A
A
C
A
A
C
C
C
A
A
C
A
G
A
A
C
3
2
3
8
2
rs3732451
rs17021319
rs9290375
rs16939382
rs2043074
C
C
A
A
C
C21orf91
PDE9A
FRMD4A
PLCL2
ANKRD55
LPHN3
MAGI1
EGFL11
LPHN3
CHN2
SLC24A4
GRM3
DAB1
LRRC31
LRRC31
16
unknown
intronic
unknown
unknown
unknown
intronic
unknown
intronic
unknown
unknown
unknown
intronic
intronic
intronic
unknown
intronic
unknown
unknown
unknown
intronic
intronic
unknown
unknown
unknown
intronic
intronic
intronic
unknown
intronic
unknown
unknown
unknown
unknown
unknown
unknown
coding
synonymous
unknown
intronic
unknown
unknown
-0.1162
-0.1053
-0.1099
-0.1145
-0.1094
0.1008
-0.1374
0.1359
0.1718
0.1568
-0.1004
-0.0925
0.0911
0.092
0.114
0.09
-0.0898
0.0907
-0.1078
0.3472
0.0908
-0.0913
0.1408
-0.1319
-0.09
0.0848
0.1101
-0.1703
-0.0845
0.1012
0.1058
-0.1101
0.1331
0.1327
-0.0911
0.0278
0.0253
0.0264
0.0276
0.0264
0.0243
0.0332
0.0329
0.0416
0.038
0.0243
0.0224
0.0222
0.0224
0.0278
0.022
0.022
0.0222
0.0264
0.0853
0.0224
0.0225
0.0348
0.0326
0.0222
0.021
0.0274
0.0423
0.021
0.0252
0.0263
0.0274
0.0332
0.0331
0.0227
2.92×10-5
3.08×10-5
3.16×10-5
3.31×10-5
3.33×10-5
3.43×10-5
3.51×10-5
3.58×10-5
3.65×10-5
3.70×10-5
3.70×10-5
3.75×10-5
3.91×10-5
3.93×10-5
4.24×10-5
4.35×10-5
4.37×10-5
4.41×10-5
4.42×10-5
4.71×10-5
4.85×10-5
4.91×10-5
5.11×10-5
5.15×10-5
5.16×10-5
5.38×10-5
5.71×10-5
5.73×10-5
5.86×10-5
5.87×10-5
5.89×10-5
5.91×10-5
6.02×10-5
6.03×10-5
6.12×10-5
-0.0915
-0.1607
-0.0914
0.0894
-0.1485
0.0228
0.0401
0.0228
0.0223
0.0371
6.12×10-5
6.15×10-5
6.27×10-5
6.28×10-5
6.38×10-5
1
4
3
11
14
18
2
3
10
2
4
X
2
1
5
2
2
21
5
9
X
11
12
4
4
8
1
5
7
3
4
7
9
rs10754671
rs2345047
rs4580521
rs4757842
rs7145790
rs11661481
rs6729973
rs1442265
rs10901838
rs10199698
rs1921564
rs1501476
rs4149510
rs9887893
rs1145123
rs10183748
rs7569678
rs462390
rs26056
rs2806096
rs17218023
rs4757841
rs1861911
rs13147555
rs4697053
rs11135681
rs10881442
rs7730085
rs802457
rs1865604
rs1369465
rs2301725
rs10818080
C
C
A
C
G
A
A
C
A
C
A
G
C
A
C
C
A
C
A
C
A
A
A
A
C
C
C
A
A
C
A
C
C
LPHN3
ULK4
NAV2
L3MBTL4
ENPP6
CHST10
NAV2
ENPP6
PEBP4
RGS7BP
WWTR1
GRID2
ZNF277
unknown
intronic
intronic
intronic
unknown
intronic
unknown
unknown
unknown
unknown
intronic
unknown
intronic
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
intronic
unknown
intronic
unknown
intronic
unknown
intronic
unknown
intronic
intronic
intronic
unknown
-0.0928
0.0893
-0.0911
-0.0887
0.1577
-0.1276
0.1596
-0.1194
-0.1001
-0.0902
-0.0879
0.1461
-0.0838
-0.1315
-0.0871
-0.0901
-0.0835
-0.1092
0.0864
0.202
0.11
-0.0878
0.095
0.0896
-0.1345
-0.0828
0.1273
-0.094
0.1051
-0.0854
-0.0976
-0.1148
-0.0865
0.0232
0.0224
0.0228
0.0222
0.0396
0.032
0.0401
0.03
0.0252
0.0227
0.0221
0.0368
0.0211
0.0332
0.0219
0.0227
0.0211
0.0276
0.0218
0.051
0.0278
0.0222
0.024
0.0227
0.0341
0.0211
0.0325
0.024
0.0269
0.0219
0.025
0.0294
0.0222
6.42×10-5
6.49×10-5
6.51×10-5
6.54×10-5
6.72×10-5
6.79×10-5
6.85×10-5
7.02×10-5
7.14×10-5
7.15×10-5
7.15×10-5
7.16×10-5
7.25×10-5
7.26×10-5
7.29×10-5
7.35×10-5
7.45×10-5
7.48×10-5
7.52×10-5
7.58×10-5
7.62×10-5
7.67×10-5
7.67×10-5
7.97×10-5
8.17×10-5
8.53×10-5
8.91×10-5
9.04×10-5
9.42×10-5
9.45×10-5
9.49×10-5
9.59×10-5
9.93×10-5
Supplementary Table 4
The estimated effect (beta), standard error (SE) and p values are shown for SNPs which achieved
a significance of p < 1 x 10-4 in the meta-analysis of the CAGES cohorts for crystallized
intelligence.
17
Supplementary Figure 1
Plot of the allele frequencies from the Cognitive Ageing Genetics in England and Scotland
(CAGES) and Norwegian Cognitive NeuroGenetics (NCNG) cohorts.
18
Supplementary Figure 2
Plot of the first two components (C1 and C2) from the multi-dimensional scaling (MDS) analysis
of the five CAGES discovery cohorts: LBC1921, LBC1936, ABC1936, and the Newcastle and
Manchester samples. A small sub-group can be seen in the bottom left corner. As a result of this
analysis, all of the genotype-cognitive phenotype association analyses were adjusted for the first
four MDS components.
19
Supplementary Figure 3
Manhattan plots showing association results for gf in each of the five CAGES discovery sample
cohorts. The -log10 P value (y axis) of each SNP is presented based on its chromosomal position
(x axis).
20
Supplementary Figure 4
Quantile-quantile plots of the association analysis P values for gf per cohort. The black circles
represent the observed data, the red line is the expectation under the null hypothesis of no
association, and the black curves are the boundaries of the 95% confidence interval. The lambda
values suggest no inflation of association signals in accordance with random expectation.
21
Supplementary Figure 5
Manhattan plots showing the gc results for each cohort. The -log10 P value (y axis) of each SNP
is presented based on its chromosomal position (x axis).
22
Supplementary Figure 6
Quantile-quantile plots of the association analysis P values for gc per cohort. The black circles
represent the observed data, the red line is the expectation under the null hypothesis of no
association, and the black curves are the boundaries of the 95% confidence interval. The lambda
values suggest no inflation of association signals in accordance with random expectation.
23
a
b
Supplementary Figure 7
Quantile-quantile plots of the association analysis P of genome-wide gene-based test of
association for gf (a) and gc (b). The black circles represent the observed data, the white line is
the expectation under the null hypothesis of no association, and the grey shading marks the
boundaries of the 95% confidence interval.
24
England
-0.020
-0.018
-0.015
-0.013
-0.010
-0.008
-0.005
-0.003
0.000
0.003
0.005
0.008
0.010
0.013
0.015
0.018
0.020
0.023
0.025
100
90
80
70
60
50
40
30
20
10
0
Genetic relationship
Genetic relationship
Combined
c
100
90
80
70
60
50
40
30
20
10
0
-0.021
-0.018
-0.016
-0.013
-0.011
-0.008
-0.006
-0.003
-0.001
0.002
0.005
0.007
0.010
0.012
0.015
0.017
0.020
0.022
0.025
Density
Density
100
90
80
70
60
50
40
30
20
10
0
Scotland
b
-0.021
-0.018
-0.016
-0.013
-0.011
-0.008
-0.006
-0.003
-0.001
0.002
0.005
0.007
0.010
0.012
0.015
0.017
0.020
0.022
0.025
Density
a
Genetic relationship
Supplementary Figure 8
Histograms of the genetic relationships estimated from all the SNPs in the English samples (a),
the Scottish samples (b) and the combined samples (c), respectively.
25