Supplementary Information
Common SNPs Explain Some of the Variation in the Personality Dimensions of
Neuroticism and Extraversion
Anna AE Vinkhuyzen, PhD1,8,9, Nancy L Pedersen, PhD2, Jian Yang, PhD1, S. Hong Lee, PhD1,8, Patrik KE
Magnusson, PhD2, William G Iacono, PhD3, Matt McGue, PhD3, Pamela AF Madden, PhD4, Andrew C
Heath, PhD 4, Michelle Luciano, PhD5, Antony Payton, PhD 6, Michael Horan, PhD7, William Ollier, PhD6,
Neil Pendleton, PhD 7, Ian J Deary, PhD5, Grant W Montgomery, PhD1, Nicholas G Martin, PhD1, Peter M
Visscher, PhD1, Naomi R Wray, PhD1,8
1
Queensland Institute of Medical Research, Brisbane, Australia
2
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
3
Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
4
Washington University School of Medicine, St. Louis, Missouri, USA
5
Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of
Edinburgh, Edinburgh, UK
6
Medical Genetics Section, University of Edinburgh Molecular Medicine Centre, Institute of Genetics
and Molecular Medicine, Western General Hospital, Edinburgh, UK
7
School of Medicine, The University of Manchester, Manchester, UK
8
The University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia
9
To whom correspondence should be addressed at: The University of Queensland, Queensland Brain
Institute (building #79), St Lucia, 4072, Queensland, Australia, Phone: +61 7 3346 6430, Fax: +61 7 3346
6301, Email: anna.vinkhuyzen@uq.edu.au
CONTENTS:
Section 1: Supplementary Notes:
-
Description of samples and phenotypes
-
Description of the analyses
Section 3: Supplementary Tables 1-3
Section 4: Supplementary Figures 1-2
Supplementary Information: Section 1
Description of samples, phenotypes, and quality control.
Samples and phenotypes
Neuroticism and Extraversion scores were derived from the Eysenck Personality Questionnaire (EPQ)1, 2,
the NEO-Five Factor Inventory (NEO-FFI)3, the International Personality Item Pool (IPIP)4, 5, and the
Multidimensional Personality Questionnaire (MPQ)6, 7, see below for details. Each scale uses a sum score
of binary item responses. To normalize sum scores on the EPQ Neuroticism and Extraversion scales, an
averaged angular transformation
8
was applied to the raw sum
scores (y) separately for each sub sample, with n the total number of items. Residuals were derived
from regression of the (transformed) Neuroticism and Extraversion sum scores on sex, age, sex*age,
age2, and sex*age2; residuals were then standardized separately for each sex and combined over all
cohorts generating the phenotypic value used in the linear model (see Analyses section below). Cohort
membership was included as a fixed effect in the model. Individuals with scores above or below 3 SD
from the mean were removed (36 individuals for Neuroticism, 20 individuals for Extraversion).
Australia
Australian data were derived from twin- and family studies that were conducted at different points in
time at the Queensland Institute of Medical Research (QIMR)9-12. Neuroticism was measured in ~1980
(EPQ-23-items: N=2947), ~1989 (EPQ-12-items; N=2871), and ~2002 (NEO-FFI-12-items; N=213).
Extraversion was measured in 1980 (EPQ-21-items; N=2906) and 1989 (EPQ-12-items, N=2839). Data
collection and cohort description of the 1980 and 1989 waves of data collection are described in more
detail by Birley et al.11, and the data collection and sample description of the 2002 collection are
described by Saccone et al.12. If EPQ data were available from more than one time point, data from 1980
were retained because this data set was based on the full EPQ rather than the abridged version used in
1989. If individuals had data on both EPQ and NEO, EPQ data were retained.
After QC, genotype and phenotype data were available for 6031 (63% female) and 5745 (63%
female) participants for Neuroticism and Extraversion, respectively.
Sweden
Neuroticism and Extraversion data were collected within the middle cohort of the Swedish Twin
Registry13 between 1972 and 1973, using the EPQ-9-items14 for both scales. After QC, genotype and
phenotype data for Neuroticism and Extraversion were available for 5794 (54% female) and 5748 (54%
females) participants, respectively.
United Kingdom
Data from the United Kingdom (UK) were derived from four different cohorts: the Newcastle and
Manchester cohorts15 and the 1921 and 1936 Lothian Birth Cohorts (LBC1921 and LBC1936,
respectively)16-18. Neuroticism was measured using the EPQ-23-items in the Newcastle and Manchester
cohorts, the IPIP-10-items in LBC1921, and the NEO-12-items in the LBC1936. Please note that
Neuroticism in the IPIP is measured as Emotional Stability, reverse scores were used for the analyses.
Extraversion was measured using the EPQ-21-items in the Newcastle and Manchester cohorts, the IPIP10-items in LBC1921, and the NEO-12-items in the LBC1936. Neuroticism data were available for 687
(72% female), 593 (73% female), 395 (60% female), and 867 (51% female) individuals from the
Newcastle, Manchester, LBC1921, and LBC1936 cohorts, respectively. Extraversion data were available
for 686 (72% female), 593 (73% female), 408 (60% female), and 870 (51% female) individuals from the
Newcastle, Manchester, LBC1921, and LBC1936 cohorts, respectively. All individuals were genotyped
and passed QC.
United States of America
Neuroticism and Extraversion data were collected as part of three ongoing studies at the Minnesota
Center for Twin and Family Research (MCTFR; Iacono & McGue, 2002) in which a 198-item version of
the MPQ was administered. The three studies are: The Minnesota Twin Family Study (MTFS), a
longitudinal study of adolescent twins and their parents19; The Sibling Interaction and Behavior Study
(SIBS), a longitudinal study of adolescent adopted and biological full siblings and their rearing parents20;
and The Enrichment Study (ES), a longitudinal study of adolescent twins selected to be at high risk for
externalizing psychopathology and their parents21. Details concerning the genotyping of the MCTFR
sample, including quality control filters on samples and markers can be found in elsewhere22. For the
present study, the MPQ higher-order Negative Emotionality scale was used for Neuroticism and the
MPQ higher-order Positive Emotionality scale was used for Extraversion; after QC genotype and
phenotype data were available for spouse pairs in the parent generation of the respective studies: 3508
(55% female) and 3507 (55% female) participants, respectively.
Combined sample:
For the combined sample, data from 17875 (58% female) individuals were available for Neuroticism
(mean age 39.07; SD=16.02, range: 14-86). After estimation of the pairwise genetic similarity using all
autosomal markers, 11961 (58% females) individuals were retained (mean age 42.36; SD=16.91, range:
14-86). For Extraversion, data from 17557 individuals (59% female) individuals were available (mean age
39.01; SD=16.18, range: 14-86) of whom 11786 (58% females) were genetically ‘unrelated’ (mean age
42.36; SD=17.09, range: 14-86).
Genotyping and quality control
Samples from the UK (Newcastle, Manchester, LBC1921, and LBC1936), Minnesota, and Sweden were
genotyped on the Illumina610-Quadv1 chip, Illumina 660W Quad array, and the Illumina OmniExpress
700K respectively. Samples from Australia were genotyped on an Illumina SNP microarray chip at
different genotype centres using different platforms (317K, 370K-array, 370-Quad, 610-Quad, and
humanCNV370-Quadv3).
Detailed information on genotyping and quality control (QC) can be found elsewhere 22-25. In
brief, QC procedures were applied separately to each individual cohort. Individuals with a call rate <0.95
(N=22), estimated inbreeding coefficient > 0.15 (N=2)26, and individuals showing evidence of nonEuropean descent from multidimensional scaling (N=298, mainly individuals from the USA cohort with
Mexican ancestry) were removed. Individuals were considered outlying from European descent if one or
more of the first four eigenvectors were more than 3SD removed from the mean. SNPs with minor allele
frequency (MAF) < 0.01, call rate < 0.95 or Hardy-Weinberg Equilibrium test p-value <0.001 were
removed. After QC, a total of 18009 individuals (before removing ‘related’ individuals) with data on
Neuroticism and/or Extraversion remained. A total of 849,801 autosomal SNPs and 21,187 X
chromosomal SNPs were retained across all cohorts. These numbers refer to all the SNPs that met the
QC criteria within each project but were not necessarily genotyped in all cohorts. 162,056 autosomal
SNPs were in common across all cohorts. See Supplementary Table S2 for number of SNPs included in
specific analyses.
Description of the analyses
The method applied in the present study is extensively described by Yang et al.26, 27 and is designed to
capture variation due to linkage disequilibrium between genotyped SNPs and unknown causal variants
in the genome. This estimate of variance explained by all SNPs is different from heritability estimates in
twin- and family studies as the latter include variance explained by all causal variants (an estimate of the
latent genetic effect).
We first estimated the pair wise genetic similarity between all the 18009 individuals that were
included in the present study cohorts using all autosomal markers that passed QC procedures. We
selected a subset of ‘unrelated’ individuals (pair-wise genetic similarity at less than 0.025,
approximately corresponding to 2nd cousins) for further analyses to ensure that the estimate of variance
explained by common SNPs is not driven by common environmental effects (e.g., non-genetic familial
effects) and causal variants not tagged by SNPs but captured by pedigree information. The related
individuals were removed selectively, rather than at random, to maximize the remaining sample size 26.
We also created a set of ‘unrelated’ individuals in each cohort for the analyses in individual cohorts (See
Supplementary Table S2). To estimate the variance explained by the SNPs, a linear model was fitted to
the data in which the SNP effects
variance
. The linear model for an individual is
the effect of the
the
SNP
were treated as random variables from a distribution with
SNP,
with
where is the phenotypic value,
the allele frequency and
is
the genotype indicator of
, and is a random environmental effect including measurement error. In
matrix notation, the linear model for all individuals is y = g + e, where var(y) = A σ2b + I σ2e, and A is the
matrix of genome-wide similarities between individuals (i.e., genetic similarity estimated from all the
SNPs). We used the following equation to calculate the genetic similarity between individuals j and k:
, where m is the number of SNPs.
We first fitted the linear model including all autosomal SNPs to estimate the proportion of the
variance explained by all the SNPs. Cohort status and the first 20 principal components (PC) from a
principal component analysis were fitted as covariates in the model to control for the effects
attributable to population structure. Analyses were repeated (i) without PC adjustment, (ii) adjusting for
imprecise LD between genotyped SNPs and causal variants (See notes Supplementary Table 2 for
details), (iii) separately for each cohort, (iv) for the total sample excluding one cohort at a time, (v)
fitting only SNPs on the X chromosome; (vi) separately for men and women, and (vii) including a
genotype-by-sex interaction term.
To investigate whether the variance explained is proportional to the length of the chromosome,
we subsequently partitioned the variance explained into individual autosomes28. To this end, all
chromosomes were simultaneously fitted in a mixed linear model and the proportion of the variance
explained by each of the chromosomes was estimated.
In a subsequent series of analyses we investigated possible discrepancy in the percentage of
variance explained by SNPs and variance explained by twin-and family studies. We mimicked the
conventional AE-model (i.e., estimation of additive genetic factors and environmental factors) using the
entire Australian sample including close. The sample consisted of 3075 families with genotype and
phenotype data from 5973 individuals among which 4168 twins (922 complete MZ twin pairs, 619
complete DZ twin pairs), 1365 siblings, and 248 parents of the twins and siblings (115 complete pairs). In
twin and family studies, estimates of heritability are based on the relationship between phenotypic
resemblance and expected genetic similarity based on pedigree information (e.g., MZ twin pairs are
expected to share 100% of their genetic material whereas DZ twins, full sibs and parent-offspring pairs
are expected to share 50% of their genetic material). SNP data, however, provide us with estimates of
the realized genetic similarity, which vary around the expected values from pedigrees (See Figure S2).
The genetic variation around the expected values (e.g., 0%, 50% or 100%) is not captured by pedigree
studies but can be captured by the SNP data.
In order to estimate the variance that is explained by SNP data, additional to the variance
explained by pedigree data, two genetic similarity matrices were created: one based on SNP data (the
realized genetic similarity,
similarity,
) and the other based on pedigree information (the expected genetic
). From the SNP model above, we know that the covariance between the phenotypes
of individuals j and k is
, with
the total genetic effect of the causal variants tagged by SNPs 27, 29. This model is exactly analogous
to using pedigree information where the covariance between individuals j and k from the pedigree
matrix is
.
The analysis consisted of seven steps:
(1) Estimation of variance explained by all SNPs based on ‘unrelated’ individuals using genome
wide SNP data, as above.
(2) Estimation of variance explained by all SNPs based on all individuals, again using genome
wide SNP data. In this situation, the estimate of variance explained by all SNPs is dominated by the close
relatives and it is expected to be similar to heritability estimates reported by twin and family studies.
(3) Estimation of heritability based on all individuals using expected genetic similarity (pedigree
similarity matrix). The estimates based on all individuals from the SNP similarity matrix (step 2) and the
pedigree similarity matrix (step 3) are expected to be highly correlated. However, observed similarity
deviates from expected similarity and SNP-based similarity include information of genetic similarity
between distant relatives not known through recorded pedigree information.
(4) Partitioning of the variance onto variance captured by the SNP similarity matrix and variance
captured by the pedigree similarity matrix. The linear model is
such that the
covariance between knowingly related individuals j and k is
and the covariance between not knowingly related individuals is
. Partitioning the
variance gives estimates of variance explained by the pedigree similarity matrix and variance that is
additionally explained by the SNP similarity matrix. This additional variance is due to variation around
the expected values of similarity for ‘related’ individuals (i.e., expected genetic similarity e.g. > .025 in
the SNP matrix) and to variation in genetic similarity of ‘unrelated’ individuals (i.e., expected genetic
similarity e.g. < .025 in the SNP similarity matrix).
(5) Variance was then partitioned further into variance captured by the pedigree similarity
matrix, and by SNP similarity matrices, one for ‘unrelated’ individuals and one for ‘related' individuals, in
order to distinguish between variation around expected values of ‘unrelated’ and ‘related’ individuals.
Off-diagonal entries in the matrices for similarity between the ‘unrelated’ and ‘related’ individuals were
set to zero. Initially, the threshold for ‘unrelated’ was set at .025, the analyses were repeated using
thresholds for ‘unrelated’ of .1 (analysis 6) and .2 (analysis 7) from the SNP similarity matrix, rather than
0.025, to account for imprecise estimation of identity by state coefficients for distant relatives and to
preserve higher similarity between the pedigree similarity matrix and the SNP similarity matrices.
Pairs of individuals that showed discrepancies in their genetic similarity between pedigree
matrix and SNP matrix larger than .2 were removed (N individuals removed = 83). Discrepancies are
likely to occur if individuals have unknown common ancestry because of missing founders in the
pedigree similarity matrix, a type of missingness that generally occurs in population based human data.
For example, a pair of individuals with unknown common ancestors is regarded as unrelated in the
pedigree matrix, while estimate of genetic similarity from SNP data indicates that they are likely to be
close relatives.
Section 3: Supplementary Tables
Supplementary Table S1. Number of participants (% females), mean age (standard deviation), and
phenotype information for each cohort and for the total sample.
Neuroticism
All individuals
N (% females)
Sweden
USA
UK-Newcastle
UK-Manchester
UK-LBC1921
UK-LBC1936
Australia
Total
Extraversion
5794 (54%)
3508 (55%)
687 (72%)
593 (73%)
395 (60%)
867 (51%)
6031 (63%)
17875 (58%)
Sweden
USA
UK-Newcastle
UK-Manchester
UK-LBC1921
UK-LBC1936
Australia
Total
Mean age (SD) N (% females)
29.67 (7.78)
43.59 (5.60)
65.86 (6.00)
64.58 (6.10)
81.15 (0.29)
69.53 (0.85)
32.51 (11.86)
39.07 (16.02)
All individuals
N (% females)
5748 (54%)
3507 (55%)
686 (72%)
593 (73%)
408 (60%)
870 (51%)
5745 (63%)
17557 (59%)
‘Unrelated’ individuals
3268 (53%)
3264 (56%)
671 (71%)
586 (73%)
373 (59%)
822 (51%)
2977 (62%)
11961 (58%)
29.73 (7.86)
43.57 (5.62)
65.86 (5.99)
64.58 (6.09)
81.15 (0.29)
69.53 (0.86)
32.87 (12.26)
42.36 (16.91)
Questionnaire
(number of items)
EPQ (9)
MPQ
EPQ (23)
EPQ (23)
IPIP (10)
NEO (12)
EPQ (23,12)*/NEO (12)**
‘Unrelated’ individuals
Mean age (SD) N (% females)
29.68 (7.78)
43.57 (5.59)
65.87 (5.98)
64.58 (6.10)
81.15 (0.29)
69.53 (0.85)
31.83 (11.84)
39.01 (16.18)
Mean age (SD)
3235 (53%)
3261 (56%)
670 (71%)
586 (73%)
385 (58%)
824 (51%)
2825 (63%)
11786 (58%)
Mean age (SD)
29.75 (7.85)
43.57 (5.61)
65.88 (5.97)
64.58 (6.09)
81.15 (0.29)
69.53 (0.85)
32.00 (12.23)
42.36 (17.09)
EPQ (9)
MPQ
EPQ (21)
EPQ (21)
IPIP (10)
NEO (12)
EPQ (21,12)*
Notes: Numbers refer to the sample including All Individuals (left panel) and the sample including
‘Unrelated’ Individuals, i.e., individuals with a pairwise genetic similarity < 0.025 (middle panel);
N=number of individuals; SD = standard deviation; USA = United States of America; UK = United
Kingdom; LBC1921 = 1921 Lothian Birth Cohort; LBC1936 = 1936 Lothian Birth Cohort; Numbers refer to
number of individuals for whom genotype data are available (after QC); EPQ = Eysenck Personality
Questionnaire; MPQ = Multidimensional Personality Questionnaire Scales (MPQ scales are higher-order
scale scores and are a function of the other MPQ scales); IPIP = International Personality Item Pool; NEO
= Neuroticism-Extroversion-Openness Five-Factor-Inventory; for each questionnaire used, the number
of items is shown between brackets; *in case individuals EPQ data were available for the full and
abridged version of the EPQ, data from the full version were used for the analyses; **if individuals had
data on both EPQ and NEO, EPQ data were retained.
Supplementary Table S2 Proportion of variance explained by autosomal SNPs for Neuroticism and Extraversion
Neuroticism
Total sample
Australia only
Sweden only
UK only
USA only
Australia only, only SNPs in common*
Sweden only, only SNPs in common*
UK only, only SNPs in common*
USA only, only SNPs in common*
Total sample except Australia
Total sample except Sweden
Total sample except UK
Total sample except USA
Total sample, no cut-off**
Total sample, only SNPs in common*
Total sample, only SNPs in common adjusted**
Total sample, no PC adjustment***
Total sample, X chromosome
Men only
Women only
Total sample, GxSex interaction
Extraversion
N SNPs
N Individuals
h2(SNP)
(s.e.)
p
N Individuals
h2(SNP)
(s.e.)
p
849,801
531,616
621,444
530,819
508,337
162,056
162,056
162,056
162,056
845,345
545,625
847,879
849,022
849,801
162,056
162,056
849,801
21,187
849,801
849,801
N SNPs
849,801
11961
2977
3268
2452
3264
2928
3163
2438
3241
8984
8693
9509
8697
17870
11770
11770
11961
11961
5016
6945
N Individuals
11961
.056
.052
.021
.000
.153
.000
.057
.000
.178
.058
.072
.041
.057
.400
.047
.067
.062
.000
.161
.057
h2(SNP)
.016
.029
.111
.107
.145
.109
.110
.102
.137
.102
.039
.040
.036
.040
.019
.028
.040
.028
.005
.070
.050
(s.e.)
.041
.027
.319
.319
.499
.080
.500
.291
.500
.040
.067
.034
.131
.076
.000
.045
.046
.013
.500
.011
.127
11786
2825
3235
2465
3261
2783
3131
2451
3239
8961
8551
9321
8525
17557
11604
11604
11786
11786
4929
6857
N Individuals
11786
.120
.233
.270
.112
.000
.181
.234
.037
.000
.127
.127
.132
.108
.448
.090
.128
.123
.007
.176
.054
h2(SNP)
.125
.030
.120
.111
.146
.108
.120
.104
.136
.099
.040
.041
.038
.041
.018
.028
.040
.029
.006
.071
.051
(s.e.)
.042
.000
.026
.008
.229
.500
.068
.013
.394
.500
.001
.001
.000
.004
.000
.001
.001
.000
.099
.006
.140
h2(SNPxSex)
(s.e.)
p
h2(SNPxSex)
(s.e.)
p
.078
.056
.078
.000
.057
.500
Notes: all individuals included in the analyses have a pair-wise genetic similarity < 0.025 except for the ** marked in which no cut-off was made; the first 20
principal components were included as fixed effects in the model, except for the *** marked; N SNPs = number of unique SNPs included in the analyses, all
SNPs met criteria for QC within each cohort but were not necessarily genotyped in all cohorts; N individuals = number of individuals included in the analyses; h2
(SNP)
= proportion of phenotypic variance explained by all the SNPs; s.e. = standard error; p = p-value; h2 (SNPxSex) = proportion of phenotypic variance explained by
genotype-by-sex interaction; PC = principal component; SNPs = single nucleotide polymorphisms; UK = United Kingdom; USA = United States of America; * =
analyses based on 162,056 SNPs that were in common between all cohorts (number of ‘unrelated’ individuals differs from analyses based on all the SNPs since
genetic relatedness was estimated from the SNPs that were in common between the cohorts only; adjusted = estimate adjusted for imprecise LD between
genotyped SNPs and causal variants for causal variants within the allelic frequency spectrum as genotyped SNPs, using the regression coefficient
equation:
from
(assuming c=0), where Ajk is the variance of the off-diagonal elements of the genetic similarity matrix, N is the number of SNPs used
to calculate Ajk. c depends on the minor allele frequency of the causal variants See 27 for details. Note that the estimates for the age-cohort separate analyses for
Extraversion are based on a bivariate model in which the genetic correlation (rG) between the young and old cohort was estimated at .23 (s.e.=.26, p=.003).
Analyses were based on the residuals derived from regression of the (transformed) Neuroticism and Extraversion sum scores on sex, age, sex*age, age2, and
sex*age2; residuals were standardized separately for each sex and combined over all cohorts. Cohort membership was included as a fixed effect in the model.
The proportion of phenotypic variance explained by genotype-by-sex interaction was not significant when analysed in the Australian cohort only: Neuroticism:
h2(SNPxSex) = .02, s.e. = .21, N = 2977, p = .46. Extraversion: h2(SNPxSex) = .00, s.e. = .24, N = 2825, p = .50.
Supplementary Table S3 Proportion of variance explained by autosomal SNPs for Neuroticism and Extraversion based on SNP (h2(SNP)) and Pedigree (h2(PED)) data
Analysis
Trait
h2(SNP-U)
(s.e.)
1. SNP analysis:
'Unrelated' individuals
Neuroticism
Extraversion
.052
.233
h2(SNP-ALL)
.111
.120
(s.e.)
2. SNP analysis:
All individuals
Neuroticism
Extraversion
.419
.418
h2(PED-ALL)
.023
.023
(s.e.)
3. Pedigree analysis:
All individuals
Neuroticism
Extraversion
.457
.449
h2(SNP-ALL)
.022
.023
(s.e.)
+
h2(PED)
(s.e.)
.143
.138
h2(SNP-R)
.062
.067
(s.e.)
+
+
+
.312
.310
h2(SNP-U)
.066
.070
(s.e.)
+
h2(PED-ALL)
(s.e.)
Neuroticism
.062
.236
+
.129
.049
+
.334
.236
Extraversion
.063
.288
+
.124
.052
+
.330
.290
h2(SNP-R)
(s.e.)
+
h2(SNP-U)
(s.e.)
+
h2(PED-ALL)
(s.e.)
Neuroticism
.067
.282
+
.127
.048
+
.330
.284
Extraversion
.011
.353
+
.124
.052
+
.382
.355
h2(SNP-R)
(s.e.)
+
h2(SNP-U)
(s.e.)
+
h2(PED-ALL)
(s.e.)
Neuroticism
.015
.282
+
.129
.048
+
.382
.284
Extraversion
.113
.347
+
.121
.052
+
.281
.349
4. Joint analysis:
SNP + Pedigree: All individuals
5. Joint analysis:
SNP-‘Relateds’ + SNP-‘Unrelateds’ + Pedigree-All
(Cut-off 'Relatedness' = 0.025)
6. Joint analysis:
SNP-‘Relateds’ + SNP-‘Unrelateds’ + Pedigree-All
(Cut-off 'Relatedness' = 0.1)
7. Joint analysis:
SNP-‘Relateds’ + SNP-‘Unrelateds’ + Pedigree-All
(Cut-off 'Relatedness' = 0.2)
Neuroticism
Extraversion
Notes: Estimates are based on the Australian cohort only; close relatives are included in the analyses; h2(SNP)= proportion of variance explained by autosomal
SNPs data (max 529,492 SNPs); s.e. = standard error; h(PED) = proportion of variance explained by pedigree data; h2(SNP-ALL) (s.e.) + h2(PED-ALL) (s.e.) = proportion of
variance explained by autosomal SNP data from all individuals + pedigree data from all individuals in a joint analyses; h2(SNP-R) (s.e.) + h2(SNP-U) (s.e.) + h2(PED-ALL)
(s.e.) = proportion of variance explained by autosomal SNP data from ‘related’ individuals + autosomal SNP data from ‘unrelated’ individuals + pedigree data
from all individuals in a joint analyses; standard errors are shown between brackets; number of individuals included in analysis 1 (‘unrelated’ individuals only) is
2977 for Neuroticism and 2825 for Extraversion; number of individuals included in analyses 2-7 (all individuals) is 5954 for Neuroticism and 5693 for
Extraversion.
Section 4: Supplementary Figures
Supplementary Figure S1 Off-diagonal (a) and Diagonal (b) elements of genetic similarity matrix using all autosomal SNPs and Off-diagonal (c) and Diagonal (d)
elements of genetic similarity matrix using autosomal SNPs in common between all cohorts.
a
b
c
d
Notes: Panels a and c show the distribution of pair-wise genetic similarity between all pairs with a genetic similarity less than 0.025 (off-diagonal
elements of the genetic similarity matrix) based on all the SNPs and only the SNPs that are in common between all cohorts, respectively. Panels b and d show
the diagonal elements of the genetic similarity matrix (1 + inbreeding coefficient). The figure is based on all individuals with data on Neuroticism and/or
Extraversion; N = 12,044 for genetic similarity matrix based on all the SNPs; N =12,039 for genetic similarity matrix based on SNPs that are in common between
all cohorts, N SNPs in common between all cohorts = 162,056.
Figure S2 Expected pair-wise genetic similarity from autosomal SNP data versus estimated pair-wise
genetic similarity from pedigree data
Notes: shown is the expected pair-wise genetic similarity from pedigree data (Y-axis) versus the
observed pair-wise genetic similarity from autosomal SNP data (X-axis). Note that the values on the Xaxis correspond to the diagonal elements of the genetic similarity matrix (1 + inbreeding coefficient).
Polyserial correlation between genetic similarity from pedigree data and genetic relationship from SNP
data is .91 (number of observations is 18,304,197). The slope of the regression line is 1.003 (p <.001).
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