Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Supplementary Material for:
APOE*E2 Allele Delays Age of Onset in PSEN1 E280A Alzheimer’s Disease
JI Vélez1,2,*, F Lopera2,*, D Sepulveda-Falla2,3,*, HR Patel1, AS Johar1, A Chuah4, C Tobón2, D Rivera2,
A Villegas2, Y Cai1, K Peng5, R Arkell6, FX Castellanos7, SJ Andrews9, MF Silva Lara1, PK Creagh1,
S Easteal9, J de Leon8, ML Wong10, J Licinio10*, CA Mastronardi1,10*& M Arcos-Burgos1,2,*,#
1.
Genomics and Predictive Medicine Group, Department of Genome Sciences, John Curtin School of Medical
Research, The Australian National University, Canberra, ACT, Australia.
2.
Neuroscience Research Group, University of Antioquia, Medellín, Colombia.
3.
Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
4.
Genome Discovery Unit, Department of Genome Sciences, John Curtin School of Medical Research, The
Australian National University, Canberra, ACT, Australia.
5.
Biomolecular Resource Facility, John Curtin School of Medical Research, The Australian National University,
Canberra, ACT, Australia.
6.
Early Mammalian Development Laboratory, Research School of Biology, The Australian National University,
Canberra, ACT, Australia.
7.
NYU Child Study Center, NYU Langone Medical Center, New York, NY, US, and Nathan Kline Institute for
Psychiatric Research, Orangeburg, NY, US.
8.
Mental Health Research Center at Eastern State Hospital, University of Kentucky, Lexington, Kentucky, US.
9.
Genome Diversity and Health Group, Department of Genome Sciences, John Curtin School of Medical Research,
The Australian National University, Canberra, ACT, Australia.
10.
South Australian Health and Medical Research Institute and Department of Psychiatry, School of Medicine,
Flinders University, Adelaide, Australia.
*
These authors contributed equally to this work.

# Correspondence
to be directed to:
Mauricio Arcos-Burgos, M.D., Ph.D.
Associate Professor and Group Leader,
Genomics and Predictive Medicine Group
John Curtin School of Medical Research,
The Australian National University,
Building 131 Garran Road,
Canberra, ACT, Australia.
E-mail: Mauricio.Arcos-Burgos@anu.edu.au
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Clinical assessment: The modified CERAD evaluation battery
As part of our clinical program of AD, patients as well as carriers and non-carriers family members
(either from the same geographical region as the E280A pedigree or carrying the PSEN1 E280A
mutation) underwent clinical, neurological and neuropsychological assessment at the Group of
Neurosciences AD Clinic. Patients were defined as affected by MCI based on Petersen’s criteria, by
AD if the DSM-IV criteria were met, 1, 2 and using a Spanish version of the CERAD evaluation battery3
adapted for the cultural and linguistic characteristics specific to this population,4-7 which includes the
following measures:
(i) Semantic Fluency (Animal Naming): Individuals are requested to name as many animals as
they can in 1 min. The dependent variable is the number of unique animals named.
(ii) Modified Boston Naming Test: Consists in asking individuals to identify 15 line drawings of
increasing complexity (high, medium and low frequency) with a maximum of 10 s for each drawing.
One point is awarded for each correct response. The dependent variable is the total number of correct
naming.
(iii) Mini-mental State Exam (MMSE): A modified version of the MMSE by Folstein (1975)8
was used. The spelling backward subtest was excluded because the spelling ability involved in this task
is not a common skill for Spanish-speaking people. Instead, we replaced it with subtraction by threes,
which is more appropriate for this population. The total score of this test was 30 points. The dependent
variable was the total number of correct points.
(iv) Word List Memory (WLM): This test assesses the ability to recall newly learned
information. Individuals are presented 10 printed words on a card at the rate of one every 2 s. They are
immediately asked to recall as many words as possible from the list. This procedure is repeated in three
consecutive trials. The maximum number of correct words is 30 for the three trials. The number of
correct words identified serves as the dependent variable.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
(v) Constructional Praxis: Individuals are presented with four line drawings of figures of
increasing complexity (circle, rhombus, pentagon and cube), one at a time, and asked to copy them.
Maximum time allowed for copying each figure is 2 min. Reproductions are scored according to
predetermined criteria and the dependent measure is the total score for the four drawings.
(vi) Word List Recall: This test assesses delayed memory by requesting individuals to recall the
list of ten words presented in the WLM task. The maximum time allowed for this recall is 90 s. One
point is awarded for each word recalled correctly with maximum of 10 points if all the words are
recalled.
(vii) Word List Recognition. This test consists of recognition of the 10 words from the WLM
task, presented in a list of 20 words (including 10 additional distractor words). A forced-choice
paradigm is used so that subjects must respond with YES to the words they consider correct (i.e., in the
list of words previously read), and NO to the words that were not on that list. One point is awarded for
each word correctly recognized. To adjust by chance, the subject’s score is calculated as the total
number of correct answers minus 10; if the result is less than 0, a score of 0 is given.
(viii) Line-Drawing Recall. Individuals are asked to recall the drawings they copied previously
and draw them from free recall on a blank sheet of paper. This tests serves to assess visual memory.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
b
70
AOO (years)
a
60
50
40
35
45
50 55 60
AOO (years)
65
70
Female
Male
Gender
d
70
70
AOO (years)
AOO (years)
c
40
60
50
40
60
50
40
None
Elementary
High
Tertiary
0
Education
5
10
15
20
Schooling (years)
Supplementary Figure 1. (a) Histogram and probability density plots for the ADAOO in 71 patients
with PSEN1 E280A AD. Analysis of the ADAOO distribution disclosed the presence of two hidden
groups with an average ADAOO of ~45 and ~57 years old, respectively. Box- and violin-plots for the
ADAOO by (b) gender and (c) education group. No difference in the average ADAOO was found in
either case. (d) ADAOO as a function of the years of education. ADAOO. AD = Alzheimer’s disease;
ADAOO = Alzheimer’s disease age of onset.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
b
80
AOO (years)
a
70
60
50
40
40
70
80
Female
Male
Gender
d
80
70
80
AOO (years)
AOO (years)
c
50
60
AOO (years)
60
50
70
60
50
40
40
Elementary
High
0
Tertiary
5
10
15
20
Schooling (years)
Education
Supplementary Figure 2. (a) Histogram and probability density plots for the ADAOO in Results 54
patients with sporadic AD. Analysis of the ADAOO distribution disclosed the presence of two hidden
groups with an average ADAOO of ~59 years and ~69 years, respectively. Box- and violin-plots for the
ADAOO by (b) gender and (c) education group. No difference in the average ADAOO was found in
either case. (d) ADAOO as a function of the years of education. Observe that individuals with < 3
years of education in the sample seem to have an earlier ADAOO. AD = Alzheimer’s disease; ADAOO
= Alzheimer’s disease age of onset.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Supplementary Figure 3. (a) Partition of phenotypic variance in the multi-locus LMEM for each
forward inclusion (steps 1 to 10) and backward elimination (10 steps after the dotted line). The yellow
vertical line marks the selected model based on the highest multiple posterior probability of association
(mPPA). (b) Manhattan plot for the CEFVs included for analysis. Markers with –log10(P)>5.08 were
significant after FDR correction. The red horizontal line corresponds to the Bonferroni threshold of –
log10(P)=6.89. (c) Beanplots for the ADAOO in variants with PFDR<0.05 (see Table 1). Pink, blue and
dotted horizontal lines correspond, respectively, to the within genotype average ADAOO, the
individuals’ ADAOO and the global average ADAOO in our 71 patients with PSEN1 E280A
Alzheimer’s disease. ADAOO: Alzheimer’s disease age of onset. CEFVs: common exonic functional
variants. LMEM: linear mixed-effects model.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Rare Functional Exonic Variants Modifying ADAOO
Supplementary Table 1. Results for the association analysis of rare variants using (a) regression- and
(b) permutation-based KBAC. (c) Top 20 genes associated genes from the SKAT-O9 analysis of rare
variants.
(a)
Chr
5
11
10
Start
31,799,031
108,093,559
98,741,041
Stop
32,111,038
108,239,826
98,745,585
Gene
PDZD2
ATM
C10orf12
m
7
4
3
G
8
5
4
T
3.395
2.423
1.817
P
1.80x10-2
2.40x10-2
5.00x10-2
(b)
Chr
5
11
17
Start
31,799,031
108,093,559
71,330,523
Stop
32,111,038
108,239,826
71,640,227
Gene
PDZD2
ATM
SDK2
m
7
4
3
G
8
5
4
T
0.200
0.143
0.107
P
1.40x10-2
2.13x10-2
5.29x10-2
Gene
m
ATM
4
PDZD2
7
RSPH6A
3
ADSSL1
3
ZNF311
3
FNDC7
3
VILL
3
XKR9
3
PLG
3
LMOD3
3
TEX35
3
ZNF646
3
CDC42BPG 3
PACS2
3
INCENP
3
C10orf12
3
SCN10A
3
SDK2
3
ERMARD
3
T
0.004
0.005
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
P
6.94x10-3
9.15x10-3
1.95x10-2
1.95x10-2
1.96x10-2
1.96x10-2
1.96x10-2
1.97x10-2
1.97x10-2
1.97x10-2
1.97x10-2
1.97x10-2
1.98x10-2
1.98x10-2
1.98x10-2
1.98x10-2
1.98x10-2
1.98x10-2
1.9x10-2
(c)
Chr
11
5
19
14
6
1
3
8
6
3
1
16
11
14
11
10
3
17
6
Start
108,093,559
31,799,031
46,298,968
105,190,534
28,962,594
109,255,556
38,035,078
71,581,600
161,123,225
69,156,039
178,482,212
31,085,743
64,591,662
105,767,148
61,891,445
98,741,041
38,738,837
71,330,523
170,151,718
Stop
108,239,826
32,111,038
46,318,605
105,213,647
28,973,037
109,285,367
38,048,676
71,648,177
161,175,085
69,171,746
178,492,635
31,094,833
64,612,041
105,864,484
61,920,635
98,745,585
38,835,501
71,640,227
170,181,680
Chr: Chromosome; m: number of markers; G: number of multi-marker genotypes; T: one-side KBAC
test statistic; P: one-sided P-value; KBAC: Kernel-based adaptive cluster; SKAT-O: Optimized
Sequence Kernel Association Test. Highlighted genes in (c) were also found using KBAC.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Supplementary Table 2. GO process and disease annotations for the network involving AOO modifier genes.
GO Processes and Disease Annotations (P-value)
Gene(s) involved
PSEN1 APOE* TRIM22 IFI16 RC3H1* DFAN5
GO Process
Negative regulation of cell proliferation (3.367x10-7)
Cell differentiation (1.4x10-5)
Regulation of apoptotic process (2.69x10-5)
Positive regulation of apoptotic process (1.57x10-3)
Regulation of neuron apoptotic process (2.37x10-4)
Regulation of intrinsic apoptotic signalling pathway (2.68x10-4)
Intrinsic apoptotic signalling pathway by p53 class mediator
(2.96x10-4)
Negative regulation of neuron death (5.22x10-3)
Positive regulation of IL-1 β production (7.56x10-5)
Immune response (2.32x10-4)
T cell activation involved in immune response (4.30x10-4)
Positive regulation of NIK/NK-kappa B signaling (1.66x10-3)
Diseases
Alzheimer's disease (3.34x10-5)
EOAD (1.68x10-2)
Delerium, Dementia, Amnestic, Cognitive disorders (3.13x10-5)
Frontotemporal lobar degeneration (4.3x10-4)
Frontotemporal lobar dementia (4.3x10-4)
Neurodegenerative diseases (5.85x10-4)
* Age of onset decelerators.
Conventions
Apoptotic-related processes
Immunological-related processes
0
Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Supplementary Table 3. GO process and disease annotations for the network involving AOO modifier genes.
Genes involved (by Network Analysis)
PSEN1, APOE
PSEN1, RC3H1
IFI16, RC3H1
IFI16, PSEN1
IFI16, PSEN1, RC3H1
IFI16, PSEN1
Processes
Organic hydroxy compound transport
Positive regulation of protein catabolic process
Regulation of axonogenesis
Positive regulation of proteolysis
Regulation of coagulation
Positive regulation of protein processing
Regulation of synaptic plasticity
Positive regulation of cellular catabolic process
T cell activation involved in immune response
Lymphocyte activation involved in immune response
Cell activation involved in immune response
Leukocyte activation involved in immune response
Negative regulation of B cell proliferation
Myeloid leukocyte differentiation
Leukocyte differentiation
Autophagy
Cellular response to starvation
Antigen receptor-mediated signaling pathway
Immune effector process
Positive regulation of intracellular signal transduction
Regulation of intracellular signal transduction
Negative regulation of response to stimulus
Regulation of signal transduction
IFI16, PSEN1, RC3H1, TRIM22
PSEN1, RC3H1, TRIM22, DFNA5
PSEN1, RC3H1, APOE, TRIM22, DFNA5
IFI16, AOAH, PSEN1, RC3H1, APOE, FCRL5
PSEN1, RC3H1, APOE, FCRL5, TRIM22, DFNA5
IFI16, AOAH, PSEN1, RC3H1, APOE, FCRL5, TRIM22,
Regulation of response to stimulus
DFNA5
1
P
PFDR
4.40x10-5
4.74x10-5
4.92x10-5
2.08x10-5
2.13x10-5
3.13x10-5
8.48x10-5
9.55x10-5
4.89x10-6
1.84x10-5
6.07x10-5
6.07x10-5
3.63x10-5
4.74x10-5
3.71x10-5
3.70x10-5
5.10x10-5
7.38x10-5
4.33x10-6
3.40x10-5
7.02x10-5
7.37x10-7
1.00x10-4
1.68x10-3
1.68x10-3
1.68x10-3
1.40x10-3
1.40x10-3
1.63x10-3
2.02x10-3
2.07x10-3
6.53x10-4
1.40x10-3
1.83x10-3
1.83x10-3
1.63x10-3
1.68x10-3
1.63x10-3
1.63x10-3
1.69x10-3
1.93x10-3
6.53x10-4
1.63x10-3
1.93x10-3
4.04x10-4
2.07x10-3
2.56x10-6
5.61x10-4
Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Calculation of Performance Measures for the ARPA-based predictive models
Supplementary Table 4. (a) Possible results when the real and predicted early- and late-onset
Alzheimer’s disease (AD) status are compared. Here, a is the number of individuals with early-onset
AD (EO) that are correctly classified, b is the number of EO individuals classified as late-onset AD
(LO), c corresponds to the number of LO cases classified as EO, and d to the number of LO cases
correctly classified. (b) Expressions for calculating the performance measures used to quantitatively
compare the CART, Random Forest and TreeNet strategies.
(a)
Prediction
EOAD LOAD
a
b
c
d
Phenotype
EO (AOO < 48)
LO (AOO ≥ 48)
(b)
Measure
Sensitivity
Specificity
Precision
Classification rate (CR)
AOO: age of onset.
Expression
a / (a+c)
d / (c+d)
d / (c+d)
(a+d) / (a+b+c+d)
0
Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Gender-specific effects of genetic variants modifying ADAOO
We performed a 2-based association analysis to assess gender-effects in the nine genetic variants
reported to modify ADAOO in our 71 patients with PSEN1 E280A AD. Results of this analysis are
presented below. Overall, no specific effects of these genetic variants by gender or education group
were found.
Supplementary Table 5. Gender-specific effects of genetic variants modifying ADAOO.
SNP
2
Gene
df
P
rs7412
APOE
0.014 1 0.906
rs36092215 GPR20 1.055 2 0.590
rs12364019 TRIM22 0.000 1 1.000
rs16838748 FCRL5 0.000 1 1.000
rs12701506 AOAH
1.019 2 0.601
rs2682585 PINLYP 2.002 2 0.367
rs62621173 IFI16
0.291 1 0.590
rs10798302 RC3H1 0.000 1 1.000
rs754554
DFNA5 1.530 2 0.465
2
 = Test statistic; df = degrees of freedom; P = P-value.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Number of variants per gene after filtering
Number of genes (x 1000)
5
4
3
2
1
0
1
3
5
7
9
11 13 15 17 19 21 23 25 27 30 32 35 38 40 65 83
Number of variants per genes
Supplementary Figure 4. Distribution of the number of common functional exonic variants (CEFVs)
within genes. On average, ~2.83 CEFVs are located within each gene (standard deviation = 3.23).
2
Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Power analysis
Oligogenic model for detecting ADAOO modifiers
Our power analysis using the pwr10 package in R11 suggests that, for a k=3 group design, 71 individuals
would be sufficient to detect 80% true positives and a large effect (defined by the Cohen’s d parameter;
d=0.82, Supplementary Figure 4) when m=100,000 variants are tested for association (a value that
certainly overcomes the final number of variants used during the LMEM analyses). The selection of
such effect is based on our hypothesis that variants of large effect (i.e., mutations) modify ADAOO in
individuals with PSEN1 E280A AD (see also the 𝛽̂ coefficients in Table 1 of the manuscript); k is
selected based on the maximum number of possible genotypes for a biallelic genetic marker; and m, as
mentioned before, to be conservative.
(b)
250
0.85
200
150
100
100
0.80
Power
0.75
0.70
50
150
0.85
1.0
0.9
0.80
0.8
0.75
0.7
0.70
0.6
Power
0.65
Cohen's d
0.5
0.600.4
0.65
50
n
2
0
250
(a)
0.4
0.6
0.8
1.0
Cohen's d
Supplementary Figure 5. (a) Sample size (n) as a function of the Cohen’s effect size (d) and power.
The type I error probability used for calculations is 0.05/100,000. In (b) the red dot corresponds to
n=71, power = 80% and a large effect d = 0.82.
It is also worth mentioning that d implies that d% of the ADAOO variance is explained by the m
independent variables (i.e., ~100,000 genetic variants being tested). Our LMEMs estimates show that
the percentage of ADAOO variance explained with only nine CEFVs (see Table 1 of the manuscript)
tested for association is > 90% for the n=71 individuals. Under these conditions (d=0.9, n=71,
m=40,000), the post hoc power estimate is of 96%, but for nine CEFVs the post hoc power estimate is
of 99.9%.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
Testing differences in ADAOO using extreme phenotypes from Genetic Isolate
In addition, we have performed a power analysis to assess what the resulting sample size would be
when a different split of the groups (in terms of the AOO) is used. As before, we utilized the pwr10
package in R11 to evaluate the power of a sample of n1 individuals with an ADAOO < 48 years and n2
individuals with ADAOO ≥ 48 years, using a two-sample t-test, for a wide range of effect sizes (f).
Cohen (1988)12 suggests that values of f=0.2, 0.5, and 0.8 represent small, medium, and large effect
sizes, respectively, where f is calculated as
𝜇1 −𝜇2
𝑓=|
𝜎
|
(1)
In our context, 𝜇1 and 𝜇2 are the population ADAOO for individuals with early- and late-onset AD,
respectively, and 𝜎 2 is the common variance. We estimated 𝜇1 and 𝜇2 with the correspondent sample
means (i.e., 44.2 and 53.5, respectively) and 𝜎 2 as
𝑆2
𝑆2
1
2
𝜎̂ 2 = 𝑛1 + 𝑛2
(2)
In this formula, 𝑆12 and 𝑆22 are the sample estimates of the variance for the group of individuals with
early- and late-ADAOO, respectively (i.e., 2.482 and 5.132 as reported in the second paragraph of the
“E280A Pedigree” section). Thus, the sample effect size is f=8.936 (which corresponds to an extremely
large effect size). The observed power of our study design is >99% when a type I error probability of
5% is used. Now, assuming a separation of eight years, no changes in the variance estimates and a
sample size of n=71 individuals, the estimated effect size is f=7.687. The corresponding power would
then be > 99%.
In order to further study the power of our design under other conditions, we used a type I error
probability of 5%, varied the sample sizes as n1 = n2 ={20, 40, 60, 80, 100} and f in the interval (0.1, 3)
to cover small, medium, large and extremely large effect sizes (Cohen, 1988). The results are presented
in Supplementary Figure 5 below. Overall, our study design has a power of >90% to detect effect sizes
f > 0.8, which is a tenth of that actually observed. Hence, the ADAOO can be safely tested using our
sample size, and our ad hoc separation of early and late onset.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
100
a
1.0
0.7
80
0.8
0.6
n1
0.6
Power
60
0.4
0.5
0.2
0.4
40
0.0
100
100
80
80
0.3
60
60
n2
40
40
n1
20
20
20 20
40
60
80
100
80
100
80
100
n2
b
100
1.00
80
0.95
0.90
0.85
n1
Power
0.80
60
0.75
0.70
0.65
100
100
0.9
5
40
80
80
60
n1
0.8
0.8 5
0
60
40
40
n2
0.9
0
20
20 20
20
40
60
n2
c
100
1.00
80
0.99
0.98
n1
Power
0.97
60
0.96
0.95
100
100
40
80
80
n1
0.9
0.9 9
8
60
60
40
40
n2
20
20 20
20
40
60
n2
Supplementary Figure 6. Power analysis as a function of n1 and n2, the number of individuals with
ADAOO<48 years and ADAOO ≥ 48 years, respectively, for a type I error probability of 5% and effect
sizes (a) f = 0.4, (b) f = 0.8 and (c) f =1.2. The red dot corresponds to the power of our study design
(n=71, n1=43, n2=28) for the effect size shown.
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Mutations Modifying the Age of Onset of Dementia
Vélez et al. 2015
a
30
20
10
0
Squared error loss
40
School
school
APOE
APOE
IFI16
IFI16
DFAN5
DFNA5
RC3H1
RC3H1
Sexsex
PINLYP
PINLYP
GPR20
GPR20
TRIM22
TRIM22
FCRL5
FCRL5
AOAH
AOAH
0
b
Iteration
40
30
20
Squared error loss
10
1000 2000 3000 4000 5000
Iteration
0
40
30
20
10
1000 2000 3000 4000 5000
Iteration
20
30
40
50
10
20
30
40
Relative influence
school
School
APOE
APOE
IFI16
IFI16
DFNA5
DFAN5
Sexsex
RC3H1
RC3H1
PINLYP
PINLYP
GPR20
GPR20
AOAH
AOAH
TRIM22
TRIM22
FCRL5
FCRL5
0
Squared error loss
0
10
Relative influence
School
school
APOE
APOE
IFI16
IFI16
DFAN5
DFNA5
Sexsex
RC3H1
RC3H1
PINLYP
PINLYP
GPR20
GPR20
AOAH
AOAH
TRIM22
TRIM22
FCRL5
FCRL5
0
0
c
0
1000 2000 3000 4000 5000
0
10
20
30
40
Relative influence
Supplementary Figure 7. Squared error loss (left) and relative importance (right) for the best fitting
generalized boosting regression model (GBRM) when the (a) out-of-bag, (b) test data set and (c) crossvalidation error are used, and the ADAOO is introduced as a continuous variable. Based on the
minimum mean squared error, a measure of model fitting, model (b) was selected. The black, red,
green and blue lines in the left panel correspond to the training data set, the test data set, the crossvalidation, and the best performing model, respectively. Note that the variable importance plots on the
right are similar to those obtained when the ADAOO was introduced as a binary variable.
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Vélez et al. 2015
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