Chronological and gestational DNAm age estimation using different methylation-based clocks
Dolors Pelegri and Juan R Gonzalez
2024-05-01
Package
methylclock 1.10.0
Contents
1 Description of implemented clocks
This manual describes how to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. The package includes the following estimators:
1.1 Chronological DNAm age (in years)
- Horvath’s clock: It uses 353 CpGs described in Horvath (2013).
It was trained using 27K and 450K arrays in samples from different tissues.
Other three different age-related biomarkers are also computed:
- AgeAcDiff (DNAmAge acceleration difference): Difference between DNAmAge and chronological age.
- IEAA Residuals obtained after regressing DNAmAge and chronological age adjusted by cell counts.
- EEAA Residuals obtained after regressing DNAmAge and chronological age. This measure was also known as DNAmAge acceleration residual in the first Horvath’s paper.
- Hannum’s clock: It uses 71 CpGs described in Hannum et al. (2013). It was
trained using 450K array in blood samples. Another are-related biomarer is
also computed:
- AMAR (Apparent Methylomic Aging Rate): Measure proposed in Hannum et al. (2013) computed as the ratio between DNAm age and the chronological age.
- BNN: It uses Horvath’s CpGs to train a Bayesian Neural Network (BNN) to predict DNAm age as described in Alfonso and Gonzalez (2020).
- Horvath’s skin+blood clock (skinHorvath): Epigenetic clock for skin and blood cells. It uses 391 CpGs described in Horvath et al. (2018). It was trained using 450K EPIC arrays in skin and blood sampels.
- PedBE clock: Epigenetic clock from buccal epithelial swabs. It’s intended purpose is buccal samples from individuals aged 0-20 years old. It uses 84 CpGs described in McEwen et al. (2019). The authors gathered 1,721 genome-wide DNAm profiles from 11 different cohorts with individuals aged 0 to 20 years old.
- Wu’s clock: It uses 111 CpGs described in Wu et al. (2019). It is designed to predict age in children. It was trained using 27K and 450K.
- BLUP clock: It uses 319607 CpGs described in Zhang et al. (2019).
It was trained using 450K and EPIC arrays in blood (13402 samples) and
saliva (259 samples). Age predictors based on training sets with various
sample sizes using Best Linear Unbiased Prediction (BLUP)
- EN clock: It uses 514 CpGs described in Zhang et al. (2019). It was trained using 450K and EPIC arrays in blood (13402 samples) and saliva (259 samples). Age predictors based on training sets with various sample sizes using Elastic Net (EN)
1.2 Gestational DNAm age (in weeks)
- Knight’s clock: It uses 148 CpGs described in Knight et al. (2016). It was trained using 27K and 450K arrays in cord blood samples.
- Bohlin’s clock: It uses 96 CpGs described in Bohlin et al. (2016). It was trained using 450K array in cord blood samples.
- Mayne’s clock: It uses 62 CpGs described in Mayne et al. (2017). It was trained using 27K and 450K.
- EPIC clock: EPIC-based predictor of gestational age. It uses 176 CpGs described in Haftorn et al. (2021). It was trained using EPIC arrays in cord blood samples.
- Lee’s clocks: Three different biological clocks described in Lee et al. (2019) are implemented. It was trained for 450K and EPIC arrays in placenta samples.
- RPC clock: Robust placental clock (RPC). It uses 558 CpG sites.
- CPC clock: Control placental clock (CPC). It usses 546 CpG sites.
- Refined RPC clock: Useful for uncomplicated term pregnancies (e.g. gestational age >36 weeks). It uses 396 CpG sites.
The biological DNAm clocks implemented in our package are:
- Levine’s clock (also know as PhenoAge): It uses 513 CpGs described in Levine et al. (2018). It was trained using 27K, 450K and EPIC arrays in blood samples.
- Telomere Length’s clock (TL): It uses 140 CpGs described in Lu et al. (2019) It was trained using 450K and EPIC arrays in blood samples.
The main aim of this package is to facilitate the interconnection with R and
Bioconductor’s infrastructure and, hence, avoiding submitting data to online
calculators. Additionally, methylclock also provides an unified way of
computing DNAm age to help downstream analyses.
2 Getting started
The package depends on some R packages that can be previously installed into your computer by:
install.packages(c("tidyverse", "impute", "Rcpp"))Then methylclock package is installed into your computer by executing:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("methylclock")The package is loaded into R as usual:
library(methylclockData)
library(methylclock)These libraries are required to reproduce this document:
library(Biobase)
library(tibble)
library(impute)
library(ggplot2)
library(ggpmisc)
library(GEOquery)3 DNA Methylation clocks
The main function to estimate chronological and biological mDNA age is called
DNAmAge while the gestational DNAm age is estimated using DNAmGA function.
Both functions have similar input arguments. Next subsections detail some of
the important issues to be consider before computind DNAm clocks.
3.1 Data format
The methylation data is given in the argument x. They can be either beta or
M values. The argument toBetas should be set to TRUE when M values are
provided. The x object can be:
A matrix with CpGs in rows and individuals in columns having the name of the CpGs in the rownames.
A data frame or a tibble with CpGs in rows and individuals in columns having the name of the CpGs in the first column (e.g. cg00000292, cg00002426, cg00003994, …) as required in the Horvath’s DNA Methylation Age Calculator website (https://dnamage.genetics.ucla.edu/home).
A GenomicRatioSet object, the default method to encapsulate methylation data in
minfiBioconductor package.An ExpressionSet object as obtained, for instance, when downloading methylation data from GEO (https://www.ncbi.nlm.nih.gov/geo/).
3.2 Data nomalization
In principle, data can be normalized by using any of the existing standard
methods such as QN, ASMN, PBC, SWAN, SQN, BMIQ (see a revision of those
methods in Wang et al. (2015)). DNAmAge function includes the BMIQ method
proposed by Teschendorff et al. (2012) using Horvath’s robust implementation that
basically consists of an optimal R code implementation and optimization
procedures. This normalization is recommended by Horvath since it improves
the predictions for his clock. This normalization procedure is very
time-consuming. In order to overcome these difficulties, we have parallelize
this process using BiocParallel library. This step is not mandatory, so that,
you can use your normalized data and set the argument normalize equal to
FALSE (default).
3.3 Missing individual’s data
All the implemented methods require complete cases. DNAmAge function has an
imputation method based on KNN implemented in the function knn.impute from
impute Bioconductor package. This is performed when missing data is present
in the CpGs used in any of the computed clocks. There is also another option
based on a fast imputation method that imputes missing values by the median of
required CpGs as recommended in Bohlin et al. (2016). This is recommended when
analyzing 450K arrays since knn.impute for large datasets may be very time
consuming. Fast imputation can be performed by setting fastImp=TRUE which is
not the default value.
3.4 Missing CpGs of DNAm clocks
By default the package computes the different clocks when there are more than
80% of the required CpGs of each method. Nothing is required when having
missing CpGs since the main functions will return NA for those estimators
when this criteria is not meet. Let us use a test dataset (TestDataset)
which is available within the package to illustrate the type of information
we are obtaining:
# Get TestDataset data
TestDataset <- get_TestDataset()
cpgs.missing <- checkClocks(TestDataset) clock Cpgs_in_clock missing_CpGs percentage
1 Horvath 353 2 0.6
2 Hannum 71 64 90.1
3 Levine 513 3 0.6
4 SkinHorvath 391 283 72.4
5 PedBE 94 91 96.8
6 Wu 111 2 1.8
7 TL 140 137 97.9
8 BLUP 319607 300288 94.0
9 EN 514 476 92.6cpgs.missing.GA <- checkClocksGA(TestDataset) clock Cpgs_in_clock missing_CpGs percentage
1 Knight 148 0 0.0
2 Bohlin 87 87 100.0
3 Mayne 62 0 0.0
4 Lee 1125 1072 95.3
5 EPIC 176 170 96.6The objects cpgs.missing and cpgs.missing.GA are lists having the missing
CpGs of each clock
names(cpgs.missing) [1] "Horvath" "Hannum" "Levine" "skinHorvath" "PedBE"
[6] "Wu" "TL" "BLUP" "EN"We can see which are those CpGs for a given clock (for example Hannum) with
the function commonClockCpgs :
commonClockCpgs(cpgs.missing, "Hannum" ) [1] "cg20822990" "cg22512670" "cg25410668" "cg04400972"
[5] "cg16054275" "cg10501210" "ch.2.30415474F" "cg22158769"
[9] "cg02085953" "cg06639320" "cg22454769" "cg24079702"
[13] "cg23606718" "cg22016779" "cg03607117" "cg07553761"
[17] "cg00481951" "cg25478614" "cg25428494" "cg02650266"
[21] "cg08234504" "cg23500537" "cg20052760" "cg16867657"
[25] "cg06685111" "cg00486113" "cg13001142" "cg20426994"
[29] "cg14361627" "cg08097417" "cg07955995" "cg22285878"
[33] "cg03473532" "cg08540945" "cg07927379" "cg16419235"
[37] "cg07583137" "cg22796704" "cg19935065" "cg23091758"
[41] "cg23744638" "cg04940570" "cg11067179" "cg22213242"
[45] "cg06419846" "cg02046143" "cg00748589" "cg18473521"
[49] "cg01528542" "ch.13.39564907R" "cg03032497" "cg04875128"
[53] "cg09651136" "cg03399905" "cg04416734" "cg07082267"
[57] "cg14692377" "cg06874016" "cg21139312" "cg02867102"
[61] "cg19283806" "cg14556683" "cg07547549" "cg08415592"commonClockCpgs(cpgs.missing.GA, "Bohlin" ) [1] "cg00153101" "cg00602416" "cg00711496" "cg01190109" "cg01635555"
[6] "cg01833485" "cg02324006" "cg02405476" "cg02567958" "cg02642822"
[11] "cg03108070" "cg03281561" "cg03337084" "cg03507326" "cg03710860"
[16] "cg03729251" "cg03773820" "cg03963689" "cg04347477" "cg04685228"
[21] "cg05053327" "cg05544807" "cg05877497" "cg06753281" "cg06897661"
[26] "cg07106169" "cg07676709" "cg07738730" "cg07749613" "cg07788865"
[31] "cg07835443" "cg08326019" "cg08620426" "cg08943494" "cg09447786"
[36] "cg10308785" "cg11124260" "cg11294761" "cg11864574" "cg12880227"
[41] "cg12999267" "cg13036381" "cg13066703" "cg13433246" "cg13641317"
[46] "cg13733403" "cg13959344" "cg13982823" "cg14276580" "cg14427590"
[51] "cg15035133" "cg15131146" "cg15165154" "cg15908709" "cg16187883"
[56] "cg16348385" "cg17022232" "cg18183624" "cg18217136" "cg18954401"
[61] "cg19057830" "cg19439123" "cg19875532" "cg20301308" "cg20303561"
[66] "cg20816447" "cg21081878" "cg21143441" "cg21155834" "cg21221899"
[71] "cg21707172" "cg21878650" "cg22761205" "cg22796593" "cg22797644"
[76] "cg23051248" "cg23346945" "cg23403099" "cg23457357" "cg24041556"
[81] "cg24087613" "cg24366564" "cg25150953" "cg25531857" "cg25639749"
[86] "cg26077811" "cg26092675"In Section 4.1 we describe how to change this 80% threshold.
3.5 Cell counts
The EEAA method requires to estimate cell counts. We use the package meffil
(Min et al. (2018)) that provides some functions to estimate cell counts using
predefined datasets. This is performed by setting cell.count=TRUE (default
value). The reference panel is passed through the argument
cell.count.reference. So far, the following options are available:
- “blood gse35069 complete”: methylation profiles from Reinius et al. (2012) for purified blood cell types. It includes CD4T, CD8T, Mono, Bcell, NK, Neu and Eos.
- “blood gse35069”: methylation profiles from Reinius et al. (2012) for purified blood cell types. It includes CD4T, CD8T, Mono, Bcell, NK and Gran.
- “blood gse35069 chen”: methylation profiles from Chen et al. (2017) blood cell types. It includes CD4T, CD8T, Mono, Bcell, NK, Neu and Eos.
- “andrews and bakulski cord blood”. Cord blood reference from
- It includes Bcell, CD4T, CD8T, Gran, Mono, NK and nRBC.
- “cord blood gse68456” Cord blood methylation profiles from
- It includes CD4T, CD8T, Mono, Bcell, NK, Neu, Eos and RBC.
- “gervin and lyle cord blood” Cord blood reference generated by Kristina
Gervin and Robert Lyle, available at
miffilpackage. It includes CD14, Bcell, CD4T, CD8T, NK, Gran. - “saliva gse48472”: Reference generated from the multi-tissue pannel from
- It includes Buccal, CD4T, CD8T, Mono, Bcell, NK, Gran.
- “guintivano dlpfc”: Reference generated from Guintivano, Aryee, and Kaminsky (2013). It includes dorsolateral prefrontal cortex, NeuN_neg and NeuN_pos.
- “combined cord blood”: References generated based in samples assayed by Bakulski et al, Gervin et al., de Goede et al., and Lin et al. It includes umbilical cord blood, Bcell, CD4T, CD8T, Gran, Mono, NK and nRBC
4 Chronological and biological DNAm age estimation
Next we illustrate how to estimate the chronological DNAm age using several datasets which aim to cover different data input formats.
IMPORTANT NOTE: On some systems we can find an error in the DNAmAge()
function when parameter cell.count = TRUE. This error is related to
preprocessCore package and can be fixed by disabling multi-threading
when installing the preprocessCore package using the command
BiocManager::install("preprocessCore",
configure.args = "--disable-threading",
force = TRUE)4.1 Data in Horvath’s format (e.g. csv with CpGs in rows)
Let us start by reproducing the results proposed in Horvath (2013). It uses
the format available in the file ’MethylationDataExample55.csv" from his
tutorial (available here). These data
are available at methylclock package. Although these data can be loaded into
R by using standard functions such as read.csv we hihgly recommend to use
functions from tidiverse, in particular read_csv from readr package.
The main reason is that currently researchers are analyzing Illumina 450K
or EPIC arrays that contains a huge number of CpGs that can take a long time
to be loaded when using basic importing R function. These functions import
csv data as tibble which is one of the possible formats of DNAmAge function
library(tidyverse)
MethylationData <- get_MethylationDataExample()
MethylationData # A tibble: 27,578 × 17
ProbeID GSM946048 GSM946049 GSM946052 GSM946054 GSM946055 GSM946056 GSM946059
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 cg0000… 0.706 0.730 0.705 0.751 0.715 0.634 0.682
2 cg0000… 0.272 0.274 0.311 0.279 0.178 0.269 0.330
3 cg0000… 0.0370 0.0147 0.0171 0.0290 0.0163 0.0243 0.0127
4 cg0000… 0.133 0.120 0.121 0.107 0.110 0.129 0.102
5 cg0000… 0.0309 0.0192 0.0217 0.0132 0.0181 0.0243 0.0199
6 cg0000… 0.0700 0.0715 0.0655 0.0719 0.0914 0.0508 0.0294
7 cg0000… 0.993 0.993 0.993 0.994 0.991 0.994 0.993
8 cg0000… 0.0215 0.0202 0.0187 0.0169 0.0162 0.0143 0.0172
9 cg0000… 0.0105 0.00518 0.00410 0.00671 0.00758 0.00518 0.00543
10 cg0001… 0.634 0.635 0.621 0.639 0.599 0.591 0.594
# ℹ 27,568 more rows
# ℹ 9 more variables: GSM946062 <dbl>, GSM946064 <dbl>, GSM946065 <dbl>,
# GSM946066 <dbl>, GSM946067 <dbl>, GSM946073 <dbl>, GSM946074 <dbl>,
# GSM946075 <dbl>, GSM946076 <dbl>IMPORTANT NOTE: Be sure that the first column contains the CpG names.
Sometimes, your imported data look like this one (it can happen, for
instance, if the csv file was created in R without indicating
row.names=FALSE)
> mydata
# A tibble: 473,999 x 6
X1 Row.names BIB_15586_1X BIB_33043_1X EDP_5245_1X KAN_584_1X
<int> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 cg000000~ 0.635 0.575 0.614 0.631
2 2 cg000001~ 0.954 0.948 0.933 0.950
3 3 cg000001~ 0.889 0.899 0.901 0.892
4 4 cg000001~ 0.115 0.124 0.107 0.123
5 5 cg000002~ 0.850 0.753 0.806 0.815
6 6 cg000002~ 0.676 0.771 0.729 0.665
7 7 cg000002~ 0.871 0.850 0.852 0.863
8 8 cg000003~ 0.238 0.174 0.316 0.206If so, the first column must be removed before being used as the input
object in DNAmAge funcion. It can be done using dplyr function
> mydata2 <- select(mydata, -1)
# A tibble: 473,999 x 5
Row.names BIB_15586_1X BIB_33043_1X EDP_5245_1X KAN_584_1X
<chr> <dbl> <dbl> <dbl> <dbl>
1 cg000000~ 0.635 0.575 0.614 0.631
2 cg000001~ 0.954 0.948 0.933 0.950
3 cg000001~ 0.889 0.899 0.901 0.892
4 cg000001~ 0.115 0.124 0.107 0.123
5 cg000002~ 0.850 0.753 0.806 0.815
6 cg000002~ 0.676 0.771 0.729 0.665
7 cg000002~ 0.871 0.850 0.852 0.863
8 cg000003~ 0.238 0.174 0.316 0.206In any case, if you use the object mydata that contains the CpGs in the
second column, you will see this error message:
> DNAmAge(mydata)
Error in DNAmAge(mydata) : First column should contain CpG namesOnce data is in the proper format, DNAmAge can be estimated by simply:
age.example55 <- DNAmAge(MethylationData) Warning in predAge(cpgs.imp, coefHannum, intercept = FALSE, min.perc): The number of missing CpGs forHannumclock exceeds 80%.
---> This DNAm clock will be NA. rows : 353 cols : 16 Warning in predAge(cpgs.imp, coefSkin, intercept = TRUE, min.perc): The number of missing CpGs forSkinclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefPedBE, intercept = TRUE, min.perc): The number of missing CpGs forPedBEclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefTL, intercept = TRUE, min.perc): The number of missing CpGs forTLclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefBLUP, intercept = TRUE, min.perc): The number of missing CpGs forBLUPclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefEN, intercept = TRUE, min.perc): The number of missing CpGs forENclock exceeds 80%.
---> This DNAm clock will be NA.age.example55 # A tibble: 16 × 11
id Horvath Hannum Levine BNN skinHorvath PedBE Wu TL BLUP EN
<chr> <dbl> <lgl> <dbl> <dbl> <lgl> <lgl> <dbl> <lgl> <lgl> <lgl>
1 GSM946… 51.8 NA -30.3 56.4 NA NA 1.08 NA NA NA
2 GSM946… 39.8 NA -29.6 42.1 NA NA 0.808 NA NA NA
3 GSM946… 26.4 NA -33.3 25.6 NA NA 0.772 NA NA NA
4 GSM946… 34.0 NA -36.0 28.0 NA NA 0.941 NA NA NA
5 GSM946… 10.1 NA -52.8 13.4 NA NA 0.456 NA NA NA
6 GSM946… 20.4 NA -42.2 16.7 NA NA 0.621 NA NA NA
7 GSM946… 6.00 NA -44.8 7.54 NA NA 0.258 NA NA NA
8 GSM946… 34.6 NA -23.2 34.6 NA NA 0.624 NA NA NA
9 GSM946… 7.91 NA -49.8 12.0 NA NA 0.237 NA NA NA
10 GSM946… 4.72 NA -48.2 6.43 NA NA 0.396 NA NA NA
11 GSM946… 29.6 NA -39.9 28.5 NA NA 0.413 NA NA NA
12 GSM946… 1.38 NA -48.3 3.48 NA NA 0.122 NA NA NA
13 GSM946… 56.0 NA -26.7 47.3 NA NA 0.714 NA NA NA
14 GSM946… 24.0 NA -39.7 23.3 NA NA 0.676 NA NA NA
15 GSM946… 9.38 NA -45.4 11.9 NA NA 0.251 NA NA NA
16 GSM946… 38.8 NA -27.5 41.4 NA NA 0.599 NA NA NAAs mention in Section 3.4 some clocks returns NA when there are more than 80% of the required CpGs are missing as we can see when typing
missCpGs <- checkClocks(MethylationData) clock Cpgs_in_clock missing_CpGs percentage
1 Horvath 353 0 0.0
2 Hannum 71 64 90.1
3 Levine 513 0 0.0
4 SkinHorvath 391 282 72.1
5 PedBE 94 91 96.8
6 Wu 111 0 0.0
7 TL 140 137 97.9
8 BLUP 319607 300192 93.9
9 EN 514 476 92.6Here we can observe that 72.1% of the required CpGs for SkinHorvath clock are
missing. We could estimate DNAm age using this clock just changing the argument
min.perc in DNAmAge. For example, we can indicate that the minimum amount
of required CpGs for computing a given clock should be 25%.
age.example55.2 <- DNAmAge(MethylationData, min.perc = 0.25) Warning in predAge(cpgs.imp, coefHannum, intercept = FALSE, min.perc): The number of missing CpGs forHannumclock exceeds 25%.
---> This DNAm clock will be NA. rows : 353 cols : 16 Warning in predAge(cpgs.imp, coefPedBE, intercept = TRUE, min.perc): The number of missing CpGs forPedBEclock exceeds 25%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefTL, intercept = TRUE, min.perc): The number of missing CpGs forTLclock exceeds 25%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefBLUP, intercept = TRUE, min.perc): The number of missing CpGs forBLUPclock exceeds 25%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefEN, intercept = TRUE, min.perc): The number of missing CpGs forENclock exceeds 25%.
---> This DNAm clock will be NA.age.example55.2 # A tibble: 16 × 11
id Horvath Hannum Levine BNN skinHorvath PedBE Wu TL BLUP EN
<chr> <dbl> <lgl> <dbl> <dbl> <dbl> <lgl> <dbl> <lgl> <lgl> <lgl>
1 GSM946… 51.8 NA -30.3 56.4 7.15 NA 1.08 NA NA NA
2 GSM946… 39.8 NA -29.6 42.1 7.09 NA 0.808 NA NA NA
3 GSM946… 26.4 NA -33.3 25.6 5.93 NA 0.772 NA NA NA
4 GSM946… 34.0 NA -36.0 28.0 6.34 NA 0.941 NA NA NA
5 GSM946… 10.1 NA -52.8 13.4 5.76 NA 0.456 NA NA NA
6 GSM946… 20.4 NA -42.2 16.7 5.79 NA 0.621 NA NA NA
7 GSM946… 6.00 NA -44.8 7.54 5.64 NA 0.258 NA NA NA
8 GSM946… 34.6 NA -23.2 34.6 5.55 NA 0.624 NA NA NA
9 GSM946… 7.91 NA -49.8 12.0 5.06 NA 0.237 NA NA NA
10 GSM946… 4.72 NA -48.2 6.43 5.48 NA 0.396 NA NA NA
11 GSM946… 29.6 NA -39.9 28.5 6.19 NA 0.413 NA NA NA
12 GSM946… 1.38 NA -48.3 3.48 4.91 NA 0.122 NA NA NA
13 GSM946… 56.0 NA -26.7 47.3 7.07 NA 0.714 NA NA NA
14 GSM946… 24.0 NA -39.7 23.3 6.23 NA 0.676 NA NA NA
15 GSM946… 9.38 NA -45.4 11.9 5.57 NA 0.251 NA NA NA
16 GSM946… 38.8 NA -27.5 41.4 6.69 NA 0.599 NA NA NAIn that case, we see as SkinHorvath clock is estimated (though it can be observed that the estimation is not very accurate - this is why we considered at least having 80% of the required CpGs).
By default all available clocks (Hovarth, Hannum, Levine, BNN, skinHorvath,…)
are estimated. One may select a set of clocks by using the argument clocks
as follows:
age.example55.sel <- DNAmAge(MethylationData, clocks=c("Horvath", "BNN")) rows : 353 cols : 16age.example55.sel # A tibble: 16 × 3
id Horvath BNN
<chr> <dbl> <dbl>
1 GSM946048 51.8 56.4
2 GSM946049 39.8 42.1
3 GSM946052 26.4 25.6
4 GSM946054 34.0 28.0
5 GSM946055 10.1 13.4
6 GSM946056 20.4 16.7
7 GSM946059 6.00 7.54
8 GSM946062 34.6 34.6
9 GSM946064 7.91 12.0
10 GSM946065 4.72 6.43
11 GSM946066 29.6 28.5
12 GSM946067 1.38 3.48
13 GSM946073 56.0 47.3
14 GSM946074 24.0 23.3
15 GSM946075 9.38 11.9
16 GSM946076 38.8 41.44.2 Age acceleration
However, in epidemiological studies one is interested in assessing whether age acceleration is associated with a given trait or condition. Three different measures can be computed:
- ageAcc: Difference between DNAmAge and chronological age.
- ageAcc2: Residuals obtained after regressing chronological age and DNAmAge (similar to IEAA).
- ageAcc3: Residuals obtained after regressing chronological age and DNAmAge adjusted for cell counts (similar to EEAA).
All this estimates can be obtained for each clock when providing chronological
age through age argument. This information is normally provided in a
different file including different covariates (metadata or sample annotation
data). In this example data are available at ‘SampleAnnotationExample55.csv’
file that is also available at methylclock package:
library(tidyverse)
path <- system.file("extdata", package = "methylclock")
covariates <- read_csv(file.path(path, "SampleAnnotationExample55.csv"))
covariates # A tibble: 16 × 14
OriginalOrder id title geo_accession TissueDetailed Tissue diseaseStatus
<dbl> <chr> <chr> <chr> <chr> <chr> <dbl>
1 3 GSM946… Auti… GSM946048 Fresh frozen … occip… 1
2 4 GSM946… Cont… GSM946049 Fresh frozen … occip… 0
3 7 GSM946… Auti… GSM946052 Fresh frozen … occip… 1
4 9 GSM946… Auti… GSM946054 Fresh frozen … occip… 1
5 10 GSM946… Auti… GSM946055 Fresh frozen … occip… 1
6 11 GSM946… Auti… GSM946056 Fresh frozen … occip… 1
7 14 GSM946… Cont… GSM946059 Fresh frozen … occip… 0
8 17 GSM946… Cont… GSM946062 Fresh frozen … occip… 0
9 19 GSM946… Auti… GSM946064 Fresh frozen … occip… 1
10 20 GSM946… Auti… GSM946065 Fresh frozen … occip… 1
11 21 GSM946… Auti… GSM946066 Fresh frozen … occip… 1
12 22 GSM946… Cont… GSM946067 Fresh frozen … occip… 0
13 28 GSM946… Cont… GSM946073 Fresh frozen … occip… 0
14 29 GSM946… Cont… GSM946074 Fresh frozen … occip… 0
15 30 GSM946… Cont… GSM946075 Fresh frozen … occip… 0
16 31 GSM946… Cont… GSM946076 Fresh frozen … occip… 0
# ℹ 7 more variables: Age <dbl>, PostMortemInterval <dbl>, CauseofDeath <chr>,
# individual <dbl>, Female <dbl>, Caucasian <lgl>, FemaleOriginal <lgl>In this case, chronological age is available at Age column:
age <- covariates$Age
head(age) [1] 60 39 28 39 8 22The different methylation clocks along with their age accelerated estimates can be simply computed by:
age.example55 <- DNAmAge(MethylationData, age=age, cell.count=TRUE) Warning in predAge(cpgs.imp, coefHannum, intercept = FALSE, min.perc): The number of missing CpGs forHannumclock exceeds 80%.
---> This DNAm clock will be NA. rows : 353 cols : 16 Warning in predAge(cpgs.imp, coefSkin, intercept = TRUE, min.perc): The number of missing CpGs forSkinclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefPedBE, intercept = TRUE, min.perc): The number of missing CpGs forPedBEclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefTL, intercept = TRUE, min.perc): The number of missing CpGs forTLclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefBLUP, intercept = TRUE, min.perc): The number of missing CpGs forBLUPclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefEN, intercept = TRUE, min.perc): The number of missing CpGs forENclock exceeds 80%.
---> This DNAm clock will be NA.age.example55 # A tibble: 16 × 24
id Horvath ageAcc.Horvath ageAcc2.Horvath ageAcc3.Horvath Hannum Levine
<chr> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
1 GSM9460… 51.8 -8.22 -4.45 -4.91 NA -30.3
2 GSM9460… 39.8 0.754 2.00 1.59 NA -29.6
3 GSM9460… 26.4 -1.59 -1.67 -1.86 NA -33.3
4 GSM9460… 34.0 -5.00 -3.76 -0.463 NA -36.0
5 GSM9460… 10.1 2.06 -0.428 2.82 NA -52.8
6 GSM9460… 20.4 -1.61 -2.42 -2.88 NA -42.2
7 GSM9460… 6.00 2.00 -0.971 -0.827 NA -44.8
8 GSM9460… 34.6 6.65 6.57 5.32 NA -23.2
9 GSM9460… 7.91 2.91 0.0589 -2.61 NA -49.8
10 GSM9460… 4.72 2.72 -0.489 1.46 NA -48.2
11 GSM9460… 29.6 -0.427 -0.268 -1.37 NA -39.9
12 GSM9460… 1.38 0.375 -2.95 -2.19 NA -48.3
13 GSM9460… 56.0 -4.01 -0.242 1.62 NA -26.7
14 GSM9460… 24.0 2.03 1.23 -0.669 NA -39.7
15 GSM9460… 9.38 1.38 -1.11 -0.885 NA -45.4
16 GSM9460… 38.8 8.76 8.92 5.85 NA -27.5
# ℹ 17 more variables: ageAcc.Levine <dbl>, ageAcc2.Levine <dbl>,
# ageAcc3.Levine <dbl>, BNN <dbl>, ageAcc.BNN <dbl>, ageAcc2.BNN <dbl>,
# ageAcc3.BNN <dbl>, skinHorvath <lgl>, PedBE <lgl>, Wu <dbl>,
# ageAcc.Wu <dbl>, ageAcc2.Wu <dbl>, ageAcc3.Wu <dbl>, TL <lgl>, BLUP <lgl>,
# EN <lgl>, age <dbl>By default, the argument cell.count is set equal to TRUE and, hence, can
be omitted. This implies that ageAcc3 will be computed for all clocks.
In some occassions this can be very time consuming. In such cases one can
simply estimate DNAmAge, accAge and accAge2 by setting cell.count=FALSE.
NOTE: see section 3.5 to see the reference panels available to estimate
cell counts.
Then, we can investigate, for instance, whether the accelerated age is associated with Autism. In that example we will use a non-parametric test (NOTE: use t-test or linear regression for large sample sizes)
autism <- covariates$diseaseStatus
kruskal.test(age.example55$ageAcc.Horvath ~ autism)
Kruskal-Wallis rank sum test
data: age.example55$ageAcc.Horvath by autism
Kruskal-Wallis chi-squared = 1.3346, df = 1, p-value = 0.248kruskal.test(age.example55$ageAcc2.Horvath ~ autism)
Kruskal-Wallis rank sum test
data: age.example55$ageAcc2.Horvath by autism
Kruskal-Wallis chi-squared = 3.1875, df = 1, p-value = 0.0742kruskal.test(age.example55$ageAcc3.Horvath ~ autism)
Kruskal-Wallis rank sum test
data: age.example55$ageAcc3.Horvath by autism
Kruskal-Wallis chi-squared = 2.8235, df = 1, p-value = 0.092894.3 Chronological age prediction using ExpressionSet data
One may be interested in assessing association between chronologial age and DNA
methylation age or evaluating how well chronological age is predicted by
DNAmAge. In order to illustrate this analysis we downloaded data from GEO
corresponding to a set of healthy individuals (GEO accession number GSE58045).
Data can be retrieved into R by using GEOquery package as an ExpressionSet
object that can be the input of our main function.
# To avoid connection buffer size
Sys.setenv("VROOM_CONNECTION_SIZE" = 131072 * 10)
# Download data
dd <- GEOquery::getGEO("GSE58045")
gse58045 <- dd[[1]]
# Restore connection buffer size
Sys.setenv("VROOM_CONNECTION_SIZE" = 131072)gse58045 ExpressionSet (storageMode: lockedEnvironment)
assayData: 27578 features, 172 samples
element names: exprs
protocolData: none
phenoData
sampleNames: GSM1399890 GSM1399891 ... GSM1400061 (172 total)
varLabels: title geo_accession ... twin:ch1 (43 total)
varMetadata: labelDescription
featureData
featureNames: cg00000292 cg00002426 ... cg27665659 (27578 total)
fvarLabels: ID Name ... ORF (38 total)
fvarMetadata: Column Description labelDescription
experimentData: use 'experimentData(object)'
pubMedIds: 22532803
Annotation: GPL8490The chronological age is obtained by using pData function from Biobase
package that is able to deal with ExpressionSet objects:
pheno <- pData(gse58045)
age <- as.numeric(pheno$`age:ch1`)And the different DNA methylation age estimates are obtained by using DNAmAge
function (NOTE: as there are missing values, the program automatically runs
impute.knn function to get complete cases):
age.gse58045 <- DNAmAge(gse58045, age=age) Imputing missing data of the entire matrix ....
Data imputed. Starting DNAm clock estimation ... Warning in predAge(cpgs.imp, coefHannum, intercept = FALSE, min.perc): The number of missing CpGs forHannumclock exceeds 80%.
---> This DNAm clock will be NA. rows : 353 cols : 172 Warning in predAge(cpgs.imp, coefSkin, intercept = TRUE, min.perc): The number of missing CpGs forSkinclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefPedBE, intercept = TRUE, min.perc): The number of missing CpGs forPedBEclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefTL, intercept = TRUE, min.perc): The number of missing CpGs forTLclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefBLUP, intercept = TRUE, min.perc): The number of missing CpGs forBLUPclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefEN, intercept = TRUE, min.perc): The number of missing CpGs forENclock exceeds 80%.
---> This DNAm clock will be NA.age.gse58045 # A tibble: 172 × 24
id Horvath ageAcc.Horvath ageAcc2.Horvath ageAcc3.Horvath Hannum Levine
<chr> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
1 GSM1399… 65.6 1.07 4.58 5.46 NA 50.7
2 GSM1399… 66.3 0.197 4.06 5.06 NA 51.3
3 GSM1399… 53.9 -5.31 -2.98 -2.42 NA 40.5
4 GSM1399… 40.6 -5.23 -5.89 -6.14 NA 31.3
5 GSM1399… 50.1 0.982 1.06 1.28 NA 41.1
6 GSM1399… 63.7 -0.895 2.64 2.92 NA 48.1
7 GSM1399… 44.7 -0.875 -1.59 -1.76 NA 29.2
8 GSM1399… 59.7 -8.55 -4.20 -3.48 NA 41.0
9 GSM1399… 48.4 -5.84 -4.63 -2.50 NA 43.8
10 GSM1399… 59.3 -3.93 -0.719 -0.609 NA 46.1
# ℹ 162 more rows
# ℹ 17 more variables: ageAcc.Levine <dbl>, ageAcc2.Levine <dbl>,
# ageAcc3.Levine <dbl>, BNN <dbl>, ageAcc.BNN <dbl>, ageAcc2.BNN <dbl>,
# ageAcc3.BNN <dbl>, skinHorvath <lgl>, PedBE <lgl>, Wu <dbl>,
# ageAcc.Wu <dbl>, ageAcc2.Wu <dbl>, ageAcc3.Wu <dbl>, TL <lgl>, BLUP <lgl>,
# EN <lgl>, age <dbl>Figure shows the correlation between DNAmAge obtained from Horvath’s method and the chronological age, while Figure depicts the correlation of a new method based on fitting a Bayesian Neural Network to predict DNAmAge based on Horvath’s CpGs.
plotDNAmAge(age.gse58045$Horvath, age)
plotDNAmAge(age.gse58045$BNN, age, tit="Bayesian Neural Network")
4.4 Use of DNAmAge in association studies
Let us illustrate how to use DNAmAge information in association studies (e.g case/control, smokers/non-smokers, responders/non-responders, …). GEO number GSE19711 contains transcriptomic and epigenomic data of a study in lung cancer. Data can be retrieved into R by
# To avoid connection buffer size
Sys.setenv("VROOM_CONNECTION_SIZE" = 131072 * 10)
# Download data
dd <- GEOquery::getGEO("GSE19711")
gse19711 <- dd[[1]]
# Restore connection buffer size
Sys.setenv("VROOM_CONNECTION_SIZE" = 131072)The object gse19711is an ExpressionSet that can contains CpGs and
phenotypic (e.g clinical) information
gse19711 ExpressionSet (storageMode: lockedEnvironment)
assayData: 27578 features, 540 samples
element names: exprs
protocolData: none
phenoData
sampleNames: GSM491937 GSM491938 ... GSM492476 (540 total)
varLabels: title geo_accession ... stage:ch1 (58 total)
varMetadata: labelDescription
featureData
featureNames: cg00000292 cg00002426 ... cg27665659 (27578 total)
fvarLabels: ID Name ... ORF (38 total)
fvarMetadata: Column Description labelDescription
experimentData: use 'experimentData(object)'
pubMedIds: 20219944
Annotation: GPL8490Let us imagine we are interested in comparing the accelerated age between cases and controls. Age and case/control status information can be obtained by:
pheno <- pData(gse19711)
age <- as.numeric(pheno$`ageatrecruitment:ch1`)
disease <- pheno$`sample type:ch1`
table(disease) disease
bi-sulphite converted genomic whole blood DNA from Case
266
bi-sulphite converted genomic whole blood DNA from Control
274disease[grep("Control", disease)] <- "Control"
disease[grep("Case", disease)] <- "Case"
disease <- factor(disease, levels=c("Control", "Case"))
table(disease) disease
Control Case
274 266The DNAmAge estimates of different methods is computed by
age.gse19711 <- DNAmAge(gse19711, age=age) Imputing missing data of the entire matrix ....
Data imputed. Starting DNAm clock estimation ... Warning in predAge(cpgs.imp, coefHannum, intercept = FALSE, min.perc): The number of missing CpGs forHannumclock exceeds 80%.
---> This DNAm clock will be NA. rows : 353 cols : 540 Warning in predAge(cpgs.imp, coefSkin, intercept = TRUE, min.perc): The number of missing CpGs forSkinclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefPedBE, intercept = TRUE, min.perc): The number of missing CpGs forPedBEclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefTL, intercept = TRUE, min.perc): The number of missing CpGs forTLclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefBLUP, intercept = TRUE, min.perc): The number of missing CpGs forBLUPclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefEN, intercept = TRUE, min.perc): The number of missing CpGs forENclock exceeds 80%.
---> This DNAm clock will be NA.We can observe there are missing data. The funcion automatically impute those
using impute.knn function from impute package since complete cases are
required to compute the different methylation clocks. The estimates are:
age.gse19711 # A tibble: 540 × 24
id Horvath ageAcc.Horvath ageAcc2.Horvath ageAcc3.Horvath Hannum Levine
<chr> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
1 GSM4919… 62.9 -5.14 -0.351 -1.10 NA 61.1
2 GSM4919… 68.8 -12.2 -2.85 -2.13 NA 57.0
3 GSM4919… 60.0 3.96 4.54 4.37 NA 43.0
4 GSM4919… 57.9 -4.13 -1.45 -1.38 NA 40.9
5 GSM4919… 59.0 -13.0 -6.79 -6.98 NA 57.0
6 GSM4919… 57.0 -4.00 -1.66 -1.09 NA 44.7
7 GSM4919… 61.9 -3.08 0.657 0.183 NA 47.9
8 GSM4919… 59.1 -11.9 -6.07 -5.53 NA 50.0
9 GSM4919… 60.7 -16.3 -8.33 -9.33 NA 47.7
10 GSM4919… 51.1 -7.93 -6.30 -6.33 NA 52.5
# ℹ 530 more rows
# ℹ 17 more variables: ageAcc.Levine <dbl>, ageAcc2.Levine <dbl>,
# ageAcc3.Levine <dbl>, BNN <dbl>, ageAcc.BNN <dbl>, ageAcc2.BNN <dbl>,
# ageAcc3.BNN <dbl>, skinHorvath <lgl>, PedBE <lgl>, Wu <dbl>,
# ageAcc.Wu <dbl>, ageAcc2.Wu <dbl>, ageAcc3.Wu <dbl>, TL <lgl>, BLUP <lgl>,
# EN <lgl>, age <dbl>The association between disease status and DNAmAge estimated using Horvath’s method can be computed by
mod.horvath1 <- glm(disease ~ ageAcc.Horvath ,
data=age.gse19711,
family="binomial")
summary(mod.horvath1)
Call:
glm(formula = disease ~ ageAcc.Horvath, family = "binomial",
data = age.gse19711)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.10995 0.09771 -1.125 0.2605
ageAcc.Horvath -0.02023 0.01154 -1.753 0.0795 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 748.48 on 539 degrees of freedom
Residual deviance: 745.25 on 538 degrees of freedom
AIC: 749.25
Number of Fisher Scoring iterations: 4mod.skinHorvath <- glm(disease ~ ageAcc2.Horvath ,
data=age.gse19711,
family="binomial")
summary(mod.skinHorvath)
Call:
glm(formula = disease ~ ageAcc2.Horvath, family = "binomial",
data = age.gse19711)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.02970 0.08617 -0.345 0.730
ageAcc2.Horvath -0.01315 0.01209 -1.087 0.277
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 748.48 on 539 degrees of freedom
Residual deviance: 747.27 on 538 degrees of freedom
AIC: 751.27
Number of Fisher Scoring iterations: 3mod.horvath3 <- glm(disease ~ ageAcc3.Horvath ,
data=age.gse19711,
family="binomial")
summary(mod.horvath3)
Call:
glm(formula = disease ~ ageAcc3.Horvath, family = "binomial",
data = age.gse19711)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.02993 0.08626 -0.347 0.729
ageAcc3.Horvath -0.01927 0.01283 -1.502 0.133
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 748.48 on 539 degrees of freedom
Residual deviance: 746.13 on 538 degrees of freedom
AIC: 750.13
Number of Fisher Scoring iterations: 4We do not observe statistical significant association between age acceleration estimated using Horvath method and the risk of developing lung cancer. It is worth to notice that Horvath’s clock was created to predict chronological age and the impact of age acceleration of this clock on disease may be limited. On the other hand, Levine’s clock aimed to distinguish risk between same-aged individuals. Let us evaluate whether this age acceleration usin Levine’s clock is associated with lung cancer
mod.levine1 <- glm(disease ~ ageAcc.Levine , data=age.gse19711,
family="binomial")
summary(mod.levine1)
Call:
glm(formula = disease ~ ageAcc.Levine, family = "binomial", data = age.gse19711)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.40956 0.17894 2.289 0.02209 *
ageAcc.Levine 0.03178 0.01133 2.806 0.00502 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 748.48 on 539 degrees of freedom
Residual deviance: 740.17 on 538 degrees of freedom
AIC: 744.17
Number of Fisher Scoring iterations: 4mod.levine2 <- glm(disease ~ ageAcc2.Levine , data=age.gse19711,
family="binomial")
summary(mod.levine2)
Call:
glm(formula = disease ~ ageAcc2.Levine, family = "binomial",
data = age.gse19711)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.02925 0.08718 -0.336 0.737225
ageAcc2.Levine 0.04430 0.01234 3.589 0.000332 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 748.48 on 539 degrees of freedom
Residual deviance: 734.49 on 538 degrees of freedom
AIC: 738.49
Number of Fisher Scoring iterations: 4mod.levine3 <- glm(disease ~ ageAcc3.Levine , data=age.gse19711,
family="binomial")
summary(mod.levine3)
Call:
glm(formula = disease ~ ageAcc3.Levine, family = "binomial",
data = age.gse19711)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.02962 0.08622 -0.344 0.731
ageAcc3.Levine 0.01679 0.01244 1.350 0.177
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 748.48 on 539 degrees of freedom
Residual deviance: 746.62 on 538 degrees of freedom
AIC: 750.62
Number of Fisher Scoring iterations: 3Here we observe as the risk of developing lung cancer increases
3.23 percent per each unit in the
age accelerated variable (ageAcc). Similar conclusion is obtained when using
ageAcc2 and ageAcc3 variables.
In some occasions cell composition should be used to assess association. This
information is calculated in DNAmAge function and it can be incorporated in
the model by:
cell <- attr(age.gse19711, "cell_proportion")
mod.cell <- glm(disease ~ ageAcc.Levine + cell, data=age.gse19711,
family="binomial")
summary(mod.cell)
Call:
glm(formula = disease ~ ageAcc.Levine + cell, family = "binomial",
data = age.gse19711)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -9.768206 4.380382 -2.230 0.025748 *
ageAcc.Levine 0.003959 0.012208 0.324 0.745746
cellCD4T -3.339693 3.833531 -0.871 0.383656
cellMono 10.165096 4.594096 2.213 0.026922 *
cellNeu 16.319534 4.584745 3.560 0.000372 ***
cellNK -0.882134 4.296498 -0.205 0.837326
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 748.48 on 539 degrees of freedom
Residual deviance: 686.56 on 534 degrees of freedom
AIC: 698.56
Number of Fisher Scoring iterations: 4Here we observe as the positive association disapears after adjusting for cell counts.
4.5 Use of DNAm age in children
dd <- GEOquery::getGEO("GSE109446")
gse109446 <- dd[[1]]controls <- pData(gse109446)$`diagnosis:ch1`=="control"
gse <- gse109446[,controls]
age <- as.numeric(pData(gse)$`age:ch1`)
age.gse <- DNAmAge(gse, age=age) rows : 353 cols : 29plotCorClocks(age.gse)
5 Gestational DNAm age estimation
5.1 Model predicion
Let us start by reproducing the example provided in Knight et al. (2016) as
a test data set (file ‘TestDataset.csv’). It consists on 3 individuals whose
methylation data are available as supplementary data of their paper. The data
is also available at methylclock package as a data frame.
TestDataset[1:5,] CpGName Sample1 Sample2 Sample3
1 cg00000292 0.72546496 0.72350947 0.69023377
2 cg00002426 0.85091763 0.80077888 0.80385777
3 cg00003994 0.05125853 0.05943935 0.05559333
4 cg00005847 0.08775420 0.11722333 0.10845113
5 cg00006414 0.03982478 0.06146891 0.03491992The Gestational Age (in months) is simply computed by
ga.test <- DNAmGA(TestDataset) Warning in DNAmGA(TestDataset): CpGs in all Gestational Age clocks are not present in your
data. Try 'checkClocksGA' function to find the missing CpGs of
each method. Warning in predAge(cpgs.imp, coefBohlin, intercept = TRUE, min.perc): The number of missing CpGs forBohlinclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefEPIC, intercept = TRUE, min.perc): The number of missing CpGs forEPICclock exceeds 80%.
---> This DNAm clock will be NA. Warning in DNAmGA(TestDataset): The number of missing CpGs for Lee clocks exceeds 80%.
---> This DNAm clock will be NA.ga.test # A tibble: 3 × 6
id Knight Bohlin Mayne EPIC Lee
<chr> <dbl> <dbl> <dbl> <lgl> <lgl>
1 Sample1 38.2 NA 35.8 NA NA
2 Sample2 38.8 NA 36.5 NA NA
3 Sample3 40.0 NA 36.6 NA NAlike in DNAmAge we can use the parameter min.perc to set the minimum missing
percentage.
The results are the same as those described in the additional file 7 of Knight et al. (2016) (link [here] (https://static-content.springer.com/esm/art%3A10.1186%2Fs13059-016-1068-z/MediaObjects/13059_2016_1068_MOESM7_ESM.docx))
Let us continue by illustrating how to compute GA of real examples.
The PROGRESS cohort data is available in the additional file 8 of
Knight et al. (2016). It is available at methylclock as a tibble:
data(progress_data)This file also contains different variables that are available in this
tibble.
data(progress_vars)The Clinical Variables including clinical assesment of gestational age
(EGA) are available at this tibble.
The Gestational Age (in months) is simply computed by
ga.progress <- DNAmGA(progress_data) Warning in DNAmGA(progress_data): CpGs in all Gestational Age clocks are not present in your
data. Try 'checkClocksGA' function to find the missing CpGs of
each method. Warning in predAge(cpgs.imp, coefBohlin, intercept = TRUE, min.perc): The number of missing CpGs forBohlinclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefMayneGA, intercept = TRUE, min.perc): The number of missing CpGs forMayneclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefEPIC, intercept = TRUE, min.perc): The number of missing CpGs forEPICclock exceeds 80%.
---> This DNAm clock will be NA. Warning in DNAmGA(progress_data): The number of missing CpGs for Lee clocks exceeds 80%.
---> This DNAm clock will be NA.ga.progress # A tibble: 150 × 6
id Knight Bohlin Mayne EPIC Lee
<chr> <dbl> <dbl> <lgl> <lgl> <lgl>
1 784 38.8 NA NA NA NA
2 1052 37.2 NA NA NA NA
3 1048 40.3 NA NA NA NA
4 1017 39.2 NA NA NA NA
5 956 38.9 NA NA NA NA
6 1038 39.2 NA NA NA NA
7 989 37.2 NA NA NA NA
8 946 35.4 NA NA NA NA
9 941 33.5 NA NA NA NA
10 1024 37.4 NA NA NA NA
# ℹ 140 more rowsWe can compare these results with the clinical GA available in the variable EGA
plotDNAmAge(ga.progress$Knight, progress_vars$EGA,
tit="GA Knight's method",
clock="GA")
Figure 3b (only for PROGRESS dataset) in Knight et al. (2016) representing the correlation between GA acceleration and birthweight can be reproduced by
library(ggplot2)
progress_vars$acc <- ga.progress$Knight - progress_vars$EGA
p <- ggplot(data=progress_vars, aes(x = acc, y = birthweight)) +
geom_point() +
geom_smooth(method = "lm", se=FALSE, color="black") +
xlab("GA acceleration") +
ylab("Birthweight (kgs.)")
p
Finally, we can also estimate the “accelerated gestational age” using two
of the the three different estimates previously described (accAge, accAge2)
by provinding information of gestational age through age argument. Notice
that in that case accAge3 cannot be estimates since we do not have all the
CpGs required by the default reference panel to estimate cell counts for
gestational age which is “andrews and bakulski cord blood”.
accga.progress <- DNAmGA(progress_data,
age = progress_vars$EGA,
cell.count=FALSE) Warning in DNAmGA(progress_data, age = progress_vars$EGA, cell.count = FALSE): CpGs in all Gestational Age clocks are not present in your
data. Try 'checkClocksGA' function to find the missing CpGs of
each method. Warning in predAge(cpgs.imp, coefBohlin, intercept = TRUE, min.perc): The number of missing CpGs forBohlinclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefMayneGA, intercept = TRUE, min.perc): The number of missing CpGs forMayneclock exceeds 80%.
---> This DNAm clock will be NA. Warning in predAge(cpgs.imp, coefEPIC, intercept = TRUE, min.perc): The number of missing CpGs forEPICclock exceeds 80%.
---> This DNAm clock will be NA. Warning in DNAmGA(progress_data, age = progress_vars$EGA, cell.count = FALSE): The number of missing CpGs for Lee clocks exceeds 80%.
---> This DNAm clock will be NA.accga.progress # A tibble: 150 × 9
id Knight ageAcc.Knight ageAcc2.Knight Bohlin Mayne EPIC Lee age
<chr> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> <dbl>
1 784 38.8 0.792 1.27 NA NA NA NA 38
2 1052 37.2 -1.05 -0.488 NA NA NA NA 38.3
3 1048 40.3 2.29 2.77 NA NA NA NA 38
4 1017 39.2 0.643 1.28 NA NA NA NA 38.6
5 956 38.9 1.75 1.99 NA NA NA NA 37.1
6 1038 39.2 1.09 1.61 NA NA NA NA 38.1
7 989 37.2 -0.774 -0.292 NA NA NA NA 38
8 946 35.4 -2.36 -1.96 NA NA NA NA 37.7
9 941 33.5 -3.18 -3.06 NA NA NA NA 36.7
10 1024 37.4 -1.12 -0.486 NA NA NA NA 38.6
# ℹ 140 more rowsOne can also check which clocks can be estimated given the CpGs available in the methylation data by
checkClocksGA(progress_data) clock Cpgs_in_clock missing_CpGs percentage
1 Knight 148 0 0.0
2 Bohlin 94 94 100.0
3 Mayne 62 61 98.4
4 Lee 1125 1125 100.0
5 EPIC 176 176 100.06 Correlation among DNAm clocks
We can compute the correlation among biological clocks using the function
plotCorClocks that requires the package ggplot2 and ggpubr to be
installed in your computer.
We can obtain, for instance, the correlation among the clocks estimated for the healthy individuals study previosuly analyze (GEO accession number GSE58045) by simply executing:
plotCorClocks(age.gse58045)
7 References
Alfonso, Gerardo, and Juan R Gonzalez. 2020. “Bayesian Neural Networks Improve Methylation Age Estimates.” bioRxiv.
Bohlin, Jon, Siri Eldevik Håberg, Per Magnus, Sarah E Reese, Håkon K Gjessing, Maria Christine Magnus, Christine Louise Parr, CM Page, Stephanie J London, and Wenche Nystad. 2016. “Prediction of Gestational Age Based on Genome-Wide Differentially Methylated Regions.” Genome Biology 17 (1): 207.
Chen, Wei, Ting Wang, Maria Pino-Yanes, Erick Forno, Liming Liang, Qi Yan, Donglei Hu, et al. 2017. “An Epigenome-Wide Association Study of Total Serum Ige in Hispanic Children.” Journal of Allergy and Clinical Immunology 140 (2): 571–77.
Guintivano, Jerry, Martin J Aryee, and Zachary A Kaminsky. 2013. “A Cell Epigenotype Specific Model for the Correction of Brain Cellular Heterogeneity Bias and Its Application to Age, Brain Region and Major Depression.” Epigenetics 8 (3): 290–302.
Haftorn, Kristine L, Yunsung Lee, William RP Denault, Christian M Page, Haakon E Nustad, Robert Lyle, Håkon K Gjessing, et al. 2021. “An Epic Predictor of Gestational Age and Its Application to Newborns Conceived by Assisted Reproductive Technologies.” Clinical Epigenetics 13 (1): 1–13.
Hannum, Gregory, Justin Guinney, Ling Zhao, Li Zhang, Guy Hughes, SriniVas Sadda, Brandy Klotzle, et al. 2013. “Genome-Wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates.” Molecular Cell 49 (2): 359–67.
Horvath, Steve. 2013. “DNA Methylation Age of Human Tissues and Cell Types.” Genome Biology 14 (10): 3156.
Horvath, Steve, Junko Oshima, George M Martin, Ake T Lu, Austin Quach, Howard Cohen, Sarah Felton, et al. 2018. “Epigenetic Clock for Skin and Blood Cells Applied to Hutchinson Gilford Progeria Syndrome and Ex Vivo Studies.” Aging (Albany NY) 10 (7): 1758.
Knight, Anna K, Jeffrey M Craig, Christiane Theda, Marie Bækvad-Hansen, Jonas Bybjerg-Grauholm, Christine S Hansen, Mads V Hollegaard, et al. 2016. “An Epigenetic Clock for Gestational Age at Birth Based on Blood Methylation Data.” Genome Biology 17 (1): 206.
Lee, Yunsung, Sanaa Choufani, Rosanna Weksberg, Samantha L Wilson, Victor Yuan, Amber Burt, Carmen Marsit, et al. 2019. “Placental Epigenetic Clocks: Estimating Gestational Age Using Placental Dna Methylation Levels.” Aging (Albany NY) 11 (12): 4238.
Levine, Morgan E, Ake T Lu, Austin Quach, Brian H Chen, Themistocles L Assimes, Stefania Bandinelli, Lifang Hou, et al. 2018. “An Epigenetic Biomarker of Aging for Lifespan and Healthspan.” Aging (Albany NY) 10 (4): 573.
Lu, Ake T, Anne Seeboth, Pei-Chien Tsai, Dianjianyi Sun, Austin Quach, Alex P Reiner, Charles Kooperberg, et al. 2019. “DNA Methylation-Based Estimator of Telomere Length.” Aging (Albany NY) 11 (16): 5895.
Mayne, Benjamin T, Shalem Y Leemaqz, Alicia K Smith, James Breen, Claire T Roberts, and Tina Bianco-Miotto. 2017. “Accelerated Placental Aging in Early Onset Preeclampsia Pregnancies Identified by Dna Methylation.” Epigenomics 9 (3): 279–89.
McEwen, Lisa M, Kieran J O?Donnell, Megan G McGill, Rachel D Edgar, Meaghan J Jones, Julia L MacIsaac, David Tse Shen Lin, et al. 2019. “The Pedbe Clock Accurately Estimates Dna Methylation Age in Pediatric Buccal Cells.” Proceedings of the National Academy of Sciences, 201820843.
Min, JL, G Hemani, G Davey Smith, C Relton, M Suderman, and John Hancock. 2018. “Meffil: Efficient Normalization and Analysis of Very Large Dna Methylation Datasets.” Bioinformatics.
Reinius, Lovisa E, Nathalie Acevedo, Maaike Joerink, Göran Pershagen, Sven-Erik Dahlén, Dario Greco, Cilla Söderhäll, Annika Scheynius, and Juha Kere. 2012. “Differential Dna Methylation in Purified Human Blood Cells: Implications for Cell Lineage and Studies on Disease Susceptibility.” PloS One 7 (7): e41361.
Teschendorff, Andrew E, Francesco Marabita, Matthias Lechner, Thomas Bartlett, Jesper Tegner, David Gomez-Cabrero, and Stephan Beck. 2012. “A Beta-Mixture Quantile Normalization Method for Correcting Probe Design Bias in Illumina Infinium 450 K Dna Methylation Data.” Bioinformatics 29 (2): 189–96.
Wang, Ting, Weihua Guan, Jerome Lin, Nadia Boutaoui, Glorisa Canino, Jianhua Luo, Juan Carlos Celedón, and Wei Chen. 2015. “A Systematic Study of Normalization Methods for Infinium 450K Methylation Data Using Whole-Genome Bisulfite Sequencing Data.” Epigenetics 10 (7): 662–69.
Wu, Xiaohui, Weidan Chen, Fangqin Lin, Qingsheng Huang, Jiayong Zhong, Huan Gao, Yanyan Song, and Huiying Liang. 2019. “DNA Methylation Profile Is a Quantitative Measure of Biological Aging in Children.” Aging (Albany NY) 11 (22): 10031.
Zhang, Qian, Costanza L Vallerga, Rosie M Walker, Tian Lin, Anjali K Henders, Grant W Montgomery, Ji He, et al. 2019. “Improved Precision of Epigenetic Clock Estimates Across Tissues and Its Implication for Biological Ageing.” Genome Medicine 11 (1): 1–11.
Appendix
utils::sessionInfo()