BACKGROUND
alzheimer’s disease  is a neurodegenerative disease and the most common form of dementia.
although its etiology is not completely understood, a genetic component of susceptibility to ad has been shown in the literature .
cerebrospinal fluid  studies  have also been conducted in ad to identify differential biomarkers.
given that one hallmark of ad pathology is a cerebral accumulation of amyloid-β 1– <dig> peptide  in amyloid plaques, the aβ1– <dig> level has been shown markedly reduced in csf.
in addition, the total tau  protein level has been shown significantly elevated in the csf of ad patients.
as a result, the csf t-tau/aβ1– <dig> ratio has also been studied as a biomarker for differentiating ad from normal older adults .
with the recent advances in high-throughput genotyping technologies, genome-wide association studies  have been applied to the analysis of csf biomarkers  to identify relevant genetic markers, such as single nucleotide polymorphisms .
while most studies examined genetic associations with csf biomarkers at the individual snp or gene level, mining higher level genetic associations using biological interaction networks is still an under-explored topic for the csf biomarker studies in ad.
recently, many studies in other domains have demonstrated that integrative analyses of gwas data and protein-protein interaction  networks can provide valuable biological insights.
some methods have been proposed to identify subnetworks enriched by gwas results .
one tool is ipinbpa , which is based on the fact that the genes identified in gwas are more likely to physically interact as well as to belong to the same or related pathways.
with these observations, in this work, we performed a genome-wide network-based pathway analysis for csf studies in ad.
we analyzed an ad cohort from alzheimer’s disease neuroimaging initiative , used the csf biomarker t-tau/aβ1– <dig> ratio as the test phenotype or quantitative trait , downloaded the ppi network from the human protein reference database  , and applied the ipinbpa analysis to the gwas findings of the csf t-tau/aβ1– <dig> ratio.
our goal was to search for subnetworks or network modules enriched by the csf gwas findings, which may offer valuable biological insights and suggest high confidence candidates for subsequent analyses.
methods
figure  <dig> illustrates the work-flow of this study.
the genotyping and csf data were downloaded from the alzheimer’s disease neuroimaging initiative  database.
gwas of the csf qt was performed using the plink software  <cit> .
this resulted in  <dig>  snps with associated p-values, which were then assigned to  <dig>  genes.
the gene assignment and gene-based p-values were calculated using the vegas <dig> software  <cit> .
the nominally significant genes  were mapped onto the hprd ppi network  <cit>  and analyzed using the ipinbpa method in order to identify the enriched subnetworks.
the enrichr pathway analysis tool  <cit>  was applied to functionally annotate the subnetwork.fig.
<dig> flow chart.
step1: gwas using plink was performed on  <dig> adni participants.
step2: vegas <dig> was used to obtain gene-level p-values, which were mapped onto the hprd ppi network.
step3: network-based analysis was performed by ipinbpa software  <dig> times, and  <dig> groups of subnetworks were obtained.
for the top subnetwork of each result, we computed a consensus module by intersecting this top subnetwork with the most similar subnetworks obtained in all the other nine results.
step4: kegg pathway enrichment analysis was performed for each consensus module by enrichr tool.
subjects
data used in the preparation of this article were obtained from the alzheimer’s disease neuroimaging initiative  database .
one goal of adni has been to test whether serial magnetic resonance imaging , positron emission tomography , other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment  and early ad.
for up-to-date information, see http://adni.loni.usc.edu/.
appropriate institutional review boards approval occurred at each adni site and informed consent was obtained from each participant or authorized representative.
in this study, our analyses were concentrated on  <dig> adni subjects whose genotyping data  and the baseline csf biomarker data including t-tau and aβ1– <dig> were both available.
this sample included  <dig> cognitively normal  subjects,  <dig> subjects with significant memory concern ,  <dig> subjects with early mild cognitive impairment ,  <dig> subjects with late mild cognitive impairment , and  <dig> subjects with alzheimer’s disease .
table  <dig> shows the demographic and clinical characteristics of these participants at the baseline, where the characteristics were analyzed with the statistical software ibm spss  <cit>  version  <dig> for differences across diagnostic groups using one-way analysis of variance  or chi-square test.table  <dig> selected demographic and clinical characteristics of 843adni participants
logical memory immediate
logical memory delayed
t-tau/aβ1-42ratio
ad alzheimer’s disease, adni alzheimer’s disease neuroimaging initiative, cdr–sob clinical dementia rating–sum of boxes, cn cognitively normal, smc significant memory concern, emci early mild cognitive impairment, lmci late mild cognitive impairment.
number  or mean  was shown in each entry.
p-values were assessed due to significant differences between diagnosis groups, which computed using one-way anova
csf biomarker measurement as quantitative trait
the amyloid-β 1– <dig> peptide  and total tau  measured in the baseline csf samples of the participants were downloaded from the adni database.
the t-tau/aβ1– <dig> ratio was computed and used as the quantitative trait in the gwas.
genotyping data and quality control
the genotyping data of the participants were collected using either the illumina  <dig>  m array  or the illumina omniquad array.
for the present analyses, single nucleotide polymorphism  markers that were present on both arrays were included  <cit> .
quality control  was performed on the adni participants using the plink v <dig>  software  <cit>  , following a similar procedure described in li et al.
<cit> .
briefly, snps not meeting any of the following criteria were excluded:  snps available on both  <dig>  m array and omniquad array,  call rate per snp ≥ 95%;  minor allele frequency ≥ 5% ; and  hardy-weinberg equilibrium test of p ≥ 10− <dig>  using control subjects only.
participants were excluded from the analysis if any of the following criteria were not satisfied:  call rate per subject ≥ 95% ,  sex check ,  identity check for related pairs .
population stratification analysis was performed using eigenstrat  <cit> , and confirmed using structure  <cit> .
it yielded  <dig> study participants who did not cluster with the remaining subjects and with the ceu hapmap samples who are primarily of european ancestry .
these  <dig> participants were excluded from the analysis.
after qc,  <dig>  snps and  <dig> individuals remained available for the subsequent gwas, network and pathway analyses.
snp-level and gene-level gwas analyses
for gwas, to examine the main effects, linear regression was implemented by plink to evaluate the association between each snp and the t-tau/aβ1– <dig> ratio.
an additive genetic model was tested with covariates, including age, gender, and diagnosis .
then, the snp-level p-values were obtained.
the vegas <dig> software  <cit>  was used to assign  <dig>  snps to  <dig>  autosomal genes according to positions on the ucsc genome browser , and to compute gene-level p values.
the software applies simulations from the multivariate normal distribution to employ information from a defined subset of markers within a gene as well as take into account linkage disequilibrium between the markers.
to save running time, we use a multi-stage approach to adaptively determine the number of simulations per gene:  we run  <dig> simulations for all the genes;  we run  <dig> simulations only for genes with stage  <dig> empirical p-values ≤  <dig> ;  we run  <dig> simulations only for genes with stage  <dig> empirical p-values ≤  <dig> .
we interpret an empirical p-value of  <dig> from  <dig> simulations as p < 10− <dig>  given  <dig>  genes included in this analysis, a bonferroni-corrected threshold is p <  <dig>  × 10− <dig> , which can be exceeded by the theoretically smallest empirical p-value shown above.
a manhattan plot was generated using r  to visualize the gene-level gwas results for our work.
network-level analysis
the human ppi data  were downloaded from the human protein reference database ; gene-level p-values obtained from the gwas of the csf t-tau/aβ1– <dig> ratio were mapped to the ppi network.
the integrative protein interaction network-based pathway analysis  software  <cit>  was used to integrate gwas findings with physical evidence of interaction at the protein level, and to detect new high-level associations  with the csf biomarker.
briefly, ipinbpa identifies enriched subnetworks using the following three steps.
in step  <dig>  using the gwas findings, the nominally significant genes  are treated as seed genes, and assigned with certain weights .
after that, a random walk with restart strategy is employed to smooth these weights over the entire network.
intuitively, the nodes in the network are weighted based on their connectivity to seed genes .
let nk be a node on the ppi network mapped with gene-level p-value pi.
let eij be the edge connecting ni and nj, and wij be the weight of eij.
all the wij’s form the adjacency matrix w. extending köhler’s approach  <cit> , ipinbpa weights the edge eij as follows: wij=1−pi+1−pj/ <dig>
in addition, it normalizes the adjacency matrix w by its columns.
after each step of random walk, a score vector is calculated as pt=1−rw·pt−1+rp <dig>
where p is the score after walking t steps, and r is the restart ratio.
in this work, we assign  <dig> to all the seed genes, and  <dig> to the rest.
upon the completion of the random walk after t steps, the vector p contains the node weights, which reflect the topological connections to the seed genes  <cit> .
in step  <dig>  given a network a containing k nodes, ipinbpa defines its score by combining the gene-level p-values with node weights described above, using the liptak-stouffer method.
specifically, the network score of a is defined, via weighted z transform test  <cit> , as follows:
za=∑i∈aptizi∑i∈apti <dig>
a random sampling of gene sets of size k∈ <cit>  for  <dig> times was applied in ipinbpa  <cit>  to determine the background distribution of the network score.
using this distribution, the adjusted network score of a is defined as:
sa=za−μkσk,
where za is the network score, and μ
k and σ
k are respectively the mean and the standard deviation of the background distribution of the network scores at size k.
in step  <dig>  a greedy algorithm was developed to search for modules with high network scores, i.e., those enriched in genes with low p-values and high weights.
details about the algorithm is available in wang et al.
<cit> .
in this work, the parameters were set in ipinbpa as follows: r =  <dig> , t =  <dig>  netscore >  <dig> , netsize ≥  <dig>  and maxnetsize ≤  <dig>  given the stochastic nature of the ipinbpa algorithm, we ran ipinbpa ten times, respectively by setting the random seed value from  <dig> to  <dig>  consequently, we obtained ten groups of enriched subnetworks  identified by ipinbpa.
below, we couple a consensus module  strategy with ipinbpa  to generate consensus findings from these analyses.
given two subnetworks m and n, we use dice’s coefficient dc to measure their similarity:
dcmn=2m∩nm+n.
in this work, we only focused on analyzing the top subnetwork  in each ipinbpa run.
let tn
a be the top subnetwork identified in run a ∈ { <dig>   <dig>  …, 10}.
we first find sn
b
, which is the most similar subnetwork to tn
a in run b ∈ { <dig>   <dig>  …, 10}\{a}.
clearly we have snbtna=argmaxsndctna,sn,
where sn is any subnetwork enriched in run b. after that, we define the consensus module  based on run a as follows:
cm
a = tn
a ∩ ).
namely, cm
a is the intersection of tn
a and its most similar subnetworks identified in all the other runs.
network module visualization and functional analysis
cytoscape  <dig>   <cit>  was used to visualize the example identified network modules.
the enrichr  <cit>  pathway analysis tool  and the kyoto encyclopedia of genes and genomes database   <cit>  was applied to functional analysis of the identified network modules.
heat map was plotted, using r  <dig> .
<dig> software, to demonstrate relations between consensus modules and relevant kegg pathways.
RESULTS
gwas and gene-level analysis
the demographic and clinical characteristics for the  <dig> adni subjects in this study are presented in table  <dig>  the summary statistics for all diagnostic groups  are given.
education level  was not significantly different across the five diagnostic groups; however, gender demonstrated a significant difference .
furthermore, as expected, age, apoe e <dig> status, clinical dementia rating–sum of boxes , mini mental status examination , logical memory immediate recall, and logical memory delayed recall exhibited significant differences across the five groups .
also as expected, the phenotype t-tau/aβ1– <dig> ratio significantly differed across the diagnostic groups .
the top snp in the gwas analysis was rs <dig> , which was previously reported by lars bertram et al.
<cit> .
the snp rs <dig> within the apoe gene on chromosome  <dig> was also significant with p =  <dig> e- <dig>  and was previously reported by soerensen et al.
<cit> .
similar to the results reported in our earlier paper  <cit> , the tomm <dig> snp rs <dig>
network search for cms
consensus modules  were identified by cm-ipinbpa network analysis strategy.
subnetwork search was conducted on the gwas findings using ipinbpa ten times by varying the seed value of random number generator, which ranged from  <dig> to  <dig>  table  <dig> summarizes the results of ten ipinbpa runs.
prkca and tp <dig> appeared to be the start nodes of the top subnetworks identified in multiple runs.
the prkca gene was previously reported as being associated with an altered amyloid precursor protein  secretion in fibroblasts from ad patients  <cit> .
culmsee et al.
demonstrated that tp <dig> was a novel gene as a biomarker of ad and was related to neurodegenerative processes  <cit> .table  <dig> results of  <dig> ipinbpa runs
srn:the seed value used for an ipinbpa run
# of subnetworks: the number of subnetworks identified in an ipinbpa run
tn size: the number of genes in the top subnetwork identified in an ipinbpa run
tn startnode: the start node of the top subnetwork
tn score: the score of the top subnetwork
table  <dig> shows the top subnetwork identified in each run, its most similar subnetworks in other runs coupled with the dice’s coefficient value, and the corresponding cm.
for example, in table  <dig>  the dice’s coefficient between tn <dig> and sn <dig>  is  <dig> .
thanks to the overlapping subnetworks, only four unique cms were identified .
these four cms are shown in fig.
<dig>  where the reddish nodes represent the known ad genes from the kegg ad pathway .
cm <dig>  shown in fig.
<dig> contains totally  <dig> genes, including kegg ad genes gsk3b, mapk <dig>  psen <dig>  calm <dig>  calm <dig> and casp <dig>  cm <dig>  shown in fig.
<dig> contains totally  <dig> genes, including kegg ad genes bace <dig>  gsk3b, mapk <dig>  psen <dig>  calm <dig>  calm <dig> and casp <dig>  cm <dig>  shown in fig.
<dig> contains  <dig> genes, including kegg ad genes gsk3b, calm <dig> and casp <dig>  cm <dig>  shown in fig.
<dig> contains  <dig> genes, including kegg ad genes bace <dig>  gsk3b, mapk <dig>  psen <dig>  calm <dig>  calm <dig> and casp <dig> table  <dig> the characteristics of the identified consensus modules in  <dig> ipinbpa runs
cm consensus module id, dc dice’s coefficient
fig.
<dig> consensus modules identified by  <dig> runs of ipinbpa.
a consensus module1; b consensus module 2;  consensus module 3;  consensus module  <dig>  the reddish color indicates genes belonging to the kegg alzheimer’s pathway.
the adjusted network scores  of these four modules are  <dig> ,  <dig> ,  <dig> , and  <dig> , respectively.
therefore, all the modules are significant
in addition, the intersection of the four cms was extracted and named as the common subnetwork.
the common subnetwork is shown in fig.
<dig>  and contains total  <dig> genes, including  <dig> kegg ad genes calm <dig>  casp <dig>  and gsk3b, and  <dig> other genes.
pathway analysis of consensus modules and the common subnetwork
to test the hypothesis that cms enriched by the gwas findings might be significantly overrepresented in ad and other relevant pathways, the enrichr method was performed for pathway analysis.
for the genes in each cm, a pathway enrichment analysis was conducted, and the nominally enriched pathways were identified with adjusted p-value ≤  <dig> .
then, these identified pathways of the four cms were plotted as a heat map shown in fig.
<dig> to summarize the relationships between the pathways and cms.
note that fig.
<dig> lists only pathways enriched by at least one cm.
table  <dig> shows top  <dig> pathway enrichments analysis of the common subnetwork.fig.
<dig> functional annotation of the four identified consensus modules  using kegg pathways.
the four consensus modules were treated as four gene sets, and went through pathway enrichment analysis based on the kegg pathway database.
the enrichment results at a nominal statistical threshold of adjusted p-value <  <dig>  are shown.
-log <dig> values are color-mapped and displayed in the heat map.
heat map blocks labeled with “x” reach the nominal significance level of adjusted p-value <  <dig> .
only top enrichment findings are included in the heat map, and so each row  has at least one “x” block
pathway: the name of kegg pathway
overlap: the number of overlapping genes compared with the number of input genes
p-value: p-value was computed using the fisher exact test
adjusted p-value: adjusted p-value was a corrected p-value to the fisher exact test
z-score: computed by assessing the deviation from the expected rank
combined score: computed by taking the log of the p-value from the fish exact test and multiplying that by the z-score of the deviation from the expected rank
genes: the overlapping genes between the input and the pathway
discussion
gene-level analysis
in the gwas analysis, the gene-level p-values were determined and shown in fig.
<dig>  the use of the csf t-tau/aβ1– <dig> ratio as a quantitative trait  in this study enabled us to examine the effect of genes previously known to be associated with the qt as well as to identify novel genes.
table  <dig> lists the top ten genes obtained by converting snps into gene-wise p-values.
given the bonferroni-corrected threshold of p <  <dig>  × 10− <dig> , we found five significant genes.
as expected, significant associations were identified between loci on chromosome  <dig> and the csf t-tau/aβ1– <dig> ratio .
among these genes, apolipoprotein c <dig>  encoded by the apoc <dig> gene is associated with amyloid β plaques; the apoe and tomm <dig>  genes code for proteins related to the clearance of aβ and mitochondrial functions  <cit> ; and the pvrl <dig> gene was previously reported as related to risk factors that contribute to ad pathogenesis  <cit> .
pdk <dig>  shows a trend towards the significance, and the overexpression of this gene may be related to cancer and diabetes <cit> .
additionally, ccl <dig> mrna is highly increased by aβ1– <dig> stimulation  <cit> .
the ccl <dig> gene was previously reported as related to the chemotaxis of pro-inflammatory cells to the inflamed location  <cit> .
the ptger <dig> gene was previously reported as being related to the inflammatory response  <cit> .
network search for cms and functional validation
although ipinbpa has been successfully applied in several previous studies , we observed that different subnetworks could be obtained by using different random seed values.
to overcome this limitation, we proposed to examine the consensus modules discovered by multiple ipinbpa analyses.
in other words, we focused on examining the shared subnetworks among multiple ipinbpa runs, which turned out to be more stable patterns.
a two-stage strategy was employed.
first, ten groups of subnetworks were generated by running ipinbpa ten times with varying random seed values ranging from  <dig> to  <dig>  after comparing these ten sets of results, we identified ten cms, one from each run .
as a result, there are four unique cms based on ten identified ones.
the genes in the cms might not show a direct statistical significance but could interact with some genes identified in our gwas.
these genes can demonstrate indirect association with the studied qt, and may potentially provide valuable biological interpretation.
for example, consensus module  <dig> contains the protein gamma-aminobutyric acid  a receptor  gene.
gabrb <dig> codes for the β <dig> subunit of gamma-aminobutyric acid a  receptors  <cit> .
the gabrb <dig> gene has been demonstrated to be involved in the thalamus structure and its interactive effects on intelligence  <cit> .
the gabrb <dig> gene had also been associated with many neuropsychological diseases, such as schizophrenia, major depression, bipolar disorder, and alzheimer’s disease  <cit> .
in this study, we hypothesize that trait prioritized cm with high replication might have strong functional associations with t-tau/aβ1– <dig> ratio.
we gathered these identified pathways for  <dig> cms to plot heatmap to explore and refine the relationships between pathways and cms .
in fig.
<dig>  it was observed that four pathways, including colorectal cancer, gliomas, renal cell carcinoma, and huntington’s disease, were commonly enriched in all the consensus modules.
the neurodegenerative symptoms of neuron death affect many diseases, including alzheimer’s, parkinson’s, and huntington’s diseases.
below we briefly discuss a few additional pathways identified in fig.
<dig>  in ad, focal adhesion complexes regulating neuronal viability can be used in treatment to adjust neuronal survival  <cit> .
the adherens junction has been demonstrated as maintaining blood–brain barrier integrity, and the adherens junction pathways is highly associated with neurodegenerative diseases  <cit> .
apoptosis is an important pathway in alzheimer’s disease that is associated with neuronal loss  <cit> .
the change in the mapk signaling pathway contributes to significant change in neurotropin  <cit> .
some cancer-related pathways were found, such as colorectal cancer, pancreatic cancer, prostate cancer, endometrial cancer, bladder cancer, and so on.
some prior studies have been performed to examine the relationship between cancer and neurologic disease  <cit> .
with these observations, the genes in the cms may provide valuable information to suggest novel molecular mechanisms for subsequent analyses.
compared with the standard ipinbpa method, cm-ipinbpa network analysis strategy for mining consensus models could offer more stable results.
common subnetwork and functional validation
the common subnetwork is the intersection of the four identified common modules, and consists of  <dig> genes .
among these genes, the gsk3b, sumo <dig>  akap <dig>  calm <dig> and dlg <dig> genes have been identified to be involved in tau phosphorylation, and over-phosphorylation of the tau protein can form the tangles in the brain of ad patients .
additionally, the casp <dig>  pik3r <dig>  ppa <dig>  parp <dig>  csnk2a <dig>  ngfr, and rhoa genes have been demonstrated to be involved in amyloid beta peptide production .
the bcl <dig>  cflar, smad <dig>  and hif1a genes have been identified to be associated with late-onset alzheimer's disease.
the common subnetwork also contains the following genes tp <dig>  ddx <dig>  ndn, mst1r, ccdc <dig>  nmt <dig>  rpa <dig>  akap <dig>  gab <dig>  ppm1a, sptbn <dig> and med <dig>  which interact with themselves and other genes.
these findings offer valuable biological insights and suggest promising candidates for subsequent analyses.fig.
<dig> the common subnetwork.
this subnetwork consists of only overlapping genes of all consensus modules.
the reddish color indicates genes belonging to the kegg alzheimer’s pathway
table  <dig> shows the top twenty pathways enriched by the common subnetwork.
some significant pathways were observed, such as glioma, apoptosis, huntington’s disease, renal cell carcinoma, melanoma, the erbb signaling pathway, focal adhesion, neurodegenerative diseases, and so on.
twelve genes  were involved in the top twenty pathways.
the gene pdgfrb has been confirmed as causative of primary familial brain calcifications   <cit> .
the gene pik3r <dig> has been shown to be involved in alzheimer’s disease  <cit> .
calm <dig> encodes for tau protein and regulates the subcellular localization and function of calmodulin in neurons  <cit> .
cflar suppresses death receptor-induced apoptosis and tcr activation, which induces cell death by inhibiting caspase- <dig> activation  <cit> .
rhoa was implicated in aβ neurotoxicity, and the activation generates cytoskeletal changes  <cit> .
ngfr ligands play an important role in preventing fundamental tau-related pathologic mechanisms in alzheimer’s disease  <cit> .
these pathways appear relevant given the results in prior studies.
for other genes identified in this pathway analysis, it warrants further investigation to demonstrate the role they play.
limitations
due to the limited number of subjects available to us, we were only able to perform a discovery study in this work.
when more data become available in the future, replication studies in independent cohorts are required to evaluate and validate the identified network modules in our study.
in addition, in this work, we reported the results using the default parameter setting provided by the software tool and according to lili et al.
<cit> , except the random seed value.
we ran ipinbpa multiple times by using different random seed values and then extracting the consensus patterns to stabilize the results.
as to other parameters, we also briefly tested each of those by varying its value.
for most of these parameters, we obtained very similar results.
the most sensitive parameter is the restart ratio used in the step called “random walk with restart” for prioritizing phenotype-associated genes.
it is excepted that different restart ratios will assign different weights to network nodes and subsequently produce different scores for network components.
the determination of the optimal restart ratio warrants a separate and focused investigation and is an interesting future direction.
CONCLUSIONS
network-based methods form a new generation of enrichment analysis strategy, and they can overcome the limitation of traditional enrichment analysis where only a fixed set of pre-defined pathways are examined.
in this study, a genome-wide network-based pathway analysis of the csf biomarker of the t-tau/aβ1– <dig> ratio was performed, using a sample of  <dig> subjects from the adni database.
to our knowledge, this is the first genome-wide network-based pathway study on the csf biomarker t-tau/aβ1– <dig> ratio in alzheimer’s disease.
due to the stochastic nature of the ipinbpa method, we employed a consensus module  strategy to run ipinbpa multiple times and aimed to identify cms from these different runs.
we identified  <dig> cms.
these cms contain not only genes from kegg ad pathways, including bace <dig>  gsk3b, mapk <dig>  psen <dig>  calm <dig>  calm <dig>  casp <dig>  and sk3b; but also interesting genes with relevant biological functions such as gabrb <dig>  mmp <dig>  cdk <dig>  and igfbp <dig>  in sum, besides confirming previous findings , this study has also suggested new susceptible genes, cms and pathways underlying alzheimer’s disease.
abbreviations
adalzheimer’s disease
adnialzheimer’s disease neuroimaging initiative
anovaanalysis of variance
aβ1–42amyloid-β 1– <dig> peptide
cdr-sobclinical dementia rating–sum of boxes
cmconsensus module
cm-ipinbpaconsensus module  strategy with ipinbpa
cmsconsensus modules
cncognitively normal
csfcerebrospinal fluid
dcdice’s coefficient
emciearly mild cognitive impairment
gnsgroups of enriched subnetworks
gwasgenome-wide association studies
hprdhuman protein reference database
ipinbpaintegrative protein interaction network-based pathway analysis
lmcilate mild cognitive impairment
mcimild cognitive impairmen
mmsemini mental status examination.
mrimagnetic resonance imaging
petpositron emission tomography
ppiprotein-protein interaction
qtquantitative trait
smcsignificant memory concern
snpssingle nucleotide polymorphisms
tntop subnetwork
t-tautotal tau

 alzheimer’s disease is a neurodegenerative disease and the most common form of dementia. although its etiology is not completely understood, a genetic component of susceptibility to ad has been shown in the literature. cerebrospinal fluid studies have also been conducted in ad to identify differential biomarkers. given that one hallmark of ad pathology is a cerebral accumulation of amyloid-β 1– peptide in amyloid plaques, the aβ1– level has been shown markedly reduced in csf. in addition, the total tau protein level has been shown significantly elevated in the csf of ad patients. as a result, the csf t-tau/aβ1– ratio has also been studied as a biomarker for differentiating ad from normal older adults. with the recent advances in high-throughput genotyping technologies, genome-wide association studies have been applied to the analysis of csf biomarkers to identify relevant genetic markers, such as single nucleotide polymorphisms. while most studies examined genetic associations with csf biomarkers at the individual snp or gene level, mining higher level genetic associations using biological interaction networks is still an under-explored topic for the csf biomarker studies in ad. recently, many studies in other domains have demonstrated that integrative analyses of gwas data and protein-protein interaction networks can provide valuable biological insights. some methods have been proposed to identify subnetworks enriched by gwas results. one tool is ipinbpa, which is based on the fact that the genes identified in gwas are more likely to physically interact as well as to belong to the same or related pathways. with these observations, in this work, we performed a genome-wide network-based pathway analysis for csf studies in ad. we analyzed an ad cohort from alzheimer’s disease neuroimaging initiative, used the csf biomarker t-tau/aβ1– ratio as the test phenotype or quantitative trait, downloaded the ppi network from the human protein reference database, and applied the ipinbpa analysis to the gwas findings of the csf t-tau/aβ1– ratio. our goal was to search for subnetworks or network modules enriched by the csf gwas findings, which may offer valuable biological insights and suggest high confidence candidates for subsequent analyses. methods figure  illustrates the work-flow of this study. the genotyping and csf data were downloaded from the alzheimer’s disease neuroimaging initiative database. gwas of the csf qt was performed using the plink software. this resulted in snps with associated p-values, which were then assigned to genes. the gene assignment and gene-based p-values were calculated using the vegas software. the nominally significant genes were mapped onto the hprd ppi network and analyzed using the ipinbpa method in order to identify the enriched subnetworks. the enrichr pathway analysis tool was applied to functionally annotate the subnetwork.fig. flow chart. step1: gwas using plink was performed on adni participants. step2: vegas was used to obtain gene-level p-values, which were mapped onto the hprd ppi network. step3: network-based analysis was performed by ipinbpa software times, and groups of subnetworks were obtained. for the top subnetwork of each result, we computed a consensus module by intersecting this top subnetwork with the most similar subnetworks obtained in all the other nine results. step4: kegg pathway enrichment analysis was performed for each consensus module by enrichr tool. subjects data used in the preparation of this article were obtained from the alzheimer’s disease neuroimaging initiative database. one goal of adni has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment and early ad. for up-to-date information, see http://adni.loni.usc.edu/. appropriate institutional review boards approval occurred at each adni site and informed consent was obtained from each participant or authorized representative. in this study, our analyses were concentrated on adni subjects whose genotyping data and the baseline csf biomarker data including t-tau and aβ1– were both available. this sample included cognitively normal subjects, subjects with significant memory concern, subjects with early mild cognitive impairment, subjects with late mild cognitive impairment, and subjects with alzheimer’s disease. table  shows the demographic and clinical characteristics of these participants at the baseline, where the characteristics were analyzed with the statistical software ibm spss version for differences across diagnostic groups using one-way analysis of variance or chi-square test.table selected demographic and clinical characteristics of 843adni participants logical memory immediate logical memory delayed t-tau/aβ1-42ratio ad alzheimer’s disease, adni alzheimer’s disease neuroimaging initiative, cdr–sob clinical dementia rating–sum of boxes, cn cognitively normal, smc significant memory concern, emci early mild cognitive impairment, lmci late mild cognitive impairment. number or mean was shown in each entry. p-values were assessed due to significant differences between diagnosis groups, which computed using one-way anova csf biomarker measurement as quantitative trait the amyloid-β 1– peptide and total tau measured in the baseline csf samples of the participants were downloaded from the adni database. the t-tau/aβ1– ratio was computed and used as the quantitative trait in the gwas. genotyping data and quality control the genotyping data of the participants were collected using either the illumina  m array or the illumina omniquad array. for the present analyses, single nucleotide polymorphism markers that were present on both arrays were included. quality control was performed on the adni participants using the plink v software, following a similar procedure described in li et al.. briefly, snps not meeting any of the following criteria were excluded: snps available on both  m array and omniquad array, call rate per snp ≥ 95%; minor allele frequency ≥ 5%; and hardy-weinberg equilibrium test of p ≥ 10− using control subjects only. participants were excluded from the analysis if any of the following criteria were not satisfied: call rate per subject ≥ 95%, sex check, identity check for related pairs. population stratification analysis was performed using eigenstrat, and confirmed using structure. it yielded study participants who did not cluster with the remaining subjects and with the ceu hapmap samples who are primarily of european ancestry. these participants were excluded from the analysis. after qc, snps and individuals remained available for the subsequent gwas, network and pathway analyses. snp-level and gene-level gwas analyses for gwas, to examine the main effects, linear regression was implemented by plink to evaluate the association between each snp and the t-tau/aβ1– ratio. an additive genetic model was tested with covariates, including age, gender, and diagnosis. then, the snp-level p-values were obtained. the vegas software was used to assign snps to autosomal genes according to positions on the ucsc genome browser, and to compute gene-level p values. the software applies simulations from the multivariate normal distribution to employ information from a defined subset of markers within a gene as well as take into account linkage disequilibrium between the markers. to save running time, we use a multi-stage approach to adaptively determine the number of simulations per gene: we run simulations for all the genes; we run simulations only for genes with stage empirical p-values ≤ ; we run simulations only for genes with stage empirical p-values ≤ . we interpret an empirical p-value of from simulations as p < 10− given genes included in this analysis, a bonferroni-corrected threshold is p <   × 10−, which can be exceeded by the theoretically smallest empirical p-value shown above. a manhattan plot was generated using r to visualize the gene-level gwas results for our work. network-level analysis the human ppi data were downloaded from the human protein reference database; gene-level p-values obtained from the gwas of the csf t-tau/aβ1– ratio were mapped to the ppi network. the integrative protein interaction network-based pathway analysis software was used to integrate gwas findings with physical evidence of interaction at the protein level, and to detect new high-level associations with the csf biomarker. briefly, ipinbpa identifies enriched subnetworks using the following three steps. in step using the gwas findings, the nominally significant genes are treated as seed genes, and assigned with certain weights. after that, a random walk with restart strategy is employed to smooth these weights over the entire network. intuitively, the nodes in the network are weighted based on their connectivity to seed genes. let nk be a node on the ppi network mapped with gene-level p-value pi. let eij be the edge connecting ni and nj, and wij be the weight of eij. all the wij’s form the adjacency matrix w. extending köhler’s approach, ipinbpa weights the edge eij as follows: wij=1−pi+1−pj/ in addition, it normalizes the adjacency matrix w by its columns. after each step of random walk, a score vector is calculated as pt=1−rw·pt−1+rp where p is the score after walking t steps, and r is the restart ratio. in this work, we assign to all the seed genes, and to the rest. upon the completion of the random walk after t steps, the vector p contains the node weights, which reflect the topological connections to the seed genes. in step given a network a containing k nodes, ipinbpa defines its score by combining the gene-level p-values with node weights described above, using the liptak-stouffer method. specifically, the network score of a is defined, via weighted z transform test, as follows: za=∑i∈aptizi∑i∈apti a random sampling of gene sets of size k∈ for times was applied in ipinbpa to determine the background distribution of the network score. using this distribution, the adjusted network score of a is defined as: sa=za−μkσk, where za is the network score, and μ k and σ k are respectively the mean and the standard deviation of the background distribution of the network scores at size k. in step a greedy algorithm was developed to search for modules with high network scores, i.e., those enriched in genes with low p-values and high weights. details about the algorithm is available in wang et al.. in this work, the parameters were set in ipinbpa as follows: r = , t =  netscore > , netsize ≥  and maxnetsize ≤  given the stochastic nature of the ipinbpa algorithm, we ran ipinbpa ten times, respectively by setting the random seed value from to consequently, we obtained ten groups of enriched subnetworks identified by ipinbpa. below, we couple a consensus module strategy with ipinbpa to generate consensus findings from these analyses. given two subnetworks m and n, we use dice’s coefficient dc to measure their similarity: dcmn=2m∩nm+n. in this work, we only focused on analyzing the top subnetwork in each ipinbpa run. let tn a be the top subnetwork identified in run a ∈ {    …, 10}. we first find sn b, which is the most similar subnetwork to tn a in run b ∈ {    …, 10}\{a}. clearly we have snbtna=argmaxsndctna,sn, where sn is any subnetwork enriched in run b. after that, we define the consensus module based on run a as follows: cm a = tn a ∩ ). namely, cm a is the intersection of tn a and its most similar subnetworks identified in all the other runs. network module visualization and functional analysis cytoscape was used to visualize the example identified network modules. the enrichr pathway analysis tool and the kyoto encyclopedia of genes and genomes database was applied to functional analysis of the identified network modules. heat map was plotted, using r. software, to demonstrate relations between consensus modules and relevant kegg pathways.  gwas and gene-level analysis the demographic and clinical characteristics for the adni subjects in this study are presented in table  the summary statistics for all diagnostic groups are given. education level was not significantly different across the five diagnostic groups; however, gender demonstrated a significant difference. furthermore, as expected, age, apoe e status, clinical dementia rating–sum of boxes, mini mental status examination, logical memory immediate recall, and logical memory delayed recall exhibited significant differences across the five groups. also as expected, the phenotype t-tau/aβ1– ratio significantly differed across the diagnostic groups. the top snp in the gwas analysis was rs, which was previously reported by lars bertram et al.. the snp rs within the apoe gene on chromosome was also significant with p =  e- and was previously reported by soerensen et al.. similar to the results reported in our earlier paper, the tomm snp rs network search for cms consensus modules were identified by cm-ipinbpa network analysis strategy. subnetwork search was conducted on the gwas findings using ipinbpa ten times by varying the seed value of random number generator, which ranged from to table  summarizes the results of ten ipinbpa runs. prkca and tp appeared to be the start nodes of the top subnetworks identified in multiple runs. the prkca gene was previously reported as being associated with an altered amyloid precursor protein secretion in fibroblasts from ad patients. culmsee et al. demonstrated that tp was a novel gene as a biomarker of ad and was related to neurodegenerative processes.table results of ipinbpa runs srn:the seed value used for an ipinbpa run # of subnetworks: the number of subnetworks identified in an ipinbpa run tn size: the number of genes in the top subnetwork identified in an ipinbpa run tn startnode: the start node of the top subnetwork tn score: the score of the top subnetwork table  shows the top subnetwork identified in each run, its most similar subnetworks in other runs coupled with the dice’s coefficient value, and the corresponding cm. for example, in table  the dice’s coefficient between tn and sn is. thanks to the overlapping subnetworks, only four unique cms were identified. these four cms are shown in fig.  where the reddish nodes represent the known ad genes from the kegg ad pathway. cm shown in fig.  contains totally genes, including kegg ad genes gsk3b, mapk psen calm calm and casp cm shown in fig.  contains totally genes, including kegg ad genes bace gsk3b, mapk psen calm calm and casp cm shown in fig.  contains genes, including kegg ad genes gsk3b, calm and casp cm shown in fig.  contains genes, including kegg ad genes bace gsk3b, mapk psen calm calm and casp table the characteristics of the identified consensus modules in ipinbpa runs cm consensus module id, dc dice’s coefficient fig. consensus modules identified by runs of ipinbpa. a consensus module1; b consensus module 2; consensus module 3; consensus module the reddish color indicates genes belonging to the kegg alzheimer’s pathway. the adjusted network scores of these four modules are,,, and, respectively. therefore, all the modules are significant in addition, the intersection of the four cms was extracted and named as the common subnetwork. the common subnetwork is shown in fig.  and contains total genes, including kegg ad genes calm casp and gsk3b, and other genes. pathway analysis of consensus modules and the common subnetwork to test the hypothesis that cms enriched by the gwas findings might be significantly overrepresented in ad and other relevant pathways, the enrichr method was performed for pathway analysis. for the genes in each cm, a pathway enrichment analysis was conducted, and the nominally enriched pathways were identified with adjusted p-value ≤ . then, these identified pathways of the four cms were plotted as a heat map shown in fig.  to summarize the relationships between the pathways and cms. note that fig.  lists only pathways enriched by at least one cm. table  shows top pathway enrichments analysis of the common subnetwork.fig. functional annotation of the four identified consensus modules using kegg pathways. the four consensus modules were treated as four gene sets, and went through pathway enrichment analysis based on the kegg pathway database. the enrichment results at a nominal statistical threshold of adjusted p-value <  are shown. -log values are color-mapped and displayed in the heat map. heat map blocks labeled with “x” reach the nominal significance level of adjusted p-value < . only top enrichment findings are included in the heat map, and so each row has at least one “x” block pathway: the name of kegg pathway overlap: the number of overlapping genes compared with the number of input genes p-value: p-value was computed using the fisher exact test adjusted p-value: adjusted p-value was a corrected p-value to the fisher exact test z-score: computed by assessing the deviation from the expected rank combined score: computed by taking the log of the p-value from the fish exact test and multiplying that by the z-score of the deviation from the expected rank genes: the overlapping genes between the input and the pathway discussion gene-level analysis in the gwas analysis, the gene-level p-values were determined and shown in fig.  the use of the csf t-tau/aβ1– ratio as a quantitative trait in this study enabled us to examine the effect of genes previously known to be associated with the qt as well as to identify novel genes. table  lists the top ten genes obtained by converting snps into gene-wise p-values. given the bonferroni-corrected threshold of p <   × 10−, we found five significant genes. as expected, significant associations were identified between loci on chromosome and the csf t-tau/aβ1– ratio. among these genes, apolipoprotein c encoded by the apoc gene is associated with amyloid β plaques; the apoe and tomm genes code for proteins related to the clearance of aβ and mitochondrial functions; and the pvrl gene was previously reported as related to risk factors that contribute to ad pathogenesis. pdk shows a trend towards the significance, and the overexpression of this gene may be related to cancer and diabetes. additionally, ccl mrna is highly increased by aβ1– stimulation. the ccl gene was previously reported as related to the chemotaxis of pro-inflammatory cells to the inflamed location. the ptger gene was previously reported as being related to the inflammatory response. network search for cms and functional validation although ipinbpa has been successfully applied in several previous studies, we observed that different subnetworks could be obtained by using different random seed values. to overcome this limitation, we proposed to examine the consensus modules discovered by multiple ipinbpa analyses. in other words, we focused on examining the shared subnetworks among multiple ipinbpa runs, which turned out to be more stable patterns. a two-stage strategy was employed. first, ten groups of subnetworks were generated by running ipinbpa ten times with varying random seed values ranging from to after comparing these ten sets of results, we identified ten cms, one from each run. as a result, there are four unique cms based on ten identified ones. the genes in the cms might not show a direct statistical significance but could interact with some genes identified in our gwas. these genes can demonstrate indirect association with the studied qt, and may potentially provide valuable biological interpretation. for example, consensus module contains the protein gamma-aminobutyric acid a receptor gene. gabrb codes for the β subunit of gamma-aminobutyric acid a receptors. the gabrb gene has been demonstrated to be involved in the thalamus structure and its interactive effects on intelligence. the gabrb gene had also been associated with many neuropsychological diseases, such as schizophrenia, major depression, bipolar disorder, and alzheimer’s disease. in this study, we hypothesize that trait prioritized cm with high replication might have strong functional associations with t-tau/aβ1– ratio. we gathered these identified pathways for cms to plot heatmap to explore and refine the relationships between pathways and cms. in fig.  it was observed that four pathways, including colorectal cancer, gliomas, renal cell carcinoma, and huntington’s disease, were commonly enriched in all the consensus modules. the neurodegenerative symptoms of neuron death affect many diseases, including alzheimer’s, parkinson’s, and huntington’s diseases. below we briefly discuss a few additional pathways identified in fig.  in ad, focal adhesion complexes regulating neuronal viability can be used in treatment to adjust neuronal survival. the adherens junction has been demonstrated as maintaining blood–brain barrier integrity, and the adherens junction pathways is highly associated with neurodegenerative diseases. apoptosis is an important pathway in alzheimer’s disease that is associated with neuronal loss. the change in the mapk signaling pathway contributes to significant change in neurotropin. some cancer-related pathways were found, such as colorectal cancer, pancreatic cancer, prostate cancer, endometrial cancer, bladder cancer, and so on. some prior studies have been performed to examine the relationship between cancer and neurologic disease. with these observations, the genes in the cms may provide valuable information to suggest novel molecular mechanisms for subsequent analyses. compared with the standard ipinbpa method, cm-ipinbpa network analysis strategy for mining consensus models could offer more stable results. common subnetwork and functional validation the common subnetwork is the intersection of the four identified common modules, and consists of genes. among these genes, the gsk3b, sumo akap calm and dlg genes have been identified to be involved in tau phosphorylation, and over-phosphorylation of the tau protein can form the tangles in the brain of ad patients. additionally, the casp pik3r ppa parp csnk2a ngfr, and rhoa genes have been demonstrated to be involved in amyloid beta peptide production. the bcl cflar, smad and hif1a genes have been identified to be associated with late-onset alzheimer's disease. the common subnetwork also contains the following genes tp ddx ndn, mst1r, ccdc nmt rpa akap gab ppm1a, sptbn and med which interact with themselves and other genes. these findings offer valuable biological insights and suggest promising candidates for subsequent analyses.fig. the common subnetwork. this subnetwork consists of only overlapping genes of all consensus modules. the reddish color indicates genes belonging to the kegg alzheimer’s pathway table  shows the top twenty pathways enriched by the common subnetwork. some significant pathways were observed, such as glioma, apoptosis, huntington’s disease, renal cell carcinoma, melanoma, the erbb signaling pathway, focal adhesion, neurodegenerative diseases, and so on. twelve genes were involved in the top twenty pathways. the gene pdgfrb has been confirmed as causative of primary familial brain calcifications. the gene pik3r has been shown to be involved in alzheimer’s disease. calm encodes for tau protein and regulates the subcellular localization and function of calmodulin in neurons. cflar suppresses death receptor-induced apoptosis and tcr activation, which induces cell death by inhibiting caspase- activation. rhoa was implicated in aβ neurotoxicity, and the activation generates cytoskeletal changes. ngfr ligands play an important role in preventing fundamental tau-related pathologic mechanisms in alzheimer’s disease. these pathways appear relevant given the results in prior studies. for other genes identified in this pathway analysis, it warrants further investigation to demonstrate the role they play. limitations due to the limited number of subjects available to us, we were only able to perform a discovery study in this work. when more data become available in the future, replication studies in independent cohorts are required to evaluate and validate the identified network modules in our study. in addition, in this work, we reported the results using the default parameter setting provided by the software tool and according to lili et al., except the random seed value. we ran ipinbpa multiple times by using different random seed values and then extracting the consensus patterns to stabilize the results. as to other parameters, we also briefly tested each of those by varying its value. for most of these parameters, we obtained very similar results. the most sensitive parameter is the restart ratio used in the step called “random walk with restart” for prioritizing phenotype-associated genes. it is excepted that different restart ratios will assign different weights to network nodes and subsequently produce different scores for network components. the determination of the optimal restart ratio warrants a separate and focused investigation and is an interesting future direction.  network-based methods form a new generation of enrichment analysis strategy, and they can overcome the limitation of traditional enrichment analysis where only a fixed set of pre-defined pathways are examined. in this study, a genome-wide network-based pathway analysis of the csf biomarker of the t-tau/aβ1– ratio was performed, using a sample of subjects from the adni database. to our knowledge, this is the first genome-wide network-based pathway study on the csf biomarker t-tau/aβ1– ratio in alzheimer’s disease. due to the stochastic nature of the ipinbpa method, we employed a consensus module strategy to run ipinbpa multiple times and aimed to identify cms from these different runs. we identified cms. these cms contain not only genes from kegg ad pathways, including bace gsk3b, mapk psen calm calm casp and sk3b; but also interesting genes with relevant biological functions such as gabrb mmp cdk and igfbp in sum, besides confirming previous findings, this study has also suggested new susceptible genes, cms and pathways underlying alzheimer’s disease. abbreviations adalzheimer’s disease adnialzheimer’s disease neuroimaging initiative anovaanalysis of variance aβ1–42amyloid-β 1– peptide cdr-sobclinical dementia rating–sum of boxes cmconsensus module cm-ipinbpaconsensus module strategy with ipinbpa cmsconsensus modules cncognitively normal csfcerebrospinal fluid dcdice’s coefficient emciearly mild cognitive impairment gnsgroups of enriched subnetworks gwasgenome-wide association studies hprdhuman protein reference database ipinbpaintegrative protein interaction network-based pathway analysis lmcilate mild cognitive impairment mcimild cognitive impairmen mmsemini mental status examination. mrimagnetic resonance imaging petpositron emission tomography ppiprotein-protein interaction qtquantitative trait smcsignificant memory concern snpssingle nucleotide polymorphisms tntop subnetwork t-tautotal tau 
this study demonstrated that integration of gene-level associations with cms could yield statistically significant findings to offer valuable biological insights and suggest high confidence candidates for subsequent analyses. four consensus modules were discovered by cm-ipinbpa, and were functionally annotated using the pathway analysis tool enrichr. the influence of genetic variation on these csf biomarkers has been investigated in candidate or genome-wide association studies. gene-level p-values were obtained by vegas genes with p-value ≤  were mapped on to a protein-protein interaction network. the intersection of four cms forms a common subnetwork of genes, including those related to tau phosphorylation, amyloid beta production, and ad. the genome-wide network analysis yielded enriched cms that share not only genes related to tau phosphorylation or amyloid beta production but also multiple genes enriching several kegg pathways such as alzheimer’s disease, colorectal cancer, gliomas, renal cell carcinoma, huntington’s disease, and others.
 the cerebrospinal fluid levels of total tau and aβ1– are potential early diagnostic markers for probable alzheimer’s disease. the influence of genetic variation on these csf biomarkers has been investigated in candidate or genome-wide association studies. however, the investigation of statistically modest associations in gwas in the context of biological networks is still an under-explored topic in ad studies. the main objective of this study is to gain further biological insights via the integration of statistical gene associations in ad with physical protein interaction networks.  the csf and genotyping data of study subjects from the alzheimer’s disease neuroimaging initiative were analyzed. plink was used to perform gwas on the t-tau/aβ1– ratio using quality controlled genotype data, including single nucleotide polymorphisms, with age, sex and diagnosis as covariates. gene-level p-values were obtained by vegas genes with p-value ≤  were mapped on to a protein-protein interaction network. we integrated a consensus model strategy into the ipinbpa network analysis framework, and named it as cm-ipinbpa. four consensus modules were discovered by cm-ipinbpa, and were functionally annotated using the pathway analysis tool enrichr. the intersection of four cms forms a common subnetwork of genes, including those related to tau phosphorylation, amyloid beta production, and ad.  this study coupled a consensus module strategy with the ipinbpa network analysis framework, and applied it to the gwas of csf t-tau/aβ1- ratio in an ad study. the genome-wide network analysis yielded enriched cms that share not only genes related to tau phosphorylation or amyloid beta production but also multiple genes enriching several kegg pathways such as alzheimer’s disease, colorectal cancer, gliomas, renal cell carcinoma, huntington’s disease, and others. this study demonstrated that integration of gene-level associations with cms could yield statistically significant findings to offer valuable biological insights and suggest high confidence candidates for subsequent analyses. keywords alzhermer’s diseasecsf biomarkert-tau/aβ1– rationetwork analysispathway analysisconsensus modulecm-ipinbpanational key scientific instrument and equipment development projects of china2012yq04014010li jin fundamental research funds for the central universitiesheucf160412liang hong the natural science foundation of heilongjiang provincef201241liang hong http://dx.doi.org/ /100000092u.s. national library of mediciner lm011360shen li http://dx.doi.org/ /100000049national institute on agingr ag19771p ag10133saykin andrew j. http://dx.doi.org/ /100000083directorate for computer and information science and engineeringiis-1117335shen li u.s. department of defensew81xwh-12-2-0012saykin andrew j. national institute of biomedical imaging and bioengineering r eb022574shen li issue-copyright-statement© the author 2017 