diff --git a/ehr/resources/pipeline/kinship/populateKinship.r b/ehr/resources/pipeline/kinship/populateKinship.r
index abffbd120..ef08381da 100644
--- a/ehr/resources/pipeline/kinship/populateKinship.r
+++ b/ehr/resources/pipeline/kinship/populateKinship.r
@@ -1,5 +1,5 @@
 ##
-#  Copyright (c) 2013-2019 LabKey Corporation
+#  Copyright (c) 2012-2017 LabKey Corporation
 #
 #  Licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0
 ##
@@ -15,7 +15,11 @@ library(methods);
 library(kinship2);
 library(getopt);
 library(Matrix);
-library(dplyr);
+library(dataCompareR);
+library(Rlabkey);
+library(lme4);
+library(parallel);
+
 
 spec <- matrix(c(
 #'containerPath', '-c', 1, "character",
@@ -36,38 +40,197 @@ allPed$Species <- as.character(allPed$Species)
 allPed$Species[is.na(allPed$Species)] <- c('Unknown')
 allPed$Species <- as.factor(allPed$Species)
 
+##### commented out after checking data
+# duplicatedId=allPed$Id[duplicated(allPed$Id)]
+# allPed[allPed$Id%in%duplicatedId,]# Check for duplication: passed
+# Id.Dam=unique(allPed$Dam)
+# allPed[(allPed$Id%in%Id.Dam)&(allPed$Gender!=2),]#Check for Dam that is not female
+# Id.Sire=unique(allPed$Sire)
+# allPed[(allPed$Id%in%Id.Sire)&(allPed$Gender!=1),]#Check for Sire that is not male:1 not passed
+
+# this function adds missing parents to the pedigree
+# it is similar to add.Inds from kinship; however, we retain gender
+addMissing <- function(ped, species){
+    if(ncol(ped)<4)stop("pedigree should have at least 4 columns")
+    head <- names(ped)
+
+    nsires <- match(ped[,3],ped[,1])# [Quoc] change ped,2 to ped,3
+    nsires <- as.character(unique(ped[is.na(nsires),3]))
+    nsires <- nsires[!is.na(nsires)]
+    if(length(nsires)){
+        ped <- rbind(ped, data.frame(Id=nsires, Dam=rep(NA, length(nsires)), Sire=rep(NA, length(nsires)), Gender=rep(1, length(nsires)), Species=rep(species, length(nsires))));
+    }
+
+    ndams <- match(ped[,2],ped[,1])# [Quoc] change ped,3 to ped,2
+    ndams <- as.character(unique(ped[is.na(ndams),2]))
+    ndams <- ndams[!is.na(ndams)];
+
+    if(length(ndams)){
+        ped <- rbind(ped,data.frame(Id=ndams, Dam=rep(NA, length(ndams)), Sire=rep(NA, length(ndams)), Gender=rep(2, length(ndams)), Species=rep(species, length(ndams))));
+    }
+
+    names(ped) <- head
+    return(ped)
+}
+
+print(paste0('Getting previousKinship'))
+library(Rlabkey);
+labkey.url.base = "http://localhost:8080/labkey/"
+labkey.url.path = "/WNPRC/EHR"
+
+#labkey.setCurlOptions(ssl.verifypeer=FALSE, ssl.verifyhost=TRUE)
+#previousRecords=NULL
+previousKinship <- function(bool){
+    print('fetching previousKinship using quarterOfCores')
+    previousRecords <- labkey.selectRows (
+    baseUrl=labkey.url.base,
+    #to run directly in R, uncomment this line.  otherwise providing a containerPath is not necessary
+    folderPath=labkey.url.path,
+    schemaName="ehr",
+    queryName="kinship",
+    colSelect=c('Id', 'Id2','coefficient'),
+    showHidden = TRUE,
+    colNameOpt = 'fieldname',  #rname
+    #showHidden = FALSE
+    )
+}
+numCores <- detectCores()
+quarterOfCores <- numCores/2
+print(quarterOfCores)
+#mclapply(TRUE,previousKinship, mc.cores = quarterOfCores)
+
 # In order to reduce the max matrix size, calculate famids using makefamid, then analyze each group separately
 # It resizes the biggest matrix from 12000^2 to 8200^2 thus reduces the memory used by half
-newRecords=NULL
-for (species in unique(allPed$Species)){
+
+
+calculationKinship <- function(species){
+    newRecords=NULL
+    filename=NULL
+    previousRecords=NULL
+    compareResults=NULL
+    summaryResults=NULL
+    mismatchResults=NULL
+    #columnKeys=NULL
+
+
     print(paste0('processing species: ', species))
-    allRecordsForSpecies <- allPed[allPed$Species == species,]
+    recordsForSpecies <- allPed[allPed$Species == species,]
+    print(paste0('total subjects: ', nrow(recordsForSpecies)))
+
+    recordsForSpecies <- addMissing(recordsForSpecies, species)
+    gc()
+    print(paste0('total subjects after adding missing: ', nrow(recordsForSpecies)))
+
+    fami=makefamid(id=recordsForSpecies$Id,father.id=recordsForSpecies$Sire,mother.id=recordsForSpecies$Dam)
+    famid=unique(fami)
+    famid=famid[famid!=0]
+    gc()
+
+    print(paste0('total families: ', length(famid)))
+    for (fam.no in famid){
+        familytemp=recordsForSpecies[fami==fam.no,]
+       # print(paste0('family size: ',species, ' ' , nrow(familytemp)))
+
+        temp.kin=kinship(familytemp$Id,familytemp$Sire,familytemp$Dam)
+        rownames(temp.kin) <- colnames(temp.kin) #add rownames to make matrix symmetric, which is required downstream
+        sparse.kin=as(temp.kin,"dsTMatrix") #change kinship matrix to symmetric triplet sparse matrix
+
+        # convert to a sparse matrix usng S4 object from Matrix library
+        temp.tri=data.frame(Id=colnames(temp.kin)[sparse.kin@i+1],Id2=colnames(temp.kin)[sparse.kin@j+1],coefficient=sparse.kin@x,stringsAsFactors=FALSE)
+        newRecords=rbind(newRecords,temp.tri)
+    }
+
+    filename <- paste0("kinship_",species,".txt");
+
+    firstRun = TRUE
+    print(paste("value of file.exists",file.exists(filename), filename))
+
+
+    if (file.exists(filename)){
+        print(paste0("opening previouskinship for ", species))
+        previousRecords <- read.table(filename, header = TRUE, sep="\t", colClasses = c("character","character", "numeric"))
+        #colnames(previousRecords)<c('Id', 'Id2', 'coefficient')
+        print(paste("removing duplicates",species,"previous", nrow(previousRecords), "newRecords",nrow(newRecords)))
+        #trimpreviousRecords <- subset(previousRecords,previousRecords$Id==previousRecords$Id2)
+        #removing coefficients for animals with same IDs
+        trimpreviousRecords <-previousRecords[(previousRecords$Id!=previousRecords$Id2),]
+        trimnewRecords <- newRecords[(newRecords$Id!=newRecords$Id2),]
+        #trimnewRecords <- subset(newRecords,newRecords$Id==newRecords$Id2)
+        #print number of records after trim
+        print(paste("after removal",species,"previous", nrow(trimpreviousRecords), "newRecords",nrow(trimnewRecords)))
+
+        compareResults <- rCompare(trimpreviousRecords,trimnewRecords,keys = c("Id","Id2"),roundDigits = 7,mismatches = NA,trimChars = TRUE);
+
+        summaryResults <- summary(compareResults)
+        print(summaryResults)
+        print(summaryResults$nrowSomeUnequal)
+
+        print(paste0('Generate mismatches for: ',species))
+        mismatchResults <- generateMismatchData(compareResults,trimpreviousRecords,trimnewRecords)
+        #print(mismatchResults)
+
+        print(paste0("nrowInAOnly ", summaryResults$nrowInAOnly, "nrowSomeUnequal ", summaryResults$nrowSomeUnequal))
+        oldFile <- paste0("previous_",filename)
+        file.rename(filename,oldFile)
+        firstRun = FALSE
+
+    }
+    print(paste("number of new records", is.null(newRecords)))
+
+    if ((firstRun || is.null(mismatchResults)) && !is.null(newRecords)){
+        print("Output table:")
+        print(str(newRecords))
+        print(filename)
+        write.table(newRecords, file = filename, append = FALSE,row.names=F,quote=F,sep="\t")
+    }
+    else if (!is.null(mismatchResults) ){
+        #&& (summaryResults$nrowInAOnly>0 && summaryResults$nrowSomeUnequal>0)
+        print("print comparison")
+        detailMismatches <- colsWithUnequalValues(COEFFICIENT,mismatchResults)
+        #print(summaryResults$colsWithUnequalValues)
+        mismatchFile <- paste0("mismatchesKinship",species,".txt")
+        saveReport(compareResults,reportName = mismatchFile, HTMLReport = FALSE)
+        write.table(detailMismatches, file = 'mismatches', append = FALSE,row.names=F,quote=F,sep="\t")
+        #write.table(mismatchResults, file = mismatchFile, append = FALSE, row.name=F,quote=F,sep="\t")
+    }
+
 
-    # Add missing parents for accurate kinship calculations
-    fixedRecords <- with(allRecordsForSpecies, fixParents(id = Id, dadid = Sire, momid = Dam, sex = Gender))
+}
+system.time({
+    #lapply(unique(allPed$Species), calculationKinship)
+    mclapply(unique(allPed$Species), calculationKinship, mc.cores = quarterOfCores)
+})
 
-    # Kinship is expecting records to be sorted IAW it's own pedigree function
-    recordsForSpecies <- with(fixedRecords, pedigree(id=id,dadid=dadid,momid=momid,sex=sex,missid=0))
 
-    temp.kin=kinship(recordsForSpecies)
 
-    # Add rownames to make matrix symmetric, which is required downstream
-    rownames(temp.kin) <- colnames(temp.kin)
 
-    # Convert kinship matrix to a triplet list of two ids and their coefficient
-    summaryDf = as.data.frame(summary(as(temp.kin, "dgCMatrix")))
-    idList <- rownames(temp.kin)
-    temp.tri = data.frame(Id=idList[summaryDf$i], Id2=idList[summaryDf$j], coefficient=summaryDf$x)
+#summary(previousRecords)
+#See size of previousKinship to determine to add initial ran.
+
+#columnKeys <- c('Id','Id2')
+#compareResults <- rCompare(previousRecords,newRecords,keys = columnKeys,roundDigits = NA,mismatches = NA,trimChars = FALSE);
+#summaryResults <- summary(compareResults)
+#print(summaryResults)
+#print(summaryResults('nrowSomeUnequal'))
+print(paste0('nrowCommon'))
+#print(summaryResults$nrowCommon)
+#print(summaryResults$nrowInAOnly)
+#print(summaryResults$nrowInBOnly)
+#print(summaryResults$datasetSummary)
+#print(paste0('Number of rows with same results: ',nrowSomeUnequal(summaryResults)))
+#print(paste0('Generate mismatches'))
+#mismatchResults <- generateMismatchData(compareResults,previousKinship,newRecords)
+#print(mismatchResults)
 
-    # Now filter out parents added for kinship calculation
-    temp.tri <- dplyr::filter(temp.tri, grepl("^(?!addin).*$", Id, perl = TRUE))
-    temp.tri <- dplyr::filter(temp.tri, grepl("^(?!addin).*$", Id2, perl = TRUE))
 
-    newRecords=rbind(newRecords,temp.tri)
-    print(paste0('total subjects: ', nrow(allRecordsForSpecies)))
-}
 
 # write TSV to disk
 print("Output table:");
-print(str(newRecords));
-write.table(newRecords, file = "kinship.txt", append = FALSE,row.names=F,quote=F,sep="\t");
\ No newline at end of file
+#print(str(newRecords));
+#print('value of mismatches ',is.null(mismatchResults))
+#if (is.null(mismatchResults)){
+#write.table(newRecords, file = "kinship.txt", append = FALSE,row.names=F,quote=F,sep="\t")
+#}
+#if(!is.null(mismatchResults)){
+#write.table('null', file = "kinship.txt", append = FALSE, row.name=F,quote=F,sep="\t")
+#}
\ No newline at end of file