ST was low (Fst = 0.04362), indicating the existence of a weak genetic differentiation between them. In addition, grouping of farms according to their geographical locations was observed. This study presents for the first time a genome-based analysis on the genetic structure of the two indigenous Greek goat breeds and identifies markers that can be potentially exploited in future selective breeding programs for traceability purposes, targeted genetic improvement schemes and conservation strategies.Goats play an important role in the livestock sector in Greece. The national herd consists mainly of two indigenous breeds, the Eghoria and Skopelos. Here, we report the population structure and genomic profiles of these two native goat breeds using Illumina’s Goat SNP50 BeadChip. Moreover, we present a panel of candidate markers acquired using different genetic models for breed discrimination. Quality control on the initial dataset resulted in 48,841 SNPs kept for downstream analysis. Principal component and admixture analyses were applied to assess population structure. The rate of inbreeding within breed was evaluated based on the distribution of runs of homozygosity in the genome and respective coefficients, the genomic relationship matrix, the patterns of linkage disequilibrium, and the historic effective population size. Results showed that both breeds exhibit high levels of genetic diversity. Level of inbreeding between the two breeds estimated by the Wright’s fixation index F The Greek national flock of goats is the largest in the European Union (E.U.), counting approximately 5 million heads . These aRearing of goats in Greece, traces back to Mycenaean times ; the first description on cheese making was reported by Homer and 500 years later, goats are found on silver coins in different Greek islands . During Herds of the Eghoria breed are found all over Greece, from isolated islands to inaccessible mountainous areas. This is a dual purpose breed, whose milk production ranges from 100 to 170 kg/lactation, depending on the rearing type. However, the genetic structure of this breed remains largely unknown, while it demonstrates large phenotypic, productive and reproductive variations. In contrast, Skopelos is a highly homogenous breed (based on phenotypic and productive traits), reared mainly at Northern Sporades island complex (Skopelos and Alonnisos islands). This breed has been intensively studied through national programs for the conservation of endangered indigenous breeds and for performance recordings, and has thus reached a milk yield of 225–260 kg/lactation. Historically, Skopelos and Eghoria breeds are assumed genetically distinct. The isolated geographical origin of Skopelos breed played a major role in supporting this premise. Yet, crossings between Eghoria and Skopelos breeds are usual, in order to increase milk performances in Eghoria and adaptability in more disadvantaged regions in Skopelos breed.Unlike the cattle industry, where all Greek indigenous breeds are extinct or are in the verge of extinction due to the advent of more productive, high yielding animals (eg. Holstein-Friesian cattle), many indigenous breeds of small ruminants are still reared in Greece. However, during the last years, many goat herds are transforming, creating nuclei of crossbred animals (indigenous x imported purebreds). This diverse national herd has only been studied with few nuclear molecular markers, as single nucleotide polymorphisms (SNPs) , 5, amplth place in E.U. in total milk yield, after France, Czech Republic, Spain and Estonia, based on official data recorded for 2015 [http://www.goatadaptmap.org/) is a valuable initiative led by international scientific groups that offers genotyping data of 130 breeds reared worldwide, so that can be used to monitor gene flow, migration events and genomic admixture levels with local or cosmopolitan breeds [Despite having the largest goat population in the E.U., Greece ranks in the 5n breeds –16. SuchGenomic characterization and protection of breeds has been extensively discussed in the past as a means to design breeding programs and future conservation strategies –19. MoreIn the present study, we report a comprehensive genome-wide analysis of the population structure and genomic diversity of two Greek goat breeds, based on the genotypes generated by the Goat SNP50 BeadChip. Furthermore, we aim to identify genomic regions that could be used as potential markers to distinguish breeds and be further used in breed identification and conservation strategies.All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. In accordance to the 2010/63/EU guide and the adoption of the Law L276/33/20.10.2010 by the Greek Government (Law 106/vol A/30.04.2013), ethical approval is not required in our study. Blood sampling was conducted by veterinarians and/or under veterinarian supervision for routine veterinary care. All samples and data in our study were collected under the consent of the breeders.A total of 72 samples of two Greek domestic goat breeds (32 of Eghoria and 40 of Skopelos) were collected from 6 farms located in Central and Northern Greece. All animals were reared under semi-intensive conditions. Four milliliters of blood were collected from the jugular vein of each female goat in tubes containing EDTA as anticoagulant and stored in a freezer (-20°C) until further use in the laboratory. Breed discrimination was based on the available pedigree, morphology and productive trait records. Farm locations are indicated in a geographical map of Greece  which waDNA was extracted from blood samples using the Nucleospin Blood Quick Pure kit  according to the manufacturer’s protocol. DNA integrity was assessed by electrophoresis on a 0.8% TAE agarose gel. DNA concentration was quantified with the Qubit® dsDNA BR assay kit . All animals were genotyped using Illumina’s Goat SNP50 BeadChip containing 53,347 SNPs. DNA samples were dried down in 1 mM Tris-EDTA and 400 ng of each sample was sent to GeneSeek  for genotyping on an Illumina’s iScan platform. Imaging and data generation were performed with the Illumina’s iScan system.mind command in PLINK v1.90 [Genotypes were generated and pre-processed by the iScan system based on an automated genotype calling feature by Illumina’s GenomeStudio software v.1.9.4). During this step a preliminary quality control on individuals (exclusion or inclusion of samples) and basic statistics for each SNP are generated e.g. call rate, allele frequencies. Filtering of samples was performed based on the call rate for each sample  generated by Illumina’s GenomeStudio software, as well as using the—NK v1.90 . Chromos.4. DurinPLINK v1.90 was used to generate summary statistics and conduct exact tests for deviation from Hardy–Weinberg equilibrium (HWE). Quality control and SNP filtering were performed using PLINK v1.90  by filteAssessment of the genetic structure of individuals was performed in autosomal filtered SNPs using the ADMIXTURE v1.3 software , assuminhttps://doi.org/10.5061/dryad.v8g21pt. Selection of the 45 goat breeds was based on the transit of goats from their putative center of domestication to Europe, both from the Danubian corridor and the Mediterranean basin. Thus, data from 29 breeds from Europe, 3 breeds from Near East, 9 breeds from Northern Africa and 4 breeds from eastern Asia were selected for comparison purposes with Greek data. Quality control and SNP filtering were also performed using PLINK v1.90, by filtering out SNPs in autosomes with minor allele frequency (MAF) <1% and call rate <0.98. Population substructure among the 47 goat populations using ADMIXTURE software was conducted assuming a number of K from 2 to 50. Visualizations of ancestry coefficients estimated with ADMIXTURE software were performed using the BITE R package [Genetic structure of the two Greek goat breeds was also analyzed including data from 45 breeds reared worldwide, in order to assess whether admixture with cosmopolitan breeds has occurred in Greek breeds. Genotypes were obtained from the AdaptMap project  and are  package .m0 (no migration event) to m15, by grouping SNPs in windows of 200. To increase robustness of analysis, we applied three replicates per migration event. The percentage of fraction of variance explained for each m, the log-likelihood value for each m and the residuals from the fit of the model among individuals were used to determine and visualize the most predictive model (mbest). Then, nodes robustness was estimated for the mbest by running 100 bootstrap replicates using the Treemix_boostrap.sh script and plotted using the treemix.bootstrap R function, both implemented in BITE R package [To further investigate historical gene flow between the populations analyzed, we used Treemix software v.1.12 . Maximum package .IS  was calculated per animal using PLINK. Genomic inbreeding was also calculated following Van Raden [GRM. Estimates of inbreeding coefficients were calculated from autosomes, for each animal separately, using the GCTA program [ST value was calculated using ARLEQUIN software to test inbreeding levels and genetic distance between breeds.Observed (Ho) and expected (He) heterozygosities were calculated for each breed separately to measure the genetic diversity within breed using ARLEQUIN 3.5.2.2 software , by applan Raden ; computa program  startingRuns of homozygosity (ROH) were defined in all autosomes to assess inbreeding levels for all animals, in each breed separately, and categorized based on their length and chromosome using PLINK v1.90. ROHs were calculated with the ‘Runs of homozygosity’ function in PLINK software with adjusted parameters for the total length of ROHs (≥1Mb), the variants in the scanning window (n = 15), the number of heterozygous SNP allowed (n = 1) and the number of missing calls (n = 1) to estimate homozygosity. The percentage of ROHs per chromosome was calculated as proposed by Al-Mamun et al. , as follROH) was calculated for each animal from the sum of ROH lengths, divided by the total length of the autosomal genome (kb) covered by SNPs as proposed by McQuillan et al. [ROH), ii) <10 Mb (FROH <10Mb), iii) 10–20 Mb (FROH 10-20Mb) and iv) >20 Mb (FROH >20Mb). In all cases, coefficients were calculated separately for each animal and then averaged within breed.Genomic inbreeding coefficient based on ROHs , using the same classification reported by the AdaptMap project for comparison purposes . ROH freTo identify SNPs that are present inside ROHs the R package ‘detectRUNS’ was used . The gen2) curve was estimated by determining the nonlinear least squares fit line using the nls function in R. The r2 coefficient was used instead of D’ as a more reliable measurement in studies with small sample sizes and more useful in predicting the power of association mapping [Linkage disequilibrium (LD) was tested for each breed separately to examine recombination of linked SNPs using PLINK v1.90 with default parameters; SNPs included in this step spanned a distance from 0.001 to 1 Mb. The squared correlation coefficient (r mapping .Effective population size (Ne) was estimated separately for each breed, using SNeP v.1.1 . Ne estiIn order to identify regions putatively under selection and SNPs that can be used for the discrimination of breeds, three different approaches were carried out:fst -within command in PLINK v1.90, using the method introduced by Weir and Cockerham (W&C) [Calculation of Fst for each marker: Fst value for each SNP was calculated with the—am (W&C) . SNPs atam (W&C) .Identification of discriminatory SNPs was performed using the whole dataset of animals per breed, denoted as ‘TRES_all’.awk srand(n) function), denoted as ‘TRES_tt’.Identification of discriminatory SNPs was performed by randomly splitting the dataset into training and test populations  software . EvaluatConsensus SNPs among the three methods  were obtained and evaluated using GeneClass2 . SNPs peDiscriminatory SNPs obtained from all methods were annotated on the ARS1 assembly  using thhttps://zenodo.org/record/3073175#.XOPaAthRWHt).No sample was excluded from further analysis; mean call rate for the 72 samples was 0.974. Quality control of SNPs was performed by filtering out non-informative SNPs for minor allele frequency (MAF), call rate  and HWE. As such, 4,506 SNPs were excluded, resulting in 48,841 SNPs kept for downstream analysis . GenotypThe genetic structure among the two Greek breeds and the 45 selected breeds reared worldwide, assessed from PCA, revealed three major clusters; one enclosing European breeds, a second enclosing only breeds from Pakistan  and a third looser cluster consisting of breeds from Asia and Africa . Resultsm10 (m0) to 95.03% (m15). The model with the 10 migration edges was chosen as the most predictive model since it explains 93.52% of the variance, and all migration events thereafter (m11 to m15) did not account for much more variance  to a high-weighted edge that links Galla breed (GAL) from Kenya to Pyrenean breed (PYR). Eghoria breed was located in a node enclosing Carpatian (CRP), Jonica (JON), Girgentana (GGT) and Ciociara Grigia (CCG) breeds. This clade was found to be linked with a high-weighted edge originating from Italian breeds Aspromontana (ASP) and Sarda (SAR).Treemix analysis verified ADMIXTURE and PCA results, since no gene flow between Alpine and Saanen and Greek breeds is observed at m10 . Overallvariance . Moreovee events . The conPopulation substructure was also assessed exclusively in Greek goats, using both ADMIXTURE and PCA analysis in autosomal filtered SNPs in order to evaluate purebreds and detect possible crossbreds. Population structure was investigated assuming a number of K from 2 to 5. Cross validation error was the lowest for K = 3 (0.63661), indicating the most likely number of different sub-populations represented in the 72 samples . At K = PCA analysis in the 48,841 autosomal SNPs revealed the same clustering profile with ADMIXTURE analysis. The first two principal components (PC1 and PC2) explained 26.02% of the total genetic variation. In particular, three distinct clusters were formed, associated with region of origin . AnalysiAfter the exclusion of admixed animals, the new dataset consisted of 64 animals in total . From the 48,841 quality-filtered SNPs, the percentage of within-breed polymorphic loci was greater than 95% for both breeds . The numIS was calculated separately for each individual; both breeds presented positive mean FIS values  indicating more homozygotes in the studied population than expected (GRM and FROH) , indicating that the variance is low. Inbreeding coefficient based on all ROHs revealed higher levels of autozygosity in Eghoria (FROH = 0.0680) than in Skopelos breed (FROH = 0.0287). Among the different FROH bins, FROH >20Mb expressed the highest values for both breeds. Moreover, at small lengths, where very short and common ROHs are located due to LD [ROH <10Mb presented very low values in both breeds. Overall, differences on levels of inbreeding reflected by FIS, FROH and FGRM were quite modest; similar levels of inbreeding were found between the two breeds. Genetic differentiation of breeds and the level of their relatedness, indicated by pairwise FST values was low (0.04362).Different estimates of inbreeding coefficients were calculated from the SNP genotyping data. Wright’s inbreeding coefficient Fexpected . Lower ind FROH) . The meaue to LD , FROH <1According to the parameters used, 765 ROHs were found in total; Eghoria had a slightly larger number of ROHs (n = 389) than Skopelos (n = 376) with an average of 12.16 and 11.75 ROHs per animal, respectively, including those that did not present any ROH . The lonROHs frequencies were further classified according to their size and breed. For both breeds, the frequency of short ROHs was higher compared to ROHs of greater size . In geneChromosome 6 had the largest number of ROHs (n = 48), followed by chromosome 10 (n = 47) and chromosome 1 (n = 46). Overall, the total number of ROHs per chromosome decreased with decreasing chromosome length , except 2 in the 48,841 autosomal SNPs, for each breed separately. The pattern of LD measured by r2 was quite similar between breeds. The most rapid decline for both breeds was observed over the first 0.1 Mbp. Eghoria breed showed somewhat higher rates of LD decay compared to Skopelos, which displayed slightly increased levels of LD  . DiscrimTRNAC-GCA (transfer RNA cysteine (anticodon GCA)) was encountered as the nearest gene of many discriminatory SNPs.The 151 discriminatory SNPs are located within or ±100kb of 381 genes. SNPs with the highest Fst value were snp30300-scaffold333-3618181, snp17056-scaffold178-6924 and snp7659-scaffold1277-264730, located on chromosomes 27, 8 and 4, respectively. The 151 identified SNPs, alongside with the detected genes in nearby regions are summarized in TRNAC-GCA was the most frequent neighboring gene of the 155 discriminatory SNPs.Identification of SNPs for breed assignment using the TRES_all approach and the algorithms implemented in TRES software led to 155 common SNPs among the three methodologies (similarity percentage 77.5%) . DiscrimThe independent identification of SNPs in the training population (TRES_tt methodology) resulted in 157 SNPs that corresponded to a similarity percentage of 78.5% among the three methodologies implemented in TRES software. SNPs were located in all autosomes; chromosomes 7, 6 and 5 presented the highest number of discriminatory SNPs with 19, 13 and 10 SNPs, respectively. Four-hundred six genes were identified in the nearby regions (within ±100kb) of the 157 SNPs . EvaluatCommon SNPs among the three methodologies were further evaluated in GeneClass2 software. An overlap of discriminatory SNPs per methodology was observed . In partBMP2 (Bone morphogenetic protein 2), PDGFRB (b-Type platelet-derived growth factor receptor) and ZAR1 (Zygote arrest 1). The 95 common SNPs were not evenly distributed across autosomes; chromosome 7 had the largest number of identified discriminatory SNPs (N = 12), followed by chromosome 21 (N = 8) and 5 (N = 8).Eighty-four out of the 95 common SNPs were found to be in ROH segments either for Eghoria or Skopelos breeds. The 95 common SNPs were located in or within ±100kb from 237 genes. Among them, some well-characterized genes were included such as ST values [ST index (FST = 0.04362) indicates that Greek goat breeds are genetically related based on the SNP dataset used; since values of this magnitude (i.e. close to 0) indicate low differentiation between breeds or that historical gene flow exists between breeds. This result is not in agreement with a previous study on the two autochthonous Greek breeds which generated an FST value of 0.10000; however, in their study only a small number of SNPs (n = 26) was analyzed [In this study the population structure, genetic diversity and inbreeding levels were examined in the two recognized Greek goat breeds, Eghoria and Skopelos, using Illumina’s Goat SNP50 BeadChip. Overall, the populations studied here possess high levels of genetic diversity according to the indices used. The use of Goat SNP50 BeadChip identified large numbers of polymorphic loci in both breeds, despite the fact that its design and initial validation was based on other breeds for different breeding purposes . Calculated heterozygosity values of Greek breeds  were in agreement with most published studies employing Goat SNP50 BeadChip. Compared to the average heterozygosity values of 43 European breeds reported by Colli et al.,  , Greek bT values . In our Analysis of Greek goat breeds using 45 breeds reared worldwide revealed that Eghoria and Skopelos cluster together with European breeds, away from African and Asian populations. The clustering of this continental subdivision, and in particular the grouping of Greek breeds with Italian  . This waPopulation structure analyses revealed that the most admixed population inhabits central Macedonia (northern Greece), which is a region featuring numerous goat farms with different production systems. ADMIXTURE and PCA analyses in farm 4 indicate that admixture between purebreds existed in the past, with exchanges of genetic material between breeds, despite the fact that their phenotypes pointed otherwise. The high levels of admixture in farm 4 in our study are possibly due to an unsupervised and random mating system between animals, which is usual in goat faming in Greece. The grouping of individuals according to the sampling locations evidenced in our results has previously been reported for several goat breeds, where gene flow was studied in their transit from Middle East to the Iberian Peninsula , 60.IS and FROH. The degree of inbreeding for the two breeds was quite similar in all inbreeding coefficients measured. Since FIS values did not deviate significantly from zero in both breeds, we cannot not claim that Greek breeds are a result of genetic subdivision (Wahlund effect). The same pattern in FIS values is observed in many cosmopolitan goat breeds reared worldwide such as Angora, Alpine, Saanen and Toggenburg [Runs of homozygosity in autosomal SNPs were determined for all samples in each breed, to assess whether regions in the genome are present, that are potentially under positive selection, therefore indicators of selective sweeps in the studied populations , 62, or ggenburg , 58, as ggenburg , Sudan [ggenburg  and Soutggenburg .ROH seems to be a more powerful approach for detecting inbreeding than any other alternative method, due to the advantage of not being influenced by estimates of allele frequency or incomplete pedigree data [ROH value, similar to breeds from East Africa , Turkey  and some European breeds from Italy (Argentata), Spain  and Romania (Carpatian) [ROH value for Eghoria compared to Skopelos breed was equivalent to autochthonous goat breeds from Europe  and Africa  [Since there is no consensus or standardized criteria to define a ROH within a species, we identified ROH segments in the goat genome, by applying the same parameters used by the AdaptMap project , 13; theree data –68. Estirpatian) , 13. The, Keffa) , 13.ROH compared to Skopelos breed. This has been also reported for the Rangeland breed reported by Brito et al. [According to our results, Greek breeds presented quite smaller number of ROHs per individual but a higher average ROH length compared to various breeds reared worldwide  or localo et al.  and can o et al. , 71. Proo et al. .CSN1S1 , CSN1S2 , CSN2 (beta casein) and CSN3 (kappa casein) [ABCG2 (ATP binding cassette subfamily G member 2) gene [BMPR1B (bone morphogenetic protein receptor) gene, which is known as a major gene for prolificacy in sheep [DGAT (Diacylglycerol O-Acyltransferase 1) gene is located, which has recently been found to affect milk fat content in dairy goats [ROH analysis revealed that many of the detected SNPs within ROHs mapped to genes with well-characterized functions. For both breeds, chromosome 6 demonstrated large homozygous regions, with SNPs within these ROHs mapping to genes affecting milk production traits such as the casein gene cluster containing  casein)  or the A 2) gene . Moreovein sheep , howeverin sheep –79. ROH ry goats .2 confirmed the results obtained from ROH analysis, although differences between breeds were quite modest. In particular, the slightly lower LD decay in Eghoria corroborated increased levels of homozygosity compared to Skopelos breed. Mean r2 values were lower than other goat breeds [2 of 0.3 to ensure successful whole genome association studies [Linkage disequilibrium analysis through rt breeds , 81, 82 t breeds –85. Howe studies  and cons studies , 87, we Despite the differences in their morphological and productive characteristics, Greek breeds seem to share a similar genetic background. Due to breeding practices (intense selection through participation in conservation programs), history and demography of Skopelos breed, we would expect increased levels of homozygosity compared to Eghoria breed. Yet, our results indicated that the two breeds demonstrate similar levels of genetic diversity. Furthermore, accepting 50–100 as a critical effective population size range for long-term viability , our resDiscrimination of goat breeds has been studied in the past using microsatellite markers ; howeverPopulation structure and breed assignment using W&C’s Fst and TRES approaches have been evaluated in goats , 69, 92 An important problem highlighted from the present study is the incorrect classification of purebreds despite their recorded pedigree and morphological characteristics, emphasizing the need for a more effective way to identify and record individuals. Preservation of indigenous purebreds is crucial since the irreversible loss of alleles could lead to decreased adaptability and survival rates especially taking into consideration the threat from the upcoming climatic change . Since gBMP2 is a well characterized gene, that has been associated in sheep with body size development [BMP2 has been identified as a putative gene involved in the formation and adaptation to the alpine environment of Swiss goat populations [MYOZ1 (Myozenin 1). This gene has been associated with different lipid traits in cattle [MYOZ2 and MYOZ3) have been studied against their role in meat quality [ZAR1 gene is identified in the nearby regions of discriminatory SNPs, in all methodologies applied. ZAR1 is a gene well known for its role on embryonic development and fertility control in mammals [TRNAC-GCA is a gene reported by many authors that is located around significant or discriminatory SNPs. Specifically, QTLs enclosing this gene have high effect in carcass weight [TRNAC-GCA has been related to affect residual feed intake in Angus breed [TRNAC-GCA has been frequently encountered in genomic regions showing evidence of positive selection for breeds of different purposes [Within ±100kb of the 95 common discriminatory SNPs many candidate genes were found. All the identified genes surrounding the 95 SNPs (237 genes), were extracted and used as a dataset to search against the KEGG database for metabolic pathways relevant to important productive traits (data not shown). Based on this analysis, only general/basic pathways, essential for life maintenance and organism homeostasis were detected e.g. propanoate metabolism, cell cycle, amino sugar and nucleotide sugar metabolism. Among the identified loci, elopment  and tailelopment . In goatulations  and as aulations . Anothern cattle  and withn cattle . In goat mammals . Yet, in mammals . TRNAC-Gs weight  and in ms weight  in Nelorus breed  and sperus breed . In goatpurposes .In this study, genetic diversity and population structure of autochthonous Greek goat breeds was assessed for the first time using the Goat SNP50 BeadChip. Our results provide a comprehensive characterization of goat genetic diversity, inbreeding levels and population structure in Greece, using ~60K SNPs. Our findings revealed that the two indigenous goat breeds maintain high levels of genetic diversity which can be further exploited in the design and implementation of breeding schemes. Eghoria presented slightly increased inbreeding levels, nevertheless, our results show that the two breeds are closely related. Moreover, first insights of Greek goat breed traceability are presented, hence, our data could be used for breed discrimination and protection of the authenticity of traditional local products. According to our results, the high levels of genetic diversity in Greek goat breeds could serve as a valuable genetic reservoir to be exploited in national breeding schemes.S1 FigSampling for the Eghoria breed was conducted from farm 1  = 15) located in Tsepelovo and farm 4  located in Thessaloniki. Sampling for the Skopelos breed was conducted from farm 2 , farm 3  and farm 6  located in Skopelos, farm 4  and farm 5  located in Alonnisos island.(TIFF)Click here for additional data file.S2 FigEach migration event was analyzed in triplicates.(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 FigEach farm is presented by a vertical bar. Different colors indicate different clustering groups. F1: farm 1; F2: farm 2; F3: farm 3; F4: farm 4; F5: farm 5; F6: farm 6.(TIFF)Click here for additional data file.S5 FigDifferent colors indicate the two breeds .(TIFF)Click here for additional data file.S6 FigEach bar represents the total number of ROHs distributed per chromosome (Number of ROH). The red line shows the average percentage of ROHs for each chromosome.(TIFF)Click here for additional data file.S7 FigAutosomes are alternately colored in red and blue. SNPs: Single nucleotide polymorphisms, ROHs: Runs of homozygosity.(TIFF)Click here for additional data file.S8 Fig(TIFF)Click here for additional data file.S9 Fig(TIFF)Click here for additional data file.S10 Fig(TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableGenes located within ±100kb of the identified SNP or nearby genes of the identified SNPs are presented in italics. Texts in bold indicate the 95 common SNPs identified among the three methods. CHR: chromosome, kb: kilo base pair; SNP: Single nucleotide polymorphism.(DOCX)Click here for additional data file.S5 TableGenes located within ±100kb of the identified SNP or nearby genes of the identified SNPs are presented in italics. Texts in bold indicate the 95 common SNPs identified among the three methods. CHR: chromosome, kb: kilo base pair; SNP: Single nucleotide polymorphism.(DOCX)Click here for additional data file.S6 TableGenes located within ±100kb of the identified SNP or nearby genes of the identified SNPs are presented in italics. Texts in bold indicate the 95 common SNPs identified among the three methods. CHR: chromosome, kb: kilo base pair; SNP: Single nucleotide polymorphism.(DOCX)Click here for additional data file.S7 TableA.P.: Assignment Probability, SNPs: Single nucleotide polymorphisms.(DOCX)Click here for additional data file.


P < 1.00 × 10−5) CpG-SNPs associations. Furthermore, the MWAS results were over-represented  among findings from three recent GWAS for MDD-related phenotypes. Overlapping loci included, e.g., ROBO2, ASIC2, and DCC. As the CpG-SNP analysis accounts for the number of alleles that creates CpGs, the methylation differences could not be explained by differences in allele frequencies. Thus, the results show that the MWAS and GWASs provide independent lines of evidence for the involvement of these loci in MDD. In conclusion, our methylation study of MDD contributes novel information about loci of relevance that complements previous findings and generates new hypothesis about MDD etiology, such as that the functional effects of genetic association may be partly mediated and/or enhanced by the methylation status in these loci.DNA methylation is an epigenetic modification that provides stability and diversity to the cellular phenotype. It is influenced by both genetic sequence variation and environmental factors, and can therefore potentially account for variation of heritable phenotypes and disorders. Therefore, methylome-wide association studies (MWAS) are promising complements to genome-wide association studies (GWAS) of genetic variants. Of particular interest are methylation sites (CpGs) that are created or destroyed by the alleles of single-nucleotide polymorphisms (SNPs), as these so-called CpG-SNPs may show variation in methylation levels on top of what can be explained by the sequence variation. Using sequencing-based data from 1132 major depressive disorder (MDD) cases and controls, we performed a MWAS of 970,414 common CpG-SNPs. The analysis identified 27 suggestively significant ( Because MDD has a lifetime prevalence of almost 15%5, tends to start early in life6, and is often chronic8, it is the leading contributor to disability worldwide10. In comparison with other (psychiatric) disorders, discerning the biological basis of MDD has been difficult. Only very recently, a number of genetic variants were identified and replicated12. However, these variants had small effect sizes and explained only a small proportion of the disease risk.Major depressive disorder (MDD) is a complex disorder that is characterized by persistent dysphoria and is often accompanied by considerable morbidity15. Therefore, methylome-wide association studies (MWAS), which test a genome-wide set of methylation sites for association with an outcome of interest, are promising complements to genome-wide association studies (GWAS) of genetic variants. Of particular interest are methylation sites (CpGs) that are created or destroyed by single-nucleotide polymorphisms (SNPs). These sites, commonly referred to as CpG-SNPs, may show variation in both methylation and sequence, and may therefore convey information beyond either of the two data types alone. However, methylation-dependent association signals in CpG-SNPs are not captured by GWAS and are very poorly captured by a regular MWAS. Therefore, a specific CpG-SNP analysis is needed to detect these signals.DNA methylation is an epigenetic modification that provides stability and diversity to the cellular phenotype. Because methylation is dynamic in nature and can be altered by environmental factors, it can potentially account for key clinical features of MDD such as its episodic nature or mediate the effects of environmental risk factors such as stressRegular GWAS studies detect differences in allele frequencies between cases and controls. In contrast, a CpG-SNP analysis tests whether groups of cases and controls with the same genotype show differences in methylation at these sites. Thus, these two analyses capture different signals. Similarly, while a regular MWAS detects differences in methylation it does not account for differences in genotype and will therefore often lack the statistical power to detect association signals for CpG-SNPs. A CpG-SNP MWAS remedies this by including information on the actual genotypes of each subject.19. In addition, in a high-density analysis of methylation quantitative trait locus (meQTL), CpG-SNPs were involved in the majority of all identified meQTLs20.Furthermore, the link between sequence variation and methylation levels at these sites may allow CpG-SNPs to function as important cis-regulatory polymorphisms that connect genetic variation to variation in methylation. For example, the alleles present and the methylation levels observed at a specific CpG-SNP have been associated with a variety of regulatory functions22, including close to 1 million CpG-SNPs. To further explore the MWAS findings and their potential relevance for MDD, we also tested for their overlap with results from recent GWASs.To study whether CpG-SNPs contribute to MDD disease risk, we used a sequencing-based approach that provides nearly complete coverage of all CpGs23. In addition, to a current MDD diagnosis, cases had a score >14 on the IDS-SR3024, a 30-item self-report measure of depression symptoms. Controls had no lifetime psychiatric disorders and an IDS-SR30 score <14. The sample selection was further based on good quality GWAS genotype information available from a previous investigation25 . For further details about NESDA, and demographic and clinical characteristics of participants used for the present study, see Table DNA from blood was obtained from 1200 individuals from the Netherlands Study of Depression and Anxiety (NESDA). MDD was diagnosed using the DSM-IV-based Composite International Diagnostic Interview (CIDI version 2.1) that was administered by specially trained research staff21. In short, we used ultrasonication to shear genomic DNA into, on average, 150 bp fragments followed by enrichment with MethylMiner™ (Invitrogen) to capture the methylated fraction of the genome. The captured fragments were eluted and used to create a barcoded sequencing library for each methylation capture. Labeled sequencing fragment libraries were pooled in equal molarities and sequenced on a NextSeq500 instrument (Illumina). To ensure consistency in the sample preparation, MethylMiner captures and library constructions were both performed using Biomek NxP robotics (Beckman Coulter). Samples were performed in a randomized order and all labtecnical procedures were performed blind to any phenotype information. The sequence reads were aligned to the human reference genome (hg19/GRCh37) using Bowtie226.We assayed the methylome using an optimized protocol for methyl-CG binding domain sequencing (MBD-Seq) that provides almost complete coverage of all CpGs in the genome27. Among these, 970,414 were common CpG-SNPs  that were used for MWAS. To identify the CpG-SNPs we used directly genotyped and imputed genotype information (Supplement) from the NESDA participants. The imputed SNPs were filtered by imputation R2 ≥ 0.9 and minor allele frequency ≥ 0.1 in cases and controls. Finally, an in silico experiment described elsewhere28 was used to remove CpG-SNPs in loci showing alignment problems.Quality control and data processing (Supplementary Note) were performed using our RaMWAS Bioconductor package, which is specifically designed for large-scale methylation studies. After rigorous quality control of samples, reads, and CpGs, 1132 subjects (320 controls and 812 cases) with an average of 48.7 million reads per sample  remained. For each of these individuals, our dataset included commonly methylated high-quality methylation information for 21,869,561 CpGs22. Second, we regressed out the demographic variables age and sex. Third, to avoid confounding due to cell-type heterogeneity, we regressed out blood cell type proportions as estimated by the methylation data29 using MBD-Seq “reference methylomes” we generated after isolating all common cell types in blood30. Fourth, principle component analysis was used to capture any remaining unmeasured source of variation. Specifically, using a scree test we selected the first principle component.To test for association between the methylation level at each CpG-SNP and MDD, we performed a regression analysis with four sets of covariates. First, we regressed out 19 assay-related variables  including the quantity of methylation-enriched DNA captured, sample batches, and peak location31Y are the CpG scores, b0 is the intercept of the regression line, b4…bk the effects of covariates, and E are the residual effects. The CpG-SNP is coded as 0, 1, and 2, which corresponds to having 0, 1, or 2 copies of the SNP allele that creates/destroys the CpG relative to the reference genome. MDD is coded 0 for controls and 1 for cases. Figure b2 and b3 parameters, when b1 is equal to 1. A non-zero value of parameter b1 indicates that the site is methylated with the amount of methylation being proportional to the number of CpGs . Parameter b2 estimates the case-control difference at the CpG-SNP site that is proportional to the number of CpGs . Parameter b3 captures case-control differences from nearby sites and thus do not depend on the number of CpG creating alleles of the SNP . MBD-Seq assays the methylation of regions that are about the size of the sequenced fragments (~150 bp). Therefore, part of the differences observed at the CpG-SNP may reflect the effects of nearby CpGs resulting in non-zero values b3. In the overall association  we tested the null-hypothesis, H0: b2 = b3 = 0.The MWAS was performed by fitting the following regression equation:P-values from the MDD CpG-SNP MWAS were corrected for the average permutation-obtained lambda.To test if the lambda observed for the MDD CpG-SNP MWAS was caused by associations to the outcome variable, or if it was caused by that the test statistic distribution did not follow the theoretical null we used permutations. Using exactly the same dataset, we performed MWAS for 100 permutations of the MDD outcome variable and recorded the lambdas. Next, the observed association 34 as implemented in the R Glmnet package. Elastic nets are akin to multiple regression analysis but suitable for our scenario where the number of predictors is much larger than the number of observations. Elastic nets were fitted by setting the alpha parameter to zero . To avoid overfitting, k-fold cross-validation is used35. That is, the sample was randomly partitioned into k = 10 equal sized subsamples. Of the k subsamples, k−1 are used as a “training” set to fit the elastic net and obtain weights for each predictive methylation site. The estimated weights are then used in the remaining “test” set to predict the outcome from the methylation data. By alternating the subjects used in the training and test sets, predictions are obtained for all subjects in the study. RaMWAS repeats the entire cycle of CpG-SNP selection through MWAS followed by estimation of prediction weights using elastic nets for each of the k-folds. Because both the selection of CpG-SNPs and estimation of their weights are not affected by the participants in the test set, we obtain unbiased predictions of the outcome for each subject. Furthermore, the score of CpG-SNPs is for an important part determined by the number of CpGs. To capture only effects associated with MDD, we removed the effect of the number of CpGs from the methylation score prior to conducting the “in sample” replication. By testing whether these methylation predictions are significantly correlated with actual MDD status, we performed an “in sample” replication of the cumulative MWAS signal.To study the significance of the cumulative MWAS signals, we used the “ramwas7riskScoreCV” function in RaMWAS. Specifically, the function uses elastic netsP-values are used to cross-classify each mapped marker in the two datasets as being in the top or bottom. Based on the resulting 2 by 2 tables as input, shiftR tests the null hypothesis that the enrichment odds ratio equals 1. To perform these test, shiftR uses circular permutations36. Specifically, through fast bitwise operations, it shifts the mapping of the two datasets by a single random integer in each permutation. This approach to generate the empirical test statistic distribution under the null hypothesis preserves the correlational structure of the data. We used 1 million permutations for each test. Multiple thresholds can be specified to define “top findings” . To account for this “multiple testing”, the same thresholds are used in the permutations where the test statistic distribution under the null hypothesis is generated from the most significant (combination of) thresholds.To perform enrichment tests of the overlap between datasets we used the “shiftR” R-package. shiftR first maps the two datasets to each other based on chromosomal location. In our analyses, no flanking regions were used. Thus, for SNPs we considered a single base position and for CpGs we considered two bases. Next, the 12 and the study by the Converge Consortium37 determined phenotype status using information about current or prior MDD diagnosis. In contrast, the GWAS meta-analysis performed by the Social Science Genetics Association Consortium (SSGAC)11 studied depressive symptoms, which for the majority of the individuals  were assessed based on self-reported frequency an individual had experienced feelings of unenthusiasm/disinterest and depression/hopelessness during the past 2 weeks. Thus, this assessment was not a clinical diagnosis of depression nor a validated method for assessing depression symptoms. In contrast, when SSGAC studied neuroticism, an MDD-related phenotype, the status for the majority of individuals was assessed using a validated questionnaire that applied different harmonized neuroticism assessment batteries  and a 12-item version of the Eysenck Personality Inventory Neuroticism38 . Therefore, for the purpose of comparison with our MWAS for MDD, we used the SSGAC GWAS meta-analysis results of neuroticism11.Three independent  GWASs were recently reported for MDD or related phenotypes. Similar to the phenotyping in the NESDA sample, the 23andMe studyP-values from the top 10,000 SNPs. To address this restriction we used SNPs retained in the multiple Psychiatric Genetic Consortia (http://www.med.unc.edu/pgc) studies after quality control. After removing the 10,000 top 23andMe SNPs, we assumed that these common and QC’ed SNPs were likely tested or were in LD with tested SNPs in the 23andMe study but yielded P-values lower than those of the top 10,000. The top 10,000 SNPs all had P-values < 10−5. To define a second threshold for the 23andMe study, we also selected the 745 SNPs with P-values < 10−8. To account for this “multiple testing”, the same two thresholds were used in the permutations. To maximize the compatibility of the analysis, all GWAS datasets were subjected to the same procedure as used for the 23andMe study.For calculating the enrichment test statistic, shiftR classifies markers as being among the top vs. bottom results. However, from the 23andMe study, we could only get access to the P-values for each site we instead control for the deviation in the theoretical null distribution. Thus, the P-values were corrected for the average permutation-obtained lambda  across the genome  than expected by chance. A negative dose effect translates to  with a positive dose effect. These sites showed a consistent pattern where the case-control difference gets bigger with more CpG-creating alleles but where the cases show higher methylation levels than the controls. The reaming 18 sites (78.3%) showed a negative dose effect. Thus, the negative dose effect occurred significantly more often , with the signal coming from the top 15,000 markers.The deviation of the observed lot Fig.  after coP = 4.9 × 10−3, OR = 5.00) followed by SSGAC  and Converge . The overlap included 55 CpG-SNP sites (Table −3) in the CpG-SNP MWAS that overlapped with the GWAS data was located in the Roundabout, axon guidance receptor, homolog 2 gene (ROBO2). The overlapping CpG-SNPs included 26 genes present in GO. These genes were overrepresented (P < 0.01) in 12 level-5 GO terms (Table −4) was “Regulation of synapse organization” which, among other genes, included ROBO2.When comparing our MWAS results with three recent GWAS, a significant, or trend toward, enrichment was observed for all three GWASs when using the top 1% of results (the 1% threshold) for the CpG-SNP MWAS and the most stringent threshold for each of the three GWAS Table . The higes Table . The mosHere we present the first MWAS of common CpG-SNPs  in MDD cases and controls. The methylation data were generated using a sequencing-based approach and involved 970,414 CpG-SNPs and 1132 individuals. Furthermore, we investigated the overlap of this study with recent GWAS for MDD, or related phenotypes. The MWAS suggested that multiple sites are potentially associated with MDD and resampling showed that the cumulative signal replicated. Furthermore, permutation-based enrichment tests suggested significant overlap with top findings from the MWAS and recent GWAS.40, this pattern would be in agreement with that a portion of potentially damaging mutations might not be properly silenced in MDD cases. Interestingly, the same pattern with less methylation observed in cases than in controls has previously been observed also in CpG-SNP studies for psychosis using both blood and brain tissue31.The majority of the associated CpG-SNPs that expressed a significant meQTL effect and a significant dose effect in the MWAS showed a distinct pattern where methylation increased with the number of CpG alleles present, but where this increase was attenuated in MDD cases compared to controls. Thus, cases often showed less methylation than controls at the differently methylated loci. Many possible explanation may exist for this pattern. However, consistent with a general function of DNA methylation that protects the integrity of the genome by inactivating DNA elementsROBO2  is critical for the maintenance of inhibitory synapses in the adult ventral tegmental area, a brain region important for the production of dopamine41, and has been implicated in schizophrenia44 and bipolar depression45. ASIC2  plays a role in neurotransmission46. DCC  upregulation in prefrontal cortex pyramidal neurons causes vulnerability to stress-induced social avoidance and anhedonia in mouse, and mutations in DCC have been associated with brain malformation47. Furthermore, DCC has been suggested to confer susceptibility to depression-like behaviors in mice and humans48 and was recently associated with mood instability, which has a strong genetic correlation to MDD49. In addition, the netrin 1 pathway, which involves DCC, has been identified as a candidate pathway for MDD50. Critically, both ROBO2 and DCC interact in opposing fashion and have strong roles in directing axon pathfinding in developing neurons52. In summary, several of the genes detected in the MWAS-GWAS overlap serve critical biological functions of likely relevance to MDD etiology.Many of the genes implicated by both the MWAS and the GWASs are of critical importance for neuronal function. Some of the overlapping gens have previously been associated with psychiatric disorders. For example, P-value = 5.28 × 10−6). Thus, the CpG-SNP MWAS and GWAS provide additional and independent lines of evidence for the involvement of these loci in MDD.The overlap between the CpG-SNP MWAS and GWAS cannot be explained by the allele frequency differences between cases and controls that produce GWAS signals. It is true that methylation levels will be higher in the group with the higher frequency of the SNP allele that creates the CpG-SNP. However, these methylation differences are fully accounted for by the effect of the SNP as a “covariate” in the model we used for the CpG-SNP MWAS. Indeed, performing a GWAS with only the SNPs that were included in the CpG-SNP MWAS showed a lambda of 0.995 without any strong association signals .Supplementary material


BDNF (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington’s disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort (N = 715). pICA identified a genetic component highlighting NTRK2, which encodes BDNF’s TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas (p < 0.001); this association appeared to be driven by participants with high or low levels of the genetic profile. The frontal GMC profile correlated with cognitive and motor variables (Trail Making Test A (p = 0.03); Stroop Color (p = 0.017); Stroop Interference (p = 0.04); Symbol Digit Modalities Test (p = 0.031); Total Motor Score (p = 0.01)). A top-weighted NTRK2 variant (rs2277193) was protectively associated with Trail Making Test B (p = 0.007); greater minor allele numbers were linked to a better performance. These results support the idea of a protective role of NTRK2 in prodromal HD, particularly in individuals with certain genotypes, and suggest that this gene may influence the preservation of frontal gray matter that is important for clinical functioning.This study assessed how  Huntington’s disease (HD) is a progressive, heritable condition characterized by chorea (involuntary motion) as well as cognitive alterations spanning executive functioning, working memory, olfactory and facial recognition, and emotional processing . HD, aloA promising way to identify treatment targets is to characterize the earliest changes before the onset of HD. Motor impairments associated with HD, such as dystonia and chorea, often lead to diagnosis because their disruptiveness prompts affected individuals to seek medical attention. However, cognitive symptoms and alterations in brain volume and morphology are already present more than a decade before diagnosis, during a period known as the prodrome . In keepHTT exon 1 locus determines future HD development. The HTT gene encodes huntingtin protein, which is widely expressed in the brain and central nervous system [HTT encodes mutant huntingtin (mHTT), which compromises numerous cellular processes including endocytosis and secretion, calcium homeostasis [Although there is no cure for HD, its cause is known. An abnormally large cytosine-adenine-guanine (CAG) expansion ≥36 repeats) at an s system . Abnormaeostasis , glutama repeats eostasis , mitochoeostasis , p53 sigeostasis , apoptoseostasis .HTT factors as modulators of prodromal progression and HD onset. Moreover, the reduced genetic complexity of HD makes it tractable to disentangle onset-protection and susceptibility factors. HTT CAG-expansion length considerably influences age at diagnosis and can be used to estimate the age of, or time to, HD onset. Despite strong prediction accuracy for many prodromal individuals, some outcomes deviate from expectations. For example, one PREDICT-HD participant with 44 CAG-repeats lacked positive diagnosis at the age of 71 years, and 13 participants with <41 repeats reached the age of 70 years with no diagnosis. HD onset prediction (based on age and CAG-repeat number) is most accurate in individuals with >44 repeats and increasingly variable as the repeat number decreases, and different disease progression rates are often observed in persons with the same number of CAG-repeats. These examples highlight the onset variability and suggest that additional genetic factors may promote or suppress HD conversion , yet little is known about non-HTT genetic factors that account for variability in the rapidity and severity of HD symptoms and onset.Both within and outside the realm of huntingtin’s interactions, several lines of evidence implicate non-The influence of such factors is likely also reflected by differences in brain structure and clinical functioning throughout the prodrome. Known polygenetic neural effects suggest that multiple genes may modulate decline; this is in keeping with the prevailing common disease-common variant model, which posits that the combined effects of multiple common nucleotide variants, or single nucleotide polymorphisms (SNPs), with weak individual effects may confer disease susceptibility or resistance . At an iInteractions among multiple genes can have complex effects on disease phenotypes . UnivariCombined effects of nucleotide-level differences (or SNPs) on gray matter concentration (GMC) across the brain can be assessed across the genome and in candidate genes using the multivariate method of parallel ICA (pICA) ,11. ThroLike other multivariate methods, pICA is optimal for examining multi-gene interactions because it considers the cumulative effects of changes at multiple loci, likely accounting for more variation than the strongest changes in single genes . ConsequGenes that interact with mHTT and are involved in mHTT-compromised processes are strong candidate HD-progression mediators. One such target is brain-derived neurotrophic factor (BDNF). BDNF mediates neurogenesis, and accumulating evidence suggests its importance in HD development and onset ,14. BDNFmHTT interferes with BDNF transcription and vesicular transport . (a) HunBDNF is particularly relevant to prodromal HD. Compared to controls, asymptomatic HD transgenic mice have reduced striatal BDNF that is lower at higher CAG-repeat numbers, indicating a prodromal BDNF deficit . FurtherNTRK2 (which encodes BDNF’s TrkB receptor) on frontal gray matter and clinical functioning. Profiles including SNPs from multiple genes were anticipated to correlate with gray matter concentration profiles and clinical functioning with maximum effects in BDNF-related, HD-compromised pathways.Using parallel ICA with reference (pICAr), this study leveraged legacy imaging, genomic, and clinical data from PREDICT-HD to investigate how SNPs in BDNF-signaling genes impact clinical functioning and patterns of brain morphology in prodromal HD. As an expansion of the pICA results, we assessed the effects of four individual SNPs in A cohort of 715 expansion-positive (≥36 CAG-repeats) prodromal PREDICT-HD participants were included in the study ). Participant data included genotyping, T1-weighted structural MR images, cognitive and motor variables, and demographics  from PREDICT-HD [ftp://ftp.ncbi.nlm.nih.gov/dbgap/studies/phs000222). Release of the specific genetic and imaging component data generated in this study is forthcoming; however, similar ICA-generated gray matter imaging components from a different PREDICT-HD study by the lead author have been made available in the latest study version . Access to the specific component data from the study may be requested from the corresponding author.SNP data included in this study are publicly available from dbGAP (Study Accession: phs000222.v4.p2). Other PREDICT-HD data, including baseline T1-weighted MR images (used as input files in this study), subcortical and cortical segmentations, and longitudinal clinical measurements, are available on the public download site , based on their established clinical reliability and sensitivity to prodromal HD progression . BecauseBriefly, the SDMT measures working memory, complex scanning, and processing speed, and is an adaptation of the Wechsler Digit Symbol subtest ,18. PartThe Stroop Color and Word Test consists of three 45-s conditions that measure basic attention and inhibition of an overlearned response ,21. For TMTA and TMTB measure visual attention and task switching ,23. For The UHDRS-TMS assesses several indicators of motor performance spanning oculomotor function, bradykinesia, chorea, dystonia, gait, and postural stability ,24. Highr2 > 0.5), the full genomic dataset consisted of 305,271 SNPs that were included in further analyses. Only one participant per family was included in the study; the family structure was determined using PLINK identity-by-descent analysis, where p > 0.18 indicates relatedness (http://zzz.bwh.harvard.edu/plink/ibdibs.shtml). The top 10 multi-dimensional scaling (MDS) factors were used to correct for population structure.Data for 1,160,231 SNPs assayed with the Illumina Human 1M platform were downloaded from dbGAP (Study Accession: phs000222.v4.p2). Data were filtered at a 5% missingness rate per sample and per SNP, and SNPs with a minor allele frequency (MAF) of >0.05 were selected. After linkage disequilibrium (LD) pruning  or 3 T. The study used a standardized acquisition protocol that was modified for each site by our MR physicist. Secondary to upgrades and acquisition changes over the 12-year study, additional variation occurred. A total of 50 site/scanner field strength combinations were analyzed, with at least three participants from each different MRI scanner. Images at each site were obtained using three-dimensional (3D) T1-weighted inversion recovery turboflash (MP-RAGE) sequences. Then, 1.5 T scans were collected using General Electric and Siemens scanners. The Siemens protocol was constructed to be similar to the General Electric scan parameters: GRAPPA factor, 900 ms TI (inversion time), 2530 ms TR (relaxation time), 3.09 ms TE (excitation time), 256 mm × 256 mm field of view (FoV), 10° flip angle, 240 coronal slices with 1 mm slice thickness, 256 × 128 matrix with 1/4 phase FoV, 220 Hz/pixel receiver bandwidth. Protocol for 3 T scanners commonly involved a sagittal localizing series followed by acquisition of an axial three-dimensional (3D) volumetric spoiled gradient recalled acquisition in a steady-state (GRASS) sequence, using the following scan parameters: ~1 mm × 1 mm × 1.5 mm voxel size, 18 ms TR, 3 ms TE, 24 cm FoV, 20° flip angle, 124 slices with 1.5 mm slice thickness, 0 mm gap, 256 × 192 matrix with 3/4 phase FoV, number of excitations (NEX) = 2.http://www.fil.ion.ucl.ac.uk/spm/software/spm8/). Images were segmented into gray matter, unmodulated (to isolate GM concentration), and normalized to the same SPM8 Montreal Neurological Institute (MNI) template. Voxels were re-sliced to 2 × 2 × 2 mm3, and images were smoothed by a 10 × 10 × 10 mm3 full-width-half-maximum (FWHM) Gaussian kernel. Processed images were 90 × 109 × 91 voxels in size. A linear regression model was applied to each GM voxel to account for the effects of age, sex, and site (inclusive of field strength); the site variable was coded using 49 dummy variables.Images were aligned with the anterior commissure-posterior commissure (AC–PC) plane, resampled with 1 mm isotropic voxels to correct for inhomogeneity , and preBDNF, NGFR, NTRK2, RCOR1, SIN3A, SORT1, HAP1, REST, and RILP , F2 , and F3  [PICA is an extension of ICA, a robust and popular method for isolating maximally independent sources from a mixed signal ,11,26. I and A2) ,11. EachWe then queried SNPs of interest . Weights for these 61 SNPs within the component (61 × 1 matrix) were multiplied by participant genotypes for these SNPs (715 × 61 matrix) to yield new participant loadings for the top 61 contributing SNPs. These new loadings were correlated with loadings for the full component at r(713) = 0.77 (two-tailed significance of p < 0.001). A list of the top 61 SNPs, associated genes, and weights in the component is available in To test whether the observed effects were explained by the entire component cumulatively or were largely the effect of heavily-weighted SNPs, restricted genetic coefficients based on the most important SNPs in the component were calculated. First, the distribution of the weights for the 305,271 SNPs in the SNP component was fitted to a logistic distribution. Based on this, SNPs with weights more than 4.25 standard deviations (SDs) from the mean were selected as top contributing SNPs  cross-validation were used to verify significant pICA component correlations . PermutaAn influence matrix was generated using the regression influence plot function in R ,30 to pip < 0.001). This association remained significant after Bonferroni correction for multiple testing. The GMC component most strongly represented supplementary motor, superior, mid, and medial frontal, and precentral/primary motor regions   were among the top 10 SNPs contributing to the component. Other top genes included CDK14, FAM114A1, and HEATR4  = 0.17,  regions . The core (TrkB) . Four ind HEATR4 .NTRK2 SNP component) was significantly correlated with four of the seven queried clinical variables, including UHDRS total motor score  = 6.8, p = 0.009, surviving Bonferroni multiple testing correction), TMTA  = 6.3, p = 0.01, passing Bonferroni), Stroop Color  = 4.7, p = 0.03), and Stroop Interference  = 3.8, p = 0.05). This component also approached a significant correlation with the other three clinical variables  = 3.5, p = 0.06; Stroop Total: F = 3.4, p = 0.07; TMTB: F = 3.1, p = 0.08)). In each case, a higher GMC was linked to improved performance.The frontal GMC component highlighted by pICA  was significantly associated with TMTB  = 6.7, p = 0.01), an effect that survived Bonferroni correction. Here, the minor allele was associated with better TMTB performance. This SNP also approached a significant correlation with Stroop Interference  = 3.6, p = 0.058), with the minor allele similarly being associated with better performance. NTRK2 SNP rs111408010 was also correlated with UHDRS-TMS  = 3.7, p = 0.05), with a greater minor allele number being associated with better functioning (lower TMS score), but this effect did not withstand multiple testing correction.The frontal-GMC-associated The pICA results passed permutation testing; the ratio of correlation values above maximally linked components was 0.013, translating to about a 1% likelihood that the results were obtained by chance. However, the results did not replicate in all the 10-fold validation runs, likely because the GMC-SNP component correlation was driven by several individuals with high or low levels of the SNP component . The visp and r2 values were slightly weaker for the top 61 weights but were essentially comparable.Similar to tests with the full SNP component, clinical associations with weights comprised of the top 61 component SNPs yielded no significant results. Derived NTRK2-weighted genetic profile may be required to significantly affect the frontal gray matter profile. The genetic effect on frontal/supplementary motor GMC in individuals in the tail of the sample was still strong enough to be significant and apparent within the larger sample. In keeping with this, all 25 individuals driving the significant SNP-GMC component relationship  had SNP component scores 2–4 SDs above or below the mean; only three individuals with SNP component values in this range were not highlighted by the regression influence test. By contrast, only six of these participants also had GMC component values ≥2 SDs from the mean in either direction, suggesting that the effect is specific to the SNP component. These participants did not stand out in any other obvious way from the full prodromal sample, and represented both sexes and a variety of ages, sites, and CAG-repeat numbers.Our results suggest that a strong or weak presence of the NTRK2 SNPs were among the top 10 SNPs contributing to the GMC-related SNP component. NTRK2 (Neurotrophic Receptor Tyrosine Kinase 2) is a large  chromosome 9 gene located between SLC28A3  and AGTPBP1 (ATP/GTP binding protein 1). NTRK2 is widely expressed in the brain , and neurotrophin-3 (NTF3), and regulates neuronal differentiation, growth, and survival as well as synaptic plasticity and the transcription of cell survival genes . Thus, iRegarding BDNF, TrkB is preferentially activated by mature BDNF and expressed in indirect pathway striatal MSNs . FollowiLike other neurotrophins, BDNF is synthesized in the endoplasmic reticulum (ER) as a large (32 kDa) precursor protein (pro-BDNF) , then trNTRK2 variations could influence this balance protectively, promoting cell survival and preserving frontal gray matter and cognitive and motor performance.Considering the roles of BDNF’s high- and low-affinity receptor types in promoting cell survival and apoptosis, respectively, a pro- to mature BDNF ratio imbalance could contribute to the abnormal apoptosis observed in HD . TrkB rep and r2 values were only slightly weaker in analyses using the top 61 SNP weights, indicating that the original results do not reflect the aggregate effects of the entire SNP component but are rather primarily due to the top contributing SNPs.Weights comprised of the top 61 SNPs contributing to the component  yielded comparable results when evaluated with the GMC profile and clinical variables. Derived The correlated GMC component, containing supplementary motor, superior, middle, and medial frontal, and precentral/primary motor cortex, was significantly related to both cognitive  and motor (TMS) performance in this cohort. Although the variance in individual clinical performance measures accounted for by the GMC component was small, such effects are typical when comparing single clinical variables to brain structural measures; the observed associations were consistent with the literature reporting structural and functional changes in these regions that correlate with altered performance on these tasks in independent samples ,34,35,36NTRK2 exons 19 and 20 , with rs2277193 (the TMTB-related SNP) being the closest to exon 19. In fact, rs2277193 is only 799 base pairs from rs11140810, the strongest NTRK2 contributor to the component, and it is 3577 base pairs from the other top SNP in this region (rs10868241). The placement of these SNPs  is fitting, as truncated NTRK2 isoforms that lack the catalytic tyrosine kinase domain  are considered the most clinically relevant [Among the top 10 SNPs contributing to the frontally-related SNP component were rs11140810, rs4877289, rs10868241, and rs2277193; no other gene was represented more than once in the top 10 SNPs. Interestingly, three of these four SNPs are between relevant .NTRK2 SNPs, rs11140810 had the strongest weight in the component. The Lieber Institute for Brain Development’s (LIBD) expression quantitative trait loci (eQTL) browser, comprised of dorsolateral prefrontal cortex (DLPFC) expression data from 237 healthy controls and 175 schizophrenia patients, identified this SNP as an eQTL for NTRK2 in the DLPFC at the expressed region level [http://www.braineac.org), an eQTL database from 134 healthy human brains [NTRK2 are strongest in the occipital lobe, thalamus, and frontal cortex. Other top affected genes include SLC28A3, HNRNPK, MIR7-1, and NAA35. Of the on level . AccordiNTRK2 [AGTPBP1 expression in the prefrontal cortex (PFC) [The nearby rs2277193 had a protective effect on TMTB, in which the minor allele was associated with improved performance. In the brain, this SNP exhibits maximum regulatory effects on many of the same genes, albeit a less pronounced influence on NTRK2 . Accordiex (PFC) .NTRK2 SNPs associated with histone enhancement and promotion, protein binding, altered regulatory motifs, and eQTL hits. For example, rs11140785 (LD = 0.94) bound to STAT3 in ChIP-Seq and is associated with altered myocyte enhancer factor (Mef2) regulatory motif and expression-enhancing chromatin changes in the hippocampus, substantia nigra, caudate, inferior temporal, cingulate gyrus, angular gyrus, and DLPFC. In the brain, this variant most strongly affects RMI1, especially in the frontal cortex [rs4877289 is correlated with H3K4me1 (enhancer activity associated) in anterior caudate and cingulate gyrus, and is in strong LD with other NTRK2 in the DLPFC at the expressed region level in control and disease populations [r2 = 0.97) is associated with alteration of 12 regulatory motifs and with histone enhancers in the temporal lobe and PFC). In the brain, rs10868241 most strongly regulates many of the same genes as the other top NTRK2 SNPs  [Like rs11140810, rs10868241 is an identified eQTL for ulations . It is aNTRK2 SNPs that contributed most to the SNP component were situated near alternative stop codons ; these SNPs act on NTRK2, its neighboring SLC28A3 and AGTPBP1 genes, and other genes  in and outside of the brain tissue. These genes are implicated in apoptosis regulation, smooth muscle cell proliferation, mRNA splicing, neurotransmission, translational control, SUMOylation, Golgi and ER retrograde transport, and the shortening of long polyglutamate chains, among other functions , followed by the thalamus, putamen, and cerebellum. In summary, the four unctions . The effr2: 0.92–0.98) and rs10868241 is in high LD with nine SNPs . Thus, the effects of the latter two SNPs in this study may be attributable to an SNP in high LD that was pruned from the dataset.It is important to consider LD pruning when interpreting these results. Although two of these SNPs (rs1114081 and rs2277193) are not in high LD with other SNPs, rs4877289 is in high LD with three SNPs . Many of these genes are implicated in cancer regulation ; rs7655305 accounted for half of these effects. HEATR4 SNP is omitted due to the absence of relevant findings). Of these SNPs, the intergenic SNPs rs548321 (LRRC55-associated) and rs427790 (MIR181A1 and NR5A2-associated) share perhaps the most overlap with the NTRK2 SNPs; rs2277193 (NTRK2) and rs548321 both alter GR (glucocorticoid receptor) and Pax (paired box) regulatory motifs (Pax-4 for rs548321 and Pax-6 for rs2277193) and are associated with histone changes in the brain. rs11140810 (NTRK2) and rs427790 alter the same Zec transcription factor and two distinct Fox regulatory motifs (Foxj1 for rs427790 and Foxa for rs11140810). Each of these SNPs are outlined in more detail below.Although gulation . OverallCDK14 variant (rs7801922) contributed to the SNP component more than any other SNP. This intronic SNP is 68,161 bp upstream of CDK14 exon 1, and is an eQTL in the lungs (GTPBP10), tibial artery (CDK14), tibial nerve (GTPBP10), and thyroid (DPY19L2P4) [STEAP2, DPY19L2P4, C7orf63) [CDK14 SNPs associated with other regulatory motif alterations [The Y19L2P4) . In the Y19L2P4) . In healC7orf63) . Supplemerations .HEATR4 (HEAT-repeat containing 4) variant was another top SNP. HEAT-repeats are amino acid domains that often have roles in protein-protein interactions, and are considered important for intracellular transport, microtubule dynamics, and chromosomal separation. Huntingtin is one of the first proteins in which HEAT-repeats were discovered  [HEATR4 SNP that was prominent in our genetic component (rs8012614) is an eQTL for ACOT4 , DNAL1 (expressed region level), and HEATR4 (expressed region level) in the DLPFC [ACOT4 only . Additionally, this SNP has more Genotype-Tissue Expression (GTEx) eQTL hits than any other top contributing SNP, although these are mostly outside the brain in adipose, muscle, skin, heart, pancreas, and thyroid tissue [DNAL1 is particularly interesting, as this gene is not only part of the Kyoto Encyclopedia of Genes and Genomes (KEGG) Huntington’s disease gene pathway but is also directly involved in aberrant BDNF transport [DNAL1 is one of three genes associated with dynein axonemal light chain 4, part of the ATP-dependent outer dynein arms complex that acts as a molecular motor for cilia [An intronic and TOR) . The HEAhe DLPFC ; in contd tissue . The effransport . DNAL1 ior cilia .FAM114A1  [TLR1 in the DLPFC (transcript and exon levels in the disease population) [http://www.featSNP.org). This SNP is also in LD with several other intronic FAM114A1 SNPs with correlated enhancer histone marks, altered regulatory motifs, and eQTL hits [The final intronic SNP, rs7655305, regulates the expression of ocampus)  and otheulation)  as well ulation) , and actQTL hits .NUS1 that is linked to H3K4me3 promotion in adipose nuclei and duodenum smooth muscle as well as the alteration of five regulatory motifs  [LRRC55, most strongly affects the expression of TIMM10, UBE2L6, and ZDHHC5 . Each of these genes exhibits the lowest expression in the cerebellum relative to other brain tissue types [MIR181A1 and NR5A2, is an eQTL hit in the testis and basal ganglia [rs112140519 is a deletion/insertion variation closest to  Zfp105) . rs54832ue types . rs42779 ganglia . Haplore ganglia .Including the CAG-repeat number or CAG-age-product (CAP) score as a covariate did not affect the significance of the GMC-SNP component correlation. Similarly, the inclusion of the CAP did not nullify the significance of the NTRK2-SNP correlation with TMTB, although it did account for some of the variance. While we can currently only speculate on this finding, it may be attributable to the regression of age effects from the imaging data prior to pICA. The CAP reflects the cumulative disease burden of age and the CAG-repeat number. Because the GMC component had the effects of age subtracted from it, CAP and CAG would be expected to display a similar relationship to the GMC component, which is what we observed. The relationships between the GMC component and the clinical variables, however, could be accounted for by the CAP, likely reflecting the sensitivity of these measures to CAG and age, both of which are highly related to the clinical variables assessed in this study.NTRK2 were associated with a GMC profile representing the supplementary and primary motor cortex, as well as other frontal regions (positive correlation).In this PREDICT-HD prodromal cohort, high or low levels of an SNP profile with substantial contributions from This frontal gray matter profile was associated with cognitive and motor performance in this population.NTRK2 SNPs had a protective association with performance on TMTB, a measure of task switching and visual attention, indicating some influence on cognition.The SNP component was not significantly associated with clinical functioning, but one of its top Correlations between the SNP component and clinical/GMC variables were mainly due to top contributing SNPs, rather than being an aggregate effect of the entire SNP component.Top component SNPs have been associated with active histone modifications in the brain  and altered regulatory motifs  and zinc finger protein (Zec)).NTRK2 SNPs in the component were close to alternative stop codons and reportedly regulated genes implicated in diverse functions .Top The study results demonstrated the following:NTRK2 has protective potential in Huntington’s disease, especially in individuals with certain genotypes. Treatments that target BDNF receptors may help preserve frontal gray matter that is important for cognitive and motor functioning.Although further investigation is warranted, these results suggest that In any study involving many participants with a rare condition, multiple scanning sites are typical and a certain degree of inhomogeneity in data collection is thus inevitable. This study incorporates data from several unique 1.5 T and 3 T MRI scanners, and every effort was made to control for possible confounds related to this. Before data collection, uniform protocols were established to ensure maximal homogeneity of data collection. The effects of collection site and scanner field strength, as well as sex and age, were regressed from GMC images before pICA.N = 903) that included controls (N = 189). However, the analysis did not extract a significantly correlated SNP-GMC component pair that withstood correction for multiple testing. The effects presented in this study may be unapparent in the combined sample with controls because they are related to other processes already aberrant in HD, such as BDNF transcription and nuclear translocation.Ideally, controls would be included in the analysis to permit comparisons with prodromal HD participants. PICA was carried out with a more extensive dataset  strains are available that permit rapid, efficient, and inexpensive querying of SNPs and genes of interest derived from human studies. The most promising candidates from these experiments can be flagged for continued research in mammals and eventual use in clinical trials. A promising direction is to examine how different ratios of BDNF TrkB and p75 receptors influence progression and onset in the context of HTT CAG-repeats. BDNF acts on TrkB receptors to increase its own continued expression; thus, an underrepresentation of these receptors may reduce BDNF. Similarly, p75 receptor overrepresentation or an overabundance of pro-BDNF may promote apoptosis and contribute to disease-related decline. Another important consideration is the impact of other BDNF exons. Huntingtin stimulates transcription from the BDNF exon II promoter, which is 60% less active in cells overexpressing mHTT [This study helped characterize the complex interactions of BDNF-signaling genes, brain structure, and clinical functioning in prodromal HD. As genetic and epigenetic factors of prodromal HD progression continue to be identified, potential gene therapies (such as DNA-methylating drugs and histone-deacetylase inhibitors) can be refined, tested, and implemented more strategically . To aid ing mHTT . Althoug


Single Nucleotide Polymorphism (SNP) in the genome has become crucial information for clinical use. For example, the targeted cancer therapy is primarily based on the information which clinically important SNPs are detectable from the tumor. Many hospitals have developed their own panels that include clinically important SNPs. The genome information exchange between the patient and the hospital has become more popular. However, the genome sequence information is innate and irreversible and thus its leakage has serious consequences. Therefore, protecting one’s genome information is critical. On the other side, hospitals may need to protect their own panels. There is no known secure SNP panel scheme to protect both.In this paper, we propose a secure SNP panel scheme using homomorphically encrypted K-mers without requiring SNP calling on the user side and without revealing the panel information to the user. Use of the powerful homomorphic encryption technique is desirable, but there is no known algorithm to efficiently align two homomorphically encrypted sequences. Thus, we designed and implemented a novel secure SNP panel scheme utilizing the computationally feasible equality test on two homomorphically encrypted K-mers. To make the scheme work correctly, in addition to SNPs in the panel, sequence variations at the population level should be addressed. We designed a concept of Point Deviation Tolerance (PDT) level to address the false positives and false negatives. Using the TCGA BRCA dataset, we demonstrated that our scheme works at the level of over a hundred thousand somatic mutations. In addition, we provide a computational guideline for the panel design, including the size of K-mer and the number of SNPs.The proposed method is the first of its kind to protect both the user’s sequence and the hospital’s panel information using the powerful homomorphic encryption scheme. We demonstrated that the scheme works with a simulated dataset and the TCGA BRCA dataset. In this study, we have shown only the feasibility of the proposed scheme and much more efforts should be done to make the scheme usable for clinical use. Single Nucleotide Polymorphism (SNP) is crucial information in medical sciences than ever before. A single aberrant nucleotide variation can incur dysfunction in a biological process, affecting individual vulnerability to certain diseases. Hence, SNP existence can be utilized to diagnose diseases. Sometimes, SNPs help determine effective treatment, especially in cancer. From The Cancer Genome Atlas (TCGA) project, numerous driver mutations are reported in many cancer types  and paneThe utility of SNPs goes beyond the medicine domain. People are diverse in the genomic content, thus the difference in genomic content among people can be used to identify a specific person. For such reason, SNPs are often used for legal and forensic purposes. There are Direct-to-Customer SNP kits that are designed for non-medical use such as pedigree search. More people measure their genomes and use the information for various purposes.As genomic data are widely used and disclosed, a potential threat to genomic privacy is a serious problem. DNA sequence is a blueprint of a human being since the information includes not only medical conditions but to the extent of even reconstructing facial model which is one of the most sensitive information of human being . Our knoAmong recent measures to protect data, homomorphic encryption is a technology that gains attention recently. It refers to an encryption scheme that allows the third party to perform computations while not knowing any content of the original source data or the private key. If such computation includes addition and multiplication, then theoretically all computations can be performed and that is called fully homomorphic encryption. The result of the computation is also returned encrypted, thus the third party can provide its inference based on the data and at the same time cannot know the data content or perform inference at all. The first fully homomorphic encryption scheme is proposed by Gentry at 2009 , and conFor example, consider that Alice is running a company and Bob provides a cloud storage service. Alice stores her encrypted data on Bob’s cloud. When Alice wants to compute aggregate data, an average revenue per month for instance, she has to decrypt the data if it is traditionally encrypted. Decrypting on the cloud storage may reveal both the private key and the source data to Bob. Downloading the encrypted data to local and decrypting it goes against the purpose of utilizing cloud service in the first place. This is when homomorphic encryption comes useful. If the data is homomorphically encrypted, Alice does not have to decrypt the source data on Bob’s cloud to do the computation needed. Instead, the encrypted form of aggregate data can be computed as ciphertexts and downloaded to Alice’s computer, and then safely decrypted. This way, Alice can exploit Bob’s resource to compute the average revenue without giving any information to Bob.p, q, and p+q with the private key. Multiplicative homomorphic encryption works likewise.In more formal notation, an encryption scheme is additive homomorphic encryption if and only if Given the importance of genome information, there has recently been active research on genome security. Naveed et al.  presenteDifferential privacy includes de-identification, which refers to making genomic data unidentifiable by either anonymizing or discarding personally differential information. This aims to perturb the information so that any leak of data itself would not possibly lead to identifying the patient. However, it is shown in many papers that generic de-identification techniques are not powerful enough to prevent reconstructing identity –13. To mThe secure system design with traditional encryption focuses on controlling the flow of sensitive information. It relies on secret sharing of private keys with multiple parties that do not collude to ensure the confidentiality of the data. Canim et al.  suggesteχ2 statistic in GWAS and edit distance of sequences in a secure way. Lu et al. ”, where all the parameters for random sequencing errors are set to none and the throughput parameter “-N” depends on the size of each panel. The sequencing depth was set to 10, meaning that 10 sequencing reads are expected to cover each SNP residue in average. So, to cover more residues, it requires more sequencing reads to simulate (hence larger “-N”). Lastly, each residue has 50% chance of nucleotide substitution to simulate SNPs by design. For instance, if we check any SNP residue, we can expect to find 5 reads having reference allele and other 5 reads having variant allele as planted. In summary, we generated 10 DNA-seq data corresponding to 10 SNP panels having different combination of SNP residues, each having average 50% substitution rate.−100)2. The computed p-value is negligible. Therefore, it is safe to merge the residues satisfying this condition. However, if an extreme case occurs that flanking sequences of two SNP residues overlap more than 150 bp, which is longer than short-read length, we treat them same as the case of partial overlap situation, hence they are discarded.There could be unwanted overlap of flanking sequences between two different SNP residues. The overlap between two SNP residues would require a large value K. Therefore, we either merge the overlapped residues or discard one of them. This problem is described in Fig. The handling of overlapping sequence enables that, even if more than two SNP residues exist near, possibly within the range of K so that there exists a K-mer which has both SNPs, the proposed SNP panel scheme finds both SNPs correctly. It only has to enumerate all possible K-mers and perform equality test on the user K-mers with corresponding SNP labels.Now we have a panel that will be used for testing our secure SNP scheme. The panel should be designed by doctors for a specific disease. What we have shown is a computational scheme to test whether a panel with many residues can be used with our secure SNP panel scheme that is proposed in this paper.The goal of this paper is to propose a patient-to-hospital secure communication protocol such that a patient would conceal its genome sequence while requesting SNP detections to a hospital with a panel. Specifically, we are interested in separating access between to the SNP panel at the hospital and to the genomic sequence of the patient. The SNP panel information is an asset of the hospital which may not be revealed to the public. On the other hand, the user does not want the hospital to know one’s sequence. The goal is to protect both.homomorphic equality tests on two K-mers. The patient would receive an encrypted result of SNP calling from the hospital. If a K-mer from patient sequence matches a K-mer from the panel sequence annotated with a specific SNP, the patient can tell which SNP one has. Further medical diagnosis can be done based on the test result.In our scheme illustrated in Fig. Note that the patient encrypts the K-mers and holds the private key so that the hospital does not gain any information about the patient’s sequence. Meanwhile, the computation is done on the hospital’s side, securing SNP panel assets from leakage.Our SNP detection scheme works under specific conditions. To determine SNP existence by performing K-mer equality testing, the corresponding K-mer should be unique throughout the whole SNP panel sequences. Otherwise, K-mer matching cannot guarantee the existence of SNP. Thus the core idea of K-mer design is to set K long enough for all K-mers that contain any panel SNP to be distinguishable. However, while long K ensures unique K-mers, longer K-mers will be computationally expensive.K-mers in set (A) are unique (have no duplicate)K-mers in set (B) do not appear in set (A)Therefore, given a panel we computed panel-specific minimum value of K such that all K-mers in the panel that have a SNP are distinct. To achieve this goal, first K-merize all panel sequences and group them into two sets: (A) K-mers with SNPs and (B) K-mers without. Then, following conditions must hold for K-mers with SNPs to be distinct. Among the Ks that satisfy the above conditions, the minimum value is chosen.the variations at the population level are considered. An actual sequence derived by SNP panel may present unannotated deviations from what is known to the panel, other than SNP residues in it. Examples include point mutation, individual genome variation and sequencing errors. Presence of such deviations may result in false positive and false negative errors. False positive error occurs when the scheme identifies a K-mer and declares a SNP existence but actually the patient does not have one. False negative error occurs when the patient indeed has a SNP yet the scheme fails to detect one. We henceforth refer to point deviation as a single nucleotide deviation from intended panel sequence, resulting from either point mutation, individual variation or sequencing error.In our system, K of K-mer is defined to make all K-mers distinguishable and thus no false positive or negative errors. However, we assumed that individual patient sequence derived from SNP panel varies only at the interested SNP residue and the flanking sequences are identical for all patients. This is not the case when Point Deviation Tolerance (PDT) level. Previously in the process of computing K, we had two conditions on K-mers. Both conditions utilized the equality test to check uniqueness of K-mers in (A) and exclusiveness of K-mers in (A) over those in (B). We generalize the equality test to the hamming distance check with its lower bound being PDT. In other words, we apply strict rules and regard similar sequences ambiguous. Here, being similar is defined by PDT point deviations. The uniqueness condition is the specific case of PDT being 0. Thus we can rewrite the K-mer conditions as below: K-mers in set (A) are distinct (any pairs’ hamming distance greater than PDT)K-mers in set (B) are distinct from K-mers in set (A)False positive occurs when the determined K-mer did not originate from the flanking sequence around the found SNP. Rather, it originated from other irrelevant parts of patient genome sequence that correspond to any other SNP residue in the processing panel. To deal with false positive errors, we devised Given PDT, one can determine a minimum value K satisfying the updated conditions. PDT works as a safety margin to K-mer ambiguity. The point deviation, namely the sum of point mutations, individual variation and sequencing errors, is allowed up to maximum PDT. Therefore, if the aggregate point deviation occurs less than or equal to PDT, false positive cases do not appear.On the contrary, we cannot prevent false negative cases. False negative happens when the system cannot determine a SNP when it is truly in the patient’s sequence. The major cause of this is also the point deviation in the sequences flanking a SNP. It is infeasible to prepare all the variant K-mers as we did to cope with the false positive errors. In this paper, we assayed TCGA BRCA data to determine the distance distribution of somatic mutations among patients. Based on the data, we estimate the probability of a point mutation lying on K-mers to estimate false negative errors.s and some error distribution χ, the relation between a and 〈a,s〉+e are computationally indistinguishable from random numbers. RLWE uses polynomial integer rings instead of vectors. Namely, n=ϕ(m), where Φ(·) is cyclotomic polynomial and ϕ(·) is Euler’s phi function. RLWE is estimated to achieve equal or less level of security compared to LWE. Other parameters for the scheme are p for message modulus, q ciphertext modulus and ring M. We further denote by Rq=R/qR and χ for error distribution. The scheme for cryptography used throughout this paper is described in detail below. SKGen(params) Choose random s(X)←χ, and set a(X),a′(X)←Rq, e(X),e′(X)←χ, and set b(X)=−a(X)s(X)+pe(X)∈Rq and b′(X)=−a′(X)s′(X)+pe′(X)∈Rq. The public key is pk=∈R2 and the evaluation key is evk=∈Rq. Enc Choose v(X),e0(X),e1(X)←χ and let c1(X)=m(X)+v(X)b(X)+pe0(X),c2(X)=v(X)a(X)+pe1(X). Return evk) For In this paper, we used HEAAN (Homomorphic Encryption for Arithmetic of Approximate Numbers) library to implement the equality test on two homomorphically encrypted K-mers. HEAAN is based on RLWE (Ring-Learning With Error) encryption scheme. RLWE is a variation of LWE (Learning With Error) problem, which is a lattice-based cryptography. LWE exploits its hardness assumption to ensure security which follows: sk1→sk2) KeySwitch → sk(X))AutomorphismRLWE-based homomorphic encryption supports batching (or SIMD) encoding and data array movement. If we call the each element of data array as slot, the scheme has permutation of message slots. This functionality can be used to make our homomorphic evaluation algorithm more efficient and split and merge DNA information. KeySwitchingMatrixGen=2×43+0×42+1×41+3=135.To perform equality tests of K-mers in numerical system, we regard each K-mer as a quaternary number via mapping each nucleobase A, C, G, T to 0,1,2,3, respectively and encode K-mers into integers. In this view, for example, if "GACT" is a K-mer of length 4, then it corresponds to L wholly as an integer is inefficient when L is large, requiring a huge set of scheme parameters. To achieve a better performance, we suggest a method of breaking K-mers into smaller blocks of same length and performing equality tests for each block simultaneously. Henceforth for N user-side K-mers and M panel-side K-mers divided into B blocks respectively, we denote the data as following: n-th user-side K-mer : Kn)m-th panel-side K-mer : b-th block of Kn)size of a block: LHowever, encoding DNA sequence of length B vectors. Specifically, b-th blocks of N user-side K-mers {Kn)([ b]·N+n. It is noteworthy that Kn)([ b]·N+n=Km) from an encryption of . We denote the rotation operation as r positions. Define and compute n∈[ N], Kn)([ b]=Krefm)(=Krefm)([ b]=Krefm)(=Krefm)( for some m and 0 if not.Note that for Step 4: Define and compute n satisfying Kn)(=Krefm) the number of SNP residues included in panels grow and (2) the higher PDT level required. The estimated value of K with the simulated data is shown below Table .However, the values of K tend to saturate very quickly. There is ignorable difference among the values of K along the number of SNP residues from 20 to 100. The saturating tendency implies that our scheme is likely to be effective even regarding the panel with large number of SNP residues. In the data preprocessing stage, we have discarded SNP residues with redundant flanking sequences to prevent very large number of K. Table Although homomorphic encryption has a good potential for protecting security of data, the encryption method combined with the current computer systems has not achieved practical performance to fully utilize the power. In the field of genomics, the usage of homomorphic encryption has been mostly limited to querying aggregate or annotated data, requiring the preprocessing of data. However, to preprocess the genomic data, the raw genome sequence should be reveled without adequate protection, thus more reliable scheme for protecting the genome information is much needed. In addition, the SNP panel at the hospital should be protected. In this paper, we propose a secure SNP panel scheme that protects both the user’s genome information and the SNP panel information owned by the hospitals. Since the current homomorphic encryption technologies are not computationally efficient, it is not trivial to develop a secure SNP panel scheme that can be used in reality. By chunking the part of genome down into K-mers, we have minimized the size of ciphertext space and overcome the current inefficiency of the homomorphic encryption. The scheme has yet many further possible improvements such as parallel processing and new algorithmic techniques. We expect our method to protect the raw sequence from possible threats and further return the control of genomic data to its owner, and at the same time protect the hospital’s SNP panel assets safely. However, we emphasize that our method shows the feasibility of our scheme. Applying our proposed method to hospitals will certainly require extensive evaluation and improvement.