Furthermore, only the 13 most informative markers were needed to achieve maximum differentiation. This simple, effective method provides robust and accurate genotypic data which allows for more efficient resource management , thus contributing to an increased knowledge on the genetic background of cacao worldwide. Notably, the described method can easily be integrated in other laboratories for a wide range of objectives and organisms.In this study, a double-mismatch allele-specific (DMAS) qPCR SNP genotyping method has been designed, tested and validated specifically for cacao, using 65 well annotated international cacao reference accessions retrieved from the Center for Forestry Research and Technology Transfer (CEFORTT) and the International Cocoa Quarantine Centre (ICQC). In total, 42 DMAS-qPCR SNP genotyping assays have been validated, with a 98.05% overall efficiency in calling the correct genotype. In addition, the test allowed for the identification of 15.38% off-types and two duplicates, highlighting the problem of mislabeling in cacao collections and the need for conclusive genotyping assays. The developed method showed on average a high genetic diversity (H Theobroma cacao L. (T. cacao) (2n = 20) are worldwide acknowledged for their application in the chocolate industry1. They are harvested in the tropical regions between 10 to 20 degrees north and south of the equator. Although the South-American Amazon region is recognized as the center of its origin, the three main cacao producing countries are Ivory Coast, Ghana and Ecuador3. The flavor characteristics of cacao are mainly influenced by genotype and origin, although growth conditions of the cacao tree and post-harvest processing factors have also shown to be of importance5. T. cacao consists of numerous morphologically distinct populations, mainly subdivided in Criollo, Trinitario and Forastero. They can all be crossed reciprocally, which is valuable for breeding and propagation. Such crop improvement programs primarily focus on cultivation of new, resistant and high-yielding cacao varieties using characterized genetic resources (germplasm)7. So far, up to 15 to 44% of the cacao accessions have been estimated to be mislabeled – called off types – as they show different genetic profiles than expected10. Therefore, more straightforward identification techniques based on genetic markers, also known as DNA fingerprinting methods, have been used.The beans of 15.Simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) are mainly used for genotyping and classifying cacao varieties. Both have been employed successfully in cacao for genetic diversity studies, detecting mislabeling and establishing the genetic relationships within and between populations or individuals. Moreover, they proved to facilitate cacao conservation and domestication, and conjointly assisted in mapping useful genes and selecting breeding parents. In short, the use of genetic markers resulted in a reduction of inbreeding, mislabeling, unwanted progeny, and genetic drift, playing a main role in long-term breeding gains by enhancing efficient and substantiated cacao management17, progeny improvement16, parentage analysis18 and diversity assessments20. Since 2004, a set of fifteen SSRs has been recognized as the international standard for genotyping cacao21. In 2008, Motamayor et al. used these markers to classify Amazonian cacao germplasm (living genetic resources) more accurately into 10 major genetic clusters, namely Amelonado, Contamana, Criollo, Curaray, Guiana, Iquitos, Marañon, Nacional, Nanay and Purús8. This technique, in combination with the proposed classification, allows to reflect on the genetic diversity of cacao varieties worldwide. Unfortunately, many disadvantages are inherently linked to SSR. Besides the lack of associations with genes and traits22, this electrophoresis-based analysis often delivers ambiguous results making inter-lab comparisons difficult. Moreover, the technique is costly and requires specialized training and instrumentation, both usually limited in cacao producing countries and therefore often outsourced and unaffordable for small scale studies13.SSRs are the most commonly used type of marker for genotyping plants and animals. These tandem nucleotide repeats are highly polymorphic, abundantly present in the genome, co-dominant and suitable for automatization. Their efficiency in cacao genotyping has been demonstrated successfully for multiple applications such as off-type detection24. Approximately 1,560 SNP candidates from a wide range of cacao organs have already been published in Tropgenedb25. Recently, Livingstone et al. (2015) identified 330,000 SNPs by mapping RNA-seq data from 16 diverse cacao cultivars to the Matina 1–6 genome26. Despite the fact that SNPs are biallelic in nature and hence less polymorphic and informative than SSRs, they have shown to be 98% as efficient in calling off-types27. Furthermore, SNP-based genotyping is more robust and less ambiguous as it requires only a (quantitative) PCR instrument facilitating data publication in databases accessible worldwide, such as the International Cocoa Germplasm Database (ICGD).Alternative molecular markers such as SNPs have been proposed. They constitute the largest class of polymorphisms observed in plant genomes and can be found in coding regions, making associations with traits feasible6 and TaqMan-based quantitative PCR (qPCR) are the golden standard for SNP detection in cacao studies28. TaqMan-based genotyping has already been applied on-field and both methods can be used in multiplex and automated high-throughput contexts12. However, Maldi-TOF MS needs specialized equipment and training, whereas TaqMan-based qPCR assays depend on fluorescently labeled probes. This increases design complexity and cost in comparison to SYBR-green-based qPCR – especially for low throughput genotyping assays. Recently, a cost-effective qPCR-based method has been proposed for SNP genotyping purposes, coined DMAS qPCR. The method is based on straightforward readout of DNA-binding dye based qPCR technology. It entails two parallel qPCR reactions, each including a different allele-specific (AS) primer harboring an artificial mismatch to increase robustness and discriminating power. The advantage of DMAS-qPCR is its ease of use, high analytical sensitivity and specificity (matching TaqMan-based methods) without the need for expensive fluorescently labeled probes29. Moreover, a similar SNP genotyping method, known as AS Primer (ASP) PCR, was published in 2010 with promising results in genotyping rice30.Currently, Maldi-TOF mass spectrometry (MS)This paper describes the development of successful DMAS-qPCR SNP genotyping assays that are useable for cacao mislabeling and genetic diversity studies. Two main objectives can be distinguished, namely (1) the establishment of cacao specific DMAS-qPCR SNP assays, and (2) the testing and validation of resulting DMAS-qPCR assays on a subset of international cacao reference accessions. In general, the applicability, reliability and fast multi-sample and multi-locus SNP genotyping capacity of DMAS-qPCR will be demonstrated.th nucleotide of this end, and a common primer. Both combinations are used in parallel qPCR reactions and allows SNP detection by analysis of Cq values retrieved. Three genotyping models were proposed, all using the ΔCq – the difference in Cq between the REF and ALT allele reaction – from validated accessions to assign classify the SNP status. The first model (1) exploits setting manual thresholds around the heterozygous samples in the difference plots. Thresholds were based on critical assessment of the scatter and difference plots, found in Supplementary Each DMAS qPCR assay, consists of reference (REF) and alternative (ALT) allele specific primers, matching the SNP at their 3′ and containing an additional mismatch at the 4st model  exploitsst model  exploits31. DMAS-qPCR assay design was attempted for these 102 targets, for which 10 failed due to the limited design space and low genomic complexity at the associated regions. The remaining 92 DMAS-qPCR assays were first tested by qPCR on 6 to 10 cacao accessions for which a corresponding reference genotype status was available – through ICGD or SBG  of the 102 remaining SNP targets, genotype status of different cacao accessions has been published in ICGD, allowing comparison with DMAS-qPCR results later on. For the other 14 SNPs, genotype status was determined by means of in-house sequencing-based genotyping (SBG)The additional 55 international cacao accessions were genotyped using the 47 successful DMAS-qPCR assays (Supplementary HOM/HET distances (i.e. abs(ΔCqHOM − ΔCqHET) values) and thus increased discriminating power and more accurate genotype calling. When focusing on heterozygous calls, 92.9% (n = 39) assays showed an average ΔCq < 0.5 and 69.1% (n = 29) <0.25. For homozygous samples, ΔCqHOM/HET values >5 could be observed for 92.9% (n = 39) and 88.1% (n = 37) of the assays in reference and alternative samples, respectively, while 69.1% (n = 29) and 57.1% (n = 24) of the ΔCqHOM/HET distances exceeded 7.5. Overall, 78.6% of the assays generated ΔCqHOM/HET >5 , allowing straightforward genotype calling. However, an assay having lower ΔCqHOM/HET distances and very low SD for both the homozygous and heterozygous calls could still achieve similar (or even better) genotyping accuracy compared to an assay with perfect ΔCqHOM/HET distances.Performance of each of these assays was assessed using their average ΔCq value and its standard deviation (SD)  was overall high, from 89.97% for the second model (automated thresholds), over 91.51% for the third model (clustering), and up to 92.16% for the first model . In general, an overall average genotyping efficiency of 88.47% was observed when combining all models. By comparing models among themselves, similar trends could be observed with average genotype overlap reaching 97.60%. When taking the first model as a reference, the clustering-based model seems to outperform the automatic threshold-based model slightly . Overlap between the second and third model is substantially lower (97.75%)  – who considered cacao clones as dissimilar when they differed on at least two loci23 – and Padi et al. (2015) – who permitted only a single mismatch to account for genotyping errors9, we only classified samples as off-type if they differed in at least four loci from the reference accession, e.g. EET 95 and U45. CEFFORT carried a greater amount of off-types in contrast to ICQC, with 24% and 9.76% respectively.During these analyses, an overall 15.38% mislabeling was detected by comparing the multi-locus SNP profiles of the accessions with their reference status. The reference status of ICS1(CEFORTT), LCTEEN62/S, IMC67, PA120, SCA9(CEFORTT), NA33, NA399 and SCA11 differed on more than 13 loci from our genotype calls and were thus classified as off-types. In contrast to Takrama 32 (Table mAF and MAF) were calculated, ranging from 0.046 and 0.945 to 0.492 and 0.508 with an average of 0.338 and 0.647, respectively. The large range of MAF suggests a good level of genetic diversity across the selected SNPs. This determines if a specific SNP is abundantly present in a population, making it useable as a selection criterion. From these frequencies expected and observed heterozygosity (He and Ho) was calculated the first represents the proportion of heterozygosity expected under random mating and is proportional with genetic variability within the population, which should be high in a diverse population as the one used here whereas the second is inversely proportional with inbreeding. The He ranged from 0.088 to 0.500  with an overall average of 0.416, while an average of 0.298 was observed for Ho, ranging from 0.062 to 0.462. For all markers, except TcSNP878, He was higher than Ho suggesting an isolate-breaking effect (the mixing of two previously isolated populations).For all validated assays, key descriptive analysis was computed using GenAIEx v6.532 Table . At leasI) – representing the informativeness of each marker – ranged from 0.187 (TcSNP786) to 0.693 for multiple loci, with a high average of 0.601. Fixation index (F) values approximating zero are expected under random mating, while substantial positive values indicate inbreeding. Negative F-values indicate excess of heterozygosity due to negative assortative mating or selection for heterozygotes32. Here, an F-value of 0.277 was observed, indicated random mating in the population. Further, the discriminating power of the SNP loci was computed through the probability of idenitity amongst siblings (PIDsib) over all analyzed loci and 65 cacao accessions counting for 3.4 × 10−22, indicating that the chance of finding two individuals with the same genotype in the population was close to zero. Identification of the previously detected off-types through pair-wise multi-locus analysis using all SNP data accessible in ICGD failed33 (data not shown). However, two duplicates could be identified, namely APA4 and SPEC41/6-18 – and – U45 and U70. Following ranking of the markers according to I, only 13 markers  were deemed necessary for differentiation with over 99.999% certainty as analyzed by matching and unique genotypes (data not shown). Furthermore, the validated assays were on average 98.05% efficient in calling the correct SNP genotype from unmislabeled cacao accessions with a 95% certainty . Different explanations for the high wet-lab failure rate can be proposed. Reaction conditions could be sub-optimal. Although the most critical factors known to affect PCR efficiency were taken into account during assay design, it is currently still difficult to accurately simulate reaction dynamics in silico. Another explanation includes unanticipated variation, such as polymorphisms, present in the primer sites. This could be confirmed for some failed assays – e.g. TcSNP90, TcSNP702 and TcSNP1439 – through targeted sequencing of the primer annealing sites. Variation in primer annealing sites has been shown to impede proper binding of a primer to its target, thus blocking or hampering efficient amplification, which could also be observed with the ASP-SNP genotyping method30. Since variation in T. cacao is less well characterized, this feature is most likely the major cause for some of the DMAS-qPCR failures.DMAS-qPCR design was attempted for 102 SNPs, which failed for 10 markers. These failures could be attributed to the setup of DMAS-qPCR requiring allele-specific primers to align with their 3′ terminal nucleotide on the SNP of interest. This leaves little room to reposition the oligo, thus only its length, desired annealing temperature and GC content can be adjusted. Secondly, the cacao genome is less well annotated, only two genome builds are currently availableHOM/HET ≥5) for most assays was sufficient to allow accurate genotyping. Evaluation of DMAS-qPCR-based genotyping accuracy was assessed by comparison with reference genotyping calls in corresponding accessions. Concordance of all tested genotyping models with reference calls was very high (88.47% on average), while overlap between the three models exceeded 97.60%. This shows genotyping efficiency and accuracy is independent of the classification method , allowing automated calling approaches to streamline and simplify genotype calling. Discrepancies between DMAS-qPCR calls and reference genotype status could be explained by the uncertainty and inaccuracy associated with any experimental method, including DMAS-qPCR, and potential errors that could have slipped in ICGD – we have already identified duplicates in this repository as have others in the same or different collections and in greater amounts40. The level of inaccuracy associated with DMAS-qPCR can only be tested by applying this method on a sufficiently large sample population, and comparing the calls with results generated using multiple different techniques . The DMAS-qPCR results have shown to be independent from technical variations however, specificity could be increased by optimizing the additional internal mismatch, as shown by Hirotsu et al.30.Quality assessment and comparison of genotype calls obtained with the final set of 42 DMAS-qPCR showed the high reproducibility and robustness of the method, having a 98.05% overall efficiency. Global standard deviation for all assays across all samples was low, while discriminating power . It appeared some of the ICGD calls were called on the reverse strand, while DMAS-qPCR results were called on the forward strand. This is most likely caused by the fact that the current ICGD data was generated using EST libraries, and ICGD genotype status thus depends on the strand from which the gene was transcribed. More extensive annotation  of genotype status in online repositories or adoption of common reporting guidelines  could prove helpful to prevent such discrepancies in future studies. As a start, the data from this study will be incorporated into the ICGD database to serve as an additional and independent source of genotype information for the corresponding samples.41. Mislabeling and the verification of genetic identity from introduced as well conserved germplasm is an acute problem, as can be seen in this study and different publications over the last ten years42. Despite the fact that different applications and strategies on tackling cacao mislabeling have been provided by Turnbull et al.43, it still seems challenging to deploy these into practice. This underscores the need for accessible genotyping methods and off-type detection such as the one described in this paper. Introducing these guidelines to minimize mislabeling would facilitate and substantiate cacao research, management and breeding, by preventing the spread of undesired traits and guiding the choice of potential parents to minimize inbreeding9. DMAS-qPCR would allow straightforward and cost-efficient genotyping in the country of origin in a more routinely manner, thereby contributing to a more profound insight into the genomic identity of the cultured cacao varieties. Although a limited set of genotyping markers would improve adoption of this method by laboratories in service of local farms, a larger set is bound to better reflect the genomic reality and improve genotyping accuracy. So it is important to find the sweet spot between the marker set size and genotyping power. More studies as the one presented here will allow for better management of any material held in plant collections. Nevertheless, it should be taken into account that this method does not allow multiplex analysis, in contrast to TaqMan based assays. Furthermore, successful design of DMAS qPCR assays does not guarantee successful genotype discrimination, we have tested 102 assays resulting in a 54% overall failure rate.Comparison of DMAS-qPCR results with publicly available status revealed 15.38% off-types in cacao accessions retrieved from CEFORTT and ICQC. This is undoubtedly less than the suspected level of mislabeling (15 to 44%) in germplasm collections worldwideet al. (2013) and Livingstone et al. (2011), 26 SNPs would be adequate to uniquely identify a cacao clone in a population of 84 accessions and 19 clones, respectively. By selecting the most informative markers and analyzing unique and matching genotypes, it could be shown that a limited set of 13 markers is sufficient to achieve maximum differentiation in this population (n = 65). To obtain a more global insight in the genetic diversity and informativeness of these SNPs, more genotypes of different origins should be analyzed simultaneously. To further validate the applicability of the described DMAS-qPCR SNP assay in genetic diversity studies, analysis using PCoA and STRUCTURE44 is recommended, next to descriptive analysis by means of GenAIEx v6.5 to gain insights in marker as well as population characteristics32.According to Ji In conclusion, we have developed a robust and accurate method for cacao genotype identification using a limited set of SNPs. It can be applied to a wide range of genetic diversity studies, either with or without the inclusion of additional markers. The ease of use and cost-efficiency of the method – without the need of specialized instruments – can contribute to the adoption of routine-based genotyping to prevent mislabeling in germplasm collections and select optimal breeding parents in cacao and other organisms. The described method can easily be implemented in any molecular biology lab in the context of genotyping, genetic diversity studies, parentage analysis, mutation detection and to facilitate gene mapping and marker-assisted selection for breeding purposes.et al.41 and seven Trinitario genotypes, were obtained from CEFORTT  and ICQC   was freeze-dried upon arrival  using the ALPHA 1–2 LDplus instrument (Christ) and further crushed with liquid nitrogen, before storage at −18 °C.Leaves from 65 international cacao reference accessions, representing the ten genetic groups of Motamayor ® Spin Plant Minikit (Stratec). The protocol was executed according to the suggestions of the manufacturer, with following adaptations: (1) the addition and incubation of RNase A  for 5 minutes at room temperature after transferring the lysis solution to the Prefilter before adding binding buffer A and (2) two consecutive elution steps of 50 µL instead of a single 100 µL elution. After extraction, DNA was qualitatively and quantitatively analyzed using the Nanodrop 1000 (Thermo Scientific) and Quantus™ Fluorometer (Promega), respectively. All samples were normalized to 0.5 ng/µL.Genomic DNA was isolated from lyophilized and crushed cacao leaf material with the Invisorb36, of which 12 have been associated with disease resistance27  was introduced to increase genotype discrimination power29 . In brief, each 6 µL reaction contained 1 µL common primer (1.25 µM), 1 µL AS primer  (1.25 µM), 3 µL SsoAdvanced SYBR Green Supermix (2×) (Bio-Rad) and 1 µL template DNA (0.5 ng/µL). The following cycling parameters were used for all assays: (1) 2 min at 95 °C, (2) 44 cycles of 5 s at 95 °C, 30 s at 60 °C and 1 s at 72 °C. After amplification, Cq values were collected from the LightCycler® 480 software to assess SNP genotype status by means of scatter and difference plots, as described by Lefever et al.29.DMAS-qPCR assays were designed for only 102 SNPs, as 13 failed to map, using the GCA_00403535.1 cacao genome build (Matina 1–6) and the in-house developed primerXL web toolFor SNPs without annotation in ICGD, a reference genotype status in the samples was determined by means of SBG using a MiSeq instrument (Illumina). In brief, primers surrounding the SNPs were designed using primerXL, followed by PCR in the samples of interest. After library preparation and sequencing, the reads were aligned to the Matina 1–6 genome using the Burrows-Wheeler aligner (BWA). Reads were sorted and indexed with SAMtools and duplicates removed using Picard. Finally, variants in the aligned reads were identified using the Genome Analysis ToolKit (GATK). Through variant allele frequency (VAF) analysis, the ratio of the number of reads harboring the variant and the total read count are calculated. Subsequently, homozygous (~100% or ~0% VAF) and heterozygous (~50% VAF) SNPs could be differentiated,32. Nucleotides were given the following aliases in the analyses: A = 1, C = 2, G = 3, T = 4 and missing = 0. The program allows the computation of the mAF, MAF (1 − mAF), I, Ho, He, F, PIDsib and the analysis of matching and unique genotypes, amongst others. Output of the analysis was used to determine the minimum SNP set required to achieve maximum genotype power, sensitivity and specificity. Through pair-wise multi-locus matching, duplicates  and off-types (homonymous mislabeling – accessions with different genotypes but same name) could be identified24. Three mismatches between DMAS-qPCR results and reference status were permitted to account for genotyping errors in each analyzed cacao accession.GenAIEx v6.5 was used to determine key descriptive statistics for the genetic markers tested on the available cacao populationSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6


In this paper we discuss the solution submitted by our team to this competition.One of the 3 tracks of iDASH Privacy & Security Workshop 2017 competition was to execute a whole genome variants search on private genomic data. Particularly, the search application was to find the top most significant SNPs (Single-Nucleotide Polymorphisms) in a database of genome records labeled with Privacy and confidentiality of genome data had to be ensured using Intel SGX enclaves. The typical use-case of this application is the multi-party computation  of the SNPs which statistically differentiate control and case genome datasets.Our solution consists of two applications: (i) compress and encrypt genome files and (ii) perform genome processing (top most important SNPs search). We have opted for a horizontal treatment of genome records and heavily used parallel processing. Rust programming language was employed to develop both applications.Execution performance of the processing applications scales well and very good performance metrics are obtained. Contest organizers selected it as the best submission amongst other received competition entries and our team was awarded the first prize on this track. In this paper we describe the solution submitted by our team to the second task of iDASH Privacy & Security Workshop 2017 competition  gives the number of control VCF files containing genome difference SNP. We recall that homozygous SNPs count twice in these presence maps.χ2 test allows to determine if there is significant difference between observed and expected frequencies in one or more categories. In the context of genome search use-case the χ2 test is used to find which SNP distributions have the largest evidence of statistical difference between case and control datasets.The Pearson’s s be a SNP found in a VCF file. Let nctrl and ncase denote the number of times SNP variation s appears in control and respectively case files (zero if not present). We have nctrl=CTRL_CNT[s] and ncase=CASE_CNT[s]. Let Nctrl and Ncase be respectively the number of control and the number of case files multiplied by 2 (as homozygous counts twice). SNP s observed frequencies O and expected frequencies E are given by: Let s frequency in both datasets.Here, χ2 test statistic value is equal to p-value is the probability that a random variable following a χ2 distribution will be larger than the above test statistic value . The SNPs which have the largest p-values are those whose distributions differ the most in case and control datasets. To find the top K most significant SNPs one needs to compute p-values for each SNP in input genome dataset and to return K SNPs with largest p-values.The In this sub-section we describe the global software architecture. We limit the discourse to genome processing part only and ignore the key-exchange part. Thus, in what follows we suppose that actors trust the SGX enclave and that the enclave has all the decryption keys.K most important SNPs (denoted the processing part). Each part is implemented in a separate application. The first application (compress and encrypt) is performed by the actors possessing VCF files and the second one by the enclave. In the following subsections we discuss in more details these applications.We split the genome processing use-case operation in two parts: (i) compress and encrypt input genome data files and (ii) build CTRL_CNT and CASE_CNT maps and compute top As expected, this application compresses and encrypts a VCF file given as input. The compression step consists in rewriting SNPs from a text format into an equivalent binary format. Each VCF file SNP (i.e. a data line) is packed into 80 bits 10 bytes). Table 0 bytes. Before proceeding to encryption a given number of contiguous SNPs are grouped into blocks. Block binary SNPs are encrypted using AES in GCM mode. The format of a block of encrypted SNPs is given in Table In a compressed VCF file all the blocks contain the same number of SNPs, except for the last one. Compressed and encrypted files are smaller when compared to initial ones, therefore the network traffic between the actors and the computation server is also lower. Another advantage is that using binary format input files, inside an enclave, is less prone to side-channel information leakage.χ2 statistic p-values.Searching for the top most important SNPs starts once all of the encrypted VCF files are received by the computation server. In the high-level algorithm given in previous section, during first step SNP counters CTRL_CNT and CASE_CNT are computed. All input VCF files must be read through before these maps are completely filled in and can be used to compute χ2 statistic for a given SNP s we need only the presence counters for this SNP . The idea of horizontal partitioning the computation follows from the previous remark. So instead of filling presence maps for all the SNPs, they are only filled for a small range of SNPs. The χ2 statistics are calculated for these SNPs. A list of top most significant SNPs is updated as a function of obtained p-values. Afterwards this procedure is repeated for a new range of SNPs.We notice that for computing the The main advantage of horizontally partitioning the treatment is that SNP maps (CTRL_CNT and CASE_CNT) size stay small. A drawback is that input VCF files need to be accessed several times. The fact that SNPs are ordered (by chromosome and position) in input VCF files allows to reduce the number of VCF files accesses.The block diagram of the processing application is shown in Fig. During each main loop iteration a specific range of SNPs is processed. The 4 blocks of the first VCF file serve as reference range (step 4 in diagram). Thus, the SNP range to treat starts at the first SNP from the first block and ends at the last SNP of the fourth block. In the inner loop (label “B”), the SNPs belonging to the reference range from each VCF file are used to update SNP presence maps: CTRL_CNT for control files and CASE_CNT for case files. Last treated block index for each VCF file is memorized so that the next time (next main loop iteration) the application knows which block to start with.χ2 statistic p-value is computed for each SNP in maps CTRL_CNT and CASE_CNT. The global list of top K most important SNPs is adequately updated as a function of newly obtained SNP p-values (step 7 in diagram). Main loop is executed till all SNPs have been treated.Once all VCF files have been treated Rust programming language was used to implement these applications. Instead of the C/C++ framework provided by Intel, a Rust framework for programming SGX based applications is used.zero-cost abstraction,type safety,guaranteed memory safety,threads without data races.Rust is a new system programming language supported by Mozilla research. The aim of Rust is to be a language for highly concurrent and safe systems. Unlike C/C++, Rust has been designed with safety in mind from ground up. Mozilla describe Rust as a “safe, concurrent and practical language”. The performance of idiomatic Rust programs is comparable to ones written in C/C++. The most important strengths of Rust as a programming language are: Rust is influenced by safe functional programming languages like Haskell and OCaml, and its syntax is very close to the ML family languages.Intel provides a Software Development Kit (SDK) for implementing SGX application. This SDK is a set of libraries and tools that allow developers to write and debug SGX applications using C/C++ language. As previously said C/C++ are unsafe languages. Developers should be very careful when implementing SGX applications in C/C++ in order to prevent memory bugs , which could lead to vulnerabilities and thus compromise enclave application security. Using Rust it is possible to circumvent this pitfall without sacrificing execution performance.Rust-SGX SDK  is a fraIn this section we describe in more details the applications we have implemented and the obtained execution results on a sample genomic dataset. The sample dataset has 1000 case and 1000 control VCF files. The size of dataset is approximatively 27 GB. It was provided by contest organizers for testing purposes. Final evaluation datasets were similar to this one.All the applications have been executed on a 5-th generation Intel(R) Xeon(R) CPU E3-1240 (3.50GHz) processor with 16 GB of RAM memory and an SSD disk.The first implemented application builds a compressed and encrypted version of each VCF file given as input. It starts by parsing a given number (i.e. block size) of VCF data lines, encoding them in binary format and encrypting them using AES. We recall that each data line is an SNP variation. In our study, we have chosen to encode 2080 SNPs per block, 2080 being a common multiple of binary format SNP size (10 bytes) and AES block size (16 bytes). A block of SNPs has a size of approximatively 20 KB. OpenSSL library  is used The compress and encrypt application uses 4 threads. Each thread treats an input VCF file. Newly obtained encrypted VCF files are written to disk. Disk input/output bandwidth is the bottleneck of this application, which has to read 27 GB and write 5.5 GB. When an SSD disk is used to store output files the execution time is approximatively 65 s. We have also tested to output files to a RAM disk. In this case execution time dropped to 50 s, representing a 23% gain.ecall functions in SGX terms) encl_init loads and initializes enclave binary module,encl_register registers a given list of VCF files labeled with control and case,encl_begin starts treatment of new SNP range ,encl_end ends main loop iteration.The processing application has two binary modules, one (the main application) is executed in the public domain and other (enclave application) in the protected domain. Enclave binary module is signed, which ensures that only authenticated modules are executed by the SGX extension. As said earlier, we ignored the key-exchange phase and the multi-party computation flavor of the studied use-case. A single AES decryption key is hard-coded into the enclave application. Communication between main application and enclave is done through a light interface . Finally, using these statistics values the global list of top K most significant SNPs is updated.Enclave application is executed on 8 threads. Each thread treats a SNP range from a file (i.e. inner loop in the block diagram). A custom thread-safe hash map is used for counting SNP variations. The hash map is cleared when treatment of a new SNP range begins . After SNP variations from all VCF files have been added to the hash map  the output list of most important SNPs is updated . The execution time of the processing application is less than 7 sec! The main part of the execution time is due to VCF file reading. No significant differences were observed when either SSD or RAM disk was used as input medium.χ2 statistic using numeric integration. Both building blocks were designed to leak as little information as possible about encrypted data (hide SNP position and contents).Hereafter, we describe two important building blocks used in the processing application. In particular, a lock-free hash-map we have implemented to count SNP variations (CTRL_CNT and CASE_CNT) and the computation of SNP variations are counted and stored in associative maps CTRL_CNT and CASE_CNT. The key of these maps is built from SNP fields: chromosome number, variation position, reference and alternate base. These fields uniquely identify a SNP variation. Hash maps  are used as implementations for SNP variations counters. A simple Fibonacci hashing method is used to map a SNP identifier to the hash table space. Hashes are XOR-ed with a random value, generated at enclave application start, in order to minimize information leakage from memory access patterns of sequel enclave application executions.Rust standard library hash table implementation (std::collections::HashMap) is not thread safe. A synchronization mechanism (e.g. mutex) is needed for write accesses. One can synchronize write accesses at the global hash map level. The issue of this solution is that the whole hash map is blocked during a write operation and thus a single thread only will be able to use it. In our solution, we have implemented a hash map from scratch where synchronization is done at element level. With this implementation each thread is able to write/update the hash map in parallel.N elements indexed by the hash of SNP identifier. N is chosen such that the hash map size is lower than processor L3 cache size (8MB in our case). In our application hash map can store up to N=282914 SNPs (approximatively 6630KB). This size was empirically chosen in order to minimize hash map memory reallocations  and to have a reasonable fill ratio (≈50−60%). Each hash map element contains 3 fields: state of the current element,element key (SNP identifier),element value (SNP variation count).In our implementation the hash map is an array of empty – element is empty and available for new entry,update – ongoing entry element creation or value update,wait – entry is initialized and can be updated.An element can be in one of the following states (given by field state): At the beginning of the execution all hash table elements are in empty state. When a new SNP entry is added or an existing SNP is updated, update state is used to synchronize concurrent threads trying to access this element. State wait means that element is free to be updated.χ2 statistic p-value computation is performed in two steps: statistic value is computed from observed and expected SNP frequencies,χ2 distribution survival function is evaluated to obtain the p-value.The p-values (i.e. top most important ones) is equivalent to finding SNPs which have the lowest χ2 statistic values. In our implementation the top most important SNPs list is updated  for this task. In order to accelerate this computation, we store an array of precomputed survival function values and perform the numerical integration for small ranges only. Our numerical integration algorithm implementation has no information leakage and the array of precomputed values is obliviously accessed.The In this paper we have discussed the solution submitted to the second track of the 2017 iDASH competition. The goal of this track was to develop an application for searching the top most important SNPs in a genomic dataset. It was requested to use Intel SGX in order to ensure the privacy and the confidentiality of genomic data.Our discourse begins with an introduction of a global view of the solution: a typical use-case and the block diagram. Afterwards we describe some implementation details and execution results on a sample dataset. Shared memory parallelism (i.e. threads) was heavily used to increase execution performance. Aggregated execution time of compression & encryption and processing applications is about 1 min. The compression & encryption step is the heaviest part, needing approximatively 50 s (mainly due to I/O bandwidth bottleneck). In the typical use-case we have discussed about, this execution time is evenly distributed over implied parties (i.e. each party encrypts its own VCF files). Data space size of our processing application was smaller than processor’s L3 cache size which contributed a lot to minimizing the number of costly page evictions.χ2 statistics computation, with small information leakage.Intel SGX enclave mechanism is not the “holy grail” for privacy preserving computations. Several works from the literature describe side-channel attacks on SGX enclave applications, all these attacks being possible because of code vulnerability. Even if it was outside of contest’s goal, we have given a particular attention to lower information leakage from application execution in SGX enclave. We have implemented our enclave application and in particular two software blocks, hash table and In perspective, we think that our compression step can be further optimized. That is to say, a larger compression ratio can be obtained without limiting the functionality of the performed computation. This will allow to further optimize communication size and the performance of the processing application. Also, we think that a more formal analysis of the information which our enclave application leaks is needed in order to better understand the challenge of the outsourced computation.


In cattle, this type of study has only been done once in Holstein. In our study we performed a genome-wide analysis of ASE in 19 Limousine muscle samples. We identified 5,658 ASE SNPs  in 13% of genes with detectable expression in the Longissimus thoraci muscle. Interestingly we found allelic imbalance in AOX1, PALLD and CAST genes. We also found 2,107 ASE SNPs located within genomic regions associated with meat or carcass traits. In order to identify causative cis-regulatory variants explaining ASE we searched for SNPs altering binding sites of transcription factors or microRNAs. We identified one SNP in the 3’UTR region of PRNP that could be a causal regulatory variant modifying binding sites of several miRNAs. We showed that ASE is frequent within our muscle samples. Our data could be used to elucidate the molecular mechanisms underlying gene expression imbalance.Allelic imbalance is a common phenomenon in mammals that plays an important role in gene regulation. An Allele Specific Expression (ASE) approach can be used to detect variants with a  It is therefore important to study this variability in order to understand gene regulation. There are different approaches to such studies: expression quantitative trait loci (eQTLs) and Allele Specific Expression (ASE) analyses. The combination of both approaches is highly effective at locating cis- and trans- regulation of gene expression.Gene regulation is a fundamental process in the development and maintenance of organisms. In mammalian genomes the variability of gene expression is a current phenomenoncis or trans. They can be identified by expression genome-wide association studies (eGWAS), an analysis method computing the likelihood of a polymorphism affecting gene expression. Unfortunately this type of analysis needs a large number of samples to minimize false-positives3. Many human eQTL mapping studies have been carried out6 including the recent Genotype-Tissue Expression (GTEx) project7. However in cattle there is a lack of studies. So far, there has been only one performed in dairy cattle, in Holstein-Friesians (HF), Jerseys (J) and HFxJ crossbreeds8.An expression quantitative trait locus (eQTL) is a DNA region with some nucleotide sequence differences  that affects the expression level of a gene in 9. This approach is complementary to identifying variants affecting gene expression with eQTL studies because we can use a smaller number of samples10. Genome-wide studies of ASE have been performed in different species  including livestock species . In addition, some ASE genes were detected to impact economically important traits17.Allele specific expression  analysis is a robust approach to quantify expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites18. In the second study, they detected 19,082 ASE SNPs  across 18 different tissues from one lactating Holstein dairy cow19.In cattle, only two studies have been performed so far, both in Holstein. In the first study, they discovered 473 ASE SNPs across 5 bovine blastocysts In our study, we performed a genome-wide investigation of ASE using 19 Limousine calf muscle samples. We distinguished between imprinting  and allele specific expression  to focus on the later. We used whole-genome sequences (WGS) and RNA-Seq data from these 19 muscle samples in our analysis. To the best of our knowledge, it is the first ASE survey in a beef breed and with the largest number of different animals.20. They were fattened in a single feedlot and fed ad libitum with wet corn silage. They were humanely slaughtered in an accredited commercial slaughterhouse when they reached 16 months. Longissimus thoracis (LT) muscle samples were dissected immediately after death and tissue samples were snap frozen in liquid nitrogen and then stored at −80 °C. The animals used in this study were beef animals raised for commercial reasons from a previous study20 and were slaughtered by certified slaughterhouses in accordance with French animal protection regulations .Nineteen Limousine bull calves were selected from a large study on the genetic determinism of beef and meat quality traits21 with the aln option with default parameters for mapping reads to the UMD3.1 bovine reference genome22. Potential PCR duplicates were removed using the MarkDuplicates tools from the Picard package version 1.4.023. Only properly paired reads with a mapping quality of at least 30 (-q = 30) were retained. The resulting BAM files were then used for all subsequent analyses.DNA was extracted from the 19 muscle samples using the Wizard Genomic DNA Purification kit (Promega). Each purified DNA sample was assessed by agarose gel electrophoresis. DNA concentration was measured with a Nanodrop ND-100 instrument (Thermo Fisher Scientific). Sequencing libraries were prepared using TruSeq SBS v3-HS Kit (Illumina) and the whole-genome sequenced using a 2 × 100  bp paired-end approach on an Illumina HiSeq2000. Sequence alignments were carried out using the Burrows-Wheeler Alignment tool (BWA-v0.6.1-r104)26. Briefly, after transfer to ice-cold RNeasy RLT lysis buffer (Qiagen), LT tissue samples were homogenized using a Precellys tissue homogeniser (Bertin Technologie). Total RNA was isolated using RNeasy Midi columns (Qiagen) and then treated with RNAse-free DNase I (Qiagen) for 15 min at room temperature according to the manufacturer’s protocols. The concentration of total RNA was measured with a Nanodrop ND-100 instrument (Thermo Scientific) and the quality was assessed with an RNA 6000 Nano Labchip kit using an Agilent 2100 Bioanalyzer (Agilent Technologies). All 19 samples had an RNA integrity number (RIN) value greater than eight.RNA extraction and sequencing was performed as previously described27.The mRNA-Seq libraries were prepared using the TruSeq RNA Sample Preparation Kit (Illumina) according to the manufacturer’s instructions. Briefly, Poly-A containing mRNA molecules were purified from 4 μg total RNA of each sample using oligo (dT) magnetic beads and fragmented into 150–400 bp pieces using divalent cations at 94 °C for 8 min. The cleaved mRNA fragments were converted to double-stranded cDNA using SuperScript II reverse transcriptase (Life Technologies) and primed by random primers. The resulting cDNA was purified using Agencourt AMPure XP beads (Beckman Coulter). Then, cDNA was subjected to end-repair and phosphorylation and subsequent purification was performed using Agencourt AMPure XP beads. These repaired cDNA fragments were 3′-adenylated producing cDNA fragments with a single ‘A’ base overhung at their 3′-ends for subsequent adapter-ligation. Illumina adapters containing indexing tags were ligated to the ends of these 3′-adenylated cDNA fragments followed by two purification steps using Agencourt AMPure XP beads. Ten rounds of PCR amplification were performed to enrich the adapter-modified cDNA library using primers complementary to the ends of the adapters. The PCR products were purified using Agencourt AMPure XP beads and size-selected (200 +/− 25 bp) on a 2% agarose Invitrogen E-Gel (Thermo Scientific). Libraries were then checked on an Agilent Technologies 2100 Bioanalyzer using the Agilent High Sensitivity DNA Kit and quantified by quantitative PCR with the QPCR NGS Library Quantification kit (Agilent Technologies). After quantification, three different tagged cDNA libraries were pooled in equal ratios and a final qPCR check was performed post-pooling. Each library pool was used for 2 × 100 bp paired-end sequencing on one lane of the Illumina HiSeq2000 with a TruSeq SBS v3-HS Kit (Illumina). After sequencing, the samples were demultiplexed and the indexed adapter sequences were trimmed using the CASAVA v1.8.2 software (Illumina). The quality of the raw sequence reads was assessed using FastQC and QualimapBos taurus reference genome sequence was downloaded from Ensembl . To align the reads to the assembled reference genome the STAR RNA-Seq (version 2.4.2a) aligner was used28. Default values were used for mapping except for the intron alignment . Reads for each sample were mapped separately to the reference genome sequence. Only paired reads were retained for alignment. The number of paired-reads uniquely aligning to transcribed regions of each transcript was calculated for all genes of the annotated transcriptome. The transcript paired-read count was calculated as the number of unique paired-reads that aligned within the exons of each transcript, based on the coordinates of mapped reads.The 30. First, reads were subjected to local realignment, coordinate sorting, base quality score recalibration and indel realignment. We then performed SNP and indel discovery and genotyping. In the GATK analysis, we used a minimum confidence score threshold of Q30 with default parameters. We also used multi-sample variant calling in order to distinguish between a homozygous reference genotype and a missing genotype among the analysed samples. SNPs were annotated with VEP31 using the transcript set from Ensembl 87.SNPs were called following the best practices from GATK (version 3.4–46) with HaplotypeCaller for DNA and RNA sequence data respectively9 to calculate read counts per allele. We performed an N-masking (replacing for each identified variant the nucleotide of the bovine genome reference sequence by N) to remove mapping bias and we only kept overlapping heterozygous SNPs from DNA and RNA to remove discordant genotypes, possibly due to imprinting or RNA editing. We only kept candidates with minimum 10 reads for at least one allele. To determine if the imbalance was significant, we used a binomial test against an allelic ratio of 0.5 with a p-value of 5% (Python).We used ASEReadCounter32 . We used HTSeq-count33 to determine the number of reads for each transcript per individual and normalised this using DESeq234. We computed the Spearman’s rank correlation coefficient between the genotypes of ASE SNPs or SNPs in LD and expression level of the corresponding transcript. We performed a correction for multiple testing, for the same transcript, using the Bonferroni correction.The SNP being tested for ASE might not be the variant regulating the expression of the gene. So in order to determine the SNPs within the regulatory regions or potentially the regulatory variant itself, we detected SNPs in linkage disequilibrium with our ASE SNPs using PLINK 1.9First-strand cDNA was synthesized from 500 ng of DNase I-treated total RNA using the SuperScript III First-Strand Synthesis System kit (Thermo Fisher Scientific) and oligo-dT primers with random hexamers, according to the manufacturer’s instructions in a total volume of 20 μl. The resulting cDNA was diluted 1:10.35. One of the forward or the reverse PCR primer had a 5′-biotin modification and was HPLC-purified. Primers were synthesized by IDT and are listed in Table 2, 200 μM of each dNTP and 0.3 μM of each PCR primer. The following touchdown cycling protocol was used: 95 °C for 2 min, followed by 13 cycles of 95 °C for 1 min, 1 min of annealing  and 72 °C for 1 min 30 s. These initial cycles were followed by 20 cycles of 95 °C for 1 min, 55 °C for 1 min and 72 °C for 1 min 30 s, and a final extension step at 72 °C for 10 min. To check the quality of the amplification 10 μl of PCR products were then analysed by gel electrophoresis with a 1% agarose gel.PCR and Pyrosequencing primers were designed using PyroMark Assay Design 2.0 (Qiagen) with sequences previously masked with RepeatMaskerBiotinylated PCR products (20 μl) were immobilized on streptavidin-coated Sepharose beads , purified, washed and denatured using a 0.2 M NaOH solution and rewashed all using the PyroMark Vacuum workstation (Qiagen) as recommended by the manufacturer. Purified single-stranded PCR product was annealed to the pyrosequencing primer (diluted to 0.3 μM) and then sequenced using the PyroMark Q24 system (Qiagen), following the manufacturer’s instructions. For validating candidate ASE SNPs, DNA and RNA (cDNA) from each sample were pyrosequenced simultaneously. The proportions of individual alleles for each SNP were obtained using the PyroMark Q24 software version 1.0.10 (Qiagen). Genomic DNA was examined to confirm the heterozygosity. The final ASE ratio for each SNP of each sample analysed was calculated using the following formula: ASE ratio =  RNA/ genomic DNA.36 for both sequences with all known bovine miRNAs using the default parameters. Bovine miRNA sequences were retrieved from the miRBase database (version 21). To finish, we selected miRNAs which could bind only one of these two sequences.Prediction of microRNA (miRNA) binding sites was done as follows: first, for SNPs within 3′UTR regions, flanking sequences (+/−100 bases) were retrieved using the whole-genome reference sequence (UMD3.1). Then we created two versions of this sequence, one with the reference allele and one with the alternate allele. Next we used miRandaSequencing of all 19 whole-genome sequences generated a total of 5.3 billion of raw paired-end reads corresponding to 537.51 Gb. Approximately, 92 to 400 million paired-end reads were obtained for each library. On average, 83% (56–92%) of the paired-end reads were properly aligned with BWA on the UMD3.1 bovine reference genome Table .26, 17 of our 19 RNA samples were sequenced and mapping was performed using BWA (version 0.5.9-r16)21. 63–76% of the mapped reads were aligned. The increase of the mapping rate (on average 17.8% more reads) indicates that STAR performs best. This is largely because STAR is a splice aware aligner. The mapping performance is comparable to other studies done in cattle with STAR and the same reference genome (UMD3.1). For instance 90% of transcripts from Holstein-Friesian peripheral blood leukocytes were mapped37.Sequencing of all 19 RNA-Seq libraries generated a total of 1.4 billion raw paired-end reads. Approximately, 35 to 180 million paired-end reads were obtained for each library. On average, 89% (86–91%) of the reads were uniquely mapped Table . In a pr33 and was normalized with DESeq234. In our samples, we found 18,206 transcripts  with an expression in at least 3 individuals among the 19.The count of transcripts was performed using HTSeq-countWe identified 11,943,766 and 269,390 single nucleotide variants (SNVs) from WGS and RNA-Seq data, respectively.We identified on average 11,344,542 +/− 7.12% SNVs per individual from WGS and on average 53,732 +/− 31.85% SNVs per individual from RNA-Seq reads. On average, 26.2% and 34.2% of the detected SNVs were heterozygous in WGS and RNA-Seq, respectively. Among the SNVs identified from WGS , 106,700 (39.61%), 14,734 180 (5.47%), 53,630 (19.91%) as intergenic, intronic, upstream gene, downstream gene variants, respectively. We identified 24,160 (8.97%) exonic variants .38 or 65.6% of intergenic and 33.6% were identified of intronic variants in Qinchuan cattle39. Interestingly, we found 20.20%  of SNPs identified from our RNA-Seq data as intergenic. These SNPs could be located in transcripts of large intergenic non-coding RNAs. Indeed, we found 7,706  intergenic SNPs from our RNA-Seq data within lincRNAs previously identified from six of our samples by Billerey and collaborators25. We also found 39.61% of SNPs identified from our RNA-Seq data in intronic regions. These SNPs could be from premature transcripts (before splicing).We found 67.8% of SNPs from WGS data as intergenic. This percentage is in agreement with the 70.4% of the intergenic part of the bovine genome. This proportion is also similar in others studies done in cattle. For instance 73% of intergenic, 26.2% of intronic, 4.26% of downstream gene and 4.14% of upstream gene variants were found in Hanwoo and Yanbian cattleWe compared SNPs detected from WGS with SNPs from RNA-Seq data for each individual. On average, we detected 11,306,326 SNPs only from WGS , 15,516 SNPs only from RNA-Seq reads , and 38,217 of the SNPs from both  and 20% of the genes with at least one heterozygous SNP . On average, we detected 574 ASE SNPs per individual  corresponding to 3.2% of the heterozygous SNPs from RNA-Seq data . However, the number of ASE SNPs per chromosome is strongly correlated with the number of coding genes  and with the number of expressed genes   were not polymorphic in our Limousine animals, 43 others SNPs (2.1%) are not showing heterozygosity among our 19 individuals and 38 SNPs (1.9%) are located on the chromosome X . For the 1,123 remaining ASE SNPs (60.0%) identified in Holstein muscle, we found at least one heterozygous Limousine animal. This discrepancy might be due to differences in ASE detection methods or in breed gene regulation.We compared our detected ASE SNPs with ASE SNPs previously identified by Chamberlain and collaborators in a Holstein muscle sample42 .In order to study the impact of genes affected by ASE on specific biological pathways, we performed a Gene Ontology (GO) enrichment. This analysis was carried out by first converting the cow gene list into a human gene list using Biomartq-value < 0.05), with many of these related to striated muscle development  and 53% for allele A (complementary base of T) in gDNA and we observed 33% and 67% in cDNA  (r2 > = 0.75) with our predicted ASE SNPs. We obtained 2,955 SNPs (including ASE SNPs) with genotypes for all the 19 individuals. For each transcript showing allele-specific expression, we calculated the Spearman correlation coefficient score between expression level of genes containing ASE SNPs and genotypes of SNPs in LD with ASE SNPs. We computed correlations between 2,794 SNP genotypes and 1,085 unique transcripts, averaging 2.74 SNP genotypes per transcript . We found 100 significant correlations with 45 transcripts  including 42 negative correlations  with a SNP  in upstream and exonic (synonymous variant) regions of APMAP respectively. The second SNP shows ASE in one individual (LIM8) among the nineteen. The genotypes of the first SNP  is significantly correlated  to the APMAP level expression. Indeed, we found on average for the 19 animals 404, 323 and 214 transcripts (read counts) for C/C, C/T and T/T animals . None of the allele-specific sequences of these SNPs were located in predicted TFBS.For example, we found one SNP  in LD  with a significant correlation in the upstream region without detecting any TFBS.We extended the TFBS search for 5 other SNPs in 5 different genes  with a SNP . Both SNPs are in 3′UTR region of the PRNP gene and show ASE in four individuals among the nineteen. The genotypes of the first SNP  is significantly correlated  to the PRNP expression level. On average, the PRNP expression level was 4,641 transcripts for G/G individuals, 4,455 for G/A individuals and 3,324 for A/A individuals  and 5 miRNAs which could bind the alternate allele (A)  in LD  Table . InteresA) Table . This co58. However, no study describes so far miRNAs expressed in Limousin animals. We found that all fourteen miRNAs impacted by the SNP rs109763272 are expressed in muscle53 including in Longissimus dorsi/thoracis53 . We found no binding sites for 3 of these SNPs and for the remaining 10 SNPs we always found allele-specific binding sites for both alleles .For most of the 45 genes for which we had a significant correlation between expression level and SNP (ASE SNP or SNP in LD with an ASE SNP) genotypes we couldn’t find SNPs altering TFBSs or the binding sites of miRNAs. It is therefore likely that epigenetic mechanisms might also play a role, rather than just AOX1) gene encodes a homodimeric protein, which produces hydrogen peroxide. In mouse, it is involved in myogenesis59. Therefore, it might play a role in muscle development in cattle. We detected eleven ASE SNPs in this gene with six also detected by Chamberlain and collaborators19. Among these 6 ASE SNPs, three had genotypes significantly correlated to the expression of this gene. In addition, we found 13 others SNPs in AOX1 with significant correlation  gene encodes a cytoskeletal associated protein, which exists as multiple isoforms60. This actin associated protein plays a significant role in regulating cell adhesion and cell motility. It is also important for the early smooth muscle cell differentiation in mouse61. In cattle, palladin might play dual roles (positive and negative) in maintaining the proper skeletal myogenic differenciation62. We detected two ASE SNPs in this gene including one experimentally validated by Pyrosequencing. Interestingly, these SNPs are within a QTL region associated with average daily gain (ADG) trait in Hereford63.The palladin (CAST) gene encodes an inhibitor of protease μ-calpain, which has a known effect on beef muscle tenderness variation64. Interestingly, a more recent study confirmed that CAST affected meet tenderness in Longissimus muscle in Limousine crossed-breed animals65. We detected seven ASE SNPs in this gene including two experimentally validated.The calpastatin (AOX1 is a missense variant. This SNP (rs109201304) modifies a glycine residue into a cysteine amino acid and is located within a protein region conserved in mammals . They don’t alter the primary sequence of the corresponding proteins however it has been shown that codon usage can vary between genes and that this codon bias can affect RNA secondary structure, splicing and translation72. Further work is needed to investigate the phenotypic impact of these variants/genes.We didn’t find any missense polymorphisms in Longissimus thoracis muscle. The high number of genes potentially impacted by allele-specific imbalance prompted us to investigate if some of these ASE SNPs could have a major impact on muscle biology.Overall we identified 5,658 ASE SNPs in 13% of genes  with detectable expression in First we looked if ASE SNPs could induce a gene loss-of-function. We didn’t find any ASE SNP that could create or remove stop codons and causing consequently protein truncations or changes in the open reading frame, respectively. However, we identified 14 ASE SNPs that according to the VEP annotation have or could perturb the splicing of the corresponding gene. Further work is needed to check this potential impact.Zfp106 in mice induces several skeletal muscle phenotypic abnormalities75, such as severe muscle wasting74, loss of muscle strength75 and degeneration of muscle fibers75 in homozygous knock out Zfp106 −/− mice. Heterozygous Zfp106 +/− mice are comparable to wild type littermates75. These results suggest that ZFP106 might not be a dosage-sensitive gene and that haploinsufficiency of ZFP106  might not impact muscle physiology. We also found a deleterious ASE SNP (rs110365838) within MAP4, a muscle-specific microtubule associated protein which is expressed in early myogenesis76 and that is required for muscle cell differentiation77. This ASE SNP was detected in two animals (LIM2 and LIM15). We didn’t find, so far, any information on potential consequences of deleterious variants within this gene. However, because of the critical role of MAP4 in muscle development, it will be interesting to investigate if the two heterozygous animals for this ASE SNP have normal amount of MAP4 protein.Second we investigated further the 421 missense ASE SNPs. According to the VEP annotation, only 37 of those missense ASE SNPs are predicted to be deleterious. 95% of these deleterious ASE SNPs are found in only one or two animals. Interestingly, we found one T/C deleterious ASE SNP  within ZFP106 in one animal (LIM18). ZFP106 encodes a zinc fingered RNA binding protein. Disruption of MYOG), a muscle-specific transcription factor required to induce myogenesis78, had in total 21 ASE SNPs, including 5 and 7 in downstream and 3′UTR regions, respectively. However, disruption of murine myogenin showed no overt effects in heterozygous Myog +/− mice79 suggesting that a potential reduction of MYOG in animals heterozygous for those 12 ASE SNPs might not have phenotypic consequences.Third, we examined if ASE SNPs could impact genes important for muscle cell development or function. We focused on ASE SNPs located in downstream, upstream, 5′ or 3′ UTR regions, as they might have an effect on the regulation of the transcription of important genes. We found that myogenin (We performed a genome-wide survey of ASE using 19 Limousine muscle samples combining WGS and RNA-Seq data. This analysis shows that ASE is pervasive in beef muscle. We identified 5,658 ASE SNPs located in 2,119 genes and 37.2% of these ASE SNPs are found within QTLs associated to meat or carcass traits. We validated 5 out of 6 selected ASE SNPs suggesting that our pipeline identify mostly true ASE SNPs. In addition, we detected SNPs with genotypes significantly associated with gene expression levels.PRNP that could be a causal mutation by modifying binding sites of several miRNAs. We showed that our in silico ASE approach can facilitate the identification of candidate cis-regulatory SNPs. However, further work is needed to validate these candidates. In the future, functional analyses of the impact of polymorphisms within TF or miRNA binding sites will try to elucidate the molecular mechanisms underlying gene expression imbalance.For example, we identified one SNP in the 3′UTR region of Supplementary informationsTable S1Table S2Table S3Table S4Table S5Table S6Table S7Table S8Table S9Table S10


We combined and re-phased the genotypes from NARD and 1KGP3 to construct a union set of haplotypes. This approach established a robust imputation reference panel for Northeast Asians, which yields the greatest imputation accuracy of rare and low-frequency variants compared with the existing panels. NARD imputation panel is available at https://nard.macrogen.com/.Here, we present the Northeast Asian Reference Database (NARD), including whole-genome sequencing data of 1779 individuals from Korea, Mongolia, Japan, China, and Hong Kong. NARD provides the genetic diversity of Korean ( During the past decade, the reference panels with population-scale whole-genome sequencing (WGS) have enabled the extensive human genetic research , 2. Theyhttp://www.worldometers.info/world-population), the majority of genetic studies and reference panels are biased to European ancestries [Despite Northeast Asians account for 21.5% of worldwide population , consisting of 1779 individuals from Korea, Japan, Mongolia, China, and Hong Kong. The goal of this study is to establish a high-quality population-specific reference panel for the genetic studies and precision medicine in Northeast Asia without the aforementioned issues.n = 850), JPN (n = 396), MNG (n = 384), CHN (n = 91), and Hong Kongese  with deep  or intermediate  sequencing coverages  and short insertion/deletion . Also, transition to transversion (Ti/Tv) ratios were consistent across the samples , Khalkha Mongols , and other Mongolians including Barga, Daringanga, Kazakh, Khoton, Uuld, Durvud, Khotogoid, and Zakhchin . They were also genetically separated into BUR and KHA/OTH  result of global human populations, individuals from the NARD were closely related to East Asians from the 1KGP3 as expected , (2) 1KGP3 (n = 2504), (3) HRC r1.1 , (4) NARD + 1KGP3 (n = 4200), and (5) NARD + 1KGP3 . To measure the imputation accuracy, we calculated the squared Pearson correlation coefficients (R2) between the true genotypes and the imputed dosages as a function of MAF in 850 KOR individuals from the NARD. The imputation performance of the NARD exceeded the 1KGP3 panel for every MAF bin , rare , and low-frequency  variants, when the haplotypes in the combined panel were re-phased by SHAPEIT3 [R2 values measured by Minimac3, as it is the standard for the quality control procedure in GWAS [R2 ≥ 0.9) compared with other panels, especially 1KGP3 (n = 7.5 million versus 6.7 million), in concordance with the imputation accuracy  [R2 = 0.71 and 0.84 for CHN and JPN cohorts, respectively), rare , and low-frequency  variants  as a reference panel for diverse Northeast Asians by performing additional imputation tests using independent cohorts of unrelated CHN and JPN individuals (ctively) . For impctively) , 14. In To investigate where the improvement of the NARD + 1KGP3 (re-phased) comes from, we divided the panel into the NARD (re-phased) and 1KGP3 (re-phased) and assessed the imputation accuracy separately. The NARD (re-phased) panel had slightly lower imputation power than the NARD + 1KGP3 (re-phased) panel, but greatly improved compared to the original NARD panel  analysis. It is known that phasing or genotype errors cause the gaps within the real IBD tracts; hence, the length of segments in phased genotype data tends to be shorter , 30. Bashttps://github.com/statgen/Minimac4). In the post-processing step, the output is assessed and provided as bgzip-compressed VCF and PLINK binary files. The server will provide the PLINK format with extra files containing predicted R2 values per variant for imputation quality check. Once imputation is finished, users will be notified by email and the result will be stored in the server for a week.We developed a user-friendly web site to provide imputation service using the NARD + 1KGP3 (re-phased) panel for researchers  based on the manufacturer’s instructions. We also used publicly available 91 CHN samples , which whttp://edicogenome.com/dragen-bioit-platform/) with the following parameters: (i) creating gVCF: “--enable-map-align-output true,” “--remove-duplicates true,” “--enable-bam-indexing true,” “--enable-variant-caller true,” and “--vc-emit-ref-confidence GVCF,” and (ii) joint calling: “--enable-joint-genotyping true.” For indels, we discarded variants greater than 49 base pairs, which are generally defined as structural variants [i)SNP: DP, QD, MQ, MQRankSum, FS, SORIndel: DP, QD, MQ, MQRankSum, ReadPosRankSum, FS, SORAnnotationsii)SNP: HapMap3.3 and 1KGP Omni2.5Indel: Mills & 1KGP gold standardTruth setiii)SNP: HapMap3.3, 1KGP Omni2.5 and 1KGP phase1 high confidenceIndel: Mills & 1KGP gold standard and 1KGP phase 1Training setiv)SNP: dbsnp138Known setRead alignment to the human reference genome (hg19) without any alternate contig, duplicate read removal, and joint calling of SNPs and indels were performed using Dynamic Read Analysis for GENomics platform  located in the low complexity regions (LCRs), which were defined by the 1KGP3 study, (ii) genotype quality < 20, and (iii) read depth < 5. After these filtration processes, SNPs and indels were phased by SHAPEIT3 (version r884.1) which provides a fast population-scale phasing with low switch-error using the following model parameters: “--states 100,” “--window 2,” and “--effective-size 15000.”We selected the 86 CHN samples in NARD with publicly available Illumina Omni 2.5 M array data from the 1KGP3, for validating variants in the NARD. A total of 1,664,330 SNPs were overlapped between NARD and Omni chip, excluding mitochondrial DNA and pseudoautosomal regions. The concordance is the cumulative sum of the matching alleles divided by the total number of loci multiplied by two, which is the maximum matching opportunity in diploid. The sex chromosomes in male are considered as diploids for this calculation.All the SNPs and indels in this study were annotated by ANNOVAR based on the RefSeq gene definition , 47. We We calculated HWE of variants in the NARD using VCFtools (version 0.1.12b) with “--hardy” option .R2 > 0.1) within 50 base pairs sliding window using PLINK.We converted VCF files of bi-allelic autosomal SNPs from the NARD and 1KGP3 into PLINK format using GotCloud (version 1.75.5). Then, we merged the two panels by PLINK (version 1.9) and extracted SNPs with genotype rate equals to 100% and MAF ≥ 1% to remove the batch effect between the NARD and 1KGP3. Finally, we pruned SNPs with linkage disequilibrium .With this processed data, we carried out PCA with Genome-wide Complex Trait Analysis version 1.91.3beta)  using  and vice versa, then they were merged into a single reference panel. In addition, the combined panel was re-phased by SHAPEIT3 using the model parameters mentioned above with “--early-stopping” and “--cluster-size 4000” parameters. We kept variants that are not located in LCRs.We separately processed 113 KOR, 79 CHN, 27 JPN, and 24 FRA individuals that are not included in the reference panels for imputation accuracy evaluation . Before imputation, the haplotypes of individuals in the four cohorts were estimated using Eagle2 (version 2.3.2). After imputation, we extracted 4,352,921, 5,427,462, 48,431,56, and 5,419,512 SNPs in the four cohorts, which were imputed by all reference panels, and none of them were present with missing genotype in the non-masked dataset. The squared Pearson correlation coefficients (R2) were calculated between the imputed dosages and true genotypes, and those values were aggregated into 11 MAF bins to measure the imputation accuracy.We performed imputation using Minimac3 with the five different types of reference panels. Imputation using the HRC panel was performed at the Michigan Imputation Server  with “length = 2.0” parameter . To evalDue to the cost-reduction and technological advancements in WGS, several groups have been focused on building the population-specific reference panels, especially for underrepresented populations in the conventional panels such as 1KGP3 –10, 13. Genotype imputation accuracy is known to be affected by several factors, and one of the major determinants is the sample size of reference panel , 36. UntRecently, the HRC panel was constructed using the genotypes of more than 30,000 individuals, mostly composed of European descent from various cohorts such as the 1KGP3 study. It is the largest publicly available reference panel, but previous studies demonstrated the poor imputation performance of this panel for CHN, admixed Africans, and Hispanic/Latino populations, even worse than the 1KGP3 panel , 55, andAs previous studies yield further increment of the imputation accuracy from their population-specific panels by combining dataset of the 1KGP3 , 8–10, wIn summary, we generated a large-scale reference panel for Northeast Asians, which will be a highly valuable resource to resolve a persistent deficiency of Asian genome data. We believe that our efforts will remarkably contribute to precision medicine in Northeast Asia.Additional file 1: Figure S1. Geographic map of the study area in the NARD. Figure S2. Correlation between the sequencing depth and number of variants. Figure S3. Transition to transversion ratio of the populations in the NARD. Figure S4. Heterozygous to homozygous ratio of the global populations. Figure S5. Number of loss-of-function variants. Figure S6. Hardy-Weinberg Equilibrium test of variants in the NARD. Figure S7. Novel variant statistics. Figure S8. Differential genetic composition of the two MNG groups. Figure S9. Imputation performance evaluation of FRA individuals. Figure S10. Imputation performance evaluation of CHN and JPN individuals. Figure S11. Length distribution of shared IBD tracts between the two individuals in each population. Figure S12. The flow chart of the pipeline consisting of four major steps for NARD imputation server. Figure S13. The cross-validation error inferred by ADMIXTURE algorithm.Additional file 2: Table S1. Variant statistics. Table S2. Variant accuracy. Table S3. Data source for imputation analysis. Table S4. Imputation performance according to types of reference panel.