Body traits are generally controlled by several genes in vertebrates (i.e. polygenes), which in turn make them difficult to identify through association mapping. Increasing the power of association studies by combining approaches such as genotype imputation and multi-trait analysis improves the ability to detect quantitative trait loci associated with polygenic traits, such as body traits.Oreochromis niloticus) using genotypes imputed to whole-genome sequences (WGS). To increase the statistical power of mtGWAS for the detection of genetic associations, summary statistics from single-trait genome-wide association studies (stGWAS) for eight different body traits recorded in 1309 animals were used. The mtGWAS increased the statistical power from the original sample size from 13 to 44%, depending on the trait analyzed. The better resolution of the WGS data, combined with the increased power of the mtGWAS approach, allowed the detection of significant markers which were not previously found in the stGWAS. Some of the lead single nucleotide polymorphisms (SNPs) were found within important functional candidate genes previously associated with growth-related traits in other terrestrial species. For instance, we identified SNP within the α1,6-fucosyltransferase (FUT8), solute carrier family 4 member 2 (SLC4A2), A disintegrin and metalloproteinase with thrombospondin motifs 9 (ADAMTS9) and heart development protein with EGF like domains 1 (HEG1) genes, which have been associated with average daily gain in sheep, osteopetrosis in cattle, chest size in goats, and growth and meat quality in sheep, respectively.A multi-trait genome-wide association study (mtGWAS) was performed to identify quantitative trait loci (QTL) and genes associated with body traits in Nile tilapia (The high-resolution mtGWAS presented here allowed the identification of significant SNPs, linked to strong functional candidate genes, associated with body traits in Nile tilapia. These results provide further insights about the genetic variants and genes underlying body trait variation in cichlid fish with high accuracy and strong statistical support.The online version contains supplementary material available at 10.1186/s12864-020-07341-z. Oreochromis niloticus) represented 8.3% of this volume [Tilapia is one of the most important fish species cultivated in the world, and is currently farmed in more than 125 countries. Total farmed finfish production reached 54.3 million tons globally in 2018, and Nile tilapia , fibroblast growth factors (FGF), disintegrin and metalloproteinase domain 12 (ADAM12), myosin light chain kinase (MYLK) and transforming growth-factor beta receptor type 3 (TGFBR3).The availability of a chromosome-level reference genome assembly  and highThe use of ultra-high-density SNPs or WGS can improve the accuracy and power of GWAS to detect QTLs associated with complex traits –30. AlthTo the best of our knowledge, no previous studies have shown the use of imputation to high-density SNP genotypes, in a combination with mtGWAS, to uncover putative causative genetic variants associated with body traits in aquaculture species. The objective of this study was to use mtGWAS and high-density SNP genotypes to increase the accuracy and power to identify both QTLs and genes associated with eight body traits in Nile tilapia.A total of 1309 animals averaging 370 days-old were phenotyped and genotyped. Average, standard deviation, minimum and maximum phenotypic values for average daily gain (ADG), body weight at harvest (BWH), waste weight (WW), head weight (HW), gutted head-on weight (HON), body length at harvest (BLH), fillet weight (FW) and fillet yield (FY) are reported in Table For WGS, the call-rate parameter excluded the highest number of SNPs (~ 12 million), whereas MAF discarded ~ 7.8 million and ~ 253 K SNPs, for WGS and imputed WGS data, respectively. The HWE filter discarded a low number of markers, ~ 1.8 million for WGS and 79 K for imputed WGS data, respectively. After quality control applied to the 50 K SNP chip, 5905, 4114 and 3665 SNPs were removed by HWE, MAF and genotyping call-rate filters, respectively, 29,587 SNPs remained for subsequent analyses. After applying sample call-rate, all samples in both WGS and 50 K SNP chip were retained .Heritability estimates calculated using the SNP-based genomic-relationship matrix (GRM) constructed with about 1 million markers ranged from 0.21 to 0.45 for the body traits analyzed here, with the lowest and the highest value determined for FY and HW, respectively  increased from 7.52 to 14.58 for ADG, from 7.63 to 14.39 for BWH, from 7.45 to 14.60 for WW, from 5.71 to 14.39 for HW, from 7.45 to 13.00 for HON, from 5.63 to 17.15 for BLH, from 7.59 to 17.75 for FW, and from 8.50 to 11.62 for FY, when comparing stGWAS against mtGWAS , associated with ADG, BWH, WW, HON and FW, and a significant SNP on LG07, in position 16,847,179 bp, for FY  common in ADG, BWH, WW, HW, and HON. The lead SNPs for FW and FY were found on LG04 and LG13, and none of those were identified in other body traits  and growth differentiation factor 6 (GDF6) on LG16 and LG22, respectively, and ankyrin repeat and SOCS box containing 2 (ASB2) associated with BWH and HON, located on LG19. The genes intercepted by lead SNPs, located in exonic or intronic regions are shown in Table A disintegrin and metalloproteinase with thrombospondin motifs 9 (ADAMTS9), located in LG05, was intercepted by a SNP in an exon region at 29,062,243 bp. Two lead SNPs for WW, located on LG09, at positions 14,670,077 and 14,674,835 bp, intercepted introns of the gene solute carrier family 4 member 2 (SLC4A2). Intronic regions of α1,6-fucosyltransferase (FUT8) and the heart development protein with EGF like domains 1 (HEG1), located on LG15 and LG16, were intercepted by lead SNPs associated with ADG and FY, respectively. Two SNPs within nucleoporin 107 (NUP107), located on LG17, were associated with both BWH and HON, on positions 19,609,147 and 19,612,729 bp, respectively, with the first SNP hitting an intronic region and the second one located in an exon region. Others genes such as Coiled-Coil Domain Containing 102A (CCDC102A), SLIT-ROBO Rho GTPase Activating Protein 1 (SRGAP1), MutS Homolog 6 (MSH6) Myosin VI (MYO6), Myosin XVI (MYO16), and Kinectin 1 (KTN1) were intercepted by one or more lead SNPs, but no clear evidence of a close association with body size and growth-related traits has been reported.The full list of genes located within 100 kb upstream and downstream of the lead SNP is available in additional file . Some lechain COLA1 and grWe found moderate to high heritability values for ADG, BWH, WW, HW, HON, BLH, FW and FY, which is consistent with previous estimates for Nile tilapia calculated using pedigree and genomic methods , 20, 21.The use of genomic information can help in the identification of QTLs controlling complex traits which are economically important for aquaculture purposes, such as growth-related traits. Previous studies have identified loci and candidate genes associated with growth-related traits in aquaculture species , 37, 38.To increase the statistical power, in order to detect genetic association between SNPs and traits of interest, recent studies have used mtGWAS, which can leverage multiple summary statistics from GWAS performed on the same trait with different measures or different traits with a high genetic correlation among them , 39, 40.A difference in the number of significant SNPs between stGWAS and mtGWAS is expected given the substantial increase in statistical power which has been documented for the mtGWAS approach. However, it has also been shown that original associations detected by single-trait GWAS can disappear when running multi-trait GWAS. For instance, in the paper describing the application of mtGWAS , the inchydhroxysteroid 17-Beta Dehydrogenase 4 (HSD17B4), a possible regulator of muscle development in Berkshire pigs, has been reported to play an important role during the early stages of myogenesis when the expression of its mRNA is significantly high [We found numerous significant markers associated with body traits, dispersed in almost all linkage groups LG; Fig. , probabltly high .COL4AI, located in LG16, associated with different body traits, including ADG, BWH, WW, HON and BLH. In catfish COL4A1 was identified within QTLs associated with body length and body length of the fish without the head. Collagen is an important component of the extracellular matrix of cartilage and bone, playing a key role in skeletal development [GDF6, located in LG22, which was associated with different traits including ADG, BWH, WW, HON, BLH. Mutations in GDF6 in zebrafish is related with reduced eye size and different skeletal defects [GDF6 gene was found to control developmental patterning of skeletal joints [GDF6 gene can cause defects in the joints, ligaments, and cartilage formation in mouse [ASB2 gene, located in chromosome 19, was associated with BWH and HON. In Atlantic salmon, the ASB2 gene is not involved in muscle differentiation but may play an important role in growth inhibition. The high expression of ASB2 observed in skeletal muscle of fasting fish is strongly downregulated in response to feeding [Some lead SNPs identified in this study were located close or intercepted several strong functional candidate genes associated with body and growth-related traits in previous studies. For instance, strong functional candidate genes were found in windows within 100 kb downstream and upstream from the lead SNP, such as elopment . We also defects . In a stl joints . Inactivin mouse . In addi feeding .FUT8 gene, which has been associated with ADG (from birth to six months-age) in a sheep population from Iran [FUT8 gene induces severe growth retardation and early mortality during postnatal development [insulin-like growth factor binding protein-3 (IGFBP-3) has growth inhibitory effects, and the alteration in the function of low-density lipoprotein receptor-related protein-1 (LRP-1) is a result of the loss of core fucosylation that might cause an elevated serum concentration of IGFBP-3 in FUT8-null mice [FUT8 has also been reported to be related to down-regulation of transforming growth factor-beta 1 (TGF-β1) receptor and epidermal growth factor (EGF) receptor, proteinase-activated receptor and integrin activity, which contributes to emphysema-like changes in the lung, and growth retardation in FUT8-null mice [We also found strong candidate genes intercepted by lead SNPs that may contribute to a better understanding of the biological mechanisms controlling body traits in Nile tilapia. Growth is considered a continuous function during the life of an animal and ADG is an important trait which can be targeted to select for rapid growth. ADG was previously reported as a selection criteria in a breeding program for Nile tilapia, which has applied selection for at least five generations . We founrom Iran . In miceelopment –52. The ull mice .NUP107 gene which plays an important role in the development of vertebrate embryos. The zygotic deficiency of NUP107 in zebrafish embryos can result in loss of pharyngeal skeletons, degeneration of intestinal and retinal epithelia, and implications in cartilage and bone formation [NUP107 is suggested to be involved in the hypo-responsiveness to growth [Two lead SNPs associated with BWH and HON were found on LG17, in an intronic and exonic region of the ormation . In seneo growth .SLC4A2 gene, a strong biological candidate for waste weight in Nile tilapia. The loss of function of this gene causes emaciation and achlorhydric condition [SLC4A2 is associated with an osteopetrosis phenotype [The waste weight is the sum of the weight of the head, viscera, bones and fins, and has been suggested as an alternative phenotypic record to improve fillet yield through the application of various index (e.g. fillet to waste ratio). However, based on simulated data of ten generations of selection using real genetic parameters of five farmed fish populations, direct selection on fillet yield was generally the best approach to improve the trait . The potondition , generatondition . Osteopeondition , 58. In ADAMTS9 and HEG1, respectively. The ADAMTS9 gene is highly expressed during embryo development and continues to be expressed in adult tissues of mice [ADAMTS9 during skeletal development of mouse was suggested by Jungers et al. (2005), including mandible, ossification centers, initial condensation of mesenchyme to form the cartilage centers, perichondrium around formed cartilage, proliferative zones of cartilage and long bones. Skeletal development may be correlated with organic growth [ADAMTS9 is responsible for the regulation of the epidermal growth factor receptor (EGFR) and TGF-β1 [ADAMTS9 associated with chest width, chest width index and chest circumference index, and a14-bp indel associated with height across the hip in cashmere goats, which suggests that ADAMTS9 is a functional molecular marker that can be used to improve growth traits in goats [Fillet traits are key economic characteristics for aquaculture species and new insights regarding the underlying genetic variants controlling them can help to enhance yield. We found two lead SNPs associated with FW and FY, intercepting an exonic and intronic region of genes  of mice , 60. A sc growth . ADAMTS9d TGF-β1 , 63. Tand TGF-β1  identifiin goats .HEG1 gene, located in LG16. The HEG1 gene was initially reported to be responsible for regulating zebrafish heart growth and the development of heart and blood vessels development [HEG1 gene was identified as one of several novel genes associated with human skeletal muscle growth, exhibiting a significant correlation with the percentage of change in lean mass [longissimus dorsi in sheep, Cheng et al. (2020) [HEG1 [A lead SNP associated with FY intercepted the elopment . Recentlean mass , 67. In . (2020)  identifi0) [HEG1 .MSH6, SRGAP1, MYO6, MYO16, KTN1 and other molecules presented in Table MSH6, for example, may increase the risk of developing colorectal carcinomas [MYO16 appears to have an important role in neural development and the function of the nervous system [Some genes such as rcinomas , 74, ands system . The funWe used dense genotypic information to refine association mapping analysis for body traits in Nile tilapia and found that mtGWAS provided substantial improvements in the number of significant SNPs identified when compared to stGWAS. These results confirm the increase of statistical power to identify trait-specific genetic associations in multi-trait analysis. Interestingly, we found several lead SNPs within or nearby genes related to cartilage, bone, skeletal growth and development in humans, mice, livestock and aquaculture species. These results can provide further knowledge and a better understanding of genetic variants and genes underlying complex body traits in Nile tilapia.We used a total of 1309 phenotyped animals from 72 families  belonging to a breeding nucleus owned by Aquacorporación International group (ACI), Costa Rica. More details about the breeding program, the origin of the Nile tilapia population and production conditions are described in detail in previous studies , 76. Brip-value 10− 6), minor allele frequency (MAF < 0.01), and a genotyping call-rate for SNPs and samples of < 0.90. After quality control 29,587 SNPs and 1309 samples were retained.Genomic DNA was extracted and purified from 1309 fin clip samples using the DNeasy Blood & Tissue Kit (QIAGEN) according to the manufacturer’s protocol. The samples were genotyped using a 50 K SNP Illumina BeadChip  and filtp-value 10− 8), MAF < 0.01 and call-rate for SNPs < 0.80. A total of 5,011,051 SNPs were retained after applying the filters described above. In order to estimate the overall accuracy of imputation and remove the variants with low imputation accuracy we used a five-fold cross validation scheme. Briefly, the 143 animals with data from the WGS-derived genotypes were randomly divided into five exclusive reference sets  and the remaining animals were used as the validation set . The accuracy of imputation was estimated as the correlation between true and imputed genotypes . A total of 1,324,420 SNPs with R2 value higher than 0.80 were used as the final ultra-dense SNP panel for imputation. The 143 re-sequenced animals and 1,324,420 SNPs were used as a reference dataset to impute the 1309 animals with medium-density SNP genotypes using the software FImpute v. 3.0 [p-value < 10− 8, resulting in a total of 992,494 SNPs available for downstream analyses.Initially, 26.6 million non-redundant SNP variants were identified through Illumina HiSeq 2500 re-sequencing performed in 143 animals from the breeding nucleus owned by Aquacorporación International group (ACI), Costa Rica . Qualitye v. 3.0 . A post-mlma option of the software GCTA v. 1.24 [yij is the phenotypic value of the j-th animal, μ is the fixed effect of the overall mean, b1 and b2 are the regression coefficients for age and the allele substitution effect for SNP, respectively, agej is the age covariate of the j-th animal and SNPi is the i-th SNP genotype of animal j, coded as 0, 1 and 2 for genotype A1A1, A1A2 and A2A2, respectively, aij is the random polygenic effect of the j-th animal G representing the genomic relationship matrix (GRM) calculated using the imputed genotypes and eij is the random residual effect I representing an identity matrix and 2) was calculated as h2 = p-value were also estimated using GCTA.The single-trait genome wide association analyses (stGWAS) were performed using the  v. 1.24 , which wvariance , 79, andg scheme . The GRMg scheme  implemenp-values from a mtGWAS, which can be interpreted in the same way as stGWAS. Significance thresholds were determined for both single-trait and mtGWAS using Bonferroni correction (0.05/ number of SNPs).The summary statistics from stGWAS were used as input for the multi-trait analysis of GWAS (mtGWAS) performed using the software MTAG v0.9.0 . In MTAG2 statistics than mtGWAS, the following equation was used [χ2 statistic for mtGWAS and stGWAS results, respectively, and NGWAS is the number of actual sample size in stGWAS .To calculate how much larger the stGWAS sample size would have to be to give the same mean χwas used :2\documOreochromis niloticus reference genome  against the latest version of the UMD_NMBU ), which Additional file 1: Supplementary Fig. 1. Manhattan plot for single-trait GWAS (stGWAS) for body traits in Nile tilapia. Manhattan plots of SNPs associated with: (A) Average daily gain. (B) Body weight at harvest. (C) Waste weight. (D) Head weight. (E) Gutted head-on weight. (F) Body length at harvest. (G) Fillet weight. (H) Fillet yield. The x-axis presents genomic coordinates along chromosomes 1–23 in Nile tilapia. On the y-axis the negative logarithm of the SNPs associated p-value is displayed. The dashed black line represents the genome-wide significance threshold after Bonferroni correction ).Additional file 2: Supplementary Table 1. Summary results from genotype quality control of whole-genome sequence (WGS), imputed WGS genotypes, and 50 K single nucleotide polymorphism (SNP) chip for Nile tilapia.Additional file 3: Supplementary Table 2. Genomic regions and candidate genes for all lead SNPs associated with body traits based on multi-trait GWAS for Nile tilapia


The challenge of decoding information about complex diseases hidden in huge number of single nucleotide polymorphism (SNP) genotypes is undertaken based on five dbGaP studies. Current genome-wide association studies have successfully identified many high-risk SNPs associated with diseases, but precise diagnostic models for complex diseases by these or more other SNP genotypes are still unavailable in the literature. We report that lung cancer, breast cancer and prostate cancer as the first three top cancers worldwide can be predicted precisely via 240–370 SNPs with accuracy up to 99% according to leave-one-out and 10-fold cross-validation. Our findings (1) confirm an early guess of Dr. Mitchell H. Gail that about 300 SNPs are needed to improve risk forecasts for breast cancer, (2) reveal an incredible fact that SNP genotypes may contain almost all information that one wants to know, and (3) show a hopeful possibility that complex diseases can be precisely diagnosed by means of SNP genotypes without using phenotypical features. In short words, information hidden in SNP genotypes can be extracted in efficient ways to make precise diagnoses for complex diseases. Among these challenges, it is of great practical significance to find methods of diagnosing complex diseases precisely based on single nucleotide polymorphism (SNP) genotypes3. This challenge has become a shackle to current genome-wide association (GWA) studies, and now it may be the time to break it such that moving beyond the initial steps of GWA studies4 will be no longer a hard work in the near future.High-throughput sequencing technology helps us get more and more molecular data, but also poses challenges on how to use these rich resources efficiently5, lung cancer8, breast cancer11 and prostate cancer15 are still the first three top cancers around the world , so we start exploration from these three cancers. Our method can be extended to more other complex diseases, and may be expected to serve for personalized diagnosis and even precise medicine17. If so, combination of precise diagnostic models with those important insights known in GWA studies shall play a substantial role in further promoting GWA studies and even in improving human health comprehensively4.According to the global cancer statistics 20187, phs000147.v3.p110, phs000517.v3.p1 and phs000306.v4.p1. Given a dataset, we first use Snp2Bin .Five dbGaP studies  related to these three cancers are studied, with the following accession numbers: phs000634.v1.p1, phs000753.v1.p1Bin Fig.  to reducBin Fig.  to get tThe lung cancer study, , consists of 946 cases and 1052 controls, involving 656,891 SNPs. Following the above procedures, we first use Snp2Bin to transform these SNPs into 656,891 binary variables, then apply IterMMPC to reduce attributes and obtain a 3274-variable subset, and finally employ OptNBC to get a 268-feature NBC Fig. A.Figure For convenience, denote this NBC model by 0% Figs. B and S3AThe two breast cancer studies, phs000147 and phs000517, consist of 1145/699 cases and 1142/667 controls, involving 546,646 and 1,288,157 SNPs, respectively. For phs000147, we get a 4128-variable subset and then a 318-feature NBC; for phs000517, we get a 1863-variable subset and then a 255-feature NBC. Denote the two NBCs by ly Figs. D and S5A21, showing the same conclusion as Yücebaş and Son14 concluded that ethnicity is the most important attribute.The prostate cancer study, phs000306, is divided into two datasets: one is for Japanese and Latinos (JL) containing 829 cases and 836 controls, the other is for African Americans (AA) containing 1431 cases and 1424 controls. JL and AA contain 657,366 and 1,199,187 SNPs, respectively. For JL, we get a 3919-variable subset and then a 242-feature NBC; for AA, we get a 24,457-variable subset and then a 352-feature NBC. Denote these two NBCs by ly Figs. F and S3D22) to measure the performance  Figs.  and S5B.2) Figs. E.Table 1As seen, for each dataset, its diagnostic models, mentclass2pt{minimJL Table I, for whFinally, Table 24 and then build corresponding models. Our results show this possibility, indicating that moving beyond those initial steps of GWA studies4 may be no longer a hard work in the near future!Sufficient and efficient exploration of rules hidden in available sequencing data is a challenge but also a key to prevention, diagnosis and treatment of complex diseases such as the three top cancers. As expected, it may be increasingly becoming urgent to find SNPs that can be used to make precise diagnoses, rather than only identifying some related or high-risk SNPsThe use of Snp2Bin is a key procedure to the performance of making classifications. Without using Snp2Bin, IterMMPC cannot get a good subset of variables, and also OptNBC cannot select proper features to make precise classifications. Taking phs000634 for example, if using Snp2Bin to transform SNPs into 2-value variables, IterMMPC can get a 3274-variable subset, and then OptNBC selects a 268-feature NBC, namely 25, the parents, children, and spouses are its theoretically optimal features26. As a special Bayesian network, NBC needs only the target’s children, which can be identified by the MMPC algorithm. An important working mechanism of MMPC is to use  independence tests to exclude redundant variables.For a target variable in a Bayesian networkp value used for testing the statistical hypothesis “the SNP is independent of cancer risks”. As seen from Fig. Numerically, for each of the six datasets, we check every SNP’s association with cancer risks by computing its (0-order) p value for every SNP when testing “the SNP is conditionally independent of cancer risks conditioned on any one of those SNPs (except itself) used by Such many superficially high associations make it hard to determine an optimal subset of SNPs used for prediction. However, these superficially high associations can be filtered to a great degree by conditioning one or more truly high associated SNPs, as MMPC does. To explain why this works so well, we take phs000517 as an illustration by computing the 1-order p values are used, MMPC can exclude many more redundant variables.By the results Figs. C and S5DOn the other hand, MMPC has an exponential complexity, so it cannot be used directly to select features for a dataset of high dimension . Instead, IterMMPC divides all variables into many parts and implements MMPC for every part to update the subset of variables, and then iterates the process until no change occurs. In short words, IterMMPC not only saves computing time, but also finds a small superset of all useful SNPs.selective Bayesian clasifier27. If the features are properly used, the resulting classifier will possess robust power of making classifications19. Considering the high dimensionality of each dataset, we use the 10-fold cross-validation score (substituting for leave-one-out score) to speed up computations. It can be seen from Fig. Our OptNBC algorithm enhances naive Bayes by using a similar idea of constructing the For a complex disease such as one of these three top cancers, there are no leading SNPs, and per SNP only carries a small amount of information about cancer risks. In some situations, such information also may be swamped by some unknown random factors, and in this case the corresponding SNP will give an opposite effect on predicting cancer risks, needing more other SNPs to offset this opposite effect.29, we believe that evolution is indeed urging humans (and other species) to mitigate the risk of getting a serious disease by dispersing it to many loci of the micro world, so a large number of SNPs associated with a complex disease have to be identified and used in a better method.On the other hand, as Matt Ridley said in summarizing the genetic annealing model of Carl Woese: “the organism was not yet an enduring entity, and the genes that ended up in all of us may have come from lots of ‘species’ of creature”about 300 (=7+10+280) SNPs are needed to dramatically improve risk forecasts for breast cancer31. The guess of Dr. Gail, however, does not mean we can improve risk forecasts substantially by simply taking Our results also confirm an early guess of Dr. Gail that As the third generation of genetic markers, SNP genotypes are expected to contain all information about what one wants to know, such as skin color, gender, ethnicity, temperament, and even sexual orientation, if data on all SNP genotypes are collected properly. For example, to see the gender information hidden in the intersected 170,571 SNPs of phs000634 and phs000753, we regard the 1998/2290 gender labels in this two dbGaP studies as the target data, and then perform Snp2Bin/IterMMPC/OptNBC to make classifications. For phs000634, we get a 385-variable subset and then a 304-feature NBC, which performs “predictions” for gender with accuracy 89.64% according to 10-fold cross-validation; for phs000753, we get a 507-variable subset and then a 311-feature NBC, performing “predictions” with accuracy 92.23%. If all SNPs are pre-collected at the data-gathering phase, the accuracy will be higher. In this sense, those phenotypical information (such as gender) useful for characterizing cancer risks are contained in some SNPs genotypes. This explains why our method can make precise classifications by using SNPs only.Besides the three top cancers, our method can also be applied to many other complex diseases, if corresponding datasets are available. On the one hand, Snp2Bin plays an important role in extracting as much useful information as possible and in making the most efficient use of IterMMPC. On the other hand, among so many SNPs, there is no any leading SNP; in this case, any potential opposite effect of a SNP on making predictions caused by random factors may be remedied by some other SNPs.https://github.com/lxq2018/dbGaP.All datasets are available through the dbGaP. The main code used in this report is available on All datasets only consist of the part with restriction of GRU . For a SNP, its missing values are regarded as chaos states of genotypes. Denote them by an imaginary genotype, instead of simply deleting them or replacing them with imputed data, because such states may stand for certain potential unknowns to be unexplored rather than consequences of some other factors such as precision of sequencers.32 of the corresponding contingency table is computed (as its score). Among all such possible coded 2-values variables, the one with the highest score is as the optimal 2-value variable for this SNP. This scheme borrows in part the idea of transforming a multi-class attribute into a binary variable33 and can increase the power of As Fig. T and X be two random variables, taking T and X are independent, Y be one of the 2-value variables of X, defined as taking 1 if T and Y are also independent. This indicates (1) unrelated SNPs will never enter our NBC models, and (2) the information that a SNP carries about the cancer will be encoded by the 2-value variable as much as possible.Moreover, it can be verified that, for any SNP independent of the cancer, the corresponding 2-value variable must also be independent of this cancer. In fact, let 25, all the features in an NBC are children of the target (status of lung cancer or breast cancer or prostate cancer). Considering the number of SNPs is very huge, up to half a million and even larger, we use IterMMPC  to reduce the search space before looking for the optimal NBC. MMPC35 is a state-of-the-art algorithm used for finding the parents (direct causes) and children (direct efforts) of the target. Its computational complexity is exponential to the number of parents and children, so we divide the feature set into a number of groups and update each group individually by applying MMPC to it. Iterate this process until no change occurs. Figure As a simple Bayesian networkIterMMPC  gets a superset of attributes of a target. Specifically, this superset contains 3274 attributes for phs000634, 1298 attributes for phs000753, 4128 attributes for phs000147, 1863 attributes for phs000517, 3919 attributes for phs000306-JL, and 24,457 attributes for phs000306-AA. Based on these filtered attributes, OptNBC starts from an empty NBC. As Fig. SubOptNBC  is an alternative algorithm to OptNBC in searching a good NBC. It simply replaces OptNBC by adding the attribute with the second highest score to the NBC in the forward phase. The NBCs searched by OptNBC and SubOptNBC can be regarded as two different experts of making diagnoses with different empirical information in a sense.Supplementary Information.


Background: Genetic testing for pedigree accuracy is critical for managing genetic diversity in North American (NA) yak (Bos grunniens), a population expanded mostly from imported zoological park specimens.  DNA testing also enhances species conservation by identifying recentB. taurus F1 hybrid ancestors (within three generations).  Biallelic single nucleotide polymorphisms (SNPs) can accomplish either task, but increases the marker count and costs necessary to achieve both.  Our aim was to identify novel, multifunctional, triallelic yak SNPs (tySNPs), with each having two alleles for yak parentage testing, and a third allele for identifying recent cattle introgression. Methods:  Genome sequences were aligned to the cattle UMD3.1 assembly and SNPs were screened for 1) heterozygosity in a NA and a Chinese yak, 2) a third allele at high frequency in cattle, and 3) flanking sequences conserved in both species.  Subsequently, tySNPs were filtered for unique alignment to the haplotype-resolved F1 yak assembly.  Allele frequencies were estimated in a subset of 87 tySNPs by genotyping 170 NA yak.Results:  We identified 610 autosomal tySNPs, distributed in 441 clusters with 5 Mb average genome spacing.  The average NA yak minor allele frequency was high (0.296), while average introgressed cattle alleles were low (0.004).  In simulations with tySNPs, 28 were sufficient for globally-unique animal identification (PI=5.81x10-12), 87 were able to exclude 19 random bulls from parentage at the 99% level without using the dam’s genotype (PE=5.3x10-4), and 87 were able to detect F1 hybridization events after three generations of yak backcrosses (1/16thB. taurus germplasm).Conclusions:  Identifying animals, determining parentage and detecting recent hybridization events was efficient with as few as 87 tySNPs.  A similar triallelic approach could be used with other bottleneckedBos species that hybridize with cattle, such as NA plains bison (B. bison). Bos grunniens) are native to the Qinghai–Tibet Plateau of Central Asia and valued around the world for their meat, fiber, milk, fuel, wool, transportation, predator protection, and as pets1. The global yak population is large and diverse with upwards of 14 million domestic yak, and 15,000 wild yak (Bos mutus)2. In contrast, the North American (NA) yak population is small and narrow with only 2,000 to 5,000 yak, all of which are domestic. This herd has mostly arisen from a few dozen animals imported from public and private European zoological parks to NA zoos around the turn of the 20th century. The lack of source diversity may have also compounded the problem. For example, the Smithsonian National Zoo (SNZ) in Washington, DC imported their first yaks in 1898 from the Zoological Society of London, England3. For 23 years the closed SNZ herd was expanded by breeding and surplus animals were sent away until the zoo received a single new yak breeding bull from Parks Canada. This Canadian bull descended from yaks kept at Woburn Abbey in England, approximately 50 miles from the original London Zoo source4. Other than zoological park sources, additional yak germplasm has not been introduced to the NA herds due to strict federal regulation barriers placed on the importation of live animals, embryos, and semen. Thus, highly related surplus yaks from limited introductions are the apparent founders of the current NA yak population. This narrow genetic base of the NA yak population is an ongoing concern for maintaining genetic diversity to protect the health and vigor of these herds. Domestic yaks (Bos taurus). The issues arising from yak and cattle hybridization predates their export from the Qinghai–Tibet Plateau. The production of yak and cattle hybrids has been practiced for 3000 years, starting with the Yin Dynasty6. These ancient hybridization events account for the 2–3%Bos taurus alleles present in domestic yak today7. The challenge of maintaining genetic diversity within NA yaks is increased by occasional hybridization with cattle  is essential for preserving authenticBos grunniens germplasm while producing healthy, genetically diverse animals.Genetic management of ancient12. Previously, yak parentage testing had usedBos taurus derived microsatellite markers 14. However, publicly available whole genome sequence (WGS) data from Chinese and NA yak, together with a haplotype-resolved F1 yak-cattle assembly, made it possible to identify informative NA yak SNPsin silico for the potential development of parentage SNP markers17. The ultimate utility of a set of parentage SNPs may be measured by their success in accomplishing the most challenging scenario: “one-parent traceback.” For example, using DNA from a lamb carcass to identify its true sire in a multi-sire mating system, but not having the dam’s DNA available to perform the trio’s analysis9. Identifying the offspring’s sire requires the genetic exclusion of all other males exposed to the dams. This approach is based on the principle that the true sire must share an allele with the offspring at every site tested18. Thus, when an offspring and a potential sire are homozygous for different alleles at the same site, the potential sire is excluded from parentage. The ideal SNP set for one-parent parentage testing has markers with a minor allele frequency (MAF) greater than 0.30, is evenly spaced across the genome, and has SNPs with highly conserved flanking DNA sequences for efficient and accurate genotyping9. If available, such yak biallelic parentage SNPs would allow producers to access commercial high-throughput SNP genotyping, use multiple-sire pasture breeding strategies, verify pedigrees, and establish unique animal identification information that could be used for tracing if needed.SNP-based tools provide new opportunities for more precise genetic management of a given livestock species. Advances in SNP discovery and testing have made it routine to verify parentage, identify animals, and traceback diseases in cattle, sheep, horse, and swineB. taurus introgression, which would require an additional set of biallelic SNPs thereby increasing cost while limiting choice of platforms suitable for lower throughput. However, based on our previous interspecies alignments of WGS19, we hypothesized that some biallelic parentage SNP sites in yak may also align with a nearly monomorphic, alternative allele inB. taurus. In this hypothetical triallelic SNP system, the evolutionary source of all three alleles could be inferred by estimating frequencies in samples ofBos species, a genus that diverged 5 million years ago. This idea is consistent with the evolution of theBos genus as a complex of genetically interconnected species with shared evolutionary trajectories20. For example, an ancestralBos allele would be common in most of the extantBos species including yak   . If a suB. taurus introgression events in NA yaks. Markers were sequentially filtered for having: 1) identical heterozygous genotypes in a NA and a Chinese reference yak, 2) identical homozygous genotypes in the other non-taurineBos species, 3) a third high-frequency allele inB. taurus, 4) conserved flanking sequences inBos species, and 5) a unique location in theB. grunniens genome. A representative trial set of 87 tySNPs were used to estimate performance in NA yak with a MALDI-TOF genotyping platform. Based on these estimates, we performed computer simulations to predict the power to determine parentage and recentB. taurus introgression in NA yak with increasing numbers of tySNPs. Our ultimate goal was to use a minimal set of tySNPs to accomplish multiple diverse genetic tests.Here we describe a panel of 610 tySNPs for use in animal identification, parentage testing, and for estimating recentAcknowledgements). The banteng DNA samples (1 ug each) were transferred for a fee under a Research Material Agreement that was executed on February 6, 2007 by the legal directors of the San Diego Zoo and the Technology Transfer Office of the USDA, ARS. The gaur blood samples were collected under Omaha Henry Doorly Zoo IACUC protocols on June 15,1999 by their Director of Animal Health, Dr. Douglas Armstrong, DVM in the presence of coauthor, MP Heaton. The animals were being evaluated for interstate transfer to another zoo which required a venous blood draw. A butterfly needle was used to fill blood tubes for required testing and DNA sample collection.This article contains no studies performed with animal subjects. The original sources of archived DNA samples used were either purchased from companies that collected them for artificial insemination and not for research (beef cattle), purchased or donated from individuals that collected them privately for their purposes such as food (bison and yak), or donated to the U.S. Meat Animal Research Center (USMARC) by private individuals that collected the samples privately for their own herd management purposes . Details of library preparation and sequencing are as described by Heatonet al.21. The NA reference domestic yak female used for WGS and alignment to theD171 QA;. QA diedB. taurus genome was QH115. Approximately 60-fold coverage of FASTQ files were obtained from BioProject accession numberPRJNA74739 . This animal’s data set was originally used because it was the only other yak data set with sufficient coverage (>10X) for both variant discovery and accurate genotyping. Subsequently, additional yak data were available from BioProject accession numberPRJNA285834, includingB. grunniens: DYS74, SAMN03766772, SRX1056027; DYS77, SAMN03766773, SRX1056028; DYX12, SAMN03766774, SRX1056029; DYX11, SAMN03766775, SRX1056030; DYY31, SAMN03766776, SRX1056031, andB. mutus: WYX01, SAMN03766777, SRX1056032; WYX02, SAMN03766778, SRX1056033; WYX03, SAMN03766779, SRX1056034; WYX04, SAMN03766780, SRX1056035; and WYX05, SAMN03766782, SRX105603622.The Chinese reference domestic yak female used for alignment to the17, was also used for evaluating the accuracy of tySNPs , BioProject accession numbersPRJNA551500 andPRJNA552915 . In addition, research and commercial genotype data from 170 NA yak samples derived from 36 sources were used to estimate allele frequencies in a subset of 87 tySNPs.WGS and the haplotype-resolved F1 yak-cattle assembly of “Esperanza,” the calf of a female NA yak and a Highland beef bull21. Pedigrees were obtained from leading suppliers of U.S. beef cattle semen and breed associations, and analyzed to identify unrelated individuals for inclusion. On the basis of the number of registered progeny, the breeds were estimated to represent greater than 99% of the germplasm used in the US beef cattle industry, contain more than 187 unshared haploid genomes, and allow a 95% probability of detecting any allele with a frequency greater than 0.01623. The breeds in MBCDPv2.9 were (in descending order of registered progeny circa 2000): Angus (n = 6), Hereford (n = 6), Charolais (n = 6), Simmental (n = 6), Red Angus (n = 6), Limousin (n = 6), Gelbvieh (n = 6), Brangus (n = 5), Beefmaster (n = 5), Salers (n = 5), Shorthorn (n = 5), Maine-Anjou (n = 5), Brahman (n = 6), Chianina (n = 4), Texas Longhorn (n = 4), Santa Gertrudis (n = 4), Braunvieh (n = 4), Tarentaise (n = 4), and Corriente (n = 4). The average genome coverage of FASTQ files for these 96 beef bulls was about 14-fold and is available in the NCBI SRA with links to BioProject accession numberPRJNA32482221.The beef cattle panel consisted of 96 unrelated individuals from 19 popular U.S. beef breeds (USMARC Beef Diversity Panel version 2.9 [MBCDPv2.9])Bos species used consisted of approximately 10 to 14-fold coverage of WGS from gaur (n = 2), banteng (n = 2), and bison (n = 1) and were obtained from BioProject accession numberPRJNA325061 includingB. gaurus: 199911001, SAMN05558794, SRX2026439-SRX2026446, SRX2026451, SRX2026462, SRX2026473, SRX2026484, SRX2026495 - SRX2026498; 199911002, SAMN05558795, SRX2026447 - SRX2026450, SRX2026452 - SRX2026461, SRX2026463 - SRX2026464;B. javanicus: 200710001, SAMN05558796, SRX2026465 - SRX2026468; 200710002, SAMN05558797, SRX2026469 - SRX2026472; andB. bison: 199912001, SAMN05558798, SRX2026474-SRX202648121.The otherTrimGalore (version 0.5.0) and mapped to theBos taurus assembly, UMD3.124, using theBurrows Wheeler Aligner (version 0.6.1)25 modulealn. The sam-formatted file was then converted to bam format and sorted with samtools (version 0.1.18)26. Samtools was also used to mark the PCR duplicates with the “markdup” function. The Broad Institute’sGenome Analysis Toolkit27 (GATK version 1.5-32-g2761da9) module “IndelRealigner' was used to ensure that any insertions or deletions were consistently aligned. Variant discovery and genotyping for mapped WGS datasets for yak, gaur, banteng, and bison animals (n = 7) were performed using the GATK module UnifiedGenotyper (version 3.4-46-gbc02625) run with genotyping_mode=DISCOVERY. The VCF file was filtered using a custom program (ParseYakCoarse.java)28 to identify tySNPs where the gaur, banteng and bison animals (n = 5) were homozygous for the same inferredBos ancestral allele 28 was written to filter the VCF record, passing only those records where 183 or more of the 192 possible beef cattle alleles (i.e. >95%) was a cattle-specific, bovine UMD3.1 reference allele 28 was written to count the number of ancestral, yak-associated andB. taurus-associated alleles present in each yak.A VCF file containing candidate tySNPs was used as the --alleles argument in the Unified Genotyper software with genotyping_mode=GENOTYPE_GIVEN_ALLELES and the bam files created for five wild and four domestic Chinese yak were genotyped. The “--intervals” option was also used with a .bed file that had a record specifying a locus of 2001 bases centered on each of the polymorphisms in order to limit the scope of the genotyping to those loci relevant to this study. A custom program 28 to extract 100 bases of flanking sequence both 3’ and 5’ of the marker position. The bam files for the five wild and four domestic Chinese yak, and the 96 beef cattle diversity panel21, were used to identify neighboring SNPs that could disrupt heteroduplex formation with oligonucleotides used in genotyping assays on a variety of genotyping platforms. The GATK UnifiedGenotyper was run with genotyping_mode=DISCOVERY and the --intervals value was set as a .bed file containing records for all loci specified with 2001 bases centered on each marker. Cattle variants discovered in the 100 bp sequences adjacent to tySNPs on either side were replaced with “N” if their allele frequencies were greater than or equal to 5% . These variant flags may be used by genotype assay design software to redirect oligonucleotide placement to more conserved flanking sequences. Similarly, yak variants flanking the tySNPs at any frequency (compared to theB. taurus UMD3.1 assembly) were also replaced with N for the same reason.TheThe last step in filtering candidate tySNPs was aligning 200 bp of flanking sequence to the haplotype-resolved F1 yak assembly . This determined the yak chromosome and position for the tySNP. Markers were excluded from the group if they did not map exactly once to the assembly. The remaining tySNPs were manually assigned to marker “bins” based on their chromosome position. Clustered markers  were typically grouped in the same bin to allow SNP assay design software choices for the most amenable target in the region. Where possible, the goal for spacing between bins was 5 Mb. With perfect 5 Mb spacing in the 2479 Mb yak autosomal genome, there would be about 496 bins plus 29 for the end bins.9. Assay development and genotyping was performed at Neogen Genomics  with the MassARRAY platform and iPLEX GOLD chemistry according to the manufacturer’s instructions . The multiplex assays were designed with the manufacturer’s assay design software and a preliminary set of 139 bins with 518 tySNPs aligned to the B. taurus UMD3.1 assembly. The software options were set for a maximum of 48 assays per plex and to select one tySNP marker available in any bin. Extension probe concentrations were adjusted empirically to optimize signal across the entire mass spectrum. The three multiplex assays were run on DNA from the NA reference yak (QA) and other NA yak. Specific SNP assays that produced low call rates or high error rates were censored from data sets. For the present report, genotype data was used for any of these 139 binned SNPs that passed the assay criteria above and had a unique mapping position in the haplotype-resolved F1 yak assembly.Prior to assay development, the cutoffs for call rate  and accuracy  were set at 97% and 99%, respectively. For the purposes of parentage exclusion the cutoff call rate of 97% means that a minimum of 94% of the tySNPs will have a genotype for each of the two animals in a pairwise comparison. Although these cutoffs are relatively high, we consider them to be the “gold standard” in SNP-based parentage testing and are within the capability of today’s DNA testing technology. Sets of parentage SNPs that meet these standards substantially increase the efficiency of testingI is an estimate for the probability of a coincidental genotype match between two animals. Assumptions used for analyzing tySNPs included Hardy-Weinberg (HW) distributions of genotypes, a negligible frequency of theB. taurus-associated allele, and that the average MAF for yak parentage alleles in NA yak is representative. Briefly, the PI for locus A with SNP alleles A1 and A2 was the sum of the squares of the three genotype frequencies: PI =  (χ11)2 + (χ12)2 + (χ22)2, where χ11, χ12, and χ22 were the relative genotype frequencies of A1A1, A1A2, and A2A2, respectively29. The combined PI for multiple SNP markers was the product of the PI for each individual marker. The underlying assumption was that the marker spacing was sufficient for meiotic recombination to cause alleles to be randomly associated with one another. However, as parentage SNP density increases, the validity of this assumption is decreased. Thus, the combined PI for more than one parentage SNP per chromosome is an underestimate of the probability of a coincidental match between random animals from the population, due to linkage disequilibrium between SNPs on the same chromosome. Also, for the purposes of estimating PI and PE, we assumed that the frequency of theB. taurus-associated allele (A3) was negligible.PE is the probability that a random animal would be excluded from parentage. PE is also the least complicated method of parentage analysis and estimates the fraction of potential adults excluded from parentage. In this report, all PE estimates stringently used only one parent’s genotype information i.e., the most challenging scenario. Thus, exclusion was based only on the frequency of the opposing homozygous SNP genotypes in the offspring and the purported parent as previously described9. Briefly, the probability of opposing homozygotes (POH) between a random offspring and a random eligible adult at SNP locus A with alleles A1 and A2, was calculated as follows: POH = (χ11offspring)(χ22adult)+(χ22offspring)(χ11adult), where χ11 and χ22 were the relative genotype frequencies of A1A1 and A2A2, respectively for the adults or offspring groups. The frequencies of homozygous SNP genotypes were assumed to be the same within a breed group regardless of age. Thus, for a single biallelic SNP, PE = POH = 2(χ11)(χ22) when one of the parent’s genotypes is unavailable. This represents the fraction of eligible adults that would be excluded from parentage at one locus, averaged over all comparisons between offspring and adults. Without using the other parent’s genotype information, the combined PE for multiple SNPs was as follows: PE(SNPn) = PE(SNP1) + R1PE(SNP2) + R2PE(SNP3) … + Rn-1PE(SNPn), where P represents the fraction of eligible adults excluded by the first SNP and R1 is the remaining fraction of unexcluded adults. R2 to Rn-1 are remaining fractions of unexcluded adults after each round of subsequent testing with n parentage SNPs. Thus, for 29 parentage SNPs (one on each autosome), the combined PE for unrelated parents is given by: PE(29) = PE(1) + R1PE(2) + R2PE(3) …+ R28PE(29). As was the case with combined PI, the combined PE for more than 1 parentage SNP per autosome is an underestimate of the probability that a random alleged parent would be excluded from parentage due to linkage disequilibrium between SNPs on the same chromosome. For related parents, the PE for each SNP was multiplied by a coefficient of relatedness (r), wherer = 0.125, 0.250, or 0.500 [34]. Thus, PE(29) for related parents  =  (rPE(1) +rR1PE(2) +rR2PE(3) … +rR29PE(29)).PB. taurus-associated alleles in some individuals. Consider the three alleles of a tySNP: theBos ancestral allele (A1), the yak parentage allele (A2), and theB. taurus-associated allele (A3). An exclusion occurs whenever the calf and the adult do not share an allele. Thus, when evaluating “one-parent” scenarios where the dam’s genotypes are unavailable, the analysis is not limited to only the opposing homozygous genotypes. Heterozygous sites may become informative when the calf or the adult possess a copy of theB. taurus-associated allele. Two common examples of these genotype configurations are when the calf is A1/A1 and the adult is A2/A3 genotype, or when the calf is A1/A3 and the adult is A2/A2. In both examples the adult is excluded from parentage. Similarly, when evaluating “two-parent” scenarios where the dam’s genotype at some sites can be used to determine the sire’s allele in the calf, a heterozygous site in the dam and the calf may become informative if one or the other possesses a copy of theB. taurus-associated allele. For example, when the calf is A1/A2 and the dam is A2/A3, the calf’s sire allele is A1 and will exclude all adults that do not carry the A1 allele. Although parentage exclusions caused by theB. taurus-associated alleles only occur in a few percent of the NA yak genotypes comparisons, it is important to account for them when processing parentage tests in commercial settings.Triallelic SNPs can lead to parentage exclusion in ways that biallelic do not, due to the presence ofB. taurus-associated allele in yak results in a negligible contribution to yak parentage exclusion. Consequently, the third allele was ignored for the purposes of these simulations. One million random offspring/adult pairings were simulated with tySNP allele frequencies inferred from genotypes of 170 NA yak with HW assumptions. The offspring/adult pair approximates the one-parent parentage testing scenario since the other parent’s genotypes were not used to phase the offspring’s heterozygous sites. An exclusion from parentage was counted for each tySNP site where the offspring and the adult were homozygous for different alleles. The frequency distribution for exclusions for a given set of tySNPs was determined by summing the exclusive sites for each offspring/adult pairing over all pairings. A second simulation was performed to test the effect of “allelic dropout,” a well-known source of genotyping error, and the systematic error we expect most often with a high-quality sample in this SNP panel. This occurs when other genomic SNPs are present in the binding sites for the three oligonucleotides used in a MALDI-TOF assay. These SNPs may disrupt heteroduplex formation in any of the three required assay primers and cause the linked target allele to be absent from the genotyped alleles. In this simulation, a yak sire was simulated by choosing alleles for each tySNP based on allele frequencies inferred from genotypes of 170 NA Yak with HW assumptions. For each tySNP, one of the sire yak alleles was assigned to the offspring with the second allele chosen at random based on the allele frequencies for the corresponding marker. A fixed genotype error rate was then applied to each genotype. For those genotypes chosen to be in error, one of the two alleles was omitted from the call, and the event was recorded if an artifactual exclusion was introduced.A triallelic SNP has an increased potential to exclude random adults from parentage compared to a biallelic SNP. However, the exceedingly low frequency of theB. taurus-associated allele at every site  heterozygous in both yak, 2) homozygous in gaur, banteng, and bison, and 3) had a thirdB. taurus-associated allele. Subsequent filtering of the less frequent cattle alleles  reduced the set of tySNPs to 1023. Aligning the flanking sequences of the 1023 candidate tySNPs to theB. grunniens haplotype-resolved yak genome assembly identified 612 tySNPs with unique chromosome coordinate positions. Two additional candidate SNPs were removed for being monomorphic after all tySNPs were genotyped in 170 NA yak in a final round with updated statistics. The remaining 610 tySNPs were grouped into 441 regional clusters  with an average distance of 5.26 Mb between bins (Table S1)30. Of the 610 tySNPs, 87 coincided with an unpublished set of tySNPs for which we previously developed MALDI-TOF MS genotyping assays 31. These 87 tySNPs were a subset of markers identified by the same process except for alignment to the haplotype resolved yak genome. Their prior selection predated the availability of this novel yak reference assembly. Thus, their unique alignment and yak genome positions relative to the yak reference assembly were previously unconfirmed. The positions of the 87 tySNPs, together with the other 523 tySNPs, are shown on the haplotype-resolved yak genome assembly  was 0.9952 for 87 tySNPs in 170 animals. TheBos ancestral allele was the major allele for most of the tySNPs in NA yak (67%). The average MAF for the yak parentage allele was 0.296 with 53% of them making the 0.30 cutoff  with more than half of the NA yaks having zero of 174 possible cattle alleles among the 87 sites tested  cutoff . The ove tested . TheB. verall. . I for one SNP was 0.427, meaning that approximately 43% of NA yak would be expected to share identical genotypes at an average tySNP. Extending this to 29 tySNPs distributed equally on 29 autosomes yielded a combined theoretical PI of 1.92 × 10-11. However, selecting the best high-MAF tySNPs gave a slightly better result with only 28 autosomes: PI = 5.81 × 10-12 , the theoretical PE for one SNP was 0.087 with the same MAF and HW assumptions. Thus, approximately 9% of candidate bulls can be excluded with one tySNP. To exclude a group of 30 candidate bulls at the 99% confidence level would require 87 tySNPs all with the same PE  and HW assumptions, the Pame PE . There win silico with WGS from yaks,Bos species, beef cattle and an F1 yak-cattle hybrid trio. By design, the two reference yak were heterozygous at all 610 sites, having exactly oneBos ancestral allele and one yak-associated allele 31. An unexpected genotype result was found in one of the five Chinese domestic yak data sets , which contained greater than 98%B. taurus-associated alleles. Additional analyses performed on its mapped WGS dataset (Table S6)33 confirmed this to be aB. taurus data set and it was eliminated from subsequent analyses. The intrinsic properties and allele distributions of the 610 tySNPs were further evaluated allele . ExcludiB. taurus-associated allele frequencies in these yaks were lower  compared to domestic yaks (0.608), while theBased on the intrinsic properties of the 87 tySNPs above, it is reasonable to extrapolate their performance in scenarios with larger sets of tySNPs. Efficient parentage exclusion depends on a number of factors including: the MAF, the number of markers, and the genotyping error rate. It also requires the majority of random non-parents to have significantly more exclusions  than the erroneous exclusions in the true parent due to genotyping errors. With the current set of 87 tySNPs and a 0.296 average MAF, most random calf-adult pairings had 5 to 10 exclusive genotypes  while most calf-parent pairs have only 0 to 2 false exclusive genotypes at a 1% genotyping error rate . At a 5%B. taurus-associated alleles detected  would be expected to have 10etected . This is animal . Doublin animal . The mod30. In addition to allele frequencies, the markers were stringently selected for reduced negative attributes such as indels, repetitive sequences, and flanking SNPs, to enhance their performance on present and future genotyping technologies. The tySNPs were distributed across the genome in 441 clusters (bins) with an average spacing of 5 Mb, based on the recently completed NA yak genome assembly17. A subset of 87 tySNPs was developed for a MALDI-TOF MS platform and their abilities to: 1) provide each animal with a unique genetic identifier, 2) exclude random adults for parentage determination, and 3) identify animals with F1 hybridization events in their recent ancestry. By selecting the best tySNPs on each available autosome, only 28 tySNPs of the 87 were needed to uniquely identify every NA yak (estimated combined PI = 5.81 × 10-12). Excluding monozygotic twins, this result means the odds of any two random NA yaks having the same genotypes at all 28 sites by chance would be 1 in 172 billion. This is enough power for tracing yaks and their products in the global food chain if needed. Our aim was to identify a set of novel triallelic SNPs. We identified a novel set of 610 tySNPs where each marker has two alleles for NA yak parentage testing, and a third allele for identifying recent cattle introgression. Assay design data for these markers are provided in Table S19. When 1000 offspring are involved, one effective strategy is to first assign the high-confidence calf-sire relationships with one-parent testing, and then use the dam’s genotypes to confirm sire exclusions on any remaining ambiguous calf-sire relationships.The power to exclude random adults from parentage with 87 tySNPs was high. Most random adults had between 5 to 10 tySNP sites that excluded them from parentage with a given offspring, i.e., did not share an allele with the offspring at those sites. However, the false exclusions in a true parent due to genotype error was only about 1 in 87 tySNPs with a genotype platform error rate of 1%. More than 99% of random adults can be excluded from parentage even with a genotyping error rate as high as 5% . Thus, most yak calves can be assigned to a single parent without having the other parent’s genotypes available. For multi-sire pasture mating situations this can reduce the cost of parentage testing by nearly 50%. Note that these estimates are for parentage exclusion with unrelated adults. The power to exclude full sibs from parentage is essentially reduced by half and thus may require use of the dam’s genotypes and/or more accuracy in the genotyping systemB. taurus species introgression in 170 NA yaks from diverse sources was low and not distinguishable from that of other yaks. With 87 tySNPs, no significant differences were detected between the averageB. taurus-associated allele frequency in 170 NA yak (0.0042) and that of nine domestic and wild chinese yak (0.0057). This suggests the genetic foundation of the NA yak population overall is not significantly influenced with introgressedB. taurus germplasm. A low background of cattle introgression in yaks facilitates the ability of tySNPs to detect F1 hybridization events in the recent ancestry of a yak. With 87 tySNPs, simulations predicted confident detection of F1 hybridization events in yak containing as little as 1/16thB. taurus germplasm . This would be sufficient to verify the accuracy of three- or four-generation pedigrees. It should be noted, however, that tySNPs are not informative with regards to whichB. taurus animal was involved in the F1 hybridization event, because nearly allB. taurus animals are homozygous for theB. taurus-associated allele. We also noted that six tySNPs hadB. taurus-associated allele frequencies between 0.01 and 0.10 in NA yak, while theB. taurus-associated allele frequencies in the other 81 tySNPs were essentially zero. Reasons for this could include: ancientB. taurus introgression and selection and/or the existence of both alleles prior to speciation. A future tySNP filter to consider would be less than 1% prevalence ofB. taurus-associated alleles in NA yaks. In spite of these exceptions, overallB. taurus-associated alleles in tySNPs were rare in NA yaks, and thus able to identify animals descended from backcrosses of recent F1 hybrids.TheThe primary areas for improving the multiplexes set of 87 tySNPs include: marker abundance, parentage MAF, and genomic distribution. The 87 tySNPs presented here were developed out of necessity, prior to the availability of the NA yak genome assembly, and without prior knowledge of their allele frequencies in NA populations. While only 28 tySNPs provide more than enough power for genetic fingerprinting, additional tySNPs would add significant power for parentage exclusion. Using future WGS from an additional 10–15 NA yak would allow MAF estimates to be confidently assigned for the 610 tySNPs presented here. With this NA yak allele frequency information in hand, choosing tySNPs with the highest MAFs and increasing the number of markers to at least 131  would increase the proportion of random adults excluded from parentage, as simulated inHomo sapiens, a set of 1,270 polymorphic tri-allelic SNPs mined from the 1000 Genomes Project was recently used in forensic identification of missing persons34. Interestingly, there are approximately twice as many triallelic sites in humans as expected by chance35. It is conceivable that at least some of these may be due to introgression of archaic species into modern humans36. Regardless of their use in humans, the multipurpose triallelic SNP approach presented here may be useful in other species that can hybridize, including bovids, cervids, odontids, camelids, equids, canids, felids, and ursids. An obvious immediate application is in NA plains bison (B. bison). Plains bison are wild animals native to NA, have experienced a population bottleneck, hybridize withB. taurus, and farmed for their meat and byproducts. Ideally, WGS from a diverse group of 15 unrelated plains bison sampled from the NA herd would be used, together with the banteng, gaur, yak, and cattle to identify suitable triallelic bison SNPs (tbSNPs). By using WGS from 15 bison, the MAF estimates for bison parentage would be knowna priori and could be more effectively used in the tbSNPs filtering process. Depending on the aims, WGS from NA wood bison (B.bison athabascae) or European wisent (B. bonasus) could also be included in filtering for MAF estimates and extend the potential utility of a tbSNP panel.Triallelic SNPs have been systematically identified in other mammals. InResults from novel tySNPs presented here demonstrate that one minimal set of markers can be efficiently and accurately used for animal identification, parentage determination, and detecting recent F1 hybridization events to support herd management and breeding decisions of yak producers. The 610 tySNPs, their assay design information, multiplex MALDI-TOF MS assays for the subset of 87 tySNPs, and all other associated information are available for world-wide use without restriction.Bos mutus strain:yakQH1 Genome sequencing and assembly. Accession numberPRJNA74739.NCBI BioProject:Bos grunniens Genome sequencing. Accession numberPRJNA285834.NCBI BioProject:PRJNA551500.NCBI BioProject: Phased trio assembly of yak and cattle genomes. Accession numberPRJNA552915.NCBI BioProject: Bos grunniens x Bos taurus Genome sequencing and assembly. Accession numberPRJNA324822.NCBI BioProject: WGS data from diverse types of U.S. cattle. Accession numberPRJNA325061.NCBI BioProject: WGS data from Cetartiodactyla. Accession numberhttps://doi.org/10.6084/m9.figshare.12472925.v130.Figshare: Table S1. Genomic locations and sequences features of 610 tySNPs in 441 bins.https://doi.org/10.6084/m9.figshare.12473087.v331.Figshare: Table S2. Genomic locations and sequence features of a subset of 87 tySNPs.https://doi.org/10.6084/m9.figshare.12473360.v137.Figshare: Table S3. In silico genotypes derived from WGS for 610 tySNPs in yak, cattle, and other Bos species. m.https://doi.org/10.6084/m9.figshare.12473492.v138.Figshare: Table S4. Oligonucleotide sequences for MALDI-TOF MS assays of 87 tySNPs.https://doi.org/10.6084/m9.figshare.12473537.v132.Figshare: Table S5. MALDI-TOF MS genotypes of 87 tySNPs for 170 NA yak.https://doi.org/10.6084/m9.figshare.12682331.v133.Figshare: Table S6. Analysis of Chinese domestic yak WGS data set DYY31 for B. taurus sequences.https://github.com/kalbflei/YakParentageAndIntrogression.All custom software used to analyze data for this project are available from the GitHub page for this project:https://doi.org/10.5281/zenodo.398845728.Archived software at time of publication:MIT license.License: Reviewer's concerns have been addressed - no further comment.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?PartlyIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?PartlyAre sufficient details provided to allow replication of the method development and its use by others?PartlyReviewer Expertise:Genetics, DNA profiling, development of parentage, forensic and population DNA panelsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors have used available genomes from wild and domestic yaks and related bovids to identify SNPs that are informative for partentage analysis in yaks, for which cattle have a nearly fixed alternate allele. Using available information on allele frequencies they estimate the power for parentage, even without data from one parent and introgression detection and conduct simulations to evaluate the number of SNPs required to reach different confidence levels for the two analyses. The paper is well written, highly detailed, and methods are adequately described.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?No source data requiredAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:population management, comparative genomicsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. et al., explores the identification and use of triallelic SNPs (tySNPs) that are diagnostic for North American yak and cattle. The markers could then be used for population and parentage studies to manage the NA yak populations, attempting to keep inbreeding at a minimum and reducing introgression from cattle breeds. Sequence and assembly data from cattle (UMS3.1) and the F1 yak assembly were used to identify unique candidate tySNPs that had conserved flanking sequence to ensure robust amplification in both species and were dispersed across the genome. Overall, 610 autosomal tySNPs were identified. Allelic frequencies were determined for 87 tySNPs by genotyping in 170 NA yak. By simulations, the power for unique identification, parental exclusions and introgression up to three backcross generations from the F1 were estimated. A unique DNA profile could be obtained with 27 SNPs and the parentage and hybridization determinations required up to 87 SNPs.The manuscript by KalbfleischBos taurus introgression. Recent, planned and unplanned cattle introgression has occurred in the yak NA population. Current SNP-based parentage panels for cattle and other species contain over 100 to several hundred SNPs for unique identification and parentage determination. The panels need to be robust and valid for use in a variety of inbred breeds for a given species. Although the global population of domesticated yak is large and diverse, NA yak have a small population derived from founders in zoo populations imported from Europe. The herd has not been diversified using artificial insemination techniques. Ancient hybridization suggests 2 – 3% The manuscript is well written and easy to understand and read. The authors provide extensive background in each section for the reader, however, some of which can be deleted to shorten the manuscript, but likely not a concern for the online publication. The study design is well planned and the bioinformatic approaches are sound. However, in the end, 87 SNPs were selected – but apparently not by the methods presented here. The presentation gets to ~610 candidate SNPs, where these 87 are amongst those candidates, but, the 87 were selected before these methods were conducted. So, how were the 87 SNPs initially selected and where are the other SNPs that likely failed in the development process? The 87 SNP panel also needs to be adjusted. This is a commercial set of SNPs that I fear are being promoted – not considering all the criteria even presented in this publication.Introduction:The background information is very informative, but perhaps the introduction is a bit too long.What is the calculated evolutionary distance (time) between cattle and the yak? Both Figure 1A and 1B are not necessary.  Perhaps use Figure 2 as Figure 1B?Methods:Sample collection – “purchased or donated from zoological parks in their normal course of caring for animals”. Most zoological parks have ACUC protocols and require “study protocol forms” for samples released – even after opportunistic collections during health examinations. Perhaps the protocols should be confirmed?Can more of the Methods be shortened by using references? Such as details on the identity of the animals used for sequencing?The use of the pedigrees to select a strong representation of the beef cattle breeds is a strong aspect of the project.Correct spacing: “TrimGalore (version 0.5.0)and”Perhaps a supplementary table could be used to represent the information in the text regarding the number of each breed and the genome accession numbers, thus, shortening the text.the cutoffs for call rate  and accuracy  were set at 97% and 99%, respectively.”“ and “For the purposes of parentage exclusion the cutoff call rate of 97% means that a minimum of 94% of the tySNPs will have a genotype for each of the two animals in a pairwise comparison” These are stringent cutoffs for the data and should produce a very robust panel of SNPs.Used MALDI-TOF mass spectroscopy for genotyping: a preliminary set of 139 bins with 518 tySNPs aligned to the B. taurus UMD3.1 assembly. 48 assays per plex using one SNP per bin – producing 3 multiplexes.Although very thorough – can the section including the “Estimating the probability of identity (PI) and probability of exclusion (PE) with tySNPs”, and “Identifying exclusions in a tri-allelic SNP system” be shortened by referencing?Overall, the bioinformatic approaches appear to be sound and robust for this study.Figure 3 is not really needed.Results:Of the 610 tySNPs, 87 overlapped an unpublished set of tySNPs for which we previously developed MALDI-TOF MS genotyping assays 31.” Thus, this panel was already developed and in use, but then verified by phased yak genome. Therefore – how were these original 87 tySNPs identified and designed?Interesting – “If starting from scratch with 610 tySNP candidates and then designing mass array multiplexes – 441 bins with 48 SNPs per multiplex = 10 assays minimum.Thus – it does not seem like the authors used the presented information to produce the 87 tySNP panel. After the fact – they are validating the panel but not suggesting any improvements from the data produced by the methods performed in the manuscript. For example, at least 5 or so of the 87 tySNPs appear to be very physically close in Figure 4 – and hence should not be in the panel.in silico analysis for the 610 SNP allele frequencies confirmed the allelic frequencies for the 87 SNPs that were genotyped in the 170 yaks. However, could a better set of tySNPs have been selected from this data?TheAll the exclusion and introgression calculations are based on the 87 SNPs.No animals with documented, known hybridization were genotyped for validation.Discussion:“Our aim was to identify a minimal set of novel triallelic SNPs to accomplish multiple diverse genetic tests in NA yaks.”a priori set of 87 SNPs that were selected using perhaps a different process as described here. The authors need to confirm the same process was used to select the 87 SNPs – short of using the phased yak assembly for confirmation and positioning, which they indicate. The authors do define a robust set of triallelic SNPs – however – the most robust or minimal set has NOT been identified because this paper confirms anB. taurus – associated allele frequencies. However, the authors do suggest that better SNPs could be selected in more yak WGS data was available. Another way to produce this data would be to make additional multiplexes from the remaining 610 SNPs and genotype the same 170 yaks. If 87 SNPs had already been produced – likely – additional data on tySNPs not selected for the panel is likely available. This manuscript does indicate the 87 SNPs developed for yak testing are robust and have good exclusion rates, but it does not select the best SNPs from the 610 to produce the panel. No modifications are suggested for the panel – including for SNPs now shown to be in close proximity or the 6 tySNPs withB. taurus introgression, thus, a standard set of SNPs would be likely sufficient, although introgression could not be monitored.In the end, the NA yak are not significantly influenced withIs the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?PartlyIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?PartlyAre sufficient details provided to allow replication of the method development and its use by others?PartlyReviewer Expertise:Genetics, DNA profiling, development of parentage, forensic and population DNA panelsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Dear Reviewer 1,     Thank you for your detailed comments on our manuscript.  Regarding the concern surrounding the 87 markers used, we revised the manuscript to highlight our aim of showing the variety of purposes to which triallelic yak SNPs could be applied, as our intention was not to validate a livestock SNP panel.  We regard the identification of the 610 tySNPs and their genomic metadata in Table S1 as a significant outcome of the present report.  We can confirm the subset of 87 tySNPs were identified by the same process as the 610 tySNPs, except for the use of the haplotype-resolved Yak reference genome for determining their correct and unique genome positions.  Like all the candidate tySNPs, their MAFs were unknown at the time of their original selection.  These 87 tySNPs were also identified a second time in the group of 610 as they met all the same selection criteria.  None of the 610 tySNPs were excluded for being close to another tySNP (hence binning), since automated assay design software needs options.  We have updated the new version of the manuscript as described below to highlight these facts.With respect to the comment “This is a commercial set of SNPs that I fear are being promoted – not considering all the criteria even presented in this publication.”  All of the genotype data presented here were produced for research purposes and predate any commercial offering of a yak tySNP test.  The data were meant to provide a statistically significant sampling of the intrinsic properties expected in 170 NA yak for the 610 markers identified here.  Although a commercial tySNP test is now available and contains the 87 tySNPs, we do not endorse it to the exclusion of other markers or test panels that may be suitable.Page 8, column 1, paragraph 1, line 13 of the first version:Methods)“Of the 610 tySNPs, 87 unpublished set of tySNPs for which we previously developed MALDI-TOF MS genotyping assays . Together, all 610 tySNPs, their bins, and their sequence information are suitable for input into SNP assay design software for a variety of genotyping platforms.”And we have modified the following within the DiscussionPage 10, column 2, paragraph 2, line 1 of the first version:Our aim was to identify a set of novel triallelic SNPs. We identified a novel set of 610 tySNPs where each marker has two alleles for NA yak parentage testing, and a third allele for identifying recent cattle introgression. As to the reviewer’s suggestion that there may be a better set of 87 markers available within the 610, we agree.  However, the MAF needs to be estimated for each tySNP in NA yak before this can be explored further.  That effort is beyond the scope of this report.  All 610 tySNPs should be considered viable targets for assay development, with anyone being free to use them in full or to identify a suitable subset within them for their specific purpose.We made the following modification to this sentence in the conclusion:Page 13, column 1, paragraph 2, line 6 of the first version:their assay design information, multiplex MALDI-TOF MS assays for the subset of 87 tySNPs, and all other associated information are available for world-wide use without restriction.The 610 tySNPs,  In addition, we have responded point-by-point to your comments below (bold text).  With respect to requests to make the manuscript briefer, since length is not a concern in this online format, and the editors had previously requested such information, we have left most of the content unchanged.  Introduction:The background information is very informative, but perhaps the introduction is a bit too long.Bos species, a genus that diverged 5 million years ago. We modified Page 3 column 2 paragraph 2 to include: In this hypothetical triallelic SNP system, the evolutionary source of all three alleles could be inferred by estimating frequencies in samples of What is the calculated evolutionary distance (time) between cattle and the yak? We believe that the Figure 1 panels show different information.  Figure 1A shows how tySNPS would arise, while Figure 1B shows the accurate species distances from the root.  Both Figure 1A and 1B are not necessary.  Perhaps use Figure 2 as Figure 1B?  Methods: The following information was added to Methods: The banteng dna samples (1 ug each) were transferred under a Research Material Agreement that was executed on February 6, 2007 by the legal directors of the San Diego Zoo and the technology transfer office of the USDA, ARS.  The gaur blood samples were collected under IACUC protocols on June 15,1999 at the Omaha Henry Doorly Zoo by the head zoo veterinarian, Dr. Doug Armstrong, during evaluation and preparation for interstate transfer to another zoo.Sample collection – “purchased or donated from zoological parks in their normal course of caring for animals”. Most zoological parks have ACUC protocols and require “study protocol forms” for samples released – even after opportunistic collections during health examinations. Perhaps the protocols should be confirmed?   This journal requires full method descriptions, efforts to shorten with previous submissions have been declined.   Can more of the Methods be shortened by using references? Such as details on the identity of the animals used for sequencing?   We agree.The use of the pedigrees to select a strong representation of the beef cattle breeds is a strong aspect of the project.   Repaired Page 5 column 2, paragraph 2Correct spacing: “TrimGalore (version 0.5.0)and”.   We have left the text as is.Perhaps a supplementary table could be used to represent the information in the text regarding the number of each breed and the genome accession numbers, thus, shortening the text.the cutoffs for call rate  and accuracy  were set at 97% and 99%, respectively.”and “For the purposes of parentage exclusion the cutoff call rate of 97% means that a minimum of 94% of the tySNPs will have a genotype for each of the two animals in a pairwise comparison”These are stringent cutoffs for the data and should produce a very robust panel of SNPs.We agree.“   This is correct.Used MALDI-TOF mass spectroscopy for genotyping: a preliminary set of 139 bins with 518 tySNPs aligned to the B. taurus UMD3.1 assembly. 48 assays per plex using one SNP per bin – producing 3 multiplexes.As described above, F1000Research editors require full method descriptions, efforts to shorten with previous submissions have been declined.Although very thorough – can the section including the “Estimating the probability of identity (PI) and probability of exclusion (PE) with tySNPs”, and “Identifying exclusions in a tri-allelic SNP system” be shortened by referencing?   We agree.Overall, the bioinformatic approaches appear to be sound and robust for this study.As this work was inspired by input from yak breeders, we wanted to include a figure that shows producers the expected loss of introgressed cattle alleles across generations of yak backcrossing. This figure relates directly to the ability of tySNPs to detect cattle introgression as shown by simulations in Figure 8.Figure 3 is not really needed. Results:Of the 610 tySNPs, 87 overlapped an unpublished set of tySNPs for which we previously developed MALDI-TOF MS genotyping assays 31.” Thus, this panel was already developed and in use, but then verified by phased yak genome. Therefore – how were these original 87 tySNPs identified and designed?Addressed in the revised manuscript in “Amendments from Version 1”. Interesting – “   Depending upon the total number of assays required by an interested party, and the assay platform/chemistry, any subset of the proposed bins could be chosen for an assay panel.  We have demonstrated with a MALDI-TOF MS platform that 3 multiplexes with 87 total assays will provide enough information to reasonably accomplish animal identification, parentage determination, and detect recent hybridization. If starting from scratch with 610 tySNP candidates and then designing mass array multiplexes – 441 bins with 48 SNPs per multiplex = 10 assays minimum.   As addressed in the revised manuscript in “Amendments from Version 1”, our aim was not to validate an elite panel of 87 tySNPs, but to use the population genotypes to estimate their MAF in NA yak.  Since all tySNPs were selected based on being heterozygous in only two yaks (1 NA and 1 Chinese), we had initial concerns that the MAF for most of the SNPs would be too low to be useful.  Using data from the 87 unmapped tySNPs and 170 yak we were pleasantly surprised there was a useful distribution of MAFs.  We document this in Figure 5A and can reasonably infer a similar distribution among all 610 tySNPs.Thus – it does not seem like the authors used the presented information to produce the 87 tySNP panel. After the fact – they are validating the panel but not suggesting any improvements from the data produced by the methods performed in the manuscript. For example, at least 5 or so of the 87 tySNPs appear to be very physically close in Figure 4 – and hence should not be in the panel.   in silico analysis for the 610 SNP allele frequencies confirmed the allelic frequencies for the 87 SNPs that were genotyped in the 170 yaks. However, could a better set of tySNPs have been selected from this data?Any new panel design starting with these 610 tySNPs would likely end with a different marker composition.  However, any randomly selected set of 87 tySNPs distributed among the 441 bins are expected to produce similar results in NA yaks. Moreover, creating specific combinations of markers into multiplex assays (up to 48 at a time) is a tremendous technical challenge.  This challenge is increased by having three SNP alleles to assay instead of just two.  In addition, flanking polymorphisms near some of the tySNPs reduce the utility of some of the 610 identified tySNPs depending on the genotyping platform and assay requirements.  All of these challenges were overcome here as part of a solution to develop as powerful of assay as possible with cost awareness.The    Since we had data on 87 SNPs, that number was used as the lower limit.  In order to demonstrate the benefit of more markers, we simulated tests with 1.5, 2, and 4-fold increases in marker count.  The balance between cost and the benefit of more markers is difficult to strike.  However, we wanted to demonstrate what could be expected with these 87 tySNPs since this is the data we had.  All the exclusion and introgression calculations are based on the 87 SNPs.   This assertion is incorrect.  We used the F1 cross between a Highland bull and a yak dam for in-silico genotyping of the full panel of 610 markers (Table 1 last row).   This hybrid female (“Esperanza”) had a 0.5049 cattle allele fraction.  The genotypes are provided in Table S3.  No animals with documented, known hybridization were genotyped for validation. Discussion:“Our aim was to identify minimal set of novel triallelic SNPs to accomplish multiple diverse genetic tests in NA yaks.”The authors do define a robust set of triallelic SNPs – however – most robust or minimal set has NOT been identified because this paper confirms an a priori set of 87 SNPs that were selected using perhaps a different process as described here. The authors need to confirm the same process was used to select the 87 SNPs – short of using the phased yak assembly for confirmation and positioning, which they indicate.We confirm.B. taurus – associated allele frequencies. However, the authors do suggest that better SNPs could be selected in more yak WGS data was available. Another way to produce this data would be to make additional multiplexes from the remaining 610 SNPs and genotype the same 170 yaks. If 87 SNPs had already been produced – likely – additional data on tySNPs not selected for the panel is likely available. all the data from all the tySNPs that met the selection criteria and converted to a reliable assay .  Figure 5A shows that some of these 87 tySNPs had low MAFs which is less than ideal for the tasks at hand but were still included.  As mentioned above, any new panel design starting with these 610 tySNPs would likely end with a different marker composition.  However, any random set of 87 tySNPs distributed among the 441 bins are expected to produce similar results in NA yaks.  The success of SNP assay design is dependent on many factors including chemistry, platform, technology, reagents, sample quality, and operator experience, to name a few.  We used This manuscript does indicate the 87 SNPs developed for yak testing are robust and have good exclusion rates, but it does not select the best SNPs from the 610 to produce the panel. No modifications are suggested for the panel – including for SNPs now shown to be in close proximity or the 6 tySNPs with    B. taurus introgression, thus, a standard set of SNPs would be likely sufficient, although introgression could not be monitored.As the reviewer indicates, introgression cannot be monitored with a standard set of bi-allelic SNPs.  This was the motivation to identify tySNPs that can detect recent introgression among NA yak without the need for a second, independent set of markers for that task. In the end, the NA yak are not significantly influenced with


Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating disease characterized by persistent fatigue and post-exertion malaise, accompanied by other symptoms , 2. The With the aim to identify putative genetic factors that could explain the pathophysiological mechanisms of ME/CFS, four genome-wide association studies (GWAS) and two targeted-genome association studies (TGAS) were conducted in the past decade –10. In tCurrent GWAS or TGAS of ME/CFS are based on data of the so-called single nucleotide polymorphisms (SNPs) located in specific positions of the human genome. These genetic markers are short nucleotide sequences that differ in a single position from each other. Each possible sequence of a SNP is interpreted as a different allele. In theory, there are up to four alleles of the same SNP given that there are only four possible nucleotides . However, by design, classical genotyping technologies can only assess the two most frequent alleles per SNP. As an alternative to classical GWAS and TGAS, studies using data from next-generation sequencing technologies are able to assess all possible alleles of a given SNP. As far as we know, these alternative studies have been never performed on ME/CFS.In general, several QC checks should be performed in the genetic data before carrying out the association analysis itself. First, it is important to determine all monomorphic SNPs and to report the respective number. These SNPs are non-informative for the subsequent genetic association analysis, because they show the same allele in all study participants. It is also important to calculate the so-called minor allele frequency (MAF) of each SNP. Statistically speaking, the MAF is defined as the frequency of the least frequent allele of a given SNP. In practice, a very low MAF is in the same order of magnitude of the underlying genotyping error rate and, therefore, SNPs under this condition should be excluded from the study. A typical threshold for a very low MAF ranges from 1 to 5%. Less stringent thresholds for the MAF can be used in studies with smaller sample sizes.2 goodness-of-fit test. In this statistical test, p-values below the specified significance level suggest evidence against the HWE. Since the HWE is supposed to be tested in data of each SNP separately, the significance level of each individual test should be adjusted in order to ensure a global significance level for this QC check. Bonferroni or Sidak-Dunn corrections are two popular methods to make such adjustment. Alternatively, one can use procedures based on the control of the false discovery rate, as proposed by Benjamini and Hochberg  should be tested in the observed genotype frequency distribution of each SNP. The HWE is a mathematical expectation for the probability of observing a given genotype under random mating (or panmixia), no selection, no migration, non-overlapping generations, and no genotyping errors. According to the HWE, the frequency of a given genotype is expected to be factorized into the product of the respective allele frequencies. The HWE is usually tested by the popular Pearson's χHochberg . In theoThird, the proportion of heterozygous genotypes  across all SNPs should be calculated for each individual sample. Excessive heterozygosity rate suggests a possible contamination of the respective biological sample, while reduced heterozygosity rate indicates genetic inbreeding. The usual practice is to exclude samples from individuals whose heterozygosity rates are not falling into a “confidence” band. This confidence band is usually defined by the average heterozygosity rate of all the samples plus/minus a given number of times the standard deviation of the heterozygosity rate. The heterozygosity of SNPs located in the X chromosome is also used to confirm the gender of a sample and to detect putative label swaps.e.g., deletion or insertion). A low genotyping rate of a given individual indicates a low quality of the DNA material used for genotyping. Again, researchers must decide what is considered a reasonable genotyping rate for their study. In addition, different exclusion criteria can be applied to the genotyping rates of SNPs and individuals.Fourth, data of SNPs or of individuals with low genotyping rates should be excluded from the analysis. The genotyping rate of a given SNP is the proportion of individuals with fully determined genotypes of that SNP, whereas the genotyping rate of a given individual is the proportion of SNPs with a fully determined genotype of that individual. A low genotyping rate of a given SNP suggests that the genomic site associated with that SNP includes another type of genetic variation  can also be performed in GWAS and TGAS, as reviewed elsewhere  out of 61 patients based on the 1994 Center for Diseases Control Criteria (Besides the partial QC checks, the investigated genetic data on ME/CFS suffer from the curse of not having an objective biomarker for disease diagnosis. Similar problem can be envisioned for other complex diseases lacking a biomarker, such as Fibromyalgia and the Gulf War Syndrome. The absence of a biomarker is likely to introduce a possible misclassification of the true disease status of the recruited patients . To illuCriteria , 18. HowIn summary, given the partial QC checks performed in current GWAS and TGAS, the question of a genetic component in ME/CFS remains open for investigation. To accelerate the discovery of promising disease-gene association, future genetic studies of ME/CFS should set data and methodological standards as high as those followed by the 1,000 Human Genome Project and the UK10K project , 20. DatNS conceptualized this research. AG and NS performed the literature review. EL and LN helped in the interpretation and discussion of all the results. All authors read, revised, and approved the final draft of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.