Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identifying the causal variants and revealing underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variant.We observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions.Our approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle.The online version of this article (10.1186/s12863-018-0620-0) contains supplementary material, which is available to authorized users. Over the last decade, the development of high density single nucleotide polymorphism (SNP) arrays and next-generation sequencing (NGS) technologies have made genome wide marker sets available in many organisms , 2. CombTo resolve these issues, we need additional information over and above association statistics. For traits with Mendelian inheritance, techniques such as homozygosity mapping and studies of recombinant haplotypes provide important clues due to the unambiguous association of at least some genotypes with phenotypic differences. For quantitative traits, no such close associations exist. However, genomic information of various types do allow relative prioritization among candidate variants. The challenges are which information to consider post-GWAS and how to combine them with GWAS statistics. Expression quantitative trait loci (eQTL) mapping can help; expression profiles as the dependent trait in a GWAS have identified causal genes in some studies . NeverthIn this study, we used an approach to separate multiple closely linked QTL in dairy cattle by fixing the lead SNP as a covariate. The analysis detects QTL chromosome by chromosome, and generates a list of lead SNPs for each QTL. The method is demonstrated by application to three milk yield traits in Nordic Holstein cattle. Many previously identified loci were also confirmed here. Furthermore, we used the mammalian phenotype database to help find the candidate genes and Variant Effect Predictor (VEP) annotations to screen for possible causal mutations.We applied a GWAS analysis approach that automatically and iteratively accounts for the effects of QTL identified in previous iteration(s), a similar approach to conditional analysis implemented in GCTA . The imp2) with the QTN in order to test whether our method introduces type I error into analysis when fixing lead SNPs detected in previous iterations as covariates [Our approach of including associated SNPs as covariates in subsequent rounds of analyses did not increase the type I error rates. We simulated one SNP as a QTN and considered ten other SNPs with different levels of LD  located within the intron of MGST1. MGST1 was previously reported associated with the milk fat content [COX15. In a human study, this gene was proposed involved in biosynthesis of heme A [SCD1 [Analyzing milk fat yield, our approach detected seven additional QTL over and above the QTL detected in the first round Fig. . In Tablin DGAT1 , BTA14:  content . On chrof heme A . Even th A [SCD1  located BOP1. The annotation of BTA14:1835440 (rs208567981) is a missense mutation, and the SIFT annotation is tolerant. However, this signal is most likely due to the known mutation in DGAT1. The lead SNP (rs208567981) was in strong LD with SNPs located within DGAT1 and the –log10(P) value of these 19 SNPs within DGAT1 were larger than 47.99 , which is close to MRPS30. A recent study showed MRPS30 to be associated with lactation persistence in Canadian Holstein cattle [GHR [GHR is BTA20:31909478 (rs385640152), and is known to affect milk yield [MGST1. A previous eQTL study showed MGST1 may affect milk composition [ABCG2. ABCG2 was previously reported to affect milk yield in dairy cattle [We applied our analysis to milk yield Fig. . A totalposition . With ouy cattle . This SNy cattle . We esti996719 rs3116343, CSN1S1, CSN2, CSN3 and DGAT1 were reported in dairy cattle and also identified in the present study. Furthermore, we identified five genes related to “abnormal in mammary gland development” , the remaining four genes are ELF5, CAT, STK3 and CHUK. ELF5 is one of the candidate genes proposed by the closest genes to lead SNP (BTA15: 65891100) associated with the fat yield  on the same chromosome associated with milk protein yield  and annotated them using VEP [GHR, the well-known causative mutation for GHR is BTA20:31909478  [As shown before, the causative mutation maybe located in the neighboring region of the lead SNP. Therefore, we extracted all SNPs in LD with leading SNPs  were found related to abnormal milk composition and DGAT1 related to mammary gland development . Moreover, in STK3, we also found tolerance missense mutations.Further, we checked whether we can detect some candidate mutations in the new candidate genes. Four genes , but also identified new candidate genes which may be helpful in finding causal factors.Post GWAS, we face the challenge of identifying the candidate genes. The conventional method is to use the nearest gene, but this may miss the target as many-a-time the lead SNP may not be from the causal gene. This could be due to imputation inaccuracies, multiple QTL in the vicinity or random chance factor. Therefore, we need to use additional information to prioritize the candidate genes. In this study, we used the mammalian phenotype database to search for candidate genes from the genes located in association regions. The mammalian phenotype is based on mouse mutation lines. As a test, we extracted all genes located within LD of the lead SNPs for all three milk yield traits and searched for related phenotype terms. Here, we searched for two phenotype terms ‘abnormal mammary gland development’ and ‘abnormal milk composition’. We successfully identified some well-known genes affecting milk related traits in cattle as well as new candidate genes Table . For theDGAT1 and ABCG2 can be detected in our lead SNP list, and the causative mutation of both can be detected in VEP annotation as missense variants. GHR was found nearby the location of lead SNPs. For ABCG2 and GHR, the SIFT score show these mutations as ‘deleterious’. For DGAT1, even though the SIFT showed these two mutations are tolerated the amino acid of the protein is changed. Therefore, the impact of moderate and high reported by VEP can be considered as possible causative mutations, while SIFT score can be used to provide additional support.We also face another challenge of identifying the causative variant once the causal gene is identified as levels of linkage disequilibrium in cattle are high . In manyIn summary, our analysis approach can distinguish nearby association signals of multiple QTL. In our study, we found candidate genes reported by previous studies. Followed by searching genes within the LD region of the lead SNPs, we can find high confidence candidate genes. Lastly, using VEP can help us to find putative causative mutations within candidate genes and provides a good source for further functional validation. However, our approach will not be able to pinpoint causal variants located in the non-coding and regulatory regions due to lack of annotation of the cattle genome.In this study, we designed an approach for detecting closely linked multiple association signals and performed the analysis in Nordic Holstein cattle for milk, fat and protein yields. The results showed we not only detected most of the well-known genes affecting these three milk yield traits but also detected additional candidate genes. Post-GWAS, we used information from the mammalian phenotype database and variant effect predictor to confirm known genes and causative mutations. In the meanwhile, we detected additional genes which might be contributing to variation in milk traits in Nordic Holstein cattle. Therefore, we concluded our approach can be used routinely for GWAS studies in dairy cattle.No animal experiments were performed in this study, and therefore, approval from the ethics committee was not required.http://www.nordicebv.info/). Breeding values for milk, fat and protein yield  are based on production figures expressed in kilograms taken from routine milk records and then combined into an index for each trait. For details on genetic evaluation for milk yield traits in Nordic countries see (http://www.nordicebv.info/production). The breeding values used for association analysis were de-regressed proof breeding values [Phenotypic records for Nordic Holstein cattle are kept in a centralized database . As the 1000 Bull Genomes Project only shares data after variant calling, some markers were not called for all animals in the combined dataset. To avoid large gaps of missing markers in the dataset, only markers that were called in both the Nordic and the 1000 Bull Genomes Project datasets were kept. For positions containing both a SNP and an INDEL, the INDEL was discarded, as the imputation methods rely on unambiguous sequences of variants. Positions with disagreements between alleles for sequence and HD data were also deleted. Reference genotype probability data was run through BEAGLE [2 value  below 0.9 were removed from the original sequence data. This was done in order to remove poorly imputed markers that might have adverse effects on the imputation procedures.Different variant calling pipelines were used for the 1000 Bull Genome Project data and the in-house Nordic data at Aarhus University. The whole genome sequence data at Aarhus University was analyzed as described by Brøndum et al. ; while th BEAGLE  and all P < 1.0− 6), 15,355,402 SNPs on 29 autosomes in Nordic Holstein cattle were retained for association analyses. The average accuracy  was 0.85 for across breed imputation. Information on the distribution of imputation accuracy as a function of minor allele frequency has previously been published [Imputation from 54 k to HD genotypes to HD and imputation to the WGS level were undertaken with IMPUTE2 v2.3.1  and Miniublished .GCTA-COJO approach in GCTA [10P value in the GWAS. The SNP with the largest –log10P value, the lead SNP, within each chromosome was identified. An experiment-wise 0.05 type I error rate after Bonferroni correction for 15,335,402 simultaneous tests corresponds to a threshold of –log10P ≈ 8.5. If the –log10P value of the lead SNP exceeded 8.5; we extracted the lead SNP’s genotype dosage, fitted it as a covariate, and scanned the whole chromosome again as the second round. If the result of second round detected another SNP with a –log10P value exceeding 8.5 and this SNP also was significant in the first round (–log10P > 8.5), we extracted the allele dosage of this SNP and fixed it as another covariate and scanned the chromosome in a third round. This same procedure was iterated until no additional SNP remained significant. The lead SNP in each round were collected to build a lead SNP list. Moreover, in each round solo SNP, that is, SNP with no other significant SNP within a 1 Mb region were removed. A boundary for each QTL peak was defined as follows: for each QTL, we scanned the 1 Mb region up- and down-stream of each lead SNP, if SNP –log10P value decreased by more than 3 units compared to the value at the leading SNP and the region is larger than 0.25 Mb we set this SNP as a boundary, otherwise we set ±0.25 Mb as the boundary. The list of candidate genes were generated from the closest annotated genome feature to the lead SNP list.We developed an analysis approach to run the conditional GWAS analysis, similar to the  in GCTA . However in GCTA  for each2) with the simulated QTN were selected. These 10 SNPs have different r2 with the QTN as follows: one with 0.9–1, one with 0.8–0.9, one with 0.8–0.7, one with 0.7–0.6, one with 0.6–0.5, one with 0.5–0.4, one with 0.4–0.3, one with 0.3–0.2 and two with less than 0.2. Allele substitution effects at the QTL were sampled from a univariate normal distribution with mean of 20% of the standard deviation of phenotype and variance equal to 1% of the phenotypic variance. We repeated this simulation and applied our analysis 100 times. Lastly, we investigated how many times we found a SNP in LD with the simulated QTN after we fix the simulated causative mutation as a covariate i.e. false positive detection.We used simulated phenotype data to test whether our approach to detecting multiple QTL on a chromosome by incorporating previously identified QTL as covariates, inflates the type I error rates . We sele2 between lead SNP and all other SNPs on the same chromosome using PLINK [2 > 0.2 with the lead SNP. All these SNPs were annotated by VEP (Variant Effect Predictor) [2 with SNPs in the chromosome. The boundary was set to the last SNP that has r2 > 0.2. Then we extracted all the genes overlapping these regions and searched them in the database. We found 411 genes located in the LD regions, of which 375 have gene symbols. These 375 genes were searched in the database and 364 have mutation lines with phenotype descriptions in the Mammalian Phenotype database. We refined results using two terms for phenotypes: ‘abnormal in mammary gland development’ and ‘abnormal in milk production’.We calculated the pairwise rng PLINK  and extredictor) . To findedictor) . We collWe used the lead SNP list to generate the genetic relationship matrix (GRM) as group 1. Then we excluded all flank 2.5 Mb SNPs of the lead SNP from the imputed HD data to generate GRM as group 2. At last, we estimated variance explained by these two groups for each trait. The whole analysis was conducted using GCTA .Additional file 1:Figure S1. The locuszoom figure of previous report causative mutation of DGAT1 of the genome-wide association result in fat content of milk in Nordic Holstein cattle and Figure S2. The locuszoom figure of previous report causative mutation of DGAT1 of the genome-wide association result in fat content of milk in Nordic Holstein cattle. BOP1 was not include in USCS refFlat file. (DOCX 551 kb)


Passive acoustic telemetry using coded transmitter tags and stationary receivers is a popular method for tracking movements of aquatic animals. Understanding the performance of these systems is important in array design and in analysis. Close proximity detection interference (CPDI) is a condition where receivers fail to reliably detect tag transmissions. CPDI generally occurs when the tag and receiver are near one another in acoustically reverberant settings. Here we confirm transmission multipaths reflected off the environment arriving at a receiver with sufficient delay relative to the direct signal cause CPDI. We propose a ray-propagation based model to estimate the arrival of energy via multipaths to predict CPDI occurrence, and we show how deeper deployments are particularly susceptible.A series of experiments were designed to develop and validate our model. Deep (300 m) and shallow (25 m) ranging experiments were conducted using Vemco V13 acoustic tags and VR2-W receivers. Probabilistic modeling of hourly detections was used to estimate the average distance a tag could be detected. A mechanistic model for predicting the arrival time of multipaths was developed using parameters from these experiments to calculate the direct and multipath path lengths. This model was retroactively applied to the previous ranging experiments to validate CPDI observations. Two additional experiments were designed to validate predictions of CPDI with respect to combinations of deployment depth and distance. Playback of recorded tags in a tank environment was used to confirm multipaths arriving after the receiver’s blanking interval cause CPDI effects.Analysis of empirical data estimated the average maximum detection radius (AMDR), the farthest distance at which 95% of tag transmissions went undetected by receivers, was between 840 and 846 m for the deep ranging experiment across all factor permutations. From these results, CPDI was estimated within a 276.5 m radius of the receiver. These empirical estimations were consistent with mechanistic model predictions. CPDI affected detection at distances closer than 259–326 m from receivers. AMDR determined from the shallow ranging experiment was between 278 and 290 m with CPDI neither predicted nor observed. Results of validation experiments were consistent with mechanistic model predictions. Finally, we were able to predict detection/nondetection with 95.7% accuracy using the mechanistic model’s criterion when simulating transmissions with and without multipaths.Close proximity detection interference results from combinations of depth and distance that produce reflected signals arriving after a receiver’s blanking interval has ended. Deployment scenarios resulting in CPDI can be predicted with the proposed mechanistic model. For deeper deployments, sea-surface reflections can produce CPDI conditions, resulting in transmission rejection, regardless of the reflective properties of the seafloor. The past three decades have seen an increase in the popularity of passive tracking of aquatic animals using acoustic telemetry systems . Due in Interpretation of the results of a telemetry study requires knowledge of the receiver’s detection region to understand the probability of a transmission’s detection across a range of potential depths and distances which a tagged individual may occupy. The passive sonar equation provides a framework for understanding factors affecting detection of transmissions.A transmission is likely to be detected when the signal-to-noise ratio of the arriving ping exceeds the receiver’s detection threshold (DT). The received level (RL) depends on the source level (SL) and transmission loss (TL), including geometric spreading and attenuation via scattering and absorption. A signal can be detected when the RL exceeds the background noise level (NL) by a level greater than the DT in the frequency range of interest . The NL Propagation conditions, TLs, and NLs differ across sites; therefore determining the detection characteristics of receivers for every study is critical. A 2013 meta-analysis of 321 acoustic tracking studies called for more comprehensive detection range testing and reporting in acoustic tagging studies, finding that only 48.6% of studies reviewed included results from equipment ranging experiments . Some ofA common finding of range testing experiments is that the probability of detecting a transmission decreases with increasing range between a tag and receiver, with the highest probability of detection occurring when tags are at distances closest to the receiver . HoweverObservations of CPDI have been noted in other acoustic ranging experiments . A cruisTo understand when and how CPDI occurs, it is helpful to understand the way Vemco tags encode and transmit data and how receivers decode and interpret those transmissions. Each transmission consists of a train of 7–10 rapid high-frequency acoustic pings with data encoded in the timing of the intervals between successive pings. The interval between the first two pings, known as the synchronization interval, defines a narrow range of possible coding schemes indicating the tag’s model, a range of potential identification numbers, and other associated data. The last interval acts as a checksum used to confirm that a series of detected pings are from a single train of a valid tag. The remainder of the inter-ping intervals encode the tag’s unique identifier and any sensor data. Each complete transmission lasts roughly 3–5 s . On receIn this manuscript, we will use the term “multipath” in place of “echo” to refer to arrivals of the signal that have been reflected off the sea surface and/or seafloor, for reasons of clarity and consistency with acoustic terminology. CPDI occurs when a ping’s multipath arrives at a receiver during the tag’s transmission sequence, outside of a prescribed blanking interval. If the RL of the multipath is sufficiently high, the receiver may misinterpret the multipath as the arrival of the subsequent ping, resulting in rejection of the transmission . The arrBroadly, reflections result when acoustic energy encounters sharp acoustic impedance contrasts such as those occurring between the water and air and (often to a lesser degree) between water and the seafloor. Acoustic energy may arrive at a receiver having been reflected one or more times off such interfaces. For fixed tag–receiver pair depths, the path length difference  between direct and multipath arrivals decreases as the range between tag and receiver increases . ConsequOur model identifies deployment depth as an important factor contributing to CPDI. Consider the simplest case of the arrival of transmission energy along the direct path and the first multipath reflected off the sea surface in an environment with a uniform sound speed  where arrival time is directly related to propagation distance of the direct and multipath. When the water surface is smooth, the sea-surface acts as a near perfect reflector with virtually no TL . In the With this study we conducted a series of sequential experiments building on the results of one another to answer the following questions: How does the shape of the detection function differ between receivers that experience CPDI and those that do not? What causes CPDI? Can we accurately predict where CPDI will occur? How does depth contribute to the CPDI phenomena and what depths are most susceptible?We performed a series of five experiments which incrementally build on the results of the prior to construct and validate our mechanistic CPDI model. The goal of the first experiment was to determine the range of distances from a receiver at which tags could be detected in a deep water (300 m) environment. The observation of CPDI in the results of this experiment led us to conduct a second range test in a shallow water (25 m) setting to determine if CPDI effects persisted. From observations of the presence/absence of CPDI in experiments 1 and 2, we developed the mechanistic model for predicting CPDI using a simplified straight-line ray-propagation model where direct and multipath arrivals are modeled as a function of sound speed, water depth, and relative receiver and tag positions. We initialized our mechanistic model with similar conditions from the results of experiments 1 and 2 and compared the observed presence and absence of CPDI during these experiments to the mechanistic model’s predictions We then developed two further field experiments comparing CPDI observations with the mechanistic model’s predictions. Finally, we used playback of a recorded acoustic tag transmission in a controlled tank setting to confirm the multipath hypothesis that arrivals occurring after the blanking interval result in missed detections (hence CPDI). The location of each of the four field experiments is shown in Following the work outlined by At a glance, the number of detected tag transmissions is significantly lower than would be expected during the first two ranging experiments. This is due to the number of tags used during these experiments and their transmission interval. As the number of tags with variable transmission intervals detectable by a receiver increases, so too does the probability that individual transmissions from two or more tags will overlap. When this occurs, the receiver will reject both transmissions. Therefore, when multiple tags are within the detection range of a receiver, even when transmissions were theoretically detectable on their own, the realized number of detections will be less than the total number of transmissions sent by all tags. This problem is exacerbated when the transmission interval of tags is short, further depressing the number of transmissions detected. For this reason, we present the number of total detections logged by receivers during each hour of the experiment without standardizing values by average number of detections sent per hour as this would be dependent on the exact detection characteristics during each transmission. Vemco’s website provides a collision calculator for estimating the expected number of detectable transmissions when a number of tags with similar transmission parameters are within detection range of a receiver, the results of which we have provided for reference .A ranging experiment was initially conducted to quantify detection probability at various distances from a receiver for a tracking study investigating the movements of a Hawaiian deep water demersal snapper. The experiment occurred offshore of the Diamond Head crater on the south shore of Oahu. This area was selected as a study site for its accessibility, moderate slope, and similarity to a nearby site involved in other ongoing passive telemetry work. It features a protruding flat shallow shelf between 0 and 100 m extending approximately 1.8 km offshore and terminating with a moderate slope to 700 m over a distance of 5 km into the Kaiwi channel between the islands of Oahu and Molokai .Three receivers were deployed from the R/V Ho’okele in 300 m depth. Receivers were suspended 1, 15 and 30 m from the seafloor on a single mooring using trawling floats, 80 kg of concrete, a polypropylene line, and an acoustic release . Acoustic tags were moored in a similar manner at 1 and 15 m above the seafloor at ranges spaced by approximately 200 m from 0 to 1,000 m . EquipmeA transmission’s detection probability across the full range of the study was estimated using a generalized additive model (GAM) to explain the number of hourly transmissions detected for each tag and receiver pair as a function of the distance between tags and receivers and the height of the receiver relative to the seafloor, as well as a number of random factors identified by other studies to affect detection distance, using a Poisson distribution to model the error distribution. Random effect variables including mean hourly wind speed and mean hourly wind gust (from NOAA buoy #161234), hourly tide height and hourly tide direction data (from NOAA tide station #1612340), and diurnal period, divided into day (6am to 6 pm) or night (6 pm to 6 am). GAMs were fit using the Mixed GAM Computational Vehicle (mgcv) package in R . From GAFrom the resulting global GAM, candidate GAMs consisting of all possible permutations of independent explanatory variables were compared to determine the best fit models using Akaike’s information criterion (AIC). Candidate models within two AIC units of the best fit GAM were used to estimate AMDR and CPDI extent. The number of expected hourly detections across the range of potential tag locations for each combination of explanatory factors were predicted using each GAM using median values for wind speed, wind gust, water level, and incoming/outgoing tides during both day and night periods. Predicted hourly detections were then used to determine AMDR and presence/extent of CPDI. AMDR was defined as the distance at which the number of detections predictedfell below a threshold of 5% of transmissions sent. In practice, this occurred when there were fewer than three predicted detections per hour. We then constructed a range including standard error around this value by also predicting the standard error values at each predicted distance and then adding and subtracting the error from our model fit. We then calculated a range inclusive of the standard error as the distance where each of our predictions incorporating the error term fell below our 5% threshold as a measure of the model’s fit. CPDI was said to affect the range from the receiver to the distance at which the predicted number of detections and their standard error first overlapped the maximum predicted value and its standard error. At this point we could be 95% confident the predicted values no longer statistically differed.A second experiment was designed to determine the relationship between detection probability and horizontal distance in a shallow water setting, and to explore whether CPDI was present in this setting. A field site was selected off Sand Island, immediately west of the Honolulu Harbor channel. Characterized by a loose sand substrate and sparse coral rubble, this location was selected for accessibility to a relatively linear swath of 25 m isobath, water properties presumed similar to the deep water ranging experiment site due to their geographic proximity, and a standing agreement between the University of Hawaii and Hawaii’s Department of Aquatic Resources for use of the area for research purposes.Nine Vemco VR2-W units were deployed on a single mooring from the R/V Hoʻoponopono. The mooring design used was similar to the one employed in the deep water ranging experiment except that the polypropylene line was reinforced with a 1/8″ braided steel cable and acoustic releases were not used. The nine receivers were suspended in groups of three at 1, 7.5, and 15 m above the seafloor. Eighteen acoustic tags were affixed 7.5 m from the seafloor, in groups of three, spaced at approximate horizontal distances of 0, 75, 150, 300, 600, and 1,200 m from the receivers, as measured by GPS during each mooring deployment .Following deployment, divers descended on the receiver mooring to assess equipment condition and measure the bottom depth which was found to be 25 m using a dive computer . Bottom depth was measured using the same dive computer during recovery of the tag moorings which ranged between 23.8 and 25.3 m. The same process for determining AMDR and CPDI extent was performed for data from this shallow water ranging experiment as was done during the deep water ranging experiment.The proposed mechanistic CPDI model uses a depth and range-independent sound speed , relative positions of the receiver and tag, water depth at the receiver, the duration of the receiver’s blanking interval, and AMDR determined from ranging experiments to calculate the path length of direct and multipath arrivals for a grid of potential tag position . All dirThe first step of the proposed mechanistic model is to grid the study area by range and depth, with each grid point representing a potential tag position and the receiver fixed at 0 m range. A resolution parameter allows the user to select an appropriate grid spacing. A ray tracer calculates both direct and multipaths lengths at each grid point using an ideal model of multipath propagation . This isTherefore, each multipath arriving at a receiver may fall into one of three categories. (1) If the relative arrival time is less than or equal to the blanking interval and the total path length is less than or equal to the AMDR, the multipath is not predicted to interfere with detection of the direct signal. (2) If the relative arrival time is greater than the blanking interval and the total path length is less than or equal to the AMDR, the multipath is predicted to interfere with the direct signal resulting in failure of the receiver to detect the transmission. (3) If the path length is in excess of the AMDR, no interference is predicted, as the multipath has experienced TLs during propagation such that it is below the threshold for detection. At each grid point, each multipath is categorized. Grid points with at least one multipath falling into the second category are predicted to experience CPDI based on our criteria; grid points where all transmission multipath are of the first and third type are predicted not to experience CPDI.Following the development of the mechanistic CPDI model, we input parameters from both deep water and shallow water ranging field experiments to compare the observed ranges affected by CPDI to predictions from the mechanistic CPDI model. We used a 260 ms blanking interval , a sound speed of 1,530 m/s , and a The first of two validation experiments was designed to test predictions of CPDI related to deployment depth. In this experiment, the mechanistic CPDI model was used to identify two depth conditions: One in which multipaths were predicted to arrive outside the receiver’s blanking interval, producing CPDI, and a second, where no detectable multipaths arrived outside the receiver’s blanking interval, and thus no CPDI effects were present. The mechanistic model’s AMDR parameter was set to 843 m, the closest whole number to the median value determined during the deep water ranging experiment (experiment 1), due to similarities in depth and deployment location. The model’s blanking interval was initialized at 260 ms and sound speed was 1,530 m/s. The mechanistic model predicted CPDI for receiver and tags on the same mooring line , when both receiver and tag were positioned 1 m above the seafloor in 215 m bottom depth. No CPDI was predicted for a similar tag and receiver pair in 50 m water depth. Latitude and longitude coordinates were selected for locations matching these depths in proximity to the location where the deep water ranging experiment was conducted using bathymetry charts . One mooThe number of tag transmissions detected hourly by each receiver was assessed for normality using Shapiro–Wilks’ test and were compared between receivers using a Wilcoxon sign-rank test due to the nonparametric distribution of data collected. To account for the independence in the number of transmissions sent by each tag, daily meta-logs for each receiver were downloaded from the VUE database. These provided the number of valid detections, valid synchronization intervals, total detected pings, and the number of detections rejected due to invalid checksums logged by each receiver. Daily performance metrics, including code detection efficiency (CDE) and the rejection coefficient (RC) were determined for each receiver from meta-logs using methods previously established . CDE is These metrics allowed receiver logs to be normalized for comparison independently of the total number of tag transmissions sent. This is important when comparing detection logs in which variations in transmission interval may have resulted in each receiver being exposed to a different number of transmission ping trains. However, both CDE and RC use the number of detected valid syncs as a proxy for the number of transmissions sent. For a receiver to recognize a synchronization interval, the time between the arrival of two pings must be of a strictly defined length. We suspect multipath arrivals of the first ping of the synchronization interval may occur before the subsequent ping, resulting in failure of the receiver to categorize these pings as defining a valid synchronization interval. If this occurred, the number of synchronization intervals would be an underestimate of the number of transmissions for a receiver experiencing the effects of CPDI. To decouple our CDE and RC receiver metrics from the number of synchronization intervals, we created adjusted CDE and RC metrics replacing the number of detected syncs with number of pings detected reduced by a factor corresponding to the number of pings composing a complete transmission train. For our tags, a complete transmission train consisted of eight pings.The second of the validation experiments was designed to test the mechanistic CPDI model with respect to depth and distance. Simulations using the mechanistic CPDI model indicated that in 300 m water depth, multipath arrivals producing CPDI conditions would persist to distances of 255 m when receivers and tags were positioned 1 m above the seafloor using a sound speed of 1,530 m/s and an 843 m estimate for AMDR. Therefore, it was predicted a receiver positioned 500 m from a group of tags would be more likely to detect a greater number of transmissions than a receiver positioned 50 m from the same tags, within the range CPDI was predicted. Three acoustic tags with 15 min fixed transmission intervals were activated 5 min offset from one another to prevent transmission overlap and moored off Diamond Head in 300 m of water. Two separate VR2-W moorings were deployed at target distances of 50 and 500 m from the transmitter tags along the 300 m isobath. GPS marks taken during deployment indicated the receiver targeted for 50 m was deployed 10 m off mark, 60 m from the tags, and that the receiver targeted for 500 m was deployed 8 m off mark, 508 m from the tags . The norA controlled tank experiment was designed to test the underlying hypothesis behind our CPDI model, that the primary driver of CPDI is spurious ping multipaths arriving after the blanking interval. A laptop running Matlab’s Data Acquisition Toolbox (MathWorks 2015) was used to playback a waveform signal recorded from a V13 acoustic tag using a digital-to-analogue converter, amplifier, and two ITC 1042 transducers (one transmitting and one recording the sound) with a relatively flat sensitivity of −200 dB re 1 V/μPa between 1 and 100 kHz and a sampling frequency of 192 kHz . The transmitter was suspended in the tank about 1 m away from a VR2-W receiver unit and the hydrophone. The output level of the transmitter was calibrated to match the output of a tag by incrementally increasing amplifier output until the peak-to-peak voltage measured by the hydrophone matched the output level produced by the acoustic tag placed in the tank at the same position as the transmitter.IArr) for each of the first 20 acoustic arrivals . Then, the RL factor for each arrival path was calculated using the formula:Recordings of the acoustic tag were processed to create a simulated tag transmission. TL for each simulated multipath was calculated using a straight-line acoustic propagation model to calculate the path length , and an experimental condition which included both the direct path and simulated multipaths. Scenarios in the experimental condition were further categorized into those in which CPDI was predicted and those in which PCDI was not predicted, according to the CPDI model criterion. All simulated transmissions were repeated five times.R2 was calculated for the GLM (Each simulated transmission was assigned an event identification based on the experiment simulated and the placement of the receiver in the water column. One of three predictive classifications were assigned to each transmission: (1) no multipath (control), (2) with multipath, no CPDI predicted, and (3) with multipath, CPDI predicted, leading to four possible outcomes (1) detection predicted, detection occurred, (2) detection predicted, no detection occurred, (3) no detection predicted, no detection occurred, and (4) no detection predicted, detection occurred. A transmission was coded 1 if it was detected by the receiver and 0 if it was not detected. A logistic regression was fit using a generalized linear model (GLM) with transmission detection/nondetection as the binary response variable. Predictor variables included the predictive classification , and the event ID representing the analogous experiment and condition simulated. Terms representing the interaction between predicted/observed and each event ID, which would identify any simulated experimental analogues where observations systematically varied from predictions were also considered. Model selection was used to identify the best GLM. A pseudo  the GLM  and hierThe shape of the detection functions for the deep water ranging experiment (experiment 1) differed from that of the shallow water ranging experiment (experiment 2) . The preDuring the deep water ranging experiment, on average, the range at which tag transmissions were detected ranged between 840 and 846 m (range including standard error: 839–847 m) with some variation arising from different factor levels of random predictor variables . The ranR2 = 0.647). The predictor variables included in the GAM with the lowest AIC were distance, receiver height, tag height, mean hourly wind speed, mean hourly wind gust, and diurnal period.There were eight GAMs with AIC values equal to or within two AIC values of the lowest, and thus best fit, model. Each of these explained 64.6% of variation in the number of transmissions per hour detected by the receivers  from the receiver . CPDI waR2 = 0.684). Predictor variables for the GAM with the lowest AIC score included distance, receiver height, diurnal period, mean hourly wind gust, mean hourly wind speed, and mean hourly water level.There were four GAMs with AIC scores equal to or within two values of the lowest, and thus best fit, AIC value. Each of these four candidate GAMs explained approximately 72.7% of the variation in the number of detected transmissions per hour  and their median AMDR estimates into our mechanistic model for CPDI. CPDI estimates from range test results were compared to the mechanistic model’s predictions  and 2. FAs the mechanistic CPDI model does not consider TLs from reflection and absorption, it was not unexpected that the CPDI model predicted a slightly larger CPDI range than that estimated by the GAMs results. Only the combination of receiver and tag both positioned 1 m above the seafloor produced GAM estimated CPDI ranges larger than those predicted by the CPDI model.p < 0.05 for the 50 m case and p < 0.001 for the 212 m case). The number of detections recorded by the two receivers differed significantly as determined by using a Wilcoxon sign-rank test (p < 0.001). The 50 m tag/receiver pair experienced mean detection rates over 5.5 times greater than that of the 212 m tag/receiver pair . There were no periods in which the deeper receiver, where CPDI producing multipaths were predicted, detected more transmissions than the shallow receiver where CPDI producing multipaths were not predicted.During this experiment, observed detections of tag transmissions by each receiver were consistent with predictions made by the mechanistic model. Shapiro–Wilks’ tests indicated that distributions for the number of hourly detections by each receiver were non-normal . When compared using the adjusted CDE metric, the difference between receivers remained significant (p < 0.05). The 50 m depth receiver had a median adjusted CDE of 1.00 while the receiver at 212 m depth had an adjusted CDE of 0.214  while the 212 m receiver had a significantly lower median CDE of 0.0865 (of 0.214 .p > 0.05). When adjusted as described above, the difference was significant (p < 0.05). The median daily adjusted RC was 0 for the receiver at 50 m depth and 0.110 for the receiver at 212 m. These daily results, which make no assumptions about the number of transmissions sent during the study period, are similarly consistent with our hourly analyses and the mechanistic model’s predictions, supporting the use of our adjusted metrics when CPDI effects are present.Median RC values for each receiver were not significantly different, with a median value of 0 for the receiver at 50 m depth (no detections were rejected) and a median value of 0.0138 for the receiver at 212 m depth . A Wilcoxon sign-rank test used to compare hourly detection counts between the receivers found that the receiver at 508 m recorded significantly more detections per hour than the receiver at 60 m, logging on average over 1.5 more detections per hour (7.67 transmissions per hour compared to 4.88) than its shallow water counterpart (p < 0.001), despite the greater distance. The receiver at 508 m range outperformed the receiver at 60 m range in 120 of the 133 h intervals and recorded the same number of transmissions during 4 of the 133 hour intervals. In the nine remaining cases, the receiver at 60 m detected more transmissions than the receiver at 508 m. Although the specific explanation for these nine cases is unknown, it is possible that it was due to fluctuating NLs.Consistent with the mechanistic model’s predictions, the receiver 60 m from the tags detected fewer transmissions than the receiver 508 m from the tags. Shapiro–Wilks’ testing indicated that the distribution of hourly detections were non-normal . Despite this, the 60 m range receiver logged fewer detections of completed transmissions during the same period . These results compare favorably to the mechanistic model, which predicted a CPDI range of 276.6 m.Of the 900 simulated tag transmissions, only 20 measured outcomes differed from CPDI predictions. Of these, there were four detections where transmissions included simulated multipaths predicted to interfere with detection. The remaining 16 discrepancies occurred when the model predicted detection but no detection was logged by the receiver.p > 0.05) so the GLM was refit with just predictive classification and event ID variables (p < 0.001). There was no statistical difference in the number of detections between the control group and the “with multipath, no CPDI predicted” group. These results indicated that the detection of transmissions with simulated multipaths where no CPDI was predicted did not differ from the control group without multipaths, for which detection was also predicted. Conversely, there were significantly fewer detections when the arrival times of simulated multipaths predicted CPDI conditions. Of the factor levels for the event ID model terms, only the condition corresponding to results of the 212 m water depth scenario from the depth-dependent model validation experiment (experiment 3) were significant (p < 0.001). Overall, the model explained approximately 81.5% of the observed variance (pseudo R2 = 0.815) with 81.8% of that total explained variance coming from our predictive CPDI classification.The binomial GLM compared detection or nondetection of a transmission logged by the VR2-W during tank testing to predictions of the CPDI model. Initially, the GLM was fit with predictive CPDI classification, event ID, and their interaction as independent variables. The interaction term was found to be statistically insignificant  of a receiver and tag can lead to conditions where acoustic energy reflected from the surface and/or seafloor may interfere with detection of the transmission’s ping train. Implementation of a ray tracing mechanistic CPDI model was able to predict when this interference occurred in multiple experiments with a high degree of accuracy.It has been noted that CPDI may be present in environments particularly amenable to acoustic reflection . This stDuring our deep and shallow ranging experiments (experiments 1 and 2), some variability in the presence and observed magnitude of CPDI effects can likely be attributed to the number of high output tags used and their variable transmission intervals. The maximum transmissions detected by a receiver of a single tag was 40 of 60 expected hourly transmissions. We believe this was partially a result of the large number of tags used during each ranging experiment , with relatively short transmission intervals (averaging 60 s) resulting in failure to detect transmissions during periods where 2 or more transmissions occurred simultaneously, reducing the overall number of transmissions detected each hour.Selecting an appropriate transmission interval and power output of study tags is often a tradeoff. The tags used in experiments 1–3 were selected for use in a deep water snapper study with receivers positioned so their detection ranges would overlap in fence/gate configurations. A relatively short transmission interval was selected so multiple transmissions would be emitted by tagged fish swimming between receivers, improving the probability of detecting the presence of an individual. For similar reasons, tags were also high output. Selecting high output tags allowed us to maximize the distance from a receiver that transmissions could be detected and construct a fence from a minimum number of receivers. However, increasing the output level of a tag also increased the received signal level of transmission multipaths which, under sufficient conditions, produce CPDI.Some hourly variation in the number of total transmissions sent by each tag was expected and may have contributed further variability to the observed hourly data. However, it is unlikely the variable transmission interval accounts for the magnitude of observed CPDI effects as each transmitter has the same variability in transmission interval; thus all tags were expected to have a similar number of hourly transmissions.Standardizing test results of the depth-dependent model validation experiment using data from receiver meta-logs allowed us to control for discrepancies in variable transmission intervals. The number of synchronization intervals and pings detected are likely underestimates of the true values due to the receiver’s inability to detect transmissions during blanking intervals . Both syEnvironmental and anthropogenic factors have been implicated as external sources of variability affecting receiver detection performance . While oLow background NLs in the tank and artificially high signal levels for simulated multipath arrivals produced CPDI at simulated distances far surpassing those observed during shallow and deep water ranging experiments. There was a higher number of detected transmissions in the tank environment for simulated tag transmissions which mimicked distances between tag and receiver where a low number of detections were observed in field experiments and for which CPDI was not expected. Low background NLs in the tank environment meant that signals of weaker intensity were detected by the receiver. In several instances, CPDI was not observed in field results, but was present in the tank experiment analogue. Multipath arrivals in the ocean undergo additional attenuation when reflecting off the sea surface and seafloor interfaces. These losses were not accounted for in the calculation for reducing signal intensity of multipath arrivals in tank simulations. When coupled with the tank’s favorable low noise conditions, we would expect more simulated multipath arrivals to arrive at the receiver with sufficient intensity for detection to produce CPDI in the tank than in the field.There are a number of study designs and analysis methods that would benefit from the consideration of CPDI. When paired with knowledge of an organism’s swimming speed, this model can be helpful in the selection of an appropriate interval for tag transmissions. An ideal transmission interval will ensure tagged individuals traveling through the detection range of the receiver have a likelihood of detection equal to or greater than some acceptable probability. This is particularly relevant to passive acoustic network arrays where the detection footprints of receivers overlap, such as full coverage, gate, and curtain designs . When apClose proximity detection interference results in the failure to detect tag transmissions when reflected acoustic energy arrives at a receiver with intensity and timing sufficient to be mistaken for a unique signal. Our results show that when CPDI conditions are present, the shape of a receiver’s detection function includes an area of low detection probability near the receiver. Conditions leading to CPDI can be reasonably predicted by incorporating knowledge of the study environment and a receiver’s detection parameters. Depth is also a key factor in the occurrence of CPDI. Assuming a constant sound speed of 1,530 m/s, CPDI may occur when relative path lengths exceed 400 m. In this example scenario, CPDI arising from the first surface reflection occurs for receivers at depths greater than 200 m. In cases where reflection off both the surface and seafloor are important, the receiver depth for which CPDI occurs will decrease relative to this surface-reflection only case. Relatively quiet and/or highly reflective environments  lead to higher signal-to-noise ratios which result in a greater number of multipath arrivals that can be detected at the receiver. These signals potentially interfere with transmission detection, increase the CPDI range, and result in fewer  detections from tagged individuals near receivers.Modeling for CPDI, therefore, is an important step for designing and interpreting acoustic tagging studies, particularly when working at greater depths. This is particularly a concern as acoustic tracking studies occurring in deeper waters become more common . Prior t10.7717/peerj.4249/supp-1Supplemental Information 1Raw data used for analysis of components 1, 3, and 4.Click here for additional data file.10.7717/peerj.4249/supp-2Supplemental Information 2A zip file containing the Matlab implementation of the mechanistic model for predicting CPDI and a README file instructing on its use.Click here for additional data file.10.7717/peerj.4249/supp-3Supplemental Information 3Code for performing analysis and generating figures presented in manuscript.Click here for additional data file.10.7717/peerj.4249/supp-4Supplemental Information 4A zip file containing the R implementation of the mechanistic model for predicting CPDI and a README file instructing on its use.Click here for additional data file.


Sorghum bicolor BTx623 individuals, that using replication to detect false positive-induced variants in next-generation sequencing (NGS) data permits higher throughput variant detection with greater accuracy. We used a lower sequence coverage depth (average of 7×) from 586 independently mutagenized individuals and detected 5,399,493 homozygous single nucleotide polymorphisms (SNPs). Of these, 76% originated from only 57,872 genomic positions prone to false positive variant calling. These positions are characterized by high copy number paralogs where the error-prone SNP positions are at copies containing a variant at the SNP position. The ability of short stretches of homology to generate these error-prone positions suggests that incompletely assembled or poorly mapped repeated sequences are one driver of these error-prone positions. Removal of these false positives left 1,275,872 homozygous and 477,531 heterozygous EMS-induced SNPs, which, congruent with the mutagenic mechanism of EMS, were >98% G:C to A:T transitions. Through this analysis, we generated a collection of sequence indexed mutants of sorghum. This collection contains 4035 high-impact homozygous mutations in 3637 genes and 56,514 homozygous missense mutations in 23,227 genes. Each line contains, on average, 2177 annotated homozygous SNPs per genome, including seven likely gene knockouts and 96 missense mutations. The number of mutations in a transcript was linearly correlated with the transcript length and also the G+C count, but not with the GC/AT ratio. Analysis of the detected mutagenized positions identified CG-rich patches, and flanking sequences strongly influenced EMS-induced mutation rates. This method for detecting false positive-induced mutations is generally applicable to any organism, is independent of the choice of in silico variant-calling algorithm, and is most valuable when the true mutation rate is likely to be low, such as in laboratory-induced mutations or somatic mutation detection in medicine.The accurate detection of induced mutations is critical for both forward and reverse genetics studies. Experimental chemical mutagenesis induces relatively few single base changes per individual. In a complex eukaryotic genome, false positive detection of mutations can occur at or above this mutagenesis rate. We demonstrate here, using a population of ethyl methanesulfonate (EMS)-treated  Forward and reverse genetics are ubiquitous methods for gene function discovery . The priin silico reconstructed reference genome sequences  derived from low-coverage NGS of 586 of these individuals. We treated these individuals as a population of replicates of sequencing error to remove false positive SNP calls. This cost-effective and accurate method effectively detects 2PO4 (pH 7.0) at 20° for 6 hr shaking at 150 rpm. Seeds were then washed 10 times with 1 L of distilled water and allowed to air dry at room temperature prior to planting. M0 seeds were planted in an isolation block at ∼20 seeds/m. Unmutagenized BTx623 rows were interspersed with mutagenized seeds to serve as pollen parents to improve seed set. Plants were allowed to open pollinate and one head was harvested from ∼12,000 mutagenized plants, dried, and threshed individually to produce M1 seed. A total of 20 M1 seeds were then planted for 7304 lines. The 10th plant in each plot was self-fertilized to produce M2 seed. A total of 40 M2 seeds from 4800 lines were planted to score for segregating phenotypes. M2 plants in each plot were self-fertilized to produce M3 seed. M3 seeds for each line were planted in the greenhouse. For each line, adult leaf tissue was harvested from a single plant for DNA preparation and stored at −20°; the same plant was then self-fertilized by controlled pollination to produce M4 seed. All DNA was prepared using a CTAB extraction as described by Xin and Chen (2012), except that incubations were done at 55° rather than 60°. The M4 seeds were also reserved for stock center deposit, phenotypic assessment, and future research.Seeds of the reference sorghum BTx623 genotype grown at Purdue University were treated with EMS (Sigma-Aldrich), by imbibing the seeds overnight in distilled water at 20° with shaking (150 rpm) and then soaking in 1 L of 45 mM EMS in 10 mM KHDNA was processed using the Illumina TruSeq DNA PCR-Free LT Library Preparation protocol, to make single indexed, 550 bp insert libraries. These were titred individually using an Applied Biosystems Step One kit with Kapa Biosystems Illumina Library Quantification Kit reagents. After flowcell clustering using a TruSeq PE Cluster Kitv3-cBot-HS kit, sequence data were generated as paired, 101 base reads by an Illumina HiSequation 2500 using SBS v3-HS reagents. “Stage 1” pools of 24 samples with compatible indices were created and a single lane of each pool was sequenced. Based on demultiplexing results from the initial lane of sequence, the library titres were refined and a new “stage 2” pool created to correct for excesses/deficiencies in numbers of reads per library from the first lane. Two lanes of the stage 2 pool sequence were then run. Reads from all three runs were merged for each library.genomeCoverageBed command in the BEDTools genome arithmetic software suite , with parameter option “VALIDATION_STRINGENCY=LENIENT.”Illumina HiSeq paired-end sequencing reads were mapped to version 2.1 of the sorghum genome reference sequences  file, which was redirected to the BCFtools view program. To produce a subset containing only the possible variant positions, we used the BCFtools view command with parameter options: “-vcg” to output a variant call (VCF) file limited to likely variants only. This file contains bona fide SNP and indel positions, as well as false positive variant calls that satisfied these low-stringency criteria, which accepted any position at which any read returned a nonreference alignment to be a variant position.Whole-genome variant calling started with the detection of positions with statistical support from nonreference sequence in each sequenced sorghum individual. We used the SAMtools varFilter command (with nondefault parameter option: “-D 100”) in the SAMtools vcfutils.pl Perl utility script to remove variants with a low-quality score (<10), low read coverage (read depth < 2), and that were possibly derived from repetitive origins (read depth > 100). We refer to this initially filtered SNPs as the “Repeat-Filtered” SNPs. We then used the SnpSift component (filter command with query string “(QUAL>=20) & (isHom(GEN[0]) & (isVariant(GEN[0]),” we retained homozygous SNPs with variant call quality of ≥ 20. This set of filtered SNPs was designate as the homozygous “Q20” SNPs. Previous studies have shown that strand-biased SNPs tend to be a source of false positive SNP detections (filter command with query string “(((DP4[0]=0) & (DP4[1]=0)) & ((DP4[2]>0)& (DP4[3]> 0)))” to retain SNPs with at least one nonreference read mapping to both the forward and reverse strands of the reference genome, which had no reference reads at the SNP position. This set is referred to as the “Standard Filtered” SNPs. We then used the SnpSift filter command with the query string “((REF=“G” & ALT=“A”) | (REF=“C” & ALT=“T”))” to catalog the subset of the standard filtered SNPs that were either G to A, or C to T, substitutions.The output of the variant-calling stage was refined in multiple steps. We initially used the filter query string “(QUAL>=20) & (isHom(GEN[0])) & (isVariant(GEN[0]))” with “(QUAL>=20) & (isHet(GEN[0])) & (isVariant(GEN[0]))” to select the heterozygous Q20 SNPs. The low read depth in our genomic sequencing required a relaxed stringency for the presence of mapped reference reads at the heterozygous SNP genomic position to retain likely heterozygous positions. We accomplished this by replacing the SnpSift filter query string “(((DP4[0]=0) & (DP4[1]=0)) & ((DP4[2]>0) & (DP4[3]> 0)))” with “((DP4[2]>0) & (DP4[3]> 0)).” This checks only for the mutant allele by determining the presence of at least a single nonreference read on both the forward and reverse strands.To detect heterozygous SNPs, we modified the above procedure for detecting homozygous SNPs. We replaced the SnpSift filter command with query string “(exists INDEL).” Homozygous and heterozygous indels with variant quality of ≥20 were retained using the filter command with query strings “(QUAL>=20) & (isHom(GEN[0])) & (isVariant(GEN[0]))” and “(QUAL>=20) & (isHet(GEN[0])) & (isVariant(GEN[0])),” respectively. To eliminate homozygous and heterozygous indels from positions with strand-biased alignment, we required variant calls on both strands using the filter query strings “(((DP4[0]=0) & (DP4[1]=0)) & ((DP4[2]>0) & (DP4[3]> 0)))” and “((DP4[2]>0) & (DP4[3]> 0)),” respectively.Indels in the VCFs were cataloged using the SnpSift Following the set of variant filters described above, the standard filtered SNPs detected in the preliminary set of 570 sequenced individuals were concatenated into a single list. These alleles were sorted by chromosome and genomic position. The number of appearances of each genomic position among the called SNPs was tallied. To the extent that EMS mutagenesis is stochastic and even in its ability to affect mutation, the same SNPs should rarely occur in more than one individual in an organism with a sufficiently large genome size. Thus, positions with a tally of two or more SNP call instances were recorded as probable error-prone positions. We refer to this set of probable error-producing positions in the sorghum reference genome (version 2.1) as the Error-Prone SNP Positions. This list of identified positions was then used to subtract the error-prone portion of the genome from the standard filtered SNP calls for each of the final 586 sequenced individuals, and included the preliminary 570 sequenced individuals. The remaining, unrepeated or sample-specific SNPs were unique to each line. The subtracted repeat SNPs and the unrepeated SNPs we refer to as the Replicate and Nonreplicate SNPs, respectively. We applied the same filtering technique to the indel variant calls using the indel event starting nucleotide position of each indel as the sort item. Indels starting at positions present in more than one line were removed from all indel lists. To detect possible false negative replicate SNPs with origins in mutational hotspots  with parameter option “–hist-indel-len” (We used the SnpEff program (version 3.1) to predict and classify the effects of the detected SNPs and indels on sorghum gene function. To use SnpEff, we had to create a custom SnpEff database and entry for the sorghum genome reference assembly (version 2.1) by using the SnpEff Sorghum bicolor BTx623 (version 2.1), maize B73 (annotation version 5b.60), and Arabidopsis thaliana (TAIR release version 10) from Phytozome (version 9.1) to annotate the SNP-encoding genes. We created protein BLAST search databases for the maize and Arabidopsis sequences using the makeblastdb command in the BLAST suite of programs (Arabidopsis protein sequences using the blastp (protein BLAST) command with nondefault parameters: “-evalue 1E-05 -num_threads 16 -max_target_seqs 5 -outfmt 6 -seg yes.” The gene function description files for sorghum (version 2.1), maize B73 (version 5b.60), and Arabidopsis (TAIR version 10) were obtained from Phytozome, and annotations were appended as additional columns to the VCFs for the affected sorghum locus, along with the gene identifiers and annotations for the best maize hit and best Arabidopsis BLAST hit. The top hits for maize and Arabidopsis were appended only when BLAST e-values were less than a threshold value of E−30.We used the predicted protein sequences of getfasta program with parameters “-bed -fo” and “-bed -tab,” respectively. A subset of 10,000 sequences was randomly selected with uniform distribution from the FASTA-formatted file containing the 21 nucleotide (nt) sequences (SNP and each flank). Using the sorghum reference genome sequences (version 2.1), we generated a third-order background Markov model using the fasta-get-markov utility program in the MEME ungapped motif-finding software suite  in the number of occurrences from the random data set to the EMS-induced SNP data set. We analyzed the tabulations of the pairs of data sets for the G and C reference base cases separately.blastn program in the BLAST software suite (version 2.2.30+) with parameter options: “-evalue 1.0E-10 -num_threads 16 -outfmt 0 -ungapped -dust no -word_size 11.” The BLAST e-value threshold was selected based on initial preliminary runs in order to exclude a high percentage of spurious matches in the alignment results. The BLAST ungapped alignment output results were initially parsed to retain only a subset of alignment results, which consists of global alignments. The stringency of the filtering was subsequently increased to only retain global alignments containing a single substitution, coinciding with the 26th position (error-prone SNP position). The alignments and the single nucleotide substitution base were then cataloged. For each of the error-prone SNP positions, we compared the DNA base in the single nucleotide substitution in the BLAST alignments with the false positive SNPs calls at the error-prone reference position, and kept track of matching and mismatching bases. We repeated this procedure for two other data sets, with each consisting separately of 58,000 randomly selected genomic positions, and likely EMS-induced homozygous SNPs. We tabulated and compared the BLAST results for the three data sets.Due to the complexity of the sorghum genome, we determined that ∼50 flanking bases are sufficient to distinguish the sequence context of a specified genomic position from another. We hence extracted the 51 nt sequence context for the detected error-prone SNP positions. These were aligned to the sorghum reference genome sequences in a BLAST-indexed database using the http://www.ebi.ac.uk/uniprot; retrieved in March, 2015). The FASTA-formatted sequences were then formatted into a protein BLAST database using the formatdb command in the BLAST package with parameters: “-i uniref90.fasta -p T –n uniref90.” The SnpEff annotated VCF-formatted files were parsed to extract the subset of likely EMS-induced SNPs, which were predicted to cause missense mutation. The output file consisted of the amino acid changes, their positions in the protein sequences, and the genes that encode them. Using this table, we generated an input file for each sorghum gene transcript with at least one missense EMS-induced SNP. The file for each gene transcript contained all the amino acid changes detected for that gene in all 586 sequenced individuals. The amino acid sequence of each affected gene transcript was downloaded from Phytozome and stored in separate FASTA-formatted files. The individual sorghum protein sequences, and the UniRef90 BLAST database and amino acid changes files for each sorghum gene transcript, were used as input to the SIFT_for_submitting_fasta_seq.csh C-shell utility script, which was provided in the SIFT program to predict the effect of missense amino acid changes on protein function. For SIFT prediction, we specified a median conservation value of 2.75 and a SIFT score threshold of 0.05. The seqs_chosen_via_median_info.csh script in the SIFT package was modified to increase the CPU time limit from 30 to 60 min (“limit cputime 60m”) and the psiblast alignment program was executed in the multithreaded mode (“-num_threads 16”). A mutation was predicted to be deleterious if the computed SIFT score was lower than the threshold. We parsed the output results of the SIFT program to retain amino acid changes with predicted “DELETERIOUS” and “TOLERATED” designations. These designations were appended to the corresponding amino change columns in the EMS-induced SNPs annotation file. The main scripts for the computational pipeline implemented for SNP detection and annotation are included as supplemental information .We followed the recommended steps for the setup and application of the SIFT program to predict the effect of missense mutations on protein function (https://doi.org/10.5061/dryad.t80gj. The Illumina NGS data sets generated for this project have been deposited in the NCBI SRA (Short Reads Archive), under the Project Title: “Purdue University: Functional Gene Function Discovery Platform for Sorghum,” with the SRA Study Accession Number SRP065118. The BioProject ID# is PRJNA297450 and the BioSample ID#s range from SAMN04128840 to SAMN04129425. Seed samples for the EMS-mutagenized sorghum population used in this study are available on the Germplasm Resources Information Network database.File S1, File S2, File S3, File S4, File S5, File S6, File S7, File S8, File S9, File S10, File S11, File S12, File S13, File S14, File S15, File S16, File S17, File S18, File S19, File S20, File S21, File S22, File S23, File S24, and File S25 are available from the Dryad Digital Repository: A total of 29,972,216,082 Illumina NGS paired-end reads were generated from 586 EMS-mutagenized sorghum BTx623 individuals . Of thesMaterials and Methods). Initially, we detected 31,178,985 putative SNPs in the 586 EMS-mutagenized individuals. The default criteria for SNP calling resulted in an unreasonably high number of positives: a polymorphism for every ∼14 kb of genome with 53,206 SNP sites per individual . For example, there were a total of 1085 positions in the sorghum genome, where the same SNP was detected in between 451 and 500 EMS-treated sorghum individuals. This group of SNPs accounted for 515,902 redundant SNP detections (see Table S1 in File S26). The vast majority of these positions are not random poor base calls. For example, for nearly all  of these replicate SNPs, the predicted change was the same for all lines (File S2). For example, in 567 lines, a C to G substitution was predicted at genomic position 37,580,764 on chromosome 5. This pointed to some structural basis for the repeated observations. Similarly, of the 7,238,377 standard filtered heterozygous SNPs, 6,775,202 SNPs were derived from replicate SNPs at only 71,294 positions. Thus, roughly 13% of the 534,469 distinct genomic positions produced replicate heterozygous SNPs accounting for 94% of the heterozygous SNP predictions in the 570 sorghum individuals . Similarly, only 36%  of the heterozygous SNPs were G:C to A:T substitutions. With <40% of the retained, high-quality standard filtered SNPs showing signatures of EMS-derived mutagenesis, it is more likely that a significant proportion of the 5,244,176 homozygous and 7,238,377 heterozygous SNPs are false positives. Remarkably, removal of the replicate SNPs showed that, consistent with the experimentally determined effect of EMS mutagenesis, 98%  of the retained nonreplicate or sample-specific homozygous SNPs were G:C to A:T transitions . The second and third largest substitution classes were A:T to G:C transitions and A:T to T:A substitutions, accounting for 0.7% (9066) and 0.5% (6194) of the SNPs, respectively. Similarly, in the case of the heterozygous standard filtered SNPs, after removal of the replicate SNPs, the remaining nonreplicate SNPs were 94%  G:C to A:T transitions .We observed that only 39%  of the standard filtered homozygous SNP predictions were G:C to A:T substitutions (File S26). Protein-coding gene loci comprised 246,062 (19%) of the likely homozygous EMS-induced SNPs and >99%  were G:C to A:T mutations  were homozygous for ≥60% of their SNPs, and only 21 samples were ≤20% homozygous (R = 0.51) between the final number of predicted heterozygous SNPs and the read mapping coverage for individuals in the mutagenized population. On the other hand, we found a negligible correlation (R = 0.07) between the number of homozygous SNPs and mapping coverage. Hence, the final number of heterozygous mutations we predicted may be overestimated, especially for the mutant individuals with lower mapping coverage and lower homozygous percentage. Overall, we predicted an average of 2177 ± 906 homozygous and 815 ± 622 heterozygous nonreplicate SNPs per individual, and observed ∼1 SNP per 243 kb (with one homozygous SNP per 334 kb).The detected probable error-prone SNP positions in the 570 unique lines were used to mask likely false positives from the standard filtered SNPs called for each of the 586 lines. The additional 16 sorghum individuals with overlapping genomic positions were excluded from the masking set, so as not to remove true EMS-induced SNPs that were due to contaminated pedigrees. Cumulatively, 1,275,872 (24%) of the 5,399,493 standard filtered homozygous SNPs were nonreplicate SNPs in the 586 sorghum individuals (File S3 and utations . Similarutations . Hence fFile S26), and this is further depicted by the point of inflection at N = 2 on the x-axis of the plot in Figure S3. Over 61% (9291) of the homozygous and 53% (5709) of the heterozygous replicate SNP positions detected in exactly two individuals were G:C to A:T substitutions. Hence, we cataloged an extra 18,582 homozygous and 11,418 heterozygous G:C to A:T substitutions as tentative false negative EMS-induced mutations (File S6 and File S7).The signal-to-noise analysis showed a precipitous drop in the G:C to A:T substitution percentages for replicate SNP positions detected in more than two individuals (Table S4 in File S26). The remaining 134,301 SNP calls were in protein-coding sequences, with 60,442 effecting changes in protein-coding capacity in 23,866 distinct sorghum genes. Homozygous mutations of high impact (loss of reading frame) were predicted for 4035 SNPs disrupting 3637 distinct genes. These mutations included 2880 nonsense alleles and 1043 mutations removing splice sites. A total of 56,407 nonreplicate homozygous SNPs were predicted to cause nonsynonymous amino acid substitutions in 23,198 distinct sorghum genes. In addition, we detected five lost stop codons associated with nonreplicate SNP changes. These five mutations were not G:C to A:T mutations, as no such mutations can induce a stop codon loss, which may explain the relatively small number relative to the 145 start codon losses. The stop codon losses included a single homozygous and four heterozygous mutations. Synonymous changes from one stop codon to another were associated with G:C to A:T mutations, and we detected 148 synonymous (silent) stop codon substitutions consisting of 114 homozygous and 34 heterozygous mutations. The silent stop-lost mutations included 92 TGA and 56 TAG codon changes to TAA codons (File S10 and Table S6 in File S26). Mutations causing loss of start codons included 111 homozygous and 34 heterozygous changes (File S26). Intergenic regions and introns contained 387,570 (81%) and 42,357 (9%) of the 477,531 nonreplicate heterozygous SNPs, respectively. The heterozygous SNPs also included 1813 high-impact and 19,800 nonsynonymous mutations  and 96 amino acid substitution alleles per individual. The average individual also encoded three high-impact and 34 moderate-impact nonreplicate heterozygous SNPs per individual. We estimated an average of 5.7 alleles per sorghum gene. In addition, the tentative false negative SNPs included 40 high-impact and 716 moderate-impact homozygous SNPs, and likewise, 14 high-impact and 326 moderate-impact heterozygous SNPs (File S13 and File S14).The majority of the SNP calls were outside predicted protein-coding regions. Intergenic regions and introns contained 1,032,427 (81%) and 109,144 (9%) of the nonreplicate homozygous SNPs, respectively . Less than 1%  of the remaining indels were retained after replicate indel filtering, indicating an unacceptably high rate of false positives . Among the nonreplicate indels, 77% were ≤ 2 bp in length and 97% of the indels were 10 bases or shorter. A total of 277 and 180 indels were predicted to have high and moderate impact on gene function, respectively. One deletion, in line SbEMS0456-2, disrupts the sorghum ortholog, Sobic.003G386900, of the Monoculm1 gene from rice, LOC_Os05g42130. The phenotype of this line is reduced height and a complete absence of tillers. Twenty-two indel variations affected splice sites and 1094 occurred in untranslated regions of sorghum protein-coding genes. Another 14,341 and 1947 indels occurred in intergenic and intronic regions, respectively.We initially detected 3,312,791 indel polymorphisms in the 586 sorghum lines and the standard filtering retained 1,989,078 indels  of the 73,346 homozygous nonsynonymous EMS-induced SNPs were missense mutations. These affected the coding sequences of 27,943 transcripts encoded by 23,227 distinct sorghum genes. Using the SIFT program in conjunction with the 32,168,296 sequences in the high-quality UniRef90 protein database, we were able to predict the impact of 56,289 missense SNPs. Of these, 25,013 (44%), affecting 12,840 genes, returned a SIFT score <0.05 and likely encode changes deleterious to protein function. The remaining 31,276 SNPs were more likely to be tolerated mutations in 14,559 genes (File S17). Similarly, 24,191 (92%) heterozygous missense mutations were found in 16,118 transcripts encoded by 13,340 (40%) distinct sorghum genes. SIFT analysis predicted the impact of 21,183 of these heterozygous mutations and 9659 (46%) of them were predicted to be deleterious changes. The remaining 11,524 heterozygous SNPs affecting 7417 genes were classified as likely tolerated mutations (File S18). The detailed summary statistics on the sequencing, mapping, SNPs prediction, filtering, annotation, and classification of the homozygous and heterozygous SNPs, as well as the indel polymorphisms, are included as supplemental information (see File S19). The detailed classification of the medium- or high-impact SNPs and indels, and annotations for the affected genes in all 586 sequenced lines, are included as supplemental information .r2 = 0.00279; r2 = 0.61069; Figure S5 and Figure S6). Transcripts containing SNPs had mean G and C counts of 361.1 and 340.4, respectively, while the mean count of Gs and Cs for transcripts with no SNPs were 133 and 128.2, respectively, indicating that, in the absence of saturation, genes without SNPs were enriched for genes that represented a smaller mutational target (File S23). Consistent with this, we found a transcriptome-wide linear correlation between the number of SNPs and the length of a transcript . Our analysis found no strong transcriptome-wide linear correlation between GC content and the number of SNPs . In the absence of uniform flanking motifs, we observed significant sequence changes in the single base immediately upstream (position −1), and two bases immediately downstream of the 608,485 mutated G-nucleotide positions, when compared to the context of random G-nucleotide positions  in the count of Cs, and a 17% decrease  in the count of A bases. At the first downstream position (+1) we observed a 32% increase  in the count of Cs and a 26% decrease  in the count of Gs. At the second downstream position (+2) we observed a 38% increase  in the G count and an 18% decrease  in the count of Cs. The reverse complement of the results of the analysis for the 606,560 mutated C reference bases yields the same pattern . Based on the nucleotide counts and the percentage changes at each flanking position, we observe an overrepresentation of a 5′-CGCG-3′ motif (with the mutagenized G position showed in bold) and an underrepresentation of 5′-AHGM-3′ . In comparison to the genome, the CG dinucleotide content was 57% higher in the 21 nt sequence context of the mutated positions (3.4% vs. 5.3%) and the 5′-CGCG-3′ motif is also clearly delineated in the positions 6–9 in the MEME motif results.The MEME ungapped motif search program found no single fixed length uniform motif associated with the 10,000 randomly selected SNPs. However, we observed enrichment for motifs that were present in clusters of SNPs. The most significant motif  was a 16 nt long GC-rich sequence, which is present in a cluster of only 834 SNPs out of the 10,000 homozygous SNPs , 37% , and 24%  of the error-prone, random genomic, and random EMS-induced SNPs, respectively, had perfect matching paralogous sequences in the reference genome. Considering only end-to-end ungapped alignments of these sequences, we detected a total of 68,568,482 near-perfect matches for 41,733 (73%) of the error-prone SNP positions. For the randomly selected genomic positions, we detected ungapped near-perfect matches for nearly as many loci  but in only 29,849,107 global alignments, indicating lower copy numbers for the paralogous sequences. This pattern of lower paralog coverage for the 51 nt SNP context was also true for the nonreplicate SNPs, where we recovered threefold fewer paralogous locations on average . Interestingly, 51%  of the ungapped alignments to error-prone SNP sequence contexts contained a mismatch at the central SNP position. In contrast, only 7%  and 11%  of the ungapped alignments for the random genomic positions and the nonreplicate EMS-induced SNPs, respectively, contained a mismatch at the central (SNP) position . The tot5 generation and the Illumina WideSeq paired-end sequencing protocol. The sequencing yielded mean genome coverage of 0.06×. These reads were aligned to the sorghum genome and the positions of previously identified SNPs in these 29 lines were assessed. Given that a mean of 2177 homozygous nonreplicate SNPs was found in each line, this should overlap with 130 SNPs per line and provide ample depth to test line identity. The low-depth sequence overlapped with 3843 nonreplicate SNP positions and 96% of these were unambiguously confirmed . This validated our SNP calling procedures, and also verified the identity of our lines and the validity of our pedigrees. There were 1745 reads that contained positions called as heterozygous nonreplicate SNPs in the M3 sequence data. For these heterozygous SNPs, our low-pass sequence confirmed 41% (710 out of 1745) of the mutations . This rate of validation again confirmed our pedigrees and line identities. The lower rate of confirmation is consistent with the expectations of segregation, where 37.5% of the M5 should now be homozygous references for the heterozygous position and of the 25% still heterozygous, we would recover the nonreference allele in half of the reads. This should have resulted in 50% nonreference alleles, or 48% if the validation rate was the same for heterozygous and homozygous SNPs. Consistent with the likely higher false positive rate of the heterozygous SNPs, suggested by the lower G:C to A:T SNP percentage in the nonreplicate SNPs, we validated fewer SNPs.A total of 29 randomly selected individuals from the collection of 586 sequenced individuals were resequenced using leaf tissues from the Min silico detection of sequence variants can be impacted by the inherent sequencing error rates of NGS technologies, nature of the variant-calling programs, genome complexities, sequence alignment programs, and the quality of reference genome assemblies . The NGS sequencing coverage depth for the 586 sorghum lines in this study was 7×, and this is lower than the 20× coverage depth often described as the minimum for accurate SNP calling . Consistent with the analysis of indels induced by fast neutron irradiation of rice  between the number of in silico heterozygous SNPs detected and the read mapping coverage for the sequenced individuals. In attempting to compensate for a lack of sufficient sequencing coverage for predicting the heterozygous SNPs, our relaxed computational stringency for predicting heterozygous SNPs may have caused an overestimation in the number of heterozygous calls. This explanation applies more specifically to samples with lower sequencing coverage. For some individuals, the consequence of pollen contamination from outcrossing to previous generations, and not sequencing coverage, may have caused an increase in the level of heterozygosity. For example, 21 of the sequenced individuals with at least 7× sequencing coverage were at most only 20% homozygous . The slightly lower percentage of G:C to A:T transitions for the heterozygous SNPs may be attributed to higher false positive rates due to the inclusion of a greater number of sites with paralogous positions within the heterozygous SNP class, as well as the lower overall stringency when non-SNP containing reads are allowed. The notion that we still have some false positives within our SNP calls is supported by the fact that within the protein-coding regions >99% of the homozygous SNPs that we detected were G:C to A:T substitutions. These positions are less likely to possess unassembled paralogous sequences than the intergenic positions. This frequency matches a previous study in Arabidopsis that found that >99% of EMS-induced mutations in a set of 192 genes were G:C to A:T substitutions . In particular, the intriguing upsurge in the C count occurring at the position immediately preceding the mutated G implied a greater proportion of CG dinucleotides among the mutated bases. These dinucleotides are underrepresented (3.6%) in the sorghum genome (version 2.1), and are the sites of DNA methylation. Methylated cytosines are known to spontaneously deaminate to thymidine. For this reason, the G to A mutational impact of EMS, which would pair perfectly with thymidine, may be more readily affected at these locations and go undetected by repair processes. We observed that the CG dinucleotide content in the 21 nt context of the mutation was 57% higher than that of the sorghum reference genome. The observation that a similar sequence occurs as an overrepresented motif was described previously in the large-scale exome sequencing of rice EMS mutants , which may be of use to others interested in the regulation of stop codons, the contribution of stop codon usage to mRNA turnover, or nonsense suppression. We observed a single homozygous and four heterozygous stop codon loss SNPs that were due to non-G:C to A:T substitutions that should result in translation read-through , and the fact that the majority of the likely EMS-induced mutations are G:C to A:T substitutions, stop codons are primarily buffered from the effects of EMS mutagenesis and we saw very few losses of stop codons. TAA codons do not contain any G and/or C nucleotide targets of EMS, while the G to A substitution of TGA or TAG converts them silently to TAA stop codons. We did observe 148 such silent stop codon substitutions in our SNP annotation . Hence, we selected an additional 18,582 homozygous and 11,418 heterozygous replicates or nonunique SNPs as likely false negative coincidental SNPs.Although the removal of the shared variants is a very effective technique for identifying likely false positive EMS-induced mutations, a few caveats need to be highlighted. First, the method should be applied meticulously to prevent the introduction of false negatives. For example, sample mislabeling and unexpected hybridization between individuals within the population can result in the sequencing of multiple DNA samples containing shared (identity by descent) mutagenized chromosomes. Sequence contamination originating from sample or library preparation stages can also negatively impact the detection of false positives. In all the above-mentioned scenarios, technical errors or contaminants would result in the erroneous purging of true DNA variants that were shared due to identity by descent from a common ancestor. In contrast to noncontaminated samples, prior to population-wide subtraction of shared variations, sample pairs connected by contamination are characterized by an abnormally high percentage of shared variation between the two individuals. Prior to the removal of common variants, pairwise comparison of detected variants in all the sequenced individuals in the population is mandated to expose contaminated samples with shared pedigrees, regardless of how certain biologists are that there is a lack of contamination in their work. Second, although the likelihood is extremely low, it is not statistically impossible for a mutation to be independently induced at the same genomic position in two or more individuals (In biological experiments, replication plays a critical role by adding statistical power to both detect differences and reject errant stochastic results, as discussed previously for NGS experiments (in silico detection of induced mutations via the identification of false positive variants. This method is generally applicable to any genetics study and is also independent of the choice of the variant-calling program. Using the method, we have detected and described the set of EMS-induced mutations in a population of S. bicolor, which is a next-generation crop species with tremendous agronomical potential. We hope that the genetics community and plant breeders will find the described genetic resource to be very useful.The prevalence of false positive DNA variants negatively impacts the efficiency of forward and reverse genetics studies. We utilized and described a cost-effective, high-throughput, and highly accurate NGS method for improving the www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.300301/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.