This reference method was developed with the greatest possible emphasis on the positional, time and repeatable accuracy of ground truth. Four phases of the measurement scenario  were used in order to compare four different types of RTK receiver horizontal operation accuracy over three measurement days. The worst result of one of the receivers was measured at SSR = 13.767% in dynamic movement. Since the same “low-cost” receiver without an INS unit had SSR = 98.14% in previous static measurements, so it can be assumed that the motion had a very significant effect on the dynamic properties of this receiver. On the other hand, the best “high-end” receiver with an INS unit had SSR = 96.938% during the dynamic testing scenarios. The median values of the deviations were always better during uniform movements than during acceleration or braking. In general, the positioning accuracy was worse in the dynamic mode than in the static one for all the receivers. Error indicators (errRMS and Me) were found several times higher in the dynamic mode than in the static one. These facts should be considered in the future development of modern agricultural machinery and technology.While the existing research provides a wealth of information about the static properties of RTK receivers, less is known about their dynamic properties, although it is clear that the vast majority of field operations take place when the machine is moving. A new method using a MRA for the evaluation of RTK receivers in movement with a precise circular reference trajectory ( The application of precision agriculture practices helps save operational costs and reduce the environmental burden, resulting in increased and improved food production while optimising the whole agricultural process . Today, Precise positioning can be assured by RTK methods that have already proved successful in a number of precise agriculture applications . The posUnmanned Aerial Vehicles (UAV) equipped with a camera can be useful in providing orthophotographs and improved resolution images. Ortho-mosaics can be generated based on images obtained from UAVs capable of flying over considerable large areas in a short time . The geoRTK receivers to guide the tractor, possibly supplemented by the collection of data on land or crops, were described, for example, in references ,13; theySSR indicator had a significant effect on the positioning accuracy. This indicates the ability of the RTK receiver to reliably process the correction signal, which has a significant effect on the accuracy of positioning. It was concluded that the phenomenon of “false fixed solution” can occur for several reasons: the already mentioned erroneous estimation of phase ambiguities and the noise in the correction data from the base station, as well as the influence of the environment in which GNSS operates [SSR indicator was described in the study of reference [SSR) was improved from 48.4% to 85.3%.In the previous study of the research team of reference , the paroperates . The evaeference , where ieference , experimeference  comparedeference  showed tRTK receivers are more often used as reference systems providing positions or trajectories for other studies ,12,23,24−1 without specifying the definition of the acceleration performed. The evaluation of four RTK receivers against a postprocessed trajectory with a stated accuracy of 0.005 m was described by the authors of reference [errRMS) in the individual ENU components, where the horizontal error was no greater than 0.013 m in the fixed mode. The study defined a measurement scenario with a duration of 10 min but did not define the limiting speeds and acceleration of the measuring platform (only mentioned the specifications of the UAV used). Using the Oxford OxTs RT3002 reference system, the authors of reference [−1. A detailed comparable motion specification for the ground truth was not given. The stability of the positioning of these pairwise comparisons of distance deviations did not exceed 0.382 m. An interesting methodological idea for verifying the dynamic properties of the GNSS was described by the authors of reference [−1 to 15.6 m s−1. A similar method was used by the study of the authors of reference [−1. Within the resolution of the reference number points used, the distances performed and carrying platform speed limits, it was reasonable to use the proposed method only to evaluate the uniform movements. Unfortunately, the GNSS technology evaluated in this investigation was unsuitable for comparison of the RTK accuracy. A very similar investigation was carried out by the team of authors of reference [The authors’ study from reference  comparedeference . Among oeference  verifiedeference  comparedeference  used theeference  evaluateeference , where aeference  using anAs it is clear from the preview literature review, the most accurate possible navigation function is essential for the further development of modern agricultural technology . Nevertheless, the existing research provides only minimal information on the influence of motion on the loss of accuracy, and the study of specific values of accuracy depending on motion is largely under-researched, although it is clear that the vast majority of field operations take place when the machine is moving, not when it is standing. The knowledge of navigation errors in the dynamic mode of operations is therefore essential for the further development of this technique.SSR) (1), positioning the deviations of and their error indicators for the whole measurement period (2), positioning the deviations of and their error indicators for the period “FIX” (3), error indicators of the static measurements (4), recovery time of the “FIX” state after its loss (5), the linear dependence between the obtained series (6), the comparison of the positioning deviations according to the current state of the system (7) and the comparison of the positioning deviations according to the scenario phase (8).Therefore, the main aim of this research was to investigate the effects of speed and acceleration on the properties of RTK receivers and compare the horizontal errors in the static and dynamic modes of operation. For these purposes, a new method was developed, based on a robotic MRA platform (Measurement Robotic Arm). The evaluation of four RTK receivers in this work was divided into the following subunits: the ability of the system to solve the problem of carrier phase ambiguities (0) on the satellite elevation angle, base station baseline and frequency. One low-cost multifrequency RTK receiver u-blox ZED-F9P was used for verification, and it was confirmed that, even in static measurements, this receiver showed SSR ranging from 43.3% to 97.9%. This study also showed a twofold degradation in the vertical accuracy when replacing the fixed base station with an identical low-cost RTK receiver.First, it is appropriate to mention the technology principle fundamentals of RTK receivers. RTK receivers calculate the absolute position of the phase centre of a GNSS antenna, like GNSS receivers, determining the position point anywhere on Earth’s surface by estimating the pseudoranges from individual satellites moving in orbit . As descThe use of so-called “moving base” technology was suggested as a suitable alternative for precision agriculture . ScientiIn general, RTK positioning errors arise from the same causes as the GNSS. As described by Laud and Cross, the so-called multipath signal propagation can have a negative effect on a system’s accuracy, especially in environments with high obstacles obscuring the view of the orbit . These aIn contrast to older technologies, today’s low-cost RTK receivers use only single-frequency and single-GNSS RTK methods . As a reIn practice, the high initial costs of multifrequency  technology have been complemented with the widely used MEMS technologies  . When a In a previous study , the devThe measurements were performed in an outside test track of the development centre , which merrRMS = 0.009 m . Both reference points were determined by the averaging of fixed positions with the same correction signal as the one used for the measurement. The first point defined the absolute location of the centre of the circular reference trajectory (CP), and the second point defined the initial absolute angle (azimuth) of this circular reference trajectory (AP). A beam of laser rangefinder Leica DISTO D810 representing a straight line came out from a tripod placed on AP, was aimed at the axis of rotation placed on CP and passed through the trolley peephole where the signal from the Hall sensor was detected. The distance between points CP and AP was 34 m.The MRA was installed in a precisely marked location before each measurement day. This location was defined after the first day of measurement by accurately surveying the two reference points . For thiThe robust construction of the rotation axis of the MRA was firmly anchored with screws and concrete blocks to the concrete surface in order to prevent possible deflection of the axis. Tilting the driven wheels in the direction of the tangent to the trajectory resulted in a reduction of the centrifugal force. The deflection of the rotation axis at the bearing height was measured in two perpendicular directions with a Hoteche HT284610 dial indicator located between the rotation axis rod wall below the bearing and the structure attached to concrete blocks at the maximum angle speed. These deflections were less than 2 mm in both directions.max = 0.7 m s−2) during the scenarios and the installation of the brake on the rear of the trolley. If, hypothetically, one of the sides of the trolley was raised to a height of 20 mm, this would mean a horizontal error (acting in the direction of the trajectory) less than 0.2 mm.The vibration and possible tilting of the trolley in the pitch direction were eliminated by using sufficiently inflated tires with a wheelbase of 1.2 m and a very smooth and flat surface panelled with oriented strand board. Another aspect of reducing the trajectory error in this direction was the smooth acceleration  at the end of the freely rotating lever, the possible deflection of the drawbar was eliminated. Another aspect minimising this possible deviation was the even distribution of the component weights on the trolley.The possible error caused by the torsion of the trolley frame and the deflection of the drawbar in the roll direction was minimised by the robust joint at the height of the trolley centroid.The MRA was constructed with emphasis on the stiffness of the frame and designed so that the linkages allowed the smoothest and most stable movement of the trolley in the direction of a circular trajectory with a radius of 3 m. The following measures were taken to reduce the reference trajectory error due to the torsion of the structure:Since the test track had a suitable tilt of the surface (less than 0.01 m to a radius of 3 m with respect to the circle created by the trolley wheels), the error according to the horizontal surface in the calculations of the location determination deviation was less than 0.02 mm.The absolute angles were obtained based on the recorded times of microprocessor hours from the incoming leading edge of the three sensors: incremental encoder, a Hall probe and clock synchronisation signal Pulse per second (PPS) from the built-in MRA GNSS. Each signal was assigned a UTC time stamp in seconds per day in a microsecond resolution, as verified in the previous study of the authors of reference . In contδ) of size 18°. Using the obtained data pairs, the values of instantaneous angular speed rv and instantaneous angular acceleration ra in relation to the trolley were calculated. Due to the high noise levels, these waveforms had to be smoothed out by a convolution filter with a core size equal to 7 for the instantaneous angular speed fv and 33 for the instantaneous angular acceleration fa.The obtained signal was further resampled using linear interpolation of the time-synchronous signal, corresponding to the clock of the given each RTK receiver. These samples were assigned corresponding coordinates in the ENU coordinate system with the same origin as the reference trajectory by subtracting the GNSS antenna angle offset : a gradual start from zero to the speed with the required value, even movements during the speed with the required value for 40 or 60 s approximately and breaking. For each speed, this approach was repeated at least four times.On each measurement day, six sets of tests were performed, and the approximate angle speed of the arm (antenna of the RTK receivers) was set at the following values: 2 m sFour receivers were selected to be evaluated in this study: Receiver A, Receiver B, Receiver C and Receiver D. It is noteworthy that these selected devices differed in terms of their acquisition costs. The cheapest receiver, Receiver D, had an approximately seven times lower acquisition cost than Receiver C (both were delivered in pairs), an approximately fifteen times lower acquisition cost than Receiver B and more than thirty times lower acquisition cost than Receiver A. RP). This signal was further divided to all RTK receivers using active splitter Antcom 4G1215S-XT4-P1. This solution made it possible that, to evaluate all of the four RTK receivers, one MRA reference trajectory was used. To determine the orientation (heading), Receiver A used the second antenna located in the front part of the trolley (HP). The heading was not evaluated in this study.Receiver A and Receiver B, containing an IMU unit, were located directly on the trolley. Receiver C and Receiver D were located on the mounting area near the centre of the MRA .SSR ratio, expressing the RTK ability to solve the problem of ambiguity of the integer part of the carrier phase, was calculated as follows:Each sample from the obtained data contained status information from the NMEA sentence of a specific RTK receiver. The information could include one of the following three values: “FIX”—when the sample belonged to a period of a known integer value of the carrier phase, “FLOAT”—when the sample belonged to a period known only to the decimal of the carrier phase value and “GNSS”—when the sample belonged to a period when the RTK receiver was not able to process the correction signal. The SSR)—ability of the system to solve the problem of integer phase ambiguity (%):fn—dataset size of the samples with solved integer phase ambiguity (samples of the “FIX” state epoch) (-), and wn—dataset size of the measured samples (samples of the whole measurement) (-).System status ratio :id)—Euclidean distance between the reference and measured points (hypotenuse) on the horizontal plane (m):refx—x-axis (east) coordinate of the reference point (m), refy—y-axis (north) coordinate of the reference point (m), rtkx—x-axis (east) coordinate of the measured point (m) and rtky—y-axis (North) coordinate of the measured point (m).Deviation —sample mean of deviations from the reference point (offset of error) (m):n—dataset size of the measured samples (-) and id—deviation from the reference point at the i index of a dataset (m).Accuracy (errs)—sample standard deviation (stability of the positioning) (m):n—dataset size of the measured samples (-), id—deviation from the reference point at the i index of a dataset (m) and errµ—sample mean of deviations from the reference point (accuracy) (m).Precision (errRMS)—value specified by the manufacturer (metric emphasising large errors) (m):n—dataset size of the measured samples (-) and id—deviation from the reference point at the i index of a dataset (m).RMS error , no method was used to remove the deviation outliers.To determine the indicators describing the properties of all the four RTK receivers  similarly to the preceding Evaluation (2). The errRMS indicator from the “FIX” period was suitable as a value to be compared with the values in the manufacturers’ catalogues (When only the samples marked as “FIX” in the NMEA messages (period when the RTK receiver was able to resolve the carrier phase ambiguity) were selected, the total set of measured coordinates that can be considered as accurate was obtained. For the evaluation, the same formulas were used to determine the error indicators  were used to calculate the error indicators for the static measurements as for the dynamic measurements in Evaluation (2) and Evaluation (3). The errRMS indicator from the “FIX” period was suitable as a value to be compared with the values in the manufacturers’ catalogues , these periods were further investigated. These observations (o) were searched in both the static and dynamic measurements, and only those with known failure time and recovery time boundaries were used. Continuous logging of the NMEA messages between these boundaries was a necessary condition. The number and duration of these outages were evaluated:on—number of detected outages from the “FIX” state (-),min(ot)—the shortest duration of the outage (s)max(ot)—the longest duration of the outage (s),µ(ot)—the mean duration of the outage (s) andMe(ot)—the median duration of the outage (s).Due to the frequent observation of the state of inability of the RTK receivers to successfully process the correction signal  was performed, using the median values from these datasets, as well as the presentation in Boxplot. The expression of the error using the median value Me was not burdened by the error of outliers as much as the expression of the error by er study , which c−2, for “UNIF” in the interval  m s−2. For the “STAT” scenario phase, a separate set identical to that in section Evaluation (4) was used. As in the preceding case of Evaluation (7), this evaluation was performed based on the median values from the datasets and presented in Boxplot. These evaluations were also performed separately for the range of deviations for the whole measurement period (“FIX” + “FLOAT” + “GNSS”) and separately for the subset of the samples from the period “FIX”.The whole dataset was also compared in terms of its current scenario phase. Four phases of the scenario were identified: static (“STAT”)—when the trolley did not move, acceleration (“ACC”)—when the trolley arm was gradually accelerated from a zero angular speed to the required angular speed, uniform movement (“UNIF”)—when the trolley arm performed a uniform movement at the required angular speed and deceleration (“DEC”)—when the trolley arm was gradually braked to a zero angular speed. Individual samples of the dynamical measurements were divided per value of instantaneous angular acceleration between three ranges: for “ACC” in the interval , as well as RTK receivers with a higher purchase price. At the same time, it was clear that Receiver A and Receiver B were best able to maintain this condition for accurate measurements even during movement. Since both of these localisation systems used an RTK receiver with a built-in INS , it could be assumed that this feature was supported by the fusion with the INS that the receivers were equipped with. This hypothesis was also proven by reference , whose rid is further shown in errµ, errs and errRMS).The graph  plots hierrµ, errs and errRMS, RTK Receiver A was undoubtedly evaluated as the best. The plotted density of the occurrence values of the positioning deviations . The occurrence density and deviation distance were even more pronounced in the errRMS error of Receiver C  most of the measured positioning deviations occurred in the area of centimetre units . However, the same cannot be said about the results of Receiver D  were the highest, which means that this receiver had, by far, the worst results in all the measurements. The errRMS value of this RTK receiver was almost 94 times higher than the best result of Receiver A.In the whole measurement period, based on the submitted error indicators viations a showed viations b. The laceiver C c. The acceiver D d. The hiid from this period is further shown in errµ, errs and errRMS).Another evaluation was performed on deviations from the period when the RTK receivers knew the integer value of the carrier phase (when in the “FIX” system state) and were measured in their most accurate mode. The graph  plots hifn datasets. The size of the datasets was mainly affected by the SSR ratio of each of the receivers, as well as by the fact that Receiver D allowed samples with only four times lower frequency (5 Hz compared to 20 Hz). It must be noted that Receiver B also had a “failure” of the measurement section in these measurements, which is not shown in this view. This anomaly was also taken into account in the evaluation.The evaluation was largely influenced by the size of the −1 and an acceleration of 0.3–1.3 m s−2), when 98 samples showed a deviation of 0.45–0.54 m. Receiver B produced the worst result, as, during the period of one of the trolley starts , it measured a deviation of 4.9–5.1 m in 436 samples. Paradoxically, the smallest error indicators were displayed by Receiver C . Its largest deviations ranged from 0.1 m to 2.6 m in the number of 21 samples occurring in two periods at high speeds . Receiver D had the least bulky dataset for this evaluation and good results of its error indicators. Its largest deviations ranging from 0.22 m to 0.26 m in the number of 50 samples and also occurred at higher speeds .The Receiver A error indicators were calculated from the largest dataset and showed worse values than Receiver C  and Receiver D. Receiver A was also loaded with a positioning error that occurred immediately from the initial start-up of the first measuring set . Receiver B and Receiver C were successful throughout all the measurements, and Receiver A was not able to resolve the ambiguity in one short period of three seconds. Receiver D was able to solve the phase ambiguity of the carrier wave in a static measurement almost three times more often than in a dynamic measurement. Nevertheless, compared to the results of a previous study [The stable unchanged position was mainly reflected in the ability to solve the ambiguity of the carrier phase  from the whole measurement period, while these errors were still smaller compared to the dynamic measurements. The error indicators determined based on the selection of samples from the “FIX” period showed smaller errors than in the dynamic measurements. A comparison of the resulting errRMS values with the catalogue values specified by the manufacturers revealed that Receiver A (errRMS = 0.01 m + 1 ppm) and Receiver D (errRMS = 0.025 m + 1 ppm) met the stated values. Receiver B and Receiver C did not narrow the stated values (errRMS = 0.01 m + 1 ppm).The Outages from the “FIX” state, which had a known period between its boundaries (outage and recovery), were not detected during the static measurements. The detected periods of outages came only from the dynamic measurements, which are presented in Based on the results above, it was possibly assumed that the RTK receivers with an INS unit (Receiver A and Receiver B) were able to return to the “FIX” state earlier than the receivers without an INS unit (Receiver C and Receiver D).dvr or on the acceleration dar from the period of all the measurements did not have a significant effect. The dependencies calculated the same way in the evaluation from the “FIX” period were even weaker in significance than from the whole measurement period. No statistically significant linear relationship was found between any of the examined series. The evaluation of the dependence of the current system state on the speed svr and the dependence of the current system state on the deviation sdr at Receiver C showed a very weak negative dependence. However, due to the proximity of the coefficient values to zero, even these dependencies could not be considered statistically significant.The graphs  below shMe(FIX) was the largest value of all of the four RTK receivers. Receiver B and Receiver D had the smallest median errors. Receiver B had the smallest value of Me(FLOAT), and Receiver C had a much higher value. In the “GNSS” period, Receiver A had the lowest Me(GNSS) value, and Receiver D had more than 101 times the Me(GNSS) value. It is evident that, for all the four RTK receivers, the deviations calculated from the median increased if the RTK receiver was in different states of the system in the following order of states: “FIX”, “FLOAT” and “GNSS”. From the median point of view, it could be assumed that, for all the RTK receivers, the positioning deviations were significantly influenced by the current state of the system.The first graph a clearlyfa from all the dynamic measurements in a percentage ratio, as well as three areas of selected scenario phases . fa  of a selected scenario . For the phases of the “STAT” scenario, a separate set obtained from the static measurements described in the section Evaluation (4) was used.The graphs  below shMe(STAT), Me(ACC), Me(UNIF) and Me(DEC), it was also clear that all the RTK receivers always performed better during the “FIX” system state. The median values of the static measurement errors Me(STAT) were always significantly better than the errors of the dynamic measurement phases Me(ACC), Me(UNIF) and Me(DEC). The best accuracy in the static measurements was achieved by Receiver A, but it should be noted that this receiver provided a reference by averaging its position.According to the evaluation of the deviations using the values of the medians errµ, errs and errRMS, which was most clearly reflected in the evaluation within the period of the “FIX” system. Paradoxically, the best median values were produced by Receiver C, with Receiver B coming second, Receiver D third and Receiver A fourth. Even in this evaluation, however, the volume of the datasets for the calculation of the median values had to be taken into account, as was the case in Evaluation (3) and Evaluation (4). In the whole measurement period of the dynamic measurements, Receiver B had the best median values, Receiver A coming second, Receiver C third and Receiver D fourth.In the dynamic measurements, Receiver B was not burdened with such a large degree of influence of outliers, as was the case with the evaluation of the error indicators Me(ACC), Me(UNIF) and Me(DEC), it was clear that the best results were systematically achieved during the phases of the uniform movement “UNIF”. Therefore, it was assumed that the starting phases of the “ACC” trolley and the braking phases of the “DEC” trolley had a negative effect on the positioning capability of all the four RTK receivers. In addition, during the whole measurement period, the “ACC” phase clearly had a more negative effect on the positioning ability of all four RTK receivers than the “DEC” phase. The same was demonstrated by Receiver A and Receiver B during the “FIX” system status period.Looking at the median values and comparing the three phases of the dynamic measurements: It is clear from SSR, errRMS and Me)  showed in the dynamic mode 4.8–6.4 times worse values of the error indicators (errRMS and Me) than in the static mode. The other receivers compared showed much worse, especially in the case of err.RMSThe results of the error indicators ( and Me)  were comBased on these results, it can be concluded that the errors of all the tested GNSS receivers with RTK correction in positioning were many times worse in the dynamic mode of measurements than in the static one.Thus, as described by collective authors of reference , with thSSR results. Receiver D achieved a much worse SSR result during the dynamic measurements than during the static measurements. (2) The errRMS value of Receiver D from the whole measurement period was 94 times higher than errRMS value of Receiver A. These error indicators from the whole measurement period (3) always had worse results than the error indicators from the “FIX” status period. Distant deviations from this period had a significant impact on the values of the error indicators. During the dynamic measurements, none of the assessed RTK receivers reached the values of errRMS accuracy for the “FIX” mode as stated by the manufacturer. (4) During the static measurements, only two of the four receivers in the “FIX” period met the stated errRMS values by the manufacturer. The static measurement error indicators were also always several times better than the dynamic measurement error indicators. (5) It was possible to assume that the RTK receivers with an INS unit (Receiver A and Receiver B) were able to return to the “FIX” state earlier. (6) A linear dependence between the current values of the deviation, speed, acceleration or the state of the RTK receiver was not confirmed. (7) The median values of the deviations always had better results in the “FIX” state than in the “FLOAT” state, and in the same vein, the devices performed better in the “FLOAT” state than in the “GNSS” state. (8) The median values of the deviations always had better results in the uniform movement phase of the testing scenario than during the acceleration or braking phases. These findings should be considered in the future development of modern agricultural machinery and technology.The comparison of RTK receivers using a newly developed method (MRA) allowed us to draw the following conclusions: (1) RTK receivers with a built-in INS (Receiver A and Receiver B) had better


Nyssorhynchus  darlingi, the major malaria vector in the Amazon region of Brazil, using low-coverage genomic sequencing data.In Brazil, malaria is concentrated in the Amazon Basin, where more than 99% of the annual cases are reported. The main goal of this study was to investigate the population structure and genetic association of the biting behavior of Samples were collected in the municipality of Mâncio Lima, Acre state, Brazil between 2016 and 2017. Different approaches using genotype imputation and no gene imputation for data treatment and low-coverage sequencing genotyping were performed. After the samples were genotyped, population stratification analysis was performed.Weak but statistically significant stratification signatures were identified between subpopulations separated by distances of approximately 2–3 km. Genome-wide association studies (GWAS) were performed to compare indoor/outdoor biting behavior and blood-seeking at dusk/dawn. A statistically significant association was observed between biting behavior and single nucleotide polymorphism (SNP) markers adjacent to the gene associated with cytochrome P450 (CYP) 4H14, which is associated with insecticide resistance. A statistically significant association between blood-seeking periodicity and SNP markers adjacent to genes associated with the circadian cycle was also observed.The data presented here suggest that low-coverage whole-genome sequencing with adequate processing is a powerful tool to genetically characterize vector populations at a microgeographic scale in malaria transmission areas, as well as for use in GWAS. Female mosquitoes entering houses to take a blood meal may be related to a specific CYP4H14 allele, and female timing of blood-seeking is related to circadian rhythm genes.The online version contains supplementary material available at 10.1186/s13071-022-05219-5. Malaria is the most impactful arthropod-borne disease in developing countries. According to the WHO world malaria report 2019 [Nyssorhynchus darlingi, the main malaria vector in Brazil, is highly susceptible to human Plasmodium and capable of transmitting the parasite inside and outside houses, even when at low density [Nyssorhynchus darlingi is both anthropophilic and opportunistic [Brazil presents a high incidence of malaria, with the majority of the 194,000 cases registered in 2018 concentrated in the Brazilian Amazon rainforest . Nyssorh density –7. Nyssotunistic . In the tunistic , 11.Ny. darlingi species complex, and the mosquitoes present in the Amazon correspond to one of three lineages within this complex [Ny. darlingi genome have demonstrated genetic differentiation that could represent phenotypic differences related to malaria transmission dynamics [Recent studies support the hypothesis of a  complex . Moreovedynamics , 14.The rapid development of technologies involved in whole-genome sequencing (WGS) has resulted in dramatic reductions in the per base sequencing cost. However, studies that require the sequencing of large numbers of samples remain costly, possibly prohibitively so in some laboratories. One low-cost strategy is genotyping-by-sequencing for low-coverage WGS (L-WGS), which is associated with imputation that provides sufficient genomic information to select markers accurately . The accLi and collaborators  demonstrNy. darlingi collected in Mâncio Lima, Acre state, Brazil. Genetic data were correlated with information such as specimen collection and location (larva or adult), adult capture time, house-to-house capture location (intra- or peridomestic) and distance of larvae capture sites from forest areas.In the present study, we used low-coverage sequencing markers to investigate the population of Larval and adult samples were captured from three different collection points in the municipality of Mâncio Lima, Acre state Fig.  during DFor DNA extraction, heads and thoraces of mosquitoes were separated from the rest of the body with a sterile scalpel. Each adult (head and thorax) and larva (whole body) were extracted individually using the Glass Fiber Plate DNA Extraction Kit  following the Center’s recommendations. DNA quantification was performed by fluorometric quantitation using QuBit dsDNA HS Assay Kit  according to the manufacturer's recommendations.DNA libraries were prepared using one fifth of the total volume recommended for the Nextera XT Library prep kit  following the manufacturer's recommendations. DNA samples were multiplexed to a total of 60 samples per run and sequenced in the NextSeq500 (Illumina) platform in a 151-cycle single-read run. Sequence quality analysis was performed using the FastQC  program,Ny. darlingi cytochrome oxidase subunit I (COI) reference sequence (available at https://www.ncbi.nlm.nih.gov/nuccore/KP193458.1/) using Burrows-Wheeler Aligner (BWA) software [COI consensus sequences were generated using the SamTools [COI consensus sequence. Only the highest matching result from BLAST was used. Specimens were discarded if e-value > 1e-100, identities < 200, and identity < 90%, and if the matching sequence was not identified as Ny. darlingi.Sequencing data was aligned with the software . After aSamTools  softwareSamTools  was usedNy. darlingi reference genome available in the VectorBase database, version AdarC3 [The n AdarC3 , was usen AdarC3 . Genotypn AdarC3  using geThe unimputed genotypic data used in secondary analyses were generated by the same variant call workflow described above, except for the imputation and post-imputation steps. VCF quality control was applied with LCVCFtools. Genotypes were removed if DP < 5 and GQ < 20 and SNPs were filtered for MAF > 0.1 and MD < 0.8 (at least 15 non-missing genotypes from each strata). HWE analysis control was also applied within strata (HWE < 0.001), including samples from Peru.r2 pairwise linkage disequilibrium for all markers, calculated by the PLINK 1.9 program [r2) were calculated by 500-bp windows for 40 adjacent windows, for a total of 20 kb. The prediction of the linkage disequilibrium decay function eres is the value of residual error. SNPs were selected by pruning, considering as window size that the average r2 value at that distance is approximately between 0.1 and 0.05.The linkage disequilibrium decay was estimated from the  program . LinkageST according to the mathematical model of Weir and Cockerham [ST value was calculated using the PLINK 1.9 program according to the model ST values were calculated using all pruned markers, and a permutation test  was performed to verify the statistical significance of the genome-wide average FST and per SNP FST values. The genome-wide average FST and per SNP FST estimates were considered significant when P ≤ 0.05 after false discovery rate  correction. Principal component analysis (PCA) was performed using the PLINK 1.9 program [Stratification signals were estimated by Fockerham . The FST program  and k-mek strata under the null hypothesis MH ~ χ2 (Chi-square) with 1 degree of freedom. The MH value can be calculated as kth stratum, a and c are equal to the total number of alleles A for the case and control, respectively. In the same way, b and d are equal to the total number of alleles B for the case and control, respectively. n is the total number of observed alleles for the kth stratum, where n = a + b + c + d.A genome-wide association study (GWAS) was performed using the Cochran-Mantel–Haenszel Test statistical model . The tesCase and control categories were considered indoor and outdoor for biting behavior, and samples were collected between 06:00 and 22:00 hours (dusk); samples collected between 02:00 and 06:00 hours (dawn) were considered as blood seeking at dusk or dawn. Stratum groups were determined by collection location . Table P-value < 0.05. Manhattan Plot images were generated by R scripting in RStudio [Ny. darlingi genome available in the gff3 format in VectorBase.The FDR multiple test correction method was applied to control for false positives, assuming statistical significance when corrected  RStudio , 31. Adj RStudio , 31 fromdarlingi BLAST result for species identification. The samples used in the population analysis are described in Table A total of 436 samples were captured and sequenced, of which 394 and 42 samples were from Mâncio Lima and Salvador, respectively. Following species identification and genome alignment, 73 samples from Mâncio Lima and three from Salvador were discarded due to minimum sequencing coverage threshold, low confidence BLAST result or non-P < 0.001) and 4.76 (P < 0.001) for R2 approximately = 0.97. At approximately 12.57 kbp away, the estimated average linkage disequilibrium was 0.1 for the lower confidence interval curve. For practical purposes, 14 kb was adopted as the window size for the pruning process. Figure The imputed genotypes panel from 321 Brazilian samples , an estimated 465 km from Mâncio Lima (Brazil), were compared with all Brazilian mosquitoes, resulting in significant FST  that was 15-fold greater than the highest FST obtained when groups within Mâncio Lima, about 2–3 km apart, were compared. The per SNP permutation analysis resulted in a subset of 34 microgeographic informative SNPs. The results from the PCA and clustering analysis shown in Fig. χ2 = 95.257, df = 4, P-value < 2.2e-16).Mean Fcyp4H14, period and prp4). For the blood-seeking period, three scaffolds had significantly associated SNPs, of which two present genes < 10 kb apart from the significant SNPs (timeless-2 and rdgC) . Considering pairwise comparisons of the groups from Brazil and the comparison between groups from Brazil and Peru, groups collected in Acre showed signs of stratification that were approximately 15-fold lower when compared to the FST values between the groups from Brazil and Loreto, Peru. Although the Mâncio Lima groups showed significance in the permutation tests, the FST values showed a relatively weak signal of stratification. Gélin and collaborators [Anopheles gambiae in Muheza, Tanzania using microsatellite markers from 172 mosquito samples . The linear distances between the studied locations ranged from 5 to 10 km. FST values of 0.001, 0.003 and 0.009 were observed at distances of 6.5, 9.2 and 3.5 km, respectively, but none of the results were significant in the permutation test. Our study presents similar observed values regarding the magnitude of the stratification signal for short distances and, interestingly, the stratification signal of our data is significant. Stratification analysis detected convergent results between the imputed and non-imputed panels, indicating that imputation does not generate significant bias in stratified populations. The groups collected for biting behavior and blood-seeking period did not show significant FST values. The PCA and clustering analysis indicated an optimal k-value of k = 3 because this value had the optimal TWSS difference when compared to k < 3; in addition the TWSS difference reached a plateau when k > 3. The three main clusters indicate a high association with the microgeographic structure, with samples from location A mostly in the third cluster (73.7%), samples from location B mostly in the first cluster (56.7%) and samples from location C mostly in the second cluster (50%). Interestingly, the mosquito population structure presented here seems to be similar with Plasmodium vivax population structure reported by Salla and collaborators [Significant Fborators  evaluateborators , where tprp4 and CYP4H14 (CYP450 superfamily). Two SNPs  were < 10 kbp apart from CYP4H14 . CYP450 is a well-known superfamily containing members that are important in determining insecticide resistance in insects [Plutella xylostella based on transcriptome analysis to identify genes that responded to these treatments. The tested insecticides were chlorantraniliprole, cypermethrin, dinotefuran, indoxacarb and spinosad. Differential expression of prp4 genes was detected, indicating the functional importance of this gene in insecticide resistance. The role of prp4 is not yet clear, but the results suggest the importance of further studies to disclose the relationship between prp4 and insecticide resistance.Our investigation of the gene regions adjacent to the four significant SNPs in the GWAS for biting behavior revealed some genes that should be highlighted, including  insects , includi insects –37. The  insects  studied P-value < 0.05) is located < 1.5 kb downstream from the retinal degeneration C protein (rdgC) gene locus, and the SNP ADMH02001945:12,409  is located approximately 1 kb downstream from the timeout/timeless-2 (tim2 or timeout) gene locus. Rhodopsin phosphatase (rdgC) plays an important role in the dephosphorylation of rhodopsin Rh1, the most abundant photosensory protein in Drosophila melanogaster. Rh1 is required for molecular synchronization with light and circadian rhythm behavior [rdgC gene function is associated with Rh1 hyperphosphorylation, leading to photoreceptor degeneration in the presence of light in D. melanogaster adults [D. melanogaster, including rdgC. Timeout is a timeless gene (tim1) paralog, and both have been described as components of the circadian cycle in D. melanogaster. Timeout is mainly involved in the perception of luminosity and circadian photoreception in adults [Ny. darlingi (from Brazil), the functional association of timeout with the light stimulus at the time that anophelines were collected is rather remarkable. Honnen and collaborators [Culex pipiens and timeout was observed in males.The results of our GWAS on blood-seeking at dusk or dawn and on adjacent gene regions related to the three significantly associated SNPs highlight two genes and their biological roles. The SNP ADMH02000929:14,323 (FDR-corrected behavior . The losr adults  describen adults . Consideborators  studied CYP4H14 allele and that female time of blood-seeking is related to circadian rhythm genes.The genetic association related to the behavior of females entering houses seems to be selection mediated by the use of indoor insecticides. On the other hand, genetic control of the blood-seeking period could be an ecological adaptation to host availability. Taken together, the data presented here suggest that L-WGS with adequate processing represents a powerful tool to genetically characterize vector populations at a microgeographic scale in malaria transmission areas, as well as for GWAS to disclose behavioral processes, such as the findings that females entering the houses to take a blood meal might be related to a specific Additional file 1: Table A. List of statistically significant taxonomy identifications using BLAST and COI gene sequence as target. Individual sample data can be accessed using the BioSample accession code on NCBI.


We combined quantitative and population genetic methods to identify loci under selection for adult plant resistance to stripe rust in an Austrian winter wheat breeding population from 2008 to 2018.P. striiformis f. sp. tritici, in wheat (Triticum aestivum L.) is both qualitatively and quantitatively controlled. Resistance genes confer complete, race-specific resistance but are easily overcome by evolving pathogen populations, while quantitative resistance is controlled by many small- to medium-effect loci that provide incomplete yet more durable protection. Data on resistance loci can be applied in marker-assisted selection and genomic prediction frameworks. We employed genome-wide association to detect loci associated with stripe rust and selection testing to identify regions of the genome that underwent selection for stripe rust resistance in an Austrian winter wheat breeding program from 2008 to 2018. Genome-wide association mapping identified 150 resistance loci, 62 of which showed significant evidence of selection over time. The breeding population also demonstrated selection for resistance at the genome-wide level.Resistance to stripe rust, a foliar disease caused by the fungus The online version contains supplementary material available at 10.1007/s00122-021-03882-3. Triticum aestivum L.) caused by the fungus P. striiformis f. sp. tritici (Pst). Breeding resistant varieties are the most effective strategy for mitigating yield losses due to stripe rust  mapping to identify QTL associated with adult plant resistance to stripe rust within and across years and assessed their dynamics in allele frequencies and effects over the 11-year period to test for selection at the locus and genome-wide levels.Here, we analyzed a historical stripe rust dataset from the winter wheat breeding program of Saatzucht Donau GmbH & CoKG . In total, 20,529 genotypes  were evaluated in 71 trials in 53 locations from 2008 to 2018 Table .Table 1TPst pathotypes. The inoculum was then propagated on seedlings of susceptible genotypes in the greenhouse at the Saatzucht Donau research station in Reichersberg, Austria. One trial per year was grown in the Reichersberg disease nursery (location ID = LOC01), where plots were spray-inoculated with urediniospore suspension at the EC29 and EC30 growth stages  provided urediniospores from a mixture of Genotypes (minimum F5 stage) with good agronomic performance , grain quality, and disease resistance  were pre-selected for DNA sequencing. From the pre-selected material, a final subset of 5233 genotypes representing the diversity of the breeding program was chosen for sequencing and downstream genomic analysis Table . Leaf saTo adjust for spatial variation in each stripe rust score (stripe rust was scored at 1–3 different timepoints) within each trial, we fit a general linear model with genotype as a random effect and row and column effects modeled as two-dimensional P-splines and then estimated heritability using the “SpATS” package  as σ2G is the genotypic variance, σ2ε is the error variance, and ph is defined as n is the number of genotypes, and pi is the number of plots for the ith genotype  GWA, we fit mixed models with spatially adjusted stripe rust values as the response, SNP as a fixed effect, and genotype (only genotypes with SNP data) and trial as random effects using the “sommer” package (Covarrubias-Pazaran σ2) of the genotype BLUEs as n is the number of observations per genotype (Online Resource 3). In the second stage, we used the “sommer” package  as a fixed effect, and trial as a random effect using the “breedR” package  analysis for each GWA model with the “qvalue” package (Storey β) as Z is the incidence matrix of the genotype random effect, Mi is the ith column of the SNP matrix, y is the response  in each year from 2008 to 2018 and extracted β from each within-year GWA model. To estimate the change in allele frequency of each SNP from 2008 to 2018, we fit a linear model for each SNP with p as the response and year as a continuous fixed effect and extracted the year coefficient from the model (Δp). Likewise, we estimated the change in allele effect of each SNP from 2008 to 2018 by fitting a linear model for each SNP with β as the response and year as a continuous fixed effect and extracted the year coefficient from the model (Δβ). In GWA, the power to detect a SNP-trait association and the absolute effect size of a SNP decrease with decreasing minor allele frequency  if Δp > 0 and Δβ > 0, there was selection for the “A” allele conferring resistance; (3) if Δp < 0 and Δβ < 0, there was selection for the alternate “a” allele conferring resistance; (4) if Δp < 0 and Δβ > 0, there was selection for the “a” allele conferring susceptibility.For each SNP, we calculated the frequency of allele “A” (Vp) and estimated the expected variance in allele frequency due to random genetic drift (Vt) as p is the initial “A” allele frequency in 2008, t is the number of generations (t = 11 generations from 2008 to 2018), and Ne is the effective population size (Ridley Ne (Ne = 149) by regressing identity-by-descent (IBD) coefficients against time (2008–2018), with Vp – Vt. We compared Vp – Vt of each SNP to the genome-wide null distribution of Vp – Vt. The null distribution was generated by subsampling Vp – Vt from 150 random SNPs in 1000 replications. The subsample size of 150 was selected because there were 150 significantly associated SNPs from GWA.We sought to test whether changes in allele frequencies were driven by selection rather than drift. For each SNP, we calculated the observed variance in allele frequency from 2008 to 2018  from the selection analysis. We then estimated the value and significance of Δj is the change in allele frequency from 2008 to 2018 for SNP j, αj is the allele effect of SNP j, and m is the total number of SNPs. To test whether the observed Δj is the change in allele frequency from 2008 to 2018 for SNP j, j, and m is the total number of SNPs  for resistance to stripe rust was generally high within years (H2 = 0.50–0.90) and was moderate across years (H2 = 0.54)  and had small to medium-effect sizes (|β|= 1.09 ± 1.23) (Online Resource 6).GWA across years and within 2009–2011, 2014–2015, and 2018 revealed 186 significant SNP-stripe rust associations (150 unique SNPs) after multiple test correction . Few genotypes (N = 47) had SNP data in 2008 and the trial and residual terms explained a larger proportion of the variance in stripe rust in years with no significantly associated GWA SNPs, which may partially explain the lack of statistical power to detect SNP-trait associations (Online Resource 5).No SNPs were significantly associated with stripe rust in 2008, 2012–2013, and 2016–2017  of each SNP against the null distribution of |Vp – Vt| , we found significant evidence of selection of the resistant allele at 38/150 significant GWA SNPs  and for selection of the susceptible allele at 24/150 significant GWA SNPs  . By comparing the variance in observed allele frequencies to the expected variance due to drift  difference between the observed To test whether genome-wide resistance or susceptibility to stripe rust was under selection in the breeding program between 2008 and 2018, we used the ues Fig. A.Fig. 3(β|) and Vp – Vt were greater at significant GWA SNPs  than at nonsignificant GWA SNPs . These results suggest that both moderate and small effect QTL were under selection, although to a lesser extent for QTL with small effects that were not detectable GWA.SNPs with larger effect sizes on stripe rust in across-year GWA had greater changes in allele frequencies from 2008 to 2018 Fig. B. FurthePst populations. Here, we combined quantitative genetic and population genetic methods to identify genomic regions that were under selection for resistance or susceptibility to stripe rust in an Austrian winter wheat breeding program from 2008 to 2018. GWA revealed 150 SNPs significantly associated with stripe rust within 2009–2011, 2014–2015, and 2018 and across 2008–2018, many of which overlapped with regions previously associated with stripe rust resistance in other populations Online Resource 2. Data for 9744 SNPs, including SNP ID, chromosome, physical position (bp), and genotypes of 5233 lines (CSV 116263 kb)Online Resource 3. Genotype best-linear unbiased estimates (BLUEs) and variances used in two-stage across-year GWA (CSV 655 kb)Online Resource 4. Plot of first and second principal components from principal component analysis of the breeding panel using SNP data. Each point represents one breeding line and is colored based on the year in which the line first appeared in the panel (TIF 34804 kb)Online Resource 5. Number of observations, variance and number of genotypes, variance and number of trials, error variance, false discovery rate  SNP p-value significance thresholds (NA = no significant SNPs), and number of significant SNPs from genome wide association of stripe rust resistance within and across years from 2008 to 2018 (CSV 0 kb)Online Resource 6. GWA results for each SNP tested within each year or across years from 2008-2018, including chromosome, physical position (Mbp), effect estimate, p-value, F-statistic, and R2 (CSV 6525 kb)Online Resource 7. Quantile-quantile plots of the expected vs. the observed SNP p-values from GWA for stripe rust within and across years from 2008 to 2018 (TIF 266227 kb)Online Resource 8. Selection tests on 9744 SNPs, including allele frequencies and effects from GWA within 2008–2018, changes in allele frequencies and effects from 2008 to 2018, observed variance in allele frequencies from 2008 to 2018, expected variance in allele frequencies from 2008 to 2018 due to drift, difference between observed allele frequency variance and expected variance due to drift, selected effect (resistant/susceptible), selected allele (A/a), and significance in GWA (CSV 3119 kb)Below is the link to the electronic supplementary material.


This would not only increase profits, but also decrease the energy needed during the storage and refrigeration of aged beef, decreasing the carbon footprint of beef production.When searching for genetic markers for the selection of more tender beef, it is important to maintain minimal environmental variation from pre-slaughter, right through to the ageing process, to ensure the accuracy of the obtained phenotypes. This is because beef quality traits have a large environmental component that can greatly alter the characteristics of the meat, which would not reflect a true genetic effect. We propose that variable ageing times are especially important in determining whether markers are associated with tenderization or not. Our analyses included candidate genes for the protein degrading enzyme system for calpains, because they contribute the most to tenderization. We were able to validate these markers in South African beef cattle, where they could be useful for selection. The timing of the collection of tenderness data was critical, as only a few (6/134) genetic markers sustained their association with tenderization over ageing to 20 days. A larger tenderization effect earlier in ageing, as shown here for the n = 166) were genotyped using the Illumina BovineHD SNP BeadChip through a gene-based association analysis targeting the cast, capn3, capn2 and capn1 genes. The Warner–Bratzler shear force (WBSF) and myofibril fragment length (MFL) of Longissimus thoracis et lumborum (LTL) steaks were evaluated between d 3 and d 20 of ageing, with protease enzyme activity in the first 20 h post-mortem. Although several of the 134 SNPs are associated with tenderness, only seven SNP in the cast, capn2 and capn1 genes sustained genetic associations, additive to the ageing-associated increases in tenderness for at least three of the four ageing periods. While most genomic associations were relatively stable over time, some genotypes within the SNP responded differently to ageing, resulting in altered genomic effects over time. The level of ageing at which genomic associations are performed is an important factor that determines whether SNPs affect tenderness phenotypes.The most important factor that determines beef tenderness is its proteolytic activity, and the balance between calpain-1 protease activity and calpastatin inhibition is especially important, while contributions can also arise from calpain-2 and, possibly, calpain-3. The meat ageing process itself affects these processes. To determine whether genotypes in the calpain–calpastatin system can enhance tenderness through a 20-day ageing period, South African purebred beef bulls ( Bos indicus genetic background [Increased tenderness with beef ageing involves the weakening of the myofibrillar structure of muscle fibers through the degradation of myofibrillar (and other) proteins ,2,3. Theckground , allowinckground  and shorckground .The aim of selection to improve the tenderness of beef  goes hanGenome-wide association studies (GWAS) are useful for determining novel associations within phenotypes, while SNPs within targeted genes give a higher power of association of analysis for quantitative traits ,28. The The duration of the ageing period may affect the estimates of heritability, with variations between breeds and ageing time, including interactions between these two variables . This isBos indicus using a limited capn1 and cast SNP pool suggested that ageing time (<21 d) may alter genomic associations [Accurate phenotypes are a prerequisite for linking genotypic data with traits related to beef quality , especiaciations , while iciations ,48. Few ciations ,45,49,50cast (chromosome 7), capn-3 (chromosome 10), capn-2 (chromosome 16) and capn-1 (chromosome 29) genes with tenderness (WBSF and MFL) up to 20 days of ageing in South African beef cattle. The objective of this study is to determine whether favorable genotypes in causative genes for tenderness could improve the phenotype over extended ageing up to 20 days and whether these associations are similar over ageing periods and between breeds.The augmentation of tenderness through post-mortem practices, such as electrical stimulation, tenderstretch or extended ageing, could eliminate differences in tenderness over time , alterinBos taurus, Charolais European Bos taurus, Brahman Bos indicus, Bonsmara as an indigenous Sanga-type composite breed and Nguni as the indigenous Sanga-type, Bos taurus africanus. Bulls were slaughtered to yield A2/A3-class carcasses [Purebred bulls, approximately nine months old, were feedlot finished for approximately 120 days at the Animal Production Unit of the Agricultural Research Council (ARC-AP) unit in Irene, Pretoria, −25°54′ 28°12′, altitude ≥ 1475 m. Five breeds were included in the study  as reprearcasses ,52, meanBulls were transported to the ARC-AP abattoir (3.9 km from the feedlot) and held overnight with access to water. Following captive bolt stunning and exsanguination, bulls were slaughtered, carcasses were halved and the right half of each carcass was electrically stimulated for 15 s  and chilled directly (ES). The left half of each carcass served as a control and chilling was delayed for 6 h post-mortem (NS) to allow for accelerated conditioning at 10 °C before chilling.Association analyses were originally performed on the left half and the right half of the carcasses separately, as well as the pooled data. Of all the association analyses performed  for each SNP and phenotype, only 0.3% exhibited genotype × treatment interactions (results not shown) and did not include any of the candidate SNPs identified here. In order to simplify data representation tables, only data from the NS + ES analysis were represented here and any notable differences between association analyses described in the text.Longissimus lumborum et thoracis  muscle was excised (boned out). Several 30 mm steaks were collected from predetermined regions of the LTL for each of the laboratory analyses for each ageing period, vacuum-sealed (70 microns) and aged for three, nine, fourteen and twenty days post-mortem at 2 ± 1 °C . The myofibril fragment length (MFL) was determined following ageing, while steaks for the determination of Warner–Bratzler shear force (WBSF) were frozen at −20°C until analyzed. Small sub-samples were collected from the lumbar portions of the LTL at 1 h and 20 h post-mortem, snap-frozen to -196 °C in liquid nitrogen and immediately stored at −80 °C until analyses of muscle calpains and calpastatin activities.Meat or tail hair samples were collected at slaughter. Meat samples were stored at −20 °C until extraction of DNA from a core sample, while tail hair was stored at room temperature until extraction of DNA from hair bulbs. At approximately 20 h post-mortem, the For WBSF measurements , steaks The MFL was determined following extraction according to Culler et al. 1978), as modified by Heinze and Brüggemann (1994) 978, as m. The meaA336) at 25 °C, while protease inhibition by calpastatin was defined as the amount required to inhibit one unit of calpain-2 activity. Enzyme activities and inhibition were expressed as units per grams of meat, while relative units of inhibition by calpastatin per units of calpain-1, or per the combined protease activities of calpain-1 and calpain-2, were also calculated.Calpain-1, calpain-2 and calpastatin were extracted from muscle tissue samples and separated by 2-step gradient ion exchange chromatography . Using a® Tissue kit  with silica membrane columns, including modifications to the basic protocol to enhance DNA yield, as per the user manual. Samples were allowed to stabilize overnight at 4 °C and DNA quality and concentration were determined at the Agricultural Research Council, Animal Genetics  using UV spectrometry , fluorometry  and ethidium bromide agarose gel electrophoresis.DNA was collected from muscle or hair samples and extracted using the NucleoSpincapn1 gene , 27 for capn2 , 18 for capn3  and 77 for cast . For each SNP, the name was abbreviated as the gene plus the last three digits of the Illumina code (gene_123), except for the SNP capn1-4751, which was abbreviated from the Illumina CANP1_2 to capn1_4751. The allele that favored tenderness was determined by comparing phenotypic means of the three genotypes, first for the genotype with the lowest WBSF and then MFL (if necessary), where these alleles were indicated with an asterisk and designated the tender allele (*A/*C/*G/*T) that coded for the tender form of the protein. This simplified designation of the alleles was possible because no dominance effects were found in the candidate SNP effects.All the animals were individually genotyped using the BovineHD SNP BeadChip  using the HiScanSQ platform and Genome Studio software  at the Agricultural Research Council, Biotechnology Platform . The National Centre for Biotechnology Information (NCBI) Genome Data Viewer , using bPHWdev ≤ 1 × 10−4) and minor allelic frequency <5%. Five SNPs were disqualified during quality control, two from the capn3 gene, two from the capn2 gene and one from the cast gene.Quality control was performed on the SNP using PLINK version 1.9 software  to exclubx) as a fixed effect in a linear model, with a variance-standardized genomic relationship matrix (g) as a random accumulated effect of all SNPs and the residual (e) or random effect of error. To determine whether these responses were similar between breeds, associations were determined within breeds, with all breeds as covariates or with each individual breed added to the simplified model as covariate fixed effects (Brcovar):A mixed linear model association (mlma) analysis  was condp-value affected the results, these false positive associations were clearly indicated in tables and described in the results .We also performed an adjusted association analysis  on the sGe) of the SNP, electrical stimulation treatment (Tmt), breed (Br), first-order interactions with genotype as well as residual (e):To determine the least squares means (LSM) of genotypes or genotypes within breeds, Statistical Analysis System  software was used  to deterBecause individual SNPs in a gene are not independent (genotypes of different closely associated SNPs are linked in inheritance or pleiotropy), a linkage disequilibrium (LD) analysis was performed using Haploview 4.2 software   default SNPs with Lewontin’s D-prime (D′) that was greater than the threshold of 0.95 were likely to also be affected when a candidate SNP was included in a selection program. All SNPs that were identified as dependent on a candidate SNP that had additive effects through extended ageing were also subjected to the gene-based association analyses to ensure the direction of change in tenderness phenotypes was also positive and that no (readily apparent) inadvertent negative effects were linked to the SNP that sustained tenderness over extended ageing.On d 3 post-mortem, Brahman cattle had the toughest beef based on the highest WBSF values, while Bonsmara was the most tender, but was not different from Angus a. The WBThe MFL on d 3 was the shortest in Bonsmara and longest in Brahman b. The MFcast gene and one in the capn2 gene, were statistically significant but biologically negligible, as the differences in the LSM between genotypes were very small.Ten candidate SNPs were identified in this study that could affect tenderness over extended ageing periods for at least three out of the four ageing periods measured (from a pool of 134 SNPs in the four genes). Of these, one SNP was eliminated in quality control measures due to a very low minor allelic frequency, which could result in false positives. Another two SNPs, one in the cast_736 SNP (rs137217365) was located 28.81 kb from the start of the cast gene. The SNP was independent, i.e., it did not exhibit LD (D′ > 0.95) with any of the other SNPs of the cast gene. It seemed, therefore, that there should not be any unintended effects of non-random inheritance from nearby loci. The genotype that favored tenderness, or the tender genotype *GG, was relatively rare, and occurred in only 19 of the 166 bulls (11%). However, data from bulls with the heterozygous genotype showed intermediate tenderizing effects, confirming the effects of the *G allele.The cast_736 genotype (p ≤ 0.1343). The 0.43 kg decrease in shear force on d 14 in *GG bulls was additive to a tenderizing effect of ageing that resulted in an almost 2 kg decrease in shear force values in all three genotypes. This meant that the tender genotype was still able to induce a 10.3% increase in tenderization in the face of the existing tenderization of ageing at 14 days of 36.0% compared to d 3. The addition of breeds to the simplified model as covariates, each breed as a covariate or within-breed analyses suggested that the effects of this genotype on the WBSF were not breed dependent. However, false discovery rate (FDR) analyses showed that these biologically small effects could be overestimated because of multiple testing, rather than a true association. The effects of the cast_736 SNP on d 3 and d 9 only tended to be significant in the FDR analysis, while the association on d 14 was not significant (p ≤ 0.2340).The genotype  was assocast_736 SNP was associated with an MFL over the ageing period between d 9 and d 20, with little advantage to adding breed to the model. However, the phenotypic response to genotypes was greatly altered by the time of the ageing period. The MFL on d 9 tended to reach significance, but the response in the *GG genotype was an increase in the myofibril length of 1.70 µm, which would not favor tenderization. At this ageing period, the only significant breed-dependent effect was identified in Charolais, which was the only breed to exhibit a decrease in the MFL of 6.9% or 1.94 µm (within-breed p ≤ 0.0404). On d 14 post-mortem, the genotypes failed to result in biologically important changes in the MFL compared to TT (+0.9%), but was 4.2% shorter than GT. The association of the MFL with genotypes on d 20 resulted in a relatively small decrease in myofibril length (1.01 µm shorter) that occurred in addition to the large tenderizing effects induced by ageing. During the period where the cast_736 SNP affected the MFL (d 9–d 20), ageing resulted in a 4.53 µm decrease in myofibril lengths in TT bulls and 4.10 µm in GT, while, at the same time, *GG bulls experienced a 7.24 µm decrease in MFL.The p-values were adjusted for multiple testing, but tended to associate with inhibition in the mixed linear model. The relative inhibition of calpain-1 by calpastatin at 1 h was decreased by 5.9% in *GG bulls, while the decrease per units of calpain-1 and calpain-2 activity was biologically negligible. The effects on relative calpastatin inhibition remained significant in the adjusted association analysis.The lack of a consistent response in the MFL and the small changes in the WBSF could be explained by the differences between genotypes in the muscle protease system. The genotype did not affect calpain protease activities, while small and inconsistent associations were noted for calpastatin inhibition at 1 h post-mortem only . The LSMcast_763 SNP (rs135465452) was located 98.91 kb from the start of the gene and was in strong LD (D′ > 0.95) with several SNPs associated with tenderness phenotypes (results not shown). When determining whether cast_763 would be a suitable candidate for selection for tenderness that was sustained in the face of ageing, it was important to note whether the closely-linked SNPs also induced positive tenderizing effects, as selection for the candidate SNP would indirectly result in selection for SNPs in high LD. These SNPs included cast_741 (−50.0 kb from cast_763), cast_770 (+17.2 kb from cast_763), cast_771 (17.9 kb), cast_772 (18.9 kb) and cast_779 (36.9 kb). Although none of these SNPs were identified as candidate SNPs for the improvement of tenderness over extended ageing periods, they were found to have associations with some tenderness phenotypes (unpublished results). The corresponding alleles for these SNPs were also favorable for tenderness and the general effects of these SNPs were mild reductions in the WBSF between d 3 and d 14 (of between 6.7 and 9.0%), with generally small improvements in the MFL on d 14 (5.0–6.5%).The cast_763 SNP did not associate with the WBSF at any of the ageing periods, but was associated with the MFL throughout the entire ageing period between d 3 and d 20 of ageing in the simplified model  was located 55.07 kb from the start of the gene. The SNP was in LD (D′ > 0.95) with other SNPs that affected tenderness in the gene-based association study, capn2_760 (−46.93 kb), capn2_763 (−35.72 kb), capn2_766 (−26.53 kb) and capn2_772 (−21.40 kb). Selection for the tender allele of the capn2_780 SNP would include indirect selection for these SNPs, all of which were linked to the corresponding tender alleles. These SNPs were found to have no association with the WBSF or MFL, but did affect changes in enzyme function, which was a general increase in calpastatin and calpains, where calpain proteolysis increased to a greater extent than calpastatin inhibition. This resulted in a decrease in the relative calpastatin inhibition per units of calpain proteolysis, which would favor tenderization (results not shown). The tough GG genotype was relatively rare (n = 15), where 10 of these bulls were Brahman, while also not being sufficient for determining the LSM. The G allele was rare in Angus, with only three heterozygotes and no GG bulls. Because few “tough homozygotes” were available for comparison, genotype differences were compared between *AA (n = 112) and AG (n = 39), with the exception of the Angus breed, where the association could not be tested .The capn2_780 genotype tended to affect the MFL throughout the ageing period  was only present on d 14 post-mortem b, where The fact that the consistent decrease in the MFL over the ageing period was not accompanied by a concomitant decrease in shear force values at any of the ageing periods was most likely due to a lack of changes in the proteolytic enzymes in the different genotypes . The adjcapn1_184 SNP (rs17871986) was located 4.51 kb from the start of the gene and exhibited a high level of LD (D′ > 0.95) with three other SNPs associated with tenderness within close proximity of the SNP. The selection for the tender *G allele of capn1_184 favored selection for the “tender alleles” for these SNPs that were closely linked to it (unpublished results). The capn1_183 (−0.76 kb), capn1_316 SNP (+1.10 kb) and capn1_185 (+2.96 kb) SNPs were all associated with increased tenderization, with similar or smaller means differences than those discussed below. There was a shortage of tough homozygous bulls (AA) in the Bos taurus and Sanga types and a lack of tender homozygotes (*GG) in Brahman. However, the addition of breed(s) as covariate(s) did not improve the simplified mixed model in any of the phenotypes tested. A greater pool of animals would be required to fully elucidate the breed differences, if any exist.The capn1_184 SNP exhibited a strong, stable association with the WBSF over the entire ageing period (p ≤ 0.1250) and this effect would need to be confirmed in a larger dataset. These genotype associations were additive to decreases in the WBSF of between 34.2 and 37.1% in all three genotypes over the ageing period  was located 17.59 kb from the start of the gene and was in strong LD (D′ > 0.95) with the capn1_4751 SNP (+6.57 kb) discussed below. The distribution of alleles for Brahman was skewed toward the tough allele, while other breeds generally experienced a shortage of CC genotypes. The discussion, therefore, focused on the differences between the tender homozygous bulls (*TT) and heterozygotes (CT) for breed dependence, although there was little or no advantage to adding breeds to the simplified model as covariates.The p-value for the WBSF on d 20 decreased from a significant p ≤ 0.0302 to a tendency p ≤ 0.0558.The SNP was associated with the WBSF over the entire ageing period. The least squares means differences between homozygous bulls were very close to 1 kg on d 3 and d 9 post-mortem (0.43–0.49 kg for heterozygous bulls), and the large gain in the tenderness of early ageing was decreased somewhat by d 20, where the shear force improved by 0.77 kg in *TT and 0.34 kg in CT. These genotype effects on shear force occurred additive to the 37% reduction in the WBSF that occurred in all genotypes by d 20 post-mortem a. Adjustcapn1_187 genotype with the MFL decayed over the time of ageing, with the most prominent effect on d 3, where an 11.78 µm decrease in length was observed in *TT bulls (26.6% shorter). As the ageing period progressed, the improvement in the MFL gradually decreased and was 5.42 µm (18.2%) on d 9 and non-significant by d 14.The association of the capn1_187 SNP and absolute calpastatin at 20 h . The means differences for relative calpastatin activity per calpain(s) were, generally, unaffected, while the relative calpastatin inhibition per calpain-1 at 1 h was tenderizing (15.5% lower).There was a tendency toward a breed-dependent association of the  at 20 h . A largeThe large consistent responses in tenderization  were, therefore, not in response to the decreased calpastatin inhibition of protease activities, but rather the large and consistent responses of the proteases themselves to genotypes b. Increacapn1-4751 SNP (rs17872050) was located 24.17 kb from the start of the capn1 gene. It exhibited strong LD (D′ > 0.95) with the capn1_187 SNP discussed above (−1.86 kb) and some downstream SNPs, with the tender allele for capn1_4751 corresponding to the tender alleles of these SNPs (results not shown). capn1_189 SNP (+6.04 kb), capn1_190 (+10.34 kb) and capn1_191 (+13.79 kb) decreased the WBSF in late ageing by between 9.0% on d 14 and up to 16.2% on d 20, and selection for the capn_4751 SNP would result in favorable responses in these tenderness phenotypes.The capn1-4751 SNP and MFL was, however, sustained from d 3 to d 14 post-mortem, although the effects on myofibril lengths were moderate. The MFL was decreased by 10.4% on d 3, 9.3% on d 9 and 8.1% on d 14 in *CC bulls compared to TT. These moderate gains were still important, as they occurred additive to the 32–33% decrease in the MFL than that which occurred between d 9 and d 20 of ageing, and the genotype effect deteriorated but did not disappear over time in late ageing as the myofibril lengths decreased. There was no advantage to adding breeds to the simplified mixed model as covariates.The SNP associated with the WBSF only between d 14 and d 20, resulting in a moderate tenderization of 0.47–0.50 kg shear force , additivcapn1_4751 genotype on tenderizing phenotypes was an increase in the calpain-1 protease activity at 1 h post-mortem, although this effect was biologically small. The association with the protease was significant in the simplified model (p ≤ 0.0340), but only tended to be significant after adjustment for multiple testing (p ≤ 0.0769). The protease activity of *CC bulls was 7.7% greater than CT, with an intermediate increase in tender homozygotes compared to TT (The only other effect of the ed to TT .p ≤ 0.10), while 38 SNPs associated significantly (p ≤ 0.05) with tenderness (results not shown). Only 12 of these SNPs, or less than 20%, associated with tenderness over extended periods of ageing, before quality control or biological importance of differences were considered. For these SNPs, it was more likely to identify associations from data collected at a single ageing period or from animals that showed variation in ageing, where 92% of the associations would be identified if any one ageing period was used to determine both the WBSF and MFL. However, more than 80% of the SNPs that could affect tenderness would likely fail to associate with tenderness from a single measurement, because they only associated with tenderness phenotypes for a short period of ageing. The least likely ageing period to identify an association in these data was d 9, where only 40% of all the possible SNPs (24/62) showed an association with the WBSF or MFL, while at the other ageing periods , approximately 50–55% of all possible associations were identified.Of the total 134 SNPs tested in the gene-based association analyses, 62 SNPs showed some association with tenderness phenotypes in the WBSF or MFL in at least one of the ageing periods  and did not extend into the d 14 intermediate ageing period [capn1 gene with larger sustained phenotypic responses in the tender genotypes  and could be linked physiologically to responses in the muscle calpain–calpastatin system.Although the effects of genotypes in the calpain–calpastatin system are well-established, few studies have formally investigated the changes induced by ageing over time, especially when it came to using high-definition (777K) genotyping in South African beef cattle. Here, the SNPs in the g period . In our cast and capn2 genes was high, 0.84 and 0.92, respectively, while greater levels of diversity (moderate D′) were present in the capn1 (0.69) and the capn3 (0.63) genes. All the SNPs in LD with the candidate SNPs for extended ageing were also linked to tender alleles that would result in responses that favored tenderness, with no apparent unintended negative effects on tenderization [Linkage disequilibrium (LD) is a requirement for effective genomic selection ,70, but rization . In addiIt must be considered that the SNPs in this study were specifically chosen to sustain an effect on tenderness (WBSF or MFL) over time. If the three genotypes of an SNP responded to ageing in the same way, the association with the phenotype should remain relatively unaffected over the ageing period. One could assume that this should be true for the SNPs identified in this study, and this was generally true, with little difference in the absolute size of the tenderizing response (kg or µm) between d 3 and d 20, within each of the genotypes .capn1_187 SNP, where the change in the MFL of the *TT and CT genotypes  was 11.93 µm and 13.53 µm, respectively (a 37–38% decrease in myofibril length to d 20). However, the change in the CC genotype was 20.3 µm or 45.8% between d 3 and d 20, and because the bulls with the “tough genotype” exhibited a greater response to ageing, the beneficial effect of the *T allele also gradually disappeared over time.If there was an interaction between the individual genotypes and tenderization over the ageing period, one genotype would tenderize more (or less) than the other genotypes over time. Longer ageing periods are generally employed to allow beef that tenderizes at a slower rate, to reach a greater tenderness before going to market. Over time, the differences between tough steaks in early ageing and those that tenderize rapidly by early ageing can decrease and even become negligible . One coucapn1_187), the effect on the WBSF was, generally, very stable and there was little difference between the responses of bulls with different genotypes and ageing for 20 days. Tender homozygous bulls (*TT) experienced a 2.22 kg reduction in the WBSF (38.2%), heterozygous bulls 2.32 kg (37.1%) and tough homozygous (CC) bulls 2.43 kg (35.7%). The differences between means of the genotypes, therefore, remained very stable over the different ageing periods, with little difference between d 3 and d 20 in the percentage difference of *TT compared to CC. This large tenderizing response, from very early ageing would be the ideal in a candidate SNP, because it would eliminate the financial and logistical costs associated with extended ageing; therefore, increasing profitability [For this same SNP, however,  for shear force identified previously, but was not one of the candidate SNPs tested by the 50K array used [The effects of the f ageing . The casray used  and was ray used ,77. The cast_763 SNP on the WBSF, previous studies failed to identify an association between the SNP and tenderization [cast_763 SNP was not included in the these analyses as a potential candidate SNP [n = 113). This is characteristic of many SNPs in the cast gene [cast gene averaged 71.3%, being the minor allele in only 9 (12%) of these SNPs. This could be the result of selection practices, as confirmed by the relatively high LD of the gene (D′ = 0.84).Although the present results showed small but persistent effects of the rization . Althougdate SNP ,79 or didate SNP ,77. Evenast gene ,81,82, wcapn2_780 SNP generally only tended to have a small effect on the MFL, but was persistent through most of the ageing periods tested. It was linked to SNPs that could favor responses in the muscle calpain system in this study that could indirectly enhance tenderization through proteolysis in selection. Although SNPs in the capn2 gene were tested as possible candidates for selection for tenderness [The nderness , this SNnderness ,76,77.capn1_184 SNP was located within QTL for tenderness phenotypes (shear force and panel score), where it was linked to the WBSF [The the WBSF , while othe WBSF ,76,78. Ithe WBSF .capn1_187 and capn1_4751 SNPs were candidates for extended effects on tenderness throughout ageing in this study. They were slightly less effective than capn1_184 in determining the WBSF, at least as effective for the MFL and had more pronounced effects on calpain protease activities. capn1_4751 SNP was also linked to downstream SNPs that caused at least similar responses in tenderization. The capn1-4751 SNP was the only one of the SNPs studied here that was identified as a causal SNP in QTL for shear force [The ar force ,79, and capn1_187 was not identified as a candidate for selection for tenderness of beef [capn1_4751 associated with several measures of tenderness in many studies. The capn1-187 SNP was located within the range of QTL for shear force, but not included as a genotype in these research articles [capn1-187 SNP with tenderness phenotypes could arise from its strong LD with the causal SNP capn1-4751, through association in inheritance rather than an effect on the calpain-1 large subunit protein itself. The capn1_4751 SNP has been shown to exhibit very low tender allelic frequencies in some Brahman [capn1_4751 and capn1_187 showed only 10% tender alleles in the Brahman breed, where the effect of the SNP remained tenderizing.Although  of beef ,77, capnarticles ,78,79. Tarticles . The ass Brahman , and botcapn1, where it was only 15.8% .The present results agreed with that of previous studies , namely,cast and capn1 genes, compared to genotypes that contained the tender alleles, progressively eroding genotype differences as the ageing was extended.One possible way of decreasing the effective ageing period is through genomic selection for SNPs that  accelerate ageing over extended time periods. Because the SNPs of the calpain system are responsible for coding the most important proteases of tenderization and their inhibitor, these genes are likely targets for selection. However, the duration of ageing can alter genomic associations over time. Some of the SNPs in the muscle calpain system sustained genomic associations with tenderness throughout extended ageing periods, while some individual genotypes also responded differently to ageing, altering these associations over time. We found that ageing was accelerated in some tough genotypes in the


Peromyscus maniculatus elusus from Santa Barbara Island and P. m. santacruzae from Santa Cruz Island exhibit significant differences in olfactory predator recognition, activity timing, aggressive behavior, morphology, prevalence of Sin Nombre virus, and population densities. We characterized variation in protein‐coding regions using exome capture and sequencing of 25 mice from Santa Barbara Island and 22 mice from Santa Cruz Island. We identified and examined 386,256 SNPs using three complementary methods . We found strong differences in molecular variation between the two populations and 710 outlier SNPs in protein‐coding genes that were detected by all three methods. We identified 35 candidate genes from this outlier set that were related to differences in phenotypes between island populations. Enrichment analyses demonstrated that patterns of molecular variation were associated with biological processes related to response to chemical stimuli and regulation of immune processes. Candidate genes associated with olfaction , circadian activity (Cry1), anxiety (Brca1), immunity , aggression , and body size  exhibited non‐synonymous mutations predicted to have moderate to large effects. Variation in olfaction‐related genes, including a stop codon in the Santa Barbara Island population, suggests loss of predator‐recognition traits at the molecular level, consistent with a lack of behavioral aversion to fox feces. These findings also suggest that divergent pathogen prevalence and population density may have influenced adaptive immunity and behavioral phenotypes, such as reduced aggression. Overall, our study indicates that ecological differences between islands are associated with signatures of selection in protein‐coding genes underlying phenotypes that promote success in those environments.Understanding the forces that drive genotypic and phenotypic change in wild populations is a central goal of evolutionary biology. We examined exome variation in populations of deer mice from two of the California Channel Islands:  Understanding the forces that drive genotypic and phenotypic change in wild populations is a central goal of evolutionary biology. We examined molecular variation in mice from two different islands that differ greatly in predators, pathogens, and competitors and found significant molecular variation in genes for olfactory predator recognition, activity timing, immunity, and aggression. These findings demonstrate the molecular foundation of selection‐mediated differences among organisms, and also help identify important genes for future studies to investigate adaptive evolution. Islands provide an outstanding opportunity to understand how ecological factors such as resource availability  are found across much of North America, including several island archipelagos . After best‐hit genomic intervals were padded to a minimum of 80 nucleotides and collapsed to contiguous intervals, the space totaled approximately 43.5 Mbp of target space. These regions were used to design probes (80 nucleotide length) spaced about every 100 base pairs across the target space. To eliminate baits that might hybridize, Arbor Biosciences then used their “ultra‐stringent” filtration method to filter probe candidates for single‐locus specificity, with additional filtration of any candidate whose best hit was 13% or more soft‐masked. This resulted in a final bait set of 378,989 unique bait sequences, which were then synthesized as part of a custom myBaits® kit.Arbor Biosciences generated the exome capture bait set using the coding sequence reference from the ® next‐generation sequencing project. After quality assessment, genomic DNA was sonicated using a QSonica Q800R instrument and size‐selected using SPRI beads to average lengths of 300 base pairs. Each sonicated, size‐selected sample was then converted to an Illumina® TruSeq‐style sequencing library, which included amplification for 7 cycles to add dual indexes in unique combinations. Up to 200 nanograms of each sample library was combined into pooled libraries of up to 8 sample libraries. These pools were enriched using the custom myBaits® kit following the myBaits® manual version 3.2 protocol. After hybrid capture and cleanup, bead‐bound enriched library pools were amplified using universal primers for an additional 10 cycles before purification and sequencing. The 19 enriched pools were then sequenced across 6 lanes of the Illumina® HiSeq® 2500 platform using PE125 sequencing protocol, and reads were de‐multiplexed using CASAVA® and the unique index pairs, while tolerating a 1‐nucleotide mismatch.Genomic DNA from tail snips was extracted using Qiagen DNeasy Blood & Tissue Kits following the standard protocol. DNA extracts were provided to Arbor Biosciences for library preparation, target enrichment, and sequencing as part of a myReads2.3Peromyscus maniculatus v. 1.0 genome using default parameters in bwa mem . Indels and multiallelic SNPs were removed before proceeding with SNP filtration. SNPs were filtered following Broad Institute's general hard filtering recommendations: MappingQuality < 40.0, QualByDepth < 2.0, MappingQualityRankSumTest < −12.5, and ReadPosRankSumTest < −8.0. Remaining SNPs were filtered to retain SNPs with minor allele frequencies >5% and no missing data.Peromyscus genome identified genes with an official gene symbol, a subset was best matched with a locus . For significant LOC genes, we used the Gene tool within NCBI and ran BLAST against the house mouse (Mus musculus) genome to gain additional information. None of the olfactory receptors that differed between islands had an official gene symbol, so these were further evaluated using the BLAST function and identified with the closest house mouse olfactory receptor .While most alignments with the 2.4ST outliers given priors such as population assignment. BayeScan 2.1 was run using default parameters aside from a prior odds value of 2, corresponding to neutrality being twice as likely as selection. Default parameters include thinning interval = 10, 20 pilot runs of 5000, and a burn‐in length of 50,000. We ran BayeScan with two prior populations defined, corresponding to the two islands. For outlier detection, a false discovery rate of 0.05 was used.We used three different analytical approaches to identify outlier SNPs between the two island populations. BayeScan (version 2.1) uses a reversible‐jump Markov chain Monte Carlo approach to identify FPcadapt detects outlier loci based on principal component analysis and does not rely on prior population assignments. Outliers identified by pcadapt contribute to population structure much more strongly than average SNPs. We report results from pcadapt using a K = 2, which was chosen after running pcadapt for K = 1–20 and evaluating the screeplot. Q‐values were determined using the qvalue R package as described in the pcadapt vignette, and a q‐value cutoff of 0.0001 was used. We used LFMM (latent factor mixed modeling) to conduct genotype‐by‐environment association tests where source island was a binary environmental variable using the R package LEA. LFMM detects outliers by identifying significant associations between SNP allele frequencies and environmental gradients or phenotypic traits, after accounting for population structure. LEA’s ancestry analysis, sNMF, indicated a most likely K value of 2, which was used for subsequent LFMM analysis. We ran LFMM for 10 repetitions, and p‐values were adjusted using a value of 1. A q‐value cutoff of 0.05 was used for outlier detection. We used pcadapt and LFMM because they account for population structure when identifying outliers. Population structure between the two islands could make it difficult to distinguish between signatures caused by drive vs. selection, but using multiple approaches that account for structure should minimize false‐positive outliers.P. maniculatus Generic Feature Format 3 (GFF3) file. Outlier SNPs that fell within genic regions were separately retained for each analysis. Per‐population allele frequencies for retained outlier SNPs were calculated in VCFtools. VariantEffectPredictor was used to determine which SNPs might have a greater effect on coding, based off the potential amino acid change or introduction of stop or splice elements (Ensembl)  within 2592 unique genes. For LFMM, we retained the top 1% of outliers, which amounted to 2957 SNPs within 1097 unique genes. 803 SNPs (0.2%) were identified as outliers by all three methods. Of these, 709 were associated with annotated genes , as well as non‐synonymous mutations in genes with a known role in olfaction . One gene with a key role in olfaction, Gfy , exhibits mutations of moderate effect on both islands. Such patterns of non‐synonymous mutations that differ among islands may signal the role of different processes on each island, such as relaxed selection on one island  and continued directional selection on Santa Cruz Island; additional studies to evaluate this possibility are warranted. Feral cats were present on both islands for a brief period from the late 1800s until they were largely eliminated by the 1950s  did not respond to olfactory cues of foxes (feces), while  Orrock, . Our resP. maniculatus, especially males  and a missense mutation (in Il12a) that are not present in the Santa Cruz Island population, where high SNV prevalence would favor selection for functional copies of these genes.Pathogens and parasites can have significant effects on host evolution  may be adaptive in settings with greater habitat complexity, such as forests compared with prairies  to contribute to genomic differences between islands. Such demographic changes may sometimes produce genomic signatures similar to those produced by selection, although demographic changes are expected to exert genome‐wide influence rather than acting on specific outliers. We did not estimate demographic parameters such as NItpkb, an IP3 kinase. Itpkb plays an important role in immune cell differentiation  receptor, connected with 8 other genes in this group  that experiences such a wide array of selective pressures. By helping to identify the source of molecular variation underlying behavioral, physiological, and morphological phenotypes of P. maniculatus on islands that differ in predators, pathogens, and population density, we hope to motivate studies that leverage the expansive geographic range of P. maniculatus, the extensive molecular tools available for this genus, and a growing literature on variation and adaptation in this genus  species are likely to be among the most threatened by global climate change ; Formal analysis (supporting); Funding acquisition (lead); Investigation ; Project administration (lead); Visualization ; Writing‐original draft (lead). Linelle Abueg: Data curation ; Formal analysis (lead); Investigation (supporting); Methodology ; Visualization (supporting); Writing‐review & editing . Stephen Gammie: Formal analysis ; Investigation (supporting); Methodology ; Visualization ; Writing‐review & editing . Jason Munshi‐South: Conceptualization ; Formal analysis ; Funding acquisition ; Investigation (supporting); Methodology (lead); Project administration (supporting); Writing‐review & editing .Appendix S1Click here for additional data file.