F ≈ 0.34). We use Optimal Contribution Selection to maximize retention of genetic diversity. However, some treatments add Coancestry Assortative Mating (CAM). This helps to avoid early dilution of immigrant genetic material, maximizing its ability to contribute to genetic diversity in the longer term. After 20 generations, this resulted in considerably increased genetic diversity, with mean coancestries 59% of what random pairing gave. To manage progeny inbreeding, common practice is to reject matings above an upper limit. As a suboptimal rules-based approach, this resulted in 26% decreased genetic diversity and 8% increased inbreeding in the long term, compared with random pairing. In contrast, including mean progeny inbreeding as a continuous variable in the overall objective function decreased final inbreeding by 37% compared with random pairing. Adding some emphasis on selection for a single trait resulted in a similar pattern of effects on coancestry and inbreeding, with 12% higher trait response under CAM. Results indicate the properties of alternative methods, but we encourage users to do their own investigations of particular scenarios, such as including inbreeding depression. Practical implementation of these methods is discussed: they have been widely adopted in domestic animal breeding and are highly flexible to accommodate a wide range of technical and logistical objectives and constraints.This article relates to breeding programs that seek to manage genetic diversity. The method maximizes a multicomponent objective function, applicable across breeding scenarios. However, this paper focuses on breeding decisions following immigration of 10 unrelated individuals into a highly inbred simulated population ( When an ex situ captive population has become highly inbred and suffers low genetic diversity, a small number of “unrelated” and possibly “outbred” individuals from the wild or a captive population can be introduced to help rectify this. There could be more diversity in the small introduced group than in the whole of the inbred population, although this is not required. Random pairing in this newly combined population means that the new genetic material can be quickly diluted by the existing genetic material, with loss of opportunity for the new genetic material to make suitably high contributions in the longer term .MK Selection method (PMx software (MK) to the full population. This solves the immigration problem by helping to extend the period of introgression of the new genetic material into the overall population.However, the rules-based Ranked n method , as commsoftware , prefereThe imported genetic material will have been built up to higher animal numbers without losing its high concentration through frequent crossing into the home population. Overall, this delayed and/or reduced crossing means that higher levels of dissemination of the new genetic material can be reached without resorting to ongoing importations. This is illustrated in In practical implementation of a zoo/conservation breeding program, many component objectives need to be accommodated, in addition to management of genetic diversity. These include management of progeny inbreeding, health and behavior issues, transport and other costs, plus genetic issues such as managing Mendelian conditions, and selection on 1 or more traits. Use of a rules-based approach to selection and mating decisions becomes problematic with such component objectives. For example, the setting of threshold conditions, whereby a mating is rejected because it breaks a declared limit, is suboptimal: matings that are well away from the limit do not benefit from that high margin, whereas matings that just escape rejection are equally accepted. Both must compete equally for the rules on the next issue. The effects of this will be shown strongly in this paper.MK Selection.Such sequential decision-making can lead to suboptimal outcomes, even within the single component of managing genetic diversity. This is illustrated by example in To solve this issue, we use a generalized method for implementing breeding programs that is not rules-based, but integrates all technical and logistical issues in a single objective function to be optimized . These iSelections that give low population mean coancestry.Matings that extend availability of immigrant genetic material.Matings that reduce progeny inbreeding.Inclusion of some emphasis on a competing priority—trait selection.The objectives of this paper are:CAM] within that method).To use the generalized method to solve the immigration problem  in the population . For this we need to exclude living animals that will not be used for breeding, and we need to account for increased contributions from any animals that are to be widely used. This means that in practice, a slightly different version of mean kinship is needed, excluding nonbreeding individuals, which we term MKi, and this is recalculated during analyses that generate new animal pairing recommendations. This is already being done for individual animals at each iteration in commonly used MK Selection methods  among all candidates . The unrelated male’s coancestry with the full selected population is increased due to his own higher contribution. In fact, the mean coancestry for each selected candidate is driven to equality, maximizing retention of genetic diversity.The methods used in this paper follow Optimal Contribution Selection OCS, . Where mOCS by altering contributions from other candidates, to “re-balance” genetic diversity. This means, for example, that a target level of population mean coancestry can be maintained while maximizing other aspects of merit.Where the objective is a balance of genetic diversity and some merit factor(s), such as increased genetic merit or reduced capture and transport costs, then the higher contribution allocated to a high-merit candidate, which reduces genetic diversity, is compensated for under In our implementation, optimal contributions are not calculated, as in CAM, invoked by adding the parameter “variation in the progeny distribution of mean coancestry/kinship” as a component of the overall objective function to be optimized. Appropriate weighting on this component of the objective function promotes solutions that show assortative mating on coancestry/MKi, with high-to-high MKi matings and low-to-low MKi matings among selected candidates, increasing variation in progeny coancestry values, as seen in MKi), and among their descendants in later generations, with increasing crossing into the main population as the number of their descendants increases through selection advantages. Maintaining this higher concentration of immigrant genetic material in the population extends its ability to inject diversity into the main population over generations, as illustrated in This paper introduces CAM is implemented with calculation of an MKi value for each individual candidate, i, against just the selected candidates in the current solution, including weightings according to the number of matings allocated to each candidate. In addition, the CAM component of the objective function is not over-riding. It must compete with all other issues in the objective function. However, in this paper the only competing issue that affects mate allocation is Progeny Inbreeding management, such that interpretations can be made with some clarity.CAM and degrade these favorable outcomes. However, the alternative policy of weighting against mean progeny inbreeding in the overall objective function competes with CAM at the level of individual matings, meaning that key matings of high value to CAM can persist in the face of relatively high progeny inbreeding values. This paper will test whether an appropriate weighting on mean progeny inbreeding can give good outcomes for both genetic diversity and progeny inbreeding.The common practice of rejecting matings that exceed a threshold inbreeding coefficient affects mate allocations, and this can disrupt h2 = 0.25 and fixed and covariable effects set to zero. Coancestry and inbreeding parameters were calculated from the known pedigree, which was also used to calculate BLUP estimates of breeding value (EBV) for the trait. Please see The population simulation program PopSim  was usedPreparation phase, years 2000 to 2021 : This is for generating an initial population at Starting in year 2000 from a population of unrelated individuals, 32 single-mate pairs were used each year, giving population From 2018, all female progeny were bred, to double population size each year, but selecting as of year 2020 to cap populations size at 100 breeding females per year thereafter, resulting in In year 2022, 5 unrelated males plus 5 unrelated females were introduced as candidates for selection. These were individuals of unknown prior pedigree, assumed to be noninbred and unrelated to all individuals in the main population. This was simply achieved by setting the parents of 5 male and 5 female progeny candidates to “unknown” in the simulation, leaving trait values as typical of the main population. The test is from this prepared point, before selection in year 2022.Treatment phase, years 2022 to 2040: Selection and mating each year using the treatments described below.x = d for a target of high diversity alone and x = td for a combined target of favorable genetic change for the trait simulated and high diversity:The treatments were different methods of constructing a mating list to be used at each year in the simulations. Treatments were as follows, where OCSxCAM.FLimit we applied an “F upper limit” as specified under Auto Pair in PMx software  matings among the prevailing candidates. Notice that this “sliding scale” is only used to choose Flimit, which is then applied as a fixed parameter across all prospective matings in the prevailing year of analysis.For software , p. 89, td) was implemented by using an intermediate 45 degrees on the frontier of possible outcomes for each selection round, where 0 degrees represents full emphasis on high genetic gain using EBVs for the simulated trait, and 90 degrees represents full emphasis on high genetic diversity, or low The combined target of favorable genetic change for the trait simulated and high diversity  gave the same results, indicating very good convergence in the mate selection algorithm: These 2 treatments targeted the same optimal outcome , but by following 2 different routes, resulting in the same individuals selected and the same allocations made to give the same results, over 5 replicates × 34 generations = 170 separate analyses per treatment. Average runtime to convergence for an analysis (400 candidates) was 4.8 min.OCSdCAM.F0.01 initially lowered progeny inbreeding compared with OCSdCAM, but followed OCSdR.F1.0 to some extent, as the 0.01 weighting on F detracted sufficiently from emphasis on CAM, and worked against it. Lowering this weighting in OCSdCAM.F0.001 allowed CAM to impact almost fully on coancestry (OCSdCAM), but with sufficient residual impact on mate allocations to lower progeny inbreeding values considerably , through 3.51 (OCStdCAM.F0.1) to 3.54 (OCStdCAM)  . Rates oOCS.CAM leads to a substantial increase in retention of genetic diversity, with only 59% of the Mean Coancestry achieved under OCS alone. An aberrant figure is 132% for mean inbreeding in the top row. This is seen in Simulations were also made for populations that started from higher inbreeding levels  over a fully heritable accumulating component (mean coancestry or MKi). However, by using a relative weighting on Flimit probably eliminates some key half-sib matings that the CAM approach promotes.Using Flimit operates as a hard constraint to exclude matings that might otherwise favor any other factor, not just coancestry. This alters the balance in opportunity and change in these other factors. Given the privilege of hard constraint, Flimit can compete harder against trait emphasis in the objective function, with a lower trait response but lower coancestry seen in 2023/2024  for MKi, could be highly related or lowly related. Applying a small weighting to Flimit, and shift mate allocations in that direction as long as there is sufficiently little impact on overall variance in progeny MKi. This is what has given good outcomes for CAM treatments that put an appropriate weighting on In our immigration scenario, MKi mating pair with a low progeny MKi values will be for somewhat different reasons. The Flimit approach will not exploit this benefit.A low–low MKi pairs would tend to be highly related, especially in smaller populations, and yet be rejected for allocation because of a limit on progeny OCStdCAM.Flimit . This is probably not optimal. For example, using low weightings on progeny inbreeding in early years would help avoid the damaging effect of crossing immigrants into the established population, but after equilibrium of these genetic groups has been approached, a higher weighting against progeny inbreeding might be warranted, as might a lower weighting on occasion . HoweverCAM, as these treatments resulted in considerably lower inbreeding coefficients.As for QTL to have some influence could have some impact on outcomes . In the current case the attribute is genetic diversity. However, the same approach could be used for other attributes, such as notably high genetic merit for valuable traits, or gene edits to be introgressed from a small experimental population into a large breeding population.OCSxCAM method could be seen as a generalized and possibly optimal version of this approach, implemented at the level of individuals rather than at the level of groups. But there is a key difference: The cycling designs aim to avoid progeny inbreeding for a number of generations ahead, whereas CAM aims to preserve “high diversity” genetic material for use over a number of generations ahead.Some breeding designs that aim to reduce inbreeding in the longer-term employ population division, with use of males cycling between subpopulations/families in some pattern . The OCSDifferent implementations of the generalized method employed in this paper relate quite closely to methods that are widely used in conservation and zoo-animal breeding. MK Selection methods all pair the male and female with the lowest MKs, followed by the male and female with the next lowest MKs, etc. This preferentially pairs immigrant animals in the current scenario, and should produce results similar to those from OCS with CAM, but only for cases not involving management of progeny inbreeding. However, these MK Selection methods all involve sequential decision-making, and are therefore generally not optimal, as later decisions can override the validity of decisions fixed earlier on:The Static, Dynamic and Ranked MK Selection, candidates that are not part of the final solution are used in MK evaluations that affect decisions. This is important, as unselected candidates do not contribute to the population and should thus not be involved in MK calculation.Under Static MK Selection, earlier pair selections are accommodated in later pair selection decisions, but candidates that are not part of the final solution still affect MK evaluations used for decisions.Under Dynamic MK Selection, MK calculations for each mating pair relate to different subsets of the full population, where all MK calculations should relate to the final selected animals alone. For example, early-selected pairs do not accommodate later-selected candidates in their final MK evaluations. A simple example with an incorrect outcome is given in MK Selection. However, in many cases that target diversity alone this suboptimality may have little impact.Under Ranked MK methods are implemented in PMx software, and have probably performed well in practice, but it seems that the Simultaneous MK Selection method should give the best outcome among methods in column 1 of MK values for solutions tested in the simulated annealing algorithm are based on all selected individuals, and their extent of use in the solution. Thus, unlike the other MK Selection methods, Simultaneous MK Selection is expected to get the correct result for the example in MK Selection was slightly outperformed by Ranked MK Selection, but that this was likely because the algorithm occasionally converged on a local rather than the global minimum of the evaluated function. As noted in Results, 170 pairs of analyses in the current paper converged to the same results. Management of convergence is described here: https://youtu.be/xs0qTmHHwNA.The Static, Dynamic and Ranked MK implementation minimizes an objective function MK an added advantage over the other MK Selection methods. However, OCSdR.Fγ in this paper, and using γ = 1.0 or 0.1, this resulted in much higher coancestry and progeny inbreeding in the medium- and long term, compared with treatments using CAM, for reasons already noted. This could be rectified by adding CAM to the Simultaneous MK objective function.Simultaneous  MK¯+γF¯ . ManaginF upper limit” in PMx may have detracted from the benefit of MK Selection in some cases, as seen in this paper. On the other hand, an appropriate weighting on progeny PMx zoo breeding software.Mating pair rejection at an “non-selected animals are part of the future gene pool of the population, and they might very well be selected as breeders in a subsequent year. Similarly, animals that are too young to be considered as available breeders certainly can contribute to the future gene pool, but are generally not included in calculation of the optimal set of current breeders identified by OCS methods.” .The simulations modeled discrete generations, for simplicity of illustration. However, under overlapping generations, some “MKi calculations for current candidates, as illustrated at https://youtu.be/JpcLgFaM0AA. This issue is most important for ongoing nonseasonal breeding systems where relatively few matings are made each week or so, with most adults not mated in any 1 wk. This gives very overlapped generations. Simulation of weekly matings over 4 yr in pigs gave a 28% lower increase in mean coancestry when using Committed Matings, compared with standard OCS on available candidates  essentially the same future contribution status as prospective new progeny from new matings, by forcing the “Committed Matings” that generated these current juveniles to be part of the solution. The analysis is expanded to cover not just the current candidates, but the full life-cycle of a complete generation surrounding these candidates, with the analysis solution forced to include all these Committed Matings in addition to the current matings to be decided. This gives more appropriate ndidates .The methods used in this paper have only been implemented at the full simultaneous/portfolio level, as in the bottom right entry in Control selections and mate allocations according to animal locations, quarantine/health risk on migration, animal age profiles, animal size compatibility issues, transport costs, etc.Favor some conservation/restitution of multiple wild subpopulation types, coat colors, etc., possibly following undesirable admixture, while managing overall diversity.Include selection for a Diversity EBV  calibratReduce animal movements when assembling mating groups, as done in poultry.Dictate any desired mean change and/or corrective matings for other factors such as mating behavior, resistance to disease, reversion of unwanted selection while in captivity, and Mendelian genetic conditions. This paper has used a single trait as an example. Wherever possible, any trait selection should be targeted at performance in the wild, for example through use of genomic selection calibrated on phenotypes observed on wild animals in the natural environment.With many such factors to manage it is valuable to explore the range of possible outcomes. This can be implemented by altering constraints and weightings during the analysis, in the light of results reported as the analysis proceeds. This can be seen in It is also possible to make selection and mating decisions among immature juveniles, set up as separate groups within the analysis, not for immediate application, but to help accommodate possible future mating decisions when making current mating decisions, as well as to invoke early culling. These and many other issues are routinely managed using this simultaneous approach in the domestic animal industries.Journal of Heredity online.Supplementary material is available at Supplementary Fig. S1. Results for treatments targeting genetic diversity alone, and for simulation of initial inbred population to Supplementary Fig. S2. Results over years for treatments that place equal emphasis on genetic diversity and genetic gain, and for simulation of initial inbred population to Supplementary Fig. S3. Results for treatments targeting genetic diversity alone, and for simulation of initial inbred population to Supplementary Fig. S4. Results over years for treatments that place equal emphasis on genetic diversity and genetic gain, and for simulation of initial inbred population to MK Selection leads to an incorrect outcome. See text for details.Supplementary Fig. S5. Simple example whereby Ranked OCSdR.F1.0 (upper diagram) and OCSdCAM.F0.001 (lower diagram). See text for details.Supplementary Fig. S6. Pedigree diagrams for treatments Fbest relates to the weighting on progeny inbreeding that gave the best result. For OCS.R.Fbest, best = 0 in all cases, for reasons described in the text.Supplementary Table S1. Summary results across initial population mean inbreeding coefficients. esad027_suppl_Supplementary_MaterialClick here for additional data file.esad027_suppl_Supplementary_Data_S1Click here for additional data file.esad027_suppl_Supplementary_Data_S2Click here for additional data file.


From 2015 to 2021, the impacts ofwinter heating on PM2.5 in Beijing and surrounding cities decreased by 5.9 μg m–3 (41.3%), whereas that in other northern cities onlydecreased by 1.2 μg m–3 (12.9%). This demonstratesthe effectiveness of stricter clean heating policies on PM2.5 in “2 + 26” cities. Overall, clean heating policiescaused the annual PM2.5 in mainland China to reduce by1.9 μg m–3 from 2015 to 2021, potentiallyavoiding 23,556 premature deaths in 2021.Heating is a major source of air pollution. To improveair quality,a range of clean heating policies were implemented in China over thepast decade. Here, we evaluated the impacts of winter heating andclean heating policies on air quality in China using a novel, observation-basedcausal inference approach. During 2015–2021, winter heatingcausally increased annual PM Quasi-natural experimental study reveals significant benefitsof clean heating policies on improving air quality and public healthin China. Xiao et al.2 estimated thatin 2014 heating from coal and biomass burning contributed 13% (5.6μg m–3) and 43% (24.9 μg m–3) of the outdoor and indoor PM2.5 concentrations in urbanChina, respectively. It is found that coal and biomass burning areoften associated with severe pollution episodes during the heatingperiods in northern China.4 Emission reductionsfrom coal and biomass burning for winter heating lead to substantialbenefits for both air quality and public health.8China’s centralizedwinter heating strategy is one of thelargest energy-consumption systems globally. In northern China 1, define9 whichaccelerated the use of centralized/district heating and encouragedthe switching to cleaner fuels including gas and electricity. In 2017,the Chinese central government issued the “Clean Winter HeatingPlan for Northern China (2017–2021)”, which aimed toincrease the share of clean heating in northern China to 50% by 2019and to 70% by 2021 compared to the base scenario in 2016. In addition,the share of clean heating in “2 + 26 cities”  should exceed 90% in urban areas, reaching 100% by 2021.1 In 2018, a new plan (3-year action plan to fightair pollution) was issued, and clean heating in northern China wasamong the major measures listed.10 Allthe above plans led to substantial emission reductions. Meanwhile,the annual average PM2.5 concentrations in Chinese citieswithin the national air quality monitoring network decreased from72 μg m–3 in 2013 to 33 μg m–3 in 2020.11 Such improvement was mainlyattributed to clean air actions including the promotion of clean fuelsin the residential sector.6In 2013, China introduced the “Air PollutionPreventionand Control Action Plan”,12 and Ebenstein et al.13 estimated that winter heating caused the total suspended particles(TSPs) and PM10 just north of the Huai River to increaseby about 247.5 and 41.7 μg m–3, respectively,using Regression Discontinuity (RD) designs. However, the impactsover the cutoff of the discontinuity cannot represent locations farnorth of the Huai River. A difference-in-differences (DID) approachhas also been applied to investigate the impacts of the clean winterheating plan.15 However, the DID model requiresparallel trends between treatment and control groups during the pretreatmentperiod. This means that the temporal trends of air pollutant concentrationsin the control and treatment groups should share similar trends duringnonheating periods. The complex weather effects and differences inemission trends often led to mismatch between two groups during nonheatingperiods. In addition, imprecise testing of the preparallel trend assumptioncan lead to biased estimation and misleading conclusions.16Followingthe 5-year (2017–2021) implementation of cleanheating in northern China, it is imperative to evaluate the effectivenessof the intensive heating-related policies. Chen et al.17 The approach uses a weighted average of the set of control unitsto construct a more suitable comparison unit for the treatment unit. Thus, the difference in the trajectories betweenthe synthetic and real treatment cities after the intervention startedcan be regarded as the causal impacts.A synthetic control approach, a recentimprovement upon the DID,has been applied in social science studies and recognized as the mostimportant innovation in the field of policy evaluation in recent years.18 to improve the originalsynthetic control approach by removing the bias from any imbalancein pretreatment outcomes.More recently, a newAugmented Synthetic Control Method (ASCM)was developedFigure S1), coupling a machine learning-based weather normalization methodwith the ASCM model.19 First, we use arandom forest-based machine learning model20 to decouple the effects of meteorology from observed air pollutantconcentrations in 189 Chinese cities (Table S1).Then the deweathered  concentrations are fed into the ASCM model to isolatethe causal impacts of winter heating on air quality. The improvedapproach provides the causal evidence on the impacts of air qualityinterventions/policies on annual average air pollutant concentrationsin multiple cities under quasi-natural experimental designs, whereasprevious studies focused on short-term interventions, such as theCOVID-19 lockdowns.21Our study estimates the causal impactsof winter heating on airquality in northern China using quasi-natural experimental designs.We follow a two-step approach see S1, coupl22.1Table S1): (1) “2 + 26” cities(24 cities) with >60% heating coverage, (2) “northern”cities (66 cities) with >60% heating coverage, (3) alternativecities(9 cities)—some southern cities with ≤60% heating coverage,(4) nonheating cities (39 cities)—southern cities with no centralheating, and (5) other southern cities (51 cities)—southerncities close to the heating cities. Note that the “northern”cities are the cities in northern China having excluded the “2+ 26” cities. Population-weighted average air pollutant concentrationsin “2 + 26” cities and “northern” citiesare defined as two separate treatment groups whereas those in nonheatingcities are regarded as the control units. The criteria of selectingcontrol units include (1) no municipal central heating, (2) latitudeslower than the designated heating area in China, which is ∼31°N, and (3) less impacts from long-range transported pollution fromthe North China Plain.22 Alternative citiesare similar to control cities in many aspects except that alternativecities are partially impacted by winter heating.The 189 Chinese cities are categorized into five groups (Table S2), respectively. The counterfactual airpollutant concentrations in all of the treatment groups are estimatedfrom a weighted average of air pollutant concentrations in the citiesof the control group using the ASCM. The heating impact is the differencein air pollutant concentrations between each treatment unit and itscorresponding synthetic counterfactual unit.Two additionaltreatment groups are analyzed in this study: northern and mainlandChina. The northern China treatment group is constructed from population-weightedaverage air pollutant concentrations in all cities of “2 +26” cities and “northern” cities whereas themainland China treatment group is constructed from population-weightedaverage air pollutant concentrations in all monitoring cities (since2014) in mainland China. To gain insights into city-level causal impactsof clean heating transition on air quality, 14 major Chinese citiesin northern China were also considered as treatment cities. The periodand heating periods in each year are defined as pretreatment and post-treatmentperiod separated by the start dates of winter heating , sulfur dioxide(SO2) and nitrogen dioxide (NO2), ozone (O3) and 8-h average O3 (O3_8h) from January2014 to December 2021 for the 189 Chinese cities were retrieved fromthe open-access repository: https://quotsoft.net/air/. The hourly total gaseous oxidant(Ox) is calculated by summing up the volumeconcentrations of NO2 and O3. The hourly measurementsof ground-level meteorological conditions  atairports in the cities were obtained from the Integrated Surface Database(ISD) of NOAA  usingthe “worldmet” R package. The hourly data on boundarylayer height, total cloud cover, surface net solar radiation and totalprecipitation in each city were collected from ERA5 reanalysis dataset (ERA5 hourly data on single levels from 1979 to present) fromthe European Centre for Medium-Range Weather Forecasts.The city-level hourly air pollutant concentrationsfor ambient PM2.323 The weather normalized concentrations are alsocalled “deweathered” concentrations in this study. Theexplanatory variables include meteorological variables and time variables.The meteorological variables include both ground-level measurements at airports in each city and ERA5 reanalysis dataset . Time variables were added tothe explanatory variables since they are proxies of time-related variables. Here, we used Unix time as a linear trendcomponent, Gregorian date (day of the year) as a seasonal component,day of the week as a weekly component, and hour of the day as a diel-cyclecomponent. In addition, we added “Lunar date”  as a time variable in the modelto capture emission changes due to Chinese traditional holidays.21A random forest(RF)-based weather normalization approach was used to decouple theeffects of meteorology from observed air pollutant concentrationsin each city. The weather normalization was implemented using the“rmweather” R package developed by Grange and Carslaw.25 a forest of 300 trees, n_tree = 300; and the minimumsize of terminal nodes, min_node_size = 5. We use 70% of the originaldata set to train the model and the remaining 30% to test the model.The RF models for all the studied groups are with low bias (closeto 0) and high correlation coefficients (>0.7), see Table S3. In addition, no obvious differencesin the modelperformance for different groups are observed. For each air pollutantin each city, the deweathered concentration at a particular hour iscalculated by averaging 1,000 predictions from the meteorologicalvariables  randomly resampled from theobservation period (2015–2021).21 The deweathered hourly concentrations are averaged into daily concentrationsfor all the air pollutants except O3_8h. Daily maximumdeweathered O3_8h concentrations are calculated—deweatheredMDA8 O3. The daily PM2.5, CO, SO2, NO2, O3, and MDA8 O3 are averagedinto weekly data for ASCM analysis.The hyperparameters for the RF model areconsistent with previous studies:2.4Supplementary Text S2. Results fromASCM show great improvements in the pretreatment match compared tothat using uniform weights for cities in the control group (Table S4). The Ridge ASCM approach is implementedusing the “augsynth” R package.18 A detailed description of the method can be found in Ben-Michaelet al. (2021).18 The treatment groups andthe control group are shown in the “Quasi-natural experimentaldesigns” section. The Ridge ASCM is used on both observed anddeweathered air pollutant concentrations on a year-by-year basis toisolate the effects from winter heating in each heating period. Toallow for sufficient pretreatment periods in each year, the pretreatmentperiod is from the end date (1 May) of the last heating period tothe start date (range from October–November) of the currentheating period in each city, and the post-treatment period is thecurrent heating period (from Oct–Nov to 30 April next year).The purpose of the synthetic control method (SCM) is to constructan artificial control city  that is comparableto the treatment city. The synthetic city is built from a weightedaverage of a group of control cities by reproducing air pollutantconcentrations in the treatment city before the heating switch-on.The ASCM applied in our study extends from the SCM and uses an outcomemodel (ridge regression model) to estimate the bias due to imperfectpreintervention fit and then directly corrects the original SCM estimateusing the outcome model. Details regarding the ASCM are attached inthe 33.1S2, higher levels of PM2.5, NO2, CO, O3, and Ox concentrations were observed in “2 + 26” citiesthan “northern” cities. In addition, significant reductionsin PM2.5, SO2, NO2, and CO concentrationsfrom 2015 to 2021 were observed across all regions. From 2015 to 2021,the annual mean observed PM2.5 concentration declined by41.5 μg m–3  at a rate of −7.0 μg m–3 yr–1 (R2 = 0.98) in “2 + 26” cities, 21.2 μg m–3  ata rate of −3.4 μg m–3 yr–1 (R2 = 0.96) in “northern”cities, 19.3 μg m–3  at a rate of −3.3 μgm–3 yr–1 (R2 = 0.99) in southern cities and 24.2 μg m–3  ata rate of −4.1 μg m–3 yr–1 (R2 = 0.99) in mainland China. The decreasingrate of annual PM2.5 concentrations in “2 + 26”cities is ∼2.1 times that in “northern” cities.The annual SO2 concentrations in all regions decreasednotably from 2015 to 2018 but less so after 2018  than in “northern”cities  during 2015–2018.Consequently, SO2 concentrations in “2 + 26”cities after 2018 were lower than those in “northern”cities. The annual Ox concentrations inall the regions increased from 2014 to 2017/2018 and then decreasedafterward.As shown in ter 2018 2. A grea2.5 concentrationsdecreased by 34.2 μg m–3  for “2 + 26”cities, 15.6 μg m–3  for “northern” cities,28.7 μg m–3  for northern China (including “2 + 26”cities and “northern” cities), and 19.6 μg m–3  for mainland China.From 2015 to 2021, the deweathered PM3.2S2). The start and end of the seasonal peaksin the deweathered concentrations coincided well with those of theheating periods (Table S2), agreeing with the enhancedemissions during heating periods. We estimated the weather effectson air pollutant concentrations by subtracting deweathered concentrationsfrom observed concentrations. As shown in Figure S3, there is no obvious enhancement of weather effects on PM2.5, SO2, and CO concentrations during the heatingperiods compared to nonheating periods.Clear seasonal cycles were found for both observedand deweathered air pollutant concentrations though observed valuesshowed much greater fluctuation than weather-normalized ones 2 and S2.2.5 concentrations during heating periods decreased by 39.6μg m–3  at a rate of −7.3 μg m–3 yr–1 (R2 = 0.97) in “2 + 26” cities, 16.8 μg m–3  ata rate of −3.1 μg m–3 yr–1 (R2 = 0.97) in “northern”cities, 17.7 μg m–3  at a rate of −3.1 μgm–3 yr–1 (R2 = 0.98) in southern cities, and 21.8 μg m–3  ata rate of −3.9 μg m–3 yr–1 (R2 = 0.98) in mainland China. The decliningrate of PM2.5 concentrations in “2 + 26”cities during heating periods is ∼2.4 times that in “northern”cities. However, such declines could not be entirely attributed toclean winter heating policies because substantial declines were alsoobserved year by year during nonheating periods (2.5 concentrations during nonheating periods are −4.7μg m–3 yr–1 (R2 = 0.96) in “2 + 26” cities, −2.3μg m–3 yr–1 (R2 = 0.94) in “northern” cities, −2.4μg m–3 yr–1 (R2 = 0.97) in southern cities, and −2.9 μgm–3 yr–1 (R2 = 0.96) in mainland China. However, the declining rates ofdeweathered PM2.5 concentrations in “2 + 26”cities, “northern” cities, southern cities, and mainlandChina during heating periods are −2.6, −0.8, −0.7,and −1.0 μg m–3 yr–1 greater than those during nonheating periods, respectively. Sucheffects are more obvious for SO2 and CO . The difference between deweathered Ox during heating and nonheating periods became increasinglysmaller for “2 + 26” cities (from 1.1 to −0.3ppbv) but increasingly larger for “northern” cities(from 0.6 to 1.2 ppbv) from 2015 to 2021, implying a larger declinein atmospheric oxidants during heating period in “2 + 26”cities than in “northern” cities. The interannual variationsof air pollutant concentrations in “2 + 26” cities weredifferent to those in “northern” cities. Therefore,“2 + 26” cities and “northern” citiesshould be studied separately regarding the impacts of winter heatingon air quality.The deweathered PM periods 2. The de3.3Table S1). The synthetic differences  in deweathered air pollutant concentrationsduring nonheating periods are close to 0 (S5 and Table S4), suggesting that the synthetic controlgroup can closely reproduce the deweathered trend  of the treatment group during nonheatingperiods. This is what we expect because there is no heating duringnonheating periods in the treatment groups. However, we observed largedifferences in the synthetic difference of observed concentrationsfor all air pollutants during nonheating periods .The “2 + 26” cities and “northern” citieswere regarded as two “treatment” groups  and the 39 nonheating cities in southern China weretreated as a control group   in “2 + 26” cities, and 7.6 ± 1.8 μgm–3 (decreased from 9.3 μg m–3 in 2015 to 8.1 μg m–3 in 2021) in “northern”cities (Table S6). During 2015–2021, such annualsynthetic difference in deweathered SO2 was 3.9 ±4.0 μg m–3 in “2 + 26” citiesand 5.3 ± 3.4 μg m–3 in “northern”cities, respectively. Note that the annual synthetic difference indeweathered SO2 declined substantially from 2015 to 2018—from9.8 to 0.6 μg m–3 in “2 + 26”cities and from 10.8 to 3.6 μg m–3 in “northerncities”—but showed a small change from 2018 to 2021.The annual synthetic difference in deweathered CO  was 0.17± 0.06 mg m–3 in “2 + 26” citiesand 0.12 ± 0.02 mg m–3 in “northern”cities, respectively. The annual synthetic difference in deweatheredNO2 concentration  was close to zero in both“2 + 26” cities (0.0 ± 1.0 μg m–3) and “northern” cities (−0.3 ± 0.2 μgm–3). And those in deweathered MDA8 O3 and Ox  were 5.3 ± 1.9 μgm–3 and 1.5 ± 0.5 ppbv in “2 + 26”cities, 4.4 ± 0.6 μg m–3 and 1.6 ±0.3 ppbv in “northern” cities, respectively.Overall, the synthetic difference in deweatheredconcentrationsduring heating periods for PM regions 4. During3.42.5  was found in Urumqi (18.8 ± 6.1μg m–3), followed by Xi’an (16.2 ±4.4 μg m–3), Harbin (13.3 ± 5.7 μgm–3), Shijiazhuang (12.0 ± 6.3 μg m–3), Changchun (10.5 ± 2.9 μg m–3), and Zhengzhou (10.3 ± 3.5 μg m–3).Among the 14 cities, the three cities with lowest annual syntheticdifferences in deweathered PM2.5 (Table S7) were Lanzhou (0.7 ± 3.0 μg m–3), Xining (2.3 ± 1.6 μg m–3), and Jinan(6.0 ± 1.8 μg m–3). Except for Lanzhou,Xining, and Jinan, the other cities all showed markable decreases,with percentage decrease greater than −40%, in annual syntheticdifferences in deweathered PM2.5 (2.5 from 2015 to2021 (–3), followed by Beijing , Urumqi , Harbin , and Zhengzhou . In 2021, annualsynthetic differences in deweathered PM2.5 were still ashigh as 8.3 μg m–3 in Xi’an, 6.4 μgm–3 in Urumqi, 6.0 μg m–3 in Changchun, and 5.9 μg m–3 in Harbin.During 2015–2021, the larger annual synthetic difference indeweathered PM2 during 2015–2021 (Table S7) were Taiyuan (14.8 ± 11.4 μg m–3), Harbin (7.7 ± 4.0 μg m–3), Shenyang(7.3 ± 7.7 μg m–3), Hohhot (7.1 ±4.2 μg m–3), and Changchun (6.4 ± 4.3μg m–3). The five cities with largest percentagedecreases in annual synthetic difference in deweathered SO2 from 2015 to 2021 were observed in Zhengzhou, Shijiazhuang, Tianjin,Shenyang, and Beijing (Table S7), withpercentage decrease greater than 93%. In 2021, annual synthetic differencesin deweathered SO2 in all the 14 cities decreased to lessthan 3.5 μg m–3, with highest values in Taiyuan(3.5 μg m–3), Hohhot (2.0 μg m–3), and Lanzhou (1.8 μg m–3).The five cities with the largest annual syntheticdifferences indeweathered SO44.126 which areoften not available especially in scenarios with abrupt emission changes,such as COVID lockdowns20 and extensiveclean winter heating policies (this study). Recently, Shi and Songet al.20 proposed a combined deweatheringand “detrending” approach to disentangle the changesdue to both meteorological and “counterfactual” variations(confounding factors) from the intervention-associated changes inemissions to understand the resulting differences in air pollutantconcentrations. In that study, deweathered changes before and afterthe intervention date in previous years served as “counterfactual”deweathered changes. However, such an approach is not suitable forevaluating the impacts of winter heating on air quality since thecounterfactual air pollutant concentrations varied day by day duringheating periods and for each year. This is because emissions are constantlychanging, making it impossible to “detrend”.To estimate the causal impacts of a policy “intervention”or “treatment”, such as those of winter heating on airquality in northern China, the key is to construct counterfactual trajectories. Traditionally, in air quality interventionstudies, the counterfactual concentrations were simulated by chemicaltransport models (CTMs). However, CTMs rely heavily on timely andaccurate emission inventory,Figure S6). This is because theemission intensity and meteorology are different for the paired groupsduring nonheating periods. Thus, the widely used DID model may leadto biased estimation. RD designs have also been utilized to studyair quality impacts from winter heating switch-on/off28 and China’s Huai River Policy on air pollution.13 However, changes in air quality over the cutoff points cannot representchanges of the whole heating period or the full year or the overallimpacts of winter heating on air quality. The intensity of the heating,and thus emissions, changes on a daily basis. Furthermore, even thedeweathered differences in the RD design cannot be attributed to winterheating due to different average meteorological conditions betweenheating and nonheating cities, partially resulting in the mismatch of air quality trends between the pairedgroups during nonheating periods .DIDis a popular model to evaluate interventions. However, by examiningthe observed and deweathered differences in air pollutant concentrationsbetween heating and nonheating cities, the paired groups  during pretreatment periods did not meet the paralleltrend assumption for the DID model, especially for “2 + 26”cities (Table S4). But this cannot be based on observed concentrations becausesuch synthetic difference is highly variable due to meteorologicalvariability  and the resultinghigh mismatch in concentrations during nonheating periods (Table S5). This is confirmed by the large differencesin the synthetic difference of observed concentrations during nonheatingperiods between paired groups, leading to large bias in the estimatedeffects during heating periods . As we can see from Table S6, conclusions made from ASCM based onobserved concentrations could be completely different to those basedon deweathered ones, and thus, they could be highly misleading forair quality management. We conclude that it is essential to removethe meteorology-induced variations in both the treatment and the controlgroup before applying synthetic control techniques.29The synthetic control approach can minimize the bias fromdifferencesin concentrations between treatment  and control  cities during nonheating periods  haswinter heating just like the treatment (“2 + 26”/“northern”)cities. We applied the same approach to the 39 control cities oneby one. The synthetic differences in each nonheating city and thecontrol cities are shown in S7. We observed a cleardistinction between the synthetic difference in PM2.5,SO2, CO, MDA8 O3, and Ox for the nonheating cities and that for “2 + 26”cities and “northern” cities from in-place placebo tests, demonstrating that our resultsfor “2 + 26” and “northern” cities arenot driven by chance and can be attributed to the winter heating-relatedemissions. In addition, the synthetic differences in NO2 concentrations in “2 + 26” cities and “northern”cities were similar to those in the nonheating cities , indicating that winter heating innorthern China has less of an impact on NO2 than the otherair pollutants, as expected.The synthetic differences in deweathered SO2.5, SO2, and Ox during heating periods than for the nonheating cities . The limitations of using aML-ASCM are discussed in Supplementary Text S2.In conclusion, ourquasi-natural experimental designs with theimproved approach by combining machine learning (ML) and ASCM (ML-ASCM),can reliably attribute the causal effects to “interventions”,such as the winter heating and clean heating policies on air qualityin northern China. No obvious effects (−0.2 ± 0.5 μgm–3) of winter heating on NO2 were observedin northern China. Averaging over mainland China, winter heating contributedto annual PM2.5 by 4.6 ± 1.0 μg m–3, SO2 by 2.3 ± 1.9 μg m–3, CO by 0.07 ± 0.02 mg m–3, MDA8 O3 by 2.6 ± 0.4 μg m–3, and Ox by 0.9 ± 0.2 ppbv. Winter heating contributed15.0 ± 2.2% and 9.5 ± 2.2% to the annual PM2.5 concentrations in northern China and mainland China, respectively.Our estimated contribution from heating to ambient PM2.5 in mainland China in 2015 was 5.7 μg m–3. This is almost the same as that estimated by Yun et al.2 based on emission inventory and CTM (5.6 μgm–3) for 2015.31 However, ourapproach is orders of magnitude more computationally efficient thanCTM. Furthermore, uncertainty concerning the ML-ASCM approach is wellquantified. Our data-driven approach may underestimate the contributionof winter heating to air pollution as there are short-term air qualityinterventions  in winter in certain areas of northernChina compared to those in southern China. However, such impacts shouldcontribute little to annual synthetic differences compared to winterheating, as shown from small annual synthetic differences in deweatheredNO2 concentrations . If other sources or interventionshave substantial impacts on our analysis, we should see non-zero syntheticdifferences in NO2.Overall, winter heating during 2015–2021 causally increasedannual PM2.5 and SO2 concentrations peaked around January/February,which coincided with the normal peak of the Heating Degree Days index,further confirming that the enhancement of heating emissions was causallydriven by increased heating supply. Our study shows that the contributionfrom winter heating to annual average PM2.5 in mainlandChina decreased from 5.7 μg m–3 in 2015 to3.8 μg m–3 in 2021, demonstrating the effectivenessof the clean winter heating policies. In particular, the heating impactson PM2.5 in “2 + 26” cities during 2015–2021decreased by 5.9 μg m–3 (41.3%). The decreasingrate (−2.6 μg m–3 yr–1 from 2015 to 2017) is higher than that estimated by Zhang et al.6 (−1.8 μg m–3 yr–1) from CTM, noting that their estimates are from 2013to 2017.The winter heating driven PM2.5 in “2 + 26” citiesare 2.4 ± 2.3 μg m–3 higher than thosein “northern” cities, though it accounted for comparablefractional contributions to PM2.5 (14.5 ± 1.7% vs14.8 ± 4.2%). The benefit of clean heating on declining PM2.5 during 2015–2021 in “2 + 26” cities,5.9 μg m–3 (41.3%), is much larger than the1.2 μg m–3 (12.9%) in “northern”cities  was rarely usedin“2 + 26” cities, but it was still used in surroundingareas, especially in rural areas. Now, the sulfur content in bituminouscoal is basically lower than 0.5%. The local standard is even lower,and the requirements for sulfur content in briquette coal in Tianjinare <0.4%. Stricter policies in “2 + 26” cities (than“northern” cities) led to lower winter heating emissionsand thus causing a reduced impact of heating emissions on SO2. These results demonstrate clear air quality benefits from the stricterclean heating policies in “2 + 26” cities.1 The lowest heating impacts on PM2.5 during the study period were found in 2018 for “2 + 26”cities and in 2017 for “northern” cities  and mainland China (12.9%) were found in 2020.One of the possible reasons is the increased usage of household fuelfor heating during the national COVID-19 quarantine in 2020.33 On the other hand, the relative contribution of winter heating toPM2.5 increased from 2017 to 2020 in “2 + 26”and “northern” cities, reflecting a rebound in heating-relatedemissions after the gas shortage during 2017–2018, as wellas the effective emission reductions from other sectors, as shownin the changes in counterfactual concentrations . However, the clean heating policies havereduced such inequalities. In 2015, the average concentration of PM2.5 in northern China is ∼40.2% higher than that inthe southern China. In 2021, this difference has reduced to ∼27.2%,partially due to the clean winter heating policies.We show that winterheating has significantly contributed to airquality and health inequalities in northern vs southern China 3 and S7.4.36 the clean winter heatingpolicies accounted for ∼10.8% (from 11.5 to 8.4 μg m–3) and ∼9.7% (from 5.7 to 3.8 μg m–3) of the decline in annual PM2.5 in northern(from 78.3 to 49.6 μg m–3) and mainland China(from 60.0 to 40.4 μg m–3) during 2015–2021 show little decrease from 2015 to2017/2018  and rebounded after 2017/2018  and then reached the maximumin 2020 but dropped thereafter. Overall, the attributable fractionfor premature deaths dropped from ∼1.7% in 2015 to ∼1.5%in 2021 (Supplementary Text S3), correspondingto ∼169,016 and ∼145,460 premature deaths, respectively,indicating that clean heating policies potentially avoided 23,556premature deaths in 2021 (using 2015 as the baseline).Of all the air pollutioncontrol measures,015–20214. Despitto PM2.5 4 and the2 and CO showed little changes after 2018,reaching 1.7 μg m–3 and 0.08 mg m–3 in 2021 in northern China. There is still potential to further reduceprimary emissions from heating in northern China  innorthern China from 2015 to 2021 is much smaller than that for SO2 (−81.1%) . The enhanced impacts from heatingon Ox during heating periods were mainlycaused by heating-related net-production of O3 since syntheticdifference in O3 has increased and that in NO2 has been stable . Such effectsmight be related to increased volatile organic compound (VOC) emissionsfrom natural gas and/or coal combustion during the heating periods36 and/or nonlinear photochemical processes inthe atmosphere.26 In addition, unburnednatural gas in gas distribution systems and residences also contributeto emissions of aromatic VOC species with high hydroxyl radical (•OH) reactivities.37 Increasein O3 contributes to higher atmospheric oxidation capacity,facilitating the formation of secondary PM2.5.43 This is consistent with a recent study demonstratingthat strict VOC control in winter is critical to mitigate PM2.5 more efficiently by reducing Ox simultaneously.38 Future modeling studies are required to furtherinvestigate the impacts of heating emissions on O3 as wellas Ox during heating periods in northernChina.The heatingimpacts on SO(−81.1%) 4. Such aFigure S8). In addition, Jinan accelerated thedevelopment of geothermal resources, in addition to nature gas andelectricity, to solve the problem of urban heating owing to its abundantgeothermal resources. The large declines in heating impacts on annualPM2.5 in all the other northern cities from 2015 to 2021further supported the substantial environmental benefits of cleanheating policies. Switching to greener energy sources, such as heatpumps and geothermal resources, would be important for reducing heating-relatedsecondary pollution  andcontributing to carbon neutrality.39Our results show low heating impacts on air pollutionin Lanzhou,Xining, and Jinan after 2015 5. This iSupplementary Text S3), respectively.In addition, winter heating may also contribute to excess mortalityassociated with O3 exposure. Thus, abating heating-relatedsecondary pollution has further potential health benefits in northernChina. Note that the population-weighted annual average PM2.5  in northern China in 2021 was stillfar above 35 μg m–3, the World Health Organizationinterim target-I (WHO IT-1). In the counterfactual scenario in 2021,population-weighted annual deweathered PM2.5 concentrationsin “2 + 26” cities, as well as northern China did notmeet the WHO IT-1  are always far above the new annual Air QualityGuideline level (PM2.5: 5 μg m–3) recommended by WHO,40 implying thatthe potential to abate heating related PM2.5 and thus improvepublic health remains significant,42 but the effectivenessof future heating policies likely depends on controlling the emissionsof gaseous precursors.Winter heating during 2015–2021 accounted for ∼2.8%and ∼1.6% of premature deaths in northern and mainland China(WHO IT-1 4, implyi


Journal of Personalized Medicine showcases the latest research on genetic counseling and genetic testing in precision medicine.Progress in genomic technologies has spurred innovation in healthcare and medicine, contributing to improved health and well-being. These improvements have been made possible by way of precision medicine, which is defined as an approach that incorporates an individual’s genomic, environmental, and lifestyle information to guide decisions related to their medical management . DeliverThe Special Issue includes 14 articles from four countries including Australia, Switzerland, Spain, and the United States. The articles demonstrate the breadth of genetic counselors’ practice, the variety of specialty areas and testing types, and a range of research participants’ perspectives. Pleasingly, to tackle this topic, researchers are using a mix of methods, with articles in this Special Issue including qualitative, quantitative, systematic review, and mixed-methods studies. Co-design methods are also represented in this special issue ,4. Co-deArticles in the Special Issue highlight the ever-evolving and expanding ways that people are obtaining genetic counseling and genetic testing. Traditionally, these genetic services were only available in specialized tertiary healthcare settings for those with suspected genetic conditions or a family history of such conditions. However, new approaches to accessing genetic counseling and genetic testing are being investigated. One such approach involves offering research participants actionable secondary findings through their participation in a genomic research study . This meWhile the majority of articles focus on genetic healthcare for individuals at risk of cancer, it is worth noting that another area experiencing significant growth in genetics is reproductive genetic carrier screening (RGCS) . RGCS inThe importance of assessing patient-reported outcomes of genetics and genomics in a range of contexts is increasingly being recognized. Three articles in this Special Issue contribute to these efforts ,5,10. ThSimply providing individuals with their genetic test results often does not lead to behavior change. Behavior change techniques and theory provide evidence-based strategies to effectively engage and motivate people. By integrating behavior change techniques and theory into the process of delivering genetic test results, health professionals can enhance individuals’ ability to implement positive behavior changes based on their genetic information. The role of both genetic counselors and oncology nurses in facilitating behavior change was highlighted in this Special Issue ,11. JacoThree papers in the Special Issue highlighted the importance of family communication ,13,14. OJournal of Personalized Medicine, we chose to focus on rigorous research by and for genetic counselors with the support of other relevant health professionals and academic fields. The majority of the articles include genetic counselor researchers leading or co-authoring the research, demonstrating clear evidence of continued growth of researcher capability. As a multi-disciplinary editorial team, we celebrate the diverse research showcased in the Special Issue and call for further development of a rigorous evidence base for genetic counseling and testing. This is particularly important as we enter a time where genomic healthcare and precision medicine expand into specialty areas where genetic and genomic testing is likely to be facilitated by healthcare professionals with limited access to genetic counselor support.In establishing the direction of the Special Issue of the


Ne), genetic diversity, inbreeding, and genetic load. The domestication and improvement processes are believed to be pivotal in diminishing genetic diversity while elevating levels of inbreeding and increasing genetic load. In this study, we performed a whole genome analysis to quantity genetic diversity, inbreeding, and genetic load across seven wild Ovis species and five domesticated sheep breeds. Our research demonstrates that the genetic load and diversity of species in the genus Ovis have no discernible impact on recent Ne, and three species within the subgenus Pachyceros tend to carry a higher genetic load and lower genetic diversity patterns. The results coincide with these species’ dramatic decline in population sizes within the subgenus Pachyceros ~80–250 thousand years ago. European mouflon presented with the lowest Ne, lower genetic diversity, and higher individual inbreeding coefficient but a lower genetic load (missense and LoF). This suggests that the small Ne of European mouflon could reduce harmful mutations compared to other species within the genus Ovis. We showed lower genetic diversity in domesticated sheep than in Asiatic mouflon, but counterintuitive patterns of genetic load, i.e., lower weak genetic load (missense mutation) and no significant difference in strong genetic load (LoF mutation) between domestic sheep and Asiatic mouflon. These findings reveal that the “cost of domestication” during domestication and improvement processes reduced genetic diversity and purified weak genetic load more efficiently than wild species.In theoretical biology, a prevailing hypothesis posits a profound interconnection between effective population size ( Ne) describes the population size caused by genetic drift under the random sampling of genetic variants in a finite population, which plays a pivotal role in molding the dynamics of genetic drift and natural selection within populations [Ne) values correlated with reductions in genetic diversity and increases in mutation burden , respectively [Ne), genetic diversity, and genetic load were changeable, as demonstrated in prior studies among certain species [The effective population size  [Ovis orientalis, AMUF) approximately 11,000 years ago in the Fertile Crescent [Ne) on the genetic diversity and genetic load of wild Ovis species and further investigate the impacts of the domestication and improvement process on the genetic diversity and genetic load on AMUF, three native breeds  and two improved breeds , of which Tibetan sheep is a native breed which has adapted to the Qinghai–Tibetan Plateau environments, and Finnsheep and Shetland sheep are primitive breeds on the periphery of northwest Europe [Wild and domesticated sheep in the genus  America , and then, EMUF) ,13. The Crescent ,15,16, aCrescent  and prodCrescent . In thist Europe .Ovis musimon, 31 Ovis orientalis, 9 Ovis vignei, 8 Ovis ammon, 8 Ovis nivicola, 6 Ovis dalli, and 6 Ovis canadensis); (ii) 32 native individuals ; and (iii) 20 improved samples (20 Chinese Merino)  . https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_002742125.1/, last accessed on 17 June 2023) via BWA v0.7.17-r1188 mem [MarkDuplicates module of Picard v2.18.12  to remove duplicates and detected single nucleotide polymorphisms (SNPs) with the GATK best-practice recommendations [HaplotypeCaller module. Secondly, all GVCF files were merged via the CombineGVCFs module, and the raw SNPs were called using the GenotypeGVCFs module. The biallelic SNPs were selected by the SelectVariants module in GATK and further by the VariantFiltering module of the GATK with the following parameters: “QUAL < 30.0||QD < 2.0||MQ < 40.0||FS > 60.0||SOR > 3.0||MQRankSum < −12.5||ReadPosRankSum < −8.0”. We further trimmed the SNPs using vcftools v0.1.17 [Whole genome sequencing (WGS) data were initially trimmed using Trimmomatic v0.3917  and subsndations . We firsNucleotide diversity (π) was calculated using all sites with VCFtools v0.1.17  for seve6 [FROH was calculated as the percentage of the autosome genome covered by ROH for each sample [ROHiL is the length of iROH of individual i; and Lautosome is the autosomal genome length.The ROH were identified using detectRUNS v0.9.6  with the6 . Then, th sample .FROH=∑iNe) of all seven wild sheep species by SNeP v1.1 [Ne based on the equation c is recombination rate and r2 is the correlation coefficient between pairs of loci [Ne 100 generations ago to scale population dynamics using the different SNP marker distance bins. We estimated the recent effective population size . First, the goat reference genomic sequences (GCA_001704415.2)  were aligned against the sheep genome (GCF_002742125.1)  using minimap2 v2.24-r1122 [Ovis and defined each SNP’s ancestral state as the goats’ allele state. To estimate mutation burden, we first inferred the ancestral allelic state of each SNP following the EPO pipeline with modification  annotation file  with the “-lof” option. Deleterious mutation SNPs were categorized as loss of function (“LOF”) and missense mutations. We first counted the total heterozygotes and derived homozygotes per individual for LoF and missense sites to investigate how mutation load varies across species and populations. We assessed the whole genetic load by calculating the number of derived alleles [Subsequently, we inferred the deleterious mutations of the polarized SNPs dataset using SnpEff v5.1d . The ana alleles . The pot alleles  were estWe collected whole genome resequencing data for 123 individuals from seven wild species and five domesticated sheep breeds . A totalOvis, we observed extensive variation of genetic diversity among species  (FROH (0.0008–0.0059), and F-index (0.8590–0.8980) were observed within three species in subgenus Pachyceros  showed the lowest FROH (0.0008), which was significantly lower than that found for the other two American wild species  (FROH and F-index) of Argali are at an intermediate level between Moufloniforms and Pachyceriforms (FROH (0.0778) among all the wild sheep (FROH (0.0003) and F-index (0.2344) and the highest π (0.00326) and heterozygosity (0.1467)  . More sichyceros , which silcoxon) C. The geeriforms . Howevereriforms A,B,D andld sheep C. In con(0.1467) . The resPachyceros showed similar patterns of genetic load and possessed lower heterozygosity-derived missense and LoF variants than Argali, Urial, and Asiatic mouflon  and similar genetic diversity and individual inbreeding coefficient patterns  showed similar values of genetic load. A comparatively similar pattern was detected in the genetic load across seven species over the last 100 generations. We did not observe any significant correlation between utations . To elimvariants . SimilarTo investigate the effects of the domestication and improvement process on genetic diversity and genetic load, we further calculated the genetic diversity and genetic load of native breeds  and improved breeds (Chinese Merino sheep). p < 0.001) and improved sheep (p < 0.001), and the native sheep possessed higher π than the improved sheep (p < 0.001) (p < 0.01), which showed lower heterozygosity than the improved sheep breeds (p < 0.001). However, the heterozygosity of the improved species was significantly higher than that of the native breeds (p < 0.001) and not considerably different from Asiatic mouflon (p > 0.05) (FROH and F-index were lower in improved breeds than in native breeds (p < 0.05) (p < 0.01) (p < 0.01) (p > 0.05) (We found that the Asiatic mouflon population presented significantly higher nucleotide diversity (π) than both native sheep (< 0.001) A. Additi > 0.05) B. FROH a < 0.05) C,D, and  < 0.05) . We obse < 0.05) . The mut < 0.01) A,C,E. St < 0.01) B,D,F. We < 0.01) C,E, and  > 0.05) D,F. The Ne) cause genetic diversity and deleterious mutations is vital to sheep breeding and wild species conservation [The domestication and improvement processes exert different evolutionary forces on sheep, such as bottleneck, artificial selection, and the relaxation of selective pressures. They may reshape genomic characteristics  ,17,38,39ervation ,42. FROH, F-index, and genetic load  .Nucleotide diversity and heterozygosity are pivotal metrics in assessing the conservation status of wild species, exerting influences on factors such as fitness, inbreeding, and genetic load . We calctic load . Signifitic load . These oicients)  and deleaccounts ,45,46, aears ago , which mPachyceros and other species within Ovis [European mouflon presents a distinctive case, with the lowest genetic diversity, the highest inbreeding coefficient, and a lower genetic load . The reshin Ovis ,44.Ne) is a critical parameter in population genetics and is indirectly related to genetic load via genetic drift [Ne might purge deleterious mutations via purifying selection and genetic drift in Island foxes and kākāpō [The effective population size . This suggests that the small Ne of European mouflon reduced the number of deleterious mutations more efficiently compared to other species within the genus Ovis [Ne among Bighorn sheep, Thinhorn sheep, and Snow sheep  B,D,F, whOvis’s genetic load and diversity have no discernible impact on recent Ne. We also observed lower genetic diversity in domesticated sheep than in Asiatic mouflon and counterintuitive patterns of genetic load, i.e., lower missense mutations in domestic sheep than in Asiatic mouflon, indicating that domestication and improvement processes lower the weak genetic load (missense) more efficiently. Collectively, this study underscores the need for a more nuanced and comprehensive understanding of these multifaceted relationships and emphasizes the critical role of genomics in species conservation and animal breeding.This study illuminates new insights into the impact of effective population size, domestication, and improvement processes on genetic diversity and mutation burden. Our research demonstrates that genus


NKX2-5 gene [Cardiomyopathy entails a broad group of diseases, acquired or genetic, which result in a similar phenotype. Furthermore, cardiomyopathy is a clinically heterogeneous disease with large differences in gender, age of onset, and rate of progression, which are thought to be explained by a complex interplay of genetic susceptibility and environmental factors ,2. The cBecause of the wide variety of conditions that can lead to cardiomyopathy, a systematic approach is needed to facilitate the identification and management of specific cardiomyopathies. The diagnosis, management, and follow-up of patients with cardiomyopathy is a multifactorial process . AlthougMultimodality imaging is an important screening tool for the identification of various cardiomyopathies and is often the first cause of clinical suspicion of a specific etiology, especially when the history and familiarity of the disease is vague . MultimoIn recent decades, our understanding of the genetic alterations that give rise to the different phenotypes of cardiomyopathy has advanced dramatically. More than 1000 mutations have been identified in various genes, and different genetic alterations or combinations of genetic alterations have been shown to cause either HCM, DCM, LVNC, or ACM. Recently, mutations in the same gene have been found in several cardiomyopathies with different and possibly overlapping phenotypes of cardiomyopathy [A wide range of imaging modalities, combined with other clinical information and genetics, is useful in the evaluation of cardiomyopathy. Further evidence is needed to improve the diagnostic capabilities of multimodality and make it part of the standard diagnostic workup.


Our study focused on the history of introgression between domesticated grapes and their European wild relative. We find evidence for a single domestication of grapevine with introgression from the wild relative to wine, but not to table, grapes. These regions are enriched for genes related to aromatic compound synthesis, suggesting that European wild grapes have been an important resource for improving the flavor of wine grapes. However, the beneficial effects of introgression carry a potential cost because introgressed regions have elevated numbers of putatively deleterious variants, which are usually maintained in a heterozygous state for this clonally propagated crop. The identification of introgressed beneficial and deleterious variants is important for genomic breeding of grapevines. Vitis vinifera ssp. sylvestris) and cultivated (V. vinifera ssp. vinifera) grapes. Based on machine learning–based population genetic analyses, we detected evidence for a single domestication of grapevine, followed by continuous gene flow between European wild grapes (EU) and cultivated grapes over the past ~2,000 y, especially from EU to wine grapes. We also inferred that soft-selective sweeps were the dominant signals of artificial selection. Gene pathways associated with the synthesis of aromatic compounds were enriched in regions that were both selected and introgressed, suggesting EU wild grapes were an important resource for improving the flavor of cultivated grapes. Despite the potential benefits of introgression in grape improvement, the introgressed fragments introduced a higher deleterious burden, with most deleterious SNPs and structural variants hidden in a heterozygous state. Cultivated wine grapes have benefited from adaptive introgression with wild grapes, but introgression has also increased the genetic load. In general, our study of beneficial and harmful effects of introgression is critical for genomic breeding of grapevine to take advantage of wild resources.Domesticated grapevines spread to Europe around 3,000 years ago. Previous studies have revealed genomic signals of introgression from wild to cultivated grapes in Europe, but the time, mode, genomic pattern, and biological effects of these introgression events have not been investigated. Here, we studied resequencing data from 345 samples spanning the distributional range of wild ( Introgression describes the movement of alleles, usually via hybridization and backcrossing, from the donor species or population into the recipient species or population , 2. It iOf course, introgression can introduce adaptive variants, too. Adaptive introgression is expected to proceed when alleles from the donor species have a positive effect on the fitness of the recipient species , 10. OneAlthough it is widely appreciated that introgression can contribute to local adaptation, our understanding of the consequences, genetic architecture, and traits affected by adaptive introgression is still quite limited. Here, we are interested in introgression between a crop wild relative (CWR) and a crop because there is a growing recognition that historic introgression events have been sources for key agronomic traits . Introgr1520Vitis vinifera ssp vinifera; hereafter vinifera) and its wild progenitor . Grapevine is among the first domesticated crops; it has been widely cultivated for both fruit (table grapes) and wine since antiquity. Historical and archaeological evidence dated the first domestication of grapevines back to 6,000 to 5,800 BC, when ancestors began to collect and propagate sylvestris in regions that may have included the South Caucasus, the northern Fertile Crescent, and the Levant  from Europe (EU), 36 wild grapes from the Middle East and Caucasus region (ME), and 231 domesticated grapes (V. vinifera ssp. vinifera), along with representatives of North American outgroup species Vitis californica (n = 3) and Muscadinia rotundifolia (n = 3). Wild sylvestris was sampled to cover the predicted distribution area in Europe and the Near East . Among the analyzed sample, 40 accessions were resequenced by this study; the remaining data were published previously . The 345 accessions had average mapping coverages of >20x.To investigate the genetic history of grapevines, including introgression events, we analyzed resequencing data from a total of 345 SI Appendix, Fig. S2; see Material and Methods). In the resulting phylogeny, domesticated grapes clustered together, suggesting a single domestication event, consistent with some but not all previous studies , those around the Caspian Sea (ME1 group), and samples from the Fertile Crescent near the Mediterranean Sea (ME2 group) . Notably, the EU sylvestris formed a distinct group but with minority admixture components shared with wine grapes  . A prince grapes A.SI Appendix, Figs. S6 and S7). These cytoplasmic phylogenies had three notable features: 1) Some domesticated grape varieties clustered with the EU group, but more obviously for wine grapes  than for table grapes (9.3% and 11.8%), thus supporting the admixture pattern based on nuclear genomes; 2) the cytoplasmic genomes of wild ME1 and ME2 groups were indistinguishable from the remainder of domesticated grapes; and 3) wine and table grapes were intercalated in clades, punctuated by short branches between them. These results, especially the grouping of wine grapes with EU sylvestris, suggest the possibility of extensive introgression.To gain more potential insight into the possibility of hybridization and introgression among groups, we constructed network phylogenies based on chloroplast and mitochondrial genome data (STF). Wine and table grapes (0.24 in both) had higher heterozygosity than wild populations . In contrast, the wild EU group had higher pairwise STF values compared to the other groups (from 0.085 to 0.120). Comparable results were obtained in sequence similarity (xyD) analysis among five populations . Notably, ME2 also had the lowest within-group nucleotide diversity (π = 0.0013 on average) at ~2/3 the value of ME1 (0.0023 on average), which had the highest within-group nucleotide diversity .Regarding divergence between groups, the average genome-wide 4 B.P. , consistent with previous studies  sequential Markov coalescent (SMC) analysis and ii) identity-by-descent (IBD) analysis. SMC++ analysis revealed that all populations experienced bottlenecks with continual decreases in population sizes until ~10 studies , 30. We  studies B.sylvestris. To address this issue more directly, we modeled demography using site frequency spectra (2dSFS) and coalescent simulations . We performed demographic estimation of each population independently  and compared six initial models without introgression to find out the most likely relationships between cultivated grapes (table and wine) and ME wild grapes (ME1 or ME2) . Similar to the SMC++ results, fastsimcoal inferred a bottleneck with subsequent recovery for the EU, table, and wine groups. In contrast, it inferred a continuous Ne decrease for both ME1 and ME2.These results might indicate an introgression history between wine grapes and EU SI Appendix, Figs. S19 and S20 and Table S4). Under the best model, fastsimcoal inferred that the EU group diverged ~4 × 104 years ago; suggested that the domestication of table grapes occurred as early as ~1.5 × 104 y ago  compared to table grapes (3.8 × 10−5) .Based on the initial model, we estimated 34 different potential models of gene flow among the table, wine, and EU populations   and domesticated grapes (table and wine) inferred from coalescent models, we also calculated the fb statistic , which wdifolia) D. The redifolia) , 28, 29.sylvestris and domesticated grapes, we also applied the fd statistic based on 50-kb nonoverlapping windows. As with fb, the triplet topologies suggested a higher genome-wide value for the EU-wine comparison (average fd of 0.26) than the EU-table comparison (average fd of 0.06), again suggesting higher levels of introgression between EU sylvestris and wine grapes compared to table grapes . However, the difference in genome-wide fd values between wine and table grapes could, in principle, reflect population parameters like Ne, recombination, and mating systems. We, therefore, used xyD to analyze potentially introgressed regions of wine-EU and table-EU pairs. We measured the difference (xyD (wine_EU)–xyD (table_EU)) of each window based on the triplet topologies between EU groups and domesticated populations . The average value of the difference between xyD (wine_EU) and xyD (table_EU) was −0.02, indicating that genomic sequences were more similar between wine and EU groups than between the table and EU groups. These results support our inference of EU introgression into wine grapes but provide no evidence for EU introgression with table grapes. The fdM statistic further confirmed this result because fdM identified 6,743 windows (50 kb per window) as admixed between EU and wine but only 651 windows between EU and table grapes .Given evidence for introgression between EU P > 0.05). However, we found more genes in the high fd regions (>= 0.4) of wine grapes, suggesting a genetic basis for successful introgression events .We also calculated the number and function of genes in high fd windows. Comparing the wine-EU and table-EU pairs, we found no difference in gene numbers within low fd regions . Strong, shared selection signals were detected on chromosomes 2, 11, 17, and 18. The regions on chromosomes 2 and 17 have been detected as sweep regions previously; they contain, respectively, the sex determination locus  to wine and table grapes separately. Interestingly, the two groups presented very similar selection patterns . We found that the most significant regions were classified as soft-linked selected regions—i.e., >21% of genome sequences in the wine and table group fell into this category (Datasets S1 and S2). The next common classification category was soft-selected regions, containing ~12% of genome sequences in the wine and table populations. Relatively, few regions were recognized as hard-selected or hard-linked selected regions . As expected, nucleotide diversity (π) of the wine population was much lower in these regions .We also used machine learning methods to characterize selective sweep regions, using Shic , 41 . Filet identified a total of 836 regions (10 kb per region) as introgressed from EU sylvestris into wine grapes , representing 1.82% of the total genome. However, only 0.11% of the EU sylvestris genome was inferred to be introgressed from wine grapes, suggesting clear directionality to introgression events. The proportion of introgressed regions also varied among chromosomes; 4.38% of chromosome 18 was inferred to be introgressed from EU wild grapes, followed by chromosome 10 with 3.47%. In contrast, chromosome 2 had the lowest percentage of putatively introgressed regions (0.47%) .To investigate the relationship between selection and introgression, we first sought to refine the boundaries of introgressed regions. Although we detected significant introgression signals with fd, it has substantial limitations: 1) It is difficult to define the threshold of fd to separate introgressed and nonintrogressed regions; 2) fd cannot distinguish the direction of introgression ; 3) relatively large windows are necessary to apply the method as recommended . For exaSTF and xyD were low for these regions. To further validate the reliability of these predictions, we generated the local phylogenetic tree in one of the introgressed regions , including 57 such genes on chromosome 18. We investigated GO functions for the genome-wide set of introgressed-selected genes and focused on three kinds of genes  . Interestingly, many flowering-related genes were selected after introgression, followed by aromatic compound-related genes. Most of the enrichment for GO categories was also related to flavor, including lignin catabolic processes, L-phenylalanine catabolic processes, and cinnamic acid biosynthetic processes . Thus, based on our analyses, adaptive introgression between wine and EU sylvestris appears to have primarily affected flavor-related traits.Altogether, our machine learning analyses identified putatively introgressed regions, putatively selected regions, and their overlaps. Out of 836 introgressed regions, 298 (or 36%) overlapped with predicted selected  regions B. Among SI Appendix, Fig. S27). Furthermore, we found that introgression affects deleterious load. The dSNPs/sSNPs ratio is significantly higher in the introgression region than the nonintrogression region  of deleterious and structural variants (SVs) in introgressed and nonintrogressed regions, using synonymous SNPS (sSNPs) as a presumably neutral control. Compared to sSNPs, the dSNP and SV frequency spectra were significantly skewed toward the left, consistent with purifying selection in wine grapes . We found that the total number of introgressed alleles, both beneficial and deleterious, increased after hybridization until they reached an equilibrium . There was nonetheless an important difference between clonal and outcrossing populations because clonal populations reached equilibrium much more quickly , generally suggesting that clonal populations may be able to capture introgressed alleles more effectively in the short term. We also examined the number of introgressed segregating sites over time, which declined after introgression before reaching an equilibrium. The number of introgressed segregating sites was lower for deleterious than for beneficial alleles at equilibrium, suggesting the effects of positive and negative selection . This decline suggested that some purging and loss of introgressed sites occurred following introgression, but the remaining sites were fixed throughout the population eventually . Interestingly, in the clonal population, the frequency of most alleles was maintained at 50% at equilibrium, reflecting maintenance of the heterozygous state under our simulation conditions (Movies S1 and S2). We also estimated the distribution of fitness effects (DFE) of introgressed alleles. Purifying selection was stronger in clonal than outcrossing populations within 100 generations after admixture , but the strength of selection continued to increase for the outcrossing population to be more prominent at generation 2000 . To sum, these simulations illustrate that the fate of introgressed alleles can differ dramatically between clonal and outcrossing systems. The accelerated dynamics in clonal populations generally suggest that introgression may be a historically important process for improving clonal systems like domesticated grapes.In order to understand the dynamics of introgressed alleles, we conducted forward simulations to investigate introgression from outcrossing populations into clonal and outcrossing populations. We simulated hybridization with both beneficial and deleterious alleles, followed by 10 generations of outbreeding before transitioning to clonality . After this merging, we obtained a total of 532 windows, ranging from 10 to 250 kb. However, 81.6% of putatively introgressed regions were <40 kb in length even after merging. Only 25 of 532 windows were >100 kb, with the largest being ~250 kb . We focused on these larger (>100 kb) regions to assess sets of potentially cointrogressed genes. For example, one 120-kb region on chromosome 14 contained a gene cluster. To better understand the history and genetic architecture of this region, we compared 11 assembled genomes consisting of six wine grapes, two EU wild grapes, and two ME wild grapes. Synteny across this region indicated that part of the region was duplicated in the ME grapes and in one EU grape, representing a region of 260 kb containing 11 genes with the malectin/receptor-like protein kinase domain. In contrast, most wine grapes (except Merlot) lacked all or part of the duplication  (lication A and B. Arabidopsis protein database. One gene in this region was identified as homologous to the Feronia (Fer) gene in Arabidopsis. Fer affects male–female gametophyte interactions during pollen tube reception, but it also influences cell growth as well as biotic and abiotic stress response in Arabidopsis and rice , with two subgroups clustering EU grapes with many wine grapes .We further examined the genes in this region by blasting them to the and rice –48. We pe grapes C. Both tAlthough there was a high probability (65%) of introgression from the EU to the wine population for this region, introgressed alleles were not fixed across wine accessions D. The reV. vinifera is the most valuable horticultural crop in the world  are distinct, in terms of both their divergence from other sylvestris populations and their population histories . The EU group has lower levels of nucleotide diversity than either the ME1 or ME2 groups , suggesting lower historical population sizes. The genetic divergence of EU populations provides an opportunity to detect introgression events.The opportunity for adaptive introgression likely occurs when a crop disperses from its center of domestication and contacts new wild populations or species , 59. TheAlthough a genomic signal of introgression had been detected between EU and wine grapes previously , 29, theSI Appendix, Fig. S24). There is thus ample evidence for introgression from EU sylvestris to wine grapes but surprisingly little corresponding evidence of introgression between EU and table grapes . Given introgression between EU sylvestris and wine grapes, an important question is whether it has had an adaptive basis. Adaptive introgression has played an important role in crop domestication, such as in maize, barley, potato, and rice  from the EU to the wine group, with only 47 segments resulting from introgression in the opposite direction   under selective sweeps , representing a substantial enrichment.sylvestris to table grapes has been relatively low—i.e., roughly 100 times less intense than gene flow to wine grapes. This result is further supported by fd analysis and chloroplast and mitochondria phylogenetic trees , where ~30% of the organelle haplotypes of wine grapes clustered with EU sylvestris, but few table grapes (~10%) clustered with EU sylvestris.We also investigated the timing and intensity of gene flow by constructing and analyzing 34 models with different timing and direction of introgression events. Our best-fit model suggests that introgression has been ongoing for the past ~1.8 thousand years, corresponding roughly to the timing of the dispersal of grapes to Europe, especially from EU wild grapes to wine grapes. These models also infer that gene flow from EU sylvestris has lower nucleotide diversity than other groups, one predicts that genetic drift may lead to a higher deleterious load in EU populations. In accordance with this expectation, we find that introgressed regions in wine grapes generally have higher numbers of putatively deleterious mutations than the genomic background. On average, the frequency of heterozygous harmful SNPs in introgressed regions is three times that of the randomly selected regions of the same length . Interestingly, most beneficial introgressed alleles are fixed in an outcrossing population eventually but not in clonal population (Movies S1 and S2). We conclude that introgression has contributed to the complement of the beneficial and deleterious variants in the heterozygous state within grapes. These beneficial and deleterious variants may prove to be major targets for genomic design of grapevine breeding . Genomic DNA was isolated from leaf samples, paired-end sequencing libraries were constructed, and libraries were sequenced using the Illumina HiSeq 4000 platform with 150-bp paired reads to a target coverage of 30x. The raw sequencing data have been deposited in the Short Read Archive at NCBI under BioProject ID: PRJNA910315 .For full materials and methods, see NA910315  and the NA910315 . We alsoe genome . The GATe genome  (SI AppexyD), and fixation indices (STF) were calculated using the Python script: popgenWindows.py  with 50-kb nonoverlapping windows.Phylogenetic trees were constructed using SNP data by iqtree with 1,000 bootstrap replicates . Vcftoolhttps://github.com/millanek/Dsuite) to calculate Patterson's D (ABBA-BABA) and f4-ratio statistics across populations to evaluate introgression probabilities across the genome  was used to further estimate the population history under complex evolutionary scenarios , 79. We e genome . We alsoe genome .SI Appendix.The forward simulation software SLiM3  was usedAppendix 01 (PDF)Click here for additional data file.Dataset S01 (XLSX)Click here for additional data file.Dataset S02 (XLSX)Click here for additional data file.Dataset S03 (XLSX)Click here for additional data file.Dataset S04 (XLSX)Click here for additional data file.Movie S1.The SFS change of introgressed alleles after hybridization in outcrossing population. The bar located directly above the movie indicates the generations after hybridization.Movie S2.The SFS change of introgressed alleles after hybridization in clonal population. The bar located directly above the movie indicates the generations after hybridizationMovie S3.The DFE change of introgressed alleles after hybridization. The bar located directly above the movie indicates the generations after hybridization.