Data were recorded on GY, HSDWT and DT50FL.Variance components, genotypic coefficient of variation (GCV), and genetic advance (GA) were used to decode the magnitude of genetic variance within and among sets. Genetic usefulness (Up) which depends on mean and variance to score the genetic merits in historically bi-parental populations was applied to groups of breeding lines with mixed parentage. Principal component analysis (PCA) was used to depict contribution of traits to observed variations. GY and DT50FL explained the variance within and between sets respectively. Genotypes were significantly different, although genotype-by-location and set-by-location interaction effects were also prominent. Genetic variance (δ2G) and GCV were high for GY in Prelim2 , HSDWT in Prelim11  and DT50F in Prelim5 . Heritability varied among sets for GY (H = 0.21 to 0.57), HSDWT (H = 0.76 to 0.93) and DT50FL (H = 0.20 to 0.81). GA and percentage GA (GAPM) were high for GY in Prelim2 , HSDWT in Prelim11 , and DT50F in Prelim10 . These sets also registered high values of genetic usefulness, suggesting potential application in non-full sib populations. These approaches can be used during preliminary performance tests to reinforce decisions in extracting promising lines and choose among defined groups of lines.The objective of this study was to determine genetic potentials in eight sets of cowpea lines for grain yield (GY), hundred seed weight (HSDWT) and days to 50% flowering (DT50FL). A total of 614 FThe online version contains supplementary material available at (10.1007/s10681-020-02763-y) Vigna unguiculata (L.) Walp.] is a key legume in the semi-arid regions of Sub-Saharan Africa (SSA) because of its significant contribution to food and nutritional security in the region. The crop provides a cheap source of quality protein and minerals to both rural and urban communities in Africa  of a population for a given quantitative trait is determined by its mean (μ) and expected genetic gain (iHσp) as follows: U = μ + i*H*σp where i is the selection intensity which depends on the selection pressure, σp is the phenotypic standard deviation, and H is the broad sense heritability (Tabanao and Bernardo 2) maize populations  for grain yield and component traits to understand the extent of genetic variability using F2 populations, although the usefulness criterion was not used. Two early generation populations of cowpea were examined based on genetic variance, heritability and genetic advance expressed as a percentage of mean to gauge the degree of genetic variability for grain yield and fodder traits , Shika, Kaduna State, Nigeria (Table 1s that were self-pollinated and between 200 -300 F2 derived lines per set were advanced by single seed descent (SSD) until F5 generation. At this stage lines were planted in a striga infested observation plot and susceptible lines within each set were dropped and resultant sets of F6 genotypes belonging to the different maturity groups were then used in the present study . Included in the study is an extra early duration set of F6 lines referred to as Prelim11 that came from the inter-mating of eight parents. The sets had variable population sizes ranging from 60 to 90 and totaling to 614 genotypes (Table Sets of lines belonging to eight cowpea populations intended for preliminary yield tests (PYT), derived from multiple crosses in the breeding program and targeting different product profiles were used in this study. The crossing structure, pedigrees and agronomic features of parental lines are presented in Supplemental File 1. The creation of the multiple sets of test lines was based on maturity duration meant to suit different agro-ecolozies in cowpea growing corridors of Northern Nigeria. Consequently, the sets were categorized as: extra early and early maturity targeting the short duration production in the Sahelian and Sudan Savanna zones of West Africa, Medium and late maturity groups meant for the Medium and late duration product profiles suitable for the Guinea Savanna zone of West Africa. These maturity groups in addition to striga resistance status of the lines gave rise to the eight sets used in the present study. Smarmily, the sets were created by making several bi-parental crosses using specific elite parents per maturity group; that is, two sets for short duration group: Prelim7 and 10, two sets of medium duration group: Prelim2 and 5, and three sets of late duration group: Prelim2, 3 and 8. The crosses generated FAt both Minjibir and Shika experimental sites, the eight populations were laid out as separate experiments in one mega experimental field per location. Materials were planted on ridges spaced at 0.75 m apart, with 0.2 m hill spacing within row. All experiments consisted of four rows per plot, each measuring 4 m long, arranged as an alpha lattice design, with two replications per experiment and the number of incomplete blocks within a replication varied depending on the number of lines within each of the eight populations Table . The expPlant stand was determined two weeks after seedling emergence and at harvest. Date to 50% flowering (DT50FL) was recorded when 50% of plants in the middle two rows in a plot had flowered and the number of days were computed with reference to the planting date. At maturity, the middle two rows in a plot were harvested, threshed and weighed to obtain grain yield (GY) in grams per plot. The grain yield per plot was then converted to kilograms per hectare (kg/ha), considering the spacing and the plot length. Seed samples were taken from each plot and used to generate the one hundred seed weight (HSDWT) data, measured in grams.The R statistical software, version 3.5.2 (R Core Team (a) Between set Model (b) Within set Model Analyses of variances (ANOVA) were performed in two steps; first with merged data of all sets, across two locations to assess differences between sets, and second for each population independently to assess variances within the sets. The following models were implemented in R using agricolae and lme4 packages  function in R was implemented using lme4 package , was computed from the variance components, expressed on an entry mean basis as:σ2G, σ2G × L and σ2e are variance components for genotype, genotype x location interaction and the error respectively while r and l are number of replications and locations respectively.Broad sense heritability (HA) and genetic advance expressed as a percentage of the mean (GAPM) for each trait was computed according to  of each population was then estimated based on the mean and genetic advances according to  that overlap with high seed weight and earliness could be extracted from these populations Fig. a. For HSP < 0.001) for all the three traits (Table P < 0.001) and consequently, the overall responses of the breeding sets were highly influenced by environment as portrayed by significant Set-by-Location interaction effects for all the three traits (Table P < 0.001) for all the three traits, signaling the apparent difference among genotypes within the sets . The interactions between genotypes and location were also highly significant in all the eight breeding sets for all the three traits  and Prelim10  displayed low magnitude of variance due to genotype-by-Location interaction relative to the genetic variance component  relative to the respective genotypic variances (δ2G), with Prelim 11 (δ2G = 7.137 vs δ2G*L = 0.39) having the highest genetic variance component  and Prelim5 (δ2G = 4.54 vs δ2G*L = 6.56) having high values of genetic variance components relative to the Genotype-by-Location interaction terms  for GY was more dependent on heritability than on genetic variance. When GA for grain yield was expressed as a percentage of population means (GAPM), Prelim2 emerged with the highest percentage of expected genetic advance . This was followed by Prelim7  and Prelim5 . Other intermediate breeding sets were Prelim8  and Prelim1 with GAPM and GA of 14.21% and 175.83Kg/ha respectively while Prelim3 had the lowest GAPM (Table Results for expected genetic advance within the eight breeding sets computed based on broad sense heritability and assuming 10% selection intensity are present in Table Up) values , while Prelim11 (Up = 874.13Kg/ha) and Prelim3 (Up = 1110.93Kg/ha) were the least useful sets for grain yield  and Prelim2 , while Prelim8  registered the lowest percentage value of expected genetic advance  to intermediate (0.49 for Prelim5) and high (0.81 for Prelim10). Consequently, breeding sets that showed high predicted genetic advance were Prelim10 , Prelim1  and Prelim5 . Prelim2 and Prelim11 had intermediate proportion of expected genetic advance while Prelim8 and 3 had the lowest prediction of genetic advance for DT50F (Table Up = 43.44 days), Prelim11 (Up = 43.59 days) and Prelim1 (Up = 45.08 days) with high genetic potential for early flowering  and Prelim3 (UP = 1110 kg/ha) with relatively low GY scores that would be dropped at this stage and lines taken back in the crossing nursery for yield improvement.To further decode the genetic potential of the eight breeding sets, total variance within each set was partitioned to reflect variances attributed to genotype, location and the interaction thereof Table . This alThe present study elucidated the structure and properties of eight sets of cowpea breeding materials that are destined for further testing, revealing the uniqueness of each set and the magnitude of expected gain from selection within each set and the genetic usefulness of each set. The variance component analysis allowed estimation of genetic and phenotypic coefficient of variation, heritability and expected genetic advance. These parameters exposed the genetic potential of eight sets of cowpea breeding lines for GY, HSDWT and DT50FL, revealing sets with high genetic variance and from which superior lines could be extracted to recommend for advanced testing. Estimates of genetic usefulness were generally consistent with results from variance components which provided additional layer of information on the score for genetic merits of the sets. The current study highlights a novel application of usefulness criteria in non-biparental populations with populations defined based on maturity groups. However, comparisons of performance among populations may be limited by the nature of traits used for grouping as in the present case where maturity may be correlated with other traits used for assessing performance. Principal component analysis depicted the relative contributions of the three traits to the variability between and within sets, revealing that more benefit would be obtained by selecting among genotypes within sets based on GY and HSDWT than on DT50FL. These approaches generated relevant information required in making decision for advancement in a conventional breeding program.Supplementary file1 (xlsx 37 kb)Supplementary file2 (DOCX 31 kb)Supplementary file3 Supplementary file4 (DOCX 423 kb)Supplementary file5 (txt 3 kb)Supplementary file6 (DOCX 16 kb)Below is the link to the electronic supplementary material.


Teleemergency doctors support ambulance cars at the emergency site by means of telemedicine. Currently, each district has its own teleemergency doctor office . This paper analyses the advantages and disadvantages of a centralized solution where several teleemergency doctors work in parallel in one office to support the ambulances in more districts.The service of incoming calls from ambulances to the teleemergency doctor office can be modelled as a queuing system. Based on the data of the district of Vorpommern-Greifswald in the Northeast of Germany, we assume that arrivals and services are Markov chains. The model has parallel channels proportionate to the number of teleemergency doctors working simultaneously and the number of calls which one doctor can handle in parallel. We develop a cost function with variable, fixed and step-fixed costs.For the district of Greifswald, the likelihood that an incoming call has to be put on hold because the teleemergency doctor is already fully occupied is negligible. Centralization of several districts with a higher number of ambulances in one teleemergency doctor office will increase the likelihood of overburdening and require more doctors working simultaneously. The cost of the teleemergency doctor office per ambulance serviced strongly declines with the number of districts cooperating.The calculations indicate that centralization is feasible and cost-effective. Other advantages  and disadvantages (lack of knowledge of the location and infrastructure) of centralization are discussed.We recommend centralization of telemedical emergency services. However, the number of districts cooperating in one teleemergency doctor office should not be too high and the distance between the ambulance station and the telemedical station should not be too large. The German emergency medical service is a two-tiered system of paramedics (1–3 years of training) and emergency physicians . While paramedics attend all emergencies, not every emergency requires an emergency physician. To increase flexibility of the emergency medical service, paramedics and emergency physicians are transported independently in separate vehicles to the emergency site in the so-called “rendezvous system” μ rate of service [completed calls per hour]k number of parallel channelsm maximum number of calls one teleemergency doctor can service in paralleln number of teleemergency doctors in the teleemergency doctor officeσ standard deviation of number of completed calls per hourwi probability that i calls are in the systemwi), we calculated the probability that a call cannot be serviced instantly asBased on the probabilities of i calls in the system . The variable costs per emergency will be identical between centralized and decentralized solutions and will therefore not be considered.We follow the cost function provided by Suess & Fleßa .\documeCd total cost of decentralized solution for n districtsCc total cost of centralized solution for n districtsn number of districtsf fixed cost of establishing a teleemergency doctor officeCv variable cost of running one teleemergency doctor for 24/365e number of teleemergency doctors working in parallel in one office, e = f(n)The cost functions assume that fixed and jump-fixed costs are identical for the centralized and decentralized TED offices. For salaries, training of staff, provision of network and depreciation of equipment, it is obvious that costs increase with the number of teleemergency doctors working in parallel in one office (e). There is no reason to assume that these items will be more or less expensive if we have one or more TED working in parallel. The fixed costs like rent, support, quality management as well as administration and supervision might jump as well if a critical number of teleemergency doctors working in parallel in one office is reached. However, the model assumes that this number is not achieved under realistic conditions.e if all ambulances are equipped with telemedical technology and are included into the teleemergency doctor services.The number of teleemergency doctors working simultaneously in one office depends on the number of telemedically equipped ambulances serviced per district. The original setup in Vorpommern-Greifswald is 6 of 26 ambulances, but the function also allows calculating the variable We used the documentation of the telemedicine office of the district of Vorpommern-Greifswald in North-Eastern Germany to estimate the rates and distribution. Digital data from April 1, 2018 to February 29, 2020 was available. Distribution fitting was done with Stat-Fit3. The maximum number of parallel calls serviced by one teleemergency doctor was estimated by interviewing the respective personnel.The cost estimates were taken from the district of Vorpommern-Greifswald  shown inFrom April 1, 2018 to February 29, 2020, the teleemergency doctor office serviced 3019 emergencies, e.g. 4.14 emergency calls per day. 253 emergencies required one minute or less than one minute and were excluded from the service time analysis resulting in 3.96 emergencies per day. Furthermore, emergencies with a total duration of more than 4 h were excluded (three emergencies). For the remaining 2763 values, the service time ranged from 1:03 min to 3:32:19 h, averaging 24:37 min. Standard deviation was 928.19 s.Occasionally, one teleemergency doctor had to service two emergencies at a time. The teleemergency doctors interviewed stated that two would also be an appropriate maximum number of emergencies to be serviced in parallel. Figure For the entire period, the rate of arrivals is λ = 0.18; for the peak period it is λ = 0.27. The service rate is μ = 2.57 for all 3019 calls or μ = 2.36 for 2823 values ≥60 s. Distribution fitting shows that the assumption of a Markov process of arrival cannot be rejected, i.e., it can be assumed that the number of arrivals per hour follows a Poisson distribution. For the service process, the exponential distribution should be theoretically appropriate , 17, but0 = 89.97%, i.e., the likelihood that the teleemergency doctor has no service provision in almost 90% of the time. The probability that a call will have to wait for service because the teleemergency doctor is already fully occupied with two emergencies is 0.03%, i.e., in reality, the teleemergency doctor is rarely overburdened. Even strong increases of the arrival rates will not make a major difference. In order to receive a rate of calls having to wait of 10%, the arrival rate has to be 2.093, i.e., 7.7 times as high as shown in the empirical data from this district. Assuming that the need for tele-emergency doctor services is unlikely to be that high, this calls for centralization and cooperation with other districts.Consequently, the following calculations for the existing teleemergency doctor office in Vorpommern-Greifswald are based on an M/M/2*m; ∞, FIFO model with λ = 0.27 and μ = 2.57, i.e., we concentrate on the peak hours of arrival to calculate the arrival rates and determine the service time under the assumption that it is independent from the time of arrival. With these parameters we calculate wCurrently, six of 26 ambulances are equipped with telemedical technology. The selection was based on an analysis of the frequency of emergency physician services and distances, i.e., we concentrated on very rural areas of the district. Our analysis shows that even if the district of VG had equipped all 26 ambulances with telemedicine technology (λ = 1.17), the likelihood that an incoming call has to wait is approx. 2.01%, i.e., even a very big and rural district can work with one teleemergency doctor.We assume that all districts follow the same distribution of arrivals with identical rates (λ). Similarly, we assume that the service time of all TEDs follows the same distribution with identical rates (μ) regardless of the district and the degree of centralization. Under these conditions we calculate the probability that a teleemergency doctor office is overburdened, i.e., that an incoming call from a paramedic has to wait. Figure As stated before, the probability that the system is overburdened is 0.03% if one TED is responsible for one district with six telemedical ambulances . The probability increases to 0.22% if telemedical emergency services of 12 ambulances  are centralized in one office, 19.81% for 60 ambulances (10 districts) and 56.96% for 90 ambulances (15 districts). With 114 ambulances (19 districts) the likelihood is 100%, i.e., the system fails to cover all incoming calls. If we deploy two TEDs in one office in parallel, the respective likelihoods are smaller at a given number of districts. As the figure shows, two TEDs can cover up to 228 ambulances , but then almost all calls have to wait. The figure also shows the probabilities for three to six TEDs in parallel clearly indicating that the number of TEDs strongly determines the likelihood of overburdening.Assuming a certain maximum probability of overburdening can also indicate the number of required TEDs. Figure If we assume a maximum probability of overburdening of 5%, one TED can handle up to 36, two TEDs up to 96, three TEDs up to 162, four TEDs up to 240, five TEDs up to 324 and 6 TEDs up to 408 ambulances . If we assume a maximum probability of overburdening of 10%, the respective figures for ambulances are 42, 114, 192, 270, 360 and 456. For a probability of 20%, it is 60, 138, 222, 318, 408 and 504. If we want, for instance, to centralize 40 districts with a total of 240 telemedical ambulances and have a maximum likelihood that an incoming call has to wait of 10%, we will need 4 TEDs.Figure For the following analyses a maximum likelihood of overburdening the system of 5% is assumed. If each district runs its own tele-emergency doctor office, the annual cost are about 696,900 € per district. The costs per district are identical irrespective of whether only 6 of 26 (23%) or all ambulances are linked to the office. This is due to the fact that one tele-medicine doctor can service up to 36 ambulances without increasing the likelihood of delaying a call above 5%.If we centralize and combine several districts in one office, the unit cost per district strongly declines. A sharp cost decrease occurs, if the number of TEDs working simultaneously in a centralized station increases. If we assume that each district runs 26 ambulances, 20 districts will require 6 emergency doctors working simultaneously. The annual costs per district are approx. €171,900. This equals 24.6% of the costs of a decentralized solution with the same maximum likelihood of putting an incoming call on hold. However, many districts are not as big as Vorpommern-Greifswald, have fewer ambulances and might not equip all cars with the respective technology. Hence, the financial advantages of centralization will even be higher. Assuming that 23% (6 of 26) of ambulances are included in the teleemergency doctor service, 60 districts will require 6 emergency physicians working simultaneously in one central office. The annual cost per district will be approx. €57,300, i.e., 8.2% of the decentralized solution.The calculations presented in this paper clearly highlight that there are strong advantages to centralization of the services of teleemergency doctors in one office for several districts. In particular, the costs for the entire system and for each district are much lower if we centralize as compared to a decentralized solution. This is mainly due to the fact that the utilization of the TED’s working place is extremely low if one TED is responsible for one district. Even if all 26 paramedic ambulances in the district of Vorpommern-Greifswald were equipped with telemedical technology, the likelihood of being put on hold is approx. 0.5%. Furthermore, even for the district with the highest number of ambulances in the state of Mecklenburg-Western Pomerania (Mecklenburg Lake-District) with 40 cars, one TED can handle all of them and the likelihood that an incoming call has to wait is less than 2%. Setting up the respective office is expensive, but the most important cost component is the salary of the teleemergency doctors. Providing the service 24 h per day for 365 days per year requires at least five doctors (each working 230 days per year á 8 h/day). As a decentralized solution leaves this valuable resource mainly idle waiting for the next call, strongly underlines the needs for centralization. At the same time, the higher workload per TED will lead to fast learning and might decrease the time required per patient .At the same time, the number of TEDs in a centralized solution might even be higher than indicated in this calculation. We used data from the peak hours of the day see Fig.  in orderFurthermore, the quality of teleemergency doctor services is likely to increase after centralization. If more than one TED works in the office, they can support each other in case of an emergency or a very difficult case. It might also be possible to have sub-specializations if more TEDs are deployed in parallel, such as a TED pediatrician. In addition, education of TED might be more efficient if provided for a greater number of staff in one office.Centralization might also lead to a situation in which managing the teleemergency doctor office and the entire rescue system might be easier and more efficient. Physicians are a scarce resource in Germany and emergency doctors with the special training in emergency medicine and telemedicine are particularly scarce . TherefoOn the other hand, a decentralized solution has a number of advantages as well . Most coAnother disadvantage of centralization might be increased stress of TEDs. The likelihood of servicing more than one call at a time increases for the centralized solution while the idle time between calls decreases. Consequently, the workload for TEDs is higher for the centralized solution. This might lead to stress, high fluctuation and difficulty attracting staff. However, long times between services might also lead to boredom and make the job as a TED unattractive. Currently, it cannot be decided which of the arguments will be stronger.Additionally, a decentralized system has the advantage that a complete breakdown of the system will only affect one district. The existential risk  is low, but significantly higher than zero. Thus, central systems are more prone to existential risks. One might overcome this hazard by providing reserve capacities, but this will induce higher costs of the centralized solution.The management of a decentralized system might also be more efficient as the TEDs and paramedics know each other. They develop trust in each other, thereby speeding up the service process . A TED mFinally, Germany is a federal republic and emergency systems are the responsibility of the federal states. This means that Germany has 16 different laws of emergency services with deviating regulations on rights and obligations of paramedics, peculiarities of the rescue chain (e.g. use of helicopter services) and limits of service times. It is difficult for a strongly centralized teleemergency doctor office to know all the regulations for different states.Without a doubt, some of the disadvantages of the centralized system can be healed by providing professional software support for the central TEDs, incl. Detailed online knowledge of weather conditions, regulations, paramedics, etc. Keeping this information updated is quite an effort not included in the calculations given above.The patients or relatives might prefer local solutions. Once the paramedic reaches the patient at the site of emergency, they explain to the patient or relatives that they will now contact the teleemergency doctor. For many people – in particular in rural areas in remote districts – it might make a difference whether the emergency paramedic explains that the TED is situated in the administrative center of this particular district or hundreds of kilometers away in a major city.2 will benefit a lot from a centralized system. Instead of having costs of approx. €696,900 per district, centralizing eight districts in one office will lead to costs per district of approx. €87,100  or some €224,100 . At the same time, the laws and regulations are identical for all districts. It might even be possible to get to know each other in one state. For other states of Germany (e.g. Bavaria with 71 districts and 25 county boroughs), it might be worthwhile to have 2–3 offices in different parts of the state in order to ensure that familiarity is not lost.Table This analysis is subject to a number of limitations. First, our analysis is based on the specific situation in the district of Vorpommern-Greifswald. Our data represents the empirical facts from this district and might have to be adjusted for other districts. For instance, office rent and salaries are lower in the Northeast of Germany than in other districts.Secondly, the queuing model has a number of assumptions which might be challenged. The assumption that arrivals and services are Markov processes is well founded in theory, but we could only prove it for arrivals, not for services. Another distribution of departures might change the respective calculations slightly. However, the financial advantage of centralization demonstrated with this queuing model is so strong that another model might lead to slightly different numerical results without any impact on the evidence provided.Thirdly, our rate of arrival was based on the peak-period from 10.00 a.m. to 11.00 a.m. with a rate of arrivals of λ = 0.27. The logic behind this assumption is that the main objective of our calculation is to avoid overburdening the system. By assuming the maximum arrival rate as the model parameter, we ensure that the risk of letting a call wait is lower for all other periods. However, this might underestimate the advantages of centralization at other times of the day, i.e., centralization might be more economical in times where occupancy of the TEDs is even lower.Fourthly, our model assumes that fixed costs  do not depend on the number of TEDs working in parallel. This is not true for very high numbers of TEDs. As we recommend establishing one central TED office in the state of Mecklenburg- Western Pomerania and 2–3 in states with more inhabitants and emergency cases, this critical point where fixed costs jump will not be reached, but the general problem that what we call “fixed” might not be “totally fixed” should be kept in mind. Otherwise, we might overestimate the advantages of centralization.Fifth, one major assumption of the model is that the traditional emergency service  are separate from the teleemergency doctor service. This was the situation in Vorpommern-Greifswald during the implementation stage of the project. However, once the teleemergency doctor services have been established as a standard, dispatchment and TEDs could be combined in one office and processes could be interrelated. This would lead to synergies with an impact on costs. However, this future event could not be reflected in this paper.Even with these limitations, we can conclude that there is clear evidence that centralization is economically wise. Several authors have shown that teleemergency systems are effective from a medical perspective –24 as we


Bubalus bubalis) is an essential farm animal in tropical and subtropical regions, whose genomic diversity is yet to be fully discovered.The domestic buffalo  from 25 swamp and river buffalo breeds. Both uniparental and biparental markers were investigated to provide the final scenario. The ancestors of swamp and river buffalo diverged ∼0.23 million years ago and then experienced independent demographic histories. They were domesticated in different regions, the swamp buffalo at the border between southwest China and southeast Asia, while the river buffalo in south Asia. The domestic stocks migrated to other regions and further differentiated, as testified by (at least) 2 ancestral components identified in each subspecies. Different signals of selective pressures were also detected in these 2 types of buffalo. The swamp buffalo, historically used as a draft animal, shows selection signatures in genes associated with the nervous system, while in river dairy breeds, genes under selection are related to heat stress and immunity.Our findings substantially expand the catalogue of genetic variants in buffalo and reveal new insights into the evolutionary history and distinct selective pressures in river and swamp buffalo. The domestic buffalo is an important farm animal in tropical and subtropical regions, which can provide milk, meat, and draught power for rice cultivation. The domestic buffalo can be divided into 2 types: swamp and river buffalo. These 2 types show differences concerning body size, outward appearance, biological characteristics, and chromosome karyotype  , 2. The Bubalus arnee) , th, th15], Syncerus caffer, showed a deep division between swamp and river buffaloes; then buffaloes from adjacent geographical regions form distinctive clades  and 2 in river buffalo   trees, principal component analysis (PCA), and ADMIXTURE were used to explore the genetic relationships among the examined 121 buffaloes. The NJ tree, rooted with des Fig. . The samdes Fig. . The PCAdes Fig.  and d shdes Fig.  separatedes Fig.  when whong Array . The PC3TA) Fig. . Within Y-chromosome and mitochondrial DNA (mtDNA) are very useful to investigate genetic origins and ancient migrations . After qWe also inferred the maternal history of buffalo, combining the novel 118 mitogenomes from this study with 107 sequences from previous studies  12]. Sw. Sw12]. As for the Y-chromosome variation of river buffalo, we identified 1 ancestral node (YR) and 2 haplogroups (YR1 and YR2). The YR haplotype was found in 1 Indian, 2 south Asian, and 9 southern Chinese buffaloes. The latter finding was probably due to recent importation of bulls in China through cross-breeding programs , which ieN) of 4 “ancestral” buffalo groups. River and swamp buffaloes underwent 2 apparent expansions and 2 bottlenecks that seemed to overlap with 3 major glacial cycles , which was the most extensive glaciation during the Quaternary Period. The ancestral eN of river buffalo recovered very quickly and reached the highest peak at ∼70 kya after a short bottleneck ∼0.23 Mya. On the contrary, the ancestors of the swamp buffalo went through a long period of population decline until the retreat of the penultimate glaciation , and then, the eN slightly increased starting from ∼0.10 Mya. During the interglacial period, both river and swamp buffalo populations reached another peak and quickly declined during the last glaciation (LG). These results confirmed that the glaciations had a strong effect on the demographic history of swamp buffalo, as already observed by analyzing complete mtDNAs [We used the multiple sequentially Markovian coalescent (MSMC) method to detect the changes in the effective population size , cross-population extended haplotype homozygosity (XP-EHH) to detect genomic regions related to selection in river and swamp buffalo. Two or more methods showed outlier signals  in overlapping regions and were therefore considered as candidate selective regions. Functional gene set enrichment was used to identify KEGG pathways and Gene Ontology (GO) terms that are statistically significantly associated with the genes.We applied 4 methods  in the Jak-STAT signaling pathway, glioma, and pathways associated with cancer   are associated with the immune system response, while MMS22L . Additional signs of selection were identified in genes coding for productive and economically significant traits, such as NUMB [SGMS2 [NRF1) [TNPO3) [In river buffalo, a total of 502 candidate regions under selection containing 569 genes were detected . Candidah cancer . The Jakh cancer . In part22L Fig.  may be auffaloes , as well as NUMB  and SGMSB [SGMS2  associat [TNPO3) .P-value < 0.05) to 4 KEGG pathways   and nervous system  involving 42 genes   . Among tIK2 Fig. –c may beIK2 Fig. .In this study, we analyzed the whole-genome sequence of 121 buffaloes . Our autosomal data reveal an ancient separation between river and swamp buffaloes ∼0.23 Mya, overlapping with previously reported data , 12 and PTPN22 encodes a negative regulator of T-cell receptor (TCR), which has been associated with human autoimmune diseases [PTPN22, AP4B1, BCL2L15, and PHTF1) were those related to bovine leukemia virus [BCL2L15 SNP A226G (acid changed: T76A) is a conserved region and almost fixed with the allele A in river buffalo (P > 0.90)  . In addir system , 33, wasr system  and inhir system . Double-r system , 37. MMSlication . Taking lication , these gHOMER1, GRIK2, DLGAP1, GNG7, and LOC102398542), which plays an important role in behavior, particularly concerning adaptation to stress and fear responses , a member of the class II HDAC proteins, plays a crucial role in neuronal differentiation during cortical development [HOMER1  encodes a member of the homer family of dendritic proteins, involved in several psychiatric disorders, such as schizophrenia [HOMER1 plays an important role in brain development and behavior; HOMER1 knockout mice showed deficits in learning and memorizing, and impairment of pain perception [HOMER1 is also an important scaffold for TRP channels and regulates mechanotransduction in skeletal muscle [HOMER1 showed myopathy with decreased muscle fiber cross-sectional area and reduced skeletal muscle strength generation [BIN1 is associated with Alzheimer disease [BIN1 is also involved in the biogenesis of T-tubules, which are responsible for the plasma membrane invaginations that allow for the excitation-contraction coupling machinery in cardiac and skeletal muscles [GRIK2  encodes for GluR6, a kainite receptor that is highly expressed in the brain and is associated with autosomal recessive mental retardation [GRIK2 knockout mice exhibited reduction in fear memory [GRIK2 was also identified as a candidate selective gene in domestic rabbits [TIAM1, RELN, DISC1, NLGN1, LOC102398542)  . The strc spines . RELN,DId memory . TherefoThis is the first population genetics study on buffalo using a large amount of whole-genome resequencing data. We reconstructed the genetic history and population structure of buffalo from all genetic perspectives using both uniparental and biparental markers. The final scenario indicates that the ancestors of swamp and river buffalo diverged ∼0.23 Mya. The swamp buffalo was then domesticated at the border between southwest China and southeast Asia, while the river buffalo was domesticated in south Asia (between northern India and Pakistan). The domestic herds then migrated to other regions and further differentiated. In fact, we were able to identify 2 ancestral and distinctive components in the current genomes of swamp (south China and southeast Asia components) and river  populations. The different genetic history of these 2 subspecies is also evident by 2 distinct selection patterns identified in their genomes. River buffalo was selected to improve milk production, while the swamp buffalo was mainly raised to provide power for rice cultivation. We were able to intercept distinctive signature of selection in genes associated with nervous and muscle development in swamp buffaloes and in genes related to economic and reproductive traits in river breeds.In summary, this is the first study providing a large amount of genomic data on river and swamp buffaloes, which was needed to describe their current genetic diversities and population structures, to reconstruct their demographic histories, and to scan for distinctive selective pressures.We sampled a total of 98 buffaloes from different locations: China (81), Laos (5), Vietnam (4), and India . Genomic DNA was extracted from ear tissue or blood samples using the standard phenol-chloroform protocol , amplifiAll cleaned reads were aligned to the reference genome (GCA_000471725.1) linked to “24+X+unplaced” pseudo-chromosomes  using BWRRID:SCR_001757) and visualized with FigTree . TreeMix software was used to construct a population-level phylogeny [RRID:SCR_004965) [RRID:SCR_001263) [NJ tree, PCA, and ADMIXTURE methods were used to explore the genetic relationships among buffalo populations . An indihylogeny . The PCA_004965)  and the _001263)  considerRRID:SCR_010228) (RRID:SCR_006086) with the following parameters: -f a -x 123 -p 23 -# 100 -k -m 132 GTRGAMMA. The phylogenies were built using pegas [After removing sites shared with female buffaloes, heterozygous sites, and sites with a genotyping rate <5%, a total of 520 male-specific SNPs were used to construct the phylogenetic tree with BEAST 1.8.0  . A totalng pegas .eN) and divergence times considering 2 samples with average coverage >16× for each population. Autosomal SNPs of each sample were identified using GATK . After removing variant outliers (with extremely low or high coverage), all sites were phased using BEAGLE v. 4.1  [μg = 1.26 × 10−8 [An MSMC was used to infer effective population sizes (_001789) . The sam6 × 10−8 .FST) values [RRID:SCR_001235) [P-value < 0.005. Two or more methods showed significant signals  in overlapping regions and were therefore considered as the candidate regions affected by selection. The KOBAS 3.0 tool  [To detect selective sweeps in swamp and river buffalo, we performed the following comparisons: (i) the swamp buffalo as a reference and the river buffalo as the object population and (ii) the river buffalo as a reference and the swamp buffalo as the object population. A total of 4 methods were used. (i) The fixation index  values  were cal_001235) . (ii) Hi_001235) . We used_001235) , which w_001235) . For the_006350)  was usedGigaScience GigaDB database [Raw sequencing data are available from EBI European Nucleotide Archive Bioproject number PRJNA547460. All supporting genotype data and additional materials are available in the database .Supplementary Note 1. Linking pseudo-chromosomes.Supplementary Note 2. The whole-genome diversity of buffalo.Supplementary Note 3. Population structure analysis.Supplementary Note 4. Y-chromosome and whole mitochondrial genome phylogeny.Supplementary Figure 1. Genome-wide distribution of nucleotide diversity of buffaloes in 6 geographical regions in 50-kb sliding windows with 20-kb steps.Supplementary Figure 2. TreeMix relationships between 25 buffalo breeds.Supplementary Figure 3. PCA of river buffaloes, with PC1 plotted against PC2.Supplementary Figure 4. PCA of swamp buffaloes, with PC1 plotted against PC2.Supplementary Figure 5. Model-based clustering of buffalo using the ADMIXTURE program with K from 2 to 5.Supplementary Figure 6. ML phylogeny of the Y-chromosome using 520 SNPs for 89 buffaloes.Supplementary Figure 7. ML phylogeny of the mitochondrial genome.P-values < 0.05) GO terms associated with immunity. The color intensity is positively correlated to the corrected P-value of the GO term. (b) Amino acid conservation of BCL2L15 (first exon) in mammals. Most amino acids are highly conserved with only a few exceptions, including SNP A226G (acid changed: T76A).Supplementary Figure 8. (a) Hierarchical graph of the over-represented  GO terms associated with the nervous system. The color intensity is positively correlated to the corrected P-values of the GO term. (b) Amino acid conservation of LOC102398542. Nonsynonymous SNP C455T (acid changed: P152L) located in the first exon. Amino acids at this site are highly conserved in other mammals.Supplementary Figure 9. (a) Hierarchical graph of the over-represented  statistics and Supplementary Table 8. Cross-validation (CV) errors for ADMIXTURE ancestry models with K ranging from 2 to 5.Supplementary Table 9. The genotype of 520 SNPs in the Y chromosome.Supplementary Table 10. Mapping results for 118 novel buffalo mitochondrial genomes plus 107 published mitogenomes.FST.Supplementary Table 11. A summary of genes from P-value < 0.5%) in river buffalo.Supplementary Table 12. A summary of genes from XP-CLR (swamp/πriver)  in river buffalo.Supplementary Table 13. A summary of genes from ln ratio in swamp buffalo.Supplementary Table 17. A summary of genes from XP-CLR (river/πswamp)  in swamp buffalo.Supplementary Table 18. A summary of genes from ln ratio, Natural Science Foundation of China (31872317) to C.Z.L., and National Thousand Youth Talents Plan to Y.J.; and the Italian Ministry of Education, University and Research (MIUR), i.e., Dipartimenti di Eccellenza Program (2018–2022)-Dept. of Biology and Biotechnology “L. Spallanzani,” University of Pavia (to A.A.).Y.J. and C.Z.L. conceived and supervised the experiments. T.S. and J.F.SH. performed the majority of the analysis with contributions from Q.M.CH., N.B.CH., and ZH.Q.ZH. T.S. and A.A. wrote and revised the manuscript. R.H.D., H.C.ZH, X.M.ZH, M.R.C., Y.ZH.H., X.Y.L., and H.CH. provided and prepared the samples. All authors reviewed the manuscript and gave final approval for publication.


Genetic characterization of breeds at risk of extinction is crucial in order to develop efficient conservation plans. In this study, for the first time to our knowledge, a set of bovine breeds summarized as Morenas Gallegas , located in the Northwest of Spain, were studied by using genomics tools. Our aim was to analyze their genetic diversity and their genetic relationships with a set of local and cosmopolitan European bovine breeds. The results evidence the specific genetic signature of the Morenas Gallegas bovine breeds. Cachena was the only breed located in the same cluster as the Spanish bovine breeds analyzed, while Caldelá, Frieiresa, Limiá, and Vianesa were clearly separated. The genetic diversity of the set of Morenas Gallegas breeds is intermediate or high, but Frieiresa breed showed the higher value of inbreeding coefficient and requires special attention in the next years. Vianesa shows two different lineages, with an evident Frieiresa component and without it. Our results are the proof of the valuable genetic reservoir that Morenas Gallegas bovine breeds is due to their uniqueness in comparison with other Spanish bovine breeds.A set of five local bovine breeds in danger of extinction named Cachena, Caldelá, Limiá, Frieiresa, and Vianesa and included in the group of Morenas Gallegas are located in the Autonomous Community of Galicia at the Northwest of Spain. Local authorities launched a conservation plan at the end of the 21th century in order to preserve this important genetic reservoir. However, Morenas Gallegas bovine breeds never have been analyzed with genomic tools and this information may be crucial to develop conservation plans. The aim of the study was to analyze their genetic diversity and genetic relationships with a set of local and cosmopolitan European bovine breeds using single nucleotide polymorphisms. Our results show own genetic signatures for the Morenas Gallegas breeds which form a separate cluster when compared to the Spanish breeds analyzed, with the exception of the Cachena breed. The genetic diversity levels of the Morenas Gallegas were intermediate or high, and low inbreeding coefficients can be found except for the Frieiresa breed (11%). Vianesa breed evidenced two lineages depending on the Frieiresa component influence. The Morenas Gallegas bovine breeds group represent an important Spanish bovine genetic reservoir and despite their classification within a single generic group, the five breeds show their own genetic uniqueness. Approximately, 200 breeds have disappeared and up to 30% are endangered . This trThe region of Galicia, in the Northwest of the Iberian Peninsula, hosts a set of local cattle breeds classified as endangered called Morenas Gallegas . This geTo date the Morenas Gallegas breeds have not been included in any DNA study to elucidate the genetic structure and relationships between them and other European cattle breeds. Although, being located in a small geographic area distribution their belonging to the same racial group is unknown. Only a few studies with biochemical markers were carried out in the last decade of the 20th century in which this set of breeds and their relationships were analyzed with other Spanish cattle breeds ,11. The A total of 106 samples belonging to the five bovine breeds called Morenas Gallegas were analyzed in the study . No ethiGenetic diversity parameters, observed and expected heterozygosity, methods of moments inbreeding coefficient, and Principal Component Analysis (PCA) were estimated using PLINK . Trends ROH) was calculated as the proportion of genome in ROH over the overall length of the genome covered by the involved SNPs .Runs of Homozygosity (ROH) were detected as described in Cortes et al. 2019  using PLIndividual ancestry proportions given a k number of ancestral populations were estimated using a maximum likelihood clustering approach implemented in ADMIXTURE . For eachttps://github.com/szpiech/asd). Reynolds’ genetic distances among individuals were estimated using ARLEQUIN software [Pairwise allele sharing distance (ASD) among Morenas Gallegas individuals were estimated using ASD program  to 0.36 , while the He was the same for all the breeds. Frieiresa showed the highest FHOM average (0.11), ranging from 0.02 to 0.06 for the remaining breeds. As expected, Ne decreased as the number of generations increase. Frieiresa breed showed the lower historical Ne values until 27 generation ago while Cachena evidenced the maximum historical Ne values  provided us the effective population size adjusted to the genealogical information and the inbreeding coefficient of those animals born since 2010. Based on genealogical information Frieiresa, Limiá, and Vianesa evidenced similar Ne  while Cachena and Caldelá’s are higher . Furthermore, inbreeding coefficients based on genealogical information for those animals born since 2010 ranged from 2.3% (Cachena) to 3.6% (Limiá and Vianesa) .PCA analysis revealed the genetic identity of the five bovine breeds . The firThe admixture plot with k ranging from 2 to 5 can be seen in Pairwise allele distance sharing (ASD) dendrogram showed a topography in agreement with admixture and PCA results A. The fiIn addition, TREEMIX phylogram without migration events showed a first separation event among Cachena and the remaining bovine breeds B. SubseqPCA and Neighbor-net dendogram of a set of local and cosmopolitan European bovine breeds analyzed jointly with Morenas Gaellgas can be found in ADMIXTURE results are shown in The six migration events evidenced in the TREEMIX phylogram  showed aThe agricultural system in the Northwest of Spain during the 19th century favored fragmented multipurpose bovine populations  adapted to local environmental conditions. Such bovine populations played an important role in familiar economies, but since the 20th century, the arrival of more productive bovine breeds and changes in the production systems led to the decline of those local breeds or even close to their extinction . ConsequIn this study, the Morenas Gallegas bovine breeds were analyzed using genomic tools in order to elucidate their genetic diversity distribution and genetic relationships not only between them but also when comparing with other local and cosmopolitan European bovine breeds for the first time.The results have evidenced the own genetic signatures of the Limiá, Frieiresa, Vianea, and Caldelá breeds, as revealed by the PCA and Reynolds genetic distance dendrograms which clearly separate them with respect to the Iberian Peninsula, Italian, and French bovine breeds analyzed that are grouped in different clusters in agreement with previous results ,24. The Frieiresa and the Mirandesa Portuguese bovine breed share part of their geographical distribution area and it is well known their close genetic relationship , our reWithin-breed genetic diversity plays an important role in the short term for genetic improvement of productive traits and in the long term for conservation purposes. In 1990, local authorities launched a conservation plan of local breeds based on rotational mating systems, the development of a germplasm collection, and several demographic initiatives . All theOur results showed a high level of genetic differentiation among Morenas Gallegas bovine breeds. Caldelá and Cachena breeds evidenced the higher genetic differentiation level as revealed by ADMIXTURE, ASD, and Reynolds genetic distance dendrogram. Caldelá is the unique Morenas Gallegas breed belonging to the Black Iberian lineage origin, one of the three main historical bovine lineages in the Iberian Peninsula  .Furthermore, Cachena farmers traditionally selected animals with big horns, strong forehead, and reduced size , well-adapted to work purposes in stony soils in which it is located, while the remaining Morenas Gallegas breeds did not have such definite selection procedures. Finally, Limiá, Vianesa, and Frieiresa breeds share the same ancestral lineage origin (Cantabrian) that may explain that they are genetically closer than to Cachena and Caldelá breeds, although preserving their genetic uniqueness.It is remarkable the two genetic lineages evidenced within Vianesa animals . One linThe first analysis developed with genomic tools in the Morenas Gallegas, revealed the genetic uniqueness of the five breeds that encompass this racial group. Except Cachena breed, the remaining Morenas Gallegas are clearly separated from the remaining Spanish bovine breeds analyzed. Additionally, the five breeds have evidenced intermediate or high levels of genetic diversity, with Ne values, estimated from genomic tools, higher than 50 in all the breeds. However, the reduced census of the Frieiresa breed and its inbreeding coefficient (11%) suggests paying special attention in this breed. Finally, Vianesa breed shows two different lineages, one with an evident Frieiresa component and the other lacking it. Although the five breeds analyzed are included in a unique racial group, our results evidence clear genetic differences among them.


Tibetan pigs, an excellent species unique to China, face serious threats, which in turn affects the development and utilization of the outstanding advantages of plateau hypoxia adaptability and reduces their genetic diversity. Therefore, a discussion of measures to conserve this genetic resource is necessary. The method, based on genetic diversity, genetic divergence and total genetic contribution rate of population, reflects the priority conservation order and varies depending on the three different purposes of conservation.We analyzed mitochondrial DNA control region (D-loop) variation in 1,201 individuals from nine Tibetan pig populations across five provinces and downloaded 564 mtDNA D-loop sequences from three indigenous pig breeds in Qinghai, Sichuan, and Yunnan Provinces distributed near the Tibetan pigs.We analyzed three different aspects: Changdu Tibetan pigs have the highest genetic diversity, and from the perspective of genetic diversity, the priority conservation is Changdu Tibetan pigs. Hezuo Tibetan pigs have the highest genetic contribution, so the priority conservation is Hezuo Tibetan pigs in the genetic contribution aspect. Rkaze Tibetan pigs were severely affected by indigenous pig breeds, so if considering from the perspective of introgression, the priority conservation is Rkaze Tibetan pigs.This study evaluated genetic diversity and comprehensively assessed conservation priority from three different aspects in nine Tibetan pig populations. Tibetan pigs, an excellent species unique to China, are mainly distributed in the Tibetan Plateau area, which is the largest continuous high altitude ecosystem in the world, with an average altitude of more than 4,000 m . These aBlood samples were collected from a total of 728 Tibetan pigs in seven different populations of Tibetan pigs where the samples had not been adequately collected before: Ganzi (79), Diqing (83), Linzhi (122), Shannan (72), Changdu (73), Hezuo (193), and Qinghai (106). All animal work was conducted according to the Institutional Animal Care and Use Committee (IACUC) and was approved by the Animal Care Committee of Gansu Agricultural University. Considering the limited quantity of testing sequences, an additional dataset of 473 samples was used to complement the data on representative Tibetan pigs to obtain high coverage of Tibetan pigs. A total of 564 mtDNA D-loop sequences from three indigenous pig breeds in Qinghai, Sichuan and Yunnan Provinces, which are located near where Tibetan pigs are distributed, were downloaded from GenBank and used as a reference set in this study. All sample summaries are listed in 2O. The cycling conditions were initial denaturation at 94°C for 5 min, followed by 33 cycles of 94°C for 30 s, 56°C for 30 s and 72°C for 30 s and a final extension for 5 min at 72°C [The D-loop region was amplified directly from the genomic DNA by polymerase chain reaction (PCR) using the primers 5′-CCAAAAACAAAGCAGAGTGTAC-3′ and 5′-CGTTA TGAGCTACCGTTATA-3′. PCR was carried out in 25 μL volumes and contained 12.5 μL of 2× Eco Taq PCR Supermix containing 1 U Taq polymerase, 500 mμ dNTPs, and 10× Taq buffer , 0.1 μg of template DNA, 0.4 μL of each primer at 10 pmol/mL and 11.6 μL of ddHOriginal sequence data were obtained using the ABI PRISM DNA sequencer software. Sequences were edited and aligned using ClustalX 1.81 . DnaSP 5The 431-bp D-loop region of mtDNA was obtained. Haplotype diversity (Hd), nucleotide diversity (Pi) and average number of nucleotide differences (K) are the basic parameters used to assess genetic diversity. PCA is a statistical procedure used to reduce the dimensionality of a dataset by transformation to a new set of variables  to summarize the features of the data . GeneticA total of 38 unique haplotypes were identified in nine Tibetan pig populations. The HT have the largest number of unique haplotypes (15) and the highest ratio of unique haplotypes (55.6%). RT have no unique haplotypes . Thus, tS(k)) and genetic distinctiveness (RD(K)) are combined to obtain the total genetic contribution (RT(K)) of the kth population.To synthesize genetic distinctiveness and diversity, Petit et al  proposedi represents the number of populations with the ith haplotype. Similarly, the rates of contribution attributed to genetic variation (CS(K)) and genetic distinctiveness (CD(k)) to the total genetic contribution rate (CT(k)) of the Kth population with RK haplotypes are obtained using the following formulas:where n represents the total number of populations studied, and nT represents the total number of haplotypes, R̄S = ∑RS(k)/n, R̄D = ∑RD(k)/n, R̄D = ∑RT(k)/n. The total contribution rate CRT(k) can be partitioned into two components, CRS(k), which is the rate of contribution of the kth population due to its own diversity, and CRD(k), the contribution due to its divergence, i.e., CRT(k) = CRS(k)+CRD(k), ∑CRS(k) = 0, ∑CRD(k) = 0, ∑CRT(k) = 0.where RS(k) and RD(K) (and therefore RT(K)) were highest for the HT and lowest for the RT (S(K), CD(K), and CT(k) provide relative overall criteria for setting priorities for conservation of Tibetan pig populations. The highest values of CS(K), CD(K), and CT(K) were in HT ) and genetic distinctiveness (RD(K)) are combined to obtain the total genetic contribution (RT(K)) of the kth population. CRS(K) and CRD(k) reflect the effects of population k in maintaining genetic diversity within the population and genetic divergence between populations, while CRT(k) reflects the combined effect of the two, which is the effect of maintaining the overall allelic richness of the species. The calculation of these three values reflects the measure of the relative contribution rate of the genetic diversity, distinctiveness and total genetic contribution by comparing the degree of difference between the genetic contribution rate of each population and the average genetic contribution among nine populations, based on genetic diversity, genetic divergence and total genetic contribution rate of population.Genetic diversity and genetic distinctiveness highlight two different aspects of genetic diversity; these two aspects are both important for the conservation of species. It is easy to misjudge using only one aspect, which is not conducive to the effective conservation of species. Therefore, considering the role of both aspects is necessary. Based on this, Petit et al  put fortThe order of priority conservation of a population based on genetic diversity and genetic distinctive were discussed respectively earlier in this study and did not take into account the combined effect of both aspects. Therefore, to fully consider the two factors of genetic diversity and genetic distinctiveness, the order of priority conservation of population was evaluated based on the model of genetic contribution rate.RD(k) is generally consistent with the CRT(k) curve, while the CRS(k) is similar to the previous two curves, but the values are quite different. This indicates that the genetic distinctiveness has a greater contribution rate to the overall genetic effect, while the contribution rate of the genetic diversity effect is smaller; that is, the genetic distinctiveness has the greatest influence and the most sensitive effect on the overall genetic effect. Ranking of nine populations prioritized based on overall genetic effects, the overall genetic effect of HT, GT, and DT was positive, indicating that the genetic contribution of Tibetan pigs in these populations is higher than the average genetic contribution, and its presence increases the overall allele richness of Tibetan pigs and is worthy of conservation. The values in HT were higher than those of other populations, indicating that the HT population could contribute the most to improving the genetic variation and haplotype richness of Tibetan pigs, followed by the GT and DT populations.As shown in RS(k), CRD(k), or CRT(k) values, the pre-existing population conservation value is large; in particular, those populations with positive CRS(k), CRS(k), or CRT(k) should be given priority protection.In summary, based on the CShared haplotypes were identified in 92.7% of Tibetan pigs on average, indicating that Tibetan pigs are severely introgressed by the indigenous pig breeds, which are distributed in the surrounding areas. In total, 38.3% (23/60) of haplotypes that were shared with indigenous pigs were identified in 90.6%  of Tibetan pigs. Shared haplotypes between indigenous and Tibetan pigs showed an unequal global distribution . The frePriority conservation order varies depending on the purpose of conservation. According to the principle of genetic diversity, the first priority in conservation is CT. According to the principle of genetic contribution rate, the first priority in conservation is HT. Tibetan pigs of different populations are threatened by indigenous pig breeds to varying degrees, RT is more affected by indigenous breeds, and the first priority in conservation is RT in introgression.


Colocasia esculenta) is an important root and tuber crop cultivated worldwide. There are two main types of taro that vary in morphology of corm and cormel, ‘dasheen’ and ‘eddoe’. The eddoe type (Colocasia esculenta var. antiquorium) is predominantly distributed throughout China. Characterizing the genetic diversity present in the germplasm bank of taro is fundamental to better manage, conserve and utilize the genetic resources of this species. In this study, the genetic diversity of 234 taro accessions from 16 provinces of China was assessed using 132,869 single nucleotide polymorphism (SNP) markers identified by specific length amplified fragment-sequencing (SLAF-seq). Population structure and principal component analysis permitted the accessions to be categorized into eight groups. The genetic diversity and population differentiation of the eight groups were evaluated using the characterized SNPs. Analysis of molecular variance showed that the variation among eight inferred groups was higher than that within groups, while a relatively small variance was found among the two morphological types and 16 collection regions. Further, a core germplasm set comprising 41 taro accessions that maintained the genetic diversity of the entire collection was developed based on the genotype. This research is expected to be valuable for genetic characterization, germplasm conservation, and breeding of taro.Taro ( Colocasia esculenta), a member of the Araceae family, monocotyledons, is one of the most important root and tuber crops. It is consumed as a staple food and vegetable by more than 500 million people in regions of the Asian-Pacific, Africa, and America (Taro ( America . The und America .Colocasia esculenta var. antiquorium). Cytological studies using flow cytometry analysis showed that taro has diploid (2 ×  = 2n = 28) as well as triploid (3 ×  = 3n = 42) cytotypes. Taro is believed to have been domesticated, possibly independently, across an area that ranges from northeastern India to the province of Yunnan in China, to New Guinea . China hAs vegetative propagation and fixation of somatic mutations are common in taro, cultivars can be quite distinct in morphotype even within the same genetic background . FurtherIn the absence of a reference genome, various kinds of next-generation sequencing strategies such as genotype by sequencing (GBS)  and RNA-A core germplasm collection is defined as a limited set of accessions representing the genetic diversity of a crop species . As taroDespite phenotypic diversity, there is little information on the genetic diversity within the Chinese taro collection based on genome-wide molecular characterization. Exploring the genetic diversity contained in the taro collection will contribute to the enhanced genetic characterization, germplasm resources conservation and introduction breeding of this species. Therefore, this study aims to characterize the population structure and genetic diversity of 234 taro accessions comprising the collections maintained in the National Germplasm Repository for Aquatic Vegetable of China, and to develop a core collection maintaining most of the genetic diversity using the genome-wide SNP identified by the SLAF-seq.A collection of 234 taro accessions from 16 different provinces of China, including 16 multi-corm type and 218 multi-cormel type , were seThe SLAF library was perpared based on the procedures described by http://www.ncbi.nlm.nih.gov/sra/) under the BioProject ID PRJNA644023. Sequences of sample with the most tags were used as the reference. BWA 0.7.17  content and the Q30 value . The cleA 0.7.17  was usedA 0.7.17  and SAMtA 0.7.17  were useA 0.7.17 . After tA 0.7.17  were idep value of HWE less than 0.0001 and LD decay with r2 great than 0.1 were filtered out through PLINK v1.90b4 , linkage disequilibrium (LD) and outlier loci. SNPs with  v1.90b4 , and out v1.90b4  and Losi v1.90b4  with ten v1.90b4 .https://itol.embl.de/)  .π) were estimated using the Populations program in Stacks pipeline (Nei’s genetic diversity index (H), polymorphic information content (PIC), MAF, and observed heterozygosity (Ho) of the population and inferred groups were calculated with the popgen function in the R package “snpReady” . The anapipeline , and onepipeline  was emplA total of 234 taro accessions, including 218 of multi-cormel type and 16 of multi-corm type from 16 provinces of China , were sup value less than 0.0001 and LD with r2 threshold of 0.1. BayeScan detected 13 outlier SNPs which were the subset of the 7,582 ones that identified by Lositan. After screening by these filters, 17,923 SNPs were set to ADMIXTURE for population structure analysis. The estimated ancestry fractions of the 234 accessions for different values of K ranged from 1 to 10 were determine d by ADMIXTURE  clearly. From K = 6 on, the genetic substructure between TMC and TNC progressively takes shape. The cross-validation error (CV) decreased to the lowest value at K = 8  from discriminant analysis of principal components (DAPC) was performed to determine the appropriate number of clusters. The BIC showed rapid decline from 1 to 8 , indicatπ), Nei’s genetic diversity index (H), observed heterozigosity, PIC, and MAF were estimated separately. The value of π and Nei’s genetic diversity index indicated that accessions in group V exhibited the highest genetic variation, whereas the accessions in group IV and VIII showed the lowest genetic variation  was performed to measure genetic differentiation among the eight inferred groups. The results revealed the highest differentiation between group IV and VII, with an Fst of 0.298. On the other hand, the lowest Fst of 0.100 were found between group VI and II, and group VI and VII, which indicated that they were most closely related . We speculate that the multi-corm type evolved from the multi-cormel taro type during the process of domestication and evolution  based on the SNPs revealed that the 234 taro accessions of China can be classified into eight groups. The majority of total variance was attributed to genetic differentiation among the eight inferred groups but not to the collected regions. Furthermore, a core germplasm set comprising 41 taro accessions were identified which highly maintained the overall of genetic diversity of entire collection to a great extent. In addition to significantly expanding the genomics and genetics information on taro, these findings will prove highly valuable for genetic characterization, germplasm resources conservation and breeding of taro.10.7717/peerj.10485/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.10485/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.10485/supp-3Supplemental Information 3TMC: the multi-corm type, TNC: the multi-cormel type; Inferred group: group revealed by ADMIXTURE; NA: not accessible. T indicates accession belonging to the core germplasm.Click here for additional data file.10.7717/peerj.10485/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.10485/supp-5Supplemental Information 5Click here for additional data file.


The proportions of educational variation explained by genetic and shared environmental factors did not differ between Europe, North America and Australia, and East Asia. When restricted to twins 30 years or older to confirm finalized education, the heritability was higher in the older cohorts born in 1900–1949  than in the later cohorts born in 1950–1989 , with a corresponding lower influence of common environmental factors . In conclusion, both genetic and environmental factors shared by co-twins have an important influence on individual differences in educational attainment. The effect of genetic factors on educational attainment has decreased from the cohorts born before to those born after the 1950s.We investigated the heritability of educational attainment and how it differed between birth cohorts and cultural–geographic regions. A classical twin design was applied to pooled data from 28 cohorts representing 16 countries and including 193,518 twins with information on educational attainment at 25 years of age or older. Genetic factors explained the major part of individual differences in educational attainment (heritability: a The education system is also the main institution reproducing social stratification3. Accordingly, the understanding of how genetic and environmental factors underlie the individual differences in educational attainment has not only scientific but also important societal implications. A meta-analysis of twin studies conducted in 10 countries found that genetic and environmental factors shared by co-twins explain about equal shares (around 40%) of the inter-individual differences in educational level4. The proportion of contribution by shared environment was substantially higher than found in a meta-analysis of performance in school subjects5. The shared environmental variation in intelligence is still substantial in childhood, but diminishes until adulthood6. Thus, in addition to cognitive skills, childhood family and other environmental factors seem to affect educational level. A previous study demonstrated the importance of social inheritance by showing that good parental socio-economic position is associated with academic success even after adjusting for cognitive ability7.Education is one of the most important intellectual, social and material resources in modern societies. It typically precedes occupation, strongly affects occupation-based social position and lifetime income5. For example, in more meritocratic and open societies, the role of individual differences derived from genetic factors may be more pronounced, whereas in socially more closed societies with less social mobility, childhood social factors may be more important. Information on how the macro-environment modifies the role of genetic and environmental factors behind inter-individual variation in education is important for the understanding of how socially and politically established barriers in the society modify the realization of genetic potential for educational outcomes. An early Norwegian twin study found that the role of genetic factors on education increased after World War II when the educational system was reformed8, and a study in Spain showed similar results on the increasing effect of genetic factors on education after an educational reform promoting equality in educational opportunities9. This is further supported by a re-analysis of the published heritability estimates of educational attainment4 finding that high intergenerational mobility in a society was associated with the higher proportion of genetic and the lower proportion of shared environmental factors of the total variation of educational attainment10. These twin study results correspond well with the findings that the effect of parental socioeconomic position on offspring education has diminished over the recent decades in Europe11. The previous literature thus suggests that a more open society, in which educational reforms make the education system more open and meritocratic, will strengthen the influence of genetic factors and decrease the influence of family background on educational attainment. An Estonian study supported this conclusion by showing that previously identified genetic polymorphisms associated with educational attainment explained a larger proportion of variation in educational attainment in the cohorts who received their education after rather than before the independence of Estonia from the Soviet Union12.The role of genetic and environmental factors in educational differences can also vary between societies and changes over time because of differences between educational systems and societies in general9, we expect that the role of genetic factors in educational variation has increased over the recent decades, especially after World War II when the educational system was expanded in many countries. We further expect that the increasing general level of education may increase the proportion of genetic variation since the decision for higher education is done later in adolescence when the children have become more independent from their parents13. Societies also differ whether education is free for students or whether tuitions are needed which may affect how parental financial resources affect possibilities for higher education. Thus, we expect to see macro-level differences between countries, which may reflect access to higher education. We classified the countries in accordance with the typology of welfare regimes presented by Esping-Andersen14; European countries follow the social democratic, conservative or mixed societal models whereas the USA and Australia follow the liberal model. The East Asian countries were difficult to classify by this typology, but they share many cultural similarities. Finally, we will analyze gender differences in the proportions of genetic and environmental variation in educational attainment, and whether they have changed over the twentieth century when opportunities for higher education have equalized for men and women.In this study, we analyzed the contribution of genetic and environmental factors to educational attainment in a large multinational twin database. Based on previous twin studiesTable 2 = 0.43; 95% CI 0.41–0.44), but environmental factors shared by co-twins were also substantial . When stratified by gender, we found that the proportion of variation in educational attainment attributable to genetic factors was larger in men than in women  – and correspondingly the proportion explained by shared environmental factors smaller . The highest heritability was found in the latest birth cohort born in 1980–1989 . This result remained when we restricted the analyses only to those of 30 years of age or older to confirm that that this was not affected by unfinished education in this birth cohort  as in North America and Australia . In East Asia, the heritability point estimate was lower but did not differ statistically significantly from the other cultural–geographic regions either ; because of the smaller sample size, the confidence interval was wide. When we stratified the results by gender, there were no differences between the cultural–geographic regions  and the role of shared environment increased over the birth cohorts . When we stratified these analyses into Europe and North America and Australia, we saw a similar decrease in the proportion of additive genetic factors and increase in the proportion of shared environmental factors in North America and Australia. In the gender-stratified analyses, women showed only minor decrease in the role of additive genetic factors. Thus, the difference between men and women in the proportion of variation explained by additive genetic factors was greater in the cohorts born in 1900–1949 than in 1950–1989 .Table Finally, we applied the genetic models separately to each twin cohort to explore whether there was a consistent, regular pattern within the countries not captured by our classification of the three cultural–geographic regions  studies, including 1.1 million participants, identified 1271 genome-wide significant loci associated with education, but even together, they explained only 3.86% of the variation of education17. When using all common genetic variants (known as SNP-heritability), 12% of educational variation could be explained18. This lower proportion of genetic variation estimated by measured genetic polymorphisms than in our estimates based on twin design reflects the multifactorial and polygenic background of educational attainment; the current GWA technology does not capture all existing genetic variation.In this dataset, pooling twin cohorts from 16 countries, we found that genetic factors explained 43% of the individual differences in educational attainment. The role of genetic factors affecting differences in educational attainment is not surprising. Intelligence, having an important effect on educational attainment, shows a substantial influence of genetic factors6 or personality factors are small or not existing in adulthood19. Further, studies have not shown shared environmental effects for other socio-economic traits, such as income20. It is, however, noteworthy that disentangling genetic and shared environmental effects needs considerable statistical power21, which may have led to ignoring shared environmental effects in some smaller studies. A large Swedish twin study of young adult men found that shared environmental factors explained 20% of variation of intelligence22. Furthermore, differences in intelligence in childhood, influenced by greater shared environmental effect, may be more important for education than differences in adult intelligence. However, we found the substantial shared environmental variance components in nearly all twin cohorts, which were larger than generally found for intelligence, even in childhood6. These estimates were also higher than found in previous studies for academic performance at school5. Previous results have shown that parental socio-economic position affects offspring education, even after adjusting the results for cognitive ability7. A recent study demonstrated that the alleles of the educational polygenic score of parents that were not transmitted to their offspring were associated with school performance at age 1723 as well as the educational years in adulthood in offspring24. This genetic nurture effect was replicated in a Dutch study, where in adults, both transmitted and non-transmitted alleles included in educational polygenic scores were associated with offspring education25. In a UK study, polygenic education scores predicted school performance in adolescence better between individuals from different families than within dizygotic (DZ) co-twins, suggesting that a part of the genetic effect on education was because of family environment created by parental genotype26. Together, these results support the conclusion that even when family environment has a limited effect on many psychological and social outcomes27, it may be important for education. However, more research is needed to reveal the mechanisms between family background and education.In addition to genetic factors, our results show the importance of shared environmental factors explaining 31% of educational variation. This variation can include the effect of home environment but also the influence of school, common peers and other environmental factors shared by co-twins. This result is interesting since many previous studies have reported that shared environmental influences on intelligence11 and another study showing no change in the correlation between parental and offspring education in 42 countries28. The previous meta-analysis on the heritability of education, including several twin cohorts included also in our study, also found decreasing shared environmental variation in the cohorts born after 1950, but this effect was not statistically significant4. We confirmed these results by limiting these analyses to those of 30 years of age or older having thus virtually finished their education. Thus, unfinished education does not affect the results, which may happen, for example, if those with higher intelligence have finished their education in a shorter period.We found that genetic factors explained a larger proportion, and shared environmental factors a smaller proportion, of educational variation in the earlier born cohorts in 1900–1949 than in the later born cohorts in 1950–1989. These results, thus, do not support our initial hypothesis that the role of genetic factors declined over the birth cohorts because of higher educational level, especially in women, reflecting the expansion of higher education after World War II, which may have led to higher meritocracy. There have been somewhat contrasting results on the intergenerational transmission of education, one study suggesting diminishing effect of parental social-position in eight European countries14, i.e. North America and Australia in this study, leading to lower heritability, because in these countries parental economic resources may be more important for higher education than in the countries where higher education is free and organized by the government. However, this was not the case: the heritability estimates were at the same level as in the European countries including in this study countries following the social-democratic or conservative models. We analyzed this in more detail in individual cohorts to assess whether there would be a systematic pattern not captured by our classification of countries. However, we did not find any evidence on systematic differences in the heritability estimates between the countries. Our results suggest that shared environment has an important role in educational differences regardless of the model of society. A study showing a higher genetic effect in Estonia after re-independence from the Soviet Union indicates that the societal structure can play a role in the genetic variation of education12. However, it may be apparently limited to specific societal systems, such as former Soviet Union, or specific times in a societal development.Our region-specific results are also against of our prior hypothesis. We expected that shared environment would be especially large in countries following the liberal model of typology by Esping-Andersen4, but is extended with a greater number of countries, thus showing the universality of the result. Importantly, there is no evidence on a gender difference in the role of genetic and shared environmental factors in intelligence29. This suggests that family background and other environmental factors shared by co-twins are more important for women than for men in choosing to continue education. Interestingly, the differences in the heritability estimates between men and women were larger in the cohorts born in 1900–1949 than in the later cohorts born in 1950–1989 when the role of shared environment increased in men. Thus, in the later birth cohorts, the role of genetic and environmental factors converged in men and women. Parallel results have been found in molecular genetic research: A US study found that the polygenic risk score of education predicted educational years more strongly in men than in women and this difference has diminished from the cohorts born in the 1930s to 1950s30. This may be associated to more equal opportunities for women to get a higher education in the later birth cohorts, which also this study showed by an increasing education in women.Our most consistent result was the larger proportion of genetic variation and consequently the smaller proportion of shared environmental variation of education in men than in women. This supports a previous meta-analysis, based mainly on published results31. If this induces a genetic correlation between spouses, the genetic correlation between DZ twins becomes higher than 0.5 as assumed by the twin model. In a sub-cohort, we had information on maternal and paternal education . The spousal correlation in the parents of twins was 0.57 after adjusting for the twin cohort and the birth year of twin children used as a proxy of the missing information on the birth years of parents. If applied to the estimates of genetic and environmental variation using the formula presented by Martin32, this spousal correlation was too high even if all common environmental variation is caused by assortative mating, i.e., it should have produced even higher shared environmental variance component than we found. However, in addition to phenotypic assortment, social homogamy, i.e. similar social environment of spouses, can affect a spousal correlation, which does not generate a correlation between genotypes of spouses33. A US study found that the correlation of polygenic risk score of education was 0.13 between spouses, which was much lower than the spousal educational correlation34. Correcting shared environmental variation by this genetic correlation reduced them somewhat, but they remained substantial (c2 = 0.19 in men and c2 = 0.30 in women). However, the polygenic risk score explains only a fraction of educational variation17, and so we should make an assumption that the spouses share the same amount of unknown genetic variants affecting education than they share the known genetic variants. In a UK study using all genetic loci affecting education, the genetic correlation between spouses was even higher (rA = 0.65) than the educational trait correlation35. When applied to our estimates, this genetic correlation should have produced even higher shared environmental variation than found in this study. The higher genetic than trait correlation in education would also expect a different mechanism behind assortative mating, i.e. selecting a spouse because of genetic liability rather than the trait itself, than previously assumed. Thus, it is too early to argue how much assortative mating has inflated shared environmental variation in this study because we do not know the mechanism behind assortment well enough to be able to estimate the genetic spousal correlation. However, we found that there were no differences in spousal correlations between cohorts born 1900–1949 and 1950–1989  or, as expected, between the parents of males and females  which could explain the differences in the genetic architecture between birth cohorts or between men and women. In contrast, the spousal correlations were thus even lower in the latter birth cohorts and in women, which thus cannot explain the larger shared environmental variation in these groups than in the earlier birth cohorts and men.One factor which may have inflated the estimates of shared environmental variation is assortative mating. There is a well-known spousal correlation in education, which may itself change over time4, our study based solely on individual level data offered several important strengths. With access to individual data, we could conduct more flexible analyses than could be done when based on published estimates. Further, our data are free of publication bias, which may lead to tendency to publish results in line of previously published heritability estimates, and we also have more data from a larger number of countries, including Asian countries, than in this previous meta-analysis4. The progress of GWA studies have allowed to estimate the role of genetic factors based on individual level data with information on common genetic variants17; GWA studies, however, lack information on rarer variants and the more complex genetic variation that is captured by whole genome sequencing. A strength of the classical twin design is that it allows separating the effects of all genetic and of environmental factors shared by co-twins, such as family background. Thus, twin studies allow for a more comprehensive understanding of how genetic and social inheritance affect the transmission of education across generations. A weakness of our data is that we have information only on accomplished education. It would be important to collect also information on school performance, intelligence, motivation and other factors affecting education. This would help to understand mechanisms behind genetic and environmental components of educational variation.Our data have important strengths but also weaknesses. As compared to the previous meta-analysis of twin studies of education based mainly on published resultsIn conclusion, our international twin data demonstrated that genetic factors are important for educational attainment regardless of the society. We also found environmental variation shared by co-twins behind inter-individual differences in educational attainment, but its estimate depends how we assume assortative mating to affect the genetic correlation between spouses. The role of shared environmental variation was higher in women than in men, especially in the cohorts born before World War II. This suggests that the influence of environmental factors on education differs between men and women and this gender difference itself may change over time.36. However, additional information was also collected on own education37. Together, 28 twin cohorts representing 16 countries provided data on education to the CODATwins database and were included in the present study. The footnote of Table 14: European countries follow the social democratic , conservative  or mixed (Netherlands and Belgium) societal models whereas the USA and Australia follow the liberal model. The East Asian countries  were difficult to classify by this typology, but they share many cultural similarities. Sri Lanka and Brazil were not included in these region-specific analyses, because they are geographically and culturally distinct from the other regions.The data for this study were derived from the international COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) database. The CODATwins project aimed to pool data from all twin cohorts in the world having information on height and weightAll participants were volunteers and gave informed consent when participating in their original study. Only a limited set of observational variables and anonymized data were delivered to the data management center at University of Helsinki. The pooled analysis was approved by the ethical committee of Department of Public Health, University of Helsinki, and the methods were carried out in accordance with the approved guidelines.37. To ensure that the educational attainment variable captured the participants’ final highest level of education, we removed those under 25 years of age from the sample. We used this age limit because a higher age limit would have led to a substantial loss of data in the latest birth cohorts. However, when making comparisons between birth cohorts, we used a stricter age limit, i.e. 30 years, to confirm that unfinished education would not affect the results. Further, we replicated the heritability analyses by birth cohort using this stricter age limit to analyze whether it would change the results systematically. Together, we had educational data from 193,518 twins (54% women) including 81,894 complete twin pairs; 40% of the complete twin pairs were monozygotic (MZ), 39% same-sex dizygotic (SSDZ) and 21% opposite-sex dizygotic (OSDZ). From these participants, 39,235 were under 30 years of age, and we thus removed them when using this stricter age limit. The birth years of the twins ranged from 1889 to 1989. However, we had data for only 70 men and 137 women born earlier than 1900, whom we removed from the analyses because the number was too small for birth cohort specific analyses. The largest number of twins were from Europe  and North America and Australia , while the number of twins was much smaller for East Asia (N = 2728). In the region-specific analyses, East Asians born before 1950 (113 men and 106 women) were removed because the number was too small for the analyses stratified by birth cohort and region.We transformed the different educational classifications used in the original surveys from each cohort into educational years by using the mean levels of educational years in each category. The original classifications and the corresponding numbers of educational years have been described elsewhere38. The twin modeling uses the different genetic relatedness of MZ and DZ twins: DZ twins share, on average, 50% of genetic variation, whereas MZ twins are virtually identical at the DNA sequence level. This principle allows decomposing the educational variation into variance attributable to additive genetic factors (A: correlated by 1.0 for MZ and 0.5 for DZ pairs), environmental factors shared by co-twins (C: by definition correlated by 1.0 for both MZ and DZ pairs), and environmental factors unique to each twin individual (E: by definition uncorrelated for MZ and DZ pairs). The unique environmental component also includes measurement error. The models were estimated using the OpenMx package (version 2.0.1) of R statistical software39. As we have reported previously37, MZ twins had slightly higher education than DZ twins, and therefore we allowed different means for MZ and DZ twins in the models.The data were analyzed using quantitative genetic twin modeling based on structural equation models37. The adjustments were done by calculating regression residuals of education by using birth year and the dummy variables of twin cohorts as independent variables in the regression models. We then analyzed temporal trends by pooling the birth cohorts into two broad time spans, 1900–1949 and 1950–1989, to increase power to detect differences in the genetic and environmental variation over the birth cohorts. We selected these cohorts because of the much higher education in the later born cohorts indicating extension in educational systems. We restricted these analyses to Europe and North America and Australia since older birth cohorts were not available in East Asia and in other countries. After these analyses by birth cohort, we conducted a second set of analyses and estimated genetic and environmental variance components separately in each twin cohort to confirm that the used classification did not hide any existing pattern. In these twin cohort specific analyses, we adjusted education for birth year separately in each twin cohort.We started the analyses by estimating the genetic and environmental variances in educational attainment in the whole dataset as well as in 10-year birth cohorts by first pooling data from all cohorts together and then stratifying them by the cultural–geographic region. In these analyses, we adjusted education by twin cohort because otherwise different educational systems between the countries and different education classifications between the surveys might have affected the results. Further, we adjusted for the effect of birth year in order to account for the effect of increasing educational level during the twentieth century in these cohortsSupplementary Tables.