The crucial role of green finance policies in advocating environmental sustainability cannot be overlooked and has resulted in an increased attention in recent years. Despite their significance, the connection between these policies and economic growth remains disputed among policymakers. This study presents an empirical analysis that aims to uncover the relationship between green finance policies and economic growth rates by analysing the data from 107 cities in China, spanning a period of 16 years from 2003 to 2019. The research focuses on the implications of green finance reforms and innovation pilot zones by employing PSM-DID model to evaluate their effects on the regional GDP by segregating the industries in primary, secondary, and tertiary industries. The results indicate that the green finance reforms and innovation pilot zones are associated with reduced growth rates of regional GDP for secondary industries. However, the growth rates of the tertiary industry are positively affected. Thus, the study offers valuable guidance and recommendations to policymakers and cities seeking to join the pilot zones for green financial reform to foster economic growth while supporting environmental sustainability. The findings of the study also contribute to the understanding of how targeted policy interventions, such as the establishment of pilot zones, can influence economic dynamics at the regional level and describes the complex relationship between policy interventions, economic growth, and industry composition, particularly in the context of environmental sustainability. While comparing the development of green finance in developed countries in the West and China, we find that their development models differ significantly. In the developed countries of the West, represented by the United States and the United Kingdom, a variety of financial institutions and non-governmental organizations are the main participants in green finance, which is mainly based on market mechanisms. Government involvement only occurs when green finance has reached a certain level of development, resulting in a “top-down” development model that is influenced by market demand. It took longer for green finance to take hold in developing countries, represented by China, and the initial creation of the green financial system primarily relied upon government power and was supported by government departments and regulators, resulting in a government-oriented “top-down” approach. There has been an improvement in China's green finance policy system over the past decade, and the author compares and contrasts the timeline of green finance policy development in China .Fig. 1TiThe relevance of green finance in promoting sustainable economic growth has become a subject of academic inquiry, leading to varying conclusions among scholars. For instance, Ref.  researchThe results highlighted a significant and noteworthy suppressive effect of the green financial reform pilot policy on regional national income growth rates. This finding was assessed through a robustness test, which further supported the validity and reliability of the findings. Overall, these outcomes could have implications for policymakers and practitioners alike, as they suggest that the implementation of green financial reform policies may have broader economic consequences within the Chinese context. Moreover, the following study provides a basis for further research to investigate and explore these findings in greater depth, to better understand the underlying mechanisms and implications for future policy making. Also, the study revealed that the primary and secondary industries were significantly suppressed, whereas the tertiary industry was significantly boosted. In light of this finding, cities with diverse industry types may use it as a reference for making policy decisions. As well, the report provides theoretical support and empirical evidence for whether they should choose to apply to become green financial reform test sites. We believe that cities with a predominantly tertiary industry profile should actively seek to become pilot areas for green financial reform.This paper comprises of three essential contributions. Firstly, it investigates the impact of establishing a pilot zone for green financial reform on regional economic growth and industrial structure growth, thereby adding to the empirical research on green pilot zones and green finance. Secondly, the use of a quasi-natural experiment approach through the exogenous event of establishing a pilot zone minimizes endogenous issues, which strengthens the estimation results and provides reliable empirical evidence concerning the connection between green financial reform and regional economic development. Thirdly, this research employs a proximity test that scrutinizes the impact of green finance reform pilot programs on similar cities, accounting for the heterogeneity and significant differences that exist amongst cities. This approach enhances our understanding of the scope and implications of green financial reform policies and their effectiveness in promoting sustainable economic growth. Overall, these findings contribute significantly to the literature on green finance, pilot zones, and the broader environmental and economic policies.The remainder of this paper proceeds as follows: Section 22.1In the past few years, in-depth discussions have taken place between international organizations and governments regarding the role that green finance plays in policymaking at the global level. These discussion reveal that the concept of green finance is understood differently by different institutions. As defined by some, green finance involves investments and financing activities that are related to the environment  and thes2.2The above definitions of green finance may differ. However, there is consensus on the concept that green finance is the financing of economic projects that are beneficial to the environment. Green finance has been proven to be effective in promoting environmental upgradation from various perspectives by scholars. On the basis of micro-enterprises, green investment is considered a successful financial strategy that positively impacts enterprise performance ,13, thus2.3In the introduction to this paper, it is stated that different researchers have come up with different methods and different verification results for determining whether green finance contributes to economic development or not. The most common method of assessing the role of green finance in promoting economic growth ,26, as wMost of the existing studies on the green finance domain focus on analyzing the impact of green financial products, such as green credit, green investment, and green financing, on the environment, enterprise development, and industrial structure. However, there are only a few studies that are conducted from the perspective of green financial policies. Furthermore, a considerable amount of literature on green financial reform pilot zones centers around the role of corporate and regional green innovation, with little attention paid to the pilot effect of green financial reform pilot zones on regional economies and industries. Therefore, this paper's main objective is to examine the effects of green financial reform and innovation pilot zones on regional economic growth and industrial structure through the Propensity Score Matching (PSM) and Difference-in-Differences (DID) modeling techniques. By doing so, this study aims to bridge the gap in the literature and provide a more comprehensive understanding of the efficacy of green financial policies in promoting sustainable economic growth and restructuring regional industries.33.1The State Council approved the first batch of green financial reform and innovation pilot zones in June 2017. Among them are Huzhou and Quzhou in Zhejiang Province, Guangzhou City in Guangdong Province, Ganjiang New Area in Jiangxi Province, Gui'an New Area in Guizhou Province, and Karamay City, Hami City, and Changji Prefecture in Xinjiang Uygur Autonomous Region, a total of eight places in five provinces. This marks the beginning of a new phase for China's green finance, combining a top-down, regional exploratory approach with a top-down, top-level design approach. As of November 2019, Lanzhou New Area in Gansu Province was approved as the country's ninth pilot zone for green financial reform and innovation. This is the only pilot zone for green financial reform and innovation in the Yellow River Basin. The Pilot Zone for Green Financial Reform and Innovation in Chongqing was officially launched in August 2022.This study suggests the use of green reform pilot zones as a natural experiment to evaluate their effect on regional economic growth. The treatment group for this experiment comprises ten cities with green financial reform pilot zones, while cities without green financial reform pilot zones make up the control group. The double difference method is utilized to measure and compare the impact of green reform and innovation pilot zones on economic growth in regions across these two groups. This approach helps in isolating the effects of green financial policies on the economy by controlling for any pre-existing differences between the treatment and control groups. By using this method, the study aims to provide a more comprehensive understanding of the effectiveness of green financial reform policies in promoting sustainable economic development.(1)Except for Quzhou, Huzhou, Guangzhou, Karamay, Changji, Karamay and Chongqing City, not all pilot areas are divided by cities in the pilot area documents. This paper contains a pilot reform area of ten cities. How were they selected? Nanchang City is used as the sample because Jiangxi Ganjiang New Area is located on the bank of Ganjiang River in the north of Nanchang City. The Gui'an New Area is located at the junction of Guiyang City and Anshun City, so Guiyang City and Anshun City are used as samples. The Lanzhou New Area is located north of Lanzhou City in Gansu Province, which is why Lanzhou City was used as a sample. This paper does not include Chongqing in its list of reform pilot zone cities since it was approved in August 2022.(2)Considering the fact that the industrial structure of big cities is complete, it is necessary to select big cities to ensure the comparability between the treatment group and the control group. According to the “2020 China Population Census County-by-County Data” published by the Office of the Seventh National Population Census Leading Group of the State Council on October 22, there are 105 big cities. Of the 105 big cities identified, four are county-level cities and only have a single industrial structure, so that should be eliminated. A total of 107 cities' sample data has been collected after adding 10 cities in the pilot area and subtracting the cities that overlap with the big cities (107 = 105-4 + 10-4).(3)To minimize the influence of external factors such as the COVID-19 pandemic, the research period for this study was selected from 2003 to 2019. The source of the data is the China Urban Statistical Yearbook and the CSMAR database collected over this period, and any missing data are interpolated to ensure completeness. Therefore, a comprehensive set of balanced panel data was compiled and covers 107 major cities in China over the period from 2003 to 2019. By utilizing this data, the study aims to provide a reliable and accurate assessment of the effects of green financial reform pilot zones on economic growth in different regions of China.In order to meet the prerequisites for using the double difference method, the following pre-processing was performed in selecting the sample in this paper:3.2In this paper, green financial policy is used as the explanatory factor and regional economic growth is used as the explained factor, where green financial policy is measured by green financial reform pilot zones and regional economic growth is measured by the growth rate of regional GDP and the share of value added of each industry in GDP. The impact effects of pilot zones on the local economy are identified by comparing the differences in the impact effects of cities before and after they become pilot zones  and cities that are not pilot zones (control group). The double difference model constructed is as follows.trolsit+εgkrit=α+αThe following study employs Propensity Score Matching (PSM) combined with a Difference-in-Differences (DID) model which is advantageous for several reasons. PSM aids addressing the problem of selection bias in observational studies as in non-experimental settings, treatment assignment is not random, and there may be systematic differences between the treatment and control groups does this by balancing the covariates between the treatment and control groups, which helps to reduce bias in the estimates. PSM also provides more accurate estimates of the causal effect of the treatment on the outcome variable as it balances the distribution of observed covariates between the treated and control groups. Since our study combines PSM with DID model so it strengthens the causal inference as it compares the pre and post treatment outcomes allowing for the estimation of causal effects and DID model further isolates the treatment effect from other factors that may affect the outcome over time.3.33.3.1The primary objective of this study is to examine the impact of green financial reform pilot zones on the domestic economy of China. In doing so, the growth rates of gross regional product (gr), the proportion of the value added of the primary industry to GDP (g1r), the proportion of the value added of the secondary industry to GDP (g2r), and the proportion of the value added of the tertiary industry to GDP (g3r) are utilized as explanatory variables. By analyzing these variables, the study aims to explore the influence of the green financial reform pilot zones on both overall economic growth and industrial development in different regions of China. The findings of the study are presented in 3.3.2The explanatory variable of this study is whether it is a green financial reform pilot area. Double differential variables are primarily used to measure it. The did statistic is calculated as the product-term of the dummy variables in the processing period and the dummy variables in the processing group, that is, did = dummy variables in the processing period * dummy variables in the processing group. As long as it is officially approved, this year and later will be valued at 1, while other years will be valued at 0. The value of dids is zero in cities that are not approved as pilot areas.3.3.3To ensure the reliability and robustness of the findings, an array of control variables  have bee44.14.24.2.1In order to further verify the results of the initial analysis, a different approach was employed to handle the time variable. Given that the establishment of the first green financial reform pilot zone occurred in the midyear of 2017, assigning a value of 1 for that year may exaggerate the policy impact on the test area. To account for this concern, the time variable was adjusted to 0.5 for the year of establishment, while the values for the remaining years were assigned by following the baseline regression equation. The regression results after the adjustment are presented in 4.2.2Since the cities selected as pilot zones are not chosen at random, they may be selected because these pilot cities have certain characteristics. Moreover, policies are generally implemented first in pilot demonstration areas, which may already have a fairly high level of economic development. Therefore, in order to further ensure the validity of the regression results and eliminate the influence of the characteristic factors of the pilot cities on the empirical results, it is necessary to find cities similar to the pilot cities in the non-pilot cities for comparison tests. After conducting PSM-DID, 4.2.3To ensure that the DID model is reliable and can be utilized for research purposes, it is imperative to acknowledge that the treatment and control groups conform to the parallel trend assumption. This assumption implies that in the absence of treatment, the difference in outcomes between the two groups is relatively constant over time. By satisfying this basic requirement, it is possible to confidently attribute any observed differences in outcome after the introduction of the treatment to the actual impact of the intervention under consideration. Thus, we carefully evaluated whether the parallel trend assumption holds before attempting to draw conclusions from the DID model results. Using 2014 as a hypothetical year for cities with pilots established in 2017, and so on, the pilots were constructed 3 years before, 2 years before, current, one year after, and two years after the policy was implemented. 4.2.4To ensure that the results obtained from the implementation of the Green Financial Reform and Innovation Test Area are free from the influence of unidentified factors, a placebo test was conducted. This study utilized data from 500 samples taken from 107 cities, with only the first period number (2003) retained for each ID. To match the previous data, a random selection of 10 cities was made from each sampling. The matched cities were then used as a dummy treatment group in the regression, while the remaining cities were considered the control group. The scatter plot in 5The above empirical results could be attributed to a variety of factors. First and foremost, the coefficient of the impact of the green reform pilot policy on regional economic growth is extremely negative, indicating that the implementation of the green financial reform pilot policy will significantly decrease the regional economy's growth rate. This may be due to the fact that under the constraint of the environmental development goal, green financial development may crowd out other financial resources. In other words, environmental requirements have been raised, and investments that previously yielded high returns but did not meet environmental standards are no longer considered viable. As a result, GDP growth slows down and shifts into green, sustainable growth, instead of the crude growth of the past.It is also evident from the second result that there is a restriction on the development of primary and secondary industries. Possibly, this is due to differences in the energy demand structure among different types of industries. Green financial policies may help to promote the transformation of old into new sources of energy. As energy and industrial structure contributes to the ecological footprint of an economy . ConsequThe hypothesis mentioned earlier has been corroborated by its impact on the tertiary industry. The regression analysis performed indicates a noticeable and positive influence of the tertiary sector's growth. Based on these observations, it can be deduced that the adoption of green financial policies seems to be conducive to the expansion and progress of the tertiary industry. Furthermore, the implementation of the green finance reform and innovation policy could aide in the transformation of primary and secondary industries into tertiary ones. While there may be implications from this shift, its potential benefits could lead to greater diversification and sustainability in the long run.The application process for becoming a green financial reform pilot zone is underway in many cities. However, based on the empirical results of this study, green financial reform pilot zones will inhibit regional economic growth. Thus, not all cities are eligible to participate in the pilot program. Aside from that, different cities have different industrial structures as well as different characteristics of their industrial pillars. It is therefore necessary to determine whether to pursue green financial policy support in conjunction with their own development objectives.6In China, ten pilot zones for green financial reform and innovation have been approved across seven provinces, autonomous regions, and municipalities directly under control of the central government. Furthermore, a growing number of cities are seeking to join these designated pilot zones. Against this backdrop, this paper seeks to evaluate whether such pilot zones have the potential to stimulate regional economic growth. To achieve this, the study employs quasi-natural experiments across 107 Chinese cities from 2003 to 2019. By examining the data from these cities, the research aims to assess the relationship between the implementation of pilot zones and their potential impacts on economic growth in the region.Based on the results we deduce that the development of both primary and secondary industries may be restricted, whereas the tertiary sector seems to have gained a significant momentum to grow. These findings held constant after a set of robustness tests were executed, which lends credibility to the empirical link between green financial reform pilot zones and short-term regional economic growth. What makes this discovery noteworthy is that it provides robust and reliable empirical evidence that local authorities can reference when considering whether cities should apply to become pilot zones. Furthermore, these conclusions could inform policy decisions related to the potential advantages and drawbacks of prioritizing the green finance sector over other industrial sectors within a region.Based on the findings of our study we suggest that the central government should designate green financial reform pilot zone based on the industrial structural and characteristics of the cities; those cities with a strong tertiary industry should be given priority for pilot zone designation as our findings suggest that tertiary industry benefits more from the establishment of these pilot zones. The growth momentum in the cities with primary focus on tertiary industries can be further leveraged by giving substantial support for green financial reform and innovation in those cities. The impact of green finance policies should not be viewed as directly promoting economic growth, as their primary goal is to support sustainable and low-carbon development. Therefore, not all cities may be suitable as green reform pilot zones, since it depends on their individual characteristics. Local authorities should exercise caution while implementing green finance policies in cities with primary or secondary industry dominance as the following study suggests that these industries may experience short-term constraints on their economic growth due to the impact of green finance policies. Therefore, policymakers should cautiously evaluate the possible adverse effects and consider alternate strategies to support sustainable development in these industries. Furthermore, it is also important to consider contextual considerations such as the designations and functional positioning of each pilot zone should be tailored to the unique characteristics, resources, industrial structures, economic sizes, and financial development bases of the respective cities as our findings reveal that the effect of pilot zones on the development across industries is subjective to the level at which an industry is operating.However, cities with a primary focus on the primary or secondary industry may be negatively impacted by green finance policies, which could pose a short-term constraint on their economic growth. Therefore, the preference for green finance may not be desirable in these cities. Moreover, governments must account for the different industrial structures, resources, economic sizes, and financial development bases of their designated green finance reform pilot zones, reflecting each unique context. As such, the designations and functional positioning of each pilot zone may require tailoring to best suit local conditions, supply capacity, and demand scales accordingly.One potential limitation of this study is that the sample data only reflects information up to 2019. Given that the first announcement of green financial reform and innovation pilot zones was only released in June 2017, this means that the analysis covered a relatively brief period. Additionally, the COVID-19 pandemic that emerged in 2020 required significant efforts to combat, which created further challenges in measuring the full impact of the pilot zones on the economy. To strengthen the accuracy of future research, longer-term observation of the impact of the pilot zones on economic growth, including more established cities, would be necessary. This would allow for a more comprehensive and precise analysis of the effects of the pilot zones on regional economic development, potentially mitigating the limitations of this study based on limited data.While this paper has made a considerable effort to analyze the impact of green financial reform pilot zones on regional economic growth, there are several potential areas for future research that could enhance the study's strength. One such area is to conduct greater in-depth analysis into the different impacts of pilot zones on regions with varying industrial structures as the following study does not consider the pilot zones based on the industrial structures. This could involve further categorization of cities based on their specific industrial makeup, allowing for more detailed and precise insights into the relationship between green finance policies and economic performance as this will extend the following study further. Furthermore, since some of the pilot cities are also part of other policy experiments, such as low-carbon city pilots, it may be difficult to entirely eliminate any possible influences of these factors in our study throughout the robustness test stage. Therefore, it would be essential to exclude potential effects of unaccounted variables more thoroughly in future studies. As some of the cities included in our study are in embryonic stage and the green finance reform and innovation pilot zone is still undergoing exploration and promotion stages, and it may take more time to determine whether its potential to provide economic growth stimuli is limited. Thus, over time, the potential complexities and multidimensional impacts of green finance policies may further unfold, and this would provide more opportunities for research expansion.Cheng Hu: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Yan Wang: Contributed reagents, materials, analysis tools or data.This work was supported by the [Young Talent Project Fund of Science and Technology Research Program of Hubei Provincial Education Department] (Q20223101).The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


We compared the effects of nine kinship matrices on genetic gain, kinship, inbreeding, genetic diversity, and minor allele frequency when applying OCS in a simulated small dairy cattle population. We used VR1 and VR2, each using base animals, all genotyped animals, and the current generation of animals to compute reference allele frequencies. We also set the reference allele frequencies to 0.5 for VR1 and the pedigree-based relationship matrix. We constrained OCS to select a fixed number of sires per generation for all scenarios. Efficiency of the different matrices were compared by calculating the rate of genetic gain for a given rate of increase in average kinship.Genomic selection has increased genetic gain in dairy cattle, but in some cases it has resulted in higher inbreeding rates. Therefore, there is need for research on efficient management of inbreeding in genomically-selected dairy cattle populations, especially for local breeds with a small population size. Optimum contribution selection (OCS) minimizes the increase in average kinship while it maximizes genetic gain. However, there is no consensus on how to construct the kinship matrix used for OCS and whether it should be based on pedigree or genomic information. VanRaden’s method 1 (VR1) is a genomic relationship matrix in which centered genotype scores are scaled with the sum of 2We found that: (i) genomic relationships were more efficient than pedigree-based relationships at managing inbreeding, (ii) reference allele frequencies computed from base animals were more efficient compared to reference allele frequencies computed from recent animals, and (iii) VR1 was slightly more efficient than VR2, but the difference was not statistically significant.Using genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals. Genetic diversity must be preserved to achieve genetic gain in future generations, and inbreeding must be managed to avoid negative effects on traits. Genetic diversity within livestock breeds is preserved and inbreeding is controlled by managing effective population size, by controlling the rate of increase in average kinship. In recent years, the implementation of genomic breeding has increased the threat of eroding the genetic diversity in dairy cattle in at least two ways. First, some genomically-selected populations have shown increased inbreeding rates and smaller effective population sizes –4. SeconOptimum contribution selection (OCS) maximizes genetic gain while it restrains inbreeding by managing long-term genetic contributions . SeveralThe cost of raising bulls and collecting semen for artificial insemination (AI) is a major part of the costs for dairy cattle breeding programs , especiaPedigree relationships have been used to implement OCS (POCS). Genomic OCS (GOCS) is possible by using a genomic relationship matrix (GRM) instead of the pedigree relationship matrix (numerator relationship matrix). Sonesson et al.  and Henrn et al.  comparedThe aim of this study was to compare the performance of nine kinship matrices for use with OCS: the numerator relationship matrix and two types of GRM, VR1 and VR2 using different RAF in a simulated dairy cattle population undergoing genomic selection. We compared (i) rate of increase in average kinship, (ii) rate of genetic gain, (iii) rate of loss of additive genetic variance and additive genic variance, (iv) rate of average inbreeding according to drift and homozygosity, and (v) change in MAF at QTL and neutral loci.The simulations were modelled after the breeding program of the Icelandic Cattle population. Simulations were performed using QMSim  for simu–5 per allele per generation. No new mutations were simulated but the allele state was altered between the two alternate alleles. Mutations were simulated only for the historical population. Genotype data of the historical population were converted into PLINK  or a combined genomic and pedigree relationship matrix [single step genomic BLUP (ssGBLUP)]. In the first three generations of genomic selection, ssGBLUP was used to predict breeding values and in the subsequent 12 generations, GBLUP was used to reduce computations because the ssGBLUP evaluation is computationally time-consuming when the number of genotyped animals is large. We used the genotypes of eight generations of animals with data for the genomic evaluations.Selection in the first generation was random, followed by four generations of PBLUP based truncation selection. In each generation, 120 males were selected, randomly mated to females, and used equally. Phenotypes were available for females when selection was performed. To build a reference population for genomic prediction, selected sires in generations zero to five were genotyped and all females in generation five were genotyped.pseudofemale and mating her to males while restricting the OCS computations to select 40 males, as described below. The 40 selected males were then randomly mated to females with an equal number of matings per bull.In each generation, the parent average  and second (VR2) methods of VanRaden  to constVR2 was similarly computed as:VR1 Base, VR2 Base, VR1 All, VR2 All, VR1 Current and VR2 Current were computed using VR1 and VR2, and RAF were computed using (i) animals in generation one (Base); (ii) all genotyped animals (All); and (iii) genotyped animals in the current generation (Current). In addition, we used VR1 with a RAF of 0.5 for all markers, i.e. VR1 0.5. Using a value of 0.5 as RAF with OCS contributes in maintaining a maximum heterozygosity rather than minimizing drift. We also used VR1 with RAF estimated from the selected and genotyped bulls in generations zero to three, VR1 Old. This scenario represented information that might be available to breeding programs by genotyping semen samples from old insemination bulls. Lastly, we used the numerator relationship matrix, Pedigree. These nine matrices represent the different studied scenarios. Eight scenarios used GOCS and one scenario used POCS. The different matrices are based on three different measures of diversity; Pedigree is based on probabilities of identity-by-descent, VR1 0.5 is based on heterozygosity, and the other genomic matrices are based on changes in allele frequency. To reduce simulation errors, we used the same base population for all scenarios in each replicate across scenarios.Bulls were selected according to OCS based on their GEBV and one of the relationship matrices described above. To reduce computations, only 1000 genotyped males with GEBV above the median were considered for selection. OCS was implemented with constraints to select a fixed number of sires. We constructed a pseudofemale based on all the females. The pseudofemale had a relationship with herself that was equal to the mean relationship among females, including self-relationships, and a relationship with each bull that was equal to the mean relationship of all female candidates to that bull. The pseudofemale was assigned 40 matings, and each of the 1000 bulls was allowed a maximum of one mating. This simplification reduced computation time drastically and is a valid approximation when there is no selection on the dam side. The genetic contributions were optimized to achieve the target rate of inbreeding, while maximizing genetic gain. This implementation means that the contribution of each candidate was fixed and thus the optimized parameter was whether the bull was selected or not. Genetic contributions qualed ½ . To achikinship.emp.fast, which uses recombination points to compute the proportion of chromosome segments between two individuals that are identical-by-descent when a haplotype is drawn at random from each individual. The mean kinship was estimated using 360,000 randomly selected pairwise relationships, out of the total of 71,994,000 pairwise relationships for each cohort of 12,000 animals. Additive genetic variance was the variance of true breeding values in each generation and additive genic variance was computed as the sum of QTL additive genetic variance at each locus as In each generation, we computed mean true breeding value, mean kinship, additive genetic variance . However, VR1 resulted numerically in less Base and All were used as RAF. Minor allele frequency followed the same trend as There was no significant difference in ,, and VR2 Base and most dissimilar in VR1 0.5.Table VR1 Base and VR1 Old achieved the largest genetic gain per unit VR2 Current and VR1 Current were the least efficient. The ranking of the efficiency of the scenarios, measured as genetic gain per unit of VR1 0.5, which resulted in relatively low values of Figure VR2 Base had the highest percentage of segregating QTL, i.e. 84.8%, and VR1 Base had the highest percentage of segregating neutral loci, i.e. 84.8%. VR1 Current had the fewest segregating QTL and neutral loci, i.e. 83.7% and 83.6%, respectively.There were only small differences among the scenarios in terms of the proportion of segregating alleles at the end of the simulation. Base or Old animals to compute RAF was more efficient at achieving genetic gain for a given rate of increase in average All animals or Current to compute RAF, and (iii) VR1 resulted in slightly less VR2, but the differences were not statistically significant.We observed the following major trends in our results: (i) GOCS was more efficient than POCS at achieving genetic gain for a given rate of increase in average Pedigree. Several studies of OCS in genetic improvement programs have found that POCS was more efficient than GOCS; Körte [In our study, GOCS was able to achieve more S; Körte , HenryonS; Körte , MeuwissS; Körte , and ZhaS; Körte  found thS; Körte  found thmentclasspt{minimaS; Körte  argued tS; Körte , 21, 34.S; Körte  did not Some OCS studies have focused on applications in conservation programs that do not include genetic improvement. de Cara et al.  and GómeVR1 Base), but also when bulls selected in the first four generations were used (VR1 Old). VanRaden’s estimators of relationship represent the probability of identity-by-descent relative to the base population, from which the RAF are computed [The benefits of using base RAF are clear in our study. Updating RAF, as implemented in our study either by using all genotyped animals or the most recent generation of genotyped animals, allowed more genetic gain compared to using frequencies of previous generations, but at the cost of substantially higher computed . When cucomputed . As showcomputed , genomicVR1 0.5 to promote heterozygosity was shown by the relatively high MAF, and low Higher selection pressure was reflected in lower MAF for scenarios using more recent RAF. The tendency of n et al. .Our results suggest that implementation of OCS in practice should estimate RAF using the oldest genotype data available. In dairy cattle, cryo-preserved semen from old bulls is often available and genotyping these animals may be useful to estimate RAF for GOCS. Alternatively, the method of Gengler et al.  may be uThis study used simulated data and looked at long-term effects but applications should also be studied using real data. A study by Eynard et al.  used reaThe simulated population in this study was closed, i.e. no imports were simulated. Admixed populations have to be investigated in a separate study. In an admixed population using GOCS with base RAF estimated from animals that were born before admixture, GOCS would tend to move allele frequencies in the population towards the unadmixed base population. Whether this is desirable depends on the goals of the genetic management of the population.Additive genetic variance is expected to decrease proportionally to mean kinship in the population , but in mentclass2pt{minimPedigree and Current RAF, more drift was observed at all types of loci, as measured by VR1 0.5 in our study. Meuwissen et al. [Pedigree resulted in roughly equal Markers used for genetic prediction), and for the neutral and QTL loci which had a MAF distribution resembling that of whole-genome sequencing data as reported by Meuwissen et al. [We used two estimators, election . We simun et al. . A simuln et al.  found sin et al. , a matrin et al.  maintainn et al.  observedn et al. . The seln et al. , the pern et al.  set a tan et al.  are due n et al. , heterozMeuwissen et al.  concludeOur study compared the effects of kinship matrices for a fixed number of sires, an equal use of them and random mating of selected males to females. We did not simulate any selection on the female side and used discrete generations. Although the assumptions of no female selection, of a fixed number of sires, an equal use of sires, and of random mating deviate from the practical situation, we do not believe they affect the results of our study. We believe that the constraints imposed on OCS in our study resemble a realistic situation for a small dairy cattle population. However, the results may not be generalizable to all breeding programs and the effects of kinship matrices should be studied with other breeding program structures. Because the kinship matrices measure kinship differently, selection resulted in different kinship rates across scenarios, which complicates the comparisons. We optimized the genetic contributions so that the target rate of kinship was computed relative to all animals in each generation. This is not possible in practice for GOCS because genotype data are only available for genotyped individuals, which in this case were 6000 females and 2000 bulls. Optimizing genetic contributions relative only to genotyped individuals will result in kinship rates exceeding the target. Thus, in real application, the realized inbreeding rate will exceed the target rate, unless the genotyping of both sexes is random or all animals are genotyped. In real genomic dairy cattle breeding programs, cow genotyping is common, which should provide a relatively unbiased sample of female genotypes, but the genotyping of bulls is only carried out to identify new AI sires. Therefore, selection will cause the genotyped bulls (which are the selection candidates) to be more related on average than the average relationship among all males in the population, resulting in a higher rate of increase in kinship than the target. Breeders can deal with this issue by simply taking into account that realized inbreeding will be higher than the target rate when genotyping of sires is biased. Further studies on OCS should consider how to deal with this issue in real populations. If not all animals are genotyped, a combined genotype and pedigree matrix could be used.We recommend that breeding programs resembling the one that is simulated in this study apply OCS using VR1 with RAF that are estimated from old animals, possibly from old artificial insemination bulls. The use of user input RAF is implemented in software such as GMATRIX . TherefoGenomic OCS is preferable to POCS if base animals are used to compute RAF and a fixed number of sires is selected in each generation. Using base animals to compute RAF for GOCS results in less inbreeding per unit of genetic gain than using recent animals to compute RAF. We did not find significant differences in the performance of VR1 and VR2. Previous studies have found contradicting results regarding the relative performance of GOCS and POCS but our study found clear benefits of GOCS. We suggest that further research should compare fixed and varying numbers of parents in OCS schemes. We recommend that for a small genomic dairy cattle breeding program, implementation of OCS should be based on VR1 with RAF estimated from base animals.


SM Study, we investigated the genetic and environmental contributions to microstructure and volume of nine subcortical gray matter regions. Microstructure was assessed using diffusion MRI data analyzed using restriction spectrum imaging (RSI) and diffusion tensor imaging (DTI) models. The highest heritability estimates  for microstructure were found using the RSI model in the pallidum , putamen , and thalamus . For volumes the corresponding regions were the caudate  and putamen . The subcortical regions displayed high genetic stability (rA = 0.743–1.000) across time and exhibited unique environmental correlations (rE = 0.194–0.610). Individual differences in both gray matter microstructure and volumes can be largely explained by additive genetic effects in this sample.Using baseline (ages 9–10) and two-year follow-up (ages 11–12) data from monozygotic and dizygotic twins enrolled in the longitudinal Adolescent Brain Cognitive DevelopmentThe online version contains supplementary material available at 10.1007/s10519-023-10142-1. Understanding how genetic variability influences brain structure in adolescence may provide insights into the pathophysiology and biological constructs that impact normal brain development, cognition, and the development of neuropsychiatric disorders. Brain structures vary in heritability, suggesting that different genes may influence the structural development of different areas  were highly heritable and entirely genetically stable from ages 9 to 12; however the sample size was limited  to investigate the heritability of both subcortical volume and microstructure in adolescents.While several studies have considered the heritability of the volumes of subcortical structures, very few have investigated their microstructure  spatial resolution, and sufficient gray/white matter contrast to enable segmentation of deep gray matter structures, and determination of their volumes. Diffusion MRI uses the motion of water molecules in the brain to provide information on the microscopic environment, and is sensitive to developmental changes related to, for example, myelination and cell density  were included in the diffusion analysis. To maximize sample size, volumetric data for twins scanned on both Siemens and Philips  scanners was included. Twin pairs were excluded if they had incomplete data, or if the structural and diffusion imaging data did not pass ABCD-recommended quality control, including a diffusion mean motion threshold of 1.5 mm. Subjects with a history of brain injury, cerebral palsy, muscular dystrophy, multiple sclerosis, or substance abuse were excluded. Demographic information for the two groups is given in Table 3. Real-time motion detection and correction using volumetric navigators was used to minimize the effects of head motion , 1000 (15 directions), 2000 (15 directions), and 3000 (60 directions) s/mmImaging data was preprocessed as described previously   model was also applied to generate measures of mean diffusivity (MD) to compare its sensitivity to the more sophisticated RSI model.Zygosity status was genetically determined. All data was residualized to account for the effects of age, sex, and site.Intraclass correlations (ICCs) between MZ and DZ twin pairs were calculated as descriptive statistics to visualize the relative twin similarity for the phenotypes. Within subject Pearson correlations for phenotypes derived at baseline and year 2 were calculated to provide a proxy measure of the test-retest reliability of the imaging metrics. The relationship between the estimated heritability for each region/metric and the within-subject stability was calculated as a Pearson correlation.The analysis presented is based on the ACE model. MZ twins share 100% of their genetic information, while DZ twins share approximately 50%. Additive genetic effects imply that the expected genetically-based correlation between DZ twins should be half that of MZ twins. Non-linear effects, such as those caused by genetic dominance, are not included in the model. The ACE model also incorporates the equal environments assumption (EEA), that the effect of the common environment is the same for MZ and DZ twins. Violation of this assumption would be expected to result in greater similarity of MZ twins relative to DZ twins, increasing the apparent heritability. The unique environment term models uncorrelated differences between twin pairs, including differences caused by measurement error.Structural Equation Modelling was used to assess the additive genetic (A), common environment (C), and unique environmental (E) contributions to tissue microstructure using the OpenMx  for MZ and DZ twin pairs for each region and metric at baseline are shown in Fig. Structural equation modelling estimates of the additive genetic, common, and unique environmental contributions to each region and metric are shown in Table The contributions to each region and metric are shown in Table At two-year follow-up, significant (p < 0.05) contributions of the common environment to MD in the nucleus accumbens  and the volume of the brainstem  were observed.The correlated factors models assessed the stability of etiological influences on subcortical regions between measures at baseline and the two-year follow-up Table . For RNIThe Pearson correlations between measures at baseline and two-year follow-up are shown in Fig. Figure The RNI metric based on an RSI model was more consistent between time points and generally yielded higher heritability estimates compared to MD derived from a diffusion tensor model. The RSI model is biologically based, and substantially more sophisticated than the DTI model, modelling the restricted, hindered, isotropic, anisotropic, and free fluid components separately. By comparison the DTI model combines these components, and the derived MD values are susceptible to contamination, for example from partial volume averaging of free fluid  with adjacent tissue due to poor tissue segmentation. RNI may be a more reliable microstructure measure to be used in the scope of twin research. A previous study  across all three imaging modalities. The putamen and thalamus displayed the possibility of novel genetic influences in all three modalities; however, given the magnitude of the genetic correlations, the genetic effects are mostly stable. All the other phenotypes demonstrated high genetic correlations but were inconsistent across modality on whether there were novel genetic influences. The putamen and thalamus displayed the possibility of novel genetic influences in all three modalities; however, given the magnitude of the genetic correlations, the genetic effects are mostly stable. All the other phenotypes demonstrated high genetic correlations but were inconsistent across modality on whether there were novel genetic influences.The large genetic correlations across time are suggestive that genetic influences on subcortical development remain relatively stable from ages 10 to 12. Besides the hippocampus and caudate, most of the genetic correlations were less than one in at least one of the imaging modalities, suggesting some novel genetic influences emerging. These findings contradict one previous longitudinal twin study which found no new genetic effects on subcortical brain volumes from ages 9 to 12 , subject motion can also be problematic, although the ABCD study benefits from the use of prospective motion correction for the structural data acquisition, combined with recommended imaging quality control criteria.Measurement error is expected to reduce the correlations between MZ twins and DZ twins similarly, resulting in lower heritability estimates for metrics that have low test-retest reliability Fig. , r = 0.8While we controlled for the effect of sex, we did not specifically investigate it. Prior studies have postulated that there are no sex differences in subcortical volumes Below is the link to the electronic supplementary material.


Tetranychus urticae to disentangle genetic vs. nongenetic correlations among traits, while varying parasite density and opportunities for transmission. A positive genetic correlation between virulence and the number of transmitting stages produced was found under continuous transmission. However, if transmission occurred only at the end of the infection period, this genetic correlation disappeared. Instead, we observed a negative relationship between virulence and the number of transmitting stages, driven by density dependence. Thus, within-host density dependence caused by reduced opportunities for transmission may hamper selection for higher virulence, providing a novel explanation as to why limited host availability leads to lower virulence.Virulence is expected to be linked to parasite fitness via transmission. However, it is not clear whether this relationship is genetically determined, nor if it differs when transmission occurs continuously during, or only at the end of, the infection period. Here, we used inbred lines of the macroparasitic spider mite  R0; Virulence, the harm inflicted by parasites on their hosts, is a trait with high relevance for human, animal, plant, and ecosystem health. Parasites replicate within their host, exploiting their resources and damaging tissues, thus virulence is often positively correlated with replication rate , a measTetranychus urticae was collected on different host plants, in Portugal in 2013 , at the University of Lisbon. In October 2015, 50 individuals from six different field populations  were collected and mixed to form an outbred population maintained at high densities . In October 2016, inbred lines were created from this population by sib mating. This procedure was repeated for 14 generations, ensuring an inbreeding coefficient above 94%  in small plastic boxes (255 mm length × 183 mm width × 77 mm height) at optimal conditions . Under these conditions, egg to adult development takes ca. 11–13 days. These same conditions were kept throughout all experiments.Prior to the experiments, cohorts of spider mites from each inbred line were created by isolating 40–50 mated females of each line during 48 hr. After 14 days, the resulting mated females (daughters) were used in the experiments. In this species, mated females are the main dispersers  and, additionally, the damage inflicted by males is negligible, thus, does not significantly contribute to virulence . Not all2 bean leaf patch, where they fed and laid eggs for 4 days. Then, adult females were removed, and a photograph of each patch was taken using a Canon EOS 70D camera. The amount of damage inflicted by spider mites was measured using ImageJ and Ilastik 1.3, using a method developed specifically for spider mites , on a 4 cmr mites . In this setup, transmission would only be possible after this measurement, i.e., at the end of the infection period , on a 4 cmn period . There wWe present correlations between different traits : virulenGenetic and environmental correlations were measured using a multiresponse generalized linear mixed model fitted with an MCMCglmm . GenetiTo measure genetic variance for traits (variance among inbred lines) and the effect of density on this variance we used per capita values, by dividing the value for each host patch by the initial density of adult females . Note thVar ((E)) and the Var ((E)).V = 1 and nu = 0.002, assigning a weak informative variance (V) and a low degree of belief (nu) , ncol = 2, nrow = 2) and nu = 0.002, meaning no covariance between traits, in accordance with the null hypothesis of no correlation. Additionally, we ran the models using an uninformative prior based on an inverse-gamma distribution (V = diag(2)*(0.002/1.002), nu = 1.002), which gave similar results (not shown)  (to assess genetic variance), ief (nu) ; for mult shown) . These pt shown) ). When mt shown) .re = 0.99) and genetic , between the number of transmitting stages produced  and transmission to uninfected hosts , parasite replication (number of eggs), and transmission (the number of transmitting stages) among the ed lines , using pines  and parasite replication (number of eggs laid) both when transmission occurred continuously during the infection period and when transmission was restricted to the end of the infection period . These rWhen transmission was continuous, there was no overall environmental or genetic correlation between virulence and transmission across densities. However, at each density (intermediate and high), we found positive environmental and genetic correlations between virulence and transmission . In thisIn this study, we show that opportunities for transmission shape the relationship between virulence and transmission, a key component of parasite fitness. We found a positive genetic correlation between virulence and transmission when the latter was continuous during infection, independently of the initial parasite density. In contrast, when transmission was restricted to the end of the infection period, the relationship between these traits no longer had a genetic basis, but instead, was modulated by density dependence: beyond a certain level of virulence, at high initial densities of mites, despite an increase in replication , within-host competition prevents most offspring from becoming adult, such that highly virulent genotypes produce fewer adult daughters. Evidence for density dependence has been found in this , and othT. urticae is a generalist parasite, hence it may frequently shift among hosts, and optimal virulence may vary with the host species , within-host density dependence may underlie the predicted optimal transmission at intermediate virulence levels, for parasites that transmit continuously, it may only come into play later in the epidemic, when uninfected hosts become limited. In any case, our results demonstrate that only by including the whole parasite life-cycle in experimental set-ups is it fully possible to understand the ecology and evolution of virulence .qrac008_suppl_Supplementary_MaterialClick here for additional data file.


Background: Socioeconomic status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for cardiovascular disease (CVD). We analyzed Mexican American Family Studies (MAFS) data to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an important determinant of variation in CVD risk factors.Methods: We employed a linear mixed model to investigate GxE in Mexican American extended families. We studied two proxies for CVD [Pooled Cohort Equation Risk Scores/Framingham Risk Scores (FRS/PCRS) and carotid artery intima-media thickness (CA-IMT)] in relation to socioeconomic status as determined by Duncan’s Socioeconomic Index (SEI), years of education, and household income.Results: We calculated heritability for FRS/PCRS and carotid artery intima-media thickness. There was evidence of GxE due to additive genetic variance heterogeneity and genetic correlation for FRS, PCRS, and CA-IMT measures for education (environment) but not for household income or SEI.Conclusion: The genetic effects underlying CVD are dynamically modulated at the lower end of the SES spectrum. There is a significant change in the genetic architecture underlying the major components of CVD in response to changes in education. Cardiovascular disease  (CVD) isResults from the Whitehall I Study, conducted over 4 decades ago, and the Whitehall II study, conducted 2 decades later, demonstrate a social gradient describing an inverse relationship between mortality  and morbidity, and socioeconomic status (SES)  The pattWe conducted a statistical genetic investigation of the potential role of genotype-by-environment interaction (GxE) in mediating the social gradient related to CVD. SES is a composite environment that dynamically modulates the genetic architecture underlying CVD mortality and morbidity phenotypes. Our approach tests predictions derived from the general hypothesis that the complex genotype-phenotype map governing CVD outcomes is dependent upon or a function of the SES environment. Using three measures of SES , we assessed the evidence for genotype by socioeconomic status in the Mexican American Family Studies (MAFS). We investigate potential genotype-by-SES interaction (GSI) influencing several atherosclerosis or CVD risk measures. Since there are no specific risk-scores for Mexican Americans, we used the established Framingham Risk Score (FRS-08), which was developed initially as a 10-year atherosclerosis risk assessment  and lateWe propose that SES acts at the cellular and molecular levels to modify gene expression regarding the risk of CVD and that this can be captured by GSI modeling.The Institutional Review Board at the University of Texas Health Science Center at San Antonio approved the MAFS study protocols. All study participants provided written informed consent.As described by our team in earlier publications, the MAFS population (1431) comprises large Mexican American extended families (42) randomly ascertained with respect to CVD or comorbidities . The pro2), waist circumference (cm), systolic blood pressure (mmHg), diastolic blood pressure (mmHg) were collected on all participants , fasting insulin (u/mL), 2-h glucose (mg/dL), leptin (ng/mL), high-density lipoprotein-cholesterol (mmol/L), triglycerides (mmol/L), total serum cholesterol (mmol/L), 2-h insulin (u/mL), body mass index , carb (g/d), saturated fat (g/d), monosaturated fat (g/d), polyunsaturated fat (g/d), cholesterol (g/d), sucrose (g/d), alcohol (g/d)]. Our SES variables obtained at interview were INC, and EDU where SEI was computed following the method in . This vaThe 10-year atherosclerosis scores were computed by applying the appropriate formulas to the requisite MAFS data for the FRS, PCE-AA, and PCE-CA . The PCEy we write:Our statistical genetic approach is a linear mixed model, where for a generic phenotype vector ctively.  The phen a trait . Each ph a trait . There w a trait .The polygenic model is used to obtain estimates of trait heritabilities and as a model reference point upon which more complex models can be elaborated. For a sample of related individuals, assuming fully uncorrelated genetic and environmental effects, the polygenic model posits that the phenotypic covariance is decomposable into additive genetic and residual environmental variance components and that inter-individual covariances will be given strictly by the additive genetic variance weighted by the genetic relatedness coefficient. The latter feature of the polygenic model makes two implicit assumptions regarding the genetic covariance: that the pairwise genetic correlation is unity, and that the additive genetic variance is homogeneous.Under the general GxE model, we relax these assumptions by expressing both the additive genetic variance and genetic correlations as continuous functions of a specific environment to capture any potential interaction between the genetic effects  and the specific environment. The null hypothesis under this extension is that the expression of the aggregate of all genotypes underlying a phenotype (polygenotype) is independent of the specific environment. Rejection of this null implies that the genotype-phenotype map for the trait in question depends on a specific environment or, in other words, is a function of the specific environment. We propose that the extent to which the genotype-phenotype interaction depends on a function of the environment can be captured by modeling the GxE variance. For the simplest case of contrasting two different SES environments, for example, high versus low levels of income, the GxE variance is zero if the following two conditions are simultaneously true: homogeneity in the additive genetic variance: There is GxE evidence if either null hypothesis is rejected . RejectiWe now extend this theory to a spectrum of such measures to model GxE for continuous SES environments as opposed to two levels of the SES variable. To this end, we employ variance and correlation functions , which wprocess,  where thWe then posit a diagonal matrix To ensure that we are highlighting the potential for SES environments to modulate the genotype-phenotype map, we employ Best Linear Unbiased Prediction (BLUP) methods to extract any associated additive genetic effects influencing EDU, INC, and SEI. . BLUP acFor the basic polygenic model, denote the parameter vector by http://solar-eclipse-genetics.org/brief-overview.html) was used to obtain the model likelihoods, maximum likelihood estimates (MLEs) of model parameters, and their standard errors (SEs)  . HypotheWe adopted a two-stage hypothesis testing approach that our team has implemented to analyze GxE models of biomedically significant trait-environment dyads . In the dom (df) .In the second stage, we examined the more specific GxE hypotheses. The full GxE model with all parameters estimated was compared with models when either th 1 df.  Regardind theory . Therefop < 1.0 s 10–5 for each).Our analysis reveals that genetic factors play a significant role in shaping the phenotypic response to various socioeconomic (SES) measures. The heritability estimates indicate that genetic factors account for a substantial portion of the variation in these traits, ranging from 41% to 51%. In the initial stage of our analysis, we compared the performance of the genotype-by-socioeconomic status interaction (GSI) model to that of the polygenic model. We showed a significant improvement of the GSI model over the polygenic model only when EDU, and not INC or SEI, served as the environmental index Further Analysis: Additive Genetic Variance Homogeneity, Genetic Correlation, and Residual Environmental variance homogeneity, specifically for the genotype-by-EDU interaction (GEdI) models.Moving to the second stage, we focused on more specific hypotheses related to additive genetic variance homogeneity, a genetic correlation of 1, and residual environmental variance homogeneity, specifically for the genotype-by-EDU interaction (GEdI) model.For FRS-08, we found that the GSE (genetic correlation) was not significantly different from 1. Under a reduced GEdI model , we discovered significant evidence of GEdI. This was indicated by a genetic correlation function that declined significantly away from 1 , 2A, as In the case of PCE-AA, we found evidence of GEdI through significant heterogeneity in additive genetic variance , 2B, whiFor PCE-CA., we identified significant GEdI due to a genetic correlation function declining away from 1 with increasing differences in the EDU index .Our analysis of CCA-IMT revealed evidence of GEdI, as indicated by a genetic correlation function declining away from 1 and significant heterogeneity in the residual environmental variance (decreasing) , 2C.p-values were slightly above the nominal significance level (p•0.05). It is worth noting that this trait had the smallest sample size among the variables examined , in the development of cardiovascular disease in Mexican Americans has also garnered significant attention from various disciplines. . RosengrRecognizing education to be crucially important in the genesis of the social gradient, Marmot hypothesized that SDoH in general  and educOur study is unique in several ways. Firstly, this is a family-based design of Mexican American families and is useful for combined linkage and association analysis. The increased genetic relationship among relatives allows the implementation of genetic variance decomposition methods . Family-The Framingham Risk Score could overestimate/underestimate risk in US-Hispanic populations as the magnitude of associations between risk factors differs between race/ethnic groups . The finSince education significantly affects genetic factors underlying CVD risk variables, future studies should prioritize education as the critical socioeconomic determinant when investigating gene-environment interactions for CVD risk. Future studies should investigate how education may directly affect genetic factors underlying CVD risk and the mechanisms through which it may exert its influence. We can also examine the genetic correlation across different education levels---the genetic correlation of CVD risk variables was found to decline with increasing differences in education levels. This suggests different genes may be involved in CVD risk at different education levels. Future studies should further examine this relationship and the specific genetic variants that may be involved. Researchers can consider the interplay between genetic and environmental factors. Our findings suggest that genetic and residual environmental factors significantly influence CVD risk variables, with environmental factors becoming less important as education levels increase. Future research should continue to investigate this complex interplay and how genetic and environmental factors interact to impact CVD risk across varying education levels.The next step in this research is to determine the genes involved in the genotype by education interaction. Once identified, established canonical pathways and molecular expression can provide intervention targets and a better understanding of how genes interact with environments to modify genetic expression in health and illness.We examined potential GxE interaction using likelihood-based statistical inference in the phenotypical expression of CVD. We assessed Social Determinants of Health (SDoH), specifically measurements of socioeconomic level, and identified significant GxE interactions for CVD and education. These findings provide evidence that genetic factors interact with the level of education to influence CVD. Future directions will focus on the specific genes involved and the nature of the interactions.


China’s and Europe’s dependence on imported protein is a threat to the food self-sufficiency of these regions. It could be solved by growing more legumes, including alfalfa that is the highest protein producer under temperate climate. To create productive and high-value varieties, the use of large genetic diversity combined with genomic evaluation could improve current breeding programs. To study alfalfa diversity, we have used a set of 395 alfalfa accessions (i.e. populations), mainly from Europe, North and South America and China, with fall dormancy ranging from 3 to 7 on a scale of 11. Five breeders provided materials (617 accessions) that were compared to the 400 accessions. All accessions were genotyped using Genotyping-by-Sequencing (GBS) to obtain SNP allele frequency. These genomic data were used to describe genetic diversity and identify genetic groups. The accessions were phenotyped for phenology traits  at two locations  from 2018 to 2021. The QTL were detected by a Multi-Locus Mixed Model (mlmm). Subsequently, the quality of the genomic prediction for each trait was assessed. Cross-validation was used to assess the quality of prediction by testing GBLUP, Bayesian Ridge Regression (BRR), and Bayesian Lasso methods. A genetic structure with seven groups was found. Most of these groups were related to the geographical origin of the accessions and showed that European and American material is genetically distinct from Chinese material. Several QTL associated with fall dormancy were found and most of these were linked to genes. In our study, the infinitesimal methods showed a higher prediction quality than the Bayesian Lasso, and the genomic prediction achieved high (>0.75) predicting abilities in some cases. Our results are encouraging for alfalfa breeding by showing that it is possible to achieve high genomic prediction quality. Its positive impact on protein autonomy through atmospheric nitrogen fixation and the environmental services it provides at the plot and rotation level have become increasingly important in recent years  or more accurately through its progeny under field or test conditions. In other species, numerous breeding programs have benefited from the advances in high-throughput genotyping technologies . With a de novo assembly of reads without a reference genome , as described on heterozygous diploid species . In the e genome  or on a uncatula . With thuncatula , more reuncatula .Linkage disequilibrium decays at short distance in allogamous species  and thisIn alfalfa, as in other species, phenology traits are the major drivers of climate adaptation. Flowers are formed at the leaf axillaries  and do ni.e. population) level, with SNP frequencies as genotyping data.In this study, we gathered ‘cultivated material’, comprising old and recent cultivated accessions mainly from Europe, North and South America, and China, and further extended the genetic diversity with breeding material of five major European breeders. From this material, we assessed whether a genetic structure has been created by preferential crosses between materials of specific fall dormancy groups. We studied how diversified breeding material of the five European breeders is, compared to the diversity found in the cultivated material. With the cultivated material, we assessed if a GWAS approach can detect QTL for phenology traits and if the genomic prediction models allow to predict phenology with a good accuracy. In this study, GWAS and GP analyses were conducted at accession  comprised of 378 cultivars and 22 landraces whose fall dormancy score mainly ranged between 3 and 7. Their origin, based on the place they have been collected (landraces) or initially selected and registered (cultivars) was Europe (318 accessions), North America (45 accessions), South America (16 accessions), China (17 accessions), Middle East (3 accessions) and Japan (1 accession). In addition, 617 accessions  representing advanced breeding material obtained by five European breeders were included: 144 accessions from breeder A, 62 accessions from breeder B, 101 accessions from breeder C, 189 accessions from breeder D and 121 accessions from breeder E. Each breeder chose their material for this study but did not mention if it was used in or representative of the breeding program of the company or institution. All the 1017 accessions were genotyped and used for genetic structure study while only the 400 ‘cultivated material’ accessions were phenotyped for phenology and used for GWAS and GP studies. The origin of the material used is available in the data repository (see the Data Availability Statement section).2.2The methodology used for the DNA extraction, the optimization of the GBS methodology and GBS sequencing has previously been reported in 2.3delineate . Due to a very dry fall in Lusignan in 2020, fall regrowth was not sufficient for a cutting and an estimation of dry matter yield.All the “cultivated material” plant material was established in two locations for phenotyping, the first one in France at the research unit (URP3F) of INRAE  and the second one in Serbia at the research unit of IFVCNS . In Lusignan, the trial was sown on the 10e blocks . The Ta2.6All traits were independently adjusted to field micro-environmental heterogeneity with the breedR package . Within G is explained in the next section. B-splines were anchored at a given number of knots for rows and columns, a high number of knots smooths out the surfaces. breedR optimized the knot numbers by an automated grid search based on the Akaike information criterion  was based on , adaptedth marker, and The denominator is a scaling parameter, corresponding to the sum of the expected SNP variance across genotypes , where  species , with =2.8The GWAS analyses were performed with the MLMM method , while thttps://bbric-pipelines.toulouse.inra.fr/myGenomeBrowser?portalname=MSAT_XinJiangDaYe&owner=sebastien.carrere@inrae.fr&key=PyG9k9tK). Three sources of gene annotation were available, one from the reference genome used for the genotype calling  is represented according to its size. We randomly sampled the remaining accessions to test the effect of the size of the training population. Nine sample sizes were tested: ranging from 10% of the remaining accessions (29 accessions) to 90% (270 accessions), ten iterations were performed for each sample size. In order to assess the effect of the training population composition on the quality of the predicting ability, a second case was studied. The validation population was composed of fifteen accessions from one group only and the training population of 210 accessions was randomly taken from the remaining groups. Ten repetitions were performed. Groups with less than fifteen accessions were excluded. For each repetition, the validation population was sampled from within a group and was predicted using two different training populations. To test the predicting ability across groups, the first training population excluded the other accessions belonging to the same group as the validation population. The second training population was a random sample of all groups. We ensured that the potential confusion between the effect of the composition of the training population and the effect of the sample size was avoided by using the same sampling but two different training populations.2.9.2First, the best linear unbiased prediction based on genomic information (GBLUP)  was usedThen, we used two Bayesian methods, the Bayesian Ridge Regression (BRR)  and the We compared the different models in the situation of the first case scenario with a validation population of 100 accessions randomly selected and a training population of 270 randomly selected accessions (90%).33.1The GBS pipeline optimization, as performed in this study, led to a genotyping dataset with little missing values among the accessions. Only five accessions out of 1017 were lost due to a poor sequencing depth . The five other groups showed a genetic continuum but each group can be related to the geographic origin of the accessions: group 3 with 139 accessions of European origin (France and Northern Europe), group 7 with 151 accessions mostly of European origin (Southern and Eastern Europe), group 4 with 61 accessions of Europe, North and South America, group 5 with 21 USA and 1 Chinese accessions and group 2 with 5 North American accessions. The European accessions as well as the American accessions were thus split into several groups. The group 4 probably illustrates the multiple origins of some varieties selected in the two continents.The number of principal components (PC) required to explain 90% of the genetic variation was 300 Figure 2falcata material (group 1), even if the latter was represented by only two accessions in this study. The distance between the group 6 with Chinese accessions and the overlapping groups 3, 4, 5, 7 containing Western accessions suggested unconnected breeding programs. The FST value between the groups were low with an average value of 0.01 and a range between 0.001 and 0.026 .Finally, the European and the North American accessions displayed little overlap, but South-American accessions interestingly overlapped with these two groups. The three accessions from the Middle East were close to the American accessions and the Japanese accession was closer to the European-American groups than to the Chinese group. All Chinese varieties resided in the Chinese group except one variety that grouped into group 5, probably revealing a selection based on American material. Accessions from China seemed to be different from nd 0.026 Table 2.falcata or possibly Chinese accessions. Nonetheless, the genetic material provided by the five European breeders in this study did not belong to the North American nor the Chinese groups.The breeding material provided by five European breeders was compared to the groups obtained with the worldwide cultivated material  increased markedly by increasing the size of the training population from 29 to 59 accessions, and reached a plateau with a training population of at least 89 accessions Figure 74We designed this study firstly to describe the genetic structure among currently grown alfalfa plant materials  and plant breeding materials from the major European breeders . Secondly, we wanted to test our hypothesis that breeding programs conducted within a narrow range of fall dormancy could induce a structure within worldwide breeding pools depending on fall dormancy as previously studied by 4.1Our study used a large number of markers (227 092 SNP) with few missing data (<5% per locus), in comparison to other studies in alfalfa . Previou4.2falcata. This separation is consistent with another study showing that the ssp falcata are clearly separated from cultivated alfalfa . Previous studies already evidenced QTL for fall dormancy. In a first attempt, QTL were found in a mapping population  but the 4.4First of all, this study showed that it was possible to reach a high predicting ability  for phenology related traits. However, this was not observed for all traits, the prediction capacity was around 0.30 for some traits . In our study, the difference between traits depended mainly on the year of measurement, with the year 2020 having a lower predicting ability on average, and on the location of measurement, with the prediction of traits measured in Novi Sad (Serbia) being less accurate than that measured in Lusignan (France). We do not have a clear explanation for these results, we suppose that it may be related to the difference between the continental climate (Novi Sad) and the oceanic climate (Lusignan) that is warmer in fall allowing a higher potential growth and therefore a better distinction of the fall dormancy of the accessions. Heritability was often presented  as a facIn this study, the different GP models showed similar results, which was expected for GBLUP and BRR, but not necessarily for the Bayesian Lasso, as the latter selected the SNPs having an effect in the model and was supposed to perform better in the case of strong QTL effects . HoweverSimilar to other authors , increasOur study has shown that for traits such as fall dormancy or flowering date, we can achieve good genomic prediction quality in a diverse panel. These results, combined with other studies on alfalfa , show thThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/MP contributed to the genotyping bioinformatics pipeline, conceived and performed the analyses and wrote the paper draft. SD performed DNA extraction, prepared libraries for sequencing. FS contributed to data collection. DK conceived the project with BJ. DM defined the trial experiments and plant material with BJ and coordinated data collection in Novi Sad. MZ set up the experimental augmented design. TR helped with bioinformatics analysis. PB covered the aspects of GWAS and genomic prediction on the project. BJ conceived the project and obtained financial support, contributed to data collection and analyses. All authors participated to the draft and revision of the manuscript and approved the final version. All authors contributed to the article and approved the submitted version.