A sizeable majority of the respondents who were interested in about learning their cancer risk also said that they were likely to participate in a PGS test that involved a clinical appointment and blood draw, but no out-of-pocket cost . Reasons for not wanting to participate included not finding test results interesting or important, concerns about costs, and feeling afraid to know the results. Overall, our results suggest that engaging and educating the general population about the benefits of learning about an inherited cancer predisposition may be an important strategy to address recruitment barriers to PGS.An emerging role for DNA sequencing is to identify people at risk for an inherited cancer syndrome in order to prevent or ameliorate the manifestation of symptoms. Two cancer syndromes, Hereditary Breast and Ovarian Cancer and Lynch Syndrome meet the “Tier 1” evidence threshold established by the Centers for Disease Control and Prevention (CDC) for routine testing of patients with a personal or family history of cancer. Advancements in genomic medicine have accelerated public health pilot programs for these highly medically actionable conditions. In this brief report, we provide descriptive statistics from a survey of 746 US respondents from a Qualtrics panel about the public’s awareness of genetic testing, interest in learning about their cancer risk, and likelihood of participating in a population genetic screening (PGS) test. Approximately of half the respondents were aware of genetic testing for inherited cancer risk  and would choose to learn about their cancer risk . Characteristics of those interested in learning about their cancer risk differed by educational attainment, age, income, insurance status, having a primary care doctor, being aware of genetic testing, and likelihood of sharing information with family ( DNA-based screening of healthy individuals has enormous, yet untapped potential to improve cancer-related health outcomes through early detection and cancer prevention before symptoms manifest. Multidisciplinary research supporting the clinical utility and validity of DNA-based population screening for certain medically actionable conditions is increasing. . A pointThe US Centers for Disease Control and Prevention (CDC) has defined two hereditary cancer syndromes as “Tier 1” based on their clinical actionability: Lynch syndrome (LS) and Hereditary Breast and Ovarian Cancer (HBOC). Evidence indicates that population screening could significantly reduce morbidity and mortality for millions of Americans each year. . Cost-efThe National Academies of Sciences, Engineering and Medicine’s Genomics and Public Health Action Collaborative has endorsed “an accelerated implementation science agenda”  for the Though the capacity for clinical PGS programs to transform personalized public health in the United States is widely acknowledged , currentIn December 2020, the UNC Lineberger Cancer Prevention and Control Program recruited an online convenience sample of US adults through the Qualtrics Online Panel platform . Participants were eligible if they were over the age of 18 and resided in the US. Qualtrics panel members are recruited from multiple sources including but not limited to, targeted email lists, gaming sites, customer loyalty web portals, and social media. Panel members were sent an email invitation or were prompted on the survey platform to respond to the online survey for a specific compensation amount. Because panel members can be reimbursed in different ways , incentive amount for this study is estimated to be $2.50 per person. Interested respondents clicked on the survey link provided. The University of North Carolina Institutional Review Board approved this study (#20–2338).Genetic tests that analyze your DNA for potential cancer risks are currently available. Have you heard or read about these genetic tests? Yes/no) ((1) awareness about genetic tests  ;Have you ever had a genetic test to determine if you have an increased risk of developing cancer? Yes/no) ((2) receipt of genetic testing (among those aware of genetic testing:  Yes/no) ;How interested would you be in learning whether you have a genetic risk factor for cancer that can be prevented or treated? Likert 1–5) ((3) interest in learning genetic risk (among those who have not received genetic testing: ert 1–5) ;To learn whether you have a genetic risk factor for cancer, you would need to make an appointment at a local clinic, get your blood drawn, and set up an online account to access your test results. Assuming there is no cost to you, how likely would you be to get this test? Likert 1–5); and(4) likelihood of getting a PGS test (among those who were interested in learning genetic risk: (4a) reasons why you selected “unlikely/very unlikely” or “neither likely or unlikely” or “likely/very likely” (open-ended response). See .Primary outcomes of interest were adapted from existing surveys and included:Given that cost is a known barrier to genetic testing among patients , we askeBecause we were interested in understanding which subpopulations are aware of, engaged in or interested in engaging in PGS programs, we collected sociodemographic information that has been associated with awareness about or use of genetic services in prior studies. These characteristics included: gender (women/men) , ethniciWe calculated descriptive statistics to examine whether there were differences between the characteristics of respondents according to our primary outcomes. We used chi-square tests to examine differences between those who were 1) aware of genetic tests versus not aware, 2) previously tested versus not tested, 3) interested  in PGS versus not interested , and 4) likely to participate in PGS versus not likely .We used thematic analysis to understand respondents free text responses to their likelihood of participating in PGS (question 4a above). We report the top five themes among those who were likely, unlikely, and neutral about getting a PGS test (MCR and LVM). Independently coders reviewed open-ended responses and identified emergent themes for each response category  and applied them to 10% of responses in each category. Coders compared themes and their application, modified the list of themes, and then independently applied the codes to 20% of responses (n = 60). We repeated this process until we achieved 100% agreement (in one round), and then we independently coded the remaining responses. We calculated the level of agreement between coders (87.6%). Conflicts were then reconciled through discussion. We reported the major themes and exemplar quotes.Of the overall sample of respondents, 377 (50.6%) were aware of genetic tests. Among those who were aware of genetic tests, a higher proportion were non-Hispanic, in the middle age categories, as well as had higher educational attainment, Medicare or other insurance, a primary care doctor and higher perceived comparative cancer risk. No significant differences between gender, race, income, rurality, and sexual orientation were identified .Among those aware of genetic tests, 110 respondents (29.2%) had received a genetic test for an inherited cancer risk in the past. Compared to the 267 respondents who had not previously received a genetic test, the tested group had a higher proportion of men, higher educational attainment, higher income, had insurance coverage, lived in urban areas, were in the middle age categories, and identified as not straight .Of the 635 respondents who had not received genetic test, 309 (48.7%) would be interested in learning whether they had a genetic risk factor for cancer. Among those interested in learning about their risk, a higher proportion were aware of genetic tests, had higher income, had a primary care doctor, had insurance coverage, were in older age categories, had higher educational attainment, and would be more likely to share information with family .Finally, of 309 respondents interested in learning whether they had a genetic risk factor for cancer, a substantial majority, 255 respondents (82.5%), would be likely or highly likely to get a PGS test . Within Despite increasing availability of direct-to-consumer (DTC) testing and PGS programs, we found that awareness of genetic testing for cancer predisposition in the general population remains at around 50%. This aligns with prior research from 2017 from the National Cancer Institute (NCI) Health Information National Trends Survey in which 57% of respondents reported being aware of genetic tests used for health reasons. . This peThough racial disparities in genetic testing utilization are well established in the literature, , we did Among those aware of genetic testing who have not already had genetic testing, almost half would be interested in learning whether they had a genetic risk factor for cancer that can be prevented or treated, which is lower than what has been reported elsewhere in public samples. . For exaAmong respondents who had not had any previous genetic testing, about half were interested in learning about a genetic risk for cancer predisposition. Furthermore, a large majority said they would be likely to participate in a PGS test in a clinical setting that required making an appointment, getting a blood draw, and creating an online account for a patient portal. All respondents provided contextual information about how and why they responded to this question and, interestingly, only one respondent mentioned mistrust or concerns about genetic discrimination, data privacy or security, which have been commonly reported in the literature. . This maintentions to learn about genetic risk and likelihood of participating in a PGS test. Intentions are associated with health behaviors, as are other factors, some of which are known, such as perceived control (data not collected) and others which are less certain. . Further, while Qualtrics survey samples can provide a diverse sample, , respondcertain. . Thus, icertain. . BecauseOur findings identified two key challenges for the implementation of population genetic screening: increasing awareness of the potential benefits of genetic testing and interest in learning one’s genetic risk for cancer. This may be especially important for subpopulations with lower socioeconomic status and those without a primary source of care. Future work to better understand and develop strategies to overcome these challenges will be essential as PGS programs are increasingly implemented into clinical practice.


Citrus sinensis L. Osbeck) is one of 30 species of citrus which is cultivated in different regions of Iran. In this study, 80 trees of 13 sweet orange cultivars of Mazandaran province were studied for genetic diversity and fingerprinting by five short simple repeat (SSR) marker.Citrus species are among the most important and widely consumed fruit trees in the world and are subjected to increasing global cultivation. Sweet orange (The studied cultivars showed a high degree of genetic variability with an average genetic polymorphism of 98.46%. Behshahr and Jadeh Ghadim2 genotypes had the highest and lowest values in Nei genetic diversity, number of effective alleles, and Shannon index, respectively. Based on k-means clustering, the studied genotypes were divided into two main different groups. The high magnitude of genetic similarity between replicates of different cultivars indicated a potential case of homonymy or synonymy. DAPC analysis showed genetic admixture among some of the cultivars. The heatmap plot illustrated the alleles involved in cultivar differentiation. The CAPs analysis of monomorphic alleles of SSR loci indicated that these alleles differ in their sequences which add up to the genetic variability of citrus germplasm.In general, SSR markers, due to their codominant nature and abundance in genome, are a good indicator for cultivar fingerprinting and hybrid prediction in orange cultivars. The present results showed the high diversity of sweet orange trees in different cultivars in the north of the country.The online version contains supplementary material available at 10.1186/s43141-022-00393-6. The exaCitrus phylogeny and taxonomy are complicated due to the occurrence of bud mutations, sexual compatibility between citrus and related genera, wide dispersion, and long history of cultivation .http://www.fao.org/faostat/en/#data/SC). However, little is known about the genetic diversity of the Iranian citrus germplasm [Climatic conditions especially in the southern and northern provinces of Iran create suitable conditions for citrus production . Therefoermplasm , 9, 20.Citrus sinensis) with 2n = 2× = 18 [Among citrus species, sweet oranges , 2617, 2Most of the sweet orange accessions showed a narrow genetic basis , 25. It C. sinensis) cultivars along with several tangerines . On  Blanco, .C. sinensis) cultivars using SSR molecular markers and (2) evaluation of monomorph allele sequences by using CAPs-SSR method.The present study aims are as follows: (1) genetic fingerprinting of sweet orange .Thermal program consisted of 94 °C for 5 min; 38 cycles of 94 °C for 40 s, with annealing temperatures for each locus; TAA27 for 52 °C, CAC15 for 52 °C, AG14 for 58 °C, CAT01 for 53 °C, and TC26 for 55 °C for 40 s; and extension segment at 72 °C for 1 min. Final extension was at 72 °C for 5 min. PCR reaction was performed by using Techne Prime thermocycler.The PCR products were visualized on 3% high-resolution ultrapure agarose gel .BamHI restriction enzyme was used due to the high abundance of its site in the genome. For that, monomorph SSR-PCR products  were mixed with 0.4 μl of 10 BamHI enzyme , 0.5 μl of the buffer, and 4.5 μl of distilled water. The reaction was incubated at 37 °C for 8–10 h. Digestion products were visualized on 3% UltraPure Agarose gel.For CAPS-SSR assay, Each band was scored as present (1) or absent (0). Genetic parameters such as polymorphism percentage (P%), Nei genetic diversity (He), Shannon index (I), and AMOVA test were estimated by using GenAlEx 4.6 software. We used different clustering methods for grouping of the studied genotypes. These dendrograms were constructed by PAST software ver 3.01. Details of genetic grouping by the SSR markers were studied by constructing a heatmap plot. The genetic admixture and assignment of the studied citrus trees were determined by DAPC . These analyses were performed in R package 4.1.In total, 5 SSR loci produced 84 alleles ranging from 100 to 300 bps Fig. , S2. TheThe highest and lowest observed heterozygosity (Ho) belonged to cultivars Fereydoon kenar (no. 8) and Jadeh Ghadim 2 (no. 13) with a mean of 0.45 and 0.067, respectively.Based on Nei’s genetic distance, the highest distance was observed between Behshahr cultivars (no. 9) and local orange (no. 4). According to the Ward clustering Fig. , genotypP-value = 0.001) with Fst = 0.110.  was almost the same with Golein’s study , which confirmed the high genetic diversity of sweet orange trees in different genotypes in the northern part of the country. Sequencing and CAPS-SSR studies have also provided more variation among monomorph alleles of the same locus can be sequentially different. SSR and CAPs could be helpful for differentiation of sweet orange genotypes.Additional file 1: Table S1. AMOVA table based on SSR alleles for 13 cultivars. Table S2. Pairwise groups Fst value between cultivars. Fig. S1. Allele patterns of CAC15 SSR locus. 50bps Ladder. Fig. S2. Allele patterns of TAA27 SSR locus. 50bps Ladder.


Mecheri sheep are a hair-type sheep breed from India, reared for meat, which play a vital part in the economic well-being of farmers. This study was conducted to evaluate Mecheri sheep by assessing sources of variation and estimating the genetic parameters for growth traits and examining genetic diversity through pedigree analysis. The estimated direct heritability for growth traits was lower in post-weaning age groups when compared to the pre-weaning stages. The intermediate heritability and moderate-to-high genetic correlations observed for the weaning weight indicated that the weaning weight could be employed as a selection criterion for enhancing later-age traits of body-weight trait. Mecheri sheep demonstrate a much lower level of inbreeding due to the widespread use of techniques such as routine genetic diversity monitoring and ongoing efforts to scientifically design and carrying out mating plans for the flock.Determining the genetic and non-genetic sources of variation in a breed is vital for the formulation of strategies for its conservation and improvement. The present study was aimed at estimating the (co)variance components and genetic parameters of Mecheri sheep by fitting six different animal models in the restricted maximum likelihood method, with a preliminary investigation on the performance of animals for non-genetic sources of variation. A total of 2616 lambs were studied, and varying levels of significance were found for the effects of period, season, parity of dam, and birth type on different body-weight traits. Direct heritability estimates derived from the best animal model for body weight at birth, three months, six months, nine months, and twelve months were 0.21, 0.24, 0.10, 0.15, and 0.09, respectively, and the maternal heritability of the corresponding traits was 0.12, 0.05, 0.04, 0.04, and 0.04, respectively. The genetic correlations between the body-weight traits were all positive and moderate-to-strong, except for the correlation between birth weight and the other body-weight traits. The significance of non-genetic factors studied in this work demanded a correction to improve the accuracy of the direct selection of lambs for body-weight traits. The estimated genetic parameters identified the weaning weight as a selection criterion for the improvement in body weight of Mecheri lambs at different ages. Inbred individuals accounted for approximately 13% of the total population in the Mecheri sheep population studied. There were 877 founders in the population, and the actual effective population size was 128.48. The population’s mean generation interval was 3.26. The mean inbreeding values ranged from 0.005 to 0.010 across generations. The population’s average relatedness ranged from 0.001 to 0.014 across generations. Individual inbreeding was found to be 0.45 per cent for the entire population and 3.4 per cent for the inbred population. Sheep farming in India contributes significantly to the livelihood of a large number of small and marginal farmers, as well as landless labourers. Mecheri sheep are one of the hair-type sheep breeds of India and are known for their relatively good growth rate, higher dressing percentage, and excellent skin quality. They are polled animals, medium-sized, and have a compact body covered with short hair. They are widely distributed in the southern state of India , especially in the north-western agro-climatic zone, which includes Salem, Erode, and parts of the Dharmapuri and Namakkal districts . MecheriIdentifying genetic and non-genetic sources of variation in economically important performance traits is a key component in designing animal-improvement programmes. The significance of maternal effects, in addition to direct genetic effects on production traits, had been studied in different sheep breeds as a part of the genetic evaluation programme. Disregarding maternal influence when it is essential skews the estimates of additive variance upwards ,4. AdditThe primary goal of population management is to maximize the long-term competitiveness of a breed. Hence, monitoring the rate of change in inbreeding and the genetic diversity within a population is critical for guiding breeding programmes. A breed’s conservation value could be enhanced by increasing its contribution to the gene pool of its species. This could include regaining its original genetic background and retaining a high level of genetic diversity. Hence, the breeding programme must ensure that the kinship does not increase quickly to maintain the desired effective size . In a smPedigree and growth details of Mecheri sheep maintained at Mecheri Sheep Research Station (MSRS), Tamil Nadu, India, were collected over a forty-year period from 1979 to 2018. The average annual rainfall and the mean maximum and minimum temperatures at this location were 894.38 mm, 34.30 °C, and 21.90 °C, respectively. At the MSRS, Mecheri sheep were allowed to graze for eight hours. When poor grazing was observed, the animals were fed with concentrates, the quantity of which was based on their age, sex, and production status. Depending on grazing conditions, each adult animal was supplemented with 200–350 g/d of concentrate. Breeding rams were fed 350–400 g of concentrate per day during the mating season. Aside from grazing, animals were also fed cultivated fodder and tree cuttings during the dry summer season. The breeding rams were maintained at a rate of 15 ewes per ram, and the mating of rams was limited to three to four consecutive breeding seasons. The number of ewes mated per rams in this study ranged between one and forty ewes, and the mating was conducted in such a way to avoid inbreeding by the careful verification of the pedigree structure. Controlled mating or hand mating was been practised to ensure the collection of pedigree data. All female lambs, excluding weak lambs, were selected and retained for breeding purposes. Lambing was limited to two seasons: the major season stretching from January to March (the main season), and the minor season from August to September (the off season). The maximum number of lambings always occurred during the main lambing season. Depending on their health condition, ewes were typically culled at around six to seven years of age.ijklmn, μ, and eijklmn are the lamb’s body weight, overall mean for the trait, and the random error associated with each observation, respectively. The effects of the non-genetic factors, period of birth, season of birth, parity of dam, sex of lamb, and type of birth are mentioned in the model as Pi, Cj, Dk, Sl, and Bm, respectively.The growth performance of the Mecheri sheep was assessed for the body-weight traits recorded at birth (BW), three (3MW), six (6MW), nine (9MW), and 12 months (12MW) of age. After eliminating outliers, a data set on the growth information of 2616 lambs with pedigree details was created. The lambs were classified for non-genetic factors: namely, the sex of the lamb , season of birth (main and off), period of birth , type of birth (single and twin), and the parity of the ewe . All the non-genetic factors were taken as fixed effects, and the weight of the dam at lambing was included as a covariate in the statistical analysis. The initial analysis revealed that the interactions among the fixed effects were not significant and hence not considered in the final analysis . A serie1, Z2, and Z3, respectively. The following is the matrix of the assumed (co)variance structure:Significantly contributing non-genetic factors identified from the least squares analyses were included as fixed effects in animal models for the estimation of the (co)variance components of growth traits. By incorporating or excluding the maternal genetic and permanent environmental effects, with or without direct maternal genetic covariances in a simple random effect model, the following six different animal models  were fit2a and Aσ2m, respectively. In the variance and covariance structure, A is the additive numerator relationship matrix, and σ2a, σ2m, and σam are the additive variances, maternal genetic variance, and the covariance between the direct and maternal genetic effects, respectively. Permanent environmental effects and residual effects were also assumed to follow a normal distribution with a mean of zero and variances of Idσ2pe and Inσ2e, respectively. In the structure of the variance and covariance matrix, Id and In are identity matrices with orders equal to the number of dams and individual records, respectively, and σ2pe and σ2e are the maternal permanent environment and residual variances, respectively. Direct additive and maternal genetic effects were assumed to be distributed normally with a mean of 0 and variances of Aσp < 0.05. Following this, the most-suited model for each trait according to the likelihood ratio test was employed in bivariate analyses to estimate the genetic, phenotypic, and environmental correlations between the studied traits. The variance and covariance components estimated from the best model in the univariate analysis were utilized as starting values in the bivariate analysis. The direct heritability (h2a = σ2a/σ2p), maternal heritability (h2m = σ2m/σ2p), genetic correlation between the direct and maternal effects , and the maternal permanent environmental variance (c2 = σ2c/σ2p) were estimated. The direct maternal correlation (ram) was computed as the ratio of the estimates of the direct maternal covariance (σam) to the product of the square roots of the estimates of σ2a and σ2m. For the purpose of estimating the inbreeding coefficient, a total of 4168 Mecheri sheep, including pedigreed individuals and their sires and dams, were used. The pedigree analysis was carried out using the Endog software, version 4.8 [Heritability or variance ratio estimates were calculated as the ratio of the variance of additive genetic to the total phenotypic variance. Sampling errors of heritability and correlation estimates were derived as per the approximation method reported by Meyer . A selecsion 4.8 .p < 0.05). The mean values of the body weight of Mecheri sheep at birth, three, six, nine, and twelve months were 2.35, 9.76, 13.72, 16.68, and 19.46 kg, respectively. On least-square analysis, the influence of the period of birth on all the body-weight traits; sex on the 9MW and 12MW; birth type on birth weight, parity on the BW and 6MW; and the season of birth on the 3MW and 12MW a,b were (Co)variance components of the growth traits and heritability estimates pertaining to additive genetic, maternal genetic, and permanent environmental effects estimated for the Mecheri sheep are presented in The genetic correlations estimated between the body-weight traits were all positive . Birth we) and effective number of ancestors (fa) for the pedigreed population were determined to be 117 and 99, respectively. It was predicted that the founders’ uneven contributions would increase inbreeding by 0.27 per cent. A significant subset of founders accounted for 50% of the population’s genetic diversity. The ratio of the effective number of founder animals (fe) to the effective number of ancestor animals (fa) was 1.18., and the mean value of the genetic conservation index (GCI) was 3.001. The mean generation interval (GI) for the gametic pathways, viz., sire–ram lamb, sire–ewe lamb, dam–ram lamb and dam–ewe lamb, were 3.02, 3.04, 3.59, and 3.45 years, respectively. The average GI for rams and ewes was 3.03 and 3.52 years, respectively, and was 3.26 years for the entire population.The descriptive statistics and parameters describing the probability of gene origin in the Mecheri sheep population are presented in The average relatedness and inbreeding coefficient were determined for each individual; their mean values for each generation are shown in The body-weight traits obtained using the least squares mean at various ages were consistent with breed standards and earlier findings ,14. HoweUnderstanding non-genetic sources of variation in the performance traits of sheep is critical for developing the most effective management strategies to support the genetic progress of the animals. The non-genetic factor, viz., the period of birth, had a significant effect on all the body-weight traits but it did not show any apparent trend. The variation in body weight among lambs over periods may be attributable to differences in management, ram selection, and environmental variables such as ambient temperature, humidity, and rainfall . A similThe parity of the dam significantly influenced the birth weight and six-months body weight. The body weight of lambs increased with the parity of their dam;therefore, the lambs born at a parity of third and above had significantly heavier bodies at birth. This could be due to the relative nutritional rivalry between the still-growing pregnant ewes and the growing fetus, as well as the fact that the uterine space and other factors necessary for lamb growth improve as the age of the dam increases ,22. Pari2) estimates of the BW, 3MW, and 6MW were moderate, whereas the estimate was low for the 9MW. Lower heritability estimates for post-weaning traits in Mecheri sheep are consistent with the findings in Kermani [The direct heritability  between the BW and 3MW in Mecheri sheep was positive and high, and the estimates were lower than the reported values of 0.56 for Columbia , 0.43 foThe weaning weight in Mecheri sheep had strong genetic correlations with the 6MW and 9MW and a moderate correlation with the 12MW. The moderate heritability and the strong genetic correlations observed for the weaning weight suggest that the selection for the trait could result in an improved response in later body-weight traits. The genetic correlation observed in Mecheri sheep between the 3MW and 6MW was similar to the correlation found in Iranian Kordi sheep (0.73) , but it The genetic correlation between sequential traits was greater than the genetic correlation between non-consecutive traits, as predicted, and the observed trend is consistent with estimates from previous studies ,50,53,54Genotypic and phenotypic correlations between body-weight traits had a similar trend in that the estimates decreased as the age interval between weight measurements increased. The observed trend is consistent with estimates from previous studies ,50,53,54The average complete equivalent generation and percent pedigree completeness provide information about the pedigree depth of a population. The average equivalent generation recorded for the population of Mecheri sheep (1.74) was lower than the values reported for Nellore , SandynoIn the Mecheri population, only a few males were used as sires; hence, the effective number of founders (117) was less than the total number of founders (877). The reduced number of founder individuals contributed to a reduction in the effective population size and associated genetic diversity to a certain extent. This indicates that incorporating the effective number of founders in a pedigree report could give important information for the better management of inbreeding . Comparae/fa) has been used to measure the degree of the genetic bottleneck in populations; and the ideal ratio would be one. It was stated that the marginal contribution from all ancestors must add up to one, and the fa must always be smaller than or equal to the fe [e/fa ratio in the Mecheri sheep population (1.18) demonstrated that unbalancing between ancestors and founders reduced the genetic variation to a minor degree and confirmed the absence of severe bottlenecks. This fe/fa ratio is similar to the estimate in Afshari sheep (1.17) [The ratio of the effective number of founder animals to the effective number of ancestor (fo the fe . The obsp (1.17)  but lessp (1.17) , Nilagirp (1.17) , and Bhap (1.17)  sheep brThe projected rise in inbreeding (0.27) as a result of the unbalanced contribution of founders in Mecheri sheep was significant, but the number of ancestors contributing to 50 per cent of the population (42) was relatively high. Thus, the genetic differences from the founders had not been lost as a result of bottlenecks, unequal contribution, or segregation. Individuals in the population had genetic conservation index (GCI) values between 0.879 and 13.06. To maintain a balanced contribution from the founders, breeding rams could be selected from the animals with a higher index .The estimated realized effective population size (128.48) was slightly higher than the reported estimates for Zandi (71) , Bharat Generation intervals in sire-based pathways (3.02 and 3.03 years) were comparatively lesser than the dam-based pathways (3.59 and 3.45 years). This could be attributed to the long-standing practise of keeping breeding ewes in a flock longer than breeding rams in order to produce offspring . In the The estimated inbreeding in the Mecheri sheep population (0.45%) was comparable to the estimates reported in the Iranian Moghani (0.40%) , SpanishThe average relatedness (AR) coefficient in the Mecheri sheep across generations showed a similar pattern to inbreeding, reaching its maximum in the fifth generation (1.4%). The average AR coefficient (0.89%) was notably greater than the mean inbreeding, which could be attributed to the comparatively good pedigree depth of the population studied. A comparable average AR (0.73%) was reported in Santa Ines sheep . ComparaThe study revealed a prominent maternal influence in the pre-weaning growth trait and indicated the importance of maternal genes in determining this trait. The moderate heritability and the moderate-to-strong genetic correlations observed for the trait, weaning weight, suggest that this trait could be used as a selection criterion for improving the later-age growth traits. The analysis of pedigree information and genetic differences in the population revealed a high level of intra-flock diversity, with the effective population size exceeding the critical point, indicating sufficient genetic diversity for breed conservation. The rate of inbreeding in the population was under control, and the introduction of genetically superior rams from other populations to reduce inbreeding could be balanced by the flock’s selection and mating of superior breeding animals with a high genetic conservation index, which may maintain the founders’ balanced contribution.


These analyses found that trotting and pacing sires were less genetically diverse than dams  and their offspring , and pacing offspring were significantly less diverse than their dams (HEPBonferroni = 2 × 10-3). Inbreeding coefficients for trotters (FIS = −0.014) and pacers (FIS = −0.012) suggest that breeding practices have maintained diversity. Moderate levels of genetic differentiation (0.066 < FST < 0.11) were found between pacing and trotting groups. Additionally, 10 of the most prolific trotting sires and their male offspring (n = 84) were genotyped on the 670K Axiom Equine HD Array. HO values higher than HE (P < 0.001), low inbreeding coefficients (mean F = −0.064), and mean FROH = 21% indicate relatively high levels of diversity in this cohort, further supporting the STR data. However, in contrast, HO values were higher for trotting sires (0.41) than their offspring (0.36). This observation warrants further monitoring of diversity over time. These data provide an updated foundation of diversity indices for further, long-term analysis in the breed.American Standardbreds were developed as a harness racing horse breed. The United States Trotting Association closed the studbook in 1973 and implemented a book size cap in 2009. This study aimed to investigate genetic diversity in the American Standardbred after the studbook cap was introduced using short tandem repeats (STRs) and single-nucleotide polymorphisms (SNPs). Sixteen STRs from horses foaled from 2010 to 2015 and their sires and dams ( The American Standardbred was developed during the 19th century as a harness racing horse breed that competes at one of 2 different gaits: the trot  or the pace . Founding horses of the American Standardbred include individuals of Trotter, Thoroughbred, Norfolk Trotter, Morgan, Barb, Canadian Pacer, and other imported bloodlines . GeneratClosing a studbook limits the introduction of new alleles into the gene pool and thus can impact genetic diversity and the health of animals. For example, in Standardbreds, 26% of the 1987 foal crop  were sired by 2% of the sires (60 horses) . A contiIn addition to reducing genetic diversity, inbreeding has been found to be correlated with reproductive failure in a few horse breeds with high inbreeding levels. For example, in Friesian horses, higher levels of inbreeding were correlated with an increase in the rate of retained placenta . Thus, eIn the 1980s, inbreeding was analyzed in American Standardbreds using pedigree analysis of 14 generations as well as short tandem repeats (STRs). Together, these studies showed higher levels of inbreeding in trotters when compared with pacers . A statiFIS), which could be attributed to increased inbreeding in the individuals sampled or substructure based on gait -based study to investigate diversity in Standardbreds utilized data from the 50K SNP array to compare diversity indices of only 15 American Standardbreds (7 trotters and 8 pacers) and 25 Norwegian Standardbreds (trotters), in addition to 35 other breeds. Unlike Norwegian Standardbreds, American Standardbreds were found to have significant excess homozygosity . Genotyping records for all foals who’s sires bred for at least 4 consecutive seasons from 2010 to 2015 (“offspring”), as well as their sires (“sires”), and dams (“dams”), were genotyped by Bureau Veritas Laboratories as part of the routine parentage verification process for registration with the USTA and data provided for use in this study. Horses used in this study were categorized by gait phenotype as either trotter or pacer. Phenotyping was performed by the USTA based on performance records.Horses were grouped according to gait (trotters or pacers) and designated subpopulations  . RecordsFST) (Ar), mean expected heterozygosity (HE), mean observed heterozygosity (HO), mean unbiased heterozygosity (HU), and mean inbreeding coefficient (FIS) were then calculated by averaging the values of each locus. T-test comparisons of HE, HO, FIS, and Ar, between and within populations, as well as linear models (lm) of offspring foaled from 2010 to 2015, were calculated using R (t-tests (PBonferroni= 1 – (1 − x)n).Sixteen STRs were analyzed: AHT4, AHT5, ASB17, ASB2, ASB23, CA425, HMS1, HMS2, HMS3, HMS6, HMS7, HTG10, HTG4, HTG7, LEX33, and VHL20. These markers are routinely used for equine parentage testing and genetic diversity studies as they are located on 12 different chromosomes and are not in linkage disequilibrium (LD) . STR datFST) . Mean al using R . A BonfeThe total number of foals sired from 2010 to 2015 was used to identify 10 of the most prolific trotting sires from the study period, excluding full-siblings and father-son pairs. These 10 sires contributed to a combined 31% of the total foal crop from 2011 to 2015. To evaluate the representative diversity of these high-book stallions and their male offspring, 6–10 offspring per sire were included in the analysis, based on sample availability, for a total of 96 individuals. Genomic DNA was isolated from mane or tail hair follicles collected at the time of registration using the Qiagen Gentra Puregene Blood Kit as previously described . Genotyphttps://www.cog-genomics.org/plink/1.9/general_usage#cite). Only autosomal SNPs were evaluated. SNPs with minor allele frequency less than 0.05, genotyping rate < 95%, and in strong LD (>0.8) were removed (--maf 0.05 --geno 0.05 --indep-pairwise 50 5 0.8). The pruned data set included 111 688 SNPs. Using the pruned SNP set, expected heterozygosity (HE), observed heterozygosity (HO), and method-of-moments inbreeding coefficient estimates (F) were calculated using the --het function in PLINK v.1.90  was also conducted in PLINK v.190 (--pca), and results were plotted using the R package ggplot2  (https://www.ncbi.nlm.nih.gov/genome/145?genome_assembly_id=22878).Genomic inbreeding was also calculated for a subset of 50 individuals with the highest quality SNP data (genotyping rate > 97%), which was comprised of offspring only. Runs of homozygosity (ROH) > 1.0 Mb in length were identified using the --homozyg command in PLINK without prior MAF or LD pruning . A totaltatistic  was calcr than low-book sires  . Even when parsed by book size, offspring have higher Ar than their respective sire groups . Ar of offspring did not differ when parsed by stallion book size or offspring year of birth (t-tests: PBonferroni > 0.05). Ar did not differ when comparing respective groups of trotters to pacers, including the comparison of all trotters when compared with all pacers (t-test: P > 0.05)  . Sires a > 0.05) .HE when compared with sires . Pacing dams had statistically significantly higher HE than that of offspring  . No significant difference in HE was found when comparing trotters and pacers, both as a whole and by group (t-test: PBonferroni > 0.05) (Trotting and pacing dams had higher average = 0.002) . Offspri > 0.05) .HU)  (Nei’s unbiased heterozygosity (HU)  was also= 0.040) .O of pacing dams was significantly higher than that of offspring as a whole and offspring of mid- and low-book sires . In trotters, only HO of dams was significantly higher than that of offspring of low-book sires . No other comparisons of HO were significantly different .FIS) below zero were observed for all groups  (FIS than mid-book sires (PBonferroni= 0.045). In pacers, only offspring of high-book sires had significantly lower FIS than offspring of low-book sires (PBonferroni= 0.03). No significant differences in FIS were observed when comparing respective trotters and pacers as a whole or by respective groups (t-test: P > 0.05) (Estimated inbreeding coefficients (l groups . In trotctively) . In trot > 0.05) .FST < 0.15) (FST was found when comparing respective groups of trotters to pacers (FST between respective groups (FST = 0.066). High-book sires had the highest pairwise FST between respective groups (FST = 0.11), with their offspring not far behind (FST = 0.099). Within the trotting and pacing subpopulations, pairwise FST was below 0.05 in all intergroup comparisons, indicating that little to no genetic differentiation occurred within gait subgroups (Moderate differentiation (0.05 <  < 0.15)  in pairwo pacers . Dams haubgroups .HE and HO than their respective offspring when grouped by year (PBonferroni < 0.029), while pacing and trotting sires had lower Ar than offspring grouped by year (PBonferroni < 0.031)   . Additioexamined . Althoug > 0.05) , these dHO) values ranged from 0.31 to 0.51, with a mean individual heterozygosity of 0.36 (HE) for the study cohort (P < 0.001). Additionally, mean observed heterozygosity was significantly higher for sires (0.41) when compared to their offspring . No significant pairwise differences in heterozygosity levels were detected between half-sibling families.Genome-wide observed heterozygosity ( of 0.36 . This vaF) based on observed and expected heterozygosity showed low estimates of inbreeding and an excess of heterozygosity in the study cohort, ranging from −0.48 to 0.086 (mean = −0.064) , but no significant pairwise differences in inbreeding levels were found between half-sibling families.Individual inbreeding coefficients ( −0.064) . Sires wFROH) for this cohort ranged from 12% to 27% (mean FROH = 21%)  . The numH = 21%) .Relationships among individuals belonging to different sire families were also investigated using PCA . PC 1 anFST values calculated between sire families were significant (P < 0.001) using 20 000 permutations. Families 4b and 8 showed the lowest level of genetic differentiation (FST  =  0.082), whereas the greatest divergence was found between families 6 and 7a (FST  = 0.13). As expected, pairwise FST values between families that shared the same grandsire  were among the lowest observed in the dataset (FST = 0.096 for both) (All pairwise or both) .In this study, genotyping records of 16 STRs in 50 621 American Standardbreds and genome-wide SNP genotypes from a subset of 94 trotting individuals were used to investigate genetic diversity metrics in the breed across 6 years from 2010 to 2015. Additionally, STRs were utilized to analyze the contribution of dams to within-breed diversity as well as diversity between pacers and trotters. The application of these markers is widespread in population genetic studies due to their robustness to discern genetic differences between individuals using a limited number of highly polymorphic loci and low-quality input DNA. Furthermore, because STR genotypes are readily available for every registered Standardbred, this approach allows for an unbiased investigation of diversity in the entire population in a reproducible and cost-effective way. In contrast, high-density SNP genotyping is rapidly evolving as an alternative strategy to investigate more global genetic diversity, albeit with increased costs.Ar) in Standardbred sires when compared with offspring and dams, indicating lower diversity in the sire population. When parsed by percentage contribution of offspring, high-book and mid-book sires demonstrated lower Ar than low-book sires. While it is possible that this difference can be attributed to a smaller number of horses in the high-book sire (n = 13 trotters and 10 pacers) and mid-book sire (n = 19 trotters and 20 pacers) groups, these may represent true differences in diversity among the groups, and this diversity metric should continue to be monitored to make comparisons across generations.Analyses utilizing STRs suggested a lack of allelic richness  was calculated to correct for sample size differences among the groups and results for HU mirror that of HE, with the exception of statistically significantly lower HU in pacing mid-book sires compared to pacing dams, which was not detected with HE. These findings suggest additional data may be necessary to determine the extent of substantial genetic differentiation between the high- and mid-book sire groups compared with dams.Although grouping sires based on book size led to small population sizes in the high- and mid-book groups, sires with at least four consecutive breeding seasons during this period were included in the analysis and thus these data represent nearly the entire population from 2010 to 2015. Nei’s unbiased heterozygosity  were responsible for 26% of the 1987 foal crop  may indicate that dams are indeed maintaining the genetic diversity of offspring and counteracting the decreasing number of sires contributing to a larger percentage of the foal crop. If the broodmare population experiences a substantial decrease in the number of individuals or a significant loss of diversity due to shifts in breeding practices, the diversity of the American Standardbred could be threatened.The higher genetic diversity found in dams compared with other groups  and expected heterozygosity (HE) were 0.13 and 0.30, respectively; here, we report a mean F of −0.064 and He = 0.34. As demonstrated by these indices, higher diversity levels in our Standardbred cohort might be attributed to greater sample size and SNP density. Furthermore, mean FROH calculated for a subset of 50 Standardbreds in this study (21%) was similar to that found for 55 Thoroughbred horses born in 1994 (FROH = 21%), after which point it increased yearly until 2017 (FROH = 23%)  . Given tThese data constitute a snapshot of diversity trends in American Standardbreds over a 6-year period and illustrate the value in utilizing available STR data to assist in monitoring population-level trends in diversity. Furthermore, the SNP data corroborated STR data from the high-book trotting stallion lines investigated and further illustrates the value in supplementing STR data with high-density SNP data for deeper evaluation. Future SNP-based studies using larger populations of trotting and pacing Standardbreds are required to explore genome-wide diversity in the breed. Together, our data provide a basis of breed-wide genetic diversity in American Standardbreds and serve as a reference point for future studies.esab070_suppl_Supplementary_MaterialClick here for additional data file.


Human birthweight is a complex, multifactorial trait. Maternal characteristics contribute to birthweight variation by influencing the intrauterine environment. Variation explained by genetic effects is also important, but their contributions have not been assessed alongside other key determinants. We aimed to investigate variance in birthweight explained by genetic scores in addition to easily-measurable clinical and anthropometric variables.We analysed 549 European-ancestry parent-offspring trios from a UK community-based birth cohort.R2) to estimate variance explained, adjusted R-squared (Adj-R2) to assess improvement in model fit from added predictors, and F-tests to compare nested models.We investigated variance explained in birthweight  in multivariable linear regression models including genetic scores, routinely-measured maternal characteristics, and parental anthropometric variables. We used R-Squared . Fetal genetic score improved variance explained  when added to maternal characteristics and parental heights.Maternal and fetal genetic scores together explained 6.0% variance in birthweight. A model containing maternal age, weight, smoking, parity and 28-week fasting glucose explained 21.7% variance. Maternal genetic score explained additional variance when added to maternal characteristics (Adj-Genetic scores account for variance explained in birthweight in addition to easily measurable clinical variables. Parental heights partially capture fetal genotype and its contribution to birthweight, but genetic scores explain additional variance. While the genetic contribution is modest, it is comparable to that of individual clinical characteristics such as parity, which suggests that genetics could be included in tools aiming to predict risk of high or low birthweights.The online version contains supplementary material available at 10.1186/s12887-022-03554-1. Birthweight is a complex trait with considerable variability. It is important to understand what contributes to this variability because babies born large for gestational age (LGA) or small for gestational age (SGA) are at a higher risk for adverse pregnancy and perinatal outcomes . There aPrevious research has shown that factors associated with the maternal intrauterine environment, such as maternal glycaemia, age, parity, weight and smoking, account for some variation in birthweight, once fetal sex and gestational duration have been accounted for . MaternaFetal genetic variation contributes to variation in birthweight independently of the intrauterine environment and is therefore important to consider. Some of the fetal genetic contribution to birthweight can be captured by measuring paternal or maternal height. Height is a highly heritable trait, and the correlation between birthweight and paternal height in particular, via fetal skeletal growth , occurs A recent genome-wide association study (GWAS) identified 190 regions of the genome where common single nucleotide polymorphisms (SNPs) are associated with birthweight variation . The assAll methods were carried out in accordance with relevant guidelines and regulations.We used data from the Exeter Family Study of Childhood Health (EFSOCH), which is a prospective cohort study based on children born between 2000 and 2004 in postcodes EX1–4 in central Exeter, UK .Inclusion criteria for the current analyses consisted of only those parent-offspring trios where the offspring was born at term (≥37 and < 42 weeks gestation ), and coFull details of data collection are found in the EFSOCH study protocol . BrieflyMaternal glucose was measured in fasting maternal blood samples (fasting for at least 10 h prior to sampling), early morning at the parents’ home. Pregnancy details such as parity were obtained from medical records and information about the mother’s smoking status was obtained via a questionnaire completed by the mother at recruitment.n = 969), fathers (n = 937), and offspring (n = 862)) genotyping had been carried out using the Illumina HumanCoreExome array, which is a tool for assessing the genotypes of approximately 500,000 variants across the genome from a single DNA sample. A total of 106 samples were excluded due to low call rate, kinship errors, sex mismatches, or ancestry outliers. The 2662 included samples were of European ancestry assessed using flashPCA [p > 1 × 10− 6, and minor allele frequency (MAF) > 1%.Parental and offspring DNA were extracted to allow molecular genetic analysis of variants implicated in fetal growth. A sample of cord blood was taken at delivery and DNA was extracted from the spun white cells. The EFSOCH sample  version r1.1 reference panel (Michigan Imputation Server) was used to impute additional genotypes in all samples. We extracted genotypes for a total of 209 SNPs from the genome-wide genotype data to construct genetic scores for our analyses. A total of 98% of the SNPs included in the scores had an imputation quality > 0.4 and a Minor Allele Frequency > 0.001 . The genetic scores were calculated according to Eq. A total of 209 SNPs, identified at 190 loci in the most recent GWAS of birthweight , were usModel 1: Genetic scores model: maternal and fetal genetic scores were included as predictors to investigate their contribution to birthweight.Model 2: Maternal clinical features (intrauterine environment) model: maternal fasting glucose, age, weight, parity, and the mother’s smoking status were used in this model.Model 3: Maternal genetic score + maternal clinical (intrauterine environment) features: The maternal genetic score was added to Model 2 to investigate the additional contribution of maternal genetics to variance explained in birthweight.Model 4: Maternal clinical features + Parental anthropometric traits (genetics) model: Maternal and paternal height are variables that capture the effects of fetal genetics and are easily measurable; these were added as predictors to Model 2 to create Model 4.Model 5: Fetal genetic score + maternal clinical features + parental anthropometric (genetic) traits: The fetal genetic score was added to Model 4 to further investigate the contributions of the fetal genetic score in addition to parental heights and clinical features.Model 6: Parental genetic score + maternal clinical features + parental anthropometric (genetic) traits: Given that the fetal genetic score for birthweight is not available prior to delivery, we analysed the contribution of the maternal genetic score for offspring birthweight and the paternal genetic score for father’s own birthweight in Model 6 in addition to clinical features and parental heights.Model 7: Fetal genetic score + maternal genetic score + maternal clinical features + parental anthropometric (genetic) traits: The maternal genetic score was added to Model 5 to further investigate the contributions of the maternal genetic score in addition to parental heights and clinical features.Additional models: Parents’ own birthweights: We additionally investigated the contribution of maternal and paternal self-reported birthweights as these may also capture information about fetal genetics. These were available in a smaller sample (n = 425 trios).We used multivariable linear regression models to model the variance in birthweight explained by several clinical, anthropometric, and genetic factors. We ensured that the regression model assumptions were met by assessing diagnostic plots of residuals and fitted values. To determine the additional variability explained by genetics, we examined the following models, with birthweight  as the outcome variable:2 statistic to assess improvement in model fit based on any added predictors, while the R2 statistic and its 95% confidence intervals were used to assess the overall explanation of variance in birthweight by the predictors in the model. An F-test was used to compare nested models and check for any improvements in the explanation of variance in birthweight. Confidence intervals were calculated by bootstrapping. Multicollinearity between predictor variables in the models was checked by using the Variance Inflation Factor (VIF).We used the Adj-R2 > 0.4. The development of the multiple linear regression models and calculation of F-tests between nested models was carried out using the statistical software R (version 3.5.2).As a sensitivity analysis to check for any potential impact of poor-quality SNP genotype data, we repeated models containing genetic scores with only those SNPs that had minor allele frequency > 0.1% and imputation quality rDescriptive characteristics for the 549 parent-offspring trios are shown in Table The genetic scores all showed strong associations with their respective phenotypes Table S.R2 = 0.020) and the maternal genetic score explained 3% of variance in birthweight (R2 = 0.030). For comparison, the variables parity, mother’s smoking status, and paternal height each explained 3% of variation.A multivariable linear regression model  was higher than that explained by genetic scores alone .A multivariable linear regression model , indicating that the maternal genetic score captured additional variance in birthweight.The addition of the maternal genetic score for offspring birthweight to Model 2 as a predictor Model 3; Table ; Table 3R2 increasing from 0.210 to 0.248 (p < 0.001).The addition of maternal and paternal height variables, which can capture the effects of fetal genetics, to Model 2  showed that the additional variables can further explain variance in birthweight that is adjusted for sex and gestational age Model 4; Table ; Table 4n = 425 available trios, we found that mother’s and father’s own self-reported birthweights explained additional variance in offspring birthweight when added to a model that included parental heights .In a subsample of 2 statistic when comparing the nested models , indicating that the fetal genetic score captured additional variance in birthweight. The fetal genetic score also improved variance explained in the model containing parental birthweights in a subsample of 425 trios .With the addition of the fetal genetic score for offspring birthweight to Model 4 as a predictor .When we added the maternal and paternal genetic scores to Model 4, .When the maternal genetic score and the fetal genetic scores were added on top of clinical variables in Model 4  genetic scores made additional, independent contributions to birthweight variance. GWAS have established that fetal and maternal genetic variants are associated with birthweight , but manMaternal and fetal genetics are known to be important determinants of fetal growth but the contribution of genetic scores to variance explained in birthweight has not been investigated previously using multivariable regression models containing other clinical and parental anthropometric characteristics. We showed, consistent with other epidemiological studies , 21, thaFetal genetic scores are not available before birth, so they are not informative for predicting birthweight at present. However, we showed that maternal and paternal genetic scores can also explain variation in birthweight. The parental genetic effects are mediated both through direct effects of genes inherited by the fetus and indirect maternal genetic effects on the intra-uterine environment. Some of these effects will have been captured by clinical features. Previous research has shown that associations between maternal height and offspring birthweight is predominantly defined by fetal genetics . PaternaR2 value for the maternal genetic score was larger than that of the fetal genetic score because previous work [2 values for maternal and fetal genetic scores were not precise enough in this relatively small sample to be able to infer confidently whether one was bigger than the other , and point estimate values of R2 fluctuated so that the fetal estimate appeared larger than the maternal estimate when the models were re-run in wider samples that did not require all family members to be genotyped  were noAnother limitation of this study is that we conducted the analyses in a UK-based, northern European-ancestry population and it is likely that the associations between birthweight and both genetics and parental clinical features will differ in samples of other ancestries and in other settings. Further studies will be necessary to investigate the contribution of genetic scores and other variables to birthweight in other populations.Since the EFSOCH study was part of the maternal GWAS study that identified SNPs associated with birthweight , there iWe have shown that maternal and fetal genetic scores explain variation in birthweight in healthy pregnancies, in addition to clinical and anthropometric variables that are routinely or easily collected. While the individual contribution of each genetic score is not large , it is comparable to the individual contributions of variables such as parity or maternal smoking status. This raises the possibility that genetic scores might be useful alongside clinical characteristics in prediction models, for example, those aiming to predict risk of LGA in pregnancies affected by gestational diabetes. Our best model explained just under 30% of variation in birth weight, and it is likely that this would be increased by additional characteristics known to explain variation in birthweight that were not included here . FurtherAdditional file 1. SNPs used to create genetic scores. This file contains a list of all the SNPs used to create the genetic scores.Additional file 2: Supplementary tables S2-S6. This file contains the supplementary tables for this paper.


In this study, we evaluated genetic diversity in a panel of 87 Indian mustard varieties using 200 genomic-SSR markers. A total of 189 SSRs resulted into positive amplification with 174 (92.06%) SSRs generating polymorphic products and 15 (7.94%) SSRs producing monomorphic amplicons. A total of 552 alleles were obtained and allele number varied from 2–6 with an average number of 3.17 alleles per SSR marker. The major allele frequency ranged from 0.29 (ENA23) to 0.92 (BrgMS841) with an average value of 0.58 per SSR locus. The polymorphic information content (PIC) value ranged from 0.10 (BrgMS841) to 0.68 (BrgMS519) with 0.39 as mean PIC value. The gene diversity per locus ranged from 0.13 (BrgMS841) to 0.72 (ENA23 & BrgMS519) with a mean value of 0.48 per SSR primer pair. Both Unweighted Neighbor Joining-based dendrogram and population structure analysis divided all the 87 varieties into two major groups/subpopulations. Analysis of molecular variance (AMOVA) inferred the presence of more genetic variation (98%) among individuals than among groups (2%). A total of 31 SSRs produced 36 unique alleles for 27 varieties which will serve as unique DNA-fingerprints for the identification and legal protection of these varieties. Further, the results obtained provided a deeper insight into the genetic structure of Indian mustard varieties in India and will assist in formulating future breeding strategies aimed at Indian mustard genetic improvement. Brassica juncea L. Czern & Coss.) is an economically important oilseed crop of Rapeseed-Mustard group, belonging to family Brassicaceae with a physical genome size of 922 Mb [B. rapa  and B. nigra , followed by subsequent chromosomal duplication in nature [Indian mustard  SSRs amplified polymorphic products, whereas 15 (7.94%) SSRs resulted into monomorphic amplicons. Various allelic diversity parameters of SSR markers used in this study including number of alleles, major allele frequency (MAF), PIC value and gene diversity are presented in clusters . Fifty-sPopulation structure analysis enhances the understanding about genetic relationship among various genotypes and also provides the basis for association mapping studies. Till now, no effort has been directed to study the population structure of Indian mustard cultivars. In the present study, the results based on K-value of 1–9 exhibited a sharp peak of delta K at K = 2  inferrinThe subpopulations as obtained by population structure analysis were subjected to analysis of molecular variance (AMOVA) to quantify the percentage of variation among and within subpopulations. AMOVA explained that most of the genetic variation of this species resides among the cultivars which accounted for 98% of the total variation, while remaining 2% of the genetic variation was attributed to the between populations genetic variation , Table 3In the present study, SSR allele size matrix was analyzed to identify unique genotype-specific alleles  to demarcate SSR-based DNA fingerprint for a particular variety. A total of 36 unique alleles were reported in the dataset with 31 SSR markers to distinguish 27 Indian mustard varieties .B. oleracea var. botrytis using 43 SSRs, inferring presence of narrow genetic diversity in the genotype panel [B. juncea were characterized using 44 SSR markers in a similar study [Evaluation of genetic diversity and population structure analysis has many positive implications for genetic resource conservation and for developing an effective breeding program. The potential of identifying a superior genotype increases with the proper estimation of genetic diversity in a given population set. In the present study, 200 SSR markers distributed over all the eighteen chromosomes of Indian mustard were used for genotyping of 87 Indian mustard varieties. The advantage of using molecular markers distributed throughout the entire genome of a crop ensures that equal chances of representation is given to all the regions of the genome, thus avoiding inaccurate estimates of the genetic similarities/dissimilarities among the individuals . A totalpe panel . Geneticar study . A lower(B. juncea var. rugosa) germplasm lines using 159 SSRs [B. carinata (Ethiopian mustard) germplasm lines using SSR markers and three subpopulations were obtained [UNJ-dendrogram divided all the 87 Indian mustard varieties into two major clusters. Structure analysis also grouped the present set of varieties into two subpopulations, which is in concurrence with neighbor joining grouping method. Though few mismatches in varieties belonging to subpopulation I and cluster I and also in varieties belonging to subpopulation II and cluster II were observed, notably varieties Prakash, RH 30, Vasundhara, Rohini, NRCHB 101, RCC 4 and HYT 33 of subpopulation I were grouped into cluster II, similarly varieties Sitara Singar and PBR 210 of subpopulation II were grouped into cluster I. We observed that out of nine varieties expressing mismatches in two grouping methods, eight had an admixture ranging between 30–50% from other subpopulation, which may be one of the reasons of such mismatches. In an earlier investigation, 31 Indian mustard varieties had been grouped into five different clusters on the basis of multivariate analysis following Euclidean distance and UPGMA method [obtained .Selection of diverse parents in a hybridization programme is the key for creating more transgressive segregants in early generations and providing better scope for selection of desired recombinants. Clustering helps in grouping genotypes into diverse groups, but it did not speak about genetic makeup of genotype. Structure analysis reveals the extent of admixture from other subpopulations, hence provides right information for selection of parents from diverse groups. Parents for hybridization should be representative of diverse gene pools and at the same time should have minimum admixture from other subpopulations. Varieties Basanti, Sitara Singar, Pusa Karishma, RLM 619 of cluster II and Varuna, Urvashi, Pusa Mustard 25 (NPJ 112) and PBR 210 of cluster I expressed more distinctness from other varieties of their respective group, as depicted by more edge length . These rB. juncea lie in the fact of its restricted geographical diversity. Cultivation of this species remained restricted largely to northern states of India, though it has shown promise in Australia and Canada. Another reason is that its large-scale cultivation in India is of recent origin. Though, as a species, B. juncea is supposed to be in existence for about 2500 years, however large-scale cultivation started only 100 years back when B. juncea due to its inherent tolerance against abiotic and biotic stresses, replaced that time prevalent B. campestris var brown sarson [B. juncea (AABB) being amphidiploids expressed better tolerance to prevalent diseases and insect-pests. Genetic diversity evaluation had been carried out in B. juncea varieties on morphological basis and distinctness in varieties developed in eastern and western states than that of the northern states was obtained. We in the present study compared the diversity at molecular level among varieties developed in different states. Present set of 87 varieties was developed at 25 different centres/locations. We observed diversity in varieties of same centre which have developed more than two varieties, as witnessed by grouping into different subpopulations of varieties developed at the same centre. Pedigree-wise analysis of varieties revealed that Varuna was used as a parent in 21 varieties of which varieties; Rohini, Kranti, Pusa Jaikisan and Krishna are direct selection from Varuna, while in other 17 varieties; Basanti, RGN 13, Shivalik, Bhagirathi, GM 3, Pusa Jagannath, JM 2, JM 3, Maya, Pusa Bahar, Pusa Bold, RCC 4, Urvashi, Vardan, Vaibhav, PBR 97 and Sarama, it was one of the parents. These derivatives of Varuna namely Krishna, Pusa Bold, Pusa Jagannath, Pusa Jaikisan and Kranti were also involved as parent in 16 varieties of B. juncea. Considering direct and indirect role of Varuna in Indian national breeding programme, it contributed in parentage of 38 varieties in the present set only, which is again a major reason of narrow genetic base of B. juncea.Pedigree, geographical diversity and trait advantage have been the criterion for selection of parents for hybridization in applied breeding since the historical times, but with the advent of molecular markers, diversity at molecular level has also been given more consideration. Reasons for narrow genetic base of n sarson . B. juncAmong the present set of varieties, most similar varieties on the basis of SSR marker variation were Patan Mustard 67 and PCR 7; Ashirwad and Aravali; RH 406 and RH 749; Krishna and Kranti. Both the varieties PM 67 and PCR 7 were derived from germplasm collected from Gujarat, hence are expected to share common gene pool. Ashirwad and Aravali had Krishna as common parent in their pedigree. RH 406 and RH 749 both were developed at same centre, though they did not share common parents in their pedigree, however, sharing of common gene pool from germplasm is expected. Krishna being a direct selection from Kranti, hence remained as a closely similar variety. Out of 21 varieties having Varuna in their pedigree, six varieties; Vardan, Vaibhav, RL 1359, Pusa Jaikisan, Urvashi and JM 2 remained in subpopulation II, while 15 varieties acquired sufficient variation to move to subpopulation I. It was observed in structure analysis that varieties having acquired admixture from other subpopulation turned to be more distinct from other varieties of the same subpopulation. Varieties; Basanti, RLM 619, Sitara Singar, Urvashi and Pusa Karishma having 30–50% admixture expressed more distinctness from other varieties of the same group. However, the other varieties with similar extent of admixture remained similar to the other varieties of the group, so it may be inferred that distinctness depends upon the alleles introgressed and not on the extent of admixture. Thus, the earlier hypothesis that the repeated use of few widely adapted cultivars as parents in hybridization lead to narrow genetic base and yield stagnation in Indian mustard [B. juncea var. rugosa) [B. carinata) [In the present study, AMOVA results inferred the presence of significant variations among the varieties (98%) than that between subpopulations genetic variation (2%). Higher levels of variation within individuals than among subpopulations were also reported while evaluating population structure of leafy mustard ( rugosa)  and Ethiarinata) , respectTo establish the newness of a crop genotype or a variety, DUS  test involves almost two years testing in field along with the reference varieties, which is quite cumbersome and time consuming . On the B. juncea, de novo derivation of B. juncea from hybridization between nonparental amphidiploids, mutation breeding and population improvement methods for broadening the genetic base of B. juncea varieties. The results of the present study can be useful in formulating breeding programs of Indian mustard as they can assist in identification of genetically diverse genotypes to be used as parents for genetic improvement of this crop. The polymorphic SSRs identified in this study would facilitate marker-assisted breeding, QTL(s) and gene mapping studies through linkage analysis and association mapping studies in Indian mustard. Further, 36 unique DNA fingerprints have been developed for 27 Indian mustard varieties, which can be used for registration under Plant Varieties and Farmers’ Rights Act-2001 for obtaining plant varietal protection and also resolving disputes in seed certification. However, due to the narrow genetic base or genetic composition similarities of Indian mustard varieties, it has been concluded that more comprehensive set of SSR markers is required to characterize these varieties to develop unique DNA fingerprints for all of them.The present study constitutes the first attempt to develop understanding about the genetic variability and development of unique DNA-fingerprints for Indian mustard varieties using SSR markers. Both cluster analysis and population structure analysis divided all the cultivars into two major groups. SSR marker variation and pedigree analysis of released varieties expressed narrow genetic base. Further, we suggest interspecific hybridization, resynthesis of


Habitat fragmentation is an important factor leading to the decline in the leopard cat population in Beijing. Habitat loss may further result in population shrinkage, which increases the risk of inbreeding and genetic decline. To reveal the segregation effects of highway construction and infrastructure expansion on population genetic variation, this study analyzed the genetic structure of leopard cats in five nature reserves in the mountain surroundings of Beijing. The results showed that a mild disparity trend exists in Baihuashan and Songshan subpopulations, due to habitat segregation and dispersal difficulties. We suggest that the genetic structures of the leopard cat population be monitored every 5 years to detect any changes. If needed, individuals can be artificially exchanged among different subpopulations to maintain the viability of this wild cat in Beijing. Prionailurus bengalensis) from five nature reserves in mountainous areas around Beijing. We focused on microsatellite markers. A total of 112 individual leopard cats were identified among 601 samples of scat, and moderate population genetic diversity was detected. Microsatellite-marker-based genetic differentiation (Fst) and gene flow (Nm) showed a weak trend toward discrepancies in the Baihuashan and Songshan subpopulations, which indicated habitat fragmentation effects on individual dispersal. Because the segregated subpopulations may suffer a high risk of genetic diversity loss, we suggest that their genetic structure be monitored with more molecular markers to detect any changes, and that female individuals be artificially introduced as needed to maintain the viability of the leopard cat subpopulations in Beijing. In the face of habitat shrinkage and segregation, the survival of wild cats looks bleak. Interpreting their population genetic structure during habitat fragmentation is critical in planning effective management strategies. To reveal the segregation effects of road construction and human settlements on the population genetic structure, we analyzed non-invasive fecal DNA samples from leopard cats ( Variation in population genetic structure can influence the interactions between a species and its environment, and populations with higher genetic diversity are more adaptable to risks brought by changing environments . MoreoveAlthough small in number, wild feline species are important predators with various body types, diverse dietary habits, and great adaptability to the surrounding environment. These species play key roles in the natural ecosystem ,8. As a Prionailurus bengalensis) is a small wild cat native to and widely distributed in East Asia, South Asia, and Southeast Asia [P. b. rabori is considered Vulnerable [P. b. iriomotensis as Critically Endangered [The leopard cat , from the Songshan national nature reserve, which detected moderate population genetic diversity [It was estimated that there were 1500 leopard cats in the Beijing region in the first national wildlife survey from 1995 to 2000 . However, few studies focused on leopard cats, except studies on food components by scat residue identification ,33, cameiversity . There in = 315), Yunmengshan (n = 43), Yunfengshan (n = 45), Xiaolongmen (n = 75), and Baihuashan (n = 123) nature reserves in Beijing  (accessed on 12 September 2019).We tested the Hardy–Weinberg equilibrium and linkage disequilibrium of each target microsatellite locus using Arlequin v3.5  and GeneThe non-Bayesian method, DAPC , was used to compare to the results from STRUCTURE analysis. This method adopted the R software platform to perform a linear discrimination analysis by running the Adegent data-analysis module and the parameter as the main number of factors was set to N ≤ N/3  . There were 601 carnivore fecal samples collected in the five sampling nature reserves. After a BLAST search of the NCBI database, 550 of the samples were identified as those of leopard cats, with a matching rate of ≥ 98%. Of those 550 samples, 508 were discriminated by sex, with 383 from females and 125 from males. The 508 samples successfully identified by sex were amplified using the microsatellite loci; 96 samples that could not be amplified at the six sites were removed. Micro-Checker detected no null alleles at any of the six loci. After microsatellite-genotype-sharing analysis and sex identification, we identified 53 individuals  in SS, 17 individuals  in YMS, 17 individuals  in YFS, 13 individuals  in XLM, and 12 individuals  in BHS.p > 0.05), and no linkage disequilibrium was detected. There were 31 alleles detected for the six microsatellite loci and the allele frequency was unevenly distributed; some of them were null in the five subpopulations . There was no significant genetic differentiation among the subpopulations in the five sampling areas. This indicates that the leopard cats in Beijing share more common genetic background and less genetic differentiation in nuclear DNA diversity .The results of the DAPC analysis showed that there was no significant genetic differentiation among the five subpopulations . HoweverBHS represents Baihuashan in red, SS represents Songshan in blue, XLM represents Xiaolongmen in green, YFS represents Yunfengshan in gray, and YMS represents Yunmengshan in orange.2 SS reserve [Accurate evaluation of the population abundance and sex structure is critical to assessing the current status and future development of local populations . From la reserve  may apprFelis chaus) in India at 1.59:1 [Panthera uncia) and the African lion (Panthera leo) [The sex ratio is an important indicator of the population structure and development trends for the effective management of endangered species . There wt 1.59:1 . This phera leo) ,53 becauera leo) ,54. Howeera leo) . Our stu−4 and 10−2, respectively. Considering there were less than 100 wild cats in each sampling area [Analyzing the genetic structure of local populations through molecular approaches may reveal clues to their genetic diversity and adaptability to the environment . Microsaing area , the sixing area . The aveing area . In thising area  but highing area . Moreoveing area , but theing area , our paring area . Ursus arctos) demostrated low number of mtDNA haplotypes and high microsatellite diversity among four analyzed subpopulations. It is suggested that this is a result of gene flow mediated by male dispersal and geographical distance [Eopsaltria australis) [Habitat loss and fragmentation increase the chance for inbreeding and pose an extinction risk for the local populations, which are becoming major threats to biodiversity conservation . In genedistance . Similarstralis) . Thus, iFrom the point of view of conservation genetics, the Beijing region is in a relatively narrow geographic range with no marked differences in weather or altitude. In addition, the leopard cat has a strong dispersal ability. We postulated at the beginning of our study that there would be no genetic differentiation among subpopulations. However, the mild discrepancy trend in the BHS and SS subpopulations was different from our expectation. We assume that this is due to female-dominated philopatry traits of the leopard cat, as well as segregation effects from natural rivers, major roads, and the expansion of human residential sites. Thus, we suggest that greater attention should be paid to the BHS and SS subpopulations in future monitoring programs, and they should each be taken as an independent conservation unit for the planning of further conservation strategies. If needed, female individuals from other areas could be introduced to maintain the integrity of genetic diversity. We also suggest further field sampling and more molecular markers be utilized to obtain a clearer genetic variation of the leopard cat population in the Beijing region.


Laboratory animal research involving mice, requires consideration of many factors to be controlled. Genetic quality is one factor that is often overlooked but is essential for the generation of reproducible experimental results. Whether experimental research involves inbred mice, spontaneous mutant, or genetically modified strains, exercising genetic quality through careful breeding, good recordkeeping, and prudent quality control steps such as validation of the presence of mutations and verification of the genetic background, will help ensure that experimental results are accurate and that reference controls are representative for the particular experiment. In this review paper, we will discuss various techniques used for the generation of genetically altered mice, and the different aspects to be considered regarding genetic quality, including inbred strains and substrains used, quality check controls during and after genetic manipulation and breeding. We also provide examples for when to use the different techniques and considerations on genetic quality checks. Further, we emphasize on the importance of establishing an in-house genetic quality program. During the last 15 years, awareness by the scientific community on the lack of reproducibility from published studies in general, but most importantly from animal studies, has increased. It has been estimated that 36% of the cost of preclinical research is spent on irreproducible experiments due to errors of reagents used and materials including animals  gives an overview of the many phenotypic differences seen in the different C57BL/6 (B6) substrains, and the most common C57BL/6 substrains are C57BL/6J and C57BL/6N. However, there are many commercial distributors which have their own C57BL/6 substrain. C57BL/6 J and C57BL/6NJ are produced by the Jackson Laboratory, C57BL/6JBomTac and C57BL/6NTac are produced by Taconic Biosciences, C57BL/6JOlaHsd and C57BL/6NHsd are produced by Envigo, etc.Crb1rd8, and a nucleotide deletion in the nicotinamide nucleotide transhydrogenase gene which causes the loss of exons 7–11 in C57BL/6J mice , independently of one another. Simon et al.  comparedneo, Pgk-neo, Tk-neo, IRES, lacZ, GFP, Cre, Flp, Puro, and Hyg, among others  cells (Evans & Kaufman oxP (Sauer and Henderson FRT (Sandowski 1995) among many other variants  containing the target complementary crRNA and the trans-activating crRNA elements. Since target recognition is mediated entirely by the sgRNA, CRISPR/Cas9 is considered as the most flexible and user-friendly platform for genome editing . This may enable expression of the required gene either early on to overcome the embryonic lethality, followed by suppression later in the mouse’s life, or if the tissue or cell type of interest is not essential for embryonic development, by conditionally knocking out the gene in that specific tissue, while leaving it fully functional in other tissues or cell types.In addition, when crossing mice with different genetic modifications, each modification will have its’ own passenger segment. The passenger segment is the portion of the donor strains DNA flanking the modification site. This flanking DNA is always carried with the mutation, even after many rounds of backcrossing, because of the low probability of close portions of DNA independently assorting due to crossing over. While these sections of donor DNA can be minimized by backcrossing, they cannot be eliminated, and are likely to contain at least one functioning gene from the donor strain . Doing so prevents the establishment of a divergent substrain in the colony. Many commercial breeders periodically refresh their breeders from a stock of frozen embryos, greatly reducing genetic drift. Cryopreservation is also recommended for any unique or genetically modified strains both to guard against loss of the colony either from a disaster or genetic contamination, and to prevent genetic drift by in house breeding. Such strains should be cryopreserved, either as embryos or sperm, and rederived every 10 generations to avoid genetic drift.Genetic monitoring is an essential part of maintaining animal colonies. While the importance of attentive animal care workers and robust husbandry procedures and documentation in preventing strain contamination cannot be overstated, a genetic monitoring program, particularly in facilities which house multiple mice with the same coat color, is an important verification step to catch any incidents of genetic contamination.Before molecular techniques became readily available, genetic background testing was carried out using phenotypic markers  as well as biochemical tests (like isoenzymes) and immunological tests, like skin grafts , are abundant sequences throughout the genome which are made of short tandem repeats (a repeated sequence of 2–6 bases), which are repeated between a few to several dozen times  and the Mouse Phenome Database . There are also published panels of SNPs, such as the one in Petkov et al.  that was originally published in 2015, the MiniMUGA Genotyping Array (Sigmon et al. Whether using microsatellites or SNPs to monitor for genetic contamination, it is important to test enough markers to distinguish between all the strains and, ideally, substrains present in your facility. Genetic monitoring is of little use if it is not sensitive enough to discover a genetic contamination. SNP or Microsatellite Panels should contain critical markers on each chromosome that are able to distinguish between a large number of inbred strains, including substrains. It is understood that cost is also a factor and therefore larger panels, while more robust in terms of detecting contamination, also tend to be more expensive to run. A smaller panel, which can be run on more mice, with greater frequency, may be preferable. If your facility has many C57BL/6 mice of multiple substrains or unknown substrains, you may want to consider adding markers that distinguish between C57BL/6 substrains. There have been several publications of SNP markers which could be used for that purpose (Mekada et al. The ICLAS laboratory Animal Quality Network Committee has as its goal to raise awareness of the scientific community on the importance of high-quality laboratory animals used for research, as well as to maintain and improve the health and genetic quality on animals used in research (Turner et al. https://iclas.org/genetic-monitoring-reference-program.These samples are available so that research institutions throughout the world could use them to compare the genetic background of their mice to established strains widely used from commercial providers. Research institutes wishing to participate in the program can sign up at the ICLAS website at: Genetic Quality is a critical component that plays a major role in animal research but is often overlooked due to its complexity. We have addressed in this review the different genetic engineering technologies that are used in generating genetically modified mice and have pointed out ways to check them for genetic compliance. As part of ICLAS goals we are committed to assist the scientific community in establishing a genetic control program in their breeding facilities and help with its compliance so that scientific publications provide all the important elements of the strain and/or genetic modifications of the mouse model used. We hope that this review clarifies and brings awareness to the genetic quality of mouse model research.


Cambodia is located within the distribution range of the red junglefowl, the common ancestor of domestic chickens. Although a variety of indigenous chickens have been reared in Cambodia since ancient times, their genetic characteristics have yet to be sufficiently defined. Here, we conducted a large-scale population genetic study to investigate the genetic diversity and population genetic structure of Cambodian indigenous chickens and their phylogenetic relationships with other chicken breeds and native chickens worldwide.A Bayesian phylogenetic tree was constructed based on 625 mitochondrial DNA D-loop sequences, and Bayesian clustering analysis was performed for 666 individuals with 23 microsatellite markers, using samples collected from 28 indigenous chicken populations in 24 provinces and three commercial chicken breeds.A total of 92 haplotypes of mitochondrial D-loop sequences belonging to haplogroups A to F and J were detected in Cambodian chickens; in the indigenous chickens, haplogroup D (44.4%) was the most common, and haplogroups A (21.0%) and B (13.2%) were also dominant. However, haplogroup J, which is rare in domestic chickens but abundant in Thai red junglefowl, was found at a high frequency (14.5%), whereas the frequency of haplogroup E was considerably lower (4.6%). Population genetic structure analysis based on microsatellite markers revealed the presence of three major genetic clusters in Cambodian indigenous chickens. Their genetic diversity was relatively high, which was similar to findings reported for indigenous chickens from other Southeast Asian countries.Cambodian indigenous chickens are characterized by mitochondrial D-loop haplotypes that are common to indigenous chickens throughout Southeast Asia, and may retain many of the haplotypes that originated from wild ancestral populations. These chickens exhibit high population genetic diversity, and the geographical distribution of three major clusters may be attributed to inter-regional trade and poultry transportation routes within Cambodia or international movement between Cambodia and other countries. Gallus) in the wild, and the original domestication event are of considerable interest with respect to establishing the evolutionary history of domestic chickens and their genetic diversity. In this regard, DNA-based analyses of mitochondrial DNA (mtDNA) D-loop sequences has revealed that the Indochinese subspecies of the red junglefowl  is the primary maternal ancestor of domestic chickens [Gallus gallus gallus, G. g. spadiceus, G. g. jabouillei, and G. g. bankiva), Liu et al [G. gallus, and also that there are at least two centers of domestication, namely, Southeast Asia and the Indian subcontinent. However, the monophyletic origin of domestic chickens from the red junglefowl remains contentious, given that there is a non-negligible genetic contribution from other junglefowl species such as the grey junglefowl  and Ceylon junglefowl  [The ancestral relationships between junglefowl  ,6. Liu eafayeti)  have sugafayeti)  phylogenIn Southeast Asia, Cambodia is located on the Indochina Peninsula, which lies within the distribution range of the red junglefowl, which is acknowledged to be the common ancestor of present-day domestic chickens ,2,4. IndmtDNA D-loop sequences have been widely used for ge netic characterization of wild and domestic chickens ,7,15, owThe aim of the present study was to assess the genetic diversity of Cambodian indigenous chickens and their phylogenetic relationships with indigenous chickens in other Asian countries and the red junglefowl, using mtDNA D-loop sequences and microsatellites as diagnostic molecular tools. We conducted a large-scale population genetic assessment of 28 populations of indigenous chickens, collected from across 24 provinces, as well as three commercial chicken breeds reared in Cambodia. To the best of our knowledge, this study represents the first attempt to determine the genetic diversity and population genetic structure of Cambodian indigenous chickens.Between 2018 and 2020, blood samples were collected from 690 chickens at locations throughout Cambodia: 646 indigenous chickens from 28 villages in 26 districts of 24 provinces (blood samples were collected from one farm population in each village) and 44 commercial chickens of three breeds [nine three-way cross hybrids between indigenous chickens and commercial chickens (hereafter referred to as the “three-way hybrid chicken breed”) and 29 Isa Brown chickens from Phnom Penh, and six Rhode Island White chickens from Kandal Province . Blood wTotal genomic DNA was extracted from 20-μL blood samples using an ISOSPIN Blood and Plasma DNA Kit  according to the manufacturer’s protocol.https://primer3.ut.ee/). PCR amplification was carried out in 10-μL reaction mixtures containing 50 ng genomic DNA, 4 pmol of each primer, and 5.0 μL of SapphireAmp Fast PCR Master Mix . The cycling conditions were as follows: an initial denaturation at 94°C for 1 min; followed by 35 cycles of denaturation at 94°C for 20 s, annealing at 61°C for 5 s, and elongation at 72°C for 10 s; and a final extension at 72°C for 5 min. The PCR products were detected by electrophoresis on 1.5% agarose gels and then purified using the 20% polyethylene glycol/2.5 M NaCl precipitation method [The complete mtDNA D-loop region was PCR amplified using the primer set: Gg_Dloop_1F (5′-AGGACTACGGCTT GAAAAGC-3′) [n method ,22. CyclGenotyping was also performed using 23 microsatellite markers, which were selected from among the 30 markers recommended by the FAO for studying the genetic diversity of chickens  Supplem. PCR ampGallus lafayettei) (NC_007239) [DNA sequences were aligned against the chicken mitochondrial reference genome (accession No. X52392)  based on_007239) ,26, and _007239) . After d_007239) . A diagr_007239) .π) [h), and Watterson estimator per sequence (Theta-w) [The nucleotide diversity (π) , number Theta-w)  were calTheta-w) .A), allelic richness (AR), the mean number of alleles per population (Na), null allele frequency (NAF), and F-statistics , were calculated for each microsatellite DNA marker, using Microsatellite Analyzer v4.05 (A and AR) [Na, FIS, FST, and FIT) [NAF) [Ne), observed heterozygosity (Ho), and expected heterozygosity (He) were also calculated using GenAlEx 6.5.Genetic diversity indices, namely, the number of alleles ( and AR) , GenAlExand FIT) , and CerT) [NAF) ,35 , and Theta-W per population were 7.89, 0.006, and 6.14, respectively. For the genetic diversity indices based on the selected 23 microsatellite markers, the average values of AR, Na, Ne, Ho, and He per population were 3.92, 5.43, 3.12, 0.62, and 0.62, respectively. Values higher than the average values of each index are underlined in F value was 0.003, ranging from −0.097 for Klengpor village in Banteay Meanchey to 0.156 for Pruok village in Ratanakiri, which indicates a low level of inbreeding within each population. Population in Klengpor village in Banteay Meanchey (Pop 2), Chamkar Ou village in Pursat (Pop 7-1), and Chamkadoung village in Kampong Speu (Pop 13-1) showed relatively low genetic diversity, as determined by both mtDNA D-loop sequences and microsatellite markers. Despite the small sample size, the three-way hybrid chicken breed showed high genetic diversity at the mtDNA level, compared with the other two commercial chicken breeds, Isa Brown and Rhode Island White.The genetic diversity of 28 village populations of Cambodian indigenous chickens collected from 24 provinces and three commercial chicken breeds was assessed using mtDNA D-loop sequences and microsatellite markers . The aveA total of 92 haplotypes were detected among the 625 sampled Cambodian chickens, which consisted of 585 indigenous chickens from 28 populations in 24 provinces and 40 individuals from the three commercial chicken breeds . Among tGenotyping of the 23 microsatellite markers was carried out for a total of 666 individuals, which consisted of 623 indigenous chickens from 28 populations in 24 provinces and 43 individuals from the three commercial chicken breeds. Structure Harvester analysis indicated that the highest and second highest delta K values were K = 3 and K =5, respectively . At K = Mitochondrial DNA D-loop sequences of the red junglefowl and domestic chickens have previously been phylogenetically classified into 15 haplogroups, namely, A to K and W to Z . The 15 Na), observed heterozygosity (Ho), and expected heterozygosity (He) were 6.09, 0.60, and 0.63, respectively. Ho and He values have also been found to be higher than 0.60 in three breeds from Indonesia  and four breeds from Laos  [Ho and He values higher than 0.60 (both 0.62 on average), which is comparable to the values obtained for indigenous chickens in Vietnam, Indonesia, and Laos, although Na was lower than 6.00 (5.43). These findings accordingly indicate that in common with indigenous chickens in other Southeast Asian countries, indigenous chickens in Cambodia have high genetic diversity.In a number of previous studies, the genetic diversity of indigenous chickens in Southeast Asia has been investigated using microsatellite DNA markers ,20,44,51Population genetic analysis using 23 microsatellite markers revealed the presence of three major genetic clusters in Cambodian indigenous chickens, one of which is distributed in almost all regions of Cambodia surveyed, whereas the other two clusters are predominant in the northern part and the central and southern parts of Cambodia. The geographical distribution of these three clusters is conceivably attributable to inter-regional trade and transportation routes of poultry in Cambodia or between Cambodia and other countries . An inteThis study is the first population genetic study on the genetic diversity and population genetic structure of Cambodian indigenous chickens. Present data show that the high genetic diversity is conserved in Cambodian indigenous chickens and many of the mtDNA D-loop haplotypes that were derived from the ancestral wild populations are retained. Further large-scale genomic analyses of Cambodian indigenous chickens should be conducted to conserve Cambodian indigenous chickens as an important resource that has unique and beneficial genetic characteristics for developing novel chicken breeds with high quality and productivity.