Bovine and buffalo are important livestock species that have contributed to human lives for more than 1000 years. Improving fertility is very important to reduce the cost of production. In the current review, we classified reproductive traits into three categories: ovulation, breeding, and calving related traits. We systematically summarized the heritability estimates, molecular markers, and genomic selection (GS) for reproductive traits of bovine and buffalo. This review aimed to compile the heritability and genome-wide association studies (GWASs) related to reproductive traits in both bovine and buffalos and tried to highlight the possible disciplines which should benefit buffalo breeding. The estimates of heritability of reproductive traits ranged were from 0 to 0.57 and there were wide differences between the populations. For some specific traits, such as age of puberty (AOP) and calving difficulty (CD), the majority beef population presents relatively higher heritability than dairy cattle. Compared to bovine, genetic studies for buffalo reproductive traits are limited for age at first calving and calving interval traits. Several quantitative trait loci (QTLs), candidate genes, and SNPs associated with bovine reproductive traits were screened and identified by candidate gene methods and/or GWASs. The IGF1 and LEP pathways in addition to non-coding RNAs are highlighted due to their crucial relevance with reproductive traits. The distribution of QTLs related to various traits showed a great differences. Few GWAS have been performed so far on buffalo age at first calving, calving interval, and days open traits. In addition, we summarized the GS studies on bovine and buffalo reproductive traits and compared the accuracy between different reports. Taken together, GWAS and candidate gene approaches can help to understand the molecular genetic mechanisms of complex traits. Recently, GS has been used extensively and can be performed on multiple traits to improve the accuracy of prediction even for traits with low heritability, and can be combined with multi-omics for further analysis. Reproductive traits are economically important for sustainable food production, especially for monotocous livestock, such as cattle and buffalo. Low reproductive capacity or infertility can be described as an extended duration between two calvings. This problem requires additional inseminations, more veterinary attention, and hormonal treatments, which consequently alters the current and subsequent lactations . Also, aIn the previous study, reproductive traits were divided into binary, interval, and continuous traits with respect to statistical distribution . To bettGenetic variation, which is a variability in breeding values within a population for a trait under selection, significantly affects the accuracy of genetic selection. Heritability measures how much of the phenotypic variation is attributed to genetic variation, and affects the rate of genetic improvement for a trait over generations. Over the past 20 years, several studies were conducted to estimate the heritability of different reproductive traits in dairy cattle , beef cah2 = 0.01)  . Likewis = 0.01) . Regardi2 for scrotal circumference was also reported to have moderate to high estimates. Excluding the Angus population (0.2)   in beef on (0.2) .The excellent milk quality and the limitation of buffalo milk yield contribute to the breeding selection focusing more on milk production traits in buffalo compared with reproductive traits. Currently, there are limited studies for estimating genetic parameters for reproductive traits in buffalo species, mainly for AFC and CI . The herComparing heritabilities between different traits in dairy and beef cattle along with buffalo, we found that:(1)Most of the reproductive traits had low habitabilities, but not all. In the dairy and beef cattle, AOP showed high heritabilities. The heritability estimates for scrotal circumference of the beef bull were medium to high. Also, the superovulation response in dairy and beef cattle was worthy of notice. These moderate to high heritability traits could be applied to the selection and breeding system.(2)The heritability estimates for calving intervals, NRR, days open, and length of reproductive life in most populations were very low, which indicated that these traits would be influenced and improved by proper management practices. The application of synchronization-timed AI protocol , body co(3)The heritability of the same trait varies greatly among different breeds. For instance, the heritability of age at first calving was as high as 0.4 in a crossbreed of dairy cows , while t(4)For most of the reproductive traits, beef cattle had higher heritability estimates compared to those estimated in dairy cattle for the AOP and CD , 3. Eith(5)The breeding progress of buffalo is slow compared to dairy and beef cattle, as a few studies have reported during the last decade. Further large-scale studies are required to accurately estimate the genetic parameters for different reproductive traits in buffalo populations.Concerning the disadvantages of the long cycle and not up-to-mark efficiency of traditional breeding, several association analyses were performed to identify genomic loci associated with the trait variation to find the possible candidate genes or to detect causative mutations. This section summarized the GWAS and candidate gene studies for bovine and buffalo reproductive traits published in the past 20 years (2000–2020) .FSHR, INHA, LHCGR, and OPN were reported to have significant effects on the buffalo superovulation responses. So far, few GWAS have been performed on buffalo reproductive traits  associated with reproductive traits such as AFC, CI, and DO in buffalo. Also, ROCK2, PMVK, ADCY2, MAP2K6, BMP10, and GFPT1 genes are the main candidates for reproductive traits in water dairy buffaloes, which may be used in the future for animal breeding programs or for gene expression studies of the species  studies on the reproductive traits of buffalo. In this regard, e traits . Previoue traits  were con species . The GFPGnRH, GnRHR, LEP, and LHCGR were studied for association with reproductive traits of buffalo bulls. Notably, genes involved in IGF1 and LEP pathways were reported to affect multiple reproductive traits. For example, IGF1 could affect a variety of ovulation- and mating-related traits. LEP and LEPR showed significant effects on both breeding- and calving-related traits. Moreover, long non-coding RNA and ribosomal RNA could be future research directions since non-coding RNAs  were reported to affect reproductive traits  in cattle related to each reproductive trait using the Cattle Quantitative Trait Locus Database (Cattle QTLdb)  Supplem. Only 11s placed   could be directions for future research. Moreover, certain areas that affect multiple traits of different species also deserve further attention. For example, Bubalus bubalis, 2n = 50) and swamp buffalo   . In addi2n = 48) . It is a2n = 48) . DespitePhenotypic records for a trait of individuals and their relatives are used to estimate breeding values by employing the best linear unbiased prediction (BLUP) to facilitate animal selection for economically important traits . It is bThe total number of SNP estimated at millions and the advent of DNA Chip technology made genotyping of many animals for many of these markers feasible and cost-effective. However, a dense marker map improved precision for QTL mapping by traditional linkage analysis . TherefoConsidering a denser marker map, not only could some markers be close to QTL but also, in linkage disequilibrium with it, it was anticipated that some markers could have a positive effect on the quantitative traits across all families and be used for selection without the need to establish a Linkage phase in each family. Close markers can also be combined into a haplotype. Chromosome bearing the rare marker haplotype is likely to be identical by descent and hence carry the same QTL allele. Quantitative traits are usually affected by many genes and, consequently, the benefits from the MAS are limited by the proportion of the genetic variance explained by the QTL. Hence, it is warranted to utilize all the QTL affecting the traits in MAS. Nevertheless, a dense marker map defines a very large number of chromosome segments and so there will be many effects to be estimated, probably more than there are phenotypic data points from which to estimate them .® Buffalo Genotyping Array (90K), GS methods are improving livestock genetic evaluation systems. They have the advantages of high accuracy, short interval between generations, and rapid genetic progress.With the emergence of high-density SNP chips, such as Illumina chips  and AxiomAt present, GS has been applied in cattle on a large scale, but mainly focus on milk production and carcass traits . The GS For AFC, the accuracy of genomic prediction was varied among different populations and methods. In the Nellore breed, the accuracy of prediction for AFC was 0.64 ; howeverGenomic selection studies on puberty  showed that the accuracy performance of different models is above 0.6 . HoweverIn the PR studies, the accuracy of prediction was 0.269 in the Angus population  and 0.64Regarding buffalo studies, genomic evaluation reports are very limited either for productive or reproductive traits. There is only one published study for AFC and CI in buffalo . Genomic1 indicated that, without severely slowing genetic gain for milk production, the daughter PR has stabilized and the declining trend has been reversing since 2003. A similar trend has also been demonstrated by Reproductive traits were depreciated during selection indexes to improve the genetic potential of livestock. Hence, the recently desired gains are being practiced to ensure that the all TMI  traits show a positive response or, at the very least, no negative response. However, the statistical data from the Council on Dairy Cattle Breeding (CDCB)Unlike dairy and beef cattle, few studies have been performed so far for reproductive traits in buffalo. Methods such as GWAS and GS require a large group size, well-structured pedigree, and accurate phenotypic records, which are big challenges for buffalo populations. The first reference for buffalo genome sequencing was released in 2017 , lackingDissimilar to dairy production traits, GWAS for reproductive traits seems to be underpowered and has difficulty in finding major QTL. It still provides genetic variability across many genome-wide genes and intragenic regions for complex trait studies, which greatly increases the understanding of complex traits’ molecular genetic mechanisms.For reproductive traits with low heritability, the genetic gain using GS is improved three to four times per year compared to traditional methods . HoweverGH contributed to the conception and design of the study. BS wrote the first draft of the manuscript and collected the data. CD, HS, MA, and YY wrote sections of the manuscript. NG, HA, SM, YZ, TD, LY, and SZ revised the manuscript and made profound suggestions. All authors contributed to manuscript revision and read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Residual feed intake (RFI) gained attention as a potential alternative to the feed conversion ratio (FCR). Thus, this study aimed to estimate genetic parameters for different feed efficiency (FE) traits  and their genetic correlation to on-test daily weight gain (ADG), backfat (BFT), loin muscle area (LMA), lean percentage (LP), and total feed intake (FI) for 603 Male Duroc (DD), 295 Landrace (LL), and 341 Yorkshire (YY). The common spatial pen effect was also estimated in these traits. Five RFI measures were estimated by regressing daily feed intake on initial testing age (ITA), initial testing weight (IBW), and ADG for RFI1; other models were the same as RFI1 except for additional BFT for RFI2; LMA for RFI3; BFT and LMA for RFI4; BFT, LMA, and average metabolic body weight (AMBW) instead of IBW for RFI5. Genetic parameters estimated using two animal models and the REML method showed moderate heritability for FCR in all breeds , low heritability for the majority of RFI measures in DD (0.15 to 0.23) and YY (0.14 to 0.20) and moderate heritability for all RFI measures in LL (0.31 to 0.34). Pen variance explained 7% to 22% for FE and 0% to 9% for production traits’ phenotypic variance. The genetic correlation revealed that selection against less complex RFI1 in DD and LL and RFI2 in YY would bring the most advantageous reduction to FI  without affecting ADG in all breeds , decrease in BFT, and increase in LP in DD  and LL . Therefore, inclusion of these breed-specific RFI measures in the future selection criteria would help improve feed efficiency in the swine industry. In the modern capital-intensive pork production system, the feed accounts for about 60% to 70% of the total cost of production . Thus, aA total of 1239 male pigs at the growing-finishing phase comprising 603 Duroc (DD), 295 Landrace (LL), and 341 Yorkshire (YY) were used in the analyses. The DD pigs were born and reared in one GGP farm (Farm B), while LL and YY were both born and reared in two GGP farms (Farm A and Farm B) located in South Korea. Pigs from three pig breeds were born from the year 2018 to 2020. Each breeding farm was equipped with a different brand of single-space electronic feeders namely: the Schauer MLP II  and Acemo Genstar  for Farm A and Farm B, respectively. The number of animals for each pig breed and farm is shown in The pigs used in the study were initially reared until they moved to testing pens with an average initial testing age (ITA) and initial testing weight (IBW) of 82.99 ± 1.72 days and 43.71 ± 4.87 kg for DD, 79.49 ± 4.15 days and 36.93 ± 5.82 kg for LL, and 79.25 ± 4.57 days and 35.68 ± 6.76 kg for YY, respectively. Moreover, these tested pigs had free access to feed for an average of 62.33 ± 0.65 days for DD, 67.94 ± 7.23 days for LL, and 68.92 ± 6.88 days for YY. Approximately 9 to 12 pigs were contained by each testing pen equipped with an electronic feed dispenser.Apart from feed intake records, other equally important production and feed efficiency traits were calculated and measured to facilitate the estimation of RFI, estimation of genetic parameters, and genetic and phenotypic correlation for all studied traits in this study. Among the calculated and measured traits are the following: average daily feed intake (ADFI), feed conversion ratio (FCR), on-test average daily gain (ADG), ultrasound backfat thickness (BFT), ultrasound loin muscle area (LMA), ultrasound lean percentage (LP) and average metabolic body weight (AMBW). The ADFI was calculated by dividing the total feed intake over the feeding days; the feed conversion ratio (FCR) was computed by dividing total feed consumed by each pig (kg) over the live weight gain (kg) during the experimental period; on-test average daily gain (ADG) during the experimental period was calculated by dividing weight gain or the difference of final body weight (FBW) and initial body weight (IBW) over the number of testing days.The BFT was measured using A-mode scanners  at three different sites (5 cm from the left and right of the midline), particularly on the shoulder or the 4th thoracic vertebrae, mid-back, or the last thoracic vertebrae, and pigs’ waist or at the last lumbar vertebrae. The BFT used in the analysis of RFI was not adjusted to 90 kg body weight; instead, actual measurements of BFT at off-test were averaged and included in the analyses. The LMA, which measures the largest muscles in pigs, was scanned at 5 cm ventral to the dorsal point of the last thoracic vertebrae using the same probe (PIGLOG 105) used to measure BFT. The same A mode scanner was also used to determine the LP in this study. Similar to BFT, the LMA and LP not adjusted to 90 kg body weight were used in the analyses.The average metabolic body weight (AMBW) was calculated based on the equation presented by Noblet et al.  and NRC 0 indicates the intercept; β1, β2, β3, β4, β5, and β6 represent the partial regression coefficients for ITA, IBW, ADG, BFT, LMA, and AMBW, respectively; e represents the residual term from the linear model above. The root mean square error (RMSE), coefficient of determination (R2), and Akaike information criterion (AIC) are the parameters used in determining the best regression model in predicting DFI for each breed. The AIC value was computed using the PROC GLMSELECT of the SAS package PC version 9.4 [The RFI was estimated by subtracting the actual ADFI from the predicted DFI (pDFI). The pDFI was obtained by regressing ADFI to production, carcass components, and maintenance requirements. The PROC GLM of SAS package PC version 9.4  was usedsion 9.4 .p < 0.001) as a fixed effect, while IBW and FBW were found significant as fixed covariates for corresponding test traits described earlier. The fixed covariates used for each trait are presented in Before analyses of genetic parameters, the genetic and phenotypic correlation, the contemporary group , the parity of dam, and potential covariates for each trait including ITA and IBW for FCR and ADG, and the final testing age (FTA) and final testing weight (FBW) for BFT, LMA, and LP were tested for significance using the PROC GLM procedure of SAS package/PC version 9.4 . After tt is the vector of observations for each feed efficiency and production trait of DD, LL, or YY; b is the vector of fixed effects of CG (testing batch for DD and testing farm- batch for LL and YY); a is the vector of additive genetic effects; c is the vector for the common litter effect which was only applied for DD pigs due to the small difference between animals and number of litters for LL and YY; p is the vector for random common spatial pen effects; e is the vector for random residual effects. The animal genetic, common litter, and spatial pen terms were assumed to be independent and distributed as a ~ 1, Z2, and Z3 are the corresponding indices matrices for the fixed effect, additive genetic effect, common litter effect, and common spatial pen effect, respectively. The number of litters and animals in the pedigree data is presented in The genetic parameters and (co)variance components for each feed efficiency and production trait per breed were estimated using two animal models with different random effects analyzed through the restricted maximum likelihood method (REML) of the WOMBAT program . Animal 2) was defined as the ratio of additive genetic The heritability estimate (hg) was calculated as:p) for each breed was calculated as:g is applied in all breeds; rp1 is applied in DD; and rp2 is applied for both LL and YY; Bivariate analyses were performed to estimate the genetic and phenotypic correlation between feed efficiency and production traits. The first animal model for each breed including the fixed covariates for each trait was used for the said analyses. The estimated genetic correlation  less than the cut-off threshold of ≤2 is considered essentially as good as the best model fit [To compare two models with a different random effect, the obtained Akaike information criterion  or AIC sodel fit , while todel fit .2 value between models used in predicting DFI. The phenotypic variability of ADFI due to RFI ranged from 22.82% to 23.72% in DD, 16.46% to 19.53% in LL, and 17.67% to 20.17% in YY. The contemporary group accounts for differences in the environmental and managerial aspect for each testing batch and testing farm-batch; fixed covariates such as ITA and IBW account for differences in the initial testing age and weight among animals, respectively; ADG represents the growth performance of pigs during the testing period; BFT, LMA, and LP represent carcass components; and AMBW represents the maintenance requirements of each pig. All of these fixed effects and covariates accounted for the variability in ADFI of approximately 76.28% to 77.18% in DD, 80.47% to 83.54% in LL, and 79.83% to 82.33% in YY. In agreement with the studies of Johnson et al. [2 value indicating the importance of including BFT in predicting the DFI of pigs. Among production, carcass components, and maintenance requirement traits included in the RFI models used in this study, only ADG, BFT, and AMBW were found significant, while LMA was not significant but remains worthy to be included in the model to determine its impact in the prediction accuracy.The partial regression coefficients for each covariate used in computing the predicted feed intake are presented in n et al.  and Hoqu2 in this study was similar to that observed by Rauw et al. [0.75), body weight gain, BFT, and ITA. Moreover, the same researchers stated that a higher R2 value could be attributed to the computation of the RFI value in pigs of same-sex, breed, and birth year. Meanwhile, the high R2 value in this study could also be associated with the report of Rauw et al. [2 could be due to the inclusion of the testing batch and testing farm-batch as a fixed effect in predicting DFI.The observed Rw et al.  with an w et al.  with the2 values. Hoque et al. [2 of 59%, 68%, 61%, and 69% for RFI1, RFI2, RFI3, and RFI4 of DD pigs, respectively. Although this study used similar covariates for RFI1 to RFI4 with that of Hoque et al. [2 might be due to the differences such as population structure, differences in fixed effects used to account for the environmental and managerial differences during the particular testing period, variations in initial testing age and weight, and nutritional content of diets fed to the animals. In addition, de Haer et al. [However, previous researchers reported lower Re et al.  reportede et al. , the obsr et al.  identifi2, the partial regression coefficient observed for each covariate in three purebred pigs had an almost similar magnitude to those observed by Hoque et al. [Despite differences in Re et al. , with the et al. ,19.Meanwhile, the partial regression coefficient for ADG ranged from 1.52 to 2.04 for DD, 1.33 to 2.09 for LL, and 1.28 to 2.00 for YY. These regression coefficients for ADG were within almost the similar range to those observed by Hoque et al.  and Mrod0.60, and 11.73 mm, respectively, were observed in YY. On the other hand, the same pig breed recorded the highest mean LMA and LP of 28.31 cm2 and 58.91%, respectively. This observed leanness or lower BFT and lower daily gain of YY compared to LL and DD were also observed by Mrode and Kennedy [The means and standard deviation (SD) for the analyzed feed efficiency and production traits are presented in  Kennedy  who reco0.60, 11.96 mm, 27.80 cm2, and 58.24%, respectively, while the poorest mean FCR was also found in this breed with 2.37 kg feed/kg gain. Most of these observations were also observed in the study of Mrode and Kennedy [Meanwhile, among the three pig breeds, the LL had an intermediate mean for ADFI, ADG, AMBW, BFT, LMA, and LP with their corresponding values of 2.14 kg/day, 905.63 g/day, 12.40 kg Kennedy , where L0.60, and 2.30 kg feed/kg gain, respectively. On the other hand, the same pig breed also recorded the lowest LMA and LP of 26.03 cm2 and 56.95%, respectively. Similar to these observations, Mrode and Kennedy [Among breeds, the DD had the highest mean value for ADFI, ADG, BFT, and AMBW, and the lowest for FCR with corresponding values of 2.48 kg/day, 1067.32 g/day, 14.23 mm, 13.43 kg Kennedy  reportedThe RFI values for all breeds in this study had the same value of zero as expected based on its definition. However, although the same mean value was observed, it was noticeable that each RFI model for each breed had considerable differences in the actual value as observed in their standard deviation (SD). The DD had the largest SD value ranging from 164.56 to 167.78 g/day, while YY had the lowest ranging from 113.92 to 121.70 g/day. Among RFI models, RFI2 had the best model in predicting feed intake in all breeds by having the lowest root mean square error (RMSE) and AIC scores . Figure The previously observed breed differences in RFI are in agreement with those observed by Do et al.  in DD haThe heritability of feed efficiency and production traits of three pig breeds are shown in The observed heritability for FCR in LL was in agreement with those observed in the study of Hoque et al. , Do et aThe estimated heritability for all RFI measures in this study was found to vary for each breed. In DD and YY, a low to moderate heritability estimates were observed in all RFI measures. Meanwhile, moderate heritability estimates were observed for all RFI measures in LL pigs. In all breeds, animal model 1 obtained the highest heritability estimates for all RFI measures due to the substantial pen effect for these traits. Among three breeds, the highest heritability was found in LL ranging from 0.30 to 0.34, where RFI measures with BFT as a covariate had the lowest heritability among all RFI measures in this breed. In DD, all RFI measures had low to moderate heritability estimates ranging from 0.15 to 0.23. Similar to LL, the DD also recorded the lowest heritability estimates in all RFI measures with BFT as a covariate . Among breeds, YY had the lowest heritability for RFI ranging from 0.14 to 0.20. Contrary to the earlier observations, the RFI measures for this breed that include BFT as a covariate had one to two points higher heritability than models without such covariate.The heritability estimates observed for RFI in LL was in agreement with those observed by Do et al.  of 0.34 In DD pigs, the observed heritability for RFI2 and RFI4 was almost similar to those observed by Hoque et al.  for RFI The estimate of heritability for RFI in YY was similarly low as reported in the study of Gilbert et al.  of 0.14 These observed differences in heritability estimates of feed efficiency traits, particularly RFI1, RFI2, RFI3, RFI4, and RFI5, could be attributed to differences in the number and sex of the tested animals, variations in the calculation of RFI such as the inclusion of a fixed CG to account for the environmental and management differences, inclusion of ITA and IBW when these traits are not fixed or with a large discrepancy among tested animals, variations in RFI as stated by de Haer et al. , and difThe heritability estimates for ADG observed in three pig breeds in this study ranged from 0.16 to 0.38. In ADG of DD and LL, animal model 1 had higher heritability compared to model 2, while identical heritability for both models was observed in ADG of YY . The higThe heritability estimate for ADG in YY was in accordance with those observed by Gilbert et al.  of 0.35 Estimates of heritability for BFT in this study were all high, ranging from 0.55 to 0.69. Both animal models 1 and 2 for each breed had obtained almost identical heritability due to the zero to very small common litter (for DD) and spatial pen effect for this trait. Among breeds, both DD and YY pigs recorded the highest heritability for BFT ranging from 0.68 to 0.69, while the lowest heritability of 0.55  was observed in LL. These observations are in agreement with several previously published research projects. Specifically, a high level of heritability in BFT was observed in the study of Cai et al.  of 0.68 A low to moderate heritability estimate for LMA was observed in this study ranging from 0.11 to 0.38. Due to a zero to very small fraction of the common spatial pen effect for this trait, almost identical heritability estimates were observed in both animal models 1 and 2 used in this study. The highest heritability for LMA was observed in LL with an estimate of 0.38 for both models 1 and 2. Meanwhile, intermediate heritability estimates of 0.18 and 0.19 for LMA were observed in YY, and the lowest heritability for LMA of 0.11 was observed for both animal models in DD pigs. The observed heritability estimate of LMA in LL in this study was similarly moderate but with far higher magnitude compared with that indicated by Son et al.  of 0.20,The heritability estimates for LP in this study were moderate to high in all breeds ranging from 0.22 to 0.42. An identical heritability for LP was observed for both animal models 1 and 2 in DD and LL, while model 1 had the highest heritability estimates in YY. Among three purebred pigs, the highest heritability for LP was observed in models 1 (0.42) and 2 of DD (0.42), and model 1 (0.42) of YY pigs, while an intermediate estimate was observed in model 2 (0.26) of YY, and the lowest heritability was observed in models 1 and 2 in LL pigs with an identical heritability of 0.22. The noticeable big difference in the heritability of LP between models 1 and 2 in YY could be attributed to a considerable fraction 9%) of the common pen effect on this particular trait. The observed high heritability for LP in model 1 of YY pigs was in agreement with the findings of Son et al.  of 0.46 % of the In general, the observed differences in the heritability estimates of production traits can be attributed to the differences in the number and sex of tested animals, differences in fixed effects, fixed covariates and the random effect included in the animal model for the estimation of genetic parameters, variation in testing duration, testing age and weight, and differences in measurement methods for carcass compositions. Given these differences, the majority of production traits of the three pig breeds used in this study had moderate to high heritability, indicating a fast improvement for these traits once they are considered as part of the breeding program.The common spatial pen effect explained a varied percentage variability for feed efficiency and production traits in three purebred pigs in this study. Compared between LL and YY which have similar animal models, the earlier breed had slightly higher percentage variance due to the common spatial pen effect for FCR, RFI1, RFI2, RFI3, RFI4, and RFI5 . MeanwhiThe common spatial pen effect accounted for about 10% to 19% and 7% to 10% of the total variability for feed efficiency traits in LL and YY, respectively. In LL, the common spatial pen effect explained zero percentage to the total variability of BFT, LMA, and LP, while it explained 6% to the total variability of ADG. Furthermore, in YY pigs, the common spatial pen effect explained zero percentage to the total variability of ADG and BFT, while it explained 3% and 9% to the total variability of LMA and LP, respectively. In DD pigs, the percentage of variance due to common spatial pen effects ranged from 17% to 22% in all feed efficiency traits, while only 0% to 2% was observed in all production traits.The observed percentage of variance due to the common spatial pen effect in feed efficiency traits of DD were slightly higher than those observed by Lu et al.  in G:F aOverall, the common spatial pen effect varies in every trait and breed used in this study. Despite such variability, most of the traits belonging to the feed efficiency category were observed to have a higher percentage of variance due to the common pen effect as compared to most traits measured during the end of the testing period such as BFT, LMA, and LP (except in YY) as also observed by Do et al. . TherefoThe AIC scores for each feed efficiency  and prodBased on the set cut-off threshold value of ≤2  and ≤6  as descrThe genetic and phenotypic correlation between feed efficiency and production traits in DD is presented in The genetic and phenotypic correlation of five measures of RFI to FCR and ADG was close to one (0.91 to 0.94 and 0.93 to 0.94) and not different to zero (0.05 to 0.06 and 0.00), respectively. The RFI measures in DD pigs with the highest genetic and phenotypic relationship to FCR were RFI1 (0.94) and RFI3 (0.94). Such a high genetic and phenotypic correlation between RFI without BFT as a covariate (RFI1 and RFI3) and FCR was in agreement with those observed by Hoque et al.  and Do eMeanwhile, compared with earlier discussed measures of RFI, the remaining measures of RFI calculated with BFT as a covariate  had a lower genetic and phenotypic correlation for the most production traits. Specifically, the RFI2, RFI4, and RFI5 showed a low and positive genetic correlation (0.21 to 0.22) and a not different to zero phenotypic correlation (0.01) to BFT. Similar RFI measures had a high negative genetic (−0.57 and −0.58) and an identical close to zero phenotypic correlation (−0.08) to LP, and also had an identical high and positive genetic (0.67) and phenotypic correlation (0.61) to FI. However, RFI2, RFI4, and RFI5 had a higher genetic correlation to LMA (−0.23 to −0.25) as compared to those observed in RFI1 and RFI3. Hoque et al.  also obsGenerally, these observations suggest that selection for DD with low RFI would result in the reduction in feed intake without adversely affecting daily weight gain. Specifically, selection for a lower RFI with no BFT as a covariate (RFI1 and RFI3) would result in the reduction in feed intake, favorable decrease in BFT, and increase in meat LP on a stronger scale than RFI with BFT as a covariate . Thus, with these observations and a comparable genetic correlation of RFI measures with and without BFT as a covariate to production traits and feed intake, the selection for low RFI1 appears to be the most advantageous among RFI measures in DD pigs due to its slightly higher heritability, less model complexity, and applicability when records of BFT and LMA are not available in the data.The genetic and phenotypic correlation between feed efficiency and production traits in LL pigs is shown in Compared to those observed in RFI1 and RFI3, the RFI measures with BFT  as a covariate in LL had a lower genetic correlation to FCR, BFT, LP, and FI, except for ADG which had a stronger genetic correlation to latter RFI measures, and LMA which had an almost equal genetic and phenotypic correlation to all RFI measures in this study. Explicitly, the RFI2, RFI4, and RFI5 had a high and positive genetic (0.90) and phenotypic (0.89) correlation to FCR, a not different to zero genetic (−0.04) and phenotypic (0.00) correlation to BFT, a moderate and negative genetic correlation (−0.45 to −0.46) and close to zero phenotypic correlation (−0.13 to −0.14) to LP, a moderate and positive genetic (0.34 to 0.35) and high and positive phenotypic correlation  to FI, respectively. The observed lower genetic correlation of RFI measures with BFT as a covariate than RFI measures without BFT as a covariate to BFT was in agreement with those reported by Hoque et al.  of 0.06 However, despite the higher correlation of FCR to RFI1 and RFI3, a weaker genetic and phenotypic correlation between RFI1 and RFI3 to ADG compared to FCR was still observed in this study with an identical genetic and phenotypic correlation coefficient of −0.11 and −0.01, respectively. These observations were lower than the genetic correlation of RFI2, RFI4, and RFI5 to ADG having an identical correlation coefficient of −0.19. The observed stronger genetic correlation between RFI measures with BFT as a covariate and ADG in this study was in agreement but with an opposite direction and larger difference than those obtained by Hoque et al.  and MrodOverall, these observations suggest that selection against all RFI measures in LL pigs would reduce feed intake without adversely affecting the daily weight gain. Specifically, selection for lower measures of RFI without BFT as a covariate (RFI1 and RFI3) would reduce FI, favorably decrease BFT, and increase LP on a stronger scale compared to RFI with BFT as a covariate . Similar to DD pigs, selection for a less complex RFI1 might be better to include in the selection criteria for improving feed efficiency, reducing BFT, and increasing LP in LL pigs.The genetic and phenotypic correlation between feed efficiency and production traits in YY is presented in On the other hand, the RFI measures with BFT as a covariate  had also a high and positive genetic  and phenotypic (0.89 to 0.90) correlation to FCR, a not different to zero genetic (0.05 to 0.07) and zero phenotypic correlation to ADG, a low and negative genetic (−0.26 to −0.28) and not different to zero phenotypic (−0.01) correlation to BFT, a high and positive genetic (0.55 to 0.57) and not different to zero phenotypic (0.01 to 0.05) correlation to LMA, a low and positive genetic (0.21 to 0.22) and not different to zero phenotypic (−0.03 to −0.04) correlation to LP, and a moderate positive genetic (0.43 to 0.44) and phenotypic (0.53 to 0.54) correlation to FI, respectively. The observed negative genetic correlation of RFI measures with BFT (RFI2 and RFI4) as a covariate and the RFI measure with BFT and metabolic body weight (MBW) as covariates (RFI5) to BFT were in agreement but with a slightly higher correlation coefficient with those observed by Nguyen et al.  in LW piFurthermore, the close to zero genetic and phenotypic correlation of RFI2 and RFI4 to ADG was in agreement but with a slightly lower genetic correlation than those observed by Johnson et al.  in LW boOverall in YY, the observed genetic correlation among feed efficiency and production traits indicates that selection against FCR, RFI1, and RFI3 decreases feed intake but would also bring a negative impact to daily weight gain and a disadvantageous change to LMA in YY pigs. Meanwhile, selection for a lower RFI with BFT as a covariate  would reduce feed intake without adversely affecting daily gain but would likely bring disadvantageous change in BFT, LMA, and LP. Given the obvious comparable genetic correlation of RFI with BFT as a covariate to production traits and feed intake, the selection against less complex RFI2 among this RFI category is the most advantageous RFI measure in YY pigs.The FCR had moderate heritability in all breeds, while the majority of RFI measures in DD and YY and all RFI measures in LL pigs had low and moderate heritability, respectively. These observations indicate a slow to moderate response of RFI measures to selection. The genetic correlation of all RFI measures to production traits and total feed intake revealed that selection against RFI2, RFI4, and RFI5 would likely result in the reduction in feed intake without affecting ADG in all breeds; advantageous change to BFT in DD, and LP in DD and LL; and disadvantageous change in BFT, LMA, and LP in YY. Meanwhile, selection against RFI1 and RFI3 in DD and LL would likely result in the reduction in FI and advantageous change in BFT and LP on a stronger scale compared to RFI2, RFI4, and RFI5. Among RFI measures, the RFI1 was found to be the most advantageous and less complex RFI measure in DD and LL, while RFI2 was the most preferred RFI measure in YY pigs. However, RFI measures with BFT as a covariate could still be applied for DD and LL when weaker genetic correlations to BFT and LP are desired and if carcass components records are available. Moreover, the inclusion of the common spatial pen effect in the animal model brings a substantial and small fraction to the total variability in most feed efficiency and production traits in three pig breeds, respectively. The obtained genetic parameters and correlation coefficients for breed-specific RFI measures in this study can be used in future studies which aim to improve the feed efficiency of the swine industry.


Colocalization is a statistical method used in genetics to determine whether the same variant is causal for multiple phenotypes, for example, complex traits and gene expression. It provides stronger mechanistic evidence than shared significance, which can be produced through separate causal variants in linkage disequilibrium. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations . We propose a new approximation to the popular coloc method that can be applied when limited summary statistics are available.Our method  imputes missing summary statistics for one or both traits using LD structure in a reference panel, and performs colocalization using the imputed summary statistics.e.g., relatively larger sample size or effect size). Applying POEMColoc to estimate colocalization of GWAS Catalog entries and GTEx eQTL, we find evidence for colocalization of 150,000 trait-gene-tissue triplets.We evaluate the performance of POEMColoc using real (UK Biobank phenotypes and GTEx eQTL) and simulated datasets. We show good correlation between posterior probabilities of colocalization computed from imputed and observed datasets and similar accuracy in simulation. We evaluate scenarios that might reduce performance and show that multiple independent causal variants in a region and imputation from a limited subset of typed variants have a larger effect while mismatched ancestry in the reference panel has a modest effect. Further, we find that POEMColoc is a better approximation of coloc when the imputed association statistics are from a well powered study (https://github.com/AbbVie-ComputationalGenomics/POEMColoc.We find that colocalization analysis performed with full summary statistics can be closely approximated when only the summary statistics of the top SNP are available for one or both traits. When applied to the full GWAS Catalog and GTEx eQTL, we find that colocalized trait-gene pairs are enriched in tissues relevant to disease etiology and for matches to approved drug mechanisms. POEMColoc R package is available at The online version contains supplementary material available at 10.1186/s12859-021-04170-z. Genome-wide association studies have identified thousands of disease and trait associated loci but as most GWAS associated loci are non-coding, their functional interpretation remains difficult. eQTL studies are a popular way to follow up on GWAS studies. By providing a link between an associated variant and a gene, they facilitate functional interpretation of GWAS results, especially when associated variants are found in noncoding regions of the genome. A simple approach is to query eQTL datasets for GWAS lead variants to determine if they are significant eQTL for any gene. However, this approach can lead to false positive GWAS-eQTL links as linkage disequilibrium can lead to shared significance at a SNP without the two traits sharing a causal variant. Colocalization analysis reduces false positive results by directly testing competing hypotheses of causal sharing.e.g. eQTL) and does not actually provide a probability of causal sharing, merely a score that can be used to prioritize the most likely causal links. Coloc ) We report a new method for performing colocalization analysis when only the summary statistics at a lead SNP are reported. We show that the method provides a close approximation to the popular coloc method in simulations and in applications to real association summary statistics from the UK Biobank and GTEx projects. We find that our method performs well even when reference panels are not perfectly matched to study populations. We find significant reduction relative to coloc in performance where there are likely multiple causal variants, when reported top summary statistics come from a limited subset of genomic variation , and under conditions of low heritability and sample size in the imputed dataset. By imputing information, POEMColoc allows for the statistically principled testing of competing hypotheses of shared causality where previously not possible.http://www.nealelab.is/uk-biobank/. We removed duplicated phenotypes by excluding sex-specific GWAS runs and excluding raw quantitative variables in favor of the inverse rank transformed alternative. For each GWAS, we selected non-overlapping windows around genome-wide significant (coloc.abf function from the coloc R package (version 3.2-1) using default priors. On the same combinations of UKBB and GTEx summary statistics, we ran POEMColoc using the full list of http://bochet.gcc.biostat.washington.edu/beagle/1000_Genomes_phase3_v5a/). We evaluated performance of POEMColoc under the scenario where the reported GWAS SNP came from a limited SNP subset by choosing the UKBB top SNP from only those SNPs contained on each of three Illumina genotyping chips that vary in total SNP content from 1 to 5 million SNPs .Full summary statistics for GWAS conducted on UKBB phenotypes were downloaded on 4 June 2019 from mentclasspt{minimaTo evaluate the performance of POEMColoc on a wider variety of GWAS types, we obtained summary statistics from the GWAS Catalog. To evaluate the effect of diverse ancestry, we obtained statistics for a single study of 15 hematological traits from meta-analysis of five global populations . To evalH3, a second, distinct causal SNP was randomly selected from variants with minor allele frequency at least 0.01 within a 1 kb window from the first causal SNP. Close proximity of the two causal SNPs ensures that many datasets will present a challenging colocalization problem, in which the causal GWAS SNP may have a statistically significant association without shared causality due to linkage disequilibrium. We randomly sampled k = 1 indicates the eQTL dataset and k = 2 the GWAS dataset) is simulated based on the causal SNP genotype Simulation studies were performed using genotype data from UK Biobank British ancestry individuals and simulated quantitative trait phenotypes. The LD structure in our simulation therefore reflects that of the UK Biobank British population. For each simulated dataset, a causal eQTL SNP was randomly selected among variants across the genome with minor allele frequency at least 0.01. Under pics.download using the EUR ancestry option. We attempted to run PICCOLO on all simulated datasets and a random sample of 100 UK Biobank phenotypes with at least one genome-wide significant association. Due to its use of PICS for estimating causal SNP probabilities and the fact that PICS uses only variants in 1000 Genomes phase I, PICCOLO is not able to run on a large fraction of analyzed datasets. All comparisons presented between PICCOLO and POEMColoc are restricted to the subset of datasets on which both could be run.POEMColoc was compared to PICCOLO using its default settings and PICS causal probabilities obtained from GWAS Catalog data were downloaded on Nov 26, 2018. For each GWAS Catalog entry, we attempted to obtain the SNP We collapsed replicate associations to obtain one colocalization posterior probability per gene-trait-tissue trio as the maximum across associations. Using a colocalization cutoff of 0.9, we determined an enrichment score for each trait-tissue combination as the We used target-indication pair approval status from the supplementary materials of  to assesIn order to identify drug mechanisms supported by colocalizations and to have a quantitative assessment of tissue-trait similarity, we used similarity in the MeSH vocabulary. Similarity is determined through odds ratio of cooccurance in article MeSH terms in the PubMed corpus , an approach based on . For ideAdditional file 1. Supplementary methods and results.Additional file 2. Supplementary dataset 1: A table of all high probability colocalizations between GWAS catalog entries and GTEX eQTL.


ST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8–0.85) compared to the prediction accuracy of either germplasm’s sources individually (0.51–0.67). CC GP proved to have potential use under the present study’s scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa’s germplasm, whereas for IITA’s it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava’s products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm’s of Embrapa Mandioca e Fruticultura  and The International Institute of Tropical Agriculture  for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ∼14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs’ estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers’ effects that were trained with data from other research institutes/country’s germplasm to estimate their clones’ GEBV. Fixation index (F Manihot esculenta Crantz) is an important staple root crop, a food source for millions of people mostly in developing countries, and used for several industrial applications. Subsistence farmers rely on cassava as a vital source of dietary energy, as it can be planted and harvested throughout the year, tolerates periods of drought, and grows on marginal soils   . Althougde (HCN) , the higHydrogen cyanide is a toxic compound that may be harmful to human and animal health, the potential toxicity depends on the amount, route, and duration of the exposure. The toxicological effects of cyanide absorption due to intake of improperly processed cassava occur in acute and chronic form, it can affect the nervous, respiratory, cardiovascular, and endocrine systems , causingCassava varieties are classified as bitter and sweet depending on the content of HCN in the roots. This component varies substantially with plant variety, environment conditions, time of harvest, and post-harvest practices . Those wTraditional cassava genetic improvement strategies are still very demanding in terms of financial resources; and, historically, cassava is a crop that receives less investment than other commodity crops . HoweverAmong the methodologies currently used to accelerate genetic gain and to shorten the breeding cycle interval, genomic selection proposed by Implementation of genomic prediction/selection schemes was recently boosted by the advent of low-cost SNPs marker platforms such as GBS . GenotypSelection decisions are based on genomic breeding values (GEBVs), which are calculated by first estimating the SNP effects in a training population (reference) with both phenotypic and genotypic information available; and then, the SNP effects are multiplied by the genotypes’ marker matrix of a testing population (target) and summed to form the GEBVs . One of Joint evaluations can be desirable under certain situations, its practice is currently more common in animal breeding as crossbred predictions . HoweverThis work aimed to achieve the following objectives: (i) evaluate the feasibility of cross-country genomic predictions between germplasm’s accessions from Embrapa  and IITA  for HCN ; (ii) identify the HCN variability in the cassava germplasm; (iii) provide an assessment of population structure in the joint dataset (Embrapa+IITA); (iv) estimate the genetic parameters of GP based on a haplotype-approach and compare them to those based on single markers. This seems to be among the first attempts to evaluate the cross-country genomic selection in cassava.1, it included the genotypic and phenotypic information of 590 cassava clones evaluated in Ibadan, Nigeria.A total of 1,230 cassava clones from the Cassava Breeding Program (CBP) of Embrapa Mandioca e Fruticultura  were genotyped and phenotyped for HCN. The set of clones consisted of landraces and modern breeding lines, collected from different Brazilian growing regions. It consisted of a selected subset from a panel of 1,536 clones, which were revealed as a unique subset out of an original set of 3,354 clones by identity-by-state analysis carried out by Field trials were carried out in Cruz das Almas  from 2016 to 2019, with the number of replications varying from three replications  to four replications (2016) in a randomized block design. The plots consisted of two rows with 16 plants per plot. Planting was performed from May to July (during the rainy season). Spacings between rows and plants were 0.9 and 0.8 m, respectively. All trial management was performed, whenever necessary, in accordance with the technical recommendations and standard agricultural practices for cassava. For the IITA dataset, phenotypic information was collected from Cassavabase (see text footnote 1), the final file consisted of 590 clones evaluated in Ibadan, Nigeria, from 1998 to 2012; the number of replications was mostly four, but it ranged from two to six for some clones. The IITA dataset was composed of 77 trials in total with varied experimental designs. This dataset originated from a previous curatorship in which 3 cut was made at the mid-root position, between the peel and the parenchyma center. The cube of root and five drops of toluene were added to a glass test tube, and they were tightly sealed with a stopper. In order to determine the qualitative score of HCN content, a strip of filter paper was dipped into a freshly prepared alkaline picrate mixture until saturation. Then, the saturated filter paper was placed above the root cube in the tube. Tubes were sealed for 10–12 h before the color intensity evaluation. HCN represents the total cyanogenic glucosides  in cassava root . Subsequent samples were sent to the laboratory for library preparation, sequencing, and bioinformatics analyses. Genotyping was performed by genotyping by sequencing (GBS) methodology, which is described in detail by ApeKI restriction enzyme and the Illumina sequencing read lengths of 150 bp. Marker genotypes were called with the TASSEL GBS pipeline V5  and discriminant analysis of principal components (DAPC) analyses were performed with the joint dataset , with the package “adegenet”  in R sofST) (The fixation index (FST)  between ST)  in R sofST) .Single nucleotide polymorphisms (SNPs) obtained after quality control were used to build the haplotype blocks. Haplotypes were identified by Gabriel’s method , implemeClone’s GEBVs for HCN were predicted based on SNP effects estimated by the following analyses: (i) genomic prediction with training population of Embrapa genotypic and phenotypic data, (ii) genomic prediction with training population of IITA genotypic and phenotypic data, and (iii) genomic prediction with training population of the joint (Embrapa+IITA) datasets. The SNP effects were estimated in the training populations. Then, these three vectors of SNPs effects were multiplied by the genotype’s incidence codes of the mutual testing populations and summed to form the GEBVs.The models used for the genomic prediction analyses by single markers and haplotypes were:y1, y2, and y3 are the vectors of phenotypic observations and b1 and b2 are the scalars of mean fixed effect, for analyses of datasets of Embrapa and IITA separately, respectively, and b3 is the vector of fixed effects of the country added to the mean for the joint dataset’s analysis. The vectors aGij refers to the random additive genomic clone effects associated with the i-th dataset , aGij ∼ N, aYRi ∼ N. εi represents the residual vector associated with the i-th dataset , εi ∼ N were undertaken to test the random effects using the chi-square distribution, with degrees of freedom equal to the difference in the number of parameters for the two models. Genomic prediction analyses were performed using the package “sommer”  and the j-th genotyping method  for the genomic prediction of single markers and haplotypes were obtained as it follows:To capture genetic relatedness, the genomic relationship matrix  centered by the mean 2pi and H is the haplotypes incidence matrix (numbered as 0 and 1). The sizes of Gij matrixes were 1,230 × 1,230, 590 × 590, and 1,820 × 1,820, for analyses (datasets) 1, 2, and 3, respectively. And the sizes of Iyri identity matrices for the combination of year and replication were 26 × 26, 60 × 60, and 86 × 86, for analyses 1, 2, and 3, respectively.Where, pThe SNP and haplotype effects vectors  and the j-th genotyping method . The GEBVs’ vectors were obtained as follows: The vector M to encompass only common SNPs that were in both datasets.For the cross-country analyses, simulating the hypothetical situation where we would not have the phenotypic information of a group of clones from a certain research institute/country, for the vector of GEBV prediction, we have used the estimated SNP effects with data from another research institute/country, after filtering the matrix fold, k = 10, with the clones being randomly assigned to each fold. The training set, composed of 9 of the 10 subsets, was used to estimate marker effects and the remaining subset was the validation set. These marker effects estimates were used to predict the genomic breeding values of the validation set individuals. This process was repeated until all 10 subsets had been used as the validation population once.Trait estimates of predictive ability, accuracy, and bias were calculated from cross-validation with the training sets for each analysis. Those parameters were estimated for each fold, and the value presented in this study is the mean of the folds. The cross-validation method used was the k-PP,GEBV was used to measure the degree of bias of the GEBV prediction. The bias relates to the size of the absolute differences between clones’ predicted genetic values and their pseudo-phenotypes. The estimated magnitude of these differences can be quantified by the PP,GEBV regression coefficient and can be overestimated  or underestimated . A regression coefficient equal to one indicates no bias. Then, here we will represent bias as one unit minus the regression coefficient PP,GEBV .The predictive ability was given by the correlation coefficient between predicted genetic values (GEBVs) and the phenotypes in the validation population. The accuracy was calculated as the ratio between the predictive ability and the square root of the phenotypic trait heritability. The phenotypes (PP) were linearly regressed on the GEBVs, and the regression coefficient We compared the top (lower GEBVs for HCN) 100 ranking lists contrasting the predicted and estimated GEBVs for both Embrapa and IITA datasets , considering the hypothetical situation that in the absence of phenotypic characterization for one research institute/country, we might want to use the marker effects obtained from the GP using TP data from the other research institute/country to predict the performance of their clones.Statistical analyses were based on the HCN phenotypes originated by the picrate method 1–9 color scale; however, for data visualization clone’s GEBV were further classified into three classes: sweet, intermedium, and bitter, as clones ranged from 1 to 4, 4.1 to 5, and 5.1 to 9, respectively.Principal components analysis in our joint dataset revealed a clear pattern of grouping clones according to their origin (Embrapa/Brazil and IITA/Nigeria), with the first three PCs accounting for 76.3% of the genetic variation  .Similar to our PCA, in our first STRUCTURE run, two large groups were found. One is predominantly composed of clones from Embrapa (pop1 – 924 clones from Embrapa and 23 clones from IITA), and the other (pop2) with 567  clones from IITA in addition to 306 clones from Embrapa . AdditioFor DAPC, 500 PCs that explained ≈ 90% of the genetic variance were kept in our dataset. The optimal number of 12 genetic clusters was suggested to explain the total genetic variability based on BIC , with clThe intergroup distance limit was 0.64 with DACP clusters 5 (7.15 ± 0.84), 7 (6.8 ± 0.97), and 12 (7.08 ± 0.71) being allocated in the same Tocher group; high chances of the bitterest clones belonging to that group of clusters. They were exclusively composed of clones from Embrapa, except for cluster 7 which had 296 clones from Embrapa and only one from IITA. On the other hand, the ‘sweetest’ (low HCN) Tocher’s group was composed by cluster 10 (3.75 ± 0.68), only, exclusively composed of clones from Embrapa.ST values between the proposed mutual training and validation sets, composed of Embrapa and IITA’s genotypic datasets, were moderate (0.072). Estimated fixation indexes with markers between the twelve clusters identified within the joint dataset (Embrapa+IITA) varied from 0.002  to 0.091 (clusters 3 and 9) (The F3 and 9) . ClusterST were expected to be among groups of clones from Embrapa and IITA, there were groups with clones exclusively from Embrapa (clusters 5 and 9) and exclusively from IITA (clusters 3 and 8) that presented moderate FST between them of 0.09, evidencing the genetic variability within the germplasms of each origin. In the same way, there were clusters from different origins that presented low FST. The heatmap of the kinship matrix G also illustrated the population structure , 5  and 12 . DAPC groups 5 and 12 were groups with the highest HCN means . These rntiation .p < 0.01) genetic and year-replication variability for HCN ’s records, which are: ranging from 1 to 4 - Sweet; from 4.1 to 5 – Intermedium; from 5.1 to 9 – Bitter. For Embrapa, IITA, and the joint datasets, 3,132, 2,678, and 3,337 haplotype blocks were found with mean lengths (Kb) of 20.4, 33.189, and 19.636 Kb and mean number of SNPs of 3.616, 4.086, and 3.569 SNPs, respectively .Although we have assumed a simplified approach to number the ‘haplotype matrix’, for the haplotype genomic prediction analyses , the accFor Embrapa clones’ GEBVs estimated by markers effects in the three analyses , high correspondences between GEBVs estimated by analyses 1 and 2, 1 and 3, and 2 and 3  were identified. For IITA clones’ GEBVs estimated by markers effects in the three analyses, weak but positive correspondences were obtained between GEBVs estimated by analyses 1 and 2, and 1 and 3 , 0.1 (1 vs. 3), and 0.78 (2 vs. 3), respectively). For analyses 2 and 3 a good correspondence was observed (correlations of 0.92).We assessed the correlation between the common SNP’s effects vectors  originated from the three analyses. For SNP effects from analyses 1 and 2 the correlation was 0.01, from analyses 1 and 3, and 2 and 3, they were 0.69 and 0.42, respectively.We also compared the top (lower GEBVs for HCN) 100 clones ranking contrasting the predicted and estimated GEBVs for both Embrapa and IITA datasets , considering the hypothetical situation that in the absence of phenotypic characterization for one research institute/country, we might want to use the marker effects obtained from the GP using data from the other research institute/country to predict the GEBVs’ performance of their clones. For Embrapa, from the top 100-lists’ comparison, we have found 12 coincident clones , while fThis study was conceptualized as a proof-of-concept to assess the feasibility of using cross-country genomic predictions to support the selection of the most suitable clones for germplasm exchange. Moreover, the germplasm characterization for preemptive breeding purposes, in case of quarantine diseases, i.e., when we might not have the phenotypes (resistance to certain disease because the screening in a greenhouse or the field is forbidden) is another important area that can benefit from the results of the present study. In such a case, the estimation of the clones’ GEBVs by using markers’ effects estimated in different training populations can be used to rank the most promising clones with resistance to the disease. However, it is well known the population structure has a strong effect on genomic prediction estimates, as observed in both animals  and planUnderstanding the population structure of the joint dataset, how the cassava clones from Embrapa-Brazil and IITA-Nigeria are related to each other, is important in order to better plan the germplasm exchange dynamic between Brazil and Nigeria, or even to understand the outcomes from the joint dataset and the mutual cross-country predictions. ST, and STRUCTURE), point to the same common point: there is clearly a population structure between the germplasm from Embrapa (Brazil) and the IITA’s (Nigeria), however, this differentiation is not strong enough that there are no clones between these research programs that are more similar than divergent clones within the own program itself. It may be related to past clone exchange (Latin America and Africa), mislabeling, among others. It is important to highlight that DAPC and STRUCTURE are ad hoc methods, then, likely (and even expected) to present different results in terms of clustering. Nevertheless, their results can be complementary. As they were in the present study, which generates speculation and perspective for future studies about the reason of the groups of clones from Embrapa with the highest content of HCN (DAPC groups 5 and 12) are those that are apparently more genetically related to a considerable part of the clones from IITA (group 2), and their respective STRUCTURE pop2a including Embrapa and IITA clones. Future studies with dry matter content and fresh root yield could shed light on this issue, as these traits appear to be more decisive in driving artificial selection over the years.Population structure exists due to geography, history of domestication and genetic background, natural or artificial selection. In general, our results, obtained by different methods to assess genetic differentiation between populations  between the proposed mutual training and validation sets, composed by Embrapa and IITA’s germplasm, were moderate (0.072), representing a weak population differentiation. When studying the population structure of Brazilian germplasm from Embrapa separately, ST of 0.073 in 10 clusters obtained by DACP. The predictive ability was higher by using the joint dataset (Embrapa + IITA) (0.64) than using the IITA population due to the training set sizes . However, the Embrapa population showed almost the same predictive ability of the joint dataset , despite the difference in the training set sizes . Consequently, the use of the Embrapa population to predict IITA genotypes is feasible, which had already been evidenced by the moderate fixation index (0.072). ST and lower predictive ability than the achieved in the present study, the use of any single population to predict another would be impractical and could compromise accurate GEBV prediction. Hence, it would be mandatory for the inclusion of individuals from other populations for GEBVs prediction and success of these breeding programs’ consortia (The fixation index (Fonsortia .Manihot esculenta) in Africa that had introgressions of Manihot glaziovii, the legacy of crosses during the 1930s to improve varieties and mitigate the effects of emerging plant diseases. Although crosses with Manihot glaziovii were not as frequent in Brazil as they were in Africa at that time, recently M. glaziovii in the Brazilian germplasm as well .To provide the comprehensive genetic architecture of HCN in cassava, Comparing the dataset’s genetic parameters estimates, Embrapa’s dataset yielded the largest genetic variance estimates for HCN compared to IITA, whereas the latter had the largest estimates for year-replication and residual variance estimates. The highest mean for HCN were observed for the Embrapa dataset. The observed differences between heritability estimates from Embrapa-Brazil and IITA-Nigeria germplasm could be attributed to several factors, e.g., environmental influence, genetically related plants, phenotypic measurement errors, imputation, and could have been potentialized by germplasm’s mislabeling. Clone mislabeling has been reported to reduce genetic gain by up to 40% in Africa . Wolfe eIn our study, pooling the two datasets together improved HCN predictive ability, accuracy as well as reducing the bias. Multi-population TP combining breeding populations can be employed to assess the potential for broadly focused GS training approaches . Multi-gFuture studies including clones that were evaluated at both IITA and Embrapa would be beneficial to assess cross-country genomic prediction in light of genotype-by-environment interaction. Another topic of interest that could be explored in future studies would be genomic predictions considering only the STRUCTURE pop2a  and to verify whether the correlations between GEBV vectors would be greater than those found in the present study (0.55 for prediction of Embrapa clones and 0.10 for prediction of IITA clones). However, restricting the most closely related groups would limit genetic variation  and that could substantially alter the genetic parameters. It is worthy to mention that pooling together datasets is expected to increase precision and accuracy, due to an increase in sample size; by its turn, cross-population/location predictions tend to decrease them, due to change in the target. That was one of the criteria we took into account to narrow down multilocation datasets to a dataset strictly focused on Ibadan in our case. Those referred increase and decrease are in comparison with the within-population prediction. Whatever strategy is adopted, it is essential to maintain sufficient genetic variation in training populations in practical genomic selection .Boxplots of estimated GEBVs group-allocated  on own country-dataset, cross-country-dataset and considering both datasets jointly  showed tk-fold cross-validation in wheat yield, that using haplotypes of 5, 10, 15, and 20 adjacent markers increased in 6.3, 2.9, 5.3, and 2.2%, respectively in the predictive ability over single markers. The authors pointed a trade-off regarding the haplotype length: on one hand, the increase of haplotype length is expected to capture LD between markers in blocks with QTL, thereby increasing the accuracy of prediction, on the other hand, this may also increase the number of haplotype “allelic” classes, which may reduce the accuracy of prediction due to smaller sample sizes representing these classes. The simplified approach proposed to build the haplotype’s matrix, could possibly explain why we observed slightly lower accuracies: our haplotypes were generated by Gabriel’s method , they could be enough to at least give some clue in clonal selection for germplasm exchange with much better probabilities than a blind-scenario, where it is not feasible to send all the clones available, and specifically when many clones demonstrate good performance for important agronomic traits.°C (within a range of 18–34°C), whereas Ibadan’s altitude of 199 m above sea level, 1,311 mm annual precipitation and an average temperature of 26.5°C (within a range of 20–34°C)2.It seems reasonable to attribute part of the success of these coincidences between the predictions of GEBVs obtained with the effects of markers from the country-specific analysis vs. from other country’s analysis to the similarities the regions (Cruz das Almas-Brazil and Ibadan-Nigeria) in terms of temperature and rainfall distribution. Both are in a low latitude range, belonging to tropical latitude zones; according to Köppen-Geiger Climate Classification, Cruz das Almas and Ibadan are lying into tropical rainforest climate (Af) and tropical savanna, wet (Aw), respectively. Other interesting variables to be mentioned: Cruz das Almas’ altitude of 224 m above sea level, 1,136 mm annual precipitation, and an average temperature of 23The poor prediction result for cross-program prediction reported here, using Embrapa’s TP to predict IITA clones’ GEBV (0.10) makes it a bit hard to conceive the cross-program predictions to be adopted in the daily routine of the referred breeding programs. However, in the lack of a better strategy, especially for the case of exchanging material in the context of quarantine diseases, which is a more delicate scenario with an existing restriction on clone movement, information at the molecular level could aid to the understanding of populations structure, diversity, and cross-country genomic predictions could corroborate to guide a germplasm selection to exchange by a reasonably accurate selection of clones since unlimited shipping would be unfeasible.Nevertheless, when using IITA’s TP to predict Embrapa clones’ GEBV a correlation of 0.55 was observed which is of relevance for such a study. This discrepancy within the mutual cross-country GP may perhaps have been influenced by experimental technical details since the same trait showed so disparate heritabilities and coefficients of variation when evaluated in different research institutes/countries. From the GWAS results for HCN obtained by Twelve clusters were revealed by the assessment of population structure in South-American and African cassava. The fixation index among the clusters identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset (Embrapa+IITA) provided an improved accuracy compared to the prediction accuracy of either germplasm’s sources individually. When using the marker effects estimated with IITA dataset as the training to predict the Embrapa clones’ GEBVs, we could say it was a successful case of cross-country prediction, taking into consideration the medium-to-high correlation of 0.55 between their vectors of estimated GEBVs; regardless, the reverse presented a low correlation of 0.1. Genomic predictions from haplotype blocks had slightly lower accuracies and higher biases compared to single markers. The correlation between the GEBVs estimated by the analyses of single markers and by haplotype blocks varied from 0.95 to 0.99, with high accuracy recovering rates of the haplotype blocks compared to single markers, ranging from 0.84 to 0.94. Also, with the haplotype blocks analyses, the model sizes were reduced, with complexity reduction rates going down to 0.2–0.22. Cross-country genomic predictions proved to have potential use under the present study’s scenario, i.e., investigated trait heritabilities higher than 0.18 and conserved genetic architecture across locations, with clones from regions that are not too much divergent in terms of meteorological conditions and with at least a minimal historical germplasm exchange.ftp://ftp.cassavabase.org/manuscripts/Torres_et_al_2021/, Cassavabase.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: LT, EO, and MR contributed to the interpretation of results and drafting of the manuscript. LM, CA, FF, and MR contributed to the brainstorming and critical review of this manuscript. EO, LT, AO, GB, and MR contributed to the conceptualization, data curation, formal analysis, investigation, methodology, and writing—original draft preparation. EO and MR contributed to project administration, supervision, and writing—review and editing. LT, AO, LM, and GB contributed to the data resources, curation, and writing—review and editing. GS contributed to data analysis. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Association studies using genetic data from SNP-chip-based imputation or low-depth sequencing data provide a cost-efficient design for large-scale association studies. We explore methods for performing association studies applicable to such genetic data and investigate how using different priors when estimating genotype probabilities affects the association results. Our proposed method, ANGSD-asso’s latent model, models the unobserved genotype as a latent variable in a generalized linear model framework. The software is implemented in C/C++ and can be run multi-threaded. ANGSD-asso is based on genotype probabilities, which can be estimated using either the sample allele frequency or the individual allele frequencies as a prior. We explore through simulations how genotype probability-based methods compare with using genetic dosages. Our simulations show that in a structured population using the individual allele frequency prior has better power than the sample allele frequency. In scenarios with sequencing depth and phenotype correlation ANGSD-asso’s latent model has higher statistical power and less bias than using dosages. Adding additional covariates to the linear model of ANGSD-asso’s latent model has higher statistical power and less bias than other methods that accommodate genotype uncertainty, while also being much faster. This is shown with imputed data from UK Biobank and simulations. Genome-wide association studies (GWASs) have classically been done to study genotype–phenotype associations. However, a slightly different design of GWAS is using low depth next-generation sequencing (NGS) data, because in such cases the genotype cannot be inferred accurately. Low depth sequencing provides a cost-efficient design, where the number of individuals studied can be increased many folds compared with high depth sequencing, since each individual will be a lot cheaper to sequence. The statistical power to detect associations increases with the number of individuals while only dropping slightly with lower depth and therefore this design provides good statistical power to detect associations . The genotype probabilities can be calculated from the genotype likelihoods, which in turn can be calculated from the NGS data  from which we can calculate the genotype probability y) given our sequencing data (x) and covariates (Z):G. N is the number of individuals, N × c matrix with the additional covariates. We see that the trait iy is conditionally independent of the sequencing data given the genotype [meaning f , for individual i for site j, across k populations:jkf is the frequency of the jth site in population k and ikq is the admixture proportion of population k for individual i. In order to estimate the individual allele frequencies, we will have to first estimate the ancestral frequencies and the admixture proportions. For NGS data this can be done using NGSadmix . p(G) can be calculated using our individual allele frequency assuming Hardy–Weinberg proportions:Another approach is  or PCAngβ from the genotype (g) and the ancestry/admixture proportions (q) with effect γ from being population 1 and SD 1, according to γ to have a value of 1. Furthermore, for the genotype effect size β, we chose a range of values that we deemed realistic. Finally, we also wanted to explore the impact of correlation between sequencing depth and the phenotype, and how to handle this. We therefore introduced sequencing depth and phenotype correlation in our simulations. Essentially, this means that having a large phenotype value or being a case will make it more likely that an individual is in the high depth group. This is described in more detail in We simulate the phenotypes under a standard additive model with a normally distributed phenotype. The mean given is given by effect We wanted to assess which prior performs best for generating the genotype probabilities when performing association studies with low depth sequencing data. We simulate different scenarios with and without population structure and with and without sequencing depth and phenotype correlation. For each of these scenarios, we both applied a sample allele frequency prior and an individual allele frequency prior and evaluated their performance with regard to false positive rate and statistical power to detect an association. Genotype dosages or the expected genotypes are often used in association studies, in order to try and account for the uncertainty on the genotype. However, dosages can be uninformative especially with low depth sequencing data. We did simulations in order to investigate the statistical power to detect an association, when we model the full genotype probabilities instead of using just the genotype dosage. We use ANGSD-asso latent model and dosage model for this. Last, we compared ANGSD-asso latent model with the similar model in SNPTEST in terms of bias, statistical power and computational speed. An overview of the simulation scenarios is shown in In scenario 1, we simulate data without genotype effect, and population structure but with sequencing depth and phenotype correlation. We observe no inflation of the false positive rate for both priors . This isIn scenario 3, we simulate population structure and sequencing depth and phenotype correlation. As shown in In scenario 5, we remove population structure but reintroduce correlation between sequencing depth and phenotype and use a range of effect sizes for the genotype. To evaluate the effect of estimating the admixture proportions from a limited number of genetic sites, we ran simulations with a varying number of sites used for estimating the admixture proportions. We observe a bias in effect size and a reduction in power when a small number (50 or 500) of sites is used. However, when the number of sites is increased , there is very little reduction in power or bias  comparedTo further explore the performance difference with dosages, we simulated a large case-control study with 100,000 individuals. All individuals have low depth sequencing data but the cases and controls have different average sequencing depths.In -method em) with genotype probabilities also using a GLM framework. We applied both methods to the imputed data of UK Biobank  = 0.049; SNPTEST  for controls and cases, where cases have more informative genetic data. This could happen if a certain population is not being represented in the reference panel used for imputation or if different reference panels or SNP-chips are used for cases and controls. A systematic difference in imputation quality is roughly equivalent to having a different average sequencing depth. However, at the same time, we have to state that our simulations and our analyses have shown us that in many instances dosages perform just as well. However, as mentioned there are certain circumstances where they do not perform as well. We have tried to explore these circumstances in this article and what the impact of them is.In Another conclusion, we can draw from our simulations is that when there is sequencing depth and phenotype correlation, our estimates of the effect size will be biased when we do not know the true frequency as shown in With ANGSD-asso implemented in ANGSD, we have made it possible to perform large association studies with low depth sequencing data retaining maximal statistical power, and also estimating effect sizes. SNPTEST is too slow for the analysis of large-scale data sets. The speed-up of ANGSD-asso’s latent model compared with SNPTEST is due to priming for faster convergence of the EM algorithm and threaded analysis using the ANGSD  frameworhttps://github.com/ANGSD/angsd.The three methods in ANGSD-asso for association analysis are implemented in the ANGSD framework. ANGSD can be downloaded from its github page: https://github.com/e-jorsboe/ANGSD-asso-scripts. Some of the simulations use population frequencies from . We conducted the research using the UK Biobank resource under an approved data request (ref: 32683).The R-scripts used for generating the simulations are available from ies from , this daG3 online.jkab385_Supplementary_DataClick here for additional data file.jkab385_Supplementary_Data2Click here for additional data file.