Striga hermonthica is influenced by multiple genomic regions with moderate effects. It is possible to increase genetic gains from selection for Striga resistance using genomic prediction.Genome-wide association revealed that resistance to Striga hermonthica (Del.) Benth., commonly known as the purple witchweed or giant witchweed, is a serious problem for maize-dependent smallholder farmers in sub-Saharan Africa. Breeding for Striga resistance in maize is complicated due to limited genetic variation, complexity of resistance and challenges with phenotyping. This study was conducted to (i) evaluate a set of diverse tropical maize lines for their responses to Striga under artificial infestation in three environments in Kenya; (ii) detect quantitative trait loci associated with Striga resistance through genome-wide association study (GWAS); and (iii) evaluate the effectiveness of genomic prediction (GP) of Striga-related traits. An association mapping panel of 380 inbred lines was evaluated in three environments under artificial Striga infestation in replicated trials and genotyped with 278,810 single-nucleotide polymorphism (SNP) markers. Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance. Heritability estimates were moderate (0.42) to high (0.92) for measured traits. GWAS revealed 57 SNPs significantly associated with Striga resistance indicator traits and grain yield (GY) under artificial Striga infestation with low to moderate effect. A set of 32 candidate genes physically near the significant SNPs with roles in plant defense against biotic stresses were identified. GP with different cross-validations revealed that prediction of performance of lines in new environments is better than prediction of performance of new lines for all traits. Predictions across environments revealed high accuracy for all the traits, while inclusion of GWAS-detected SNPs led to slight increase in the accuracy. The item-based collaborative filtering approach that incorporates related traits evaluated in different environments to predict GY and Striga-related traits outperformed GP for Striga resistance indicator traits. The results demonstrated the polygenic nature of resistance to S. hermonthica, and that implementation of GP in Striga resistance breeding could potentially aid in increasing genetic gain for this important trait.The online version contains supplementary material available at (10.1007/s00122-020-03744-4). Striga hermonthica (Del.) Benth., is the most widespread parasitic weed posing a serious threat to maize production in sub-Saharan Africa (SSA)  lacks its own root system and survives by drawing water and nutrients from host plants like maize for its own growth and has a potent phytotoxic effect  enables genetic dissection of complex traits. GWAS offers high mapping resolution and could effectively identify favorable genomic regions associated with the trait of interest (Yu and Buckler Striga resistance. Genomic prediction (GP) facilitates prediction of best-performing lines and can potentially accelerate the breeding cycle with optimal resources  evaluate the diverse array of 380 tropical maize inbred lines for response to Striga under artificial infestation; (ii) detect main-effect QTL and putative candidate genes associated with Striga resistance; (iii) assess the utility of GP for Striga resistance with different cross-validation methods in the tropical maize panel; and (iv) evaluate multivariate predictions of Striga resistance indicator traits using other correlated agronomic traits based on the IBCF approach. We evaluated the IBCF approach for predicting a given target trait  by incorporating information from other traits that can affect the target trait(s). In this study, users represent the target trait while items represent other related traits evaluated in different environments, that are expected to have some correlation with the target trait.While several studies have reported the use of GWAS to understand the genetic architecture of some biotic stresses, e.g.,  association mapping panel . Both Kibos  and Alupe  have a bimodal rainfall distribution. Striga seeds collected from sorghum fields were stored for few months which helps to break the seed dormancy which was later used for the infestation. Artificial infestation of the S. hermonthica trials at Kibos and Alupe was carried out by adding viable Striga seeds to each planting hole. Each maize plant was exposed to a minimum of 2000 viable S. hermonthica seeds. A standard scoop calibrated to deliver specified amount of germinable Striga seeds per hill was used for the artificial infestation  under artificial tion Kim . Striga u et al.  to ensurStriga damage syndrome rating and emerged Striga plant counts were recorded in the field trials. Host plant Striga damage syndrome rating (SDR) was visually rated for each plot at 10 and 12 weeks after planting (WAP) using a scale of 1–9, where 1 to 3 = no visible or mild damage symptoms; 4 to 5 = some leaf blotching, wilting and stunting; 6 to 8 = extensive leaf scorching, noticeable stunting and reduction in plant growth; and 9 = all leaves completely scorched and dead , using the formula for “area under the disease progress curve” (AUDPC)  was calculated from ear weight and grain moisture, assuming a shelling percentage of 80% and adjusted to 12.5% grain moisture content. These agronomic traits data were collected to understand their correlations and their usefulness in the breeding for Striga resistance. Further other traits are also used in multivariate predictions for Striga-related traits and GY.Other agronomic traits including days to anthesis  and days to silking  were recorded. Plant height , ear height , number of ears per plant , husk cover , plant aspect  and grain moisture at the time of harvest were also recorded. All the ears harvested from each plot were weighed, and representative samples of ears were shelled to determine percentage moisture using a Dickey John moisture meter at all locations. Grain yield kj is the effect of the kth replication at the jth environment, B(R.E)ojk is the effect of the oth incomplete block in the kth replication at the jth environment and eijko is the residual. Environments and replications were treated as fixed effects and the other effects as random. Heritability on an entry-mean basis was estimated from the variance components on a progeny mean basis as described by Hallauer and Miranda (https://hdl.handle.net/11529/10201). Pairwise Pearson’s correlation coefficients (r) among the traits were calculated using R software version 3.2.5 (https://www.r-project.org/).All quantitative genetic parameters were estimated based on the performance of the 380 inbred lines in the IMAS association mapping panel. Residuals for all traits were normally distributed. Individual location analyses were performed, and data from locations with significant genotypic variation and good repeatability were selected for across location analyses. We removed the outliers and did the across location analyses. Analyses of variance within and across environments were undertaken by the restricted maximum likelihood method using the software ASREML-R  markers was described earlier , as well as population structure and kinship (Q + K) using the mixed linear model (MLM) algorithm .BLUPs calculated for Striga-associated traits in individual environments using its similarity to other traits measured at different environments. The detailed steps involved in IBCF approach implementation are described in earlier studies  denotes the items rated by user (line) j, We used the IBCF approach for multivariate prediction of GY and G-matrix) calculated from markers  scheme and was found very useful in breeding programs, this cross-validation called incomplete field trials (CV2) was the practical method when some lines were evaluated in some environments but not in others; the goal here was to predict the performance of these lines in environments where they had not been phenotyped  genotypic and genotype-by-environment (G × E) interaction variances  for Striga plant counts and AUSNPC, and high for SDR (0.84) and GY (0.70). The distribution of phenotypic values was unimodal for the number of emerged Striga plants at different intervals, AUSNPC and SDR indicating the quantitative nature of Striga resistance .The strongest positive and significant correlations were observed between number of emerged Striga infestation are shown in Table Striga plants at 10 and 12 WAP. Inbred lines with high GY and supporting a low number of emerged Striga plants in addition to lower SDR were considered as resistant/tolerant to Striga.The top 16 lines in terms of GY and their performance for other traits under Striga plants at 8, 10 and 12 WAP, AUSNPC, SDR and GY. The results for the six traits are shown in Manhattan and Q–Q plots of P values comparing the expected -log10p values to observed  − log10p values . Eleven, fourteen and six significant SNPs individually explained 8–10%, 8–11% and 8–10% of the total phenotypic variance for number of emerged Striga plants at 8, 10 and 12 WAP, respectively. For AUSNPC, SDR and GY, sets of 11, four and nine significant SNPs individually explaining 7–10%, 6–7% and 7–8% of the total phenotypic variance, respectively, were detected. The most significant SNP across the six traits was S5_56842787 on chromosome 5 which explained 10% of the total phenotypic variance for number of emerged Striga plants at 8 WAP. SNPs S5_56842787 on chromosome 5, S7_70368510, S7_7160192 and S7_144538472 on chromosome 7, S1_298988628 on chromosome 1 and S7_165944748 on chromosome 7 were found to be the most significantly associated for Striga count at 8, 10 and 12 WAP, AUSNPC, SDR and GY under Striga, respectively. A set of putative candidate genes associated with the significant markers was identified (Table r2) between adjacent pair of markers and distance in kb . The average physical distance was 6.53 kb and 18.82 kb at a cut-off value of r2 = 0.2 and 0.1, respectively.GWAS was performed for the number of emerged Striga plants at 8, 10 and 12 WAP and AUSNPC compared with the other environments, whereas for SDR and GY, Alupe2013 performed better. There were slight increases in the prediction correlations for models with the G × E interaction in cross-validation scenarios for most traits and environments. Prediction correlations were also obtained for the measured traits using BLUEs across locations which showed values similar to those observed for CV0 and CV2  and number of emerged Striga plants 12 WAP (0.68) observed in this study corroborate previous reports under artificial Striga infestation  indicating that the IMAS association mapping panel has significant diversity, mimicking a natural population, and thus was appropriate for conducting GWAS. Previous studies on the population structure of the IMAS panel used in this study showed confounding structure in the panel .Correcting for population structure is an important step in GWAS to reduce the false positives that could arise from it without overcorrecting and further causing false negatives , we identified a total of 57 marker trait associations distributed across the genome and controlled by few major and many minor effect QTL  suggesting the complexity of resistance to Striga infestation. For SDR and number of emerged Striga plants at 8 WAP, we observed four and 11 SNPs and Adewale et al  detected for number of Striga-emerged plants were overlapped with four QTL detected for SDR in an earlier study  involved in the post-translational targeting of the nuclear encoding light harvesting chlorophyll-binding proteins (LHCPs) to the thylakoid membrane Below is the link to the electronic supplementary material.


Mycoplasma hyopneumoniae (MH), porcine circovirus (PCV2), and Actinobacillus pleuropneumoniae . Ab response was measured in blood at entry into gilt rearing, post-acclimation , and parities 1 and 2. Heritability estimates for Ab response to IAV, MH, and PCV2 ranged from 0 to 0.76. Ab response to APP ranged from 0 to 0.40. The genetic correlation (rG) of Ab response to IAV with MH, PCV2, PRRSV, and APPmean  were positive (>0.29) at entry. APPmean was negatively correlated with PCV2 and MH at entry and parity 2 but positively correlated with MH at post-acclimation and parity 1. Genomic regions associated with Ab response to different APP serotypes were identified on 13 chromosomes. The region on chromosome 14 (2 Mb) was associated with several serotypes of APP, explaining up to 4.3% of the genetic variance of Ab to APP7 at entry. In general, genomic prediction accuracies for Ab response were low to moderate, except average Ab response to all infectious pathogens evaluated. These results suggest that genetic selection of Ab response in commercial sows is possible, but with variable success depending on the trait and the time-point of collection. Future work is needed to determine genetic correlations of Ab response with disease resilience, reproductive performance, and other production traits.Losses due to infectious diseases are one of the main factors affecting productivity in the swine industry, motivating the investigation of disease resilience-related traits for genetic selection. However, these traits are not expected to be expressed in the nucleus herds, where selection is performed. One alternative is to use information from the commercial level to identify and select nucleus animals genetically superior for coping with pathogen challenges. In this study, we analyzed the genetic basis of antibody (Ab) response to common infectious pathogens in health-challenged commercial swine herds as potential indicator traits for disease resilience, including Ab response to influenza A virus of swine (IAV),  Infectious diseases are well known to cause productivity losses in the swine industry , motivatA common limitation for genetic selection of improved host response to infectious pathogens is that these traits are not expected to be expressed in the nucleus, where selection is performed, because of high biosecurity . Disease2 = 0.38–0.46) has been reported for Ab response to this disease in commercial gilts  but not for porcine circovirus type 2 . In their study, pigs were vaccinated to PRRSV and co-infected with field strains of both viruses. However, other common pathogens, such as influenza A virus of swine (IAV), Mycoplasma hyopneumoniae (MH), and Actinobacillus pleuropneumoniae (APP) are also involved in the porcine respiratory disease complex  base on the Guide to the Care and Use of Experimental Animals, vol. 1, Olfert ED, Cross BM .1. The data included 2,848 commercial F1 (Landrace × Large White) replacement gilts sourced from 17 high-health multipliers from seven breeding companies, all members of PigGen Canada. Replacement gilts were introduced to 23 commercial farms with historical occurrences of natural disease challenges, following the standard acclimation procedures of each farm, including each farm’s routine vaccination protocols, in contemporary groups (CG) of 10 to 63 animals , with a total of 107 CG. The summarized information of the vaccination protocols provided by each farm is provided in The datasets used in this study were provided by a consortium of pig breeding companies (genetic suppliers) that operate in Canada (PigGen Canada)Blood samples were collected from all replacement gilts at four time-points: when entering the commercial herd (Entry), after the acclimation period (Post-acclimation), and during parity 1 (P1), and parity 2 (P2). The average time (± standard deviation) between Entry and Post-acclimation sampling was 40.8 ± 16.3 days, ranging from 29 to 88 days. Sample collection for P1 and P2 occurred between farrowing and weaning, but the exact date of collection was not available. Animals were not deliberately infected with any of the pathogens in the study; therefore, the level of exposure (if present) to these antigens was unknown and was likely variable, which further supports this study as a model for evaluating the overall genetic basis of response to pathogens in commercial swine populations.® for IAV, and INgezim CIRCO IgG® for PCV2. All analyses were performed at GREMIP. Since antibody response to IAV was the only pathogen measured in the opposite direction (S/N instead of S/P), we recalculate this measurement as S/P for analyses to facilitate the interpretation of the results. Two summaries of Ab response traits were also created: (1) APPmean, as the mean of S/P for all APP serotype; and (2) MEAN, as the mean of standardized Ab response (S/P ratio divided by the standard deviation) to all infectious pathogens, to summarize the overall Ab response.Antibody response to PRRSV, IAV, MH, PCV2, and 8 serotypes of APP  were measured as sample-to-positive  or sample-to-negative  ratios. Antibody measurements were performed using ELISA  for PRRSV, LC-LPS ELISA, developed by the Groupe de Recherche sur les Maladies Infectieuses en Production Animale  for all serotypes of APP, IDEXX ELISA for MH; IDEXX Influenza A virus Ab test kitFollowing A total of 316 animals were genotyped with the Illumina PorcineSNP BeadChip  at Delta Genomics , of which 48, 1710, and 1857 were genotyped using versions 60 K v.2, 60 K v.2B, and 80 K, respectively . These versions include 62163, 61565, and 68528 single-nucleotide polymorphisms (SNP), respectively. A total of 42145 SNP was common to all three versions, and 38191 SNP that passed quality controls were used for the genomic analyses, based on gene call (GC) score >0.5, animal call rate of 80%, and genotype call rate of 99.48%. GC scores measure the quality of the genotyping call for each genotyped SNP within an animal. Of the 3516 genotyped animals, 668 were parents of the gilts and did not have Ab response phenotypes. Still, we kept their genotype information in the dataset to make use of their genomic relationships. A full description of the genotypic data can be found in An animal model with a genomic relationship matrix (GRM) from the first method described by yij is the phenotype of the jth individual of the ithCG; μis the intercept; CGiis the effect of the ith level of the fixed effect of CG; uijis the breeding value of the jth individual of the ithCG, withGRM is the genomic relationships matrix based on 38191 SNP and 3516 individuals, with SNP genotypes coded as 0/1/2 and averaged and centered within multiplier herd; and eij is the random residual effect, withI is the identity matrix. The GRM was created separately for pigs from each breeding company, and relationships between breeding companies were assumed to be zero. The fixed effect of CG was included in the model to account for environmental effects due to the farms and other possible environmental effects confounded within the farms , and not for comparisons between CG.where 2) and genetic correlations(rG). The same fixed and random effects as used for the univariate model were also used for the bivariate analyses.Bivariate analyses were performed between Ab response to two pathogens within a time-point, and between two time-points for the same pathogen. Co-variance components were estimated for each of the %SCD and were used to estimate heritabilities . The estimate of π was 0.99 for all datasets. The final GWAS were based on the BayesB method, with π equal to 0.99. One-Mb SNP windows that explained at least 1% of total genetic variance explained by the markers (TGVM) and that had a posterior probability of inclusion (PPI) greater than 0.7 (Sus scrofa 11.1 assembly. Candidate genes within 1-Mb in each direction of the identified SNP were identified using Ensembl BioMart (Genome-wide association studies (GWAS) were performed using Bayesian variable selection methods  using Gethan 0.7  were con BioMart .2 using the whole dataset. For the seven-fold cross-validation, the accuracy was weighted by the number of individuals in the validation dataset.Genomic prediction was performed using BayesC0, BayesB, and BayesC . AnalyseThe proportion of positive animals in each dataset is shown in 2) for each trait for each dataset are presented in 2 estimates for these traits were low to moderate, ranging from <0.01 ± 0.05 (PCV2 at P1) to 0.76 ± 0.07 . In general, h2 estimates numerically increased for datasets with a higher proportion of seropositive animals. This trend was more evident at entry. In contrast, APP serotypes had overall greater h2 estimates; APP10 showed the highest average h2 estimate (∼0.25), peaking at P2 (h2 = 0.38 ± 0.08), while APP2 had overall the lowest estimate (∼0.06). Among all traits analyzed, APPmean had the highest average h2 estimate, ranging from 0.29 ± 0.06 at P1 to 0.55 ± 0.07 at P2. For overall Ab response (MEAN), h2 estimates ranged from low  to moderate . Overall, results indicate that selection for Ab response to some of these infectious pathogens is possible, depending on the time of collection.Heritability estimates . Between PCV2 and PRRSV, the estimate of rG was low to moderate and negative at all time-points. The estimate of rG between APPmean and PRRSV was positive at all time-points. Overall, rGestimates of APPmean with all pathogens were consistent across time-points but among the other pathogens they were more variable, suggesting that genetic changes in one Ab trait may result in complex correlated responses to selection.Estimates of phenotypic and genetic correlations are shown in rG between time-points for a given pathogen are presented in rG between time points, ranging from 0.71 ± 0.17 between entry and P1 to 0.99 ± 0.08 between P1 and P2. On average, APPmean had the highest estimate of rG (0.77) compared to IAV (0.60), MH (0.62), PCV2 (0.51), and MEAN (0.61). Overall, these results indicate that selection for increased Ab response at one time-point would increase Ab response at all time-points.Estimates of Sus scrofa chromosomes (SSC) 8, 9, 12, and 14 (2 Mb). The same QTL on SSC 14 was identified at post-acclimation. For APP5 at entry, 6 QTL on SSC 1, 4, 6, 9, and 13 and for at post-acclimation 5 QTL were located on SSC 2, 14, 6, and 8. For APP7 at post-acclimation, 2 QTL were identified on SSC 6 and 14. For APP 10 at entry, 1 QTL was identified on SSC 16. For APP13 at entry, there were 2 QTL on SSC 1 and 9; at post-acclimation, 2 QTL on SSC 14 and 16; and at P2, 2 QTL on SSC 6 and 7. For APPmean, there was 1 QTL on SSC 6 at entry; 3 QTL on SSC 7, 11, and 19 at post-acclimation; and 2 QTL at P2 on SSC 6 and 12. No QTL was identified for the other traits. The region on SSC14 (2 Mb) was associated with four different serotypes at entry and post-acclimation, suggesting that this is a key pleiotropic region associated with Ab response to APP.Genomic regions that explained at least 1% of TGVM and that had a PPI > 0.7 are presented in Genomic prediction results are presented in mean, AGP ranged from 0.10 (P1 and P2) to 0.16 (post-acclimation). When analyzing all serology traits together, the AGP for MEAN were moderate to high .In this study, we performed genetic and genomic analyses of Ab response to common infectious pathogens in pigs  that, along with PRRSV, have been identified as the main agents causing porcine respiratory disease complex, which causes considerable economic losses in the swine industry . Few stuSummarizing, although there were some limitations on the study, such as the lack of confirmation of whether animals were vaccinated and/or naturally infected, the existence of several confounding factors , and the lack of information on how long after the exposure the Ab was collected, the advantages prevailed over the disadvantages. The applicability of these results in commercial settings, the possibility of using crossbred performance for selection, identifying novel traits for selection of resilience in pigs, and the use of relevant pathogens common in the swine production are valuable to the pig industry.2 were low to moderate for all Ab responses analyzed. Estimates of h2 for Ab response to IAV were, in general, low, except at entry (0.46), when 75% of the animals within a CG were positive. Previously, an estimate of h2 for Ab response to IAV of 0.37 has been reported in F1 (Landrace × Large White) piglets (∼76 days old) after vaccination (two doses) to IAV  to IAV . It is i2 of Ab response. 2 estimate of 0.23 for lesion score of mycoplasma pneumonia measured in slaughtered pigs that were experimentally inoculated with MH. In their study, 59% of the animals were considered positive based on lung lesions  to 0.40 (APP7 at post-acclimation). In general, APP13 showed higher h2 estimates (average of 0.25). Although all APP serotypes can cause the same disease, some serotypes may be more virulent than others  indicates that selection for this trait in sows under healthy challenge may be more successful than selection for Ab response for specific pathogens.For APP, estimates of hn others  and crosn others . The inc2. Similarly, the estimate of 2 estimates indicate a great variation in the practicability of the use of Ab response traits in commercial swine populations for genetic selection purposes. Altogether, these results indicate that, in order to obtain high genetic variation for Ab response to common infectious pathogens in commercial sows, exposure to these pathogens must happen, via either vaccination and/or natural infection.In general, an increase in the proportion of positive individuals in the dataset for IAV, MH, and PCV2 increased the estimate of expected , and in me study . The lowrG with Ab response to MH and PRRSV at entry, when the proportion of positives for IAV, PCV2, and MH was higher. At post-acclimation, this relationship became negative, coinciding with the increase in the number of positives for PRRSV. A previous study has shown low interactions between MH and IAV  between PRRSV and PCV2 viral load in pigs that were PRRSV-vaccinated and co-infected with both pathogens, but a near-zero rG in non-PRRSV vaccinated pigs [rG = 0.04 (0.09)]. However, our estimates for rG for Ab response to PCV2 and PRRSV were negative, suggesting that the immune response to one pathogen is compromised by co-infection with the other pathogen. PCV2 natural infection tends to inhibit innate immune response, which is the initial response to fight against PRRSV infection  with IAV and PCV2. The rG of MEAN with PRRSV at entry, and with MH at P1 and P2 were negative, indicating that selection for MEAN is possible but should be done with care. Genetic selection over total Ab response is expected to have a correlated response with antibody response to individual pathogens. Thus, it can affect the genetic capacity of the organism to deal with these pathogens, which should be taken in consideration when selecting for immune response-related traits.At entry, the estimate of l stress . APP is l stress , which m2 estimates for Ab response to each pathogen within a time-point. We also evaluated the rG and rP for the 0% SCD, which had low h2 and rG and obtained an overall similar direction, but lower estimates and greater SE  gene which has been associated with surface immunoglobulin (Ig)M complexes and appears to stimulate the signaling cascade in B lymphocytes via an antigen receptor  gene has been reported to affect the ovulation rate in the pig (SOAT1), which has been shown to be upregulated in pigs infected with APP7 in comparison to healthy animals (VTN) and fucosyltransferase 2 (FUT2) genes, which were less expressed in the liver of healthy animals compared to pigs infected with APP , 9 (121 Mb), and 12 (47 Mb). On SSC 8, the ubiquitin C-terminal hydrolase L1  and upregulated  in pigs infected with APP7 (UPK1B is part of the innate immune system and has been associated with urinary tract infection by gram-negative bacteria in humans (CCR9), c–x–c motif chemokine receptor 6 (CXCR6), c–c motif chemokine receptor 2 (CCR2) and 5 (CCR5), and c–c motif chemokine receptor-like 2 (CCRL2). These genes are most related to cytokine–cytokine receptor interactions. Of those, CCRL2 has recently been implicated in the regulation of reproductive functions in pigs (SPP1) gene, which was less expressed in the liver of healthy animals compared to pigs infected with APP , 13 (30 Mb), and 4 (63 Mb) include genes that were previously found to be down-  gene, which along with lymphocyte antigen 96 (MD2) and toll-like receptor 4 (TLR4), mediates the innate immune response to bacterial LPS, leading to NF-κB activation, cytokine secretion, and the inflammatory response  is located. This gene encodes for a protein that has been shown to regulate autophagy in response to Staphylococcus aureus infection.For APP5 at post-acclimation, besides the region on SSC 14 (2 Mb), the region on SSC 2 (129 Mb) has been previously associated with APP natural infection in swine . This reresponse . LPS is ate gene . BesidesC1Q) A chain (C1QA), C1Q B chain (C1QB), and C1Q C chain (C1QC). Complement activation is one of the mechanisms of defense stimulated by APP  and 11 (61 Mb), have previously been associated with IgG2 and eosinophil counts, respectively, in Meishan vs. Pietrain pigs infected with Sarcocystis sp. (TCRA), interferon-stimulated transcription factor 3 gamma (IRF9), and ribonuclease RNase A family 4 (ANG) genes, which have previously been associated with APP natural infection  contains several complement genes, such as the complement C1q . The performance of BayesC and BayesC0 relative to other methods depends on the actual distribution of the marker effects  to −0.05 (0% SCD). Therefore, the high AGP found for PCV2 has little implication for selection purposes.For Ab response to IAV, the low AGP are in accordance with the fact that no QTL were identified for this trait across all time-points, and its low hmean, indicating a limitation for the use of this trait for selection purposes. Similar to h2, the low number of positive animals for APP may limit the genetic expression of these traits among the animals in this dataset; therefore, studies involving Ab response to vaccination or natural infection to APP should not be excluded from future works.For all APP, although some QTL were identified for most serotypes, the AGP were low, including for APPThe AGP for MEAN were higher than for the other traits, especially at entry and post-acclimation. Although no major QTL was identified for these traits, the overall sum of small QTL effects captured by the markers suggests that genomic prediction can be used to identify animals with overall better acquired immune response to the pathogens included in this study. This may happen because the SNPs spread along the genome are capturing QTLs with small effects, resulting in overall greater accuracy, even in the absence of major QTLs. This corroborates that selection on the overall mean of Ab response to common pathogens may be more efficient than selection on Ab response to individual pathogens. These results suggest that the genomic predictive ability of most of these traits is limited, but some of them  have the potential to be further explored.mean and MEAN. In addition, important genomic regions were identified for some APP serotypes. Most of the Ab response traits had low to moderate genomic predictive ability, especially when no QTL were identified. However, MEAN had moderate to high genomic prediction accuracies. These results suggest that genetic progress by selection on Ab response to these pathogens is possible but may be slow and that selection on the average Ab response to common pathogens in pigs may be an alternative strategy. The use of specific sample collection time-points can result in higher heritabilities, as well as datasets with a higher proportion of seroconverted animals, to increase the genetic variance. Some disadvantages such as the lack of confirmation of whether animals were vaccinated and/or infected with these pathogens, the existence of several confounding factors , and the lack of information on how long after the exposure the blood was collected and the course the pathogen could limit the interpretation of the results obtained. However, this variability is a strength for the application of these results in commercial settings, as the ability to test for infection with all of these pathogens may not be realistic. Other advantages of this study include the possibility of using crossbred performance for selection, the identification of novel traits for selection of resilience in pigs, the use of commercial populations reared in true commercial conditions, and the use of relevant pathogens that are easy to be measured. New studies including commercial performance, such as reproductive performance, are needed to better understand the relationship between Ab response to these pathogens and commercially important traits in the swine production.For the first time, the genetic basis of Ab response to a range of pathogens in pigs was explored in commercial sows. Differences in the Ab response exist for different pathogens; however, this trait may be still a proxy for resilience in commercial sows. Our results revealed that these traits have low intermediate heritabilities, with exception of APPThey participated in project and protocol development and implementation, coordinated the sources of sows and collection of associated data, and contributed to the project through regular discussions during execution of the gilt acclimation project: Mr. D. Vandenbroek and Mr. B. DeVries, Alliance Genetics Canada, St. Thomas, ON, Canada; Dr. N. Dion and Ms. S. Blanchette, AlphaGene, Saint-Hyacinthe, QC, Canada; Dr. T. Rathje, DNA Genetics, Columbus, NE, United States; Mr. M. Duggan, FastGenetics, Saskatoon, SK, Canada; Dr. R. Kemp, Genesus, London, ON, Canada; Dr. P. Charagu, Hypor, Regina, SK, Canada; and Dr. P. Mathur, Topigs Norsvin, Helvoirt, Netherlands.The data that support the findings of this study are not publicly available. Data may be available from authors upon request and authorization from the company that generated the data.The animal study was reviewed and approved by the Canadian Council on Animal Care (2020).LS performed the data analyses, interpreted the results, and drafted the manuscript. PC, JH, SB, GP, and JD developed the research project. BM coordinated the data collection. PW coordinated the database. LS, JD, and NS conceived the statistical analyses. LS and NS prepared the first draft of the manuscript. All authors contributed to the final manuscript, and read and approved the final manuscript.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.


The connections between genes and multifactorial polygenic age-related traits are not trivial due to complexity of metabolic networks in an organism, which were primarily adapted to maximize fitness at reproductive age in ancient environments. Given this complexity, pleiotropy in predisposition to complex traits appears to be common phenomenon. Identifying mechanisms of pleiotropic predisposition to multiple age-related traits can be a key factor in developing strategies for extending health-span and lifespan. Correlation between complex traits may be a factor shedding light on these mechanisms. Recently, we used an omnibus test leveraging correlation between multiple age-related traits to gain insights into pleiotropic predisposition to them. The analysis using individual-level data identified large number of new pleiotropic loci and highlighted a novel phenomenon of antagonistic genetic heterogeneity, which was characterized by antagonistic directions of genetic effects for directly correlated traits. Here, we demonstrate feasibility of our approach using summary statistics from univariate genome-wide (GW) association studies (GWAS). Our analysis focused on the results for high density lipoprotein cholesterol (HDL-C) and triglycerides (TG) from the Global Lipids Genetic Consortium, which reported 94 GW significant loci (p≤5×10-8). The traits’ correlation was estimated from the individual level data. Our approach identified 28 loci with pleiotropic predisposition to HDL-C and TG at p≤5×10-8, which did not attain univariate GW significance with either of these traits. Fifteen of them (53%) demonstrated antagonistic heterogeneity. These results show that our approach can be efficiently used in the analysis of summary statistics from published studies to identify novel pleiotropic loci.


Plasmodium parasites, Anopheles mosquitoes, and global human populations, this approach yields median test errors of 16.9km, 5.7km, and 85km, respectively.Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of known geographic origin. Here, we describe a deep learning method, which we call Locator, to accomplish this task faster and more accurately than existing approaches. In simulations, Locator infers sample location to within 4.1 generations of dispersal and runs at least an order of magnitude faster than a recent model-based approach. We leverage Locator’s computational efficiency to predict locations separately in windows across the genome, which allows us to both quantify uncertainty and describe the mosaic ancestry and patterns of geographic mixing that characterize many populations. Applied to whole-genome sequence data from  In natural populations, local mate selection and dispersal create correlations between geographic location and genetic variation – each individual’s genome is a mosaic of material inherited from recent ancestors that are usually geographically nearby. Given a set of genotyped individuals of known geographic provenance, it is therefore possible to predict the location of new samples from genetic information alone . This taThe most common approaches to estimating sample locations are based on unsupervised genotype clustering or dimensionality reduction techniques. Genetic data from samples of both known and unknown origin are jointly analyzed, and unknown samples are assigned to the location of known individuals with which they share a genotype cluster or region of PC space . HoweverSPASIBA  but results were similar at 100 or 10,000 SNPs. .Windowed analyses for the three empirical systems we studied are shown in the bottom panels of We summarize genome-wide window predictions in two ways: 1) by taking a kernel density estimate of the predictions and then finding the point of maximum density, and 2) by computing the centroid of the windowed predictions. These estimates are similar in spirit to ensemble prediction methods , but shoLocator to empirical population genomic datasets. In Anopheles gambiae and A. coluzzii, estimated in 2Mbp windows across the genome. The location with highest kernel density across all windows had a median error of 5.7 km, and the centroid of the per-window predictions had a median error of 36 km  has predicted locations in Europe and Africa. In these windows, the Maya sample clusters with other individuals from the regions predicted by Locator – western Europe and Africa, respectively – rather than with other individuals from the Americas . This suLocator predictions generated from different regions of the genome. A priori we might expect recombination rate to affect accuracy because in regions of the genome with higher recombination, there are a greater number of distinct genealogies, and hence a given sample has inherited from a larger subset of the possible ancestors. Test error was estimated as the distance in kilometers from the true sampling location to the geographic centroid of the cloud of per-window predictions, and is shown in Locator’s prediction accuracy varied widely along the human genome. To assess the sources of this variation, we first examined how recombination rate interacts with the accuracy of HGDP samples using a 10-centiMorgan window size, which yields approximately the same number of windows across the genome. We found the distribution of centroid prediction errors across windowing methods was very similar , but does not extrapolate outside the bounds of the training set. Sampling the full landscape, or at least a sufficient portion thereof, is thus an important consideration in running our method.In most cases, predicted locations appear to project toward the nearest locality included in the training set . This isAnopheles . In this report, we present a new tool, Locator, which uses a deep neural network to predict the geographic location of a sample on the basis of its genotype. We show that Locator is highly accurate, computationally efficient, and can scale to thousands of genomes.The correlation of genealogy and geography leaves genetic signals of ancestral location across the genome that one can leverage for practical inference. For instance, tracking the migratory routes of disease vectors such as nopheles  could innopheles , and effSPASIBA  that maLocator transforms input data in VCF or Zarr format to vectors of allele counts per individual using the scikit-allel ; remaining samples with known locations are kept aside as ‘validation’ samples. The validation set is used to tune the learning rate of the optimizer and set the stopping time of model training, but does not directly contribute to the loss used to fit model parameters. Throughout this manuscript, we use ‘validation loss’ to refer to error estimated on the validation set, and ‘test error’ to refer to error calculated on a set of samples entirely held out from the model training procedure.https://github.com/kern-lab/locator).For datasets with small sample sizes the random train/test split may lead to some regions being under- or overrepresented in the training sample. To mitigate this we suggest fitting multiple models with different random seeds, yielding an ensemble of models trained on different subsets of the original dataset. Predictions from this ensemble can then be summarized in the same way as windows or bootstrap samples (see below). An example of this approach is included in the Locator documentation . Although this procedure does not reduce the number of marginal trees represented in the data, it does allow us to assess uncertainty associated with model training and prediction. In both cases, we summarize uncertainty in predicted locations by estimating a two-dimensional kernel density surface over a set of predicted locations, and provide plotting scripts to visualize the 95%, 50%, and 10% quantiles in geographic space . 500 individuals were randomly sampled from the final generation of each simulation for use in model fitting.We first evaluated our method on genotypes from populations simulated by SLiM v3 , using tod size’ , definedWe selected 50 individuals from each simulation as a validation set and ran Locator while varying the number of training samples from 10 to 450 and the number of SNPs from 100 to 100,000. The SNPs used were a subset sampled from the full genotype matrix without replacement and thus mimic the semi-random distribution of genome-wide SNPs generated by reduced-representation sequencing approaches like RADseq . To compAnopheles gambiae/coluzzii species complex collected across sub-Saharan Africa  765 mosquitoes from the n Africa , (2) 596 America  and (3)  America . GenotypPlasmodium falciparum dataset is unusual relative to our other empirical examples in that sequencing libraries were prepared from blood samples without filtering for coinfections or isolating individual Plasmodium. Sequence reads returned from short read sequencing then reflect the population of Plasmodium present in a human blood sample, or even multiple lineages of parasite if an individual is co-infected with multiple strains , then calling SNPs in GATK .For the Locator’s performance with empirical data when the true location is not represented in the training sample. To do this, we ran a series of models on 10,000 SNPs randomly selected from the first 5Mbp of chromosome 2L in the Anopheles data. For each run, we held out all samples from a given sampling locality from the training set, then predicted the locations of these individuals using the trained model. We also tested this approach while holding out all samples collected in a given country, which eliminates even nearby localities from the training set.We also tested www.github.com/kern-lab/locator. SNP calls for the Anopheles dataset are available at https://www.malariagen.net/data/ag1000g-phase1-ar3, for P. falciparum at https://www.malariagen.net/resource/26, and for the HGDP at ftp://ngs.sanger.ac.uk/production/hgdp. Code to run continuous-space simulations can be found at https://github.com/kern-lab/spaceness/blob/master/slim_recipes/spaceness.slim ; Arbel Harpak (Reviewer #2); Leo Speidel (Reviewer #3).Thank you for submitting your article "Predicting Geographic Location from Genetic Variation with Deep Neural Networks" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This paper describes an elegant and highly efficient method for assigning samples to geographic location based on polymorphism data.Essential revisions:The reviewers were unanimous in liking this paper, but were equally unanimous in thinking it needed more discussion/clarification of what the advantages and disadvantages of this machine learning approach is compared to explicitly model-based approaches. How should we interpret the results, what are suitable applications (beyond examples given), etc.As the reviews are consistent and self-explanatory, they are attached in full, rather than condensed here. This also goes for minor comments.Reviewer #1:The authors present a new, neural network (NN) based approach for predicting the geographic location of a sample based on its genetic information and a reference dataset of multiple samples with known genetic and geographic information. The new method, Locator, is compared to a probabilistic approach that explicitly models the geographic distribution of alleles.eLife. However, I think the manuscript could improve by providing a better presentation of advantages and disadvantages of this type of machine learning approach compared to model-based  approaches. I suspect that model-based approaches such as SPASIBA should work better for out-of-sample prediction (e.g. for geographic regions that are not covered by the training sample) and have a clearer approach to quantifying uncertainty , at least when model assumptions are correct. Specific comments and examples are below.One major advantage of the new approach is the dramatic increase in speed, particularly when dealing with very large datasets. The simulations and empirical analyses make a lot of sense. I further appreciate the theoretical and simulation-based insight that dispersal distance is the single limiting factor for the accuracy of the inference, assuming datasets are moderately large. I strongly support publication of the study in 1) I wonder about cases where the distribution of the species is continuous across space, but the sampling of the training sample is strongly clustered, which seems highly relevant for many empirical cases where samples are rarely sampled uniformly across species range . Further, it seems that the clustering of the training samples affects the prediction of Locator almost like a strong "prior", i.e. as if it is implicitly assumed that the new sample most likely also comes from one of these clusters. This might explain why Locator is superior to SPASIBA for the Anopheles data but not for the simulations  since SPASIBA does not have a prior on location but assumes that the new sample could come from any arbitrary location. However, this might also imply that SPASIBA is better for out-of-sample prediction, i.e. when the spatial location of the investigated sample is different from the locations of the clustered training data. Related to this, in the discussion the authors state that they found that "the greatest increase in performance relative to the model-based approach is in empirical data for which the assumption of smooth variation in allele frequencies across the landscape is unlikely to hold, such as the complex multi-species Anopheles sample analyzed here ". My alternative interpretation here is that this has nothing to do with smooth variation in allele frequency, but is a result of the strong clustering of the training sample, and the fact that the test samples are also sampled from these cluster locations. In fact, one of the results (subsection "Effects of Unsampled Populations") is that prediction error is much higher for individuals from regions excluded from training, and that "predicted locations appear to project towards the nearest locality included in the training set", which is supporting my interpretation. I thus would change the discussion regarding the mentioned statement and bring up the problem of clustered sampling locations of the training sample. It might be out of the scope of the current study, but testing both SPASIBA and Locator on spatial simulations with clustered training samples but uniformly sampled test samples would also be informative regarding this question.2) The statistical method SPASIBA allows to compute a likelihood surface of the location of the tested individual, i.e. it provides a statistical way of quantifying uncertainty in the estimate. The Locator method does not allow such a quantification. However, the authors suggest to run Locator on multiple 2 Mb windows across the genome and use the predicted locations from such multiple runs to quantify the uncertainty, for example by plotting contours of the 95% quantiles of a two-dimensional kernel density of these locations. This approach certainly gives some idea about the spatial extent of uncertainty . However, it seems important to point out that these contours are not proper confidence or credibility intervals to avoid any confusion. There is no guarantee that the 95% contours capture the true location 95% of times. Further, the contours might look very different depending on the choice of the window size. The authors should provide more explicit guidelines about how to (or not to) interpret these contours, and also guide the choice of window size .Reviewer #2:Battey et al., develop a software to predict geographic location from genotype data. They use the fact that their method is fast to quantify individual prediction uncertainty and describe "mosaic ancestry" by predicting locations separately in windows across the genome.The manuscript and code are overall clear and the work is well-rounded, with applications to simulations and three real data sets and a thoughtful discussion of the method's performance on each. The fact that LOCATOR works directly on a VCF and a coordinates file, is very fast, and quantifies individual level uncertainty-should all be very appealing to users.I have some doubts are about the utility of the method and about the neural net approach. What are some possible motivations / applications beyond the trafficked elephant ivory example? Assuming that there are some important applications, is the neural net approach really preferable to a generative model? Throughout, the authors articulate interesting insight  that they cannot fully quantify with their method due to its black-box nature. Relatedly, the authors demonstrate that the performance is superior to SPASIBA when the input is raw, unphased genotype data. While this may represent the current state of the art, it seems to me plausible that in the future we will separate the learning of genealogical trees from learning dispersal patterns. I wonder, for example, if feeding the network with inferred tree sequences (e.g. tsInfer) would lead to much better performance, as it disentangles these two learning steps. To be clear, I am not asking the authors to develop a new method, but only to modify their discussion to either explain the benefits of their steering away from the generative model; or alternatively say that down the line, a generative approach is more fruitful.Reviewer #3:The manuscript describes a method for predicting geographic location using a simple, fully connected deep neural network which is trained on labelled samples of known geographic location. I am particularly impressed that the method can be applied to genomic subregions, quantifying uncertainty about predictions as well as variation of ancestry along the genome due to recent admixture or other evolutionary processes influencing genetic structure.The presented method is a significant advance from existing methods, which are substantially slower and impose stronger modelling assumptions. Accuracy appears comparable to existing methods on simulated data , while on real data the presented method outperforms existing approaches.The manuscript is very well written; details of the method were clear. While I did not attempt to do so, I believe that analyses were explained in sufficient detail for reproducing the results.eLife and I would recommend it for publication. Below, I am listing a few comments I have after reading the manuscript.I believe that the work is of interest to the readership of - Prediction of geographic location has many similarities with (recent) ancestry inference, and can be seen as a projection of genetic structure onto a geographic landscape (where genetic structure may be affected by many processes). It would be great if the authors could include a discussion putting this work into context with previous work on genetic structure, in particular methods that also don't require discrete populations, e.g., PCA on various types of matrices  (genes mirror geography!). I do think and like that projecting structure on geography can improve interpretability compared to these methods, however perhaps adds constraints on the projection space that other methods may not have/or differ from other methods?- I believe that a potential weak point of the current manuscript is that it is unclear how to interpret these predictions (apart from the naive interpretation of geographic origin of ancestors). In particular, I believe that the impact of this paper would be very much elevated if the authors could include examples where geographic location can be used for inference of some underlying biological process, e.g., selection acting on the genome or regions with reduced genetic diversity. Is there a difference in predicted geographic location in coding/non-coding regions (such as coding regions have lower variance of prediction for instance)?- Is it possible to estimate dispersal rates from these predictions and their uncertainty? (which may be the obvious parameter one might want to learn about an organism/group of interest).- I like that the authors provided an analysis where some samples were dropped from the training step, to illustrate the effect of unsampled groups. In practise, is there a way to learn from the data whether the reference/training samples are representative for the sample I want to do prediction for? E.g., one could think about having multiple neural networks that are trained on subsets of samples and compare predictions.- What is the reason for windowing according to number of base-pairs instead of recombination rate (cM), especially given that Figure 8 shows a higher prediction error in regions of lower recombination rate .Overall, I very much enjoyed reading this paper. Out of interest and with no need to mention or implementing this in the current paper – If the underlying genealogies were known, would you be able to predict geographic locations of most recent common ancestors? You could then extract information about directional migration from this for instance I believe? Essential revisions:The reviewers were unanimous in liking this paper, but were equally unanimous in thinking it needed more discussion/clarification of what the advantages and disadvantages of this machine learning approach is compared to explicitly model-based approaches. How should we interpret the results, what are suitable applications (beyond examples given), etc.As the reviews are consistent and self-explanatory, they are attached in full, rather than condensed here. This also goes for minor comments.Thanks to the editors and reviewers for your thorough comments. In this revision we have added two new analyses looking at (1) fixed- vs recombination-distance-based windows, and (2) the EDAR locus as an example of a well-studied genomic region with a steep geographic cline. We also added to the end of our Discussion section to explore the general utility of machine learning approaches in population genetics. We also made minor figure edits and clarified several methodological points and syntax issues. Specific responses for each reviewer comment are printed below.Reviewer #1:The authors present a new, neural network (NN) based approach for predicting the geographic location of a sample based on its genetic information and a reference dataset of multiple samples with known genetic and geographic information. The new method, Locator, is compared to a probabilistic approach that explicitly models the geographic distribution of alleles.One major advantage of the new approach is the dramatic increase in speed, particularly when dealing with very large datasets. The simulations and empirical analyses make a lot of sense. I further appreciate the theoretical and simulation-based insight that dispersal distance is the single limiting factor for the accuracy of the inference, assuming datasets are moderately large. I strongly support publication of the study in eLife. However, I think the manuscript could improve by providing a better presentation of advantages and disadvantages of this type of machine learning approach compared to model-based  approaches. I suspect that model-based approaches such as SPASIBA should work better for out-of-sample prediction (e.g. for geographic regions that are not covered by the training sample) and have a clearer approach to quantifying uncertainty , at least when model assumptions are correct. Specific comments and examples are below.Thank you for your comments. We have expanded the general discussion of the utility of machine learning approaches in population genetics at the end of the Discussion section, and respond to specific comments below.1) I wonder about cases where the distribution of the species is continuous across space, but the sampling of the training sample is strongly clustered, which seems highly relevant for many empirical cases where samples are rarely sampled uniformly across species range . Further, it seems that the clustering of the training samples affects the prediction of Locator almost like a strong "prior", i.e. as if it is implicitly assumed that the new sample most likely also comes from one of these clusters. This might explain why Locator is superior to SPASIBA for the Anopheles data but not for the simulations  since SPASIBA does not have a prior on location but assumes that the new sample could come from any arbitrary location. However, this might also imply that SPASIBA is better for out-of-sample prediction, i.e. when the spatial location of the investigated sample is different from the locations of the clustered training data. Related to this, in the discussion the authors state that they found that "the greatest increase in performance relative to the model-based approach is in empirical data for which the assumption of smooth variation in allele frequencies across the landscape is unlikely to hold, such as the complex multi-species Anopheles sample analyzed here ". My alternative interpretation here is that this has nothing to do with smooth variation in allele frequency, but is a result of the strong clustering of the training sample, and the fact that the test samples are also sampled from these cluster locations. In fact, one of the results (subsection "Effects of Unsampled Populations") is that prediction error is much higher for individuals from regions excluded from training, and that "predicted locations appear to project towards the nearest locality included in the training set", which is supporting my interpretation. I thus would change the discussion regarding the mentioned statement and bring up the problem of clustered sampling locations of the training sample. It might be out of the scope of the current study, but testing both SPASIBA and Locator on spatial simulations with clustered training samples but uniformly sampled test samples would also be informative regarding this question.This is a good point, and the analogy to a strong prior given spatially concentrated sampling seems right to us. We have edited the second paragraph of the Discussion section to include this point.2) The statistical method SPASIBA allows to compute a likelihood surface of the location of the tested individual, i.e. it provides a statistical way of quantifying uncertainty in the estimate. The Locator method does not allow such a quantification. However, the authors suggest to run Locator on multiple 2 Mb windows across the genome and use the predicted locations from such multiple runs to quantify the uncertainty, for example by plotting contours of the 95% quantiles of a two-dimensional kernel density of these locations. This approach certainly gives some idea about the spatial extent of uncertainty . However, it seems important to point out that these contours are not proper confidence or credibility intervals to avoid any confusion. There is no guarantee that the 95% contours capture the true location 95% of times. Further, the contours might look very different depending on the choice of the window size. The authors should provide more explicit guidelines about how to (or not to) interpret these contours, and also guide the choice of window size .We added a note to the relevant Results section explaining differences between window prediction intervals and confidence intervals .Reviewer #2:Battey et al., develop a software to predict geographic location from genotype data. They use the fact that their method is fast to quantify individual prediction uncertainty and describe "mosaic ancestry" by predicting locations separately in windows across the genome.The manuscript and code are overall clear and the work is well-rounded, with applications to simulations and three real data sets and a thoughtful discussion of the method's performance on each. The fact that LOCATOR works directly on a VCF and a coordinates file, is very fast, and quantifies individual level uncertainty-should all be very appealing to users.I have some doubts are about the utility of the method and about the neural net approach. What are some possible motivations / applications beyond the trafficked elephant ivory example? Assuming that there are some important applications, is the neural net approach really preferable to a generative model? Throughout, the authors articulate interesting insight  that they cannot fully quantify with their method due to its black-box nature. Relatedly, the authors demonstrate that the performance is superior to SPASIBA when the input is raw, unphased genotype data. While this may represent the current state of the art, it seems to me plausible that in the future we will separate the learning of genealogical trees from learning dispersal patterns. I wonder, for example, if feeding the network with inferred tree sequences (e.g. tsInfer) would lead to much better performance, as it disentangles these two learning steps. To be clear, I am not asking the authors to develop a new method, but only to modify their discussion to either explain the benefits of their steering away from the generative model; or alternatively say that down the line, a generative approach is more fruitful.Thank you for your comments. We have expanded our Discussion section to talk more generally about the utility and desirability of machine-learning vs statistical approaches – see the new final two paragraphs.Reviewer #3:The manuscript describes a method for predicting geographic location using a simple, fully connected deep neural network which is trained on labelled samples of known geographic location. I am particularly impressed that the method can be applied to genomic subregions, quantifying uncertainty about predictions as well as variation of ancestry along the genome due to recent admixture or other evolutionary processes influencing genetic structure.The presented method is a significant advance from existing methods, which are substantially slower and impose stronger modelling assumptions. Accuracy appears comparable to existing methods on simulated data , while on real data the presented method outperforms existing approaches.The manuscript is very well written; details of the method were clear. While I did not attempt to do so, I believe that analyses were explained in sufficient detail for reproducing the results.I believe that the work is of interest to the readership of eLife and I would recommend it for publication. Below, I am listing a few comments I have after reading the manuscript.Thank you for your comments. In this revision we have expanded our discussion to talk more generally about the utility of process-agnostic models in population genetic inference, and made a number of small edits to syntax and figures. Specific responses are below.- Prediction of geographic location has many similarities with (recent) ancestry inference, and can be seen as a projection of genetic structure onto a geographic landscape (where genetic structure may be affected by many processes). It would be great if the authors could include a discussion putting this work into context with previous work on genetic structure, in particular methods that also don't require discrete populations, e.g., PCA on various types of matrices  (genes mirror geography!). I do think and like that projecting structure on geography can improve interpretability compared to these methods, however perhaps adds constraints on the projection space that other methods may not have/or differ from other methods?We like this suggestion. To address this we have added the following text to the Discussion section: “The correspondence between our explicitly geographic method and unsupervised clustering or dimensionality reduction methods highlights the implicit prior assumption of genetic-geographic correlation often made when interpreting the output of STRUCTURE or PCA. Rather than mapping population structure to geography as a post-hoc interpretation, Locator and other continuous assignment methods directly incorporate space in the model.”- I believe that a potential weak point of the current manuscript is that it is unclear how to interpret these predictions (apart from the naive interpretation of geographic origin of ancestors). In particular, I believe that the impact of this paper would be very much elevated if the authors could include examples where geographic location can be used for inference of some underlying biological process, e.g., selection acting on the genome or regions with reduced genetic diversity. Is there a difference in predicted geographic location in coding/non-coding regions (such as coding regions have lower variance of prediction for instance)?Thanks for this suggestion. We have now expanded on our analysis of association or error with recombination rate to look at different windowing designs, and pulled out a case study of the EDAR locus in Eurasia as an example of a highly geographically differentiated region of the genome that readers familiar with the human genetics literature in particular may be familiar with.- Is it possible to estimate dispersal rates from these predictions and their uncertainty? (which may be the obvious parameter one might want to learn about an organism/group of interest).In simulations we found that error was roughly constant when expressed in units of population mean dispersal distance , so it may be possible to work backwards from observed validation error in empirical data to estimate a dispersal kernal for the organism. However, we didn’t design the method with this use in mind and haven’t tested extensively enough to be confident in using it that way, so we decided to leave this out of the main text.- I like that the authors provided an analysis where some samples were dropped from the training step, to illustrate the effect of unsampled groups. In practise, is there a way to learn from the data whether the reference/training samples are representative for the sample I want to do prediction for? E.g., one could think about having multiple neural networks that are trained on subsets of samples and compare predictions.<40 training samples), where randomly splitting training/validation samples can result in some regions under- or overrepresented in the training set. On the Locator documentation  we now included a section suggesting users fit models in a loop with different seeds to get an ensemble of models trained on different subsets of their reference data. This doesn’t quite get at whether or not each training set is representative, but does help average over some of the noise from the training / validation split. In this revision we added a paragraph to the Materials and methods section suggesting this approach to users with small sample sizes.The multiple networks idea is an interesting one that we hadn’t considered. The closest approach that we’ve worked on here is in small samples (i.e. - What is the reason for windowing according to number of base-pairs instead of recombination rate (cM), especially given that Figure 8 shows a higher prediction error in regions of lower recombination rate .Good point. We have now added a section comparing fixed versus per-centimorgan windowing, and looking a little deeper at genomic factors that could affect prediction accuracy. Perhaps unsurprisingly the error of centroid estimates is extremely similar using bp or cm windows, but the distribution along the genome is quite different in a way that seems to reflect the association with recombination rate. Overall, the variance in per-window prediction error is lower for recombination-distance based windows. This mostly seems to reflect centromeres, whose presence predicts significant proportions of variance in error using fixed windows but not in centimorgan windows.Overall, I very much enjoyed reading this paper. Out of interest and with no need to mention or implementing this in the current paper – If the underlying genealogies were known, would you be able to predict geographic locations of most recent common ancestors? You could then extract information about directional migration from this for instance I believe?Interesting idea – I think the answer is yes if you make some assumptions about the dispersal kernal (the model we sketch out in the appendix would get you part of the way there). We are also aware of another group with a method in the works that does something like this and are excited to see it!


Triticum aestivum L.) germplasm under terminal drought stress and well-watered conditions in semi-arid environments. An association panel including 87 bread wheat cultivars and 199 landraces from Iran bread wheat germplasm was planted under two irrigation systems in semi-arid climate zones. The whole association panel was genotyped with 9047 single nucleotide polymorphism markers using the genotyping-by-sequencing method. A number of 23 marker-trait associations were selected for traits under each condition, whereas 17 marker-trait associations were common between terminal drought stress and well-watered conditions. The identified marker-trait associations were mostly single nucleotide polymorphisms with minor allele effects. This study examined the effect of population structure, genomic selection method , training set size, and type of marker set on genomic prediction accuracy. The prediction accuracies were low (-0.32) to moderate (0.52). A marker set including 93 significant markers identified through genome-wide association studies with P values ≤ 0.001 increased the genomic prediction accuracy for all traits under both conditions. This study concluded that obtaining the highest genomic prediction accuracy depends on the extent of linkage disequilibrium, the genetic architecture of trait, genetic diversity of the population, and the genomic selection method. The results encouraged the integration of genome-wide association study and genomic selection to enhance genomic prediction accuracy in applied breeding programs.The present study aimed to improve the accuracy of genomic prediction of 16 agronomic traits in a diverse bread wheat ( T. aestivum) has approximately 10.67 grams of proteins and 47.54 grams of carbohydrates (per 100 grams of seeds), which is remarkably higher than other cereals and makes bread wheat one of the most strategic crops [The world wheat production in 2020 is estimated at 762.7 million tons . Bread wic crops .Wheat is a highly adapted plant species which can grow between the latitudes of 30° to 60°N and 27° to 40°S . Droughthttps://triticeaetoolbox.org; http://plants.ensembl.org). However, some early known QTL were not suitable for identifying candidate genes or even marker-assisted selection (MAS) due to the unsatisfactory marker density or limited recombination rate [Genetic studies have identified many quantitative trait loci (QTL) for wheat traits  [Oryza sativa L.) [Avena sativa L.) [Zea mays L.) [Panicum virgatum L.) [Hordeum vulgare L.) [Thinopyrumn intermedium) [Genomic prediction (GP)  will booivum L.) , rice (Otiva L.) , oat (Avtiva L.) , maize (mays L.) , switchgatum L.) , barley gare L.) , and whermedium) .The present study determines GP accuracy using alleles derived from a mixed population of 87 cultivars and 199 landraces of Iran bread wheat germplasm. The goal was to optimize GP accuracy using different population structures, GS methods, TS sizes and types of MSs for 16 agronomic traits under terminal drought stress (TDS) and well-watered (WW) conditions.A collection of two hundred and eighty-six Iran breed wheat accessions including 199 landraces (collected during 1931–1968 in Persian plateau) and 87 cultivars (released during 1942–2014), was kindly provided by the University of Tehran (UT) and Seed and Plant Improvement Institute (SPII), Karaj, Iran. The detailed information about the landraces and cultivars is given in S1 and S2 Tables in The genotyping-by-sequencing (GBS)  method whttp://taylor0.biology.ucla.edu/structureHarvester). Two subpopulations (SBP-I and SBP-II) were identified within the association panel. The SNP calling was performed for each subpopulation, and 7714 SNPs for SBP-I and 5873 SNPs for SBP-II were identified. The 4785 markers were common between SBP-I and SBP-II, which were systematically separated for further analysis. The population structure matrix (Q-matrix) was obtained for the association analysis of the whole population. In addition, principal component analysis (PCA) was conducted on the SNP data set with the prcomp function using the tidyverse [The population structure was evaluated by the Bayesian clustering approach with the use of an admixture model in STRUCTURE software . The numidyverse  package idyverse  package idyverse  was achiidyverse  embeddedidyverse  package r2) [r2 > 0.02) was chosen following Sukumaran et al. [The distribution of molecular markers and LD estimates was calculated for the whole association panel (WAP) and each subpopulation, separately. LD among markers was estimated for each chromosome using the full matrix option in TASSEL software . Pairwisr2) . The cutn et al. . The pern et al. .2 (GY) and thousand kernel weight (TKW), seed length (SEL), seed width (SEW), seed number per spike (SN), spike length (SPL) and spike weight (SPW), flag leaf length (FLL), flag leaf width (FLW), peduncle length (PL), shoot diameter (SHD) and awn length (AWL). For details on measurements and time of assessments, please refer to the manual “Physiological breeding II: a field guide to wheat phenotyping” [Phenotypic measurements included days to heading (DTH), days to maturity (DTM), duration of heading-to-maturity (DHTM) and plant height (PH), grain yield/motyping” .proc mixed procedure in SAS version 9.4 [μ represents the total mean, gi represents the genetic effect of the ith genotype, lj indicates the effect of the jth environment, and (gl)ij indicates the interaction effect between the ith genotype and the jth environment. In addition, rm(j), bk(mj) and εijmk represent the effect of the mth replication within the jth environment, the kth block effect within mth replication within the jth environment, and the residual effect following H2) estimates were calculated based on each plot mean with an assumption of independence of effects using the following equationk and r represent genotypic variance, genotype by environment interaction variance, residual variance, the number of environments, and the number of replications, respectively. The estimation of variance components was performed by the proc varcomp procedure, whereas all effects were considered as random.The phenotyping data of Zanjan and Alborz provinces were pooled for TDS and WW conditions separately to have a wide range of phenotypic variations in semi-arid climate zones. Then, analysis of variance (ANOVA) was conducted for the WAP under TDS and WW conditions separately using the sion 9.4 . The mod10 ≥ 3.0 (P ≤ 0.001).The best linear unbiased predictions (BLUPs) were estimated for each accession using the same model described for phenotypic analyzes by the lme4 package  in the Rhttp://plants.ensembl.org/Triticum_aestivum/Tools/Blast). The predicted candidate genes were selected from the local LD which included the identified MTAs. The annotated genes in IWGSC v1.0, TGAC v1.0, and TAIR10 were used to predict the biological function of the candidate genes (http://plants.ensembl.org and https://triticeaetoolbox.org). The prediction of candidate genes was referred to the following criteria: a) genes identified by the peak markers, and b) genes with known biological functions for the trait under study in wheat (T. aestivum), and Arabidopsis .The peak markers were used to perform BLAST searches on the IWGSC v1.0 RefSeq reference genome . The SMMS included 93 common markers identified by the K, K+Q, and K+PCA matrices for all traits and both conditions. The GP was assessed for each trait under TDS and WW conditions, separately. The accuracy of the GP was estimated as Pearson’s correlation coefficient (r) among GEBVs and BLUPs over TS and VS. The average of accuracies was reported across folds and repeats [GP was estimated by three different methods: ridge regression-best linear unbiased prediction (RR-BLUP) , genomic repeats .The existence of two main subpopulations  was idenr2 > 0.02 at about 5.24 cM in the A genome, at about 4.29 cM in the B genome, at about 9.95 cM in the D genome , while chromosomes 2B and 3B had the highest number of markers   estimates for all traits were calculated under TDS and WW conditions, separately (S6 Table in G) was observed for all 16 agronomic traits under both TDS and WW conditions, whereas the genotype-by-environment effect (G×E) was not significant for SEL, SEW, SPL, SPW, FLL, FLW, PL, SHD, and AWL under both TDS and WW conditions  to 0.86 (DTH) under TDS conditions, and from 0.52 (DTM) to 0.87 (DTH) under WW conditions  under TDS conditions and eleven phenotypes  under WW conditions when 90% of accessions applied in TS (S15 Table in The SMMS (93 significant markers) showed the highest prediction accuracy for all traits under both conditions  results revealed differences in the prediction accuracies which were only explainable by population structure. If we assume the SBP-I as a half-pure population (77 cultivars and 71 landraces), the SBP-II as a diverse population (128 landraces and 10 cultivars), and the WAP as a mixed population (87 cultivars and 199 landraces), it is expected that a mixed population will perform more reliably because of additional diversity in TS and more cultivars in VS during CV repeats , 19. A mIt was reported that RR-BLUP works well for genetic architectures containing many loci with small effects . If heriThis study assumed that probably some closely related accessions were present in the association panel. If the related individuals are present in both TS and VS, the inflation produced by half and full-sib families may lead to false results during the CV repeats. Differences among prediction accuracies decrease in larger TSs . The BayThe ability of GS to enhance plant breeding is based on the fact that genotyping will soon become cheaper, and consequently, breeders will be able to save time and reduce phenotyping tasks , 15. AltThis study concluded that obtaining the highest GP accuracy depends on the extent of LD, the genetic architecture of trait, genetic diversity of the population, and the GS method.S1 File(XLSX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(PDF)Click here for additional data file.S4 File(DOCX)Click here for additional data file.S5 File(DOCX)Click here for additional data file.S6 File(DOCX)Click here for additional data file.S7 File(XLSX)Click here for additional data file.S8 File(DOCX)Click here for additional data file.S9 File(XLSX)Click here for additional data file.