Along with the traditional traits, swine breeding programs for Italian dry-cured ham production have recently aimed to include novel phenotypes. The identification of the genomic regions underlying such new traits helps to untangle their genetic architecture and may provide useful information to be integrated in genetic selection. With this aim, we estimated genetic parameters and conducted a single step genome wide association studies (GWAS) on untrimmed and trimmed thigh weight considering two pig crossbred lines approved for Italian Protected Designation of Origin ham production. Quantitative trait loci (QTLs) were characterized based on the variance of 10-SNP sliding windows genomic estimated breeding values. In particular, we identified interesting QTL signals on several chromosomes, notably on chromosome 4, 6, 7 and 15. A high heritability and genetic correlation were observed for the two traits under investigation and although independent studies including other pig populations are required to disentangle the possible effects of specific linkage disequilibrium in our population, our findings suggest that such QTL could be investigated in future pig breeding programs to improve the reliability of genomic estimated breeding values for the dry-cured ham production.Protected Designation of Origin (PDO) dry-cured ham is the most important product in the Italian pig breeding industry, mainly oriented to produce heavy pig carcasses to obtain hams of the right weight and maturity. Recently, along with the traditional traits swine breeding programs have aimed to include novel carcass traits. The identification at the genome level of quantitative trait loci (QTLs) affecting such new traits helps to reveal their genetic determinism and may provide information to be integrated in prediction models in order to improve prediction accuracy as well as to identify candidate genes underlying such traits. This study aimed to estimate genetic parameters and perform a single step genome wide association studies (ssGWAS) on novel carcass traits such as untrimmed (UTW) and trimmed thigh weight (TTW) in two pig crossbred lines approved for the ham production of the Italian PDO. With this purpose, phenotypes were collected from ~1800 animals and 240 pigs were genotyped with Illumina PorcineSNP60 Beadchip. The single-step genomic BLUP procedure was used for the heritability estimation and to implement the ssGWAS. QTL were characterized based on the variance of 10-SNP sliding window genomic estimated breeding values. Moderate heritabilities were detected and QTL signals were identified on chromosome 1, 4, 6, 7, 11 and 15 for both traits. As expected, the genetic correlation among the two traits was very high (~0.99). The QTL regions encompassed a total of 249 unique candidate genes, some of which were already reported in association with growth, carcass or ham weight traits in pigs. Although independent studies are required to further verify our findings and disentangle the possible effects of specific linkage disequilibrium in our population, our results support the potential use of such new QTL information in future breeding programs to improve the reliability of genomic prediction. The production of Protected Designation of Origin (PDO) dry-cured ham is the most important product in the Italian pig breeding industry with remarkable economic benefits for producers . Parma aIn the present study, leg traits were collected from the entire population of two pig crossbred lines approved for the ham production of the Italian San Daniele PDO circuit. Although many studies have been focused on purebreds, pig industries favor crossbreeds and few association studies exist ,14,15,16The study did not imply modification of the farm and slaughter protocols and data were provided by the farmers and abattoir personnel. Samples were collected from the green and trimmed thigh with the permission of farmers. Animal care and slaughter of the pigs were under the supervision of the veterinarians of the National Health Service. The study followed the guidelines of the Animal Care Committee of the Department of Animal Science of the University of Udine .http://www.anas.it, accessed on 18 February 2021) and the Goland C21 is a commercial line approved for the ham production of the PDO . The litters of Duroc × Large White and Goland C21 × Large White were born from January 2013 to December 2014 and, according to the prescription of the PDO, were reared in the farms for 9 months. The farms had implemented a traceability system, through the application at the birth of a radio-frequency identification (RFID) object tags in the thighs, allowing to individually track pigs in the farm and the slaughter and processing phases. The final live weight of pigs ranged from 145 to 170 kg and during the whole period pigs were housed in groups of 15 animals in a pen with access to an external paddock. Starting from 35 days of age pigs were fed a post-weaning commercial supplement. At around the age of 80 days, pigs were fed diets formulated by the Breeding Association of the Friuli Venezia Giulia (FVG) Region, in compliance with the PDO prescription. In particular, diets were offered to a semi-libitum and formulated for an initial phase of growth (from 80 to 115 days), an intermediated phase of growth (from 115 to 195 days), and for the finishing phase (from 195 to 270 days). Ingredients and chemical composition of the diets are reported in For the study, 1810 pigs reared in five Italian commercial farms, all within the circuit of the PDO for the productions of San Daniele ham, were used. Initially, a group of 230 Large White farrows were introduced in the farms and were mated with Duroc and Goland C21 boars. Large White and Duroc were enrolled in the Italian Registry of the Italian Pig Breeder National Association , inserting the probe between the third and fourth last rib on the left hot carcass at 8 and 10 cm off the dorsal midline. The carcass lean percentage was calculated and cold carcass weight was recorded for each selected pig, as by the Council Regulation (EC) n. 1234/2007, Annex V, Part B. Backfat thickness was used to grade the carcass in the EUROP grid. Only the U, R, and O carcasses were considered suitable according to PDO production. Moreover, the weight of the right thigh was recorded before and after trimming, which was performed 24 h after slaughter. Trimming consisted of removing part of the fat and the rind to obtain the typical round ham shape. The RFID tracking system was used to collect these data from the animals and guarantee traceability information during the whole pig slaughter line and 1810 untrimmed thigh (UTW) and 1202 trimmed thigh (TTW) weight values were obtained. p > 0.00001). All quality control procedures were performed using the PLINK v.1.07 toolset  were randomly chosen for genotyping. The DNA was extracted from the tissue samples using the ExgeneTM Clinic SV commercial kit . After extraction, the quality and quantity of nucleic acid were assessed by electrophoresis and spectrophotometry with the Nanodrop instrument. The DNA was stored at −20 °C. Four hundred nanograms of genomic DNA were used for marker analysis with the PorcineSNP60 Genotyping BeadChip  followin toolset . After ty is the vector of investigated traits; X is the incidence matrix linking records to fixed effects and b is the related vector including crossbred (2 levels), sex (2 levels), contemporary group (12 levels), farm (5 levels) and abattoir (5 levels), slaughter age as covariate; Z is the incidence matrix for random genetic effects, relating records to animals, and a is the vector of the individual additive genetic values ; and e is the vector of random residuals distributed as I is an identity matrix. The additive genetic effect was modelled according to the (co)variance structures in the single-step framework (ssGBLUP), which is the blended genomic and pedigree relationship matrix (H) according to Aguilar et al. ; a is the fixed effect of the SNP genotype; u and ε are vectors of random additive polygenic effects and random residuals, respectively, u ∼N and ε ∼N, where A is the additive genetic relationship matrix estimated from SNP data using the ibs function in GenABEL [I is an identity matrix, and σ2a and σ2ε are the additive genetic and residual error variances, respectively. X, S, and Z are the related incidence matrices.In order to support the better performance of WssGWAS, a genome-wide association analysis was also carried out based on regression of phenotypes on the genotypes of animals for one SNP at a time, using mixed model and score (GRAMMAS) in GenABEL  as descr GenABEL , I is anBased on association mapping outcomes, gene annotations for candidate QTL windows  were obtained using the Biomart platform on Ensembl  through p-values were corrected using the Benjamini-Hochberg procedure (FDR) and pathways with FDR less than or equal to 0.05 were considered as significantly enriched.To obtain a list of possibly overrepresented pathways among the list of positional candidate genes identified, a pathway enrichment analysis was performed using the enrichment function in the PANEV tool  which esA matrix .Descriptive statistics of the two traits selected for the GWA analysis are provided in A matrix . This reA total of 240 pigs were genotyped with the Illumina PorcineSNP60 (~64 K SNPs). After quality control, the final data set consisted of 36,569 SNP for 236 animals. We identified genomic regions associated with the two traits under investigation considering the variance explained by 10 consecutive SNP window estimated by WssGWAS approach. Manhattan plots showing the proportion of genetic variance explained by the 10 SNP window (~0.55 Mb) are in Sus scrofa 11.1 reference genome map [A total of nine and eight relevant genomic regions were found to be associated with UTW and TTW traits, respectively. The genomic regions were mainly located on chromosomes 1, 4, 6, 7, 11 and 16 in both traits. Whereas specific QTL signals were detected on chromosome 8, 9, 13 for UTW and chromosome 5 for TTW trait. The most important windows explained the 15.3% and 18.1% of the genetic variance of each trait . Same sinome map , and a tnominal value < 1.37 × 10−6) for both traits. However, considering the complexity of investigated traits and according to other GWAS focusing on similar traits in pig [nominal value < 5.00 × 10−5 and < 5.00 × 10−4 thresholds  gene was reported in literature to be correlated with feed/gain ratio in pig [KCNA3 (Potassium Voltage-Gated Channel Subfamily A Member 3) gene is known to affect carcass traits in swine [PROK1 (Prokineticin 1) gene has been reported as candidate gene associated with body weight [SLC16A4 (Solute Carrier Family 16 Member 4) associated with feed efficiency in cattle [CSF1 gene (Colony stimulating factor 1), which encodes a myokine, is known to be associated with skeletal muscle in human [GSTM3 (Glutathione S-Transferase Mu 3) was reported as a functional candidate gene in pig with different backfat thicknesses [AMPD2 (Adenosine Monophosphate Deaminase 2) gene is noteworthy since AMPD was reported to be a candidate gene for meat production trait in pig and beef cattle [SLC16A4 gene was mapped as potential candidate for body length in indigenous cattle breeds [ADGRL2 (Adhesion G Protein-Coupled Receptor L2), CNOT1 (CCR4-NOT Transcription Complex Subunit 1) and SLC38A7 (Solute Carrier Family 38 Member 7) were already described as candidate genes for weight [CCDC113 (Coiled-Coil Domain Containing 113) and CFAP20 (Cilia And Flagella Associated Protein 20) were recently identified in a genome-wide association study on carcass and meat quality traits in Italian Large White pigs [KATNB1 (Katanin Regulatory Subunit B1) gene was reported as a potential candidate gene for muscle fibre characteristics in pigs [NDRG4 (NDRG Family Member 4) and COQ9 (Coenzyme Q9) genes deserve particular mention. Indeed, NDRG4 plays a key role in regulating the myogenic differentiation via Akt/CREB activation [COQ9 was reported in association with water holding capacity and with several quality traits in pigs [PRKACB  gene is intriguing considering the role played by cAMP-dependent protein kinase in skeletal muscle adaptation [TDP1 (Tyrosyl-DNA Phosphodiesterase 1) and CALM1  genes were recently described as candidate genes associated with ham weight loss at first salting [PSMC1  on growth and carcass traits was also already reported and the use of its polymorphisms in marker-assisted selection for improving beef cattle was suggested [FOXN3 (Forkhead Box N3) gene was proposed as candidate gene affecting intramuscular fat content in swine [EFCAB11  gene was recently identified as candidate gene for ham weight at end of salting [MGAT5  gene is known to be associated with intramuscular fat in pig [IFT74 (Intraflagellar Transport 74) was identified as potential candidate gene, which was already reported in an association study for intramuscular fat content in heavy pigs [Over the years, several reports have been focused on dissecting the genetics of pig quantity and quality production traits in many countries ,43,44,45o in pig  whereas in swine  and cattin swine . PROK1  and candidate QTL regions explaining more than 1% of additive genetic variance were identified. In particular, significant QTL signals were highlighted notably on chromosome 1, 4, 6, 7, and 15. A total of 249 unique candidate genes were pinpointed. Notably, some of them were reported in literature in association with growth, carcass, feed efficiency or skeletal muscle development traits in swine or beef cattle. In particular, among the others,


P < 0.001) and GBLUP  but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds.We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248,  Genomic selection (GS)  has becoAfter these initial studies, an increasing number of research articles have been published effectively testing the integration of GS into conventional plant breeding pipelines for different traits measured in different environments . The appgj expressed as a linear regression of the line phenotype on marker covariates . The matrix G is a genomic relationship matrix with markers centered and standardized  probabilities.Genomic selection is affected by a range of factors occurring at different levels. For example, one complexity arises while incorporating genotype × environment (G × E) interaction into statistical models. Also important are the genome interactions related to G × E interactions for multi-traits and the complexity of the traits (complex vs. simple) evaluated in multiple environments. Some of these complexities can be addressed using parametric models where the effect of phenotypic lines can be replaced by dardized , which lSemi-parametric genomic regression methods are efficient for capturing non-additive variation. The Reproducing Kernel Hilbert Space (RKHS) method was initially used in animal breeding  and in wWhile GS is routinely deployed in the stage 1 yield trials of the CIMMYT Global Wheat Program, genomic prediction has not yet been applied in early generations due to a number of factors including, but not limited to, genetic complexity of the crop, logistics, and expense of establishing a faster cycle integrated into the existing shuttle breeding method, which involves moving seed within and/or outside Mexico each breeding generation. However, from the 2009–2010 to 2014–2015 seasons, a large GS proof-of-concept experiment was carried out with the objective of incorporating genomic prediction for increased yield in the early stages of population improvement in the context of the standard methodology applied in the CIMMYT Wheat Breeding Program in Mexico. Here, we present the results of this initial experiment, which is the first reported in wheat applying GS as early as the F2 generation. Note that the genome-based models incorporating G × E were not yet available during the time this experiment was conducted, so were not applied in this study.The training set was comprised of 1,334 entries from the 17th and 18th Semi-Arid Wheat Yield Trials (17th and 18th SAWYT), and International Bread Wheat and Semi-Arid Wheat Screening Nurseries , leaf rust (Puccinia triticina), and septoria tritici blotch (Zymoseptoria tritici).This study sought to incorporate genomic prediction for increased yield in the early stages of population development in the context of the standard breeding methodology applied at CIMMYT in Mexico. This method used selected bulks and two field generations per year alternating between the CIMMYT Experimental Station in Toluca  and the Campo Experimental Norman E. Borlaug (CENEB) station at Cd. Obregon . The phenotypic selection in segregating generations was for semidwarf plant height, phenology equivalent to parents and checks and disease resistance; notably stripe rust  comprised lines derived from a random sample of approximately 1,000 F2 seeds per cross. These Con-BP F2s were each sown in a 10 m × 1.6 m plot at the CENEB station in the 2011–2012 season, and approximately 50 F2 plants with desirable height, phenology, and disease reaction were selected to form an F3 bulk. Approximately 1,000 seeds from each F3 bulk were planted in 10 m × 1.6 m plots in Toluca in May 2012, and 50 plants with desirable plant type and disease reaction were selected and harvested to form an F4 bulk. Again, 1,000 seeds of each F4 bulk were planted in the same plot configuration, 50 plants per plot were selected for plant type and disease reaction and each harvested individually to form F4:5 single plant selections. These were increased in single 2 m double-row beds over summer 2013 at the Toluca Station. We aimed to select 20 lines from each cross based on uniformity, plant type, and disease reaction. Selected rows were individually harvested and threshed to generate the F4:6 lines that were planted in field trials at the CENEB station in the 2013–2014 and 2014–2015 seasons.Genomic biparental populations (Gen-BPs) were formed from 50 F2 plants per cross that were space planted at the same time and in the same field location with the Con-BP F2 subpopulations. DNA was extracted from leaf tissue of F2 individuals for genotyping-by-sequencing (GBS) and calculation of Genomic Estimated Breeding Values (GEBVs). Individuals from each cross were selected on the basis of GEBV, plant type, and disease reaction. As GEBVs from the different prediction methods were not highly correlated (see section “Results”), with no way to know which was most predictive, F2s with the highest average GEBV across the three prediction methods were selected. Selfed seed from selected F2 plants within each cross was combined to form F3 bulks. Gen-BPs were advanced from F3 bulk to F4:6 line concurrently, with the same methods and in the same field nurseries as the Con-BPs. In other words, selection methodologies and intensities were identical for Gen-BPs and Con-BPs from the F3 bulk stage. Similar numbers of lines were derived from the Gen-BP and Con-BP subpopulations of most crosses. Six crosses did not produce progeny with acceptable combinations of plant type and disease reaction for Gen-BP and Con-BP subpopulations.Rapid cycle intercross populations (RCIs) were generated by crossing selected F2 individuals, those with the highest average GEBVs across prediction methods, within and between Gen-BP subpopulations. For intercrosses within a population, average GEBV and genetic distance based on the kinship matrix  among inSingle plant-derived lines (SPLs) were developed from a subset of 240 F2 plants from across the Gen-BP subpopulations. The selection of F2 plants was based on a visual assessment of acceptable plant height, phenology, and agronomic type, without consideration of disease reaction or GEBV. F3 seed from each selected plant was sown in a single row at CIMMYTs El Batan Research Station in May of 2012. Rows were sprayed with a fungicide to control diseases and were assessed for uniformity, height, and phenology. Rows were discarded only if they expressed excessive height, slow phenological development, or high levels of within-row variability. From the 240 rows, 213 F2:4 SPLs were selected for field testing to assess response to selection for F2 GEBV using each of the three different GEBV calculation methods. SPLs were obtained from 36 of the 38 Gen-BP subpopulations and tested in field trials at CENEB in the 2012–2013 and 2013–2014 seasons.Phenotypic data for the training set of 1,334 lines were generated in field trials at CENEB, Cd. Obregon over the 2008–2009, 2009–2010, and 2010–2011 growing cycles under irrigated conditions with management to achieve high yield according to local best practice. Summary data for these trials are outlined in 2 (3 m × 1.6 m). Each trial was conducted in two consecutive seasons. Trials in each growing season were planted in late November or early December and harvested in early May. Data were collected for grain yield, plant height, and heading date. Details specific to the trials related to each of the following components of our research are provided in the following sections and summarized in Field trials of the developed populations were conducted at CENEB across three growing cycles  with equivalent management to that applied to the training set. Plots were of 4.8 mH2 × S). The latter was calculated by dividing the mean of the selected lines by the mean of the full population.Phenotypic data for the Con-BP, Gen-BP, and RCI-derived lines  were generated in field trials at CENEB, Cd. Obregon over the 2013–2014 and 2014–2015 crop cycles . EntriesSingle plant-derived lines were tested in field trials at CENEB, Cd. Obregon in the 2012–2013 and 2013–2014 crop seasons. Experiments were two replicate row-column designs. Grain yield data for the F2:4 lines were examined for correlation to GEBVs of their respective, individual F2 progenitor plant.2 plants, indexed by their genotypic identification number (GID), were characterized using GBS following the same procedure as described in The wheat genotypes included in the training set and FThis part of the analysis was performed on the six field trials that included the 1,334 entries in the training set which are outlined in Yijkl is the phenotype of wheat line i-th at location j-th in replicate k-th within the block l-th, μ is the overall mean, Yearj is the fixed effect of the year j-th, Rk(j) is the fixed effect of the k-th replicate within year j-th, Bl(kj) is the random effect of the incomplete block l-th within replicate k-th and year j-th assumed to be independently and identically normal distributed (iid) with mean zero and variance i is the fixed effect of genotype i-th, (g × L)ij is the fixed effect of the genotype × year interaction, and eijkl is the random error assumed to be iid normal with mean zero and variance H2) was computed on an entry-mean basis according to where y is the number of years, and r is the number of replicates. Note that different trials had different numbers of testing years, 17–18 SAWYT data had trials in years 2009 and 2010, whereas the other three trials had 3 years of testing (where  testing .p) greatly exceeds the number of data-points (n) available; thus, implementing regression methods poses important statistical and computational challenges. However, new developments in the area of shrinkage estimation procedures allows the implementation of whole-genome regression methods.We considered three different models: GBLUP using additive genomic relationships , the GK i = 1, 2,…, n) is given by:The regression model for wheat lines  estimates the genomic relationship by means of its linear kernel  be used . The mod  matrix, which depends on the markers and the bandwidth parameter h > 0, n × 1 vector of homoscedastic and independent normal errors.The basic idea underlying the GK approach to GS  is to usent of ε , Kh is aThis general approach requires choosing an RK, for example, a GK functionxi and xj are the marker vectors for the i-th and j-th individuals, and q0.05 is the fifth percentile of the squared Euclidean distance where ce dij2 .The marker-based, additive relationship matrix was calculated with the function A.mat in R package rrGBLUP, version 4.1 , which cA fivefold cross-validation was performed to evaluate the prediction performance of the models on the training set. The full dataset was randomly divided into five mutually exclusive subsets, four of which formed the training set for fitting the model, and the fifth was used as a test set. Predictive abilities were calculated as the Pearson’s correlation coefficient between the predicted values and the observed phenotypic values of the test set.Predictions with GBLUP, rrGBLUP_imp, and GK in the training population had similar levels of predictive ability for a yield of 0.42–0.43 as determined by a fivefold cross-validation . The GEBBecause populations were advanced through a selected bulk method to develop the material tested in Experiment 1, this experiment could not address the question of whether one method was superior to another in F2 GEBV calculation. Therefore, a random subsample of F2 plants was chosen to develop single F2 plant-derived lines so a correlation between the yield of a derived line and GEBV of an F2 could be measured. This set of lines was the basis for Experiment 2.A total of 1,857 lines were derived from conventional, phenotypically selected biparental (Con-BP), Gen-BP, and RCI breeding methods with roughly similar numbers from each . All metP < 0.001), Gen-BP lines yielded an average of 1.5% more than RCI lines (P < 0.01), and there was no significant difference between Gen-BP and Con-BP . From these, it is clear that correlations between F2 GEBVs and F2:4 yield for GK and GBLUP are not strong but are not driven by outliers with high leverage. In both cases, the selection of F2s with the highest GEBVs would avoid the selection of the lowest yielding F2:4 lines.Within the subset of 213 F2s which were randomly sampled to produce F2:4 bulks for yield testing, the correlations between prediction methods were also low . FiguresThe proof-of-concept Experiment 2 reported here demonstrates the potential of early generation genomic prediction to increase genetic gain over conventional selection methods by allowing the ability to increase the number of crossing cycles per year. In Experiment 2 of our study, F2 GEBVs generated by GK and GBLUP methods showed significant positive correlations with the yield of derived lines. The highest 10 and 20% of GEBVs from the GK method showed 4.7 and 4.2% increases, respectively, and the top 10 and 20% of F2s GEBVs predicted by GBLUP showed realized gains of 3.68 and 2.60% over a 50× random sample of the same proportion of lines from the same populations. In contrast, a similar analysis of F2 GEBVs from the rrGBLUP_imp method showed no difference from the mean of the 50× random sampling.Each of the three prediction methods used in this study produced highly correlated GEBVs in inbreds and the same levels of predictability of inbred performance based on cross validation in a training set of elite CIMMYT inbreds. In contrast, predictions in F2s derived from crosses between inbreds that were part of the training set for the model showed little to no correlation and differing levels of predictive ability compared with a realized yield of F2 SPLs. These differences are likely due to the different abilities of the prediction methods to handle heterozygosity, which is generally not accurately characterized with a GBS genotyping platform and the importance of non-additive variation in wheat. This may be reflected in the much narrower distribution of GEBVs from rrGBLUP_imp compared to GBLUP and particularly GK. The difference in the distribution of the GEBVs between the GBLUP and GK methods is likely due to the different shrinkage applied in each method. On the other hand, differences between GBLUP and rrGBLUP_imp are likely due to the imputation method used.In Experiment 1, we attempted to incorporate F2 genomic prediction into a selected bulk breeding methodology closely mirroring the typical breeding methodology in the CIMMYT spring bread wheat program. The three different prediction methods generated F2 GEBVs that showed little correlation with one another. It should be noted that the low correlations between the rrGBLUP_imp with GBLUP and GK were considered as a rare result, especially knowing the equivalence between the GBLUP and the rrGBLUP_imp. The reasons for the failure of the rrGBLUP_imp in generating similar predictions to GBLUP are unknown but may be attributable to different factors. For example the nature of the imputation algorithm or convergence issues with the Expectation-Maximization algorithm in rrGBLUP_imp. Since the three methods had similar ability to predict yield of inbreds and predictions were correlated, it was difficult to discard one of the models based on observed phenotypes and we decided to use an average of the methods. If we had conducted additional research to confirm that GK was the most predictive method or that GBLUP also showed a useful level of predictability, we would likely have made better selections of F2 individuals to form selected bulks and to make early generation intercrosses. Given that our selections were probably no better than random and the number of F2s selected was less than in parallel conventionally selected populations, it is hardly surprising that a lower level of genetic variance (presumed by planting only 5% of the number of F2s in Gen-BP vs. Con-BP) did not result in a yield advantage in the genomically selected biparental-derived inbreds (Gen-BP) and the early generation intercross derived (RCI) inbred populations; both on average and in the highest yielding 20% of lines from each of the population types.When comparing genome-based predictions, we should also emphasize that in this study the accuracy of the three methods  for predicting F2 plants was measured at the F2:4. Therefore, any attempt to make a precise estimate of errors among the three methods and benchmarking results from genome-based methods with those under conventional breeding methods in terms of biases and errors are complex and out of the scope of this research. It would be worthwhile to investigate further methods to optimize prediction power in early generation wheat populations. If a robust method can be determined, there are useful increases in genetic gain from early generation genomic prediction in wheat, particularly, in populations that are not varying for some of the obvious drivers of yield that are easily selected phenotypically, such as height or flowering time. Considering there are roughly one million F2 plants generated per year in the CIMMYT spring bread wheat program, early generation genomic prediction will likely be best targeted to certain types of populations that provide the greatest probability of higher response to selection or where there is little obvious variation amenable to phenotypic selection.These evaluations give the first indications of genetic gains from early generation GS for a highly complex trait in a practical wheat breeding program.https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10548576.The original contributions presented in the study are publicly available. This data can be found here: All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.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.


ST) and the cross-population extended haplotype homozogysity (XPEHH) were applied to detect selection signatures for these five traits. Finally, a total of 427 and 307 trait-specific selection signatures were revealed by FST and XPEHH, respectively. Further bioinformatics analysis indicates that some genes, such as USF1, NDUFS2, PIGM, IGSF8, CASQ1, and ACBD6, overlapping with the trait-specific selection signatures are responsible for the phenotypes including fat metabolism and muscle development. Among them, a series of promising trait-specific selection signatures that were detected in the high IMF subpopulation are located in the region of 93544042-95179724bp on SSC4, and the genes harboring in this region are all related to lipids and muscle development. Overall, these candidate genes of meat quality traits identified in this analysis may provide some fundamental information for further exploring the genetic basis of this complex trait.In past decades, meat quality traits have been shaped by human-driven selection in the process of genetic improvement programs. Exploring the potential genetic basis of artificial selection and mapping functional candidate genes for economic traits are of great significance in genetic improvement of pigs. In this study, we focus on investigating the genetic basis of five meat quality traits, including intramuscular fat content (IMF), drip loss, water binding capacity, pH at 45 min (pH45min), and ultimate pH (pH24h). Through making phenotypic gradient differential population pairs, Wright’s fixation index (F The most important purpose of pig breeding is the genetic improvement of important economic traits . In pastPRKAG3, MC4R, and PIT1, which have been identified in different populations to be related to meat quality traits   . HoweverTo further explore the genetic mechanism of meat quality traits, we put forward a hypothesis: although the allele frequency has increased underlying human-driven selection, the loci related to meat quality traits are still polymorphic . Here, wIn this study, a total of 233 castrated large white pigs were used. The experimental pigs were raised in the same farm, had a common diet, and drank water freely. The same standard management conditions were applied in the whole process of the experiment. Antibiotics are banned in the 3 months before slaughter. Finally, healthy individuals were chosen and slaughtered at around 90 Kg weight. The meat samples of longissimus lumborum from all pigs were collected for measuring meat quality traits, including IMF, DL, water binding capacity (WBC), pH45min, and pH24h .Here, IMF was measured as percentage of lipid (lipid weight per 100 g of muscle tissue). Correspondingly, the Soxhlet extraction method was used following the standard AOAC Official method in foods . The pH p > 10e–6), iv) SNP minor allele frequency ˃0.05, v) autosomal SNPs with known positions extracted. After quality control, 11,624 SNPs with minimum allele frequency less than 0.05 were deleted, and 42 markers were deleted after the Hardy–Weinberg test (p ≤ 10e-6). Finally, the data set contained 37,061 autosome SNPs with an average inter-marker spacing of 62.06 kb. The genotype data can be downloaded from Figshare (https://figshare.com/s/cd815d8930c75561392c). The BEAGLE software was then applied to impute the missing genotypes and infer haplotypes . Then, quality control was performed using the following criteria: i) SNP missing rate <0.05, ii) individual call rate >0.90, iii) SNPs in Hardy–Weinberg equilibrium  ranking by phenotypic values of each trait; ii) based on the rank of phenotypic values, we equally divided the source population into high and low phenotypic subpopulations and recorded them as first population pair; iii) based on step ii, we chose 75 individuals with a higher phenotype from the high phenotype subpopulation and 75 individuals with a lower phenotype from the low phenotype subpopulation to create the second population pair; iv) similarly, we further chose 45 individuals with a higher phenotype and 45 individuals with a lower phenotype from the second population pair subpopulations to create the third population pair; v) population differentiation–based methods XPEHH and Fon pairs .p-values were generated by genome-wide ranking of FST and XPEHH scores, respectively. The trait-specific selection signatures were defined using the following two criteria: i) the SNPs with p-value < .01 were considered as significant loci; ii) from first to second to third population pair, the FST or XPEHH scores of each significant locus displayed a gradient change.In this analysis, the XPEHH scores do not need to be standardized. The empirical https://www.ensembl.org/index.html) was employed to search the genes and their orthologous genes of mouse harboring the trait-specific selection signature regions. Then, the database of Mouse Genome Informatics (MGI) was used to perform functional annotation (https://david.ncifcrf.gov/)  . The GO p < .01).To explore the influence of population division in this analysis, minor allele frequency, and linkage disequilibrium were investigated among different populations. As expected, the distribution of the minor allele frequency (MAF) in each subpopulation is similar to the results using all 233 large white pigs . The proST were employed to identify positive selection signatures. In this analysis, about 370 positive selection signatures were detected by each method in each population pair for each trait . Enrichment analysis showed that no significant biological terms may be associated with five meat quality traits after multiple correction in this study . Neverthnotation . ENSSSCGrphology . The preysiology . Both ofST simultaneously is associated with high IMF content. Based on the pigQTLdb, we found that 10 QTLs related to meat quality and fat metabolism are harbored in this genomic region  that was detected by XPEHH and Fc region . Furthertabolism  (Kim et tabolism .In this study, we planned to map some candidate genes of meat quality using population differentiation–based selection signature methods through constructing phenotypic gradient difference population pairs. In theory, meat quality traits are quantitative traits, controlled by minor-effect polygenes, and their genetic mechanisms are complex . AlthougSCIMP, ENSSSCG00000000550, GFRAL, and FNBP1L, are related to the body weight and lean body weight of mice (ENSSSCG00000010936 and PTPN13. Simultaneously, the orthologous gene of ZNF367 that was considered an IMF-specific selection signature is related to the increase in circulating levels of creatinine in mice, which is consistent with the fact that creatinine has flavor properties. After the annotation of pigQTLdb, we note that trait-specific selection signals are mainly associated with QTLs for meat quality traits, but there are also QTLs related to backfat thickness, body weight, and body length (The genetic mechanism of meat quality traits is complex. The candidate genes related to meat quality traits discovered in this study may have pleiotropism effects. As shown in  of mice . We founy length . In geneCASQ1 and ENSSSCG00000010936 genes identified in the high and low IMF subpopulations are abnormal muscle physiology and abnormal lipid homeostasis based on MGI annotation (Because the XPEHH method can identify the selected population, this study can further study the complex genetic mechanism of meat quality traits . In genenotation . In addinotation . This fuUSF1, NDUFS2, PIGM, IGSF8, and CASQ1, which provide a reference for future pig breeding.In this study, we propose a new strategy to identify trait-specific selection signatures. The application in five meat quality traits in large white pigs indicate that this strategy is promising in gene mapping. Furthermore, we detected a series of genomic selection signatures and identified some genes related to meat quality traits, such as


DWT1 and grain yield, the DELLA protein gene SLR1 and heading date, the Ethylene overproducer 1-like protein gene ETOL1 and thousand-grain weight, protein and starch content, as well as the Lectin receptor kinase SIT2 and plant height. A Leucine-rich repeat receptor protein kinase and a Xyloglucan alpha-1,6-xylosyltransferase count among the cross-validated genes associated with water-extractable arabinoxylan content. This study demonstrates the power of GWAS, hybrid breeding, and the reference genome sequence in rye genetics research to dissect and identify the function of genes shaping genetic diversity in agronomic and grain quality traits of rye. The described links between genetic causes and phenotypic variation will accelerate genomics-enabled rye improvement.Rye is the only cross-pollinating Triticeae crop species. Knowledge of rye genes controlling complex-inherited traits is scarce, which, currently, largely disables the genomics assisted introgression of untapped genetic variation from self-incompatible germplasm collections in elite inbred lines for hybrid breeding. We report on the first genome-wide association study (GWAS) in rye based on the phenotypic evaluation of 526 experimental hybrids for plant height, heading date, grain quality, and yield in 2 years and up to 19 environments. We established a cross-validated NIRS calibration model as a fast, effective, and robust analytical method to determine grain quality parameters. We observed phenotypic plasticity in plant height and tiller number as a resource use strategy of rye under drought and identified increased grain arabinoxylan content as a striking phenotype in osmotically stressed rye. We used DArTseq™ as a genotyping-by-sequencing technology to reduce the complexity of the rye genome. We established a novel high-density genetic linkage map that describes the position of almost 19k markers and that allowed us to estimate a low genome-wide LD based on the assessed genetic diversity in elite germplasm. We analyzed the relationship between plant height, heading date, agronomic, as well as grain quality traits, and genotype based on 20k novel single-nucleotide polymorphism markers. In addition, we integrated the DArTseq™ markers in the recently established ‘Lo7' reference genome assembly. We identified cross-validated SNPs in ‘Lo7' protein-coding genes associated with all traits studied. These include associations of the WUSCHEL-related homeobox transcription factor  Secale cereale L.) is the only allogamous crop in the Triticeae tribe of the grasses. Natural genetic diversity in outbreeding rye enabled to achieve a series of technological advances that ultimately facilitated the establishment of hybrid breeding  assess the genetic variation for plant height, heading date, tiller number, grain yield, grain weight, grain protein, and starch, as well as total and water-extractable arabinoxylan content in rye, (ii) identify QTL for these traits by GWA mapping and estimate their effects, (iii) investigate the co-localization between QTL for grain yield and yield parameters, as well as between grain quality parameters. Furthermore, we used the recently released ‘Lo7' genome assembly  cytoplasm , plant height , thousand-grain weight , heading date , tiller number (TIN), grain protein , and starch , as well as total arabinoxylan , and water-extractable arabinoxylan  content. For TGW data across 10 environments  were recorded. GPC, STC, TAX, and WAX were assessed in eight environments , while TIN was assessed in three environments . Grain yield was adjusted to a moisture content of 140 g H2O kg−1. We have used a near-infrared spectroscopy (NIRS) calibration , which is determined from soil sampling data and that ranges from 0 (very low) to 100 (very high). The soil type at Kleptow is a loam, partly with loess covering , while Kleptow-Sand is a heavy-to-clayey loam , just like at Wulfsode . The soil at Wulfsode-Sand is a sandy loam . Thus, the locations comprise (1) Kleptow (KLE) N53.4°, E13.9°, (2) Kleptow-Sand (KLS) N53.4°, E13.9°, (3) Wulfsode (WUL) N53°, E10.2°, (4) Wulfsode-Sand (WLS) N53°, E10.2° (5) Cappeln (WES) N52.8°, E8°, (6) Bornhof (BOH) N53.5°, E12.9°, (7) Granskevitz (GKV) N54.5°, E13.2°, (8) Sulejów (SUL) N51.4°, E19.8°, (9) Poznań (POZ) N52.4°, E16.6°, and (10) Uhnin (UHN) N51.6°, E23°. In 2012, the location WLS was not included. The location × year combinations are subsequently referred to as environments. The year 2011 and, in particular, the period from January to May was characterized by natural drought stress in Europe with many areas receiving <40% of long-term mean annual precipitation [DWD (Deutscher Wetterdienst), For each data set, best linear unbiased estimators (BLUEs) of the experimental hybrids were calculated over locations according to model Equation (1):yijkl is the phenotypic observation of the ith hybrid at the jth location in the lth in complete block of the kth replication; μ, the intercept; gi, the genetic effect of the ith hybrid; αj, the effect of the jth location; δij, the genotype-by-environment interaction; βjk, the effect of the kth replicate at the jth location; φjkl, the effect of the lth incomplete block of the kth replication at the jth location; and εijkl, the residual. μ and gi were regarded as fixed, all other effects as random. For estimation of variance components, gi was defined as random. Broad-sense (entry-mean) heritability (H2) was estimated based on Equation (2):where with DNA profiles were established for a panel of 180 ‘Petkus' and 34 ‘Carsten' genotypes as well as 91 plants of the RIL-population L2039-N x DH , gene diversity , as well as the Analysis of Molecular Variance  > 0.05 were selected using the R package snpReady. The basic population genetic statistics observed heterozygosity  explained by individual SNPs were plotted using ggplot2 in R.To adjust raw Hochberg  using thHochberg . Phenotyp < 0.05) for all traits except TIN in 2011 (p < 0.05) genotype x environment (GxE) interaction variances. The GxE interaction variances of GYD in both years, as well as of WAX in 2011 and HDT in 2012, were larger than the variances of the genotype (2 = 0.3). Q-Q plots are fairly straight and indicate that normal distribution is a reasonably good approximation for all traits  revealed that genotypic effects were statistically significant ( in 2011 . All tragenotype . The avel traits .p < 0.001) and consistent correlations were found between STC and PHT, STC and GPC, STC and TAX, as well as between TAX and WAX. The significant (p < 0.01) correlations between GYD and STC as well as GPC are in opposite directions in both years. GYD, TGW, and HDT, as well as TGW, STC, GPC, TIN, and PHT, revealed significant (p < 0.001) correlations in 2012 only. Similarly, the correlation between STC and WAX was significant (p < 0.001) in 2012 only. Plant height was significantly (p < 0.001) correlated with TGW in 2012 and starch content in both years but neither correlated with GYD in 2011 nor in 2012. A very strong negative correlation (r = −0.9) could be observed between GPC and STC in 2011 only. No moderate correlations were found except the correlations previously mentioned  could be mapped in the ‘Lo7' reference genome sequence and revealed an equal distribution on the seven ‘Lo7' pseudomolecules. The average PIC of these SNPs was 0.258, ranging from 0.09 to 0.38. A detailed list of these informative SNP loci, including physical map position in the ‘Lo7' genome assembly, base change, MAF, heterozygosity, gene diversity, and PIC, is provided in ockerham  emphasizockerham  chord geockerham . The majockerham . PCoA onockerham  showed aBased on Evanno's DeltaK method, we determined k = 4 as the most probable number of groups in each of the two sets of experimental hybrids . The pri in 2011 . In the llection . The meapadj <0.05) associated with PHT, HDT, and the studied agronomic traits (padj <0.05) associated SNPs for quality traits (padj <0.05) in both years. For all remaining traits, this value ranged between 15 and 132 significant SNPs (2) of the individual QTL ranged from 1.4 to 29.9% for GYD, 1.3 to 31.3% for PHT, 1.4 to 31.6% for HDT, 1.4 to 26.3% for TIN, 1.4 to 42.7% for TGW, 1.4 to 27.3% for GPC, 1.4 to 24.7% for STC, 1.3 to 38.8% for TAX, and 1.4 to 30.1% for WAX  are covered by the Rye 600k genotyping array  and 10,7c traits . We ideny traits . For GYDant SNPs . The exp for WAX . In tota for WAX . Between for WAX . Nine ofSLR1, the epigenetic regulator Decrease in DNA Methylation 1 (DDM1), the lectin receptor-like kinase SIT2, the phosphate transporter PT8, and the transmembrane ABC transporter STAR2   on TGW could be observed for 9 of the 525 SNPs (1.7%) associated in 2011, including the predicted sucrose transporter SECCE7Rv1G0456720 and for 535 of the 1,465 (36.5%) SNPs associated in 2012 (We detected 685 SNPs associated with TGW in ‘Lo7' protein-coding genes . FourteeOsETOL1) . The OsEG0479810 . The exp in 2012 . The croDWT1, the Zinc-finger transcription factor YABBY1, the gene DTH2 promoting heading under long-day conditions, the cellulose synthase bc6, the soluble starch synthase IIa, the Cytochrome P450CYP96B4, the metal-nicotianamine transporter YSL2, the amino acid transporter AAP6, the ATP-binding cassette protein, encoding gene ABCB14, the putative potassium efflux antiporter Albino midrib 1 (AM1), the Zinc finger family protein encoding gene sor1, and the transmembrane ABC transporter STAR2. While the SNP representing the DWT1-ortholog in rye was associated in both years, the SNPs in the rye orthologs of YABBY1 and ABCB14 were associated in 2011 and the remaining SNPs in 2012. Among the 337 SNPs associated in 2011, eight (2.4%) explained more than 10% of the phenotypic variance. Three of these SNPs represent genes encoding a cytochrome P450 protein, a putative late embryogenesis abundant (LEA) hydroxyproline-rich glycoprotein, as well as an ARM repeat superfamily protein. Likewise, 101 (20.2%) among the 500 SNPs in protein-coding sequences associated in 2012 had major effects (R2 > 10%) on GYD. Except for 6, the 38 SNPs associated in both years had minor effects (R2 < 10%) on grain yield. The genes represented by these major effect SNPs include SECCE3Rv1G0183650 encoding, a WUSCHEL homeobox protein, SECCE5Rv1G0323860.1, a HAESA-like LRR receptor protein kinase, SECCE6Rv1G0412350, a flavonol synthase, SECCE6Rv1G0416090, a putative Snf1-related kinase interactor, SECCE7Rv1G0469060, a Gibberellin 2-beta-dioxygenase, and SECCEUnv1G0531960, a BEL1-like homeobox protein.We identified 19 (3.8%) out of 500 SNPs associated with GYD in protein-coding genes of the ‘Lo7' assembly as orthologs of cloned rice QTL . These icZOGT1, an embryo and endosperm development-involved cyclin and the transmembrane ABC transporter STAR2 (2 > 10%) on TAX and WAX in 2011 were observed for 17 and 23 SNPs, respectively. Corresponding protein-coding sequences represented, among others, the receptor protein kinases SECCE2Rv1G0135820 and SECCE4Rv1G0223400, and the hexosyltransferase SECCE5Rv1G0328330. In 2012, 249 and 202 SNPs revealed major effects (R2 > 10%) on TAX and WAX. These SNPs described, among others, the α-glucosidase SECCE3Rv1G0149680 and the xyloglucan α-1,6-xylosyltransferase SECCE6Rv1G0407620. Major effects (R2 > 10%) on TAX were observed for SNPs in the Glycosyltransferase-encoding genes SECCE3Rv1G0197970 and SECCE7Rv1G0456850, and minor effects (R2 < 10%) on TAX and WAX for SNPs in six further Glycosyltransferase-encoding genes  for major target traits in a rye improvement program. The observed significant correlations reveal that complex target traits of rye breeding do not evolve independently. However, as weak and moderate correlations between traits dominate, the substitution of an inexpensive measurement for a correlated expensive phenotype in variety development is no constructive option. The strong to moderate negative correlation (r = −0.9 and r = −0.39) between GPC and STC in both data sets is well-known from studies in wheat  for GYD, PHT, and TGW compare well with previously published heritability estimates in biparental  negative correlation between PHT and STC, the higher sink activity of grains under drought suggests a mobilization and reallocation of stem reserves to the grain, as has been observed in Sorghum  positive correlations with PHT in both years indicate that HDT might be involved as a confounding variable. Although early heading date and a shorter vegetative phase were significantly (p < 0.001) correlated with grain yield under less-osmotic stress conditions in 2012, drought escape through earlier heading could not be identified as an adaptive mechanism of rye in 2011, as indicated by a correlation coefficient between GYD and HDT of almost zero. Further research directed to the collection of drought tolerance traits that are not easy to measure under field conditions, and the assessment of rye using state-of-the-art phenotyping in controlled environments -sensitive dwarfing gene FST) among the ‘Petkus' and ‘Carsten' populations, which coincides with the identified heterotic pattern in rye  estimated from single nucleotide polymorphisms between elite and exotic wheat lines  mapping to a region with a high ratio of physical to a genetic distance of 122.4 Mbp/cM on chromosome 7R , gene discovery and isolation, and, as demonstrated in the present study, GWAS. Furthermore, functional markers can explain a large part of the genetic variance, which may improve the predictive ability of genomic selection models in plant breeding programs  in genetic association studies has become an increasingly appealing and accepted target for multiple-comparison adjustment  and KW7 have been genetically  and tg (thick grain) identified by Skoryk et al.  genes are of central importance for cereal hybrid breeding, both for minimizing ergot infestation  transcription factor that promotes tiller growth downstream of SLR1 in rice  breeding  of a SNP in the receptor protein kinase-like gene model SECCE4Rv1G0223400 on WAX. This observation supports previous research highlighting the impact of receptor protein kinases function in stress responses  negative correlation between GYD and WAX in 2011 refers to a changed carbon allocation and metabolism that resulted in energy dissipation in terms of declined yield. Recent progress in developing random mating feeding rye with low content of WAX . We gratefully acknowledge support by the Open Access Publication Funds of the Julius Kühn-Institute.DS and FF are employed by HYBRO Saatzucht GmbH & Co. KG, and AK is employed by Diversity Arrays Technologies. The companies have a commercial interest in the results for application in variety development and for the provision of molecular genetic services. This does not alter the adherence of the authors to all Frontiers in Plant Science policies on sharing data and materials. The remaining 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.


Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research. Edwardset al reported the first GWAS for age-related macular degeneration in 2005. In the last decade, many GWAS have been reported in scientific databases and these studies have revealed the presence of various single nucleotide polymorphisms (SNPs). For instance, GWAS Catalog reported 4865 publications and 247051 associations in 2021. These reported SNPs could be used to better understand the molecular mechanisms of common diseases and biological pathways of interesting traits.Genome-wide association studies (GWAS) have been used to identify genetic variants associated with specific traits or diseases of interest. One of the advantages of performing GWAS is that they do not require prior knowledge of the biological hypothesis underpinning the genetic machinery of the investigated trait. Many GWAS have revealed hundreds of common variants that are associated with various phenotypes, including common diseases. However, most complex diseases that have been targeted by GWAS are known to be caused by multiple genes that could be influenced by many other factors. It is known that GWAS typically report SNPs as statistically significant when their associatedp-values are less than 5e-08. Accordingly, GWAS might not detect genetic variants with low or moderate risk. Thus, the ability of GWAS to detect a significant genomic position depends on heritability on phenotype, minor allele frequency (MAF), and sample size. Traditional GWAS could therefore be faced with the challenge of not being able to detect variants that are associated with low disease risk, implying that traditional GWAS results are prone to unreliable findings due to their false negative results. Furthermore, GWAS might fail to detect a significant signal if the effect of a variant in another gene is not taken into consideration. It is also limited in identifying changes in genotype as a response to environmental changes, i.e., detecting the impact of genotype interaction with the environment. These limitations of GWAS occur primarily because of the undetected effect of gene polymorphism. More specific challenges of GWAS have been reported in many scientific papers. Given all of the reported limitations of GWAS, it is crucial to perform post-GWAS (pGWAS) analysis. The overall goal of pGWAS analysis is to use the result of the association between the genotype and phenotype (summary statistics), with the following objectives:•Transferability of previous result,•Identification of new significant functional variants, i.e. lead SNPs,•Identification of novel disease susceptibility genes, genotype-phenotype associations, and biological pathway network, and•Building a polygenic risk score using summary statistics.Over the last decade, most GWAS have been known to detect the genetic signals of a single gene or a single genetic marker, i.e., interpreting genes based on detected signals (positions) on genomic coordinates using a specified Genome Assembly.So far, the most common approaches to perform pGWAS analysis include the following three approaches: (i) Single-variant approach; (ii) Gene-scoring approach; and (iii) Pathway-sub-network-based approach. However, pGWAS approaches could be categorized into many classes based on their usage (see section below). Also, there are several methods/tools for pGWAS reported in different articles.In this article, we provide an overview to performing pGWAS analysis and discuss the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis. Finally, we include a custom pGWAS pipeline to guide new users when performing their research.GWAS have become an indispensable approach in providing insight into understanding genotype-phenotype associations of complex disease. While the design for GWAS experiments are well established as bench-work, the computational methods for the analysis of GWAS data are still evolving. The typical computational pipeline to analyse any GWAS data consists of two essential tasks: a) upstream GWAS analysis (classic GWAS methods), and b) downstream GWAS analysis. The latter step is known as pGWAS analysis. After obtaining raw data-sets with high quality scores, the next step is to report the statistically significant SNPs. Many statistical tests are available for this, including Chi-square test, Fisher’s exact test, Cochran-Armitage trend test, Odds ratio, Logistic regression, ANOVA, Transmission Disequilibrium test, Bonferroni correction, and many other methods.The upstream GWAS analysis is a multi-step task resulting in a list of statistically significant SNPs. This step often starts by checking the quality control of raw GWAS data which is a crucial step for the task of pGWAS analysis. It is obvious that unclean data lead to unreliable results. Therefore, many parameters such as quality control per sample, relatedness, replicate discordance, SNP quality control, sex inconsistencies, and chromosomal anomalies should be checked. PLATO, EIGENSOFT, and STRUCTURE. Several other tools are also available as packages within R software, the most popular open-source software for statistical computing.Many free tools are available to perform GWAS upstream data analysis such as: Plink,Genome wide significant threshold. This approach provides the statistics at a SNP level. This approach aims to score the association between SNPs and the target trait. Usually, the score defined by aOR), and its standard error (SE) gives an idea of the genotype’s effect on the phenotype. Effect strength is computed usingOR. Also, scientists usep-value as a measure of how likely this effect is to occur by chance. Statistically, many researchers usep-value to evaluate the hypothesis that there is no statistical evidence for SNP-trait associations. However, this hypothesis will be rejected whenp-value is less than a predetermined threshold that is adjusted for a multi-test. Many methods are used for multiple testp-value correction. These methods include Bonferroni correction and false discovery rate (FDR). However, some of these methods do not consider linkage disequilibrium (LD) and they may be too stringent. In general, a threshold of This value has been computed using independent signals in genomes. However, this value is not absolute and it depends on many factors, includingLD (and diversity), array type, whole genome sequencing or whole exome sequencing, position number in array or sequencing, imputation panels, and positions finally analyzed. For trans-ethnic populations, it is recommended to estimate the threshold based on population diversity andLD. For instance, analyzing 1000 Genomes data, the suggested significance thresholds were Although, a recent study using an African population proposed a threshold ofSuggestive threshold. Some authors consider GWA threshold very stringent, so studies often include a “suggestive threshold”. This is superior to a genome-wide significant threshold, and values found in the literature areLD block” from 1000 Genomes used The code below contains an R command to select significant SNPs using a cutoff value of## library need : data.table#open files with fread functiondata.gwas<-fread("result.gwas")# head to obtain the header of gwas filehead(data.gwas)# selected lines using threshold value of 5E-8data.gwas# obtained information about min pvaluedata.gwasInflation factors. Population and cryptic relatedness can cause spurious associations in GWAS withp-values higher than random leading to false positive signals. Genomic control (GC) approach is extensively used to effectively control false positive signals. Genomic inflation factors ## computed lambdamedian/qchisqGlobal visualisation of results. A common way to visualize GWAS results is the Manhattan plot. The Manhattan plot demonstrates the physical location of SNPs distributed by chromosome in thex-axis with the degree to which a SNP is associated with the target trait, i.e.p-value scaled bylog10 in they-axis. A Manhattan plot can be done inR software using the qqman package, which includes functions for creating Manhattan plots andq-q plots from GWAS results. The code below demonstrates how to create Manhattan plots, andq-q plots the qqman R package.## do a qqplot and Manhattan plot using library qqmanlibrary('qqman')## used function qqplot from qqmanqqplot## used function manhattan from qqmanmanhattanLocal visualisation of results. Local visualisation can be done using the method described by Pruimet al. In this method, information ofLD, genes, and previous results of GWAS can be added. Zoom version 2 offers a virtual analysis of local GWAS results. On the other hand, LocusTrack from the UCSC genome-browser adds more annotation compared to LocusZoom. Furthermore, BigTop is capable of providing three-dimensional visualisation of data using allele frequency as a third dimension. The code below demonstrates how to use LocusZoom to visualize SNPs considering LD information.## use R to reformat your file to be used by locuszoomR -e "library(data.table);data.gwas<-fread('result.gwas');data.gwas<-data.gwas;names(data.gwas)<-c;write.table "## use locus zoom to do a plot,locuszoom/bin/locuszoom --epacts gwas.epacts \--delim tab --refsnp rssnp \--flank 10000 --pop EUR \--build hg19 --source 1000G_Nov2014\-p rs7412 --no-date \This approach considers the association between a trait and all SNPs within a predefined window around genes rather than each marker individually. In many cases, this approach is more powerful than traditional individual-SNP-based GWAS.p-values and fold changes could be considered gene scoring values. For pGWAS analysis, GS is defined as the sum of all statistically significant alleles  of the selected SNPs present in each individual under investigation. Various algorithms have been developed to calculate GCs based on GWAS summary statistics. It is a common approach to encode and adjust SNPs values prior to calculating GC during analysis. The process of SNP encoding aims to ensure that all SNPs are positively correlated with the outcome. One more essential aspect to consider while calculating GS is the effect size. Variation in the effect size reflects reduction of predictive power of GS. The GS method mostly used in pGWAS analysis is the gene-basedp-value.A gene score (GS) is a value given to a gene representing some measures related to a genetic trait. Thus, all the statistical summary values such asp-value.Gene level In many pGWAS pipelines, the first step in downstream GWAS analysis is to assign the SNPs to functional genomic features. The latter includes: coding genes, non-coding RNAs, 5’UTR, 3’UTR, proximal promoters, regulatory element, and enhancer elements. and VEGAS. GLOSSI is available as an R package, while VEGAS is available as an online tool as well as a stand-alone tool to be run on a local machine. Besides VEGAS and GLOSSI, many pGWAS tools provide procedures to calculate genes’p-values. For instance, ancGWAS provides four different methods to calculate the genesp-values: Simes, Smallest, Fisher, and Gwbon. Similar to ancGWAS, MAGMA provides three methods to calculate genes’p-values. However, one of the MAGMA methods is similar to the VEGAS method. The other two methods are by considering either the smallest SNPp-value, or the highest SNPp-values.Two common methods for assigning SNPs to their corresponding genes are GLOSSIThis approach considers the fact that complex biological phenomena addressed by GWAS, including the molecular basis of the rare disease, often arise due to gene interaction rather than single gene effect. Therefore, scientists analyse GWAS based on the biological network theory to understand the disease-causing genes and mechanisms involved in traits and complex diseases, such as in rare diseases. This approach is based on the results obtained from the gene-based association test and provides a higher level of complexity by considering biological networks and/or genes ontology. Analysing GWAS based on biological networks allows us to capture biological interactions between various molecules such as proteins, functional DNA motifs, coding and non-coding RNA, as well as disease mechanisms and to consider epigenetic changes, including methylation states or other modifications . Also, this approach aims to map genes that are associated with significant SNPs into known pathways/Gene Ontology terms. The result of this approach provides information about the over-represented pathways in a given set of genes/SNPs.LD with each other as being associated with the phenotype. The lead SNPs are those with the most significantp-value – they may be causal or not. The other SNPs in this region may only have an association because they are inLD with the causal SNP or they may be independently associated. This GWAS limitation may be observed when integrating information of variability in allele frequency, SNPs inLD block and imputation. For instance, when simulating GWAS results using OR value of On the other hand, changing the previous parametersOR value of Therefore, changing the previous parametersOR value ofGWAS will often identify a number of SNPs inLD and functional annotations. There are two fine-mapping methods: (i) heuristic method that penalizes regression models, and (ii) Bayesian fine-mapping methods.Therefore, fine-mapping methods are used to identify causal variants that are associated with the target trait and the number of putative causal variants from GWAS data. The fine-mapping approach integrates summary statistics from GWAS data,Lead SNPs. Lead SNPs are defined as independent SNPs that have reached a minimump-value threshold, i.e. they are independent of each other at theLD threshold. It is common to measureLD as PLINK has implemented a function called’ld-clump’ that clumps independent SNPs. Moreover, FUMA tool identifies lead SNPs by double clumping method. The first clumping is used for clumping SNPs withp-valuep-value, i.e.p-value## define lead snps using plink# --clump-p1 Significance threshold for index SNPs# --clump-p2 Secondary significance threshold for clumped SNPs# --clump-r2 LD threshold for clumping# --clump-kb Physical distance threshold for clumping# --clump-snp-field your snp field must be same than in your assoc file# --clump-field: p value headerplink --bfile plinkbase --clump result.gwas --clump-snp-field rsid\--clump-field p.value --clump-p1 0.001 --clump-p2 0.1 \--clump-r2 0.1 --clump-kb 250 --out assocHeuristic fine-mapping approaches. These are used to identify potential causal SNPs. They are proposed to filter SNPs around lead SNPs considering the value of their pairwise correlationLD block by direct extraction of block and the selection of the same position on the block or by the visualisation representation ofLD using LocusZoom or Haploview. Nonetheless, the described method is not a statistical approach to define putative causal variants, such as Bayesian methods or regression models. The code below demonstrates how to combine Plink tool and Haploview to perform heuristic fine-mapping.### use haploview to plot your data#### haploview has also an web interfaceplink --keep list.ind -bfile $baseplink --recode tab --out fileres \--chr chr --from-kb begin --to-kb end --maf 0.01 \# execute Haploview in the previous rangejava -jar Haploview.jar -n -minMAF $cut_maf -missingCutoff 0.01 \-pedfile fileres.ped -map fileres.map -png -blockoutputBayesian methods: framework. The Bayesian method is commonly used to identify causal variants in a predefined SNPs window containingUsers first define an initial number of causal variantLD and GWAS summary statistics.Most of the tools are aimed to find a maximum probability to have the best combination of the causal variants. To model Bayesian statistics, many tools integrate various data such asBayesian methods: posterior inclusion probability. This approach is used to compute post inclusion probability (PIP) at each SNP The PIP approach should be used with caution to identify causal variants in the highLD regions. Therefore, it is recommended to estimate the posterior expected number of causal SNPs by summing the estimated PIPs for all SNPs in the region.Using the rank of PIP is an excellent way to select putative causal variants.Bayesian methods: credible sets. This approach is used to define a set of variants that could have a good candidate. One way to estimate a credible set is to use PIP by ranking the values and doing the cumulative sum of PIP from the largest to the smallest. Then select all variants where the sum is less than the predefined The code below demonstrates how to combine Plink tool and FINEMAP to perform Bayesian-based fine-mapping analysis.# extract a range of interestplink -bfile $plk --keep-allele-order --chr chr--from-kb begin --to-kb end --make-bed -out plink_range#compute LDplink --r2 square0 yes-really -bfile plink_range -out ld_range# format ldsed 's/\t/ /g' tmp.ld > $outld## Finemaping#create a config fileecho "z;ld;snp;config;cred;log;n_samples" \> fileconfig.finemapecho "$filez;$ld;${out}.snp;${out}.config;${out}.cred;${out}.log;${params.n_pop}" >> $fileconfigFINEMAP --cond --in-files fileconfig  --log \--cond-pvalue 0.0000001 --n-causal-snps $ncausalsnp### caviarbfcaviarbf -z ${filez} -r $ld -t 0 -a ${params.caviarbf_avalue} \-c $ncausalsnp -o ${output} -n ${params.n_pop}nb=`cat ${filez}|wc -l `modelsearch -i $output -p 0 -o $output -m \$nbBayesian methods: trans ethnic fine-mapping. This is used to perform trans-ethnic fine-mapping studies using simulation results in similar fine-mapping resolution among the European and Asian ancestries. However, the inclusion of samples with African ancestry in meta-analysis leads to a significant improvement in fine-mapping resolution due to the lowestLD in the African ancestry population. The probability that the lead GWAS variants were also the causal variants increased using trans-ethnic GWAS data. It is a process that relies on disparateLD patterns in populations of diverse genetic ancestries to localize the causal variants. This approach has been successfully implemented to fine-map and leads to several common GWAS findings.Integrating annotation into fine-mapping. Functional annotations have been shown to improve the discovery power and fine-mapping accuracy. Therefore, some tools such as CausalDB, PAINTOR and BIMBAM have integrated expression quantitative trait locus (eQTL) information in fine-mapping approach.Bayesian methods: software. Several Bayesian-based fine-mapping tools have been developed using summary statistics,LD and eQTL  could be used to predict an individual’s likelihood to develop a specific trait or to estimate the level of predictive power that the trait is associated with a particular set of variants. Although PRS methods are classified into Bayesian-based methods and non-Bayesian methods, there are more classifications of underlying PRS methods. PRS is calculated by aggregating effects from a large set of causal SNPs. Several tools were developed to calculate PRS. For performing PRS studies, we highly recommend this recent review paper. PRS is usually computed after the challenges associated with GWAS are carefully addressed. Here, we demonstrate key quality control (QC) measures and include sample bash scripts.QC measures and contains the guidelines on specific thresholds. Thresholds can differ depending on the study’s unique features.This method is used to predict disease risk using GWAS summaries.GWAS effect allele. As datasets for PRS come from different GWAS experiments, it is critical to ensure consistency. Knowing which allele is considered the effect allele, it is vital to get an accurate PRS score. However, the effect allele is not labeled clearly in many datasets. Different allele coding schemes exist, including Illumina’s TOP/BOTTOM coding concept, ALT/REF, effect/other, HapMap’s forward allele coding, Illumina’s A/B allele coding, Affymetrix’s A/B allele coding, REF (reference)/ALT , PLINK’s 1/2 allele coding, A1 /A2 , A0 /A1 , effect allele/non-effect allele, effect allele/other allele, and many others. To avoid allele inconsistency, researchers should carefully read the documentation of GWAS datasets.SNPs level errors. TheQC assessment at the SNP level is crucial to avoid misleading PRS. SNPs level errors include (i) mismatching SNPs, i.e., inconsistent SNPs due to position difference in genomic position or nucleotide type, (ii) existence of duplicate SNP, (iii) ambiguous SNPs, i.e., researchers have no idea about SNP strand , and (iv) missing alleles.Chip heritability. The chip heritability  and confounding biases, including cryptic relatedness and population stratification.The code below demonstrates how to use Plink to perform quality control checks and calculating PRS.# $bfile: Target data set in Plink binary format.# $QC: Base data set i.e., base GWAS summary statistic. This file contains P-value information# $p1: P-value threshold for a SNP to be included as an index SNP. Choose value of 1 if you want to##include all SNPs are include for clumping.# $r2: Cutoff for r2 value i.e., SNPs having value higher than given r2 will be removed# $kb: cutoff value window size in kilobase, i.e., SNPs within $kb of the index SNP are considered for clumping.# $SNP: Name the column SNP that containing the SNP IDs# $P: Name of the column that containing the P-value information# $Output: Output file################### Quality Control of Target Samplesplink\  --bfile $bfile \  --maf 0.05 \  --mind 0.1 \  --geno 0.1 \  --hwe 1e-6 \  --make-just-bim \  --make-just-fam \  --out $Output.qc################### Clumpingplink \  --bfile $bfile \  --clump-p1 $p1 \  --clump-r2 $r2 \  --clump-kb $kb \  --clump $QC \  --clump-snp-field $SNP \  --clump-field $P \  --out $Output############### calculating PRS# $listpvalue: Threshold values# $valid.snp: valid SNPs# Here we are assuming that 3 column for SNP ID; 4 for effective allele information;### the 12 for effect size estimate.#### Moreover, this file has a header.plink \  --bfile $bfile \  --score $QC 3 4 12 header \  --q-score-range $listpvalue \  --extract $valid.snp \  --out $OutputThe meta-analysis approach can be used to evaluate the association between SNPs and biological traits by combining various GWAS summaries from different studies. The following paragraphs will provide the key concepts for performing a meta-analysis. To have concrete information about the meta-analysis approach, we recommend this review article.Heterogeneity of source. Heterogeneity in data could be derived using GWAS summary statistics. The standard variables to estimate heterogeneity in data include odds ratios, standardized effect sizes, other metrics along with their uncertainty  and the accompanyingp-values. However, there might be many other variables for each dataset that are important to deal with in order to estimate heterogeneity in data.Standard meta-analysis. This is used to perform the meta-analysis approach which is to sum theZ-scores across all studies and weigh them appropriately using the sample sizes. SeewhereIndependence of the samples. Conventional meta-analysis has an assumption that assumes that effect sizes are independent. Simulation studies demonstrate that failure to account for overlapping samples could greatly inflate type I error. If accounting for the overlap is unavoidable, the overlap/covariance can be estimated usingZ’s covariance between summary statistics. Some tools can account for overlapping samples, such as METAL software and ASSET.Correcting for population structure with genomics control. The presence of population structure in the GWAS study can impact an over-dispersion of the corresponding association test statistics. One approach to limit this problem is to correct statistics of each summary using genomic control. This correction factor is given as the inflation factor  to balance weight used in meta-analysis. On the other hand, Metasoft proposed two other different methods to take into account the heterogeneity, which are Random Effects model and binary effects model withm-value, i.e. weight of each summary study in summary statistics.Heterogeneity test. Heterogeneity at a locus can be reflected in the variability in population or environment. It can be relevant to gene-environment interaction and the reason behind the variability in GWAS approach of each data-set, i.e. covariable, model and approximation of GWAS between summary statistics. The heterogeneity is computed between two sets of summary statistics rather than one locus.Cochran’s statistic provides a test of heterogeneity of allelic effects at SNPswhere SeeAlternatively, we use Q statistic to quantify the extent of heterogeneity in allelic effects across studies.Other meta-analysis approaches. Traditionally, meta-analyses of GWAS have focused on combining results of multiple studies for similar traits. The Bayesian framework has been tested to estimate MetABF tool has implemented a method to perform meta-analysis across genome-wide association studies of diverse phenotypes. It is important to note that a recent review on cancer suggests that it is possible to obtain “noteworthy” Bayesian results at higherp-values that are not considered statistically significant in GWAS.Study alignment and error trapping. Meta-analysis aggregates various summary statistics. Therefore, any error to designate the effect allele and other allele or strand issue can cause an error in estimateEffect size. By conducting a meta-analysis, researchers often neglect the sample size variation among different studies “true effect sizes are the same across studies”. However, in some cases, researchers introduce the correct effect size by considering the posterior probability for each study. Some software for estimating effect size are given inMeta-analysis output. Meta-analysis provides a new set of summary statistics. For each position that has not been discarded, new statistics will be calculated. These statistics include new values forp-value. Users should be aware of theLD and the reference population used.More resources for meta-analysis. We recommend h3bionet/h3agwas for meta-analysis pipeline. In addition, those interested in meta-analysis are directed to a review published by Zegginiet al. and another review article by Evangelou and Ioannidis. The code below demonstrates how to use the Metasoft tool to perform meta analysis.# example with metasoft## reformatting input files.### File 1R -e "library(data.table);data.gwas<-fread('$File 2');data.gwas<-data.gwas;names(data.gwas)<-c;data.gwas<-data.gwas[data.gwas[['P']]<1];write.table"### File 2R -e "library(data.table);data.gwas<-fread('$File 1');data.gwas<-data.gwas;names(data.gwas)<-c;data.gwas<-data.gwas[data.gwas[['P']]<1];write.table"## metasoft### Metasoft with binary effectjava -jar Metasof/Metasoft.jar \-input file_merge_all.meta\-output meta.meta \-pvalue_table Metasof/HanEskinPvalueTable.txt\-binary_effects\Colocalization. Colocalization is an approach used to integrate annotations with GWAS results. The annotations resources include gene expression (eQTLs), protein expression (pQTLs), exon splicing (sQTLs), DNA methylation (mQTLs), and chromatin acetylation and chromatin accessibility (caQTLs).Statistics for colocalization studies. Several parametric and non-parametric statistics can be done for colocalization studies.Resources for colocalization studies. We recommend the following scientific resources for those who are beginners in this field:•From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases.•Colocalization analyses of genomic elements: approaches, recommendations and challenges.•Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.•LocusFocus: web-based colocalization for the annotation and functional follow-up of GWAS colocalization of GWAS and eQTL signals detects target genes.•A powerful and versatile colocalization test.Using summary statistics from multiple phenotypes and traits. The methods for GWAS are mostly focused on single variant analysis with a single phenotype or trait. Increasing evidence shows that pleiotropy, one gene’s effect on multiple phenotypes, plays a pivotal role in many complex traits. Therefore, associating different GWAS results for multiple phenotypes can provide an extensive power by aggregating multiple weak signals. Different approaches have been developed to integrate dependentp-values to assess the association between a gene and multiple correlated phenotypes. Several tools exist to perform multi traits analysis, including Multi-Trait Analysis of GWAS (MTAG) and CPASSOC package. For instance, one trait (phenotype or disease) might be affected by confounding or reverse causation rather than a conventional observational variable. Therefore, MR aims to provide a statistical frame to verify the causality between locus and phenotype and exclude pleiotropy. Such methods will provide a reliable explanation of the results.Mendelian randomisation (MR) is a statistical approach that can be defined as “the use of genetic variants as instrumental variables to investigate the effects of modifiable risk factors for disease”.Assumption of MR. There are three main assumptions for MR. These are (i) the genetic variant that is associated with the exposure (significant association), (ii) the genetic variant that is independent of the outcome given to the exposure and all confounders (measured and unmeasured) of the exposure-outcome association, (iii) the genetic variant that is independent of factors (measured and unmeasured) that confound the exposure-outcome relationship.Statistical methods for MR. MR general strategy is to comparep-value of the same position of two or more different GWAS using related/confounding phenotype. Several methodologies have been developed for MR analysis, an example is the ratio of coefficients estimator, which can be modeled usingwhereMR software. Several tools have been developed to compute MR, e.g GSMR  from GCTA, TwoSampleMR (version 0.4.20), MR-PRESSO, and MR-LDP which is integrated LD information. We recommend the following tutorial:https://bioconductor.org/packages/release/bioc/vignettes/GMRP/inst/doc/GMRP.pdf.The code below demonstrates how to use gcta64 tool to perform Mendelian randomisation analysis.#reformat file 1R -e "library(data.table);data.gwas<-fread('$File');data.gwas<-data.gwas;names(data.gwas)<-c;data.gwas[['N']]<-10000;data.gwas<-data.gwas [data.gwas[['p']]<1];SNPtmp<-table(data.gwas[['SNP']]);UniqSNP<-names(SNPtmp)[SNPtmp==1];write.table"#reformat file 2R -e "library(data.table);data.gwas<-fread('$File');data.gwas<-data.gwas;names(data.gwas)<-c;data.gwas[['N']]<-10000;data.gwas<-data.gwas[data.gwas[['p']]<1];SNPtmp<-table(data.gwas[['SNP']]);UniqSNP<-names(SNPtmp)[SNPtmp==1];write.table# createecho "P1 FilePheno1" > exposureecho "Exp FilePheno2" > outcome## gcta need a plink file or bgen file## GSMR analyses,###forward-GSMR analysis (coded as 0),###reverse-GSMR analysis (coded as 1)###and bi-GSMR analysis .gcta64 --bfile plkfile --gsmr-file exposure outcome \--gsmr-direction 0 --out test_gsmr_resultMR and gene expression. Recently, some methods have integrated MR into GWAS and eQTL to test if the effect of gene expression is zero on the trait. These methods provide a promising way to combine GWAS summary statistics and expression data.Our proposed pGWAS pipeline consists of three main steps: preprocessing, visualization, and the downstream pGWAS analysis refer to.1, the preprocessing step, aims to control checks and ensure a correct input file format for the downstream pGWAS analysis. The main purpose of this step is to ensure that SNPs’ positions in the GWAS summary file accurately match the genomic coordinates in the downstream reference panel if any is available. The UCSC LiftOver tool (http://genome.ucsc.edu/cgi-bin/hgLiftOver) is widely used to correct genomic position mismatches between the GWAS summary file and the reference panel. However, other options exist, including: Bioconductor rtracklayer package, Assembly Converter, NCBI Remap, and the CrossMap tool.Step2, the visualization step, aims to visualize the raw input GWAS summary data pictorially, primarily through two scatter plots: Manhattan plot and quantile-quantile (Q-Q) plot. The Manhattan plot is widely used in genomics to visualize the results of GWAS studies. In the Manhattan plot, the X-axis represents the positions on chromosomes, while the Y-axis reflects genomic association strength with a given trait. TheQ-Q plot is used to check the normality of data -mainly the normality ofp-values distribution. Step2 can be completed using theqqman R package.Step3, of downstream pGWAS analysis, can be divided into three approaches based on their underlining data heterogeneity. First, if there is homogenous data, researchers can perform a single variant pGWAS analysis such as while pathway analysis could be done using the PASCAL  tool. Researchers can also undertake fine-mapping analysis and MR for homogenous GWAS summary files. Second, researchers can perform PRS analysis and genetic risk score if individual-level data is available. Third, if there is heterogeneous data from numerous independent studies, a meta-analysis can be performed.StepThis articles demonstrates various pGWAS methods. The advancement in these pGWAS techniques solves a significant problem in our efforts to understand the vast amount of data generated and explore fundamental biology. However, several issues should be taken into consideration when performing pGWAS analysis across trans-ethnic GWAS studies. Some of these issues include: (i) heritability of the trait, (ii) GWAS sample size, (iii) polygenicity of the traits, (iv) genetic architecture of the trait, and (v) genotype-environments interactions. Furthermore, we expect that in the future many pGWAS methods will be developed to address these limitations either for a particular ethnic group or for multi-ethnic groups.No data is associated with this article. This review paper entitled “Performing post-genome-wide association study analysis: overview, challenges and recommendations” provides a practical guide to post-GWAS analyses in human complex traits and diseases. To be honest, I am not a human geneticist, but find this paper helpful for a non-human geneticist to overview a wide variety of post-GWAS analyses. While the principles are not so deeply discussed, the example code helps us use software and interpret its result. I have a few concerns on the step 3 of downstream analyses.Major comments:Usage of the heritability estimation The authors mention “…, the ability of GWAS to detect a significant genomic position depends on heritability on phenotype, minor allele frequency…” (Introduction) and “Higher values of heritability indicate that the phenotype is explained best by the genotype, i.e., set of SNPs” (Chip heritability section). This understanding seems correct, but I am wondering why the heritability estimation should be conducted ‘after’ GWAS. If the SNP heritability is a good proxy to know the extent to which a target trait is genetically controlled, we should perform the heritability estimation ‘before’ GWAS analysis. Some web-based GWAS pipelines provide SNP heritability before performing association mapping .Gene-set analysis Because a gene-set or gene ontology enrichment analysis is widely performed in omics analyses , further introduction to the gene-set analysis would gain a value of this manuscript. As far as I know, due to the LD among SNPs, gene-set enrichment analyses in GWAS are not so straightforward as those in RNA-Seq and the other omics analyses. For example, Gowinda is designed for an unbiased gene-set enrichment analysis for GWAS (Kofler & Schlötterer 2012 Bioinformatics).Minor Comments:Page 4 of 20 “Plink”, Page 6 of 20, and elsewhere: PLINK is described as “PLINK” in some lines but also as “Plink” in the other lines. The capital letters would be better.P(M) should be a numerator.Page 5 of 20: The equation of the Bayesian rule is incorrect.Page 13 of 20 “Statistics for colocalization studies”: This subsection is not informative as it consists of only a single sentence.Page 13 of 20 “Resources for colocalization studies”: This seems a list of papers and thus repetitive of References. Please explain the points of these papers, otherwise this list is unnecessary.Page 15 of 20: Letters in Figure 1 are too small to read.As this review focuses on tools rather than theory, it would be great if the authors could deposit their tutorial (incl. codes and example data) on some open repository.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?No source data requiredAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:plant ecology and geneticsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors of the manuscript entitled “Performing post-genome-wide association study analysis: overview, challenges and recommendations” give a detailed overview to the methods and tools of post GWAS analysis. The principles, key factors, tools, resources, suggestions and codes were provided to facilitate researchers who need to conduct post GWAS analysis. Overall, the manuscript is well organized and well written. It would be helpful for all target readers. There are few of my concerns that should be addressed by the authors. Minor comments: “Introduction” section: the authors divided common pGWAS analysis approaches into “the following three approaches ...”. Actually, nine approaches were mentioned in the main text. Hence, rewording of this paragraph is suggested.   “Bayesian methods: framework” section: the equation for Bayes’ rule is not correct. “Colocalization analysis” section: Providing more information is suggested, since the content in the current version of this section is a bit too simple. Such as “Several parametric and non-parametric statistics…”.Is the rationale for developing the new method (or application) clearly explained?YesIs the description of the method technically sound?YesAre the conclusions about the method and its performance adequately supported by the findings presented in the article?YesIf any results are presented, are all the source data underlying the results available to ensure full reproducibility?No source data requiredAre sufficient details provided to allow replication of the method development and its use by others?YesReviewer Expertise:statistical genomics, animal breedingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.