GxE) interaction can be underestimated for low to moderate heritability traits. As high-throughput genotyping becomes economically affordable, we recommend expanding the use of genomic selection to obtain more accurate estimates of genetic parameters and gains.Forest tree improvement helps provide adapted planting stock to ensure growth productivity, fibre quality and carbon sequestration through reforestation and afforestation activities. However, there is increasing doubt that conventional pedigree provides the most accurate estimates for selection and prediction of performance of improved planting stock. When the additive genetic relationships among relatives is estimated using pedigree information, it is not possible to take account of Mendelian sampling due to the random segregation of parental alleles. The use of DNA markers distributed genome-wide (multi-locus genotypes) makes it possible to estimate the realized additive genomic relationships, which takes account of the Mendelian sampling and possible pedigree errors. We reviewed a series of papers on conifer and broadleaf tree species in which both pedigree-based and marker-based estimates of genetic parameters have been reported. Using metadata analyses, we show that for heritability and genetic gains, the estimates obtained using only the pedigree information are generally biased upward compared to those obtained using DNA markers distributed genome-wide, and that genotype-by-environment ( Compared with crop or animal breeding, forest tree breeders initiated their research activities quite recently, i.e., in the late 1950’s3. Since then, genetic improvement programs have been initiated for a large number of forest tree species from around the world4. The breeding process in forest tree species is often slow due to the limited resources available and to its inherent complexities. Among these complexities, are: (1) the necessity to set up more or less long-term progeny tests to assess the traits of interest, (2) the fact that many of these traits are often mature traits, thus requiring many years or even decades of testing before their appropriate assessment can be conducted, and (3) some of the traits are difficult and expensive to assess, such as microfibril angle and cell wall thickness.Forest tree breeding traditionally aimed to increase volume production and improve both adaptive traits and fiber attributes of forest tree plantations. Ongoing environmental and market changes are currently shifting the selection focus towards seedstock production for enhanced carbon sequestration capacities and resistance to biotic and abiotic stressors. Those multiple breeding goals and the long-lived perennial nature of trees demand for the most precise estimation of genetic parameters and exact selection of individuals that best combine the desired properties of various nature . The model is characterized by the fitting of a random component for the breeding value of each individual, which can be obtained using Best Linear Unbiased Prediction (BLUP)8. The animal model can also account for other environmental and genetic effects9. The additive genetic relationships between individuals is of crucial importance in the prediction of breeding values. The probability of identical genes by descent occurring in any pair of individuals is called the coancestry10 and the additive genetic relationship is twice the coancestry. The genetic evaluation using BLUP is heavily dependent on the genetic covariance among individuals, both for accuracy and unbiased results8, and genetic covariance among individuals includes the additive genetic variance, the dominance variance and the epistatic variance. In the present article, we will focus on additive genetic variance only.Traditionally, the selection of trees with desirable attributes for breeding or propagation has been based on both pedigree and phenotypic information. The genetic merit of candidate trees in a breeding population is now estimated using individual tree model or the so-called animal model in a mixed model framework11 proposed a recursive method to compute this matrix. The method is known as ABLUP. The A-matrix is symmetric. Its diagonal elements are equal to 1 + Fi, where Fi is the inbreeding coefficient of the individual i, and its off-diagonal elements equal the numerator of the coefficient of relationship between individuals i and j12. The covariance among breeding values can be obtained by multiplying the additive genetic variance by the A-matrix. As the additive genetic relationship among pairs of relatives is estimated using registered pedigree information, all members of a family receive the same expected relationship . Hence, it is neither possible to take account of Mendelian sampling, which is due to the random segregation of the alleles of the parents13 and may cause a deviation of actual relationship from the expected one, nor of potential pedigree identification errors or contamination (e.g.17). Moreover, Askew and El-Kassaby18 reported that for relatively undomesticated forest tree species, the average relationship does not allow detecting unknown population structure and/or inbreeding, as often shown for tree species (e.g.19). Thus, forest tree breeding values as well as heritability and genetic gain estimates obtained only with pedigree information could be biased.The additive genetic relationships among individuals are usually represented by a matrix called the Numerator Relationship Matrix or A-matrix. The inverse of this matrix is needed for solving the mixed model equations and for obtaining the best linear unbiased predictions of breeding values. Henderson.20, in their seminal paper, proposed using genome-wide distributed markers to model the entire complement of QTL effects across the genome, whether these effects are significant or not, and to obtain genomic-estimated breeding values (GEBVs). This method is called genomic selection (GS). Since then, it has been tested for the selection of complex traits in numerous species, such as maize and wheat21, trees23, and cattle24. In the last two decades, there has been a rapid development in DNA marker technologies. The availability of large numbers of markers distributed genome-wide and relatively inexpensive high-throughput genotyping technologies offers the possibility to improve the efficiency of tree breeding at a reasonable cost29. Hence, depending on availability of markers for a given species, a large number of individuals can be genotyped for up to many thousands of markers. It is then possible to obtain genotypes from many different loci well distributed across all the chromosomes of a species (e.g.30).In the early 2000’s, Meuwissen et al31. The methods of the first group are based on the concept that it is possible to predict the genetic value of individuals by using a regression model that relates phenotypes to all available markers. When the number of available markers generally exceeds the number of individuals in the population used to solve the regression, and the predictors (markers) are highly correlated, variable selection or shrinkage estimation procedures are required. Hoerl and Kennard32 proposed a method called ridge regression, which introduces a little bias so that the variance can be substantially reduced, which leads to a lower overall mean squared error. Tibshirani33 introduced the least absolute shrinkage and selection operator (LASSO) as an alternative to ridge regression. Since then, Bayesian estimation procedures of the shrinkage estimation methods have also been proposed to address the problem of multi-collinearity. With these regression methods, the genetic effect of each marker can be estimated and the summation of these marker effects for a given individual corresponds to its GEBV. The second group of methods uses the genomic relationships between individuals of a population (G-matrix), which are derived from their multi-locus genotypes, in a linear mixed or animal model framework to directly estimate the GEBV of any individual. It is usually referred to as Genomic Best Linear Unbiased Prediction or simply GBLUP. It is possible to use it in the context of an additive infinitesimal model where the standard pedigree-based numerator relation matrix (A-matrix) is replaced by the marker-based or realized genomic relationship matrix (G-matrix)34. Thus, this second marker-based method is more familiar to tree breeders and quantitative geneticists, and its results are easier to interpret for them.Various statistical methods have been developed for GS and they can be classified in two main groups35. This method is referred as to the single-step GBLUP. A relationship matrix called H-matrix is generated using information from both the A-matrix and the G-matrix, and then again, as for GBLUP, it is possible to work in the context of an additive infinitesimal model, by simply replacing the A-matrix by the H-matrix.More recently, a new method, which makes it possible to combine in a single GS analysis trees that were genotyped and trees for which only the pedigree information is known, has been proposedGxE) interaction for several quantitative traits based on additive effects only, and additive and non-additive effects (e.g.39). It is thus possible to make comparisons between results obtained with the pedigree-based and the marker-based approaches. Using a meta-analytical approach, our objective was thus to verify, for a large number of conifer and broadleaf tree species and traits, whether the estimates of narrow-sense heritability of quantitative traits as well as the expected genetic gains derived from selection were biased following an upward or downward trend when using registered pedigree information only, as compared to using marker-based information. We also wanted to compare the estimates of GxE interaction obtained with both approaches to determine if they provided the same results.Forest geneticists and tree breeders have used all these methods to analyze their data over the last decade. Depending of the genetic structure of the populations studied and the experimental design used, they reported estimates of heritability and genotype-by-environment  when all the traits in all studies were considered, with pedigree-based estimates being higher than marker-based ones  . Indeed, when we removed results of studies on open-pollinated families (22 study-trait pairs) in our metadata analysis, the Wilcoxon signed-rank test still remained highly significant , thus confirming the observed general bias of using registered pedigree information to represent the genetic relationships existing between the individuals making up the breeding population analyzed. The same upwardly biased trend was also observed for genetic gain estimates derived from pedigree information only, with important implications for conventional tree breeding programs.Results of the present meta-analytical study provides clear evidence that quantitative genetic analyses based on registered pedigree information only, resulted in upwardly biased estimates of narrow-sense heritability for growth, insect resistance, and wood quality traits in forest tree species, as compared to estimates obtained with realized genomic relationships based on DNA markers sampled genome-wide. For open-pollinated families, additive genetic variance estimates can be biased upwardly as the assumption of true half-siblings cannot be confirmedE. grandis x E. urophylla full-sibs51. After a thorough analysis, these authors concluded that putative pedigree errors (pollen contamination and mislabeling) negatively affected their ability to estimate accurate heritability estimates of traits based on pedigree information only. This might be the best explanation, knowing that in another study on the same hybrid species44, such lower pedigree-based heritability estimates were not observed. As this study provided half of the 8 species-trait pairs that did not follow the general trend of higher pedigree-based heritability estimates, it had some influence on the lower significance of the statistical test.The same significant trend was observed in both conifers and broadleaf species, although it was weaker in the latter. Several reasons may explain this difference. For conifers, 48 out of the 57 species- trait pairs compared (84%) showed higher pedigree-based heritability estimates than marker-based estimates. For broadleaf species, 22 of the 30 species-traits pairs (73%) showed such results. Thus, the proportion of significant higher pedigree-based estimates was marginally reduced in broadleaf species. The smaller number of species-trait pairs available for broadleaf species as compared to those of conifers may also partly explain the reduction in the significance of the statistical test. Indeed, the Wilcoxon matched-pairs signed-ranks test gives more weight to a pair that shows a large difference between the two conditions compared than a pair that shows a small difference. For many of the species-trait pairs of the broadleaf species, the differences between both the pedigree-based and the marker-based heritability estimates were quite small, but the trend remained statistically significant, as that observed for conifers. In addition, one can observe that the heritability estimates obtained with the pedigree information only were lower to those from marker information for all species-trait pairs in a single study on 6-yr old 52, forest tree breeding programs are generally only in their second or third generation at best4, and the available pedigree records and performance data are scarce. Thus, the presence of any preexisting inbreeding or relatedness among the ancestors of the current generation in typical forest genetic experiments is most often unknown unless marker-based assessment is used19. Consequently, using the current generation average  additive genetic relationships between individuals does not allow taking account of ancestral effects. This may partly explain the systematic overestimation of the real additive genetic relationships observed in this study when using the pedigree-based approach to estimate breeding values and associated genetic parameters in forest trees.While pedigree records and performance data for dairy cattle date back to the late 1800s, together with widespread collection of performance data shortly thereafter35. Hence, more accurate additive genetic variance and breeding values can be obtained. As indicated by several authors53, GS can have a substantial impact on the rate of genetic gains, especially because the use of realized genomic relationships is associated with increased accuracy in estimating the additive genetic variance and the breeding values.The DNA markers used to estimate identity-by-state relatedness between individuals represent the observed  rather than the average  relationship values and thus, make it possible to potentially capture distant relationships and the variation around close relationships due to Mendelian sampling35. This approach would likely be useful when the number of genotyped individuals is limited. In such non-optimal conditions, the marker-based estimates would likely not be more accurate than those obtained with the registered pedigree information would, and combining both types of data might be somewhat advantageous, but we would not recommend it outright. When the marker density and genome coverage are inadequate or the number of genotyped individuals is small relative to the non-genotyped individuals, estimates obtained would likely be closer to the upwardly biased pedigree-based genetic parameters, because the information derived from genomic data would be insufficient to counterbalance the bias from using pedigree information. Thus, the best option would still be the use of genomic-based approaches applied to most or all of the population even in a situation where all pedigree errors could be recovered by pedigree reconstruction, given that genomic-based approaches also take into account the effects of Mendelian sampling.One could wonder if the combination of both the pedigree and marker information would help obtain more accurate genetic parameter estimates. Single-step GBLUP analysis or the use of blended relationship matrices (H-matrix), which makes it possible to carry out an analysis combining both genotyped individuals and those for which only the registered pedigree is available, was proposed to take advantage of all the information availableGxE interaction as compared with marker-based methods for traits that respond more strongly to variation in environmental conditions. To delineate breeding zones and select superior trees to assemble their breeding and production populations, tree breeders have traditionally based their decisions mainly on growth and adaptive traits. These traits are generally under low to moderate genetic control and thus, are more influenced by the environment. Hence, the use of a marker-based approach to estimate more precisely GxE interaction would be beneficial, especially to tree breeders who have to address the reforestation and plantation needs of land or territories of more heterogeneous nature. The accurate prediction of trait value and genetic merit to specific environments is becoming even more important for some tree breeders given the context of deploying efficient climate change mitigation measures such as seed transfer and assisted migration.One interesting finding of the current metadata analysis is that the pedigree-based approach appears to underestimate the importance of 2. It also allows considering forward selection of superior individuals at an earlier stage and thus to hasten breeding cycles55. This increased flexibility and efficiency will also be proving particularly important in the context of climate change, to allow tree breeders to adjust their selection goals more rapidly. Consequently, while being more accurate, the genetic gains per time unit provided by the use of GS are considerably increased as well as the benefits and profitability of tree breeding programs56. Although our study was specifically on forest tree species, we believe that our results might also have similar implications for breeders working with other plant species that are in their first steps of domestication.In addition to obtaining more precise estimates of genetic parameters and gains, GS offers other significant advantages over conventional breeding based on registered pedigree information. It indeed makes it possible to practice higher selection intensities or facilitate multi-trait selection by screening large number of candidates without phenotyping all of them or even phenotyping only a fraction of themOverall, tree breeders should take advantage of the reduction of breeding cycles and the increase in accuracy of genetic parameters and genetic gain estimates resulting from the use of GS approaches. Over the last decade, the cost of genotyping offered by commercial high-throughput genotyping platforms has also been regularly decreasing so that such an investment should now be viewed as affordable and essential for the rightful management and renewed progress of tree breeding programs.Picea mariana (Mill.) B.S.P.) that we incorporated in the present analysis (see Table 57.We conducted a meta-analytical review of scientific papers on tree genomic selection in which both marker-based and pedigree-based estimates of narrow-sense heritability were reported. We found 22 studies that were carried out over the last 10 years (Table 58 is a non-parametric test procedure that gives more weight to a pair that shows a large difference between the two conditions compared than a pair that shows a small difference. This test makes it possible to tell which member of a pair is greater and to rank the differences in order of absolute size. With such a test, we could identify for each pair which member is greater, and we could make that judgment globally for the entire sample of matched pairs as well.We opted for a non-parametric test procedure because the genetic parameters compared were obtained using different genetic material as well as experimental designs and marker types. Use of a parametric test would have required that data meet some assumptions, such as that differences in the matched-pairs follow a normal distribution and that the sample of pairs is a random sample for its population. In the context of the present metadata analysis, these assumptions could not be met adequately. The Wilcoxon matched-pairs signed-ranks testSupplementary Information.


GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.Genomic selection ( GS) is now implemented in all major farm animal species. Its main purpose is accelerating genetic trends by increasing the accuracy of selection, particularly for young animals, and reducing generation interval. The increase in selection accuracy may be particularly important for low heritability traits where conventional selection is slow. Many of the reports indicate that genetic trends under GS have indeed accelerated.Genomic selection  and Bayesian via Gibbs sampling could analyze large datasets because the mixed model equations were sparse (BLUP). If unbiased estimation under GS requires all genomic, pedigree, and phenotypic data used for selection to account for genomic preselection . They frequently find many associations  need to be used, realistic models should be applied, and methods should account for population structure.Many of the studies use small genomic datasets for a genome-wide association study  are harmful to the industry or they are just an integral part of GS.Genetic predictions with BLUP before GS were stable even when accuracies were low, except when animals acquired new phenotypes or new daughters with phenotypes. The industry was pleased with the stability of BLUP. However, genomic predictions are less stable; a top-ranked animal can easily drop in the next evaluation. Thus, the industry has a crisis of confidence in genomic evaluations. Genomic evaluations change because all animals with genotypes are connected. In particular, new phenotypic data for one animal cause small but correlated changes in all animals. Additionally, model modifications such as blending level of genomic relationships with pedigree relationships and scaling of the genomic relationships cause further changes. While correlations between consecutive genomic evaluations are high, outliers can have substantially bigger differences. The question is whether fluctuations in genomic estimated breeding values (The objectives of this study were: 1) to examine theoretical issues affecting genetic selection, 2) to discuss strategies for parameter estimation under GS, 3) to look at GWAS from the perspective of large datasets, 4) to look at GWAS from the perspective of small datasets when the use of all available information is crucial to eliminate spurious responses, and 5) to discuss degrees of fluctuation of GEBV and ways the industry can manage these fluctuations.LD) is an important phenomenon relevant to both GWAS and GS. The focus of research until now has been on LD between two loci.Linkage disequilibrium  a third-order LD coefficient could appear :pABC is the frequency of the ABC triplet; pA, pB, and pC are the gene frequencies; and DAB, DBC, and DAC are the pairwise disequilibrium coefficients. The third-order LD can be interpreted as nonindependence among single-nucleotide polymorphism (SNP) triplets unaccounted for by pairwise coefficients. The maximum third-order LD occurs when the three loci are at intermediate allele frequencies and second-order disequilibria are zero and range between −1/8 and 1/8.where There are few estimates of third-order LD in livestock species. only. The first model consists of two quantitative trait loci  and one marker (M/m); if there is epistatic variance in the QTL, then a dominance variance would appear in the marker. The second model includes one QTL (Q/q) and two markers (M/m and N/n). If the QTL is additive, only an apparent additive by additive (A × A) variance would be suggested by the markers, and if the QTL is dominant, only an apparent A × A and an apparent dominance by dominance  and demonstrated that the magnitude of the problem was even greater with large sample sizes.Imperfect LD can generate the illusion of epistasis even when the underlying genetic architecture is purely additive. Amark > VAqtl (one QTL and two markers) and in 1.7% of cases V′ Amark > VAqtls (two QTLs and one marker), where V′ A is the apparent variance explained by the markers and VA the true additive variance, respectively. Recently, Arabidopsis and rice datasets illustrated that, due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low.Phantom dominance or epistasis creates inferential problems limiting the possibility to learn about causal effects because either epistasis or dominance is detected when they do not exist. However, models that do not accurately represent the genetic architecture of a trait could be useful for improving prediction models. In 10,000 simulations of random values of gametic frequencies and genetic effects, we found that in 0.5% of cases V′ LE), thus the genetic correlation was zero; but because of LD between markers, there was a nonzero genomic correlation. In case 2, the only source of genetic correlation between traits was LD between QTLs, which could be completely lost in a genomic analysis. In case 3, two QTLs affected both traits (pleiotropy), and genetic and genomic correlations differed in magnitude and even in sign depending on the pattern of LD. In conclusion, speculating about genetic correlations, and even about their causes , using genomic data is often conjectural.In an additive context, we could expect a missing heritability for one trait, but could we also expect a missing genetic correlation between two traits? This is a difficult question, and, according to The search for more uniform animal performance has increased its economic importance in livestock. Genetic control of environmental variance offers opportunities to increase this uniformity. Classical models normally assume that the environmental or residual variance is constant. However, there is evidence that environmental variance can also be under genetic control with a heritability of about 0.10. This has been shown in pigs , sheep (litter size), poultry and beef cattle (weight), and dairy cattle (milk yield). Therefore, it is possible to decrease environmental variance by selection, but large phenotypic and genomic datasets are needed . Thus, the rate of inbreeding per generation in Holstein increased by 1.4% in the last 10 yr. A higher rate of inbreeding could compromise fitness traits and long-term genetic gains. There are methods to manage inbreeding in a GS program. Genomic information can be utilized to identify and eliminate lethal recessives and to perform optimal contributions and optimal mate allocations.2/16.It is well known that directional selection creates negative LD . This is not obvious because ++++ chromosomes are the most favored, and thus one might expect positive LD. However, in the simplest demonstration, Reductions in selection response with BLUP due to the Bulmer effect are independent of heritability and depend only on selection intensity. Their values have ranged from 0.22 to 0.27. Reduction in selection response is greater with BLUP than with phenotypic selection. However, the selection response is still larger with BLUP than with phenotypic selection.Drosophila where suppression of crossing-over reduced the long-term phenotypic selection response, especially for small effective population size; 2) in many simulation results; and 3) in analyses of empirical data. For example, in Manech Tête Rousse dairy sheep , the loss of genetic variance was about 13% for females. The Bulmer effect had more impact (10%) than the buildup of coancestry (3%) in 30 yr (The Bulmer effect has been shown: 1) in old experiments (1970s) with in 30 yr . A positive aspect of GS is that we can act on the within-family variance (Mendelian sampling variance). Thus, we can better use the Mendelian sampling variance by selecting parents on an index composed of GEBV and gametic variabilities of selection candidates. According to The other side of the coin of the Bulmer effect is that if we increase recombination rate, we can enhance selection efficiency. A higher recombination rate would reduce LD between causative variants and release genetic variance.Drosophila and Tribolium where selection succeeded in both increasing and decreasing recombination rates; 2) domesticated mammals had higher chiasma frequencies than wild mammals; 3) heritability estimates of recombination rates were substantial ; and 4) QTLs and genes affecting recombination rate have been detected either for general and for specific regions. For example, Ring Finger Protein 212 acts as a regulator for crossing-over during meiosis. Lastly, targeted recombination is taken seriously by plant breeders.Now the question is if there is genetic variance for the recombination rate. The answer is positive as shown in various lines of evidence  and almost every character is aﬀected by many genes. Although the core genes are the relevant genes, most of the heritability is determined by variation in peripheral genes. The last model will impose a higher cost on multitrait selection.How do we expect the genome to be organized as a consequence of the joint action of pleiotropy, epistasis, recombination, and selection? There are two main proposals: 1) a modular model , which can accommodate both genotyped and ungenotyped animals directly.When genomic information is available, parameters could be obtained using SNP-only models with software such as GS3 . In thisssGREML) leads to dense equation systems. The inverse of the pedigree relationship is usually constructed using Henderson’s rules , and using all information (ssGREML). Biases with unselected populations were small especially with more than one generation of data. Biases were particularly strong for GREML when the best animals were genotyped. ssGREML was relatively unbiased but it had the highest cost.High computing times with genomic information raises the question of whether parameter estimation should exclude genomic information, especially when computations are expensive. When genomic information is not used for selection, the only benefit from genomic analyses would be reduced SE of estimates . When geAnother study  looked aOne consequence of GS could be faster changes of genetic parameters. In particular, if the additive genetic variance is decreasing and unfavorable genetic correlations are increasing, expected genetic gains based on prior parameters will not be realized. However, estimating parameter changes is difficult. Under an additive model, when all data used for selection are included in the estimation, the estimated parameters correspond to those of the base population. Therefore, if the base population does not change, the estimated parameters will be the same even if the parameters for the youngest generations change.RRM) over time or using data slices  and studies involving industry-generated databases in livestock animals  approach to implement P-values for single-marker GWAS studies within the ssGWAS framework. Still, LD between SNPs may impact a full-model GWAS approach more than a single-marker mixed model test; thus, a full-model GWAS could be less powerful but more accurate than a single-marker test.In addition to single-marker tests, GS models are often used in livestock GWAS . InsteadMAFs) at 5% or 1% levels. Studies with small sample sizes need to use more stringent MAF cutoff levels to ensure that there are enough individuals carrying a minor allele in the sample .Like any other data analysis research, data quality is essential to ensure a valid GWAS. Quality checks are especially important for small-sample GWAS because data issues can easily lead to many more false-positive than true-positive results. Quality assurance procedures can be applied both before and after a GWAS analysis. We can check phenotype and genotype data before performing an association test. Generally, phenotypic values need to approximately follow a normal distribution, and outliers removed. We can check the quality of genotype data by checking Mendelian inheritance in the pedigree, perform a Hardy–Weinberg equilibrium test, and, depending on sample size, filter minor allele frequencies (QQ plot) or a Manhattan plot of P-values. In a QQ plot, we expect to see a uniform distribution of P-values by assuming that the majority of SNPs are not associated with the trait. In a Manhattan plot, we expect the top GWAS signals to be supported by clusters of SNPs in LD with the underlying causal variant(s). Those singled out significant SNPs in a Manhattan plot are likely due to data quality or analysis issues that sneaked through the quality control procedures.After GWAS, we can still check the quality of the results using a quantile–quantile plot (https://www.uscdcb.com/). While GS has been successful with these data, they also provide uniquely powerful opportunities for GWAS and other genetics research. To date, this database has enabled many large-sample, powerful GWASs that revealed QTLs and genomic regions for many production, reproduction, health, and body conformation traits in dairy cattle  in the United States maintains a dairy genomics database with millions of genotyped cattle and hundreds of millions of phenotypic records on a variety of economically important traits    project An easier target after GWAS is to generate knowledge on where the causal/functional variants are in the genome. This knowledge can help fine-mapping studies and provide useful prior information for GS. We have conducted a few preliminary studies by integrating GWAS results and functional genomics datasets in cattle. Firstly, we explored transcriptome data across multiple tissues in cattle . SpecifiGBLUP is extensively used in the animal breeding industries for incorporating genomic information into the genetic evaluation of livestock species. Single-step GBLUP  additionSeveral methods have been proposed and successfully applied for embedding GWAS into genomic prediction models. Most of the methods commonly avoid formal hypothesis testing and resort to the estimation of SNP effects, relying on visual inspection of graphical outputs to determine candidate regions . This apWith the widespread usage of ssGBLUP for genomic prediction across the industry, it is important that any GWA model incorporates information not only from genotyped individuals but also from their ungenotyped relatives that have been extensively phenotyped.When all phenotyped animals are also genotyped, the animal-centric GBLUP model is represented by equation 4:y is the vector of phenotypic values, β is the vector of fixed effects, X is the incidence matrix of fixed effects, a is the vector of breeding values, and a and G is the genomic relationship matrix :a from equation 4, and then dividing the back-solved estimated SNP effects by their SEs (equation 7). For further details, see the section on Significance testing below:where It is not necessary to compute the whole matrix product in equation 6, only the products involving diagonal elements. This can be easily parallelized even for a very large number of SNP. Also, Alternatively, consider augmenting the model in equation 4 by including a single fixed SNP effect, one at a time to test for association (equation 8):zi is the column of the standardized genotype matrix Z corresponding to the ith SNP, and bi is the fixed effect of the allelic dosage of the ith SNP on the phenotype, and all the other quantities are as described for equation 4.where EMMAX) procedure :The variance explained by a putative QTL is an important genetic parameter that is often reported in association studies. The variance associated with a QTL peak can be computed in two ways with the procedure described above. The most common way consists of selecting a window around the association peak. This can be done, for instance, using the confidence interval method presented above or by selecting an arbitrary interval  or by inwhere There are two computational bottlenecks when using this procedure. The first one is the recomputation of the two relationship matrices, especially the computation of A computationally less onerous approximation  consistsZ matrix and where GS introduced considerable changes in the field of animal breeding and genetics since its first implementation in 2009. The most important change was the increase in genetic gain resulting from the decrease in generation interval and greater accuracy of prediction of GEBV. However, the widespread adoption of GS brought some challenges, namely high computational demands and the need for new methods. Additionally, reports have emerged on differences in variance components  and inbrAdditional changes that have been reported since GS started being used relate to GEBV. Fluctuations in GEBV in subsequent evaluations have been observed even for animals that have no phenotypes added to the evaluation. Questions on the reason for such fluctuations are frequent in the livestock industry. This is because, under traditional BLUP, animals without new data in subsequent evaluations had stable EBV even when their accuracy was low. However, changes in EBV could still be observed for some animals when fixed effects were redefined, independently of accuracy levels. Provided that no model changes are made, EBV changes are limited to animals that have new data and those directly related to them through the pedigree. In fact, under BLUP, the EBV of an animal is conditioned only on the breeding value of its sire and dam. As pedigree links are sparse, EBV can be considered stable because most of the animals will have no changes. The high stability of EBV from BLUP generated great confidence in the method. Additionally, stability is an important factor because bulls are priced according to their genetic merit, and large, unexpected changes in EBV would create oscillations in the semen market.Pedigree relationships in BLUP are only expectations of the proportion of alleles shared among individuals, whereas in GBLUP or ssGBLUP the genomic information is used to better capture relationships at the gene level. Relationships based on SNPs are termed realized relationships, being, therefore, based on the observed proportion of shared alleles that are identical by state. Because of that, all genotyped animals can have some level of relationship even though they do not share common ancestors in the current population.A) is sparse, the genomic relationship matrix (G) is dense, which means animals are more connected. Because of the stronger connections, when phenotypes are added to a portion of the genotyped animals in the evaluation, changes in GEBV are more frequent because this information is shared among nearly all genotyped animals. When only a portion of the pedigree animals is genotyped, ssGBLUP (H). Another feature of ssGBLUP is that genomic information is back propagated  to non-genotyped animals that are related to genotyped animals through the pedigree  and the second evaluation used 4.38 M phenotypes until December 2017 (Dec2017). The number of animals in the pedigree and with genotypes was kept constant between the two subsequent evaluations at 10,661,517 and 509,072, respectively. A total of 54,798 genotyped and 69,996 non-genotyped animals had phenotypes added to the Dec2017 evaluation. For each evaluation, EBV and GEBV were computed by BLUP and ssGBLUP, respectively, using the BLUPF90 software suite . Figure SDa), respectively. The small differences between the distribution of EBV and GEBV can be attributed to the contributions of genotyped relatives to non-genotyped animals . When looking at genotyped animals without added phenotypes in subsequent evaluations  than for GEBV (up to 0.8 SDa). The distribution of changes for EBV and GEBV for non-genotyped animals with added phenotypes was very similar EBV are bounded by the accuracy of (G)EBV. As more information is used to compute GEBV than EBV, their accuracy is higher, and consequently, their possible changes are less extreme. When changes from Jul2017 to Dec2017 for all genotyped animals were investigated as a function of individual accuracy and SE of prediction, we observed more changes in EBV and GEBV for animals with lower accuracy .SEP)  . The 1.9SEP) , values G in the algorithm for proven and young  with high average accuracy instead of individual sires. Another way is to recognize that these changes are based on the GS theory and are part of the system.Although GS has been widely successful, many remaining issues are still being identified and addressed. More theoretical studies following analyses of large field datasets are needed to fully understand the effects of long-term GS. Parameter estimation with genomic information requires careful selection of data to minimize computations and biases. Methods for GWAS need to include strong LD and effects of inadequate modeling, especially with small datasets. Fluctuations of genomic predictions reflect limited prediction accuracies and can be managed to reduce risk and achieve high genetic gains.


To meet the challenge of feeding almost 10 billion people by 2050, wheat yield has to double by 2050. However, over the past 20 years, yield increase has slowed down and even stagnated in the main producing countries. Similar to what has been observed in maize, hybrids have been suggested as a solution to overcome yield stagnation in wheat. However, wheat heterosis, i.e., the fact that a progeny surpasses the performances of its parents, is still limited and poorly understood. To better characterize this phenomenon, we developed and phenotyped for physiological and agronomic traits 91 hybrids and their nineteen female and sixteen male parents. We showed that hybrids had a longer grain filling phase that led to bigger grains and an increased thousand kernel weight. This resulted in a better yield for 86% of hybrids compared to the average yield of their parents. In addition, hybrids appeared to be less affected by the negative correlation between protein content and yield compared to pure lines. These results shed light on the physiological bases underlying yield heterosis in wheat, paving new ways to breed for better wheat hybrids that can help to meet agriculture’s challenges.To meet the challenge of feeding almost 10 billion people by 2050, wheat yield has to double by 2050. However, over the past 20 years, yield increase has slowed down and even stagnated in the main producing countries. Following the example of maize, hybrids have been suggested as a solution to overcome yield stagnation in wheat. However, wheat heterosis is still limited and poorly understood. Gaining a better understanding of hybrid vigor holds the key to breed for better varieties. To this aim, we have developed and phenotyped for physiological and agronomic traits an incomplete factorial design consisting of 91 hybrids and their nineteen female and sixteen male parents. Monitoring the plant development with normalized difference vegetation index revealed that 89% of the hybrids including the five higher yielding hybrids had a longer grain filling phase with a delayed senescence that results in larger grain size. This average increase of 7.7% in thousand kernel weight translated to a positive mid-parent heterosis for grain yield for 86% of hybrids. In addition, hybrids displayed a positive grain protein deviation leading to a +4.7% heterosis in protein yield. These results shed light on the physiological bases underlying yield heterosis in wheat, paving new ways to breed for better wheat hybrids. Triticum aestivum L.) is one of the most important crops worldwide and the staple food for one third of the world population [With 216 million hectares and an annual production of 765 million tons, bread wheat  evaluate heterosis for agronomic and physiological traits and (ii) assess the potential of physiological traits estimated from NDVI to explain heterosis. Our study is based on an incomplete factorial design including 19 females, 16 males, and 92 F1 combinations, which were grown at three locations in northern France.Triticum timopheevii, referred to as ‘females’ [® iSelect 15K wheat SNP array [2 = 1) using the plink software [A set of 136 wheat varieties were selected within the genetic pool from Syngenta breeding company comprising 43 lines carrying fertility restorer (Rf) genes, hereafter referred to as ‘males’, and 93 lines carrying a cytoplasmic male sterility (CMS) derived from females’ . GenotypNP array . Heterozsoftware . Eventuacor function for each male x female combination (use = “pairwise.complete.obs”) [Pearson coefficient correlations were calculated with the R te.obs”) . Dissimite.obs”) . Principte.obs”) . Individte.obs”) .® 2.0 Assay Manual Workflow User Guide Rev3, Santa Clara, CA, USA). Allele calling was carried out using a modified version of the Affymetrix proprietary software packages named Affymetrix Power Tools (APT) and SNPolisher™ (http://www.affymetrix.com/estore/partners_programs/programs/developer/tools/devnettools.affx (accessed on 22 February 2021)). The objective was to take into account the specificities of the hexaploid wheat genome. For all SNPs, HomRO and HomFLD were calculated. The HomFLD filter was set to 3.6 (http://media.affymetrix.com/support/developer/downloads/Tools/SNPolisher_User_Guide.pdf (accessed on 22 February 2021)). As a first step, all the probesets were processed with a mild inbred penalty equal to 4 on all the samples. As a second step, the SNPs failing the QC criteria (“Other” and “NoMinorHom”) were reprocessed using an inbred penalty of 16. Probesets classified as Off-Target Variants (OTVs) by SNPolisher were analyzed with OTV_caller in the two steps.The 35 parents selected from the genetic panel were genotyped with an improved Axiom array based on the TaBW280K SNP chip  and compCrosses between 19 females with cytoplasmic male sterility  and 16 mThe 92 hybrids, 16 males, and 19 maintainer lines, i.e., isogenic to CMS female lines except for the CMS gene , were tePrevious studies performed in five locations in northern France demonstrated that no difference was observed on hybrid yield between a normal and a 15%-reduced sowing density (data not shown). For this reason, hybrids were sown at an 85% density relative to their parents.Field evaluations were conducted during season 2019/2020 in three locations in France . TreatmeFor all 92 hybrids, 35 parents, and the checks in all locations, eleven agronomic traits were measured for each plot . Grain p® crop sensing system . For each plot, between 60 and 80 measurements were performed 50 cm above the canopy. The number of timepoints was on average 13 for Pomacle and 14 for Moinville-la-Jeulin and Arvillers.Normalized Difference Vegetation Index (NDVI) was monitored from sowing to harvest with a GreenSeekera1 and a2 are the slopes, and b1 and b2 the intercepts. finalNDVI is the lowest NDVI value before harvesting and amplitudeNDVI corresponds to the difference between the NDVI at the beginning of senescence and finalNDVI. The exponential term of Equation (3) is composed of a linear model with a3 being the slope and b3 the intercept. Thermal time (TT) was centered on the heading date (0 °C days). Functions for Equations (1) and (2) were calculated with the “LINEST” Microsoft Excel function. Parameters a3 and b3 were determined with the Excel “Solver” add-in using the nonlinear least-squares minimization method.For each genotype, NDVI values were used to establish a plant development model. The developmental cycle was divided in three phases: the “growing phase”, from seedling to maximum NDVI (Nmax); the “flowering phase”, from Nmax to beginning of senescence; and the “senescence phase” from beginning of senescence to end of senescence . NDVI meamplitudeNDVI was reached [Several physiological traits linked with stay green were calculated from these functions: slopes, thermal time when 10%, 50%, 90%, and 99%  of  reached . The begij is the phenotypic trait for the ith genotype in the jth block and εij the residual error. μ corresponds to the intercept term of the model; αi is the genotypic effect of the ith genotype; βj is the jth block effect. The best linear unbiased estimators (BLUE) were calculated in R with function “emmeans” from the package “emmeans” for each genotype [All traits were analyzed for each environment separately with the following fixed ANOVA model for estimating adjusted means:genotype .ij is the phenotypic trait for the ith genotype in the jth block and εij its residual error. μ corresponds to the intercept term of the model; αi is the random genotypic effect of the ith genotype; βj is the jth block fixed effect.All traits were analyzed for each environment separately with the following mixed ANOVA model for calculating heritability:Broad sense heritability was calculated as the ratio of genotypic variance to phenotypic variance with the following formula :(6)h2=σGijk is the phenotypic trait for the ith genotype in the jth block at kth location and εijk the residual error. μ corresponds to the intercept term of the model, αi is the genotypic effect of the ith genotype, βjk is the jth bloc effect at kth location, Lk is the location effect of the kth location, γik is the interaction effect for the ith genotype at the kth location. The best linear unbiased estimators (BLUE) were used in the linear models for estimating the genotypic effect for each genotype.The traits were also analyzed for estimating adjusted means by including the location effect in a fixed model called “combined environments”:ijk is the phenotypic trait for the ith genotype in the jth block at kth location and εijk the residual error. μ corresponds to the intercept term of the model, αi is the genotypic effect of the ith genotype, βjk is the jth bloc effect at kth location, Lk is the location effect of the kth location, γik is the interaction effect for the ith genotype at the kth location.All traits were analyzed for combined environments with the following mixed ANOVA model for calculating heritability:n is the average number of repetitions, and l is the number of locations.Broad sense heritability was calculated as the ratio of genotypic variance to phenotypic variance with the following formulas :(9)h2=σGcor.test function. Correlations matrices were plotted in R using the ggpairs function from the GGally package [p-value was fixed to α = 0.05.Pearson coefficient correlations were calculated with the R  package . Traits i, i, i. No heterosis was calculated for hybrids for which the value for at least one parent was missing.Heterosis was calculated as the percentage of either mid-parent or best parent heterosis as :(10)Mid-Triticum timopheevii [In order to produce a panel of genetically diverse wheat hybrids, we selected a set of 136 wheat lines comprising 43 genotypes carrying fertility restorer genes (Rf), hereafter referred to as ‘males’, and 93 lines carrying a cytoplasmic male sterility (CMS) derived from opheevii , hereaftopheevii  and a seopheevii . The denOut of the 304 possible female x male combinations, 92 hybrids (30%) were selected for further analyses . These 9In order to evaluate the physiological and agronomic performances of hybrids, the 92 aforementioned combinations as well as their 35 parents and six checks were sown in three different locations in northern France: Moinville-la-Jeulin, Arvillers, and Pomacle. These three locations were chosen in major wheat production areas and close to the official field trial sites for wheat varieties registration. Average temperatures were similar for the three locations and differences between sowing and harvesting dates did not exceed three weeks. The number of days with high temperature (above 25 °C) was higher in Pomacle (n = 21) than in Arvillers (n = 16) and Moinville-la-Jeulin (n = 13) . To char2 = 0.57) and Arvillers (h2 = 0.66), and high for Pomacle (h2 = 0.80) and in the combined environments (h2 = 0.69). GPC heritability was moderate to high in all three locations (0.66 to 0.81). Thousand Kernel Weight (TKW) and specific weight (SW) had a high heritability in all environments (0.78–0.97). A similar trend was observed for traits controlled by major genes including plant height and heading date (0.84–0.99). Heritability for the different areas under the curve corresponding to plant phases were environment-dependent and varied from low to high values (0.35–0.92). Regarding the senescence indicators (TFN), their heritability was variable for each individual location (0.35–0.78) except for TFN50 and TFN10 in Moinville-la-Jeulin (0.87 and 0.90) and moderate to high in the three combined environments (0.62–0.82) . As expected, we also found a negative correlation between TKW and the number of grains per square meter  Finally, stay-green traits were strongly and negatively correlated with heading date (−0.76 to −0.80). A linear model showed that the onset of senescence (TFN90) was significantly correlated to heading date .There was a strong negative correlation between grain yield and grain protein content .p-value = 0.04)  A. Conseqp-value = 9.5 × 10−8) .Despite a 15% reduction in sowing density, hybrids had only 3% less grains per square meter at harvest compared to their parents. This decrease was largely compensated by an average +7.7% heterosis for TKW, with 97% of hybrids performing better than their mid-parent in combined environments . A linea × 10−8) . No signThe analysis of the plant development dynamics revealed that hybrids grew faster than their parents until the maximal NDVI value (+1.2% heterosis for the area of the growing phase) and had a delayed beginning of senescence (+5.8% for TFN90). In addition, the rate of senescence was lower for hybrids compared to pure lines with a positive heterosis observed for the area of the senescence phase (+3.1%).Finally, hybrids displayed a negative heterosis for heading date (−2.3%) that translated into heading data that were approximatively 30 °C days earlier.These tendencies were also observed when only the five higher yielding hybrids were analyzed. Indeed, grain yield heterosis and TKW heterosis was more pronounced for the best hybrids and they were earlier than their parents . HoweverAnother method to estimate heterosis is to compare hybrids to the best parent line. For this best-parent heterosis, sometimes referred to as heterobeltiosis , we did Grain yield, grain protein content, and yield stability are three of the most important traits for farmers and the most important targets for breeders. However, not only has wheat yield been stagnating and fluctuating in the main producing countries over the past 20 years, but also grain yield and grain protein content are negatively correlated . HybridsA positive mid-parent heterosis for grain yield was observed for the vast majority of our hybrids (86%), with an average increase of 5% and up to 23%. In addition, 54% of our combinations displayed a higher grain yield than their best parent, 5% being over the +13% threshold of hybrid profitability compared to pure lines . This reAs expected from the grain protein concentration-grain yield negative relationship , this poOverall, this led to a positive heterosis for final protein yield and suggested hybrids broke down the negative balance between high yield and nitrogen metabolism involving less protein in grains  resultinTo decipher the bases of grain yield heterosis, we analyzed yield component traits. TKW appeared as one of the main explanatory factors and an average positive heterosis of 7.7% was observed, consistent with the results of Askander and collaborators on a small dataset composed of 12 hybrids . HoweverBesides measuring agronomic traits, we also used NDVI measurements, a non-destructive and heritable method which provides information on leaf area and radiation intercepted by the canopy ,66. MoniFinally, while the selection of parental lines relied on SNP-based genetic distances, it would be interesting to characterize the 16 male and 19 female lines for genomic structural variations (SVs). Indeed, it is now widely admitted that SVs play a role in hybrid vigor through the combination of complementary gene and allele contents ,76. WhilHybrid wheat produced with chemical hybridization agent has been widely studied over the past decades. Unfortunately, with this technique, the production of hybrids is fastidious and difficult. The new generation of hybrids produced by cytoplasmic male sterility has helped to decrease these difficulties and to increase the number of combinations tested. Our study focused on heterosis in 92 CMS hybrid combinations compared to their 35 parents. It appears that a majority of hybrids have a mid-parent heterosis for grain yield with a very limited decrease in grain protein content thanks to a positive GPD. Grain yield heterosis is made possible thanks to a positive heterosis on the thousand kernel weight due to a longer filling time for hybrids.


The aim of this study was to estimate genetic parameters of 26 individual and four composite type traits in first parity Cika cows. An analysis of variance was performed with the generalized linear model procedure of the SAS/STAT statistical package, where the fixed effects of year of recording, cow’s age at recording and days after calving as a linear regression were included in the model. The variance components for the direct additive genetic effect and the herd effect in all type traits were estimated using the REML method in the VCE-6 software package. The estimated heritabilities ranged from 0.42 to 0.67 for the measured body frame traits, from 0.36 to 0.80 for the scored autochthonous traits, from 0.11 to 0.61 for the scored body frame traits, and from 0.20 to 0.47 for the scored udder traits. The estimated heritabilities for the composite traits called “autochthonous characteristics”, “muscularity”, “body frame” and “udder” were 0.55, 0.19, 0.19, and 0.26, respectively. The estimated genetic correlations among the measured body frame traits were positive and high, while the majority of them among the scored body frame traits were low to moderate. The estimated proportions of variance explained by the herd effect for the composite traits “autochthonous characteristics,” “muscularity,” “body frame” and “udder” were 0.09, 0.28, 0.14, and 0.10, respectively. The estimated heritabilities for the type traits of first parity Cika cows were similar to those reported for other breeds where breeding values have been routinely predicted for a long time. All estimated genetic parameters are already used for breeding value prediction in the Cika cattle population. Cika cattle is a Slovenian autochthonous dual-purpose breed. The breeding goal is to preserve the original type traits of the breed and to prevent an increase of inbreeding. Cika cattle are widespread all over Slovenia, especially in their regions of origin . The coat color pattern is red pied sided, which is typical for this breed and very different from other cattle breeds in Slovenia. Some Cika animals have a coat color pattern similar to Pinzgauer cattle and some to Tux-Zillertaler cattle from Austria . Based oCika cattle breeding is based on the recording of type traits of all first parity cows . The agewww.fao.org/dad-is), which is lower than in all other breeds in Slovenia. Therefore, all breeding animals are scored by a single expert. Compared to the small population size, a large number of sires (98 in the year 2020) is used for natural service and artificial insemination in the population. The effective population size is estimated to be 117 animals. Of the animals born in 2016, 96.6% had complete pedigree data in the second generation, while 65.7% of the animals had a complete pedigree in the sixth generation. The inbreeding coefficient in the population is estimated to be 2.1%.The current population size of Cika cattle is 5,531 animals  with type traits and their known ancestors were included in the additive relationship matrix among all animals . Almost 98% of the first parity cows had a known sire and 96% had a known dam. In total, 95% of the first parity cows had both parents known.yijk was the type trait, μ was the mean of the population, Yi was the fixed effect of the recording year , bI was the linear regression coefficient for the age at recording, xijk was the age at recording (days), bII was the linear regression coefficient for days after calving, zijk was the number of days after calving, eijk was an error.To investigate the non-genetic effects to be included in the model, an analysis was performed using the GLM  procedure in the SAS/STAT statistical package  for all included type traits in the first parity Cika cows ranged between 0.11 and 0.80, while the proportions of variance explained by the herd effect ranged between 0.01 and 0.28. Considering the six different trait groups, the heritabilities for the measured body frame traits ranged from 0.42 to 0.67, for the scored autochthonous traits from 0.36 to 0.80, for the scored body frame traits from 0.11 to 0.61, and for the scored udder traits from 0.20 to 0.47. The estimated heritabilities for the composite traits of autochthonous characteristics, muscularity, body frame, and udder were 0.55, 0.19, 0.19, and 0.26, respectively , American Brown Swiss cows (0.64 ± 0.02), and Red Holstein cows (0.74 ± 0.03) in Switzerland , American Brown Swiss (0.35 ± 0.02) and Red Holstein cows (0.36 ± 0.02) in Switzerland  in the first parity Cika cows were between 0.63 and 0.80.The estimated h8 ± 0.03 . A lower5 ± 0.05 . In Astu3 ± 0.02 . The est2 for the top line in the first parity Cika cows was 0.19 ± 0.05  ranged from 0.01 to 0.28 (2 was on average higher for measured body frame traits (0.10–0.25) compared to the scored body frame traits (0.01–0.23). Likewise, the scored autochthonous and udder traits had a low c2 . The estimated c2 for the composite traits  were 0.09, 0.28, 0.14, and 0.10, respectively. This might reflect the effect of the huge variability of production systems on the farms that cause variability in the body condition of the animals in the herd. The proportion of residual variance ranged from 0.18 to 0.74. The standard errors of the variance components varied between 0.007 and 0.05.The proportion of variance in the type traits of the first parity Cika cows explained by the effect of the herd (c to 0.28 . InteresThe estimated genetic and phenotypic correlations between the measured body frame traits  were posThe estimated genetic correlations between the traits describing parts of the head  were lowThe estimated phenotypic and genetic correlation between the neck and dewlap  was posiThe estimated genetic and phenotypic correlations between the scored traits describing the coat color and white stripes patterns  were posThe majority of the estimated correlations among the body frame traits  were lowThe estimated phenotypic correlations between udder traits  were modFore udder attachment and udder depth were relatively strongly correlated (0.78), as well as front teat length and teat thickness (0.78). Cows with a genetic predisposition for weakly attached udders usually have deeper udders as well. All 26 individual type traits as well as the four composite traits were already introduced in the genetic evaluation of first parity Cika cows in the year 2016. The composite traits, which include all individual traits, are widely used. There is a plan to decrease the number of traits in the future since some of the traits within each group are highly correlated. Nevertheless, Comparing the estimated heritabilities for the majority of included type traits with heritabilities from the literature, it was found that they were within the expected range and similar to those reported for other breeds. Among the measured body frame traits, the highest heritability was estimated for rump height. The heritabilites of scored body frame traits in Cika were similar to those of other breeds as well. In the scoring form for Cika, only four scored udder traits are included . Nevertheless, their heritabilities were very similar to those reported for cows of dairy breeds. The estimated heritabilities of the scored autochthonous traits, which describe the breed characteristics, were moderate to high. This suggests the possibility to breed first parity Cika cows with unique type traits known for autochthonous Cika cattle. Unfortunately, according to the best of our knowledge, no autochthonous type traits like those included in the analysis have been investigated before. Consequently, comparison with the literature was not possible.All above estimated genetic parameters are already used for breeding value prediction in the Cika cattle population. According to the genetic trends, first parity Cika cows born in the last years showed more typical autochthonous type traits of purebred Cika cows, which distinguished them from Cika cows admixed with Pinzgauer in the past. Likewise, first parity Cika cows nowadays have more excellent body frames and udders.


Low-coverage whole genome sequencing is a low-cost genotyping technology. Combined with genotype imputation approaches, it is likely to become a critical component of cost-effective genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low-coverage whole genome sequence data and genomic prediction based on the imputed genotype data. The specific aims were as follows: 1) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, minor allele frequency (MAF), and imputation pipelines and 2) to assess the accuracy of genomic prediction under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single-vs. multi-trait models. We found that a high imputation accuracy (>0.95) can be achieved for sequence data with a sequencing depth as low as 1x and the number of sequenced individuals ≥400. For genomic prediction, the best performance was obtained by using a marker density of 410K and a G matrix constructed using expected marker dosages. Multi-trait genomic best linear unbiased prediction (GBLUP) performed better than single-trait GBLUP. Our study demonstrates that low-coverage whole genome sequencing would be a cost-effective approach for genomic prediction in Dezhou donkey. Dezhou donkey, originating from Dezhou area, Shandong Province, China, is one of the major donkey breeds in China. It is famous for its large body size (and thus good meat production ability) and excellent skin quality (for producing donkey-hide gelatin). It has been introduced as a breeding stock into many areas of China and has also brought considerable economic benefits to farmers . TherefoStarting with the pioneered work of Recently, along with the rapid development of next-generation sequencing technology and reduction of sequencing cost, GS using genotypes revealed by whole genome sequencing (WGS), instead of SNP array, has drawn interests of animal GS community . The motA critical issue of lcWGS-based GS is the accuracy of imputation of missing genotypes, which is affected by several factors, such as sequencing depth, sample size, minor allele frequency (MAF), and imputation method. A number of imputation methods for lcWGS data have been proposed . HoweverIn this study, we evaluated the imputation accuracy of lcWGS data with respect to different sequencing depths, sample sizes, MAFs, and imputation pipelines using 617 Dezhou donkey animals that were sequenced with an average depth of 3.5x. We then used the imputed genotypes to investigate the performance of genomic selection for birth weight (BW) and weaning weight (WW) in the Dezhou donkey population under different marker densities, strategies for constructing genomic relationship matrices, and single-vs. two-trait models.The animals used in this study were from the Dong-E E-Jiao Donkey Farm in Shandong Province, China. Animals that had records on both BW and WW were selected. These animals along with their known parents formed the study population for this research, which consisted of 617 animals, of which 594 had records on both traits. These 594 animals  were born between 2015 and 2019. The animals were weaned at 6 months after birth, and their weaning weight was measured at the age of 6 ± 1 month. Weaning weight recorded outside this age range was regarded as invalid record. The means and standard deviations of the two traits were 30.507 ± 4.235 kg (ranging from 15.0 to 42.2 kg) and 116.752 ± 18.227 kg (ranging from 63.5 to 165.5 kg), respectively.Blood samples were collected from all these animals. Total DNA was isolated using the QIAamp DNA Investigator Kit  and following the manufacturer’s instruction. DNA quality was evaluated by spectrophotometry and agarose gel electrophoresis.All of the above experiments were carried out according to the guideline of the experimental animal management of Shandong Agricultural University (SDAUA-2018-018).DNA templates were ultrasonically sheared using a Covaris E220  to yield to 150-bp fragments and then prepared for sequencing libraries following the workflow of the NEBNext Ultra DNA Library Preparation Protocol. Multiple Ampure Bead XP cleanups  were conducted to remove any adapter dimer that might have developed. The quality and concentration of libraries were determined on an Agilent Bioanalyzer 2,100 . The genomic library for each sample was PE150 sequenced using the Illumina NovaSeq 6,000 sequencing system.https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) with focus on base quality scores (q > 30), GC content (skewness <5%), N content (<5%), and sequence duplication levels (<100). The resulting data reached a nucleotide length of 150 bp and a base quality score of higher than 30 and were aligned to the donkey reference genome  between expected dosages  and typed genotypes (https://broadinstitute.github.io/picard/). For the depths of 0.5x, 1x, and 1.5x, three repeated samplings were performed. To test the effect of sample size  on imputation accuracy, three different sample sizes  were considered. The samples with sizes of 200 and 400 were randomly sampled from the 617 animals, and three repeated samplings were performed. To test the effect of MAF on imputation accuracy, we restored the SNPs that were previously filtered out with MAF >0.01 and divided the SNPs into 15 MAF bins: (0–0.001), (0.001–0.002), (0.002–0.005), (0.005–0.01), (0.01–0.02), (0.02–0.05), (0.05–0.1), (0.1–0.15), (0.15–0.2), (0.2–0.25), (0.25–0.3), (0.3–0.35), (0.35–0.4), (0.4–0.45), and (0.45–0.5). The average imputation accuracy in each bin was calculated.We evaluated the imputation accuracy under different sequencing depths, sample sizes, and MAFs using the sequence data of additional 18 Dezhou donkey animals provided by the Donkey Research Institute, Liaocheng University, Shandong Province, China. The average sequencing depth of the 18 animals was 13.5x (ranging from 11.2x to 16.3x). Chromosomes 1, 19, and 30, which represented the long, short, and medium chromosomes among the donkey chromosomes, respectively, were chosen to evaluate the imputation accuracy. The imputation accuracy was measured with two criteria, i.e., genotypic concordance and genotypic accuracy. Genotypic concordance is defined as the proportion of correctly imputed genotypes , and genenotypes . To evalThe imputation-based sequence data was used to investigate the performance of genomic prediction using the 594 animals having records on both BW and WW. The genomic estimated breeding values (GEBVs) were obtained by using the genomic best linear unbiased prediction (GBLUP) method  under siy is the vector of observed phenotypes of BW or WW; b is the vector of fixed effects, which include the effects of sex, year-seasons when the trait was measured , and age  when the trait was measured; u is the vector of genomic breeding values with distribution of N ] was constructed using the method of i for SNP j, and j.Since STITCH provides for each SNP and each individual the imputed genotype (the most likely genotype) as well as the expected genotype dosages (posterior expectation of the genotype dosages), the G using expected dosages [denoted as G(d)], following the idea of the formula for G(g), we proposed the following formula:For constructing G matrices. From the original sequence data with an average depth of 3.5x, we obtained 2.3M SNPs after imputation and quality control. We then reduced the marker density by down-sampling SNPs from the 2.3M SNPs. We applied linkage disequilibrium (LD) pruning with three LD levels: r2 = 0.2, 0.4, and 0.8, by PLINK .We used GMAT  to constyc) and GEBVs. The corrected phenotype for each animal was calculated as the original phenotypic value corrected for fixed effects , which were estimated by conventional BLUP using the DMU package (http://dmu.agrsci.dk). The model for conventional BLUP was the same as that for GBLUP except that the G matrix was replaced by the pedigree-based A matrix. The bias of predictions was assessed by the regression of yc on GEBV  was applied to assess the accuracy of the genomic prediction. The 594 animals were divided into 12 subsets. One of them was taken in turn to be used as a validation population, and the remaining 11 subsets used as a training population. For the two-trait model analysis, we left out the observations on both BW and WW for the animals in the validation set and calculated their GEBVs for both traits simultaneously. The accuracy of genomic prediction for the validation animals was assessed by ediction .The two genotype imputation pipelines, BaseVar + STITCH and Bcftools + Beagle, were compared using the original sequencing data of the 617 animals with an average sequencing depth of 3.5x. We compared the genotypic accuracy and genotypic concordance for imputation with different sample sizes  and sequencing depths  . In all G matrix [G(g) and G(d)] constructed using five different marker sets . For the 3.5x sequence data, the estimates under the four marker sets were very similar, with the additive variance and heritability estimates from the 2.3M marker set being consistently slightly smaller than those from the other three marker sets. However, the additive variance and heritability estimates from the 1.4M marker set were all smaller than those from the other marker sets. For all marker sets, the estimates of additive genetic variances and heritabilities based on G(d) were consistently smaller than those based on G(g), although the differences were very small and not significant.G matrix and the 410K marker set  and G(d)] constructed using the five different marker sets. The average accuracies and biases derived from 12-fold cross-validation are given in G matrices. For the two-trait model, only the G(d) matrix constructed using the 410K marker set was used  and reduced the biases (0.020 vs. 0.062 for BW and 0.117 vs. 0.169 for WW). The difference tendencies mentioned above were quite consistent across the 12 folds  for which a SNP array is not available. In this study, we investigated the strategies for genotype imputation and evaluated the performance of genomic prediction using imputation-based sequence data in a donkey population.Imputation is necessary for lcWGS data due to the high missing rates, which involves two steps, i.e., SNP calling and imputation. A proper pipeline is essential to ensure high imputation performance. In this study, we compared two pipelines, Bcftools + Beagle and BaseVar + STITCH. In the first pipeline, both Bcftools and Beagle have been widely used for SNP calling and imputation for sequence data, respectively. However, it is not clear whether they are suitable for lcWGS data. On the other hand, BaseVar and STITCH were designed specifically for lcWGS data. We demonstrated that BaseVar + STITCH outperformed Bcftools + Beagle . FurtherG matrices [G(g) and G(d)], five different marker sets , and single-vs. two-trait GBLUP model.Using the imputation-based sequence data, we evaluated the performance of genomic prediction using GBLUP with respect to two types of G matrices were almost the same in all scenarios. Note that the variance component estimates from the two types of G matrices were also very similar. This implicates that for our given data, the two types of G matrices did not lead to different results. It remains to be seen whether this results also holds for other data sets.We found that the accuracies and biases of genomic prediction derived from the two types of For the four marker sets from the 3.5x sequence data, the prediction accuracy increased slightly  when the marker density increased from 130 to 410K, but did not further increase when the density increased to 2.3M. The densities of 130, 220, and 410K correspond to medium to high density of SNP array, while the 2.3M corresponds to the density of sequence data. Some studies showed that, in the frame of GBLUP, the genomic prediction accuracy could be improved using high-density SNP array compared to using medium-density array , but theG matrix and the 410k marker set derived from the 3.5x sequence data. However, such advantage should hold for other scenarios. It has been shown in several incidences that a multi-trait model can increase the accuracy of breeding value estimation, either by conventional BLUP or by GBLUP  to sequence density with millions of SNPs did not increase the accuracy of genomic prediction. The multi-trait model GBLUP improves the accuracy of genomic prediction over the single-trait model, which would be particularly meaningful for the situation where the reference population size is limited.


With the advances in genotyping technologies and electronic health records (EHRs), large biobanks have been great resources to identify novel genetic associations and gene-environment interactions on a genome-wide and even a phenome-wide scale. To date, several phenome-wide association studies (PheWAS) have been performed on biobank data, which provides comprehensive insights into many aspects of human genetics and biology. Although inspiring, PheWAS on large-scale biobank data encounters new challenges including computational burden, unbalanced phenotypic distribution, and genetic relationship. In this paper, we first discuss these new challenges and their potential impact on data analysis. Then, we summarize approaches that are scalable and robust in GWAS and PheWAS. This review can serve as a practical guide for geneticists, epidemiologists, and other medical researchers to identify genetic variations associated with health-related phenotypes in large-scale biobank data analysis. Meanwhile, it can also help statisticians to gain a comprehensive and up-to-date understanding of the current technical tool development. With the advances in genotyping technologies and electronic health records (EHRs), large biobanks genotype and extensively phenotype hundreds of thousands of individuals . For exaPhenome-wide association studies (PheWAS) utilize large numbers of measured phenotypes and can explore the associations between one genetic variant and the entire phenome . BenefitTo date, several PheWAS have been performed on biobank data . More thIn this paper, we review challenges in biobank data analysis and regression approaches to addressing these challenges with the goal of providing a practical guidance to statisticians, epidemiologists, and other medical researchers. In section “Statistical and Computational Challenges in Biobank Data Analysis and Approaches to Addressing Them,” we discuss statistical and computational challenges of genome-wide association studies (GWAS) and PheWAS on large-scale biobank data. In section “Scalable and Robust Association Testing Methods,” we summarize recently developed scalable and robust regression approaches. In section “Phenome-Wide Biobank Data Analysis Results and Phewebs,” we introduce existing phenome-wide analyses results. In section “Future Challenges,” we mention potential future challenges which require more advanced methods and tools.In this section, we give a brief discussion about statistical and computational challenges in large-scale biobank data analysis and useful strategies to address these challenges see .Increasing sample size contributes to more statistical power to identify novel marginal genetic effects and gene-environment interaction (G × E) effects. Meanwhile, it also results in a larger computational burden, which should be carefully handled. In GWAS, most regression approaches include covariates such as age, sex, and top SNP-derived principal components (PCs) to adjust for. Wald and likelihood ratio tests require fitting full models and thus both genetic and covariate effects are simultaneously estimated for all variants. If the number of covariates is large, it will take a substantial amount of time. For example, suppose that 20 covariates were adjusted to fit a standard logistic model, as the sample size increases from 5,000 to 500,000, the computation time increases from 0.02 to 2.55 s. If projected to a PheWAS with 10 million genetic variants and 100 phenotypes, the corresponding computation time increases from 238.3 CPU days to 80.8 CPU years see . Hence, p values for each variant  fitting a model under the null hypothesis; (2) calculating score statistics and iant see . When teiant see , binary iant see , 2020, tiant see , and ordiant see .p value > 5e-3), the matrix projection is used. Otherwise, regular approaches are used to test the marginal G × E effect.Score test is successful in reducing computation time since it avoids duplicated computation to adjust for covariates. This strategy can also be applied to other cases. Recently, scalable methods were proposed for a genome-wide G × E analysis . When teFor most of the population-based biobanks, individuals are recruited following a cohort study design, that is, a representative sub-population of the source population are recruited . For exaBased on a penalized likelihood function, the Firth’s approach can correct the first-order asymptotic bias of parameter estimates . Firth bIf the phenotypic distribution is unbalanced, the underlying null distribution of score test statistics could be highly skewed. Thus, regular normal distribution approximation often fails since only the first two cumulants  are used . As an aS, under the null hypothesis, i.e., K(t) = log(EH0(etS)). When analyzing binary or ordinal categorical traits, the score test statistic is the sum of multiple random variables, each of which follows a Bernoulli distribution  , around Using genome-wide genetic data, the genetic relatedness among individuals can be characterized by a genetic relationship matrix (GRM) . The offn2), where n is the sample size. Suppose that sample size n = 408, 961 (white British participants in UK Biobank), then it takes 669 Gb of memory to store the GRM given a float-precision format. Instead of precomputing a GRM and then storing it into the memory, an alternative approach is to store the raw genotype (used to construct GRM) into a bitwise binary vector and then load it when in usage  calculation. Instead, a linear system solver, such as the preconditional conjugate gradient (PCG) approach can be used to provide scalable computation, which requires O(mn1.5)  . PCG is O(mn1.5)  and RcppO(mn1.5) , can be Although scalable to analyze hundreds of thousands of individuals, the mixed model approaches using full (dense) GRM are still computationally intensive. A straightforward approach is to use a sparse GRM in which values less than a pre-given cutoff  were set to 0 . This apWhen analyzing a candidate variant, the variants in linkage disequilibrium with it (including the candidate variant itself) should not be used to construct GRM to avoid modeling effects twice . To avoiInstead of the mixed effect model framework, a fixed effect model with a penalty can be used to account for genetic relatedness. A recent developed REGENIE  used twoG denote the genotype of genetic variant and X denote the confounding covariates. The corresponding coefficients are βG and βX, respectively. For mixed models, we let Φ denote GRM, σ denote variance component, b denote the random effect and assume that b follows a multivariate normal distribution N. The trait of interest is denoted as Y.In this section, we introduce regression methods that are scalable and robust in large-scale biobank data analysis see . We let Linear regression is the most widely used approach when the trait of interest is measured quantitatively. When analyzing unrelated individuals, the regular linear model isH0:βG = 0 is implemented in several tools including GCTA  or the controls (Y = 0). Logistic model and logistic mixed model can model the dependence of a binary trait on covariates and genetic variants as below.For complex disease research, individuals are usually divided into two groups: the cases  (Suppose that  (POLMM)  has beenj = Pr is the cumulative probability of the phenotype Y ≤ j and ϵ: ϵ1 < … < ϵJ = ∞ were used to categorize the data. POLMM supports both full (dense) GRM and sparse GRM when fitting the null model and is scalable to analyze biobanks with hundreds of thousands of individuals. In addition, POLMM uses SPA and thus is robust when testing low-frequency and rare variance even if the phenotypic distribution is highly unbalanced  for the failure time associated with genotype and covariates as below.Cox proportional hazard (PH) model is widely used to analyze time-to-event data . The Cox0(t) is a baseline hazard function. R package gwasurvivr . Recently, a fast and accurate method called SPACox was proposed to use an empirical SPA to calibrate p values  isp values, which makes it more powerful and robust to analyze low-frequency and rare variants.Existing methods such as COXMEG  and GATEY, the full logistic model for G × E is as follows.Gene-environment interaction (G × E) plays an important role in the etiology of many complex traits . For a bE is environmental factor and G × E is the interaction term. One strategy to reduce computation time is to test both marginal and interaction effects of G: βG = βG × E = 0, which share the same null hypothesis in the entire genome and hence can greatly reduce computation time if the score test is used. However, since the marginal genetic effect is usually larger than the G × E effect, the association identified by the joint test is mostly driven by the marginal genetic effect, which is not the major interest in a G × E study.where p values to test marginal genetic associations  or the dependency between E and G. Then, the variants with significant screening p values are selected to test G × E effect in the next step. In addition to the single-variant tests, various set-based methods have been proposed to test G × E effect  , combinebalanced . SMMAT ibalanced . SMMAT cbalanced  can incobalanced . If the In this section, we highlight existing phenome-wide analyses results. The usage of PheWeb facilitates the sharing and organizing of genetic association results.•1.Oxford Brain Imaging Genetics (BIG) Server version 2.0 can browse GWAS results for UK Biobank Brain Imaging Phenotypes and other traits/diseases. The primary source included results from 3,144 GWAS of Brain Imaging Derived Phenotypes (IDPs) measured on 9,707 participants of the UK Biobank study. Currently, the server has loaded more GWAS results including the GWAS results of ∼ 2,000 phenotypes in the UK Biobank processed by Ben Neale•2.The Michigan Genomics Initiative (MGI) is a collaborative research effort among physicians, researchers, and patients at the University of Michigan (U-M). Since most of the PheWAS are based on UK Biobank, the PheWAS data analysis based on MGI is an important supplementary although its sample size  is less than UK Biobank•3.The BBJ project has collected around 200,000 individuals with diseases cases consisting of 47 various diseases. These subjects were recruited from 12 medical institutes in Japan. The analysis results of total 244 phenotypes including both binary and quantitative traits have been released•4.SAIGE method can better control type I error rates when the case-control ratio is unbalanced. Using SAIGE, GWAS on 1403 ICD-based traits were performed based on the White British participants of the UK Biobank•5.POLMM is an extension of SAIGE on ordinal categorical data analysis. PheWAS of 258 ordinal categorical phenotypes on UK Biobank has been conducted, in which 150 phenotypes are to describe food and other health-related preferences•6.Based on the fastGWA method, PheWAS was applied to 2,173 traits on 456,422 array-genotyped as well as 49,960 whole-exome-sequenced individuals of European ancestry in the UK Biobank. Since linear mixed model approaches were used to analyze binary traits and ordinal categorical data, the PheWAS only analyze variants with MAF > 0.01•7.Using the robust SKAT-O approach for binary phenotypes, a total of 18,360 genes were analyzed based on 45,596 independent European samples across 791 binary phenotypes with at least 50 cases. The PheWAS are based on UK Biobank 50K exome data processed by FE pipeline•8.PathWeb displays results for associations between over 10,000 pathways (gene-sets) and phenotypes derived from ICD billing codes of White British participants of the UK Biobank. GWAS summary-statistics obtained using SAIGE for 1,403 binary phenotypes derived from ICD billing codes have been used in the analysisThe recent success in methodology development has greatly facilitated the large-scale biobank data analysis on a genome-wide and phenome-wide scale. In the future, it is expected that more comprehensive information, in terms of both genome and phenome, will be collected and shared through the continuously upgrading biobanks. The rapid development of biobanks provides a basis for precision health and medicine. Meanwhile, it also brings new challenges, which requires more advanced methods and tools. Here we list some of these challenges.The current biobanks usually recruit half million participants. In the future, we are likely to encounter biobanks with millions of and even tens of millions of participants. For example, All of US biobank in US aims to recruit 1 million individuals and UK announced a plan to recruit 5 million individuals . The incIn the coming decades, whole genome sequencing (WGS) will replace GWAS chips and become the most widely used genotyping platform. Since WGS can accurately identify and genotype rare variants, more scalable and powerful strategies and methods to evaluate rare variant associations in whole genome are increasingly needed. The evolving availability of new technologies will provide us with rich multi-omics data resources. Effectively incorporating additional information, such as epigenetics, is also important to boost powers and to increase interpretability in WGS studies.In the past decades, GWAS mainly focus on univariate phenotypes, that is, the phenotype of interest has only one variable. Recently, multivariate and high dimensional phenotypes are increasingly available. For example, longitudinal data track the same sample and collect repeated observations at different time points. Image phenotypes, such as Magnetic Resonance Imaging (MRI) of brain and other organs, are collected to better diagnose and treat diseases. Developing scalable and robust methods to appropriately analyze these complex phenotypes is important to fully utilize these data.The current phenome-wide analyses are mainly to test single phenotype and then look at the association patterns across phenome. This strategy does not utilize the correlation and causal relationship between phenotypes. Effectively aggregating associations in large numbers of phenotypes can boost statistical powers and gain better phenome-wide understanding. Existing joint tests of multiple phenotypes include MultiPhen , MANOVA Population structure and family relatedness are major confounders in genetic association studies. The recently proposed mixed effect and fixed effect models are usually applied to individuals from the same ancestry group, as it isn’t clear whether they can accurately analyze multiethnic individuals. Meta-analysis is commonly applied to combine analysis results from different ancestry groups, but it may need more research if individuals of different ancestry groups are related. For admixed population, specialized approach is needed to construct GRM, such as a method using individual specific allele frequency, but scalable mixed model to use this type of GRM is under-developed . As biobThe emergence of biobanks allows researchers to explore extensive associations between genetic variants and thousands of complex traits. In this paper, we discussed statistical and computational challenges in large-scale biobank data analysis and reviewed available methods and tools to address these challenges. In addition, we also briefly introduced possible challenges in the future. Benefit from the continuous biobanking efforts to connect genome-wide variants and phenome-wide traits, several PheWAS have been performed and it is expected that more PheWAS results will be available in the coming decade. Scalable and robust statistical approaches will certainly play an essential role in the success. In addition, developing user-friendly software that makes full use of the computing capacity is also important.WB and SL conceived the study and drafted the manuscript. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.