Environmental stress hampers pea productivity. To understand the genetic basis of heat resistance, a genome-wide association study (GWAS) was conducted on six stress responsive traits of physiological and agronomic importance in pea, with an objective to identify the genetic loci associated with these traits. One hundred and thirty-five genetically diverse pea accessions from major pea growing areas of the world were phenotyped in field trials across five environments, under generally ambient (control) and heat stress conditions. Statistical analysis of phenotype indicated significant effects of genotype (G), environment (E), and G × E interaction for all traits. A total of 16,877 known high-quality SNPs were used for association analysis to determine marker-trait associations (MTA). We identified 32 MTAs that were consistent in at least three environments for association with the traits of stress resistance: six for chlorophyll concentration measured by a soil plant analysis development meter; two each for photochemical reflectance index and canopy temperature; seven for reproductive stem length; six for internode length; and nine for pod number. Forty-eight candidate genes were identified within 15 kb distance of these markers. The identified markers and candidate genes have potential for marker-assisted selection towards the development of heat resistant pea cultivars. Pisum sativum L., 2n = 14) is a major pulse crop widely grown in the temperate regions primarily for its nutritional values as a source of protein, slowly digestible starch, essential minerals, high fiber and low fat; and soil fertility benefits as it fixes atmospheric nitrogen [Pea , determined from different wavelengths of spectral reflectance, have been used as proxies to quantitatively and qualitatively assess traits linked with vegetation cover and plant vigor, pigment abundance and composition, and plant water status ,19. ThusIn pea and other crops, lodging is one of the plant factors that exacerbates heat stress by making the plant hold more heat in the canopy, and thereby leading to increased CT ,20. HeatUnderstanding of the genetic base of traits involved in pea stress response would assist breeders in developing heat resistant varieties. Genome-wide association study (GWAS) has been used as a tool for dissecting the genetic bases of various traits using the naturally occurring genetic diversity a species has accumulated over many generations ,26. LinkIn pea, association and linkage mapping has been employed to uncover the genetic bases of several traits including agronomic and seed quality traits ,35, diseThe weather condition of the five environments during the pea growing season described by the average of daily maximum, minimum, 24 h mean temperatures, number of days when the daily maximum temperature was greater than 28 °C during the growing season, and total monthly precipitation is summarized in Variance components of genotype (G), environment (E), and G × E interaction together with their significance on the six traits used in this study is presented in Descriptive statistics for minimum, maximum and mean values of phenotypic measurements on the traits of the GWAS panel across five environments are summarized in Chlorophyll concentration, measured by a SPAD meter, was affected by genotype, environment and their interaction; and the variance component analysis showed that maximum variation (67.9%) among the GWAS panel was due to the genotype effect, and the broad sense heritability was 0.95. Overall, genotype chlorophyll concentration ranged from 26.6 to 57.6 SPAD values under heat stress, and 30.0 to 67.5 under control conditions . On averFor canopy temperature (CT), the GWAS accessions significantly varied due to both genotype (G) and environment (E) effects, but not by the G x E interaction . In geneReproductive stem length was also affected by genotype and environment main effects and their interaction. The reproductive stem length under the stressed environments ranged from 13 to 99 cm, whereas under the control environments the range was from 14 to 117 cm, suggesting heat stress decreased the reproductive stem length. Analysis of variance components showed genotype and environment main effects respectively contributed to 63.4% and 7.6% of the variation in the GWAS panel. The broad sense heritability for reproductive stem length was 0.92. Seven SNP markers  were associated with reproductive stem length in at least three of the five environments, and four of these SNPs were consistent in at least four of the five environments. SNP marker Chr4LG4_29062302 was found to be associated with the trait in all five environments with an average R-square of the model of 0.60. Overall, the R-square value of the model with SNP ranged up to 0.71 for reproductive stem length .Internode length was another trait significantly affected by genotype and environment main effects and their interaction. Under heat stress, the internode length ranged from 1.6 to 11.3 cm with a mean value of 11.0 cm, whereas under control conditions, the range was 1.9 to 14.9 cm with a mean value of 14.8 cm. Variance component analysis showed genotype and environment respectively contributed 43% and 4.8% of the variations to the GWAS panel. The broad sense heritability was 0.90. Six SNP markers  were associated with internode length in at least three of the five environments. These markers were significantly associated with internode length in at least three of the five environments with the R-square value of the model with SNP ranged up to 0.63. SNP marker Chr5LG3_566189271 was identified in all five environments with an average R-square of 0.49.Pod number was also significantly affected by genotype and environment main effects and their interaction. Variance component analysis showed genotype and environment, respectively, contributed 36.6% and 12.4% to the overall pod number variance in the GWAS panel. Compared with the three control environments, pod number under the heat stress environments decreased by 14.6%. The broad sense heritability in pod number was 0.88. Eight SNP markers  were associated with pod number in at least three of the five environments explaining 7% to 9% of PV, with an average R-square value of 21.9.Manhattan plots showing the association of SNP markers with plant chlorophyll concentration and reproductive stem length in multiple trials, and the corresponding Q-Q plots are presented as examples from this research in Of all the MTAs that were observed in > 60% of the environments, the following markers had the greatest percent variation averaged over the selected environments for the respective traits: Chr5LG3_566189589 (13% PV) and Chr5LG3_449362407 (11% PV) for SPAD; Chr6LG2_469101917 and Chr7LG7_263964018 each with 9% PV for PRI; Chr4LG4_415994869 and Chr5LG3_309595819 each with 6% PV for CT; Chr3LG5_18678117 (6% PV), Chr5LG3_572669963 (5% PV), and Chr7LG7_96157380 (5% PV) for reproductive stem length; Chr4LG4_63111072 (6% PV), Chr5LG3_566189271 (7% PV) and Chr4LG4_62461234 (6% PV) for internode length; and seven markers, Chr2LG1_105547608, Chr2LG1_370541780, Chr2LG1_385949935, Chr2LG1_389336188, Chr3LG5_216337201, Chr5LG3_530537682, and Sc04062_32372 each with 9% PV for pod number .Forty-eight unique genes were identified within a 15 kb region of the selected 32 SNP markers and are considered as candidate genes. The candidate genes identified for various traits included those encoding for transcription factor, translation initiation factor, heat shock protein, ribosomal protein, protein kinase, transmembrane protein, and others as listed in Principal component analysis (PCA) based on the correlation of traits revealed the overall traits association and the genotype response across the five environments A,B. The As a cool season crop, pea is sensitive to heat stress which causes a significant yield loss. However, there exists substantial genetic variation among pea genotypes for heat tolerance ,12,24,28The present GWAS was undertaken to identify SNP markers associated with traits related with pea heat response using a panel of 135 genetically diverse pea accessions. The accessions were from breeding programs of major pea growing areas and, thus accounted genotypes with a wide range of heat sensitivity. Genotyping by sequencing (GBS) identified 16,877 good quality SNPs, of which 15,609 were distributed across seven chromosomes of pea and the remaining 1268 were non-chromosomal SNPs . Pisum accessions into 16 groups. The above groupings indicated the extent of genetic variability among pea accessions. The clustering did not necessarily correspond solely with the geographic origins of the individuals, but depended on additional factors of variability such as the objectives in different breeding programs [Linkage disequilibrium patterns of population structure and genetic relatedness information are important for association mapping to minimize the number of false positive associations , thus thprograms . In the present GWAS, we evaluated ten heat stress-responsive traits. The first six were: chlorophyll concentration by SPAD, PRI, CT, reproductive stem length, internode length, and pod number. The other four were: plant height, lodging, pod to node ratio, and water band index (WBI). From the latter four traits, five SNP markers on Chr 1 (LG6), Chr 2 (LG1), Chr 3 (LG5), Chr 5 (LG3) for lodging, and four SNP markers on Chr5 (LG3) for plant height were previously reported by Gali et al. , and no The five environments were grouped into ambient (three environments) and heat and or drought stress (two environments) conditions based on weather data and threshold temperature for heat stress in the field . All traIn this study, the SPAD value was used to estimate chlorophyll concentration, a major component of chloroplasts, and can be used as a factor to determine crop adaptation to environmental stresses by retention of greenness ,13,43. Rhttps://www.ncbi.nlm.nih.gov/nuccore/LN810021) location in the pea genome assembly spanned between the base pair positions Chr5LG3_151800929 and Chr5LG3_151804253, and is within close proximity (858 Kbp) of the SPAD associated marker ChrLG3_150942510. Using GWAS on soybean, Dhanapal et al. [We identified six MTAs that were related to SPAD value in repeated tests. All of the MTAs identified for SPAD were from Chr 5 (LG3). Bell et al.  reportedSimilarly, two loci were identified for association with PRI. One of these loci is on Chr 6 (LG2) and the second is on Chr 7 (LG7), and in both cases the markers were consistent in three environments. There are only a few reports that have used GWAS to identify markers associated with vegetation indices, namely, in soybean and wheat. In soybean, Herritt et al.  identifiafila gene have cooler CT [Two MTAs, one each on Chr 5 (LG3) and Chr 4 (LG4), were detected for CT, a trait consistently used as an indicator of stress mainly of drought and heat stresses ,14. Geneooler CT ,49. Tar’ooler CT  identifiWe identified seven MTAs associated with reproductive stem length on chromosomes 3 (LG5), 4 (LG4), 5 (LG3), and 7 (LG7); and six MTAs associated with internode length on chromosomes 4 (LG4), 5 (LG3), 6 (LG2) and 7 (LG7). The markers associated with these two traits mostly were positioned on the same linkage groups, and a SNP marker Chr5LG3_566189271 was associated with both the traits. Using the current GWAS panel, Gali et al.  identifiWe identified nine loci associated with pod number, of which six were on Chr 2 (LG1), one each on Chr 3 (LG5) and 5 (LG3), and one on a non-chromosomal scaffold. Plant pod number is the number of flower-bearing nodes multiplied by the average number of flowers per node. Previously, Jiang et al.  identifiIn conclusion, in this GWAS we identified 32 MTAs and 48 candidate genes for traits associated with pea heat response. These results are expected to enhance the understanding of genetic loci controlling these traits. The identified candidate genes are involved in various biological functions and require further functional validation. The detected MTAs and candidate genes should be useful for marker-assisted selection for heat tolerant pea varieties. A panel of 135 diverse field pea accessions, as described by Gali et al. , were gr−2, targeting 80–85 plants m−2 on 0.25 m row spacing) was used. Weed control was achieved by management practices used in pea production in Saskatchewan as described by Tafesse et al. [The experimental design at each environment was a randomized complete block with two replications. Plot size was 1.37 m width × 3.66 m length, and the recommended seeding rate  recorded by the nearest stations to the trial sites. For Saskatoon, data from central Saskatoon station, and for Rosthern the mean of data from Saskatoon international airport and Prince Albert stations were used. The daily maximum air temperatures, amount of precipitation and number of days when air temperature exceeded 28 °C during the growing seasons were used to determine the degree of stress in each environment at different growth stages. The categorization of growth stages into vegetative (germination to end of vegetative growth) and reproductive (beginning of flowering to maturity) was conducted using the phenology data reported by Gali et al. [Weather data for 2015 Saskatoon starting from June 11 to the end of the growing season, 2016 Rosthern starting from June 21 to the end of the growing season, and 2016 Saskatoon starting from July 21 to the end of the season were collected from weather stations  established at each site. Weather data of 2017 and the remaining 2015 and 2016 were obtained from Environment Canada database . The SPAD value is a unitless index, calculated as the ratio of the intensity of light transmittance at red (650 nm) to infrared (940 nm) and gives a value that corresponds to the relative amount of chlorophyll present in the leaf. Hereafter, the chlorophyll concentration estimated by SPAD meter is referred to as ‘SPAD’. The SPAD readings were taken four to six times each season, and for each measurement day the mean SPAD value was calculated by the instrument from three readings taken from three plants per plot on fully expanded stipules at the second or third node counting down from the apex of a main stem.Similarly, spectral measurement was conducted repeatedly on leaf stipules using a portable spectroradiometer PSR-1100F . This device enabled hyperspectral readings with a range of 320-1,126 nm, and 1.6 nm sampling interval, and a total of 512 discrete narrow bands. PRI was calculated from the reflectance data according to Gamon et al.  as:PRI =g stress . Canopy temperature (CT) was measured four to six times in each location in a season using a hand held infrared thermometer  as described by Tafesse et al. . MeasureThe other measurements taken at physiological maturity were: reproductive stem length (vine length from first flowering node to the tip of the main stem); internode length (determined as the ratio of reproductive stem length to reproductive node numbers); and pod number per plant . For these, each measured variable was the mean of three plants per plot sampled at random and lengths were measured in cm. 2) was calculated as:2 is the genetic variance, and σge2 is the variance of genotype and environment [Before employing analysis of variance (ANOVA), homogeneity of variances and normality of residuals were tested using checked using Levene and Shapiro-Wilk tests, respectively ,56. Variironment .Over environments, combined ANOVA on SPAD, PRI, CT, reproductive stem length, internode length, and pod number was carried out using the Mixed procedure of SAS . Genotype, environment and G x E interaction were considered as fixed, and blocks as random factors. Principal component analysis (PCA) was performed with the multivariate function of Minitab  using means of traits to infer overall association among traits and genotype for the five environments. Based on significant eigenvalue (> 1), the first two principal components (PC) were selected for the minimum number of PCs to explain the greatest total variation in the data set.10P values. Marker–trait associations were selected based on P value (P ≤ 0.001) and repeated occurrence of the association in at least three of the five trials. The genes within 15 kb of the identified markers are reported as the candidate genes. The pea genome sequence reported by Kreplak et al. [Genotyping of the 135 GWAS panel was performed by genotyping-by-sequencing , and 16k et al.  was used


Efforts to improve dairy production in smallholder farming systems of East Africa over the past decade have had limited impact because of the lack of records on performance to guide targeted breeding programs. Estimates of genetic parameters in these systems are lacking. Using data generated through a project (“Germplasm for Dairy Development in East Africa”) in Kenya and a genomic relationship matrix from genotypic records, we examined the potential impact of different models handling contemporary groups or herd effects on estimates of genetic parameters using a fixed regression model (FRM) for test-day (TD) milk yields, and the covariance structure for TD milk yield at various stages of lactation for animals using a random regression model (RRM). Models in which herd groups were defined using production levels derived from the data fitted the data better than those in which herds were grouped depending on management practices or were random. Lactation curves obtained for animals under different production categories did not display the typical peak yield characteristic of improved dairy systems in developed countries. Heritability estimates for TD milk yields using the FRM varied greatly with the definition of contemporary herd groups, ranging from 0.05 ± 0.03 to 0.27 ± 0.05 (mean ± standard error). The analysis using the RRM fitted the data better than the FRM. The heritability estimates for specific TD yields obtained by the RRM were higher than those obtained by the FRM. Genetic correlations between TD yields were high and positive for measures within short consecutive intervals but decreased as the intervals between TD increased beyond 60 d and became negative with intervals of more than 5 mo. The magnitude of the genetic correlation estimates among TD records indicates that using TD milk records beyond a 60-d interval as repeated measures of the same trait for genetic evaluation of animals on smallholder farms would not be optimal. Although each individual smallholder farmer retains only a few animals, using the genomic relationship between animals to link the large number of farmers operating under specified environments provides a sufficiently large herd-group for which a breeding program could be developed. EA), smallholder dairy farmers keep animals of mixed breed-types; however, productivity levels are low and the countries are not able to meet their national demands for milk production  was implemented from 2011 to 2013. The project was designed to determine the breed composition and levels of animal performance under different smallholder farm environments to identify the optimum breed combinations for smallholders and then to develop options to sustainably supply the optimal germplasm for improvement of dairy production in the smallholder system .In an attempt to generate baseline information and test the use of genomic information as a tool to guide the breeding of improved dairy cattle for small holder farming systems in East Africa, a project titled “Germplasm for Dairy Development in East Africa”  subtropical smallholder dairy systems as described in Moll et al.  and leguminous plants such as calliandra  and desmodium (Desmodium intortum and Desmodium uncinatum), some maize stover, and occasionally some commercial dairy meal. Calves born on the farms were separated from their dams after the first week. The calves were then fed milk using buckets for 4 to 5 mo depending on the farmer’s resource endowment.TD) yield. The field data collectors visited the farms every 2 wk to measure and verify the quantities of milk produced and recorded by the farmers. To determine the genotypes of the animals on the farms being monitored, hair samples were collected from 1,157 of the crossbred animals and 212 reference animals that were believed to be pure indigenous.Data were collected on 1,292 cows from 610 farms over 2 yr from January 2011 to December 2012. Data included farm characteristics, herd structure, cattle management practices, access to services, and animal productive and reproductive performance. An animal characterized by the field data collectors as either crossbred or pure exotic had to be lactating or at an advanced stage of gestation to be monitored. Calving dates recorded for lactating animals at the start of the project were based on farmer recall. As monitoring progressed, subsequent dates of calving for different animals were recorded. It was initially not clear whether the parity reported as first parity was actually the first parity of the animal in its lifetime or the first parity of that animal on the specific farm. Each farmer was provided with a jar for measuring milk and a booklet for recording milk produced by individual animals in the morning and in the evening on 1 day within each week of lactation to constitute a test-day . The TD records were thus grouped into 100-d stages and evaluated using linear regression . Through these analyses, it was evident that more than 80% of the TD records were within 310 DIM (lactation lengths ranged from 250 to 550 d). Out of 31,657 TD records, a final data set of 21,877 TD records from 1,038 animals in 566 herds were used to evaluate the 305-d milk production of animals of different breed-types on smallholder farms. Lactation length was set at 305 d in line with the worldwide practice of evaluating dairy cattle on a 305-d lactation period.B. indicus and African B. taurus because prior analyses have shown that East African indigenous breeds are ancient hybrids of B. indicus and African B. taurus cattle , as outlined in Ojango et al. . The resH) or herd-year-season (HYS) were fitted as either random or fixed. Four seasons were defined within each year as green or dry based on the rainfall pattern of the sites  obtained are presented in Given the small herd sizes, 2 other definitions of contemporary herd groups were examined. First, using principal components analysis, we estimated the proportion of variance contributed by different variables . This was followed by optimal extraction of number of principal components using K-means selection. Extracted factors were then used for discriminant analysis of the principal components (DAPC), leading to posterior predictions for group membership for each household using the R statistical package , parity k , percent dairy group l (l = 1 to 5); lactation stage in 100-d intervals m (m = 1 to 4), u = random cow effects, sf = random herd effects, e = random residual effects, X is the incidence matrix relating records to fixed effects, and Q and W are incidence matrices that relate records to cows and farms. The variance of random cow effects, I is the identity matrix. Solutions for random herd effects from this model were ranked into bottom, middle, and top to create 3 herdlevel (HL) classes. This approach was taken so that the data defined the farm production environment experienced by the cow; in essence, the association of animals in herd groups depended on their level of milk production and was not random. Initial results from this analysis were presented in Ojango et al.  fitting several models. Various analytical models were considered to explain the shape of the lactation curves iteratively. Preliminary data analysis using various lactation functions gave the Legendre polynomial as the best fitting curve. The order of polynomials used in this study was the best possible in terms of being able to achieve convergence and ensure that a curve with at least 3 parameters was fitted for the random animal component. A higher order for the animal component did not converge because of inadequate information, and a lower order was inferior.Genetic parameters were estimated using a genomic relationship and a fixed regression model ; iFixed are the ith fixed effects consisting of contemporary herd group , yearseason of test-day, parity group, percent dairy (kmβ are kth fixed regression coefficients for percent dairy nested within a contemporary herd group (when HG or HL was included); φtjkm is the vector of the kth Legendre polynomials (k = 1–4), for the TD record of cow j on day t; uj and pej are vectors of animal additive genetic and permanent environmental effects, respectively, for animal j; and tije is the random residual. It was assumed that I is the identity matrix and R is the matrix of residual variances. Matrix G, the genomic relationship matrix, was constructed from the genotype file consisting of 745,059 SNP as in Brown et al. , either with or without groups for percent dairy;model group 3: including a random herd-yearseason (HYS), with or without groups for percent dairy;model group 4: including herds as a contemporary group defined using HG, with or without the percent dairy;model group 5: including herds as a contemporary group defined using HL, with or without the percent dairy.AIC) and Schwarz’s Bayesian (BIC) information criteria . The RRM fitted wasThe best FRM identified using AIC and BIC was then used to compute lactation curves for animals in the contemporary groups defined by the model, and to model the covariance structures among the test-day yields using random regression analyses (ukj here is a vector of kth random regression coefficient for cow j. The same structure was fitted for permanent environment (pe) effects because there was a convergence problem with fitting a higher order of equations for this effect due to the limited data size. Different residual variances associated with different stages of lactation  were fitted. The var(pe) was defined as in equation [2] and var(u)=GM, where M is the genetic covariance of order 3 between the random regression coefficients.where terms were as defined in equation [2] but gii) of a given DIM (ti) and the genetic covariances (covgij) between different DIM (ti and t′i) were obtained from the RRM analysis as M is the co-variance matrix obtained from the ASREML analysis. The phenotypic (co)variance was calculated as the sum of the genetic, permanent environmental, and residual (co)variances. The estimate of heritability for day i was obtained as aCor) between days i and j was obtained asThe genetic variances  yielded a better fit than when herds were grouped depending on management practices (HG) or were random. Residual variance was also higher when herd groups were defined using HL. Inclusion of herd grouping based on the adoption of different management practices (HG) resulted in a substantially higher heritability estimate and slightly lower permanent environmental variance than when herds were grouped by HL . InclusiThe lactation performance of animals with different percent dairy derived from the best FRM is presented in The genomic (g)EBV ranks for animals with different percent dairy from the 2 best FRM models are presented in The analysis using the RRM fit the data better than the FRM models based on the AIC and BIC . Estimat2) for specific TD yields obtained by the RRM were higher than those obtained by the FRM  for the smallholder farmers did not display the typical peak yield in the first months of the lactation followed by a gradual decline . Only inResults from the FRM analyses illustrate how the magnitude of the parameter estimates is highly dependent on the model used. Theoretically, inclusion of the herd as a random effect for multiple smallholder farm environments should maximize the effective number of records for evaluation and increase the accuracy of breeding values. However, with the small herd sizes in this data set, it was likely difficult to separate the random herd effect from the random animal genetic and permanent effects, and the errors attached to parameter estimates may be substantially underestimated. Nevertheless, it is intriguing that the estimate of permanent environment variance was little affected by fitting a random herd variance, whereas the estimate of additive genetic variance was substantially reduced.Fitting HG as the contemporary group, although statistically significant as a fitted effect, had almost no effect on AIC and BIC and had a trivial effect on estimates of variance  strongly influence milk production, potentially explaining the higher residual variances in early lactation. In the smallholder farming systems of this study, many farmers continued to milk their animals until the last month before calving, even though production levels were low (0.5 kg). This practice negatively affects milk production in the subsequent lactation.Genetic correlations between TD yields decreased as the time between the TD measures increased. Negative genetic correlation estimates between early and later TD records, as obtained in this study, have been reported in studies on dairy animals raised in tropical environments .Patterns of phenotypic variances across lactation stages similar to those in this study have been reported for both crossbred The results from this study show that in Kenya, smallholder farmers—through a combination of natural and artificial selection pressures—have retained dairy cows of mixed breeds with different levels of various exotic breed-types. The very small herd sizes for milking animals  and the lack of pedigree and performance data on animals in smallholder farming systems have restricted their genetic evaluation in the past. Selection and breeding for improved milk production in the herds is not practiced. The lack of an organized selection program for dairy production under the smallholder farming system leads to random fluctuations in the genetic level of the population and a general reduction in productivity of the dairy sector. With the ancestral breed composition of each animal determined through Admixture analysis combined with the use of the genomic relationship matrix, it was possible, for the first time, to implement a genetic evaluation of the dairy population from different smallholder farm environments using accurate information on their breed composition. Our results are supported by a simulation study on the use of genomic BLUP for genetic evaluation of phenotypic data from smallholder farming systems (Powell et al., Although individual smallholder farmers keep only a few animals, the large number of farmers operating under specified environmental parameters provide a sufficiently large herd-group for a breeding program. Advances in mobile phone technologies can facilitate farm data collection. In addition to milk production, information is required on other adaptive traits and non-income functions of the cattle reared. Different “dairy” lines could be developed for different smallholder environments. To obtain accurate information on the dairy cattle within smallholder production systems, longer-term monitoring of the livestock population is required.G matrix in the absence of pedigree information.Results from this study highlight the impact of alternative definitions of contemporary herd groups on genetic parameter estimates. The optimal contemporary herd group definition depends greatly on individual smallholder herd size and the overall sample size of the data used for analyses. Although each individual smallholder farmer retains only a few animals, the large number of farmers operating under specified and often similar environments provides a sufficiently large herd-group for which a breeding program could be developed. Genetic correlations between consecutive TD measures with intervals <60 d were high and positive and then decreased as the TD interval increased, implying that in the smallholder systems studied, TD milk data for genetic evaluation of animals should not be recorded at intervals >60 d for adequate modeling of lactations. The study demonstrated that estimation of genetic parameters, and therefore genetic predictions, are feasible in smallholder systems using the


It is still a challenge to find common gene networks related to these traits. Therefore, here, a genome-wide association study (GWAS) was conducted to explore candidate genomic regions responsible for Feed Intake (FI), Body Weight Gain (BWG) and Feed Conversion Ratio (FCR) traits and their gene networks. A total of 1430 broilers from an experimental population was genotyped with the high density Affymetrix 600K SNP array. A total of 119 associated SNPs located in 20 chromosomes were identified, where some of them were common in more than one FE trait. In addition, novel genomic regions were prospected considering the SNPs dominance effects and sex interaction, identifying putative candidate genes only when these effects were fit in the model. Relevant candidate genes such as ATRNL1, PIK3C2A, PTPRN2, SORCS3 and gga-mir-1759 were highlighted in this study helping to elucidate the genomic architecture of feed efficiency traits. These results provide new insights on the mechanisms underlying the consumption and utilization of food in chickens.Chicken feed efficiency (FE) traits are the most important economic traits in broiler production. Several studies evaluating genetic factors affecting food consumption in chickens are available. However, most of these studies identified genomic regions containing putative quantitative trait  Furthermore, improving grow-finish feed efficiency will reduce production cost and increase profitability. Thus, feed efficiency (FE) is one of the most important selection criteria implemented in breeding programs, playing a decisive role in the economic benefit of animal meat production3. The FE depends on the relation between input—feed intake (FI) and output—growth or body weight gain (BWG) of an animal, and the feed conversion ratio (FCR) is widely used to measure it5. Broiler chickens are the most efficient among the farm animals in terms of FE and body weight gain (BWG). Over the last few years, the broilers BWG has increased by 30.2 g per year at the same time that the FCR has reduced annually by 0.036%, contributing to an increase of 167% in poultry meat production, responsible for 35% of the total meat production in the last 30 years7. All this success is due to the efforts of the poultry industry and breeding programs directed to meet market demand4.A major concern in current animal production is to increase productivity to meet the growing demands for animal protein in a more sustainable and efficient way, minimizing their impact on the environment and natural resources. An effective way to address this challenge is to improve the animal performance in terms of food consumption and its better use8. In the last 20 years, quantitative trait loci (QTL) mapping studies have identified several chromosome regions that influence the phenotypic variability for many economically important traits in chicken14. Several approaches such as linkage analyses, candidate gene association and gene expression studies have been performed to elucidate the genetic background underlying complex traits18. For FI, FCR and BWG, 260 QTL have been mapped on various chromosomes . However, FE traits have a complex biology and are mediated by a large number of genes acting at the molecular level as signaling molecules5, thus traditional QTL mapping studies and candidate gene approaches are insufficient to detect all the genetic mechanisms underlying their variation.Understanding the genetic mechanisms underlying quantitative traits can be useful for defining priors for variances in genomic selection and for identifying candidate genes for marker assisted or gene based selectionloci in farm animals. To date, GWAS has been widely used to map an increasing number of QTL, SNPs and candidate regions associated with FI, BWG, and FCR in beef cattle20 and swine21. In chickens, several GWAS have revealed many genes and genomic regions associated with production24, morphological traits25, aggressive behavior10, disease resistance26, and even with the intestinal microbial interaction27. Despite advances in knowledge of regions of interest in the chicken genome, few GWAS has been carried out to evaluate the genetic architecture of FE traits, and most of them ignore dominance, epistasis and sex interactions effects. Taken these effects into account may allow the identification of new candidate genes related to FE traits.In the last decade, the development of chicken SNP panels provided a powerful tool for genome-wide association studies (GWAS) allowing to detect small associated chromosomal regions, providing more precise estimates of the allele effects at the associated Therefore, this study aimed to identify new chromosomal regions influencing FE traits in a paternal broiler line through genome-wide scale analyses using the high density 600K SNP array. A single-marker GWAS was performed to identify genomic regions and candidate genes with additive, additive + dominance, dominance and sex interaction effects associated with FE traits comprised by FCR, FI, and BWG from 35 to 41 days of age.rg) between FCR and BWG was high, negative and favorable. The FI was not correlated with FCR and the rg between FI and BWG was not accurate (Table rp) between FI and BWG was positive, indicating a linear association between both traits, as well as the rp between BWG and FCR, which also had a favorable linear association  were identified with all levels of significance for FE traits considering the different SNP effects fit in the model  gene. When the SNP additive (A) effect was fit within each sex separately (A_sex) in the model, one significant SNP was identified in GGA 7 (rs317066057)  candidate gene , ENSGALG00000029785, ENSGALG00000033533 and ZNF384 genes  gene that was associated with this trait appears to interact with several other genes identified as regulating FCR and FI in the gene network analysis , which was lower than that reported by Gaya et al.29 (0.20 ± 0.03). The heritability estimate for BWG (0.133 ± 0.048) was similar to those reported by Pakdel et al.30 (0.16 ± 0.05). Furthermore, the heritability estimate of FCR (0.18 ± 0.056) was similar to that reported by Gaya et al.29 (0.16 ± 0.03) evaluated from 35 to 49 and Argentão et al.31 (0.19) from 35 to 42 days of age. Cruz et al.32 also observed low magnitude of the heritability estimates of FCR (0.09 ± 0.04), BWG (0.10 ± 0.04) and FI (0.18 ± 0.05) from 35 to 41 days of age when evaluating these and other performance and carcass traits in the same paternal broiler line used in the present study. Analyzing the genomic heritability of an F2 Chicken Resource Population, Moreira et al.12 observed lower estimates (from 0.0002 ± 0.0002 to 0.07 ± 0.0143) than those found in the current study for FCR, BWG, and FI.In this study, the heritability estimates for all the FE traits were low. This could possibly be due to the short period that the records were measured. Individual FCR is a difficult and expensive trait to measure in poultry breeding programs, because it needs the evaluation of individual records of feed intake in addition to body weight gain. Therefore, a short evaluated period is commonly used comprising the pre-slaughtered period. Nevertheless, these traits remain as important selection criterion in breeding programs because feed costs represent about 70% of the total cost of the broiler production. According to some authors28 comparing residual feed intake and BWG.Feed efficiency is a complex trait affected by feed and growth factors. FCR, defined as the amount of feed consumed per unit of weight gain, is a trait composed by the initial and final body weights and FI. Based on the heritability and genetic correlation estimates, selection for low FCR will improve FE with an expected correlated response in increased BWG. The positive genetic correlation between FI and BWG indicates that fast growing broilers have been consuming increased amount of feed due to their greater appetite, as also shown by Aggrey et al.33, but similar to that found by Cruz et al.32 (0.39 ± 0.23). These differences may be due to the nature of the pleiotropic relationship between FI and FCR, which may be dependent on age. Consequently, the molecular, physiological and nutritional factors that control FI and FCR may also depend on the time of development2. As with other studies33, the correlations of both FI and FCR cannot be predicted accurately because of the inherent problem of FCR being a ratio trait28.The genetic correlation found for FI and FCR, considering the period from 35 to 41 days, were different than those found for these traits by other authors34, FCR35, and Body Weight or Weight Gain12. However, many of the differences between screened loci that putatively influence FE are breed-, age-, or breeding area-specific34. In our study, new regions and candidate genes not yet described were identified, demonstrating the complexity and the large number of biological pathways involved in the regulation of these traits. In addition, most of the studies used approaches to identify only the additive genetic variation, due to their applicability in breeding programs, forgetting the other forms of gene interaction that regulate these traits, such as dominance and sex interaction effects which are addressed in our study. According to Hayes et al.36, when non-additive effects are considered in the model, predictions could be improved, because the genetic architecture of traits contributes to the accuracy of models. With the emergence of new technologies such as gene editing, the identification of genes with non-additive genetic influence on the regulation of FE traits may now have an application in animal breeding programs of the future37. To our knowledge, the study presented here is the first GWAS exploring different genetic effects in feed efficiency traits in chickens with the 600K SNP array.The poultry geneticists have used GWAS extensively as a powerful tool for elucidating the genetic mechanisms that determine FE. In recent years, several studies have identified different QTLs controlling traits in chickens such as FI38 showed that additive model and the combined additive and dominance models produced the majority of significant SNPs in feed-related traits. Recently, Darwish et al.39 demonstrated for eggshell blueness chickens that the contribution of genotypic effects of some SNPs were further divided into additive and dominant effects depending on the trait.In the present study, several SNPs with dominance effect related to FI, BWG and FCR were identified. Among these markers, two had significant dominance effects for FCR Fig. e, and onSORCS3), were identified only when this effect was taken into account. The SORCS3 gene encodes a type-I receptor transmembrane protein that is expressed in neurons of the arcuate nucleus of the hypothalamus and regulate biological processes such as control energy balance and orexigenic peptide production. A study in mice showed that combined deficiency of SORCS3 and SORCS1 may result in a state of chronic energy excess40.Our results have shown that additive or additive + dominance effects of several SNPs were influenced by the sex of the chicken, highlighting their importance in FE traits, mainly for BWG and FCR Figs. f, 4f. WhTrafficking Protein Particle Complex 9 (TRAPPC9) that may function in neuronal cells differentiation and has been associated with diseases related to Intellectual Disability-Obesity-Brain in humans41. It is already known that FI and energy metabolism regulate the energy balance and expenditure, as well as the hormone secretion, where the brain has the important role of inducing and maintaining the homeostasis 42.Several biological processes related to the development and functioning of the brain were enriched with the candidate genes identified in the GWAS for these traits Fig. d. For FCPIK3C2A. This gene showed several potential interactions with other genes associated with FCR and FI  gene that has been associated with FI  associated with feed conversion efficiency traits in a commercial broiler line. Both the PTPRG and PTPRC genes were identified as possible controllers of body weight traits (36 and 46 days).One of the predicted interactions of the Sortilin Related VPS10 Domain Containing Receptor 3 gene (SORCS3). This gene is involved in trafficking the tropomyosin-related kinase B receptor (TrkB) attenuating the brain-derived neurotrophic factor, which plays an important function in the energy homeostasis. It has been demonstrated that the loss in the SORCS1 and SORCS3 joint actions led to a chronic energy excess state, which is characterized by enhancing food intake and adiposity, and decreasing locomotor activity, within other characteristics40.Moreover, some candidate genes were associated with more than one trait in our study, evidencing pleiotropic effect regulating feed efficiency in chickens. Among the genes identified in common between BWG and FCR Fig. e, we higSORCS3 gene, the genes PDZD847, ERC248 and ATRNL1 were also identified for both BWG and FCR , generated from the expansion of a paternal broiler pure line TT, developed for genomics studies by the Embrapa Swine and Poultry National Research Center. For the expansion of this line, 20 males were mated with 92 unrelated females (1:5) to generate approximately 1500 chicks from five incubations, resulting in approximately half of each sex. A complete pedigree with 18 generations from 1991 to 2008, containing a total of 2139 individuals was used. The chicks were identified with metallic rings at birth for pedigree control and raised in collective boxes until 35 days of age according to the procedures for commercial broilers. For the evaluation of individual FE traits, chickens were transferred to individual feed cages, where they remained from 35 to 41 days of age. The chickens had free access to water and a corn and soybean meal-based diet throughout the experimental period. Broiler diet containing 3150 kcal/kg of metabolizable energy (ME) and 21% of crude protein (CP) was provided from 1 to 21 days, 3200 kcal/kg ME and 20% CP from 22 to 35 days, and 3200 kcal/kg ME and 18.5% CP from 36 to 41 days of age. Previous genomic studies have been performed in this population, and more details can be found in Marchesi et al.55.Recorded phenotypic data were individual start and final weights (BW35 and BW41), total FI, and total BWG from 35 to 41 days of age. The FCR was calculated as the ratio of FI and BWG. The removal of discrepant values and the descriptive statistics of the phenotype file were conducted in the R softwareThe extraction of the genomic DNA was performed using PureLink Genomic DNA  kit. The extracted DNA was quantified using a Qubit 2.0 Fluorometer  and diluted to a concentration of 10 ng/μL.56 in the “Centro de Genômica Funcional, ESALQ”, University of São Paulo, Brazil. The genotypes were initially read and edited using the Axiom Analysis Suite from Affymetrix with a DishQC removal value of 0.82. In this study, the data quality control was performed using PLINK v1.07 software57, excluding SNPs with call rate below 98%, with significant deviations (p < 10–6) from the Hardy–Weinberg equilibrium, and with minor allele frequencies (MAF) lower than 2%. Samples that presented call rates lower than 90% were also excluded. The classical multidimensional scaling (MDS) analysis was used to detect population structure in PLINK v1.07 software57.Genomic DNA samples from 1430 chickens , of which 113 (20 sires and 93 dams) are the progenitors (born in 2007) and the remaining are the offspring (born in 2008), were genotyped with the 600K Affymetrix Axiom high-density (HD) chicken genotyping arrayThe environmental effects considered in the genetic analysis model were studied through the least square method. The group effect (sex and hatch) and the linear effect of the covariate body weight at 35 days of age (BW35) were significant (P < 0.05) regarding the traits related to FE from 35 to 41 days of age.58. The animal model proposed for the multitrait analysis was:y is the vector of the dependent variable; X is the incidence matrix for fixed effects, correlating elements from b and y; b is the fixed effects vector containing the groups of animals from the same incubations (1 to 5) and sex (1 or 2), and the linear covariate BW35; Z is the incidence matrix for the direct genetic random effect, correlating elements from a and y; a is the random effect vector for the direct additive genetic effect, and e is the random residual effects vector. Both Genetic parameter estimates for the traits studied were performed using the restricted maximum likelihood method (REML) in a multitrait animal model using the WOMBAT software described by Meyer2 values for the TT population for each chromosome using PLINK v1.07 software57.The linkage disequilibrium (LD) between SNP pairs with a maximum distance of 5 Mb were measured by r59, which employs a maximum likelihood approach to test the association between SNP and trait, performed one SNP at a time. Qxpak allows both the additive and dominance fixed effects of the SNP to be fitted in the model. Thus, the GWAS was carried out according to the analysis procedure described by Bolormaa et al.60 and Li et al.38, based on the following linear mixed model:ith SNP, respectively, and fit as covariates, Genome-wide association analysis using single marker information was performed for FI, BWG, and FCR using the Qxpak 5.0 softwareThree separate analyses were performed for each of the 375,776 SNPs: (I) the additive effect of the SNP was fit in the model while the fixed dominance effect 59 computes the likelihood for each SNP and retains the one with the maximum likelihood. The difference in likelihoods between the complete and the reduced model were tested using likelihood ratio test (LRT) with 1 degree of freedom (df) if only the additive effect was fit or 2 df if the dominant effect was also included in the model.The hypothesis tests were performed comparing the full model  with the reduced model (with no SNP effect as null hypothesis) when the additive and the additive + dominance effects of the SNPs were fit in the model. In addition, we also used the SNP additive effect as reduced model to test for the dominance effect when fitting the additive + dominance effects in the model. QxpakTo describe sex differences in the genetic effects and in the amount of variance explained by each SNP, the additive and the additive + dominace effects of the SNPs were fit within each sex separately in model (2), and were denoted as A_sex and A + D_sex. To test the hypothesis of SNP × sex interaction, a reduced model with no SNP effect was compared to the previously mentioned full model.58 with a window size of 25 SNPs, a step of 5 SNPs, and an r2 threshold of 0.2, resulting in 24,959 independent tests. Therefore, the threshold for the Bonferroni 5% genome-wide significance was a P-value of 2 × 10–6 . We also used the P-value of 1 × 10–5 for a moderate association as recommended by the Wellcome Trust Case and Control Consortium (WTCCC)61 and a suggestive association with P-value of 4 × 10–5 . Manhattan plots and quantile–quantile plots (QQ-plot) were created using the R software55. Inflation factor was also calculated using R.Since Bonferroni correction is highly conservative due to the high LD in genetic data, it may produce too stringent a threshold and many false negative results. Therefore, we calculated the number of effectively independent SNPs using the—indeppairwise parameter in the PLINK v1.07 software2 ≥ 0.3) with other significant SNPs were identified using a Perl homemade script. The identification of the genes near to significant SNPs was obtained using the Ensembl tool Variant Effect Predictor (VEP) and the UCSC genome browser (https://genome.ucsc.edu/). The classification of genes according to its biological function, identification of metabolic pathways and enrichment of genes was performed using DAVID v. 6.8 (https://david.ncifcrf.gov/tools.jsp), and statistically significant results were considered when false discovery rate (FDR) was lower than 0.20. A gene network of predicted functional proteins was constructed using the STRING 10.0 software62.The SNP positions were updated according to the newest release from Ensembl annotation of the Gallus gallus 5.0 genome version. From the LD data analysis, the SNPs with the lowest p-value in each region in LD  from the Embrapa Swine and Poultry National Research Center under the protocol # 011/2010.Supplementary Information.


As a novel type of genetic marker, the microhaplotype has shown promising potential in forensic research. In the present study, we analyzed maternal plasma cell-free DNA (cfDNA) samples from twin pregnancies to validate microhaplotype-based noninvasive prenatal testing (NIPT) for paternity, zygosity, and fetal fraction (FF). Paternity was determined with the combined use of the relMix package, zygosity was evaluated by examining the presence of informative loci with two fetal genome complements, and FF was assessed through fetal allele ratios. Paternity was determined in 19 twin cases, among which 13 cases were considered dizygotic (DZ) twins based on the presence of 3~10 informative loci and the remaining 6 cases were considered monozygotic (MZ) twins because no informative locus was observed. With the fetal genomic genotypes as a reference, the accuracy of paternity and zygosity determination were confirmed by standard short tandem repeat (STR) analysis. Moreover, the lower FF, higher FF, and combined FF in each DZ plasma sample were closely related to the estimated value. This present preliminary study proposes that microhaplotype-based NIPT is applicable for paternity, zygosity, and FF determination in twin pregnancies, which are expected to be advantageous for both forensic and clinical settings. In recent years, the identification of fetal cell-free DNA (cfDNA) in the maternal circulation has facilitated noninvasive prenatal testing (NIPT) for investigating monogenic diseases , identifIn the field of forensic science, the validation of single-nucleotide polymorphism (SNP)-based NIPT in twin pregnancies and the performance to assign zygosity and paternity have been reported in several studies previously ,14,15. HThe microhaplotype, a novel type of genetic marker, refers to a locus with two or more closely linked SNP loci in a short segment of DNA  that generates more than three alleles and exhibits discriminating power comparable to short tandem repeats (STRs) . Additiog and then at 16,000× g within 8 h to isolate maternal plasma first and then remove the remaining white blood cells (WBCs). cfDNA from maternal plasma samples and genomic DNA (gDNA) from the blood cells were extracted as previously described [Five milliliters of peripheral blood were derived from 19 pregnant women who had signed consent forms. All samples were from twin pregnancies confirmed by ultrasound scanning. Peripheral blood samples from pregnant women from 10 weeks of gestation onwards were collected into K3-EDTA tubes. Moreover, dual amniocentesis was performed among twin pairs from each amniotic cavity at 16 to 24 weeks of gestation, and 2 ML blood samples from the husbands  were collected. Maternal blood samples were then subjected to two sequential rounds of centrifugation at 1600× Both extracted cfDNA and gDNA were used to prepare the DNA library using a custom designed MPS-based assay targeting 60 microhaplotypes as previously described . The purHypothesis 1 (H1).The alleged father (AF) is the biological father of both twin fetuses.Hypothesis 2 (H2).AF and a random man are the biological fathers of the twin fetuses .Hypothesis 3 (H3).A random man or two random men are the biological fathers of the twin fetuses.Regarding relationship interference in twin pregnancies, the weight of evidence was assessed by likelihood ratios (LRs) that consider a number of mutually competing hypotheses as follows:LRH1,H3, LRH2,H3, and LRH1,H2 be the LRs for hypotheses H1 vs. H3, H2 vs. H3, and H1 vs. H2, respectively. The LR values were then calculated using the R package relMix (http://cran.r-project.org/web/packages/relMix) [c of 0.01 and mutation rate r = 10−8 were chosen, with silent allele s and theta correction θ set to 0. Allele frequency data for LR calculation are shown in our previous study [We could then define likelihood ratios (LRs) using the hypotheses listed above. We let /relMix) ,28. A drus study , and a fIn theory, every child inherits one allele from its mother  and one from its father . With the maternal genomic sequencing results as a reference, nonmaternal alleles in the plasma sample could be considered paternal alleles when the allele fraction satisfied a threshold of 1.0%. For an MZ pregnancy where the twin fetuses are genetically identical, the interpretation of the microhaplotype pattern is essentially the same as for a singleton pregnancy; thus, no more than one paternal allele was expected in the maternal plasma at a single locus (“Type 1” and “Type 2”). For a DZ pregnancy, the nonmaternal alleles are interpreted as being derived from both fetuses. In addition to the possible presence of “Type 1” and “Type 2” loci, two additional nonmaternal alleles at one locus were assumed to be present (“Type 3”) when the twins inherited two distinct paternal alleles that were different from the maternal alleles . TherefoFF (l) or higher FF value FF (H) was therefore estimated with the following equations:d (l)i or d (H)i represents the fetus-specific allele with the lower or higher sequencing depth at the informative locus i, iD denotes the total sequencing depth of locus i, and N is the total number of informative loci observed in a DZ maternal cfDNA sample.Furthermore, the fetal fraction (FF) in maternal cfDNA was determined based on the allele ratios. For MZ twins, a combined FF was estimated with the same methodology as for singletons, and for DZ twins, two distinct assumed paternal alleles were utilized for individual FF calculation. As previous studies have shown that each fetus in a DZ gestation contributes different amounts of cfDNA in maternal circulation, the difference can be nearly 2-fold. Assuming that the fetus-specific allele with the lower sequencing depth at each informative locus was constantly derived from the fetus with lower FF and vice versa, the fetal allele with higher depth was derived from the fetus with the higher FF, and the averaged lower FF value ® DNA ID 25A system according to the manufacturer’s protocols. Based on the autosomal STR genotyping results, PI and CPI were calculated following the Chinese national standards recommended for paternity testing (GB/T 37223-2018) to determine paternity.As a separate experiment, conventional STR typing was performed to confirm the paternity and zygosity of each family case using invasive samples from each twin fetus with the GoldeneyeLRH1,H3 and LRH1,H2 values were all above 100  for 18 cases except case 2, with an assumed equal dropout probability d for both twins (ranging from 0.001 to 0.9), while the calculated values of LRH1,H3 and LRH2,H3 for case 2 were all below 1  ranging from 7.451 to 17.682 in the log10 form)  . In contectively .LRH1, H3 and LRH2, H3 values of the 98 unrelated males were below 1 as an equal dropout probability in a reasonable range , a fraction of unrelated subjects (4 in case 2 and 1 in case 16) exhibited LRH2,H3 values above 1, which may result in a misleading conclusion , whose genotypes were retrieved from the 1000 Genomes Project . The resle range . Howevernclusion .With the maternal DNA profile obtained from peripheral blood cells in each case as the reference, there were 31~46 nonmaternal alleles (fetus-specific) with allele ratios ≥ 1.0% at the 60 targeted microhaplotypes in the 19 studied cases, among which 3~10 informative loci with two additional nonmaternal alleles, indicating DZ twin pregnancy, were detected in 13 maternal cfDNA samples  . In contFor MZ twins, a combined FF was estimated with the same methodology as for singletons and ranged from 9.34% to 18.08% . For DZ The microhaplotype sequencing results of the fetal gDNA samples were retrieved and served as references for the identification of real fetus-specific alleles  in the maternal cfDNA. The results demonstrated that 94.44%–100.00% of actual fetal alleles were successfully identified based on the predefined threshold (1.0%), with 0–2 dropout alleles and 0–2 drop-in alleles. Moreover, the actual number of DZ informative loci with two distinct fetal alleles was 3–10 in the 13 DZ cases, whereas none were present in the remaining 6 MZ cases . Additio2 = 0.889, 0.960 and 0.944, respectively)  . Accordictively) . Howeverd values were unrealistically high, this model considered the mismatches between the unrelated males and the cfDNA mixture to be caused by dropout rather than nonpaternity, which may result in a false inclusion of paternity. Further study is needed to incorporate more polymorphic markers to ensure sufficient discriminative power and to establish a dropout probability prediction model for twin pregnancies. Additionally, based on the previously reported combined FF value in twin pregnancies (ranging from approx. 7% to 35%) [d = 0 for individual fetus because it is fallacious for the minor contributors of cfDNA mixtures. Instead, we set the min. d value to 0.001 for individual fetus in line with the error rate of current MPS platform (approx. 0.1%) [d = 0 for the maternal portion (major contributors of cfDNA).As a preliminary study, we report the validation of microhaplotype-based NIPT methodology to determine paternity, assign zygosity, and evaluate fetal fraction in maternal plasma as early as 10 weeks of gestation in twin pregnancies. With the combination of the R package relMix for data interpretation, the paternity of 19 twin fetuses was successfully determined in a noninvasive way without prior knowledge of zygosity. In contrast to conventional PI calculations that consider paternity or nonpaternity, the kinship LR calculation in the present study was based on multiple competing hypotheses, including the probabilities of the alleged father being the biological father of both, one, or none of the twin fetuses. Moreover, a total of 98 unrelated men were excluded as alleged fathers in 2 sample cases when the dropout probabilities were in a reasonable range of values, which further verified the specificity of the approach. It should be noted that, as a qualitative model, the R package relMix has been validated in kinship analysis in NIPT ,27,28 an to 35%) , we did x. 0.1%) . In contWe also report on the performance of microhaplotype-based NIPT in zygosity determination, where a paternal sample was not required. Knowledge of the zygosity status for evaluation of paternity in twin pregnancy is advantageous because the risk of heteropaternity superfecundation (HS) could be potentially eliminated when the zygosity status is diagnosed as monozygotic, indicating that the twins are genetically identical. Likewise, accurate determination of zygosity is also clinically important, particularly when chorionicity remains unconvincing based on an early ultrasound examination. Indeed, monochorionic (MC) twins share their blood supply, and up to 15% are affected by twin-to-twin transfusion syndrome (TTTS)  or otherAdditionally, the estimation of fetal fraction (FF), including combined FF for MZ twins and individual FFs for DZ twins, is an equally important determinant of the performance of NIPT for both forensic and clinical settings. For a DZ pregnancy, studies have shown that the circulating fetal DNA from both twins can vary by nearly 2-fold   in twin pregnancies, as a preliminary study. The algorithm for paternity analysis uses a Bayesian approach, and additional information such as stochastic effects or mutation rate can be easily incorporated into the model. Moreover, the microhaplotype-based approach is also expected to be advantageous in zygosity assignment and individual FF estimation for DZ pregnancies because the absence of stutter burden has made microhaplotypes a more straightforward tool for the detection of fetal alleles and therefore improves the estimation of nonpaternity or aneuploidy risk because the prior risk is different based on zygosity status. Additionally, the detection threshold of individual lower FF for DZ twins (or combined FF for MZ) should also be considered as a quality control of the performance of NIPT before making a confident conclusion. Further investigations based on larger samples and more sophisticated validation are required to determine the minimum number of microhaplotype markers in the total dataset as well as the minimum number of informative loci for data interpretation in DZ pregnancies.


In this study, we conducted a potential assessment of genomic selection (GS) in strawberry F1 hybrid breeding. A total of 105 inbred lines were developed as candidate parents of strawberry F1 hybrids. In addition, 275 parental combinations were randomly selected from the 105 inbred lines and crossed to develop test F1 hybrids for GS model training. These populations were phenotyped for petiole length, leaf area, Brix, fruit hardness, and pericarp color. Whole-genome shotgun sequencing of the 105 inbred lines detected 20,811 single nucleotide polymorphism sites that were provided for subsequent GS analyses. In a GS model construction, inclusion of dominant effects showed a slight advantage in GS accuracy. In the across population prediction analysis, GS models using the inbred lines showed predictability for the test F1 hybrids and vice versa, except for Brix. Finally, the GS models were used for phenotype prediction of 5,460 possible F1 hybrids from 105 inbred lines to select F1 hybrids with high fruit hardness or high pericarp color. These F1 hybrids were developed and phenotyped to evaluate the efficacy of the GS. As expected, F1 hybrids that were predicted to have high fruit hardness or high pericarp color expressed higher observed phenotypic values than the F1 hybrids that were selected for other objectives. Through the analyses in this study, we demonstrated that GS can be applied for strawberry F1 hybrid breeding.Cultivated strawberry is the most widely consumed fruit crop in the world, and therefore, many breeding programs are underway to improve its agronomic traits such as fruit quality. Strawberry cultivars were vegetatively propagated through runners and carried a high risk of infection with viruses and insects. To solve this problem, the development of F Fragaria × ananassa) is an allo-octoploid (2n = 8x = 56) species that originated from an interspecific hybridization between Fragaria virginiana and Fragaria chiloensis (1). Because of its economic importance, breeding programs for cultivated strawberries are underway to improve fruit quality, disease resistance, and yield performance  increases. Therefore, the pre-selection of promising F1 hybrids  by using GS considerably contributes to efficient F1 hybrid breeding  is now widely used for genetic improvement of quantitative traits . In GS, breeding . In GS, breeding . Recentlbreeding . These rbreeding . The gen1 hybrid breeding. For this objective, we used 105 inbred lines that were developed as candidate parents in an F1 hybrid-breeding program. In addition, we developed 275 test F1 hybrids whose parental combinations were randomly selected from the 105 inbred lines. The genotype and phenotype data for these populations were used to construct GS models for vegetative and fruit-related traits. Then, the GS models were used for phenotype prediction of 5,460 possible F1 hybrids from the 105 inbred lines. Finally, we conducted breeding selection for the characteristics of fruit hardness and pericarp color to demonstrate the efficacy of GS in strawberry F1 hybrid breeding.In this study, we conducted a potential assessment of GS in strawberry F1 hybrids were developed for GS model training . Validity of the selected parental combinations as a GS training population was confirmed using the genetic relationship between the 275 test F1 hybrids and the other possible parental combinations  and hue  were measured using a colorimeter, CR-20 . Pericarp color value was calculated as L∗ × b∗/a∗, where lightness (L∗) and hue  were measured using the colorimeter. The distribution of the phenotypic values are shown in Strawberry agronomic traits analyzed in this study are summarized in 2. The filtered reads were mapped onto the reference sequence of the strawberry genome  using Bowtie 2 version 2.3.2 . The DNA was physically sheared into approximately 350bp fragments using Covaris S220  and fragments of 300–400 bp in length were fractionated with the Sage Science BluePippin . The fractionated DNA was used for DNA library construction with Illumina TruSeq DNA PCR-Free Library Preparation Kit  in accordance with the manufacturer’s protocols. Sequencing of the DNA library was conducted with Illumina HiSeq X with 2 × 150 paired end reads. The obtained reads were subjected to quality control as follows. Bases with quality scores less than 10 were filtered using PRINSEQ version 0.20.4  and adapon 2.3.2  with paron 2.3.2 . The varon 2.3.2  with theon 2.3.2  with paron 2.3.2 . The SNPon 2.3.2 . FinallyIn this study, we estimated trait heritability using two methods. One is the additive effect heritability (where A) and dominance (D) relationship matrices, respectively  was heterozygous, then we determined the genotype of the F1 hybrids as {P1, P2, F1} = {Aa, aa, Aa}, {P1, P2, F1} = {Aa, Aa, Aa}, and {P1, P2, F1} = {aa, Aa, Aa}.The SNP genotypes of the 5,460 possible F hybrids  were det3). Because the SNPs were almost evenly distributed on the subgenomes, we evaluated the homozygosity of the 105 inbred lines as the ratio of homozygous SNP genotypes of each line . To assess the validity of GS for strawberry population in this study, we evaluated GS model accuracy using three methods. The first and second methods were within population cross-validation for the 105 inbred lines and the 275 test F1 hybrids, respectively. The third method was across population prediction that predicted phenotypes of the 275 test F1 hybrids by using the 105 inbred lines as a training population and vice versa.In this study, we have two populations that can be provided as a training population for GS model construction  high-pericarp color, (2) low-pericarp color, (3) high-fruit hardness, (4) low-fruit hardness, and (5) intermediate phenotype. 1 hybrids and the F1 hybrids from the five classes. We then developed F1 hybrids from the five classes and performed phenotyping . This result was consistent with a previous study that indicated the random location of subgenome-specific loci on the entire genome  under accession number DRA011267.The datasets and R scripts presented in this study can be found in 1 hybrids. EY and SK wrote the manuscript. All authors contributed to the article and approved the submitted version.EY conducted modeling of genomic selection, while SK conducted phenotyping and breeding selection based on genomic selection models. KS and SI conducted genotyping of the materials in this study. SK and YN constructed the 105 inbred lines and the 257 test FThe 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.


Poor understanding of the genetic and molecular basis of heat tolerance component traits is a major bottleneck in designing heat tolerant wheat cultivars. The impact of terminal heat stress is generally reported in the case of late sown wheat. In this study, our aim was to identify genomic regions for various agronomic traits under late sown conditions by using genome-wide association approach. An association mapping panel of 205 wheat accessions was evaluated under late sown conditions at three different locations in India. Genotyping of the association panel revealed 15,886 SNPs, out of which 11,911 SNPs with exact physical locations on the wheat reference genome were used in association analysis. A total of 69 QTLs (10 significantly associated and 59 suggestive) were identified for ten different traits including productive tiller number (17), grain yield (14), plant height (12), grain filling rate (6), grain filling duration (5), days to physiological maturity (4), grain number (3), thousand grain weight (3), harvest index (3), and biomass (2). Out of these associated QTLs, 17 were novel for traits, namely PTL (3), GY (2), GFR (6), HI (3) and GNM (3). Moreover, five consistent QTLs across environments were identified for GY (4) and TGW (1). Also, 11 multi-trait SNPs and three hot spot regions on Chr1Ds, Chr2BS, Chr2DS harboring many QTLs for many traits were identified. In addition, identification of heat tolerant germplasm lines based on favorable alleles HD2888, IC611071, IC611273, IC75240, IC321906, IC416188, and J31-170 would facilitate their targeted introgression into popular wheat cultivars. The significantly associated QTLs identified in the present study can be further validated to identify robust markers for utilization in marker-assisted selection (MAS) for development of heat tolerant wheat cultivars. Wheat is an important staple food crop supplying about 20% of the calories needed by the world population . In the Enhanced tolerance to heat stress and increase in yield potential should remain priorities for the genetic improvement of wheat in response to climate change. Recent studies of grain set under high temperature indicate that considerable genetic variability exists, with little apparent yield penalty associated with improved heat tolerance . To broaStagonospora nodorum blotch resistance  based association mapping that is genome-wide association (GWA) mapping . Associasistance , Russiansistance , grain ysistance , seedlinsistance , spot blsistance  and strisistance . In recesistance . HoweverKeeping the above in mind, the present study was conducted with the aim to identify genomic regions/QTLs associated with agronomical traits under late sown conditions using a diverse panel of wheat genotypes, mainly Indian wheat germplasm lines. The genomic regions identified in this study could be useful targets for the development of heat tolerant lines by wheat breeders.A wheat association mapping (AM) panel of 205 diverse genotypes including Indian landraces, indigenous and exotic germplasms, advance breeding lines, and some Indian wheat varieties released after 1960 Table S1viz., ICAR-IIWBR, Karnal , ICAR-IIWBR regional station Hisar  and ICAR-NBPGR, New Delhi . Field trials were carried out for three consecutive years from 2015 to 2017 during winter season (popularly known as Rabi season in India). Planting was done in three replications at each location. The planting was done from January 5th to 15th  with 5 days interval between the sowing of early, medium, and late maturing germplasm so that all the genotypes with different phenology included in AM could be exposed to heat stress at same growth phase. The experiment was laid out in Augmented Block Design with five checks  repeated in each block consisting of 46 germplasm lines. Each accession was sown in standard plot size that is 3.00 m2 under irrigated conditions. Recommended fertilizer doses (120 kg N: 60 kg P2O5: 40 kg K2O in per hectare area) were applied in field. The daily maximum and minimum mean temperatures were recorded for the entire crop season. Mean of minimum and maximum temperatures before and after heading was calculated by taking into consideration for each line, the minimum number of days to heading and maximum number of days to maturity. Propicanozole, a fungicide was applied to protect the crop from fungal disease.The wheat AM panel was evaluated at three different locations in the Indo-Gangetic plain region of India −2), grain yield , grain filling rate (GFR), and harvest index  to find out the variation among studied wheat accessions.Data were recorded for phenological, physiological, grain yield, and its component traits such as days to heading (DH), days to anthesis (DA), days to physiological maturity (DM), chlorophyll fluorescence (CFL), cell membrane stability (CMS), grain filling duration (GFD), grain weight/spike , grain numbers/spike (GN), grain numbers/m (GNM), productive tillers/m (PTL), plant height , 1,000 grain weight , biomass  II reaction center, to be fully oxidized and enabled us to measure the minimal fluorescence (FO). Three readings per genotype per replication were recorded. The CMS was determined from three flag leaves of each genotype using methodology given in The phenological traits were estimated at 50% growth stage on whole plot basis. GFD was estimated as difference in days between anthesis and physiological maturity. Five random main spikes were harvested from each plot followed by hand threshing to determine GN and GW estimation. TGW was measured by taking random samples of 1,000 grains/plot from plot yield and weighed. CFL was measured using fluorometer . The fluorescence data were recorded from three competitive plants tagged in the middle rows of the plot. The measurements were taken on the abaxial surface of fully expanded flag leaf at different growth stages (GS), flowering (GS65), end of flowering (GS70), medium milk (GS75), and early dough (GS83) as explained by 2), scatter plot matrix and principal component analysis for all 16 morpho-physiological traits were analyzed using SAS software v9.3 program. The biplots based on two principal components were also generated to depict the accession scores as well as loading of characters. Genotype and genotype by environment (GGE) biplot analysis was performed to determine genotype stability, superiority and also ‘which-won-where’ plot for trait based best genotypes at a particular location in relation to important agronomical characters under heat stress using R program . Heritability of the analyzed traits was estimated using restricted maximum likelihood (REML) method. For each location, trait mean and ranges of each accession were calculated. BLUP values were calculated using R package for further statistical analysis. The phenotypic correlation coefficients . All the SNP markers with a call rate < 90% and minor allele frequency (MAF) < 10% across all genotypes were excluded from the analysis. To assign the physical location of each SNP on wheat chromosomes, the SNP probe sequences were BLAST searched against the wheat genome assembly RefSeq v1.0 (https://wheat-urgi.versailles.inra.fr/Seq-Repository/Assemblies).The genomic DNA was extracted from 15 days old seedlings using a CTAB procedure . Genotypr2) using software TASSEL v. 5.0 . The MLM approach was used as it takes into account population structure (Q) and kinship matrix (K) as covariates (MLM: Q + K) thus minimizing the chances of getting spurious associations. Initially, the significant associations were detected at threshold P-value < 0.0001. Further, raw P-values were adjusted with Bonferroni correction  to detect significantly associated QTLs with traits. In addition, LD among the clustered SNPs was estimated, and those with high LD were considered single QTLs. The R package qqman (https://cran.r-project.org/package=qqman) was used to draw Manhattan plots. The favorable alleles for each QTL region were identified by comparing the extreme phenotypic values in association mapping panel.Marker trait associations (MTAs) were estimated using Mixed Linear Model (MLM) implemented in TASSEL 5.0  based on individual evaluation data of three years  and three locations revealed significant differences among the genotypes for all the studied traits Table 1.The correlation coefficient between all the studied traits was also analyzed Figure 1vs. PC2 , IC539292, and IC128573. Stable genotypes for GN were EC190950 and IC536060, whereas for GY, IC82460, IC335690, and IC539600 showed stable and above average performance. For TGW and HI the stable genotypes were IC36761A, HGP1-107, and IC547662, IC543417, IC112049 and 11-F1-8, respectively.In mean Ranking of genotypes on the basis of GGE biplot may be determined as distances from ideal genotype. For PTL, IC93335 was the most favorable genotype followed by IC539599 (RefSeq v1.0 (https://wheat-urgi.versailles.inra.fr/Seq-Repository/Assemblies) using BLASTN program with default parameters. A total of 11,911 SNPs was assigned exact physical location on wheat genome which included 3,729 on subgenome A, 4,760 on subgenome B, and 3,422 on subgenome D. The number of SNP markers on individual chromosomes ranged from 210 on Chr4D to 883 on chromosome Chr2B. The chromosomal level distribution of 11,911 SNPs on three subgenomes showed that subgenome A possessed maximum SNPs on Chr2A (780) followed by Chr1A (708) and Chr7A (618). Subgenome B contained maximum SNPs on Chr2B (883) followed by Chr1B (842) and Chr5B (807), whereas subgenome D contained maximum markers on Chr2D (712) followed by Chr1D (673), and Chr5D (524). The SNPs that could be assigned exact physical locations on the wheat reference genome were only considered for the further analyses in order to exclude any conflict with homoeologous SNPs that might be present across the three wheat subgenomes.Genotyping of the AM panel using 35 K Axiom SNP array revealed a total of 23,053 scorable SNPs after filtering for various quality parameters. SNPs with MAF ≤ 0.1 were then excluded, which left 15,886 high quality SNPs for further analyses. These SNPs (probe sequences) were searched against the reference wheat genome assembly, IWGSC  2) across 21 wheat chromosomes ranged from 0.078 (Chr4D) to 0.349 (Chr1B). In contrast, the subgenome level LD estimate did not differ much: 0.15 for subgenome A, 0.17 for subgenome B, and 0.16 for subgenome D. The summary statistics of 11,911 markers including chromosome wise distribution, average LD, average marker distance, and markers with perfect LD are presented in 2 value for the marker pairs reached 0.167 at 3.2 Mb as compared to 4.3 Mb in subgenome B and 6.8 Mb in subgenome D  containing 98, 68, and 39 genotypes, respectively ; subgenome B ; subgenome D   Table 2.Ten significantly associated QTLs were represented by five important traits; three QTLs for HI , two QTLs each for GY (Chr3AL: AX-95099434 and Chr7AS: AX-94616006), PHT , DM (Chr5DL: AX-94853198 and Chr7BL: AX-95078558) and one QTL for GFR Table 2.Furthermore, in order to identify consistent MTAs across environments, GWAS was separately performed for the three evaluated locations. The results are presented in Association of the same genomic region with multiple traits is a very common phenomenon in plants. We observed that a total of 12 SNPs was associated with more than one agronomical trait Table 2.Besides multi-trait SNPs, some genomic regions contained QTLs for more than one trait. The genomic regions harboring QTLs for more than one trait included Chr1DS (27–31.69 Mb), two QTLs for GY and one QTL for HI; Chr2BS (184–205 Mb), two QTLs each for GFR and GY, and Chr2DS (30.2–32.2 Mb), one QTL each for DM, GFD, PTL and PHT. The pair-wise LD among the clustered SNPs was low, and so these were considered independent QTLs.Favorable alleles: We have also examined promising heat tolerant lines for the presence of favorable alleles of QTLs identified in the present study. Most of the heat tolerant accessions possessed favorable alleles for the majority of QTLs associated with the analyzed traits. The accessions where favorable alleles were present for maximum number of traits were RAJ403  followed by HD2888 , WH1142 , and 11-F1-3 . The accessions with highest number of favorable alleles were RAJ4083 (24) followed by HD2932 (22) and WH1142 (22). Moreover, for the trait PTL, 12 favorable alleles were present in HD2932 followed by RAJ403 (11), and DBW93 (10) whereas six favorable alleles were present in DBW93 and HD3118 for the GFR trait. However, a maximum of four favorable alleles was found in IC416188, IC443636, IC335732, IC536375, IC534306, IC252655, RAJ4083, HD2932, K8027, HD3118 followed by C306 for GY, and five favorable alleles were observed in HGP1-306, HD3118, WH1142 followed by J31-170 for HI and DBW107, C306 followed by RAJ4083 for TGW Table S5Wheat is one of the most important food and feed crops in the world, and terminal heat stress is the major factor affecting wheat productivity globally. Therefore, improving terminal heat tolerance for wheat cultivars will have a huge economic impact worldwide. Globally, there have been many efforts to understand physiological, genetic, and molecular basis of terminal heat stress tolerance in wheat . The preetc.) that are well adapted to diverse climatic conditions and possess traits that might confer adoption to abiotic stresses including heat stress. Various other studies have reported large variations in agronomic traits in the diverse germplasm lines of wheat under heat stress conditions . Many other studies have showed rapid decay of LD in subgenome A . In our Although GWA analysis enables high resolution mapping of targeted traits, it might also reveal spurious associations if the confounding factors such as population structure and genetic-relatedness among individuals are not accounted for. Therefore, in our analysis, a MLM based method which accounts both these factors was used . The popGenome-wide association analysis revealed a total of 69 QTLs (10 significantly associated and 59 suggestive) for 10 agronomical traits including PTL, DM, BM, GFD, GNM, GY, GFR, PHT, TGW, and HI recorded under late sown conditions. Surprisingly, a large number of QTLs (17 suggestive QTLs) were associated with PTL. PTL is the count of the tillers that produces spikes and seeds and is thus considered a key determinant of wheat yield under abiotic stress conditions such as heat and drought. For this reason, genes controlling PTL are among the most important targets for yield improvement under heat stress conditions . The 17 Rht8 was previously reported, suggesting that some of the exotic germplasm lines included in our analysis carried this gene. Unlike the Rht1 and Rht2, this semi-dwarfing gene is GA (gibberellic acid) sensitive and has shown to reduce wheat plant height in the hot and dry environment without any yield penalty  and were found to be located in the same genomic regions where QTL for PHT were reported in a previously study  for PHT. Two significantly associated QTLs were present on Chr2D and Chr3B. The significantly associated QTL on Chr2D is located on its short arm and in the same genomic region where the semi-dwarfing gene  penalty . The QTLt height . Associaly study . Since iGY is the most desirable agronomic trait; however, its genetic regulation is very complex, controlled by a number of genes whose expressions are influenced by environmental conditions . Our anaGFR is an important determinant of heat tolerance in wheat . Under lHI is an important yield associated trait which determines the performance of wheat genotypes under abiotic stress conditions such as heat and drought. Previous studies have identified QTLs for HI on Chr1A, Chr2B, Chr5B, and Chr6B . In our GNM is another component of yield, which is severely affected due to accelerated physiological maturity under late sown conditions. Though a good number of QTLs have been identified for grain number per spike under heat stress, there exists limited information on genomic regions/genes associated with GNM. Our study revealed three novel genomic regions for GNM on Chr5B, Chr1D, and Chr7B. A QTL for grain number on Chr5B but on its long arm was identified by All genomic regions identified in this study should be validated in an independent biparental mapping population between parents with opposite alleles and also be tested for stability under many environmental conditions before they could be exploited in wheat breeding programs through MAS.Validation is of most interest for the total of twelve SNPs/genomic regions that were associated with two or more agronomical traits, suggesting that genomic regions controlling two or more desirable agronomical traits might have been selected during wheat domestication. Interestingly, one of these was associated with five traits GFD, DM, TGW, PHT, and HI. Co-localization of QTLs for agronomical and grain related traits has been demonstrated in earlier studies as well . This isTwo wheat germplasm lines (IC75240 and IC321906) that had few favorable alleles for the QTLs identified in this study but showed a high level of heat tolerance might be because of the presence of other favorable alleles/genes which could not be identified in the present study. Hence, some other traits related to heat tolerance may also be explored in addition to the ten traits explored in the present investigation. Our study has provided novel genomic regions for various heat tolerant associated traits These must be validated through other approaches such as biparental mapping and functional genomics to define their role, as it has been previously reported in various studies that the GWAS approach may result in false positive associations which require further validation  prior toIn conclusion, our study provided an insight into the genetic architecture of 10 agronomic traits in wheat under late sown conditions. The wheat association panel used in this analysis revealed wide variability for most of the agronomic traits under late sown conditions. Some stable genotypes for late sown conditions were also identified. Genotyping using a high density 35 K array, revealed a large number of SNPs that could be used for the GWAS analysis. In total, 69 QTLs were identified for the ten agronomic traits, which included some novel genomic regions. The potential loci/QTLs and genotypes identified in this study may be further validated to determine their use in wheat breeding programs for development of heat tolerant wheat genotypes.The original contributions presented in the study are included in the article/SundK: Conceptualization, data curation, formal analysis, investigation, drafted the manuscript. JK: Investigation, data curation, data analysis. NBh: Investigation, data recording. AP: Performed genomic predictions. NBu: Analyzed the data, manuscript writing. DM: Analyzed the data, manuscript writing. DC: Analyzed the data. SuneK: Formal analysis, revised and edited the manuscript. AS: Formal analysis, scientific inputs for manuscript preparation, revised and edited the manuscript. MR: Conceptualization, data curation, formal analysis, investigation. GS: Writing—review and editing. KS: Writing—review and editing. SS: Conceptualization, data curation, formal analysis, investigation, revised the manuscript.Financial assistance from USAID through CIMMYT, Mexico under ARCADIA—CIMMYT—USAID project Development of heat tolerant wheat for South Asia (Grant No. OAAA-13-00001).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.