The aim was to estimate genetic factors affecting the first lactation milk production traits in Kankrej cattle of North Gujarat.The 475 first lactation records of Kankrej cows that were maintained at the Livestock Research Station, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar, Gujarat, over a period of 35 years from 1980 to 2014 were studied. The least squares maximum likelihood program was used to estimate genetic parameters of first lactation traits. Heritability was estimated through paternal half-sib analysis in adjusted data.The heritability estimate for production traits was 0.40±0.17, 0.45±0.17, 0.35±0.18, and 0.20±0.14 for standard 300 days milk yield (F300Y), total lactation milk yield (FLY), wet average (FWA), and lactation length (FLL), respectively, in the first parity. All the genetic and phenotypic correlations among different production efficiency traits were high and positive. Genetic correlations between F300Y and FLY, FLL, and FWA were 0.80±0.20, 0.59±0.16, and 0.81±0.32, where as the phenotypic correlations were 0.969, 0.688, and 0.868, respectively. Genetic correlations of FLY with FLL and FWA were 0.60±0.13 and 0.79±0.20, whereas the phenotypic correlations were 0.777 and 0.817, respectively. Genetic and phenotypic correlation between FLL and FWA was 0.63±0.28 and 0.31, respectively.The heritability estimate of all first parity lactation traits waslow to medium (0.20-0.45) indicated the scope for further improvement in this trait through selection as well as managemental practice. Higher genetic and phenotypic correlation between thefirst lactation milk production traits gives theidea that genetic gain due to selection for one trait also givesmorecorrelated response of selection for other traits which is economically advantageous. Kankrej cattle breed of India is considered as the best dual purpose cattle breed, i.e., female animals are used for milk production as well as male used for draught purpose in temperate region. Cattle in the tropics have, on average, lower milk yields and shorter lactations length than cattle in temperate countries. The difference is due to genetic and non-genetic factors.Information of first lactation trait enables the breeder to predict the later lactation performance of the animals as it is highly correlated with the future performance traits .The genetic composition of a population can be studied by considering the relative importance of heredity and environmental factors affecting the performance of individuals in that population . PreciseThe present investigation was made to explore the genetic parameters such as heritability, genetic, and phenotypic correlations between first lactation milk yield (FLY) of first lactation traits in Kankrej cattle. It is envisaged that this information will be useful for the formulation of future breeding strategy for the genetic improvement of Kankrej cattle.The data are collected from Livestock Research Station from the history sheet so no need of ethical approval.The study was conducted at the Livestock Research Station, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar, Gujarat (India), about 27 km from Palanpur city (23.81° 24.70’ N 71.10° 73.00’ E). The herd was established, in 1978, with Kankrej cattle, and the animals were kept in loose housing system. Calves were grouped according to age or live body weight while the adult cows were divided into lactating, advance pregnant, and dry group according to their reproductive physiological status. Feed requirement was determined according to milk yield, pregnancy status, and body weight.The data on 475 normal first lactation records of Kankrej cattle sired by 75 sires maintained at over a period of 35 years from 1980 to 2014 were included in the present study. Data of abnormal lactations such as abortion, mastitis and below 150 days milk yield, specific or non-specific diseases, reproductive disorder, and physical injury were excluded from the study.The data of milk production were analyzed for heritability, genetic, and phenotypic correlation coefficients by the method of paternal half-sib correlation using mixed model least-squares and maximum likelihood (LSMLMW) computer program developed by Harvey . Heritabij= μ +Si+ eijYij is the observation on jth trait of the daughter of the ith sire, Si= the effect of ith sire, and eij = the random error peculiar to individual within the group.Where, μ = is the population mean, YGenetic correlation between two groups was calculated by the following formula:Where, rg is the genetic correlation, cov is covariance between sires, Vars (x) is the between sire variance of traits x, and Vars (y) is between sire variance of trait y.The following formulae were used for calculating between and within sire variances:Varw = MSWWhere, MSS is the between sires mean squares, MSW is the within sire mean square, and K is the average number of progeny per sire.Phenotypic correlation between two groups was calculated by the following formula:Where, rp is the phenotypic correlation, covw is phenotypic covariance within sires, covs is the phenotypic covariance between sires, Varw (x) is the phenotypic variance of traits x within sires, Vars (x) is phenotypic variance of trait x between sires, Varw (y) is phenotypic variance of trait y within sires, and Vars (y) is the phenotypic variance of trait y between sires.The phenotypic variances between and within sire for different traits were calculated as mentioned for calculating heritability. Phenotypic covariance between sires and within sires was calculated using the following formulae:Covw = MCPwWhere, MCPw is mean cross products within sires and MCPs is mean cross products between sires.viz., first standard 300 days milk yield (F300Y), FLY, first wet average (FWA), and first lactation length (FLL)were 0.40±0.17, 0.45±0.17, 0.35±0.18, and 0.20±0.14, respectively.The heritability estimates for first milk production traits are presented in The heritability estimate for F300Y in the present study was 0.40±0.17, which indicates that 40% of variation in F300Y is due to heredity. Therefore, emphasis may be given in selection programs for improvement of this trait. The present estimate of heritability is comparable with the earlier findings in Tharparkar breed , KankrejThe heritability estimate of first lactation yield was medium 0.45±0.17) in Kankrej cattle, which is in accordance with the earlier findings in Tharparkar , Kankrej in KankrThe heritability estimate for FLL obtained in this study was low (0.20±0.14) in Kankrej cattle. The present estimate of heritability was comparable with earlier finding in the same breed  and in Set al. and Dhaka et al. reported lower estimate of heritability of FWA in Sahiwal breed [Heritability estimate of FWA was 0.35±0.18 in present study. Choudhary al breed  of cattlGenetic correlations between standard F300Y with total milk yield, FLL, and FWA were 0.805±0.206, 0.590±0.167, and 0.807±0.324, whereas the phenotypic correlations were 0.969, 0.688, and 0.868, respectively, which were all positive and highly significant indicating that higher F300Y were associated with higher FLY on phenotypic scale essentially due to both genetic and environmental reasons. Similar positive correlations were obtained in Tharparkar cattle . High geFLY had high positive genetic and phenotypic correlation with FLL and FWA. The genetic and phenotypic correlations between FLY and FLL were 0.60±0.13and 0.777 and with FWA were 0.79±0.20 and 0.817, respectively. The present estimate of the correlation between the FLY and FLL is comparable to the estimates reported by earlier in Kankrej cattle , SahiwalThe genetic and phenotypic correlations between FLL and FWA were 0.63±0.28 and 0.311, respectively.FLY was positively correlated with the other production traits, which indicated that any increase in slandered 300 days milk yield or lactation length would simultaneously bring about an increase in total lactation milk yield which could be due to the same set of genes responsible for the expression of these traits. A positive and significant genetic correlation between standard lactation milk yield and other milk production traits under study giveidea that selection for higher Standard lactation milk yield will increase the lactation length and wet average which is economically advantageous. The medium heritability estimate of FLY and FWA indicated the wide scope for further improvement in this trait through selection. However, lactation length likely to achieve slow progress due to its low heritability. As a result of the moderate heritability estimates for total lactation milk yield and standard 300 days milk yield, it can be concluded that genetic development of milk yield can be attained through selective breeding. Therefore, these two traits may be preferred by breeders as selection criteria for development of effective genetic improvement program.The data on first lactation production traits were collected by KJA, MPP, and BSR under the supervision of KBP and JBP. The data were compiled and analyzed by NKP. The manuscript was prepared by NKP and MPP under guidance of KBP. All the authors read and approved the final manuscript.


These genes all play key roles in development and growth patterns. To improve the quality of cultured fish strains, natural genetic variation must be examined and understood. A mixed linear model has been widely used in association mapping, taking the population structures and pairwise kinship patterns into consideration. In this study, a mass cross population of orange-spotted grouper (Epinephelus coioides) was examined. These candidate genes were found to harbor low nucleotide diversity (θw from 0.00154 to 0.00388) and linkage disequilibrium levels (delay of 50% within 2 kbp). Association mapping was employed, and two single-nucleotide polymorphisms (KR269823.1:g.475A>C and KR269823.1:g.2143T>C) were found to be associated with growth , explaining 9.0%–17.0% of the phenotypic variance. The association of KR269823.1:g.2143T>C was also found via haplotype-based association (p < 0.05). The identified associations offer new insights into gene functions, and the associated single-nucleotide polymorphisms (SNPs) may be used for breeding purposes.Growth hormone-releasing hormone (GHRH) and the receptor, GHRHR, constitute important components of the hypothalamus-pituitary growth axis and act on the downstream growth hormone (GH). PACAP-related peptide/pituitary adenylate cyclase activating polypeptide (PRP-PACAP Epinephelus coioides) belongs to the Serranidae family and is broadly found in tropical and subtropical open oceans and seas [et al. [et al. [et al. [Leptin gene and growth patterns.Orange-spotted grouper . Thus, thaliana  and to mthaliana .GH synthesis and secretion from the pituitary by binding to its receptor, GHRHR [Marker-assisted selection (MAS) methods always focus on genes that control important phenotypes in the agricultural sector. The growth hormone-releasing hormone (GHRH), PACAP-related peptide/pituitary adenylate cyclase activating polypeptide (PRP-PACAP), and GHRH receptor (GHRHR) genes have been widely studied in the field of physiology (including in the orange-spotted grouper ) and arer, GHRHR . PRP andr, GHRHR  and PACAr, GHRHR . Mutatior, GHRHR , GHRHR [r, GHRHR , and PRPr, GHRHR  genes haIn recent decades, sequencing technologies have altered research methods. Several markers have been used to dissect genomes, including those of restriction fragment length polymorphism (AFLP), random amplified polymorphic DNA (RAPD), simple sequence repeat (SSR), and single-nucleotide polymorphism (SNP). Since 2005, next-generation sequencing approaches have accelerated research on genomic sequences. SNPs markers have grown popular owing to the advantages of high-density in genome and to their ease of development with the assistance of robust high-throughput sequencing tools. Ion Torrent Personal Genome Machine (PGM) is one of these high-throughput sequencing platforms and was released at the end of 2010 . With noBased on limited research previously conducted on orange-spotted grouper population and genetic attributes, the GHRH, GHRHR and PRP-PACAP genes were used to examine nucleotide diversity and LD levels at the gene scale. MLM-based association mapping was performed to dissect growth traits in individuals of a mass cross breeding population. This basic study on these four genes will provide information on population characteristics while promoting the selection breeding of this species.p-value > 0.05) among the sampled individuals with four exceptions: caudal peduncle depth (CPD), snout length (SNL), eyeball diameter (ED) and interorbital distance (ID). The eleven recorded traits were significantly positive correlated . Condition factor (K) showed highly significant negative correlations  with total length (TL) and standard length (SL), and was significant negative correlated  with head length (HL), caudal peduncle length (CPL), and interorbital distance (ID).The morphometric characteristics of 159 individuals were examined in this study. The maximum value was roughly two to three times that of the minimum value for each trait. Among the 12 traits examined, eight traits were normally distributed (The structure of four targeted fragments is illustrated in r2) of 0.94 was found, denoting that the relationship is well depicted by the model and that the estimated function may be used to predict the range of sensitivity (x0 and dx were estimated as 19.342 ± 0.244 and 11.631 ± 0.238 (mean ± SE), respectively. When the mean coverage depth exceeds 50-fold, sensitivity levels should be larger than 0.80, and when the mean coverage depth exceeds 100-fold, the sensitivity should increase to 0.90.The relationship between sensitivity and the mean coverage depth was fitted to a non-linear regression model. A correlation coefficient  in the four fragments were found to be relatively low, ranging from 0.00154 to 0.00388. The diversity of non-synonymous nucleotide substitutions (θw) was found to be comparable to that of synonymous nucleotide substitutions in the four fragments. Although Tajima’s D value was found to be negative with the exception of PRP-PACAPa, none of the values were found to be significant. Z-tests showed an absence of selective pressure on the four fragments. Fu’s Fs statistic showed significant signals in all four fragments, revealing a pattern of haplotype diversity that deviated from the expectation under neutral conditions.Nucleotide diversity levels  values of at least 10%. Twenty-seven, one, 30 and 23 SNPs remained for PRP-PACAPa, PRP-PACAPb, GHRH and GHRHR, respectively, and the PRP-PACAPb fragment was excluded. Average values of ned data . “Half-lK values was largest when the number of subpopulations (K) was two. Moreover, once the presumed subpopulation quantity reached a value of two, Ln P(D) reached a plateau and increased slightly. Thus the presumed number of subpopulations was set to two. An association was performed, and two markers (KR269823.1:g.475A>C and KR269823.1:g.2143T>C) in the GHRH gene were found to be significantly associated with growth traits )) between successive h traits . The KR2h traits . Two marp = 0.0389) and BWT (p = 0.0294)  in the GHRH gene was found to be associated with BL ( 0.0294) . Three cNew sequencing methods allow us to process hundreds and thousands of DNA sequences at low costs for each base. However, it is still not feasible to sequence numerous genomes in their entirety due to cost, time constraints, and infrastructure and data storage requirements, especially for non-model genomes. Conversely, linkage and linkage disequilibrium mapping finally focus on sequences of hundreds of thousands to several million base pairs in length, and follow-up fine mapping aims to focus on limited functional genes. Therefore, candidate gene strategies serve as an alternative means for obtaining related markers in non-model species .PCR-based capturing methods are highly sensitive, consistent and require limited equipment . In thisZ-test, and negative values may be attributable to demographic history characteristics, e.g., population expansion. Rather, the number of samples was clearly larger than the number of parents, and the population significantly expanded as a result. However, Fu’s Fs method is particularly sensitive to demographic effects [Nucleotide variation serves as the basis of selective breeding, and in this study, nucleotide diversity levels were examined for the four candidate fragments. Nucleotide diversity (π) levels ranged from 0.00099 to 0.00352, and non-synonymous site mutations were far less common than those in the silent site. As reported, the diversity of cultured stock would decrease compared with wild stock . Samples effects , and Fu’ effects . Thus, aThe mixed linear model has been used for association mapping, which makes it more robust in breeding and the random population. A common concern regarding association mapping pertains to the fact that population structures may trigger false-positive results if not correctly controlled. To control for this risk, population structures must be taken into consideration. However, if a functional allele is correlated with a population structure, population structure control may generate a false negative, particularly for a small sample . Thus, mK) was calculated using the following formula: K = 100 × BWT/SL3. Tissues were cut from the caudal fin of each individual and were preserved in 95% ethanol at −20 °C immediately thereafter. Analysis of variance (ANOVA), normal distribution fit, and correlation tests on phenotypic traits was performed using the IBM SPSS Statistics program . Total DNA were extracted according to a modified proteinase K/phenol extraction protocol [An exhaustive description of the sample has already been presented . In brieprotocol . DNA qua2+Plus), 4 μL of dNTPs (2.5 mM each), 2 μL of each primer (10 μM), 1 μL of PrimeSTAR GXL DNA Polymerase  and 1 μL of genomic DNA (100–500 ng/μL). PCR reactions were performed in an ABI 2720 thermal cycler  under the following thermal cycling conditions: denaturation at 95 °C for 3 min; 10 cycles of amplification for 10 s at 98 °C, for 15 s at 63 to 53 °C (with temperatures decreasing by 1 °C for each circle) and for 6.5 min at 68 °C; 25 cycles of amplification for 10 s at 98 °C, 15 s at 53 °C, and 6.5 min at 68 °C with a final extension at 68 °C for 10 min. Production levels were confirmed using 1% agarose gels, and bands of the predicted size were cut and purified using a Universal DNA Purification Kit  Co., Ltd.). The quality of purified amplicons was determined using a UV spectrophotometer . Then, amplicons from the same individual were mixed in equimolar amounts. The enriched target sequences of each individual were sequenced on an Ion Torrent Personal Genome Machine platform at Invitrogen Life Technologies. The single-end library of 200 bp was barcoded and constructed according to requisite instructions. The library was sequenced on PGM using chip 318. Raw sequencing read filtering and trimming tasks were performed using Torrent Suit 3.6 with cut-off values and minimum lengths of reads set to Phred quality 18 and 50 bp levels, respectively. Clean read mapping was performed using BWA version 0.7.5 against DNA sequences of the four fragments.GHRH, PRP-PACAPa, PRP-PACAPb and GHRHR  genes were selected as candidate genes. DNA sequences were retrieved from the genomic sequence of the orange-spotted grouper (unpublished). mRNA sequences were collected from the NCBI database but not for the PRP-PACAPa gene. The mRNA of PRP-PACAPa was predicted in the genomic sequence program of the species and was confirmed through the translated protein. Primer pairs were designed using the Primer Premier 5.0 software program  . Four pax) was fitted to a non-linear regression model based on empirical uses of the Boltzmann function in OriginPro software version 9.0 . The Boltzmann function was determined as:x0 is the mean coverage depth at which sensitivity levels reach average values between initial and final sensitivity levels and dx is a parameter that describes the shape of the curve between upper and lower asymptotes [The sensitivity and mean coverage depth of each amplicon varied greatly. Sources of variance from individuals and fragments were examined via a two-way ANOVA. Furthermore, the relationship between sensitivity levels (S) and mean coverage depths  base quality levels should be greater than or equal to 20 ; (2) map quality levels (MAQ) should be greater than or equal to 20 ; (3) SNP coverage level should be greater than or equal to 10-fold; (4) SNP quality levels should be greater than or equal to 20; (5) a mutation with a ratio of variant coverage  from 10% to 90% should be defined as a heterozygous variant; and (6) INDEL (insertion and deletion) should not be included. Physical coverage (sensitivity) is defined as the percentage of the segment length with a depth of at least 10-fold.w [w was defined as the average number of segregating sites per site. Allele frequency spectra were estimated as Tajima’s D. Fu’s Fs were used to detect deviations from neutrality [Z-test available through the MEGA program version 6.0 [The missing genotypes and haplotypes were inferred using PHASE 2.1.1  for eachw  using Dnw . π was dutrality . The numutrality . The nulsion 6.0 .r2) were estimated between sites at an MAF value of greater than 0.1. r2 Decay with distance was evaluated via nonlinear regression. The expected value of r2 under a mutation-recombination drift model was determined as:n is the sample size and where C = 4Nc. The nonlinear model contains a single coefficient that is the least-squares estimate for C for each bp distance between sites [r2 levels dropped by 50%.Linkage disequilibrium (LD) patterns between each pair of detected SNPs in the same fragments were assessed using the Haploview 4.2 program . Squareden sites . The “haK value were performed using the admixture ancestry model and correlated allele frequency model. For each run, the initial burn-in period was set to 750,000 and was followed by 500,000 MCMC iterations. The most likely K value was denoted by ΔK, which showed the change in estimated data log probability levels (Ln P(D)) between successive K values [Q values of <0.05 were deemed significantly related sites after SNPs were filtered based on the false discovery ratio [R2) explained by each SNP was estimated using the following formula: R2 = SSt/SST × 100%, where SSt is the variance explained by the marker and where SST is the total variance. The gene action model was qualified using the ratio of dominance (d) to additive (a) effects [The detected SNPs were extracted using the following criterion: (1) MAF > 0.05; (2) the ratio of missing data <20%. Structure association mapping was performed using a mixed linear model (MLM) available through the TASSEL software program version 5.1.0 . PairwisK values . Polymorry ratio . The per effects .The SNPs around significantly associated sites were combined to determine the block associated with growth. A haplotype-based association analysis was performed via haplotype trend regression (HTR) . HaplotyEpinephelus coioides) marine food species were examined. An SNP (KR269823.1:g.2143T>C) located in the second intron of GHRH was found to be significantly related to growth patterns and may be used for breeding purposes. These genes were found to harbor relatively low nucleotide diversity levels and low LD levels in the population.In this study, the effects of PRP-PACAPa, PRP-PACAPb, GHRH and GHRHR polymorphisms on phenotype in the important orange-spotted grouper (


Detailed analysis identified a total of 524 highly significant MTAs  with wide phenotypic variance (PV) range (5.81–90.09%) for 36 traits. These MTAs after validation may be deployed in improving biotic resistance, oil/ seed/ nutritional quality, drought tolerance related traits, and yield/ yield components.Peanut is an important and nutritious agricultural commodity and a livelihood of many small-holder farmers in the semi-arid tropics (SAT) of world which are facing serious production threats. Integration of genomics tools with on-going genetic improvement approaches is expected to facilitate accelerated development of improved cultivars. Therefore, high-resolution genotyping and multiple season phenotyping data for 50 important agronomic, disease and quality traits were generated on the ‘reference set’ of peanut. This study reports comprehensive analyses of allelic diversity, population structure, linkage disequilibrium (LD) decay and marker-trait association (MTA) in peanut. Distinctness of all the genotypes can be established by using either an unique allele detected by a single SSR or a combination of unique alleles by two or more than two SSR markers. As expected, DArT features  showed lower allele frequency and polymorphic information content (PIC) than SSRs . Both marker types clearly differentiated the genotypes of diploids from tetraploids. Multi-allelic SSRs identified three sub-groups (K = 3) while the LD simulation trend line based on squared-allele frequency correlations (r Arachis hypogaea L., 2n = 4x = 40) is the mainstay to livelihood of millions of small-holder farmers residing in semi-arid tropic (SAT) regions of the world. This crop is cultivated in 24.6 million ha with the total production of 41.3 million tons and productivity of 1676 kg/ha during 2012. Asia with 11.6 million ha (47.15%) and Africa with 11.7 million ha (47.56%) hold maximum global area. The productivity of Asia (2217 kg/ha) and Africa (929 kg/ha) remained very poor as compared to Americas (3632 kg/ha) Peanut or groundnut  in cultivated peanut, very few informative and good quality single nucleotide polymorphisms (SNPs) are available in peanut Resource-poor farmers of SAT region with small land holdings need a strong support from genomics and breeding for sustaining their livelihood through increase in their profitability and better health. Limited success could be achieved through genetic improvement approaches in developing improved cultivars performing better under adverse climatic and soil conditions. This situation is going to be further worsened due to fast changing environmental conditions, soil health, limited water and land resources. In addition, inability to identify desirable alleles from germplasm collections and limited use of unique/ rare alleles in breeding programmes led to narrow genetic base in modern cultivars and varieties The peanut ‘mini core collection’ (184 genotypes) or the ‘reference set’ (300 genotypes) represent the global diversity of about 14, 000 accessions conserved in ICRISAT genebank . This geLarge scale genotyping  of the ‘reference set’ provided a great insight in genetic relatedness, identification of most informative markers and other features such as allele number, gene diversity and observed heterozygosity. A total of 9,194 alleles were identified at 4,597 polymorphic DArT loci , while 1Unique molecular IDs for the different accessions are useful in effective germplasm maintenance and plant variety protection. Therefore, multi-allelic data for SSR markers were used to assign unique molecular IDs to all the accessions . It is iMulti-allelic SSR markers have always been more effective in revealing the genetic structure of a natural population consisting of diverse genotypes 2) was found to decay with the genetic distance of 15 cM  with large LD blocks. Similar large LD blocks were also been detected in many self-pollinated crops such as durum wheat The present study provided most comprehensive insight on population structure and LD decay in a large germplasm set in peanut. Mapping positions for 139 SSR loci mapped onto 20 linkage groups of peanut genome of 15 cM . A complA total of 524 highly significant MTAs for 36 agronomically important traits were identified using most accepted MTA analysis method (P3D mixed linear model with optimum compression) and most stringent multiple test correction method (Bonferroni correction) to filter the false positives . PhenotyAspergillus flavus (aflatoxin contamination), early leaf spot (ELS), late leaf spot (LLS), rust and groundnut rosette disease (GRD) are among the most devastating diseases in several parts of Asia, Africa and parts of Americas. GRD is endemic in Africa and does not occur in the other continents. A total of 24 season data was collected for these five important diseases at seven locations in 6 countries including India , Malawi, Mali, Senegal, Tanzania and Vietnam (Aspergillus (24.69% PV), 31 MTAs for GRD (10.25–39.29% PV), six MTAs for ELS (9.18–10.99% PV), and single MTA for LLS (18.10% PV). The marker associated to one MTA for LLS (Seq1B09) identified in the present study was different than the markers identified in earlier study using biparental populations Aspergillus flavus Vietnam . Associa Vietnam . Of the Role of high oil content in increasing profitability along with increased awareness towards health benefits of improved oil and nutritional quality have gained much importance in recent years among producers, consumers and traders. MTA analysis in present study included several oil and nutritional quality traits such as oil content (OC), oleic acid (OLE), oleic / linoleic acid ratio (OLR), protein content (PC), zinc content (ZC), iron content (IC) and sound mature kernels % (SMK%). A total of 32 seasons data on five important traits at six locations in India  and Vietnam  were genA total of 25 MTAs were detected for OC for which PV ranged from 5.84% (gnPt-714399) to 40.37% (TC4G10) . FurtherA maiden attempt was made here for identifying MTAs for three important nutritional quality traits which resulted in detection of 11 MTAs (11.63–36.1% PV) for PC and single MTA for ZC (15.63% PV) while no MTA for IC . In addi13C, harvest index (HI), haulm weight (HLMWT), leaf dry weight (LDWT), leaf area (LA), leaf length (LLN), leaf weight (LWT), leaf width (LWD), root / shoot ratio (RSR), rate of water loss (RWL), root length (RTL), root volume (RTVOL), root weight (RWT), shelling percentage (ShP), shoot length (SLN), shoot weight (SWT), specific leaf area (SLA), total leaf area (TLA), total leaf weight (TLWT), SPAD chlorophyll meter reading (SCMR), total dry matter (TDM), TDM/LA, days to flowering (DF), days to maturity (DM), emergence (EMR) and first flowering (FFL). A total of 208 seasons data on these 26 important physiological traits characterised in four countries including India , Niger (ICRISAT Sahelian center), Thailand and Vietnam , SLA , TLA , TLWT , SCMR , RTVOL , RWL , ShWT , HLMWT , HI  and ShP  . So far Yield and yield component traits have been the prime target of improvement in all the breeding programmes. Total 12 yield component traits  were analysed. A total of 50 MTAs could be identified for four yield component traits with PV ranging from 9.74% (PYLD) to 37.6% (PYLD). Significant MTAs could be identified only for SDL , SDWD , SDWT  and pod yield  . One of Aspergillus infection resulting into aflatoxin contamination of the produce and thus making it unsafe for consumption. Small-holder farmers of SAT regions have been among the most affected with drought stress and hence, continuously posing threat to their health, income and sustainable livelihood. With an objective to detect MTAs for physiological traits affecting drought tolerance significantly for use in GAB, an experiment was conducted under both the conditions (well watered-WW and drought stress-DS) to understand the genetic mechanism and common genomic regions controlling these physiological traits.Drought stress has been the most devastating abiotic stress resulting in huge yield loss along with high level of A total of 152 MTAs were detected under both the conditions (WW and DS) for six traits namely LA, LDWT, SCMR, HLMWT, HI and SDWT . A totalIn the past, only few studies have been conducted on trait mapping for economically important traits in peanut and therefore, very limited information is available on the markers linked to the traits analysed in this study. Furthermore, use of different kind/set of markers in these studies don't allow a possibility to compare results of this study with the previous studies. Nevertheless, some SSR markers used in the present study were also used in the previous linkage mapping based marker-association studies The main objective of this study was to identify MTAs for agronomical traits of complex nature using diverse panel of genotypes. As a result, a total of 134 MTAs were identified with PV >20% for 15 important traits. A total of 30 significant allele effects for these 15 traits were identified associated with 24 markers showing significant impact on these traits while nine markers were found to be associated with multiple traits . Fifty nIn view of making peanut crop more resilient to stresses with high pod and oil yield and improved oil and nutritional quality, this study is the timeliest and most comprehensive marker-trait association study conducted so far in peanut using thousands of markers and multiple season phenotyping data generated on wide range of economically important traits. Thus, several MTAs detected for many disease resistance, oil content and quality, drought tolerance related  traits, yield components and yield in the present study based on multiple season phenotyping data will facilitate their improvement through GAB. To achieve this, these MTAs upon validation may be deployed in marker-assisted improvement of peanut leading to development of improved cultivars with higher resilience to drought tolerance and disease resistance, increased yield and, improved oil and nutritional quality. Such improved cultivars will ensure sustainable livelihood to the farmers of SAT regions of Africa and Asia, and better nutritional supply to the consumers' worldwide.The peanut ‘reference set’ (300 genotypes) along with four additional elite genotypes was genotyped with 154 SSRs (genomic/genic) spanning complete peanut genome . The ‘re2, 1X amplification buffer and 0.1 U of Taq DNA polymerase . Primers were amplified using touchdown PCR amplification profile which had initial denaturation step for 3 min at 94°C followed by first 5 cycles of 94°C for 20 sec, 65°C for 20 sec and 72°C for 30 sec, with 1°C decrease in temperature for each cycle, followed by 35 cycles of 94°C for 20 sec with constant annealing temperature (59°C) for 20 sec and 72°C for 30 sec, followed by a final extension for 20 min at 72°C. After getting the amplified PCR products, agarose gel (1.2%) was used for checking the amplification of markers. The PCR products with good amplification were then used for estimating fragment size.A total of 154 SSR markers were used in the present study and details of these markers have been provided in For estimating amplicon length size, amplified PCR products were diluted to 60-100 folds in order to use them for multiplexing SSRs based on their fluorescent labels and amplicon length. SSRs with different labels and allele size ranges were considered together to get good multiplexes. The PCR products (1 µl) with GeneScan 500 LIZ standard (Applied Biosystems) from all the SSRs of a single multiplex were then mixed with formamide (1 µl) in each well. Capillary electrophoresis  was then used to analyse amplified products. Result files were then transferred to computer to do allele sizing using GENEMAPPER v4.0 software (Applied Biosystems). In addition, few SSRs which were not amenable to ABI genotyping, PCR products of these markers were analysed on 6% non-denaturing polyacrylamide gels (PAGE) (29∶1 acrylamide/bisacrylamide) and visualized by silver staining.http://www.diversityarrays.com/molecularprincip.html), however, the method is briefly described below. DArT technology consists of several steps such as complexity reduction of the DNA of interest, library creation, microarraying libraries onto glass slides, hybridisation of fluoro-labelled DNA onto slides, scanning of slides for hybridisation signal and data extraction for analysis. The complexity of a DNA sample was reduced to obtain a ‘representation’ of that sample and then variation for that representation is determined which reflect sequence variation. DArT markers detect variations of its presence vs. absence in a genomic ‘representation’ through hybridisation to DArT array consisting of a library from peanut. In this case, earlier a library for peanut representing mixture of genomic ‘representations’ from a pool of individuals covering the genetic diversity of the species is amplified. These fragments were then cloned into a vector that was introduced into E. coli to form a library and each colony contained one of the fragments from the genomic ‘representation’. Currently the high-throughput capability of DArT is based on a microarray platform and, selections of clones from the library are arranged into a plate format  after library creation. The fragments within the library were amplified and spotted onto glass slides using a microarrayer to form a genotyping array. After washing and processing of these hybridised slides to remove unbound labelled DNA, the slides are then scanned using a scanner to detect fluorescent signal emitted from the hybridised fragments. Finally, the result from each fluorescent channel is recorded and the data from the scanned images was extracted and analysed using the DArTsoft software and the information is managed by the DArTdb Laboratory Information Management System.DArT arrays in peanut have been developed by DArT Pty Ltd, Australia in collaboration with ICRISAT (India). The peanut ‘reference set’ has been genotyped with a DArT array consisting of 15,360 features. A total of 4,597 markers/features were polymorphic which were used for population diversity and association study. The detailed method of genotyping is available in the website of , oil and nutritional quality , physiological traits , yield and its components traits . Evaluation and characterization was done in a total of 467 environments at 157 locations . These 14 locations from eight countries included Patancheru, Bangalore, Dharwad, Raichur, Jalgaon, Durgapura and Coimbatore in India, Malawi, Mali, Senegal, Tanzania, Niger (ICRISAT Sahelian Centre), Vietnam and Thailand. The details on standard procedure for phenotyping are explained in several earlier published literature The peanut ‘mini core collection’ is a subset of the ‘reference set’ and either complete ‘reference set’ or ‘mini core collection’ was characterised for a total of 50 agronomic traits . These tThe polymorphism information content (PIC), major allele frequency, number for observations, availability and gene diversity were calculated using the software PowerMarker ver. 3.25 The genetic structure and number of subgroups of this germplasm set was estimated using the model-based Bayesian clustering method implemented in STRUCTURE software version 2.1 Estimated likelihood values [LnP(D)], log likelihood of the observed genotype distribution in K clusters obtained from STRUCTURE runs against K was used to predict the most probable number of subgroups in the population. The delta-K value best describes the population structure based on the criteria of maximizing the log probability of data or in other words the value at which LnP(D) reaches a plateau and hence, delta-K was calculated in order to have appropriate subgroups in this population.2) between the pairs of loci were used for calculating LD 2 values were plotted against the genetic distance (cM) between markers.Linkage disequilibrium (LD), which provides an estimate for number of markers required for conducting genetic/QTL mapping and GWAS, sometimes influenced by population structure and subgrouping derived based on the demographic and breeding history of the accessions included in the germplasm set. Genomewide LD in the present set was estimated by pair-wise comparisons among the genome anchored 139 SSR markers. Squared allele frequency correlations (r2) by the marker is reported In order to conduct precise marker-trait association analysis, population structure and Q values from the software STRUCTURE while principal components (PCs) obtained from TASSEL were used as covariates during MLM analysis. Further among different options available within MLM, the widely adapted approach called “optimum levels of compression in combination with P3D” for variance component estimation was used for association analysis. For MLM analysis, marker-based kinship matrix (K) obtained using TASSEL was used along with the Q matrix to correct for both family and population structure and the phenotypic variation explained (r−6 at a significance level of 1% after Bonferroni multiple test correction (0.01/4751). The denominator in the Bonferroni correction is the total number of markers tested.Of the several methods suggested to correct false positive in association analysis even keeping stringent p-value benchmark, the most stringent correction method called “Bonferroni Correction” was used in the present analysis. The threshold was found to be 2.1×10Table S1Details on the peanut ‘reference set’ in terms of genome type, origin and biological status. This table contains list of genotypes used in this study. It also contains information on species, market types (runner or bunch), genome information, geographical region and biological status for all the genotypes.(XLS)Click here for additional data file.Table S2Summary on phenotyping data collected on the peanut ‘reference set’. This table contains information on names of locations where phenotyping experiment was conducted. It also contains information for each trait about number of environments and locations.(XLS)Click here for additional data file.Table S3Major allele frequency, gene diversity and PIC content of 4597 DArT loci assayed on the peanut ‘reference set’. This table contains information on major allele frequencies estimated, number of alleles scored, gene diversity calculated and polymorphic information content (PIC) calculated for each polymorphic DArT marker.(XLS)Click here for additional data file.Table S4Major allele frequency, gene diversity and PIC content of 154 SSR loci screened on the peanut ‘reference set’. This table contains information on major allele frequencies estimated, number of alleles scored, gene diversity calculated, heterozygosity observed and polymorphic information content (PIC) calculated for each polymorphic SSR marker. The information on sequence and source of development of these SSRs have also been provided in this table.(XLS)Click here for additional data file.Table S5Marker allele unique to the genotype identified for all the genotypes of the peanut ‘reference set’. This table contains information on unique allele identified for selected SSR markers which can differentiate each genotype from the other genotypes. Majority of the genotypes could be identified by unique allele detected by a single marker and the remaining genotypes could be discriminated by using a combination of unique alleles for two or more than two SSR markers. Alleles highlighted with green colour indicate uniqueness to a single genotype. Dark orange colour indicates that this allele is present only in two genotypes; blue colour alleles are present in three genotypes while purple colour indicates presence in four or five genotypes.(XLSX)Click here for additional data file.Table S6List of significant marker trait association (MTA) identified after Bonferroni correction for different agronomical traits. This table contains information on the entire 524 marker trait associations (MTAs) identified for different traits. Information on linked marker, F Value, p-value, phenotypic variance (PV) explained, genetic variance, residual variance and likelihood have been provided for all the MTAs.(XLS)Click here for additional data file.


Missing data are an unavoidable component of modern statistical genetics. Different array or sequencing technologies cover different single nucleotide polymorphisms (SNPs), leading to a complicated mosaic pattern of missingness where both individual genotypes and entire SNPs are sporadically absent. Such missing data patterns cannot be ignored without introducing bias, yet cannot be inferred exclusively from nonmissing data. In genome-wide association studies, the accepted solution to missingness is to impute missing data using external reference haplotypes. The resulting probabilistic genotypes may be analyzed in the place of genotype calls. A general-purpose paradigm, called Multiple Imputation (MI), is known to model uncertainty in many contexts, yet it is not widely used in association studies. Here, we undertake a systematic evaluation of existing imputed data analysis methods and MI. We characterize biases related to uncertainty in association studies, and find that bias is introduced both at the imputation level, when imputation algorithms generate inconsistent genotype probabilities, and at the association level, when analysis methods inadequately model genotype uncertainty. We find that MI performs at least as well as existing methods or in some cases much better, and provides a straightforward paradigm for adapting existing genotype association methods to uncertain data. Genetic research has been focused at analysis of datapoints that are assumed to be deterministically known. However, the majority of current, high throughput data is only probabilistically known, and proper methods for handing such uncertain genotypes are limited. Here, we build on existing theory from the field of statistics to introduce a general framework for handling probabilistic genotype data obtained through genotype imputation. This framework, called Multiple Imputation, matches or improves upon existing methods for handling uncertainty in basic analysis of genetic association. As opposed to such methods, our work furthermore extends to more advanced analysis, such as mixed-effects models, with no additional complication. Importantly, it generates posterior probabilities of association that are intrinsically weighted by the certainty of the underlying data, a feature unmatched by other existing methods. Multiple Imputation is also fully compatible with meta-analysis. Finally, our analysis of probabilistic genotype data brings into focus the accuracy and unreliability of imputation’s estimated probabilities. Taken together, these results substantially increase the utility of imputed genotypes in statistical genetics, and may have strong implications for analysis of sequencing data moving forward. Genome-wide association studies (GWAS) have become a primary tool to elucidate the correlations between SNP genotypes and complex phenotypes in large cohorts. Association studies initially assumed the existence of genotype calls: for each sample at each assayed variant, either reference allele homozygote, heterozygote, or alternate allele homozygote. As such, methods developed for analyzing GWAS assumed the existence of such perfect-confidence genotype data. The association study design and related analysis methods have remained in force even as the field has transitioned into the sequencing era and more complete data have become available. In all situations, due to technical and financial limitations, association studies only partially assay the set of common variants in any organism. The variants included on a SNP array typically only include a small fraction of the total pool of variants present, and even sequenced variants are called incompletely and inconsistently. Furthermore, due to the low magnitude of effect of most trait-associated variants, studies prioritize sample size via multisite meta-analysis, involving genetic samples assayed on different technologies. This study design results in a complicated missingness pattern across the entire conceptual set of common variants in a sample. Yet due to shared linkage disequilibrium between different samples, this missingness can be overcome with the addition of external reference data.Genotype imputation probabilistically estimates unknown genotypes for a study sample by leveraging external reference haplotypes ascertained at a superset of SNPs . GenotypThree primary methods have been developed for the handling of uncertain data in genetic association studies. For the first method, and at one extreme, probabilities may be converted to call-like integral counts by choosing the class with the largest probability. This paradigm requires no additional modification of the analysis method for genotype calls, but all meaningful information about uncertainty is lost. For the second method, in some situations, genotype probabilities may be converted into expected counts of the reference allele: the “allelic dosage.” This strategy attempts to maintain some of the uncertainty of the genotype estimate by allowing non-integral values: for example, when the heterozygote and reference allele homozygote classes are equiprobable and the alternate allele homozygote has 0 probability, a sample is considered to have 1.5 alleles. Unfortunately, this strategy is only useful when the underlying algorithm extends to nonintegral data ; furthermore, there is no rigorous proof of the degree of bias or information loss incurred using this method. Finally, as a last method, the algorithm may be modified to directly operate on probabilities . This opβ and standard error estimate s. Importantly, MI is not a method of estimating hidden data, but rather a method of handling existing estimates. The performance of MI, and indeed of all imputed data analysis methods, is reliant on the quality of the underlying genotype imputation. Imputation accuracy, the agreement between predicted and true genotype, tends to vary across both imputation “quality,” as estimated by most imputation software, and minor allele frequency. The extent of this variable performance has not, to our knowledge, been rigorously assessed.In the statistics literature, Multiple Imputation  is the In this study, we seek to rigorously evaluate this variable genotype imputation performance. We show a significant deviation between genotype probabilities generated by imputation, and empirical probabilities estimated at the same sites. This failure of probability consistency is an important confounding effect in imputed data analysis. We show that Multiple Imputation matches or improves upon performance of existing imputed data analysis regimes by better prioritizing true positive associations, while additionally being straightforwardly extensible to future analysis algorithms.r2”). We observe that a single quality metric r2 masks significant deviations in mean quality between different genotype classes and different allele frequencies. For imputed SNPs reported to be of high quality . Our−8. Nevertheless, if the variants are associated with the trait at all, one expects the variants to be relatively better ranked in the final list of variants than variants chosen at random from the study. We conducted a BMI genome-wide association study in Health ABC, as discussed in Methods. Using these SNP association results, we computed the rank percentile of each published variant, comparing these results for two existing imputed data analysis algorithms and MI.We next sought to evaluate whether the ability to prioritize verified trait-associated SNPs in an association study ranking was detectably different using MI or other existing algorithms. We considered 73 replicated loci from a large  GWAS for BMI . As expep < 2 ⋅ 10−16). We repeat this analysis with a height GWAS in NFBC66 (p < 0.0003764). We conclude from this analysis that significant information loss may occur when uncertainty is incompletely handled in imputed data analysis. There is no evidence in this comparison that Multiple Imputation is inferior to other methods.We evaluated the receiver operating characteristic curves for PLINK dosage, SNPTEST score, and MI . MI signn NFBC66 , and finr2, implying that different association tests may lead to different expected distributions of variants in a ranked association study across the quality spectrum, whether variants are trait-associated or not.Improved signal detection by MI may be attributed to several causes. In observing the additional variance component in the multiple imputation model, sB2 see , we notep < 2 ⋅ 10−16; comparable results for nonparametric tests). PLINK and SNPTEST have indistinguishable magnitudes of effect . Multiple Imputation shows a significantly stronger correlation than the other tests . MI produces test statistics that much more significantly incorporate uncertainty, through in particular the between-draw variance component.We sought to evaluate the null distribution of variant ranks across imputation quality. We calculated the percentile rank of each variant in the BMI GWAS and sorted the variants into bins across imputation quality. Results from this investigation are shown in r2 ± 0.01 and frequency ±0.01 of the true variant. Here we are drawing variants from the association study itself; this null allows us to detect effects specifically affecting the significant tail of the distribution over the bulk of variants. For most traits, these variants will overwhelmingly be unassociated with the outcome, and thus this null will correspond to a true null of no genetic association; in the case that this condition is untrue, this null will lead to relatively conservative assumptions about the added performance of MI. We then regenerated ROC curves for each analysis, this time controlling for the null rank of SNPs matched to the published variant list based on these parameters. These results approximately correspond to trait-specific enrichment effects caused by the various analysis methods. We find that adjustment for trait-secular shifts in variant quality affects BMI and height differently. For BMI, MI continues to outperform PLINK and SNPTEST (one-tailed DeLong p < 1.177 ⋅ 10−10). For height, null adjustment reverses the previously-observed trends, such that MI tends to underperform PLINK and SNPTEST (one-tailed DeLong p < 0.0025). We note that for this trait in particular, the null derived from drawing from the association study itself may be conservative given the broad genetic basis for human height )The use of MI is not completely foreign to the field of statistical genetics. We note, however, that its use is very limited, and has not been extensively compared to other, prevalent methods of handling genotype probabilities. We evaluate MI in comparison to existing methods and show MI performance is typically comparable to existing methods, and in certain contexts significantly outperforms other existing algorithms. Furthermore, we emphasize the ease with which MI is extended to, conceptually, all existing and future genotype call analysis methods with little additional effort. We foresee MI as a simple and effective component of all probabilistic genotype analysis.For this project, we applied for access to and downloaded two SNP array and phenotype datasets from the Database of Genotypes and Phenotypes (dbGaP) . The Whoμ = 27.4, σ = 4.77) determined by age  and sex . For NFBC66, we conducted a height association study with height in centimeters stratified by sex ; age was not included for this single-year birth cohort.The phenotype data released under this collection are quite limited. For this study we limited analysis to simple anthropometric traits. For Health ABC, we conducted a BMI association study with BMI  + P(Aa) is included in a generalized linear model . This method projects the two parameter posterior probability into a single dimension, thus losing information in many cases: for example, this would consider the posterior probabilities {0, 1, 0} and For standard association models under generalized linear models (in this case linear regression), various methods currently exist for analyzing probabilistic genotypes. We selected two widely-used methods of analysis for the purposes of this study. The genetics software PLINK, as of version 1.07, has an “allelic dosage” method of genotype imputation, in which the additive predictor https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.v2.pdf. For this investigation, we solely use SNPTEST’s frequentist methods, which can explicitly handle uncertainty.The IMPUTE2 software used for imputation in this study has an accompanying analysis software package, called SNPTEST [Y containing all phenotype and covariate data for a study. Classical statistical analysis assumes Y is complete, containing no null entries, and that all entries are known with perfect confidence. Study designs with null entries can be compelled into this format by removing all null datapoints before statistics are performed, resulting in a so-called “complete-case analysis.”Genotype imputation is a discipline-specific solution to a general statistical problem called “informative missingness” . ConsideY is the conceptual matrix containing all true datapoints with perfect confidence. Missingness observed in realistic studies is encapsulated by a second matrix, M, where each entry Mij is 1 if Yij is missing in the true study, and 0 otherwise.The effect of deviations from these assumptions vary depending on the characteristics of the missingness itself. Now assume that M is independent of Y, the missingness is called “missing completely at random” (MCAR). In this situation, corresponding to the classical model, all missingness can be safely ignored in downstream analysis, with a potential loss in statistical power but no introduction of bias.Using this framework, missingness can be partitioned into three general classes. If the distribution of M is dependent on the data matrix Y, missingness can be classified in two separate cases. If the dependency can be reduced to simply the observed subset of Yobs, where Y = Yobs ∪ Ymis, then the missingness is, somewhat misleadingly, termed “missing at random” (MAR). Data that are MAR cannot be ignored while safely avoiding the introduction of bias. However, due to the restriction on Yobs, the missing values can be probabilistically imputed from Yobs and added into downstream statistical analysis.If instead the distribution of M is irreducibly dependent on Y; such missingness is called “not missing at random” (NMAR). NMAR data cannot be removed without potentially introducing bias, and furthermore cannot be predicted solely from the observed data Yobs. The case of genetic data collection is invariably NMAR, as missingness created by collection technologies such as SNP arrays and sequencers exhibit different performance at different underlying genotypes, and the selection of variants for SNP arrays is itself biased by numerous factors including but not limited to predominant ancestry in early variation projects such as HapMap, variant location in the genome and neighboring sequence content, and allele class at the site of interest.In the worst case, the distribution of Y matrix). The resulting partition of this matrix Yobs now ideally contains sufficient information to probabilistically estimate missing genotype data in the original dataset at both typed and untyped variants.Genotype imputation is an attempt to project the NMAR missingness of genetic data collection into an MAR condition. Due to linkage disequilibrium (LD), the nonrandom segregation of neighboring variants over a limited number of generations, one can add externally collected, ancestrally related haplotypes to the data  may be found at d individual MI draws, the following meta-analysis regime applies [With Multiple Imputation, a simple regime for seamlessly correcting for different proportions of uncertainty in the contributing analyses is available. Extending the logic used when combining  applies , for M cnested model multiple imputation is dm) may be used for the weighting factor in the total variance and degrees of freedom.The new variance component S1 FigData are estimated from 10% of the original chip masked from imputation. Discordance of predicted allelic dosage (y-axis) is the fraction difference between dosage computed from imputation probabilities and dosage based on masked genotype data: for example, if the true genotype is reference homozygote and the allelic dosage from imputation is 1.4, the discordance is (TIFF)Click here for additional data file.S2 Figx-axis: IMPUTE2 info  metric; y-axis: proportion of full set of variants within this quality bin. Distribution is left-truncated at common quality threshold.(TIFF)Click here for additional data file.S3 Figr2 metric; y-axis: proportion of full set of variants within this quality bin. Distribution is left-truncated at common quality threshold. Final bin with quality greater than 1 indicates small percentage of variants where empirical variance exceeds that of the expected binomial distribution.x-axis: minimac3 (TIFF)Click here for additional data file.S4 FigData are estimated from 10% of the original chip masked from imputation. Discordance of predicted genotypes (y axis) is the fraction of best guess genotypes for a given bin that do not match the corresponding masked genotype. Left panel: imputation quality greater than 0.9; right panel: quality between 0.8 and 0.9. Clusters correspond to minor allele frequencies of 10%; individual bars represent quality stratified by masked genotype. Error bars represent 95% confidence intervals of mean discordance estimate.(TIFF)Click here for additional data file.S5 FigData are estimated from 10% of the original chip (59808 SNPs) masked from imputation. x-axis: 0.02-width bins of imputation probabilities; y-axis: mean deviation between expected and observed accuracy. Data series correspond to results stratified by genotype class. Left panel: minor allele frequency greater than 0.4; right panel: minor allele frequency less than 0.1. Error bars represent 95% confidence intervals around mean consistency estimate.(TIFF)Click here for additional data file.S6 Figx-axis: number of MI draws; y-axis: observed Pearson correlation coefficient between regression coefficient estimates from masked data and estimates from MI on imputed estimates over masked sites. Horizontal line corresponds to correlation between masked data and “best guess” genotypes using imputation probabilities.(TIFF)Click here for additional data file.S7 Figx-axis: number of MI draws; y-axis absolute change in between-draw variance component from one MI round to the next. Note this is a traditional boxplot but the boxes are tightly clustered around y = 0, leading to the boxes rendering as the small thick back lines for each x value at y = 0. The emphasis of these results is on the distribution of outliers, corresponding to low-quality imputed variants with trait-correlated uncertainty.(TIFF)Click here for additional data file.


Infinium SNP data analysed as continuous intensity ratios enabled associating genotypic and phenotypic data from heterogeneous oat samples, showing that association mapping for frost tolerance is a feasible option.Oat is sensitive to freezing temperatures, which restricts the cultivation of fall-sown or winter oats to regions with milder winters. Fall-sown oats have a longer growth cycle, mature earlier, and have a higher productivity than spring-sown oats, therefore improving frost tolerance is an important goal in oat breeding. Our aim was to test the effectiveness of a Genome-Wide Association Study (GWAS) for mapping QTLs related to frost tolerance, using an approach that tolerates continuously distributed signals from SNPs in bulked samples from heterogeneous accessions. A collection of 138 European oat accessions, including landraces, old and modern varieties from 27 countries was genotyped using the Infinium 6K SNP array. The SNP data were analyzed as continuous intensity ratios, rather than converting them into discrete values by genotype calling. PCA and Ward’s clustering of genetic similarities revealed the presence of two main groups of accessions, which roughly corresponded to Continental Europe and Mediterranean/Atlantic Europe, although a total of eight subgroups can be distinguished. The accessions were phenotyped for frost tolerance under controlled conditions by measuring fluorescence quantum yield of photosystem II after a freezing stress. GWAS were performed by a linear mixed model approach, comparing different corrections for population structure. All models detected three robust QTLs, two of which co-mapped with QTLs identified earlier in bi-parental mapping populations. The approach used in the present work shows that SNP array data of heterogeneous hexaploid oat samples can be successfully used to determine genetic similarities and to map associations to quantitative phenotypic traits.The online version of this article (doi:10.1007/s00122-016-2734-y) contains supplementary material, which is available to authorized users. Avena sativa L.) is an important crop grown on nearly 10 million hectares worldwide , which is an orthologue to the ArabidopsisFlowering Locus T responsible for flower initiation  and Frost Resistance-2 (Fr-2) are the main QTLs for frost tolerance in Triticeae and were mapped on the homoeologous group 5 chromosomes  transcription factors  were identified for Fr-A2, based on sequence polymorphisms in the region CBF-A12/CBF-A15 and copy number variation of CBF-A14. An increased copy number of Vrn-A1 enhanced frost tolerance but only when the T allele was present at the Fr-A2 locus  was identified on the barley chromosome 1H accounting for 48 % of the phenotypic variation in two double haploid populations originating from winter and facultative types —opens new perspectives to the study of adaptive traits in oats.Knowledge on molecular pathways underlying frost tolerance, sensitivity to photoperiod and vernalization in oat is still poor. To this date, the main studies on oat winter hardiness were based on linkage mapping in bi-parental populations, using a limited number of AFLP, SSR or DArT markers. Recently, the Infinium 6K oat SNP array was developed  interpretation errors involved in genotype calling, which is particularly challenging in allopolyploid genomes. The objectives of the work were: (1) to study genetic diversity in European oat germplasm, (2) to show the feasibility of GWAS based on continuous SNP signal ratios in a collection including heterogeneous samples for simple traits such as lemma color and hullessness, whose phenotypic variation is explained by few major genes, (3) to use GWAS and chlorophyll fluorescence data for identifying QTLs affecting frost tolerance in oat.A. strigosa accessions and 133 hexaploid A. sativa accessions, including landraces and cultivars used for spring and winter sowing and representing about 100 years of European oat breeding history. Further information about genebank holder, taxonomy, country of origin, growth habit and year of registration can be found in Online Resource 1.A collection of 138 oat accessions, originating from 27 European countries, was analyzed in the present study. It contains a few diploid To take into account intra-varietal genetic variability, young leaves from 10 plants for each accession were pooled and genomic DNA was extracted using a CTAB solution followed by chloroform extraction.R) and intensity ratios were used for marker and sample quality checking. Single data points with extreme R values (R < 0.25 or R > 2.5) were set as missing and markers with more than 25 % missing values were excluded. Variance of intensity ratios per marker was used to remove uninformative markers (variance < 0.001). After these filters, remaining missing values were replaced by the mean ratio per marker. Moreover, seven samples were removed, since the intensity ratio frequency distribution per sample indicated high levels of heterogeneity. These quality checks resulted in a dataset of 131 samples, including five diploid A. strigosa samples, and 3567 SNPs.About 5000 SNP markers were assayed using the Infinium 6K Oat array —was divided into two sets, hereafter referred as AVEQ08 and AVEQ09 containing 75 and 62 accessions, respectively, which were evaluated in eight European locations  either in 2008 (AVEQ08) or in 2009 (AVEQ09). Eleven accessions were included in both sets as standard cultivars. The two sets were spring-sown in randomized block experiments and were phenotyped for hull percentage and days to heading. In addition, lemma colour was evaluated in the Fiorenzuola d’Arda  field trials for both populations, using the standard 1–6 scale .The mapping population—containing 126 hexaploid samples , as described in Rizza et al. . a et al. . The meahclust function of the R package stats (R Core Team prcomp (package stats). Genetic variation within and between the identified clusters was tested by Analysis of Molecular Variance  of the marker signal ratios. Ward’s clustering was performed using the algorithm implemented in the  Paradis .Fv/Fm data, ranging from 0 to 0.77, were bimodally distributed with highest frequencies close to extreme values of the scale, as expected for freezing tolerance experiments that comprise susceptible damaged and tolerant non-damaged plants  or a more extreme value to identify especially the most tolerant accession (0.6) as threshold, values above the threshold being 1 (tolerant) and below the threshold being 0 (susceptible). These 1/0 scores were subjected to logistic regression using the function glm of the R package stats , including accession and environment as fixed factors. Accession adjusted values were used to perform association mapping. Chlorophyll n principal components calculated using all markers: p values from the association analyses were plotted versus the expected p values, which are assumed to be uniformly distributed, under the null hypothesis.Genome-wide association analyses were performed using and comparing alternative models, all fitted in GenStat  before association analysis, as is usual in standard approaches using discrete genotypes.LD.Measures from the R package LDcorSV, correcting for population stratification using a covariance matrix between individuals  within chromosomes were calculated and grouped in 1 cM large bins. Then, the 90th percentiles of the R squared distributions per bin were smoothed by a spline function.Analysis of Linkage Disequilibrium (LD) decay as function of the pairwise distance between markers was conducted using the function A. strigosa accessions were included in a first PCA and then removed from the following analyses, as they clearly clustered far from hexaploid oat samples (data not shown). Ward’s hierarchical clustering revealed the presence of two main groups, which can be further subdivided giving a total of eight subgroups. In Fig. In this study, hybridization intensities from the Illumina 6K Oat SNP array were used to determine population structure in a collection of 131 European oat accessions  found a significant difference between the Mediterranean/Atlantic Europe (A) and the Continental Europe (B) groups, explaining 21.9 % of the total variance  oats were represented by six accessions in AVEQ08 and three accessions in AVEQ09, two of them in common to both groups as standard cultivars. The accession adjusted values for hull percentage were clearly distributed into two classes, in the range 6–18 % for naked and multiflorous oats and 36–62 % for covered oats (Online Resource 3).Lemma colour scores of the populations ranged from 1 to 5, with a similar frequency distribution. The pigmented categories  together represented 20 % of the total number of accessions (Online Resource 3).Days to heading was in the range 56–82 for AVEQ08 and 60–92 for AVEQ09 (Online Resource 3).p ≤ 0.0001). Frequency distribution plots of frost tolerance scores can be found in Online Resource 3. Residual plots were performed and no substantial deviations were observed from the assumption of normally distributed residuals with constant variance.Chlorophyll fluorescence measurements were used to evaluate frost tolerance. The AVEQ08 and AVEQ09 accessions were separately phenotyped in five experiments per set. Frost tolerance scores ranged from 0.11 to 1 in AVEQ08 and from 0.07 to 0.98 in AVEQ09. Logistic regression provided a good description of the data for both populations and accession effects were highly significant  for AVEQ08, AVEQ09 and the whole dataset, respectively.The GWAS results presented below were obtained using the model including a kinship matrix based on 302 uniformly spaced markers, since in most of the analyses it outperformed the other models in controlling false positives (Online Resource 7). The choice of this kinship matrix was also supported by the analysis of LD decay, indicating that the subset of 302 markers contains most of the genetic information; by adding more markers, only a slight improvement of LD correction is obtained Fig. . The genP higher than 7) and Mrg04 (seven markers co-mapping at 45.6 cM with −logP ranging from 7 to 9) . Moreover, four markers with unknown map position were significantly associated (Online Resource 4). Although phenotypic variation in AVEQ09 was low (only three naked oats) four regions were found significantly associated and three of them co-localized with the QTLs found in AVEQ08 on Mrg21, Mrg04 and Mrg09 (Online Resource 4). By mapping the naked condition  as a qualitative trait across the whole population (AVEQ08 plus AVEQ09), the two QTLs in Mrg21 and Mrg04 were confirmed and two other markers were found significant in Mrg21 (at 178.3 cM) and Mrg08 (at 132.4 cM)  and GMI_ES_LB_8315 with unknown position, as they were significant for both mapping populations (Online Resource 5). Two other putative QTLs in linkage groups Mrg03 (56.2 cM) and Mrg17 (84.3 cM) were detected in AVEQ08 and four QTLs in Mrg02 (27.4), Mrg03 (5.1 cM), Mrg04 (32.7 cM) and Mrg12 (35.3 cM) in AVEQ09 (Online Resource 5). Eleven significant markers for AVEQ09 were excluded because of the presence of outliers.P values for marker effects for AVEQ08 are shown in Fig. P greater than six. Association mapping results were not very sensitive to the threshold used for the binomial transformation of Fv/Fm values (data not shown). If separately analysed, frost tolerance scores from the two hardening conditions  were associated to different regions  and Mrg11 (8.8–9.8 cM) and three for AVEQ09 in Mrg12 (58.5 cM), Mrg20 156.7 cM) and Mrg21 (205.7 cM) , Mrg02 (33 cM) and Mrg13 (58.6 cM) for AVEQ08 and in Mrg20 (18 and 115.6 cM) and Mrg21 (122.8 cM) for AVEQ09 (Online Resource 6).In oat genetic diversity studies, dominant and co-dominant markers such as AFLP, SSR and DArT have been used to analyse DNA samples extracted from bulks of several individuals, to take into account the within-accession genetic variability  including principal components as fixed cofactors, similar to the approach implemented in EIGENSTRAT .Our association model including a kinship matrix, based on 302 equally spaced markers, considerably reduced the deviation of the observed Principal components analysis and Ward’s clustering clearly revealed the presence of population structure, mainly related to the geographical origin of the accessions, which is in agreement with several studies of large oat germplasm collections using various genotyping methods  clustered only in group A from Mediterranean/Atlantic Europe, where fall-sowing is permitted by milder winter temperatures. In particular, they mainly clustered into two branches of the Ward’s dendrogram in subgroups 6 and 7, and the available pedigrees lead to the ancestors Winter Turf  and Grise d’hiver, two European winter landraces with many similar features  may be related to growth habit (spring vs. winter type). Indeed, although growth habit classification was not available for all accessions, the most frost-tolerant accessions  and Avena sativa spp. byzantina K. Koch (red oat) is another source of stratification detected by many genetic diversity studies including hexaploid oat  proposed for genome-wide association studies in oat, based on the analysis of LD in a large set of 1205 oat lines  and most importantly was consistent across the two mapping populations, supporting our finding. These results showed that our GWAS approach based on SNP array intensity ratios was effective to detect significant associations for simply inherited traits.The association we found for lemma colour with the marker GMI_ES15_c2369_181 in linkage group Mrg20 (14.7 cM) has never been identified earlier. However, this association showed a very significant Significant associations for frost tolerance were found in linkage groups Mrg01 (101.5 cM) and Mrg11 (9.8 cM) for population AVEQ08 and in linkage groups Mrg12 (58.5), Mrg20 (156.7 cM) and Mrg21 (205.7) for AVEQ09 (Table Vrn-1 and Ft-1 (Vrn-3). Vrn1-T4 showed the highest homology to the wheat gene Vrn-A1. In the Kanota x Ogle map  .Previous studies have identified several QTLs related to frost tolerance or winter hardiness. Nava et al.  cloned tFr-2, affecting frost tolerance in Triticeae, has not been identified yet in oat. Although one of the QTLs we found could potentially represent Fr-2, currently we do not have enough data to discuss it. According to the 6 K SNP array annotation  described above. Six other QTLs with minor effects were located in KO linkage groups 21 + 46_31 + 4, 16_23, 25, 3 + 38, 11_41 + 20 and 22_44_18. The QTL found in KO 21 + 46_31 + 4, and in particular marker BCD1230B, maps in Mrg11 at 40.8 cM in the new consensus map. The most robust association we found in Mrg11 maps at 9.8 cM, which therefore could be considered a new QTL.Using the KO population Wooten et al.  identifiThe two hardening condition had an effect on association mapping as different QTLs were detected, suggesting that the accessions interact differently with the different hardening conditions. Significant associations for frost tolerance in Mrg23 (28.5 cM) and Mrg33 (26.3 cM) were detected only for sub-optimal acclimation condition experiments OR02. PCA plots based on 3567 SNPs for the two main clusters detected by Ward’s method: (a) group A and (b) group B. Accessions are represented by numbers indicating Ward subgroup assignments (PDF 32 kb)OR03. Frequency distribution of the phenotypic variables used for GWAS. Lemma colour accession means (c), accession adjusted values for hull percentage (a) and heading date (e) and frost tolerance scores (g) in AVEQ08 and AVEQ09  are shown (PDF 6 kb)OR04. Manhattan plots of –log P values (ordered by chromosome) calculated by GWAS for hull percentage in AVEQ08 (a) and AVEQ09 (b). Genetic position of markers is relative to the most recently available consensus map for oat OR06. List of associated markers for heading date in AVEQ08 and AVEQ09 (DOCX 12 kb)OR07. GWAS observed p -values versus expected p -values (Quantile–quantile plot) for frost tolerance (a for AVEQ08 and b for AVEQ09), heading date (c for AVEQ08 and d for AVEQ09), lemma colour (e for AVEQ08 and f for AVEQ09), and hull percentage (g for AVEQ08 and h for AVEQ09). The red line represents the p -values distribution for simple association and the blue line represents the p -values for the model that corrects for kinship using a subset of 302 uniformly spaced markers (PDF 365 kb)Below is the link to the electronic supplementary material.


Genomic best linear unbiased prediction (GBLUP) is a statistical method used to predict breeding values using single nucleotide polymorphisms for selection in animal and plant breeding. Genetic effects are often modeled as additively acting marker allele effects. However, the actual mode of biological action can differ from this assumption. Many livestock traits exhibit genomic imprinting, which may substantially contribute to the total genetic variation of quantitative traits. Here, we present two statistical models of GBLUP including imprinting effects (GBLUP-I) on the basis of genotypic values (GBLUP-I1) and gametic values (GBLUP-I2). The performance of these models for the estimation of variance components and prediction of genetic values across a range of genetic variations was evaluated in simulations.Estimates of total genetic variances and residual variances with GBLUP-I1 and GBLUP-I2 were close to the true values and the regression coefficients of total genetic values on their estimates were close to 1. Accuracies of estimated total genetic values in both GBLUP-I methods increased with increasing degree of imprinting and broad-sense heritability. When the imprinting variances were equal to 1.4% to 6.0% of the phenotypic variances, the accuracies of estimated total genetic values with GBLUP-I1 exceeded those with GBLUP by 1.4% to 7.8%. In comparison with GBLUP-I1, the superiority of GBLUP-I2 over GBLUP depended strongly on degree of imprinting and difference in genetic values between paternal and maternal alleles. When paternal and maternal alleles were predicted , accuracies of the estimated total genetic values in GBLUP-I1 and GBLUP-I2 were 1.7% and 1.2% lower than when paternal and maternal alleles were known.This simulation study shows that GBLUP-I1 and GBLUP-I2 can accurately estimate total genetic variance and perform well for the prediction of total genetic values. GBLUP-I1 is preferred for genomic evaluation, while GBLUP-I2 is preferred when the imprinting effects are large, and the genetic effects differ substantially between sexes. IGF2 (insulin-like growth factor 2) in pigs [IGF2, although the product level of the maternally-derived gene was lower than that of the paternally-derived gene in all cases [Genomic imprinting is an epigenetic process that involves DNA methylation and histone modifications that distinguish the expression of maternal and paternal alleles . The exp in pigs . Moreovell cases . Over 70ll cases , and 24 ll cases . Furtherll cases . Thus, iThere are several statistical methods for modeling imprinting effects. Using a mixed model, Schaeffer et al.  replacedA1 and A2 at frequencies 1−q and q, respectively, in the population. Allele frequencies of males and females were assumed to be the same and under Hardy-Weinberg equilibrium. By denoting a genotype, AiAj, Ai and Aj, are the paternally- and maternally-derived alleles, respectively. Following the approach of Spencer [A1A1, A1A2, A2A1, and A2A2 are then given by a, d1, d2, and -a, respectively. In this study, the mean of two heterozygotes and the difference between two heterozygotes were defined as δ and ε:Spencer  first ex Spencer , the genandε and −ε deviations from δ, i.e., d1 and d2 can be rewritten as δ + ε and δ − ε, respectively , the genotypic value of A2A1 is the same as that of A2A2, whereas the genotypic value of A1A2 is the same as that of A1A1. If the paternally-derived A1 allele randomly combines with maternally-derived alleles from a population, the frequencies of the genotypes produced will be 1−q for A1A1 and q for A1A2. The genotypic values of A1A1 and A1A2 are a and d1, respectively. Taking in account the proportions at which they occur, the mean value of genotypes produced from the paternally-derived A1 allele is (1−q)a + qd1. The mean genotypic value in the entire population (μ) is as follows:In this model, the heterozygous genotypic values were + A1 allele is calculated from the difference between the mean value of the genotypes produced and population mean as follows:αm is the average effect of the allele substitution in the paternal gamete and is equivalent to the male breeding value of Spencer [A1 allele is as follows:αf is the average effect of the allele substitution in the maternal gamete and is equivalent to the female breeding values of Spencer [Thus, the average effect of the paternally-derived  Spencer . Similar Spencer . The aveA1A2 is as follows:The genotypic deviation of a particular genotype can be calculated from the difference between its genotypic value and the population mean. For example, the genotypic deviation of α = a + (2q − 1)δ When there is no imprinting (d1 = d2 = δ and ε = 0) α is the same as the average effect of the allele substitution [q − 1)α and 2(1 − q)qδ are the same as the breeding value and dominance deviation of the traditional genetic model. By using δ and ε, a genotypic deviation can be divided into three terms. Under imprinting, the breeding values and dominance deviations are no longer uncorrelated, which means that the total genetic variance cannot be partitioned into the usual additive and dominance variance [α, δ, and εwhere titution  and (2q variance . Therefoα and δ terms (σa ' d ') is equal to 0, as follows:When there is no imprinting, α and ε terms (σa ' i ') is also equal to 0, as follows:The covariance between the δ and ε terms is also equal to 0.Similarly, the covariance between the Alternatively, paternal and maternal gametic variances  and gametic values (GBLUP-I2) are proposed here.y is the vector of the phenotypes; β is the vector of the fixed effects; a, d, and i are the vectors of α, δ, and ε terms, respectively; X, Za, Zd, and Zi are incidence matrices linking the phenotypes to β, a, d, and i, respectively; and e is the vector of errors. The variances of a, d, and i are as follows:First, GBLUP-I1 is defined as follows:andGa, Gd, and Gi are the genomic relationship matrices relevant to α, δ, and ε terms, respectively. These matrices describe the relationships among genotyped individuals and can be constructed by using the information from genome-wide SNPs. Let A1j and A2j be two alleles at the jth SNP and qj be the frequency of Aj2. Ga and Gd are the same as the genomic relationship matrices for breeding values and dominance deviations without imprinting. Thus, Ga and Gd can be calculated as described previously [where eviously ,22:\docuMa and Md are n × Nsnp matrices ; the elements of Ma and Md for the ith individual at the jth SNP are calculated as follows:andandMi is assumed to be a n × Nsnp matrix, and the element of Mi for the ith individual at the jth SNP can be calculated as follows:Similarly, Ma, Md, and Mi describe the coefficients of the α, δ, and ε. terms in Table i and its variance can be derived as follows:ε is the Nsnp dimensional vector of which the jth element is εj. Thus, the variance of i is calculated as follows:The elements of Gi can be calculated using Mi:Consequently, In general, GBLUP includes only breeding values. The statistical model of GBLUP is as follows:Therefore, without imprinting and dominance, the GBLUP model is the same as GBLUP-I1.pat and mat are the vectors of paternal and maternal gametic effects, respectively; and pat and mat, respectively. The variances of pat and mat are as follows:Second, GBLUP-I2 is defined as:n × Nsnp matrices that specify the coefficients of am and af in Table ith individual at the jth SNP are calculated as follows:andmentclass2pt{minimandpat and mat are as follows:Therefore, andαm and αf are the Nsnp -dimensional vectors of αm and αf, respectively. The variance of pat is equal to:where αm at all SNPs as follows:The variance of the paternal gametic effect pat) can be rewritten as follows:From this equation, Var to reach mutation-drift equilibrium [A historical population was simulated to establish mutation-drift equilibrium. The simulated genome comprised 10 chromosomes, each 1 Morgan long, containing 100 000 randomly spaced SNPs and 1000 biallelic quantitative trait loci (QTL). In the first generation of the historical population, the initial allele frequencies of all SNPs and QTL were assumed to be 0.5. A recurrent mutation process was applied with a mutation rate for SNPs and QTL of 1.0 × 10ilibrium .Nsnp was equal to 10 000. Let Q1 and Q2 be two alleles at each QTL. The genotypic values of Q1Q1, Q1Q2, Q2Q1 and Q2Q2, are given by a, d1, d2 and -a, respectively. The value of a was drawn from a gamma distribution with a shape parameter of 0.42 and its sign was drawn at random with equal chance. For QTL with imprinting, the values of d1 and d2 were determined as the product of a and the degree of imprinting (τ). Let Nm and Nf be the number of QTL that are silencing the paternal alleles and maternal alleles. The total number of QTL with imprinting (Ni) was 60 (Nm + Nf = Ni), which were randomly chosen from the 200 QTL. The total genetic effect (gj) of the jth animal was calculated by summing all QTL genotypic values, and its variance NQTL is the number of QTL. To obtain phenotypic values, an environmental effect was added to the true genetic value, which was sampled from the normal distribution, H2 is broad-sense heritability; narrow-sense heritability was set to 0.3. The phenotypic variance was finally standardized to be equal to 1.After 20 000 historical generations, the base population (G0) was generated. In G0, the population size decreased to 300 . 10 000 markers and 200 QTL were randomly selected from the segregating SNPs and QTL with minor allele frequencies greater than 0.05. Therefore, After G0, the subsequent five generations (G1 to G5) were generated. In G1 to G5, 30 males were selected by BLUP on the basis of estimated breeding values and randomly mated to 150 dams to produce 300 offspring . The reference population with both phenotypes and genotypes comprised 1200 individuals from G1 to G4, and the test population with only genotypes comprised 300 individuals from G5.d1 and d2, the number of QTL with imprinting (Ni), and Nsnp were varied to investigate their effects on the performance of GBLUP-I. In the base simulation scenario, τ = 1.0, Ni = 60,  = , Nsnp = 10 000, and paternal and maternal alleles were known. In this scenario, only maternal alleles were silenced. Six alternative scenarios were simulated in addition to the base scenario. In scenario 1, τ = 0.5, 0.75, and 1.0 to meet the condition that − a ≤ d1, d2 ≤ a. In scenario 2, Ni = 20, 60, and 100. In scenario 3,  = , , and . In scenario 4, H2= 0.1, 0.3, and 0.5. In scenario 5, Nsnp = 2000, 10 000, and 50 000. In scenario 6, the paternal and maternal alleles were assumed to be unknown. Parameter settings are outlined in Table The range of In the base scenario, the paternal and maternal alleles were assumed to be known. However, such information is unknown when using real data, because only genotypes are available. In scenario 6, the maternal and paternal origins of specific alleles (phase) were predicted using genotype and pedigree information processed by AlphaImpute software . The phaρ) was assessed as the correlation between estimated and true values. The regression coefficients of total genetic value on its estimate (b) was calculated to assess unbiasedness.Here, we estimated variance components and genetic values using GBLUP and two types of GBLUP-I. Variance components were estimated by average information restricted maximum likelihood (AI-REML) . The refτ and Ni in scenarios 1 and 2. Total genetic variance was underestimated by GBLUP when the degree of imprinting was high. With GBLUP, the estimated total genetic variances were equal to 97.6%, 91.3%, and 82.1% of true variances for τ of 0.5, 0.75, and 1.0, respectively, and 99.3%, 82.1%, and 78.2% of true variances for Ni of 20, 50, and 100, respectively. The estimated total genetic variances by GBLUP-I1 and GBLUP-I2 were almost the same as the true variances regardless of the degree of imprinting.Tables ρ obtained with GBLUP-I1 exceeded those obtained with GBLUP by 1.4%, 3.1%, and 7.8% for τ of 0.5, 0.75, and 1.0, respectively, and by 0.2%, 7.8%, and 8.2% for Ni of 20, 50, and 100, respectively. Compared to GBLUP-I1, the ρ values obtained with GBLUP-I2 were more affected by the degree of imprinting. When Ni was equal to 60 and 100, the ρ values obtained with GBLUP-I2 exceeded those obtained with GBLUP by 6.8% and 11.0%; while, when Ni was equal to 20, ρ was smaller with GBLUP-I2 than with GBLUP. For all values of τ and Ni the b values obtained with GBLUP-I1 and GBLUP-I2 were closer to 1 than with GBLUP. In scenario 3, the predictive abilities of GBLUP-I1 were not affected by the values of Nm and Nf whereas the ρ values with GBLUP-I2 decreased as the difference between Nm and Nf decreased  SNPs may not be necessary for genomic evaluation, even when imprinting effects exist.In a previous study with bovine data, when the number of SNPs was greater than 50 000, reliabilities of genomic evaluations remained almost unaltered as the number of SNPs increased . In thisIn scenario 6, prediction accuracies obtained with GBLUP-I1 and GBLUP-I2 were higher than those obtained with GBLUP when paternal and maternal alleles were predicted. Thus, both GBLUP-I methods can be applied to real livestock data. The phasing accuracy was improved by increasing sample size , number τ varied from 0.5 to 1.0 and Ni from 20 to 100, imprinting variances were equal to 1.4% to 6.0% and 0.4% to 6.9% of the phenotypic variances (1.0), respectively.GBLUP-I1 and GBLUP-I2 could accurately capture the total genetic variance, whereas GBLUP underestimated the total genetic variance. The difference in estimated total genetic variance between GBLUP-I and GBLUP is caused by the imprinting effect. Here, we calculated imprinting variance as the difference in the estimated total genetic variance between GBLUP-I1 and GBLUP. When de Vries et al.  were theNQTL ranged from 50 (Ni = 15) to 1000 (Ni = 300) and QTL were evenly spaced throughout the genome. The value of a was drawn from a normal distribution. In these conditions, the accuracies obtained by GBLUP, GBLUP-I1, and GBLUP-I2 were the almost the same as in the base scenario  in GBLUP-I1. However, there is no biological meaning for these genetic effects. In order to estimate a breeding value and a dominance deviation, the genetic values should be defined by sex, as presented by Spencer [This study partitioned the total genetic value into three estimated genetic effects  prij (A1A2) prij (A2A1) and prij(A2A2) be the probabilities of the genotypes A1A1, A1A2, A2A1, and A2A2 for the ith offspring of future matings and the jth marker. In GBLUP-I1, the elements of coefficient matrices for these offspring  can be calculated from the products of coefficients and the genotype probabilities. For example, the element of Mi for offspring is as follows:When a dominance effect exists, assortative mating or mate allocation can boost the field performance of livestock ,33. SimiLikewise, in GBLUP-I2, the elements of This study proposed two GBLUP methods i.e., GBLUP-I1 and GBLUP-I2, which include imprinting effects at the genotypic and gametic levels, respectively. The GBLUP-I1 and GBLUP-I2 methods accurately estimated the variance components and improved unbiasedness regardless of parameter settings. The accuracies of estimated total genetic values in GBLUP-I1 and GBLUP-I2 increased with increasing degree of imprinting and broad-sense heritability. Compared to GBLUP, the accuracies of estimated total genetic values obtained with GBLUP-I1 were always higher. Thus, in general, GBLUP-I1 should be applied for genetic evaluation. However, GBLUP-I2 is preferred when the imprinting effect is large and the genetic effects differ substantially between paternal and maternal gametes. After predicting the total genetic value by both GBLUP-I methods, assortative mating or mate allocation could be used to boost the field performance of livestock.