The 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation.To address these questions, we perform Hi-C on lymphoblastoid cell lines from 20 individuals. We identify thousands of regions across the genome where 3D chromatin conformation varies between individuals and find that this variation is often accompanied by variation in gene expression, histone modifications, and transcription factor binding. Moreover, we find that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality, and density of local cis contacts. We map hundreds of quantitative trait loci associated with 3D chromatin features and find evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk.Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized. Three-dimensional 3D) organization of chromatin is essential for proper regulation of gene expression –3 and plD organizPrevious studies have used Hi-C to profile 3D chromatin conformation across different cell types , 16, 21,In the present study, we set out to characterize inter-individual variation in 3D chromatin conformation by performing Hi-C on lymphoblastoid cell lines (LCLs) derived from individuals whose genetic variation has been cataloged by the HapMap or 1000 Genomes Consortia . LCLs haTo generate maps of 3D chromatin conformation suitable for comparison across individuals, we performed “dilution” Hi-C on LCLs derived from 13 Yoruban individuals (including one trio), one Puerto Rican trio, and one Han Chinese trio  to idenlimma  . In the two individuals with strong upstream DI signal at the central variable DI region, we found that the center probe is closer to the upstream than the downstream probe, as expected . However, in individuals without upstream DI signal, the center probe is closer to the downstream probe   to examine whether variable regions detected by Hi-C are consistent with distance measurements from imaging data. Focusing on a variable DI region on chromosome 15 . To facilitate this search, we used a method called Bandwise Normalization and Batch Correction (BNBC) that we recently developed to normalize Hi-C data across individuals . BNBC takes contact distance into account as a covariate because batch effects in Hi-C data can be distance-dependent. To identify variable matrix cells, we performed a variance decomposition on Hi-C contact matrix cells, resulting in a measure of biological variability for each bin in the contact matrix , and anti-correlated with the repressive H3K27me3 histone modification  approach that incorporates Hi-C replicates for each LCL  to matrix cells that showed significant biological variability in our samples, as described above. We tested for association in our discovery set between the BNBC-normalized interaction frequency in these variable matrix cells and the genotype of test SNPs in either of the two anchor bins. We selected at most one QTL SNP per matrix cell, using association p = 1.8e−14 for FIRE-QTLs, p = 2.5e−7 for DI-QTLs at positive DI bins, p = 0.008 for DI-QTLs at negative DI bins, p = 3e−4 for INS-QTLs, p = 4.1e−9 for C-QTLs; Fig. p < 0.001 for FIRE-QTLs, p < 0.001 for DI-QTLs at positive DI bins, p = 0.041 for DI-QTLs at negative DI bins, p = 0.005 for INS-QTLs, p = 0.006 for C-QTLs; Fig. To evaluate the reproducibility of each of these QTLs sets , we examined Hi-C data from six individuals who were not included in our discovery set  genetic architecture may have few direct QTL overlaps due to limited power or different study designs, but the shared signal should become apparent when results below the level of genome-wide significance are considered. We thus calculated the fraction of QTLs for a given phenotype X that exceed a nominal level of significance (p < 0.05) when tested for association with a different phenotype Y. We refer to this value as the “nominal fraction” below and in Fig. There is little direct overlap between our different QTL sets  in a large set of 65 YRI LCLs , DNase-sp = 2.5e−16 and bootstrap p = 0.024; INS-QTL with IBD chi-square p = 5.5e−17 and bootstrap p = 0.018; Fig. p = 7.6e−6 and bootstrap p = 0.090; FIRE-QTL with IBD chi-square p = 4.2e−8 and bootstrap p = 0.056; Fig. Finally, we sought to examine whether 3D chromatin QTLs might contribute risk for complex diseases. There are 44 direct overlaps between our 3D chromatin QTLs (or SNPs in perfect LD in the same 40-Kb bin) and NHGRI-EBI GWAS catalog  . (2) Our QTL searches, like most molQTL studies, are not truly genome-wide because testable regions and testable SNPs are pre-selected to limit the search space. These selection criteria can differ widely across studies, making direct QTL-to-QTL comparisons challenging. The observation of genotype-dependent epigenetic signal at 3D chromatin QTLs suggests that at least some 3D chromatin QTLs could also be detected as other types of molQTLs if those studies had enough statistical power. However, the limited overlap between 3D chromatin QTLs and published molQTLs  suggests that the QTLs with largest effects on 3D chromatin conformation are not necessarily the same as those with large effects on other molecular phenotypes, and vice versa. Therefore, it is likely that QTL studies directed toward different types of molecular phenotypes (including 3D chromatin features) are likely to be complimentary rather than redundant.Another key finding of our study is that regions which vary in 3D chromatin conformation between individuals also tend to vary in measures of transcriptional and regulatory activity. This supports the existence of shared mechanisms that underlie variation in 3D chromatin conformation, transcription, and epigenomic properties. Our data does not resolve whether variation in higher-order chromatin conformation leads to variation in gene expression and histone modification state, or vice versa. However, we suspect that no single mechanism or causal hierarchy applies to all regions of the genome with variation in one or more of these molecular phenotypes. Our QTL results suggest that, in at least some cases, genetic variation is likely the underlying mechanism that leads to the observed multi-omic variation. This raises the question of whether 3D chromatin QTLs are fundamentally the same as QTLs previously described for other molecular phenotypes  or, rather, represent a separate set of QTLs not detectable with other methods. This question is difficult to answer in the present study for two main reasons: (1) Our power is limited, and thus, we cannot say with confidence that a given SNP is We expect that future studies with higher resolution Hi-C data and larger sample size will be important to identify functional variants modulating 3D chromatin conformation, and to further dissect the mechanistic relationships between genetics, 3D chromatin conformation, and other molecular phenotypes. We anticipate that these studies will continue to reveal cases in which perturbation of 3D chromatin conformation is a molecular mechanism through which disease-associated genetic variants confer disease risk. The present study provides initial discoveries of genetic influence on 3D chromatin conformation and an analytical framework that we believe will facilitate future efforts to unravel the molecular basis of genetic disease risk.Hi-C was performed as previously described . We notehttp://broadinstitute.github.io/picard/) with default parameters. To ensure that our adapted WASP pipeline removed allelic mapping biases effectively, we simulated all possible 100-bp single-end reads spanning SNPs in our LCLs for chromosome 22 and aligned them back to the genome using our adapted WASP pipeline. We found that no SNPs depart from 50/50 mapping ratio between reference and alternative allele in these simulations.Read ends were aligned to the hg19 reference genome using BWA-MEM  v0.7.8 aWe also took steps to remove any potential artifacts due to HindIII polymorphisms. Hi-C data was obtained by cutting the genome with HindIII, so we reasoned that SNPs or indels that disrupt existing HindIII sites or create novel HindIII sites could lead to differential cutting of two alleles and thus the appearance of differential contact frequency. To mitigate these potential artifacts, we identified all HindIII sites that would be disrupted or created by genetic variants present in our samples, and removed all reads within 1 Kb of these polymorphisms in all individuals.Matrices were generated and normalized as previously described , 61. BriPC1 scores were computed using methods defined previously . BrieflyDirectionality index was computed as previously described . BrieflyInsulation scores were computed as previously described  with somTADs were called using the same approach as described previously . DI valuZ-score to create a mean of 0 and standard deviation of one. To identify significant FIRE bins in each individual, we used one-sided p value < 0.05. Ultimately, merging across all individuals, we identified 6980 40-Kb bins which are FIRE bin in at least one of 12 YRI individuals. Consistent with our previous findings [We first calculated FIRE score for each of 20 individuals, as described in our previous study . Specififindings , we obsefindings  further To estimate variability between replicates, we computed the Pearson correlation coefficient for all pairs of replicates for each score . Replicate pairs then can be divided into two groups based on whether they are from the same individuals or different individuals, as illustrated in Additional file p < 0.05) in any of 40 replicates. DI scores were only tested for bins where strong biases are observed (abs(DI) > 10.82757, which correspond to chi-squared test p value 0.001) in any of 40 replicates. INS were only tested for bins where strong insulation is observed (z-score transformed INS score < − 1) in any of 40 replicates. No similar filterers were performed for PC1 scores. Third, we filtered out any bins that overlapping large SVs  to avoid effect caused by SVs. Specifically, for FIRE, INS, and DI scores, bins that are within 200 Kb, 400 Kb, and 2 Mb respectively upstream or downstream of large SVs were removed. For PC1 scores, bins directly overlapping large SVs were removed. Lastly, we applied limma standard model with individual as a fixed factor and eBayes correction. To estimate empirical false-positive rate (FDR), we bootstrapped replicates to calculate the number of false positives in random background. Briefly, we randomly selected 40 or 22 replicates with replacement for LCL20 and YRI11 respectively and identified variable regions as mentioned above. We performed 1000 permutations and calculated empirical FDR as the average positive hits in 1000 permutations divided by number of hits in real data.To identify regions that are variable across genomes, we used a linear normal model with an empirical Bayes variance estimator using the “eBayes” in limma  functionhttps://www.biorxiv.org/content/10.1101/214361v1). A brief description follows. For each chromosome and for each strata of distance between loci , we correct for unwanted variation by taking the log counts-per-million-transformed values of all samples and generating a matrix whose entries are the observations for that chromosome’s matrix band across all samples (columns indexes samples and rows indexes contact matrix cells with anchor bins separated by a fixed distance). We then quantile normalize this matrix and regress out the impact of known batches  using ComBat [http://www.bioconductor.org/packages/bnbc). Correction of contact matrices was performed on replicate-level data using the following LCLs: GM18486 (YRI-1), GM18505 (YRI-2), GM18507 (YRI-3), GM18508 (YRI-4), GM18516 (YRI-5), GM18522 (YRI-6), GM19099 (YRI-7), GM19141 (YRI-8), GM19204 (YRI-10), GM19238 (YRI-11), GM19239 (YRI-12), GM19240 (YRI-13), HG00731 (PUR-1), HG00732 (PUR-2), HG00512 (CHS-1), HG00513 (CHS-2). We note that NA19239 (YRI-12) replicate 1 and NA19240 (YRI-13) replicate 2 were excluded because the BNBC algorithm requires multiple samples from a given experimental batch to estimate batch effect parameters.To directly compare individual Hi-C contact matrix cells across samples, we sought to remove unwanted per-matrix-cell variation owing to date of processing or other unknown “batch” effects. To this end, we developed Bandwise Normalization and Batch effect Correction (BNBC), described and evaluated in a separate manuscript (preprint on bioRxiv g ComBat  (specifiF test) to test whether there was significant between-individual variation. We used the IHW framework [To identify contacts with significant levels of between-individual variability, we employed the following procedure, which mimics the analysis for INS, DI, FIRE, and PC1, on contact matrices normalized by BNBC see the “” sectionramework  with theTo examine correlation between 3D genome organization and other genome features, we re-identified variable regions with the same pipeline mentioned above using only individuals for which data is available for other genome features, and then computed the Spearman correlation coefficient between 3D genome metrics  and other genome features  for each variabel 40-Kb bin. Signals for each 40-Kb bins were calculated by averaging signals for the bin. Specifically, signals for ChIP-seq were the average signal of all peaks within the bin, signals for RNA-seq were the average FPKM of all genes in the bin, and DNase signals were average signal for each base pair in the bin. In some cases, serval consecutive bins were identified as variable. In these cases, we only kept the bin with strongest signal for other genome features among consecutive bins. To generate random backgrounds, we permutated individual labels for the same set of bins and recomputed Spearman correlation coefficient. Ten thousand such permutations were used to calculate the statistical significance of departure from the null hypothesis in which the median value of true correlation values and permutated correlation values are equal. Similar analysis was performed for variable matrix cells with the following modifications. First, we used the variable matrix cells in the preceding section “Phasing of variants was performed based on HaploSeq pipeline . BrieflyN = 9448 HiCCUPS loops). We limited our analysis to autosomal cis loops in which a CTCF motif in one of the anchor regions overlaps a SNP (N = 572). To evaluate the impact of motif disruption, we first identified eight “key” positions in the CTCF PWM (Jaspar MA0139.1) [N = 142 loops with a motif disrupting SNP in a convergently oriented CTCF motif, which we refer to below as testable loops. For each testable loop, we extracted the Hi-C interaction frequency in the loop bin from each LCL and classified as either “WW,” “SW,” or “SS” depending on the individual’s genotype at the corresponding motif disrupting SNP. To enable aggregation of data across different SNPs, we set the mean “SW” interaction frequency for each SNP to 1 and normalized all values for that SNP accordingly. These values are plotted in Fig. GM12878 loops and motif positions were obtained from Rao et al.  (GSE6352A0139.1)  in whichftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/phase3/integrated_sv_map/ALL.wgs.integrated_sv_map_v2.20130502.svs.genotypes.vcf.gz) and removed bins which overlap one or more structural variants of any size previously annotated in these individuals , or within 200 Kb of large structural variations . These filtering steps yielded a set of 51,511 testable bins, which represent a common starting point for FIRE-QTL, DI-QTL, INS-QTL, and C-QTL searches as described below.To identify testable bins for FIRE-QTL, DI-QTL, INS-QTL, and C-QTL searches, we began with 72,036 autosomal 40-Kb bins based on reference genome hg19. We eliminated “unreliable” bins with effective length, GC content, or mappability equal to zero , resultiN = 4,132,791 SNPs remaining). Finally, where multiple SNPs in the same bin were in perfect LD among 11 unrelated YRI individuals, we selected one with the smallest genomic position (to avoid the introduction of a random selection that would not be perfectly reproducible), ultimately yielding 1,304,404 potentially testable SNPs that served as a common input set to all QTL searches.We began with a list of 15,765,667 variants among all 20 LCL individuals  the minor allele frequency of a variant, (2) the effect size of genotype (a fixed effect), (3) the variability between subjects (a random effect), and (4) the variability of the residuals. For contact QTLs, we also varied the mean of the Hi-C contact frequency in question. For analyses reported, we fixed the number of replicates per subject to be 2 (consistent with our study design). We explored a variety of settings for these parameters to assess power as each variable changes , and the subset of testable SNPs therein .We limited our FIRE QTL search to the subset of testable bins that were called as FIRE in at least one YRI LCL  showed only minor genomic inflation . The linear mixed effects model identified 476, 315, 315, and 1092 SNPs with false discovery rate (FDR) less than 0.20 for FIRE, upstream-biased DI, downstream-biased DI, and INS, respectively. When more than one SNP in the same bin was identified, we selected the SNP with lowest p value among them to be included in the final QTL sets. After this filtering, we ended up with 387 candidate FIRE-QTLs, 268 candidate upstream-biased DI-QTLs, 277 downstream-biased DI-QTLs, and 911 candidate INS-QTLs. As a control for each of these QTL searches, we randomly shuffled the score in question  among all 11 YRI individuals and performed QTL searches on this permuted data. In each of these tests, we found no SNPs associated with the permuted scores at FDR < 0.20.For each test SNP, we identified the 40-Kb bin it belongs to, and fitted a linear mixed effects model, using FIRE, DI , and these missing data points were eliminated from validation analysis. We examined the distributions of scores for each genotype. For each QTL type , we found that the same direction of effect observed in the discovery set is observed on average in the validation set. To assess the significance of this observation, we approximated the null expectation as follows. For FIRE-QTLs, for example, we started from all 128,137 FIRE test SNPs and 5822 FIRE test bins. Note that in our discovery set, we identified 387 FIRE-QTLs, each in a different 40-Kb bin. To create a random control SNP group, we first randomly selected 387 40-Kb bins from all 5822 FIRE test bins. Next, within each select bin, we randomly selected one SNP and combined all these 387 selected SNPs into a control SNP group. We then tested their SNP effect on the six additional individuals. We repeated such sampling with replacement 1000 times, to create a null distribution of positive and negative SNP effect, respectively. We performed the same type of permutations for DI and INS. Similar analysis was performed for C-QTLs with a few modifications. First, we only used replicates from NA19420, HG00512, HG00513, HG00731, and HG00732 as explained in the “To examine epigenetic variation at 3D genome QTLs, we re-analyzed DNase-seq data from 59 LCLs , histonep values from FIRE-QTLs along with all SNPs tested in the FIRE-QTL search (black dots). We also used all tested SNPs in the DI-QTL search (light green dots) as a control set. To assign significance to the overlap, we compared the fraction of SNPs with nominal significance (p < 0.05) in each set: (1) DI-QTL tested SNPs that were not significant QTLs and (2) DI-QTLs. We calculated p values for this comparison by chi-square test. To rule out the effect of sampling bias when selecting a small number of SNPs, we also performed permutation. In each permutation, we randomly selected the same number of SNPs as the real QTL set (from the full set of tested SNPs) and calculated the fraction with nominal significance. We then computed bootstrap p values using 10,000 such permutations under the null hypothesis that the fraction of nominal significance is the same between QTLs and random selected SNPs. For C-QTLs, one SNP may be tested against multiple matrix cells, so we only kept the most significant p value for each SNP to avoid biases toward SNPs with multiple tests.To compare between different 3D chromatin QTLs, we took the raw test results for each QTL set and projected other 3D QTLs into the test results. For example, in Fig. p values) from previous molQTL studies and projected 3D chromatin QTLs into those test results. We calculated the fraction of nominal significance and used chi-square test to evaluate significance between 3D-QTLs and non-3D-QTLs. Similarly, we performed bootstrap to estimate significance empirically. One modification is that we extended our QTL sets by incorporating all SNPs in perfect LD with the same 40-Kb bin because we may not use the same tagging SNP in our study as used in other studies. To ensure a fair comparison, we performed the same extension for the control sets of all tested SNPs.Similar approaches were used to assess overlap between 3D chromatin QTLs and other molQTLs. We obtained full test results  by placing 100 uL of cells at 1 × 10  cells/mLAll BAC clones were ordered from the BACPAC Resource Center at the Children’s Hospital Oakland Research Institute: “U” probe is RP11-74P5, “C” probe is RP11-337 N12, and “D” probe is RP11-248 M23. BAC DNAs were labeled with either Chromatide Alexa Fluor 488-5 dUTP  or Alexa Fluor 647-aha-dUTP  using nick-translation kit , and incubated in 15 °C for 4 h. The nick-translation reaction was deactivated using 1 μL of 0.5 M EDTA, pH 8.0, and heated for 10 min at 65 °C. The probes were then purified using illustra ProbeQuant G-50 Micro Columns  and eluted to a concentration of 20 ng/μL. Probes were mixed with Human Cot-1 DNA  and salmon sperm , and precipitated with 1/10th volume of 3 M sodium acetate, pH 5.2, and 2.5 volume of absolute ethanol for at least 2 h at − 20 °C. Probes were then spun down, washed with cold 70% ethanol, resuspended in formamide and 40% dextran sulfate in 8X SSC, and incubated at 55 °C.Cells on coverslips were blocked with 5% BSA and 0.1% Triton X-100 in PBS for 30 min at 37 °C and washed twice with 0.1% Triton X-100 in PBS for 10 min each with gentle agitation at room temperature. Cells were permeabilized with 0.1% saponin and 0.1% Triton X-100 in PBS for 10 min at room temperature. Next, they were incubated in 20% glycerol in PBS for 20 min, freeze-thawed three times with liquid nitrogen, and incubated in 0.1 M hydrogen chloride at room temperature for 30 min. Cells were further blocked for 1 h at 37 °C in 3% BSA and 100 μg/mL RNase A in PBS. Cells were permeabilized again with 0.5% saponin and 0.5% Triton X-100 in PBS for 30 min at room temperature. Lastly, they were rinsed with 1× PBS and washed with 2× SSC for 5 min. For hybridization of probes, the prepared probes were denatured at 73 °C for 5 min in water bath. Cells were denatured in a two-step process in a 73 °C water bath: 2.5 min in 70% formamide in 2× SSC and 1 min in 50% formamide in 2× SSC. Denatured probes were transferred onto microscope slides, and coverslips were placed on top with cell-side facing down. The coverslips were sealed with rubber cement and incubated overnight at 37 °C in a dark, humid chamber. Next day, coverslips were carefully removed and transferred onto a 6-well plate. Cells were washed at 37 °C with gentle agitation, twice with 50% formamide in 2× SSC for 15 min and three times with 2× SSC for 5 min. The cells were then stained with DAPI , mounted on microscope slides with ProLong Gold Antifade Mountant , sealed with nail polish, and imaged.in cis from those in trans. Distances were measured from the center of one color focus to the center of the closest focus of the other color.Images were acquired with DeltaVision RT Deconvolution Microscope at UC San Diego’s department of neuroscience (acquired with award NS047101). Captured images were processed using the TANGO [http://broadinstitute.github.io/picard). MACS2 [Raw fastq files were downloaded from SRA database for each experiment . Reads were aligned to hg19 reference genome using BWA MEM (Kasowski) or BWA ALN , 45 to e). MACS2  v2.2.1 wRaw fastq files were downloaded from SRA database (SRP030041). Reads were aligned to hg19 reference genome using STAR  v2.4.2a Raw fastq files were downloaded from SRA database for each experiment (SRP007821). Reads were aligned to hg19 reference genome using BWA ALN with the WASP pipeline in single-end mode to eliminate allelic mapping bias. Only reads with high mapping quality (> 10) were kept. PCR duplicates were removed using Picard tools. Bigwig files were generated using makeUCSCfile commands in homer tools  v4.9.1.Raw fastq files were downloaded from SRA database for each experiment (SRP004714). Reads were aligned to hg19 reference genome using BWA MEM v0.7.8 with the WASP pipeline to eliminate allelic mapping bias. Only reads with high mapping quality (> 10) were kept. PCR duplicates were removed using Picard tools. We performed quality control for CTCF ChIP-seq data by FRIP (Fraction of Reads In Peaks) and used datasets with FRIP > 10. Bigwig files were generated using bamCoverage commands in deepTools  v2.3.3. Bigwig files and peaks for H3K27ac, H3K4me1, and H3K4me3 were downloaded from GEO database (GSE62742). Peaks for each mark were merged and then used to compute the averaged signal.Additional file 1: Figure S1. Hi-C derived molecular phenotypes measured across 20 LCLs. FigureS2. FIRE measures density of local interactions. Figure S3. Aggregate looping interactions in each sample. Figure S4. 3D chromatin variation among 20 LCLs and H1-derived lineages. Figure S5. Characterization of variable regions of 3D chromatin conformation. Figure S6. Additional characterization of variable regions of 3D chromatin conformation. FigureS7. Coordinated variation between 3D chromatin conformation and multiple molecular phenotypes. Figure S8. Correlations between 3D genome phenotypes and other molecular phenotypes. Figure S9. 3D chromatin QTLs. Figure S10. Influence of 3D chromatin QTLs on epigenomic and disease phenotypes.Additional file 2: Tables S1-S3. Public datasets re-analyzed in this study, LCLs included in the study, and Hi-C mapping statistics.Additional file 3: Table S4. TAD and boundary calls for each sample.Additional file 4: Table S5. Regions showing evidence of biological variability in 3D chromatin conformation.Additional file 5: Table S6. Matrix cells showing evidence of biological variability.Additional file 6: Table S7. Summary of phasing results.Additional file 7: Table S8. Power calculations at standard p < 0.05 and at p-values corresponding to FDR < 0.2 .Additional file 8: Table S9. 3D chromatin conformation QTLs.Additional file 9: Table S10. Overlaps between 3D chromatin QTL and GWAS catalog.Additional file 10. Review history.


MSX2 and DRD1, were mapped to the narrow significant region, which was involved in embryonic and ovary development and found to be related to egg production, respectively. Moreover, three interesting genes, RHOA, SDF4 and TNFRSF4, identified from three significant loci, were considered to be candidate genes for egg shell colour. Findings in our study could provide worthy theoretical basis and technological support to improve late-stage egg quality for breeders.With the extension of the egg-laying cycle, the rapid decline in egg quality at late laying period has aroused great concern in the poultry industry. Herein, we performed a genome-wide association study (GWAS) to identify genomic variations associated with egg quality, employing chicken 600 K high-density SNP arrays in a population of 1078 hens at 72 and 80 weeks of age. The results indicated that a genomic region spanning from 8.95 to 9.31 Mb (~0.36 Mb) on GGA13 was significantly associated with the albumen height (AH) and the haugh unit (HU), and the two most significant SNPs accounted for 3.12 ~ 5.75% of the phenotypic variance. Two promising genes,  However, the rapid decline in egg quality at the end of the laying cycle , decreased albumen height, and shortened egg storage time) has badly hindered the achievement of this goal. Therefore, improving egg quality plays an important role in realizing this programme and extending the laying cycle.Eggs are rich in protein, fatty acids, vitamins and minerals, etc. and are considered as an excellent source of animal protein. With the improvement of laying hens’ production performance, the laying age of commercial laying hens has been extended from the original 72 weeks to 80 weeks, and some breeding companies have even extended the laying cycle to 100 weeks, proposing the breeding programme “Breeding for 500 eggs in 100 weeks”6, mostly based on F2 crossed populations. Currently, there are 430 QTLs reported to be associated with egg quality in the Animal QTL database 7. Although many studies identified QTLs, they had wide confidence intervals for position and were rarely replicated9. A new era began with the subsequent advance in SNP chip and sequencing technology, and the genome wide association study (GWAS) has become one of the most effective methods to detect genetic variations in livestock. Liu et al. carried out the first GWAS with the Illumina 60 K SNP array10 to uncover the genetic associations with egg quality traits in chickens11. Wolc et al. and Sun et al. subsequently reported many genes related to egg quality that were identified by genome-wide association studies13. Despite all this, the genetic improvement of egg quality is slow because of relatively low heritabilities and intensively artificial selection on egg production.Egg quality is a comprehensive concept that encompasses both internal and external quality and includes many aspects. External quality is composed of eggshell colour, egg shape index, eggshell thickness and eggshell strength, while internal quality refers to albumen height, egg yolk colour and haugh unit. All of these are quantitative traits. With the development of molecular genetics, many studies have been carried out to reveal the genetic determination for egg quality. Microsatellite markers were first employed, and many QTLs were reported through linkage analysisTo our knowledge, little research reported the genetic analysis for egg quality in the late laying period of chickens. In the present study, we employed the commercial chicken 600 K SNP chip to detect the genetic variations associated with egg quality in a population of 1078 hens at 72 and 80 weeks of age by genome-wide association analysis (GWAS) to provide a theoretical basis and technological support for improving late-stage egg quality.The descriptive statistics for eggshell colour (ESC), egg shape index (ESI), eggshell thickness (EST), eggshell strength (ESS), albumen height (AH), yolk colour (YC) and haugh unit (HU) at 2 age points were presented in Table The estimates of SNP-based heritability and genetic or phenotypic correlations among egg quality traits at the two points are shown in Table The analysis of the population structure was shown in Fig. The Manhattan and Quantile-Quantile (QQ) plots of seventeen separate GWASs using the univariate model or the bivariate model for egg quality traits are shown in Fig. As shown in Fig. MSX2), while the SNP rs15695238 was located 170 Kb downstream of dopamine receptor D1 (DRD1). Egg shell colour has high heritability and is affected by polygenes. Five candidate genes, namely, ras homologue family member A (ROHA), stromal cell derived factor 4 (SDF4), TNF receptor superfamily member 4 (TNFRSF4), tubulin tyrosine ligase-like 10 (TTLL10), and LOC419425, and one microRNA (MIR429) were found close to the significant SNPs associated with ESC (Table The annotation of significant SNPs using the Variant Effect Predictor (VEP) tool supplied by Ensembl could help us identify promising genes associated with egg quality traits. The detailed information about the genes was summarized in Table MSX2, DRD1, SDF4, TNFRSF4, TTLL10 and LOC419425, respectively. The effect allele frequency (EAF) ranged from 0.356 to 0.465, which meant that these SNPs were separate. For AH and HU, the phenotypic variance explained by two significant SNPs ranged from 3.12 to 5.75%. The substitution of one copy of EA at the rs31594320 site caused the highest decrease  in haugh unit at 72 weeks of age. The remaining four SNPs accounted for 3.28~4.15% of the variance in ESC. In addition, we compared actual phenotypic differences among the three genotypes of the above SNPs , a total of 67 QTL_IDs were reported for AH and HU. For example, four putative QTL regions were detected on GGA 1, 9, 13 and 23 that were associated with albumen height11, one was detected in the centre of GGA 7, identified by its proximity to a candidate gene18, and 5 regions were reported on GGA2, 3, 4, 9 and Z12. The published QTL regions on GGA13 were close to the genomic region in our study. By annotating the significant SNPs, two promising genes (MSX2 and DRD1) near this region were found to be associated with AH and HU. The MSX2 gene is a member of the msh homeobox family and is expressed in many embryonic tissues. In the developing chick, MSX2 is expressed in the apical ectodermal ridge and the ectoderm of the genital tubercle, and it plays a crucial role in the growth and patterning of the limb mesoderm19. In addition, the expression of MSX2 could influence the function of TNF-α (tumour necrosis factor-α), which is known to suppress adipocyte differentiation and to activate the Wnt/β-catenin pathway21. The Wnt signalling pathway regulates crucial aspects of cell fate determination, cell migration, and cell polarity during embryonic and ovary development22. In addition, a previous proteomic study reported that, in eggs, a portion of the functions of the proteins were associated with embryonic development13. We know that the egg proteins are secreted by the magnum during the egg-laying process. Therefore, we believe that MSX2 gene has an indirect effect on AH and HU during the process of protein formation. Another gene, DRD1, belongs to the D1-like type of dopamine receptor. In birds, dopamine has been revealed to be involved in both stimulating and inhibiting prolactin (PRL) secretion, which has been demonstrated to play a crucial role in the onset and maintenance of incubation behaviour25. Dopamine stimulates PRL secretion by activating DRD1 at the hypothalamus level and inhibits PRL secretion through DRD2 at the pituitary level29. All these studies have indicated that the dopamine receptor participates in the system of regulating avian reproductive behaviour. Like other D1-like members, the chicken DRD1 is an intronless gene and belongs to the rhodopsin family. Recently, Schnell et al. and Chaiseha et al. demonstrated that the DRD1 gene is widely expressed in the hypothalamus and pituitary and that the expression is associated with the functioning of the reproductive system in turkeys31. Moreover, previous findings have revealed that the DRD1 gene and its haplotypes are associated with some egg production traits in chickens32. We suggest that DRD1 could be treated as a candidate gene related to egg quality in further analyses.Genome-wide association analysis of egg quality traits were carried out separately. A significant genomic region of 0.36 Mb on chromosome 13 (GGA13) that harboured 85 unique SNPs (Supplementary Table SLCO1B3 Causes Blue Eggshell in the Chicken)34, but there still exists much uncertainty about the molecular mechanism underlying brown eggshell colour, even though it has been reported to be controlled by multiple minor genes36. Li et al. used quantitative PCR to determine the expression levels of 8 genes encoding enzymes in the liver and shell gland in a Rhode Island Red pure line. The interesting gene ABCG2 might facilitate the accumulation of protoporphyrin IX, which is the main pigment resulting in the brown coloration of eggshell36. In addition, Wolc et al. found two genomic regions located on GGA4 and GGA12 that affect the early and late eggshell colour, respectively, in a pureline Hy-line population12. In the present study, one gene (RHOA) on GGA12 was related to ESC at 72 weeks of age, and four genes, namely, SDF4, TNFRSF4, TTLL10 and LOC419425, were identified by GWAS as being associated with egg shell colour (ESC) at the two periods. The SNP rs315306430 located 4.73 Kb upstream of RHOA overlapped with a previously identified QTL12. RHOA is a small GTPase and belongs to the ras homologue (Rho) family. The Rho family of small GTPases are molecular switches that control a wide variety of cell functions, including cytoskeletal reorganization, cell motility, and gene expression37. In addition, it has been revealed that the RHOA signalling system plays a role in the modulation of actin stress fibres and chondrogenesis38. SDF4 is the stromal cell derived factor 4, and its human orthologue is known as Cab45. A 45-kDa Ca2+-binding protein, Cab45 is important because it can regulate cell migration through various molecular mechanisms39. Another gene, TNFRSF4, encodes proteins and can be used to specifically modulate the expression of other genes that directly stimulate effector T-cell activity40. All the above three genes are associated with body growth and phylogenesis. Recently, a very interesting study reported that eggshell pigment deposition and eggshell coloration were strongly and positively correlated with phylogenesis41. Therefore, we conclude that RHOA, SDF4, and TNFRSF4 affect eggshell colour through the process of phylogenesis. Unfortunately, little information about TTLL10 and LOC419425 could be found in the NCBI GenBank.There is extensive scientific literature regarding eggshell colour, and some studies have concluded that the green colour of the eggshell is controlled by a single dominant gene  in GGA13 was detected to be significantly associated with albumen height and haugh unit. Two promising genes, All the blood sample collections were performed in accordance with the Guidelines for Experimental Animals established by the Ministry of Agriculture of China . The whole study was approved by the Animal Welfare Committee of China Agricultural University (Permit Number: SYXK 2007-0023).th generation population of Rhode Island Red chickens from the Beijing Huadu Yukou Poultry Breeding Co., Ltd. were the experimental animals used in this study. This pure line has been selected for egg production over many generations. Thus, a total of 1,078 hens with accurate pedigrees were chosen for SNP genotyping. The hens were housed in individual cages in the same area with free access to feed and water.An 11https://www.r-project.org/).Egg quality traits including eggshell colour (ESC), egg shape index (ESI), eggshell thickness (EST), eggshell strength (ESS), albumen height (AH), yolk colour (YC) and haugh unit (HU) were measured at 72 and 80 weeks of age. Due to the late laying period, we collected fresh eggs for three successive days to ensure one egg per hen. ESC was measured with a CM-2600D reflectometer  using the three following parameters: L* represents lightness, a* measures the balance of red and green, and b* describes hue of blue-yellow scale. Here, we only used the L* as the eggshell colour phenotypic value. The long and short diameters of each egg were measured with a Vernier calliper, and the egg shape index was calculated as the ratio of the long and short diameters. The eggshell strength (pole to pole) of each egg was measured vertically using an EFG-0502 gauge . Then, we broke the eggs to collect the internal contents, and the AH, YC and HU were measured with the EMT-5200 multi-functional egg analyser . And the HU is also a calculation from AH with egg weight. Finally, we cleaned the eggshell membrane and measured the EST of each egg with an eggshell thickness gauge . Descriptive statistics of all phenotypic records were handled with R version 3.3.1 software , Z is an incidence matrix for random additive effects, a and e are the additive effect and random residual, respectively.Pedigree-based hereditability for ESC, ESI, EST, ESS, AH, YC and HU in the two periods was calculated with the average information restricted maximum likelihood (AI-REML) method supplied by the DMU v6.0 software43, 6,550 SNPs with unknown physical position and 43 markers with repeated genomic coordinates were excluded. The genotype calling and quality control were carried out by the Affymetrix Power Tools v1.19.0 (APT) software, following the pipeline of the Axiom Genotyping Solution. Only individuals with dish quality control (DQC) >0.82 and call rate >97% were included in the downstream analyses. A package of ps-metrics supplied by the APT software was run to calculate the SNP quality, and the lower quality SNPs were filtered out using an R script. After the above QC steps, 1063 individuals and 517,856 SNPs remained. We also discarded SNPs on the sex chromosomes because of the low detection power for associations between phenotypes and sex chromosome genotypes. Moreover, PLINK v1.90 software was used for further quality control  >0.01, Hardy Weinberg equilibrium (HWE) <1e–6)44. The remaining SNPs were used to impute some missing genotypes with the Beagle v4.0 procedure45. Finally, 1063 individuals and 294,705 SNPs distributed among 28 autosomes and two linkage groups . From a preliminary set of 580,961 SNPs46, considering the over-conservation of the Bonferroni method. After the simpleM test, a total of 31,589 effective independent tests were obtained. Then, the genome-wide significance and suggestive significance were calculated as 1.58e–6  and 3.17e–5 , respectively.Prior to the genome-wide association study (GWAS), a principal component analysis (PCA) supplied by the PLINK package was conducted to evaluate the population stratification. We pruned all SNPs to obtain independent SNPs via the option of -indep-pairwise 25 5 0.2. In addition, the kinship matrix was built through the independent SNP markers. The principal components were calculated from the linear combination of markers by the eigenvectors of the kinship matrix, which were treated as covariates and included in model of GWAS as fixed effects to control the population structure effects. We adjusted the P-value threshold of genome-wide significance using the simpleM package47. The statistical model was as follows:α is a vector of corresponding effects that compose the intercept; x is the marker genotypes; β is the corresponding marker’s effect; u is a vector of random polygenic effects with a covariance structure as u~N , where K represents a known n x n genetic relatedness matrix derived from SNP markers and Vg is the polygenic additive variance; and ε is vector of random residuals. The Wald statistical test was applied to test the alternative hypothesis H1:β ≠0 against the null hypothesis H0: β = 0 for each SNP, as Fwald = The univariate GWAS was first implemented using a linear mixed model to account for the associations between each trait and the effective SNPs, which was supplied by GEMMA softwarehttps://cran.r-project.org/web/packages/gap/) and “qqman” (https://cran.r-project.org/web/packages/qqman/) packages in R software, which described the transformed −log10 of the observed P-values against the marker locations on the genome or the expected −log10 . Moreover, the genomic inflation factor (λ) was calculated to judge the extent of false positive signals by the “GenABEL” package in R48.The Manhattan and quantile-quantile (Q-Q) plots were generated for each trait using the “gap”  as covariates into the univariate and multivariate models to elucidate independent signals in the step-wise conditional analysis. The information of significant SNPs were obtained with the annotation of Gallus-gallus 4.0, and candidate genes within 500 kb regions flanking the associated SNPs were identified using Variant Effect Predictor (VEP)50 supplied by Ensembl (http://www.ensembl.org).We performed linkage disequilibrium (LD) analysis in order to characterize causal SNPs in a strong LD region where many significant SNPs were identified by the solid spine algorithm in Haploview version 4.2 as being clustered2snp)51 and pair-wise genetic correlations of egg quality traits were calculated using the restricted maximum likelihood (REML) method with GCTA v1.24 software52. A matrix of genetic relationships was constructed from all eligible SNPs on autosomes and two linkage groups. We then calculated the phenotypic variance contribution of those genome-wide significant SNPs based on the genetic matrix.All SNP-based heritability (hSupplementary Dataset


Bemisia tabaci, is considered one of the most destructive insect pests of vegetables and ornamental crops globally. Synthetic chemical pesticides are mainly used to control B. tabaci, however, their extensive usage has led to a series of detrimental concerns to human health and environmental contamination. It is therefore of significant interest to develop a safer and eco-friendly alternative for controlling B. tabaci. Here, we review the use of entomopathogenic fungi as a proven, biologically sustainable method to effectively control B. tabaci. The development of entomopathogenic fungi in an integrated pest management strategy against B. tabaci can reduce our reliance on chemical pesticides, and help us to secure food safety while preserving nature.The whitefly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae), consists of genetically diverse species known to cause significant destruction in several crops around the world. Nymphs and adults of B. tabaci cause damage to plants during feeding, and they can act as a virus vector, thus causing significant yield loss to crops in the tropical and subtropical regions. Chemical pesticides are widely used to control B. tabaci due to their immediate action, but this approach has several drawbacks including food safety issues, insecticide resistance, environmental pollution, and the effect on non-target organisms. A biological control agent using entomopathogenic fungi (EPF) has therefore been developed as an alternative against the conventional use of chemical pesticides in an integrated pest management (IPM) system to effectively control B. tabaci. It is apparent from this review that species of hyphomycetes fungi are the most common EPF used to effectively control B. tabaci, with the second instar being the most susceptible stage of infection. Therefore, this review article focuses specifically on the control of B. tabaci with special emphasis on the use of EPF as biological control agents and their integration in IPM.Whitefly,  B. tabaci remains one of the most economically important pests of vegetable and ornamental crops worldwide ,13589,13The excessive use of chemical pesticides in agriculture has led to environmental contamination, as well as harmful effects on non-target organisms, including beneficial insects (pollinators), livestock, and humans. The application of EPF may, therefore, provide an alternative to the conventional use of chemical pesticides to effectively control insect pests, while preserving the natural environment, which is one of the main goals of sustainable agriculture .The regulation of insect pests and arachnid populations has been significantly enhanced by the use of EPF, due to their major benefits over the use of chemical pesticides, including broad host range, the absence of harmful side effects for non-target organisms, easy mass production, low cost, and eco-friendliness ,137,138.Although EPF offer several advantages over chemical pesticides, there are some limitations to the application of EPF in controlling insect pests. One of the major limitations of EPF is the lengthy duration of 2–3 weeks to kill the insect pest population, whereas chemical pesticides may take only 2–3 h . EnvironBemisia tabaci is considered a destructive insect pest of numerous crops around the world. Crops are affected directly by B. tabaci during feeding, and indirectly through the transmission of viral diseases, which can cause severe crop damage and yield losses worth millions of dollars. The control of B. tabaci relies heavily on pesticides despite several drawbacks, such as insecticide resistance and health risk to farmers and consumers. Hence, the IPM approach is deemed a safer and effective control measure to control B. tabaci, and includes the use of biological control, based exclusively on the effective use of natural parasites (parasitoids), predators, and entomopathogens.B. tabaci has been well demonstrated in various studies. The most common and popular EPF used to control B. tabaci are Ashersonia spp., B. bassiana, I. fumosoroseus, M. anisopliae, and Verticillium lecanii, each subject to various reviews. High populations of EPF and their effectiveness against B. tabaci can be sustained by improving conidia formulation and substrate. These efforts may increase the stability of EPF propagules and reduce the time required to kill B. tabaci. Moreover, some EPF, such as B. bassiana and M. anisopliae are associated with plants as symbiotic endophytes, which may help in the development of more effective insect pest management strategies. Endophytic inoculation of EPF in different parts of plants  to effectively control B. tabaci could be utilized for the development of new IPM strategies. Taken together, EPF have a promising future in the sustainable control of B. tabaci.The application of EPF as an effective biological control method of


Understanding the genetic structure of adaptation and productivity in challenging environments is necessary for designing breeding programs that suit such conditions. Crossbred dairy cattle in East Africa resulting from over 60 years of crossing exotic dairy breeds with indigenous cattle plus inter se matings form a highly variable admixed population. This population has been subject to natural selection in response to environmental stresses, such as harsh climate, low-quality feeds, poor management, and strong disease challenge. Here, we combine two complementary sets of analyses, genome-wide association (GWA) and signatures of selection (SoS), to identify genomic regions that contribute to variation in milk yield and/or contribute to adaptation in admixed dairy cattle of Kenya. Our GWA separates SNP effects due to ancestral origin of alleles from effects due to within-population linkage disequilibrium. The results indicate that many genomic regions contributed to the high milk production potential of modern dairy breeds with no region having an exceptional effect. For SoS, we used two haplotype-based tests to compare haplotype length variation within admixed and between admixed and East African Shorthorn Zebu cattle populations. The integrated haplotype score (iHS) analysis identified 16 candidate regions for positive selection in the admixed cattle while the between population Rsb test detected 24 divergently selected regions in the admixed cattle compared to East African Shorthorn Zebu. We compare the results from GWA and SoS in an attempt to validate the most significant SoS results. Only four candidate regions for SoS intersect with GWA regions using a low stringency test. The identified SoS candidate regions harbored genes in several enriched annotation clusters and overlapped with previously found QTLs and associations for different traits in cattle. If validated, the GWA and SoS results indicate potential for SNP-based genomic selection for genetic improvement of smallholder crossbred cattle. Exotic dairy breeds have been extensively imported to Kenya since the 1950s for use in crossbreeding aimed to improve the productivity of indigenous cattle by combining the environmental adaptation features of the latter with the high milk yield potential of the former. This has resulted in a large population of admixed cattle that, for several decades, have been subject to natural selection in response to different environmental stresses, such as harsh climate, low-quality feeds, poor management, and strong disease challenge. Smallholder farmers retain the admixed cattle typically in herds of size one to five cows and breed them mainly through natural mating to local bulls. A small proportion of matings (~10%) are made by AI to imported and locally produced purebred dairy bulls. There is no record of pedigree or performance of smallholder cattle and no current genetic improvement program for crossbred cattle. Genomic technologies can aid smallholder dairy farmers to develop genetically improved animals when the genetic improvement by traditional breeding schemes is impossible  if alleles in the admixed population can be correctly assigned to their ancestral origin. The latter can be done through methods that infer the ancestry of haplotypes, such as LAMP-LD  difference in milk production potential.With high-density SNP markers, it is possible to identify genomic regions that may be useful in future selection. This can be done through genome-wide association (GWA) analysis, which relies on linkage disequilibrium (LD) between SNPs and causal variants and requires phenotype plus genotype data, and by detection of signatures of selection, which only requires genotypic information. In admixed populations, the LD between SNP markers and causal variants can arise from the LD that existed in the parental populations that contributed to the admixed population and from When a beneficial allele increases in frequency by natural or artificial selection, the allele frequencies of neighboring loci in LD are also altered, and this creates extended blocks of haplotypes with increased LD and reduced variation. The changes in allele frequencies, LD, and genetic variation accumulate over time and generate unique patterns at specific regions of genome, which are referred to as signatures of selection  a GWA analysis that separates breed origin SNP effects from effects due to within-population LD to find SNPs associated with milk yield and (2) a signature of selection (SoS) analysis to detect signals of post-admixture selection. The GWA and SoS analyses are complementary because in relatively young populations, SoS are not expected to have led to fixation of alleles, and therefore, the results from one can be used as partial validation of the other.Bos indicus animals (IND) from 12 Indian breeds were obtained by stratified sampling of the larger data set analyzed by Strucken et al. (n = 24) and (ii) European taurine (EUT) ancestors of admixed cattle: Holstein , Jersey , Guernsey , British Friesian , and Ayrshire . All genotypes except BF and AY, which were provided by the Scottish Rural University College (SRUC) and Canadian Dairy Network (CDN), respectively, were obtained from the Bovine HapMap Consortium (http://bovinegenome.org). These genotypes were obtained post-quality control, so only the common SNPs between them and African and Indian genotypes were extracted. We sampled an equal number of 21 animals from each EUT breed and considered the five EUT breeds as recent ancestors of Kenyan admixed dairy cattle. SNPs with a MAF less than 0.01 across the whole sample were excluded. Animals were also required to have genotypes for more than 90% of SNPs. These controls resulted to 521,362 SNPs on 1,475 admixed, 19 EASZ, 105 IND, 24 AFT, and 105 EUT animals distributed over 29 autosomes based on the UMD3.1 bovine reference genome. Details of the cattle populations in this study are presented in The genotypic data included 1,475 admixed and 19 East African Shorthorn Zebu (EASZ) cattle sampled in Kenya between 2010 and 2014 and genotyped for 777,962 SNP markers using Illumina BovineHD BeadChip . More information on collection of samples can be found in Aliloo et al. . We retan et al. . ReferenMilk yield deviations (MYD) were obtained for the individual test-days of 1,034  Kenyan admixed cows in smallholder farms from the analyses of Brown et al. . In theiG) constructed using the VanRaden  based on all SNP genotypes after quality control  was implemented. The PCA was applied to a (co)variance matrix between all animals' genotypes  frequency of crossovers in haplotypes with lowest number of crossovers was used to rank the admixed animals.y is the vector of MYD of size n, 1n is a vector of ones, μ is the population mean term, β is a 2 × 1 vector containing the ancestral origin of allele effect and residual SNP effect, u contains polygenic effects assumed to be distributed as G being the genomic relationship matrix based on all SNP genotypes except the SNPs on the chromosome of the marker for which the association is tested  simultaneously estimated the effect of the ancestral origin (exotic vs. indigenous) of the SNP and the residual effects of SNP alleles after accounting for the ancestral origin. The local ancestry inferences obtained above were used to assign the ancestral origin of SNP alleles with ancestral origin coded as 0, 1, and 2 for no copies, one copy, or two copies coming from the EUT ancestor, respectively. The GWA model was as follows:p-values were supplied to the q-value package  and FDR thresholds.The above model was fitted by WOMBAT Meyer, . The SNPWe used two complementary haplotype-based methods to scan the genome of the admixed cattle for candidate regions under selection. The integrated haplotype score (iHS) is an intra-population measure of the extent of haplotype homozygosity  and the derived (iHHd) alleles, and then, the iHS was calculated as Kas,t is the number of distinct haplotypes from the core SNP s to SNP t carrying the core allele as, nk is the number of times the kth haplotype is observed, and nas is the total number of haplotypes carrying as and is calculated as p-values of “no selection” hypothesis according to Gautier and Naves  and derived (as = 2) alleles, and Kas,t is the number of distinct haplotypes from the core SNP s to SNP t carrying the core allele as. The iES was defined as the area under the EHHS curve at a core allele within a chromosome using a homozygosity decay threshold of 0.05. The Rsb score between admixed and EASZ cattle populations was defined as medRsb and σRsb are the median and standard deviation of Rsb across all SNPs within genome. The p-values for Rsb(s) were calculated according to Gautier and Naves (Rsb(s)) represents the Gaussian cumulative distribution function of Rsb(s) values. The qvalue package  by generating the corresponding q-values and FDR thresholds.The Rsb values between admixed and EASZ cattle populations were calculated within each chromosome according to Tang et al.  using thnd Naves :pRsb(s)We calculated measures of selection signatures in two scenarios. In the first scenario, all the admixed samples were used to obtain estimates of iHS. In the second scenario, admixed cattle with less than three crossovers were removed prior to iHS and Rsb analyses because they were deemed to be recently admixed individuals in which selection has not had enough time to leave a signature on their genome.q value < 0.1 and then searching within the 500-Kbp interval downstream and upstream (1 Mbp window) of the identified SNP for SNPs with q value < 0.5 and q value < 0.25 for iHS and Rsb analyses, respectively. We extended the detected region (with a 500-Kbp step size) until there was no SNP with a q value less than the suggestive thresholds within the 500-Kbp interval from the last identified SNP. The boundaries of the candidate region were determined based on the base pair positions of the last-identified SNP in each direction. The same procedure was used for iHS and Rsb analyses. Where GWA results were used for partial validation of SoS analyses (see below), we used a suggestive p-value threshold of 10−3 to define the candidate regions from GWA, and to define the boundaries of each candidate region, we searched the 500-Kbp upstream and downstream intervals for SNPs whose p-values were smaller than 10−3 and extended the region until there was no SNP p-value less than our suggestive threshold. The candidate regions designated by iHS and Rsb analyses were then annotated using the Ensemble Biomart 94 based on the UMD v3.1 bovine genome assembly for the underlying genes, and the biological functions of the discovered genes were evaluated and compared to the existing literature. We also calculated the ancestral allele dosages for the identified candidate regions in order to track the candidate regions under selection to each of the ancestral populations described above. In an attempt to validate SoS regions in the admixed cattle, we looked for overlap between the candidate regions identified in each of the SoS analyses, i.e., iHS or Rsb, and those identified by GWA.A candidate region detected by the SoS analyses was defined by first identifying SNPs with a https://www.animalgenome.org/cgi-bin/QTLdb/BT/index) on July 8, 2019, and was used to compare the results of the present study with the reported QTL regions in the literature. We compared the genes within our identified candidate regions for selection from iHS and Rsb analyses to the whole bovine genome background using functional annotation clustering by DAVID online bioinformatics resource v 6.8  were used to infer the local ancestries of the admixed cattle at the individual loci level. The majority of haplotypes in the admixed cattle were found to be originated from EUT ancestor (≈0.73), and IND and AFT ancestral populations contributed smaller proportions of admixed haplotypes . The genome-wide average ancestries of the three ancestral populations for each crossbred animal are shown in The distribution of number of recent crossovers on haplotypes with the lowest number of crossovers in different chromosomes is shown in The distribution patterns for the average local ancestries of admixed cattle with three or more recent crossovers per Morgan in haplotypes carrying the lowest number of crossovers are shown in The Manhattan plots of SNP allele effects and ancestral origin effects for MYD are presented in p-values for genome-wide iHS scores calculated using all samples of admixed cattle as well as when using only the admixed cattle with three or more crossovers per Morgan on the chromosomes with lowest frequency of crossovers are given in The Manhattan plots of p-values from Rsb analysis between the admixed cattle with a minimum number of three crossovers on the haplotype carrying the lowest number of crossovers across their genome and the EASZ population is shown in q value and highest peak in the Manhattan plot of The distribution of Because the SoS showed lower FDR than the results from the GWA analysis, for the purpose of investigating possible candidate genes, we chose to cross-validate the SoS that passed FDR < 0.1 with the GWA results. We used only the estimates of SNP allele effects because the confidence intervals for ancestral origin effects were very large. Four candidate regions from GWA, on BTAs 1, 7, and 20, overlapped with four candidate regions for selection obtained from iHS and Rsb analyses  appeared to work very well, yielding similar levels of indigenous vs. exotic admixture to previous Admixture analyses  are real effects. The distribution of ancestral origin effects  showed tThe distribution of estimated SNP effects  shows thWork is currently underway to phenotype and genotype much larger populations of crossbred dairy cows in East Africa. This will provide a future opportunity to validate the GWA results presented here. If some of these results are validated, it will be possible to identify groups of SNPs that track genomic region effects due either to within-population LD with causal variants or to ancestral genomic effects. In the latter case, SNPs can be chosen to provide a high accuracy of assigning ancestral haplotype allocation to the relatively large confidence regions encompassed by the ancestral QTL effects.When mapping signatures of selection, the null hypothesis of “no selection” typically implies a lack of statistical significance in situations where there is no disturbance from common demographic factors. Therefore, the ability to clearly distinguish positive selection from neutral effects is a challenge given the high sensitivity of the test statistics for detection of selection  productivity and (2) adaptation, recognizing that some regions might have pleiotropic effects in both categories. In the following, we characterize the functions of our identified regions in more detail.Several candidate regions from iHS and Rsb analyses intersected with previously reported QTLs and associations for milk and meat production traits in the literature. Milk and milk composition encompassed the highest number of overlaps among all traits for both methods. Given that there is no genetic improvement program for milk yield in the population of smallholder cows analyzed here, this might be due to phenotypic selection by farmers who preferentially keep progeny from their best yielding cows. However, it should be noted that the milk production under these poor-quality environments relies on other factors, such as the ability of cows to achieve acceptable growth and reproductive performance with restricted feed and in the presence of disease pathogens.GHR) gene that has been proved to play a central role in variation of milk production in dairy cattle , regulation of lipid metabolism (THEM5), and cell cycle progression and differentiation (S100A10) where the latter gene has also been reported as a candidate gene for residual feed intake in Angus . MSTN, also known as growth and differentiation factor-8 (GDF-8), has an important role in muscle development in cattle  and 21 (CSPG4). A candidate region on BTA 7 with a dominant IND ancestry extends from 41.40 to 42 Mbp and harbors the NLRP3 gene. This gene encodes a protein that is involved in regulation of inflammation, immune response, and apoptosis. It is also a candidate gene for Crohn's disease  that are involved in spermatogenesis and early embryonic development in humans  and regulate some important functions for reproduction. These findings suggest an advantage for inheriting genes of IND origin for fertility under heat stress conditions.Heat stress can have adverse effects on reproductive performance of cattle  are advantageous in different regions of the genome. If confirmed, it may be possible to use beneficial haplotypes in genetic improvement of crossbred performance.http://data.ilri.org/portal/dataset/dgea1-data-used-in-aliloo-et-al-2020. The reference genotypes used in this study can be accessed through direct requests to the respective data owners as indicated in the acknowledgments section.The phenotypic and genotypic data for the study population were collected by the Dairy Genetics East Africa (DGEA) project. The genetic data were collected under host country agreements that anticipated Article 5 of Nagoya Protocol of the United Nations Convention on Biodiversity (CBD). The data underlying this study is archived at the International Livestock Research Institute (ILRI) and request to access the data set can be made to Ethical review and approval was not required for the animal study because in the current study we accessed to already produced genotypes and no live animals were used.AO and JG designed the data collection program, including the phenotypes used for GWA. HA and JG conceived and designed the current analyses. RM undertook the initial analyses and produced the corrected data used for GWA. HA conducted all other analyses and drafted the manuscript. JG assisted with the interpretation of results and edited the manuscript. All authors read and approved the manuscript before submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The array was designed after filtering 645,662 genic-SNPs identified by re-sequencing of 45 diverse genotypes and has 62,053 SNPs from 9629 genes belonging to five different categories, including 4314 single-copy genes unique to pigeonpea, 4328 single-copy genes conserved between soybean and pigeonpea, 156 homologs of agronomically important cloned genes, 746 disease resistance and defense response genes and 85 multi-copy genes of pigeonpea. This fully genic chip has 28.94% exonic, 33.04% intronic, 27.56% 5′UTR and 10.46% 3′UTR SNPs and incorporates multiple SNPs per gene allowing gene haplotype network analysis. It was used successfully for the analysis of genetic diversity and population structure of 95 pigeonpea varieties and high resolution mapping of 11 yield related QTLs for number of branches, pod bearing length and number of seeds per pod in a biparental RIL population. As an accurate high-density genotyping tool, ‘CcSNPnks’ chip array will be useful for high resolution fingerprinting, QTL mapping and genome wide as well as gene-based association studies in pigeonpea.Pigeonpea is the second most important pulse legume crop for food and nutritional security of South Asia that requires accelerated breeding using high throughput genomic tools. Single nucleotide polymorphisms (SNPs) are highly suitable markers for this purpose because of their bi-allelic nature, reproducibility and high abundance in the genome. Here we report on development and use of a pigeonpea 62 K SNP chip array ‘CcSNPnks’ for Affymetrix GeneTitan Cajanus cajan L. Millsp.) despite being the second most important pulse crop and an important source of dietary protein in South Asia, faces serious challenges in terms of its productivity because of inadequate availability of seeds of improved varieties and various environmental stresses including pod borer (Helicoverpa armigera), pod fly (Melanoagromyza obtusa), spotted borer (Maruca vitrata), Fusarium wilt, sterility mosaic disease and water logging at early establishment stage. Other limitations of the extant pigeonpea varieties are their long duration, tall stature, low harvest index, spreading plant type and non-synchronous maturity, which make it difficult to manage the crop through mechanical operations. Hence, there is an urgent need to develop varieties which can overcome these limitations. The gap was fulfilled to some extent with the publication of the first draft genome of pigeonpea variety Asha1 but there is need to develop a high quality reference genome of pigeonpea to facilitate efficient gene discovery and molecular breeding. The SSR markers have also been developed for pigeonpea but the level of polymorphisms is very low2. In recent years molecular linkage maps and QTLs for important agronomic traits have been reported but more SNP markers and high-density linkage maps are required5. Establishing high throughput genotyping assays are important for gene discovery and molecular breeding in pigeonpea. High-density SNP arrays and genotyping by sequencing (GBS) have come as attractive genotyping tools. GBS using next generation sequencing can unravel unknown sequence information but its experimental operation and data analysis are beyond the reach of average breeders. In contrast high throughput genotyping arrays can be used to genotype large number of samples within a short time and the data analysis is much simpler. Several medium-density arrays have been developed for genetic analysis and breeding applications in rice10. Similarly, high-density chip arrays have been developed for rice14, sunflower15, soybean16, oil palm17, maize19, wheat21 and pigeonpea22 as well as animal species including chicken23, cattle24 and human25. However, all these arrays incorporate genome wide mostly intergenic SNPs and single SNP per locus, except for the 50 K genic-SNP chip of rice which is based on single-copy (SC) genes14. The available pigeonpea SNP array by Saxena et al. (2018) is also based largely (70.53%) on intergenic regions of the genome. Only 5166 of the total 56,512 SNPs in this chip belong to exonic regions22. The SC genes based SNP array of rice has proven efficient for studying evolutionary relationships, haplotype analysis and QTL mapping of complex traits in rice14.Pigeonpea . Out of total 56,888 protein-coding genes predicted from the improved draft of pigeonpea genome 26 a reference set of 17,125 agronomically useful and SC genes were used in SNP discovery for the chip design. We demonstrate here the usefulness of this chip in analysis of genetic diversity, population structure, gene haplotype networks and high-resolution QTL mapping in pigeonpea. The 62 K SNP chip provides an opportunity to pigeonpea geneticist and breeders for identification of novel QTLs for yield, nutritional quality and resistance to various environmental stresses using bi-parental mapping population and association mapping. The chip array can also be used for background selection in marker-assisted backcross breeding for development of new pigeonpea varieties in a way similar to rice14.Realizing the tremendous potential of SC genes based arrays in crop genetics and breeding, we identified such SNPs in pigeonpea by re-sequencing of 45 diverse varieties for development of a 62 K genic-SNP chip for Affymetrix GeneTitan® platform. Here we report on design, validation and use of this pigeonpea 62 K Axiom genic-SNP chip array named ‘CcSNPnks’  AxiomGTv1 algorithm in order to ensure a high-quality array27. For each SNP, both forward and reverse probes were assigned with p-convert values, derived from a random forest model to predict the probability of SNP conversion on the array. This model is based on factors including probe sequence, binding energy and expected degree of non-specific hybridization to multiple genomic regions. The SNP probes having high p-convert values are expected to convert on the SNP array with a high probability. After QC check 62,053 (62 K) SNPs in 9629 genes represented by 71,816 probes with p-convert values of >0.50 were included in the chip, showing very high conversion of our designed Axiom assays into the final chip. The 62 K SNP chip array named ‘CcSNPnks’ includes 4314 SCP, 4328 CSCSP, 156 AGCP, 746 DRDRP and 85 MCP genes  with a high cut-off value of 0.92 against the recommended 0.82. Actually the maximum DQC value was 0.99 with an average of 0.98. Over 95% of the samples showed high genotyping call rates of >95.5%, with an average call rate of 99.27%  with a low haplotype diversity (Hd) of 0.1418 Fig. . HaplotyConserved single-copy gene CSCSP-4864 coding for ‘DNA primase large subunit’ with 73 SNPs showed the lowest haplotype diversity of 0.1225 with only five haplotypes (CSCSP-4864-H1 to CSCSP-4864-H5) of which CSCSP-4864-H1 was the ancestral and most predominant haplotype present in 89 of the 95 varieties, the remaining four haplotypes all derived directly from this were present in one or two varieties each Fig. , Table 2Agronomically important gene AGCP-21, coding for ‘lysM domain containing receptor kinase, LYK3’ with 128 SNPs, showed 12 haplotypes (AGCP-21-H1 to AGCP-21-H12) and moderate haplotype diversity of 0.3418 Fig. , Table 2Disease resistance gene DRDRP-234 coding for a ‘serine threonine kinase’ with 116 SNPs showed 14 haplotypes and the highest haplotype diversity of 0.7380 among the eight genes analysed Fig. , Table 2Further, we examined if genotyping data on all of the large number of SNPs in a gene was needed to get the full haplotype information by taking AGCP-21 gene with 128 SNPs as an example. For this we selected three different random sets of 10 SNPs each and one random set each of 20, 40, 60, 80 and all 128 SNPs for the haplotype analysis. The number of haplotypes for the AGCP-21 gene ranged from two to four with different sets of 10 SNPs and increased successively with increasing number of SNP to maximum 12 with all the 128 SNPs , each explaining 13% of the phenotypic variance were mapped on LG3 and LG5 in narrow QTL intervals of 1.34 cM and 3.04 cM, respectively. Additive effects for both the QTLs for number of primary branches were contributed by Pusa Dwarf. A major QTL for pod bearing length on the main axis (qPBLm3.1), contributing 12% of the phenotypic variance was identified in a 0.47 cM map interval on LG3 with additive effect contributed by the parent H2001-4. There were five QTLs for pod bearing length on the primary branches explaining phenotypic variance in the range of 13–24% and all of these except one were contributed by H2001-4 , pod bearing length on main axis (PBLm), pod bearing length on primary branches (PBLp) and number of seeds per pod (SP) were mapped based on phenotypic data from 66 RILs  no interfering SNP within10 bp on either side of the target SNP (ii) sequence information of 72 bp with SNP at the 36th bp position. Average SNP density in different categories of genes ranged from 6.9/Kbp in the highly conserved CSCSP genes to 111.8/Kbp in the MCP genes, whereas average number of SNPs per gene ranged from 22.1 for CSCSP genes to 234.1 for MCP genes. Surprisingly, there was very low inclusion of only 42.9% of the SCP genes in the array as compared to 74.2–85.5% of the genes from the other four categories. Likely reason for this is high SNP density of the SCP genes (35.2 SNPs/kbp) as compared to the CSCSP genes (6.9 SNPs /kbp) resulting in large number of interfering SNPs during assay design. The exclusion of SNPs was directly related to the SNP density because of interference from the neighboring SNPs in the assay. Therefore, the maximum inclusion of 28.5% SNPs was for the CSCSP genes, which have the lowest SNP density of 6.9 SNPs/Kbp , SNP genotyping call rate (99.9%) and assay reproducibility (99.9%) because of largely SC genes based assay design, as clearly seen by the lowest proportion of only 86.8% SNPs having call rates call rates of >95.5% in the MCP genes used as control. The MCP genes based SNPs showed 5% lower call rates thus clearly demonstrating the advantage of SC genes based array.A major limitation in genetic dissection of complex traits in pigeonpea has been the unavailability of high-density genotyping assays that can be utilized effectively for the genetic improvement of the cropet al. 2015). Hence, although conserved genes are largely of single-copy or low copy in nature, all single-copy genes are not conserved. Pigeonpea and soybean are estimated to have evolved from a common ancestor approximately 20–30 million years ago33. Therefore, these genes are an important resource for deciphering the evolutionary history of pigeonpea. Also, comparative analysis of SCP and MCP genes showed that SNPs in MCP genes have significantly lower call rates underlining the higher success rate of the SC genes based array. The MCP genes have very high SNP density with almost one SNP every ten base pairs of the gene, but >95% of these were dropped during assay design due to interfering SNPs.The CSCSP genes showed almost one-fourth the number of SNPs per gene than the SCP genes due to their highly conserved nature. Here we did a comprehensive analysis of all single-copy genes in the pigeonpea genome and found that they belonged to two different categories: (i) SCP genes, which are unique to pigeonpea, and (ii) CSCSP genes, which are conserved between pigeonpea and soybean. We have reported similar results earlier in case of rice, which also has a set of single-copy genes that are unique to rice . Bayesan model based population structure analysis using a subset of 33 genome wide unlinked SNPs with 100% call rates revealed two sub-populations. Genetic diversity analysis of the same set of 95 genotypes based on 30,426 SNPs with 100% call rates showed clear correspondence between NJ phylogenetic tree and population structure. Varieties in two of the four clusters of NJ phylogenetic tree showed correspondence with sub-population I, whereas those in the remaining two clusters belonged to sub-population II with very few exceptions. The comparison of clusters obtained with 62 K SNP array clearly showed that even by using a limited number of genome wide unlinked markers, varieties can be grouped according to their ancestry in an effective manner. We have also incorporated in this chip pigeonpea homologs of 156 agronomically important cloned genes in related species, which will be useful for geneticist trying to identify novel gene haplotypes linked to quality and agronomic performance of the crop. Haplotype analysis of red pericarp (Rc1), grain size (GS3), granule bound starch synthase (GBSS1) and soluble starch synthase  genes of wild and cultivated rice using a similar 50 K SNP array provided clear evidence for convergent evolution of the GBBS1 gene in wild rice and a polyphyletic orgin of the cultivated rice 34. Similar analysis can be conducted using 62 K SNP array in pigeonpea germplasm. High-density linkage map of pigeonpea with 2078 SNP markers developed in the present study will be useful for the anchoring of pigeonpea genome, apart from the high resolution mapping of QTLs for important yield related traits. Most of these QTLs were mapped to a narrow genetic interval of less than 1.0 cM with high LOD scores, which is needed for use in marker-assisted selection. Transgressive segregation with normal frequency distribution in the RIL population was observed for all four traits, indicating that trait enahancing genes were present in both the parents that can by pyramided for yield improvement. Incorporation of 746 disease resistance and defense response genes in the array with average 10.8 SNPs per gene will be useful for pathologists and breeders in identifying genes for biotic stress resistance in pigeonpea35.This is the first genic-SNP genotyping chip array for pigeonpea, the available 56 K SNP chip incorporates largely intergenic SNPs36. Despite availability of a range of multiplexing platforms, like genome wide GBS, targeted GBS, high-density arrays, medium-density arrays, low level multiplexing and single marker assays (e.g. Sequenom (Agena), KASP and Taqman platforms), high throughput genotyping platforms are not routinely used for crop breeding applications partly due to cost considerations. Cost per sample is directly proportional to the level of multiplexing which in turn depends on the exact requirement of the number of markers and samples. Despite high per sample cost high-density SNP arrays and whole genome GBS platforms have much lower costs per data point as compared to targeted GBS and medium to low multiplexing platforms36. High-density SNP arrays are required for specific crop breeding applications, particularly high-density fingerprinting, association studies and background selection. Thus, the 56 K SNP array developed by Saxena et al.22 and the present 62 K SNP array will be useful for pigeonpea genetic studies and breeding programme. Advantage of the 62 K SNP array is that it is a fully genic-SNP array based on single-copy genes, disease resistance genes and agronomically important genes. Another advantage is that it incorporates multiple SNPs per gene allowing scoring of haplotype  information on the genes, which is necessary for allele based association mapping and selection strategies.To date more than fifteen GBS platforms and 50 SNP arrays have been developed and utilized in 25 different plant speciesIn conclusion the high-density 62 K genic-SNP array ‘CcSNPnks’ described here is a unique genotyping platform for pigeonpea. It has 62,053 SNPs from 9629 genes belonging to five different categories with more than 99% sample success rate and 99.9% SNP call rate. The usefulness of the array was demonstrated in population structure analysis, diversity analysis, phylogentic study, construction of high density linkage map, QTL mapping and gene haplotype analysis. Incorporation of multiple SNPs per gene is a unique feature of the array that allows gene haplotype analysis not possible with other available chips. Hence, it is an excellent platform for genetic and evolutionary studies as well as breeding applications in pigeonpea. It is an efficient tool for generating high-density linkage maps suitable for anchoring of the pigeonpea genome because of large number of markers genotyped across samples. In addition, the array would be useful for association mapping studies for correlating DNA polymorphism with phenotypic trait variation.8 recombinant inbred line (RIL) population derived from cross between Pusa Dwarf and H2001-4 used for QTL mapping was developed by Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi.A set of 95 diverse pigeonpea varieties used for the validation of 62 K SNP chip, diversity analysis and population structure analysis were obtained from Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, ICAR-National Bureau of Plant Genetic Resources, New Delhi and ICAR-National Institute for Plant Biotechnology, New Delhi . We performed BLASTN search of all the pigeonpea SC genes against a locally created database of CDS sequences of the three legumes using pre-optimized search parameters37. The SCP genes did not show significant homology with any of the three species whereas CSCSP genes showed homology between pigeonpea and soybean. The AGCP genes were identified by BLASTN search of a manually curated list of cloned genes for agronomic traits in major legumes and cereals obtained through literature search against the database of 56,888 pigeonpea genes at a cutoff bit score of ≥100. In addition, 874 DRDRP genes were selected from the annotations of the first draft of the pigeonpea genome1. Further, we included 96 MC genes as a control to make the final set of 17,125 reference genes. In order to get information on cis-regulatory elements the reference genes were extracted in full-length along with 500 bp of upstream and 100 bp of downstream sequences from the translation start and stop codons, respectively.CDS sequences of 56,888 protein-coding genes predicted from the second draft of the pigeonpea genome were used for extracting five categories of genes for SNP mining and chip designin-silico probe converting test, which evaluates the quality of each SNP on the basis of p-convert score using Affymetrix power tool (APT) (http://www.affymetrix.com/estore/partners_programs/programs/developer/tools/powertools.affx). Forward and reverse probes for each SNP assay were assigned p-convert values in the range of 0.0 to 1.0. Based on the p-convert values, probes were designed for each SNP and designated as ‘neutral’, ‘recommended’, ‘not recommended’ and ‘not possible‘ to help easy filtration of SNP data. Probes for the selected SNPs were synthesized directly on the array chip using Affymetrix proprietary photolithography technology. SNPs with p-convert values of <0.50 were excluded.To identify SNPs in the reference set of genes we generated Illumina Hiseq pair-end sequence data for 45 pigeonpea genotypes , fragmented, hybridized on the chip followed by single-base extension through DNA ligation and signal amplification. Affymetrix GeneTitan® Multi-Channel Instrument was used for staining, washing and scanning of the chip signals as per the manufacturer’s protocol . SNP allele calling was done using Axiom™ Analysis Suite version 2.0 using its three workflows i.e., Best Practices, Sample QC, Genotyping and Summary  on the Affymetrix Gene Titan.CEL files. The Axiom Analysis Suite requires stored library files to convert.CEL files into genotype calls. SNPs with low call rate across the samples were removed and only good quality SNPs with a DQC of >0.85 and call rates of >95% were retained for further analysis.Pigeonpea genomic DNA was isolated from young seedlings using CTAB method39 .ped and .map format and imported in the TASSEL 3.2.1 software40. A NJ phylogenetic tree of the 95 varieties was constructed based on 30,426 SNPs with hundred percent call rates using an improved version of neighbor-joining algorithm and visualized using iTOL software41. From this a subset of 33 genome wide unlinked SNPs, three from each chromosome located near the telomeres and centromere and having minor allele frequency of ≥0.3, were selected for analysis of population structure of the 95 genotypes. The model-based Bayesian approach was employed to infer population structure using STRUCTURE v. 2.3.4 software42. The project was run with the admixture model and correlated allele frequency using a burn in period of 50,000 and 50,000 Markov chain Monte Carlo (MCMC) replications. Five independent runs were performed with each K value ranging from 1 to 10. Evanno’s Δk value was obtained by processing the STRUCTURE result using STRUCTURE HARVESTER software43.Genotyping data were extracted and converted to PLINK40 in a sliding window of five nucleotides length. Haplotype networks were constructed for analysis of genealogical relationship using Network software44 and haplotype diversity was calculated using DnaSP software version 5.1045. We also analyzed the effect of taking different numbers of SNPs on the haplotype patterns of a given gene with large number of SNPs. For this we selected AGCP-21 gene with 128 SNPs and took random sets of 10, 20, 40, 60, 80 and 128 SNPs for haplotype network analysis. Three sets of 10 random SNPs were analyzed to check the effect of sampling on the haplotype.This analysis was performed to show the utility of 62 K SNP chip in generating gene haplotypes, which is not possible with most of the available SNP chips. For this we took two genes from each of the SCP, AGCP, CSCSP and DRDRP categories present in the chip with high number of SNPs and call rates of ≥95%. Total eight genes were selected and their SNP haplotypes were generated using TASSEL 3.2.1 software8 RILs derived from cross Pusa Dwarf/ H2001-4 was used. For genotyping using 62 K SNP chip the DNA from 94 RILs along with the two parents were amplified, fragmented and hybridized on chip for SNP allele calling using Axiom™ Analysis Suite version 2.0. The genotyping data was generated for 94 RILs, alleles assigned by comparing with the two parents and linkage maps were constructed using JoinMap 4.0 software46. Chi-square analysis was performed on the genotyping data to test the goodness of fit against the expected 1:1 segregation ratio. The locus genotyping frequencies of JoinMap were used to identify and discard markers with aberrant segregation ratio at a cutoff P value of 0.05. Linkage groups were constructed by grouping of markers at a minimum LOD threshold of 3 to a maximum of 10 with a step of 0.5. The groups were converted to maps at LOD 3 using regression algorithm with a cutoff recombination frequency of 0.20 performing a ripple after adding 2 loci. Kosambi mapping function was used for converting recombination frequency to map distance in cM.A set of 94 F47 using adjusted means of phenotypic data for the four traits from 66 RILs evaluated in an augment design. To find marker-trait associations, composite interval mapping (CIM)48 was performed using the ZmapQTL standard model 6 with a window size of 10 cM and 2 cM walk speed. A 1000-permutation test was performed for estimating genome-wide LOD score threshold for QTLs (P = 0.05)49. The additive effect and the percentage of phenotypic variation explained by each QTL were also estimated by the CIM method.QTL analysis was performed for four yield related traits segregating in the RILs using WinQTL Cartographer v2.5Supplementary informationSupplementary information2Supplementary information3Supplementary information4