Based on the Diversity Arrays Technology (DArT) seq assay, 58,719 highly informative SNPs were filtered for association mapping. Population structure analysis showed six major genetic groups (K = 6). A total of 78 MTAs were identified, of which 18 were associated with Fe, 43 with Zn, and 17 with PC. Four SNPs viz., Pgl04_64673688, Pgl05_135500493, Pgl05_144482656, and Pgl07_101483782 located on chromosomes Pgl04 (1), Pgl05 (2) and Pgl07 (1), respectively were co-segregated for Fe and Zn. Promising genes, ‘Late embryogenesis abundant protein’, ‘Myb domain’, ‘pentatricopeptide repeat’, and ‘iron ion binding’ coded by 8 SNPs were identified. The SNPs/genes identified in the present study presents prospects for genomics assisted biofortification breeding in pearl millet.Pearl millet hybrids biofortified with iron (Fe) and zinc (Zn) promise to be part of a long-term strategy to combat micronutrient malnutrition in the arid and semi-arid tropical (SAT) regions of the world. Biofortification through molecular breeding is the way forward to achieving a rapid trait-based breeding strategy. This genome-wide association study (GWAS) was conducted to identify significant marker-trait associations (MTAs) for Fe, Zn, and protein content (PC) for enhanced biofortification breeding. A diverse panel of 281 advanced inbred lines was evaluated for Fe, Zn, and PC over two seasons. Phenotypic evaluation revealed high variability (Fe: 32–120 mg kg In the African region, West and Central Africa has the largest area under the crop—15 million hectares—and has an annual production of 14.1 million tons. Pearl millet is a diploid (2n = 14) cross-pollinating crop (> 80%) with a genome size of ~ 1.79 GB2. Its domestication occurred in regions with low fertility soils, heat, and drought, making it naturally adapted to face the challenges associated with climate change.Pearl millet is a climate-resilient crop that accounts for two-thirds of the global millet production. The crop covers more than 31 million hectares worldwide and is grown in more than 30 countries in the arid and semi-arid tropical as well as subtropical regions of Asia, Africa, and Latin America. In Asia, India is the largest producer of pearl millet, where it is grown on 9 million hectares with a production of 8.3 million tons3. Iron and zinc are two important micronutrients that play a vital role in human health. Iron is required for psychomotor development, maintenance of physical activity and work capacity, and resistance to infection4, whereas zinc is required for the growth and maintenance of the human immune system; hence it aids in both the prevention of and recovery from various diseases5. Apart from Fe and Zn, pearl millet is also rich in grain protein content (8–19%) that is almost at par with that in wheat (11.6 vs 11.8 g/100 g) and considerably higher than that in rice (6.8 g/100 g), sorghum (10.4 g/100 g) and maize (4.7 g/100 g)6. High-quality proteins are essential for the physical and mental well-being of humans, especially children8.Pearl millet grains are naturally nutritious and contain high fiber (1.2 g/100 g) and low starch. They are the richest source of grain Fe and Zn compared to other cereals9. Anaemia is alarmingly high, especially among pregnant women (40%) and children (42%) below 5 years10. In addition to this, cereal proteins deficient in essential amino acids such as methionine, lysine, and tryptophan are a matter of concern in developing countries11. Kwashiorkor, oedema, and marasmus are some of the severe forms of protein deficiency12. To combat hidden hunger, biofortification, wherein grain micronutrients along with grain protein contents are genetically enhanced through either conventional or molecular breeding, is gaining popularity. Genomics-assisted breeding holds potential for the rapid improvement of varieties using diagnostic markers14.Diets deficient in Fe and Zn  or protein alone or in combination lead to malnutrition which is also known as ‘hidden hunger’. It has been estimated that over 2 billion people across the world suffer from micronutrient deficiencies in developing countries like Africa and India15. Biofortification using conventional breeding is time-consuming and incurs a high cost in terms of screening hybrid parental lines for micronutrients and protein in every generation. Hence, it is important to develop a cost-effective strategy to improve nutritional traits in pearl millet breeding programs. Furthermore, Fe and Zn are complex traits governed by additive genes and are affected by G × E interactions. Nutritional traits are very complex and governed by a group of genes. It is a challenge to track the genomic regions/genes that are either directly or indirectly responsible for Fe and Zn loading in the grains. Genome analysis tools provide access to thousands of genomic polymorphisms, considerably broadening the ability to monitor and effectively utilize genetic diversity16. Quantitative trait loci (QTL) mapping based on linkage analysis provides the high power of QTL detection of a trait of interest; it has a very low mapping resolution because of the few recombination events that it takes into consideration which would ultimately lead to long linkage blocks17.The wide variability for grain Fe and Zn content in pearl millet unveils the great prospect of developing biofortified pearl millet varieties and hybrids. The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) has been working towards developing biofortified hybrids and has successfully delivered high-Fe pearl millet varieties and hybrids with high yield potential in India and Africa18, DArT19, and GWAS have enabled the use of these powerful approaches in dissecting quantitative traits20. GWAS is a robust approach that has been successfully applied in the past to identify genomic regions controlling grain/kernel Fe and Zn contents in maize21, rice22, and wheat23. GWAS has been successfully applied in wheat and maize to identify grain PC. The availability of the draft genome of pearl millet2 provides the advantage of single nucleotide polymorphism (SNP) and candidate gene discovery. Single nucleotide polymorphism markers are desirable for GWAS, genomic selection, and QTL mapping24. GWAS exploits millions of SNPs generated across the whole genome through GBS, whole-genome re-sequencing (WGRS), DArT, and DArT seq using a diverse group of germplasm lines. GWAS is very effective in pearl millet due to faster LD-decay2. The discovery of SNP markers and their validation will help in developing diagnostic markers that can be deployed to develop biofortified pearl millet varieties/hybrids with elevated Fe and Zn content. This study aims to evaluate genetic variability for grain Fe, and Zn and PC among GWAS panel to discover the genomic regions associated with Fe, Zn, and PC in order to develop diagnostic markers for use in the pearl millet biofortification breeding program.Advances in high throughput genotyping technologies such as genotyping-by-sequencing (GBS)P < 0.01) mean squares for Fe, Zn, and PC among the inbred lines. Descriptive statistics revealed the presence of significant variability  for three traits studied among 281 GWAS panel of pearl millet (Table −1 with an average of 74 mg kg−1 (SEm = 2.72). The Zn content in grains varied from 19 to 87 mg kg−1 with an average of 46 mg kg−1 (SEm = 1.39), whereas the PC varied from 8 to 16% with an average of 11% (SEm = 3.06). Among the 281 inbred lines evaluated, 19%, 15%, and 14% of inbred lines belonging to the seed parents whereas, 24%, 18%, and 20% of inbreds belonging to restorer parents recorded higher Fe, and Zn, and PC, respectively in comparison with the overall trial mean. Furthermore, significant (P < 0.01) G × E interaction was recorded for all three traits. Pearson’s correlation coefficient revealed high significant  positive association between Fe and Zn, whereas PC recorded significant but moderate positive association with Fe  and Zn  , seed parents (B-lines), germplasm progenies and population progenies. The DArT seq markers were subjected to filtering and data quality check. All the SNP loci with > 30% missing data and rare SNPs with < 10% minor allele frequencies (MAF) were filtered and a total of 58,719 high-quality SNPs (derived from the DArT seq platform) were considered for further analysis  genetic groups at the corresponding least cross validation error (CV error) of 0.659 Fig. A. Among 2). A set of 58,719 SNPs with identified physical positions were used for LD analysis  ranged from 0 to 1 with the average LD across the genome being 0.116. Furthermore, chromosome-wise average LD varied in the order of 0.151 > 0.138 > 0.129 > 0.118 > 0.107 > 0.087 > 0.081 for chromosomes Pgl03, Pgl07, Pgl04, Pgl06, Pgl02, Pgl01, and Pgl05, respectively. The LD for 18,80,476 pairwise combinations obtained from 58,719 marker loci across the genome showed that 57% of SNP pairs showed < 0.01 R2, whereas 37% of SNP pairs showed 0.01–0.05 R2, and only 6% of SNP pairs showed 0.06–0.1 R2. Linkage disequilibrium-decay (LDD) across seven chromosomes was determined using the entire set of 58,719 DArT seq markers. The LDD was plotted as LD (R2) between the adjacent pair of markers on the Y-axis against the distance in base pairs (bp) on the X-axis . Among the seven chromosomes, the shortest LDD was observed in chromosome 1 with 0.2 kb  and the longest LDD was observed in chromosome 6 with 9 kb .The linkage disequilibrium (LD) between each pair of SNPs across each chromosome was evaluated by the squared Pearson correlation coefficient  considering only population structure (Q) showed high genomic inflation . The 18 markers that were identified for Fe were distributed across chromosome Pgl01 (1), Pgl02 (4), Pgl04 (7), Pgl05 (3), Pgl06 (2), and Pgl07 (1). No SNPs were found associated with chromosome Pgl03. Pgl05_135500493 was identified with the highest phenotypic variation of 8.23% for Fe with a ‘P’ value of 1.79 × 10–5.A total of 61 highly significant MTAs for grain micronutrients were identified. Of the 61 MTAs, 18 were identified for Fe Table ; Fig. 7 P’ values ranging from 2.24 × 10–5 to 9.78 × 10–4. Furthermore, the phenotypic variation explained by these SNPs ranged from 5.09 to 8.00% for Zn. These 43 markers identified were distributed across chromosomes Pgl01 (5), Pgl02 (1), Pg103 (3), Pgl04 (6), Pgl05 (12), Pg106 (5) and Pgl07 (11), respectively. Pgl07_101483782 for Zn was identified with the highest phenotypic variation of 8.00% with a ‘P’ value of 2.24 × 10–5. A total of four SNPs  located on three different chromosomes  were found common among grain Fe and Zn contents , Pgl02 (5), Pgl04 (1), Pgl05 (1), Pgl06 (1), and Pgl07 (2). No SNPs were found associated with chromosome Pgl03. Pgl06_71295563 was identified with the phenotypic variation of ~ 6% for PC with a ‘P’ value of 3.46 × 10–4.A total 17 MTAs were identified for PC with ‘Pearl millet genome sequencing reported a total of 69,398 genes and unraveled the involvement of several genes in the control of both agronomically and nutritionally important traits. The physical positions of each SNP marker from the present study were compared against the pearl millet genome sequence to determine the function of the gene underlying the respective SNP. A total of 18 SNPs associated with Fe were found linked Table  to diffe25. It is highly feasible to develop biofortified inbred lines through inbreeding which accumulates more of additive variances in subsequent generations. The strong epigenetic influence on these traits expression and sample contamination during handling of breeding materials is a challenge for biofortification in pearl millet27. The process of identifying molecular markers, preferably SNPs tightly linked to genomic regions of Fe, Zn and PC, will enhance the efficiency of biofortification using genomics assisted breeding. Recently, several genomic regions controlling the inheritance of Fe and Zn have been identified through QTL mapping28 using DArT and SSR markers and also through LD-based association mapping29 by SSR markers in pearl millet. Though SSRs are preferred markers, their resolution is relatively low17. None of the previous studies have reached the gene level; therefore, the present study aimed to dissect the genetic nature of Fe, Zn and PC in pearl millet using GWAS by exploiting the DArT seq markers to discover the genomic regions and candidate genes influencing Fe, Zn and PC.Developing biofortified hybrids in pearl millet requires high Fe and Zn content in both the parents since it’s governed by additive gene–1 Fe and 0.6 to 1.0 mg kg–1 Zn) required for normal growth and development31. Three to fourfold significant variations for Fe (32–120 mg kg−1), Zn (19–87 mg kg−1) and twofold variation for PC (8–16%) in 281 elite inbred lines prospects the breeding feasibility  across the genome decreased rapidly against the increasing distance (bp). Rapid LDD has been reported in earlier studies in pearl millet38. Chromosomes Pgl01, Pgl02, Pgl03, Pgl05, and Pgl07 showed relatively more rapid LDD (~ 0.64 kb) compared to Pgl04 and Pgl06, suggesting that a larger number of markers are required for chromosomes Pgl01, Pgl02, Pgl03, Pgl05, and Pgl07 for GWAS. The gene-rich genomic region tends to have a higher rate of recombination. Thus the LDD would be higher in such genomic regions, requiring a higher marker density for LD analysis in such regions. Of 18,80,476 pairwise LD analysis, 57% of the SNP pairs showed an LD of less than 0.01 (R2 = 1%), indicating that the LD in the current GWAS panel is relatively low. This could probably be because pearl millet is a highly cross-pollinated (> 80%) species, wherein some portion of the genome is bound to have heterozygosity (not every locus is heterozygous) as genetic load by the inbreeding process39. The low LD is also due to frequent recombination and higher inbreeding depression by virtue of being a cross-pollinated crop. The low value of LD in turn gives the high resolution of mapping but requires a large number of markers40.LD is the non-random association of alleles at two or more loci and acts as a critical genetic force in determining population structure41 and MLM43, were used for the MTA. The MLM model is more efficient and superior in reducing false positive associations by correcting for both population structure (Q) and kinship matrix (K) which can be further visualized through Quantile–Quantile (Q–Q) plots to show low genomic inflation for MLM compared to GLM . In the present study, two extensively used statistical models, GLMGLM Fig. . However29 on LG 3, LG 5, and LG 7. QTLs responsible for Fe and Zn have been co-mapped on LG 1 and LG 728; these probably indicate that chromosome Pgl05 and Pgl07 are likely to control Fe and Zn transport and accumulation in pearl millet. Though no common MTAs were identified for PC with Fe and Zn, the positive significant correlation of PC with both Fe and Zn suggested that the selection for high Fe/Zn expected to increase PC as an associated trait.Among the significantly associated SNPs for Fe, marker Pgl05_135500493 on chromosome Pgl05 explained the highest phenotypic variation (8.23%). For Zn, markers Pgl07_101483782, Pgl07_101483780, and Pgl07_147179490 exhibited more than 7.5% of phenotypic variation. However, the SNPs identified for PC explained the relatively lower phenotypic variation, wherein the highest phenotypic variation was explained by the SNP Pgl07_71295563 on chromosome Pgl07 (~ 6%). Interestingly, there were four SNPs discovered to be common for both Fe and Zn content on chromosomes Pgl04, Pgl05, and Pgl07 that cumulatively explain about 27.12% and 25.32% of phenotypic variation for Fe and Zn, respectively. The co-localization of both Fe and Zn and highly significant positive correlation between them further suggested some common genes and pathways involved in Fe and Zn homeostasis in plants i.e., from root absorption to till deposition in grains. A common set of markers for Fe and Zn has been reported in pearl millet29 reported that Fe was highly influenced by the genes on chromosomes Pgl05 and Pgl07, whereas Kumar et al.28 identified genomic regions for Zn on chromosomes Pgl01 and Pgl04 in pearl millet. Zn content was also influenced by the SNPs on chromosome Pgl03, Pgl04, Pgl05, Pgl06 and Pgl07. Similar results were reported earlier by Anuradha et al.29 while Kumar et al.28 reported genomic regions on LG 1, 4, 5, and 7. This evidence suggests that the SNPs identified on chromosomes were consistent with the previously reported markers which might have a significant role to play in the expression of Fe and Zn content. This calls for fine mapping of these genomic regions that would ultimately provide candidate SNPs for use in marker-assisted breeding to improve grain Fe and Zn. Apart from pearl millet, genomic regions were also discovered for grain Fe and Zn content in other millets and cereals such as rice22, foxtail millet50, maize21, wheat23, through genome-wide association mapping. Genetic mapping studies have discovered genomic regions for grain Fe and Zn content in sorghum51, maize52, and wheat53. Hence different genomic regions in this study can be introgressed for trait improvement in pearl millet based on the targeted environment, depending on common MTAs. This is the first report on the discovery of genomic regions using GWAS for PC in pearl millet. The findings will generate research interest to further investigate the regulation of grain PC in pearl millet. A total of 17 MTAs were identified on six chromosomes  of pearl millet, among which Pgl06_71295563 showed the highest phenotypic variation of 5.86% with a ‘P’ value of 3.46 × 104. Similar genomic regions have been reported for PC in previous studies in maize54, rice56, and wheat57.To know the conformity of the identified MTAs in this study, they were compared to previous genetic mapping studies for Fe and Zn in pearl millet. SNPs were identified for Fe and Zn in this study were concomitant to reported studies in pearl millet Table . For ins.58 observed that the MYB-domain plays a significant role in plant survival under Fe deficiency conditions, and is the most highly induced transcription factor which acted early in the Fe deficiency regulatory cascade to drive gene expression of NAS4. Shen et al.59 isolated MYB gene MxMYB1 from Malus xiaojinensis. The expression of MxMYB1 was up-regulated by Fe starvation in the roots but not in the leaves, signifying that MxMYB1 likely to play more in iron absorption from soil to roots and not likely from root to leaves. The SNP Pgl04_190105720 corresponding to the Zinc finger plays a crucial role in preventing toxic ion damage and hence performs an important role in maintaining cellular osmotic adjustment and enzyme activities, leading to significantly improved salt stress tolerance60.Gene annotation was performed by comparing the sequence reads of significantly associated SNPs at their respective physical positions against the reference genome of pearl millet. The genes identified in the present study and their functional roles in Fe and Zn metabolism in plants reported through previous studies are presented in Table 2/F3s) in order to avoid expensive phenotyping and G × E effects in future. Eight MTAs that were identified for Fe and Zn were found to be involved in Fe mobilization. Thus, the promising MTAs identified in the present study merit further validation in different genetic backgrounds of breeding lines and populations. Eleven inbred lines had ≥ 80 mg kg−1 of Fe, > 60 mg kg−1 Zn, and > 13% of PC that meet global targets and will serve as trait sources in elite backgrounds. Such lines will be easily converted into CMS (maintainers) to make hybrids with high-Fe/Zn/PC restorers for fast-track product development. The inbred panel studied that is part of hybrid parents at ICRISAT. This will enhance the introgression of these traits to develop high-yielding hybrids through marker-assisted back-crossing (MABC) in India where hybrid cultivars are dominant, while in Sub-Saharan Africa where open-pollinated varieties (OPVs) are predominant, it will be done through marker-assisted recurrent selection (MARS) and marker-assisted population improvement (MAPI).The significant phenotypic variability observed in the association panel coupled with high marker density across all chromosomes provided a strong case for whole-genome association mapping of the three  important nutritional traits in pearl millet. This GWAS study which identified marker-trait associations for Fe, Zn, and PC using the genotyping-by-sequencing platform presents greater prospects for utilization and traits mainstreaming. Rapid LDD observed in the current GWAS panel indicates that the SNPs identified through genome-wide association mapping are more reliable and complement previously reported QTLs in pearl millet. Pgl05_135500493 and Pgl05_144482656 SNPs for Fe; Pgl07_101483782, Pgl07_101483780 and Pgl07_147179490 SNPs for Zn, and Pgl06_71295563 SNPs for PC were found promising. Significant phenotypic correlations between Fe and Zn support simultaneous selection and improvement. This linkage and the identified co-localized MTA suggest there is a common physiological pathway. These MTAs help to move towards fine mapping and discovering a set of diagnostic markers to screen segregating population , 110 seed parents (B-lines), 32 advanced progenies derived from breeding population/composites, and 27 direct derivatives of germplasm accessions (Supplementary Table −1 of diammonium phosphate (18% N and 46% P) was applied at the time of field preparation and 100 kg ha−1 of urea (46% N) was applied as top dressing within 2 to 4 days of thinning. The trial was irrigated at 7–10 days intervals during the summer season 2018 and as required during the rainy season 2017 to avoid moisture stress. All the recommended agronomic practices were followed for good and healthy crop growth. Observations were recorded for five random plants per plot in each replication for Fe, Zn, and PC.The trials were planted in alpha lattice experimental design with three replications in two contrasting environments, rainy season 2017 and summer season 2018 at ICRISAT, Hyderabad . Each replication comprised of 20 incomplete blocks with 10 entries in each block, and every entry planted in two rows of 2 m length. Sowing was done by tractor-mounted 4-cone planter (7100 US model) with a spacing of 75 cm between rows during the rainy season 2017 and 60 cm in the summer season 2018. Overplanted plots were thinned 15 days after sowing to single plants spaced 15 cm apart within each row. A basal dose of 100 kg ha.61. Grain samples were finely ground and oven-dried at 60 °C for 48 h before analyzing them for Fe and Zn. A ground sample of 0.2 g was transferred into 25 ml polyprophelene PPT tubes with 2.0 ml of concentrated nitric acid (HNO3) and 0.5 ml of 30% hydrogen peroxide (H202). These samples were wetted and predigested overnight at room temperature. Samples were placed in the digestion block and heated at 80 °C for 1 h, followed by digestion at 120 °C for 2 h. After digestion, each sample digest was turned into 25 ml using distilled water. The digests were filtered using Whatman no.1 filter paper and the filtrate was used to estimate Fe and Zn content using ICP-OES.For grain sampling, open-pollinated main panicles from five representative plants per plot were harvested at physiological maturity (85–90 days after planting). These panicles were stored separately in a cloth bag and sundried for 10 to 15 days, and then hand threshed to produce clean grain samples for micronutrient analysis (Fe and Zn). Utmost care was taken to avoid contact with iron equipment while threshing and handling of threshed samples. Grain Fe and Zn content were analyzed using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) at Flinders University, Australia, following the method described by Wheal et al62 content was measured in percentage. The grain samples collected were cleaned thoroughly and about two to three grams of whole grain samples were poured in a small cup. The cup was then placed in the NIRS machine and the sample was run for a minute. The readings were then noted.Grain protein content was analyzed using Near-Infrared Spectroscopy (NIRS) at ICRISAT. The quantified grain protein63. The mean soil Fe and Zn content extractable with Diethylene Triamine Pentaacetic Acid (DTPA) were 3.8 mg kg−1 and 2.0 mg kg−1 during the rainy season 2017 and 5.0 mg kg−1 and 1.6 mg kg−1 during the summer season 2018, respectively.The soil samples collected from the top 30 cm layer in the field were analyzed for extractable Fe and Zn content by Atomic Absorption Spectroscopy (AAS)64. The quality and quantity of the extracted DNA were checked on 0.8% agarose gel using gel electrophoresis at 80 V using λ-DNA standards. The DNA was subsequently diluted to a volume 50 μl of concentration 50 ng/μl. The samples were then sent to the Diversity Arrays Technology (DArT) Pty Ltd, Australia65 for genotyping using DArT markers. The DArT seq assay, an efficient genotyping-by-sequencing platform was employed in the present study. In brief, the DNA samples were digested and ligated primarily with two different adaptors accompanying to overhang by two different restriction enzymes66. The Illumina flow cell attachment sequence, sequencing primer sequence, and varying length barcode regions were included while designing the PstI-compatible adapter. The PstI-MseI fragments were amplified for 30 Polymerase Chain Reaction (PCR) cycles using the following reaction conditions: 94 °C for 1 min, followed by 29 cycles of 94 °C for 20 s (s), ramp 2.4 °C/s to 58 °C, 58 °C for 30 s, ramp 2.4 °C/s to 72 °C, 72 °C for 45 s. Amplicons were held at 72 °C for 7 min and then at 10 °C. All PCR amplicons from the 96-well were multiplexed in equimolar amount and kept to c-Bot (Illumina) bridge PCR after that sequenced on Illumina Hiseq2000. Single lane sequencing was followed for all the amplicons; the single read sequencing was run for 77 cycles. All the generated sequences from each lane were subjected to proprietary DArT analytical pipelines. Poor-quality sequences were filtered out from the FASTQ files in the primary pipeline. In the barcode region, more stringent selection criteria (≥ Phred pass score of 30) were employed in comparison with the rest of the sequence. The sequence assignments are authenticated to specific samples. In marker aligning, about 2,000,000 identified sequences per barcode/sample were used. Finally, identical sequences were broken into FASTQ call files. In the secondary proprietary pipeline of DArT P/L, the FASTQ call files were used to detect presence/absence markers (PAM) through SNP calling algorithms (DArTsoftseq)68.Genomic DNA was isolated from tender leaf tissues of 30 day-old seedlingsWhole-genome genotyping data of 87,748 DArT seq markers on 281 pearl millet inbreds was generated using DArT genotyping platform. DArT seq SNP-derived markers were further filtered to remove SNPs of low quality with > 30% missing data and rare SNPs with < 10% MAF using TASSEL v 5.3.1 .70 in SAS University Edition 71. Heritability72 was determined using the following formula:https://www.r-project.org). The standard error of the mean (SEm) was determined in a simple excel program using the following formula:The analyses of variance was performed over the rainy season 2017 and summer season 2018 using generalized linear model procedures following a random-effects model73. The number of genetic clusters (K) was predefined as 1 to 10 to explore the population structure of the tested accessions. This analysis provided maximum likelihood estimates of the proportion of each sample derived from each of the K populations. The optimum K value was selected based on the graph plotted using the respective K value from 1 to10 against cross-validation error (CV-error). The optimal number of sub-population (K) was determined with the lowest cross-validation error. Genetic relatedness or K matrix was generated from TASSEL V 5.3.1.74. LD was quantified as adjacent pairwise R2 values 75 and was estimated for 58,719 SNPs in TASSEL V 5.3.1.Population structure was determined using ADMIXTURE 1.23 software76:Marker trait association was performed using two different models, GLM and MLM, as given below2) by the marker is reported77. Quantile–Quantile (Q-Q) plots were developed by plotting observed negative Log10 ‘P’ values against expected negative Log10 ‘P’ values for all the available SNPs in R package CMplot78. A deviation from ‘P’ values at the initial stage may display the existing population stratification. Manhattan plots were used to visualize chromosome-wise SNPs obtained through the marker-trait association study performed across the genome. -Log10 of the ‘P’ value for each SNP was plotted against seven chromosomes for the respective trait. Based on the SNP distribution, the threshold for significance of associations between SNPs and traits was fixed at [− log 10 (p) < 10−03] which gave the optimum number of reliable SNPs. SNP density plots, Q-Q plots, and Manhattan plots were generated using R package CMplot v 3.4.078.The GLM principally considers the population structure (Q) while MLM considers both Q and Kinship (K). Further, among the different options available within MLM, the widely adapted approach called ‘optimum levels of compression in combination with P3D’ for variance component estimation was used for association analysis. For the MLM analysis, marker-based kinship matrix (K) obtained using TASSEL was used along with the Q matrix generated through ADMIXTURE to correct for both family and population structure and the phenotypic variation explained (R2; and thus the functions of the respective SNPs were determined.The corresponding genes of associated SNPs or marker-trait associations were identified by using the physical positions of SNPs in gene annotations available in the pearl millet reference genome sequence2 sequence annotations. The SNP subsiding start and end positions of a gene or exons were explored for candidate genes based on their biological function annotation related to the trait of interest (Supplementary Fig. 79.The candidate genes corresponding to the significantly associated SNPs were identified using the pearl millet genomeSupplementary Information.


However, their classification, population structure and genomic feature remain elusive.Neo-tetraploid rice (NTR) is a useful new germplasm that developed from the descendants of the autotetraploid rice (ATR) hybrids. NTR showed improved fertility and yield potential, and produced high yield heterosis when crossed with japonica subspecies, which maybe the reason for high heterosis when NTRs crossed with indica ATRs. Haplotype patterns of 717 key genes that associated with yield and other agronomic traits were revealed in these NTRs. Moreover, a novel specific SNP variation was detected in the first exon of HSP101, a known heat-inducible gene, which was conserved in all NTRs but absent in ATRs, 3KRG and RiceVarMap2 databases. The novel allele was named as HSP101–1, which was confirmed to be a heat response factor by qRT-PCR, and knockout of HSP101–1 significantly decreased the thermotolerance capacity of NTR. Interestingly, HSP101–1 was also specifically expressed in the anthers of NTR at pre-meiotic and meiosis stages under optimal environment without heat stress, and its loss-of-function mutant showed significant decrease in fertility of NTR.Here, high-depth genome resequencing data of 15 NTRs and 18 ATRs, together with 38 publicly available data of diploid rice accessions, were analyzed to conduct classification, population structure and haplotype analyses. Five subpopulations were detected and NTRs were clustered into one independent group that was adjacent to The construction of first genomic variation repository and the revelation of population structure provide invaluable information for optimizing the designs of tetraploid rice breeding. The detection of specific genomic variations offered useful genomic markers and new directions to resolve high fertility mechanism of NTR.The online version contains supplementary material available at 10.1186/s12284-021-00459-y. Whole-genome duplication (WGD) or polyploidization played vital role in evolution, speciation and biological complexity of higher plants  classification and population structure of neo-tetraploid rice, (2) to construct the haplotype patterns of agronomically important genes in tetraploid rice population, (3) to mine the novel specific alleles of neo-tetraploid rice, and (4) to validate the function of neo-tetraploid rice specific genes.In the present study, the genomic re-sequencing data of neo-tetraploid rice and autotetraploid rice lines was used to reveal the genome features, population structure and haplotype patterns. By these analyses, we planned (1) to determine the subspecies (The genome re-sequencing data of 15 neo-tetraploid rice lines (NTRs) and 18 autotetraploid rice lines (ATRs) with about 46-fold sequencing depth were used in this study, which included about 2 billion high quality pair-end sequencing reads with an average ratio of Q30 score of 93.98%  per 100 Kb in indica ATRs, javanica ATRs, japonica ATRs and NTRs were detected, respectively  against two reference genomes.Variation density analysis revealed 829.28, 385.75, 304.89 and 501.01 variations (including SNPs and InDels) on an average per 100 Kb in Oryza sativa L.) subspecies, indica and japonica. Mostly, the mapping rate is high and variations are less when the sequencing sample and the reference genome belonging to the same subspecies. Subspecies classification of NTRs was detected using the difference of mapping rate and genomic variation number between NTRs and two reference genomes . With minimal coverage of 5X, the average mapping rates were 92.39% and 89.13% with MSU7 and R498 in all NTRs, respectively. On the contrary, the average variation numbers of all NTRs were 1,903,495 and 2,818,017 against MSU7 and R498, respectively. The tendency was consistent with the known japonica materials in the previous studies, which suggested that the genome sequences of NTRs were similar to japonica , indica, japonica, javanica  and NTR, and the classifications of most samples were in accordance with their known subspecies features  ranging from 2 to 8, and k = 5 was chosen as the best subgroup number because the lowest cross-validation error was detected when k = 5  and phylogenetic relationship analysis classified all materials into five different groups, namely s Table Sa, b. To = 5 Fig. . These rds Fig. S.Haplotype pattern of 55,801 annotated genes in rice whole genome MSU7) was determined to evaluate the genetic diversity of tetraploid rice population. A total of 432,290 haplotypes were identified, with an average of 7.75 haplotypes per gene. The distribution of number of haplotypes per gene revealed that 40,013 out of 55,801 genes have 2 to 15 haplotypes , NAL1 (LOC_Os04g52479) and sd1 (LOC_Os01g66100) have 4, 6 and 9 haplotypes, while TAC1 (LOC_Os09g35980) gene only have one haplotype.In order to harness the genetic diversity of tetraploid rice for breeding, the haplotype variations of previously cloned genes associated with yield and other agronomically important traits were identified in tetraploid rice population Table S. In 717 The results of haplotype analysis were further used to identify NTR specific alleles compared to ATRs. The haplotype patterns of 55,801 annotated genes of MSU7 reference genome among 18 ATRs, 15 NTRs and 30 diploid rice lines (DRs) were compared. A total of 269 shared unique haplotypes were found in the NTRs, but no unique haplotype was detected in the ATRs when compared with the DR genomes, suggesting that NTRs may contain novel variations, which is neither present in DRs, nor in ATRs Table S. MoreoveLOC_Os05g44340  and LOC_Os10g25730   and japonica (HSP101-j) types. However, HSP101 allele was different and uniform in all NTRs, with a unique base substitution from G to C that caused a missense mutation in exon 1 and amino acid change from proline to alanine  Fig. . Of thes101 Fig. , while t101 Fig.  and Rice101 Fig.  databaseHSP101–1 in neo-tetraploid rice was first validated at seedling stage. qRT-PCR results displayed that HSP101–1 was up-regulated in leaf, sheath and root at seedling stage in Huaduo 1 after heat treatment, and the expression was up-regulated by 653.19-, 1204.37- and 1843.17- fold change in leaf, sheath and root, respectively. While, the expression decreased gradually after heat shock in Huaduo 1 , Huaduo 1 and Huaduo 2543, and Huanghuazhan (HSP101-i), Huanghuazhan-4x (HSP101-i), Taichung 65 (HSP101-j), Taichung 65-4x (HSP101-j), and the NTR parental lines, Jackson-4x (HSP101-j) and 96,025 (HSP101-j), were used as controls. The results showed that NTRs with the haplotype of HSP101–1 have enhanced thermotolerance capability and produced stronger seedlings than all controls. The heat sensitivity indexes of seedlings were 0.072 and 0.084 in Huaduo 1 and Huaduo 2543, while the heat sensitivity index in controls were ranged from 0.115 to 0.203, with an average value of 0.159. The heat sensitivity indexes of roots were 0.126 and 0.193 in Huaduo 1 and Huaduo 2543, while the heat sensitivity index in controls were ranged from 0.242 to 0.419, with an average value of 0.365 , MA I (meiosis I) and MA II (meiosis II) under optimal conditions (without heat stress)  Fig. a. Knockoge Fig. Sg and asyge Fig. Sp. These on Fig. S. These rThe high-quality genome sequencing and genomic variation data provided a way to precisely resolve the population structure of plants, which also facilitated the detection of functional genes in rice  rice accessions into five subpopulations, which were consistent with their features of subspecies classification. Together with the Brassica napus , meiosis I and meiosis II stages of neo-tetraploid rice. Moreover, loss-of-function mutant of HSP101–1 showed significant reduction in seed setting of neo-tetraploid rice. Hence, the novel allele of HSP101–1 is indispensable for high fertility in neo-tetraploid rice. These results revealed enriched allelic diversity of HSP101 gene and suggested promising potential function of this novel allele in heat stress and maintaining high fertility of neo-tetraploid rice.The identified novel allele of japonica subspecies with independent subpopulation position. Haplotype patterns of whole genome genes, especially yield related key genes, were analyzed in tetraploid rice population. Functional validation indicated that the novel haplotype of HSP101–1 plays important role in both seedling heat stress and fertility in neo-tetraploid rice. Here, the construction of first large-scale genomic repository of autotetraploid and neo-tetraploid rice, and the revelation of classification and population structure will play important role in optimizing the designs of tetraploid rice breeding. The specific variations in NTRs will facilitate further marker-assisted selection and functional genomics of tetraploid rice.Large-scale genomic variations of autotetraploid rice and neo-tetraploid rice lines, and population structure analysis classified neo-tetraploid rice lines into indica rice lines and 15 japonica rice lines) were downloaded from the NCBI SRA database with the bioproject accession number PRJNA396096 . The quantity and quality of raw sequencing data was evaluated using the FastQC (v0.11.6) software  and 18 autotetraploid rice lines (ATRs) were collected from our bioproject PRJNA526117. The previously published diploid rice genome sequencing data of 38 accessions  and R498  reference genome using BWA (0.7.17-r1188) . The population structure was analyzed using ADMIXTURE (version 1.3.0) software (Alexander and Lange k (the number of groups) ranged from 2 to 8, and the k = 5 was a sensible modeling choice with the lowest cross validation error. The ancestry distributions of individuals were visualized using R script.SNP variations were selected using “SelectVariants” from GATK (3.8–0)  were used to construct the haplotype by our python script. The haplotypes with homozygous variations were considered as main haplotypes, while the haplotypes with heterozygous bases were still been counted in the total haplotype numbers. Firstly, a total of 717 agronomically important genes for yield that previously reported , leaf sheath (S) and root (R) after each hour were collected and kept in liquid nitrogen and stored at − 80 °C. The samples from untreated seedlings were also collected as CK, and all the samples were taken in three biological replications. Total RNA from each sample was extracted and the reverse transcription was conducted using Evo M-MLV RT Kit with gDNA Clean for qPCR (Accurate Biotechnology). Primers used for qRT-PCR were designed using Primer Blast software in NCBI. The reaction of reverse transcription was as follow: incubation for 2 min at 42 °C, followed by RT process of 37 °C for 15 min, 85 °C for 5 s and then stored at 4 °C. The qRT-PCR was performed on Lightcycler480 (Roche) using the Hieff qPCR SYBR Green Master Mix (YEASEN). The reaction conditions were as follow: 30 s at 95 °C, 40 cycles of 95 °C denaturation for 5 s and 58 °C annealing and extension for 20s. Three biological replications were performed in this experiment. Actin was used as an internal reference for qRT-PCR. The relative gene expression was calculated by the 2HSP101–1, using its genomic DNA sequence (http://skl.scau.edu.cn/home/) (Table Shsp101–1 was checked in T1 generation by thermotolerance assay (see below). Seed setting of 16 T1 plants were investigated and compared with wild type, Huaduo 1. Pollen fertility was observed by using 1% I2-KI method with Motic BA200 microscope  Table S. The con) Table S was tran) Table S. The theIndica autotetraploid Huanghuazhan-4x and its diploid counterpart, japonica autotetraploid Taichung65-4x and its diploid counterpart and the parental lines of NTR were used as controls. To test basal thermo-tolerance ability, three-day-old seedlings that grown in an incubator at 28 °C in water were treated at 48 °C for 1 h, and then the seedlings were transferred to growth chamber at 28 °C for about 72 h. After that, the root and seedling lengths of treated and untreated samples (CK) were measured and their morphology was photographed. The index of heat sensitivity was calculated based on the decrease in seedling length compared to the untreated samples.Heat treatment of two neo-tetraploid rice (NTR) lines, Huaduo 1 and Huaduo 2543, were performed based on the previously established protocol for diploid rice . A total of 21 samples, including leaf and stem at meiosis stage, bicellular pollen stage and 5 days after flowering, young panicle at the length of 0.5 cm, 1 cm and 5 cm, anther and ovary at meiosis I, meiosis II, microspore early, microspore late and bicellular pollen stages, were used to illustrate the expression pattern of HSP101–1 in neo-tetraploid rice Huaduo 1. The expression pattern of HSP101 in Nipponbare were retrieved from RiceXPro database  density of different types of tetraploid rice against MSU7 reference genome. Figure S2. Circos plot illustrating the variation (including SNPs and InDels) density of different types of tetraploid rice against R498 reference genome. Figure S3. Difference of 5-fold mapping coverage ratio between japonica reference genome MSU7 and indica reference genome R498. Figure S4. Comparison of genomic variation counts that generated based on japonica reference genome MSU7 and indica reference genome R498. Figure S5. Phenol reaction of neo-tetraploid rice lines. Figure S6. Sequencing coverage of wide compatibility gene S5 (LOC_Os06g11010) in autotetraploid rice lines and neo-tetraploid rice lines. Figure S7. Distribution of haplotypes per gene of the 55,801 annotated genes in tetraploid rice. Figure S8. Distribution of haplotypes per gene in the 717 agronomically important genes in tetraploid rice. Figure S9. The phylogenetic tree of LOC_Os04g09310 (a), LOC_Os05g02840 (b), LOC_Os05g43000 (c) and LOC_Os10g25730 (d) in autotetraploid rice and neo-tetraploid rice lines. Figure S10. The expression pattern analysis of HSP101 and the impact of different temperatures during flowering to the seed setting in tetraploid rice lines. Figure S11. The expression pattern analysis of HSP101 in diploid rice Nipponbare using the RiceXPro database. Figure S12. Sanger sequencing chromatogram of hsp101–1 mutants. Figure S13. Cytological observation of pollen development of hsp101–1 and wild type Huaduo 1.Additional file 2: Table S1. Summary of quantity and quality of genome re-sequencing data. Table S2. Genome coverage of sequencing reads to MSU7 and R498 reference genome. Table S3. Number of genomic variations against MSU7 and R498 reference genome. Table S4. Number of genomic variations from different effect levels against MSU7 reference genome. Table S5. Variation density of different types of autotetraploid rice against MSU7 and R498 reference genome. Table S6. Cross-validation error of different k values in ADMIXTURE analysis. Table S7. GO enrichment analysis of zero-diversity genes in tetraploid rice population. Table S8. Annotation of 717 agronomically important genes that previously reported in diploid rice. Table S9. Haplotype patterns of 717 agronomically important genes in autotetraploid rice (ATR) and neo-tetraploid rice (NTR) lines. Table S10. Neo-tetraploid rice (NTR) specific haplotypes compared with 30 diploid rice cultivars. Table S11. Validation of neo-tetraploid rice specific variation in HSP101–1 using Sanger sequencing. Table S12. The genomic variations in 3024 diploid rice accessions of HSP101 from rice functional genomics and breeding (RFGB) database. Table S13. The genomic variations in diploid rice accessions of HSP101 from RiceVarMap2 database. Table S14. Two target-site primers for NTR specific gene, HSP101–1. Table S15. Mutations of HSP101–1 knockout plants.


CSN1S1, CSN2, CSN1S2, and CSN3 have obtained substantial attention since they affect the milk protein yield, milk composition, cheese processing properties, and digestibility as well as tolerance in human nutrition. Furthermore, milk protein variants are used for breed characterization, biodiversity, and phylogenetic studies. The current study aimed at the identification of casein protein variants in five domestic goat breeds from Sudan  and three wild goat species . High-density capture sequencing of 33 goats identified in total 22 non-synonymous and 13 synonymous single nucleotide polymorphisms (SNPs), of which nine non-synonymous and seven synonymous SNPs are new. In the CSN1S1 gene, the new non-synonymous SNP ss7213522403 segregated in Alpine ibex. In the CSN2 gene, the new non-synonymous SNPs ss7213522526, ss7213522558, and ss7213522487 were found exclusively in Nubian and Alpine ibex. In the CSN1S2 gene, the new non-synonymous SNPs ss7213522477, ss7213522549, and ss7213522575 were found in Nubian ibex only. In the CSN3 gene, the non-synonymous SNPs ss7213522604 and ss7213522610 were found in Alpine ibex. The identified DNA sequence variants led to the detection of nine new casein protein variants. New variants were detected for alpha S1 casein in Saanen goats (CSN1S1∗C1), Bezoar ibex (CSN1S1∗J), and Alpine ibex (CSN1S1∗K), for beta and kappa caseins in Alpine ibex (CSN2∗F and CSN3∗X), and for alpha S2 casein in all domesticated and wild goats (CSN1S2∗H), in Nubian and Desert goats (CSN1S2∗I), or in Nubian ibex only (CSN1S2∗J and CSN1S2∗K). The results show that most novel SNPs and protein variants occur in the critically endangered Nubian ibex. This highlights the importance of the preservation of this endangered breed. Furthermore, we suggest validating and further characterizing the new casein protein variants.Genetic variations in the four casein genes  Goats play an essential role in rural areas of developing countries. They provide food security, preservation of basic nutrition, and economic income . FurtherMilk composition influences its nutritional value, technological properties, and the quality of dairy products . The proCSN1S1, CSN2, CSN1S2, and CSN3, respectively. The casein genes are located in a 250-kb region between 85,978 and 86,211 Mb on Capra hircus chromosome 6 in the following order: CSN1S1 , CSN2 , CSN1S2 , and CSN3  , eight for beta casein , 10 for alpha S2 casein , and 23 for kappa casein  have been reported  . Casein ,211 Mb) . They arreported . Additioreported .In this study, we assessed the genetic variation in and between five different goat breeds from Sudan and three wild species. The four major domestic goat breeds in Sudan are Nubian, Desert, Nilotic, and Taggar . In SudaCapra.High-density capture sequencing was used to identify genetic variants in the casein genes. Since we did not examine proteins, we predicted protein variants from DNA polymorphisms using bioinformatics tools. The identification of such variations on the DNA and protein levels is the first step for subsequent association studies, which will provide further information about the effects of specific protein variants on milk characteristics and offer their application for breed improvement. Moreover, the information can be used for conservation decisions and further elucidation of the evolution of n = 7) were sampled from four locations in the three states along the river Nile. Desert goats (n = 5) from the Bara and Abu Zabad area in the North Kordofan state, Nilotic goats (n = 7) from the Kosti and Rabak areas in the White Nile state, and Taggar goats (n = 7) from the Nuba Mountains and Dalang area in the South Kordofan state. Saanen goat samples (n = 2) were obtained from a goat improvement farm in Khartoum state. Blood was taken from the jugular vein through the use of vacutainer tubes containing EDTA as an anticoagulant (5 ml). DNA was extracted using the Puregene core kit A . Additional DNA samples were obtained from Bezoar ibex (n = 2), Nubian ibex (n = 2), and Alpine ibex (n = 1). DNAs of Bezoar and Alpine ibex were obtained from the DNA and tissue bank of the Leibniz Institute for Zoo and Wildlife Research Berlin, Germany. Nubian ibex originated from the Red Sea state of Sudan, as described previously  and the advisory group regarding animal genetic diversity of the Food and Agriculture Organization of the United Nations , as desceviously . DetaileCSN1S1, CSN2, CSN1S2, and CSN3 were enriched by hybridization to a custom tiling array  and sequenced on a Hiseq1500 instrument  in paired-end mode with a read length of 100 nt. The gene-specific tiling array was created using the goat reference sequences (version LWLT01)   availablFor the generation of sequencing libraries, 500 ng of genomic DNA was sheared by sonication  for 75 s  and further processed with the Accel-DNA 1S kit  according to the manufacturer’s instructions. The resulting whole genome libraries were barcoded and pooled in equimolar amounts and hybridized to the tiling array. Briefly, the libraries were hybridized for 65 h at 65°C, washed, and eluted with nuclease-free water for 10 min at 95°C. The eluted DNA was concentrated in a vacuum centrifuge, amplified by PCR  and purified with Ampure XP beads.Fastq sequencing files were demultiplexed based on their barcodes and reads were trimmed using trimmomatic, after which the trimmed reads were aligned to the LWLT01 reference genome using BWA. The BAM files containing the raw aligned reads per sample formed the input for our variant calling pipeline. Variants were called using BCFtools, and the resulting raw single nucleotide polymorphism (SNP) calls were filtered using the varFilter tools from the vcfutils package to remove all off-target calls .q) in the mpileup step was increased to 30 , and varFilter in the vcfutils step was called with a minimum depth (-d) of 10 reads (default: 2) in a sample before a SNP was to be called. All other settings for SNP filtering were left to their default values. This means that if a SNP is called in a sample, at least 10 reads with a read quality of 30 were available to support the detected SNPs. All SNPs have a minimal QUAL score in the resulting VCF file of 300. The average read depth across all SNPs was visualized and is available in Two settings in the SNP calling phase were adjusted: read quality  and are reported in this paper with their rs identification number and novel SNPs with their European Variant Archive (EVA) ss identifier.Subsequently, novel high confident SNPs were combined and annotated using a custom R script that relies on the “seqinr” and “Biostrings” R packages. In short, the GFF3 file containing the gene, exon, and cds locations for goat was combined with the SNPs from the VCF file obtained from SNP calling using R 4.0.2. Amino acid changes were determined by translating codons using the “Biostrings” package  using “T2 and are available in 3. The isoelectric focus (IEF) information for the new protein variants in the kappa casein was not experimentally tested, but predicted using the ExPASy tool whether the variant belonged to the AIEF or the BIEF group in the kappa casein protein , followed by variants in the upstream gene region (14.84%) and the non-synonymous variants (3.40%). The remaining SNPs were synonymous variants (2.01%) and variants located in the 3′-UTR (2.01%) and the 5′-UTR (0.92%)  . SNP genCSN1S1 (accession no. NC_030813) represents the alpha S1 casein variant ∗ACSN1S1 (XP_017904616), which includes the signal peptide. Sequence analysis of 22,807 bp revealed 226 SNPs with 9.9 SNPs per 1,000 sequenced base pairs. Among the identified SNPs, six were non-synonymous  and led to the amino acid substitution Ile44Val in the mature alpha S1 casein protein. The known non-synonymous SNPs rs155505536 and rs268293072 were found in all Sudanese breeds, Saanen goats, and Bezoar ibex; rs155505532 segregated in Sudanese breeds and Saanen, and the SNPs rs268293069 and rs655973384 were found in Saanen goats only .CSN1S1 gene, five out of seven synonymous SNPs were novel . The SNPs ss7213522443, ss7213522421, ss7213522397, and ss7213522438 revealed synonymous mutations in the codons for the amino acids Lys102, Asn139, Tyr165, and Pro168 of the mature protein, respectively. The additional synonymous SNP ss7213522449 is located in the codon for the amino acid Leu6 in the signal peptide. Three out of the five novel synonymous SNPs  segregated in Sudanese breeds, Saanen, and Bezoar ibex (In the oar ibex . The novoar ibex .CSN2 gene (accession no. NC_030813) represents the beta casein variant CSN2∗C (XP_005681778), which includes the signal peptide. Sequencing of 15,071 bp revealed 109 SNPs with 7.23 SNPs per 1,000 sequenced base pairs. Among the identified SNPs, five were non-synonymous  was the only novel synonymous SNP in the codon Gln13 of the signal peptide. It was found in Nubian ibex and Alpine ibex.In the CSN1S2 (accession no. NC_030813) represented the ∗ACSN1S2 variant of the alpha S2 casein protein (XP_013820127), which includes the signal peptide. On average, 8.5 SNPs were detected per 1,000 sequenced base pairs. In the sequence of 22,694 bp, 193 SNPs were found in comparison to the reference sequence. Among them, six were non-synonymous SNPs , rs659163710 , and rs665830654 , which lead to the amino acid substitutions of Phe4Ser in the signal peptide and Ala119Pro and Glu127Lys in the mature alpha S2 casein protein. No synonymous SNP was detected in the CSN1S2 gene in this study.The reference sequence of the ous SNPs , 38 wereous SNPs . In thisous SNPs . All throus SNPs . AlthougCSN3 (accession no. NC_030813) encodes the CSN3∗B kappa casein protein variant (NP_001272516), including the signal peptide. Sequencing of 20,113 bp of CSN3 revealed 119 SNPs compared to the reference sequence with 5.9 SNPs per 1,000 sequenced base pairs. Among the identified SNPs, five were non-synonymous  in the codon for the amino acid Asp20 of the mature protein in Nubian ibex. The known synonymous SNP rs663488235 (exon 3) segregated in Desert, Nilotic, Taggar, and Saanen goats, as well as in Bezoar ibex, the SNP rs155505563 (exon 4) in Nilotic goats, the SNP rs268293107 in Nilotic and Taggar goats, and the SNP rs268293108 in Nubian, Desert, Taggar, and Saanen goats as well as in Nubian and Alpine ibex , leading to the amino acid Gln13 in the signal peptide. The two known CSN2∗A and CSN2∗C variants were found in Sudanese breeds, Saanen goats, and Bezoar ibex. In addition, CSN2∗C was also found in Nubian ibex . Since glutamine and glutamic acid are both polar, and arginine is similar to lysine (both contain long and flexible side chains with a positively charged end), we do not expect that the CSN1S1∗J variant has significantly different biochemical properties compared to the CSN1S1∗A variant; however, this expectation needs to be further investigated. Another new variant, CSN1S1∗K, detected in Alpine ibex has the amino acid substitution Iso44Val (compared to CSN1S1∗A). Both amino acids have large rigid aliphatic hydrophobic chains, and the biochemical properties of isoleucine and valine are similar. As such, major biochemical differences between the CSN1S1∗K and CSN1S1∗A variants are not expected. Interestingly, the protein variant CSN1S1∗B3 detected at high frequency in all Sudanese breeds and Saanen goats has been associated before with increased milk protein yield and high amounts of alpha S1 casein in milk. This, in turn, could alleviate the gross yield and quality of cheese production  occurring in Nubian ibex and Alpine ibex were found in the signal peptide sequence. The mature protein is not affected by these SNPs. Because the encoded mature protein variant is not changed, no new variant name was assigned. Not assigning names to amino acid variants in the signal peptide, in the opinion of the authors, could lead to underestimating the role of the signal peptide. For example, the signal peptide changes might cause the protein to be mistargeted, leading to the protein not being excreted in the milk.CSN2∗A, which is believed to be the ancestral allele of CSN2  in comparison to CSN2 null alleles . So far, 10 variants have been identified for the goat alpha S2 casein  pattern of the new variant CSN3∗X was not experimentally tested, but it was predicted using ExPASy. The predicted IEF was 5.53. If true, CSN3∗X belongs to the BIEF group, while CSN3∗B was classified in the AIEF group. Since the BIEF group is favorable for improving milk protein content and cheese-making properties, the new variant is an interesting target for milk and cheese production.With respect to kappa casein, research . The CSNm Europe . The CSN Somalia . The newMost of the novel protein variants detected in our study were found in Nubian and Alpine ibex. This underlines the necessity to pay more attention to the study and conservation of endangered species in order to protect valuable genetic resources.CSN1S1 gene were identified in Saanen goats, Bezoar ibex, and Alpine ibex. In the CSN2 and CSN3 genes, one additional protein variant was detected in Alpine ibex in each gene. Four new protein variants that were found in the CSN1S2 gene occurred in all studied goat breeds and species. The identified novel protein variants are of interest not only for their effect on protein and milk composition but also for evolutionary studies on milk protein genes. Unfortunately, neither RNA nor milk samples of the studied goat breeds were available. Therefore, further investigation is necessary to examine the expression of the nine new variants on the protein level to validate and confirm the outcomes of this study.In this study, novel genetic variations of goat casein genes were discovered by capture sequencing. Most of the genetic variations, especially the non-synonymous polymorphisms, were identified in the critically endangered Nubian ibex and Alpine ibex. Therefore, we would like to emphasize and highlight the importance of preservation and studying rare and endangered species. It is noteworthy that nine new protein variants were found for the first time in the DNA sequences of the casein genes. Three protein variants in the https://www.ebi.ac.uk/ena/data/view/PRJEB42077. The DNA sequencing dataset is available at the NCBI Short Read Archive under BioProject ID: PRJNA683771. The dataset in this study can be found at http://www.ncbi.nlm.nih.gov/bioproject/683771. Further inquiries can be directed to the corresponding authors.The original contributions presented in the study are included in the article/All samples were collected with permission from the owners of the animals and according to the animal protection law in Sudan. Written informed consent was obtained from the owners for the participation of their animals in this study.SR, DA, and GB conceived and designed the study. SR, MR, and LH provided the samples. SR, MR, and SK performed the experiments. SR and DA analyzed the data. SR interpreted the data and drafted the manuscript. DA and GB helped to draft the manuscript. AS, LH, MR, and SK did critical revision of the manuscript. All authors read and approved the final manuscript.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.


In depth studies of quantitative trait loci (QTL) can provide insights to the genetic architectures of complex traits. A major effect QTL at the distal end of chicken chromosome 1 has been associated with growth traits in multiple populations. This locus was fine-mapped in a fifteen-generation chicken advanced intercross population including 1119 birds and explored in further detail using 222 sequenced genomes from 10 high/low body weight chicken stocks. We detected this QTL that, in total, contributed 14.4% of the genetic variance for growth. Further, nine mosaic precise intervals (Kb level) which contain ancestral regulatory variants were fine-mapped and we chose one of them to demonstrate the key regulatory role in the duodenum. This is the first study to break down the detail genetic architectures for the well-known QTL in chicken and provides a good example of the fine-mapping of various of quantitative traits in any species. Yuzhe Wang, Xuemin Cao et al. report the fine-mapping of a major growth trait QTL in chicken using genome-wide association and haplotype association analyses. They discover multiple mutations cumulatively contribute to the previously-reported QTL and identify one of a regulatory mutation that contributes to the variation in the measured traits. In contrast, complex quantitative traits tend to follow Fisher’s4 minor-polygene hypothesis, and are difficult to disentangle due to small allelic effects for each locus, complex patterns of linkage disequilibrium in associated regions, limited ability to screen for putative functional polymorphisms, the balance between artificial and natural selection for complex traits, and variable effects of loci across populations and environments7. In human genetics, genetic variants contributing to complex traits such as human height are often distributed across the entire genome and an infinitesimal model seems to hold8. In domestic animals, although some prominent examples including Insulin-like growth factor 2 (IGF2)9 and Myostatin (MSTN)11 prove that artificial selection has resulted in fixation of large effect mutations, the on-going grand challenge in the field is still to provide a better empirical understanding of the basis for polygenic traits.The molecular mechanisms of many monogenic traits have been identified in agricultural animals and plants, facilitated by their distinct genotype to phenotype associations21. Many candidate genes reside in the associated region, including Retinoblastoma 1 (RB1), Forkhead box O1 (FOXO1)21 and an insertion mutation of miR-16 have been suggested as possible causal polymorphism15. However, there is still no conclusive consensus among the detailed genetic architectural, biological mechanisms of this locus. In our previous study, an F2 intercross between a slow growing meat type chicken, the Huiyang Bearded  has been mapped in many chicken populations at the distal end of chromosome 1, where it has been confirmed to be significantly associated with several growth-related traits including body weight, abdominal fat, muscle weight, and feed conversion ratio15 advanced intercross line (AIL) derived from the previously reported F2 population and we use the large F9 population for QTL mapping and birds from the F15 generation for functional studies. The strongest association was detected to a mosaic haplotype that was further fine-mapped and functionally explored by evaluating the relationship between the haplotype detected in the HQLA-HB population and in two independently selected broiler breeds, six ancient low body weight breeds and two jungle fowl populations  gene. This study reports the fine-mapping and functional dissection of a complex quantitative trait locus, contributing new insights to the genetic and biological mechanisms underlying growth in chicken.Here, we report the construction of an F2 and F9 generations from the HQLA × HB intercross population using in total 41,758 and 291,772 SNPs24, respectively. In the F2 generation (n = 493), a single, genome-wide 1% significant (p = 8.1 × 10–7) association signal was detected for 11 of 15 evaluated traits at the distal end of GGA1  for the combined weight of the ventriculus and the proventriculus  analyses for several body weight and carcass traits , the strongest association was to BW8 . The top SNP (S1_168536487) was significantly associated with 10 traits, including weight at 2–14 weeks of age, growth rates (0–4 and 4–8 weeks of age) and intestinal length (IL) , containing genes including Insulin-like growth factor 2 binding protein 1 (IGF2BP1) and Phosphoethanolamine/phosphocholine phosphatase (PHOSPHO1).In the F Mb Fig. . In addiP-values and the difference in allele-frequency between HQLA and HB at the significant SNPs (ΔAF(HQLA-HB) were highly correlated | > 0.95). Therefore, we sequenced the 31 F0 founders of the AIL to 10× individual coverage to identify additional markers for tracing the recombination events between the HQLA and HB founder haplotypes in the associated GGA1 QTL region at high resolution. The 3.1 Mb peak QTL region was screened for polymorphic sites in the sequence data (Supplementary Table (HQLA-HB)| > 0.95 were identified. Next, 31 of these 46 SNPs were selected for genotyping using Fluidigm® in from the F9 generation . An IBD analysis identified four breed-level recombination breakpoints in the QTL interval. In total, 503 F9 individuals carried unrecombined progenitor chromosomes  in the 3.1 Mb region and significant differences in BW8 were detected between individuals carrying the three different founder-breed origin genotypes  for further analysis.The GWAS pes Fig. . The oth(HQLA-HB)| > 0.95) were located in the 1.2 Mb candidate region. For this, an additional 76 SNPs with |ΔAF(HQLA-HB)| > 0.75, including the peak SNP S1_168536487 in the previous F9 GWAS, were selected in this interval (detail in “Methods” section). These were genotyped in the same F9 cohort as before for use in additional association analysis and exploration of common, segregating haplotypes. Of these, 60 SNPs were selected as TagSNPs using a 1% genome-wide Bonferroni corrected significance threshold in the single SNP F9 association analysis across these markers. Nine of these SNPs were more significant in this analysis than the earlier top-SNP S1_168536487 (Supplementary Data 0 to F9) and the recombination rate in chicken. Instead, it is considered more likely to have arisen from variable allele frequencies of the SNPs in the founders  were selected to control for genetic background effects. In total, this analysis identified 8 SNPs . The remaining 84 were grouped .To identify the haplotypes contributing to the association signal, a multilocus backward-elimination analysis was performed across the 60 tag SNPs in the 1.2 Mb regionHB = 0.70) and one from HQLA (AFHQLA = 0.04), had bigger weight decreasing/increasing effects, respectively, than the other founder line haplotypes. The recombinant haplotypes had intermediate substitution effects to those of the contributing founder haplotypes. Inheriting a partial founder-line haplotype segment is thus not sufficient to reach their full weight increasing/decreasing effects, respectively. The part of the 1.2 Mb segment transmitted to a recombinant haplotype appears to matter. Inheriting a “HB” segment in proximal part of the haplotype provides most of the “low-weight” effect, with only a smaller contribution by the “HB” segment in the distal part. In contrast, the “high-weight” effect appears to be more evenly contributed by the corresponding “HQLA” segments in the proximal and distal parts. The results from the association analyses therefore lead us to propose the hypothesis that this 1.2 Mb segment contain at least two loci contributing to the differences in weight between HB and HQLA, and that both founder lines are likely to segregate for at least two haplotypes across this region with different effects on weight.The additive haplotype substitution effects on BW8 were estimated, and there was a gradient distribution of haplotype allele effects from decreasing BW8 by 59 g to increasing it by 65 g Fig. . The fouA haplotype-sharing analysis across multiple high-weight and low-weight chicken breeds was next used to refine the 1.2 Mb region further. We here assumed that one or more of the multiple favorable regions of the large-effect, multi-locus QTL haplotype identified in the cross between HB × HQLA (the “Q-haplotype”) would also have been selected in other high-weight breeds and that these breeds would therefore, at least in parts, share the Q-haplotype in the region. However, this analysis is also a potential source of false negative variant discrimination. The 253 re-sequenced individuals were divided into two groups  were selected for gene expression analysis detected by quantitative real-time PCR, since phenotypes relating to these tissues were associated to this region in the GWAS analyses  which may influence gene expression at local or long distances and in tissue-specific manners. To examine the potential functional effect of the variants in these Q-haplotypes, the expression levels of the 23 genes located in the 1.2 Mb interval Fig.  were eva9 was IL and duodenum  is known to have important roles in digestion, appetite regulation and growth29. We conducted ATAC-seq to profile the accessible chromatin in duodenum samples from two chickens with high and low body weights  from the F12 generation of the intercross. We obtained 86.9–131.7 million unique mapped reads and 7,303–29,724 peaks from each sample (Supplementary Table CAB39L and SETDB2 genes (within Q4 haplotype), was found compared with the haplotype-sharing results  and ~97/218-fold (qq/QQ) in CAB39L direction (R). The luciferase activity was significantly higher for the QQ constructs than for the qq constructs in CAB39L direction (R) . For enhancer activity detection, only the QQ constructs showed significantly increased luciferase activity compared with enhancer less vector and the corresponding qq constructs (~1.4/1.2-fold increase in SETDB2 direction (F)/CAB39L direction (R)). This example shows the Q4 haplotype contains functional element affecting gene regulation which may affect growth.We use ATAC-seq to identify chromatin accessibility, which is a crucial component of genome regulation. Here we first focus on chicken duodenum as an example of in-depth research because the most significant associations of the evaluated carcass traits (dressed weight (DW), abdominal fat weight (AFW), eviscerated weight (EW) and intestinal length (IL)) in Fts Figs. . Within 2 and F9 generations of a cross between native Chinese (HB) and commercial broiler (HQLA) populations. The results strongly indicated that the associated region contained multiple segregation haplotypes in both founder populations harboring regulatory mutations cumulatively contributing to a major QTL affecting chicken growth traits.Deep intercross-populations between divergent lines is a powerful experimental design for identifying and fine-mapping chromosomal regions (QTL) contributing to complex traits via the accumulation of recombination events in each generation. A series of analyses were performed in the F2 population identified a 54-bp insertion on miR-16 that was significantly associated with increased body weight15. This mutation does, however, not segregate at significantly different allele frequencies in the HQLA and HB founders of this cross, and also not in a sample of 50 additional HQLA/HB samples and other 240 high/low body weight individuals collected for diagnostic tests | < 0.4 in this study. Another association detected in this region is that of Yuan et al. reporting the top association to a SNP (rs13553102) located at upstream of miR-15a (in Block2 of this study) resulting in a significant decrease in feed conversion ratio12. A relatively high |ΔAF(HQLA-HB)| was found for this SNP (0.65), but for the other breeds in the low body weight chicken breed cluster the trend for this polymorphism was inconsistent  is activated upon phosphorylation of Thr172 within the catalytic alpha-subunit by the LKB1–STRAD–CAB39L complex37. AMPK plays a critical role in hormonal and nutrient-derived anorexigenic and orexigenic signals and in energy balance38. Interestingly, selection for muscle growth in pigs resulted in a high frequency of a missense mutation in a muscle-specific isoform of the AMPK gamma chain39. However, the sample size in the differential expression analysis (n = 19) is small for this quantitative trait and it can therefore not be expected that other than the largest effects are detected.The segregation of multiple haplotypes across the fine-mapped 1.2 Mb segment in the two founder-lines, and the differential expression of multiple genes in this segment between high-weight and low-weight birds, together reflect the complexity in the genetics of growth trait regulation even in a small fragment of the genome. Functional experiment of the open chromatin region located in Q4 haplotype highlighted two SNPs inside a regulatory element as candidate causal mutations and 41. In the Virginia chicken lines, the large selection responses for body-weight has been shown to be due to a highly polygenic genetic architecture, and where most contributing extended QTL were complex and fine-mapped to multiple linked loci42. Earlier work has shown that most of the selection in that experimental population has been due to selection on standing genetic variation26. Here, an evaluation of the region in the ancestral Red junglefowl and other breeds showed that the sub-haplotypes contributing to high weight in the commercial populations were present also in the RJF and several local breeds  were founded by birds from the F2 population and bred using random mating. The number of individuals produced in the generations varied from 144 to 294 , a broiler line bred by Guangdong Wiz Agricultural Science and Technology, Co. , and Huiyang Bearded chicken (HB), a native Chinese breed. In brief, HQLA is a Chinese commercial broiler line founded by the commercial Anak broiler breed and a Chinese indigenous chicken line, followed by strong artificial selection over 10 generations, according to a weight-based selection index. HB is a breed characterized by slow growth and high meat quality. The body weight of HB chickens at 7 weeks of age were, on average, less than one-third of that of chickens from HQLA . Weights at 14 weeks of age (BW14) were measured in F9 only. Growth rates were calculated as the weight gain during the periods 0–4, 4–8, and 8–12 weeks of age . In total, 499 F2 and 602 F9 individuals were slaughtered at week 13 (F2) or 14 (F9). After slaughter, dissections were performed to measure dressed weight (DW), Abdominal fat weight (AFW), eviscerated weight (EW), the combined weight of the ventriculus and the proventriculus 18, all in grams, and intestine length  in centimeters. In the F9 individuals, Cholesterol , Triglyceride  and lactate dehydrogenase  were also measured. Descriptive statistics for these phenotypes were provided in18 for F2 and in Supplementary Table 9.For the FAll animals used in this study were cared for, and experiments conducted using procedures, that complied with the requirements of the Animal Welfare Committee of Agrobiotechnology of China Agricultural University .0 founders for the HQLA-HB AIL (HQLA n = 16 and HB n = 15), two commercial broiler lines , and four Chinese native breeds 22, Indonesian native breeds (SUM n = 15 including Sumatera n = 5 and Kedu Hitam n = 10)23 and two jungle fowls . These chickens were divided into two groups (high and low body weight), with the high-weight group containing the commercial broiler lines  whose weights were much higher than those of other breeds  or the Qiagen DNeasy Blood and Tissue Kit  according to the manufacturer’s instructions . All animals used in this study were cared for, and experiments conducted using procedures, that complied with the requirements of the Animal Welfare Committee of Agrobiotechnology of China Agricultural University .DNA was extracted from EDTA-anticoagulated blood from the wing vein using the phenol-chloroform method , containing in total 57,636 SNPs50. SNPs on the sex chromosomes (Z/W), mitochondrial SNPs, and SNPs that could not be assigned to known chromosomes were excluded from the raw data. SNPs that failed to meet the following criteria were removed: individual call rate (>0.9), individual SNP call frequency (>0.9), and minor allele frequency (MAF > 0.05). In total, 493 F2 samples and 41,758 autosomal SNPs were retained for further analysis.In total, 493 individuals from the F9 generation, double-enzyme digestion genotyping by sequencing (ddGBS) was performed. An EcoRI- MseI library was prepared as in ref. 24 and sequencing performed on an Illumina Nextseq500 sequencer. Qualified reads were aligned to the chicken reference genome Gallus gallus 5.0 (released 2015) and the TASSEL-4.0 GBS analysis pipeline was used to discover SNPs51. The SNP filtering options used in TASSEL are described in detail in ref. 24. In total, 595 F9 samples and 291,772 SNPs that were evenly distributed across the genome in F9 were retained for further analyses.In the Fn = 211 new samples described above, and sequences from n = 42 other birds where downloaded from earlier studies, for a total of n = 253 processed samples. Approximately 100 ng of genomic DNA (for each of n = 211 samples) was fragmented to a mean size of about 300–400 bp using Covaris E210. The sample preparation workflow was complied with TruSeq Nano DNA Library Prep for NeoPrep Reference Guide (15049722 v01). The quality and concentration of the libraries were assessed with a Qubit2.0 Fluorometer  and an Agilent 2100 Bioanalyzer . Paired-end libraries were sequenced on the HiSeq X platform with 2 × 151 cycles.Whole genome sequencing was performed for https://software.broadinstitute.org/gatk/best-practices/) were employed to analyze the n = 253 chicken genomes. Prior mapping, adapter sequences were deleted and then the reads which contained more than 50% low quality bases, or more than 5% N bases, were removed. Qualified reads were aligned to the chicken reference genome  with BWA-MEM (version 0.7.10)52 using ‘-t 10 -M’ as parameters. Initial BAM files were further processed with reordering, sorting and duplicates marking utilizing the Picard (picard-tools-1.56) package followed by base quality recalibration using the BaseRecalibrator tools in the Genome Analysis Toolkit 53. Raw variants were called for individual bases using HaplotypeCaller. This process resulted in 253 individual *.gvcf files which were subjected to joint variant calling using GenotypeGVCFs. The VariantFiltration command was employed to exclude potential false-positive variant calls with the parameter ‘–filterExpression “QD < 2.0 || FS > 60.0 || MQ < 40.0 || ReadPosRankSum < −8.0” for SNPs and “QD < 2.0 || FS > 60.0 || MQ < 40.0 || ReadPosRankSum < −8.0” for Indels’. Ultimately credible SNPs were identified after using strict filtering criteria with parameters: minor allele frequency <0.05 and call rate >90% in each population. The SNPEff program54 was used, with the chicken reference genome sequence and GTF annotation files downloaded from Ensembl (version 5.0.89) to annotate variants. In GGA1, structural variations (SV) including deletions (DEL), insertions (INS), inversions (INV) and tandem duplications (DUP) were discovered using the Pindel (V0.2.4) software55 and copy number variation (CNV) were detected using the CNVnator (V0.3) software56.GATK best practices  as quantitative covariate. For all other traits, only the sex and batch were included as discrete covariates. A quantile-quantile (Q-Q) plot generated in R (v3.0.2) was used to assess the potential impact of population stratification on the genetic association studies  between the genotypes was calculated using the vcftools ‘--geno-r2’ command60. Consequently, for the F2 generation, the genome-wide 1% significance threshold was determined as p-value < 8.12 × 10–7 , and a suggestive association as 4.06 × 10–6 . For the F9 generation, the thresholds were 1.84 × 10–7  and 9.19 × 10–7 , respectively. The genotype in major QTL on GGA1 (165–175 Mb) of F9 generation was shown in Supplementary Data 2 and F9 were shown in Supplementary Data A mixed linear model (MLM) approach was used for the genome-wide association analyses as implemented in the GCTA package v1.24)457. The 9 individuals were traced back to facilitate identity-by-descent (IBD) mapping of the segment assuming divergent QTL-allele fixation in the founders. The absolute allele frequency differences (ΔAF) between HQLA and HB were calculated for all markers across the segment using the SNPs identified using whole genome resequencing of the F0 founders (see above). A subset of 46 segregating SNPs (|ΔAF(HQLA-HB)| ≥ 0.95, i.e., almost completely fixed for alternative alleles in the two lines) from candidate QTL region were selected to discriminate between alleles inherited from HQLA and HB. These were then genotyped on the Fluidigm platform (SNPtype Assays for SNP Genotyping on the 96.96 Dynamic Array IFCs) in 602 F9 individuals using the run thermal cycling protocol ‘SNPtype 96×96 v1’. SNPs and individuals were excluded due to bad calling quality (default parameters), individual call rate < 0.9 and SNPs call frequency < 0.9. In total, 31 SNPs and 575 F9 individuals passed this filtering and missing genotypes were imputed and corresponding recombinant IBD blocks deduced using BEAGLE V4.061. For each recombinant block, the distributions of BW8 and BW14 were evaluated in the 72 individuals carrying recombinant segments . The significance of weight differences between chromosomal IBD segments of HQLA-origin and HB-origin were calculated using an unpaired T-test | ≥ 0.75 . When multiple SNPs met this criterion and were separated by less than 100 bp, all but one were removed. We selected 61 SNPs in this step (including 9 SNPs genotyped earlier using the GBS method). Second, if a detected SNP was predicted as a missense/splice mutation or was located in the 3′/5′ UTR region of a gene, a softer selection criteria was used as |ΔAF(HQLA-HB)| ≥ 0.3. 18 SNPs were selected under this condition. Third, 10 SNPs that reached the genome-wide significance in the earlier F2 Chip-GWAS, but were not part of the F9 GBS set were also included. These 89 SNPs were genotyped using matrix-assisted laser desorption/ionization-time of flight technology . Thirteen SNPs were excluded after quality control due to bad calling, resulting in 76 SNPs being appended to the third GWAS in the same F9 cohort.Stage II: The two blocks with the strongest associations to weight  were subjected to a second round of targeted genotyping in the 602 available individuals from the F62 and the genetic map for our population was 2.8 cM/Mb for GGA118. Haplotype diversity (H) was calculated for each population as described by Nei and Tajima63. The H statistic was calculated in bins with 2 Kb.To investigate the signatures of selection in the three sequenced broilers and other breeds, five statistical tests were used including the frequency spectrum-based Tajima’s D, π and haplotype diversity (H) methods, the linkage disequilibrium-based XP-EHH method and the population differentiation-based Fst method. Three different genomic segments were analyzed: (1) GGA1:168.6–169.8 Mb, (2) GGA1: 165–175 Mb, and (3) the whole GGA1. For these three intervals, the Tajima’s D statistic was obtained in bins of 250 bp or 25 Kbp. Calculations of Fst between HQLA and HB were done on a per-site basis or in bins of 20 Kbp. The nucleotide diversity (π) was measured in sliding windows, with a window size of 500 bp and a step size of 250 bp or a window size of 50 Kbp and a step size of 25 Kbp. XP-EHH value normalization at each locus between HQLA and HB was estimated by selscan programQ(High-Low) ≥ 0.4. If the haplotypes in two consecutive bins both reach this criterion, the windows will be merged to create a longer Q haplotype sub-segment.Genome resequencing data was used to construct haplotypes. Eleven populations were divided into a high-weight group  and a low-weight group . Haplotype block estimation was performed using PLINK with 2 Kb bins across the 1.2 Mb candidate region. The regions defining the Q-haplotype of HQLA was identified using a criterion of ΔAF25:Y is a column vector containing the BW8 of the F9 individuals. X is the design matrix including the coding for the sex of the birds. Z is the design matrix for 17 columns containing the each haplo-genotype number  of each individual. β is a vector with the estimate of the fixed effect of sex. u is a column vector with the estimates for allele substitution effects for each haplo-genotype, and e is the normally distributed residual.In total, 100 haplotypes were detected of which 16 existed at a frequency > 0.01 (“Major haplotypes”) and the remaining 84 were grouped   online tool66. All SNPs were pruned using the indep-pairwise option in PLINK, with a window size of 50 SNPs, a step of 5 SNPs, and r2 threshold of 0.1.The population-level phylogeny was inferred using the Neighbor–Joining method15 generation of the deep intercross line were used for the quantitative real-time PCR  was the reference gene for other target genes. For all genes relative expression levels were calculated using the 2−ΔΔCt method. Primers used for Q-PCR are listed in Supplementary Table Nine birds with high 7-week weight (BW7: 882.56 ± 18.66 g), and 10 with low 7-week weight (BW7: 717.60 ± 21.15 g) from the F0 individuals. PCR amplification primers were forward: tcctcagtaaatacccacata and reverse: gaactgcattaactacaaaatc according the reference15. For more confirmation, another 25 unrelated individuals of HQLA and 25 unrelated individuals of HB were used for additional diagnostic tests.PCR sequencing was performed to identify reported variations of miR-15a-16 in all F2. Using Lipofectamine 2000  according to the manufacturer’s recommendations, we transfected per well cell in a 24 well plate (~80%–90% confluency) with a mixture comprising 720 ng of the pGL3 firefly luciferase reporter construct, 90 ng of the pRL-TK Renilla luciferase construct (Promega Corporation) and 3 µl Lipofectamine 2000. The luciferase assay was performed 48 h after transfection using the Dual Luciferase Reporter Assay system (Promega Corporation) and an Infinite F200 Luminometer . Ratios of firefly luminescence/Renilla luminescence were calculated. For each test construct, one expression value was obtained as the average of three technical replicates in each plate  of the 1010 bp open chromatin fragment were synthesized and cloned into the pGL3-basic and pGL3-promoter luciferase reporter vectors (Promega Corporation). The sequence and orientation of the inserts were confirmed by sequencing. For cell culture, DF1 (a chicken fibroblast cell line) cells were cultured in 24-well plates with DMEM  supplemented with 10% FBS  in a 37 °C incubator with 5% COn = 4) were used for ATAC-Seq. All experiments were performed based on the method of Jason D. Buenrostro67. Sequencing was performed on Illumina HiSeq platform. Clean reads were obtained from the raw reads by removing the adaptor sequences. The clean reads were then aligned using the bwa program. Peak calling was conducted using macs2 software with cutoff q value < 0.05.Duodenum of high and low body weight chickens at 7 weeks . All analyses are reproducible with access to genetic data (see “Data availability”).Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary DataReporting SummaryPeer Review File