Mangifera indica L., an important tropical fruit crop, is grown for its sweet and aromatic fruits. Past improvement of this species has predominantly relied on chance seedlings derived from over 1000 cultivars in the Indian sub-continent with a large variation for fruit size, yield, biotic and abiotic stress resistance, and fruit quality among other traits. Historically, mango has been an orphan crop with very limited molecular information. Only recently have molecular and genomics-based analyses enabled the creation of linkage maps, transcriptomes, and diversity analysis of large collections. Additionally, the combined analysis of genomic and phenotypic information is poised to improve mango breeding efficiency.Mango, Anacardium occidentale. Two regions, one on LG4 and one on LG7 containing 28 candidate genes, were associated with the commercially important fruit size characteristic in the mapping population.This study sequenced, de novo assembled, analyzed, and annotated the genome of the monoembryonic mango cultivar ‘Tommy Atkins’. The draft genome sequence was generated using NRGene de-novo Magic on high molecular weight DNA of ‘Tommy Atkins’, supplemented by 10X Genomics long read sequencing to improve the initial assembly. A hybrid population between ‘Tommy Atkins’ x ‘Kensington Pride’ was used to generate phased haplotype chromosomes and a highly resolved phased SNP map. The final ‘Tommy Atkins’ genome assembly was a consensus sequence that included 20 pseudomolecules representing the 20 chromosomes of mango and included ~ 86% of the ~ 439 Mb haploid mango genome. Skim sequencing identified ~ 3.3 M SNPs using the ‘Tommy Atkins’ x ‘Kensington Pride’ mapping population. Repeat masking identified 26,616 genes with a median length of 3348 bp. A whole genome duplication analysis revealed an ancestral 65 MYA polyploidization event shared with The availability of the complete ‘Tommy Atkins’ mango genome will aid global initiatives to study mango genetics.The online version contains supplementary material available at 10.1186/s12870-021-02858-1. Mangoes are an important fruit crop grown in over 103 countries across the tropical and subtropical zones. The common mango is typically a large, tropical, and evergreen tree with an upright to spreading dense canopy that can reach up to 30 m in some climates if not pruned. Mango production is estimated to be over 50 million metric tons (Mt) per annum from an area of over 56.8 million hectares . India iMangifera indica L.) belongs to the family Anacardiaceae. Based on morphological characters there are thought to be from 45 [Mangifera genus originating mainly in tropical Asia, with the area of highest diversity found in western Malesia [M. indica, was domesticated at least 4000 years ago, and further developed from an origin in the Assam Valley close to the western border of the Myanmar-Indochinese area in the Quaternary period and spread throughout the Indian subcontinent [M. altissima, M. caesia, M. foetida, M. kemang, M. laurina, M. odorata, M. pajang and M. pentandra being traditionally consumed in various Southeast Asian communities [Mango  due to long juvenility, polyembryony, and very low fruit retention that reduce breeding efficiency and add time to the breeding generation cycle . As a re‘Tommy Atkins’ comes from a relatively recently developed group of cultivars that originated in Florida, USA, as chance seedlings in the early part of the twentieth century –16. TheiM. indica varieties and other species growing in north-east India, the Andaman Islands and throughout South East Asia [The size of fruit on the earlier domesticated mango varieties was typically small as can be seen in older ast Asia . Fruit sast Asia , 18–21. ast Asia . In addiast Asia . Texturaast Asia . Among tast Asia .Recent studies have improved our genetic and genomic information on mangoes including molecular analyses of germplasm collections –30, singA high-quality genome can greatly aid mango breeders. Candidate gene homologs for important horticultural traits can be identified through combined “omics” approaches including comparative genomics with other tree crops. Single nucleotide polymorphism (SNP) markers for the candidate genes can be designed to screen hybrids at the seedling stage to identify associations between marker genotypes and horticultural traits. This will improve breeding efficiency and reduce the cost of selection by discarding inferior seedlings that do not carry favorable alleles. This report describes the creation of a chromosome-level genome for the primary commercial mango cultivar ‘Tommy Atkins’. The utility of the genome is shown by the identification of candidate genes for fruit size. The genome will serve as a resource for the global research community to advance the study of mango genetics and breeding.M. indica ‘Tommy Atkins’ mango genome was sequenced and de novo assembled using a combination of short reads, NRGene De-Novo Magic, 10X Genomics long read sequencing, and a phased SNP map. The result was a haploid sequence consensus assembly, TA4, with a total sequence length of 377 Mb in 571 scaffolds . Transposable elements (TE) class I were the most abundant elements accounting for the 34% of the genome . As it is common in plant genomes, LTR/Copia and LTR/Gypsy were the most abundant TE Class I representing 13 and 14% of the assembly, respectively. TE Class II represented 12% of assembly , and Helitrons represented 2% of the assembly   was used as an outgroup species  to calibrate the divergence between Anacardiaceae and Rutaceae at 81 MYA [Pistachia vera from which it diverged 61 MYA (Ks = 0.200) in agreement with other phylogenetic studies  [Mangifera and Anacardidium, they went through reduction in the number of chromosomes until twenty and twenty one pairs resulted, respectively.The mango genome was compared against itself and other selected genomes in order to elucidate part of the evolutionary history. t 81 MYA . A Ks di2, and B . Genera  2x = 40 , 2n = 2x 2x = 40 , and 2n  2x = 40 , respect2x = 30) –49. UndeC. sinensis (39% of the genome assembly) to P. vera (66% of the genome assembly). The content of LTR/Copia are smaller than the content in LTR/Gypsy, although in the mango genome they are close to each other (13 and 14% respectively), compared with C. sinensis (10 and 14%) and P. vera (20 and 32%). The Class II TE was significantly higher in M. indica (12%) compared with the other three species (9% for C. sinensis and P. vera)  and ‘Kensington Pride’ (polyembryonic) were mapped onto the TA4 diploid consensus assembly to identify variants and effects using snpEff. For ‘Tommy Atkins’, a total of 5,394,956 variants comprised of 3,946,854 SNPs, 1,051,633 MNPs (multi-nucleotide polymorphisms), 138,977 insertions, 146,654 deletions, and 110,838 mixed variants were identified. The average variant rate for ‘Tommy Atkins’ was one variant every 69 bp. Similarly, the number of variants for ‘Kensington Pride’ was identified by mapping reads from the ‘Kensington Pride’ mango cultivar onto the TA4 assembly. The total number of variants was 9,030,142 comprised of 6,291,666 SNPs, 568,959 MNPs, 223,249 insertions, 245,632 deletions, and 700,636 mixed variants. ‘Kensington Pride’ has 1.67 times the number of variants of ‘Tommy Atkins’ and an average variants rate of one variant every 41 bp. Variant effects by impact, functional class, type, and region along with the full snpEff summary are reported in Supplemental Data Arabidopsis, four species of climacteric fruits , and four species of non climacteric fruits . There were 17,382 orthogroups identified containing 219,431 genes  using Orthofinder [The proteomes of eight additional plant species were used for a comparative study with mango including hofinder . Orthogrp-values of 0.001. One region was identified on LG4 from bp position 8,275,233 to 8,495,231, and one on LG7 from bp positions 3,831,615 to 3,914,160.The availability of a genome enables genetic dissection of important traits. While ‘Tommy Atkins’ and ‘Kensington Pride’ are similar for many similar traits Table , their pUsing the position boundaries of the QTL regions, a total of 28 genes were found in the LG4 region and seven genes in the LG7 QTL region Table . All genThe haplotype of each parent contributing the minor allele for each SNP associated with fruit weight was identified using the phased genetic map of ‘Tommy Atkins’ x ‘Kensington Pride’. The genotypes of each individual in the ‘Tommy Atkins’ x ‘Kensington Pride’ population for all SNPs was inferred from the phased genetic map and haplotype data. PLINK analysis identified SNPs in the QTL regions and their associated haplotype. The four possible diplotypes (haplotype from each parent) were determined  for each individual in the QTL region. Only two haplotypes . Mangobase contains a genome browser that displays the mango reference sequence with gene sequences, annotations, repeat locations, SNP tracks, and other data. Other tools including BLAST are available for use with datasets related to the genome sequence, CDS, and protein datasets. Genetic maps can also be viewed on mangobase.org. The system is based on the SGN platform (https://solgenomics.net/) [ftp://ftp.mangobase.org/.An online genome resource was created to support mango research (cs.net/) , which iGenerating an annotated genome assembly for a tree crop like mango provides an essential genetic and genomic tool for improvement through plant breeding. The final ‘Tommy Atkins’ diploid consensus assembly, TA4, consisted of 20 pseudomolecules that were congruent with the two available genetic maps of mango. A phased map made by skim sequencing of 87 individuals of the ‘Tommy Atkins’ x ‘Kensington Pride’ hybrid population identified ~ 3 M SNP variants with known nucleotide positions in the genome. The result is a publicly available resource for the mango community, and for those interested in comparative genomics. Advances in sequencing and data analysis are enabling scientific discovery even for highly valued, yet historically under researched, species like mango.Recently, the genome sequence of a mango cultivar ‘Alphonso’ was published . The metIn general, major differences in gene family analysis results between the ‘Alphonso’ mango genome and ours arise due to number of differences in input data and focus of our analysis. There were a total 41,251 protein coding genes annotated for ‘Alphonso’ in comparison to the 26,616 high quality genes in the ‘Tommy Atkins’ genome. This difference led to a reduced number of valid gene families used as input for Café analysis for the ‘Tommy Atkins’ genome compared ‘Alphonso’ . Additionally, the parameters invoked for the gene family analysis for ‘Tommy Atkins’ were very strict when eliminating possible transposable elements and were more conservative when including any gene families represented by more than 100 genes. For example, several disease resistance gene families (eg. NB-LRR) can be misrepresented by TE gene families inflating actual numbers and this can impact gene family results. Additionally, instead of using gene families automatically created by Orthofinder based on a reciprocal blast, manually consolidated counts based on shared pfam domain were used in this study. Finally, the focus of the current study was to compare differences between climacteric and non climacteric fruits and removing inflated gene families made the analysis in this study more accurate at identifying these differences. These differences may have resulted in contrasting results for assigning expanding versus contracting gene family results.Using the renumbered pseudomolecules, Circos plots for both genomes were compared and generally found to be congruent. A good example is the synteny between pseudomolecules 14 and 15 for ‘Tommy Atkins’ and the corresponding Min11 and Min19 for ‘Alphonso’. The methods for generating the inferred phylogenetic tree and frequency distributions for synonymous substitutions to allow estimation of occurrence of whole genome duplication were different for ‘Alphonso’ and ‘Tommy Atkins’. Considering only data for the commonly included genomes, there were no significant differences in the inferred phylogenetic tree or in the estimation of the whole genome duplication event for mango. The analysis of expansion or contraction of gene families after the whole genome duplication event for mango gave different results for ‘Alphonso’ where the majority of gene families showed expansion in comparison to other genomes and ‘Tommy Atkins’ where gene families exhibited contraction in comparison to other genomes as described below.Brassica rapa [A. thaliana, Brassica rapa, Solanum tuberosum, Solanum lycopersicum, Populus trichocarpa, Glycine max, Citrullus lanatus and Cucumis sativus have 68, 99, 49, 54, 76, 98, 62 and 53 genes, respectively [Comparative genomic analyses between mango and fruit bearing species (climacteric and non-climacteric) revealed that there were no significantly expanded gene families in mango, only contractions. The pectin invertase/pectin methylesterase inhibitor gene family underwent the greatest contraction in the mango genome. Pectin invertase/pectin methylesterase inhibitor proteins are known to be involved in fruit ripening and softening. Other gene families that were contracted are glycosyl hydrolases family 28 (GH28) that includes polygalacturonases , glycosyl hydrolases family 1 (GH1) that includes beta-galactosidase and (1–4)-beta-glucanase and other proteins and unknown gene products Table . Pectin ica rapa , while iica rapa  due to rectively . Howeverectively ; previouectively , 58. Eveectively . Thirty ectively .The availability of the ‘Tommy Atkins’ genome will enable greater insights from previous genomics research in mango. For example, a previous study identified transcripts for proteins involved in metabolic pathways related to mango fruit ripening and fruit quality . Mappingp-value <= 0.001). Candidate genes in the LG4 region included the E3 gene encoding ubiquitin protein ligase. This same gene (Prupe.6G045900 E3 ubiquitin-protein ligase) was found in the QTL for fruit weight on chromosome 6 in peach (Prunus persea) [We demonstrated the utility of the genome assembly and the ‘Tommy Atkins’ x ‘Kensington Pride’ map by analyzing average fruit weight phenotypic data. This is a highly valuable trait for both breeders and growers. Fruit weight of the parents of the mapping population was almost identical ~ 400 g), whereas fruit weight of the hybrids ranged from ~ 165 g to ~ 965 g . Our genetic recombination map with defined haplotypes for each SNP is unique to our mango mapping population. Thus, our analysis of the association of diplotype with fruit weight in the two QTL regions is also unique. It enables us to develop strategies that will improve the efficiency of identifying progeny with optimal commercially suitable fruit size from open pollinated progeny of either parent.Associating fruit weight with particular diplotypes in the QTL regions implicated the LG4 region as a significant indicator of fruit weight. In the LG4 QTL region, diplotype TA2KP1 had the lowest average fruit weight (329 g) and TA2KP2 had the highest average fruit weight (499 g). As the TA2 haplotype is present in both the largest and smallest fruit, the major effect on fruit size is due to the KP parent with the KP1 haplotype decreasing fruit weight and KP2 haplotype increasing fruit weight. No significant distinction between diplotypes in the LG7 QTL was observed, but the two highest average fruit weights were for TA2KP1 (450 g) and TA2KP2 (468 g) while the two diplotypes TA1KP1 and TA1KP2 were almost identical, 352 and 359 g respectively. Thus, at the LG7 QTL, the TA parent has the greatest effect with TA1 decreasing fruit weight and TA2 increasing fruit weight. Diplotypes for each QTL region can be confidently predicted with 24 SNPs. Thus, ‘Tommy Atkins’ open pollinated progeny may be screened at the seedling stage for the presence of the TA2 haplotype at the LG7 QTL region. ‘Kensington Pride’ open pollinated progeny may be screened for the presence of KP2 at the LG4 QTL region. Identification of QTL for other horticultural traits is in progress and could lead to a suite of markers for advantageous haplotypes that could further improve selection efficiency at the seedling stage in open pollinated progeny of ‘Tommy Atkins’ and ‘Kensington Pride’.mangobase.org will lead to additional discoveries in the future.The Mango Genome Consortium successfully developed a mango genome for the most commercially important cultivar, ‘Tommy Atkins’, as a step towards a global and integrated initiative to study mango genetics. The sequencing of the ‘Tommy Atkins’ genome proved to be useful in identifying QTLs, genes, and diplotypes associated with fruit weight. We anticipate that the availability of the ‘Tommy Atkins’ genome and related resources at The aim of this study was to create a high-quality mango genome and demonstrate its utility using fruit weight as an example for trait dissection.Fruit weight was measured in fully mature fruit at harvest on a sample of ten randomly picked fruit from the ‘Tommy Atkins’ x ‘Kensington Pride’ parents and each of the 99 individual progeny within the ‘Tommy Atkins’ x ‘Kensington Pride’ hybrid population. The measurements were repeated over 5 years between 2007 and 2012.The ‘Tommy Atkins’ mango cultivar was curated and maintained at the USDA Subtropical Horticultural Research Station in Miami, Florida, USA. A hybrid population from the cultivar ‘Tommy Atkins’  and cultivar ‘Kensington Pride’  consisting of 104 individuals was generated by hand pollination and maintained at the Department of Agriculture and Fisheries, Mareeba, Australia . High mo‘Tommy Atkins’ high molecular weight DNA was subjected to library construction, sequencing, and assembly at NRGene (Israel). High molecular weight DNA quality was verified by pulsed-field gel electrophoresis. DNA fragments longer than 50 Kb were isolated to construct a Gemcode library using the Chromium instrument . This library was sequenced on HiSeqX platform to produce 2 × 150 bp reads. Five size-selected genomic DNA libraries ranging from 470 bp to 10 Kb were constructed and two shotgun libraries were made with size selection of ~ 470 bp with no PCR amplification. This fragment size was designed to produce a sequencing overlap of the fragments on the Hiseq2500 v2 Rapid mode as 2 × 265 bp, thus creating an opportunity to produce ‘stitched’ reads of approximately 265 bp to 520 bp in length. The genomic library of 800 bp DNA fragment sizes was prepared using the TruSeq DNA Sample Preparation Kit version 2 with no PCR amplification according to the manufacturer’s protocol . To increase sequence diversity and genome coverage, three separate MP libraries were constructed with 2–5 Kb, 5–7 Kb and 7–10 Kb jumps using the Illumina Nextera Mate-Pair Sample Preparation Kit . The 800 bp shotgun library was sequenced on an Illumina HiSeq2500 as 2 × 160 bp reads (using the v4 illumina chemistry) while the MP libraries were sequenced on HiSeq4000 as 2X150 bp reads. For the 10x Chromium library, PE and MP libraries construction and sequencing were conducted at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign. 10X Chromium library construction and sequencing were conducted at HudsonAlpha Institute for Biotechnology, Huntsville, Alabama.The newly assembled scaffolds were ordered into linkage groups using the high density preliminary maps created from the ‘Tommy Atkins’ x ‘Kensington Pride’ mapping population. The assembly was further improved using data from 10X Genomics sequencing . The consensus assembly (TA4) was generated and reduced to 20 pseudomolecules (linkage groups) plus unassembled scaffolds. The assembly was validated and revised by comparison to SNP order in two mango SNP maps , 33.Genome assembly was conducted using DeNovoMAGIC™ software platform . This is a DeBruijn-graph-based assembler, designed to efficiently extract the underlying information in the raw reads to solve the complexity of the DeBruijn graph due to genome polyploidy, heterozygosity and repetitiveness. This task is accomplished using accurate-reads-based traveling in the graph that iteratively connected consecutive phased contigs over local repeats to generate long phased scaffolds –65. The For read pre-processing, PCR duplicates, illumina adaptors, and Nextera linkers (for MP libraries) were removed. The PE 450 bp 2 × 265 bp library overlapping reads were merged with minimal required overlap of 10 bp to create the stitched reads. Following pre-processing, merged PE reads were scanned to detect and filter reads with putative sequencing error . Contig assembly consisted of building a De Bruijn graph (kmer = 127 bp) of contigs from the all PE & MP reads. Next, PE reads were used to find reliable paths in the graph between contigs for repeat resolving and contigs extension. 10x barcoded reads were mapped to contigs ensure that adjacent contigs were connected only in case there is an evidence that those contigs originate from a single stretch of genomic sequence (reads from the same two or more barcodes were mapped to both contigs).For split phased/un-phased assembly processes, two parallel assemblies took place to complete the phased and un-phased assembly result. The phased assembly process utilizes the complete set of contigs. In the un-phased assembly process, the homologous contigs are identified and one of the homologous is filtered out, leaving a subset of the homozygous and one of the homologous contigs in heterozygous regions. The linking information of both homologous contigs is kept through the assembly process of the un-phased assembly, usually enabling longer un-phased scaffolds.For scaffold assembly, all the following steps were done in parallel for both the phased and un-phased assemblies. Contigs were linked into scaffolds with PE and MP information, estimating gaps between the contigs according to the distance of PE and MP links. In addition, 10x data was used to validate and support correct phasing during scaffolding. A final gap filling step used PE and MP links and De Bruijn graph information to detect a unique path connecting the gap edges. 10x barcoded reads were mapped to the assembled scaffolds and clusters of reads with the same barcode mapped to adjacent contigs in the scaffolds were identified to be part of a single long molecule. Next, each scaffold was scanned with a 20 kb length window to ensure that the number of distinct clusters that cover the entire window  was statistically significant with respect to the number of clusters that span the left and the right edge of the window. In case where a potential scaffold assembly error was detected the scaffold was broken at the two edges of the suspicious 20 kb window. Finally, the barcodes that were mapped to the scaffold edges were compared (first and last 20 kb sequences) to generate a scaffolds graph with a link connecting two scaffolds with more than two common barcodes. Linear scaffolds paths in the scaffolds graph were composed into the final scaffolds output of the assembly.The assembly of the TA4 scaffolds (including the 10X Genomics data) produces a partially phased assembly in the sense that each scaffold is originating from a single haplotype. To group these phased scaffolds into their haplotypic groups (e.g. find the linkage between scaffolds and place them in the same LG), a phased recombination genetic map was produced.Illumina sequencing (5x coverage) of DNA from ‘Tommy Atkins’, ‘Kensington Pride’, and each of the ‘Tommy Atkins’ x ‘Kensington Pride’ hybrids was conducted by NRGene. Unique heterozygous SNPs from each of the parental lines of the cross were identified. All SNPs were homozygous in one parent and heterozygous in the other which allowed identification of the parental haplotype in each region of the map and the recombination points for each individual. Parental maps were generated by NRGene using a pseudo-testcross approach as described in . SNPs were mapped to the partially phased scaffolds of the TA4 assembly to identify their haplotype origin and location. SNPs were named by their nucleotide position on a pseudomolecule in the final TA4 assembly. A phased SNP map of the ‘Tommy Atkins’ x ‘Kensington Pride’ population with 20 linkage groups was created with ~ 3.3 M SNPs. The genotype and haplotype of each of the hybrid individuals at each SNP was determined from the Illumina sequence data and inference from the TA4 phased assembly described above.http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/Repeat_Library_Construction—Basic). RepeatModeler 1.0.8 (http://www.repeatmasker.org/RepeatModeler.html) and prerequisites RepeatMasker and libraries (http://www.repeatmasker.org), RECON (http://www.reatmasker.org/RECON-1.08.tar.gz), RepeatScout (http://repeatscout.bioprojects.org) TRF (http://tandem.bu.edu/trf/trf.html) and NSEG (ftp://ftp.ncbi.nih.gov/pub/seg/nseg/) were downloaded from the appropriate websites. The mango-specific repeat library and the RepeatMasker libraries cited above were used with RepeatMasker to mask repeats on the TA assembly. Hand curation of potential genes that may have been incorrectly masked revealed only retroviral genes and were subsequently masked prior to annotation.A mango-specific repeat library was created using the strategy described in Repeat Library Construction-Basic in the Maker wiki . Gene finders were Augustus trained on mango and Evidence Modeler. BUSCO genes were analyzed in the assembly as previously described [Transcript evidence was from RNA sequence data used in the development of SNP markers  as well escribed .p-value of association. The permutated p-value estimates were filtered to produce a subset of SNP markers associated with fruit weight at a p-value of <= 0.001 that defined the QTL regions for fruit weight.Mean fruit weight data for parents and hybrid progeny of the ‘Tommy Atkins’ x ‘Kensington Pride’ population were collected as previously described . The genArabidopsis along with four species of climacteric fruits and four species of non-climacteric fruits, namely apple , peach (Prunus persica), tomato (Lycopersicum esculentum), mango (Mangifera indica), orange (Citrus sinensis), strawberry Fragaria x ananassa), olive (Olea europaea), and grape (Vitus vinifera). Transposable element-related genes create noise for gene family expansion and contraction analysis, so those genes were identified using TransposonPSI (http://transposonpsi.sourceforge.net) and filtered out. Genes were functionally annotated in orthogroups with Pfam domain ids using kinfin [Proteomes of eight species were used for a comparative study against mango. The nine species included are g kinfin  in orderg kinfin  for estiThe nucleotide positions of SNPs in the QTL regions were used to search the annotation of the mango genome assembly to identify candidate genes. The phased genetic map was used to determine the haplotype of the parent donating the heterozygous allele at each SNP in the region and parent haplotypes were summed over the entire QTL region to determine the contribution of each parent. The most common result was that a single haplotype from each parent was observed for the entire QTL region. Presence of more than one haplotype from either maternal or paternal parent was evidence of a recombination event in the region. Perl scripts for this analysis are available upon request. Association of fruit weight with a haplotype was done by ANOVA and a post hoc Tukey’s multiple comparisons of means test.P. vera – Pisver_v2, GCA_008641045.1 [C. sinensis – v1.1, REF JJOQ01000000). WGD  was used to estimate the Ks distribution [http://timetree.org/) was used to estimate the divergency age between C. sinensis and the Anacardiaceae [Sequence assembly, CDS and gene annotation GFF files were downloaded from NCBI (641045.1 ) and Phyribution . Timetrerdiaceae .Transposable elements (TEs) in the four genomes  were annotated by combining homology-based and de novo-based approaches. For the de novo approach, we used RepeatModeler , LTR_FINDER , LTRharvAdditional file 1: Supplemental Data S1. snpEFF report 'Tommy Atkins'Additional file 2: Supplemental Data S2. snpEFF report 'Kensington Pride'Additional file 3: Supplemental Data S3. Fruit weight LG4 QTL report. Supplemental Data S4. Fruit weight LG7 QTL report. Supplemental Table 1. Summary of the M. indica genome assembly by chromosome. Supplemental Table 2. BUSCO analysis for the consensus diploid genome assembly TA4. Supplemental Table 3. Occurrence and distribution of repetitive DNA sequences in the mango genome. Supplemental Table 4. Protein domain content. Supplemental Table 5. Number of homologs pairs with Ks between 0.1 and 0.6 per chromosome. Supplemental Table 6. Repetitive elements comparative analysis. Supplemental Table 7. Comparison of the SNP variants and variants rate for ‘Tommy Atkins’ and ‘Kensington Pride’ by pseudomolecule. Supplemental Table S8. Concurrence of linkage groups between ‘Tommy Atkins’ and ‘Alphonso’. Supplementary Figure 1. Repeat composition for M. indica, P. vera and C. sinensisAdditional file 4: Supplemental Table S9, S10. Orthofinder reports


OsSTL1  and OsSTL2  were confirmed using sequence analysis. Haplotype and sequence analysis revealed that gene OsSTL1 was a homolog of salt tolerance gene SRP1 (Stress associated RNA-binding protein 1) in Arabidopsis. The hap1 of OsSTL1 was identified as the superior haplotype and a non-synonymous SNP was most likely to be the functional site. We also determined that the level of salt tolerance was improved by combining haplotypes of different genes. Our study provides a foundation for molecular breeding and functional analysis of salt tolerance in rice seedlings.Soil salinity is a major factor affecting rice growth and productivity worldwide especially at seedling stage. Many genes for salt tolerance have been identified and applied to rice breeding, but the actual mechanism of salt tolerance remains unclear. In this study, seedlings of 664 cultivated rice varieties from the 3000 Rice Genome Project (3K-RG) were cultivated by hydroponic culture with 0.9% salt solution for trait identification. A genome-wide association study (GWAS) of salt tolerance was performed using different models of analysis. Twenty-one QTLs were identified and two candidate genes named  Oryza sativa L.) is an important food crop1 and soil salinity is a major factor affecting its growth and productivity worldwide2. Approximately one-third of the land area on which rice is grown is affected by salinity5. Rice seedlings are particularly sensitive to biotic and abiotic stresses6. High salinity impedes water and nutrient absorption from the soil. This inhibits seedling growth and ultimately decreases yield8. Therefore, development of salt-tolerant varieties in rice is a very critical target of rice breeding programs in coastal areas10.Rice  using 295 accessions13. Because molecular marker-assisted selection (MAS) demands explicit genetic architecture of agronomic traits19 identification of new QTLs/genes related to salt tolerance in rice and confirmation of elite alleles is necessary for their utilization in modern molecular breeding.Genome-wide association studies have been widely used to identify genetic variants affecting complex traits, either by comparative analysis or correlation analysis, and have identified many SNPs associated with target traits1. Integrated gene annotation, genetic variation, homology and haplotype analysis were performed to identify candidate genes and possible causal polymorphisms for salt tolerance traits. Our results provide insight into the genetic architecture of salt tolerance and markers derived from the newly identified genes will be useful in improving salinity stress tolerance in rice seedlings.In the present study, we performed a GWAS using 664 cultivated rice accessions from the 3,000 Rice Genome (3K-RG)20 included 226 genotypes from the mini-core collection that were selected from an original core set of 4,310 accessions21, and 438 lines from the International Rice Molecular Breeding Network22. Both collections have accessions from 44 countries representing major rice-growing regions of the world (Table ). In order to analyze evolutionary aspects we studied an additional publicly available set of 446 wild rice accessions from a previous report23. The public transcriptomic data of IR29 was download from NCBI (https://www.ncbi.nlm.nih.gov/gds), series accession ID: GSE119720.The rice diversity panel comprising 664 cultivated rice accessions from the 3K Rice Genome (3K-RG)ld Table . In orde24. From July 5 to September 10 in 2015, the diversity panel was cultivated in hydroponic culture with open-air environment that the average temperature from 14 °C to 32 °C at Xinjiang academy of agricultural sciences with 0.9% salt solution for two months for trait identification. First, the rice seeds were surface-sterilized and soaked in water to promote germination for 2 days at 30 °C. For each variety, seven seeds with normal budding and the same growth potential were transplanted to a gauze-based foam board (30 cm × 42 cm). Each foam board containing 10 varieties was placed in a plastic box containing a 0.9% salt solution. The salt solution was replaced once every two weeks for about 2 months . Phenotyping was based on the International Rice Research Institute (IRRI) salt tolerance/alkali identification criteria 27. After salt stress, the symptoms of plant leaves and roots were observed and recorded on the basis of seven criteria (Shoot length (SL), Root length (RL), Shoot fresh weight (SFW), Shoot dry weight (SDW), Root fresh weight (RFW), Root dry weight (RDW) and Salt tolerance level (STL)). Salt tolerance level (STL) was based on the percentage of dead leaf blade tissue.After treatment with salt solution, the symptoms of salt damage in the roots and leaves of seedlings were assessed visuallyths Fig. . Phenotyhttp://www.ncgr.ac.cn/RiceHap323.Genetic variation (single nucleotide polymorphism (SNP)) data for the 664 accessions were obtained from the 3K-RG publicly available database, which included approximately 17 million highly credible SNPs and 2.4 million indels aligned to the cv. Nipponbare IRGSP 1.0 reference genome. Publicly available SNP set for the 446 wild rice accessions were downloaded from indica and japonica populations. Principal component (PC) and kinship matrix analyses were performed in order to account for population structure. A neighbor-joining tree was constructed using 68,376 SNPs evenly distributed throughout the genome. The first three PC were used to construct the PC matrix. We performed a GWAS of salt tolerance traits of the 664 accessions using a compressed mixed linear model (CMLM) and general linear model (GLM) using the SNP set and default settings in GAPIT software. We also performed FaST-LMM using FaST-LMM software28 in order to compare three different models. Due to the non-independence of SNPs caused by strong LD, the thresholds derived from the total number of markers were usually too rigorous for detection of significant associations. Suggestive thresholds were calculated using the formula “-log10(1 / effective number of independent SNPs)” as described previously30 and effective numbers of independent SNPs were determined by PLINK31  to be 117,880, 94,282 and 42,831 in the full population as well as in indica and japonica subpopulations, respectively. We divided the STL (Y) of each accession into the original genotypic effect (G) and the fixed effect of population structure (P). Through regression analysis of each PC on Y, we calculated the average effect of each PC in the PC matrix on each individual (P); G was excluding P from Y. Then G was randomly reshuffled as G-new, and the P + G-new was reconstructed to the novel phenotype of each accession. The conditional permutation test of total of 1,000 sets of P + Gr was performed using GLM with the same parameters and PC matrix. The threshold was therefore set at -log (P) = 4 to identify significant associations. LD heatmaps surrounding peaks in the GWAS were constructed using “LD heatmaps” in the R package32.We identified 3,513,863, 2,280,242 and 1,855,669 SNPs as a credible SNP set after removing SNPs with missing rates >30% and minor allele frequencies <5% in full, P < 0.01). Nucleotide diversity (π)34 and Tajima’s D35 were calculated using an in-house Perl script.Differences in phenotypic values between alleles of each non-synonymous SNP were assessed by Student’s t-tests. The sequence alignment of each gene was determined using non-synonymous SNPs associated with STL, and differences in phenotypic value between haplotypes of each gene were calculated by one-way ANOVA or Student’s t-tests. Duncan’s multiple range tests were conducted to make comparisons if the results of the one-way ANOVA were significant (indica (428 accessions) and japonica (236 accessions) subpopulations explaining 44.6% of the total genetic variation. The kinship matrix and neighbor-joining tree also showed similar results .Prior to GWAS we performed a PC and kinship matrix analysis based on 3.5 million SNPs to characterize the population structure Fig. . There wts Figs.  and S2.Findica and japonica subpopulations we performed statistical analyses of all seven salt tolerance-related traits. There were significant differences between indica and japonica for RFW, RDW and SFW but no differences in RL, SL, SDW and STL . The descriptive statistics for the two sub-populations are provided in , whereas the STL was not significantly correlated with RL (0.002) Table . The STLindica, japonica and full populations, using the STL data and 3.5 million SNPs. Manhattan plots of STL show that detection using all three models was consist, especially CMLM and FaST-LMM .The general linear model (GLM), compressed mixed linear model (CMLM) and Factored Spectrally Transformed Linear Mixed Models (FaST-LMM) were performed using GAPIT and FaST-LMM to identify association signals in the MM Figs.  and S5–6P) significance levels of 4 in the full population, indica and japonica subpopulations, respectively, when using CMLM , and 15, 6 and 5 QTLs were identified using FaST-LMM (Table ) in the full population, indica and japonica, respectively  overlapped with those found previously36 supported the reliability of the present results (Table ).By minimizing false positives caused by population structure we determined that CMLM was the most suitable of the three models for detecting STL QTL in our populations by comparing the quantile-quantile plots from the three models in each population Figs.  and S5–6ts Table .RSS1 . The RSS1 protein has a putative role in maintenance and viability of meristematic cells in rice plants under salt stress37. There were as many as 28 and 1 cloned genes related to salt tolerance that corresponded to present QTLs detected by the GLM and FaST-LMM, respectively .One of the QTLs detected by CMLM probably corresponded to the cloned salt tolerance gene SS1 Fig. . The RSSely Fig. -5.qSTL8-1 was identified in the full population and japonica subpopulation, and was also identified in a previous report36 whereas qSTL4-1 was detected in all three populations implying that they were worthy of pursuing to the candidate gene level .We chose two QTLs for detailed study; el Figs.  and 5a.FqSTL4-1 contained 154 SNPs at suggestive thresholds of four. The candidate region of 0.49 Mb to 0.62 Mb in qSTL4-1 was evaluated using pairwise LD correlations with a threshold r2 > 0.638  value greater than two and significant SNPs within the promoter. There was no non-synonymous SNP with a -log (P) value greater than 2 in two genes .638 Fig. . The cannes Fig.  and the ce Figs. .indica and japonica were LOC_Os04g01780 and LOC_Os04g01920. According to the gene annotations LOC_Os04g01780 is an uncharacterized expressed ACR (Act Domain Repeat), COG1399 (Clusters of Orthologous Groups 1399) family gene, and LOC_Os04g01920 is an expressed protein gene. Many genes for salt tolerance have been annotated as zinc finger family members. The third gene in the group, LOC_Os04g02000, was annotated as a zinc finger family protein gene and therefore was more likely to be the candidate functional gene for salt tolerance. According to the public transcriptomic data (The public transcriptomic data of IR29 was download from NCBI (https://www.ncbi.nlm.nih.gov/gds), series accession ID: GSE119720), the RPKMRSalt/RPKMControls  of LOC_Os04g02000 expression in IR29 was greater than 1.4, indicating differential gene expression between salt and controls. This candidate gene was finally named as OsSTL1.Two of the genes differing between qSTL8-1 contained total of 14 significant SNPs. The candidate region of 19.74 Mb to 19.85 Mb in qSTL8-1 was evaluated using pairwise LD correlations (at threshold r2 > 0.6)  whereas LOC_Os08g31870 was annotated as a cell division cycle protein gene, and showed significant differences within the japonica subpopulation, but not in indica ..6) Fig. . Three gons Fig.  whereas ca Figs. .https://www.ncbi.nlm.nih.gov/gds), series accession ID: GSE119720), the RPKMRSalt/RPKMControls  of LOC_Os08g31870 expression in IR29 was greater than 2, indicating differential gene expression between salt and controls. This candidate gene was finally named as OsSTL2.LOC_Os08g31870 is putatively expressed cell division cycle protein 48 and this led us to hypothesize that LOC_Os08g31870 was more likely to be the candidate functional gene. According to the public transcriptomic data  located on RanBP2-type domain and 3 SNPs in the promoter region; two haplotypes (hap1 and hap2) were identified  was lower than that for hap2 . Therefore, hap1 of OsSTL1 was a superior genotype and its frequency in japonica could be increased in that subpopulation in order to increase its overall salt tolerance  in Arabidopsis was involved in salt tolerance39. Stress associated RNA-binding protein 1 (SRP1) encodes a C2C2-type zinc finger protein in Arabidopsis. A knock-out mutation in the srp1 allele reduced sensitivity to ABA and salt stress at all growth stages whereas SRP1-overexpressing seedlings were more sensitive to ABA and salt than wild type.We compared OsSTL1 we investigated 446 wild rice (O. rufipogon) accessions using publicly available sequence data23 to determine the origin of OsSTL1. We compared the nucleotide diversity (π)34 and Tajima’s D35 values for wild rice with those for the indica and japonica subpopulations. The π value for OsSTL1 increased from wild rice to japonica, πjaponica/ πwild > 2, D = 4.67, and deviated significantly from zero. It was possible that balanced selection occurred in japonica during its domestication (Table ). The π value for OsSTL1 in indica was less than that for wild rice, πwild/πindica > 2, D = −1.33 indicating positive selection in indica. Although the overall diversity in cultivated rice was slightly increased it was clear that indica and japonica had undergone different selection directions in adapting to different environments during domestication.For confirmation of the superior haplotype and functional site of on Table . The π vOsSTL2 in which there were three non-synonymous SNPs  and three haplotypes  were identified  suggesting no past selection of OsSTL2 in the different populations.Nucleotide diversity (π) and Tajima’s ty Table  suggestiOsSTL1 and OsSTL2, we performed combination haplotype analysis of the two genes. The co-hap1 (combination with hap1 of OsSTL1 and hap1 of OsSTL2) and co-hap2 (combination with hap1 of OsSTL1 and hap2 of OsSTL2) represented the superior allele; the co-hap3(combination with hap1 of OsSTL1 and hap3 of OsSTL2), co-hap4 (combination with hap2 of OsSTL1 and hap1 of OsSTL2) and co-hap5 (combination with hap2 of OsSTL1 and hap2 of OsSTL2) represented the intermediate allele, and co-hap6 (combination with hap2 of OsSTL1 and hap3 of OsSTL2) represented the inferior allele. The mean STL of accessions with co-hap1 in japonica had significant lower values (p < 0.05) than those with co-hap4 and co-hap6, whereas, the mean STL of accessions with co-hap6 in japonica were significantly higher (p < 0.05) than those with other haplotypes as determined by one-way ANOVA . Clearly, the overall salinity tolerance level in breeding populations can be achieved by increasing the frequency of co-hap2.We screened 11 varieties with STL = 1 from the salt tolerance assays. Two were p2 Table . Clearly41. However, only a few reported genes for salt tolerance have been applied in rice breeding14. Gene pyramiding has become a powerful strategy to address complex traits6. Based on those studies, the salt tolerant genes will be used for new variety improvement by molecular assisted selection. However, molecular design breeding has not been made a great of progress because there have been no enough accurate genetic loci of the important agronomic traits and precise superior haplotypes dissected so far.In recent years, more than 170 genes for salt stress response have been cloned by forward or reverse genetic strategies in rice42. We detected the previously known salt stress tolerance gene RSS1 in our QTL analysis using CMLM. Many previously salt stress response genes identified using mutants and gene differential expression with no mutations or few variations in our populations were not detected in this model. Therefore, different populations may be required to detect different functional genes.The resequencing based genotype provides vast natural variations, and the functional genes and elite alleles (superior haplotype) in natural populations could be explored by GWASOsSTL1 and OsSTL2. We confirmed that hap1 of OsSTL1 was superior haplotype, while hap1 and hap2 of OsSTL2 were superior haplotypes. In addition, we identified the functional sites of the two candidate genes as the SNP Chr4_619903 and the Chr8_19760379, in which bases A and C caused the respective superior alleles.Among 664 diverse rice accessions, we identified functional candidate genes OsSTL1 in japonica was 23% (43 of 185 accessions), indicating that the utilization level of this allele was relatively low. Hence, the frequency of this allele can be increased to improve overall salt tolerance in japonica. By contrast the frequency of the same allele in indica was almost 98.5% and overall improvement in salt tolerance in this subspecies would require exploitation of the different genes. The frequency of the C base allele in Chr8_19760379 of OsSTL2 was high in both indica (99.3%) and japonica (78.0%) indicating far less gains in overall salt tolerance being obtained by selection of alleles at this locus  at each locus38. We identified the lead SNP in each of 21 QTLs and confirmed its superiority by phenotype. We then examined the correlation of the number of superior SNPs with STL values and found that STL increased with number of superior SNPs; the R2 values for the full population, indica and japonica were 0.78, 0.94 and 0.84, respectively . These results indicate that as for many other traits, pyramiding of superior alleles of important genes for salinity stress will lead to new varieties with improved tolerance. Obviously the precise identification of superior haplotypes will have a critical bearing on the outcomes of molecular breeding.Many major genes that significantly contribute to important agronomic traits can be pyramided to develop elite rice varietiesely Fig. . These rSupplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.Supplementary Table S7.Supplementary Table S8.Supplementary Table S9.


Arabidopsis thaliana the genetic bases of robust resistance mechanisms to the devastating pathogen Ralstonia solanacearum under heat stress. A local mapping population was phenotyped against the R. solanacearum GMI1000 strain at 27 and 30 °C. To obtain a precise description of the genetic architecture underlying natural variation of quantitative disease resistance (QDR), we applied a genome‐wide local score analysis. Alongside an extensive genetic variation found in this local population at both temperatures, we observed a playful dynamics of quantitative trait loci along the infection stages. In addition, a complex genetic network of interacting loci could be detected at 30 °C. As a first step to investigate the underlying molecular mechanisms, the atypical meiotic cyclin SOLO DANCERS gene was validated by a reverse genetic approach as involved in QDR to R. solanacearum at 30 °C. In the context of climate change, the complex genetic architecture underlying QDR under heat stress in a local mapping population revealed candidate genes with diverse molecular functions.Plant immunity is often negatively impacted by heat stress. However, the underlying molecular mechanisms remain poorly characterized. Based on a genome‐wide association mapping approach, this study aims to identify in  Arabidopsis thaliana response to Ralstonia solanacearum under heat stress revealed resilient quantitative disease resistance underlain by a complex genetic network of additive and epistatic quantitative trait loci.A genome‐wide association study of the natural variation of  No germination was observed for six accessions that were therefore discarded from the study  ; in particular at the interchromosomal level  effects Figures , and S2. Figures , and S2. Figures . It shou Figures , preclud Figures , with a  Figures . QTL8 sh Figures , whereas Figures , and S2.2.3At1g60140)  belonging to a GDSL‐like Lipase/Acylhydrolase protein superfamily  Figure . QTL2 coy Figure .http://bar.utoronto.ca/ntools/cgi‐bin/ntools_classification_superviewer.cgi) using the MAPMAN classification as source  that encodes an atypical meiotic cyclin‐like protein  and At4g19030 , respectively, encoding a sulphate and an aquaporin transporter, respectively  involved in cell wall modification and a lignin peroxidase (At5g05390) involved in vascular development, respectively . For instance, the top SNPs of QTL7A (SNP‐1‐26655520) and QTL10 (SNP‐2‐13134129) are located in genes At1g70700 (JAZ9) and At2g30830 , which are involved in jasmonate (JA), ethylene (ET), and gibberellin (GA) hormonal metabolisms, respectively  . Col‐0 and Ws‐4 are both susceptible to GMI1000 at 30 °C while Ws‐4, but not Col‐0, is resistant at 27 °C. At 27 °C, the sds‐2 mutant response was not significantly different from Col‐0 except at 6 dai  collection  within those populations. This would provide valuable information on the local dynamics of QDR to a bacterial pathogen in A. thaliana at the metapopulation level  are often driven by the temporal regulation of QTLs  epistatic QTLs was clearly unbalanced in the TOU‐A population. While it may reflect the maintenance of haplotypes with extreme phenotypes  by selective processes , suggesting that the regulation of trehalose‐6‐phosphate synthesis participates in the plant response. This is consistent with previous data showing that the production of this metabolite by the R. solanacearum effector RipTPS plays an important role in pathogen virulence   to balance plant defence response and growth  repair  SDS acts together with CYCB3;1 in suppressing unscheduled cell wall synthesis  agar as previously described  were kindly provided by Raphaël Mercier   and 1 ml of triphenyltetrazolium chloride (1%), and incubated at 28 °C for 48 hr then left at room temperature for 24 hr. One day before inoculation, one colony was grown in liquid BG medium and grown overnight at 28 °C under shaking. Plants were inoculated with a bacterial suspension at OD600 nm between 0.8 and 1. Before inoculation, roots were cut with scissors 1 cm from the bottom of the Jiffy pot . Disease symptoms were scored daily from 3 to 9 dai using a disease index scale from 0 to 4 as previously described  and the susceptible Col‐0 accession was placed in the same three positions within each tray (n = 6). In each block, the remaining 10 positions in the trays were kept empty. Note that plants of the third block were not scored at 9 dai.For each temperature treatment, 624 plants were used and arranged by following a randomized complete block design (RCBD) of three temporal experimental blocks. Each block was represented by two trays of 104 positions, corresponding to one replicate per accession , and “ε” is the residual term. The factor “block” was considered as a fixed factor and the factor “accession” as a random factor. The significance of the random effect was determined by likelihood ratio tests of model with and without this effect. Residuals were normally distributed so no transformation was applied on raw phenotypic data. For GWA mapping analyses, we used best linear unbiased predictors (BLUPs) obtained for each natural accession. Because A. thaliana is a highly selfing species  at each time point of phenotyping were estimated from the variance component estimates of the “block” and “accession” terms obtained with the VARCOMP procedure in SAS v. 9.4 (SAS Institute Inc.).For each temperature treatment, a mixed model  was used to explore the natural genetic variation of the disease index at each time point of phenotyping, as follows:4.3.3et al., K based on the 1,902,592 SNPs identified in the TOU‐A population  − ξ, where ip is the p value of marker i and ξ is a tuning parameter with an optimal value that can be fixed at 2 or 3 in a GWAS context  along the chromosome, with h0 = 0. Significant SNP–phenotype associations were identified by estimating a chromosome‐wide significance threshold for each chromosome  between two SNP genotype vectors. For each QTL, we obtained a distribution of LD estimates between the bait top SNP and the other 981,616 SNPs of the population (MARF > 7%). In order to exclude strong LD values due to physical proximity, SNPs located in a 100 kb window surrounding a bait top SNP were not included in the calculation. To estimate whether the bait top SNP of a given QTL  was significantly in LD with the bait top SNPs of the remaining 13 QTLs, we estimated in the LD distribution  the quantile q for each bait top SNP of the 13 QTLs. To be conservative, an LD estimate between a focal bait top SNP and another bait top SNP was declared significant if (1 − q) < .01.In order to detect epistatic interactions among our set of 14 top QTLs identified with additive effects by combining a GWA mapping approach with a local score analysis, we followed the procedure adopted in Brachi et al. . We firs4.3.5R environment 3.6.1  were adjusted by fitting them with a kinship matrix. Kinship‐adjusted BLUPs were computed under the 4.4sds‐2 and sds‐3 mutants with their corresponding wild‐type background Col‐0 and Ws‐4, respectively.For each temperature treatment, an experiment of 288 plants was set up according to a RCBD of three temporal experimental blocks. Each block was represented by one tray of 96 positions, corresponding to 24 replicates of each genotype, that is, the µ is the overall mean of the phenotypic data, “block” accounts for differences in microenvironmental conditions between the three experimental blocks, “genotype” corresponds to the genetic differences between the T‐DNA mutant and its corresponding wild‐type background, “block × genotype” accounts for variation between genotype differences among blocks, and “ε” is the residual term. All factors were considered as fixed.For each temperature treatment, we tested whether each null mutant differs from its corresponding wild‐type background along the infection stages by using the following model :R.B. and F.R. supervised the project. N.A., R.B., and F.R. designed the experiments. N.A. conducted the phenotyping experiments. N.A. and F.R. analysed the phenotypic traits. F.R. performed the GWA mapping. M.B. performed the genome‐wide local score analysis. L.B. performed the LD analyses. H.D. estimated the allelic effects of the QTLs. N.A., L.D., R.B., and F.R. wrote the manuscript. All authors contributed to the revisions. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Nine out the top 10 interactions, protein coding genes were partnered with non-coding RNA (ncRNA) which suggested a new alternative insight into interaction biology other than the frequently sought-after protein–protein interaction. Therefore, we extended to look for replication among the top 10,000 interaction SNP pairs and high proportion of concurrent genes forming the interaction pairs were found. The results indicated that an enrichment of signals over noise was present in the top 10,000 interactions. Then, replications of SNP–SNP interaction were confirmed for 14 SNPs-pairs in both replication datasets. Biological insight was highlighted by a potential binding between FHIT (protein coding gene) and LINC00969 (lncRNA) which showed a replicable interaction between their SNPs. Both of them were reported to have expression in brain. Our study represented an early attempt of exhaustive interaction analysis of GWAS data which also yield replicated interaction and new insight into understanding of genetic interaction in schizophrenia.In this study, we looked for potential gene-gene interaction in susceptibility to schizophrenia by an exhaustive searching for SNP–SNP interactions in 3 GWAS datasets  using our recently published algorithm. The search space for SNP–SNP interaction was confined to 8 biologically plausible ways of interaction under dominant-dominant or recessive-recessive modes. First, we performed our search of all pair-wise combination of 729,454 SNPs after filtering by SNP genotype quality. All possible pairwise interactions of any 2 SNPs (5 × 10 Schizophrenia is a highly heritable disorder and it affected about 1% of the population worldwide . Twins sSingle SNPs often have a small effect on the phenotype and they cannot account for all the genetic susceptibility of diseases. Many researchers explored various ways to re-analyse the GWAS data using approaches on top of the prevailing single SNP analysis, commonly used in GWAS analysis. For example, sub-classification of the phenotypes , integraGenome-wide association studies is an important tool to identify SNP associating with a variety of diseases. However, only marginal effects of SNPs were detected. SNP–SNP interaction played an essential role in the pathogenesis of complex diseases . To examp-value could be interpreted as the interactions between protein coding genes and non-coding RNA (ncRNA) genes which suggested the importance of interactions other than that of the traditional protein-protein interactions. After that, we investigated the replication among the top 10,000 SNP-SNP interactions and there was a high proportion of concurrent genes among the gene-gene interaction predicted from these SNP–SNP interaction. Therefore, there was an enrichment of signals over noise among these interactions. Finally, 9 SNP–SNP interactions were successfully replicated in both replication datasets. Among these SNP–SNP interactions, one of them could be interpreted as the interaction between FHIT (protein coding) and LINC00969 (lncRNA). Both of them were reported to have expression in brain.We developed an algorithm which generated eight biological plausible SNP-SNP interactions  and idenThe distribution of different genotypes of every 2nd order SNP combination across cases and controls can be measured and visualized as a 3 × 3 genotype table. Each genotype is represented as a cell in the 3 × 3 genotype table and can be labeled as high-risk or low-risk through statistical or heuristic algorithms like multi-factor dimensionality reduction (MDR) algorithm and its derivatives . However1.SNP1 and SNP2 are found in two different functional sites namely Site1 and Site2 respectively. .2.SNP1 and SNP2 can affect their respective functional sites and cause each site to generate at most two different subtypes of bio-molecules. For example, bio-molecules pA is generated from Site1 with SNP1 having a major allele.3.Site1 and Site2 can randomly dock with each other to form at most four different bio-molecule complexes For example, bio-molecules pA and pB can combine with each other to form complex ApBp.The bio-molecules generated from 4.A bio-molecule complex is considered to be associated to the genetic disease if any one of the following two conditions is satisfied:a.Its dominant interaction can either promote or inhibit a disease.b.Its recessive interaction presence can either promote or inhibit a disease.ApBp is the only bio-molecule complex associated to the genetic disease. If the dominant presence of ApBp can either promote or inhibit the risk of a genetic disease, samples carrying genotype {AA, BB}, {AA, Bb}, {Aa, BB}, and {Aa, Bb} obviously would have a different disease risk level comparing to other samples. Pattern 1 is hence derived after labeling these genotypes with two different colors to reflect their difference in risk level. On the other hand, if only the recessive presence of ApBp can either promote or inhibit a disease, samples carrying genotype {AA, BB} would have a different disease risk level comparing to other samples. Pattern 5 is hence derived after labeling these genotypes with two different colors to reflect their difference in risk level. Other patterns shown in Those eight SNP–SNP interaction patterns are shown in D,B)SNP is calculated, statistical test like 1 d.f. chi-square statistical test can be performed to calculate the pairwise p-value of SNPi and SNPj.After the corresponding 2 × 2 contingency tables of a 2nd order SNP combination We downloaded three schizophrenia GWAS datasets  from the database of Genotypes and Phenotypes (dbGaP). These three datasets were cleansed to remove low quality SNPs and samples with Plink  followinAfter data pre-processing, there were 729,454 SNPs and 2,306 samples  in dataset phs000021:phg000013; 761,628 SNPs and 1,726 samples  in dataset phs000021:phg000014; 767,002 SNPs and 2689 samples  in dataset phs000167. The genotypes of every SNP in these three datasets were encoded as 0, 1, 2, 3 according to the encoding scheme shown in p-value, high ranking interactions would be selected for further analysis.To identify potential biologically plausible and statistically significant 2nd order SNP–SNP interactions, we performed search on these three cleansed datasets. As phg000013 got a slightly larger sample size and was restricted to Caucasian subjects, it was chosen as the discovery dataset and the other two were used for replication of findings obtained from phg000013. First, we performed our search on these three datasets after filtering out SNPs in chromosome X, Y and mitochondrial DNA. After SNP–SNP interactions were ranked by their pairwise p-value, only a handful of interactions found in datasets phs000021:phg000013 had a p-value better than 10–11 which could be used as a cut-off value for statistically significant and the top 10 pairs of interaction were listed in After sorting SNP–SNP interactions found in the discovery datasets in terms of their p-values was too conservative and important interactions would be missed. Then we looked at the extend of replication among top ranked interaction found in the discovery dataset. Five sets of top ranked interaction found in phg000013 were checked if their component genes were also found to have high ranking in the two replication datasets n SNP–SNP interactions in terms of p-value from each dataset after each stage of our experiment and we predicted gene–gene interactions from these SNP–SNP interactions with CADD , gene-gene interactions could then be predicted from the SNP–SNP interactions which we found.According to existing literature, gene-gene interactions could be discovered through analyzing SNPs which are acting as conditional eQTLs . After rIf there were non-coding genes closest to the SNP-SNP interaction pairs, LncRRIsearch  was usedp-value. Top 10 SNP–SNP interactions are listed in To reduce bias and confounding, we tried to analysis the discovery dataset in two different stages by using two sample filters. In our stage one experiment, we performed exhaustive search after filtering out SNPs in chromosome X, Y and mtDNA. Interactions were then ranked according to their p-value. Top 10 SNP–SNP interactions were selected from each dataset for further analysis and are listed in In our stage two experiment, we performed exhaustive search only using female samples and every SNP in autosomal chromosomes and chromosome X. Interactions were then ranked according to their p-value of 2.03 × 10–7 on single SNP association analysis. In another word, many SNPs were also significant by itself and formed one of the interacting partner. In fact, 7 out of top 10 interacting pair was formed by exactly the same SNP, rs1277747. We believed that such GWAS significant SNP would dominate the interaction analysis and masked the true interacting pairs as they ranked lower in the list.However, as it was more evidence in the female only analysis, genes or SNPs that were already significant predisposition gene by itself were mostly ranked among the top 10. For example, rs1277747 of TACC2 got a th  SNP–SNP interactions in terms of p-value from each stage of our experiment independently and the corresponding gene-gene interactions of these SNP–SNP interactions could then be derived. First, we counted the number of gene-gene interactions predicted from each dataset and the number of individual component genes involved in those interactions. Then, we compared the gene–gene interactions predicted from each dataset against every other dataset and the number of common gene-gene interactions and common component genes between every pair of datasets were counted accordingly. The p-value on the number of occurrences of gene-gene interactions given the null hypothesis of no enrichment of signal were also calculated. Furthermore, the proportion of common component genes and the proportion of common gene-gene interactions in each dataset were calculated. These results are shown in p-value on the number of common component genes, we believed that the occurrence of common component gene among datasets could not be simply explained as random cooccurrence by chance and genuine interaction signals must be enriched in the top ranked list. Furthermore, we observed that the proportion of common component genes under different pairs of datasets is roughly increasing linearly with the number of top ranked nth interactions selected until n = 1000. When n = 5000, the increase trends in the proportion of common component genes under different pairs of datasets might start to reach the plateau. Therefore, we believed that top 10000th of SNP–SNP interactions should already cover most of disease-associated SNP–SNP interactions in all three datasets. The top 10,000 gene–gene interactions found in the discovery dataset were visualized as circos diagrams as shown in We selected the top nG in every predicted gene-gene interaction was annotated through the Molecular Signatures Database of Gene Set Enrichment Analysis (GSEA) such that every functional gene set containing G could be found. In our analysis, if both component genes of a gene-gene interaction I belonged to a common functional gene set F, then we believe that interaction I was associated to the biological function represented by functional gene set F. Among the gene-gene interactions predicted from top nth  SNP–SNP interactions, the number of predicted gene–gene interactions associated to each GSEA functional gene set was counted and shown in Every component gene G in this table, direct interactions involving G were validated by the following three external biological interaction databases StringDB  which were reported to be associated to schizophrenia in existing literature as shown in Using phg000013 as discovery dataset and the other 2 as replication datasets, the top 10,000 interaction pairs were mined to see if they were replicated in other datasets. Only replication by the exactly identical pattern out the 8 interactions were counted. After filtering out interactions with at least one component SNP which could not be mapped to a gene according to CADD, 14 interactions were replicated in both replication datasets in the exactly the same way as in the discovery dataset. These interactions were shown in While we and most researchers are expecting to find protein interacting partners and investigate our discoveries with protein-protein interaction databases such as StringDB , PINBA , and DMSin silico identified binding sites. We screened the adjacent 10 lncRNAs located both upstream and downstream in the chromosomal region of the specific interacting lncRNA to see if these adjacent lncRNAs might interact with the protein-coding gene and its mRNA transcript. Among the three lncRNA–RNA interactions, only LINC00969-FHIT interaction is specific, with the mRNA of FHIT showing no interaction or weaker interaction with the adjacent lncRNAs of LINC00969  and rs2550266 (LINC00969). The 3 × 3 genotype table of this interaction is visualized in p-values were not significant at only 5 × 10–3 and 1 × 10–1. Secondly, the interaction protective effect was 0.5 . So such genetic risk profile is more useful in clinical setting. Finally, the interaction was observed in all 3 datasets. It is of note that replication was seen across ethnic groups (as phg000014 was an African dataset). The reduction of risk could be found even though the allelic frequencies of rs2550266 were very different between Africans and Caucasians. The pan-ethnic variation of allele frequency can be found in dbSNP1. Other potential interacting SNP pairs that could be replicated in one dataset but not both were shown in Interaction of this profile might be genuine and representing how real interaction would look like. Genuine interaction profile should have these characteristics: (1) neither SNPs is significant by itself, or both interacting SNPs are not GWAS hits, (2) interaction should have a stronger effect size than single SNP GWAS hits, which usually have odds ratios in the range of 1.1–1.2 and (3) replication of interaction is evident. The example of rs3856662 (FHIT gene) and rs2550266 (LINC00969) indeed fulfills these expectations. Firstly, neither of them was significant by single SNP, and single SNP association p-value might represent interactions between protein coding genes and long non-coding RNA (lncRNA) genes. These results indicate the importance of interactions of other bio-molecules  in addition to that of the traditional protein–protein interactions. Then, we showed there was a high proportion of concurrent genes among the gene–gene interactions predicted from the top 10 SNP–SNP interaction in terms of p-value. Therefore, these interactions were replicable under gene-level analysis and there was a strong enrichment of signals among these interactions. Finally, 9 SNP–SNP interactions were successfully replicated in both replication datasets. We discovered that one of these SNP–SNP interactions can be interpreted as the interaction between FHIT (protein coding) and LINC00969 (lncRNA). Both FHIT and LINC00969 were reported to have expression in brain and might be a new discovery.In conclusion, after exhaustively search for every 2nd order SNP-SNP interaction from the discovery schizophrenia dataset phs000021:phg000013 with eight different biological plausible SNP–SNP interaction patterns, we first reported here that 9 out the top 10 SNP-SNP interactions in terms of http://www.ncbi.nlm.nih.gov/gap.Publicly available datasets were analyzed in this study. This data can be found here: K-YL conducted the project, developed the algorithms used in this study, and ran the experiments under the supervision of M-HW and K-SL. K-YL and DH interpreted the biological meaning of the experimental results under the guidance of NT and HS. K-YL, SM, and DH wrote the main manuscript text. All the authors discussed the results and reviewed the manuscript thoroughly.NT and K-SL are directors and hold shares of the company Cytomics Ltd. Cytomics Ltd holds a licence to use the patent related to single cell type specific gene expression analysis. The remaining 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.


Identifying causal variants and genes from human genetic studies of hematopoietic traits is important to enumerate basic regulatory mechanisms underlying these traits, and could ultimately augment translational efforts to generate platelets and/or red blood cells in vitro. To identify putative causal genes from these data, we performed computational modeling using available genome-wide association datasets for platelet and red blood cell traits.Tropomyosin 1 (TPM1) among our top-ranked candidate genes. CRISPR/Cas9-mediated TPM1 knockout in human induced pluripotent stem cells (iPSCs) enhanced hematopoietic progenitor development, increasing total megakaryocyte and erythroid cell yields.Our model identified a joint collection of genomic features enriched at established trait associations and plausible candidate variants. Additional studies associating variation at these loci with change in gene expression highlighted Our findings may help explain human genetic associations and identify a novel genetic strategy to enhance in vitro hematopoiesis. A similar trait-specific gene prioritization strategy could be employed to help streamline functional validation experiments for virtually any human trait. Elucidating genetic mechanisms governing hematopoiesis has broad value in understanding blood production and hematologic diseases . Given iIn vitro hematopoietic systems might be improved by identifying novel factors from human genetic studies. Genome-wide association studies (GWAS) have linked hundreds of single nucleotide polymorphisms (SNPs) with platelet and/or red cell trait variability , 6. BecaTropomyosin 4 promotes platelet development [Tropomyosin 1 (TPM1) in human platelet trait variation [TPM1 impacts human hematopoiesis.Actin cytoskeletal dynamics play key roles in hematopoiesis –20. Tropelopment . Geneticariation , though TPM1 expression. To validate this putative candidate gene and to explore its function, we used CRISPR/Cas9 genome editing to discover that cultured TPM1-deficient induced pluripotent stem cells enhanced hematopoietic progenitor cell formation. In turn, this increased functional megakaryocyte (MK) yield. Thus, our framework stratified relevant functional loci and helped identify TPM1 manipulation as a novel strategy to enhance in vitro hematopoiesis.Here, we utilized penalized regression to construct a model that predicted platelet GWAS associations based on epigenetic datasets as features for the prediction. Our model built from platelet trait GWAS loci reliably distinguished sentinel GWAS SNPs, as well as platelet-relevant genes and loci. Among these prioritized sites were SNPs that regulate GWAS have linked hundreds of single nucleotide polymorphisms (SNPs) with variability in human platelet traits . To focuOur “platelet trait model” was trained on 580 genome-wide-significant platelet trait-related SNPs from a large recent GWAS of human blood trait variation , along wThe resultant predictive model comprised 38 epigenomic features that best distinguished platelet trait GWAS SNPs from controls  had significantly higher average scores compared to SNPs that were not obviously associated . We reasoned that functional SNPs would (i) be in high linkage disequilibrium (LD) with established platelet trait GWAS loci, (ii) score highly relative to other SNPs within that LD block, (iii) regulate target gene(s) as expression quantitative trait loci (eQTLs), and (iv) overlap GATA binding sites , 39. We 0. We reaany site meeting these stringent criteria could form the basis for interesting biological follow-up experiments.This stratification approach identified 15 loci and related genes, including SNPs known to impact hematopoiesis, megakaryocyte, and/or platelet biology  gene locus, also attracted our attention. This sentinel GWAS SNP scored highly compared to linked SNPs (EUR r2 > 0.7) and overlapped accessible chromatin in hematopoietic cells [TPM1 expression [A second variant, rs11071720, found within the 3rd intron of the ic cells   clones from 2 separate genetic backgrounds. Karyotype and copy number variation analyses confirmed that engineering these clones did not introduce any de novo genomic aberrancies  emerged were also normal  as well as cells destined for a purely endothelial fate.TPM1 protein was present during early iPSC differentiation, but downregulated in non-adherent hematopoietic progenitor cells and differentiated MKs Fig. b, c. Ear+/CD31+ as well as CD43+ HPCs  in a translationally relevant iPSC model. Our data support a model whereby TPM1 deficiency enhances in vitro formation of multilineage HPCs  driving enhanced hemogenic endothelium and/or HPC formation will be of considerable biologic and translational interest, and such studies are ongoing. TPM1 KO-related increases in HPC formation may complement or synergize previously described approaches that enhanced later stages of hematopoiesis [Given the scope of the present study, the most important functional result was enhanced yield of HPCs and functional megakaryocytes. Our results were directionally consistent with human genetic data , findingopoiesis , 62, 63.TPM1 was unexpected based on blood genetics [TPM1 could have separate functions in early and late hematopoiesis, akin to GATA2 [Early hematopoietic function for genetics . Our modgenetics  as well to GATA2 .Though a lack of robust detection methods precluded accurate platelet production quantitation in our culture system, normal function of derived megakaryocytes suggests an overall increase in megakaryocyte yield would translate into higher platelet production. Importantly, our findings do not exclude additional effects on terminal megakaryopoiesis or erythroid development in vitro, nor in vivo effects outside the scope of our iPSC model.TPM1KO iPSCs contrasts detrimental effects of TPM1 deficiency on organism fitness in other contexts [D. rerio thrombopoiesis with tpma-directed morpholinos [TPM4 deficiency [TPM1 deficiency. This highlights the importance of species-specific genetic validation, particularly given inter-species disparities in hematopoiesis [Enhanced hematopoiesis in contexts , 65, 66.ficiency  rather topoiesis .TPM1 in constraining in vitro hematopoiesis. Recent advances increasing per-MK platelet yields [In conclusion, using a penalized regression modeling approach to functional variant identification led us to define a role for t yields  have fochttps://github.com/thomchr/2019.PLT.TPM1.Paper). Human genome version hg19 was used for all analyses, and we utilized the LiftOver script when necessary (https://bioconductor.org/packages/release/workflows/html/liftOver.html). GWAS summary statistics are publicly available (http://www.bloodcellgenetics.org/).Relevant datasets and coding scripts can be found on GitHub (r2 > 0.9) were identified using PLINK. From this set of SNPs, eQTLs and affected genes were identified from GTEx V7 [To estimate the number of eQTLs implicated by prior platelet trait GWAS, SNPs in high LD with established GWAS loci  (EUR r2  GTEx V7 . Numbersp < 5E−8) affecting platelet count, platelet-crit, mean platelet volume, and/or platelet distribution width [r2 ≥ 0.8), and minor allele frequency. We identified ~ 100 matched controls for each training SNP, all with a minor allele frequency > 10%. This minor allele cutoff was necessary to limit the effects of very low control SNP frequencies on the resultant model.From a total of 710 genome-wide significant GWAS SNPs  affecting red cell count, hematocrit, mean corpuscular volume, and/or red cell distribution width [From a total of 1003 genome-wide significant GWAS SNPs . Columns representing our 3 baseline parameters  were also included in this data table for each SNP. This chromatin feature overlap data file was then analyzed using the least absolute shrinkage and selection operator  , 24 withhttps://cadd.gs.washington.edu/download, http://www.sanger.ac.uk/resources/software/gwava, http://deepsea.princeton.edu). For each model, we identified scores for platelet trait GWAS SNPs and a random selection of ~ 100 control SNPs for each GWAS SNP. We then used ROCR [We used public databases to obtain SNP scores for alternative models .For sites in Table https://bedtools.readthedocs.io/en/latest/content/tools/closest.html) to identify the nearest gene to each of these SNPs. Genes and position were defined by BioMart (http://www.biomart.org/). We then used the Gene Ontology resource (http://geneontology.org/) to analyze pathway enrichment. Input analysis settings were binomial tests and calculated FDR for GO Biological Process complete. Pathways identified with FDR < 5% are presented in Fig. To assess biological specificity, we identified the top 1% highest-scoring SNPs from each model  after excluding all red cell or platelet trait-associated GWAS loci. We then used closestBed (Enhancer regulatory regions were defined according to the FANTOM5 dataset . Presenthttps://archive.broadinstitute.org/mpg/snap/ldsearch.php), LDlink , and 1000 Genomes Project (phase 3) data were used to measure linkage disequilibrium in the EUR population.The SNP Annotation and Proxy Search tool  with the following guide sequences: 5′ (1) ATGACGAAAGGTACCACGTCAGG, 5′ (2) TGAGTACTGATGAAACTATCAGG, 3′ (1) CCCTTTTCTTGCTGCTGTGTTGG, and 3′ (2) GGAGAGTGATCAAGAAATGGAGG.iPSC models were generated as described from peripheral blood mononuclear cells . The “CHChromosomal G-band analyses were performed by Cell Line Genetics .Copy number variation (CNV) analysis was performed with the Children’s Hospital of Philadelphia Center for Applied Genomics. CNVs were called using PennCNV  based oniPS cell cultures and primitive hematopoietic differentiations were performed as per published protocols , 75–77. 2 conditions.About 1 week prior to differentiation, iPSCs were transitioned to a “feeder-free” state by culturing on Matrigel-coated wells  in ESC medium under atmospheric O2, 5% O2, and 90% N2. All media were supplemented with 2 mM glutamine, 50 μg/ml ascorbic acid , 150 μg/ml transferrin (Roche Diagnostics), and 4 × 10−4 M monothioglycerol (Sigma). Media and cytokines were changed daily as follows [Throughout hematopoietic differentiation, cells were maintained at 37 °C in 5% CO follows : days 0–By days 6–7, non-adherent floating hematopoietic progenitor cells (HPCs) appeared. HPCs were collected on days 7–9 and either frozen or used directly for further culture and/or analyses. HPCs cultured in 50 ng/ml thrombopoietin and 25 ng/ml SCF to generate megakaryocytes, 2 U erythropoietin and 25 ng/ml SCF to generate erythroid cells, or 200 ng/ml granulocyte/macrophage colony stimulating factor to generate myeloid cells.+/SSEA4+), hemogenic endothelium (KDR+/CD31+), hematopoietic progenitors (CD43+ and CD41+/CD235+), and terminal lineages have been previously validated [Flow cytometry gating strategies for pluripotency . Flow cytometry data were analyzed using FlowJo 10 . The following antibodies were used for flow cytometry: FITC-conjugated anti-CD41 (BioLegend), PE-conjugated anti-CD42b (BD Biosciences), APC-conjugated anti-CD235 (BD Biosciences), PB450-conjugated anti-CD45 (BioLegend), AF488-conjugated anti-SSEA3 (BioLegend), AF647-conjugated anti-SSEA4 (BioLegend), PE-conjugated anti-KDR (R&D Systems), PECy7-conjugated antiCD31 (BioLegend), PECy7-conjugated anti-CD34 (eBioscience), and FITC-conjugated anti-CD43 (BioLegend).TATA-Box Binding Protein (TBP).Total RNA was prepared using PureLink RNA micro kits (Invitrogen) in which samples were treated with RNase-free DNase. The reverse transcription of RNA (100 ng–1 μg) into cDNA was performed using random hexamers with Superscript II Reverse Transcriptase (RT) (Life Technologies), according to the manufacturer’s instructions. Real-time quantitative polymerase chain reaction (PCR) was performed on QuantStudio 5 Real-Time PCR Instrument (Applied Biosystems). All experiments were done in triplicate with SYBR-GreenER pPCR SuperMix (Life Technologies), according to the manufacturer’s instructions. Primers  software.For microarray analysis, 50,000 cells were FACS-sorted directly into TRIzol. RNA was extracted from using a miRNeasy Mini Protocol (Qiagen). Samples passing quality control were analyzed using the human Clariom D Assay (ThermoFisher Scientific) and analyzed using Transcriptome Analysis Console (ThermoFisher Scientific) Software and Gene Set Enrichment Analysis .Cell pellets were resuspended in Laemmli buffer, sonicated for 5 min, and boiled for 5 min at 95 °C. Lysates were centrifuged at 10,000 rpm for 5 min at room temperature, and supernatants were used for analysis. Lysate volumes were normalized to cell counts. Samples were run on 4–12% NuPAGE Bis-Tris gels (Invitrogen) and transferred onto nitrocellulose membranes  at 350 mA for 90 min. Following blocking in 5% milk for 1 h, membranes were incubated with primary antibodies overnight at 4 °C. After washing thrice in TBST, membranes were incubated with secondary horseradish peroxidase-conjugate antibodies for 1 h at room temperature, washed in TBST thrice, and developed using ECL western blotting substrate (Pierce) and HyBlot CL autoradiography film (Denville Scientific). The following antibodies were used for western blotting: Rabbit anti-TPM1 , Mouse anti-TPM1/TPM2 , Mouse anti-TPM3 , Rabbit anti-TPM4 , and Mouse anti-β Actin . Western blot band quantitation was performed using FIJI   with 0.1% bovine serum albumin (BSA) containing FITC-conjugated PAC-1 (BD Biosciences), PacBlue-conjugated CD42a (eBioscience), and APC-conjugated CD42b (eBioscience) at a concentration of roughly 100,000 cells per 50 μl. Following addition of Convulxin (Enzo Biochem) or Thrombin (Sigma), cells were incubated at room temperature in the dark for 10 min. Cells were then incubated on ice for 10 min. An additional 100 μl Tyrode’s Salts containing 0.1% BSA was added, and cells were immediately analyzed by flow cytometry.Genome-wide SNP scores were loaded as custom tracks into the UCSC Genome Browser . Images Statistical analyses were conducted using R or GraphPad Prism 6.All materials, data, code, and associated protocols will be promptly available to readers upon request.Additional file 1:Table S1. The 860 chromatin feature tracks included in our LASSO analysis. These data were obtained from ENCODE [Table S2. Chromatin features and coefficients comprising our penalized regression-based platelet scoring model. Coefficients for background parameters are included at the bottom of this list, but were not included in subsequent genome-wide SNP scoring. Table S3. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by platelet model scores. Presented pathways had false discovery rate (FDR) < 5%. Table S4. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by GWAVA scores. Presented pathways had false discovery rate (FDR) < 5%. Table S5. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by CADD scores. Presented pathways had false discovery rate (FDR) < 5%. Table S6. CRISPR/Cas9-edited Tropomyosin 1 knockout (KO) iPSC lines did not incur any additional CNVs compared to the parent line. Analyses of wild type CHOP14 and CHOP10 ‘parent’ lines, and derivative TPM1KO ‘child’ lines, are shown. Karyotype and copy number variation (CNV) analyses for all child lines were consistent with parental iPSC lines. Table S7. Dysregulated molecular pathways in TPM1KO MKs. FACS-sorted MKs were analyzed by microarray, and gene set enrichment was performed. Upregulated Gene Ontology [Table S8. Chromatin features and coefficients comprising our penalized regression-based red cell scoring model. Coefficients for background parameters are included at the bottom of this list, but were not included in subsequent genome-wide SNP scoring. Table S9. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by red cell model scores. Presented pathways had false discovery rate (FDR) < 5%. Table S10. Penalized regression-based fine-mapping identifies eQTLs in established platelet and/or red cell trait GWAS loci that overlie GATA binding sites. Listed SNPs are within platelet or red cell trait GWAS LD blocks (EUR r2>0.7), scored in the top 5% by both our platelet trait and red cell models, overlap canonical or near-canonical GATA binding sites, and are eQTLs for at least 1 gene [Table S11. Semi-quantitative RT-PCR primers used in this study.m ENCODE , ChromHMm ENCODE , and anam ENCODE . Table SOntology  pathwayst 1 gene   for platelet trait model. Penalized regression results depicting the regularization parameter (λ) vs. AUC. Top axis shows how many features were identified at each level of λ. Variation in AUC at each λ reflects 10-fold cross-validation. The λmin  and λse  are shown, with λse model incorporating the indicated number of features. The final model, with 41 total features, included 38 chromatin features and 3 background characteristics . The AUC at λse was 0.726. Note that this AUC includes background characteristics, which were not used in subsequent genome-wide SNP score applications. Figure S2. High SNP scores for platelet trait model capture information from sub-genome-wide significant loci. a,b Higher SNP scores correlate with lower GWAS p-values for variation in a mean platelet volume (MPV) or b platelet count (PLT). SNPs were scored genome-wide and plotted against arbitrarily binned –log10 GWAS MPV or PLT variation values. A value of 7.3 for –log10 correlates with a p-value of 5x10-8. Box-and-whisker plots show 25th-to-75th percent interval (box) and standard deviation (whiskers). ****p < 0.0001 vs Column 1 . Significant linear correlations existed between higher values of –log10 and SNP scores (Pr(>|t|)<2e-16 by linear regression significance test). c,d SNPs that nearly missed genome-wide significance for c MPV or d PLT were enriched for high SNP scores. SNPs that did not meet genome-wide significance were stratified into non-significant  and marginally significant . Bars represent mean±SEM. ****p < 0.0001 by Wilcoxon Rank Sum test. Figure S3. Platelet trait model gives high scores to SNPs marking hematopoietic enhancer regions. Hematopoietic enhancer regions are enriched for high SNP scores based on our platelet trait model. FANTOM5-defined enhancer regions for adult bone marrow (BM) CD34+ (CNhs12553), K562 , and CMK  hematopoietic cells were compared with enhancer regions from random non-relevant cell types . Bars represent mean±SEM. ****p<0.0001 by 1-way ANOVA vs Controls. Figure S4. Additional putatively active eQTLs implicated through fine-mapping with LASSO-based SNP scores and by direct overlap with GATA binding sites. In each panel, the top portion shows GWAS SNP in black and linked SNPs (EUR r2>0.7) in cyan. Bar heights depict SNP scores. Gene exons are shown in yellow. Accessible chromatin regions (ATAC-Seq) are shown for hematopoietic stem cells (HSC), CD34+ hematopoietic progenitor cells, megakaryocyte-erythroid progenitors (MEP), and erythroblasts (Ery) [a rs11240368 is an eQTL for CNTN2 and TMEM81. b rs3771535 is an eQTL for GMCL1 and SNRNP27. c rs10180681 and rs10180682 are eQTLs for EPB41L5, PTPN4, and RALB. d rs9646785 is an eQTL for GAD1 and GORASP2. e rs6771578 is an eQTL for PDCD10, SERPINI1, and WDR49. f rs12652692 is an eQTL for LHFPL2 and SCAMP1. g rs72793280 is an eQTL for ACSL6, P4HA2, PDLIM4, SLC22A4, and SLC22A5. h rs1741820 is an eQTL for HSF2 and PKIB. i rs13265995 is an eQTL for LYN, TGS, and TMEM68.j rs9704108 is an eQTL for IFITM2.k rs2316513 is an eQTL for DPH1, SMG6, and SRR.l rs1654439 is an eQTL for GP6, NLRP2, and RDH13. Scale bars, 50 kb. Figure S5. The SNP rs11071720 is an expression quantitative trait locus (eQTL) for TPM1. Individuals with the rs11071720 minor ‘C’ allele have decreased Tropomyosin 1 expression in tibial artery tissue . Data obtained from GTEx V7 [Figure S6. DNA sequencing and western blot confirmation of TPM1 deletion. a Shown are TPM1 exons (numbered light blue boxes) in and around the proposed deletion site. 5’ and 3’ guide RNA sites are marked. Deleted areas in each clone are indicated as ‘empty’ bars, with flanking present DNA in dark red. b Western blot of CHOP14 or CHOP10 iPSC lysates showing no TPM1 protein in KO clones. Middle lane in CHOP10 blot depicts a suspected heterozygous clone. Figure S7. Karyotype analyses of iPSC clones were normal. a,b,c Analyses of a wild type CHOP14 performed at the time of genome editing, b CHOP14-derived TPM1 knockout clone 1 (KO1), and c CHOP14-derived TPM1 knockout clone 2 (KO2) show normal human female karyotypes. d,e Analyses of d wild type CHOP10 karyotype analysis performed at the time of genome editing and e CHOP10-derived TPM1 knockout clone (KO3) show normal human male karyotypes. These results reflect analyses and interpretations from Cell Line Genetics . Figure S8. KO cells show normal kinetics of pluripotency marker loss in early differentiation. a Representative gating strategy for flow cytometry analysis. Singlet cells were analyzed directly for all presented studies. b On days 0-4, TPM1 KO iPSCs show normal loss of pluripotency markers SSEA3 and SSEA4, with kinetics identical to WT. Figure S9. CHOP10-derived TPM1 KO iPSCs yield more single cells after differentiation. There were more hematopoietic progenitor cells  in CHOP10-derived TPM1 KO clone 3 following 7-8 hematopoietic differentiation. **p<0.01. Figure S10. Non-adherent cells (HPCs) from TPM1 KO cultures show normal cell surface markers. WT and TPM1 KO iPSC clones 1-3 all display relatively normal cell surface marker patterns after 9 d differentiation. Multiple experiments show no consistent lineage preference across all clones. Figure S11.TPM1 KO MKs have normal morphology. Following 8 d differentiation and 5 d MK expansion culture, wild type (WT) and TPM1 KO CD41+/CD42b+ primitive MKs were FACS-sorted and analyzed by Cytospin. Scale bar represents 20 μm. Figure S12. Microarray analysis shows no significant differences in MK genes. a Volcano plot showing gene expression changes in WT and KO MK microarray analysis. TPM1 is circled. b Hierarchical clustering for microarray gene analysis data of FACS-sorted WT and KO MKs. Red, high expression. Blue, low expression. c Heat map shows the most highly upregulated (top) and downregulated (bottom) genes in KO MKs. d Expression of representative MK genes are not significantly (ns) changed in WT vs KO MKs. PF4, Platelet factor 4. PPBP, Pro-platelet basic protein. SELP, P-selectin. NFE2, Nuclear factor erythroid 2. e Gene set enrichment analysis (GSEA) for MK pathways were not significantly changed. Shown are GO pathways for MK differentiation  and Regulation of MK differentiation . f GSEA plots for select significantly upregulated pathways in KO MKs. Figure S13.TPM1KO HPCs retain normal myeloid lineage expansion potential. HPCs obtained after 8d differentiation were put into lineage expansion media and cultures were analyzed by manual cell counting and flow cytometry over 3-5 d. Mature myeloid cells were CD45+. Points represent lineage-specific cell percentage multiplied by total cell count, normalized to cell count on day 0. *p<0.05 by ANOVA vs WT. Figure S14. Hematopoietic trait associations of SNPs near and within the TPM1 gene locus. Aggregated GWAS platelet, red cell, or white cell trait p-values for SNPs near and within the TPM1 gene locus in LD with rs11071720. The p-values for these SNPs reach genome-wide significance for platelet traits . Figure S15. Penalized regression identifies epigenetic features that discriminate red blood cell trait GWAS SNPs from matched controls. a Area under the receiver operator curve (AUC) for red cell trait model. Penalized regression results depicting the regularization parameter (λ) vs. AUC. Top axis shows how many features were identified at each level of λ. Variation in AUC at each λ reflects 10-fold cross-validation. The λmin  and λse  are shown. The λse model incorporated 81 total features, including background characteristics . The AUC at λse was 0.732, though it is important to note that this included background characteristics . b Penalized regression (LASSO) analysis identified 78 chromatin features from the indicated cell types that best discriminated red cell GWAS SNPs, after controlling for background characteristics. Bar heights are LASSO coefficients, indicating the relative importance of each feature. Subsequent application of this model was based only on these 78 chromatin features and associated coefficients. Ery, peripheral blood derived erythroblasts. MK, primary megakaryocytes. MK/Ery, K562 cells. Lymphoblast, GM12878 or GM12891. Figure S16. Penalized regression model identifies genes relevant to erythroid and hematopoietic biology. a SNP scores for red cell trait model training SNPs, or a set of validation red cell trait GWAS SNPs, were significantly higher than genome-wide SNP scores. Bars represent mean±SEM, ****p<0.0001 by ANOVA. b Performance comparison of our red cell trait model to DeepSEA [c Performance comparison of the indicated methods for validation red cell GWAS SNP identification. AUC values are shown in the legend corresponding to model accuracy in predicting validation SNPs  vs. ~15,000 random controls. d Erythroid and hematopoiesis pathways [ Figure S17. High SNP scores for red cell trait model capture information from sub-genome-wide significant loci. a,b Higher SNP scores correlate with lower GWAS p-values for variation in a mean corpuscular volume (MCV) or b red blood cell count (RBC). SNPs were scored genome-wide and plotted against arbitrarily binned –log10 GWAS MCV or RBC variation values. A value of 7.3 for –log10 correlates with a p-value of 5x10-8. Box-and-whisker plots show 25th-to-75th percent interval (red box) and standard deviation (whiskers). ****p < 0.0001 vs Column 1 . Significant linear correlations existed between higher values of –log10 and SNP scores (Pr(>|t|)<2e-16 by linear regression significance test). c,d SNPs missed genome-wide significance for c MCV or d RBC were enriched for high SNP scores. SNPs that did not meet genome-wide significance were stratified into non-significant  and marginally significant . Bars represent mean±SEM. ****p < 0.0001 by Wilcoxon Rank Sum test.+ CNhs125, K562 (h DeepSEA  for traipathways  identifi