LOC_Os03g48970 for qTSN3b, LOC_Os06g04710 for qFLL6a, and LOC_Os07g32510 for qTGW7 were considered as the most likely candidate genes based on functional annotations. These results provide a basis for further study of candidate genes and for the development of high-yield rice varieties by balancing source–sink relationships using marker-assisted selection.The source-sink relationship determines the ultimate grain yield of rice. In this study, we used a set of reciprocal introgression lines (ILs) derived from Xuishui09 × IR2061 to map quantitative trait loci (QTLs) that were associated with sink-, source-, and grain yield-related traits. A total of 95 QTLs influencing eight measured traits were identified using 6181 high-quality single nucleotide polymorphism markers. Nine background-independent QTLs were consistently detected in seven chromosomal regions in different genetic backgrounds. Seven QTLs clusters simultaneously affected sink-, source-, and grain yield-related traits, probably due to the genetic basis of significant correlations of grain yield with source and sink traits. We selected 15 candidate genes in the four QTLs consistently identified in the two populations by performing gene-based association and haplotype analyses using 2288 accessions from the 3K project. Among these,  Oryza sativa L.) is one of the most important staple cereals in the world, especially in Asia. Due to continuously increasing population, diminishing cultivated area, and other factors, a nearly 40% increase in grain yield will be necessary to meet global food demand during the next 40 years [Rice  [To date, a number of efforts have been made to identify QTLs underlying source-, sink-, and yield-related traits by using genome-wide association and linkage analyses –5. A larGn1a , DEP1 [7P1 [Ghd7 , GNP6 [9P6 [GNP1  for spikNP1 [GW5 , qTGW3 [5 [qTGW3 , and GS3GW3 [GS3  for graiS3 [NAL9  for leaf LSCHL4) –18. NotaQTL mapping results largely depend on genetic background . Many QTjaponica (geng) rice variety from China while IR2061-520-6-9 (abbreviated as IR2061) is a drought tolerance indica (xian) breeding line introduced from the International Rice Research Institute (IRRI), Los Baños, Philippines. The two parents are genetically significant differences in grain yield, leaf trait, panicle trait, cold tolerance, drought tolerance and heat tolerance which are certainly worth of studying. Therefore, we chose Xuishui09 and IR2061 as the two parents to construct a set of reciprocal introgression lines (ILs) [1 plants were simultaneously backcrossed to the two parents to develop two BC1F1 populations, with each having around 100 plants. Twenty-five individuals randomly selected from BC1F1 plants were then used as the male parent to backcross with the corresponding parent to produce two BC2F1 populations. The BC progenies were selfed for five consecutive generations without selection, yielding lines from BC2F2 to BC2F6. Finally, two sets of ILs were developed, consisting of 226 BC2F6 lines in the Xiushui09 background (XS-ILs) and 233 BC2F6 lines in the IR2061 background (IR-ILs).Xiushui09 is a high-yield semi-dwarf es (ILs) . Specifi2) was calculated as FLL × FLW × 0.75 [All of the ILs and their parents were planted in Sanya , Hainan province during December 2016–April 2017 and Hangzhou , Zhejiang province during May–October 2017. Each line was grown in a plot of two rows with 12 plants in each row at a spacing of 25 × 17 cm, with three replicates. The fields were managed according to the local standard practices. At the full heading stage, flag leaf width , flag leaf length  were measured on the main stems of five uniform plants from each replicate of each line. Flag leaf area , where VG, VGEI, and Ve are the variances of genotype, genotype-by-environment interaction variance, and residual error, respectively; s is the number of environments, and r is the number of replicates. All of the analyses were conducted with the PBTools package (http://bbi.irri.org/products) developed by IRRI. A linear mixed model was used to integrate the phenotypic data from two environments; the line’s genotype effect was treated as the fixed effect, and the environment and replicate within the environment were treated as random effects. The lme4 in R package was used to calculate the best linear unbiased predictors (BLUPs) of each line. BLUP values of each line were used for analysis of phenotypic correlation using the rcorr function implemented in the R package Hmisc [To dissect the effects of genotype-by-environment interaction (GEI) on the tested traits, variance components were estimated using multiple-site analysis, with all effects treated as random. Heritability across environments was computed using the estimated variance components as ge Hmisc .Genomic DNA for SNP genotyping was isolated from approximately 100 mg fresh leaf samples of five-week-old seedlings from the two sets of ILs using a modified cetyltrimethylammonium bromide (CTAB) method . GenotypBLUP values of each line were used for QTL mapping of source-, sink-, and grain yield-related traits. A total of 6181 high-quality SNP markers were used to conduct the QTL identification. Linkage maps of XS-ILs and IR-ILs were constructed separately using the Map functionality in IciMapping . Firstlyxian accessions , 628 geng accessions , 176 aus/boro, 52 basmati/sadri and 71 highly admixture (adm) accessions . Then, all available SNPs located inside these genes were searched from 4.8 M SNPs data generated from 3K RGP in the Rice SNP-Seek Database [P-value for declaring a significant marker-trait association was 10−5. Thirdly, haplotype analyses were conducted using all of the available non-synonymous SNPs located inside the gene CDS region searched from 18 M SNPs data generated from 3K RGP in the Rice SNP-Seek Database [The following four steps were conducted to identify candidate genes for QTLs consistently detected in the two sets of ILs. Firstly, the genes located in the candidate regions for QTLs consistently detected in the two sets of ILs were extracted, and gene functions were annotated by the Rice Annotation Project Database , whereas IR-ILs shared on average 90.7% of the genome with the recurrent parent IR2061 (range from 58.5 to 100%) . ObviousAlthough Xiushui09 exhibited significantly greater FLW than IR2061, the FLA value did not differ between the two parents, because the former had significantly smaller FLL than the latter in SY . In HZ, VG), and the heritabilities of the five traits ranged from 0.38 for FLL to 0.70 for TGW, and from 0.33 for TSN to 0.64 for TGW in XS-ILs and IR-ILs, respectively. FGN, PN, and GY were more sensitive to environmental factors, with gene and environment together potentially affecting phenotypic variation. The heritability of the three traits ranged from 0.04 for GY to 0.23 for PN, and 0.14 for GY to 0.31 for FGN in XS-ILs and IR-ILs, respectively  were controlled mainly by genotypes  for FLW, two QTLs (qFLL5c and qFLL6a) for FLL, one QTL (qFLA4c) for FLA, two QTLs (qTGW5b and qTGW7) for TGW, and three QTLs  for TSN. The IR2061 alleles at the qTGW7 and qTSN6b loci were associated with increased TGW and TSN, and they accounted for 4.6% and 4.3% of TGW, and 10.9% and 4.7% of TSN in XS-ILs and IR-ILs, respectively. The Xuishui09 alleles at the remaining loci resulted in increased corresponding trait values, and they accounted for 2.5% to 26.5% of the phenotypic variance , 31 QTLs influencing sink capacities (TGW and TSN), and 21 QTLs affecting grain yield (GY) and its related traits (FGN and PN) revealed seven chromosomal regions simultaneously influencing source and sink/yield-related traits in the same population . These st values .qTSN6b/qFLW6b/qFLA6a located in the region of 27.40–27.97 Mb on chromosome 6 but had no IR2061 allele introgressions at the other QTLs for TSN, FLW, or FLA; otherwise the line showed 93.7% of the genome of Xuishui09  simultaneously influencing source- and sink- related traits were identified in the same population . Among tuishui09 . XC10 shuishui09 , indicathttps://shigen.nig.ac.jp/rice/oryzabase/) for known rice genes that co-localized with the nine QTLs. Three known genes  at five QTLs  were related to the corresponding traits.In this study, nine QTLs in the seven chromosomal regions were consistently identified in the two sets of ILs. We searched the Oryzabase online resource  using 2288 accessions of 3K. The 2288 accessions presented substantial variations for the FLL, TGW, and TSN in SY  according to the Rice Genome Annotation Project Database (RAP-DB). Of 2366 SNPs available in the 184 genes, a total of 32 significant SNPs located inside the 22 genes were identified to significantly associate with the corresponding traits  by gene-based association analysis . A total of 1231 SNPs in 63 genes were used for association analysis. Based on gene-based association analysis results, four genes  were used for haplotype analysis  transcription factor in rice [OsNF-YA4 were found, and Hap1 (G) was associated with significantly more TSN than Hap2 (A)  .qFLL5c, 750 SNPs in 60 genes were used for association analysis. According to the gene-based association analysis results, six genes  were selected for the haplotype analysis   .qFLL6a, 249 SNPs in 51 genes were used for association analysis. Based on gene-based association analysis results, a total of 10 genes were used for haplotype analysis  , encoding an F-box protein in rice [ddf1-1 were found, and Hap 1 and Hap 3 exhibited significantly larger FLL than the other two haplotypes  . Among tplotypes .qTGW7 was mapped in the region of 19.32–19.42 Mb on chromosome 7 containing 10 annotated genes. A total of 136 SNPs in 10 genes were used for association analysis. According to gene-based association analysis results, two genes (LOC_Os07g32470 and LOC_Os07g32510) were used for haplotype analysis  on grain yield varied with the backgrounds [qFLL5c, qFLL6a, qFLW4b, qFLA4c, qTSN3b, qTSN4b, qTSN6b, qTGW5b, and qTGW7) consistently identified in the two different genetic backgrounds might certainly be made a priority for enhancement of rice production via MAS.QTLs influencing grain yield and its related traits are usually sensitive to genetic background and environment . Among tkgrounds , 32–34. qTSN3b contains four candidate genes, including OsNF-YA4 (LOC_Os03g48970), encoding a Nuclear Factor Y (NF-Y) transcription factors in rice [OsNF-YA4 is involved in regulating the development of the rice panicle, affecting panicle number, grain number per panicle, grain filling, and grain weight. OsNF-YA4 overexpressed transgenic plants exhibited abnormal yield component values, where grain filling rate, TSN, PN, and TGW were significantly decreased compared to the control [OsNF-YA4 were identified, and haplotype G showed significantly more TSN than haplotype A (OsNF-YA4 (LOC_Os03g48970) is considered as the most likely candidate gene for qTSN3b.The region 27.70–28.07 Mb on chromosome 3 harboring  in rice . OsNF-YA control . In the lotype A . TherefoqFLL6a, dwarf and deformed flower 1  that encodes an F-box protein in rice is the most likely candidate gene. The ddf1-1 mutant exhibited significantly shorter and thinner stems, leaves, and roots than the wild type [ddf1-1 mutant showed that all the mutant phenotypes were rescued, including plants with dwarf stature and smaller stems, leaves, roots, and panicles, as well as aberrant spikelets [ddf1-1 were detected, of which Hap 1 and Hap 3 were associated with significantly larger FLL than the other two haplotypes (Hap 2 and Hap 4) (For ild type . Geneticpikelets . In the d Hap 4) .qTGW7, LOC_Os07g32510 is a Dof zinc finger domain protein. The Dof proteins, a class of zinc finger domain transcription factors, have been reported to participate in regulation of gene expression in seed storage protein synthesis in developing endosperm [Arabidopisis due to overexpressing one of two soybean Dof transcription factor genes GmDof4 and GmDof11 [Dof daily fluctuations 1 (RDD1) encoding a Dof transcription factor gene [LOC_Os07g32510 were detected, with Hap 1 and Hap 2 being associated with significantly larger TGW values than the other two haplotypes , the sink , and the PN in diverse genetic backgrounds [qTGW3c in the region 35.37–35.77 Mb on chromosome 3 and qTGW5b in the region 5.29–5.41 Mb on chromosome 5 were co-located with qTGW3 [GW5 [qFGN7 in the region 9.11–9.21 Mb and qTSN7d in the region 29.57–29.67 Mb on chromosome 7 were close to Ghd7 [Ghd7.1 [qTSN6b for TSN was co-located in the region 27.40–27.97 Mb on chromosome 6 harboring APO1 for the number of primary rachis branches that ultimately increase the harvest index and grain yield in rice [Of the 95 QTLs for source-, sink- and grain yield-related traits, ten QTLs were located in the same or adjacent regions containing previously reported cloned genes in rice . For exakgrounds , 39; qTGth qTGW3  and GW5 GW3 [GW5  associat to Ghd7  and Ghd7 [Ghd7.1  regulatiSPIKE (NAL1) located at a QTL cluster (qFLW4b/qFLA4c/qTSN4b/qFGN4b) in the region of 31.15–31.50 Mb on chromosome 4 showed pleiotropic effects in regulating the development of multiple traits related to source  and sink (spikelet number per panicle) in different genetic backgrounds [NAL1 allele achieved 18.7% and 18% yield increases compared with their controls 9311 and IRRI146 [qFLW3b/qFLA3c/qTSN3b, qFLW4a/qFLA4b/qTSN4a, qFLW4b/qFLA4c/qTSN4b/qFGN4b, qFLW6b/qFLA6a/qTSN6b, and qFLW6c/qFLL6b/qTGW6c) into geng or xian varieties to develop super high-yield rice cultivars, as demonstrated by the IL XC10 from this study  and sinks  plays an important role in guiding the breeding of super high-yield rice varieties. The significant positive correlations of FLL, FLW and FLA with TSN in the two populations were in agreement with previous studies . Howeverkgrounds . NILs wi IRRI146 , respectis study .In general, large-panicle cultivars exhibit greater plant size, larger FLL and FLW, and fewer numbers of tillers . In the qTSN3b, qFLL6a, and qTGW7) were inferred from gene-based association analysis, haplotype analysis and functional annotation. Favorable alleles of genetic background-independent QTLs for source-, sink, and grain yield-related traits were mined. These results provide an effective guide to developing varieties with high yield potential by balancing source–sink dynamics via marker-assisted selection.We identified 95 QTLs for the yield- traits, nine of which were consistently detected in seven chromosomal regions in the two set of ILs, and seven QTL clusters that simultaneously affected source-, sink-, and grain yield-related traits were detected in the same population. A total 15 candidate genes for the four QTLs consistently detected in the two populations were identified and the most likely candidate genes for three QTLs (S1 Table(XLSX)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.


Relative to non-significantly heritable TGs, heritable TGs had a greater number of associations with gene transcripts, not directly associated with lipid metabolism, but with immune function, signalling and transcriptional regulation. Genome-wide average DNA methylation (GWAM) levels accounted for variability in some non-heritable lipids. We reveal a complex interplay of genetic and environmental influences on the ageing plasma lipidome.The critical role of blood lipids in a broad range of health and disease states is well recognised but less explored is the interplay of genetics and environment within the broader blood lipidome. We examined heritability of the plasma lipidome among healthy older-aged twins (75 monozygotic/55 dizygotic pairs) enrolled in the Older Australian Twins Study (OATS) and explored corresponding gene expression and DNA methylation associations. 27/209 lipids (13.3%) detected by liquid chromatography-coupled mass spectrometry (LC-MS) were significantly heritable under the classical ACE twin model (h As the field of lipidomics has grown, hundreds to thousands of complex lipids have been characterised , with maWhile there are substantial data to suggest that levels of traditional lipids and lipoproteins such as high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL), cholesterol and triglyceride levels are heritable , few stuLPL, LIPC and CETP respectively) have been associated with HDL, and genes encoding cadherin EGF LAG seven-pass G-type receptor 2, apolipoprotein B and translocase of outer mitochondrial membrane 40  have been associated with LDL (APOE) variants have been established as a strong risk factor for cardiovascular disease and Alzheimer’s disease (Previous genome-wide association study (GWAS) data implicate many genetic loci associated with traditional lipid levels. For example, the genes encoding lipoprotein lipase, hepatic lipase and cholesteryl ester transfer protein  and shared (C) and unique environmental (E) influences on a given trait by comparing correlations within monozygotic and dizygotic twin pairs . One majWe applied the classic twin design to estimate heritability using 75 pairs of MZ twins and 55 pairs of DZ twins from the Older Australian Twin Study (OATS) , aged ben = 330 individuals, 260 of these were used for heritability analyses. Characteristics of the MZ  and DZ  twins with available plasma for heritability analyses are presented in Plasma lipidomics was performed on h2 = 0.427, 95% C.I. = , 0.404, 95% C.I. = , 0.419, 95% C.I. = , and 0.427, 95% C.I. =  respectively). HDL had a substantial C component . For individual lipid species measured, 27 out of 203 (13.3%) were significantly heritable with a median heritability of h2 = 0.433, ranging from 0.287 for TG (18:0/17:0/18:0) to a maximum of 0.59 for Cer (d17:1/24:1).Heritability of lipids was computed using the classical ACE model. Classical lipid measures of total cholesterol, LDL, HDL and triglycerides were significantly heritable (h2 = 0.433–0.59), where 9 out of 20 species were significantly heritable. For triglycerides (TG), 12 of out 59 species measured were heritable (range h2 = 0.287–0.495). Among diacylglycerols (DG), 3 species out of 10 were heritable (range h2 = 0.422–0.544). Only three phospholipids were heritable, including 2 of 58 phosphatidylcholines (PC) and 1 out of 5 phosphatidylethanolamines (PE), (range h2 = 0.327–0.413). Cholesteryl ester (CE), lysophosphatidylcholine (LPC), phosphatidylinositol (PI) and SM (sphingomyelin) species were not significantly heritable, with median heritability for non-significant lipids at h2 = 0.23, and near zero heritability for LPC species. Heritability estimates obtained for summed lipid groups , as were TG (rg = 0.81) and DG (rg = 0.73) species. DG and TG were also highly genetically correlated with each other (rg = 0.70), as were Cer species with monounsaturated SM (rg = 0.83). Median phenotypic correlations between Cer species, between TG species and between DG species were rp = 0.85, 0.61, and 0.53 respectively, and rp = 0.51 between TG and DG species, and rp = 0.83 between Cer and monounsaturated SM. Median unique environmental correlations were moderately lower than corresponding genetic correlations , indicating that heritable lipids of similar class have a strong shared genetic basis relative to the unique environment. Further, traditional lipids  had poor genetic and phenotypic correlations with individual lipid species, apart from traditional triglyceride measures, which was highly correlated with individual TG and DG species. A genetic correlation matrix heatmap is shown in Genetic and environmental correlations were estimated for significantly heritable lipid species and lipid classes. Median genetic correlations between Cer species were high (rThe association of lipids (n = 209) with probe level gene expression  was analysed using linear mixed models via the R package nlme . We founSince the bulk of significant gene expression associations were with TG, we examined the relationship of gene expression associations for TG species by degree of saturation, classifying each TG species as being saturated (no fatty acyl double bonds), monounsaturated (possessing one double bond), or polyunsaturated (possessing two or more double bonds). We then investigated how many transcripts were associated with a low, medium and high number of lipids, by counting the number of gene transcripts significantly associated with either 1–2 lipids, 3–8 lipids, and over eight lipids in that class (in the case of polyunsaturated TG). Generally, only a few gene transcripts were associated with many lipids, regardless of saturation level. There were 282 gene transcripts associated with 1–2 TGs in the saturated TG class, but only six were associated with at least three different TGs in that class.RPL4P2), A disintegrin and metalloproteinase domain-containing protein 8 (ADAM8) and Adipocyte Plasma Membrane Associated Protein (APMAP) were uniquely associated with saturated TG when considering heritable TG lipids.HDC) and carboxypeptidase A3 (CPA3) to be significantly associated with all TGs irrespective of the number of total carbons and number of double bonds. In fact, HDC and CPA3 were also significantly associated with other lipids including DG and HDL-C . In a majority of cases, these associations were driven by a specific TG lipid , among monounsaturated TGs, this was TG(16_0/14_0/18_1) and for polyunsaturated TGs, these were TG(19_1/18_1/18_2), TG(16_0/18_1/23_1), TG(16_0/22_6/22_6) and TG(25_0/18_1/18_1)). These lipids tended to have a medium to high total carbon count (i.e. >55 carbons). By contrast, our analysis also found gene expression of histidine decarboxylase , and only 2/17 were non-protein coding RNAs/pseudogenes. By contrast, the majority of positive lipid-transcriptome associations were for non-protein coding pseudogenes (9/11) and only 2/11 were protein coding.We examined whether gene transcripts significantly associated with lipids included genes responsible for structural changes by a diversity of lipid modifying machinery, such as families of elongase and desaturase enzymes responsible for modifying fatty acid chain length and saturation level , as wellInstead, the transcripts reveal genes which regulate other physiological and cellular functions particularly those involved with immune and vascular functions , with poDirections of arrows indicate either positive (upwards facing) or inverse (downwards facing) lipid-gene transcriptome associations. Even though our transcriptomic data was for the blood transcriptome, some of these genes also have functions in the CNS or associations with neurodegenerative diseases (far right column).To gain insight into the relationships between lipid levels and DNA methylation of CpGs at specific genes, we selected gene transcripts significantly associated with lipids and identified associations between DNA methylation at CpG sites within close proximity to these gene transcripts, and lipid expression . We founWe also examined the relationship between gene expression and DNA methylation at specific CpG sites of genes whose transcripts were associated with significant heritability . We foun2), with a maximum of two significant gene expression associations (for TG18:1_17:1_22:6 and TG18:1_20:4_22:6).We then explored associations of genome wide average methylation with lipid levels and found significant associations of all five LPCs  with GWAM  which compares well with an estimate of 35.4% provided for metabolites from a genome-wide genotyping study in subjects aged 60 years and over  yielded a similar range of heritabilities of 0–62% (2 > 0.40), not dissimilar to the proportion identified in the present study (27 of 207). However, the heritability of various classes often did not corroborate our findings. For example, NUGAT reported LPC and PE to be moderately heritable (0.25<h2<0.35), while SMs had high heritability, as opposed to ceramides which were reported to be lowly heritable. By contrast, our study found high heritability of ceramides, and no significantly heritable SMs and virtually zero heritability of LPCs. A more recent publication of a Finnish population based study (FINRISK) reported SNP-based heritability of lipid species to be in the range 0.10–0.54  to the base cytosine of 5’-cytosine-phosphate-guanine-3’ (CpG) dinucleotides  suggest similar genetic influences between lipids of the same class. Further, Cer species and monounsaturated SM also exhibited high genetic correlations, as did TG and DG. Metabolically, Cer and SM belong to the sphingolipid class where SM can be converted to Cer via sphingomyelin phosphodiesterase , while TTranscriptome associations of both heritable and non-heritable triglycerides, which represented the largest component of our lipidomics dataset, were assessed. We anticipated that both heritable and non-heritable lipids would have gene transcript probe associations, since endogenous triglycerides are derived from essential dietary fatty acids, such as linoleic acid, or other fatty acids substantially derived from dietary sources (such as linolenic acid and docosahexaenoic acid). Gut microbiota (microbiome) can also have an effect on the dietary lipidome, prior to absorption, representing another ‘environmental’ contributor, to lipid abundance and structure . AlthougSLC45A3), CPA3 and HDC were previously reported in a study of lipid and immune response . Very few heritability studies have focused on the lipidome in this age bracket. It is thereby important to stress that the findings of this study may not necessarily generalise to the whole population. We suspect that in our older cohort, environmental factors would dominate given the time in which these exposures are allowed to accumulate and shape the lipidome. Some of the heritabilities reported may vary longitudinally, owing to the dynamic contribution of genetic and environmental factors, and their interaction, across the lifespan . In partTranscriptomics data obtained through the Illumina microarray provides a broad overview of many potential gene transcript associations with measured lipids from the same individuals. However, these data were obtained using RNA from blood cells, which presents potential biases in the types of associations uncovered and could account for some of the immune regulatory genes uncovered. Nevertheless, given the strict cutoff p-value employed in the analyses, it is likely these associations reflect true roles of these lipids in immune function, and the genes we uncovered have previously been identified in other lipid-transcriptomic studies . We mustAnother major limitation is the fact that only average levels of DNA methylation (i.e. GWAM) were considered when associating with lipids, rather than DNA methylation at specific sites. This approach was necessary in order to avoid multiple testing correction for over 450,000 CpG methylation sites. The result is that the associated lipids showed at best suggestive significant associations with DNA methylation. The associations that we did find were for non-heritable lipids only, especially the least heritable LPCs, and were largely inverse. It is likely that based on previous studies, more significant associations with DNA methylation sites could be determined using greater selectivity of methylation sites at certain genomic regions. Further, as our analysis only showed that a small subset of non-heritable lipids were associated with GWAM, there is a still a lot variation in the lipidome not accounted for. CpG site specific analysis for particular genes did not find a relationship between DNA methylation and gene expression of these transcripts, though this analysis may lack power to detect these relationships. Other epigenetic mechanisms such as histone modification and chromatin structural changes could be implicated in regulating lipid metabolism, but are beyond the scope of this study.We also acknowledge that our lipidomic analyses was conducted in whole plasma as opposed to within lipoprotein fractions, which limits insight into biological properties of lipid species. For example, serum albumin contains free fatty acids, which were not analysed in the present study, and would require separate analysis through gas-chromatography mass spectrometry. Since free fatty acids incorporate into phospholipids and triglycerides, their function may reveal greater complexity in lipid regulation at the gene level than we have described. Nevertheless, plasma is a common biological matrix for lipidomic study and likely includes both free lipids and plasma bound components, and represents a good comparative source for further, more detailed studies. Additionally, we advocate for increased focus on assay of individual lipids. Despite some redundancy between traditional measurements and the individual species , substantial variance in many lipid classes is not well represented using traditional measurements, and heritability of a lipid class may differ from heritabilities of individual lipids of that class, likely owing to the broad range of heritability estimates obtained, as previously reported .In the context of guidelines for lipid health and therapeutic targets, individual lipids may strengthen genetic associations with lipid loci that could be useful for assessing cardiovascular disease risk . Non-herIn our study of older Australian twins combining lipidomics, transcriptomics and DNA methylation data, a small subset of plasma lipids was heritable and included largely Cer, TG and DG species. Most phospholipids, especially LPCs, were not significantly heritable. Significantly heritable lipids exhibited high genetic correlations between individual Cer, TG and DG species, as well as between Cer and SM, and between DG and TG, indicating shared genetic influences between lipids of the same class or metabolic pathways. Heritable lipids, especially TGs and DGs, were associated with a greater degree of gene transcript probe associations relative to the non-heritable lipids, and these transcripts were related to immune function and cell signalling rather than lipid metabolism directly. Thus, genes not related to lipid metabolism may still be associated with plasma lipid levels. Finally, associations of genome-wide average DNA methylation with highly non-heritable lipids, especially LPCs, suggest a potential mechanism by which environmental influences on lipids are conveyed. Overall, this study shows that a vast majority of plasma lipids are controlled by the environment, and hence modifiable, with genetic control still a major contributor to Cer, DG and TG lipid levels. Further, our study suggests a complex interaction between lipids, environment, DNA methylation and gene transcription.n = 330 participants who had available plasma from Wave 3; plasma from this wave collected within a period of up to 3 years apart. Of these, 260 participants were eligible for heritability analyses, including 150 monozygotic twins , and 110 dizygotic twins . The study protocol for OATS has been previously published , established in 2007. The study recruited participants from three states in eastern Australia . The OATS collection included; patient data, including blood chemistry, MRI, neuropsychiatric assessment/cognitive tests, and medical exams performed over several visits (waves), each taken at an interval of 16–18 months, with the first visit denoted as ‘Wave 1’, second visit denoted as ‘Wave 2’ and so on. From OATS, we selected ublished . ParticiBlood collection, processing and storage were performed under strict conditions to minimize pre-analytical variability . FastingPlasma total cholesterol, LDL-C, HDL-C and TG were measured by enzymatic assay at SEALS pathology  as previously described , using aAPOE single nucleotide polymorphisms  was performed using Taqman genotyping assays  to determine the APOE haplotype, which has three alleles .DNA was extracted from samples using established procedures . GenotypAPOE genotype  were purchased from Avanti  and diluted ten-fold in 1-butanol/methanol (1:1 v/v). Plasma extraction was performed in accordance with a single phase extraction as previously described . Brieflygenotype  reportedLipid analysis was performed by LC ESI-MS/MS using a Thermo QExactive Plus Orbitrap mass spectrometer  in two experimental batches separated by a month. A Waters ACQUITY UPLC CSHTM C18 1.7 um, 2.1 × 100 mm column was used for liquid chromatography at a flow rate of 260 µL/min, using the following gradient condition: 32% solvent B to 100% over 25 min, a return to 32% B and finally 32% B equilibration for 5 min prior to the next injection. Solvents A and B consisted of acetonitrile:MilliQ water (6:4 v/v) and isopropanol:acetonitrile (9:1 v/v) respectively, both containing 10 mM ammonium formate and 0.1% formic acid. Product ion scanning was performed in positive ion mode. Sampling order was randomised prior to analysis.Lipidsearch software v4.2.2  was applied to perform searches on raw files using the databases ‘General’ and ‘labelled standards’. For peak detection, recalc isotope was set to ‘ON’, RT interval = 0.0 min. We used product search for LC-MS method and the precursor and product tolerances were set at 5.0 ppm and 8.0 ppm respectively. The intensity threshold was 1% parent ion, and the m-score threshold was set to 2.0. For quantitation, mz tolerance was set at −5.0 ppm to 5.0 ppm, and the retention time range was set at −0.5 to 0.5 min. The m-score threshold was 5.0, and all lipid classes were selected for inclusion. Ion adducts included +H, +NH4 for positive ion mode.The raw data were aligned, chromatographic peaks selected, specific lipids identified and their peak areas integrated using LipidSearch. Owing to the large number of RAW files being processed, the alignment step was performed in four separate batches, with a maximum of 100 samples aligned at any one time, and the data collated and exported to an Excel spreadsheet for manual processing and statistical analysis. Only lipids that were present in all four alignment batches were included in our analysis. The raw abundances (peak areas) were normalised by dividing each peak area by the raw abundance of the corresponding internal standard for that lipid class; for example, all phosphatidylcholines were normalised using 15:0-18:1(d7) PC. The intra-assay coefficient of variation (CV) was calculated by dividing the standard deviation of the normalised abundances by the mean across lipid species. Lipid ion identifications were filtered using the LipidSearch parameters rej = 0 and average peak quality >0.75. Furthermore, identifications with CV < 0.4 from repeated injections of quality control standards every 20 runs were included. These controls included: (i) blank, to check for column and chromatography background levels, (ii) internal standards only, to check on system performance across a long sequence of runs, (iii) quality control plasma, to check for between run performance and enable calculation of between run and within run assay CV%. Where duplicate identifications were found on LipidSearch , the lipid ID with the lowest CV%, and highest peak quality score was used. When necessary, the average m-score  was also used to differentiate closely related lipid species, with the lipid having the highest m-score selected. All other duplicates were excluded from analysis. Lipid groupsums were produced by adding lipids within a defined class/subclass together, such as total monounsaturated triglycerides (TG), total ceramides (Cer) etc.Fasting blood samples for gene expression analyses were collected. The methods for gene expression data collection analyses have previously been described . Brieflyminfi package was used to generate methylation beta values (ranging from 0 to 1)  for each sample across all the probe level beta values were calculated.Genome-wide DNA methylation data for 113 monozygotic twin pairs was generated using an established genomics provider using peripheral blood DNA collected at baseline . Randomi 0 to 1) . Quantil 0 to 1) . Followi 0 to 1) , implemeAPOE ε4 carrier status, which were then inverse normal transformed using the R package RNOmni , shared environmental (C) and unique environmental (E) components. In the narrow sense heritability is defined as the ratio of additive genetic variance [Var(A)] to the total phenotypic variance [Var(A)+Var(C)+Var(E)]. The model containing these three parameters  is known as the ACE model. For model parsimony and test concerning the variance parameters, models with only A and E components, known as AE model, and the models with CE and E components would be fit and compared with the full ACE model . GeneticDifferences in heritability between the two sexes were examined using the sex heterogeneity model. This model is similar to the general ACE model but additional parameters are used to represent the genetic and environmental effects of male and female samples. The male and female path coefficients are used to model the opposite sex pair in DZ twins. The full likelihood is the sum of likelihoods under MZ, DZ and opposite sex pairs. Test of equality of the parameters under male and female samples were examined by likelihood ratio test comparing the heterogeneity model against a constrained homogeneity model assuming the same set of parameters for male and female samples. Age-related change in heritability was examined using a gene-environment interaction model . The patAPOE ε4 carrier status) were used as independent and dependent variables respectively in these models. A p-value threshold of 1.39 × 10−6  was used to define significant associations of lipids with probe level gene expression.Test of association of lipids with probe level gene expression were performed using the linear mixed model and the lme function in R package nlme . Gene exSimilarly, lipid residuals were used as dependent variable and average methylation value was used as the independent variable to test the association of lipids with methylation. The proportion of variance in lipids explained by the gene expression variation and methylation variance were estimated based on the log-likelihoods as implemented in the R package rcompanion . For mos2 is McFadden’s pseudo-r2. p-value for h2 is the p-value for test of significant additive genetic effects (h2 = heritability). Thus p-value for h2 <0.05 indicates significant heritability. Regression coefficients are based on average methylation at CpG sites excluding any with known SNPs influencing lipid levels.For analysis of GWAM , r2 is MLipids are named according to the LIPID MAPS convention . Lipid aR code scripts for major analyses described in the Data Analysis are available by author request. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This study represents a comprehensive analysis of the heritability of the aging plasma lipidome in healthy older twins enrolled in the Older Australian Twins Study (OATS). Exploration of gene expression and DNA methylation profiles showed heritability under the classical ACE twin model. Heritable triglycerides were associated with gene transcripts relating to immune and cell signaling molecules, whereas variability in non-inheritable lipids was accounted for by genome-wide average DNA methylation. In all, these studies reveal interacting genetic and environmental regulation of the plasma lipidome.Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Mone Zaidi as Reviewing Editor and Matthias Barton as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Genetic and environmental determinants of variation in the plasma lipidome of older Australian twins" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife, either of which would be linked to the original paper.Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in Summary:This is an extensive study of older Australian twins (OATS) reporting the heritability of numerous lipid species in plasma of a collection of 75 monozygotic and 55 dizygotic twins (aged 69 – 93 years). They examine lipidomics, transcriptomics and DNA methylation to determine if there are certain lipid profiles that appear inheritable and are driven by underlying RNA levels or DNA methylation. The major findings are that overall 3% of lipid species displayed significant heritability estimates, with the largest fractions being among TGs, ceramides, and diacylglycerols.Essential revisions:Overall the body of work is substantive and the data has been analyzed with sufficient statistical rigor. The authors rightly note in the manuscript the scientific shortcomings of their DNA methylation analysis. Four key issues need to be addressed.1) The manuscript's major shortcoming is related to the analysis. This is a descriptive study that makes a long-winded cursory attempt at analysis in the Discussion. The manuscript should be revised to condense the analysis into clearer, bolder conclusions and placed into the context of prior studies. Perhaps a supplementary figure could be generated comparing the findings of the current study with those of prior studies so that commonalities and differences can be more readily appreciated by readers. For example there is a publication on heritability of ceramides and other lipids that should be referenced and included in the analysis: McGurk et al., 2019.2 for HDL-C of 0.419, which is much lower than that reported in other studies . This seems a more likely explanation rather than this being a unique feature of Australian twins as the authors suggest.2) From a methodological standpoint, the authors may wish to acknowledge that the study population is probably not ideal for the research question. For many traits, heritability is greatest studied in younger individuals (e.g. PMID: 8127331), and this cohort of older twins may therefore significantly underestimate heritability. This is further suggested by the authors' finding of h3) A second methodological issue is that the lipidomic profiles were assessed in whole plasma, which provides comparatively less insight into the biological properties of these lipid species compared to approaches that assay lipid concentrations within relevant compartments . The authors should discuss this limitation and how it may influence their results. This is particularly important given that many of the lipid species assessed are highly correlated with traditional lipid values (e.g. between TG and DG), and therefore the question arises as to whether the lipidomic measurements are really just an indirect means of assessing traditional lipid and lipoprotein fractions.4) Were there differences in heritability observed for male vs. female MZ twins? Essential revisions:Overall the body of work is substantive and the data has been analyzed with sufficient statistical rigor. The authors rightly note in the manuscript the scientific shortcomings of their DNA methylation analysis. Four key issues need to be addressed.1) The manuscript's major shortcoming is related to the analysis. This is a descriptive study that makes a long-winded cursory attempt at analysis in the Discussion. The manuscript should be revised to condense the analysis into clearer, bolder conclusions and placed into the context of prior studies. Perhaps a supplementary figure could be generated comparing the findings of the current study with those of prior studies so that commonalities and differences can be more readily appreciated by readers. For example there is a publication on heritability of ceramides and other lipids that should be referenced and included in the analysis: McGurk et al., 2019.We consolidated the Discussion, especially in relation to comparing heritability results against previous studies, placed an overarching summary at the beginning of the Discussion, and as suggested, a table (Table 5) has now been included to summarise this information. Some statements originally written under “lipid-transcriptome associations” of the Discussion have now been relocated into the Results section to make reading easier, and the Discussion more to-the-point.2 for HDL-C of 0.419, which is much lower than that reported in other studies . This seems a more likely explanation rather than this being a unique feature of Australian twins as the authors suggest.2) From a methodological standpoint, the authors may wish to acknowledge that the study population is probably not ideal for the research question. For many traits, heritability is greatest studied in younger individuals (e.g. PMID: 8127331), and this cohort of older twins may therefore significantly underestimate heritability. This is further suggested by the authors' finding of h2 value. However, we disagree “that the study population is probably not ideal for the research question”. The research question sought to address the heritability of plasma lipids in older age groups, so selecting an older age twin group to address this question was appropriate.The reviewers point out an interesting observation re PMID: 8127331 and a possible reason why we have identified a lower HDL-C hNevertheless, we are aware that heritabilities can fluctuate as a function of age, which has been previously reported for traditional lipids. Though our study size was underpowered for a robust analysis of variation in heritability by age, however we explored this possibility, and have included an analysis (Supplementary file 2C), which supports a decrease of HDL with age, from 0.479 (age 65) to 0.38 (age 95).Regarding PMID: 8127331, the “trait” studied was death from coronary artery disease in twins ranging between 36 – 75 years of age at baseline. The study did indeed demonstrate quite nicely that risk of death of a monozygotic twin was reduced, the older the age of death of the twin. It seems that early death from this kind of disease likely carries with it genetic risk factors. However, the trait we study is simply inheritance of specific lipid profiles – some of which may be advantageous to health, as well as the converse. We have no reason to expect that heritability decreases with age as a general rule. Actually, there is evidence to the contrary, as follows: The literature points out that there is high heritability of traits that lead to disease . Since it would be a fair expectation that disease also leads to earlier death, then it should also not be too surprising that for lethal traits, heritability is reduced with age.Mol Psychiatry. 2010 Nov; 15(11): 1112–1120. The heritability of general cognitive ability increases linearly from childhood to young adulthood). Furthermore heritability of longevity increases with age, particularly after age 60 : 1323–1338 Genetics of healthy aging and longevity, Hjelmborg et al. Genetic influence on human lifespan and longevity. Hum Genet. 2006;119:312–321).On the other hand, heritability could stay stable or even increase with age for salutary traits. For instance, heritability of intelligence/cognitive ability increases with age : 98–108 , Haworth et al. 2 estimates were calculated at various  ages within our cohort, and identified both increases and decreases in heritability with age of different lipids (Supplementary file 2C), supporting the reviewers’ suggestion of age related variation in heritability, though not necessarily in a downwards direction with age. This has been acknowledged in the “Limitations and Future Perspectives” section of the Discussion (first paragraph).These studies suggest that heritability seems to decrease with age for harmful traits but to increase with age for salutary traits. We anticipate a mix of age-trends for lipids, some disease associated while others are possibly associated with good health/longevity. To satisfy the reviewer, we have performed heritability analysis where h“In particular, heritability estimates may decrease where unique environmental exposures accumulate with time and become a dominant force in lipid modulation. By contrast, heritabilities may also increase where certain genes become more active in older age to shape a given phenotype, potentially relating to lipids that may convey protective effects with ageing, as opposed to harmful effects.”3) A second methodological issue is that the lipidomic profiles were assessed in whole plasma, which provides comparatively less insight into the biological properties of these lipid species compared to approaches that assay lipid concentrations within relevant compartments . The authors should discuss this limitation and how it may influence their results. This is particularly important given that many of the lipid species assessed are highly correlated with traditional lipid values (eg between TG and DG), and therefore the question arises as to whether the lipidomic measurements are really just an indirect means of assessing traditional lipid and lipoprotein fractions.We thank the reviewers for this suggestion. Our focus on plasma lipids was a limitation related to the additional amount and cost of processing and analysis required if compartmental experiments were performed considering the additional omics (transcriptomics and DNA methylation) data already presented within the paper, and also concern about the conclusions we could make based on our limited sample size. Even so, our extraction technique likely recovers free lipids as well as any bound within plasma components such as lipoproteins and albumin.We provide our results as a good starting point that could lead towards further, detailed examination in future studies of larger cohort size. We have addressed this point within the Discussion:“We also acknowledge that our lipidomic analyses was conducted in whole plasma as opposed to within lipoprotein fractions, which limits insight into biological properties of lipid species. […] Nevertheless, plasma is a common biological matrix for lipidomic study and likely includes both free lipids and plasma bound components, and represents a good comparative source for further, more detailed studies.”As to the question of whether the lipidomic measurements are really just an indirect means of assessing traditional lipid and lipoprotein fractions, there is an expected degree of redundancy considering that total triglycerides are comprised of the sum of individual species. However we have noted here and in previous work that total cholesterol, LDL-C and HDL-C have only modest phenotypic correlations with individual lipid species, and levels of phospholipids and sphingolipids are not represented well by variance in total triglycerides. Thus there is a lot of variance in plasma lipids not explained by traditional lipids. Further, a recent study  using the FINRISK cohort showed that individual lipid species can strengthen genetic associations at known lipid loci and hence give more statistical power compared against traditional lipids with the same sample size. We have incorporated these considerations in the last two paragraphs of the Discussion.4) Were there differences in heritability observed for male vs. female MZ twins?To address this question, we ran a heritability analysis under a sex heterogeneity model. Here, four lipids had suggestive levels of significance  to indicate differences in heritability estimates between male and female samples among significantly heritable lipids. However, due to the skewed female-male distribution, the sample size is not adequate to run a robust heterogeneity model. We have nevertheless included this information as Supplementary file 2B.


Gibberellin 2-oxidases (GA2oxs) genes showed strong involvement in controlling the spikelet sterility, which had not been reported previously in wheat. SNP-gene haplotype analysis confirmed that these SNPs influence spikelet sterility, especially the SNP co-located on the exon of the GA2ox gene. Interestingly, these genes were highly expressed in the grain and spike, demonstrating their pivotal role in controlling the trait. The integrative analysis strategy applied in this study, including GWAS, SNP-phenotype network, SNP-gene haplotype, expression analysis, and genome-wide prediction (GP), empower the identification of functional SNPs and causal genes. GP outputs obtained in this study are encouraging for the implementation of the traits to accelerate yield improvement by making an early prediction of complex yield-related traits in wheat. Our findings demonstrate the usefulness of the ancient wheat material as a valuable resource for yield-boosting. This is the first comprehensive genome-wide analysis for spikelet sterility in wheat, and the results provide insights into yield improvement.Revealing the genetic factors underlying yield and agronomic traits in wheat are an imperative need for covering the global food demand. Yield boosting requires a deep understanding of the genetic basis of grain yield-related traits . Here, we have detected much natural variation among ancient hexaploid wheat accessions in twenty-two agronomic traits collected over eight years of field experiments. A genome-wide association study (GWAS) using 15 K single nucleotide polymorphisms (SNPs) was applied to detect the genetic basis of studied traits. Subsequently, the GWAS output was reinforced via other statistical and bioinformatics analyses to detect putative candidate genes. Applying the genome-wide SNP-phenotype network defined the most decisive SNPs underlying the traits. Six pivotal SNPs, co-located physically within the genes encoding enzymes, hormone response, metal ion transport, and response to oxidative stress have been identified. Of these, metal ion transport and  Triticum aestivum L.) is the second most important cereal crop that has a direct impact on daily food consumption for much of the world population1. Current climate change scenarios predict more abiotic and biotic stress events in the areas of wheat production 2. It is, therefore, necessary to develop cultivars with high yields and better adaptation to stress conditions, in order to ensure food security and social ease3 by fine-tuning the genetically complex yield traits. This needs a better understanding of the genetic basis of such traits.Bread wheat , for instance, total number of spikelets per spike (TSS)7, grain number per fertile spikelet (GNFS)8 and thousand kernel weight (TKW)11. Accordingly, promising approaches to exploit the genetic variation and gene identification of these traits is the target for improving yield.Improving yield-related traits is one of the main aims of wheat breeding programs since inception. Improvement in such complex quantitative traits is difficult because it is a polygenic trait controlled by several genes and are influenced by environmental conditions ). Wheat grain yield improvement is mainly determined through a combination of many traits controlling final grain yield number e.g. spikes number per plant or area and grain weight. Wheat grain yield was successfully increased by introducing 12 make genome-wide analyses more beneficial that empowers researchers for deep genetic analysis, of complex traits in wheat. It is also helps breeders incorporate the discovered causative allele(s) efficiently in breeding programs, to improve yield and adaptation to specific regional conditions. Such progress in wheat production and acclimation is an imperative need to meet the demands of human population growth.Recently published wheat genome sequences with the high-quality annotated reference genome13. Genome-wide association study (GWAS) is a powerful tool for dissecting complex traits, by finding causative allelic variation at individual SNP markers or loci (multi-SNPs within linkage disequilibrium [LD] range) that are associated with natural phenotypic variation14. In contrast to GWAS, genome-wide prediction (GP) incorporates whole genotypic information from all available marker sequences to predicate the genomic estimated breeding values (GEBVs). Fitting all markers simultaneously avoids multiple testing and the need to identify markers-trait associations based on an arbitrarily chosen significance threshold. In wheat, many reports have used such advanced genome-wide analyses with high-density SNPs e.g. GWAS to understand the natural variation of yield-related traits15, and e.g. GP to show that these traits, which were evaluated under multi-environmental conditions, can be predicted17. GWAS revealed shared QTL between assimilate partitioning efficiency, floret fertility, yield potential, and spike morphology under controlled conditions (greenhouse), suggesting a potential genetic association that controls these complex traits8. Allelic variation at Rht-D1 and Ppd-D1 has an impact on the natural variation of grain number per spikelet, spikelet fertility, and TKW under field conditions6.Genome-wide analyses considering multi-traits, multi-years/environments, and multi-loci, along with the high-density SNP (single-nucleotide polymorphism) array are promising to boost power and accuracy in identifying aggregate effects of the locus/gene, and complement plant breeding programs7, which were phenotyped under field conditions for several years, and genotyped with high-density gene-based SNP (15 K Infinium SNP) arrays. To achieve our objectives, GWAS was carried out to identify the natural genotypic variation and genetic basis of the traits. Use the recent published wheat physical map12, SNP arrays provided an unprecedented resolution to identify candidate genes underlying the studied traits19. The most important SNPs controlling the studied traits had been detected by a genotype-phenotype network20 as the first report in cereals including wheat. Moreover, we tested the potential of GP on yield-related traits, as it enhances the genetic gain of quantitative traits, by accelerating the breeding cycle and increasing selection intensity, which in turn contributes to yield improvement. Interestingly, newly identified candidate genes controlling spikelet sterility, which is annotated as hormone response, metal ion transport, and response to oxidative stress, have been identified. The function of candidate genes was further validated by SNP-gene haplotype and expression analyses.In the current study, we analyzed the natural phenotypic and genetic variation of 22 agronomic and spikelet fertility-related traits in one of the oldest association collections in wheatSignificant natural phenotypic variation (at P < 0.05) was identified among the accessions for all of the 22 agronomic traits across the environments based on best linear unbiased estimato (BLUE) values Fig. . The sig2 (SN), and spike length (SL) were significantly correlated with most of the agronomic traits. GY had a positive significant correlation with yield-related traits  and TKW), but was negatively correlated with FL, HD, and sterile spikelets per spike (StS). HD and FL are tightly correlated with each other, and positively correlated with SL, spikelets per spike (SPS), and StS, whereas both are negatively correlated with HI and fertile spikelet per spike (FSS). A significant negative correlation at P < 0.05 was found between HI with peduncle length (PL), plant height (PH), SPS, and StS, whereas HI had a significant positive correlation with GW, TKW, and FSS%. SN was also significantly and negatively correlated at P < 0.05 with most of the grain yield-related traits , grain number per spike (GN), GW and SPS; Supplementary Table The Pearson’s correlation coefficients, based on BLUEs for the 22 agronomic traits, ranged from −0.95 to 0.95 Fig. . Grain yH2) ranging from 0.70 to 0.97 were obtained for each trait  or ~15 GbP (Giga base pairs), with an average marker density of three SNPs per cM. Information on the number of markers for each chromosome, with the map length and marker density for each chromosome, are presented in Supplementary Table The core collection of wheat accessions belongs to seven geographical regions. In total, 13 K polymorphic SNPs were obtained for the investigated accessions, and 11,220 SNPs passed the filtration criteria . Of these markers, 10,653 SNPs were genetically mapped and anchored by physical positions according to International Wheat Genome Sequencing consortium (IWGSC), 2 values for the whole hexaploid wheat genome gradually decreased with increasing distance between SNPs  ≥ FDR) was used to identify significant marker-trait associations (MTAs). Out of 10,653 SNPs used in the GWAS, 118 SNPs had significant −log10  ≥ passing FDR at 0.01  model had a strong power to control false positive associations with informative output in all studied traits, compared with general-linear model (GLM), and compressed MLM (CMLM). MLM, which specifies the number of PCs and kinship matrix as a correction for population structure leads to avoiding false positive associations, had been used. The strength of the models had been visualized by a cumulative Quantile-Quantile (QQ) plot of expected vs. observed p-value Fig. . The fal10(≥4.5) were distributed over 8 chromosomes, and mostly associated with grain yield-related traits , 3B (12 SNPs), 1A and 5B (11 SNPs each), and less than 10 SNPs for the rest of chromosomes  was detected for the marker (Kukri_rep_c68594_530), located at 4D (12773232 bp), associated with PL and showing a negative effect of −3.0 cm compared to the wild-type allele (A). Interestingly, the second highest significant marker (BS00047691_51) on 7 A (118326946 bp), associated with PL, showed a positive effect of allele C demonstrating the natural phenotypic and genotypic variation of PL. Another very significant marker (RAC875_c11899_366), located at 1 A (58704752 bp), and associated with GN, had a positive effect of five extra grains.The highest −logFurther associated genomic regions 1B (142834452–278473822 bp), 2 A (61286655–61287719 bp), 2D (650322702–650327159 bp), 3B (205465371–222192694 bp), 4B (561513452–561513452 bp), 4D (54447055–69850689 bp), 5B (678917307–678917517 bp), 6A (605564351–605564351 bp), and 7B (450280079–451655622 bp), showed high and consistent effects of SNPs on agronomic traits  and Exon4, within the genes TraesCS1B01G144500 and TraesCS1B01G145500, respectively  at 2D were associated with HI by two SNPs located in CDS regions, demonstrating the impact of the allelic variation of these SNPs on HI  in the organs compared with the aforementioned genes , which is the most robust model for predicting single yield-related traits. The accuracy of GEBVs ranged from 54% for spike index (SI) to 83% for PL and SW  was more informative in the context of natural variation compared with previous studies11. The association results showed that SNP array density (15 K) and GWAS analysis strategy were strong enough to identify highly significant marker-trait associations, compared with previous associations identified using DArT23 and SNP22 markers. The power of GWAS can be attributed to the utilization of high-density SNPs, and a statistical model that includes PCA and a kinship matrix for controlling the population in addition to the validation of the significant associations.The wheat collection used in the current study is considered as a source for genetic studies in breeding programs across the world, since it includes a founder genotypes13, and in the most cases, prediction accuracies are sufficient to make GP more efficient than phenotypic selection. The obtained moderate to high accuracies of predication for various traits in this study support the feasibility of applying genomic prediction as a cost-effective means to enhance genetic gain. In particular for traits like spikelet fertility, which is important for wheat grain yield improvment but difficult to determine in a large population. Breeders can utilize GEBVs for genotypes to make selction of parents with high breeding value for the purpose of designing crosses in the breeding program. Such approach leds to speed-up breeding and accelerate the rate of genetic gain of high spikelet fertilty that in turn boosting wheat grain yield.Decreasing costs of genotyping using high-density SNP-arrays, and development of statistical methods to accurately predict marker effects, have led to the breakthrough in GP. Most previous studies reported that selection decisions based on genome-wide prediction results could improve the accuracy of selection and speed of genetic improvement20, which had never been used in wheat. Such analysis empowered us to define the most important SNP(s) underlying complex traits. The newly added spikelet related-traits in this study demonstrated the importance of studying such traits which showed high natural variation. The natural variation of spikelet fertility/sterility traits and grain-yield-related traits has been recently explored in wheat24.In the current study, we used the recently developed statistical and bioinformatics analyses, that so-called “genotype-phenotype network”, for multi-loci and multi-traits24 might be a consequence of trade-offs between GN and TKW with BY and HI. Such negative correlation restricts the grain yield and genetic gains of these traits25, or a competition for assimilates between spikelets that leads to unbalanced distribution of GN within FSS and florets along the spike8. The positive relationships between FSS (or FSS%), GNFS with BY, TKW, HI, SPS and GN that found in the current study, indicate that improving grain yield through enhancing spikelet fertility together with TKW, SPS and BY is plausible. It might be attributed to the genetic material (ancient cultivars) used in the current study which are carrying allele(s) that not present, or present at low frequency in breeding lines or elite/modern cultivars used in the previous studies25. The elite/modern cultivars were selected for a specific purpose based on origin8, and always characterized with low number of kernels per spikelet, and less resistance/tolerance to biotic/abiotic stresses compared with old cultivars26. Even though this panel was not chosen to study such traits (i.e. spikelet fertility/sterility), it shows high natural variation in these traits, and confirms that the population presents a good donor for improving such traits. The findings are also important in the identification of associated alleles/loci with these traits that could be a valuable source for wheat improvement.It has been shown that a negative correlation between TKW with SPS and GN in Chinese wheat56. The structure and function of these candidate genes were analyzed /iron(Fe) transport, 13 for copper (Cu), and 10 belong to the Natural Resistance-Associated Macrophage Protein (NRAMP) family29. Several transporters belonging to different protein families  and NAC) have been shown to regulate growth and development. Cu has a large role in the basic metabolic processes, especially photosynthesis and respiration, while Cu deficiency showed a high impact on plant growth30. Cu had a strong effect on reproductive growth and development in cereals by reducing pollen viability and increasing spikelet sterility in wheat31. Therefore, it is plausible to suggest that the Cu deficiency in the spike and/or spikelet may result in increasing spikelet and floret sterility/abortion. Therefore, understanding the mechanisms that may optimize Cu in the spike/spikelet is needed.We found that NAM  that encodes a transcription factor of the NAC family, is known to be involved in the spike architecture and spikelet sterility32, in addition, to enhance salt and drought tolerance33. The NAM-B1 allele, which is solely present in wild emmer wheat, accelerates senescence and increases nutrient remobilization from leaves to developing grain. while the modern wheat cultivars carry the nonfunctional allele34. The expression analysis of NAM alleles showed high expression of NAM-A1, -B1 and -D1 in the stamens and spikelets, while NAM-B1 is also expressed in the flag leaf35. This can explain the spikelet/floret sterility in the modern cultivars that are carrying alleles NAM-A1 and NAM-D1, and demonstrates the role of NAM-B1 allele in spikelet development in our collection, because of improving nitrogen use efficiency and grain yield.The TraesCS1B01G145500 that are annotated as gibberellin 2-oxidases (GA2oxs) shows a strong impact on the spikelet sterility and TKW  in many wheat developmental organs at different developmental stages suggests its role in wheat growth and development. RPL19 was highly expressed in the shoot and root of rice under drought stress, suggesting its responsibility for drought tolerance and improvement of water use efficiency44. In accordance with our results, the gene was associated with HD and expressed in grain and spike, indicating that RPL19 is involved in the stress-tolerance pathway through earliness of the development, which might have influenced the grain yield.Highly significant expression of the TraesCS2D01G598200 that annotated as C2 calcium/lipid-binding (CLB), and Glucosyltransferases, Rab-like GTPase activators and Myotubularin (GRAM) domain protein, showed its influence on HI. The gene was highly expressed in grain, spike, and stem, indicating that the gene is involved in grain and biomass production which are the main components of HI 45. The gene was highly expressed in the leaf, root, stem, and flower of Arabidopsis. Here we found the same trend of expression in our collection, suggesting the role of this gene in abiotic stress tolerance in wheat. It had been shown that the gene encodes a C2-GRAM domain-containing protein, Oryza sativa no pollen (Osnop) gene plays a critical role in male gametophyte development during late development of pollen and its germination that might affect grain yield in rice46. The pivotal role of C2-CLB and -GRAM families genes might emanate from its crucial function in controlling many complex traits  that need to be discovered in wheat.The allelic variation at the locus of the gene  HI Fig. . The losIn conclusion, we showed the value of using such wheat germplasm as donors in breeding for improving grain yield through enhancing TKW, SPS and BY into elite wheat cultivars. High genomic prediction accuracy for the studied traits suggest their usefulness in breeding programs. Particularly, medium to high genomic prediction accuracies (>70%) for spike fertility-related traits  suggests that GP can be implemented to predict breeding values, and facilitate rapid gains from a selection of these traits, in order to boost yield. Applying the GWAS and network analyses presented here enabled us to uncover the key SNPs and yield traits in wheat. The identified SNPs associated with the traits  could be valuable for wheat improvement. Moreover, the bioinformatics analysis to detect the candidate genes controlling complex traits provide a solid starting point for functional studies. Our findings here empowered us to tentatively suggest the effective role of these genes in wheat grain improvement through controlling the spikelet sterility. SNP-Gene based haplotype and expression analyses of the candidate genes demonstrate their role in many biological processes, including spikelet development.18. Based on their phenotypic and genetic variation, a subset collection of 96 hexaploid wheat accessions had been selected and analyzed in the current study. The selected accessions showed high diversity in the 26 developmental, quality, yield and yield-related traits and are originally from 21 countries across five continents18. More information regarding the accessions of the population including the method of evaluation and selection is available in Kobiljski, et al.18. The population was grown under field conditions for up to eight growing seasons (1993–2001) at Novi Sad, Serbia, to phenotype 18 most important agronomic traits, from three plots per season of each accession. Based on these data, four highly spikelet fertility-related traits were calculated. FSS was calculated as the differences between the total SPS and StS. The division of total GN by FSS gives GNFS. FSS and StS were also presented as a percentage, to show their contribution to the SPS. H2 for each trait over environments (years) was calculated using the following equation:σ2G is the variance of the genotype, σ2E is the variance component of the environments, σ2G × E is the variance component of the interaction G × E, σ2e represents the variance of the error, and E is the number of the environments, and r is the number of replicates.A set of 710 wheat accessions was selected from 2,500 accessions  and phenotyped for more than 54 traits47. Correlation matrix analysis between the traits and boxplots of each trait were calculated using PAST software48.Analysis of variance was calculated to check whether there were a significant effect of genotypes and/or environments, considering G × E interaction. BLUEs value of each accession for each trait had been calculated, applying residual maximum likelihood (REML) in MLMs to estimate phenotypic means. The accessions were considered as a fixed factor, whereas year was treated as a random factor, and the G × E interaction was incorporated into the analysis. All of these phenotypic analyses and calculations were implemented using GenStat v18http://www.traitgenetics.com). The 15 k SNP-array is an improved version of the 90 k iSELECT SNP- array described by49. For our GWAS analyses, we used 10,653 SNPs, which had genetic and physical positions based on IWGSC RefSeq v1.012. SNPs with MAF ≥ 5%, and passing quality checking, filtering, and evaluating criteria were used for association analyses. The population structure was determined using principal component analysis (PCA) for obtaining clusters of the accessions, based on their country and/or origin of the region50. A kinship matrix was also calculated using the EMMA algorithm within GAPIT51, to show the clusters among accessions and familial relatedness based on the polymorphic markers.The wheat core collection was genotyped using a 15 k Infinium SNP array that was developed by TraitGenetics GmbH  plot was used for assessing how strong the used model in GWAS. FDR was calculated for each trait at the significance level of 0.01 separately. Association signals that passed the threshold of FDR at 0.01  were considered as significant SNPs and used in further analyses as recommended by14The association analysis between individual markers of the 10,653 SNPs with the BLUE value of each accession for each trait was performed using the GAPIT R package52. GEBVs were predicted by first estimating the effects of allele substitutions at the marker loci with statistical models that used phenotypic and genotypic information from the training population (TP). GEBVs for each entry of the validation set (VP) were then estimated by summing up the effects according to the individual’s genotypic makeup. To validate the prediction accuracy, a training population  was created to estimate the GEBVs for the remaining 25% of the whole population  based on the marker effects of the used SNPs. The division of the population into subsets (TP vs. VP) and the selection of accessions in each subset were randomly done, according to the PCA numbers that determine the population structure. Five-hundred cycles of iterations, with the function mixed.solve to optimize the parameter scales in marker-based GP considering the REML method, were used to estimate the variance components52. Prediction accuracy was computed as Pearson’s correlation between the predicted values and BLUEs of lines in the validation set within each fold.The genome-wide prediction was made using rrBLUP. The model was fitted in R , using rrBLUP package, v4.420. The genotype-phenotype network is a complex network analysis that takes into account the genetic data of markers , phenotypes of agronomic traits, and the interaction among each genetic markers and with traits. The intra- and inter-chromosomal interactions network was also calculated. The most significant SNPs that were directly connected with the agronomic traits were used to identify the high-confidence (HC) putative candidate genes, based on their physical positions using the recently published wheat genome sequence12. WheatMine web-based platform, which contains gene annotations, gene models, and the transposable elements, was used to identify the gene ontologies (GO) and InterPro number and description for the potential candidate genes based on IWGS v1.0 and v1.1 (https://urgi.versailles.inra.fr/WheatMine/begin.do).To better understand the genetic basis, and to detect the most important loci/SNPs (passing the FDR at 0.01) that are directly responsible for the studied traits, the genotype-phenotype network of multi-loci/SNPs and multi-traits were calculated using Network-Based Genome-Wide Association Studies (NETGWAS) R packaget-test statistics, to show the impact of each allele on the associated trait(s)56. Expression analysis has been done through the WheatExp; an expression database for polyploid wheat (https://wheat.pw.usda.gov/WheatExp/#) that include the RNA-seq expression database for wheat grain layers53; wheat grain at three different stages of development54; wheat spike, root, leaf, grain and stem organs at different developmental stages55; and other databases. RNA-seq are presented as TPM (Transcripts Per Kilobase Million).Only HC candidate genes that have the associated SNPs were used for further validation, applying SNP-Gene based haplotype analyses and expression analyses approaches. Such analyses allowed us to reveal the impact of alleles on the associated traits. The significant differences test between alleles was calculated using Supplemental information.