The objective of this study was to identify significant loci associated with the intensity of greenness of the soybean [Glycine max (L.) Merr.] canopy as determined by the Dark Green Color Index (DGCI). A panel of 200 maturity group IV accessions was phenotyped for canopy greenness using DGCI in three environments. Association mapping identified 45 SNPs that were significantly (P ≤ 0.0003) associated with DGCI in three environments, and 16 significant SNPs associated with DGCI averaged across all environments. These SNPs likely tagged 43 putative loci. Out of these 45 SNPs, eight were present in more than one environment. Among the identified loci, 21 were located in regions previously reported for N traits and ureide concentration. Putative loci that were coincident with previously reported genomic regions may be important resources for pyramiding favorable alleles for improved N and chlorophyll concentrations, photosynthesis rates, and N2 fixation in soybean.Nitrogen (N) plays a key role in plants because it is a major component of RuBisCO and chlorophyll. Hence, N is central to both the dark and light reactions of photosynthesis. Genotypic variation in canopy greenness provides insights into the variation of N and chlorophyll concentration, photosynthesis rates, and N Glycine max (L.) Merr.] is one of the most widely grown crops in the world, and the economic value is primarily derived from the high oil and protein concentrations of the seed. With a protein concentration of around 40%, soybean plants must acquire a large amount of nitrogen (N)2. In the absence of inorganic N in the soil, symbiotic N2 fixation provides N to soybean. Nitrogen fixation reduces N2 into biologically useful ammonia (NH3) and is carried out by Bradyrhizobium japonicum bacteria that live symbiotically in root nodules.Soybean [3. A large amount of N is allocated to the chloroplast (approx. 75%) for synthesis of the photosynthetic apparatus4. Leaf N and chlorophyll concentrations are positively correlated across a large range of plant species including maize (Zea mays L.)5, rice (Oryza sativa L.)6, soybean8, cotton (Gossypium hirsutum L.)9, and wheat (Triticum aestivum L.)10. Likewise, there are clear positive relationships between leaf N concentration and photosynthetic rate15. On one hand, a positive correlation between leaf photosynthetic rate and chlorophyll and N concentrations indicates that greener plants would expectantly have higher photosynthesis12. On the other hand, reduced chlorophyll concentration can be positively associated with canopy photosynthetic rates16 and leaf photosynthetic rates17. Recently, Walker et al.18 used a modelling approach to simulate canopy photosynthesis of genotypes with a range of chlorophyll concentrations, including a chlorophyll-deficient mutant, and found that while canopy photosynthesis may not increase when chlorophyll concentration is reduced, reducing chlorophyll concentration and thus leaf N should be possible while maintaining canopy photosynthetic rates. Variation in canopy greenness among genotypes may provide indirect information on the variation in chlorophyll and N concentrations, leaf photosynthetic rates, and, in legumes, N2 fixation. Thus, it may be useful to explore genotypic variation in canopy greenness and associated genetic markers to improvement canopy photosynthesis and/or N2 fixation.Nitrogen plays a key role in leaf physiology and metabolism because it is a major component of RuBisCO, Photosystems I and II, and chlorophyll; hence, N is central to both the dark and light reaction of photosynthesis20; however, Karcher and Richardson21 found that the intensity of red and blue may alter how green an image appears overall. As such, use of a Dark Green Color Index (DGCI) ) avoids problems from using RGB-derived indices. The DGCI-based measurements of aerial digital images are inexpensive, need little technical expertise, are higher throughput, and allow data acquisition over a much larger area than the small sensor of a SPAD meter.A portable chlorophyll meter  is commonly used to determine leaf greenness and indirectly infer leaf chlorophyll concentration. An alternative method evaluates digital images. In previous research, red, green, and blue (RGB) color components have been used to infer N status of crop plants2 fixation capability and allow increasing the frequency of favorable quantitative trait loci (QTLs) for DGCI alleles. Favorable QTLs can be identified using either genome-wide association mapping or linkage mapping (LM) methods. Major advantages of association mapping over LM include increased mapping resolution, reduced research time, and greater allele number22. Advancements in nucleotide sequencing and high-throughput genotyping technologies have facilitated the development of dense molecular-marker datasets, which are almost exclusively composed of single nucleotide polymorphism information (SNPs)23. Genotyping diverse lines at thousands of SNPs across the genome is now routine, and permits fine-level genetic mapping through exploiting ancient recombination events24. In soybean, 20,087 entries from the USDA germplasm collection  have been genotyped using the SoySNP50K iSelect Beadchip . This unique soybean genetic resource is proving invaluable for assessing soybean genetic diversity and has opened the door for application of powerful genome wide association mapping methods25.Understanding the genetic basis of canopy greenness using DGCI could be important for developing cultivars with high N concentration and N6 and maize26). In soybean, other QTL studies have mapped chlorophyll27, N28, and ureide concentrations (related to N2-fixation)29. Our objectives were to use genome wide association mapping to characterize variation of canopy greenness using DGCI in a panel of 200 diverse maturity group (MG) IV accessions, to explore the genetic architecture associated with DGCI, and to predict genotypes with extreme values of DGCI within each MG in the USDA soybean germplasm collection based on the presence of favorable QTLs discovered in the present research.To our knowledge, there has been no report of mapping canopy greenness via DGCI with either bi-parental populations through linkage mapping or association mapping in soybean. However, there are mapping studies of greenness or DGCI in other crop species , and plots were 1.52 m wide and 4.57 m long. At the PT and RH, the experiment was conducted as an augmented incomplete experimental design with six replications. The FY experiment was conducted with one replication.The panel of 200 MG IV soybean accessions used for this study consisted of 100 accessions, representing the most genetically diverse accessions (out of 373 accessions) used for previous mapping studies by Kaler www.dji.com/phantom-4-pro) unmanned aerial system (UAS) which was flown approximately 30.5 m above the ground. The UAS was programmed to collect images with an 80% overlap on the front and sides using Ground Station Pro software from DJI  operating in the ‘3D Map’ mode. The shutter speed was set to ‘auto’ and was programmed to take images at equal time intervals (2 s) with the camera in the nadir position. Image resolution with these settings was approximately 0.8 cm pixel−1. Measurements were made 54 (RH), 48 (PT), and 55 (FY) days after sowing when plants were in full bloom and canopies were completely closed. Flights were made between 1100 and 1400 h on days with clear skies. Images were stitched together to form an orthomosaic using Agrisoft Photoscan Professional (www.agrisoft.com). Also included in the image were boards painted with dark green or yellow circles measuring 1 m in diameter. The painted boards had known DGCI values of 0.5722 (green) and 0.0733 (yellow) and served as internal standards for DGCI determination8. Orthomosaic images were analyzed using FieldAnalyzer software , which was used to extract DGCI values for each plot. Software used the hue (H), saturation (S), and brightness (B) values from a digital image to determine the DGCI value21 as shown in the equation below:Aerial images were captured using the factory-installed camera  of the DJI Phantom 4 Pro  of SAS version 9.4  were used for descriptive statistics and Pearson correlation analysis, respectively. We used the PROC MIXED procedure (α = 0.05) of SAS 9.4 for analysis of variance (ANOVA) using a model suggested by Bondarih2) was estimated to understand the variation and trend of predictive ability across traits34 using the GAPIT R35 package.Broad sense heritability on an entry-mean basis was estimated using PROC VARCOMP of SAS 9.4 and the Restricted Maximum Likelihood Estimation method. For RH and PT, and across all environments, the Best Linear Unbiased Prediction (BLUP) values were estimated using the PROC MIXED procedure, and BLUP values were used in association mapping analysis. Marker-based narrow sense heritability (www.soybase.org), providing 42,509 SNPs36. Genotypic data were cleaned to remove monomorphic markers, and markers with minor allele frequency (MAF) < 5%. Markers with a genotype missing rate >10% were also removed and remaining missing markers datasets were imputed using an LD-kNNi method, which is based on a k-nearest-neighbor-genotype method37. A total of 34,680 SNPs were left for association mapping. Linkage disequilibrium (LD) between these markers was measured based on squared correlation coefficients (r2) of alleles in the TASSEL 5.0 software38. A separate LD was calculated for euchromatic and heterochromatic regions. The LD decay with distance was estimated using nonlinear regression, as described by Hill and Weir39. The decay rate of LD was determined as the physical distance between markers where the average r2 dropped to a value of 0.25.Single nucleotide polymorphism markers for all 200 accessions were obtained from Soybase , was used as in previous studies51 and based on the formula developed by Kaler and Purcell50. To identify the common significant SNPs present in more than one environment, a threshold value of P ≤ 0.05 was allowed but only if the representative SNP had an association of P ≤ 0.0003 in at least one additional environment. Using the GAPIT package, we estimated marker based narrow sense heritability using an MLM model as described previously50.Several statistical models are used for genome wide association mapping. A key consideration for selecting a model is how well it can effectively control false positives that arise from population structure and family relatedness. The Mixed Linear Model (MLM) has often been considered the most popular approach as it considers population structure and family relatednessG. max genome assembly version Glyma. Wm82.a1.v1.1 and with NCBI RefSeq gene models were obtained from SoyBase (www.soybase.org), and three major GO categories  were assessed. Genes were further classified to be associated with photosynthesis, N metabolic processes and leaf development including aging.Significant SNPs were used to identify candidate genes for DGCI. Genes located within the same LD block that were near SNPs associated with DGCI were considered as potential causative candidate genes. The gene ontologies (GO) associated with candidate genes in the et al.32, which were calculated using the allelic effects and favorable alleles estimated from results of our association-mapping. The difference in mean DGCI between genotypes with the major allele and those with the minor allele was taken as the allelic effect. Alleles were considered as favorable if they were associated with an increase in DGCI, regardless if they were drawn from major or minor allelic classes. SNP effects were expressed as a positive value if the allelic effect increased DGCI. Otherwise, if the allelic effect decreased DGCI then it was expressed as a negative value. All positives and negatives allelic values were summed to estimate the true breeding value of each accession. Based on true breeding values, extreme genotypes were identified from the entire genotyped USDA Soybean Germplasm Collections as having predicted very high or low DGCI values within each MG. the presence of multiple favorable QTLs is associated with a high true breeding value whereas the presence of multiple unfavorable QTLs would be associated with a low true breeding value.We extrapolated DGCI breeding values for the entire soybean germplasm collection based on calculation of true breeding values as described by Kaler r = 0.46 between PT and FY to r = 0.59 between RH and FY (data not shown).We observed a broad range of DGCI values within a single environment and when averaged across all environments Table . VisuallBroad sense heritability indicates the proportion of phenotypic variation that is explained by genetic effects as a combination of additive effects, dominant/recessive effects, and epistasis. However, marker based narrow sense heritability indicates the proportion of phenotypic variation that is explained by additive genetic effects, and, therefore, is important in plant breeding because the response to selection depends on additive genetic variance. Broad sense heritability for DGCI was moderate to high, ranging from 57% (RH) to 59% (PT) Table . Averager2 = 0.25 averaged across all chromosomes at 175 kb in the euchromatic region as compared to 5,100 kb in the heterochromatic region. These results were consistent with previous LD decay rates reported for soybean54.A total of 34,680 SNP markers were used for association mapping. These SNPs were more dense in euchromatic regions  than heterochromatic regions . The SNP distribution in the euchromatic region ranged from 45 SNPs per Mb (Gm19) to 68 SNPs per Mb (Gm09). In the heterochromatic region, SNP distribution ranged from 5 SNPs per Mb (Gm20) to 38 SNPs per Mb (Gm18). LD decayed to P-value distribution in the tail area, indicating that false positives and negatives were adequately controlled50. In contrast, the Q-Q plot for the MLM model did not show a sharp deviation from the expected P-value distribution in the tail area  ≥ 3.5; P ≤ 0.0003  ≥ 3.5; P ≤ 0.0003  to introgress those favorable alleles into elite backgrounds.Allelic effects of all significant loci were used to calculate the true breeding values for DGCI of the entire USDA soybean germplasm collection. Table r2 = 0.25 in the euchromatic and heterochromatic regions. There were 58 candidate genes associated with DGCI, and these genes are annotated for their gene ontologies  in Supplementary Table S254. Among the interesting annotated biological functions associated with DGCI, there were eight genes annotated for nitrate transport, six genes annotated for chlorophyll, six genes annotated for photosynthesis, six genes annotated for purine transport, six genes annotated for leaf aging and development, three genes annotated for N metabolic processes, and three genes annotated for ammonium metabolism  and affected by environment. Low narrow sense heritability estimates indicate that selection for phenotypes in traditional breeding programs would be optimally carried out on pure-lined material and with testing in multiple replications and environments. However, the putative markers identified in this study for DGCI may allow for more rapid progress in breeding than would be expected from traditional approaches.The phenotypic variation of canopy greenness using aerial DGCI measurements was determined in a panel of 200 MG IV soybean accessions in three environments. The DGCI varied widely among genotypes, which is important for successful association mappinget al.31, the distribution of SNP markers for these 200 accessions varied across genomic regions having fewer gaps in euchromatic regions than in heterochromatic regions. The extent of LD decay in euchromatic and heterochromatic regions was used in this study for gene identification, as was used previously56 whereby genes within the same LD block as a QTL were considered as potential candidate genes.Similar to the previous studies by Kaler Glyma15g40911, which encodes a protein for 2-oxoglutarate and Fe (II)-dependent oxygenase that has a biological function associated with nitrate transport  was located close to 57, N traits28, and ureide concentration29 in soybean. Loci identified as associated with DGCI in this study were compared with previously reported genomic regions associated with N traits and ureide concentration. We found 21 chromosomal regions that coincide with previously reported genomic regions on Gm01 (1), Gm02 (1), Gm03 (1), Gm05 (1), Gm07 (2), Gm09 (1), Gm10 (2), Gm11 (1), Gm12 (2), Gm13 (1), Gm14 (2), Gm15 (1), Gm16 (1), Gm18 (1), Gm19 (2), and Gm20 (1)  or ammonium transport (locus 11), photosystems  or response to light , leaf senescence , chlorophyll biosynthetic processes , stomatal complex morphogenesis , and purine transport   or nitrogen fixation (in soils with low amounts of available N). During seedfill, aerial DGCI measurements in soybean decline2 fixation ability in soybean breeding programs.This was the first study to map soybean canopy greenness using aerial DGCI measurements. Moderate to high broad sense heritability indicated that DGCI was a relatively stable trait across environments and can be used in soybean breeding programs. We found 45 significant SNPs associated with DGCI in three environments and 16 significant SNPs associated with DGCI averaged across environments. These SNPs likely tagged 43 putative loci. We confirmed 21 chromosomal regions associated with DGCI that were coincident with previously reported genomic regions for chlorophyll a/b ratio, N traits, and ureide concentration. We found 58 candidate genes and 38 of these genes had biological functions associated with nitrate transport, chlorophyll, photosynthesis, purine transport, leaf aging and development, N metabolic process, and ammonium transport. Significant loci that were coincident with previously reported genomic regions, and significant loci that were present in more than one environment, may be an important resource for pyramiding favorable alleles to improve N concentration, leaf and/or canopy photosynthesis rates, and NSupplementary information.


Kernel row number (KRN) is an important trait for the domestication and improvement of maize. Exploring the genetic basis of KRN has great research significance and can provide valuable information for molecular assisted selection.ARF29  and CKO4  were significantly related to KRN, based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) of KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate all of the KRN-associated tagSNPs identified by all GWAS models.In this study, one single-locus method (MLM) and six multilocus methods  of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, the seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, These results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding. Zea mays L.) arose from a single domestication event from its wild progenitor, teosinte, in southern Mexico approximately 9000 years ago and is now one of the most important cereal crops worldwide [Maize , spikelet pair meristems (SPMs), spikelet meristems (SMs) and floral meristems (FMs) . To dateKRN4 [KRN1 [Although many quantitative trait loci (QTLs) related to KRN were identified by linkage mapping in biparental segregating populations, few have been successfully cloned due to their small genetic effects, except for KRN4  and KRN1N4 [KRN1 . Genome-N4 [KRN1 –16. At tN4 [KRN1 . TherefoN4 [KRN1 , 19 and N4 [KRN1 , ISIS EMN4 [KRN1 , pLARmEBN4 [KRN1 , FASTmrEN4 [KRN1 , pKWmEB N4 [KRN1 , and FASN4 [KRN1 . The MLMN4 [KRN1 , 19. To N4 [KRN1 . The mulN4 [KRN1 . There aN4 [KRN1 –26. ReceN4 [KRN1 , maize [N4 [KRN1 , flax [2N4 [KRN1 , bread wN4 [KRN1  and uplaN4 [KRN1 , 32.1 hybrids between recombinant inbred lines [Previous studies have revealed that KRN is quantitatively inherited and that the effects of a single genetic locus are generally small, which poses challenges for genetic improvement in maize breeding. Therefore, the best approach is to improve the ability to predict KRN by integrated analysis of more markers distributed throughout the whole genome. Genomic selection (GS), or whole-genome prediction (WGP), has the capacity to use full-genome data to increase breeding efficiency . In preved lines , intercoed lines  and 339 ed lines , all of ed lines  showed ted lines –39. ThisIn this study, we performed a GWAS of an association panel including 639 maize inbred lines based on the MaizeSNP50 BeadChip by using one single-locus method, the MLM method, and six multilocus methods, mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO. The common significant QTNs codetected by different methods and across different environments were analyzed, and the candidate genes related to KRN were further predicted. WGP was also performed using various KRN-related tagSNPs to dissect the genetic architecture of KRN.H2 = 0.90, Table KRN was measured within our association panel, which included 639 maize inbred lines, in XX , BJ  and GZL  in 2011  by these individual QTNs ranged from 1.84 to 4.01% , FEA3 (Zm00001d040130), BAD1 (Zm00001d005737), RA1 (Zm00001d020430), and VT2 (Zm00001d008700)  and in at least two environments were used for the next analysis. Finally, seven QTNs controlling KRN were obtained Table . The sevarge KRN ; the kn1pikelets . This reARF29 and the 10-kb promoter and 10-kb region downstream of ARF29 were obtained from maize HapMap3 [ARF29 were significantly related to KRN  promoter by recognizing the TTTCGG motif [UB3 regulates KRN by the cytokinin pathway and CLAVATA-WUSCHEL pathway [CKO4  was detected as being located within 200 kb upstream of PUT-163a-110,967,306-138 on Chr. 3 by four GWAS methods , which was located within 200 kb downstream of PZE-110106563 on Chr. 10 and was detected by the MLM method and all six multilocus GWAS methods Table . Candida HapMap3 . The KRNGG motif , 45. TheGG motif . For exa pathway . In thisr2 > 0.2 and obtained 27,688 tagSNPs in our association panel. Then, we randomly selected different numbers of tagSNPs, from 5 to 27,000, in the whole genome to calculate the prediction accuracies for KRN of the inbred lines, which was calculated as a correlation between predicted and true values from the simulations. The results showed that the prediction accuracies increased as the number of tagSNPs increased  than randomly selected tagSNPs (ranging from 0.22 for 100 random tagSNPs to 0.33 for 700 random tagSNPs) Fig. .The tagSNPs representing the significant QTNs detected by different models based on BLUP were collected and used to calculate prediction accuracies for KRN in our association panel. The results showed that these tagSNPs identified by different methods had different prediction accuracies ranging from 0.43 (FASTmrEMMA) to 0.60 (ISIS EM-BLASSO) Fig. . We alsoAdditionally, to improve the prediction ability, we put the KRN-related tagSNPs detected by seven methods together in a single environment , namely, M-total tagSNPs, to conduct KRN prediction. As a result, we found that the prediction accuracies were improved sharply and reached 0.74 in XX, 0.66 in BJ, 0.75 in GZL and 0.75 for BLUP Fig. . These pTo date, the GWAS approach has been widely used to investigate the genetic basis of important traits in many species by calculating the association between genotypic and corresponding phenotypic variations . To idenCT2 (Zm00001d027886), FEA3 (Zm00001d040130), BAD1 (Zm00001d005737), RA1 (Zm00001d020430), and VT2 (Zm00001d008700). Among these genes, CT2 [FEA3 [BAD1 [RA1 [VT2 [Comparing our GWAS results with those of previous studies, we found that some important genes controlling inflorescence architecture in maize were located within 200 kb of significant QTNs Table S, includiT2 [FEA3  functionA3 [BAD1  and RA1 AD1 [RA1 , both ofRA1 [VT2  functionRA1 [VT2 , 15.WGP is also an effective method in animal breeding and plant improvement . BecauseBased on the results of this study, we suggest that KRN is controlled by many additive loci and that the rrBLUP model can be used for KRN prediction in maize inbred lines. The combined utilization of different GWAS methods is helpful for predicting candidate genes and KRN in maize breeding.ARF29  and CKO4  were significantly related to KRN, based on expression analysis and candidate gene association mapping. WGP of KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate the total KRN-associated tagSNPs identified by all GWAS models. These results will facilitate our understanding of the genetic basis of KRN and provide important candidate genes for further research on this important trait.In this study, multiple GWAS methods were used to identify significant QTNs for KRN in maize. The seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, An association panel of 639 maize inbred lines, representing a wide range of genetic diversity of temperate inbred lines in China , was colAll the accessions were planted following a randomized block design of three replicates in three environments in 2011: Gongzhuling in Jilin Province , Xinxiang in Henan Province  and Beijing  in 2011. For descriptive purposes, the three environments were designated GZL, XX and BJ, respectively. At each location, the field experiments include in a single row 3 m in length, with 0.6 m between adjacent rows and 12 individual plants per row. The Institute of Crop Science of the Chinese Academy of Agricultural Sciences has established experimental field bases at all the above locations. The Institute of Crop Science approved the field experiments, and field management followed local maize management practices. In this study, the field studies did not involve endangered or protected species.H2) of KRN was calculated according to Wu et al. [CV (%) = SD/mean, where SD and mean refer to the standard deviation and mean, respectively, of KRN in each environment [Five ears were harvested from each line, and KRN was evaluated in the middle part of the ears . BLUP vau et al. . The coeironment .Young leaves of five plants of each maize line according were collected for genomic DNA extraction. We extract the genomic DNA followed the cetyltrimethylammonium bromide (CTAB) method . All samhttp://127.0.0.1:5846/) [10P ≥ 3, and the logarithm of odds (LOD) score for the six multilocus methods was set at ≥3 [One single-locus method, MLM, and six multilocus methods, including mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO, were used in this study. Alleles of each polymorphic locus with a minor frequency > 0.05 were used for further analysis. A kinship matrix was calculated and principal component analysis (PCA) was performed with the TASSEL 5.2 program . An MLM 1:5846/) . All paret at ≥3 .https://www.maizegdb.org/). Expression data for these genes were collected from previous studies [10P > 3.The LD decay with physical distance in our association panel was calculated in TASSEL 5.2 to be 200 kb  package was used to perform genomic prediction in R [To predict the KRN of the inbred lines, we estimated predictability by WGP. We grouped the LD blocks in PLINK software  using thion in R . We randion in R . We usedAdditional file 1: Figure S1. Common QTNs codetected with different models and in different environments. a, The common QTNs codetected by different methods. The X-axis represents different environments. The Y-axis represents the corresponding number of significant QTNs detected by only one method and by at least two, three, four, five, six or seven methods. b, The common QTNs codetected across different locations.Additional file 2: Figure S2. LD decay with physical distance in our association panel.Additional file 3: Figure S3. Whole-genome prediction of KRN in the inbred lines. The bars with different colors represent prediction accuracies for the KRN when using tagSNPs identified by different models. P-values were estimated based on the two-tailed Student’s t-test. ***: P-value < 0.0001; NS: P-value > 0.05.Additional file 4: Table S1. A list of material information in our association panel.Additional file 5: Table S2. Descriptive statistics of KRN from the subgroups in the association panel.Additional file 6: Table S3. The significant QTNs for KRN identified by the MLM method.Additional file 7: Table S4. The significant QTNs for KRN identified by six multilocus methods.Additional file 8: Table S5. The common QTNs codetected by different methods.Additional file 9: Table S6. The common QTNs codetected across different locations.Additional file 10: Table S7. The expression of the candidate genes in different maize tissues.Additional file 11: Table S8. Genes related to spike mutation in maize.Additional file 12: Table S9. KRN prediction accuracies for different numbers of randomly selected tagSNPs .Additional file 13: Table S10. KRN prediction accuracies for different training population sizes.Additional file 14: Table S11. Comparison of prediction accuracy between the top tagSNPs and random tagSNPs.Additional file 15: Table S12. The prediction accuracies for KRN of the inbred lines obtained using the tagSNPs representing the significant QTNs identified by different methods.Additional file 16: Table S13. Comparison of our GWAS results with QTNs detected in previous studies.


The rapid development of sequencing technology and simultaneously the availability of large quantities of sequence data has facilitated the identification of rare variant associated with quantitative traits. However, existing statistical methods depend on certain assumptions and thus lacking uniform power. The present study focuses on mapping rare variant associated with quantitative traits.In the present study, we proposed a two-stage strategy to identify rare variant of quantitative traits using phenotype extreme selection design and Kullback-Leibler distance, where the first stage was association analysis and the second stage was fine mapping. We presented a statistic and a linkage disequilibrium measure for the first stage and the second stage, respectively. Theory analysis and simulation study showed that (1) the power of the proposed statistic for association analysis increased with the stringency of the sample selection and was affected slightly by non-causal variants and opposite effect variants, (2) the statistic here achieved higher power than three commonly used methods, and (3) the linkage disequilibrium measure for fine mapping was independent of the frequencies of non-causal variants and simply dependent on the frequencies of causal variants.We conclude that the two-stage strategy here can be used effectively to mapping rare variant associated with quantitative traits. Thanks to the rapid development of sequencing technology and the lowering of sequencing costs in the last decade, the availability of large quantities of sequence data provides an unprecedented opportunity for researchers to investigate the role of rare variants in complex traits –4. But dIn fact, rare variant association analysis in the past several years mainly focused on the qualitative trait. Only a few statistical methods have been developed for the quantitative trait , 18–21. In this report, we use phenotype extreme selection design and Kullback-Leibler distance (KL-distance)  to propoKL for the extreme sample with sample-selection threshold value of 20, 10, and 5% and with sample size of 1000 and 1500. It can be seen that, under various genetic parameters, type I error rates of TKL are not appreciably different from the nominal alpha levels, which indicates the validity of the statistic TKL.Table KL with the sample size of 1500 is nearly 0.20 larger than that with the sample size of 1000, indicating that the power of the statistic TKL significantly increase with the increasing of the sample size. It can be seen that, under the same sample size, the powers of the statistic TKL with the low 5% samples and the up 5% samples are highest and the powers with the low 20% samples and the up 20% samples are lowest, which indicates that the powers of the statistic TKL increase with the stringency of the sample selection. It is observed from scenarios {1, 2, 3} that, when rare variant effects are in the same direction, the powers of the statistic TKL increase with the increasing of the number of causal variants. The same above conclusions are observed when 80% causal variants have positive effect and 20% causal variants have negative effect  and when there is the same number of causal variants with positive effects and negative effects . By comparing the powers under scenarios {1, 4, 7} with 10 causal variants, the powers under scenarios {2, 5, 8} with 20 causal variants, and the powers under scenarios {3, 6, 9} with 50 causal variants, we found that, when the number of causal variants with negative effect increases, the power of the statistic TKL decreases slightly. From Fig. KL, the burden test, the SKAT, and the SKAT-O, the power of TKL is higher than that of other three statistics. The burden test, the SKAT, and the SKAT-O are severely affected by the number of non-causal variants and the opposite effect variants, especially when there are the same number of opposite effect variants. Although non-causal variants and the opposite effect variants affect the power of the TKL, the impact is slight. For example, when the sample size is 1500 and the number of causal variants is 50 for 10% sample-selection threshold value (B2), as the number of variants with negative effect increases from zero to 25, the powers of the burden test, the SKAT, and the SKAT-O decrease from ~ 0.80, ~ 0.79, and ~ 0.84 to ~ 0.23, ~ 0.63, and ~ 0.74, respectively, with the decline rate of 71.2, 20.2, and 12.0%. Nevertheless, when the number of variants with negative effect is 25, the TKL still achieves ~ 0.83 power, with the decline rate of just 7% comparing to ~ 0.90 when the number of variants with negative effect is zero.Figure lKL. So we sample 10 times from each of 100 simulation populations where each sample includes 750 individuals with the up-extreme phenotype of Y > U and 750 individuals with the lower-extreme phenotype with Y < L. For each sample, we calculate the value of the measure lKL for each variant. In order to guard against noisy distributions of the measure lKL, we adopt the 5-point moving-average method to determine the maximum value. We count the number  of the maximum values that locate at variant 10 or variant 11. Then the probability that the maximum values of lKL locate at variant 10 or variant 11 is B/1000. We refer this value as the power of lKL, which measure the likelihood of fine mapping the QTL. Table lKL. It can be seen that the power of lKL for fine mapping under dominant model is highest and the power of lKL for fine mapping under recessive model is lowest. The power of lKL increases with increasing of the heritability h2 of the causal variant and the stringency of the sample selection. For example, power of lKL under dominant model with the heritability h2 of 0.01 is 0.52, 0.62, and 0.67 at 20, 10, and 5% sample-selection threshold value, respectively; power of lKL under dominant at 5% sample-selection threshold value increase from 0.67 to 0.83 with the heritability h2 of the causal variant increasing from 0.01 to 0.10. We also investigate the effect of different sample sizes . As expected, power of lKL increases with the increasing sample size (data not shown). In order to assess the performance of lKL, we compare it with two LD measures l . The population then evolved for 50 generations under random mating and genetic drift. A hundred populations are simulated for analyses.To assess the performance of the LD measure rly work , 28. We ze (2 N) . We let


We investigated how the COVID-19 crisis and the extraordinary experience oflockdown affected young adults in England and Wales psychologically. One month afterlockdown commenced (T2), we assessed 30 psychological and behavioural traits in 4,000twins in their mid-twenties and compared their responses to the same traits assessed in2018 (T1). Mean changes from T1 toT2 were modest and inconsistent: just as many changeswere in a positive as negative direction. Twin analyses revealed that genetics accountedfor about half of the reliable variance at T1 and T2. Genetic factors correlated onaverage .86 between T1 and T2 and accounted for over half of the phenotypic stability.Systematic environmental influences had negligible impact on T1, T2 or T2 change. Ratherthan the crisis fundamentally changing people psychologically, our results suggest thatgenetic differences between individuals play a fundamental role in shaping psychologicaland behavioural responses to the COVID-19 crisis. It is rare for such massive and abrupt social change to occur as the world hasexperienced with the COVID-19 pandemic and lockdown. COVID-19 disease can be a life or deathissue for those infected with the virus, but the psychological responses of those infectedand of the many more people in lockdown who had not contracted the disease are also ofconcern. For example, a recent review of 24 studies on the effects of quarantine concludedthat ‘the psychological impact of quarantine is wide-ranging, substantial, and can belong lasting’ . Low mooThis and other research suggestsHere we investigated genetic as well as environmental influences on individualdifferences in psychological and behavioural traits before the COVID-19 crisis and lockdown(T1) and one month after lockdown had commenced in the UK (T2). In addition to askingparticipants how they think the crisis affected them, we compared the same psychological andbehavioural traits obtained at T1 and T2 on the same individuals with data at both T1 andT2. To assess the aetiology of individual differences, we used the classical twin designbased on the resemblance of identical and non-identical twins. From 17 April to 4 May 2020,we collected online data from 4000 twins in our Twins Early Development Study TEDS;  from whoThe twins were born between 1994 and 1996. They were thus in their early twentiesduring T1 and T2. Few twin studies have focused on this age when the twins are completingtheir studies and beginning their adult life, entering the workforce, and forming long-termrelationships. At this tipping point in their lives, it could be argued, have the most tolose from the crisis personally, socially and economically.We describe mean changes from T1 to T2, hypothesizing that changes will be modestand inconsistent, with some positive as well as negative changes (Hypothesis 1). However,our focus is on individual differences and their genetic and environmental origins at T1 andT2 and in changes from T1 to T2. On the assumption that the COVID-19 crisis affected peopledifferently, we hypothesised that variance will be greater at T2 than T1 (Hypothesis 2). Wepredicted that phenotypic correlations will be substantial between T1 and T2 (Hypothesis3).Our overall hypothesis is that genetics, by which we mean inherited DNAdifferences, is the major systematic force governing how people respond psychologically tothe COVID-19. Specifically, we expected that all traits will show substantial geneticinfluence at T1, as indicated by a large body of genetic research on psychological traits. We alsoGenetic correlates of psychological responses to COVID-19 lower for T1 to T2change scores because they only capture genetic effects at T2 that are independent ofgenetic effects at T1. We operationalised change by regressing T1 scores from T2 scores sothat T2 scores are independent of scores at T1, which we refer to as ‘changescores’. Crucially, we predicted that genetic correlations between T1 and T2 will besubstantial (Hypothesis 5), indicating that, despite the COVID-19 crisis, individualdifferences at T2 are largely governed by the same genetic factors that affect T1.Environmental factors are important too, but we predicted that their effects onindividuals are not the systematic effects of family environment. The twin design can beused to distinguish systematic environmental influences attributable to growing up in thesame family, called ‘shared’ environmental influences, from otherenvironmental influences . DespiteWe predicted that similar results will be obtained from bivariate model-fittinganalysis (Hypothesis 7). That is, most of the genetic effects on T2 scores will be accountedfor by genetic effects in common with T1, although there will be some novel genetic effectsat T2 independent of T1. Environmental effects due to shared rearing or living circumstancesduring lockdown will be negligible.Finally, we predicted that these results for T2 change will not interactsignificantly with potential moderators (Hypothesis 8). Lockdown presents aquasi-experimental test of contemporary shared environments by comparing results for twinsliving together during lockdown and those living apart. If such shared experiences wereimportant, twins locked down together should be more similar than twins living apart duringlockdown. On the basis of the generally weak effects of shared rearing environment, wepredicted that environmental effects due to living together during lockdown will benegligible. We also investigated other possible moderators of genetic and environmentalinfluences on individual differences in psychological traits before and during the COVID-19crisis, such as conditions of lockdown, having COVID-19 symptoms, socioeconomic status andgender, although power to detect such interactions is limited.https://osf.io/r58be/.) In summary, they were: Mean changes from T1 to T2 will be modest andVariance will be greater at T2 thanPhenotypic correlations will be substantial between T1andHeritability of individual differences will be substantial for alltraits at T1 and T2. Heritability will be lower for T1 to T2 changeFor all traits, genetic correlations between T1 and T2 willbeEnvironmental influences due to shared rearing or current livingcircumstances will be negligible for all traits at T1 and T2 as well as for T2changeSimilar results will be obtained from a bivariate model-fittinganalysis across T1 andThese results for T2 change will not interact significantly withpotential moderators.All of our hypotheses were preregistered prior to analysis with Open ScienceFramework: dstatistic, which is the ratio of the mean difference to the standard deviation , which isalmost certainly due to less opportunity for volunteering during lockdown.d of 0.5, considered a medium effect size, accounts for about9% of the variance. Medium-sized mean differences in the negative direction emerged forthree variables. Prosocial Behaviour declined (0.44), which, like Volunteering, might bedue in part to reduced opportunity. Achievement Motivation decreased (0.47), which isworrying because emerging adults are our next generation of workers.Hyperactivity-Inattention increased (0.42), which seems to fit with reports in the mediathat people feel less able to concentrate. Other effect sizes were modest(d = 0.20).A lower than at T1 (1.79).These mean differences mask a wide range of individual differences. If theCOVID-19 crisis affected people in more extreme ways, we would expect to see increasedvariance at T2. The standard deviations  and 2 doFor these analyses and the following analyses of individual differences, wefocused on variables that showed sufficient variability and approached normaldistributions, including Achievement motivation, Alcohol, Community satisfaction, Conductproblems, Depression, Emotional problems, General anxiety, Healthcare,Hyperactivity/inattention, Importance of relationships, Love and relationships, Media use,Money attitudes, Peer problems, Physical activity, Prosocial behaviour, Purpose in lifeand VolunteeringIf the COVID-crisis re-shuffled the rank order of individual differences, wewould expect to see little stability from T1 to T2. Pearson correlations from T1 to T2 areshown in total variance of the measures was stable from T1 to T2. Takingtest-retest reliability into account (through dividing the correlation estimate by thetest-retest coefficient) suggests that 68% of the reliable variance ofthe measures was stable from T1 to T2.Reliability of the measures represents a ceiling for stability. In TEDS, weobtained two-week test-retest reliability from TEDS twins on most measures as part of ourpreparatory work for the 2018 (T1) assessment . The aveDespite the substantial stability from T1 to T2, T2 change scores revealed someindividuals who changed dramatically in positive as well as negative directions, asillustrated in At T1, the average twin correlations for identical and non-identical twinswere 0.35 and 0.16, respectively. Because identical twins are identically geneticallywhereas non-identical twins are only 50% similar genetically, the difference in theircorrelations indexes genetic influence on individual differences, called heritability.Doubling the difference between these correlations suggests a rough estimate ofheritability of 35% at T1 because heritability cannot exceed the identical twincorrelation. At T2, the average twin correlations for identical and non-identical twinswere similar, 0.31 and 0.16, as was the average heritability of 30%, despite theCOVID-19 crisis and lockdown.Twin resemblance not explained by zygosity can be attributed to sharedenvironment (C). In other words, the extent to which heritability does not account forthe identical twin correlation is a rough index of C. On average, C was negligible at T1(2%) and T2 (4%).The rest of the variance is attributed to a residual component of variance (E)that includes non-shared environment plus unreliability of measurement. The average Ewas 63% at T1 and 66% at T2. Test-retest reliabilities suggest that non-sharedenvironment accounted for about half of E at T1 and T2.reliable variance at T1 and T2 can be attributed toinherited DNA differences. In other words, of the total variance at T1and T2, about 40% can, on average across the measures, be attributed to genetic factors,about 30% to non-shared environmental factors, and about 30% to unreliability ofmeasurement. Shared environmental influence has negligible impact.Deducting the component of variance due to unreliability indicates that abouthalf of the T2 change scores show lower heritabilities, 16% on average. Because T2 changeis a residualised score independent of scores at T1, stable genetic influence from T1 toT2 is removed from T2 change scores.Thus, heritability of T2 change scores represents novel genetic influence atT2 that does not affect T1. Shared environment, which includes not only shared rearingenvironment (the twin pairs grew up together in the same family) but also sharedexperiences during the COVID-19 crisis, has negligible effects on T2 change, 3% onaverage. Most of the variance of T2 change scores is due to the E component of variance,81% on average. We cannot separate E of T2 change scores into non-shared environment andunreliability of measurement because test-retest reliability at T1 cannot be assumed torepresent the reliability of T2 change scores.These results about variance and covariance gleaned from the twin correlationsare highly similar to the results of univariate model-fitting analyses of variance forT1, T2 and T2 change measures, as shown in The Cholesky Decomposition bivariate model-fitting model separates A, C and Ecomponents of variance at T2 into variance in common with variance at T1 and variance atT2 independent of variance at T1. As explained in Methods, the model yields estimates ofthe extent to which the phenotypic correlation between T1 and T2 is accounted for by A,C and E. The genetic correlations are shown in the top panel of The Cholesky model also estimates A, C and E components of variance at T2independent of their respective A, C and E components of variance at T1. These A, C andE estimates of T2 change –21 are, Finally, we investigated possible moderators of the univariate results. Themost novel moderator is whether the twins were locked down together or living apartduring lockdown. Lockdown presents a quasi-experimental test of contemporary sharedenvironments by comparing results for the 28% of twins living together during lockdownand those living apart. If shared lockdown experiences were important, twins locked downtogether should be more similar than twins living apart during lockdown. On the basis ofthe generally weak effects of shared environment, we predicted that environmentaleffects due to living together during lockdown are negligible.At first this prediction seemed wrong because the average twin correlation fortwin pairs locked down together (.30) was higher than the correlation for twin pairsliving apart during lockdown (.23), although this difference was not significant (p =.051). However, this possible effect of shared environments might be a genetic effect indisguise because identical twins locked down together more often than non-identicaltwins (32% vs 25%). Results of univariate model-fitting separately for twins locked downtogether vs apart  are consPower to detect significant differences for such small effects is negligible.Nonetheless, further support for the hypothesis that the apparent C effect of beinglocked down together is not really C comes from finding nearly identical A and Cestimates pre-existing at T1: A and C are .33 and .06 for twins together and .30 and .03for twins apart. Results of T2 change scores provides additional confirmation in that asimilar pattern emerged: A and C are .19 and .04, respectively, for twins together and.14 and .02 for twins apart.We also considered other potential moderators. For example, similar to beinglocked down together or apart, gender is a dichotomous variable that is the same forboth members of a twin pair .Separate univariate analyses for male and female twins yielded similar results. Thesemodel-fitting results are presented in For the continuous moderator of family SES and for moderators that can bediscordant for members of a twin pair , we corrected T2 and T2 change scores forthese moderators and repeated the analyses. ACE estimates were similar when we comparedestimates before and after correction for these moderators. These model-fitting resultsare included in d of 0.24. Although we expected that thecrisis would affect some individuals more than others, we found no increase in variance atT2. It is possible that the effects of the crisis will hit harder later or that longerlockdown or the economic aftermath of the crisis will have a greater effect. We willinvestigate these possibilities with three follow-up surveys during 2020.How much has the COVID-19 crisis changed young adults psychologically followingthe unprecedented social experiment of one month of lockdown? As expected, the 30 measuresin our study yielded many statistically significant changes in means. The largest changes inthe negative direction were reduced volunteering and achievement motivation and increasedhyperactivity-inattention. However, there were as many changes in the positive direction,most notably, reduced verbal peer victimisation. Changes were similar in direction andmagnitude for males and females, with the single exception of general anxiety, whichincreased more for females than males. However, most of these mean changes have modesteffect sizes, with an average Why do these young adults in Great Britain show modest negative effect on averageafter being in lockdown for one month when it is generally assumed that the psychologicaleffects will be substantial? Part of the answer is that research often focuses onstatistical significance and mean differences rather than considering effect size andindividual differences. With our large sample size, nearly all variables show significantmean differences, but they don’t make much of a difference, accounting for less thantwo percent of the variance on average. Another reason might be methodological. In thepresent study we did not focus on participants’ subjective reports of how theCOVID-19 crisis changed them. Instead, at T2, we asked participants to report, for example,how depressed they felt during the month following lockdown, which we compared to theirreports of depression on the same measures in 2018. We found no difference in depression onaverage.Other reasons why we found few negative effects of the COVID-19 crisis could bethat the lockdown was so widespread  or that ourparticipants are British (stiff upper lip?) or that they are young adults (resilience?insouciance?). Concerning the insouciance hypothesis, we asked participants at T2 how muchthey were worried about their physical health and mental health during the month sincelockdown. The frequency of those reporting that they were moderately, very, or extremelyworried was 38% for physical health and 57% for mental health. In other words, they were,quite reasonably, worried, although on average they did not change psychologically,including their symptoms of general anxiety. This can be viewed as a hopeful message thatyoung people on average, are resilient psychologically to an experience as seismic asCOVID-19 and lockdown, although these mean differences mask individual differences to theCOVID-19 and lockdown. It remains to be seen if similar results emerge in other countries,at other ages and after longer exposure to the crisis and its aftermath.The focus of our study was on individual differences rather than mean differences.How much has COVID-19 shuffled the deck of individual differences? The rank order ofindividual differences was largely stable from T1 to T2, with stability accounting for about70% of the reliable variance at T1 and T2 on average across the measures. From a geneticperspective, the most interesting finding was that the average genetic correlation was 0.86,indicating that genetic effects at T1 were highly correlated with genetic effects at T2,despite the intervening COVID-19 crisis and lockdown. It is also interesting that T2changes, which are independent of T1, show genetic influence.We conclude that inherited DNA differences are the major systematic force shapingindividual differences in psychological traits at T2 as well as at T1. Genetic effectsaccount for about half of the reliable psychological differences between people at T1 andT2. The environment accounts for the rest of the variance, but it is not the systematiceffect of environmental factors often assumed to be important, such as shared familyenvironment. Environmental factors of this systematic sort had negligible effects onvariance at T1 and T2 and for T2 change. The environmental effects that make a differenceare those that are not shared by twin siblings growing up in the same family or, in ourstudy, by twins locked down together. These idiosyncratic ‘non-shared’environmental factors are likely to be unsystematic, chance experiences .https://osf.io/r58be/) hypotheses. This speaks to the replicability offindings from behavioural genetic research on which these hypotheses were based, which isnoteworthy given the replication crisis in science in general and in psychology inparticular  and data collected 17 April – 4 May 2020, approximately one month afterthe lockdown in response to the COVID-19 pandemic had started (T2). For the COVID-19assessment, we only invited the subsample of twins for whom we had email addresses, whichincluded many unpaired twins as well as pairs. The twins were invited by email and given alink and code to use to log on to the survey, a platform created and supported by QuoditLtd. The survey began with an information sheet and consent mechanism. Incentives includeda prize draw for iPads and shopping vouchers. Ethical approval was received fromKing’s College London Research Ethics Committee (Reference number PNM/09/10-104).Although 4 May was the cut-off used for the present analyses, we continued to collectdata, which will be used in future papers.For our analyses for this paper, we selected twins with data both at T1 and T2.Sample sizes for each measure at T1 and T2 is reported in The sample for the current data collection at T2 for whom we also have data atT1 remains reasonably representative of the population in England and Wales for some keydemographic characteristics. For example, our sample was similar to UK equivalents  for ethnCoRonavIruS Health ImpactSurvey .Our statistical analysis plan was registered in the Open Science Framework, priorto creation of the dataset and prior to analysis . Change scores were calculated by correcting T2scores for T1 scores using the regression method . Descriptive statistics were calculated for theentire sample, and separately for males and females. Cohen’s d was used to obtainan estimate of the effect sizes of the mean differences . UnivariPhenotypic correlations were calculated between T1 and T2 scores for the wholesample and for males and females separately as an index of stability. We then compared thestability to test-retest reliability that was obtained in 2018 prior to T1 data collection. Inall The twin design was used for univariate and bivariate genetic analyses. Thetwin method offers a natural experiment capitalising on the known genetic relatedness ofidentical  and non-identical  twin pairs. MZ twins aregenetically identical and share 100% of their genes, while DZ twins share on average 50%of their segregating genes. Both MZ and DZ twins are assumed to share 100% of theirshared environmental influences growing up in the same family. Non-shared environmentalinfluences are unique to individuals, not contributing to similarity between twins.Using these known family relatedness coefficients, it is possible to estimate therelative contribution of additive genetic (A), shared environmental (C), and non-sharedenvironmental (E) effects on the variance and covariance of the phenotypes, by comparingMZ correlations to DZ correlations. Heritability can be roughly calculated by doublingthe difference between MZ and DZ correlations, C can be calculated by deductingheritability from MZ correlation and E can be estimated by deducting MZ correlation fromunity  .These parameters can be estimated more accurately using structural equationmodelling, which also provides 95% confidence intervals and estimates of model fit. Thestructural equation modelling program OpenMx was used for all model-fitting analyses.Here we present twin correlations and ACE estimates for T1, T2 and changescores for all variables. The difference in the intraclass correlations between MZ andDZ twin pairs can guide the decision of conducting an alternative to the ACE model, theADE model. The ADE model partitions the variance into additive genetic (A), non-additive(or dominant) genetic (D) and non-shared environmental (E) effects. This model is fittedin cases when intraclass correlations for DZ twins are below 50% of the MZ intraclasscorrelation – indicating non-additive genetic influences. Although for a fewtraits the DZ correlation suggested the possibility of non-additive genetic effects, weopted for running ACE models across all variables for three key reasons: first, despiteour large sample size, we lacked power to detect non-additive variance reliably; second,conducting the same models across all traits allowed us to meaningfully compare theresults across all measures; third, even in studies equipped with the necessary power todetect non-additive genetic effects, it is rare to find a significant contribution of D(and C) for self-reported psychological traits measured in adulthood .rG), which is an index of pleiotropy, indicatingthe extent to which the same genetic variants influence two traits or measures of thesame trait at two times. The shared environmental correlation (rC) andnon-shared environmental correlation (rE) are estimated in a similarmanner , shared environmental (C), andnon-shared environmental (E) components by comparing the cross-trait cross-twincorrelations between MZ and DZ twin pairs. This method also enables estimation of thegenetic correlation , we calculated ACE estimates separatelyfor each group and compared the univariate ACE estimates between groups. For continuousmoderators (SES) and for moderators that were mostly discordant for members of twinpairs , we corrected the trait scores for themoderator using the regression method and repeated the analyses. We then compared theunivariate ACE estimates before and after the correction.The Cholesky decomposition see  allows fSupplement


Red blood cell (RBC) transfusion and erythropoiesis‐stimulating agent administration are cornerstones of clinical practice, yet concerns exist as to potential increased risk of thrombotic events. This study aims to identify RBC traits most relevant to venous thromboembolism (VTE) and assess their genetically predicted effects on VTE in the general population.We used multivariable mendelian randomization with bayesian model averaging for exposure selection. We obtained genetic variants predicting any of 12 RBC traits from the largest genome‐wide association study of hematological traits (173 480 participants of European ancestry) and applied them to the UK Biobank (265 424 white British participants). We used univariable mendelian randomization methods as sensitivity analyses for validation. Among 265 424 unrelated participants in the UK Biobank, there were 9752 cases of VTE (4490 men and 5262 women). Hemoglobin was selected as the plausible important RBC trait for VTE . The best‐fitting model across all RBC traits contained hemoglobin only (posterior probability=0.46). Using the inverse variance–weighted method, genetically predicted hemoglobin was positively associated  with VTE. Sensitivity analyses  gave consistent estimates.Endogenous hemoglobin is the key RBC trait causing VTE, with a detrimental effect in the general population on VTE. Given men have higher hemoglobin than women, this finding may help explain the sexual disparity in VTE rates. The benefits of therapies and other factors that raise hemoglobin need to be weighed against their risks. ESAerythropoiesis‐stimulating agentGWASgenome‐wide association studyICD‐9International Classification of Diseases, Ninth RevisionICD‐10International Classification of Diseases, Tenth RevisionIVWinverse variance weightedMAFminor allele frequencyMCHmean corpuscular hemoglobinMIPmarginal inclusion probabilityMRMendelian randomizationMR‐BMAmultivariable mendelian randomization based on bayesian model averagingMR‐PRESSOmendelian randomization pleiotropy residual sum and outlier testPPposterior probabilityRBCred blood cellVTEvenous thromboembolismRed blood cell attributes are highly correlated genetically and phenotypically, making it difficult to disentangle causal drivers of disease.This study used multivariable mendelian randomization with bayesian model averaging to select and prioritize between 12 red blood cell traits, which suggested endogenous hemoglobin is the key factor for venous thromboembolism, with a detrimental effect in the general population.This study is consistent with randomized controlled trials showing that increasing hemoglobin in patients with anemia, via blood products, erythropoiesis‐stimulating agents, or blood transfusion, increases the risk of thromboembolic events.This study also suggests relevance to the general population as well as to patients.The benefits of therapies and other factors that raise hemoglobin need to be weighed against their risks.Red blood cell (RBC) transfusion is the most readily available method to alleviate anemia and bleeding resulting from a variety of clinical conditions, yet concerns exist as to the risk of adverse effects, with several trials ongoing to establish the optimal transfusion threshold in patients.Several observational studies have assessed the role of RBC attributes in thrombosis in the general population.Mendelian randomization (MR), using genetic variants randomly allocated during conception as instrumental variables, is less prone to confounding than traditional observational studies, and can help ascertain causal effects.https://github.com/verena-zuber/demo_AMD).The UK Biobank received ethical approval from the research ethics committee (11/NW/0382), and participants provided written informed consent. Summary statistics were generated from publicly available data that had previously received appropriate ethics and institutional review board approvals, and further sanction was therefore not required. The individual‐level data in the UK Biobank are available by application directly to the UK Biobank. The data that support the findings of this study are available from the corresponding author on reasonable request. The statistical code in R for implementing MR‐BMA can be obtained from the open‐source code from Github , the within‐batch call rate was <97%, the across‐batch call rate was <75%, or they were nonautosomal biallelic.Genetic predictors of RBC traits were extracted from summary statistics generated from an existing publicly available genome‐wide association study (GWAS) of hematological traits conducted in 173 480 participants of European ancestry without any blood cancer or other major blood disorder.P<8.31×10−9, a recent threshold for genome‐wide analyses of common, low‐frequency, and rare variants) and independently (r2<0.001) predicted any of the 12 RBC traits . These variants were checked for imputation quality and validity as instrumental variables using individual data from the UK Biobank, with the following exclusion criteria: (1) imputation information score <0.3 for MAF >3%, information score <0.6 for MAF 1% to 3%, information score <0.8 for MAF 0.5% to 1%, and information score <0.9 for MAF 0.1% to 0.5%; (2) departure from Hardy‐Weinberg equilibrium at Bonferroni‐corrected significance; (3) associated with potential confounders (described below) of the variant‐outcome relation at Bonferroni‐corrected significance; (4) in the ABO gene, which is well known to be highly pleiotropic;We obtained genetic variants that robustly , and International Classification of Diseases, Tenth Revision (ICD‐10), codes. Causes of death were classified using ICD‐10 codes. Genotyping was undertaken with 2 similar arrays, the UK Biobank Lung Exome Variant Evaluation Axiom array (49 979 participants) and the UK Biobank Axiom array (438 398 participants).The UK Biobank recruited ≈500 000 participants intended to be aged 40 to 69 years from 2006 to 2010 at 22 recruitment centers across Scotland, Wales, and England in the United Kingdom.The exposures were 12 genetically predicted RBC traits .ICD‐9 415.1, 416.2, and 451–453 and ICD‐10 I26 and I80–I82) or underlying and contributory causes of death (ICD‐10 I26 and I80–I82). Incident and prevalent cases of VTE were combined to maximize statistical power, under the implicit assumption that all events occur incident to a genetic exposure.We developed classification algorithms for VTE following the recommendations of the UK Biobank.2). Smoking and alcohol drinking status were categorized as never, previous, current smoker/drinker, and prefer not to answer. Educational level was categorized into degree/professional, nondegree, none of the above, and prefer not to answer, derived from the questionnaire. Townsend deprivation index (a composite indicator of socioeconomic status) was based on preceding census data for area of residential postcode at the baseline visit.To check the randomization, we assessed the association of each genetic variant with potential confounders  were used to assess whether each genetic variant was associated with the potential confounders. The association of each variant with VTE was obtained using an additive genetic model, adjusted for sex, age, genotyping array, and 40 principal components of genetic ancestry.Analysis of variance (continuous) and χExposure selection was performed using MR‐BMA. On the assumption that one of the models considered is true, MR‐BMA ranks all these submodels from the larger model where all 12 RBC traits could have a causal effect on VTE ,P=0.1, corresponding to a priori expecting 1.2 causal factor (p×d). On the basis of a simulation study,2=0.25, corresponding to the priori for the variance of RBC traits. To check the impact of the prior selection, we varied the prior probability of selecting a causal factor from P=0.2 to 0.4, reflecting 2.4 to 4.8 expected causal factors.As recommended,We also excluded the top‐ranking exposure from each model to check if any alternative exposure had equally strong probability of causality. Finally, as platelets may play a role in the development of VTE, we additionally included 4 platelet traits  as alternative potential exposures to assess if these play a role.P<0.05, indicates presence of a pleiotropic effect, suggesting the IVW estimate is invalid. MR‐Egger can have low statistical power, so we concentrated on the direction and effect size rather than statistical significance. MR pleiotropy residual sum and outlier test (MR‐PRESSO), which assumes instrument strength is independent of the direct effect and at least 50% of the variants are valid, is a statistical method for detecting and, if necessary, correcting for horizontal pleiotropic outliers.P<0.05 (on the basis of 10 000 simulations) indicates horizontal pleiotropy. The MR‐PRESSO uses the difference between the observed and expected distribution of RSS residual sum of squares for each variant to identify potentially horizontal pleiotropic outliers, and provides a corrected estimate by removing these outliers.P<5×10−8).To verify our finding from MR‐BMA, we used several univariable MR methods. We used an inverse variance–weighted (IVW) multiplicative random effects meta‐analysis of the genetic variant–specific Wald estimates. IVW provides unbiased estimates as long as all genetic variants are valid instruments. The weighted median provides valid estimates if at least 50% of the weight comes from valid variants.https://sb452.shinyapps.io/overlap/).In 2‐sample MR, sample overlap may introduce bias and inflate type I error rate, when weak instrument bias is present.SNPTEST v2.5.4 program. MR‐BMA was performed using the open‐source code from Github (https://github.com/verena-zuber/demo_AMD). Univariable MR analyses were performed using the TwoSampleMR and MR‐PRESSO packages in the R version 3.4.4 software platform . Two‐sided P values are reported throughout.Genetic associations with the outcome were estimated using the ABO gene, or being equivocally palindromic , the exposure most relevant to VTE on the basis of MIP was hemoglobin (MIP=0.90); all other RBC traits had MIP <0.24 Table . To checr=0.91), and models including both had relatively low probability . Again, the most relevant RBC trait was hemoglobin (MIP=0.91), which was followed in relevance by hematocrit MIP=0.28) ; Dr Luo is supported by the Bau Tsu Zung Bau Kwan Yeun Hing Research and Clinical Fellowship from the University of Hong Kong (grant *200008682.920006.20006.400.01); Drs Burgess and Zuber are supported by Sir Henry Dale fellowship, jointly funded by the Wellcome Trust and the Royal Society (grant 204623/Z/16/Z). The funders have no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.None.Tables S1–S8Figures S1–S6Click here for additional data file.