Cucurbita maxima trimonoecious line ‘2013–12’ and subandroecious line ‘9–6’ were collected as materials, and transcriptome sequencing was performed using an Illumina HiSeqTM 2000 System. 496 up-regulated genes and 375 down-regulated genes were identified between shoot apices containing mostly male flower buds and only female flower buds. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the differentially expressed genes were mainly enriched in the ethylene and auxin synthesis and signal transduction pathways. In addition, shoot apices at the 4-leaf stage were treated with the ethylene-releasing agent 2-chloroethylphosphonic acid (Ethrel), aminoethoxyvinyl glycine (AVG), AgNO3 and indoleacetic acid (IAA). The number of female flowers up to node 20 on the main stem of ‘2013–12’ increased significantly after Ethrel and IAA treatment and decreased significantly after AVG and AgNO3 treatment. The female flowers in ‘9–6’ showed slight changes after treatment with the exogenous chemicals. The expression of key genes in ethylene synthesis and signal transduction  was determined using quantitative RT-PCR, and the expression of these four genes was positively correlated with the number of female flowers in ‘2013–12’. The variations in gene expression, especially that of CmaACS7, after chemical treatment were small in ‘9–6’. From stage 1 (S1) to stage 7 (S7) of flower development, the expression of CmaACS7 in the stamen was much lower than that in the ovary, stigma and style. These transcriptome data and chemical treatment results indicated that IAA might affect pumpkin sex expression by inducing CmaACS7 expression and indirectly affecting ethylene production, and the ethylene synthesis and signal transduction pathways play crucial roles in pumpkin flower sex expression. A possible reason for the differences in sex expression between pumpkin lines ‘2013–12’ and ‘9–6’ was proposed based on the key gene expression. Overall, these transcriptome data and chemical treatment results suggest important roles for ethylene in pumpkin sex expression.Sex expression is a complex process, and in-depth knowledge of its mechanism in pumpkin is important. In this study, young shoot apices at the one-true-leaf stage and 10-leaf stage in  Cucumis melo L.) and cucumber (Cucumis sativus L.), floral primordia are initially bisexual, and sex determination occurs by developmental arrest of either the stamen or the carpel whorl, resulting in unisexual flowers [In flowering plants, sex expression is an important process that differentiates male and female flowers. Many factors are involved in the complex process of sex expression, such as temperature, daylength, ethylene, auxin, cytokinin, brassinolide (BR) and nitrogen metabolism . In addi flowers . Genes s flowers ,4,5,6.CsACS2 (the M (andromonoecious) loci in cucumber), CmACS7 (the M loci in melon), ClACS7 and CitACS4 can inhibit stamen development in female flowers and determine andromonoecy [CsACS11 (the A (androecy) loci in cucumber) and CmACS11 loss-of-function mutants lead to male plants [CsACS1G  loci) bear only female flowers, although the molecular mechanisms remain to be investigated [CitACS4 is expressed specifically in carpel primordia in watermelon (Citrullus lanatus sp.), which indicates that CitACS4 also plays a role in sex determination [CsACO2 is essential for carpel development and interacts with CsACS11 to promote female flower development in cucumber [Because the Cucurbitaceae includes seven sex forms, it is a model dicotyledonous plant family for sex determination research. The sex determination mechanism of Cucurbitaceae has been thoroughly studied, and ethylene is a key hormone that promotes female flower development in Cucurbitaceae plants. The enzymes 1-aminocyclopropane-1-carboxylate (ACC) synthetase (ACS), which catalyzes the rate-limiting step in ethylene biosynthesis, and ACC oxidase (ACO), which converts ACC into ethylene, are key in ethylene biosynthesis . The genomonoecy ,10,11,12e plants ,10. Cucustigated ,13. CitAcucumber .CS-ETR2 and CS-ERS, which are ethylene receptor-related genes, may play roles in the development of female flowers in gynoecious cucumbers under high levels of endogenous ethylene [ETR1 is known as the negative ethylene perception gene. CsETR1 was demonstrated to be involved in stamen development in female cucumber flowers through the induction of DNA damage [etr1-1 transgenic melon plants had a higher number and earlier appearance of carpel-bearing flowers on the main stem. This phenomenon indicates that ETR1 also plays a role in sex determination in melon. Recently, with the help of yeast one-hybrid and ChIP-PCR assays, CsERF110 (ethylene-responsive factor 110) and CmERF110 were shown to respond to ethylene signaling and enhance CsACS11 and CmACS11 promoter activity in cucumber and melon [CsERF31 (ethylene-responsive factor 31) also responded to the ethylene signal derived from F and positively regulated ethylene feedback by activating M expression in cucumber [Ethylene signaling is perceived by a family of ethylene receptors. ethylene . ETR1 isA damage ; etr1-1 nd melon . CsERF31cucumber .Cucurbita, is an annual vine. Pumpkin includes the three most economically important cultivated species, pumpkin (Cucurbita maxima Duch.), zucchini (Cucurbita pepo L.) and squash (Cucurbita moschata Duch.). Hybrids show strong heterosis, but hybrid production requires cross-pollination every year. Therefore, it is necessary to manually remove males, which takes time and effort. Thus, it is important to explore the sex determination mechanism of pumpkin to provide a basis for pumpkin cultivation techniques. Typical monoecious pumpkin varieties have three different developmental phases: only male flowers are produced in the first phase, the production of female and male flowers alternates during the second phase, and only female flowers are produced in the third phase [C. pepo is determined by a single recessive gene [C. pepo is ethylene insensitive, and this trait is controlled by an ethylene-response pathway gene named weak ethylene insensitive (CpWEI) [CpCTR1 and CpCTR2, were cloned as negative regulators of ethylene signaling in flowers [CpACS27A, a gene homologous to CmACS7 and CsACS2, was cloned and observed to regulate stamen development in C. pepo [C. maxima was mapped to an interval of 35.2 Kb, and the possible candidate gene was identified as a pentatricopeptide repeat-containing family gene [Pumpkin, a monoecious plant that belongs to the genus rd phase . Accordiive gene , while give gene . Androec (CpWEI) ,23. Two  flowers . Through flowers . In addi C. pepo . In receCucurbita species [Cucurbita transcriptome was generated using 454 GS FLX Titanium technology [RNA sequencing (RNA-seq) is a useful transcriptome profiling tool for rapid gene discovery, exploring gene expression, elucidating phylogenetic relationships, molecular marker development and related gene expression analyses that has been used in  species ,29,30. Ichnology . Subsequchnology ,34,35,36C. maxima as materials, and differentially expressed genes involved in the ethylene synthesis and signal transduction pathways were identified. The number of female flowers in trimonoecious C. maxima changed significantly after 4 chemical treatments, and the expression levels of CmaACS7, CmaACO1, CmaETR1 and CmaEIN3 were positively correlated with the number of female flowers. The number of female flowers in subandroecious C. maxima changed slightly, and the variations in gene expression, especially that of CmaACS7, were small in amplitude. The expression of CmaACS7 in the ovary, stigma and style at different stages of flower development indicated that CmaACS7 plays an important role in sex differentiation. The transcriptome data and results of chemical treatment indicated that the ethylene synthesis and signal transduction pathways play a crucial role in pumpkin flower sex expression.The sex expression mechanism in pumpkin is less well known than that in other Cucurbitaceae species, such as cucumber and melon. Therefore, in this study, RNA-seq was carried out using subandroecious and trimonoecious Flowers on the main stem of the subandroecious line ‘9–6’ were mainly male unisexual flowers; however, one or two female flowers appeared at nodes 15–16; fewer than 10% female flowers were observed up to node 20 of the main stem. Several male and bisexual flowers were present at the base of the main stem in the trimonoecious line ‘2013–12’, and all flowers produced after the first female flower, which appeared at node 10, were female unisexual flowers. The female flower proportion of ‘2013–12’ was approximately 50% up to node 20 on the main stem . ConsideIn ‘9–6’, shoots at one-true-leaf stage (LS1) (MS1-type sample) contained only male flower buds, whereas those at 10-leaf stage (LS10) (MS10-type sample) contained male flower buds and one or two female flower buds. In ‘2013–12’, shoots at LS1 (FS1-type sample) contained male flower buds and one or two bisexual flower buds, and those at LS10 (FS10-type sample) contained only female flower buds. There were obvious sex expression differences in FS1 vs. FS10 and in MS10 vs. FS10, but no significant differences were observed in MS1 vs. MS10 and MS1 vs. FS1.In this research, the molecular mechanism of sex expression was explored using an RNA-seq approach; young shoots of ‘2013–12’ and ‘9–6’ at the LS1 stage and LS10 stage were used as materials. Through sequencing, a total of 525.41 Mb raw reads and 509.23 Mb clean reads, which showed valid bases from 96.22% to 97.17%, were obtained. In the clean reads of all 12 samples, the Q30 scores ranged from 93.89% to 94.60%, and the GC content ranged from 44.5% to 45% , which d−5. In total, 57,118 unigenes, 43.28% of the total assembled unigenes, were matched in the NR database, and 38,487 (29.17%), 32,257 (24.44%), 13,828 (10.48%), and 33,927 (25.71%) of the unigenes were matched in the Swiss–Prot, KOG, GO and KEGG databases, respectively.All clean reads were subsequently subjected to de novo assembly with the Trinity program and produced 747,168 unigenes with a total length of 969.15 Mb. The average number of unigenes was 1297.09 bp. Of these, the longest unigene was 20,293 bp, and the shortest unigene was 201 bp . ApproxiThe correlation of gene expression between samples is important to test the reliability of the database and the rationality of sample selection. According to the unigene expression level, the correlation coefficients between replicate samples were approximately 1 A, and th2FoldChange and p-value of DEGs between two sample types  were listed in Non-published Material 1. As shown in To characterize transcriptional variations that occur in response to sex determination, the transcripts between two different sample types were compared, and the number of differentially expressed genes (DEGs) including > two-fold up-regulation or > two-fold down-regulation was counted. logp-value < 0.001). The expressed of these DEGs was significantly different between the shoot apices containing different numbers of female flower buds . This result indicated that pumpkin floral organ formation in the main stem is controlled through changes in the transcript levels of key genes involved in hormone signal transduction.To further characterize the genes identified as being differentially expressed in association with sex, 871 DEGs were mapped to GO classifications, and KEGG enrichment was determined. The GO annotations of the DEGs are shown in R2 = 0.9  analysis was performed with specific primers using FS1 and FS10 samples. The results showed that the gene expression patterns of FS1 vs. FS10 examined by qRT-PCR analyses were similar to those revealed by RNA-seq A. Most oR2 = 0.9 B.2-fold change >1 or <−1 in FS1 vs. FS10 and MS10 vs. FS10 as specific queries, 25 DEGs are listed in CmaCh01G009990, CmaCh01G016830 and CmaCh09G005960) were down-regulated in FS10. These results indicated that genes related to ethylene synthesis and signal transduction positively regulate female sex expression in pumpkin. Two ethylene synthesis genes, CmaCh03G005620 and CmaCh02G000660, were analyzed for expression in different organs. CmaCh02G000660 (named CmaACO1) showed flower-specific expression and significantly differential expression between female and male flowers , so we selected it for further study.According to the KEGG pathway results, the majority of DEGs were enriched in the plant hormone signal transduction pathway related to ethylene synthesis and signal transduction. Based on log3 and indoleacetic acid (IAA). 3 treatment compared with the control. The trimonoecious ‘2013–12’ control produced female flowers at 51% of the 20 nodes and bisexual flowers at 27.5% of the nodes, and the first female node was node 10.1. When Ethrel was applied to the shoot apices at the four-leaf stage, female flowers were produced at 72.5% of the nodes, and bisexual flowers were produced at 3% of the nodes. The first female node was node 5.9, implying a significant increase in female flower development and a significant decrease in bisexuality as well as a distinctly early appearance of female flowers relative to the control. IAA treatment exerted a similar effect to Ethrel. When AgNO3 was applied, female flowers were produced at 33.5% and bisexual flowers were produced at 33.5% of the nodes, and the first female node was node 13.7, implying a significant decrease in female flower development. AVG treatment had a similar effect to that of AgNO3.To further demonstrate the effects of ethylene and auxin on the sex expression of pumpkin, shoot apices were treated with four chemicals: Ethrel, aminoethoxyvinyl glycine (AVG), AgNOACS genes in pumpkin were cloned, and gene expression patterns in different tissues were analyzed. Among them, CmaACS7 (CmaCh10G007020) was specifically expressed in female flower buds . CmaACO1 was significantly up-regulated in FS10 in the RNA-seq data, and its female-specific expression was confirmed using qRT-PCR. CmaETR1 (CmaCh05G007380.1) and CmaEIN3 (CmaCh04G024920) were highly homologous to ETR1 and EIN3 in melon and cucumber, and were significantly highly expressed in the shoot apices of ‘2013–12’ compared with ‘9–6’ [CmaACS7, CmaACO1, CmaETR1 and CmaEIN3 were detected using qRT-PCR. As shown in CmaACS7 was elevated significantly after Ethrel and IAA treatment, whereas the mRNA level was significantly reduced after AVG treatment. AgNO3, an inhibitor of the ethylene biological response, had a nonsignificant effect on CmaACS7 expression. These results indicated that CmaACS7 expression in ‘2013–12’ could be affected by chemical treatment. However, in the ‘9–6’ control, the mRNA levels of CmaACS7 were not significantly changed after the 4 chemical treatments  was determined using qRT-PCR analyses. Pumpkin flower buds 7 days before flowering (dbf) (lengths of 2–4 mm) were considered to be at stage 1 (S1); 6 dbf (lengths of 6–8 mm), stage 2 (S2); 5 dbf (lengths of 10–12 mm), stage 3 (S3); 4 dbf (lengths of 15–17 mm), stage 4 (S4); 3 dbf (lengths of 20–25 mm and a yellow stigma), stage 5 (S5); 2 dbf (lengths of 30–35 mm), stage 6 (S6); and 1 dbf, stage 7 (S7). As shown in CmaACS7 expression in the ovary, stigma and style decreased significantly during flower development, and the highest expression level was observed at S1. CmaACS7 expression in the stamen increased over the first five developmental stages and then decreased; the highest expression level was observed at S5. CmaACS7 expression in the stamen was much lower than that in the ovary, stigma and style throughout S1 to S7, and this lower expression of CmaACS7 was significant at S1. The trend of CmaACS7 expression shown in CmaACS7 has an important role in early sex differentiation.To examine whether The experiments in this study were carried out with the subandroecious line ‘9–6’ and the trimonoecious line ‘2013–12’. Based on observations up to node 20 on the main stem, ‘9–6’ produced only one or two female flowers at the top node, while ‘2013–12’ produced approximately 10 consecutive female flowers. These two lines showed significant differences in flower sex and are therefore good materials for studying sex expression mechanisms.The study of floral sex determination using RNA-seq has previously been reported in Cucurbitaceae plants, such as cucumber and melon ,39,40, a3, an inhibitor of the ethylene biological response, and AVG, an inhibitor of ethylene biosynthesis, reduced the incidence of female flowers. These results are consistent with those for melon and cucumber [Exogenous chemical treatment could induce plant sexual transition. In our research, the loss of bisexual flowers in ‘2013–12’ was accompanied by an increase in female flowers after treatment with the exogenous auxin IAA and the ethylene-releasing agent Ethrel. Some female flowers transformed from bisexual flowers retained underdeveloped stamens , distinccucumber ,44 but icucumber ,45. Thus3 treatment. We noticed that the changes in flower sex in ‘2013–12’ were greater than those in ’9-6’ when the lines were treated with the same chemicals at the same concentration. According to our previous research, ’9–6’ shows delayed abscission of ethylene-treated male flowers and a weak triple response in five-day-old seedlings compared to those of ‘2013–12’ [After the 4 different chemical treatments, a wide variation (14–21.5%) in female sex expression was observed in the trimonoecious line ‘2013–12’. In the subandroecious line ‘9–6’, the number of female flowers was not significantly changed after Ethrel, IAA, AVG or AgNO2013–12’ , indicatCmaACS7, CmaACO1 and CmaETR1 was significantly promoted by Ethrel. Ethrel could increase ethylene level and amplify ethylene signaling in the apex by upregulating the expression of ethylene synthesis and ethylene receptor genes, leading to a significant increase in female flowers (a 21.5% increase) and advancing the first female flower appearance. After IAA treatment, ‘2013–12’ showed a significant increase (16.5%) in female sex expression, a significant decrease (22%) in bisexuality, and advancement of the first female flower appearance; Ethrel treatment led to similar tendencies. The expression of ethylene biosynthetic genes  and ethylene signaling genes (CmaEIN3) was significantly increased. IAA treatment promoted ethylene production and the expression of ethylene-related genes. Similar conclusions have been reached in zucchini (Cucurbita pepo L.) and cucumber [ACS, causing fruit softening [CmaACS7 expression. We suggest that IAA might affect pumpkin sex expression by inducing CmaACS7 expression and indirectly affecting ethylene production. Ethylene- insensitive (EIN)3 is continuously degraded in plant cells [3 treatment inhibited female sex expression and delayed the first female flower in ‘2013–12’, but they had different effects on gene expression. AVG treatment reduced the expression of the ethylene synthesis genes CmaACS7 and CmaACO1, whereas AgNO3 reduced the expression of only the ethylene signal transduction gene. These results indicated that reduced ethylene levels and weakened ethylene signaling intensity could affect pumpkin sex expression.In ‘2013–12’, the expression of cucumber ,47. Auxioftening . In our nt cells , and thent cells ,51. IAA CmaACO1 and CmaEIN3 changed under IAA treatment in ‘9–6’, the percentage of female flowers and the node of first female appearance were not significantly affected compared with those of the ‘9–6’ control. IAA had no influence on the expression of the upstream regulator CmaACS7. In fact, variations in CmaACS7 expression were not obvious after Ethrel or other chemical treatments in ‘9–6’, which corresponded to no significantly changed number of female flowers. These results indicate that the expression level of CmaACS7 plays a crucial role in sex expression in pumpkin. Activated ACS can be induced by ethylene and IAA to regulate downstream genes in cucumber [CmaACS7 in pumpkin ‘9–6’ lost its capability to respond to IAA or ethylene after chemical treatment. The sequences of CmaACS7 were highly similar to those of CsACS2 (87.47%) and CmACS7 (87.47%) [CmaACS7 were developed to analyze 200 F2 individuals derived from a cross of ‘2013–12’ and ‘9–6’. Notably, markers closely linked to CmaACS7 were not genetically linked to female flower proportion. We speculated that another key gene influencing CmaACS7 response might be mutated in ‘9–6’. Ethrel caused no significant changes in the expression of ethylene synthesis or receptor genes compared to the ‘9–6’ control, which indicated that ‘9–6’ plant had lacked its normal function of responding to Ethrel signal. It is interesting that, although the expression of cucumber . In our sACS2 87.7% and Cm2 individuals derived from a cross between the trimonoecious line ‘2013–12’ and the subandroecious line ‘9–6’, a gene controlling high female flower proportion (higher than 45% up to node 20) was mapped and located in a narrow region. Several transcription factor genes were found in the located region, which did not contain any genes directly associated with ethylene synthesis and signaling transduction. Thus, we predicted that a transcription factor gene could be stimulated by hormone signaling (ethylene and IAA) and then the transcription factor protein could specifically be combined with CmaACS7 promoter to led high expression of CmaACS7 gene in ‘2013–12’ flowers. When the sequencing of transcription factor gene changed, CmaACS7 gene could not be regulated. So CmaACS7 had lost its positive feedback capability with respect to the ethylene pathway in ‘9–6’. This phenomenon could explain the different sex expression between pumpkin lines ‘2013–12’ and ‘9–6’, and ethylene sensitivity was reduced in ‘9–6’. Further research about the lack of increase female flowers after chemical treatment in ‘9–6’ is being planned to carry in our lab. The transcriptome data and chemical treatment results in our research benefited for key genes selection and gene interactions research regulated to pumpkin sex expression.Using FThe experiment was carried out with the subandroecious inbred line ‘9–6’ and the trimonoecious inbred line ‘2013–12’ . The seeIn subandroecious line ‘9–6’, shoots at LS1 contained only male flower buds, whereas those at LS10 contained male flower buds and one or two female flower buds. In trimonoecious line ‘2013–12’, shoots at LS1 contained male flower buds and one or two bisexual flower buds, and those at LS10 contained only female flower buds. There were obvious sex expression differences in FS1 vs. FS10 and in MS10 vs. FS10, which could explore the sex expression related genes. There was no significant differences were observed in MS1 vs. MS10 and MS1 vs. FS1, which could remove DEGs by virtue of different genetic background.2 and were stored at −70 °C for RNA-seq analysis. Total RNA was were extracted using TRIzol reagent  and were treated with RNase-free DNase. mRNA was isolated from the total RNA using Dynabeads Oligo (dT) , fragmented into small pieces and then copied into cDNA using reverse transcriptase. After purification, the cDNA fragments were connected using sequencing adapters. The fragments were sequenced from directions on an Illumina HiSeqTM 2000 System , and then the quality and purity of the constructed library were determined using an Agilent 2100 Bioanalyzer  [Young shoot apices of ‘9–6’ and ‘2013–12’ at LS1 and LS10 were collected for RNA extraction to study the transcriptome . All samples were immediately frozen in liquid NA, USA.) .ftp://www.cucurbitgenomics.org/pub/cucurbit/genome/Cucurbita_maxima/v1.1/Cmaxima_v1.1.chr.fa.gz) by the TopHat2 program [ftp://ftp.ncbi.nih.gov/blast/db), Swiss–Prot (http://www.uniprot.org/downloads) and clusters of orthologous groups for eukaryotic complete genomes (KOG) (ftp://ftp.ncbi.nih.gov/pub/COG/KOG/kyva) databases using BLAST with an E-value of 1e−5.The raw reads from each library were assembled separately through base-calling analysis. After quality assessment using FASTQC software , the tri program . The defUnigene expression was determined based on fragments per kilobase per million reads (FPKM) . To discp-value < 0.05 for multiple tests using the Benjamini-Hochberg method [p-value; http://www.geneontology.org/) using Blast2GO [p-values by searching against the KEGG database (http://www.genome.jp/kegg/pathway.html) [Differentially expressed genes (DEGs) were identified with an adjusted g method  for contBlast2GO . Three cay.html) .–ΔΔCt method and were normalized to the control gene actin. The primer sequences used in qRT-PCR are listed in Shoot apices of ‘2013–12’ at LS1 and LS10 were collected, and RNA was extracted for real-time quantitative polymerase chain reaction (qRT-PCR) analysis. cDNAs were reverse transcribed using 2 µg of total RNA with the PrimeScript RT Reagent Kit . For qRT-PCR, 20 µL samples were run in triplicate on an ABI Prism 7000 Sequence Detection System and Applied Biosystems software using SYBR Green PCR Master Mix . Thermal cycling was performed using an initial denaturation step of 95 °C for 3 min followed by 40 cycles at 95 °C for 10 s, annealing temperature for 20 s, and 72 °C for 20 s. The relative expression levels of the genes were calculated by the 2C. maxima, 4 different chemical solutions, i.e., 100 mg/L 2-chloroethylphosphonic acid (Ethrel) (an ethylene-releasing agent), 100 mg/L aminoethoxyvinyl glycine (AVG) (an inhibitor of ethylene biosynthesis), 200 mg/L AgNO3 , and 200 mg/L IAA, were added to H2O containing 0.1% (v/v) Tween 20 were applied as treatments, and ddH2O with 0.1% Tween 20 was used as the control. The shoot apices of ‘2013–12’ and ‘9–6’ at the four-true-leaf stage (S4) were sprayed four times (continuously) with these chemical solutions once per day, and each treatment consisted of 15 plants with three biological replicates. After treatment for 1 d, the shoot apices of ‘2013–12’ and ‘9–6’ that had been subjected to the different chemical solutions were selected for transcript level detection. The primer sequences used in qRT-PCR are listed in To study the effects of ethylene and auxin on the sex of Pumpkin flower buds were divided into seven different stages of flower development. Buds with lengths of 2–4 mm were considered to be at stage 1 (S1); those with lengths of 6–8 mm, stage 2 (S2); 10–12 mm, stage 3 (S3); 15–17 mm, stage 4 (S4); 20–25 mm and a yellow stigma, stage 5 (S5); 30–35 mm, stage 6 (S6); and those one day before flowering were considered to be at stage 7 (S7). Male and female flower buds from S1 to S7 were collected from the trimonoecious line ‘2013–12’, and the stamen, ovary, stigma and style were detached from male and female flower buds for qRT-PCR. The morphologies of female and male flower buds at S1–S7 are shown in CmaACS7, CmaACO1, CmaETR1 and CmaEIN3 were positively correlated with the number of female flowers in ‘2013–12’, and IAA might affect pumpkin sex expression by inducing CmaACS7 expression and indirectly affecting ethylene production. The variations in gene expression in ‘9–6’, especially CmaACS7, were small in amplitude. CmaACS7 expression in the ovary, stigma and style in flower development also indicated that CmaACS7 plays an important role in sex differentiation. Based on the expression of key genes, a possible reason for the different sex expression between pumpkin lines ‘2013–12’ and ‘9–6’ was determined. In the present study, we investigated different transcript expression patterns between trimonoecious and subandroecious pumpkins. The DEGs between shoot apices containing male buds and female buds were mainly enriched in the ethylene and auxin synthesis and signal transduction pathways. The numbers of female flowers in ‘2013–12’ changed significantly, while the numbers in ‘9–6’ changed slightly after exogenous hormone treatments. The expression levels of


Lactuca sativa L.) is sensitive to salinity, which reduces plant biomass, and causes leaf burn and early senescence. We sought to identify physiological traits important in salt tolerance that allows lettuce adaptation to high salinity while maintaining its productivity. Based on previous salinity tolerance studies, one sensitive and one tolerant genotype each was selected from crisphead, butterhead, and romaine, as well as leaf types of cultivated lettuce and its wild relative, L. serriola L. Physiological parameters were measured four weeks after transplanting two-day old seedlings into 350 mL volume pots filled with sand, hydrated with Hoagland nutrient solution and grown in a growth chamber. Salinity treatment consisted of gradually increasing concentrations of NaCl and CaCl2 from 0 mM/0 mM at the time of transplanting, to 30 mM/15 mM at the beginning of week three, and maintaining it until harvest. Across the 10 genotypes, leaf area and fresh weight decreased 0–64% and 16–67%, respectively, under salinity compared to the control. Salinity stress increased the chlorophyll index by 4–26% in the cultivated genotypes, while decreasing it by 5–14% in the two wild accessions. Tolerant lines less affected by elevated salinity were characterized by high values of the chlorophyll fluorescence parameters Fv/Fm and instantaneous photosystem II quantum yield (QY), and lower leaf transpiration.Salinity is a rising concern in many lettuce-growing regions. Lettuce ( Global agriculture is under increasing threat from rising levels of salinity in soil and irrigation water. Salinity affects soil at nearly 10–20% of all land surface but is particularly problematic at irrigated land where almost 50% of all area is affected ,4. Econo−1 [Lettuce is an economically important vegetable crop cultivated in many countries around the world, with the highest production value in the U.S., Europe, and China . It is a−1 . Salinit−1 ,16,17,18−1 ,25,26,27a fluorescence kinetics has become an important tool in plant science, which allows non-invasive determination of plant health [a fluorescence of a dark-adapted photosynthetic system upon illumination with an actinic light source, followed by a gradual decline, ultimately reaching a steady state. Measurement of these transitions from a dark-adapted state to light-adapted state and back, representing the complex dynamics between chlorophyll fluorescence and photochemical yield, allows studying the photosynthetic reactions in great detail. After dark adaptation, the photosynthesis reaction centers are considered open and available for photosynthesis at their full capacity. In this state, the chlorophyll fluorescence is minimal, also called F0, and photosynthetic yield is essentially zero [0 and Fm is the variable fluorescence, Fv. The ratio of Fv/Fm has been shown to be a robust indicator of the maximum quantum yield of PSII chemistry [2 fixation rate, and is also referred to as plant vitality [Measurement of chlorophyll t health ,29,30. Pt health . Chloropt health ,33 is thlly zero . If a stlly zero ,36,37. Themistry ,39. Fv/Fhemistry . Damage hemistry . Coeffichemistry ,43. Insthemistry . The ratvitality ,46,47.Recent advances in sensing technology have led to the development of precision phenotyping instruments with highly accurate, fast operating sensors and streamlined, automated, high-throughput workflows ,49. The L. serriola) is a wild lettuce sexually compatible with cultivated lettuce. This species is used in breeding programs as a donor of certain desirable genes into cultivated lettuce [Cultivated lettuce is classified into several horticultural types with distinct phenotypes. Prickly lettuce , and PI 491154 . “Eruption” is a Latin type cultivar, but due to similarity of Latin and romaine types they can be directly compared. Cultivars and accessions from the same/similar horticultural type (or wild species) were compared to each other in order to minimize the effect of horticultural types on observed differences in their performances. Classification into salinity tolerant and sensitive groups were based on a previous study [Lettuce cultivars and germplasm accessions used in this study were: “Laura” , “Early Bird” , “Morgana” , “Mayfair” , “Eruption” , “Parris Island Cos” , PI 171676a , “Shining Star” , PI 253468  lined with coffee filters, was used as the growth substrate. Pots were placed in “1020 trays” (53.34 cm × 27.3 cm × 6.35 cm) to accommodate 18 pots per tray. Control plants were watered with Hoagland nutrient solution without NaCl (EC 2093 µS cm−1), while treatment plants were watered with NaCl/ CaCl2 -containing Hoagland solution. Salt-stress was gradually introduced so as to prevent salt-shock. To plants in the treatment trays, Hoagland solution containing 10 mM/ 5 mM NaCl/ CaCl2 (EC 5690 µS cm−1) was applied right after transplanting, followed by 20 mM/ 10 mM NaCl/ CaCl2 (EC 6320 µS cm−1) in the second week and 30 mM/ 15 mM NaCl/ CaCl2 (EC 8620 µS cm−1) in weeks 3 and 4. Trays were watered by reverse irrigation to avoid leaf injury due to contact with high-salt solutions. During watering, 3 L of the respective solution was applied to each tray and allowed to soak for 30 min. Excess liquid was then discarded from the trays.Seeds were germinated in distilled water in sterile Petri dishes for two days to ensure synchronized and uniform germination. Three uniform, 2-day-old seedlings from each cultivar were then transplanted into pots. Humidity domes were used for one week to help survival of seedlings after transplantation. After the first week, seedlings were thinned to keep only one per pot. All plants were grown in a Conviron CMP6050 growth chamber  at 20 °C, 200 µmol m−1 , which is a low salinity for lettuce. The EC of tap water was 800 µS cm−1, while that of distilled water was 1 µS cm−1. Therefore, for accuracy and consistency of salt concentrations, all solutions were prepared using distilled water. Sand moisture during experiments was tracked using ECH2O EC-5 soil moisture probes . Pots were always kept moist to avoid sudden and drastic increases in salinity due to drying, and were not allowed to fall below 0.1 m3m−3 water/soil volumetric water content (VWC).Before the beginning of the experiment, salinity in sand, tap water, distilled water, and Hoagland solution  was measExperiments were repeated up to five times, with each experiment consisting of at least four biological replicates per cultivar/accession per condition in a completely randomized design.2 assimilation was measured using the Licor LI-6400 XT Photosynthesis System . Photosynthesis measurements were performed on three leaves of intermediate age per plant and averaged. Light intensity was set to 200 µmol m−2 s−1, and 50–70% relative humidity, similar to that experienced by plants in the growth chamber. Flow rate was set at 400 µmol m−2 s−1 and 400 ppm CO2, at near-atmospheric levels, was supplied for photosynthesis. Light intensities were measured using the LiCor LI-250A Light Meter . Plant FW was determined by cutting off the entire plant at the base (root junction) and immediately weighing. After determining FW, plants were dried at 40 °C for one week and weighed to determine their DW.All physiological measurements were conducted four weeks after transplanting, just before the first plants reached their bolting stage. The leaf chlorophyll index was measured with a SPAD-502m  hand-held meter. Measurements from five intermediate-aged leaves of similar age per plant were averaged. Photosynthetic COa fluorescence was measured using the PlantScreenTM Transect XZ system, a custom setup based on the PlantScreenTM Robotic XYZ system  consisting of an imaging station mounted on a robotic arm with an LED light panel and charged coupled device (CCD) camera positioned in the middle of the light panel  saturating cool-white light with maximum intensity of approximately 5000 µmol m−2 s−1. Actinic light intensity was set to 50%, corresponding to approximately 250 µmol m−2 s−1, similar to that experienced by plants in the growth chamber. Far-red light and saturating light intensities were set at 30% and 20%, respectively. Total intensity of the saturating actinic light pulse at these settings corresponded to approximately 1200 µmol m−2 s−1. All measurements were performed after 30 min of dark adaptation. Data acquisition was staggered, including a 5 min dark adaptation after every measurement, in order to prevent light overflow to adjacent trays during measurement, potentially affecting dark adaptation. Electronic shutter was set at 20 µs and sensitivity at 10%, to maintain measurements within a dynamic range and prevent pixel overflow. Minimum fluorescence in the dark-adapted state (F0) was measured using a 5 s light flash. This was followed by a saturation pulse of 1000 µmol m−2 s−1 for 800 ms to determine maximum fluorescence in the dark-adapted state (Fm). Following a 17 s dark relaxation step, actinic light was turned on for 70 s to drive photosynthesis and measure the rise in peak fluorescence (Fp) during the initial phase of the Kautsky effect. During this period, saturation pulses were applied at 8, 18, 28, 48 and 68 s intervals as indicated in A)and allowing us to measure minimum fluorescence in the light-adapted state (F0_Lss). Actinic light period was followed by dark relaxation, maintained for 100 s. Dark relaxation responses were measured during this time by applying saturating light pulses at 30, 60, and 90 s, corresponding to D1, D2, and D3 states, respectively (0 level. The level of chlorophyll fluorescence just before application of the saturation pulse was recorded as steady-state fluorescence in the light-adapted state (Ft). Automation of plant masks for data analysis was difficult because of the large variation of shapes and sizes of the different lettuce genotypes  different from the overall mean. In addition, group means were also analyzed by Analysis of Variance (ANOVA) for Type II sum of squares, using the R package Companion to Applied Regression (car) [All statistical analyses were conducted using R . Pairwis ggplot2  and ggpu ggplot2 . Multipl ggplot2 , and sub ggplot2  was usedon (car)  and applon (car)  and corron (car)  were useAll percent change calculations were performed as percent reduction in “salt” relative to “control”, using the following formula: /control) * 100. Thus, negative numbers indicate an increase in “salt” values relative to the “control”, while positive numbers indicate a decrease in “salt” values relative to the “control”. Calculated leaf thickness was determined using the FW/LFA  formula , which hIn control conditions, FWs ranged from 2.4 g (PI 253468) to 14.8 g (“Shining Star”)  with FW DWs ranged from 0.76 g (PI 253468) to 2.49 g (PI 171676a) in control conditions, and from 0.59 g (PI 491154) to 1.91 g (PI 171676a) under salinity . PI 2534LFA  was calculated using the PlantScreen system. The smallest total leaf area in control was observed in PI 253468 , whereasL. serriola accessions PI 491154 and PI 253468.The chlorophyll index (SPAD) under control conditions was significantly higher than the overall mean in “Early Bird”, “Parris Island Cos”, and PI 491154, and significantly lower than the overall mean in “Laura”, “Mayfair”, “Eruption”, and “Shining Star” . Under s2 assimilation in “Early Bird” and “Eruption” was significantly lower, whereas the CO2 assimilation in PI 253468 and PI 491154 was significantly greater than the overall mean  was significantly lower than the overall mean in “Mayfair”, and significantly higher in “Laura”, PI 171676a, and “Shining Star” under control conditions, suggesting more open stomates in “Mayfair” than “Laura”, PI 171676a, and “Shining Star”. Under salinity treatment, VpdL was significantly lower in “Mayfair” and “Eruption”, and significantly higher in “Shining Star” than in the overall mean. VpdL showed a small (16–20%) but statistically significant increase in “Early Bird”, “Morgana”, “Mayfair”, and “Parris Island Cos” under salinity treatment , suggest2 concentration (CI) for any cultivar (2O (Cond) decreased a significant 61% in “Morgana” in salinity compared to control and decreased a significant 77% in “Mayfair” under control compared to salinity. Cond decreased 43% and 44% in PI 253468 and PI491154, respectively, under salinity compared to control  indicates photosynthetic rates, with higher ratios corresponding to higher quantum photochemical conversion . Rfd gra control .2 assimilation was observed to increase 10% under salinity compared to control in “Morgana”  to 2.26 (±0.04). qN also similarly increased significantly in “Parris Island Cos”, from 0.76 (±0.01) to 0.80 (±0.004) E. TogethAn approximately 10% reduction in Fv/Fm_Lss, from 0.44 (±0.01) to 0.40 (±0.02) in PI 171676a and from 0.45 (±0.003) to 0.41 (±0.01) in “Shining Star” was observed. NPQ_Lss levels were significantly higher in PI 171676a under salinity compared to control, rising from 1.80 (±0.03) to 2.12 (±0.06) while remaining largely unchanged in “Shining Star”. Similarly, qN_Lss levels had a small but significant increase, from 0.74 (±0.01) to 0.77 (±0.01), while remaining largely unchanged in “Shining Star”. Rfd_Lss levels increased significantly, 15% in PI171676a under salinity compared to the control, from 2.72 (±0.09) to 3.12 (±0.07), suggesting a reduction in photosynthesis. “Shining Star” did not show any significant change in Rfd_Lss between treatment, indicating no effect of elevated salinity on photosynthesis.2 assimilation under salinity in PI 253468  cluster together, characterized by high values for SPAD, stomatal conductance, leaf transpiration, and photosynthesis. PI 171676a and “Laura” cluster together, characterized by low values of VpdL, leaf area, Fv/Fm_L1, Fv/Fm_L2, Fv/Fm_L3, Fv/Fm_L4 and, Fv/Fm_Lss. “Eruption” and “Early Bird” cluster together, characterized by high values of CI, NPQ_D1, NPQ_D2, NPQ_D3, NPQ_L1, NPQ_Lss, Rfd_Lss, qN_D1, qN_D2, and qN_Lss. “Shining Star”, “Morgana”, “Mayfair” and “Parris Island Cos” cluster together, characterized by high values of QY_L1, QY_L2, QY_L3, QY_L4, QY_Lss, Rfd_L1, Rfd_L2, qN_L2, Fv/Fm_L2, FW, DW, QY_D1, D2, D3, QY_max, and Fv/Fm_D1 . When a To establish a starting point for understanding the complex relationship between the different measured variables, correlation analysis was conducted to determine the association and strength of association between all the variables in cultivars with a 16% to 31% reduction in FW under salinity  Tolerance to osmotic stress due to rapid shutdown of growth. In this rapid response to salinity, osmotic stress causes immediate reduction of cell expansion in root tips and young leaves, and stomatal closure. Tolerant plants have a more subdued response to osmotic stress, and are thus able to maintain growth and increased leaf area compared to sensitive ones, provided water supply is not an issue [+ ions from leaf blades. This process is slower, and takes place over many days or weeks [+ from the surrounding medium, thus limiting the accumulation of Na+ in the leaves. Sensitive plants, which are unable to do so, exhibit toxic effects of Na+ accumulation. Depending on the species, it can take anywhere from days to weeks to observe this effect. (3) Tissue tolerance, the mechanism in which tolerant plants can sequester Na+ and Cl− away from the cytoplasm. The ions are most commonly repossessed into cell wall or vacuoles, thus delaying and/or reducing their toxicity effect [+ than younger ones and are retained longer on the tolerant plant compared to sensitive one. Salt-tolerant plants or halophytes, have evolved efficient and effective tolerance mechanisms, and can survive in the presence of extreme salt concentrations, from 300–500 mM [+ and Cl− ions away from actively transpiring leaves (where these concentrations can be higher), to other parts of the plant, by translocating via the phloem. The end result of all these mechanisms is that salt concentration is kept reasonably low and constant for extended periods of time. Another mechanism by which euhalophytes tolerate extremely high salt concentrations is by accumulation of soluble carbohydrates in their cell sap, which reduces the osmotic potential relative to the soil solution [Effects of salinity on plants are well studied ,25,26,70an issue . (2) Excor weeks ,25. Rooty effect . Older l0–500 mM ,74. Euhasolution . Most cr+ ion accumulation in the leaves over time, and also as a response mechanism by the plant to deal with high salt stress. The plant needs to optimally regulate the light energy absorbed, so as to limit the photooxidative damage resulting from reduced photosynthesis, and maintain plant growth. Excess light energy that the plants absorb, if unquenched by photoprotective mechanisms or photochemistry, can lead to formation of the excited state of chlorophyll (triplet chlorophyll) in the photosystems, which can react with molecular oxygen to produce highly damaging reactive oxygen species (ROS) singlet oxygen (1O2) or superoxide (O2•−) [1O2 or O2•− come into contact with water, they can react to produce the equally damaging ROS hydrogen peroxide (H2O2). ROS can cause damage to other cellular components, including photosystem I and photosystem II. Scavenging ROS molecules requires a lot of energy, and is counter-productive to the already stressed plant trying to maintain growth. In addition, ROS can activate genetic pathways leading to cell death [Photosynthetic performance is a key aspect of how plants adapt to their growth conditions. Most stresses, including salinity stress, affect photosynthetic activity, and chlorophyll fluorescence can be used as a reliable, rapid and non-invasive indicator to analyze photosynthetic function ,75. Salie (O2•−) ,77. If 1ll death ,79, leadAutomated and semi-automated phenotyping allows for fast and non-destructive measurements of leaf area and a wide range of chlorophyll fluorescence parameters, including Fv/Fm, NPQ, qN, and qP, that report the condition of the photosystems ,80,81,823NH4 at 285 ppm). No significant change in SLW was also reported in arugula (Eruca sativa L.) [Phaseolus vulgaris, Gossypium hirsutum, Atriplex patula, Carrizo citrange, Cleopatra mandarin, Arbutus unedo, Myrtus communis, Eugenia myrtifolia, Viburnum tinus, zucchini squash, tobacco, and tomato [As has been reported before, FW was more sensitive to salinity than DW ,17,54. Stiva L.)  under 40d tomato ,87,88), d tomato ,85,88,89d tomato  to the ad tomato .Reduction in FW correlated with reduction in leaf area , suggestL. serriola .Similar to a previous study , we alsous study , it is pThe PCA indicates the wild lettuce genotypes segregating in a cluster clearly separated from the other lettuce types. Wild lettuce, and to an extent leaf-type lettuce, are characterized more by physiological properties of leaf transpiration, stomatal conductance, photosynthesis, and SPAD than the chlorophyll fluorescence values, which tend to distinguish butterhead, romaine, and crisphead in separate clusters .Based on fresh weight that is the main determinant of lettuce yield , our cla


Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods. Polygenic risk scores (PRS) have the potential to predict complex diseases and traits from genetic data. Here, Ge et al. develop PRS-CS which uses a Bayesian regression framework, continuous shrinkage (CS) priors and an external LD reference panel for polygenic prediction of binary and quantitative traits from GWAS summary statistics. Although this approach has advantages in terms of computational and conceptual simplicity, and has been used to predict genetic liability across a broad phenotypic spectrum, recent studies have shown that this conventional method for PRS construction discards information and limits prediction accuracy4. More sophisticated Bayesian polygenic prediction methods that rely on GWAS summary statistics, including LDpred4 and the normal-mixture model recently developed6, can incorporate genome-wide markers and accommodate varying genetic architectures, and thus have enhanced performance and flexibility. However, the type of prior on SNP effect sizes used in these methods, known as discrete mixture priors, imposes daunting computational challenges and may result in inaccurate adjustment for local LD patterns.To date, most applications calculate PRS from a subset of the genetic markers after pruning out single nucleotide polymorphisms (SNPs) in LD and applying a 12. However, all previous work required individual-level data and was limited to small-scale analyses (both in term of the sample size and number of genetic markers). PRS-CS only requires GWAS summary statistics and an external LD reference panel, and therefore can be applied in a broader range of settings.In this work, we present a polygenic prediction method, PRS-CS, which utilizes a Bayesian regression framework and places a conceptually different class of priors—the continuous shrinkage (CS) priors—on SNP effect sizes. Continuous shrinkage priors allow for marker-specific adaptive shrinkage , and thus can accommodate diverse underlying genetic architectures. In addition, continuous shrinkage priors enable conjugate block update of the SNP effect sizes in posterior inference , and thus can accurately model local LD patterns and provide substantial computational improvements. Several special cases of continuous shrinkage priors have been applied to quantitative trait prediction or gene mapping14, and demonstrate that PRS-CS dramatically improves the predictive performance of PRS over existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six curated common complex diseases (breast cancer (BRCA), coronary artery disease (CAD), depression (DEP), inflammatory bowel disease (IBD), rheumatoid arthritis (RA), and type 2 diabetes mellitus (T2DM)) and six quantitative traits  in the Partners HealthCare Biobank15, and further demonstrate the potential of PRS-CS for the clinical translation of polygenic prediction.We conduct simulation studies using the UK Biobank genetic dataN and M denote the sample size and number of genetic markers, respectively, y is a vector of traits, X is the genotype matrix, β is a vector of effect sizes for the genetic markers, and ε is a vector of residuals. By assigning appropriate priors on the regression coefficients β to impose regularization, additive PRS can be calculated using posterior mean effect sizes.We consider a Bayesian high-dimensional regression framework for polygenic modeling and prediction:β can be represented as scale mixtures of normals:N is a normal distribution with mean μ and variance σ2, and G is a mixing distribution. For example, if G places all its mass at a single point, i.e., δ• is the Dirac delta measure, then marginally 16. To create a more flexible model of the genetic architecture, a discrete mixture of two or more point masses or densities can be used, which allows for a wider effect size distribution than a normal prior can produce. For example, π is the mixing probability , produces the point-normal prior on effect sizes, βj ~ (1−π)δ0 + πN, which was used in LDpred4. Although discrete mixture priors offer a natural and intuitive approach to model non-infinitesimal genetic architectures, posterior inference requires a stochastic search over an exponentially large discrete model space, and does not allow for multivariate block update of effect sizes, which limits computational efficiency and may result in inaccurate modeling of local LD patterns.Essentially all widely used prior densities for ϕ is a global scaling parameter that shares across genetic markers and controls the degree of sparseness of the model, and g is an absolutely continuous density function, in contrast to a discrete mixture of atoms or densities. By appropriately choosing the continuous mixing density g, this modeling framework can produce a variety of shapes of the prior distribution on βj. In particular, g can be designed to introduce a prior distribution on the SNP effect sizes that has a sizable amount of mass near zero to impose strong shrinkage on noise, while at the same time has heavy tails to avoid over-shrinkage of truly non-zero effects. The marker-specific local shrinkage parameter ψj can then adaptively squelch small noisy estimates towards zero, while leaving data-supported large signals unshrunk. In this work, we investigate a specific g , and present two versions of the algorithm, which differ in the way to learn the global scaling parameter ϕ. In PRS-CS, we search a small number of fixed ϕ, select the ϕ value that produces the best predictive performance in a validation data set, and evaluate the algorithm in an independent testing set. In the second version of the algorithm, which we call PRS-CS-auto, we use a fully Bayesian approach and place a standard half-Cauchy prior on the global shrinkage parameter20: ϕ1/2 ~ C+, such that ϕ is automatically learnt from data and no validation data set is needed.In this work, we investigate a conceptually different class of priors—the continuous shrinkage priors. In particular, we consider the following prior on SNP effect sizes, which can be represented as global-local scale mixtures of normals:22. Here we enable posterior inference of SNP effect sizes from GWAS summary statistics under continuous shrinkage priors using an efficient Gibbs sampler with multivariate block update of the effect sizes (see Methods section).Individual-level Bayesian regression models (1) with a prior on SNP effect sizes can often be approximated using an external LD reference panel and turned into summary statistics based methodsP-value thresholding (P+T), LDpred and LDpred-inf4. Throughout the paper, we use the 1000 Genomes Project (1 KG) European sample (N = 503) as the external LD reference panel, but also assess the impact of using an in-sample LD reference panel on prediction accuracy in Supplementary Information.We compare PRS-CS and PRS-CS-auto with four polygenic prediction methods that rely on GWAS summary statistics in both simulations and real data analyses: polygenic scoring based on all genetic markers (unadjusted PRS), informed LD-pruning  and P-value threshold in P+T, fraction of causal SNPs in LDpred, and global shrinkage parameter in PRS-CS) were selected in a validation data set (N = 3000). Prediction accuracy for all methods was quantified by R2 between the observed and predicted traits in an independent testing set (N = 3000).We first compared the predictive performance of six polygenic prediction methods across different genetic architectures and training sample sizes  in simulation studies Fig. . SNP effFigure In addition to prediction accuracy, we assessed the calibration of polygenic prediction methods by regressing the true phenotype onto the PRS predictor and inspecting the regression slope. A slope close to one indicates that a predictor is correctly calibrated. Consistent with predictive performance, as the training sample size grows, our Bayesian approach provides the best calibration among all methods examined  a point-gamma model with different numbers of causal variants produced similar patterns of prediction accuracy  as an in-sample LD reference panel in the point-normal simulations produced, in general, slightly higher prediction accuracy for methods making use of LD information  the point-normal model with different total heritability 0.2 and 0.8); (2) a point- the poin and 0.8;23 are presented in Supplementary Table We applied PRS-CS, PRS-CS-auto, and alternative methods to predict six curated common complex diseases , and six quantitative traits  in the Partners HealthCare Biobank. Large-scale GWAS summary statistics for each disease and trait were downloaded from public domains Table . SNP herR2 (for disease phenotypes) and R2 (for quantitative traits) is summarized in Fig. Predictive performance measured by Nagelkerke’s R2 compared to LDpred) and rheumatoid arthritis (28.62% relative increase in Nagelkerke’s R2 compared to LDpred). For coronary artery disease, depression and type 2 diabetes mellitus, LDpred and PRS-CS had similar predictive performance, and both performed dramatically better than P+T. PRS-CS was only inferior to LDpred in the prediction of inflammatory bowel disease (10.24% relative decrease in Nagelkerke’s R2). However, we note that inflammatory bowel disease has the smallest training sample size among all diseases and traits  instead of the 1KG reference panel slightly increased the prediction accuracy but the improvement was marginal , and the Bayesian computation with 1000 MCMC iterations on the longest chromosome can be completed within an hour using one Intel(R) Xeon(R) CPU core and 2 GB of memory. Expanding the size of LD blocks may improve prediction accuracy but increases computational cost (as each MCMC iteration requires inverting an L × L matrix where L is the block size), while reducing the size of LD blocks has the potential risk of missing long-range LD. Therefore, the partition we chose represents a balance between modeling accuracy and computational burden. Including multi-million SNP predictors may increase prediction accuracy28 but requires further work.Although continuous shrinkage priors enable multivariate modeling of the LD structure, simultaneous updating of the effect sizes for genome-wide markers remains computationally infeasible. In this work, we used a genome partition computed and validated by prior work30, the normal-inverse-gaussian prior29, the generalized t  prior32, and the normal-exponential-gamma prior34, among others. In addition, most frequentist regularization procedures, such as LASSO, elastic net and bridge regression, have a Bayesian counterpart that can be represented as global-local scale mixtures priors in combination with posterior mode inferences. Each of these priors uses a different continuous mixing density to produce a different marginal prior on the SNP effect sizes. These alternatives may perform equally well or better than the Strawderman-Berger prior for certain genetic architectures. However, we found that as long as the prior on the effect sizes places a sizable amount of mass around zero and has heavier-than-exponential tails, variation in the shape of the prior does not seem to have a large impact on prediction accuracy. Therefore, we believe that the primary gain of PRS-CS over existing methods lies in its more accurate multivariate modeling of local LD patterns and its block-updated Gibbs sampling that can improve the mixing and convergence rate of the Markov chain. We thus recommend using the Strawderman-Berger prior as a default choice. A systematic investigation and comparison of different continuous shrinkage priors is a direction of future work.We note that the prior we investigated in this work, i.e., the Strawderman-Berger prior on the local marker-specific shrinkage parameter, is only one of the possible choices within the class of continuous shrinkage priors, which includes the normal-gamma prior40, BayesR42, BVSR43, BSLMM44, and DPR45, have used discrete mixture priors on SNP effect sizes, we expect that PRS-CS can provide substantial improvements in computational efficiency and prediction accuracy for genomic prediction that leverages individual-level data. Second, jointly modeling multiple genetically correlated traits and including functional annotations in polygenic modeling are expected to increase the predictive performance of PRS, as shown by recent studies47. Lastly, current research on polygenic prediction has largely been restricted to European samples. Heterogeneity between the GWAS, LD reference and testing samples may reduce prediction accuracy as recently demonstrated in genetic correlation analysis and fine-mapping49. Expanding genomic prediction methods to handle unknown ancestry of the target sample  and enable transethnic risk prediction is critical to maximize the value of PRS in a diverse population.We note several additional directions for further technical developments that may be useful. First, although this paper is focused on polygenic prediction methods that only require GWAS summary statistics, PRS-CS, and PRS-CS-auto can be straightforwardly applied to individual-level data. Given that a majority of the existing Bayesian genomic prediction models, including Bayes alphabetic methodsR2 less than 1%), likely due to a combination of its relatively low heritability, extremely polygenic genetic architecture, and the heterogeneous nature of the disorder. A recent study projected that a GWAS with multi-million subjects is needed to identify genetic variants that explain 80% of the SNP heritability for major depressive disorder5. In contrast, it may be easier to produce a clinically useful prediction for some autoimmune diseases or late-onset chronic diseases , due to the existence of SNPs with moderate to larger effect sizes. With these being said, as the GWAS sample size continues to grow, we believe that the predictive value of PRS will keep increasing, and PRS-CS(-auto) will demonstrate bigger advantages over existing methods with larger training sample sizes.Although PRS-CS provides a substantial improvement over existing methods for polygenic prediction, current prediction accuracy of PRS is still lower than what can be considered clinically useful, and much work is needed to further improve the predictive performance and translational value of PRS. In theory, the utility of PRS depends on multiple factors, including the GWAS sample size, and the heritability and genetic architecture of the disease. For example, among the six complex diseases we analyzed, depression had the lowest prediction accuracy , β is a vector of effect sizes, ε is a vector of independent environmental effects, and we have assigned a non-informative scale-invariant Jeffreys prior on the residual variance σ2. In contrast to discrete mixture priors, we consider a conceptually different class of priors:βj scales with the residual variance and the sample size, ϕ is a global scaling parameter that is shared across all effect sizes, ψj is a local, marker-specific parameter, and g is an absolutely continuous mixing density function. This type of prior is known as global-local scale mixtures of normals.We consider the following phenotype model:σ2, ϕ and ψj, j = 1,2,…, M, and the marginal least squares effect size estimates of the regression coefficients β isT = diag{ϕψ1,ϕψ2,…, ϕψM} is a diagonal matrix, and D = ZΤZ/N is the LD matrix. It can be seen that the posterior mean is a matrix shrinkage version of the least squares estimate. In the degenerative special case where ψj ≡ 1, the model becomes Ridge regression and all effect sizes are shrunk towards zero at the same constant rate controlled by the overall shrinkage parameter ϕ. The introduction of the local shrinkage parameter ψj thus allows heterogeneity in the scales of effect sizes.We first note that, given variance parameters D = I and thusτj = 1/(1 + ϕψj) is the shrinkage factor for the j-th marker, which relies on both ϕ and ψj, and describes the amount of shrinkage from the marginal least squares solution towards zero; τj = 0 indicates no shrinkage while τj = 1 yields total shrinkage. Therefore, ϕ controls the overall sparsity level of the model and plays a similar role as the regularization parameter in penalized regression, while ψj adaptively modifies the amount of shrinkage for each marker. By assigning a prior on ψj, which can produce a marginal prior density on βj that has both a sharp peak at zero and heavy tails, the model can pull small effects towards zero, while asserting little influence on larger effects.To provide further intuitions, assuming that all genetic markers are unlinked , we have ψj:α,β) denotes the gamma distribution with shape parameter α and scale parameter β. By using change of variables, it can be verified that placing a gamma-gamma prior on ψj is equivalent to placing a three-parameter beta (TPB) prior on the shrinkage factor τj33:x < 1, a > 0, b > 0 and ϕ > 0. When ϕ = 1, the TPB distribution becomes a standard Beta distribution. For a fixed value of ϕ, a controls the behavior of the TPB prior near one, and thus the behavior of the prior on βj around zero; b controls the behavior of the TPB prior near zero, and thus affects the tails of the prior on βj. Figure τj (upper panel) and βj (middle and lower panels) with ϕ = 1, b = 1/2, and three different values of a: a = 1/2, a = 1 and a = 3/2. It can be seen that when a = 1/2 and b = 1/2, the TPB prior has substantial mass near zero and one  can be easily derived. By using LD information from an external reference panel, the method can be applied to GWAS summary statistics and does not require individual-level data. We describe the Gibbs sampler in ψj : a = 1 and b = 1/2. This particular specification is known as the Strawderman-Berger prior18 or the quasi-Cauchy prior51, and appears to work well across a range of simulated and real genetic architectures.For all continuous shrinkage priors that take the general form in Eq. , Gibbs s27 [http://bitbucket.org/nygcresearch/ldetect-data], and conduct multivariate update of the effect sizes within each LD block . We use ρ = 1 throughout this paper.In practice, we partition the genome into 1703 largely independent genomic regions estimated using data from the 1KG European samplelock see . To avoiϕ, and setting ϕ1/2 roughly to the proportion of causal variants52 works well. If a prior guess of the sparseness of the genetic architecture is not available, we provide two ways to learn ϕ. In PRS-CS, we search a small number of ϕ values: ϕ1/2 ∈ {0.0001, 0.001, 0.01, 0.1, 1}, and select the ϕ that produces the best predictive performance in a validation data set, which is independent of the testing set where prediction accuracy of the algorithm is evaluated. In PRS-CS-auto, we use a fully Bayesian approach and assign a standard half-Cauchy prior on ϕ1/220, such that ϕ is automatically learnt from GWAS summary statistics and no validation data set is needed. See ϕ.We find that the predictive performance of the model is not sensitive to the global shrinkage parameter For both PRS-CS and PRS-CS-auto, the Gibbs sampler usually attains reasonable convergence after 1000 Markov Chain Monte Carlo (MCMC) iterations and produces prediction accuracy close to what can be achieved by much longer MCMC runs. We thus use 1000 MCMC iterations with the first 500 steps as burn-in in simulation studies to reduce computational cost. In practice, we recommend using longer MCMC runs when time and computational resources permit. In the Partners HealthCare Biobank analysis, we report the predictive performance of PRS-CS and PRS-CS-auto based on 10,000 MCMC iterations in total and 5000 burn-in steps.i-th individual is M is the total number of genetic markers, Xij is the genotype for the i-th individual and the j-th SNP, and j-th SNP.The unadjusted PRS is the sum of all genetic markers across the genome, weighted by their marginal effect size estimates. More specifically, the unadjusted polygenic score for the P-value thresholding. In this study, we use the implementation of the P+T method in the software package PRSice-253 [https://choishingwan.github.io/PRSice] and its default parameter settings. Specifically, for any pair of SNPs that have a physical distance smaller than 250 kb and an R2 greater than 0.1, the less significant SNP is removed. The polygenic score is then calculated as the sum of the remaining, largely independent SNPs with a GWAS association P-value below a threshold PT, weighted by their marginal effect size estimates. We consider PT ∈ {1E−8, 1E−7, 1E−6, 1E−5, 3E−5, 1E−4, 3E−4, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1} in this paper. The PT value that produces the highest prediction accuracy in a validation data set is selected, and the predictive performance is assessed in an independent testing set.The P+T method refers to the calculation of PRS using informed LD-pruning  and https://github.com/bvilhjal/ldpred] is a method that infers the posterior mean effect size of each genetic marker from GWAS summary statistics while accounting for LD, using a point-normal prior on the SNP effect sizes and LD information from an external reference panel4. Consider the linear model y = Zβ + ε, where both the phenotype y and the genotype matrix Z have been standardized. LDpred places an independent point-normal prior on each regression coefficient βj:π is the fraction of causal variants. Given π and an estimate of 23 to the GWAS summary statistics, LDpred employs an MCMC sampler to approximate the posterior mean of βj, conditioning on marginal least squares effect size estimates and LD information from a reference panel. In this paper, we consider π ∈ {1E−5, 3E−5, 1E−4, 3E−4, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1}. The π value with the highest prediction accuracy in a validation data set is selected, and the predictive performance is assessed in an independent testing set.LDpred .The genetic data for the UK Biobank comprises 488,377 samples and was phased and imputed to ~96 million variants with the Haplotype Reference Consortium (HRC) haplotype resource and the UK10K + 1KG reference panel. We leveraged the QC metrics provided by the UK BiobankN = 503) as an external LD reference panel. SNP effect sizes were simulated using (1) a point-normal model as specified in Eq. , the simulation was repeated 20 times.We performed simulation studies using real genetic data from the UK Biobank and the 1KG European sample (d in Eq.  with dift model with heavy tails (a mixture of a point mass at zero and a Student’s t-distribution with 4 degrees of freedom); (3) sampling SNP effect sizes using a point-gamma model (a mixture of a point mass at zero and a gamma distribution with the shape parameter set to 2), which produces an effect size distribution that is asymmetric about zero and positively skewed with the right tail being long and thin and the left tail being short and fat; (4) using the combined UK Biobank validation and testing data sets (N = 6000) as an in-sample LD reference panel in the point-normal simulations. For each setting and training sample size considered , and the simulation was repeated 20 times.In order to systematically compare polygenic prediction methods across a wide range of settings, we conducted a number of secondary simulation studies: (1) sampling SNP effect sizes using a point-normal model with heritability fixed at 0.2 or 0.8; (2) sampling SNP effect sizes using a point-https://biobank.partners.org] is a collection of plasma, serum, DNA and buffy coats samples collected from consented subjects, which are linked to their electronic health records (EHR) and survey data on lifestyle, environment, and family history55. To date, Partners Biobank has enrolled more than 96,000 participants, and released genome-wide genetic data for 25,482 subjects. A study protocol is not required for Partners investigators to obtain de-identified data sets from Partners Biobank.The Partners HealthCare Biobank [P < 1 × 10−6 were discarded. We then removed SNPs that showed significant batch associations with P < 1 × 10−6, and merged genotyping batches for subsequent processing and analyses.We performed QC on each genotyping batch separately with the following steps: (1) SNPs with genotype missing rate >0.05 were removed; (2) samples with genotype missing rate >0.02 or absolute value of heterozygosity >0.2, or samples that failed sex checks were excluded; (3) SNPs with missing rate >0.02, or HWE test N = 503) included TSI, IBS, GBR, CEU, and FIN subpopulations. The random forest model was then applied to the Partners Biobank participants, and identified 19,136 unrelated subjects  of the genotype data in all the 1KG samples, and trained a random forest model using the top 4 PCs on the super population labels , in which EUR , RA, and T2DM—for which there are more than 500 cases in the Biobank that have been genotyped, and external large-scale GWAS summary statistics are publicly available. For all the diseases, cases have an algorithm-based positive predictive value (PPV) of having current or past history of the disease greater than 0.90, and controls have a negative predictive value (NPV) of having no history of the disease greater than 0.99.In addition, we selected six quantitative traits—height (HGT), body mass index (BMI), high-density lipoproteins (HDL), low-density lipoproteins (LDL), cholesterol (CHOL), and triglycerides (TRIG)—that have been measured in the Partners Biobank healthy control population with a Charlson age-comorbidity index 0–2 and the predicted 10-year survival probability greater than 90%. We predicted these quantitative traits in a relatively heathy population to avoid measurements affected by severe diseases or medications. For participants that have multiple measurements of a trait of interest, we used the median value. Table 23. Heritability estimates for diseases on the observed scale were transformed to the liability scale as described in Lee et al.58 using the assumed population and sample prevalences shown in Supplementary Table GWAS summary statistics for all the diseases and quantitative traits are publicly available  were selected in the validation set, and the predictive performance was evaluated in the testing set. We use the average R2 between the observed and predicted phenotypes across 100 random splits to assess the predictive performance for the quantitative traits, and report the average Nagelkerke’s R2 metric across 100 random splits for disease (case–control) phenotypes. Nagelkerke’s R2 is defined as N is the sample size. We define the relative increase or decrease in R2 of a polygenic prediction method A compared to method B as R2 or Nagelkerke’s R2, we also report area under the ROC curve (known as AUC), area under the precision-call curve, and the odds ratio (OR) comparing top 10% of the participants having high polygenic risk with the remaining 90% of the sample. We adjusted for current age, sex and top 10 PCs of the genotype data in the calculation of all predictive performance metrics.For each curated disease and quantitative trait, the Partners HealthCare Biobank sample was repeatedly and randomly split into a validation set comprising 1/3 of the data and a testing set comprising 2/3 of the data. Tuning parameters (Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Greater access to family support has been shown to positively affect the management of complex patients . However, patients’ availability of family support is not easily obtainable from medical records. We aim to identify administrative variables that can be used as indicators of family support. We investigated secondary next-of-kin  and marital status as administratively defined family support availability in a Veteran sample (n=2210). A subsample (n=329) was further evaluated with documented responses to questions “Are there any friends/family members you would like to involve in any aspect of your health care?”  and “Does anyone help you with your daily activities?” . We performed a logistic regression analysis to evaluate the association between administratively defined and “actual” or “obtained” availability of support controlling for age, race/ethnicity, and gender. The sample was 90% male, mean age 63 years, 50%White and 44% African American. We found that 32.9% had administratively defined availability by being married, and 32.5% by listing secondary next-of kin. Married Veterans were significantly more likely to report greater actual availability (p=0.01) and obtained (p=0.04) support. Veterans listing a secondary next-of-kin were significantly more likely to report “actual” availability (p= 0.04) but not on “obtained” (p=0.08) support. Marital status may be a useful proxy of actual family support and listing a secondary next-of kin may be an alternate indicator for complex patients. Our study provides guidance on the use of administrative data in understanding caregivers.


Candidatus Nitrosocosmicus franklandus” C13 is an ammonia-oxidizing archaeon (AOA) isolated from soil. Its complete genome is 2.84 Mb and possesses predicted AOA metabolic pathways for energy generation and carbon dioxide fixation but no typical surface layer (S-layer) proteins, only one ammonium transporter, and divergent A-type ATP synthase genes.“ Candidatus Nitrosocosmicus franklandus” C13 is an ammonia-oxidizing archaeon (AOA) isolated from soil. Its complete genome is 2.84 Mb and possesses predicted AOA metabolic pathways for energy generation and carbon dioxide fixation but no typical surface layer (S-layer) proteins, only one ammonium transporter, and divergent A-type ATP synthase genes.“ Candidatus Nitrosocosmicus” is placed within the Nitrososphaerales, one of four orders of AOA (Candidatus Nitrosocosmicus” strains are widely distributed in soil (–Ammonia-oxidizing archaea (AOA) belonging to the phylum Thaumarchaeota play a central role in the global nitrogen cycle . The gens of AOA , 3. “Can in soil  and have in soil –7.Candidatus Nitrosocosmicus franklandus” C13 was isolated from a pH 7.5 arable soil sampled from an agricultural plot at the Scottish Rural College, Craibstone Estate, Aberdeen, United Kingdom (UK grid reference NJ872104)  and 16S rRNA gene sequences possessed 92 and 99% sequence similarity, respectively, to those of strain “Ca. Nitrosocosmicus oleophilus” . For MinION sequencing, DNA was sheared to approximately 8 kb using a g-TUBE  and a library prepared with the SQK-MAP006 kit, according to the manufacturer’s instructions . Sequencing on a MinION flow cell produced 139,341 reads with an average length of 1,511 bp.DNA was extracted from pelleted cells using a standard SDS buffer and phenol:chloroform:isoamyl alcohol chemical lysis method  and sequhttps://github.com/jts/nanopolish) was used to improve the consensus sequence. The assembly resulted in three large contigs which were aligned against the closest reference genomes (those of “Ca. Nitrosocosmicus oleophilus” [Ca. Nitrosocosmicus exaquare” [The genome was assembled using SPAdes  and Canuophilus”  and “Ca.xaquare” ) using Mrrn operon. The ammonia monooxygenase (AMO) of AOA is likely encoded by four subunits . The “Ca. N. franklandus” C13 genome contains one copy each of amoA, amoB, and amoX and three identical copies of amoC. While the amoA and amoX genes are adjacent, other amo subunits are dispersed throughout the genome, as is typically observed in Nitrososphaerales (Ca. N. franklandus” C13 and other “Ca. Nitrosocosmicus” genomes (amt), in contrast to members of other thaumarchaeal genera, which possess at least two nonidentical amt genes.The closed genome was annotated using the MaGe platform  and cons genomes , 7 posseCa. N. franklandus” C13. The key genes for the 3-hydroxypropionate-4-hydroxybutyrate carbon fixation pathway were found, as expected, as “Ca. N. franklandus” C13 grows autotrophically on bicarbonate as a sole carbon source  category N (motility) are predicted to be involved in sensing rather than movement.Typical horseshoe-type tricarboxylic acid cycle genes previously reported in Thaumarchaeota are present in “n source . All keynaterra” . “Ca. N.Ca. N. franklandus” C13, along with other characterized “Ca. Nitrosocosmicus” and “Candidatus Nitrosocaldus” genomes, lacks the main S-layer protein (encoded by slp1), which was previously considered to be the main component of the thaumarchaeal cell wall (Ca. N. franklandus” C13, but many cell wall-associated genes are putatively involved in amino sugar transformations, which warrants future investigation.Surprisingly, the genome of “ell wall , 16. It LR216287. The raw sequencing reads are available in the NCBI Sequence Read Archive under accession number PRJNA530370.The genome sequence was deposited in the European Nucleotide Archive under accession number


No significant learning effect was identified between the first and second sessions  nor was there a significant treatment order effect (p = 0.40). Caffeine’s ergogenic effect is present during CrossFit; however, future investigations should establish caffeine’s efficacy during other CrossFit protocols and among female athletes.Caffeine’s ergogenic effects persist during various exercise modalities; however, information establishing its efficacy during CrossFit protocols is limited. Our study aimed to determine the effects of caffeine supplementation on CrossFit performance. Thirteen CrossFit-trained men  were randomized in a double-blind, crossover design. Participants completed two sessions separated by a seven-day washout period, 60 min after consuming 5 mg/kg body mass of caffeine or a placebo. In each session, participants completed as many rounds as possible in 20 min of 5 pull-ups, 10 push-ups, and 15 air squats. CrossFit performance was the total number of repetitions completed in 20 min. Paired samples t-tests were used to compare CrossFit performance between caffeine and placebo conditions and to test for a potential learning effect between the first and second sessions. CrossFit performance was significantly higher during the caffeine condition compared to the placebo (461.4 ± 103 vs. 425.0 ± 93.5 repetitions,  Caffeine supplementation is pervasive in sporting disciplines with 74% of elite athletes consuming caffeine prior to competition for its ergogenic effects . SupportIn the most recent meta-analysis of the effects of caffeine supplementation on muscle function, the authors reported significant improvements (+6–7%) in muscular endurance after caffeine supplementation . A majorThe recent dramatic increase in high-intensity functional training—which aims to enhance multiple domains of physical fitness by temporally exposing athletes to varying modes of exercise  within and between each session, for varying durations , at a relatively high-intensity —has beenTo the best of our knowledge, no investigations have examined the effect of caffeine supplementation on CrossFit performance. Ostensibly, caffeine represents an ideal candidate for investigation with ‘good evidence’ establishing its ergogenicity across a variety of exercise protocols  and muscle groups ,26,27. HThis study used a randomized, double-blind, crossover design to determine the effects of caffeine on CrossFit performance. Inclusion criteria were: ≥6 months of CrossFit experience; previously completing ‘Cindy’ or a workout with similar repetition volumes for the included movements; male sex; and 18–45 years of age. Participants were excluded if they had any known health-problem , answered ‘Yes’ to any physical activity readiness questionnaire items, reported allergic or negative side-effects with caffeine use, were unable to perform ‘Cindy’ as prescribed, or were taking medication for seizures. This research project was approved by Kansas State University’s Institutional Review Board (#9100). All subjects completed a brief online survey to determine eligibility and provided written informed consent for the study in person.−1 body mass [Participants were asked to refrain from caffeine, alcohol, vigorous exercise, and nicotine for 24 h, maintain their normal diet, adequately hydrate, refrain from eating 3 h prior to testing, and to otherwise maintain their usual training regimen throughout the study. Anthropometric measures were taken on the first laboratory visit. Height was measured using a stadiometer. Body mass and percent body fat were determined using a bioelectrical impedance analysis in standard mode . Following anthropometric measures, participants  were randomized to consume either caffeine or the placebo. To determine treatment order (placebo then caffeine vs. caffeine then placebo), participants were randomized using a random number generator (0–99) with odd numbers being placed into the placebo condition and even numbers being placed into the caffeine condition for the first visit . The caffeine pill(s) (Prolab) were provided at 5 mg·kgody mass ,22,29. Bp < 0.05.Data were entered into SPSS 25  for analysis. Dependent variables were tested for normality using the Kolmogorov-Smirnov test. The variables were normally distributed and were described as mean and standard deviation. Paired samples t-tests were used to determine differences between the caffeine and placebo conditions in the total number of repetitions and RPE. An additional paired samples t-test was used to determine if a learning effect was present between the first and second sessions. An analysis of covariance was used to determine differences in the total number of repetitions performed during the caffeine condition between treatment order groups (placebo-caffeine vs. caffeine-placebo) while controlling for the total number of repetitions performed during the placebo condition. Descriptive data are provided as mean ± standard deviation. Degrees of freedom (df), critical value (cv), level of significance (p), and effect size (ES) were reported for each paired samples t-test (t). The magnitude to treatment effects (ES) were estimated with Cohen’s D and classified as “trivial” (<0.19), “small” (0.20–0.49), “moderate” (0.50–0.79), and “large” (>0.80) . A signit(12) = −3.928, p = 0.002, ES = 0.39 (t(12) = −0.562, p = 0.584 (t(12) = 0.348, p = 0.73). After controlling for the total number of repetitions performed during the placebo condition, no significant treatment order effect was observed for the total number of repetitions performed during the caffeine condition between treatment order groups  = 0.760, p = 0.40). Participants significantly improved CrossFit performance  during the caffeine trial (461.4 ± 103 repetitions) as compared to placebo (425.0 ± 93.5 repetitions), S = 0.39 . No sign = 0.584 . No signThe purpose of our investigation was to determine the effects of acute caffeine supplementation on CrossFit performance and perceptual responses to exercise in CrossFit-trained males. Because of the well-documented ergogenic effects of caffeine that are likely due to central mechanisms ,3,4,5, w−1 body mass) increased mean CrossFit performance by 8.9%. Caffeine’s ergogenic effect has been reported to improve performance by 5.5–8.5% during other repeated-high-intensity efforts in team sports athletes, and by 6–7% during muscular endurance exercise [In the current study, caffeine supplementation (5 mg·kgexercise ,32. Prevexercise ,17,33. Oexercise ,33,34. Hexercise .Our study assessed CrossFit performance via ‘Cindy’, which has been the most studied CrossFit workout to date ,22,29. BStrengths of the current investigation include a robust study design with subjects serving as their own controls, the recruitment of trained male CrossFit participants who were able to complete ‘Cindy’ as prescribed as a high-volume muscular endurance workout, and it is the first study to document an ergogenic effect during a CrossFit workout. However, our study does not go without limitation. Although 13 CrossFit-trained men completed our study, our sample size is reasonably small. However, the average sample size reported in a recently published systematic review on the effects of caffeine in trained soccer players was 15 participants; therefore, our investigation reflects similar sample sizes compared to other studies regarding caffeine’s ergogenic effect in trained populations . Partici−1 body mass ingested 60 min prior to the CrossFit workout ‘Cindy’ increased the number of repetitions performed in male CrossFit-trained individuals. However, despite the central effects of caffeine, we found no significant changes in perceptual responses during the exercise bout. Caffeine’s ergogenic effects are well documented in the literature and further supported by our investigation, yet practitioners and athletes should be aware that some ‘non-responders’ may exist; thus, evaluating caffeine’s effectiveness prior to competition for a specific athlete is encouraged.In conclusion, we have provided evidence that caffeine supplementation at 5 mg·kg