Two of the important traits for wheat yield are tiller and fertile tiller number, both of which have been thought to increase cereal yield in favorable and unfavorable environments. A total of 6,349 single nucleotide polymorphism (SNP) markers from the 15 K wheat Infinium array were employed for genome-wide association study (GWAS) of tillering number traits, generating a physical distance of 14,041.6 Mb based on the IWGSC wheat genome sequence. GWAS analysis using Fixed and random model Circulating Probability Unification (FarmCPU) identified a total of 47 significant marker-trait associations (MTAs) for total tiller number (TTN) and fertile tiller number (FTN) in Iranian bread wheat under different water regimes. After applying a 5% false discovery rate (FDR) threshold, a total of 13 and 11 MTAs distributed on 10 chromosomes were found to be significantly associated with TTN and FTN, respectively. Linked single nucleotide polymorphisms for IWB39005 (2A) and IWB44377 (7A) were highly significantly associated (FDR < 0.01) with TTN and FTN traits. Moreover, to validate GWAS results, meta-analysis was performed and 30 meta-QTL regions were identified on 11 chromosomes. The integration of GWAS and meta-QTLs revealed that tillering trait in wheat is a complex trait which is conditioned by the combined effects of minor changes in multiple genes. The information provided by this study can enrich the currently available candidate genes and genetic resources pools, offering evidence for subsequent analysis of genetic adaptation of wheat to different climatic conditions of Iran and other countries. Triticum aestivum L., genomes AABBDD, 2n = 6x = 42), is a major cereal crop, supplying 20% of the total energy and protein of the world’s diet1. Its production and productivity, especially in arid and semiarid regions such as Iran, are considerably constrained by extreme drought and heat stresses. Breeding for grain yield is the final step to produce stress-tolerant crop plants, since grain yield is a complex trait with low heritability, which is controlled by multiple genes and is affected by a lot of environmental factors, other traits such as yield components can be employed to overcome the limitations. Tillering is a crucial factor for wheat yield because of its involvement in grain weight and grain number determination. Moreover, it is a determinant of grain yield, since the tiller number is key in regulating competition between the tillers and the main shoot for assimilating supply3. The plant architecture and its tillering system impact many factors, including the light-harvesting and yield potential of the plant, the flowering and grain set, and last but not the least, the plant’s reproductive success. Therefore, genetic elucidation of tiller numbers at various stages of plant growth has become a major focus of wheat breeding research programs4.Bread wheat 8. Recently, QTL mapping and genome-wide association studies (GWAS) have become two key approaches to understanding the genetic bases and dissection of complex genes and controlling important features such as tillering traits. In spite of the success of QTL mapping in detecting QTL, the genetic variation of the population has been so far limited only to the genomes of the parents. Additionally, the genetic markers for the identified QTL can not necessarily be transferred to other populations. Such transferability would be desirable, given that genome-wide association study is a high-resolution and cost-effective method that depends on high-density marker and trait associations, employing genetically varied populations like landraces, elite breeding lines, and cultivars to elucidate the genetic architecture of agronomic traits9.Tiller number is inherited quantitatively in most cases and is affected by soil fertility and environmental factors, especially temperature and day length. Although four single genes  responsible for tiller inhibition are mapped on the wheat chromosomes 1A, 2A and 3A10. This method has been used to determine consensus regions of the genome across multiple QTL studies for their effects and consistency across different genetic backgrounds and environments, also to refine and confirm QTL positions on a consensus map via mathematical models. Reducing the CI of the MQTLs in comparison with QTLs is another indispensable perks of MQTL analysis15. More recently, a number of studies have applied the QTL meta-analysis method to different traits in wheat, including root morphological traits16, grain traits11, fusarium head blight resistance17, adaptation to drought and heat stress18 and leaf rust resistance19.In addition to the benefits of GWAS to QTL mapping, the meta-analysis as a statistical technique was developed to combine consensus loci from many individual QTL studies for any number of traits into a single dataset to identify most likely position and confidence interval (CI) of QTL regions20. Iranian bread wheat landraces possess rich genetic diversity, with a large number of rare alleles or potentially new alleles so that they can display high levels of resistance to many different abiotic stresses such as drought. However, most Iranian germplasm genotypes have not yet been characterized or utilized in modern plant breeding21. Hence, the examination of genetic variations and the distinction among Iranian wheat landraces and cultivars will be of great value. Not only may it identify new sources of resistance to drought and other stressors, but it may increase the biodiversity of the materials available for wheat breeding. Moreover, novel alleles will be identified that may be of value for Iranian wheat geneticists and breeders.In the past, wheat improvement has been based on selections from landraces, followed by crossing between landraces, introducing varieties, and finally crossing between elite varieties23. Furthermore, to confirm and strengthen the results of MTAs, QTL meta-analysis was conducted to identify wheat genome regions that are consistently associated with tiller traits.While some GWAS studies have used SNPs to examine the association of some traits in wheat under water-limited conditions, to the best of our knowledge, no GWAS study identifying the association of different agronomic traits in Iranian wheat under drought stress has been reported. Therefore, the objective of the present study, using 15 K SNP array markers, is to identify the structure of population and genome-wide marker-trait associations of Iranian wheat total tiller number (TTN) and fertile tiller number (FTN) under different water regimes, and environmets with breeding system design such as drought-tolerance improvement. In this regard, we used a multi locus GWAS model named fixed and random model circulating probability unification (FarmCPU) method as a new and more efficient recently developed methodh2 < 50%).A combined analysis of variance (ANOVA) was performed and descriptive statistics including the min, max and mean of the tiller number traits were estimated , days to flowering (0.23*), days to maturity (0.26*) in normal and all environments and also between TTN and days to maturity (0.21*) in drought condition  there was significant correlation in normal condition.For FTN, means in the six field environments ranged from 4.22 in the NIGEB drought condition to 12.83 in the normal condition at the Shahed farm during 2013 to 2014 (Supplementary Table https://urgi.versailles.inra.fr/). Estimated PIC values and overall SNP diversities, for these 6,349 SNP markers ranged from 0.263 to 0.321 with an average of 0.306, where; the PIC values for about 70% exceeded 0.20. Among the 21 chromosomes, chromosome 2B had the maximum number of markers (n = 599), followed by chromosome 5B (n = 491), while chromosome 4D had the fewest loci (n = 23), among sub-genomes of A, B, and D. The B sub-genome carried the most loci , followed by A sub-genome , and the D sub-genome .Out of the 13,006 SNPs in the Illumina iSelect 15 K SNP assay, a total of 10,054 SNPs were polymorphic among the 92 wheat genotypes. Eliminating the markers with minor allele frequencies (MAF < 0.1), the dataset was narrowed to 6,349 SNPs mapped to 21 wheat chromosomes; this dataset was later used for GWAS analysis. The analysis detected 12,698 alleles . About 60% of the SNPs (those with MAFs > 0.20), were then chosen as normal allele frequency markers, while 8% of SNPs displayed approximately equal allele frequencies (MAFs ~ 0.1). The physical positions of 6,349 SNP were determined based on the Chinese Spring reference assembly IWGSC RefSeqv1.0  values were determined using STRUCTURE to investigate the genetic diversity and population structure of the Iranian wheat panel and to classify the subgroups (K). The value of ΔK was plotted against the number of presumptive subgroups = 2 Fig. a, indica= 2 Fig. c. Most c= 2 Fig. . To exam= 2 Fig. b. The re23, The fixed and random model circulating probability unification (FarmCPU) provides a robust model for association mapping of complex traits in plants which can controls both false positives and false negatives and consistently identify a single significant SNP closest to these known published genes. Both computer simulation and real data analyses demonstrated that FarmCPU is the highly efficient method to reduce confounding issues arising due to kinship, population structure, multiple testing correction than MLM, stepwise regression, etc. Therefore, FarmCPU approch were used in this study to determine the marker-trait associations controlling TTN and FTN under both normal and stress conditions along with all environments using the best linear unbiased estimator (BLUEs) values calculated across the three locations of the experiment. Piepho et al.24 previously discovered that MTAs are more stable if BLUEs data are used in the absence of environmental effects.In line with Kaler et al.10  ≥ 3.0) were identified for the traits in the three environments examined. All chromosomes carried the MTAs, except for chromosomes 3A, 3D, 4A, 4D and 5B. Of all significant SNPs, in the three environments examined, 25 and 22 were significantly associated with total tiller number and fertile tiller number, respectively, and among these, two SNPs were significantly associated with both traits , 24 MTAs remained significant for SNP markers (FDR ≤ 0.05) for both traits in the three environments  was extracted from 217 QTLs from the literatures. Among the 217 initial QTLs, 140 (65%) and 77 (35%) QTLs were found for TTN and FTN, respectively. The 95% confidence interval (CI) ranged from 2.26 to 56.94 cM, with an average of 15.90 cM. About 91 (42%) of the collected QTLs had a CI lower than 10 cM, and 158 QTL (73%) had a CI lower than 20 cM  for total tiller number and fertile tiller number in wheat were collected from studies published since 2002 , and due to stability of these genotypes in different environments.In the present study, we evaluated the effects of two traits, total tiller number and fertile tiller number, in a collection of 92 landraces and cultivars of Iranian wheat. Significant variation in both traits appeared to be complementary between the genotypes and drought-stressed environments. In all tested environments, these traits decreased when drought stress occurred during the anthesis period. As Sareen et al.27, Al Rabi28 depicted similar results. Begum29 and Qaseem et al.30 also reported that marker IWB44377 (which was associated with TTN and FTN in our study) had significant association with the days to heading and days to maturity traits, respectively. There was only a significant correlation between FTN and grain yield in normal conditions; this shows that increase in number of fertile tillers may result in proportionate enhancement in grain yield per plant. However, because of the complicated relationships between the majority of the traits (with each other and with yield), simple correlation coefficients may not provide a comprehensive information about the relationships between different traits, and it is critical to apply multivariate statistical methods, such as factor analysis, in order to better understand these relationships. Tillering capacity with moderate heritability is widely agreed to be the trait with the greatest effect on the yield potential of cereal31, even if there was no positive correlation between these in the initial analysis32. In the previous paper, we have reviewed this issue for some of these genotypes33 and found that, for instance, the first factor, which was named as yield components, was composed of some of the constituents of total tiller number, fertile tiller number, plant height, peduncle lenght, biological yeild and harvest index. Similar results were also observed in a study by Arminian et al.32.A significantly positive correlations was observed between TTN and FTN in the normal and drought and all growing environments. Due to the importance of the relationship between tillering traits and other agronomic traits, such as grain yield and phenological characters, PCA biplots and linear correlation analysis also was performed , high amount of genetic variability and diversity has been observed among Iranian landraces and cultivar wheat collected during different years, and in different geographical regions. These variations can be practical gene sources for use in breeding to deal with climate change challenges. The structure analysis in this study confirmed the clustering result of Alipour et al.21; our genotypes had similar stratification. They conducted a cluster analysis for 369 Iranian hexaploid wheat genotypes including 99 cultivars and 270 landraces using 16,506 GBS-SNP markers and reported that accession pedigree was the principal factor affecting the separation of Iranian cultivars. Most wheat cultivars originating from Iran or with one Iranian parent evidently vary from those originating from CIMMYT, resulting in a varied genetic makeup for Iranian wheat compared to CIMMYT wheat. This point could possibly justify the high-yielding cultivars derived from Iranian and CIMMYT wheat genotype crosses. For instance, the most widely planted cultivars in Iran, Parsi and Pishgam, have been obtained from a cross between CIMMYT and Iranian genotypes.GWAS is highly dependent on the presence of linkage disequilibrium in the population, although genetic drift, population structure, and natural selection have always been influential as well. Structure and PCoA analysis classified the 92 Iranian wheat genotypes into two groups with approximately 20 genotypes belonging to both groups. These intermediate genotypes were mostly the cultivars that could be placed in the landrace group since they had been selected from Iranian landraces. Based on recent investigations . It is presumed that the sequence polymorphism is a result of older genomes undergoing gene duplication and accumulating more mutations. While considerable early gene flow might have happened between T. aestivum and T. turgidum (AABB), it seems improbable between the T. aestivum and Aegilops tauschii (DD); otherwise, there could have been less sequence diversity in the D genome compared to B and A genomes35. As the results of present study showed, roughly twice the number of the SNPs mapped to D genome, mapped to the A or B genomes. While these results are in line with those of other studies37, they are contrary to some previous results39 that have shown that the number of SNP markers mapped to the B and A genome were five times greater than those mapped to the D genome. This difference suggests a comparatively high level of SNP variation in the D genome for Iranian wheat genomes when compared with other genotypes. As Jia et al.40 state higher variations in the D genome could prepare new useful and elite alleles that can control important agronomical traits to cope with global climate changes.Similar to previous results reported by Berkman et al.41 and Wang et al.42 state that in QTL mapping approaches the two main factors involved in detecting significant MTAs are high values of heritability and abundance of polymorphic markers. However, Tavakol et al.43 indicated that a small population size is inevitably a limiting factor in detecting related loci with low effects. Despite the comparatively small size of our studied panel and the moderate value of heritability, the results of our survey seem reliable because the data were collected under six different conditions and also the BLUEs data based on these environments were more stable.Varshney et al.P < 0.05 after applying FDR-correction) for both traits in two or more environments; these results suggest pleiotropy, but may also suggest linkage. The SNPs significantly associated with TTN and FTN were determined on nearly all of the chromosomes tested, and were mainly distributed on chromosomes 1A, 2A, 2D, 6A, 6D, 7A and 7D , IWB53633 and IWB74344 (Chromosome 1A), which were associated only with TTN under stress condition. In contrast, the expression of a consistent MTA is less affected by environmental factors. An MTA stable across the different environments is of great value to marker-assisted selection (MAS) in breeding genotypes adapted to diverse ecological environments4. Overall, in this study, five MTAs  were consistently associated with TTN or FTN in two water regimes environments  ≥ 4.5), and the result considered relatively stable loci controlling tiller traits.In general, most MTAs are identified in a single environment and affect certain traits differently under different growing environments. As Chen et al.30. In this study, pleiotropic MTAs  were identified which were associated with both TTN and FTN under all conditions. High phenotypic trait correlations  might explain this pleiotropic effect. Mwadzingeni et al.45 said this is supported by the presence of different multiple MTAs in which one gene pleiotropically affects highly correlated characteristics and a common QTL often controls highly correlated traits. A review of other studies showed that Marker IWB39005 at 36.63 Mbp on Chromosome 2A was previously reported to be associated with tiller dry weight44. Marker IWB44155 at 323.74 Mbp on chromosome 7A  on chromosome 5D at 489.77 Mbp, associated with number of grains per ear (reported by Amer49) and anther extrusion . Furhrmore, Mohajeri Naraghi et al.51 reported one association i.e., IWA4483 for end-use quality. Zanke et al.52 also identified IWB28961 marker which was associated with thousend grain weight. A region (AX-95138710) near marker IWB55568, (on chromosome 2D) was reported in another study by Guo et al.44 and found to be significantly associated with spike length and total spikelet number. Ideally, the effects of such pleiotropic loci may not be affected by changes in the external environment. When breeding for broad adaptation, such loci or genomic regions could be valuable in gene introgression or breeding programs. Therefore, these MTAs could be employed to begin mining tillering genes via bioinformatics analysis and to develop cleaved amplified polymorphic sequence (CAPS) markers for MAS.A pleiotropic locus influences the expression of more than one phenotypic trait and the loci controlling these traits can be in the same genomic position. Pleiotropic effects are especially beneficial in crop breeding, as they permit the breeder to choose multiple traits simultaneously10. In this study, based on the integration of a consensus map with 14,548 molecular markers and through meta-analysis, we combined 217 initial QTLs located on chromosomes 1A, 2A, 2B, 2D, 5D, 6A, 6B, 6D, 7A and 7D into 30 meta-QTLs. Intriguingly, we found that five significant MTAs i.e., IWA6592, IWB9024, IWB55568, IWB25250 and IWA1406 for the tiller number traits were located in the MQTL2A-3, MQTL2A-3, MQTL2D-4, MQTL6A-4 and MQTL6D-4 regions, respectively  associated with two traits: total tiller number and fertile tiller number under two irrigation regimes conditions at chromosomes 1A, 2A, 2B, 2D, 5D, 6A, 6B, 6D, 7A, and 7D. Among these loci, markers IWB44377 and IWB39005 were consistently associated with both TTN and FTN in all environments; thus, they could be used to develop CAPS markers. In addition, a meta-analysis of QTLs associated with TTN and FTN validated the GWAS results. The results of the current study, as well as the MTAs detected in the population, can add to the presently available genetic resources, gene pools, and candidate genes for wheat breeding. They can also provide evidence for further examinations and studies on the genetic bases of wheat adaptation under various climatic conditions both in Iran and other countries.The study employed a set of 92 Iranian hexaploid wheat genotypes , 1,130 MASL , and a research field at National Institute of Genetic Engineering and Biotechnology (NIGEB) located at Vardavard, northwest of Tehran, 35°44′ N, 51°10′ E, 1,305 MASL . The temperature at the first research location varied from a minimum of -15° C to a maximum of 41 °C, and the climate was characterized by a mean annual precipitation of 224 mm (the highest of which is 49% in winter and 21% in spring). Whereas in the second research field, the minimum and maximum temperatures were − 20 °C and 42°C, respectively, with annual precipitation of 247.3 mm . In both research fields, sowing was done by hand in plots of four two-meter rows at 25 cm apart. All field plots were tilled before being sowed. Fertility was constrained by low organic matter and phosphorus contents, with application of 50 kg haμ represents an overall mean, both environment and genotype are fixed effects, and e is a residual term.Five plants were randomly selected from each experimental unit, and the collected data were averaged and recorded for subsequent analysis. Later, the intended traits, total tiller number per plant (TTN) and fertile tiller number per plant (FTN) were evaluated. For all the environments , the datasets were balanced; hence, the best linear unbiased estimates (BLUEs) equaled the arithmetic means across environments, places and irrigation regimes. Initially, separate analyses of data for each environment were conducted. A linear mixed model including the effects of places, genotypes, and replications was used to examine the data and BLUEs of the genotypes for each condition and across environments and the results served as input for the second step of analysis. The second step employed a linear mixed model that included the effects of G × E variance, calculated by the GenStat software package (14th edition):54 and the results displayed in a biplot. Pearson’s linear correlation was used to assess the strength and direction of association among the quantitative traits and the results were visualized using the ‘PerformanceAnalytics’ package implemented in the R enviroment.To identify discriminating traits, principal component analysis (PCA) was carried out using the ‘factoextra’ packagehttps://urgi.versailles.inra.fr/) and later included in analyses. Moreover, PowerMarker V3.25 software55 was employed to estimate the number of alleles for each locus, MAF and polymorphism information content (PIC) values.For this step, approximately 1.0 g of young wheat leaf tissue was collected from each of the genotypes before the elongation stage and the total genomic DNA was extracted, using the Cetyl Trimethyl Ammonium Bromide (CTAB) method. DNA dissolved in TE buffer was shipped to TraitGenetics company  for high-throughput genotyping using a set of 15 k Illumina Infinium iSelect SNP array (Illumina Inc). After filtering out SNPs with 10% missing data and 10% minimum allele frequency (MAF), a total of 6,349 SNP markers were determined based on the Chinese Spring reference assembly IWGSC RefSeqv1.0 (56. The results were processed using Structure Harvester web version v0.6.94 (https://taylor.biology.ucla.edu/StructureHarvester/). Principal coordinates analysis (PCoA) was used to examine the population structure of the panel for later use in GWAS, generated through the 6,349 SNP markers utilizing the Paleontological Statics (PAST).An analysis of the population structure for the association panel was estimated by a Bayesian model-based approach accomplished in STRUCTURE software V2.3.4 with 6,349 SNP markers located at least two cM apart in the genome. For each subpopulation value K, ranging from 1 to 8, STRUCTURE was run 10 times with a burn-in of 100,000 iterations and 50,000 iterations for the analysis. The inference of true K, using an ad-hoc statistic ΔK, was calculated based on the second-order rate of change in the log probability of data between successive values22. in the present study, Genome-wide association scans for tiller number traits were performed by FarmCPU model on the BLUEs values calculated for each condition , as well as for the BLUEs values of all conditions (based on 6 environments) using R package ‘FarmCPUpp’57 to calculate P-values for Manhattan plots and Q-Q plots.Fixed and random model Circulating Probability Unification (FarmCPU) is a Genome Wide Association Study (GWAS) method, which utilizes both Fixed Effect Model (FEM) and a Random Effect Model (REM), iteratively, to control false positives. FarmCPU substitutes kinship with a set of markers associated with the causal genes to remove the confounding between kinship in a mixed linear model (MLM) and genes underlying interested trait10  ≥ 3.0, which means one false positive is expected in one-thousand events. A second, more stringent threshold was also included: false discovery rate (FDR) ≤ 0.05 threshold  ≥ 4.5). Thus, SNPs with corrected p-value ≤ 0.05 were considered significantly associated with TTN and FTN traits. The GWAS results were illustrated in a circular Manhattan plot using the R package ‘SOFIA’58. All the mentioned packages were implemented in R environment version 3.5.359.Because many MTAs were found, we selected an overall cutoff significance level of − logR2 values were integrated for analysis. A new integrated consensus map . Using the approach of Veyrieras et al.62 BioMercator estimates the most likely assumption based on model choice criteria from AIC, AICc, AIC3, BIC and AWE. Based on the values for five models, the best meta-QTL model with the lowest value was considered the best fit. The probability of QTL for every segment of 0.5 cM on the consensus map was estimated following the approach described as ‘QTL-overview index’25.A QTL dataset for the total tiller and fertile tiller (or spike) number on ten chromosomes  was compiled from a review of 30 independent studies published from 2002–2020. Those QTLs with available map positions, log of odds ratio (LOD) scores and Supplementary Tables.Supplementary Figures.


Findings include missense variants in established drug targets for sleep disorders , genes with roles in arousal , and genes suggesting an obesity-hypersomnolence pathway . Association signals are concordant with accelerometer-measured daytime inactivity duration and 33 loci colocalize with loci for other sleep phenotypes. Cluster analysis identifies three distinct clusters of nap-promoting mechanisms with heterogeneous associations with cardiometabolic outcomes. Mendelian randomization shows potential causal links between more frequent daytime napping and higher blood pressure and waist circumference.Daytime napping is a common, heritable behavior, but its genetic basis and causal relationship with cardiometabolic health remain unclear. Here, we perform a genome-wide association study of self-reported daytime napping in the UK Biobank ( The genetic basis of daytime napping and the directional effect of daytime napping on cardiometabolic health are unknown. Here, the authors perform a genome-wide association study on self-reported daytime napping in the UK Biobank and Mendelian randomization to explore causal associations. In human adults, daytime napping is highly prevalent in Mediterranean cultures and is also common in non-Mediterranean countries including the United States3. In modern society, napping is encouraged in sleep-deprived populations, such as night shift workers4 and airline pilots5, to acutely improve performance and alertness. Although an acute benefit of napping on increased arousal in the setting of sleep deprivation is well-established6, the long-term effects of habitual napping on chronic disease risk remain controversial. Indeed, cross-sectional studies have provided conflicting evidence on the effects of habitual napping on cognition, blood pressure, obesity, metabolic traits, and mortality13. As napping behavior may be confounded by inadequate nighttime sleep or underlying poor health15, causal inference from these observational studies is limited.Naps are short daytime sleep episodes that are evolutionarily conserved across diverse diurnal species ranging from flies16. Indeed, up to seven genetic loci for daytime napping have been discovered in genome-wide association study (GWAS) of self-reported napping or related accelerometer-derived sleep measures19. Discovery of additional genetic loci may reveal biological pathways regulating sleep, elucidate genetic links with other sleep and metabolic traits, and clarify the potential causal effects of habitual napping on cardiometabolic disease.Genetic variation constitutes an important contributor to inter-individual differences in napping preference. A twin study estimated heritability of self-reported napping and objective daytime sleep duration to be 65% and 61%, respectively, demonstrating heritability similar or even higher than heritability found for other sleep traits such as nighttime sleep duration and timingn = 452,633), and an independent replication sample from 23andMe research participants of European ancestry , to define the genetic architecture of daytime napping and to assess links with other sleep and cardiometabolic traits. We identify 123 loci of which 61 replicate in the 23andMe research cohort, including variants in established drug targets for sleep disorders , genes with roles in arousal , and genes suggesting an obesity-hypersomnolence pathway . Cluster analysis identifies three distinct clusters of nap-promoting mechanisms and Mendelian randomization shows potential causal links between more frequent daytime napping and higher blood pressure and waist circumference.In this work, we leverage the full UK Biobank dataset of European ancestry, including related individuals , 38.2% and 5.3% of participants reported sometimes and always napping, respectively  or adjusting by a modified STOP-BANG risk scale21 did not influence findings  (Supplementary Data rg) of male and female stratified GWAS of 0.94 (standard error = 0.03). We conducted association analyses on the X chromosome to further examine whether common variants on the X chromosome contribute to sex differences in daytime napping and identified five additional loci for daytime napping (Supplementary Table P = 0.006), and no additional GWAS signals were identified on the X chromosome in sex-stratified analysis.We found no evidence of sexual dimorphism in the autosomal genetic determinants of daytime napping behaviorn = 386,577)18 retained genome-wide significance in our analyses 19 and LIFE Adult Study (n = 956)17 showed evidence of association in the current analysis.Five of seven loci for daytime napping reported in earlier GWAS in a subset of unrelated UK Biobank participants of European ancestry n = 86,57718 P < 4.6 × 10−4), of which 18 of the 61 loci were genome-wide significant . All 109 tested loci showed consistent direction of effect with the effect estimated in the UK Biobank  , 94 of the 109 lead variants remained genome-wide significant  and variants at ASCL4 and SNAP91 were strongly associated with longer duration of daytime inactivity (Padj < 0.05) (Supplementary Data P = 4.21 × 10−12) longer duration of daytime inactivity, but had no effect on other accelerometer-derived sleep duration, timing, or quality phenotypes  and long sleep duration , and weaker correlations with other sleep duration, timing and quality phenotypes (Supplementary Table Padj > 0.05), suggesting that these variants reflect mechanisms specific to daytime napping   and identified 33 shared signals (of which 25 corresponded to a genome-wide significant daytime napping locus)  = 0.98], (ii) in a cytoplasmic domain of HCRTR1 , and (iii) a cytoplasmic domain of TRPC6 , which encodes a subunit for transient receptor channels that maintains hypocretin/orexin neurons in a depolarized state29. Although an intronic lead variant in HCRTR2 was previously reported in GWAS of daytime sleepiness24 , the traits in the colocalization cluster excluded the daytime sleepiness phenotype, suggesting that the observed napping signal is driven by a distinct causal variant in HCRTR2 . This further suggests that the identified signal for daytime napping is distinct from the previously reported daytime sleepiness signal in the HCRTR2 region.First, missense variants were identified in components of the wake-promoting orexin/hypocretin neuropeptide signaling pathway: (i) in a transmembrane helical domain of PNOC and PATJ with effects on napping, daytime sleepiness, and BMI, suggesting a potential obesity-hypersomnolence pathway. An intronic candidate causal variant in PNOC  associated with more frequent napping, more daytime sleepiness, and higher BMI. PNOC encodes a preproprotein that is proteolytically processed to generate the nociceptin neuropeptide, which opposes the effects of hypocretin to reduce arousal and spontaneous activity in zebrafish31. The colocalization of daytime napping with BMI at this locus is consistent with known pleiotropic effects of PNOC in feeding behavior32 , that is associated with more frequent napping, longer sleep duration, and increased daytime sleepiness.Third, colocalization analyses refined genetic effects previously described at the KCHN8), rs10875606 (KCTD16)], (b) involved in glutamate transmission , and (c) previously associated with periodic leg movements37 and restless legs syndrome38 [rs4236060 (BTBD9)].Fourth, several genetic variants were prioritized at or near genes (a) coding for proteins constituting or interacting with potassium channels , 2 for spring [April–June], 3 for summer [July–September], and 4 for fall [October–December], as previously conducted66. Participants rated their overall health in response to the question, In general how would you rate your overall health?, with responses excellent, good, fair, poor, do not know, and prefer not to answer. Cases of sleep apnea were determined from self-report during nurse-led interviews or health records using International Classification of Diseases (ICD)-10 codes for sleep apnea (G47.3). For each participant, a modified STOP-BANG risk scale21 we have previously developed for sleep apnea in the UK Biobank to account for undiagnosed sleep apnea, was calculated67. The modified STOP-BANG risk scale for sleep apnea is missing the question, Has anyone observed you stop breathing during sleep? and replacing neck circumference with waist circumference dichotomized to the threshold for metabolic syndrome. Insomnia symptoms were ascertained from self-report to the question, Do you have trouble falling asleep at night or do you wake up in the middle of the night? with responses never/rarely, sometimes, usually, prefer not to answer. Participants who responded usually were set as insomnia cases, and remaining participants were set as controls. Smoking status  was further self-reported. Missing covariates were imputed by using sex-specific median values for continuous variables .At baseline assessment, all study participants reported their daytime napping frequency , and midpoint of the most-active 10 h of the day (M10 timing). Specifically, daytime inactivity duration was estimated by the total daily duration of estimated bouts of inactivity that fell outside of the sleep period time window. These inactivity bouts are any inactivity lasting ≥30 min. Inactivity bouts that are <60 min apart are combined to form inactivity blocks. This measure captures very inactive states such as napping and wakeful rest but not inactivity such as sitting and reading or watching television, which are associated with a low but detectable level of movement19.A subset of 103,711 participants from the UK Biobank wore actigraphy devices (Axivity AX3) for up to 7 days, ~2.8–9.7 years after their study baseline visits. Details on quality control and data processing have been described previously69. In brief, blood, saliva, and urine were collected from participants, and DNA was extracted from the buffy coat samples. Participant DNA was genotyped on two arrays, UK BiLEVE and UK Biobank Axiom with >95% common content and genotypes for ~800,000 autosomal SNPs were imputed to two reference panels. Genotypes were called using Affymetrix Power Tools software. Sample and SNPs for quality control were selected from a set of 489,212 samples across 812,428 unique markers. Sample quality control (QC) was conducted using 605,876 high-quality autosomal markers. Samples were removed for high missingness or heterozygosity (968 samples) and sex chromosome abnormalities (652 samples). Genotypes for 488,377 samples passed sample QC . Marker-based QC measures were tested in the European ancestry subset , which was identified based on principal components of ancestry. SNPs were tested for batch effects (197 SNPs/batch), plate effects (284 SNPs/batch), Hardy–Weinberg equilibrium (572 SNPs/batch), sex effects (45 SNPs/batch), array effects (5417 SNPs), and discordance across control replicates . For each batch  markers that failed at least one test were set to missing. Before imputation, 805,426 SNPs pass QC in at least one batch (>99% of the array content).Genotyping was performed by the UK Biobank, and genotyping, quality control, and imputation procedures are described in detail previouslyPopulation structure was captured by principal component analysis on the samples using a subset of high-quality (missingness < 1.5%), high-frequency SNPs (>2.5%)  and identified the subsample of white British descent. In addition to the calculated population structure by the UK Biobank, we locally further clustered subjects into four ancestry clusters using K-means clustering on the principal components, identifying 453,964 subjects of European ancestry. For the current analysis, individuals of non-white ethnicity were excluded to limit confounding effects. The UK Biobank centrally imputed autosomal SNPs to UK10K haplotype, 1000 Genomes Phase 3, and Haplotype Reference Consortium (HRC). Autosomal SNPs were pre-phased using SHAPEIT3 and imputed using IMPUTE4. In total ~96 million SNPs were imputed. Related individuals were identified by estimating kinship coefficients for all pairs of samples, using only markers weakly informative of ancestral background.n = 452,633) using BOLT-LMM70 linear mixed models and an additive genetic model adjusted for age, sex, 10 principal components of ancestry, genotyping array and genetic correlation matrix with a maximum per SNP missingness of 10% and per sample missingness of 40%. We used a SNP imputation quality threshold of 0.80 and a MAF threshold of 0.001. X chromosome data were imputed and analyzed separately using the same analytical approach in BOLT-LMM as was done for analysis of autosomes. A rare signal at IGSF1 on the X chromosome driven by one rare variant  was identified, potentially attributed to genotyping artifact or false-positive association and therefore was excluded.Genetic association analysis for daytime napping  was performed in related subjects of European ancestry with self-reported daytime napping data . Lambda inflation (λ) values were calculated using GenABEL in R, and estimated values were consistent with those estimated for other highly polygenic complex traits. Furthermore, follow-up GWAS for daytime napping were conducted using BOLT-LMM70 and included sensitivity analyses restricted to participants self-reporting excellent or good overall health51 , GWAS adjusting for BMI in addition to baseline adjustments, GWAS adjusting for BMI and BMI × BMI in addition to baseline adjustments, to account for sleep apnea, GWAS excluding participants with diagnosed sleep apnea (n = 5553 excluded) and GWAS adjusting for a modified STOP-BANG risk scale67 in addition to baseline adjustments, GWAS adjusting for self-reported daytime sleepiness, and sex-stratified GWAS .Trait heritability was calculated as the proportion of trait variance due to additive genetic factors measured in this study using BOLT-REMLP < 5 × 10−8) and pairwise independence (r2 < 0.6) within a 1 Mb window. Annotation of the lead variants, including predicted sequence consequence, was obtained from the FUMA output. We determined the PICS probability for each lead variant being the causal variant at the locus71.Distinct genomic risk loci were defined using FUMA v1.3.3 on the basis of genome-wide significance  using linear regression and an additive genetic model. Interaction analyses were adjusted for age, sex, 10 principal components of ancestry, genotyping array, and season to determine SNP interaction with season on daytime napping. In addition, for each lead variant, corresponding summary statistics for other self-reported and accelerometer-derived sleep measures were obtained from the Sleep Disorder Knowledge Portal (http://sleepdisordergenetics.org/). As earlier UK Biobank GWASs were restricted to the HRC-imputed variants, if the lead signal was unavailable, a proxy SNP (r2 > 0.8) was used instead.For the 123 lead variants, we tested for gene-by-season interaction in PLINK73 with the UK10K imputation reference panel74. For each chromosome, Minimac375 was used to impute the reference panels against each other, reporting the best-guess genotype at each site. Ancestry was determined through an analysis of local ancestry76. A principal component analysis was performed independently for each ancestry, using ~65,000 high-quality genotyped variants present in all five genotyping platforms. In addition, a maximal set of unrelated individuals was chosen for each analysis using a segmental identity-by-descent estimation algorithm. All individuals included in the analyses provided informed consent and answered surveys online according to human subject protocol, which was reviewed and approved by Ethical & Independent Review Services, a private institutional review board (http://www.eandireview.com).23andMe, Inc. is a personal genetics company. DNA extraction and genotyping were performed on saliva samples by National Genetics Institute, a CLIA licensed clinical laboratory and a subsidiary of Laboratory Corporation of America. Samples were genotyped on one of five genotyping platforms. Samples that failed to reach 98.5% call rate were re-analyzed. A single unified imputation reference panel was created by combining the May 2015 release of the 1000 Genomes Phase 3 haplotypesn = 267,271), sometimes if 2 to 5 , and usually if 6 or 7  to more closely resemble the UK Biobank categories. Replication for the 123 daytime napping loci or proxy for lead SNP (r2 > 0.80) were generated through linear regression (using an additive model) of the phenotype against the genotype, adjusting for age, sex, the first four principal components, and a categorical variable representing genotyping platform. Furthermore, meta-analysis of UK Biobank and 23andMe associations for the daytime napping loci was performed using METAL77 by weighting effect-size estimates using the inverse of the corresponding squared standard errors (version released 25 March 2011).For the present daytime napping replication, we restricted analyses to 541,333 participants of European ancestry with survey responses to a question on frequency of daytime napping. Participants were asked, How many days per week do you take naps during the day? (15 min or more) with a response on a continuous scale. Responses in days per week were scaled to never/rarely if 0 or 1 26, insomnia symptoms 25, chronotype 23, snoring , ease of awakening , and daytime sleepiness . Although these GWAS were conducted in the UK Biobank, the algorithm is robust to inclusion of studies with overlapping participants28. Colocalization analysis was performed in pre-defined, approximately independent LD blocks across the genome (1.6 Mb on average)78. We used the default variant-level prior probability of a SNP associated with a trait of p1 = 1 × 10−4 (prior probability of a SNP being associated with one trait) and y = 0.98 . With these settings, 1 in 200,000 variants are expected to be causal for two traits. Consistent with prior work28, we conservatively set both the regional and alignment probabilities to 0.80 so that a cluster of colocalized traits would only be identified if PRPA > 0.64. The outputs from the algorithm include: (i) colocalized traits, (ii) the posterior probability of colocalization, (iii) the regional association probability , (iv) the candidate causal variant, and (v) the proportion of the posterior probability of colocalization explained by the genetic variant (interpreted as a multi-trait fine-mapping probability). We report loci with posterior probability (pp) for colocalization >0.7, as this cutoff corresponds to a false discovery rate of <5%28.To identify genomic regions which harbor causal variants that influence multiple sleep traits, we performed multi-trait colocalization using the Hypothesis Prioritization Colocalization (HyPrColoc) packagen ~ 700,000) of UK Biobank and the GIANT consortium79, we performed genome-wide colocalization of naps with BMI. To link gene expression to the naps associations, we performed colocalization for all genes located within 1 MB of the top signals identified in the naps GWAS. We used summary statistics for expression quantitative trait loci (eQTL) associations identified in the Genotype-Tissue Expression project v780. We prioritized gene expression in the frontal cortex, which was identified by FUMA analysis of GTEX v7 to be the most highly enriched tissue for the naps signals.We performed two additional colocalization analyses. Using summary statistics from a meta-analysis . We conditioned on rs2653349 using the --cojo-cond function.To determine independence of the daytime sleepiness and napping association signals at the 83 with the aim of collapsing identified naps loci into subgroups of variants based on patterns of association with other sleep traits. The inputs for the bNMF algorithm were the set of the 122 naps GWAS signals  oriented to naps-increasing alleles and corresponding association statistics for 17 self-reported and accelerometer-derived sleep traits from the UK Biobank. We generated standardized effect sizes for variant-trait associations from GWAS by dividing the estimated regression coefficient by the standard error, using the UK Biobank summary statistic results (variant-trait association matrix [122 by 17]). To enable an inference for latent overlapping modules or clusters embedded in variant-trait associations, we modified the existing bNMF algorithm to explicitly account for both positive and negative associations as was done previously83.We applied the bNMF clustering algorithmk = 3 for 63% of 1000 iterations in this analysis, with those same 3 clusters present in an additional 34% of iterations with k = 4). The results of the bNMF algorithm provide cluster-specific weights for each variant and trait. Variants and traits defining each cluster were based on a cutoff of weighting of 1.09, which was determined by the optimal threshold to define the beginning of the long-tail of the distribution of cluster’s weights across all clusters (top 5% were considered to be significant).The defining features of each cluster were determined by the most highly associated traits, which is a natural output of the bNMF approach. bNMF algorithm was performed in R for 1000 iterations with different initial conditions, and the maximum posterior solution at the most probable number of clusters was selected for downstream analysis . A significance threshold was set after Bonferroni correction accounting for all pathways tested . Gene-based analysis was also performed using Pascal43. Pascal gene-set enrichment analysis uses 1077 pathways from KEGG, REACTOME, BIOCARTA databases, and a significance threshold was set after Bonferroni correction accounting for 1077 pathways tested (P < 0.05/1077). We performed single-cell enrichment analysis (Cell Type function) in FUMA44 using our MAGMA gene analysis result and multiple human-specific single-cell expression datasets86.Functional annotation was carried out using ANNOVAR in FUMA89 in LDHub88. This was conducted using all SNPs from the UK Biobank discovery GWAS found in HapMap3 and included publicly available data from 257 published genome-wide association studies. LDSC estimates genetic correlation between two traits from summary statistics (ranging from −1 to 1) using the fact that the GWAS effect-size estimate for each SNP incorporates effects of all SNPs in LD with that SNP, SNPs with high LD have higher statistics than SNPs with low LD, and a similar relationship is observed when single study test statistics are replaced with the product of z-scores from two studies of traits with some correlation. Significance was considered at the Bonferroni correction for all tests performed (P < 0.05/257 tests). In addition to publicly available summary statistics from LDHub, we also used publicly available summary statistics from earlier UK Biobank GWASs for self-reported and accelerometer-derived sleep traits from the Sleep Disorder Knowledge Portal (http://sleepdisordergenetics.org/) and computed genome-wide genetic correlations using LDSC89. Finally, we calculated genetic correlations between the sex-specific napping GWAS to determine the similarity in male and female genetic architecture.Genome-wide genetic correlation analysis were calculated using the implementation of cross-trait LD Score Regression (LDSC)The Mass General Brigham Biobank (formerly Partners Biobank) is a hospital-based cohort study from the Mass General Brigham healthcare network in Boston, MA with electronic health record (EHR) and genetic data. Recruitment for the Mass General Brigham Biobank launched in 2010 and is active at participating clinics at Brigham and Women’s Hospital, Massachusetts General Hospital, Spaulding Rehabilitation Hospital, Faulkner Hospital, McLean Hospital, Newton-Wellesley Hospital, and North Short Medical Center. All patients provided consent upon enrollment and the study protocol was approved by Mass General Brigham Institutional Review Board. To date (07/2019), a total of 104,965 subjects were consented.P < 10−6). The exclusion criteria for samples were: (1) sex discordances between the reported and genetically predicted sex, (2) missing call rates per sample ≥0.02, (3) subject relatedness , and (4) population structure showing more than four standard deviations within the distribution of the study population, according to the first four principal components. Phasing was performed with SHAPEIT290 and then imputations were performed with the Haplotype Reference Consortium Panel91 using the Michigan Imputation Server75. Written consent was provided by all study participants. Approval for analysis of Biobank data was obtained by Mass General Brigham IRB, protocol #2018P002276.Genomic data for 30,683 participants were generated with the Illumina Multi-Ethnic Genotyping Array. The genotyping data were harmonized, and quality controlled with a three-step protocol, including two stages of genetic variant removal and an intermediate stage of sample exclusion. The exclusion criteria for variants were: (1) missing call rate ≥0.05, (2) MAF < 0.001, and (3) deviation from Hardy–Weinberg equilibrium (92 and the Human Genome Diversity Project (HGDP)93 as a reference panel. Principal component analysis outliers were determined by using a principal component analysis projection of the study samples onto the HGDP reference samples, and subsequently excluded from analysis. To correct for population stratification, we computed principal components using TRACE92 in the subset with genetically European ancestry. Furthermore, sample relatedness was determined using PLINK72, and subsequently one sample from each related pair was excluded.Participant ancestry was determined using TRACEn = 11,397 cases) and 745 . A total of 951 distinct phecodes had at least 100 cases in the biobank.In aggregate, participants had a total of 7,422,726 ICD-9 and ICD-10 diagnostic codes corresponding to 784,878 instances of phecodes with at least 2 distinct diagnostic codes. The most prevalent codes were 401.1  were retained and all those in linkage disequilibrium, r2 > 0.1, were removed. Following LD clumping, the GPS included 995,188 SNPs.We generated a genome-wide polygenic score (GPS) for each individual by summing naps-increasing risk alleles across the genome, each weighted by the beta estimate for that allele from the discovery GWAS, using PRSicen = 30,683) as determined from EHR. Same-day duplicated diagnoses , non-ICD-9/10 codes , codes from participants of non-European ancestry  were removed, and a total of 8,353,253 ICD-9/10 diagnoses were kept in the analysis. Similar ICD-9 and ICD-10 were consolidated and then further collapsed to 1857 phecodes based on clinical similarity95. A total of 88.9% of the 8,353,253 ICD-9/10 codes mapped to a phecode. Participants with at least 2 codes for a specific phecode were considered cases for that respective category, whereas participants with no relevant code for that category were considered controls. Codes with at least 100 prevalent cases were kept in the analysis. The association between the daytime napping GPS and each of 951 disease code was tested using logistic regression with adjustments for age, sex, genotyping array, and 5 principal components, using the PheWAS R package96. Phenome-wide significance was considered at the Bonferroni threshold for 951 tested diseases outcomes and a less stringent FDR correction.A total of 20,054,591 physician diagnoses were obtained for genotyped participants  and sub-scores restricted to cluster-specific variants (3 clusters) with a range of cardiometabolic traits using publicly available data . An analogous approach was taken for reverse MR of adiposity measures  on daytime napping.Mendelian randomization (MR) can be conceptualized as a naturally randomized experiment whereby individuals are randomized to more or less liability for an exposure on the basis of their inherited genetic variation. This approach rests on the random assortment of alleles at gametogenesis, which substantially reduces the effect of confounding on causal estimates, and eliminates the potential for reverse causal effects of outcomes on the exposure. We created a genetic instrument from the lead daytime napping variants. These variants were further clumped at a between-SNP99 whereby we randomly split the UK Biobank sample of unrelated participants of White British ancestry into two subsets. We then re-estimated genetic associations of napping with the top variants identified in the discovery GWAS within each subset, as well as the association of those variants with SBP and DBP within each subset. In order to reduce regression dilution bias, SBP and DBP were averaged over two measurements and adjusted for self-reported antihypertensive use as done in prior GWAS100. We then performed MR utilizing exposure and outcome associations measured in different strata . MR effect estimates of daytime napping on blood pressure were combined across the two estimates using fixed-effects meta-analysis, and standardized using the sample standard deviations for SBP (11.25 mmHg) and DBP (20.65 mmHg) in the UK Biobank. As a sensitivity analysis, we used daytime napping variant association statistics from the 23andMe replication sample as the exposure, and a meta-analysis including UK Biobank and the International Consortium for Blood Pressure  as the outcome48.In the case of systolic blood pressure (SBP) and diastolic blood pressure (DBP), for which independent, non-overlapping summary statistics are not readily available, we undertook a split-sample MR approach101, the simple and weighted median102, MR-PRESSO103, and multivariable Mendelian randomization. MR Egger models a pleiotropy parameter by fitting an intercept term and adjusts the causal estimates accordingly. Estimation of this additional parameter greatly reduces power in the Egger regression. The median estimators yield valid causal effects provided that <50% of the information comes from invalid instrumental variables. Regression-based multivariable MR analyses were performed to adjust the napping proxies for their associations with insomnia and sleep duration104. We considered consistent effects across multiple methods to strengthen causal evidence.After data harmonization, we used the random-effects inverse-variance weighted (IVW) method as the main analytic approach. To account for multiple comparisons, we used a conservative Bonferonni-adjusted alpha threshold (0.05/19 = 0.0026). As the IVW approach assumes no unbalanced horizontal pleiotropy, we utilized a range of sensitivity analyses robust to violations of this assumption: MR EggerHCRTR1 and HCRTR2 (rs2271933 and rs2653349) associated with cardiovascular outcomes and risk factors, we extracted variant associations from the largest available GWAS for these phenotypes (Supplementary Table 13). As a broader investigation, we used data from a phenome-wide association study of 1402 ICD-code based phenotypes in UK Biobank50, accessed through the following web browser: http://pheweb.sph.umich.edu/SAIGE-UKB/ (Supplementary Data To assess whether missense variants in Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13


Background: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder that is influenced by both genetic and environmental factors. However, the etiology of PCOS remains unclear.Methods: We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal effects of genetically determined metabolites (GDMs) on the risk of PCOS. We used summary level data of a genome-wide association study (GWAS) on 486 metabolites  as exposure and a PCOS GWAS consisting of 4,138 cases and 20,129 controls as the outcome. Both datasets were obtained from publicly published databases. For each metabolite, a genetic instrumental variable was generated to assess the relationship between the metabolite and PCOS. For MR analysis, we primarily used the standard inverse variance weighted (IVW) method, while three additional methods—the MR-Egger, weighted median, and MR-PRESSO  methods—were performed as sensitivity analyses.Results: Using genetic variants as predictors, we observed a robust relationship between epiandrosterone sulfate (EPIA-S) and PCOS . Similarly, 3-dehydrocarnitine, 4-hydroxyhippurate, hexadecanedioate, and β-hydroxyisovalerate may also have causal effects on PCOS development.Conclusions: We identified metabolites that might have causal effects on PCOS development. Our study emphasizes the role of genetic factors underlying the causal relationships between metabolites and PCOS and provides novel insights through the integration of metabolomics and genomics to better understand the mechanisms involved in human disease pathogenesis. Polycystic ovary syndrome (PCOS) is the leading cause of female infertility worldwide, affecting 6–20% of the female population of reproductive age , 2. HypeModern omics-based technologies, including genomics, transcriptomics, proteomics, and metabolomics, have greatly advanced our understanding of the pathophysiological process of human complex diseases and successfully identified a series of biomarkers that could lead to earlier diagnosis of diseases or therapeutic targets for disorders . TherefoMendelian randomization (MR) is a novel genetic epidemiological approach that uses genetic variants as instrumental variables to assess the causality of an agent on clinical outcomes of interest . The bashttps://www.metabolon.com/). A total of 486 metabolites, assigned to eight broad metabolic groups , were included in the final GWAS analysis. Among these, 196 (37%) were classified as “unknown,” which meant that their chemical identity had not been clearly determined thus far. We included these “unknown” metabolites in our study as they still attracted attention from other researchers and might provide further useful information in the future  associated with specific metabolites to ensure that the generated instrument could explain a larger variance in the corresponding metabolite. Next, we selected independent SNPs (r2 < 0.1 within ± 500 kb) to generate the instrumental variable using a clumping procedure with the European 1,000G as reference panel. We further tested whether these genetic instruments could explain the variation of the corresponding metabolites to avoid instruments with a weak first stage. The proportion of variability (R2) and F statistic was calculated to assess the strength of these instrumental variables .The IVW approach referred to the primary MR analysis and successfully aided in inferring the causality of an exposure for outcome. However, there were still several concerns. One important concern was the existence of horizontal pleiotropy. Horizontal pleiotropy occurs when any variants were invalid instruments and acted on the outcome through other ways (not through the concerned exposure). To control for horizontal pleiotropy, we next applied additional MR methods for sensitivity analyses: the weighted median method, which allowed a subset of genetic variants (<50%) to be invalid instrumental variables ; MR-EggeFollowing standard procedures, we obtained the instrumental variables containing 3–675 independent SNPs for the 486 metabolites. The variables explained 0.8–83.5% (median 4.7%) of the variance for their corresponding metabolites. The minimum F statistic for representing the strength of the predictive instrumental variable was 20.33. All instrumental variables for the 486 metabolites were sufficiently informative for MR analysis.P = 0.0007). The risk of developing PCOS increased 5-fold for a 1-s.d. increase in the level of 3-dehydrocarnitine . Two other carnitines with causal associations with PCOS were hexanoylcarnitine  and 2-tetradecenoyl carnitine . Notably, 2-tetradecenoyl carnitine had an inverse association with PCOS, unlike 3-dehydrocarnitine and hexanoylcarnitine. 4-Hydroxyhippurate, a xenobiotic, was associated with an increased risk of developing PCOS. β-Hydroxyisovalerate, classified as an amino acid, also appeared to be a pathogenic risk factor for PCOS .With the use of genetic variants as proxies, the IVW identified 24 metabolites with causal effects on PCOS, among which 13 (54.2%) were known metabolites while the remaining 11 belonged to the “unknown” subgroup . We focuPIVW = 0.0186; PMR−Egger = 0.0111; PWeighted−median = 0.0154; and PMR−PRESSO = 0.0290), and there was no evidence of horizontal pleiotropy . Using 15 genetic predictors as instrumental variables , we observed a 50% higher risk of developing PCOS for each 1-s.d. increase in the level of EPIA-S , although the expression levels are also high in the thyroid gland, fallopian tube, and ovary (We focused on genetic variants that contribute to variation in the target metabolites. The SNP rs13222543, which is located at the intron region of ZCWPW1, was the most significant variant for EPIA-S. ZCWPW1 is a candidate gene for Alzheimer's disease; however, no link had previously been established between ZCWPW1 and EPIA-S , 47. Intnd ovary . This suThe present study has several limitations. First, the MR identified causal metabolites associated with the risk of developing PCOS using genetic variants as instrumental variables. Further experimental studies should be conducted to verify these findings. Second, multiple testing was not adjusted, but robustness of the results had been supported by using multiple MR algorithms. Third, the accuracy of MR depends on how well the genetic instruments explain the exposure. The current GWAS analysis on metabolites is based on European populations with a limited sample size. Effort should therefore be made to collect more samples across a broader swath of the population to provide a more accurate assessment of the influence of genetic factors on metabolites. Finally, the findings of our study might be only limited to the European population, not necessarily generalizable to others.The present study adopted an MR approach to identify PCOS-related metabolites. The MR approach used genetic variants as instrumental variables to provide unconfounded estimates of the causal relationships between serum metabolites and PCOS. EPIA-S was identified as a causal metabolite that was robustly associated with PCOS development. Some other metabolites, such as 3-dehydrocarnitine, 4-hydroxyhippurate, hexadecanedioate, and β-hydroxyisovalerate, may also have causal effects on the development of PCOS. We emphasized the role of genetic factors underlying the causal relationships between metabolites and PCOS. We provided novel insights by integrating metabolomics with genomics to better understand the mechanisms underlying the pathogenesis of human disease.http://metabolomics.helmholtz-muenchen.de/gwas/, https://doi.org/10.17863/CAM.27720.Publicly available datasets were analyzed in this study. This data can be found here: XL and SS were responsible for the study conception and study design. MJ, CH, and QZ were involved in data acquisition and study execution. SS and MJ analyzed the data and drafted the manuscript. WS and JS contributed to interpretation and editing of the manuscript. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cataglyphis niger ants. We evaluate the accuracy of allele frequency estimation from sequencing a pool of individuals (pool-seq) from each colony using either whole-genome sequencing or reduced representation genomic sequencing. Based on empirical measurement of the experimental noise in sequenced DNA pools, we show that reduced representation pool-seq is drastically less accurate than whole-genome pool-seq. Surprisingly, normalized pooling of samples did not result in greater accuracy than un-normalized pooling. Subsequently, we evaluate the power of the alternative approaches for detecting quantitative trait loci (QTL) of colony-level traits by using simulations that account for an environmental effect on the phenotype. Our results can inform experimental designs and enable optimizing the power of GWAS depending on budget, availability of samples and research goals. We conclude that for a given budget, sequencing un-normalized pools of individuals from each colony provides optimal QTL detection power.Social insect colonies exhibit colony-level phenotypes such as social immunity and task coordination, which are produced by the individual phenotypes. Mapping the genetic basis of such phenotypes requires associating the colony-level phenotype with the genotypes in the colony. In this paper, we examine alternative approaches to DNA extraction, library construction, and sequencing for genome wide association studies (GWAS) of colony-level traits using a population sample of  Genomic mapping techniques are used to map phenotypes to genotypes. Mapping is of general interest in any biological system, including fundamental studies of biological traits, clinical studies of genetic predisposition to disease, and agro- and bio-technological studies of domesticated plants and animals. Typically, such studies associate phenotypic measurements of individuals with their genotypes. Here we evaluate methodological approaches for genomic mapping of phenotypes that are expressed at the level of a group rather than that of individuals. We demonstrate that genomic sequencing of a DNA pool from multiple samples provides increased statistical power within a limited budget. Our results facilitate more efficient use of resources in genomic mapping studies that investigate group-level phenotypes. Social insect colonies depend on the collective performance of a large number of individual workers. Such collective performance gives rise to extended, colony-level phenotypes such as social immune responses to pathogens and parasites, coordinated defense against intruders, information transfer and processing, a colonial pheromonal odor that facilitates nestmate recognition, alternative social structures, etc. –4. TheseIn genome-wide association studies (GWAS), polymorphic genetic loci are associated with variation in phenotypic traits. These traits are often quantitative traits, and the genetic elements determining them are known as quantitative trait loci (QTLs). For many decades, researchers used QTL mapping approaches that make use of a mapping population consisting of full siblings, usually from a controlled cross of known parental genotypes. In social insects, this approach was employed mainly in honey bees, which are readily crossed in the lab. For example, lines that were artificially selected for high or low pollen foraging  and for Next generation sequencing methods allow for highly cost-effective genotyping. However, sequencing whole genomes in large numbers is still cost prohibitive despite the continuing and dramatic reductions in sequencing costs over the last decade. A project’s sequencing budget is still a significant limiting factor because sufficient statistical power for QTL detection often requires many thousands of samples . ConsequAllele frequency estimation by pool-seq may suffer from experimental noise and biases in various stages of the protocol, including DNA pooling, library construction, sequencing, and data analysis. Previous studies measured the experimental noise in pool-seq by comparing allele frequencies estimated from individually sequenced samples with allele frequencies in their pooled DNA ,16,17. GDrosophila melanogaster. They used simulations to show that their approach is mainly suitable for detecting QTLs with high frequency alleles and reasonably large effect. The pool-seq approach in these studies is suitable for individual phenotypes, where each pool consists of samples with similar phenotypic measurements.Several theoretical and empirical studies investigated the application of pool-seq to mapping quantitative traits –26. ThesThe motivation for using pool-seq to map colony-level traits is different: the genotypes of a colony may be assayed as a pool that represents the ‘collective genome’ of the colony, which is then tested for association with the collective phenotype. This scenario is distinct from the abovementioned studies in that there may be substantial within-colony phenotypic variation. More importantly, the pooled individuals are not independent samples from the population. They are related individuals , and they are sampled from the same nest/hive so their phenotypes were affected by similar environmental conditions.niger ants for this empirical evaluation. We compared WG-seq vs RAD-seq, individual sequencing vs. pool-seq, and their combinations. Thereby, we provide a broad overview of the expected results of different sequencing approaches. Using simulations, we report the minimum number of individually sequenced samples per colony that is equivalent to a pooled sample in terms of the statistical power of GWAS. Our simulations also take into consideration environmental factors that affect the phenotype to various degrees (i.e. different levels of heritability), which substantially affect the power of the GWAS. We also used the simulation study to generalize our results to different levels of polyandry, as well as monandry (singly-mated queens). Our results enable optimization of experimental designs for GWAS of colony-level traits. We conclude that pool-seq is a significantly more powerful approach for mapping colony-level traits, although depending on availability of samples and scientific goals, individual sequencing may be advantageous in some scenarios.Here we describe a comparative methodological study evaluating the contribution of alternative approaches to the statistical power of GWAS for mapping colony-level traits. We used colony samples from monogyne (i.e. having a single queen per colony), polyandrous (i.e. queens are multiply mated) C. Cataglyphis drusus [C. niger, because these populations are not differentiated by their nuclear genomic DNA [Our study population was sampled from Betzet beach on the northern Israeli coastline. This is a population that was previously described as s drusus , but ouromic DNA . Colonieomic DNA . We chosomic DNA .Thirty workers were sampled from each of three colonies  and every ant was individually frozen in -80°C on the evening of the same day. Workers of all sizes were included in the sampling. DNA was extracted individually from whole bodies, using TRIzol (Invitrogen Life Technologies) and kept in -20°C. Quantity and quality of extracts were determined by NanoDrop 2000 Spectrophotometer and gel electrophoresis was used to verify sample consistency, i.e., DNA yield, degradation, and phenol/salts residues. Pooled samples from individually extracted samples were prepared by normalizing DNA quantities based on NanoDrop measurements, for equal representation of every individual sample in the colony’s pool. Pipetting was done using a manual Gilson pipette (2-20ul).A reduced representation genomic library was constructed according to a modified double-digest Restriction-site Associated DNA sequencing (ddRAD-seq) protocol, based on protocols from Parchman, et al.  and PeteIn addition, for 29 individuals of colony BZT50, DNA was extracted from three body segments, i.e., heads, thoraces and abdomens, but without legs. DNA was also extracted from all legs pooled together (174 legs). One pooled sample of the 29 individuals was normalized for equal quantitative representation of each sample and two additional normalized pools were independently prepared from only 10 individuals out of the same 29. Normalization was based on NanoDrop measurements. Uniquely barcoded genomic libraries were constructed for each of these 33 individual/pool samples (Illumina TruSeq Nano DNA Library Prep kit). Briefly, with this method, a Covaris S220 sonicator was used for DNA shearing of 550bp inserts, ends were repaired, and the fragments were size-selected and purified using magnetic beads (supplied in kit). Then, an adenosine was added to the 3’ end of the blunt fragments, individually barcoded adaptors were ligated, and ligated fragments were amplified (eight PCR cycles). Libraries were normalized and pooled so that the four pools were represented five-fold more than individual samples. The library was sequenced in one lane of a paired-end, 150bp reads, on a HiSeq4000. Sequenced data was submitted to NCBI SRA database (Accession: PRJNA494296).C. niger reference genome version Cnig_gnA (NCBI accession number pending) using BWA-MEM and those with low alignments scores or multiple mapping were filtered out, leaving 121,343,143 reads  is required to call the genotype of a particular sample in a particular SNP locus. We set a higher minimum for the pooled sample  than for individual sample . SNPs were required to have called genotypes for a minimal percentage of individual samples (r), ranging between 75 and 95, and a minor allele frequency (MAF) of at least 1.25%, 3%, or 9% (min_maf). For the WG-seq dataset that contained fewer samples (from only one colony) we implemented an alternative to the MAF cutoff: we required that the minor allele appear in at least two samples. Thus, we permit cases of two heterozygous, but not a single homozygote for the minor allele. A total of 400 different filtering options were tested. Spearman ranked correlations were calculated between the individuals and each of the pooled samples. The following filtering criteria were chosen based on the higher correlation obtained between the pooled and the individual genotypes, so long as the filtering did not discard most of the SNPs  or ten mates, for 50 simulated monogyne colonies . Note that there is no population structure in our simulations since each colony is an independent sample of genotypes. Then, genotypes of 30 workers were randomly generated for each colony based on the parents’ genotypes. Three replicates of pool-seq results (for the 30 worker samples of each colony) were simulated according to the empirically observed relationship between the individual genotypes and the pool allele frequency in our WG-seq data from C. niger (above). Specifically, for every SNP in a simulated colony we randomly chose a SNP in the C. niger data that had a colony allele frequency  that was similar to the allele frequency in the simulated colony, and we assigned the respective C. niger pool-seq allele frequency as the simulated pool-seq allele frequency. Finally, we designated a single SNP as a QTL—a causal SNP affecting a single quantitative trait at the colony level. The trait of interest was affected by the genotypes of the colonies such that one allele gives a higher phenotypic value and the other gives a low value. If all the colony was homozygous to the “high” allele than its colony-level phonotype was 1 unit higher than the colony-level phenotype of a colony entirely homozygous to the “low” allele. Intermediate values were assigned based on the proportion of “high” alleles in the colonies (i.e. assuming an additive model). We conducted association tests to evaluate power for detecting that QTL. We examined 30 alternative scenarios for mapping the QTL using the genotypes of between 1 and 30 individuals per colony. Additionally, we examined the power of using the average of triplicates of pools from each colony.The purpose of the simulations was to compare the power of GWAS using the pool-seq approach to that of sequencing of individual samples. ire ants . Based o2 of the regression).Simulation were done under the assumption that there is no linkage disequilibrium between the simulated SNPs. Environmental factors affecting the quantitative trait were accounted for by adding varying levels of “noise” to the phenotype—a randomly chosen number from a normal distribution with a mean of 0 and standard deviation of 0, 0.5, 1, 1.5, 2, 2.5 or 3. To measure the resulting ratio between the genetic and environmental factors we empirically measured the percent of phenotypic variation explained by the genotype in a linear regression analysis (the Rlm) in each of the scenarios: 1,2,…, or 30 individuals per colony, or a pool for each colony (with either 50 or 150 colonies). When dealing with real population samples, GWAS methods need to use a linear model that accounts for population structure and/or kinship [e.g., p-value < 0.05, after false discovery rate (FDR) correction for 10,000 tests [For each simulation scenario with a given level of noise, we simulated 100 data sets of 50 colonies and 100 sets of 150 colonies. Each time, a single SNP was chosen out of the 10,000 SNPs, and 10 phenotypes were simulated using a given level of environmental noise. In total, 2000 simulated data sets were generated for each noise level. We attempted to predict which of the 10,000 SNPs affects the phenotype using linear regression  were extracted separately and yielded DNA concentrations of 44±32 ng/ul , but the highest correlation was achieved with very strict filtering criteria that left very few SNPs. Therefore, we chose a more relaxed filtering criteria: minimum of 4 reads covering a SNP in individual samples and 10 reads in the pool sample, and no more than 25% of missing data per SNP. The different pooled samples showed similar rho values, between 0.931–0.938, and the number of SNPs were between 833,080–833,479 , sub-sampling by a factor of 2/3, 1/2, or 1/3 resulted in correlation coefficients of 0.913, 0.838, or 0.786 over 179073, 22634, or 12086 SNPs, respectively .For both RAD and WG libraries, a comparison of the allele frequency distributions between individual sequencing and pool-seq data shows that the pool-seq analysis assigns 100% allele frequency to many alleles that have less than 100% frequency in the individual genotypes . FurtherTo investigate this gap, we plotted in C. niger whole-genome sequencing data. One SNP was assumed to be a QTL of a single colony-level phenotype. Various levels of noise were added to the phenotype, to represent an environmental effect on top of the genetic effect of the QTL. For example, a noise level of 1 means that the environmental effect is on the same order of magnitude as the genetic effect of the QTL. The percent of phenotypic variation explained by the QTL was measured by linear regression. For noise levels of 1, 2, and 3, the median percent of phenotypic variation explained by the genotype was 52.4%, 23.1%, or 11.5%, respectively .Next, the statistical power of QTL detection was evaluated in a simulation study, for each of the various approaches described above. Genotypes of workers from 50 colonies were simulated, as well as simulated pool-seq allele frequencies that incorporated the experimental error as measured from the ectively . A lineaectively . Also asThe power of pool-seq (63.4% with noise level of 1) is on par with the power of sequencing seven individuals (64.1%). In this case, sequencing seven or more individuals per colony would provide greater power than pool-seq. As noise increases, the pool’s power is equivalent to the power of a larger number of individuals. For example, for a noise level of 2 it is equivalent to eleven individuals. However, if pools of 150 colonies are used, the power of pool-seq increases dramatically to 97.8% for a noise level of 1 .C. niger: monogyne, polyandrous colonies with five matings per queen. To investigate the effect of polyandry, we repeated the simulations for the case of monandry , as well as for the case of higher polyandry with ten matings per queen. The simulation results presented in To aid future studies in determining what would be the favorable DNA extraction, library construction and sequencing approach, we estimated the costs for the various alternatives . WG-seq In this study, we report the first use of empirical measurements of experimental error in pooling DNA from multiple individuals for the purpose of evaluating the power of GWAS for detecting QTLs of colony-level traits. By evaluating the use of pool-seq vs. individual sequencing and WG-seq vs. RAD-seq, our study provides a broad overview on the experimental errors and biases in allele frequency estimations. Such pool-seq experimental biases can result from DNA quantification errors before normalization, pipetting errors, and various biases and artifacts in library construction and sequencing. We quantified the correspondence between allele frequencies in the individual genotypes and the pool-seq data by their correlation. Using simulations, we estimated, for the first time, the statistical power for detecting a QTL of a colony-level trait by taking into consideration the additive effects of environmental noise and experimental noise.rho = 0.94 for WG-seq and rho = 0.77 for RAD-seq. Surprisingly, the correlation was not affected by either pool size  or normalization following DNA extraction. It may be that DNA quantification error and pipetting errors did not allow for sufficient reduction in the variation of sample representation in the pool. Furthermore, subsequent stages of the library construction, sequencing and bioinformatic analysis may contribute to the error more than the initial variation in DNA extraction yield. For example, library construction includes PCR amplification that introduces variation in the amplification of different samples, and may also introduce bias toward one allele vs. the other.WG-seq of pools provides considerably more accurate allele frequency estimates than RAD-seq of pools. The correlation between allele frequencies in individuals and in pool-seq was We evaluated the importance of sequencing depth by sub-sampling our sequencing depth of the pool from approximately 30X down to 20X, 15X, and 10X. The reduced accuracy of the pool-seq allele frequency that we observed, from 0.938 to 0.913, 0.838, and 0.786, suggests that 30X is advantageous over 20X, but not by a large margin. It appears that pool sequencing depth of 30X is close to the asymptotic limit and further increase in depth (e.g. 40X) would produce only a minor improvement in the accuracy of allele frequency estimation by pool-seq. This conclusion is broadly in agreement with previous pool-seq studies—see comparisons below.rho = 0.932) over RAD-seq (rho = 0.699), which was very similar to the difference that was measured for the full dataset. Therefore, the difference is due to the lower quality of the pool genotype data at each locus that is generated by RAD-seq vs. WG-seq.The weaker correlation in the pools of RAD-seq relative to WG-seq may be due to RAD-seq artifacts. For example, polymorphism at restriction sites would result in alleles that are not cleaved and sequenced , which would bias allele frequency estimation for adjacent SNPs. This effect was shown to result in up to 9.4% of heterozygous loci being incorrectly called as homozygous in RAD-seq of individual samples ,38. Gaut29 = 0.41. Thus, we conclude that technical artifacts and biases in the pool-seq procedure lead to missed rare alleles in almost all such loci. Our analysis of these loci show that they do not suffer from lower sequencing depth compared to loci that were not affected by allele dropout. Therefore, we speculate that some other technical artifacts in library construction and sequencing introduce noise and/or bias towards one of the two alleles at each locus, which result in rare alleles being completely missed below a certain allele frequency (for a given level of sequencing depth). It should also be noted that pooling methods are disadvantageous when genotyping dominant/recessive markers. In such cases, pool-seq masks heterozygosity whereas individual genotyping enables distinguishing between two heterozygotes and a recessive homozygote.Our study also revealed what appears to be a technical artifact in pool-seq. For low frequency alleles (<5% of the colony genotypes), pool-seq misses the rare allele completely. That is, no reads were sequenced from the rare allele in the pooled samples and so the allele frequency estimate is 100%. This cannot be explained simply by low sequencing depth, since the pool had an average sequencing depth of 30X. At this depth, the probability of not obtaining any reads for an allele with 3% frequency is only 0.97Evaluation of QTL detection power in GWAS revealed that as the environmental noise level rises, the success rate of identifying a colony-level QTL drops dramatically. Note that our simulated noise levels 1–3 correspond to QTLs that explain between 10% and 50% of the phenotypic variation. This range includes, for example, two QTLs for pollen foraging that were mapped in honey bees, which had effect sizes of 38% and 33% . We condOur results show that sequencing more individuals per colony can compensate for poor QTL detection power, but only to a limited extent. For example, at a noise level of 1, sequencing seven individuals per colony gives a detection power of 64.1%, but larger numbers of individuals per colony increase the power by less than 10% . When usWe have concentrated on the case of a monogyne, polyandrous colony with five patrilines (five mating per queen). However, the level of polyandry is variable in social insects, with many monandrous species (singly mated queens) and some extreme cases of polyandry with 20 patrilines or more . TheOverall, our results demonstrate that using colony pools facilitate substantial power increase within a given budget. In general, it would be our recommendation to use pool-seq. Pool-seq is more profitable for the case of polyandrous colonies, but also profitable to a lesser degree for the case of monandrous colonies . However, in some scenarios, individual sequencing may be still be preferable. For example, if the number of sampled colonies is limited then individual sequencing would provide greater power in the polyandrous scenario  and other pools sequenced at 20X or more (rho > 0.9). Further differences between pools of more individuals (22 to 90) or greater depth (40X) were less pronounced (rho = 0.91–0.95). These previous empirical results are roughly in agreement with our results when comparing pool-seq with similar depth. With respect to the number of samples in the pool, previous studies reported somewhat increased accuracy with increased pool size. Conversely, our results show that 10 samples are sufficient to represent a colony, because this pool gave comparable accuracy to a pool of 30 samples. This result may be explained by the relatedness of individuals in the pools. In our study the pooled samples are either sisters or half-sisters, whereas previous studies used pools of unrelated samples.Previous studies investigated biases in pool-seq by computer simulations as well as empirical measurement of the error in allele frequency estimation from pool-seq relative to individual genotyping. Using simulation studies, Futschik and Schlötterer  conclude, et al.  obtained, et al.  evaluateWe conclude that for a given budget, pool-seq is preferable over individual sequencing for maximizing the QTL detection power in GWAS of colony level phenotypes. However, selection of library construction and sequencing approaches should consider the expected environmental noise, availability of samples, and manpower. We also conclude that when using pool-seq, sequencing whole genomes is preferable over sequencing reduced representation libraries and that, at least in social insects’ colonies, increasing the size of the sequenced pool is of little advantage. We also found that sequencing un-normalized pools may be of great advantage as it increases the number of colonies that can be analyzed, the power of GWAS, and reduces labor dramatically.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fig(TIFF)Click here for additional data file.