Reading and writing are crucial life skills but roughly one in ten children are affected by dyslexia, which can persist into adulthood. Family studies of dyslexia suggest heritability up to 70%, yet few convincing genetic markers have been found. Here we performed a genome-wide association study of 51,800 adults self-reporting a dyslexia diagnosis and 1,087,070 controls and identified 42 independent genome-wide significant loci: 15 in genes linked to cognitive ability/educational attainment, and 27 new and potentially more specific to dyslexia. We validated 23 loci (13 new) in independent cohorts of Chinese and European ancestry. Genetic etiology of dyslexia was similar between sexes, and genetic covariance with many traits was found, including ambidexterity, but not neuroanatomical measures of language-related circuitry. Dyslexia polygenic scores explained up to 6% of variance in reading traits, and might in future contribute to earlier identification and remediation of dyslexia. Genome-wide analysis of self-reported dyslexia identifies 42 associated loci, including 27 not previously associated with cognitive traits. Dyslexia shows genetic correlation with ambidexterity but not neuroanatomical measures of language-related circuitry. Dyslexia is a neurodevelopmental disorder characterized by severe reading difficulties, present in 5–17.5% of the population, depending on diagnostic criteria3. It often involves impaired phonological processing  and frequently co-occurs with psychiatric and other developmental disorders4, especially attention-deficit hyperactivity disorder (ADHD)6 and speech and language disorders8. Dyslexia may represent the low extreme of a continuum of reading ability, a complex multifactorial trait with heritability estimates ranging from 40% to 80%10. Identifying genetic risk factors not only aids increased understanding of the biological mechanisms, but may also expand diagnostic capabilities, facilitating earlier identification of individuals prone to dyslexia and co-occurring disorders for specific support.The ability to read is crucial for success at school and access to employment, information and health and social services, and is related to attained socioeconomic status11 or modest  association studies of diagnosed children and adolescents12. Candidate genes from linkage studies show inconsistent replication, and genome-wide association studies (GWAS) have not found significant associations, although LOC388780 and VEPH1 were supported in gene-based tests12. Larger cohorts are vital for increasing sensitivity to detect new genetic associations of small effect. Here, we present the largest dyslexia GWAS to date, with 51,800 adults self-reporting a dyslexia diagnosis and 1,087,070 controls, all of whom are research participants with the personal genetics company 23andMe, Inc. We validate our association discoveries in independent cohorts, provide functional annotations of significant variants (mainly single-nucleotide polymorphisms (SNPs)) and potential causal genes, and estimates of SNP-based heritability. Lastly, we investigate genetic correlations with reading and related skills, health, socioeconomic, and psychiatric measures, and evaluate the evidence for previously implicated dyslexia candidate genes in our well-powered results.Previous genome-wide investigations of dyslexia have been limited to linkage analyses of affected familiesP < 5 × 10−8) and 64 loci with suggestive significance (P < 1 × 10−6)  and consistent with polygenicity  in males and females, and 0.97  between younger and older adults.The full dataset included 51,800  participants responding ‘yes’ to the question ‘Have you been diagnosed with dyslexia?’ (cases) and 1,087,070  participants responding ‘no’ (controls). Participants were aged 18 years or over (mean ages of cases and controls were 49.6 years (s.d. 16.2) and 51.7 years (s.d. 16.6), respectively). We identified 42 independent genome-wide significant associated loci  in the main analysis. Intergenic SNP rs57892111 (located between TFAP2B and PKHD1 on chromosome 6p) was not among the significant or suggestive SNPs of the main analysis, and so may represent a female-specific variant. There is no evidence from existing GWAS that this SNP is associated with any other human trait. Of the six genome-wide significant variants in the male GWAS  in the GenLang consortium GWAS meta-analysis of reading  and spelling  ability14. At P < 0.05, 18 were associated in GenLang, 3 in the NeuroDys case-control GWAS12 , and 5 in the Chinese Reading Study (CRS) of reading accuracy and fluency , 3  were significant at a Bonferroni-corrected level (h2SNP = 0.152 (standard error = 0.006) using the 23andMe sample prevalence of 5%, and h2SNP = 0.189 (standard error = 0.008) using a 10% prevalence of dyslexia, which is more typical of the general population3.Gene-based tests identified 173 significantly associated genes  percentile was 0.39). For the 42 associated loci, the most probable gene targets of each were estimated by the Overall V2G (Variant-to-Gene) score from OpenTargets (Supplementary Table C1orf87) and rs3735260 (AUTS2)) could be causal because they had combined annotation dependent depletion (CADD) scores suggestive of deleteriousness to gene function according to Kircher et al.15  were located near significant SNP associations. Of the 12 brain regions assessed, gene expression was generally highest in the cerebellar hemisphere, cerebellum, and cerebral cortex, consistent with the results of gene property analysis.Of the 173 significant genes from genome-wide gene-based tests in MAGMA (see Supplementary Table P < 4.17 × 10−3)  with quantitative reading and spelling measures ranged from −0.70 to −0.75 , and were −0.62  and −0.45  with phoneme awareness and nonword repetition measures, respectively. The childhood/adolescent performance  intelligence quotient (IQ) rg was lower  than that for adult verbal-numerical reasoning19  but similar to that for childhood IQ20  and educational attainment21 . Traits showing positive rg included jobs involving heavy manual work21 ), work-related/vocational qualifications21 , ADHD22 , equal use of right and left hands21  and pain measures21 . Of the 11 ENIGMA measures tested, only intracranial volume was significantly correlated with dyslexia . Targeted investigation of 80 structural neuroimaging measures from UK Biobank, including surface-based morphometry and diffusion-weighted imaging for brain circuitry linked to language, were nonsignificant at a Bonferroni-corrected significance level for number of independent traits. Phenotype independence was estimated by spectral decomposition of the phenotypic correlation matrix implied by the bivariate LDSC intercept from GWAS summary statistics of these traits, using the PhenoSpD toolkit23  and 5.6%  of variance in word recognition tests.Dyslexia polygenic scores (PGS) based on the 23andMe dyslexia GWAS were computed in four independent cohorts and, overall, higher PGS were associated with lower reading and spelling accuracy  showed association surviving Bonferroni correction that accounted for LD (P < 0.05/68.7). In gene-based tests of 14 candidate genes from the literature25, association at a Bonferroni level (P < 0.05/14) was seen for KIAA0319L (P = 1.84 × 10−4) and ROBO1 (P = 1.53 × 10−3) (Supplementary Table CNTNAP2 association approached corrected replication-level significance (P = 0.004). Targeted gene set analysis of three pathways previously implicated in dyslexia (Supplementary Table P = 2.00 × 10−3) for the axon guidance pathway (comprising 216 genes).Of 75 previously reported dyslexia associations, none showed genome-wide significance in our analyses  ranging from 1.04 to 1.12). Our estimated SNP-based heritability of 19%  was equal to that reported in a smaller GWAS12, but lower than heritability estimates from twin studies (40–80%)27. This difference may be due partly to effects of rare and structural variants28, which have been implicated in reading and related traits30.In the largest GWAS of dyslexia to date , we identified 42 significant independent loci. Of these, 27 represent new associations that have not been uncovered in GWAS of related cognitive traits; 12 of the new associations were validated in the GenLang consortium GWAS meta-analysis of reading/spelling in English and other European languagesAUTS2 has been implicated in autism31, intellectual disability32 and dyslexia33, the variant we uncovered (rs3735260) represents the strongest AUTS2 SNP association with a neurodevelopmental trait to date. Amongst our findings were other known neurodevelopmental genes, such as TANC2  and, especially, GGNBP2  with variant rs34349354 supported in all our validation cohorts. However, rs34349354 is also associated with cognitive performance38, and based on expression quantitative trait loci (eQTL) evidence is more likely linked to ZNHIT3, colocalizing with molecular QTLs (opentargets.org). Notably, none of the more established candidate genes for dyslexia approached genome-wide significance in our results.Whereas 39. We further observed enrichment in the histone mark H3K4me1 , and at H3K4me1 and H3K4me3 clusters in the CNS . Since reading/writing systems are built on our capacities for spoken language, it is plausible that evolutionary changes on the human lineage helped shape the underlying genetic architecture41. However, we did not find enrichment of significant associations for curated annotations spanning different periods of hominin prehistory.Like other human complex traits, partitioning of SNP-based heritability revealed enrichment in conserved regions42, suggesting that hearing problems at an early age could affect acquisition of phonological processing skills.Our self-reported dyslexia diagnosis binary trait showed strong negative genetic correlations with quantitative reading and spelling measures, supporting the validity of this measure in the 23andMe cohort, and suggesting that reading skills and disorder are not qualitatively distinct. The positive genetic correlation between hearing difficulties and dyslexia is consistent with genetic correlations reported for childhood reading skill43. Educational attainment correlations were also not strong, which might reflect school adjustments and other support that counteract disadvantage in academic learning.Dyslexia showed moderately negative genetic correlations with adult verbal-numerical reasoning, but there was a lack of a strong genetic correlation of dyslexia with  performance IQ. This would be consistent with phenotypic observations that individuals with dyslexia are disadvantaged on verbal IQ tests44. Left-handedness and ambidexterity show small genetic overlap with each other45 yet are both phenotypically linked to neurodevelopmental disorders/cognitive abilities47. We report a significant genetic correlation between dyslexia and self-reported equal hand use, but not left-handedness, supporting theories linking ambidexterity and dyslexia48.There was little evidence of common genetic variation in dyslexia being related to interindividual differences in subcortical volumes, or structural connectivity and morphometry for brain regions implicated in language processing in adults. Thus, the phenotypic correlations previously reported between dyslexia and aspects of neuroanatomy may in large part reflect environmental shaping of the brain, perhaps through the process of reading itself6 often co-occur (24% reporting ADHD in our cases versus 9% in controls), and we show a moderate genetic correlation between the two, potentially reflecting shared endophenotypes like deficits in working memory and attention49. Although we did not find significant genetic correlations between dyslexia and ASD, the GWAS for the latter encompassed diverse neurodevelopmental phenotypes, including subgroups with varying educational attainment and IQ40. Genetic correlations with pain-related traits suggest that individuals with dyslexia may have a lower threshold for pain perception. Links between pain and other neurodevelopmental disorders have been reported51.Dyslexia and ADHD52.Dyslexia polygenic scores were correlated with lower achievement on reading and spelling tests in population-based and reading-disorder enriched samples, especially for nonword reading, a measure of phonological decoding that is typically impaired in dyslexia. Polygenic scores could become a valuable tool to help identify children with a propensity for dyslexia, enabling learning support before development of reading skills. However, a limitation of our study is the potential for collider bias arising from sample selection , which we were unable to quantify; thus, care should be taken in future research when using polygenic scores based on many variantsIn summary, we report 42 new independent genome-wide significant loci associated with dyslexia, 27 of which have not been associated with cognitive-educational traits and should be prioritized for follow up as dyslexia candidates. Functional annotation of the variants highlights the importance of conserved and enhancer regions of the genome for this trait. Dyslexia shows positive genetic correlations with ADHD, vocational qualifications, physical occupations, ambidexterity and pain perception, and negative correlations with academic qualifications and cognitive ability; family-based methods are needed to dissociate pleiotropic and causal effects.www.eandireview.com). They included 51,800  participants who responded ‘yes’ to the question ‘Have you been diagnosed with dyslexia?’ (cases) and 1,087,070  participants who responded ‘no’ (controls). Age ranged from 18 to 110 years, with the prevalence of dyslexia higher for younger participants (5.34% in those aged 20–30 years) than older participants (3.23% in those aged 80–90 years). The negative linear relationship between dyslexia prevalence and participant age was expected given that screening for specific learning difficulties has only become commonplace in more recent decades. Moreover, this aligns with findings from the subsample (4.3%) of participants who reported age of diagnosis: younger participants were diagnosed at an earlier age  for 20- to 30-year-olds) than older participants  for 80- to 90-year-olds). The prevalence of dyslexia in our sample was similar for women (4.51%) and men (4.6%), although the slightly higher prevalence in males in this very large sample was statistically significant (P < 8.7 × 10−6). Such a prevalence lies at the lower end of the range typically reported in the US population3 and might represent the more severe cases of dyslexia given that a formal diagnosis was required; additionally, people with dyslexia might opt out of survey research that requires reading, further restricting the sample range.Participants were drawn from the customer base of 23andMe, Inc., a consumer genetics company. Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical and Independent Review Services (53 and the 54).DNA was extracted from saliva samples and genotyped on one of five genotyping platforms by the National Genetics Institute (NGI). In the present analysis, only participants with European ancestry were included. Details about the genotyping arrays, quality control of samples and ancestry derivation can be found in Fontanillas et al.P < 5 × 10−8 (suggestive significance level as P < 1 × 10−6). Only reliably imputed SNPs (r2 > 0.80) and those with minor allele frequency (MAF) > 0.01 are presented . We define associated regions by first identifying all variants with P < 5 × 10−8, then grouping these variants into regions separated by gaps of at least 250 kb. Index variants are the variants with smallest P value within each associated region. We use the same approach for regions with suggestive associations, but by first identifying all variants with P < 10−5. Subsidiary genome-wide association analysis of separate male  and female  groups, and younger  and older  groups was performed. The latter was to check whether reliability of diagnosis  affected the GWAS signal.Association analysis was performed on genotyped and imputed SNP dosage data using logistic regression and assuming an additive model of allelic effects. For X-chromosome analysis, male genotypes were treated as homozygous diploid. Covariates included age, age squared, gender, the first five ancestry principal components and genotype platform. SNP significance was evaluated by a likelihood ratio test, and genome-wide significance was determined as 55; (2) a population sample  of children measured on quantitative traits of reading accuracy and reading fluency  and spelling  skills measured in cohorts of children and adolescents from Europe, the United States and Australia, and representing seven European languages, of which English was the most common14.We also looked to independently validate our genome-wide significant variants within (1) a published GWAS meta-analysis of 2,274 dyslexia cases from nine European countries representing six different languages (NeuroDys) by Gialluisi et al.Top SNPs are reported from the more conservative GWAS results adjusted for genomic control Fig.  and 10, P values for association with dyslexia by performing the gene analysis in MAGMA v.1.08 (ref. 56) through the FUMA interface57 using standard settings. In total, 19,039 genes were tested, and P values were judged based on a Bonferroni-corrected significance threshold of P < 2.63 × 10−6. We also performed gene set analyses for association of biological pathways  terms and curated gene sets from the Molecular Signatures Database (MsigDB)59) with dyslexia in MAGMA through the FUMA interface. The total number of pathways tested was 15,486, and P values were judged based on a Bonferroni-corrected significance threshold of P < 3.23 × 10−6.The GWAS results were used to calculate gene-based 60 assuming a prior variance of 0.1, and using the method of Maller et al.61 to define these sets. Variant effect prediction of these was done in ENSEMBL (release 104)62. For genome-wide significant variants, we considered: gene context ; deleteriousness (Combined Annotation Dependent Depletion (CADD) score); functionality (RegulomeDB (RDB) category); chromatin state (minimum and common 15-core chromatin state); and SNP-trait associations reported in the NHGRI GWAS Catalog13.Genome-wide significant variants and nearby gene(s) were annotated using external reference data and evaluated for functional or regulatory impact. A 99% credible set of potentially causal variants for SNPs in significant regions was based on approximate Bayes factor (ABFs)63, which draws on evidence from QTL and chromatin interaction experiments, functional predictions and distance from a gene’s transcription start site. For genome-wide significant genes, we considered: loss-of-function intolerance (probability of loss-of-function Intolerance (pLI) score); variation intolerance ; variation intolerance in noncoding regions ; evolutionary constraint of noncoding regions (noncoding genomic evolutionary rate profiling (ncGERP) score); evolutionary constraint of protein-coding regions (protein-coding genomic evolutionary rate profiling (pcGERP) score); deleteriousness across noncoding regions (noncoding CADD (ncCADD) score); combined functionality of variants in noncoding regions (noncoding genome-wide annotation of variants (ncGWAVA) score); and expression in 12 brain tissues . All annotations were obtained through FUMA57 except RVIS, ncGERP, pcGERP, ncCADD and ncGWAVA, which were taken from Petrovski et al.64. Details of each annotation including original sources are in the For each variant, the most probable gene target was identified using the Open Target Genetics portal39, to determine whether SNPs that share the greatest proportion of the heritability are also clustered in specific functional categories in the genome. Overall, we performed 266 different tests, which would give a very conservative Bonferroni-corrected significance level of 1.88 × 10−4, but because there will be overlap among annotation groups, we also report corrections to significance within different classes of annotation, each of which we now describe. Partitioning was performed for the 24 main functional annotations defined by Finucane et al.39. LD scores, regression weights and allele frequencies are from European ancestry samples and were retrieved from https://alkesgroup.broadinstitute.org/LDSCORE. Heritability estimates were considered statistically significant if the P value surpassed an α level of 2.08 × 10−3, derived by Bonferroni correction based on 24 tests.We partitioned SNP heritability of dyslexia using stratified LDSC, as described by Finucane et al.65 and promoters66, respectively. Enrichment is the proportion of SNP heritability divided by the proportion of SNPs. For the brain cell types, we estimated enrichment for heritability of dyslexia for genes expressed in neurons, astrocytes, and oligodendrocytes using data from Cahoy et al.67. Enrichments were considered statistically significant if the P value surpassed an α level of 0.017, derived by Bonferroni correction based on three tests. The gene expression data used to estimate the enrichment of heritability in genes expressed in certain brain regions was from the GTEx database68, and the Bonferroni-derived α level for enrichment was 4.17 × 10−3 (based on 12 tests). Chromatin annotations include data from the Roadmap Epigenomics consortium17 and EN-TEx70. For H3K4me1, the Bonferroni-derived α level for enrichment was 4.63 × 10−4 (based on 108 tests) and, for H3K4me3, the Bonferroni-derived α level for enrichment was 4.39 × 10−4 (based on 114 tests).We also estimated the enrichment for heritability of dyslexia for tissue-specific annotations, while controlling for the annotations in the baseline model, including gene expression in three brain cell types, gene expression in 12 brain regions, and chromatin marks H3K4me1 and H3K4me3 in multiple tissues  since these marks are enriched at enhancers18. These analyses capture a range of periods in an evolutionary timeframe on the lineage that led to humans, from approximately 30 million years ago to 50,000 years ago.Although reading and writing is a human cultural invention, it builds on fundamental pathways involved in language processing. Therefore, we investigated whether annotations related to human evolution were significantly enriched for heritability of dyslexia by applying an evolutionary analysis pipeline adapted from Tilot et al.71, fetal brain HGEs72, ancient selective sweep regions73, Neanderthal-introgressed SNPs74 and Neanderthal-depleted regions75 78.Genetic correlation between self-reported dyslexia diagnosis in males and females, and between younger (<55 years old) and older (≥55 years old) adults was calculated using LDSC79—a centralized database and web interface that automates the LDSC regression analysis pipeline. A selection of brain magnetic resonance imaging measures obtained from the ENIGMA-3 consortium83, and measures of reading and spelling accuracy, and performance IQ from the GenLang Consortium14 were analyzed locally using LDSC. Word reading accuracy in GenLang was measured by the number of correct words read aloud from a list in a time restricted or unrestricted fashion. Examples of tools that include this measure are Test of Word Reading Efficiency (TOWRE), the British Ability Scales (BAS) and the Wide Range Achievement Test (WRAT). Spelling accuracy in GenLang was measured by the number of words correctly spelled orally or in writing. The words were dictated as single words or in a sentence. Examples of tools that include this measure are the BAS, WRAT and Wechsler Objective Reading Dimensions (WORD). Performance IQ in GenLang was based on subtests of IQ tests that did not depend on verbal cues, as included for example in the BAS and Wechsler Intelligence Scale for Children (WISC). Trait descriptions and summary statistic sources are in Supplementary Table P value of 5.1 × 10−4 from 98 independent tests.We present the pairwise genetic correlation of dyslexia with 98 traits. Summary statistics for most of these traits are publicly available through LD Hub84. Structural brain imaging traits encompassed both diffusion tensor imaging and surface-based morphometric phenotypes85 where selected tracts or regions of interest had a known link to language. For diffusion tensor imaging, fractional anisotropy values derived from both tract-based-spatial statistics and probabilistic tractography were used for available tracts spanning the extended language network86. For surface-based morphometric  GWAS, summary statistics for regions of interest derived from the Desikan-Killiany atlas (white surface) were used, again selected for their relevance in language processing, based on previous literature90. To correct for multiple testing, phenotypic correlations between the UK Biobank imaging indices were derived and analyzed by PhenoSpD23 to obtain the number of independent variables (36.08) to use for Bonferroni correction .Genetic correlations were further estimated in a targeted analysis of structural brain magnetic resonance imaging measures from UK Biobank, which were more comprehensive than those currently available from ENIGMA, along with further advantages such as hemisphere-specific data and greater homogeneity in cohort and scanning procedures. GWAS summary statistics from brain imaging-derived phenotypes for 33,000 participants were downloaded from the Oxford Brain Imaging Genetics ServerP values from the 23andMe GWAS . They were calculated in four independent cohorts. Two were general population cohorts from Australia: n = 1,640 (772 families) adolescents/young adults (Brisbane adolescents)91; n = 1,165 (966 families) older adults (Brisbane adults)25. The other two were family-based samples selected for dyslexia: one from the United Kingdom (UKdys), n = 930 (595 families); the other from the United States , n = 717 (336 families)92. In the Australian samples, polygenic scores were calculated on 1000 Genomes Phase 3 (v.20101123) imputed genetic data using PLINK93. Only reliably imputed SNPs (R2 > 0.80) and those with a minor allele frequency >0.01 were included, and the default clumping procedure was used where index SNPs formed a clump with other SNPs in LD (R2 > 0.1) and within a 250 kb distance. In the UKdys and CLDRC samples, polygenic scores were calculated on Haplotype Reference Consortium imputed genetic data using PRSice94, with the same imputation quality and MAF exclusions for the base (23andMe GWAS) sample, and clumping parameters.Dyslexia polygenic scores were based on increasingly larger numbers of SNPs corresponding to their association 95, and two nonword repetition tests which are sensitive to developmental language disorders—Dollaghan and Campbell96, Gathercole and Baddeley97; UKdys and CLDRC: word recognition). All quantitative traits were preadjusted for sex, age and ancestry principal components . Further adjustments were made for imputation run (separate runs for different genotyping arrays) in the Australian samples, and for nonverbal IQ in all samples , and for hearing difficulties in the Australian older adults. Because the cohorts included related family members (twins or siblings), linear mixed models (lme) were specified in RStudio98, with family membership modeled as a random effect and the dyslexia polygenic score as a fixed effect. Where monozygotic twins were present, their trait scores were averaged and they were used as a single case.Polygenic scores were then used as predictors in linear models of quantitative trait outcomes , irregular word  reading and spelling tests from an extended version of the Components of Reading ExaminationWe used the results of the 23andMe dyslexia GWAS to assess variants, genes and biological pathways previously associated with or implicated in dyslexia and/or variation in reading and spelling ability in past association studies, linkage analyses and other studies.P < 7.28 × 10−4, derived by Bonferroni correction based on 68.7 independent tests derived through matrix spectral decomposition, taking into account LD . The sources for each variant are provided in Supplementary Table We assessed 75 previously reported variants within our summary statistics, adopting a replication/validation significance threshold of 56) for overrepresentation of genome-wide significant variants from the 23andMe dyslexia GWAS within the loci of 14 candidate genes from earlier literature: CMIP, CNTNAP2, CYP19A1, DCDC2, DIP2A, DYX1C1, GCFC2, KIAA0319, KIAA0319L, MRPL19, PCNT, PRMT2, S100B and ROBO1. The rationale for this selection is detailed by Luciano et al.24 and Doust et al.5. The critical P value, based on Bonferroni correction for 14 tests, was 3.57 × 10−3.We evaluated gene-based results from MAGMA v.1.08  two biological pathways previously suggested to play a role in dyslexia susceptibility101—axon guidance  and neuron migration . An adjusted critical P value of 0.017 was derived using Bonferroni correction based on three independent tests.We performed a gene set analysis in MAGMA to test for overrepresentation of genome-wide significant variants within (1) a set of transcriptional targets of Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical and Independent Review Services. Participants were included in the analysis on the basis of consent status as checked at the time data analyses were initiated.Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01192-y.Supplementary InformationSupplementary Note and Fig. 1.Reporting SummaryPeer Review FileSupplementary Table 1Supplementary Tables 1–27.


Crossbreeding is an important way to improve production beef cattle performance. Pinan cattle is a new hybrid cattle obtained from crossing Piedmontese bulls with Nanyang cows. After more than 30 years of cross-breeding, Pinan cattle show a variety of excellent characteristics, including fast growth, early onset of puberty, and good meat quality. In this study, we analyzed the genetic diversity, population structure, and genomic region under the selection of Pinan cattle based on whole-genome sequencing data of 30 Pinan cattle and 169 published cattle genomic data worldwide. ST, and XP-EHH, was computed to detect the signatures of selection in the Pinan cattle genome. A total of 83 genes were identified, affecting many economically important traits. Functional annotation revealed that these selected genes were related to immune , growth traits , embryo development , hornless traits (C1H21orf62), and climate adaptation (ANTXR2).Estimating ancestry composition analysis showed that the composition proportions for our Pinan cattle were mainly Piedmontese and a small amount of Nanyang cattle. The analyses of nucleotide diversity and linkage disequilibrium decay indicated that the genomic diversity of Pinan cattle was higher than that of European cattle and lower than that of Chinese indigenous cattle. De-correlated composite of multiple selection signals, which combines four different statistics including θπ, CLR, FIn this paper, we elucidated the genomic characteristics, ancestry composition, and selective signals related to important economic traits in Pinan cattle. These results will provide the basis for further genetic improvement of Pinan cattle and reference for other hybrid cattle related studies.The online version contains supplementary material available at 10.1186/s12864-022-08645-y. Bos taurus and Bos indicus [. Or according to geographical region, it can also be divided into five groups: European taurine, Eurasian taurine, East Asian taurine, Chinese indicine, and Indian indicine [Cattle are one of the most important domestic animals for human beings. According to the origin of cattle, domestic cattle can be simply divided into two subspecies:  indicus . Or accoindicine . The earWhole-genome sequencing (WGS) has been used to find a large number of variations that can be used as molecular genetic markers. The application of WGS plays an important role in the study of human diseases, the breeding of animals and plants, as well as the origin and domestication of species. WGS studies have yielded many important results in the field of livestock, including domestication, adaptive mechanisms of indigenous breeds, candidate genes responsible for important economic traits, and historical population dynamics , 4. In eIn the current study, we sequenced the genomes of 30 Pinan cattle to detect genetic variation, population structure, and selective sweep. Our result will lay a foundation for further studies on the genetic basis of important economic traits, provide ideas and basis for future improvement in Pinan cattle, and provide a reference for related studies in other hybrid breeds.Bos taurus reference genome (ARS-UCD1.2) with an average depth of 9.67X using Sentieon [A total of 5,402,336,411 clean reads were generated after genome sequencing in 30 Pinan cattle samples. The clean reads were aligned to the latest To place Pinan cattle into a global context, we also detected the single nucleotide polymorphisms in five "core" cattle populations , and twoBos taurus and Bos indicus. The analysis revealed clear geographical patterns of cattle distribution. The proportions of individuals in Pinan cattle inferred by the ADMIXTURE are presented in Fig. Bos taurus and Bos indicus; when K = 4, European taurine and Zebu cattle have a single genomic composition. We can see that the ancestry proportions inherited from Bos taurus are greater than that from Bos indicus in Pinan cattle. To be more precise, Pinan cattle composition is more similar to Eurasian taurine which indicates that the genetic influence of Piedmontese cattle was greater than that of Nanyang cattle. The results of the NJ tree  trees and principal component analysis (PCA) were performed Fig. . The firree Fig.  and PCA ree Fig. C analysiTo elucidate the genomic characteristics in Pinan cattle, nucleotide diversity was calculated Fig. A. The reC1H21orf62), growth traits , Disease-related traits (MACC1 and SNX14), climate adaptation (ANTXR2), lipid metabolism (ANGPTL4), embryo development , immune response , sperm flagella (DNAH2), milk yield .Within-population and cross-population statistics were combined separately in a single score using the De-correlated composite of multiple signals (DCMS) statistic . After cThe characterization of population structure and genetic diversity can help us to evaluate bovine genetic resources and play an important role in utilization and conservation. In this study, the population genetic structure of Pinan Cattle was studied in the background with different cattle breeds. As can be seen from the ADMIXTURE analysis Fig. , the ancBos taurus and Bos indicus, it has high genomic diversity. In addition, cattle breeds with higher genetic diversity tend to have lower LD decay, such as Qinchuan cattle. Interestingly, the LD decay level of Pinan cattle was higher than that of Nanyang cattle and Piedmontese, which may be due to the greater selection intensity experienced by the Pinan herd than Nanyang cattle and Piedmontese.Although Pinan cattle are closer to the Piedmontese lineage, the number of genetic variations inherited from Nanyang cattle is much higher than that inherited from Piedmontese cattle. The nucleotide diversity of Pinan cattle is lower than that of Chinese indicine and other native Chinese cattle breeds, but about twice that of European cattle breeds such as Angus, Hereford, and Piedmontese. The higher genomic diversity of Pinan cattle may be due to the influence of female parent Nanyang cattle. Because Nanyang cattle is a hybrid between REV3L, NT5E, CDX2, KDM6B, ADAMTS9) in the Pinan cattle genome show selectivity for embryo development. To better understand the selection pressure, we explored the biological function of these genes. REV3L is involved in the DNA repair process, and it was upregulated in cattle embryos derived from depleted cells [REV3L resulted in chromosome instability and embryogenesis failure in mice [REV3L is associated with embryonic development in Nellore cattle [NT5E produces a protein that encodes an enzyme that converts adenosine monophosphate into adenosine [NT5E gene affects the cleavage rate of cattle [NT5E can be useful for improving Inosine 5’-monophosphate which contributes to the umami taste in beef in Japanese Black beef [CDX2 is a transcription factor that is localized in the nucleus of the trophectoderm [CDX2 is essential for early development and gene expression involved in the differentiation of inner cell mass and trophectoderm lineages in cattle embryos [KDM6B, also known as JMJD3, is a histone demethylase expressed in bovine cleavage embryos [KDM6B is transiently upregulated around embryo developmental stages when the embryo genome is activated [KDM6B is required for normal bovine embryo development and reprogramming gene expression during the embryonic genome [ADAMTS9 is widely expressed during mouse embryo development [ADAMTS9 is necessary for ovarian development and may promote the rupture for follicular rupture in zebrafish [ADAMTS9 was associated with chest size in goats [In addition, we found important selective scanning markers in Pinan cattle. Over the past 30 years, Pinan cattle have been used for intensive beef breeding, leading to genetic improvement of production traits, especially for their early onset of puberty. Several genes . BOLA-DQA2 and BOLA-DQB both belong to the Bovine Leukocyte Antigen (BOLA) class II genes which are involved in the immune response. For example, BOLA-DQA2 plays an important role in resistance to mastitis in dairy cows [LSM14A plays a critical role in antiviral immune responses [NAALADL2 is reported to be responsible for immune homeostasis [ANTXR2) associated with climate adaptation [Native Chinese cattle tend to be more resistant and adaptable than foreign breeds. In a selective sweep of Pinan cattle populations, we identified several immune system-related genes  related to growth traits. CYP4A11 is involved in fatty acid metabolism, blood pressure regulation, and kidney tubule absorption of ions [CYP4A11 affects growth and fat deposition in cattle [RPL26, a ribosomal protein gene, is reported to be related to residual feed intake in Holstein cows [RPL26 is associated with average daily gain. There is a report shows that MYH10 is strongly associated with milk production and body conformation traits of dairy cattle [MYH10 is a candidate gene for yearling weight in Chinese Simmental beef cattle. In addition, two other positive selection genes ODF4 and ARHGEF15 have been reported to be involved in the milk yield of dairy goats. This suggests they may have a similar role in cattle.In addition, we found several genes  [Bos taurus reference genome annotation file, Annovar [After obtaining the fastq file of the raw data, Trimmomatic software (v0.36) was used to trim sequence reads with the parameters: “LEADING:20, TRAILING:20, SLIDINGWINDWOE: 3:15, AVGQUAL:20, MINLEN:35, TOPHRED33”. The clean reads were aligned to the V202010) . After tV202010) . The raw Annovar  softwarehttp://tree.bio.ed.ac.uk/software/figtree/).PLINK (version 1.9)  softwareST) and cross-population extended haplotype homozygosity (XP-EHH) were used for comparing the genomic difference between Pinan and Piedmontese cattle. We used VCFtools to analyze FST with a 50 kb window and a 20 kb step. Within each population, haplotype phasing was performed using BEAGLE 5.0 [In order to detect selective signals in the Pinan cattle, we used different strategies to scan the genomes of Pinan cattle. The nucleotide diversity (θπ) and the composite likelihood ratio (CLR)  method wAGLE 5.0 , and selAGLE 5.0  was usedST, and XPEHH into a single DCMS framework as described in Ma et al. [stat_to_pvalue function of the R MINOTAUR package, and appropriate unilateral tests were performed, i.e., a left tailed test for the θπ and CLR  and a right tailed test for the FST and XPEHH . Then, the n × n correlation matrix between the statistics is calculated using the covNAMcd function  of the R rrcovNA package [DCMS function of the R MINOTAUR package to calculate the DCMS value of the whole genome. After calculating the DCMS value, these DCMS values were transformed to a normal distribution with the robust linear model using the rlm function (dcms ~ 1) of the R MASS package [pnorm R function to calculate the p-value of DCMS statistics. To control for multiple test false discovery rate (FDR) in the rejected null hypothesis, the p.adjust R function ) was adopted to convert the p-value of DCMS into the corresponding q-value according to Benjamini and Hochberg methods.In this study, we combined four statistics including θπ, CLR, Fa et al. . The anaa et al. , 83–85. a et al. . The gen package . The mat package . The outThe region with DCMS q-value lower than 0.05 was regarded as the statistically significant region, and the genes contained in the significant region were annotated through the reference genome annotation file based on the ARS-UCD 1.2 reference genome. The function of these candidate genes was further determined through a literature review.Additional file 1: Figure S1. Phylogenetic tree constructed by neighbor-joining method from Pinan cattle and some other breeds.Additional file 2: Table S1. Summary of sequencing data. Table S2. List of additional cattle samples for analysis of genetic background in Pinan cattle. Table S3. Distribution of SNPs identified in cattle breeds within various genomic regions annotated by ANNOVAR. Only the breeds with a sample size no less than 5 were calculated in the table. Table S4. CV error corresponding to different K values. Table S5. 92 selective signal candidate regions identified by DCMS.


Several studies have analyzed the relationship between general personality traits and attitudes and behaviors, indicating that a person is more committed to the community. After raising the question of whether malevolent traits might also be related, the aim was to analyze the relationship between civic engagement and personality, delving into the contribution of the Dark Triad  and controlling for the association with the Big Five. The Civic Engagement Questionnaire, the Short Dark Triad, and the Big Five Inventory-10 were administered to 1175 Spanish students (convenience sampling). After performing statistical analyses using SPSS statistical software, it was obtained that the three Dark Triad traits explained 11% of the total explained variance of civic engagement, while 19% was reached when the Big Five were included. Narcissism and openness were the factors most strongly associated with engagement. The positive relationship between narcissism and general personality traits could explain why narcissistic people have more favorable attitudes. Furthermore, people with narcissistic traits may display these attitudes for their own benefit. This study provides further evidence of how the narcissistic personality trait differs from the other two malevolent traits. Given that these traits are also associated with maladaptive behaviors, knowing all their characteristics could facilitate the design of prevention programs aimed at reducing such maladaptive behaviors. Civic engagement is the attitude of believing that one can and should make a difference for the betterment of the community, requiring knowledge, attitudes, skills, and values to achieve that betterment ,2. It coThe different authors who have considered civic engagement in their studies have done so from different approaches, analyzing it in a more specific way and asking about certain behaviors  or in a more general way  e.g., ,6,7). Th,7. Th6,7Several studies have analyzed its relationship to broader personality traits to identify the main characteristics that are related to community engagement. The interest has been in analyzing which general traits are related to those attitudes and behaviors that indicate that a person is more engaged in their community ,13,14,15Overall, there does not seem to be a consensus on which traits are related to civic engagement, as different relationships have been obtained in each study and the same traits are not always positively associated ,13,14,15A cross-sectional study in 24 countries that examined the association of personality with political and civic participation found that the effects of the Big Five vary considerably across countries and that the results also depend on exactly which variables are measured. Furthermore, they consider that the effects of the Big Five on participation may also be mediated or moderated by other variables yet to be studied . Perhapsage = 49.20; SD = 15.20; 58% female).The question of the relationship between civic participation and malevolent  personality traits remains. Are there people who, despite having malevolent traits, are also committed to their community? So far, only one study has been located that has analyzed the relationship between attitudes towards good citizenship and civic duty (being a good citizen) and malevolent traits. This is the study by Pruysers et al. , who setThe Dark Triad was defined in  by PaulhTherefore, considering these three traits and their relationship with civic engagement, Pruysers et al.  obtainedThese results, which indicate that people with narcissistic traits do actively engage with citizenship, question the meaning of narcissism since it is considered a malevolent or “dark” personality trait. In addition, the relationship between narcissism and other characteristics considered socially desirable, such as well-being or emotional intelligence, has also been examined and positive relationships have been found ,25,26,27These relationships are interesting because it is important not to forget that Dark Triad traits have been associated with a wide variety of violent behaviors  ,31,32,33Taking these results into account, the recent interest of many authors has been in finding an answer to why narcissism behaves differently from the other two malevolent traits (Machiavellianism and psychopathy). For example, Van Groning et al.  considerHowever, following Weinschenk’s indications , the effAs stated, there are currently no studies that have analyzed the relationship between civic engagement and personality traits, both general and malevolent, and that have considered analyzing the possible relation of some traits with others in determining this engagement .Considering the few results in the literature and the controversy with the narcissistic trait, our main concern is to know the association of malevolent traits with civic commitment and to know if there are differences between the association with malevolent traits when they are the only traits included in the statistical model and when they are included together with the more general traits. Therefore, based on these ideas, this paper aimed to analyze the relationship between civic engagement and personality traits, delving into the specific contribution of the Dark Triad traits and controlling for the association with the Big Five personality traits in a sample of Spanish young adults.Consistent with previous literature , we expeThe sample consisted of 1175 students  from two Spanish universities (Miguel Hernandez University of Elche and San Antonio Catholic University of Murcia). The mean age was 20.51 years .To determine the sample size (convenience sampling), we allocated several observations 6 to 10 times greater than the variables . AccordiThe CEQ  is a subThe BFI-10  is a shoThe SD3  is a 27-Participants were recruited through institutional outreach and the survey was carried out using the online data collection platform DetectaWeb  during tWhen participants accessed the survey link from their cell phones, tablets, or computers, they were first shown the instructions, then asked for their consent to participate, and then filled in the different scales of the study, for which they needed approximately 20 min. Participants did not receive compensation for participating in the study.At the time of the project, many more questionnaires were administered than those used for this study. We administered measures for mental and socioemotional health, positive and negative affect, level of distress, anxiety, and depression, emotional intelligence, avoidance and fusion, suicidality, internalizing and externalizing symptoms and prosocial behavior, self-esteem, quality of life, healthy behaviors, and sincerity. However, only variables measuring civic engagement, the dark triad, and the Big Five were considered as variables of interest for this study. Consequently, the present study was part of a larger study in which all the measures described above were tested.A cross-sectional study  was designed for the study. First, the descriptive statistics and the scores of the sample in the different questionnaires administered were calculated to obtain the profile of the participants and the mean of the different scores. The internal consistencies of the Civic Engagement Questionnaire and the Short Dark Triad were also estimated by calculating Cronbach’s alpha and McDonald’s omega coefficients following the recommendations of Kalkbrenner  for instsr²) for each of the variables was also calculated.Secondly, to test the first two hypotheses of the study (H1 and H2), the correlations between the different variables under study were calculated to delimit the magnitudes and the positive or negative direction of the relationships between the different variables. For this same purpose, a multiple regression analysis was conducted to determine the associations of both dark traits and general personality traits with civic engagement (criterion variable). For this purpose, and to test H3, first, the specific contribution of gender as a sociodemographic variable (first block) was considered. Age was not included in the model due to the small variance in the sample (limited range of 17 to 30), anticipating that its effect would be null; in the second block the three Dark Triad traits were considered; and in the third block, the Big Five personality traits were considered. The percentage of total variance explained  by the number of analyses performed, i.e., nine). Data were analyzed using SPSS  and Jamovi  statistical software. This study’s design and its analysis were not pre-registered.Given the large sample size, correlations and magnitudes of association in the regression model were interpreted after Bonferroni correction to obtain more accurate results  between the different variables of interest for testing the first two hypotheses of the study (H1 and H2) are presented in The relationship between the Dark Triad traits and the Big Five is more heterogeneous. Narcissism correlates significantly with all five traits, but positively with extraversion, conscientiousness, and openness to experience, and negatively with agreeableness and neuroticism. Psychopathy is only significantly and negatively related to agreeableness and conscientiousness, and Machiavellianism is negatively related only to agreeableness.p < 0.001) was observed, which reached 19% (p < 0.001) when the Big Five were included as a third step (third block).The regression model (with the Bonferroni fit) testing the three hypotheses of the study is presented in p < 0.0056, Bonferroni fit), but when the Big Five were introduced into the model, only narcissism remained significant (positive relation). In turn, of the Big Five, only agreeableness, conscientiousness, and openness to experience presented a significant (and positive) association . In that third block , it was observed that of the Dark Triad traits, narcissism was the factor with the highest specific contribution (sr² = 3.42%). In the case of the Big Five, openness to experience was the factor with the highest specific contribution (sr² = 4.37%).More specifically, in the second block, all three dark traits were found to be significantly associated with civic engagement , a correlational analysis and a regression model were conducted to test the associations between civic engagement and personality traits, both general and malevolent. The results showed the null association of the socio-demographic variable of gender with civic engagement, contrary to what was found in previous studies ,11,12. TOn the one hand, in relation to the three traits of the Dark Triad, the (positive) association of civic engagement with narcissism stands out, being weaker than the (negative) association with Machiavellianism and psychopathy, when only the Dark Triad is included in the model. Thus, H1 is only partially confirmed, since we expected to obtain significant associations only with narcissism (positive) and psychopathy (negative) and, therefore, a non-significant association with Machiavellianism.These results do not coincide with those obtained in the only localized study that has analyzed these associations using a regression model, since that study did not obtain a relationship with Machiavellianism : PruyserOn the other hand, in relation to the Big Five traits, the association of civic engagement with openness to experience is the most prominent, being the trait with the highest association compared to agreeableness and conscientiousness (with no association with extraversion and neuroticism). In this case, H2 is also only partially accepted, as we expected to obtain significant associations with all five traits , but civic engagement was only significantly (and positively) associated with three of the traits .These results do not coincide with those obtained in previous literature, since no study has obtained the same associations with the same traits ,13,14,15It is important to note that in the present study, the associations between civic engagement and the Big Five were also controlled for the association with the Dark Triad, which may have led to this discrepancy with the results of previous studies . In addiThird, the regression model allowed us to test the last hypothesis of the paper (H3). In this case, H3 is accepted, since it was expected to obtain differences in the magnitudes of association with civic engagement when malevolent traits are associated alone and when they are associated with more general personality traits, and this is what was obtained. The contribution of all traits together (the Dark Triad together with the Big Five) being greater for civic engagement. Furthermore, when the Big Five are included in the model, the association with Machiavellianism and psychopathy is null and only narcissism (from the Dark Triad traits) is associated (and positively) with civic engagement. The association with the three dark traits is greater when they do so without the contribution of the Big Five.These latter results cannot be compared with previous literature since this is the first study to analyze the association between civic engagement and personality in the same regression model, i.e., both the more general and the more malevolent traits. Perhaps, as Weinschenk  says, thIt is important to highlight the association obtained between civic engagement and narcissism , as the results point to the fact that people with narcissistic traits do seem to engage with their community. Again, these results may be explained by the association between some traits and others, that is, by the relationship with general personality traits . MoreoveThese findings agree with the ones reported by Van Groningen et al. . In theiThese results highlight the need to study the subclinical narcissistic personality trait included in the Dark Triad. As with previous studies, this trait, despite being considered a malevolent personality trait, maintains a positive relationship with other variables considered socially desirable, such as emotional intelligence and well-being ,25,26,27Another possible explanation for the relationship between civic engagement and narcissism is the one pointed out by Pruysers and colleagues . They coMoreover, it is well known that people with narcissistic traits constantly need to boost their self-esteem and ego ,59,60,61A possible limitation of this study is the difficulty in generalizing the results in terms of civic engagement. As has already been mentioned, depending on how this concept is treated , the results may vary and stronger or weaker relationships with other variables may be obtained. Therefore, as a future line of research, it might be interesting to compare these results using another questionnaire that measures civic engagement more specifically.Another possible limitation of this study is that no internal consistency calculation is available for the instrument used to assess the Big Five personality traits, and the correlation between the two items of some of the factors in the present sample, especially of the agreeableness factor, is very low. Similarly, Cronbach’s alpha in the case of narcissism and psychopathy is not too high, which could indicate certain reliability problems.A final limitation is, being a cross-sectional study, there is difficulty in generalizing the results and in establishing causality between variables. As a future line of research, we propose to analyze longitudinally the associations between civic engagement and general and malevolent personality traits.This is the first study to jointly analyze (in the same regression model) the association of malevolent personality traits (Dark Triad) and the Big Five with civic engagement. It highlights, on the one hand, that the Dark Triad traits do associate with civic engagement, and, on the other hand, it highlights the specific contribution of narcissism and openness to experience (both positively associated). Following the indications of Van Groning  and WeinHowever, it is important not to forget that these dark traits have been linked to a wide variety of violent behaviors  ,31,32,33This study provides further evidence of how the narcissistic personality trait differs from the other two malevolent traits that make up the Dark Triad. Given that these traits are also associated with maladaptive behaviors, as just discussed, knowing all the characteristics of these malevolent traits could facilitate the design of prevention programs aimed at reducing such maladaptive behaviors.As future lines of research, we suggest the need to continue investigating the subclinical narcissistic trait, since it could be considered as a factor of self-protection against the other two traits of the Dark Triad (Machiavellianism and psychopathy). In addition, future research could further investigate the question of whether people with narcissistic traits are more likely to participate in collective actions because they seek their own benefits. In general, further analysis of all the relationships discussed in this study and establishing the causality of the different variables is encouraged.


Psidium friedrichsthalianum, and Shweta had the highest fruit weight (359.32 g), ascorbic acid content (197.27 mg/100 g fruit), total phenolic content (186.93 mg GAE/100 g), titratable acidity (0.69 percent), and antioxidant capacity (44.49 μmolTrolox/g), respectively. Fruit weight was positively correlated with ascorbic acid content; however, titratable acidity was negatively correlated with fruit weight. The principal component analysis (PCA) was 84.2% and 93.3% for qualitative and quantitative traits, respectively. Furthermore, K-mean clustering was executed; the population was grouped into three clusters for both traits. Additionally, the dendrogram using agglomerative hierarchical clustering (AHC), where all the germplasm were grouped into four clusters, revealed that among the clusters, clusters III and IV were highly divergent. The high variability, diversity, and structure could be utilized for the breeding programme of guava and also explored for molecular analysis using next-generation technology to enhance the guava yield and nutrition properties and also develop the climate resilient technology to fulfill the existing demand gap and nutrition availability, which could not only mitigate the nutrition requirement but also enhance the easy availability of fruits year-round.Amid environmental crises, a galloping population, and changing food habits, increasing fruit production with nutritional quality is a global challenge. To address this, there is a necessity to exploit the germplasm accessions in order to develop high-yielding varieties/hybrids with good adaptability and high quality fruit under changing environmental and biological conditions. In the study, a total of 33 morpho-biochemical traits enabled an assessment of the genetic variability, diversity, and structure in a collection of 28 diverse germplasm lines of guava. Results showed that highly significant genetic variability existed in the studied traits in the guava germplasm. The coefficient of variation values for the qualitative and quantitative traits varied from 23.5–72.36 to 1.39–58.62%, respectively. Germplasm Thai, Lucknow-49, Punjab Pink,  Contemporary field-crop-centric agricultural practices challenge the nutritional security of the growing population and cause severe health issues. The health-related problems are further aggravated due to changing climatic conditions. Human beings with nutrient deficits are supposed to be more vulnerable to changing climates; the situation will be more alarming if the same trends continue. The impacts of anthropogenic climate change on the climate at local, regional, and global scales are now beyond dispute. It is noted that there have been variations in the frequency and length of extreme climatic occurrences , vitamin A , carbohydrates (13%), and other minerals like calcium, phosphorus, and iron, which are also present in the fruit. Guava fruits have strong antioxidant and free radical scavenging properties , with an average rainfall of 797.3 mm per year, 39 mean rainy days, and an average temperature of 340 C.The ICAR-IARI, New Delhi is located in the northern region of India  such as fruit size (FS), shape (FSH), base shape (FBS), shape at the stalk end (FSS), cavity (FC), pulp texture (FPT), puffiness (FP), calyx persistence (FCP), a diameter of calyx concerning that of fruit (DC), fruit ridged collar around calyx cavity (FRC), surface (FSU), peel color (FPC), pulp color (FPU), absence/presence of fruit dots (FD), fruit surface covered in crimson or cherry red color (FSC), taste (FT), and pulp flavor (FPF) were recorded as per .i.e., fruit length/breadth ratio (FI), pulp thickness (PT), seed core cavity (SC), pulp thickness: seed core cavity ratio (PS), pulp weight (PW), pulp percent (PP), pulp: seed weight ratio (PSW), vitamin C or Ascorbic acid (AA), total soluble solids (TSS), titratable acidity (TA), total soluble solids: titratable acidity ratio (TT), antioxidant capacity (AC), and total phenolic content (TP) were recorded in the guava germplasm. For each trait, samples were analyzed in five replicates.A total of 16 morpho-biochemical parameters (quantitative) such as fruit weight (FW), fruit length (FL), fruit breadth (FB), fruit index The fruit weight and the pulp weight were recorded using an electronic weighing balance  and expressed in grams. The fruit's length, breadth, pulp thickness, and seed core cavity were measured using Vernier calipers  and expressed in centimeters. A drop of guava juice was placed on the prism of the digital refractometer  to determine TSS and expressed in Brix. Titratable acidity was determined by titrating the aliquot of a known quantity of sample against 0.1N NaOH solution to a pink endpoint using 1% phenolphthalein indicator . The rec−3 M neocuproine solution, 10−2 M CuCl2 solution, and distilled water. After 30 mins, the absorbance of the sample was recorded at 450 nm with a double beam UV-VIS spectrophotometer  against the reagent blank, and the findings were expressed as Trolox equivalent (μmol Trolox/g FW).The antioxidant capacity in guava fruits was determined by . A test Total phenolic content was determined according to the Folin-Ciocalteu reagent  using a Descriptive statistics including the minimum, maximum, mean, standard deviation, and coefficient of variation were analyzed using Web Agri Stat Package-2 (WASP-2) developed by ICAR Complex Goa, India. Qualitative data was exposed to non-parametric Spearman correlations, whereas quantitative data was submitted to parametric Pearson correlations. Principal component Analysis (PCA) and K-mean cluster plots were used to analyze data. Data sets may be divided into K clusters, which are represented by their centroids, using the K-means clustering technique .A dendrogram was created by using both qualitative and quantitative traits. Based on Ward's approach and Euclidean distance, respectively, aggregative hierarchical clustering (AHC) and genetic dissimilarity component analysis were performed.The R statistical software  was used to analyze the reported data for correlation, PCA, K-mean cluster plots, and dendrogram of 33 attributes from the 28 guava germplasm.Genetic variability and genetic diversity are of prime importance for the selection of the desirable lines, which serve as the basis for designing the breeding programme in the guava crop for improved nutritional status.The variation of CV values for the traits ranged from 23.5 to 72.36 % in the studied qualitative traits. The higher CV values were observed for the FP (72.36%), FSH (58.12%), and FPC (53.91%). FSS (42.36%), FSU (35.71%), FBS (34.68%), FPT and FD (33.95%), FCP (33.09%), FRC (32.07%), FPC (31.81%), and FT (30.15%) all had medium CV values. Similarly, low CV values included FS (28.85%), FSC (28.33%), DC (27.56%), FPF (27.33%), and FC (23.5%). The variation of CV values showed a higher variation prevalence in the studied germplasm.According to , all gerFurthermore, large fruit size was observed in Thai, Thai variant, Allahabad Safeda, and Allahabad Safeda variant. Similarly, the reddish peel color was observed in Black guava and Lalit. Pinkish pulp color was observed in Arka Kiran, Black guava, Hisar Surkha, Hisar Surkha variant, Lalit, Punjab Pink, Red type I, and Red type II, whereas yellow pulp color was seen in the Yellow type germplasm. These descriptions will help to identify and easily trace the germplasm during the breeding programme.Significant correlations (P 0.05) were found in the studied qualitative traits see . The FS PCA was carried out using 17 qualitative traits; these traits were divided into eight components, which account for 84.2% of the total variation observed in the studied germplasm .viz., FSS, FCP, DC, FRC, FSU, FPC, and FD. The third component explains 11.9% of the total variation, which includes FS, FSH, FPT, FP, DC, FPU, FD, and FSC. The fourth component, the total variation, accounts for 9.9% of the traits like FSH, FPC, FT, and FPF. The fifth component covers 9.6% of the total variation for the traits . The sixth component, explaining 7.7% of the total variation, is comprised of FBS, FSS, FPT, DC and FSU. The seventh component accounts for 5.6% of the total variation for FBS and FT. The eight components explained 5.4% of the total variation for the traits .The first component accounts for 18.2% of the total variation, which encompasses 11 qualitative traits such as FS, FSH, FSS, FC, FP, FRC, FSU, FPC, FSC, FT, and FPF. The second component explained 13.04% of the total variation for the traits (07), P. pumilum, P. friedrichsthalianum, Thai, Red type I, Red type II, and Sasni. Cluster III consists of Arka Mridula, Snow White, Yellow type, Kasipur collection, Soft Seeded variant, and VNR. These results suggested that the K-means clustering approach was efficient to explain the genetic material was widely scattered throughout the plot based on the phenotypic composition and attributes.The k-means clustering was carried out for 17 qualitative traits of germplasm (a set of 28 germplasm), and this germplasm was grouped into three clusters see . ClusterIn the study, a total of 16 quantitative traits were examined in a set of 28 germplasm lines of guava. The descriptive statistics revealed an enormous amount of variation present among the studied germplasm. The coefficient of variation (CV) ranged from 1.39 to 58.62%. The traits such as PW (58.62%) and FW (58.17%) had high CV values, the trait PSW (33.05%) had a medium CV value, and the rest of the traits showed lower CV values .i.e., the maximum FW (359.32 g), FL (9.32 cm), and FB (8.82 cm) in the Thai variety; FI (1.29) and TP (186.93 mg GAE/100 g) in Punjab Pink; PT (1.85 cm) in Allahabad Safeda variant; SC (5.4 cm) in Thai variant; PS (91.58) and TSS (15.02 0B) in Trichy; AA (197.27 mg/100 g fruit) in Lucknow-49; TA (0.69%) in P. friedrichsthalianum; TT (49.35) in Allahabad Safeda; and AC (44.49 μmol Trolox/g) in Shweta.Similarly, the morpho-biochemical variability was observed among the studied traits in the germplasm lines 0B) and TT (13.85) were recorded in P. friedrichsthalianum; FL (2.43 cm), FI (0.82), PP (88.34) and PSW (20.16) in P. pumilum; AA (110.44 mg/100g fruit) in the Sour type; AC (17.53 μmol Trolox/g) in Kasipur collection; and TP (105.63 mg GAE/ 100 g) in Yellow type.In contrast, the minimum FW (17.04 g), FB (2.43 cm), PW (15.37 g), PT (0.58 cm), SC (1.58 cm), TSS (9.49 Highly significant positive as well as negative correlations (p 0.05) were observed among studied quantitative traits in the germplasm see .The following traits showed highly significant positive correlations: FW and PW , FB and Similarly, significantly positive correlations were reported among the following traits: FW and PP (0.591), FW and AA (0.49), FW and TT (0.538), FL and AA (0.543), FL and TT (0.653), FB and AA (0.593), FB and TT (0.641), PW and PP (0.595), PW and AA (0.488), PW and TT (0.538), PP and SC (0.567), PP and TT (0.578), PT and SC (0.642), PT and PSW (0.520), PT and AA (0.599), SC and AA (0.503), SC and TT (0.503), PSW and TT (0.49), AA and TSS (0.598), TSS and AC (0.612), and TT and AC (0.517).Significant negative correlations were observed between the following traits: TA and FW (-0.693), TA and FL (-0.815), TA and FB (-0.766), TA and PW (-0.694), TA and PP (-0.7), TA and PT (-0.806), TA and SC (-0.628), TA and PSW (-0.565), TA and AA (-0.627), TA and TSS (-0.559), TA and TT (-0.934), and SC and PS (-0.497).In the present investigation, the PCA was performed using 16 quantitative traits. The results revealed that 93.3% of the total variation was observed in the six components of PCA. This shows a large amount of variation exists in the studied germplasm .viz., FI, PP, PST, PSW, AA, TSS, and TP. The fourth component showed an 8.1% total variation (FI and TP). The fifth component covers 5.8% of the total variation, which includes FI and PSW. The sixth component showed 3% of the total variation, and the traits included AA and AC.The first component, explaining 50.6% of the total variation, was observed in traits such as FW, FL, FB, PW, PP, PT, SC, PSW, AA, TSS, TA, and TT. The second component accounts for 16.9% of the total variation and includes FW, PW, SC, PST, TSS, TT, AC, and TP. The third component, explaining 8.9% of the total variation, is the traits, P. friedrichsthalianum, and P. pumilum, but these germplasm have lower FW, FL, FB, and PW. The germplasm such as Sasni, Kasipur collection, Hisar Safeda, Sasri, Pant Prabhat, VNR, Lucknow-49, Arka Mridula, Hisar Surkha, Lalit, Hisar Surkha variant, Arka Kiran, Black guava, Shweta, Punjab Pink, and Trichy fall under the cluster III.The k-means cluster was constructed using the same quantitative traits, and it was grouped into three clusters see . ClusterBased on Euclidian distance and agglomeration using the Wards technique, the genetic dissimilarity of the 28 germplasm samples was performed see . Based oi.e., Thai and Thai variant, which were the most divergent. On the other hand, related species of guava, i.e., P. pumilum and P. friedrichsthalianum were present in cluster IV. Interestingly, all pink pulp color germplasm falls into clusters I and II.Cluster II consists of eleven germplasm . Cluster III included two germplasm, Guava is an important and emerging fruit crop because of its nutritional properties. Its area is expanding in India and it is an excellent source of raw materials for the processing industry. Ascorbic acid, pectin, phenolic compounds, and antioxidants are abundant in guava, which provides health advantages. Researchers have paid a lot of attention to improving the nutritional quality of most fruit crops such as apples, mangos, bananas, and citrus, but guava has high nutritional values even though they have paid less attention to improvement. Hence, there is immense scope for researchers to improve not only the yield potential but also enhance the nutritional value of guava. To produce novel varieties with farmer and market-preferred product profiles, the genetic diversity found in the examined guava germplasm may be investigated for quantity and quality attributes with modern tools. Therefore, this study was initiated to provide a thorough genetic diversity analysis to reveal the performance and nutritional quality attributes among germplasm collections.In the study, a total of 33 qualitative as well as quantitative traits were studied in a set of 28 guava germplasm to determine the morphological traits that could be useful for genotype identification. It is necessary to characterize and assess germplasm before selecting the required genetic resources for genetic improvement projects. The majority of the traits under investigation had potential economic value, particularly those contributing to fruit yield and fruit quality. These traits can be further targeted for the improvement of guava varieties and hybrids.The results from the present investigation support the view that morphology and chemical composition in fruits can be utilized efficiently for cultivar discrimination and estimating the genetic relationships in the diverse groups of guava germplasm. These results, which follow those of earlier research, show that both quantitative and qualitative traits are essential for cultivar identification and evaluation in guava germplasm for traits like fruit dots, puffiness, texture, surface, cavity, pulp taste, etc., which are of interest to taxonomists or breeders , 32. SimFrom the perspective of farmers, consumers, export purposes, and processing industries, quantitative traits such as fruit weight, length, breadth, pulp weight, pulp thickness, pulp percent, pulp: seed core cavity ratio, pulp: seed weight ratio, TSS, antioxidants, and so on are some of the most important. observedi.e., calyx persistence and fruit ridged collar around the calyx cavity. Positive correlation ensures simultaneous improvement in two or more variables, and negative correlation brings out the need to obtain a compromise between the desirable characteristics  were the most divergent for studied traits, hence the selection of such germplasm is important for effective utilize in crop improvement programmes in guava. Similar results were observed by  and cluster IV (Overall, the results revealed the presence of significant genetic variation among the 28 guava germplasm for both qualitative and quantitative traits, which provides an opportunity for the selection of superior genotypes for breeding programmes as parents in hybridization for nutritionally rich variety development. Among the studied germplasm, Thai, Thai variant, Trichy, Lucknow-49, Allahabad Safeda, and Shweta showed promising for some traits. These germplasm can be explored in a breeding programme to enhance the yield as well as nutritional quality.Even though morphological traits are typically used to infer genetic links, such assessments are better with constraints. Unfortunately, the environment and a plant's stage of development have a big impact on these morphometric properties, which may lead to an unanticipated variety of agronomically useful characteristics. Future marker-assisted breeding and next generation sequence analysis could be used for guava crop improvement may benefit from a combinational approach to identifying and associating DNA-based molecular markers targeting promising fruit characteristic loci. Species that have great genetic diversity are especially capable of overcoming difficulties when new pests, illnesses, and climatic changes develop.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.NG, CA, MV, AN, and MT contributed to the organization of the work plan for the manuscript. NG, AN, and CS drafted the manuscript and composed the outline. VY, MV, KR, and MT contributed to the data analysis. AS, RS, and MS wrote the sections of the manuscript. All the authors had full access to the data and revised and approved the manuscript for publication.This work was funded by the Post Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Several behavioral traits show high heritability, including the selection trait: exploratory locomotion (EL) in a novel environment. There were significant phenotypic and genetic correlations between tests that capture facets of EL and anxiety. There were also correlations with Pavlovian conditioned approach (PavCA) behavior despite the lower heritability of that trait. Ten significant and conditionally independent loci for six behavioral traits were identified. Five of the six traits reflect different facets of EL that were captured by three behavioral tests. Distance traveled measures from the open field and the elevated plus maze map onto different loci, thus may represent different aspects of novelty-induced locomotor activity. The sixth behavioral trait, number of fecal boli, is the only anxiety-related trait mapping to a significant locus on chromosome 18 within which the Pik3c3 gene is located. There were no significant loci for PavCA. We identified a missense variant in the Plekhf1 gene on the chromosome 1:95 Mb QTL and Fancf and Gas2 as potential candidate genes that may drive the chromosome 1:107 Mb QTL for EL traits. The identification of a locomotor activity-related QTL on chromosome 7 encompassing the Pkhd1l1 and Trhr genes is consistent with our previous finding of these genes being differentially expressed in the hippocampus of bHR vs. bLR rats. The strong heritability coupled with identification of several loci associated with exploratory locomotion and emotionality provide compelling support for this selectively bred rat model in discovering relatively large effect causal variants tied to elements of internalizing and externalizing behaviors inherent to psychiatric and substance use disorders.Common genetic factors likely contribute to multiple psychiatric diseases including mood and substance use disorders. Certain stable, heritable traits reflecting temperament, termed externalizing or internalizing, play a large role in modulating vulnerability to these disorders. To model these heritable tendencies, we selectively bred rats for high and low exploration in a novel environment [bred High Responders (bHR) vs. Low Responders (bLR)]. To identify genes underlying the response to selection, we phenotyped and genotyped 538 rats from an F The offspring of this intercross (“F1”) were then bred with each other to help the re-emergence/divergence of the phenotypes in the F2 generation. To amplify divergence, F1 rats that had a lower response to novelty were bred together, and F1 rats that had a higher response to novelty were bred together. The resulting 48 F2 litters generated a total of 538 male and female rats, of which 216 were tested as adolescents (about 1 month old) in the open field, since our typical locomotor response to a novel environment test is geared towards adult rats, and sacrificed at this age to collect tissue. The remaining 322 rats grew to young adulthood at which time they were phenotyped and then sacrificed for tissue collection (about 4 months old). The sacrifice age, sex, and number of rats phenotyped for each behavioral test are shown in Rats selectively bred for a high or low propensity to explore a novel environment (bHRs and bLRs) at 2 months of age were established at the Michigan Neuroscience Institute, University of Michigan . The twooF) and humidity (30%–50%)-controlled room with a 12/12 light/dark cycle . All procedures were conducted in accordance with the guidelines outlined in the National Institutes of Health Guide for the Care and Use of Animals and were approved by the Institutional Animal Care and Use Committee at the University of Michigan.All rats were weaned at postnatal day 21, housed 2-3 rats per standard rat cage (140 sq. inches), and provided ad lib water and standard rat chow  in a temperature . This tracking system was used to sub-divide the floor of the OF into a total of 16 zones: the inner four zones were defined as the anxiogenic region of the apparatus; the time spent in these inner zones was recorded (converted to % time of 5 min). Additional measures during the test  were also recorded. The number of fecal boli was also counted as an anxiety-related index  test. The OF is a 100 cm × 100 cm × 50 cm white Plexiglas box with a matte black floor. All testing was performed between 9:00 a.m. and 4:00 p.m. under dim light conditions (40 lux). The test began by placing each rat into one corner of the OF and then allowing 5 minutes for free exploration of the apparatus. During the test, the rat’s behavior was monitored ed index .Adult rats were initially tested at approximately 2 months of age to assess their EL in a novel environment that was the same size as a home cage (23 cm × 43 cm) but located in a different room with novel cues. The number of beam breaks (counts) was measured over 60 min for both lateral and rearing movements, deemed the lateral and rearing locomotor scores. The cumulative lateral and rearing locomotor scores over the 60-min test were combined to generate the total locomotor score.Spontaneous anxiety-like behavior was evaluated in the EPM, which consists of four black Plexiglass arms  elevated 70 cm from the floor, arranged in the shape of a cross. Two opposite arms are enclosed by 45 cm high walls, and the other two opposite arms are open. The test began by placing animals in the intersection of these arms, a 12 × 12 cm square platform which allows access to all four arms. During the 5-min test period, the room is dimly lit (40 lux) and animals’ behavior monitored using a video tracking system  that records the amount of time spent in the arms (converted to % time of 5 min), the distance traveled in cm, and the time immobile in sec. As in the OF for the adolescent rats, the number of boli was counted as an additional measure of anxiety.2 rats were later tested for PavCA behavior, as described in  and the bLRs showing preference for approaching the location of reward delivery  . Thus, aribed in , to dete2 spleen samples was isolated via the MagAttract HMW DNA Kit (Qiagen) at the University of Michigan and transferred to the University of California San Diego for further processing. Multiplexed sequencing libraries were prepared using the RipTide kit (iGenomX), and then sequenced on a NovaSeq 6000 (Illumina), according to the manufacturer’s instructions. We produced an average of ∼2.8 million reads per sample. The reads were aligned to the rat reference genome Rnor_6.0 (rn6) from Rat Genome Sequencing Consortium (GCA_000001895.4 GCF_000001895.5).High molecular weight (HMW) genomic DNA from 538 F0 generation to identify SNPs to genotype in the larger cohort. For these 20 rats, liver tissue was sent to HudsonAlpha Discovery (Discovery Life Sciences) for HMW genomic DNA extraction and long-range and deep sequencing. Following 10X Chromium linked-read sequencing library preparation, samples were sequenced on one NovaSeq 6000 (Illumina) S4 PE150 flowcell. An average of 999.8 million reads per sample were generated, with an average coverage of 47.25X. Variants in the 538 F2 rats were called using a two-step procedure. First, we used STITCH v1.6.6  that segregate between bHR and bLR were not known, we deeply sequenced 20 rats from the F.6 R. W. , which iGLE v4.1 , to impu2 individuals, we examined mendalization errors for all F1 mothers and fathers to identify the true parent, 4) we performed principal component analysis to reveal family structure, 5) for the 20 F0 rats that were also used for Whole genome sequencing (WGS), we compared the genotypes obtained from WGS to those obtained from the low-pass genotyping. Out of 538 samples, these steps identified 15 problematic samples. For 8 samples, the problems could be confidently resolved; however, we chose to exclude the remaining 7 samples from further analyses.Once the genotypes were obtained, we performed several quality control steps to identify potential sample mix-ups due to sample handling errors: 1) we compared genotypes to the earlier microarray-derived genotypes  that wer0 founders). We then removed SNPs with a Mendelian error rate < 0.075. Finally we removed SNPs with a Hardy Weinberg Error rate p < 1 × 10–10. The number of SNPs excluded by each filter are as follows: 15,879 excluded due to low MAF, 174,063 excluded due to high Mendelian Error Rate, and 55,491 excluded due to excessive HWE. After applying these filters, we were left with 4,425,349 SNPs.To obtain our final set of autosomal variants, a final round of variant filtering was performed by first removing SNPs with minor allele frequency (MAF) < 0.005  = 6.664 and for traits with sample sizes less than 300, the threshold was slightly lower: −log10(p) = 6.254.Significance thresholds  were calculated using permutation in which no GRM was used. Because the phenotype values are quantile normalized, we did not need to perform a separate permutation for each trait , howeverp-value that was within 2 − log10(p) units of the index SNP. Other QTLs on the same chromosome were tested to ensure that they were conditionally independent of the first. To establish conditional independence, we used the top SNP from the first QTL as a covariate and performed a second GWAS of the chromosome in question. If the resulting GWAS had an additional SNP with a p-value that exceeded our permutation-derived threshold, it was considered to be a second, independent locus. This process was repeated , until no more QTLs were detected on a given chromosome. Intervals for each identified QTL were determined by identifying all markers that had a high correlation with the peak marker >0.6 (r2 = 0.6). We performed variant annotation for all SNPs within a QTL interval using SNPEff (To identify quantitative trait loci (QTLs), we scanned each chromosome to determine if there was at least one SNP that exceeded the permutation-derived threshold, which was supported by a second SNP within 0.5 Mb that had a g SNPEff .2 animals were initially tested for their EL in a novel environment, as typically performed in the bHR and bLR selectively bred lines  as part of their selection criterion, followed by EPM and PavCA testing. The OF was the only test used to evaluate the adolescent F2 animals. Both the EPM and OF tests contain measures that reflect EL and anxiety-like behaviors. All measures were checked for possible outliers using the criteria: ± 4 SDs from the mean; no values were removed. Adult FUsing the procedure described in the Methods section, we obtained genotypes for 4,425,349 SNPs on the autosomes. The density of the SNPs across each chromosome are depicted in The SNP heritability estimates for all the phenotypes ranged from 0.06 to 0.79 . Across The EL measures revealed notably high heritability estimates across multiple tests. The highest heritability measures (>0.7) were seen across all 3 facets of EL in the primary screening test. Although the standard error estimates were higher, the “distance traveled” phenotypes for both the EPM and OF also exhibited high SNP heritability (0.4–0.6).Two indices of emotionality exhibited high heritability. The first was OF Immobility (>0.4), a measure of anxiety-like behavior. The other is fecal boli counts measured during both EPM and OF. This represents another index of emotionality or anxiety-like behavior and was notably high in the OF (>0.4). EPM percent time in open arms, a classic measure of anxiety-like behavior, showed significant but lower SNP heritability (0.22).The lowest SNP heritability estimates were found with the PavCA variables, although the response bias scores exhibited significant heritability (0.24–0.32).2 animals  sample size, we identified significant genetic associations for all facets of EL, OF, and EPM behaviors, but not for PavCA behavior. We also discovered that different measures of EL were influenced by pleiotropic loci, which was expected given the strong phenotypic and genetic correlations among those traits. The loci were relatively small and contained several interesting genes, including some that harbor coding variants with modest impact.2 animals. We have previously identified high heritability in the total locomotor score trait of our bHR/bLR animal model, for which EL was defined only by the total measure of locomotion in a novel environment  in the OF test, consistent with the identification of this locus in association with EL in the current study. In a study that investigated the long-term impact of early life adversity on the ability to cope in adulthood, it was found that chronic early life stress in mice generally blunted the translational response to an adult acute stress challenge. In a previous study, Plekhf1 was one of only 18 genes identified as being upregulated in the hippocampus by acute-swim stress in mice that had experienced early life stress  between the bHR and bLR animals and how augmentation with FGF2 early in life rescues the anxiety-like phenotype in the bLRs that have low FGF2 expression  that may also be influenced by developmental age . The chromosome 1:135 Mb locus contains many genes. Several of these genes have been previously associated with neurodevelopmental disorders in humans. disorder  and varid autism . Ntrk3 iisorders , panic disorders  and onseisorders . Ntrk3, in brain . The idepression .Pkhd1l1 gene for the chromosome 7:84 Mb locus for lateral locomotor score in response to novelty. Pkhd1l1 has been associated with bipolar disorder in humans  identified in our previous exome sequencing study (We identified a missense variant within the n humans . This lobehavior . Our groactivity . Both Trng study .Pik3c3 which is involved in phosphoinositide lipid metabolism, a potential target for the therapeutic effect of lithium in bipolar disorder. A promoter variant of this gene has been identified in humans with bipolar disorder and schizophrenia (2 animals lends support for a genetic basis for this trait.The locus on chromosome 18 at about 23 Mb that was associated with OF fecal boli contains the gene ophrenia . Fecal b0 bHR and bLR rats (We looked at the allele frequency for each QTL topSNP in the 20 FbLR rats . As expebLR rats  in which0-F1-F2 cross of the bHR/bLR rat lines and discovered several genetic loci associated with complex behavior traits. Although we found strong genetic correlation between EL and PavCA measures, no significant loci for PavCA traits were identified. Remarkably, while the breeding program selected for high and low EL in a novel environment, our analyses revealed loci of relevance to different facets of anxiety, including freezing behavior in an open field and the number of fecal boli under stress. We also discovered putative pleiotropic loci that influence EL traits across different behavioral assays, thus expanding upon our previous finding of several loci tied to EL in only our main behavioral assay used for selective breeding. The strong heritability coupled with identification of several loci associated with EL and emotionality provide compelling support for this selectively bred rat model in discovering causal variants tied to elements of internalizing and externalizing behaviors inherent to psychiatric and substance use disorders. The candidate genes located in the significant loci associated with these sensation-seeking and anxiety-like traits provide avenues for further discovery into the genetic basis of stress, affect regulation, and addiction-related traits.In conclusion, we have demonstrated strong heritability in the sensation-seeking trait, depicted by the EL measures, through the F