Recent Observations on Deep-Rooted Polymorphism Patterns of Language/Cognition Genes

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In this study, we analyzed 223 single nucleotide variations (SNVs) in language-associated genes and 240 SNVs in cognition-associated genes across whole-genome sequences from 471 samples, encompassing: Archaic humans (including Neanderthals and Denisovans), Modern humans, and Other vertebrates (fish, amphibians, reptiles, birds, rodents, mammals).Computations using four genetic distance metrics and six clustering methods revealed that the most ancient Language/Cognition Gene Polymorphism Patterns (LCGPP) predominantly persist in fish, with minor representation in amphibians/reptiles and Laurasiatherian mammals. Within our sample scope: The archaic human sample closest to these ancient LCGPPs is sd1 (Africa, Sudan, 4,000 year BP); Subsequent proximity is observed in nd1n (Europe, Okladnikov Cave, Russia, 44,000 year BP) and dg2 (Asia, Devils Gate, Russia, 7,700 year BP); Both sd1 and nd1n show the strongest genetic affinity to marine fish samples. This implies that foundational frameworks for language and cognition genes may have been established as early as the piscine evolutionary stage. language gene cognition gene single nucleotide variation language/cognition gene polymorphism pattern Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION It is now widely recognized that basic linguistic capacity is not unique to humans, a well-established consensus supported by extensive literature. Phenomena such as vocal mimicry in parrots, avian vocalizations, and wailing calls of giant salamanders all relate to fundamental language abilities—particularly oral communication [ 1 ]. Hauser et al. reinforce this view by proposing the distinction between the "Faculty of Language in the Broad Sense" (FLB)—a suite of foundational abilities shared with animals—and the "Faculty of Language in the Narrow Sense" (FLN)—recursive grammar unique to humans [ 2 ]. Higher-order linguistic capacities, however, transcend basic language functions. Examples include: memorizing thousands of distinct vocabulary items or concepts (e.g., Pepperberg’s work on grey parrots [ 3 ]), or mastering reusable syntactic structures. These advanced abilities are intrinsically linked to cognitive processes and abstract thought. Notably, the act of vocalization itself constitutes a cognitive learning process. While basic cognitive capacities are undeniably not exclusive to humans (another consensus with robust empirical support), higher-order faculties—especially abstract reasoning—have traditionally been considered uniquely human. Nevertheless, emerging research challenges this paradigm: Gentner et al. demonstrated that zebra finches process nested syntactic patterns (e.g., "(AB)n sequences"), contesting the dogma that complex grammar is exclusively human [ 4 ]; A growing body of literature confirms that abstract thinking manifests across diverse animal taxa at varying levels of sophistication [ 5 – 8 ]. As distinct biological functions, linguistic and cognitive capacities inherently constitute phenotypic traits in biology, necessitating the deciphering of their underlying genotypes. However, such complex abilities represent emergent properties within the framework of systems biology—sophisticated higher-order functions characteristic of complex systems. They are not determined by a few isolated genes but inevitably involve interactions across gene networks or cohorts. While human linguistic studies have evolved into extensive disciplines, research on "language genes" remains remarkably scarce. This scarcity stems fundamentally from technological limitations: we have yet to reach the stage enabling large-scale investigation. Put differently, only with advances in systems biology can we achieve quantitatively precise characterization, modeling and prediction of complex phenotypes like language and cognition. Currently, this field remains in the observational and foundational data-collection phase. This study represents one such foundational effort. Table 1 Selected human language/cognition genes in this study [9–15] Gene Languagegene Cognition gene Function or Compromised ability (example) when mutated 1 ARHGAP11B √ Hominin-specific development and evolutionary expansion of the brain neocortex 2 ASPM √ √ Associated with microcephaly 3 MCPH1 √ √ Associated with microcephaly, primary, autosomal recessive and lymphatic malformation; 4 CHRM2 √ A nervous system gene associated with depression disorder 5 IGF2R √ Insulin-like growth factor gene associated with behavior/neurological phenotype 6 DTNBP1 √ Aprotein coding gene likely associated with schizophrenia 7 Snap25 √ A gene associated with neurotransmitter release 8 Fads2 √ A member of the fatty acid desaturase, associated with craniofacial abnormalities 9 Dab1 √ A gene linked with nervous system development 10 NBPF8 √ A gene associated with macrocephaly, autism, schizophrenia, cognitive disability 11 HAR1A √ A gene whose expression levels associated with memory and cognitive abilities 12 GNB5 √ Associated with language delay and cognitive Impairment 13 NRXN1 √ Neurexin 1required for efficient neurotransmission and formation of synaptic contacts 14 DCC √ Associated with impaired intellectual development 15 GRID2 √ Predominant excitatory neurotransmitter receptors in the mammalian brain 16 EP300 √ Associated with rare neurological diseases and impairment of intellectual development 17 KMT2D √ Lysine Methyltransferase 2D, associated with intellectual disability and eye diseases 18 NOTCH2NL √ Neural progenitor proliferation and evolutionary expansion of the brain neocortex 19 THSD7B √ Associated with eye diseases/neuronal diseases 20 CASC5 √ Potentially associated with brain size of East Asian 21 FOXP1 √ Expressive language 22 FOXP2 √ Speech 23 CNTNAP2 √ Early language development 24 RBFOX2 √ Reading, language 25 TPK1 √ Syntactic and lexical ability 26 DCDC2 √ Reading, dyslexia 27 KIAA0319 √ Reading, dyslexia 28 TM4SF20 √ Language delay; communication disorder 29 FLNC √ Reading, language 30 ATP2C2 √ Memory 31 ROBO1 √ Phonological buffer 32 ROBO2 √ Expressive vocabulary 33 CMIP √ Reading, memory 34 DYX1C1 √ Reading, dyslexia 35 NFXL1 √ Speech 36 SRGAP2 √ Vocal learning, vital for cortical neuron development In this study, we jointly investigated known language-associated and cognition-associated genes. Our analysis focused on their single nucleotide variation (SNV) patterns and the key concept: Language/Cognition Gene Polymorphism Pattern (LCGPP). The evolutionary trajectory of these polymorphic sites and their patterns—from fish to progressively advanced animals, archaic humans, and modern humans—serves as a powerful lens to examine aspects of marine organisms' evolution into diverse animal lineages (including humans). This approach helps identify humanity’s most archaic LCGPPs and, at least within our sample scope, determines which specimens retain these ancestral patterns. We incorporated a set of living fossil samples—extant lungfish (Dipnoi) and coelacanths (Actinistia)—which provide critical references for recognizing the most ancient LCGPPs in our dataset. MATERIALS AND METHODS Genome sequences Genome sequences were downloaded from the ENA database, SRA database and Ensembl genome browser. A total of 471 whole genomes (including 111 ancient genomes, Table 1 s) from 5 continents (Africa, Asia, Europe, North America, and South America) were collected. The six representative animal groups include Laurasiatherians (L), amphibians/reptiles (R), fish (F), birds (b), primates (p), and rodents (d), plus miscellaneous taxa (x). The above ENA genome sequences have fastq format, whereas the Ensembl/SRA genome sequences are all assembled full genomes in fa, fn or fna formats, and all can be read and scanned with python-based hash07plus03 software. Language/Cognition genes and their SNVs For all human cognition genes, single nucleotide polymorphisms (SNPs) or Single Nucleotide Variants (SNVs) sites in the dbSNP database were selected such that each whole gene region was relatively equally spanned by the selected sites plus those already with known clinical effects (seen in the GeneCards database; Fig.8s1, Fig.8s2). Table 1 lists 36 human language/cognition genes, and a total of 239+223 =462 SNVs were selected for this study (Table 2 s, Table 3s). Genome sequence analysis software development and SNV abstraction SNP/(SNVs) loci finding software, which is based on hash tables, primarily processes biological whole-genome files and rapidly identifies target loci within the genome via a search algorithm to obtain the specific values of the mutated bases. The software is written in Python [16]. Initially, it processes three different formats of whole-genome files—fastq, fna, and fa—on the basis of their unique characteristics, extracting gene sequences and generating standard format files that include all lines containing only ATCGN five bases. During use, the software can process multiple genome files in batches and impose restrictions on the matching length and the number of matches. After extensive validation, the speed of the software hash07plus03 has significantly improved compared with that of conventional matching algorithms and other software programs based on the Knuth–Morris–Pratt (KMP) algorithm. One of the search algorithms in the custom-developed software hash07plus03 involves constructing a 31-base string (15 flanking bases on each side of an SNV locus combined with the central base N, i.e., 15 + N + 15) to perform exact matching searches across whole-genome sequences. If a precise match is found, the software extracts the central base N as the SNV data; if no match exists, it outputs "0". For example, if exact bilateral matches yield three central bases N (e.g., T, G, and C), the output would be "TGC". Any SNV site has one of the following 16 genotypes: 0, A, T, G, C, AT, AG, AC, TG, TC, GC, ATG, ATC, AGC, TGC or ATGC. The authors used 010Edit software to extract SNV information from genome files, but most SNV information was extracted with hash07plus03 software. In all 471 genomes, the sizes ranged from 200 M to 120G. Genomes in fastq format but less than 18G were generally neglected or used only as a reference, and not used for analysis. All SNV data for 471 samples can be seen in Table 4s. Nei's Genetic Distance Nei's Genetic Distance [17-18] is a genetic distance metric based on allele frequencies, reflecting the degree of genetic differentiation between populations. It is suitable for evolutionary analysis but requires allele frequency data and is sensitive to low-frequency mutations. In this study, due to the cross-species and spatiotemporal nature of the SNV data, allele frequencies were temporarily assumed to be equal. For example: A heterozygous site AT was recorded as *A=50%, T=50%; A tri-allelic site ATC was recorded as *A=33.3%, T=33.3%, C=33.3%, G=0%; A homozygous site C was recorded as *C=100%, A=T=G=0%. DICE coefficient DICE coefficient (Sørensen-Dice coefficient) [19-20] measures similarity for binary data as a set similarity coefficient (a variant of geometric distance). It is well-suited for high-dimensional sparse data and sensitive to rare variants. Converting SNVs to binary presence/absence data facilitates rapid clustering to identify shared mutations. Rogers’ Distance Rogers’ Distance [21-22] is a geometric distance metric for categorical variables. It directly processes categorical data and reflects the proportion of differing loci, making it inherently compatible with categorical SNV data in this study (e.g., genotypes: 0, A, T, G, C). Tanimoto coefficient Tanimoto coefficient (generalized Jaccard coefficient) [23-24], as a set similarity coefficient (information-theoretic distance), is optimal for sparse binary data. Although it requires forced binarization of multi-base combinations, its complete ignorance of missing sites offers distinct advantages. Nei's Genetic Distance, DICE coefficient, Rogers’ Distance and Tanimoto coefficient were all performed via R packages. PCA/PCoA/t-SNE/UMAP analysis In this study, the basic clustering analyses of samples were primarily performed using PCA/PCoA/t-SNE/UMAP methods [25-32] plus their combination(s). While PCA (Principal Component Analysis) preserves global structures, it may inadequately reveal certain local patterns in complex samples, being suitable for linear relationships but potentially losing complex nonlinear patterns. PCoA (Principal Coordinates Analysis), based on distance matrices, maintains global distance relationships between samples and is sensitive to distance metrics, yet struggles to reflect high-dimensional local structures. In contrast, t-SNE (t-distributed Stochastic Neighbor Embedding), frequently applied in transcriptome studies, effectively handles nonlinear associations between samples by emphasizing the preservation of local similarities. It models neighborhood relationships through probability distributions and excels at capturing high-dimensional complex manifold structures (e.g., cell differentiation trajectories, subpopulation delineation), yielding clearer visual clustering. UMAP (Uniform Manifold Approximation and Projection), similar to t-SNE but grounded in topological theory, balances local and global structures with faster computational speed and improved preservation of global relationships, gradually emerging as an alternative to t-SNE. PCA, PCoA, t-SNE and UMAP were all performed via R packages. RESULTS 1） The African sample sd1 potentially represents the most archaic language/cognition gene polymorphism pattern observed in ancient humans Figure 1 illustrates these findings. Across panels A–D (employing distinct distance metrics), the animal species closest to sd1 vary—yet collectively demonstrate sd1’s strongest affinity to fish, amphibians, reptiles, and notably cetaceans (e.g., hu1 in Fig. 1D). The sd1 originates from a 4,000-year-old mummified hair sample from Sudan, Africa. This implies that humans retaining such ancestral LCGPPs persisted in Africa merely four millennia ago. In principle, this genetic architecture should correspond to a human evolutionary stage predating Homo erectus by a significant margin. Its survival in Africa until 4,000 yr BP is remarkable. 2） African sample mo1l holds a special LCGPP evolution position All four genetic distance curves exhibit inflection points at the African sample mo1l, potentially corresponding to the evolutionary transition from Homo erectus to Homo sapiens. However, only Nei's Genetic Distance reveals 3–4 inflection points in the LCGPP trajectory. Whether these inflection points correlate with encephalization surges during the hominin evolution from ancient apes to H. erectus, H. sapiens, and modern humans [33–36] remains unclear. Figure 1 integrates language and cognition genes. Complementary analyses include Nei's Distance curve for Language genes alone (Fig. 1s1) and Nei's Distance curve for Cognition genes alone (Fig. 1s2), in which Inflections are sharply defined in Cognition genes (Fig. 1s2) but less pronounced in Language genes (Fig. 1s1). Peak rate-change positions also differ between cognition/language gene curves. Besides, European/Asian samples occupy critical inflection zones in Fig. 1s2 while same regional samples reside in relatively inconsequential segments in Fig. 1s1. 3） General landscape of LCGPP evolution In Figure 2, the leftmost cluster aggregates marine fish, amphibians, reptiles, birds, rodents, and Laurasiatherian mammals—representing the densest module. Progressing rightward, transitional specimens emerge, including lu3 (lungfish), dp2/dp1 (dolphins), and some archaic human samples. Further right, multiple distinct clusters appear, comprising transitional hominins between archaic and modern humans, and diverse primate lineages. Critically, numerous transitional LCGPPs persist in extant populations. The figure reveals that each evolutionary stage contains samples from Africa, Asia, and Europe. From the archaic-human-enriched left to the modern-human-dominated right, samples from these continents consistently co-occur across all phases. This implies intercontinental migration outpaced LCGPP evolutionary rates and unimpeded population diffusion channels existed across continents throughout hominin evolution. 4） The extant lungfish appears to retain the most archaic hominin LCGPP signature This study analyzed 3 lungfish samples: lu1 (South American lungfish, Lepidosiren paradoxa), lu3 (West African lungfish, Protopterus annectens), and lu4 (South American lungfish, L. paradoxa); Plus 4 coelacanth samples: lc1 (Latimeria chalumnae, Tanzania), lc5 (African coelacanth, fLatCha1), lm1 (Indonesian coelacanth, Latimeria menadoensis) and x19 (Ensembl reference, L. chalumnae). For genetic affinities, sd1 shows closest LCGPP similarity to lu1; while lu4 clusters with archaic humans dg2 (Asia) and nd1n (Europe). For evolutionary trajectories, lu3 lies closer to modern humans than lu4; while Coelacanths (lc1/lc5/lm1/x19) align with reptiles, birds, and Laurasiatherians (Fig. 3). Deep divergence is observed for lungfish and coelacanths. Fig. 3 suggests the most ancient hominoid LCGPP originated from lu1-related ancestors rather than coelacanths; Fig. 4 indicates early LCGPP similarity between lungfish and coelacanths at lu1's evolutionary stage; Subsequent marked divergence occurred in their LCGPP evolutionary paths. 5) The evolutionary position of lungfish-coelacanth LCGPPs is remarkably distinct Figures 3 through 6 demonstrate the pivotal role of lungfish and coelacanths in preserving ancestral human LCGPP signatures from multiple perspectives: 1) Fig. 3: sd1 originates from ancient fish near lu1, while dg2 and nd1n evolved toward lu4. 2) Fig. 4: lu1-lu3-lu4-lc1 cluster adjacent to sd1-nd1n but distant from dg2; lc5-lm1-x19 diverged toward other animal lineages (not ancestral hominins). 3) Fig. 5: Though sd1-lu1 represents the most archaic LCGPP, modern humans may plausibly trace back to dg2-nd1n. 4) Fig. 6: Supports sd1-nd1n-dg2 as the core ancestral LCGPP. Figure 7 reveals tripartite divergence in lungfish-coelacanth LCGPP evolution: 1) Module 1: lu1-sd1 (deepest ancestry); 2) Module 2: lc1-lc5-lu4-nd1n-dg2 (transitional hominins); 3) Module 3: lu3-mo1l (proximal to modern humans). Each module likely corresponds to fossil records, Hominin remains and extant descendant populations. Critically, Module 3 (lu3-mo1l) exhibits the closest evolutionary proximity to modern human emergence. 6） The most archaic LCGPP originates in marine fish Across all analytical figures, samples closest to sd1 are consistently fish—not amphibians, reptiles, birds, or rodents. This indicates that as vertebrates evolved from fish toward tetrapods (amphibians → reptiles), their LCGPPs diverged progressively. Paradoxically, piscine LCGPPs align more closely with archaic humans than later-diverging lineages. Though taxonomically fish, lungfish exhibit transitional tetrapod-like traits: Lungfish possess soft-tissue morphology, developmental pathways, physiology, and behaviors convergent with extant amphibians but distinct from other fish. The massive lungfish genome (≈40 Gb) likely underlies their enhanced evolutionary plasticity compared to coelacanths—potentially resulting from ancient genomic crises. Ecologically, lungfish bridge aquatic and terrestrial niches; Phylogenetically, lungfish represent the extant sister group to tetrapods [37-41]. DISCUSSION African sd1 likely originated from a lungfish-coelacanth lineage, specifically related to lu1. Theoretically, a cohort of transitional LCGPP samples shall exist between lu1 and sd1, bridging the sd1-lu1 and nd1n-dg2 clusters and also awaiting future discovery. Lungfish/coelacanth-derived ancestral species may have independently evolved archaic humans across Europe and Asia. Critically, Module 3 in Fig. 7 suggests that lu3 (African lungfish) and its descendants potentially represent direct ancestors of modern humans. This module initiates with jm2 (East Asia, 3,000 year BP) and lu3 (African lungfish); Its expansive distribution includes sc1(Spirit Cave, USA, 10,000 year BP, Ancient North American), mo1l (Morocco, Africa, Key African transitional), mg1 (Mongolia, 34,000 year BP, Northeast Asian archaic), de2 (Denisova Cave, 100,000 year BP, Siberian Denisovan), ch1 (Ayayema, Chile, 4,700 year BP, Early South American), and us2 (Anzick, USA, 12,500 year BP, Clovis-culture infant). This transcontinental LCGPP distribution-spanning Africa, Asia, Europe, and the Americas—implies sustained gene flow during this evolutionary phase (potentially Homo erectus or earlier hominoids). It supports the possibility of regionally parallel hominin evolution across continents. Given Europe's lack of extensive continuous fossil records (unlike Africa and China), the most plausible scenarios are two as follow. The first is the Early African-Eurasian Dispersal: Archaic humans migrated from Africa through Europe-Russia-Northeast Asia at an exceptionally early stage. A subset reached the Americas during this initial expansion. The second is the Reverse Northeast Asian-Westward Dispersal: Populations moved from Northeast Asia toward Northwest Eurasia-Europe-Africa. Critical gap: Northeast Asia itself shows no robust continuous fossil record to support local continuity. Consequently, Africa remains the most probable source of the deepest-rooted language/cognition gene patterns—unless these patterns originated around the Tibetan Plateau (where fossil evidence rivals Africa/Europe in temporal span and continuity). This possibility remains unexplored. In fact, mounting evidence [ 42 – 46 ] indicates that early hominoids globally may have descended from populations near the southern foothills of the Tibetan Plateau. Thus, the "Out-of-Africa" model for modern humans likely represents merely one subset of a far more complex, globally distributed evolutionary process. The establishment of foundational LCGPP frameworks in marine fish holds profound implications. Marine fish exhibit a unique capacity for rapid transoceanic dispersal across vastly larger territories than terrestrial organisms, enabling ancestral transitional species (fish- amphibians- reptiles- rodents) to achieve near-global distribution and exploit ecological refugia during extinction crises—ensuring survival in isolated hospitable zones when continental habitats became uninhabitable. Crucially, species like lungfish embody syncretic phenotypes integrating piscine, amphibian, and reptilian traits. This pre-adaptation likely reduced evolutionary constraints on LCGPP inheritance, presenting significantly lower barriers than those imposed by local environmental adaptation. Study Limitations: Samples with FASTQ-formatted whole-genome files < 18 GB are included in our figures but excluded from analytical interpretations or conclusions. While key samples underpinning our findings—sd1 (21 GB), nd1n (55 GB), and dg2 (18 GB)—support general conclusions, their file sizes fall below the 80–300 GB range typical of high-coverage contemporary standards. Further enhancements in sequencing depth and quality by originating laboratories are essential to strengthen these inferences. Future work will expand the catalog of ancient human samples and language/cognition-associated genes, increase SNV data density, and enable in-depth mining of evolutionary trajectories. CONCLUSIONS Investigation into Language/Cognition Gene Polymorphism Patterns (LCGPP) unveils novel insights into human evolution, particularly by illuminating the pivotal role of marine fish in establishing primordial frameworks for these patterns. Through integrated analyses of > 470 whole-genome samples spanning diverse species and spatiotemporal contexts, we identified the most archaic LCGPP representative: African sd1 (4,000 year BP), and subsequent key archaic carriers: European nd1n (44,000 year BP) and Asian dg2 (7,700 year BP). Critically, the foundational architecture of these ancient LCGPPs has been established in lungfish (Lepidosiren/Protopterus), coelacanths (Latimeria), and numerous marine fish lineages. This evidence positions marine vertebrates as cornerstone taxa in the emergence of human language and cognition. Declarations Funding: This study was supported by a State Language Commission Research Grant (YB135-117), Association of Chinese Graduate Education Grant (B-2017Y0505-079), National Research Center for Foreign Language Education Grant (ZGWYJYJJ10A042) and funds from the Marine Antifouling Engineering Technology Center of Shandong Province. Author contributions: ZZ: Instructor of this study, manuscript writing, software testing; SZ: Writing software for this study, plus software testing; YX: Instructor for writing software for this study, plus software testing Conflict of interest: The authors declare that they have no conflicts of interest. Data availability: The data underlying this article are available in the article and its online supplementary material. Ethics declaration : not applicable. Consent to Publish declaration: not applicable. Consent to Participate declaration: not applicable. References Doupe AJ, Kuhl PK. Birdsong and human speech: common themes and mechanisms. 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Late middle Eocene epoch of Libya yields earliest known radiation of African anthropoids. Nature. 2010;467(7319):1095–9. doi:10.1038/nature09425. Additional Declarations No competing interests reported. Supplementary Files fig2s1PCADim1vsDim2LG.png fig2s2PCADim1vsDim2CG.png fig3s1PCoAJaccardPCoA1PCoA3.png fig3s2PCoAJaccardPCoA2PCoA3.png fig4s1tSNEtSNE1vstSNE2.png fig4s2tSNEtSNE2vstSNE3.png fig5s1UMAP2D1vs2.png fig5s2UMAP2D2vs3.png fig7s1BCUMAP12.png fig7s2BCUMAP13.png fig8s1LGSNVdistribution.png fig8s2CGSNVdistribution.png Table1s471genomesemployedinthisstudy.xlsx Table2sTested239SNPSNVsof18languagegenes.doc Table3sTested223SNPSNVsof18cognitiongenes.doc Table4sSNVdatafor471samples.xlsx Table5smetadata.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:36:35","extension":"xml","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117571,"visible":true,"origin":"","legend":"","description":"","filename":"48d95d84da354b4f8035b64f2b8adb541structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/f95060d85a0b6f58523ebc37.xml"},{"id":92275496,"identity":"ae7548d6-0f61-41ab-9261-ebc74d279b2c","added_by":"auto","created_at":"2025-09-26 15:28:34","extension":"html","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133207,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/a140d3e380fc1901139de0e9.html"},{"id":92277080,"identity":"ab70f781-0875-4086-8862-a90eb5992bce","added_by":"auto","created_at":"2025-09-26 15:36:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":298891,"visible":true,"origin":"","legend":"\u003cp\u003eAll four distance curves position \u003cem\u003esd1\u003c/em\u003eat the evolutionary forefront of archaic humans (within our sample scope).(A) Nei's Genetic Distance; (B) DICE coefficient; (C) Rogers' Distance; (D) Tanimoto coefficient. All curves concurrently indicate African sample \u003cem\u003emo1l\u003c/em\u003eat a \u003cstrong\u003eprominent inflection point \u003c/strong\u003ein hominin evolution.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/c85139f9d5fd5a918f721d30.png"},{"id":92275644,"identity":"0e2ba6c9-5353-40dd-b040-1ded14d55470","added_by":"auto","created_at":"2025-09-26 15:28:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218492,"visible":true,"origin":"","legend":"\u003cp\u003ePCA illustrating LCGPP patterns across 471 genomic samples spanning diverse taxa. Also see \u003cstrong\u003eSupplementaryFigure 2s1, 2s2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/078240ea1d3321f028412fd1.png"},{"id":92275489,"identity":"43f7ddac-2756-48ec-8b65-ff8d64c1dbbe","added_by":"auto","created_at":"2025-09-26 15:28:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":217296,"visible":true,"origin":"","legend":"\u003cp\u003ePCoA (Jaccard distance) reveals divergence/convergence in LCGPP evolution, with \u003cem\u003esd1\u003c/em\u003e, \u003cem\u003end1n\u003c/em\u003e, and \u003cem\u003edg2\u003c/em\u003e occupying the \u003cstrong\u003eearliest separation stage \u003c/strong\u003efrom fish, reptiles, and the 'other' group (containing amphibians). Also see \u003cstrong\u003eSupplementaryFigure 3s1, 3s2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/30c9f18232cd20d597f3ea65.png"},{"id":92275573,"identity":"679ddf47-786f-47f1-b277-0550b3cacdf5","added_by":"auto","created_at":"2025-09-26 15:28:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":250724,"visible":true,"origin":"","legend":"\u003cp\u003et-SNE results showing \u003cem\u003esd1\u003c/em\u003eand \u003cem\u003end1n\u003c/em\u003e LCGPPs at the \u003cstrong\u003etransitional node\u003c/strong\u003e from animals to hominoids. Also see \u003cstrong\u003eSupplementary Figure 4s1, 4s2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/02890c75fcc909dc41752664.png"},{"id":92275664,"identity":"733b6420-faec-4407-a320-55bd62ecc58b","added_by":"auto","created_at":"2025-09-26 15:28:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":221662,"visible":true,"origin":"","legend":"\u003cp\u003eUMAP analysis suggests \u003cem\u003esd1\u003c/em\u003eand dg2/nd1n represent \u003cstrong\u003etwo earliest hominin lineages,\u003c/strong\u003e both potentially linked to lungfish/coelacanths. Specifically, a lungfish/coelacanth-derived species \u003cstrong\u003eevolutionarily adjacent to basal hominins\u003c/strong\u003e may have dispersed to Africa, Europe, and Northeast Asia. Also see \u003cstrong\u003eSupplementaryFigure 5s1, 5s2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/cff3c027541f0518c703a727.png"},{"id":92275687,"identity":"36a48c27-4e24-4a60-95e1-cac7cfa689df","added_by":"auto","created_at":"2025-09-26 15:28:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":168368,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS results corroborate that LCGPPs originated in fish, followed by Lineage 1: \u003cem\u003esd1- nd1n- dg2 - mo1l-\u003c/em\u003e modern humans, and Lineage 2: Reptiles - birds - rodents -primates - humans. \u003cem\u003eHigh congruence with Figure 3 due to shared Jaccard distance metric.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/74109817e79bee1d8641aeb1.png"},{"id":92275435,"identity":"29f14c3c-c2b9-46e1-b2aa-547a2bc6fbc7","added_by":"auto","created_at":"2025-09-26 15:28:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":231915,"visible":true,"origin":"","legend":"\u003cp\u003ePCoA + UMAP (Bray-Curtis distance) indicates lungfish/coelacanth LCGPPs diverged into \u003cstrong\u003ethree evolutionary modules.\u003c/strong\u003e Also see \u003cstrong\u003eSupplementaryFigure 7s1, 7s2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/a793871c8b47958967b9ab68.png"},{"id":92277085,"identity":"b7e459c4-071e-42b0-8a77-7078bc16443e","added_by":"auto","created_at":"2025-09-26 15:36:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2449550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/20204f38-0479-4945-b179-ab2c7bc2171c.pdf"},{"id":92275431,"identity":"b6d3fc13-65bf-4c0e-90f6-469c429d5a06","added_by":"auto","created_at":"2025-09-26 15:28:26","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":983973,"visible":true,"origin":"","legend":"","description":"","filename":"fig2s1PCADim1vsDim2LG.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/1e7070b630864cca68f52754.png"},{"id":92277079,"identity":"b802b1d2-1375-4f86-b1c1-cae8180700c7","added_by":"auto","created_at":"2025-09-26 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15:28:31","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1076885,"visible":true,"origin":"","legend":"","description":"","filename":"fig3s2PCoAJaccardPCoA2PCoA3.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/11c0659b3cd90e3f46f21670.png"},{"id":92275517,"identity":"8a6363f5-06c6-4e76-a324-917b5e9c6531","added_by":"auto","created_at":"2025-09-26 15:28:35","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3405598,"visible":true,"origin":"","legend":"","description":"","filename":"fig4s1tSNEtSNE1vstSNE2.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/b59697c1cb16202cac60f4c3.png"},{"id":92275467,"identity":"3d32bfb1-f7d1-4ae3-ba40-4c3b127f2c99","added_by":"auto","created_at":"2025-09-26 15:28:31","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2999988,"visible":true,"origin":"","legend":"","description":"","filename":"fig4s2tSNEtSNE2vstSNE3.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/fe65933b4bf0b9fe82e67cf0.png"},{"id":92275456,"identity":"ecdeaa25-3d70-4b60-a429-81868d26c58d","added_by":"auto","created_at":"2025-09-26 15:28:29","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1177623,"visible":true,"origin":"","legend":"","description":"","filename":"fig5s1UMAP2D1vs2.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/719a326dace8871bda7cf497.png"},{"id":92275465,"identity":"db6d7780-ac02-4c53-a917-442ecee65b15","added_by":"auto","created_at":"2025-09-26 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15:28:39","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":5255905,"visible":true,"origin":"","legend":"","description":"","filename":"fig8s2CGSNVdistribution.png","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/5fee0f0953c7a4d6bb3f4e83.png"},{"id":92275527,"identity":"72ff0d98-9cd1-47b3-99b9-154dbb5f1601","added_by":"auto","created_at":"2025-09-26 15:28:35","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":42700,"visible":true,"origin":"","legend":"","description":"","filename":"Table1s471genomesemployedinthisstudy.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/7ff94a69b5c7647517314660.xlsx"},{"id":92275495,"identity":"78979c56-ba25-47ed-b58f-787b74c5a794","added_by":"auto","created_at":"2025-09-26 15:28:34","extension":"doc","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":140288,"visible":true,"origin":"","legend":"","description":"","filename":"Table2sTested239SNPSNVsof18languagegenes.doc","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/cb5220450a7574878c8350ff.doc"},{"id":92275429,"identity":"77beb7d5-8772-4384-bf1b-ea012fd55168","added_by":"auto","created_at":"2025-09-26 15:28:26","extension":"doc","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":128000,"visible":true,"origin":"","legend":"","description":"","filename":"Table3sTested223SNPSNVsof18cognitiongenes.doc","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/ef1ac31a0f5a41bab44954d2.doc"},{"id":92277083,"identity":"72d830e7-60cf-4981-83a5-095a87dfacb3","added_by":"auto","created_at":"2025-09-26 15:36:43","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":801690,"visible":true,"origin":"","legend":"","description":"","filename":"Table4sSNVdatafor471samples.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/8b15fb3788d97e96ea8ff76a.xlsx"},{"id":92275433,"identity":"b755a7db-ea37-4ed2-81d8-e4b9fc658081","added_by":"auto","created_at":"2025-09-26 15:28:26","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":18576,"visible":true,"origin":"","legend":"","description":"","filename":"Table5smetadata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6910300/v1/6946efd3a0f7d85dc5db4fce.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Recent Observations on Deep-Rooted Polymorphism Patterns of Language/Cognition Genes","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIt is now widely recognized that basic linguistic capacity is not unique to humans, a well-established consensus supported by extensive literature. Phenomena such as vocal mimicry in parrots, avian vocalizations, and wailing calls of giant salamanders all relate to fundamental language abilities\u0026mdash;particularly oral communication [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Hauser et al. reinforce this view by proposing the distinction between the \"Faculty of Language in the Broad Sense\" (FLB)\u0026mdash;a suite of foundational abilities shared with animals\u0026mdash;and the \"Faculty of Language in the Narrow Sense\" (FLN)\u0026mdash;recursive grammar unique to humans [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHigher-order linguistic capacities, however, transcend basic language functions. Examples include: memorizing thousands of distinct vocabulary items or concepts (e.g., Pepperberg\u0026rsquo;s work on grey parrots [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]), or mastering reusable syntactic structures. These advanced abilities are intrinsically linked to cognitive processes and abstract thought. Notably, the act of vocalization itself constitutes a cognitive learning process. While basic cognitive capacities are undeniably not exclusive to humans (another consensus with robust empirical support), higher-order faculties\u0026mdash;especially abstract reasoning\u0026mdash;have traditionally been considered uniquely human. Nevertheless, emerging research challenges this paradigm: Gentner et al. demonstrated that zebra finches process nested syntactic patterns (e.g., \"(AB)n sequences\"), contesting the dogma that complex grammar is exclusively human [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; A growing body of literature confirms that abstract thinking manifests across diverse animal taxa at varying levels of sophistication [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs distinct biological functions, linguistic and cognitive capacities inherently constitute phenotypic traits in biology, necessitating the deciphering of their underlying genotypes. However, such complex abilities represent emergent properties within the framework of systems biology\u0026mdash;sophisticated higher-order functions characteristic of complex systems. They are not determined by a few isolated genes but inevitably involve interactions across gene networks or cohorts. While human linguistic studies have evolved into extensive disciplines, research on \"language genes\" remains remarkably scarce. This scarcity stems fundamentally from technological limitations: we have yet to reach the stage enabling large-scale investigation. Put differently, only with advances in systems biology can we achieve quantitatively precise characterization, modeling and prediction of complex phenotypes like language and cognition. Currently, this field remains in the observational and foundational data-collection phase. This study represents one such foundational effort.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSelected human language/cognition genes in this study\u003csup\u003e[9\u0026ndash;15]\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLanguagegene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCognition gene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFunction or Compromised ability (example) when mutated\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eARHGAP11B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHominin-specific development and evolutionary expansion of the brain neocortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASPM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAssociated with microcephaly\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMCPH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAssociated with microcephaly, primary, autosomal recessive\u0026nbsp;and\u0026nbsp;lymphatic malformation;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHRM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA nervous system gene associated with depression disorder\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIGF2R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInsulin-like growth factor gene associated with behavior/neurological phenotype\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDTNBP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAprotein coding gene likely associated with schizophrenia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSnap25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA gene associated with neurotransmitter release\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFads2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA member of the fatty acid desaturase, associated with craniofacial abnormalities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDab1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA gene linked with nervous system development\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNBPF8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA gene associated with macrocephaly, autism, schizophrenia, cognitive disability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHAR1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA gene whose expression levels associated with memory and cognitive abilities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGNB5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAssociated with language delay and cognitive Impairment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNRXN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNeurexin 1required for efficient neurotransmission and formation of synaptic contacts\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAssociated with impaired intellectual development\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGRID2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePredominant excitatory neurotransmitter receptors in the mammalian brain\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEP300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAssociated with rare neurological diseases and impairment of intellectual development\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKMT2D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLysine Methyltransferase 2D, associated with intellectual disability and eye diseases\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNOTCH2NL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNeural progenitor proliferation and evolutionary expansion of the brain neocortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTHSD7B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAssociated with eye diseases/neuronal diseases\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCASC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePotentially associated with brain size of East Asian\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFOXP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExpressive language\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFOXP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpeech\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCNTNAP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarly language development\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRBFOX2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReading, language\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTPK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSyntactic and lexical ability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCDC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReading, dyslexia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKIAA0319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReading, dyslexia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTM4SF20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLanguage delay; communication disorder\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLNC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReading, language\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATP2C2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMemory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROBO1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhonological buffer\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROBO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExpressive vocabulary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCMIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReading, memory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDYX1C1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReading, dyslexia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNFXL1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpeech\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSRGAP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVocal learning, vital for cortical neuron development\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this study, we jointly investigated known language-associated and cognition-associated genes. Our analysis focused on their single nucleotide variation (SNV) patterns and the key concept: Language/Cognition Gene Polymorphism Pattern (LCGPP). The evolutionary trajectory of these polymorphic sites and their patterns\u0026mdash;from fish to progressively advanced animals, archaic humans, and modern humans\u0026mdash;serves as a powerful lens to examine aspects of marine organisms' evolution into diverse animal lineages (including humans). This approach helps identify humanity\u0026rsquo;s most archaic LCGPPs and, at least within our sample scope, determines which specimens retain these ancestral patterns. We incorporated a set of living fossil samples\u0026mdash;extant lungfish (Dipnoi) and coelacanths (Actinistia)\u0026mdash;which provide critical references for recognizing the most ancient LCGPPs in our dataset.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eGenome sequences\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenome sequences were downloaded from the ENA database, SRA database and Ensembl genome browser. A total of 471 whole genomes (including 111 ancient genomes, Table 1 s) from 5 continents (Africa, Asia, Europe, North America, and South America) were collected. The six representative animal groups include Laurasiatherians (L), amphibians/reptiles (R), fish (F), birds (b), primates (p), and rodents (d), plus miscellaneous taxa (x). The above ENA genome sequences have fastq format, whereas the Ensembl/SRA genome sequences are all assembled full genomes in fa, fn or fna formats, and all can be read and scanned with python-based hash07plus03 software.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLanguage/Cognition genes and their SNVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all human cognition genes, single nucleotide polymorphisms (SNPs) or Single Nucleotide Variants (SNVs) sites in the dbSNP database were selected such that each whole gene region was relatively equally spanned by the selected sites plus those already with known clinical effects (seen in the GeneCards database; Fig.8s1, Fig.8s2). Table 1 lists 36 human language/cognition genes, and a total of 239+223 =462 SNVs were selected for this study (Table 2 s, Table 3s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome sequence analysis software development and SNV abstraction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSNP/(SNVs) loci finding software, which is based on hash tables, primarily processes biological whole-genome files and rapidly identifies target loci within the genome via a search algorithm to obtain the specific values of the mutated bases. The software is written in Python [16]. Initially, it processes three different formats of whole-genome files\u0026mdash;fastq, fna, and fa\u0026mdash;on the basis of their unique characteristics, extracting gene sequences and generating standard format files that include all lines containing only ATCGN five bases. During use, the software can process multiple genome files in batches and impose restrictions on the matching length and the number of matches. After extensive validation, the speed of the software hash07plus03 has significantly improved compared with that of conventional matching algorithms and other software programs based on the Knuth\u0026ndash;Morris\u0026ndash;Pratt (KMP) algorithm. One of the search algorithms in the custom-developed software hash07plus03 involves constructing a 31-base string (15 flanking bases on each side of an SNV locus combined with the central base N, i.e., 15 + N + 15) to perform exact matching searches across whole-genome sequences. If a precise match is found, the software extracts the central base N as the SNV data; if no match exists, it outputs \u0026quot;0\u0026quot;. For example, if exact bilateral matches yield three central bases N (e.g., T, G, and C), the output would be \u0026quot;TGC\u0026quot;. Any SNV site has one of the following 16 genotypes: 0, A, T, G, C, AT, AG, AC, TG, TC, GC, ATG, ATC, AGC, TGC or ATGC. The authors used 010Edit software to extract SNV information from genome files, but most SNV information was extracted with hash07plus03 software. In all 471 genomes, the sizes ranged from 200 M to 120G. Genomes in fastq format but less than 18G were generally neglected or used only as a reference, and not used for analysis. All SNV data for 471 samples can be seen in Table 4s.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNei\u0026apos;s Genetic Distance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNei\u0026apos;s Genetic Distance\u0026nbsp;[17-18] is a genetic distance metric based on allele frequencies, reflecting the degree of genetic differentiation between populations. It is suitable for evolutionary analysis but requires allele frequency data and is sensitive to low-frequency mutations. In this study, due to the cross-species and spatiotemporal nature of the SNV data, allele frequencies were temporarily assumed to be equal. For example: A heterozygous site AT was recorded as *A=50%, T=50%; A tri-allelic site ATC was recorded as *A=33.3%, T=33.3%, C=33.3%, G=0%; A homozygous site C was recorded as *C=100%, A=T=G=0%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDICE coefficient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDICE coefficient\u0026nbsp;(S\u0026oslash;rensen-Dice coefficient) [19-20] measures similarity for binary data as a set similarity coefficient (a variant of geometric distance). It is well-suited for high-dimensional sparse data and sensitive to rare variants. Converting SNVs to binary presence/absence data facilitates rapid clustering to identify shared mutations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRogers\u0026rsquo; Distance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRogers\u0026rsquo; Distance\u0026nbsp;[21-22] is a geometric distance metric for categorical variables. It directly processes categorical data and reflects the proportion of differing loci, making it inherently compatible with categorical SNV data in this study (e.g., genotypes: 0, A, T, G, C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTanimoto coefficient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTanimoto coefficient\u0026nbsp;(generalized Jaccard coefficient) [23-24], as a set similarity coefficient (information-theoretic distance), is optimal for sparse binary data. Although it requires forced binarization of multi-base combinations, its complete ignorance of missing sites offers distinct advantages. Nei\u0026apos;s Genetic Distance, DICE coefficient, Rogers\u0026rsquo; Distance and Tanimoto coefficient were all performed via R packages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA/PCoA/t-SNE/UMAP analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the basic clustering analyses of samples were primarily performed using PCA/PCoA/t-SNE/UMAP methods [25-32] plus their combination(s). While PCA (Principal Component Analysis) preserves global structures, it may inadequately reveal certain local patterns in complex samples, being suitable for linear relationships but potentially losing complex nonlinear patterns. PCoA (Principal Coordinates Analysis), based on distance matrices, maintains global distance relationships between samples and is sensitive to distance metrics, yet struggles to reflect high-dimensional local structures. In contrast, t-SNE (t-distributed Stochastic Neighbor Embedding), frequently applied in transcriptome studies, effectively handles nonlinear associations between samples by emphasizing the preservation of local similarities. It models neighborhood relationships through probability distributions and excels at capturing high-dimensional complex manifold structures (e.g., cell differentiation trajectories, subpopulation delineation), yielding clearer visual clustering. UMAP (Uniform Manifold Approximation and Projection), similar to t-SNE but grounded in topological theory, balances local and global structures with faster computational speed and improved preservation of global relationships, gradually emerging as an alternative to t-SNE. PCA, PCoA, t-SNE and UMAP were all performed via R packages.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e1）\u003cstrong\u003eThe African sample sd1 potentially represents the most archaic language/cognition gene polymorphism pattern observed in ancient humans\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates these findings. Across panels A\u0026ndash;D (employing distinct distance metrics), the animal species closest to sd1 vary\u0026mdash;yet collectively demonstrate sd1\u0026rsquo;s strongest affinity to fish, amphibians, reptiles, and notably cetaceans (e.g., hu1 in Fig. 1D). The sd1 originates from a 4,000-year-old mummified hair sample from Sudan, Africa. This implies that humans retaining such ancestral LCGPPs persisted in Africa merely four millennia ago. In principle, this genetic architecture should correspond to a human evolutionary stage predating Homo erectus by a significant margin. Its survival in Africa until 4,000 yr BP is remarkable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2）\u003cstrong\u003eAfrican sample mo1l holds a special LCGPP evolution position\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll four genetic distance curves exhibit inflection points at the African sample mo1l, potentially corresponding to the evolutionary transition from Homo erectus to Homo sapiens. However, only Nei\u0026apos;s Genetic Distance reveals 3\u0026ndash;4 inflection points in the LCGPP trajectory. Whether these inflection points correlate with encephalization surges during the hominin evolution from ancient apes to H. erectus, H. sapiens, and modern humans [33\u0026ndash;36] remains unclear. Figure 1 integrates language and cognition genes. Complementary analyses include Nei\u0026apos;s Distance curve for Language genes alone (Fig. 1s1) and Nei\u0026apos;s Distance curve for Cognition genes alone (Fig. 1s2), in which Inflections are sharply defined in Cognition genes (Fig. 1s2) but less pronounced in Language genes (Fig. 1s1). Peak rate-change positions also differ between cognition/language gene curves. Besides, European/Asian samples occupy critical inflection zones in Fig. 1s2 while same regional samples reside in relatively inconsequential segments in Fig. 1s1.\u003c/p\u003e\n\u003cp\u003e3）\u003cstrong\u003eGeneral landscape of LCGPP evolution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Figure 2, the leftmost cluster aggregates marine fish, amphibians, reptiles, birds, rodents, and Laurasiatherian mammals\u0026mdash;representing the densest module. Progressing rightward, transitional specimens emerge, including lu3 (lungfish), dp2/dp1 (dolphins), and some archaic human samples. Further right, multiple distinct clusters appear, comprising transitional hominins between archaic and modern humans, and diverse primate lineages. Critically, numerous transitional LCGPPs persist in extant populations. The figure reveals that each evolutionary stage contains samples from Africa, Asia, and Europe. From the archaic-human-enriched left to the modern-human-dominated right, samples from these continents consistently co-occur across all phases. This implies intercontinental migration outpaced LCGPP evolutionary rates and unimpeded population diffusion channels existed across continents throughout hominin evolution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4）\u003cstrong\u003eThe extant lungfish appears to retain the most archaic hominin LCGPP signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed 3 lungfish samples: lu1 (South American lungfish, Lepidosiren paradoxa), lu3 (West African lungfish, Protopterus annectens), and lu4 (South American lungfish, L. paradoxa); Plus 4 coelacanth samples: lc1 (Latimeria chalumnae, Tanzania), lc5 (African coelacanth, fLatCha1), lm1 (Indonesian coelacanth, Latimeria menadoensis) and x19 (Ensembl reference, L. chalumnae). For genetic affinities, sd1 shows closest LCGPP similarity to lu1; while lu4 clusters with archaic humans dg2 (Asia) and nd1n (Europe). For evolutionary trajectories, lu3 lies closer to modern humans than lu4; while Coelacanths (lc1/lc5/lm1/x19) align with reptiles, birds, and Laurasiatherians (Fig. 3). Deep divergence is observed for lungfish and coelacanths. Fig. 3 suggests the most ancient hominoid LCGPP originated from lu1-related ancestors rather than coelacanths; Fig. 4 indicates early LCGPP similarity between lungfish and coelacanths at lu1\u0026apos;s evolutionary stage; Subsequent marked divergence occurred in their LCGPP evolutionary paths.\u003c/p\u003e\n\u003cp\u003e5) \u003cstrong\u003eThe evolutionary position of lungfish-coelacanth LCGPPs is remarkably distinct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigures 3 through 6 demonstrate the pivotal role of lungfish and coelacanths in preserving ancestral human LCGPP signatures from multiple perspectives: 1) Fig. 3: sd1 originates from ancient fish near lu1, while dg2 and nd1n evolved toward lu4. 2) Fig. 4: lu1-lu3-lu4-lc1 cluster adjacent to sd1-nd1n but distant from dg2; lc5-lm1-x19 diverged toward other animal lineages (not ancestral hominins). 3) Fig. 5: Though sd1-lu1 represents the most archaic LCGPP, modern humans may plausibly trace back to dg2-nd1n. 4) Fig. 6: Supports sd1-nd1n-dg2 as the core ancestral LCGPP.\u003c/p\u003e\n\u003cp\u003eFigure 7 reveals tripartite divergence in lungfish-coelacanth LCGPP evolution: 1) Module 1: lu1-sd1 (deepest ancestry); 2) Module 2: lc1-lc5-lu4-nd1n-dg2 (transitional hominins); 3) Module 3: lu3-mo1l (proximal to modern humans). Each module likely corresponds to fossil records, Hominin remains and extant descendant populations. Critically, Module 3 (lu3-mo1l) exhibits the closest evolutionary proximity to modern human emergence.\u003c/p\u003e\n\u003cp\u003e6）\u003cstrong\u003eThe most archaic LCGPP originates in marine fish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all analytical figures, samples closest to sd1 are consistently fish\u0026mdash;not amphibians, reptiles, birds, or rodents. This indicates that as vertebrates evolved from fish toward tetrapods (amphibians \u0026rarr; reptiles), their LCGPPs diverged progressively. Paradoxically, piscine LCGPPs align more closely with archaic humans than later-diverging lineages. Though taxonomically fish, lungfish exhibit transitional tetrapod-like traits: Lungfish possess soft-tissue morphology, developmental pathways, physiology, and behaviors convergent with extant amphibians but distinct from other fish. The massive lungfish genome (\u0026asymp;40 Gb) likely underlies their enhanced evolutionary plasticity compared to coelacanths\u0026mdash;potentially resulting from ancient genomic crises. Ecologically, lungfish bridge aquatic and terrestrial niches; Phylogenetically, lungfish represent the extant sister group to tetrapods [37-41].\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAfrican sd1 likely originated from a lungfish-coelacanth lineage, specifically related to lu1. Theoretically, a cohort of transitional LCGPP samples shall exist between lu1 and sd1, bridging the sd1-lu1 and nd1n-dg2 clusters and also awaiting future discovery.\u003c/p\u003e\u003cp\u003eLungfish/coelacanth-derived ancestral species may have independently evolved archaic humans across Europe and Asia. Critically, Module 3 in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e suggests that lu3 (African lungfish) and its descendants potentially represent direct ancestors of modern humans. This module initiates with jm2 (East Asia, 3,000\u0026nbsp;year BP) and lu3 (African lungfish); Its expansive distribution includes sc1(Spirit Cave, USA, 10,000\u0026nbsp;year BP, Ancient North American), mo1l (Morocco, Africa, Key African transitional), mg1 (Mongolia, 34,000\u0026nbsp;year BP, Northeast Asian archaic), de2 (Denisova Cave, 100,000\u0026nbsp;year BP, Siberian Denisovan), ch1 (Ayayema, Chile, 4,700\u0026nbsp;year BP, Early South American), and us2 (Anzick, USA, 12,500\u0026nbsp;year BP, Clovis-culture infant). This transcontinental LCGPP distribution-spanning Africa, Asia, Europe, and the Americas\u0026mdash;implies sustained gene flow during this evolutionary phase (potentially Homo erectus or earlier hominoids). It supports the possibility of regionally parallel hominin evolution across continents.\u003c/p\u003e\u003cp\u003eGiven Europe's lack of extensive continuous fossil records (unlike Africa and China), the most plausible scenarios are two as follow. The first is the Early African-Eurasian Dispersal: Archaic humans migrated from Africa through Europe-Russia-Northeast Asia at an exceptionally early stage. A subset reached the Americas during this initial expansion. The second is the Reverse Northeast Asian-Westward Dispersal: Populations moved from Northeast Asia toward Northwest Eurasia-Europe-Africa. Critical gap: Northeast Asia itself shows no robust continuous fossil record to support local continuity. Consequently, Africa remains the most probable source of the deepest-rooted language/cognition gene patterns\u0026mdash;unless these patterns originated around the Tibetan Plateau (where fossil evidence rivals Africa/Europe in temporal span and continuity). This possibility remains unexplored. In fact, mounting evidence [\u003cspan additionalcitationids=\"CR43 CR44 CR45\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] indicates that early hominoids globally may have descended from populations near the southern foothills of the Tibetan Plateau. Thus, the \"Out-of-Africa\" model for modern humans likely represents merely one subset of a far more complex, globally distributed evolutionary process.\u003c/p\u003e\u003cp\u003eThe establishment of foundational LCGPP frameworks in marine fish holds profound implications. Marine fish exhibit a unique capacity for rapid transoceanic dispersal across vastly larger territories than terrestrial organisms, enabling ancestral transitional species (fish- amphibians- reptiles- rodents) to achieve near-global distribution and exploit ecological refugia during extinction crises\u0026mdash;ensuring survival in isolated hospitable zones when continental habitats became uninhabitable. Crucially, species like lungfish embody syncretic phenotypes integrating piscine, amphibian, and reptilian traits. This pre-adaptation likely reduced evolutionary constraints on LCGPP inheritance, presenting significantly lower barriers than those imposed by local environmental adaptation.\u003c/p\u003e\u003cp\u003eStudy Limitations:\u003c/p\u003e\u003cp\u003eSamples with FASTQ-formatted whole-genome files\u0026thinsp;\u0026lt;\u0026thinsp;18 GB are included in our figures but excluded from analytical interpretations or conclusions. While key samples underpinning our findings\u0026mdash;sd1 (21 GB), nd1n (55 GB), and dg2 (18 GB)\u0026mdash;support general conclusions, their file sizes fall below the 80\u0026ndash;300 GB range typical of high-coverage contemporary standards. Further enhancements in sequencing depth and quality by originating laboratories are essential to strengthen these inferences. Future work will expand the catalog of ancient human samples and language/cognition-associated genes, increase SNV data density, and enable in-depth mining of evolutionary trajectories.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eInvestigation into Language/Cognition Gene Polymorphism Patterns (LCGPP) unveils novel insights into human evolution, particularly by illuminating the pivotal role of marine fish in establishing primordial frameworks for these patterns. Through integrated analyses of \u0026gt;\u0026thinsp;470 whole-genome samples spanning diverse species and spatiotemporal contexts, we identified the most archaic LCGPP representative: African sd1 (4,000\u0026nbsp;year BP), and subsequent key archaic carriers: European nd1n (44,000\u0026nbsp;year BP) and Asian dg2 (7,700\u0026nbsp;year BP). Critically, the foundational architecture of these ancient LCGPPs has been established in lungfish (Lepidosiren/Protopterus), coelacanths (Latimeria), and numerous marine fish lineages. This evidence positions marine vertebrates as cornerstone taxa in the emergence of human language and cognition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by a State Language Commission Research Grant (YB135-117), Association of Chinese Graduate Education Grant (B-2017Y0505-079), National Research Center for Foreign Language Education Grant (ZGWYJYJJ10A042) and funds from the Marine Antifouling Engineering Technology Center of Shandong Province.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eZZ: Instructor of this study, manuscript writing, software testing; SZ: Writing software for this study, plus software testing; YX: Instructor for writing software for this study, plus software testing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe data underlying this article are available in the article and its online supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration: \u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDoupe AJ, Kuhl PK. 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Nature. 2010;467(7319):1095\u0026ndash;9. doi:10.1038/nature09425.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"language gene, cognition gene, single nucleotide variation, language/cognition gene polymorphism pattern","lastPublishedDoi":"10.21203/rs.3.rs-6910300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6910300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResearch into how the evolution of language-related and cognition-related genes shaped the fundamental attributes distinguishing humans from other animals remains in its nascent stages. In this study, we analyzed 223 single nucleotide variations (SNVs) in language-associated genes and 240 SNVs in cognition-associated genes across whole-genome sequences from 471 samples, encompassing: Archaic humans (including Neanderthals and Denisovans), Modern humans, and Other vertebrates (fish, amphibians, reptiles, birds, rodents, mammals).Computations using four genetic distance metrics and six clustering methods revealed that the most ancient Language/Cognition Gene Polymorphism Patterns (LCGPP) predominantly persist in fish, with minor representation in amphibians/reptiles and Laurasiatherian mammals. Within our sample scope: The archaic human sample closest to these ancient LCGPPs is sd1 (Africa, Sudan, 4,000\u0026nbsp;year BP); Subsequent proximity is observed in nd1n (Europe, Okladnikov Cave, Russia, 44,000\u0026nbsp;year BP) and dg2 (Asia, Devils Gate, Russia, 7,700\u0026nbsp;year BP); Both sd1 and nd1n show the strongest genetic affinity to marine fish samples. This implies that foundational frameworks for language and cognition genes may have been established as early as the piscine evolutionary stage.\u003c/p\u003e","manuscriptTitle":"Recent Observations on Deep-Rooted Polymorphism Patterns of Language/Cognition Genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 15:09:07","doi":"10.21203/rs.3.rs-6910300/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea12ba86-9ffd-48e5-baa8-d0a5d94411ae","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-26T15:09:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-26 15:09:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6910300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6910300","identity":"rs-6910300","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}