GNPTAB and DCDC2. Furthermore, we searched for a selection target site among the SNPs in these LD regions, because a causal site is not necessarily a reported SNP but could instead be a tightly linked site. In both LD regions, we found candidate target sites, which may have an effect on expression regulation and have been selected, although which genes these SNPs affect remains unknown. Because most people were not engaged in reading until recently, it is unlikely that there has been selective pressure on reading ability itself. Consistent with this, our results suggest a possibility of genetic hitchhiking, whereby alleles of the reported SNPs may have increased in frequency together with the selected target, which could have functions for other genes and traits apart from reading ability.Dyslexia, or reading disability, is found to have a genetic basis, and several related genes have been reported. We investigated whether natural selection has acted on single nucleotide polymorphisms (SNPs) that were reported to be associated with risk/non-risk for the reading disability of Chinese characters. We applied recently developed 2D SFS-based statistics to SNP data of East Asian populations to examine whether there is any sign of selective sweep. While neutrality was not rejected for most SNPs, significant signs of selection were detected for two linkage disequilibrium (LD) regions containing the reported SNPs of  Dyslexia, or reading disability, is found to have a genetic basis ,3,4,5,6,DCDC2), although alleles for risk or non-risk are not always the same between the populations studied, as found in rs4504469 on KIAA0319 [DCDC2 [DYX1C1 and rs1079727 on DRD2 [While genetic research on dyslexia was initially conducted in populations that use alphabetic languages, genetic factors of dyslexia in Chinese populations have been investigated in the last decade ,13,14. TKIAA0319 ,16 and r9 [DCDC2 . Among t on DRD2 ,18. HoweFrom the perspective of human evolution, reading and writing are quite new activities, and have different histories to that of speaking. Writing systems were developed just a few thousand years ago and used by only a limited number of people before modern times; therefore, reading ability is unlikely to have been shaped by natural selection ,29,30,31If natural selection has acted, at least two scenarios can be considered. The first scenario is proposed as the neuronal recycling hypothesis ,33 or cuROBO1 after the divergence of the orangutan [KIAA0319 in the human lineage [ROBO1, ROBO2, and CNTNAP2 showed signatures of selective sweeps within modern human populations, where the derived alleles significantly increased in frequency after the separation from archaic hominins, although they do not reach fixation [Evolution of dyslexia-related genes has been investigated by comparing sequences of primates, which found a change in selective pressure on rangutan  and sign lineage . Some sifixation . As mentfixation ,39 did nfixation ,29,30,31Among various writing systems in the world, Chinese characters showed the earliest form around 1200 BCE, and have also been used at least once during history in other East Asian countries , where spoken language systems are different from China . ChinesenSL) [nSL, are powerful at detecting signs of recent selective sweep, where linkage disequilibrium (LD) is expected to be relatively maintained [We performed neutrality tests on the SNPs associated with the reading/writing ability of Chinese characters . BecausenSL)  and 2D SnSL) ,48, whicintained . MeanwhiWe focused on 15 SNPs that were found to be associated with dyslexia of Chinese characters in previous studies . HereaftWe examined East Asian populations, expecting if natural selection has acted on genes associated with dyslexia of Chinese characters, the signature would be seen in these populations. At present, publicly available data of these populations were East Asian populations (EAS) in the 1000 Genomes Project phase 3 (1 KG)  and the ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/, and from ftp://biodisk.org/Release/KPGP/KPGP_Data_2017_Release_Candidate/WGS_VCF_89_KOREAN_JOINT_CALL/, respectively. 1 KG was comprised of 2504 individuals from 26 global populations, and KPGP was comprised of 88 individuals (one sample of KPGP-00349 was removed because it was reported as a non-Korean sample on the ftp site). For KPGP, only SNP data with a filter status of “PASS” were used.We downloaded 1 KG and KPGP data from The unphased KPGP data was phased using Eagle2 . As the After the phasing procedure, we merged KPGP with 1 KG. The merged data includes only sites that exist in both 1 KG and KPGP. From the merged data, we extracted data of individuals in EAS and KPGP. The extracted data  was comprised of 594 individuals.2D SFS-based statistics ,48 requinSL [nSL to EAS-KPGP, using the selscan program [nSL values, only biallelic SNPs with a minor allele frequency nSL computation than the default of 100. The total number of SNPs in the data was 6,143,039. nSL values were normalized in 100 frequency bins, which is the default setting. One-tailed p-values were obtained to check neutrality on derived alleles.We used nSL  as a sum program . For calIn order to examine the neutrality of core SNPs and the surrounding regions, we conducted the 2D SFS-based statistics recently developed by Fujito et al.  and Satto et al.  and Satto et al. , and a gWe considered segregating sites in a region with high LD, which contains a core site. We assumed The SFS for the entire sample is expressed as:a et al. , and anaa et al. .The statistics of ore site . The numore site ,55,56,57{ξk}=θ/k , each fro et al. ; Equatioo et al. , where kThe statistics of  A group . The Gc0a et al. .In both statistics, the values are expected to be relatively small under selective sweep.p-values of ms [p-values of To obtain ns by ms . We assuns by ms , followins by ms  and Sattns by ms . We sampScreening for further analysis was carried out to examine whether there is a sign of selective sweep in each high LD region containing a core SNP (“core region”). We collected neighboring SNPs that had ositions . Note thpAfter identifying candidate core regions under the selective sweep from screening  located 7.5 kb downstream from the core SNP. We used this neighboring SNP because the We removed SNPs containing missing genotypes because nSL could not be properly calculated for the core SNP of rs2255526 on DIP2A. This SNP was located at the edge of chromosome 21, and extended haplotypes reached the end of the chromosome before EHH decayed entirely.Moreover, nSL and their p-values of the core SNPs (rs11264175 representative of rs28366021 on KIAA0319L), except rs2255526 on DIP2A. For all 14 SNPs, normalized nSL values were not significant  had some linked SNPs with global derived allele counts smaller than that of their core SNPs, and that these linked SNPs were classified into lower  frequency classes than their core SNPs. The global derived allele count of rs4535189 on ROBO1 is 2280 and belonged to frequency class 9; in the core region, 16 of the 23 linked SNPs were classified into the same class 9 as the core SNP, but 7 linked SNPs were classified into class 8. Similarly, the global derived allele count of rs1091047 on DCDC2 was 3871 and belonged to class 10; 7 of the 16 linked SNPs were also classified into class 10, but 9 were classified into class 9. Moreover, the global derived allele count of rs3743205 on DYX1C1 was 517 and classified into class 8, whereas the classes of the 97 linked SNPs varied: 7 were classified into a class 7, 87 into class 6, and 3 into class 5. No linked SNPs were classified into the same class 8 as the core SNP.For this reason, we checked the global derived allele count  of a core SNP and its linked SNPs, in addition to the count in EAS. Next, each SNP was classified into a “frequency class” (see the methods section). We found that three core SNPs  on DCDC2, and rs79024225  on DYX1C1.In each of these three cases, in addition to the core SNPs, we analyzed one linked SNP in a younger class, because these linked SNPs should have different evolutionary depths and therefore different polymorphism levels from their core SNPs. For each of the three cases, among the several linked SNPs, we selected a linked SNP that showed the smallest derived allele count as the “younger SNP”: rs73129039  on We also found that some linked SNPs were classified into a globally older frequency class than their core SNP. However, we ignored such cases. The extent of polymorphism at an “older SNP” should be greater than that at a core SNP due to the difference in age. Although the p-values were obtained from simulations (GNPTAB (p = 0.038) and rs3789228 (younger SNP for rs1091047) on DCDC2 (p = 0.068).We screened core regions for detailed analysis. The ulations , and twoGNPTAB and rs3789228 on DCDC2), we searched for the target site of natural selection using On the two core regions that contained SNPs that passed screening  and SYCP3. CHPT1 encodes cholinephosphotransferase [SYCP3 encodes a component of the synaptonemal complex, which is involved in the pairing and crossover of homologous chromosomes during meiosis [The derived allele count at rs17031962 is 296 out of 1008 chromosomes in EAS. The reported risk allele is the ancestral allele . The cornsferase , and SYCp-value than the case of We found one possible phasing error for one of the SNPs (rs78494298). The derived allele count at rs78494298 was 15, and only 14 alleles were linked to the derived allele at rs17031962 (core SNP). For the calculation of 2D SFS, this was counted as er event , and supAmong the linked SNPs, we found that three consecutive SNPs  showed different patterns from the other linked SNPs . These tp-values from simulations  was one of the core region boundaries, and that of the third region (p = 0.0031) was located close to the other core region boundary, where LD seemingly began to break down  is the highest among the significant SNPs.The range of the smallest region for each candidate SNP, as well as the h region B, when wSYCP3 and its upstream region. To elucidate the possible biological trait under selection, we investigated the functional significance of the SNPs by checking the Ensembl Variant Effect Predictor (VEP) [The candidate SNPs in the fifth region were located in or (VEP)  for GRChDCDC2. The number of derived alleles of rs3789228 (younger SNP) is 782 out of 1008 chromosomes in EAS, while that of rs1091047 (core SNP) is 824. The reported risk allele of the core SNP is the ancestral allele [rs3789228 is the “younger SNP” of rs1091047 on l allele . For thip-value for these pp = 0.0003) and the second SNP (p = 0.0004) shared the same smallest region and were grouped together as the first to second region. We also grouped other SNPs together that were in the same smallest region and with the same p-value. In total, five regions were detected  in the core region, we selected the region with the smallest ulations A. Among detected B, which pGNPTAB), the first to second region was overlapped with both the third and fourth regions, and partially overlapped with the sixth region. Based on this, we considered that either the top or second SNP may be the target site. On VEP [p-value, we inferred that the target site could be the second SNP.Only the top and second SNPs showed . On VEP  for GRChnSL (as an EHH-based test) and 2D SFS-based statistics. For most of the core SNPs, neither statistics detected any signatures of selective sweep, thus neutrality was not rejected. Previous studies found signs of natural selection on dyslexia-related genes by phylogenetic analyses [In order to investigate whether natural selection has acted on the core SNPs of interest, we conducted two types of neutrality tests on the derived alleles: analyses ,39. A sianalyses . While aanalyses ,29,30,31analyses ,65. DiffGNPTAB. The derived allele of this core SNP is the non-risk type [GNPTAB, this region also contains genes of CHPT1  and SYCP3. We searched for the target site using the p < 0.01 (the fifth region), because it is located in an open chromatin region. However, even if this SNP has some biological function, it is still unknown which trait is affected. There are two possible scenarios where natural selection has acted on this SNP. The first scenario is the selection for the prior functions explained by the neuronal recycling hypothesis and cultural neural reuse [GNPTAB has been found to be related to stuttering [GNPTAB for efficient infection [GNPTAB, then pleiotropy would explain this situation. However, it is unknown which gene is affected by a mutation on the target site (rs3751248). Because this SNP (rs3751248) is located in an open chromatin region, neither of the two scenarios can explain the case whereby the target site has a functional effect on genes other than GNPTAB. In such a case, our findings may be attributed to genetic hitchhiking, where alleles in dyslexia-related genes may increase their frequency together with the linked target site, which could have functions for other genes and traits other than reading ability. Thus, we consider this third scenario based on our results, and there may be other scenarios; however, it remains unclear which scenario actually occurred because of the current lack of understanding about the effect of mutations on the target site.The first case is the core region of rs17031962 on isk type . In addial reuse ,31,33,34al reuse , GNPTAB uttering ,66,67. Buttering . In addinfection . If rs37SYCP3 and its upstream region. SYCP3 is involved in the pairing and crossover of homologous chromosomes during meiosis [Although we focused on and analyzed only East Asian populations in this study, it may be valuable to look at the distribution of the derived allele among populations in the world. The derived alleles of the core SNP and its linked SNPs, such as rs3751248, are mainly observed in Asian populations , support meiosis . Such a DCDC2. This SNP was distinguished as the younger SNP to the core SNP of rs1091047, based on the global derived allele count. To date, there is no study investigating whether the derived allele of this younger SNP itself is risk or non-risk for dyslexia of Chinese characters, but the derived allele of the core SNP is a non-risk type [pGNPTAB, even if the target site has some biological function, it is unknown which gene is affected by mutations at this site and which trait is affected. Therefore, if natural selection has acted, any of the three scenarios mentioned above would also be possible for this case .The second case is the core region of rs3789228 on isk type . Based oLooking at the distribution of the derived alleles among populations in the world  and the DCDC2 was found to be positively correlated with the number of consonants [ASPM and Microcephalin in populations was found to be correlated with use of linguistic tone [ASPM and Microcephalin are genes related to brain size, and it is arguable whether they have or have not been under positive selection for brain growth [Although the present study did not investigate the relationship between allele distribution and writing systems, there are cases showing a correlation between human genetic variation and certain features of the spoken language. The frequency of an allele group of the READ1 regulatory element in nsonants . Moreovetic tone . ASPM ann growth ,74,75,76nSL. Several reasons could be considered for this discrepancy. One of the possibilities is recombination rate variation, which should affect the haplotype length [GNPTAB, nSL. Another possibility is that LD is broken down by recombination events over time, which renders it difficult to detect selection signals [nSL declines in a subpopulation of structured populations [nSL, we found that the result of nSL for rs3789228 (the younger SNP for the core SNP of rs1091047 on DCDC2) was also not significant . The derived allele frequency of rs3789228 in EAS is 77.6%, and therefore, this frequency could be relatively too high for nSL to detect sweep signals.Distinct from these previous studies, we focused on examining whether natural selection has acted on the alleles of SNPs reported to be risk/non-risk for reading ability. While 2D SFS-based statistics suggested that two core regions could be under selective sweep, this was not supported by the results of e length . In the  signals . An SNP ulations , such asGNPTAB and DCDC2. These effects are not biologically confirmed but were speculated based on annotation data. Future experiments are necessary to verify whether these target sites actually have a functional effect and which gene is affected. The findings in our study should be the results seen only in our study populations, i.e., EAS in 1 KG. In order to check sampling effects, follow-up studies are required when other East Asian data become available. In addition, although beyond our study, the validity of the association between core SNPs and reading ability needs to be confirmed by replications.In particular, we searched for the target site in two core regions, which could be under selective sweep. Our study supported the possibility of genetic hitchhiking: The target sites could have functional effects on genes other than dyslexia-related genes, Modern society has introduced public education and demands universal literacy ,28. So,


ST), as well as STRUCTURE analyses showed a very strong consistency between the two types of markers. Microsatellites gave pictures that were largely concordant with SNPs, although less accurate. The best concordance was found in the most complex dataset, which included 17 French sheep breeds . The use of microsatellites reduces the cost and the related analyses do not require specific computer equipment  infrastructure able to provide adequate computing and storage capacity). Therefore, this tool may still be a very appropriate solution to evaluate, in a first stage, the general state of livestock at national scales. At a time when local breeds are disappearing at an alarming rate, it is urgent to improve our knowledge of them, in particular by promoting tools accessible to the greatest number.Knowledge of population structure is essential to improve the management and conservation of farm animal genetic resources. Microsatellites, which have long been popular for this type of analysis, are more and more neglected in favor of whole-genome single nucleotide polymorphism (SNP) chips that are now available for the main farmed animal species. In this study, we compared genetic patterns derived from microsatellites to that inferred by SNPs, considering three pairs of datasets of sheep and cattle. Population genetic differentiation analyses (Fixation index, F According to the “Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture” , indiscrIn such a context, knowledge of population structure appears to be essential for managing and conserving Farm Animal Genetic Resources (FAnGR). This is particularly important in developing countries, where local breeds adapted to environment, disease and social uses, represent an invaluable reservoir of genetic diversity that is threatened by agricultural modernization.In the last few decades, microsatellites  or simple sequences repeats (SSRs)) have been the marker of choice in animal population genetics. In recent years, the use of single nucleotide polymorphisms (SNPs) has become increasingly important in this area. Nowadays, advances in high-throughput sequencing have identified thousands of SNPs, leading to the development of whole-genome SNP chips for the major farm animal species . IlluminA significant amount of work has been dedicated to the comparison between microsatellites and SNPs considering animal and plant models. If we limit the overview to vertebrates alone, we can distinguish different types of research. Some studies have compared the effectiveness of the two markers: (i) for the purpose of performing kinship analyses ,11,12,13In this study, we consider the case of farm animals for which the amount of SNP available is now very high. The question asked is whether a standard set of microsatellites can provide a picture of the population genetic structure that is comparable to the pattern deduced from a set of several tens of thousands of SNPs. From this perspective, we empirically compared the performance of microsatellites and SNPs to infer the relationship among breeds, considering datasets of sheep and cattle from the literature. For each breed, genotypes were extracted from studies conducted with microsatellites and from studies performed with SNP chips. The datasets were chosen to consider limited geographical scales  so as to be able to conduct fine genetic analyses. The conclusions of this study may lead to reconsider the possible interest toward microsatellites in current and future management of FAnGR.IS) obtained with PLINK v1.07 [We studied three pairs of datasets including (i) five local Algerian sheep breeds, (ii) 17 French sheep breeds , and (iii) seven local French cattle breeds. All datasets were available online except the microsatellite dataset for French sheep . For allNK v1.07  and GeneNK v1.07 ,24 softwThe considered datasets included between 21 and 30 microsatellites (see details in O) and effective number of alleles (Ae) were estimated for all markers using Molkin software (version 2.0) [Polymorphic information content (PIC), observed heterozygosity (Hion 2.0) .p ≤ 0.001).Cattle genotypes  were obtST values and the associated 95% confidence intervals using the Hierfstat package [ST computations. NeighborNet graphs based on Reynolds’ [We computed Nei’s  pair-wis package  availabl package . Followi package , no linkeynolds’  genetic eynolds’ .2 > 0.5).To ensure that uncorrected LD did not distort the analysis, SNP pruning was used to identify a subset of SNPs using the --indep option of PLINK with the following settings: 50 SNPs per window, a shift of five SNPs between windows, and a variation inflation factor threshold of two .Microsatellite datasets included 113 (Algerian sheep dataset) to 425 (French sheep dataset) individuals . The totThe SNP datasets included 36 (Algerian sheep dataset) to 346 individuals (French sheep dataset). The number of SNPs available after filtration ranged from 36,493 (Algerian sheep dataset) to 47,286 (French cattle dataset) .ST obtained from microsatellite datasets were very close to those obtained from SNP datasets  to 0.95 (French sheep dataset). All correlation coefficients were significantly different from zero , made it possible to fully appreciate the usefulness of microsatellite data. It must be noted that, for each pair of datasets (whatever the species considered), it is not the same individuals in each breed who have been genotyped with microsatellites and SNPs. Thus, it is quite remarkable to find such a correlation in the obtained patterns, while the variability introduced by this “sampling effect” constitutively limits the overlap of results from the beginning.Faced with the power of SNP chips, which are able to capture even tenuous relationships between breeds and to estimate confidence intervals with the highest precision, etc., it turns out that microsatellites gave pictures that were largely concordant, although with less accuracy. Microsatellite markers were able to capture genetic patterns  considering national scales, which is one of the first requirements for defining and prioritizing conservation measures.According to the Food and Agriculture Organization (FAO) , the risIt should also be noted that microsatellite kits are still widely used in the private sector for paternity testing. As a result, there is a significant amount of data, especially for cattle and horses, that could be interesting to collect and analyze as part of studies focusing on genetic diversity.By publishing microsatellite sets for different species of domestic animals , the FAOIn conclusion, this study highlights the role that microsatellites still play in the management of FAnGR. Indeed, our results show that microsatellites provide a picture of the genetic structure, and even though it is less accurate than that obtained with SNPs, it is absolutely relevant. Therefore, microsatellites are a suitable tool to make an initial evaluation of the situation. Because the effects of climate change are becoming more pressing, it does not seem appropriate to wait until access to high throughput techniques is widespread to characterize the breeds of developing countries. These breeds, which are largely adapted to their environments, constitute an invaluable heritage that can help us to face climate changes .


Advances in next-generation sequencing technologies have provided an opportunity to perform population-level comparative genomic analysis to discover unique genomic characteristics of domesticated animals. Duck is one of the most popular domesticated waterfowls, which is economically important as a source of meat, eggs, and feathers. The objective of this study is to perform population and functional analyses of Korean native duck, which has a distinct meat flavor and texture phenotype, using whole-genome sequencing data. To study the distinct genomic features of Korean native duck, we conducted population-level genomic analysis of 20 Korean native ducks together with 15 other duck breeds.A total of 15.56 million single nucleotide polymorphisms were detected in Korean native duck. Based on the unique existence of non-synonymous single nucleotide polymorphisms in Korean native duck, a total of 103 genes related to the unique genomic characteristics of Korean native duck were identified in comparison with 15 other duck breeds, and their functions were investigated. The nucleotide diversity and population structures among the used duck breeds were then compared, and their phylogenetic relationship was analyzed. Finally, highly differentiated genomic regions among Korean native duck and other duck breeds were identified, and functions of genes in those regions were examined.This is the first study to compare the population of Korean native duck with those of other duck breeds by using whole-genome sequencing data. Our findings can be used to expand our knowledge of genomic characteristics of Korean native duck, and broaden our understanding of duck breeds. In recent years, next-generation sequencing (NGS) technologies have dramatically improved in terms of cost, speed, and productivity . This trThe duck is one of the most common domesticated waterfowls and is economically important as a source of meat, eggs, and feathers . As a reKorean native duck (KD), called Woorimatori, is a domesticated duck that originated from the hybridized ducks between mallard duck and indigenous Pekin duck, and has been continuously improved since 1997 at National Institute of Animal Science, Republic of Korea by selecting individuals with excellent appearance, weight, and productivity –16. TheyTo address this, we apply a population-level genome analysis based on whole-genome sequencing data from populations of various duck breeds including KD. Specifically, we sequenced the whole genomes of 20 KDs, collected whole-genome sequencing data of 14 phenotypically diverse duck breeds  at a mean coverage of 25.7x see . We alsoPNPLA8 and ENO1) are shown as examples in Fig. PNPLA8 , several breeds including KD had different amino acids as alternative allele caused by missense variant. Similarly, the ENO1 gene had a locus which exhibited a G > C nsSNP only in KD. This G > C nsSNP at position 5,912,196 in the eighth exon leads to arginine as an alternative allele with glycine as a reference allele in KD , we found 3062 KD genes containing unique non-synonymous SNPs (nsSNPs) not observed at the same position in other duck breeds  and pigmentation (GO:0043473), and the B3GALT1 gene associated with lipid glycosylation (GO:0030259)  values based on SNPs in 40 Kb sliding genomic regions with 10 Kb steps see . We inveGO:003025  using high-throughput next-generation sequencing technologies, and conducted a comparative analysis with 14 duck breeds based on single nucleotide polymorphism (SNP) data. The 14 duck breeds were selected because they are well categorized according to their phenotypes, and their relationship among Pekin duck (PK), mallard (MD), and indigenous duck breeds is well studied .PNPLA8 gene is involved in energy mobilization and lipid storage in adipocyte tissue [ENO1 gene plays a role in the glycolysis pathway as an enzyme which is related to fatty acid synthesis [GRIK2 gene, which encodes a subunit of a glutamate receptor, (i) has a crucial role in synaptic plasticity, (ii) is involved in learning and memory, and (iii) plays an important role during rabbit domestication [Similar to previous studies –26, we ce tissue , and theynthesis . Althougtication .The mean nucleotide diversity (π) of duck breeds was between 0.1028 to 0.1384, and MD showed exceptionally higher nucleotide diversity than domesticated breeds. KD has a higher nucleotide diversity (0.1338) than PK (0.1221), which may be because the breeding history of KD (since late 1990s) is shorter than PK (since the Ming Dynasty) , 23. We B3GALT1, FHIT, and MITF. The B3GALT1 gene is related to lipid glycosylation (GO:0030259) which affects lipid accumulation [FHIT gene is associated with body mass index [MITF gene plays an important role in the melanogenesis pathway [MTNR1A and ITPR2, in highly differentiated regions between KD and MD. Sequential genetic variation in the MTNR1A gene is associated with the reproductive behavior of a local Greek sheep breed and goat [ITPR2 gene plays a crucial role in the regulation of intracellular calcium transportation and the process of eggshell calcification related to eggshell quality [ABHD17A, SSH2 and TGFB3 associated with palmitoyl-(protein) hydrolase activity (GO:00008474), hydrolase activity (GO:0016787) and growth factor activity (GO:0008083). Genes related with these functions may have created difference between KD and other 14 duck breeds. Zhou et al. compared populations of MD, PK, and indigenous-breed ducks, and found a regulatory mutation in a long-distance upstream region of the IGF2BP1 gene [IGF2BP1 gene, which is related to large body size in PK. The long-distance regulatory region and the IGF2BP1 gene were not included in the differentiated genomic regions obtained from the comparison between KD versus PK and KD versus MD in our study.We conducted differentiation analysis to discover what makes the difference among KD, PK, and MD breeds, and identified several candidate regions covering highly differentiated SNPs with respect to KD versus PK and KD versus MD. In the highly differentiated regions between KD and PK, we found several phenotype-related genes such as mulation . The FHIion GO:00259 which pathway , 32 and  pathway , 23. Alsand goat , 34, andand goat . The ITP quality . We suggIn summary, our study represents the first population-level analysis of 15 duck breeds including Korean native duck (KD) based on whole-genome sequencing data. Our results include candidate genes associated with unique characteristics of KD, and the genetic relationship among the 15 duck breeds. As a result, our research provides a comprehensive overview of the population structure and genetic diversity of 15 duck breeds, and will help further investigate the genetic information underlying commercially valuable traits in the KD breed.N = 20). The Korean duck samples were collected from Myeongbawinongsan  in compliance with relevant guidelines, using protocols approved by the Committee on the Ethics of Animal Experiments of the National Institute of Animal Science (Permit Number: NIAS2015–775). Each sequenced sample was prepared according to the Illumina protocols (TruSeq DNA Sample Prep Kit v2 Support (FC121–2001)). Briefly, one microgram of genomic DNA was fragmented by Covaris, the fragmented DNA is repaired, and an ‘A’ is ligated to the 3′ end. Illumina adapters are then ligated to the fragments, and the sample is size selected aiming for 400 ~ 500 base pair products. The size selected product is PCR amplified, and the final product validated using the Agilent Bioanalyzer. After that, selected DNA was sequenced using the HiSeq2000 platform  by Macrogen .We generated whole-genome resequencing data from a population of Korean native duck , Mallard; MD (N = 37), and twelve Chinese indigenous breeds; Gaoyou (GY), Longsheng (LS), Shaoxing (SX), Ji’an red (JA), Sansui (SS), Putian black (PT), Mawang (MW), Youxian (YX), Shan (SM), Jinding (JD), Taiwan (TW) and Liancheng white (LC) (N = 3 for these breeds)) from the NCBI SRA database . Local realignment was performed using the Genome Analysis ToolKit (GATK v3.8.1) tool [To generate single nucleotide polymorphism (SNP) data, we collected public sequencing data of various breeds of duck  tool . Because. We also calculated the transition-to-transversion ratio (Ti/Tv) to evaluate the quality of the SNPs. Using the annotated SNP information, KD genes with unique non-synonymous SNPs (nsSNPs), which were not observed at the same position in other duck breeds, were identified. Note that these genes can have unique nsSNPs of other duck breeds or common nsSNPs among other duck breeds. Therefore, we further reduced those KD genes to KD-specific genes which have only KD unique nsSNPs, not with nsSNPs of other duck breeds. Functional analysis of the above KD genes and KD-specific genes was performed by g:Profiler with default parameters [We built a database with the NCBI RefSeq gene annotation data (duck annotation release 103) of the reference duck assembly (IASCAAS_PekingDuck_PBH1.5) , and perrameters .μ Fst)/ σ Fst, where Fst is the Fst in a window, μ Fst is an average Fst over all windows, and σ Fst is a standard deviation of Fst values of all windows [SNP data was filtered with PLINK (v1.90) using the following criteria: “--geno 0.01 –maf 0.05 --hwe 0.000001” . For eac windows . Genes i windows . Functio windows .Additional file 1: Table S1. Phenotypic information of 15 duck breeds.Additional file 2: Table S2. Read mapping, coverage statistics and SRA accession number of 123 duck individuals.Additional file 3: Table S3. Functional categories of annotated SNPs of 15 duck breeds.Additional file 4: Table S4. A list of 3062 genes containing locus where only KD had non-synonymous SNPs compared to other duck breeds.Additional file 5: Table S5. The results of gene set enrichment analysis of 3062 genes containing locus where only KD had non-synonymous SNPs.Additional file 6: Table S6. A list of 103 genes containing only non-synonymous SNPs of KD.Additional file 7: Figure S1. The principal component analysis plot of 15 duck populations for all pairs of four components.Additional file 8: Figure S2. Maximum likelihood phylogenetic tree of 15 duck breeds with Muscovy duck as an outgroup. Color of each branch corresponds to the color in the PCA plot  between Korean native duck and other 14 duck breeds in sliding window 40 Kb with 10 Kb steps across the autosomal chromosomes. Red line denotes a threshold of ZFst at 5. Genes located in differentiated genomic regions are indicated by their gene symbols.


Arachis hypogaea) is an important oil, food, and feed crop worldwide. The USDA peanut germplasm collection currently contains 8,982 accessions. In the 1990s, 812 accessions were selected as a core collection on the basis of phenotype and country of origin. The present study reports genotyping results for the entire available core collection. Each accession was genotyped with the Arachis_Axiom2 SNP array, yielding 14,430 high-quality, informative SNPs across the collection. Additionally, a subset of 253 accessions was replicated, using between two and five seeds per accession, to assess heterogeneity within these accessions. The genotypic diversity of the core is mostly captured in five genotypic clusters, which have some correspondence with botanical variety and market type. There is little genetic clustering by country of origin, reflecting peanut’s rapid global dispersion in the 18th and 19th centuries. A genetic cluster associated with the hypogaea/aequatoriana/peruviana varieties, with accessions coming primarily from Bolivia, Peru, and Ecuador, is consistent with these having been the earliest landraces. The genetics, phenotypic characteristics, and biogeography are all consistent with previous reports of tetraploid peanut originating in Southeast Bolivia. Analysis of the genotype data indicates an early genetic radiation, followed by regional distribution of major genetic classes through South America, and then a global dissemination that retains much of the early genetic diversity in peanut. Comparison of the genotypic data relative to alleles from the diploid progenitors also indicates that subgenome exchanges, both large and small, have been major contributors to the genetic diversity in peanut.Cultivated peanut ( Arachis hypogaea) was domesticated in central South America by early agriculturalists, following tetraploidization of a hybrid involving the merger of two progenitor diploid species: A. duranensis and A. ipaënsis . As a nitrogen-fixing legume, peanut is also important as a rotation crop that restores soil nitrogen.Cultivated peanut  provide genotype data for each accession, 2) assess genetic diversity of the collection, 3) analyze population structure, 4) estimate the incidence of heterogenous or mixed accessions, and 5) assess relationships between genotypic groups and common traits and phenotypic classes.The U.S. Peanut core collection of 831 accessions was developed in the 1990s from the US collection of peanut cultivars and landraces. Of these, 44 were unavailable at the time of this study. This project genotyped the 787 accessions which were available (Supplementary File S1) and 12 commercial varieties used in many U.S. breeding programs. These included Tifguard / PI 651853 , Bailey https://www.grin-global.org), and previous evidence of heterogeneity , which limited the number of samples available for replicate genotyping for some accessions. Of the 253 accessions; 193 accessions were genotyped twice, 43 were genotyped three times, 16 accessions had four samples and one had five samples genotyped. In total, 1145 samples were available for genotyping.For all accessions, whether grown to maturity or to seedling stage, leaf tissue was sampled between 2 and 4 weeks after germination and immediately frozen in liquid nitrogen. DNA was extracted using Qiagen  DNeasy 96 Plant Kits (#69181) and 3 mm Tungsten Carbide Beads (#69997) as recommended by the manufacturer. Initial concentration and purity of 12 DNA samples/plate was estimated using a Thermo Fisher Scientific NanoDrop ND-1000 Spectrophotometer . Concentrations ranged from 26 to 75 ng/ul, with an average 43 ng/ul. A260/A280 ratios ranged from 1.882 to 1.984, with an average ratio of 1.931. A260/A230 ranged from 1.84 to 2.681, with an average ratio of 2.206. Samples were then shipped to Thermo Fisher for additional quality control and genotyping. DNA concentration and quality for all samples was confirmed using a ‘PicoGreen’ assay. Average DNA concentration was about 47 ng/μL for 926 high-quality samples. The remaining 219 samples had a concentration of 15 ng/ul and were considered of sufficient quality and quantity for genotyping. Samples were then genotyped using the 48k Thermo Fisher ‘Axiom_arachis2’ SNP array. Of the 1,145 samples, 25 replicate samples were not successfully genotyped.https://github.com/cannongroup/peanut_core_collection_genotyping).Raw SNP intensities from Affymetrix were analyzed using the ‘Best Practice Workflow’ available in the Axiom Analysis Suite. A total of 47,837 SNPs was obtained, of which 14,430 were categorized as ‘Poly High Resolution’, 15,528 were ‘Mono High Resolution’, 11,008 were ‘No Minor Homozygote’, and the remaining 6,871 were of low quality. Poly High Resolution SNPs were processed into a standard VCF format (Supplementary Files S2 and S3) using custom bash scripts for downstream analyses . SNPs from Affymetrix were called using Best Practices Genotyping Analysis Workflow algorithm on Axiom analysis Suite. This classifies SNPs into following categories: MonoHighResilution, PolyHighResolution, NoMinorHom, Other, CallRateBelowThreshold and OTV. SNPs from only the PolyHighResolution category were selected and these were the high-confidence SNPs. The matrix contains the 14,430 high-confidence SNPs, for 1,120 samples. Relative positions of the SNPs were also determined from the consensus genomic locations from five Arachis genome assemblies, as described below. This sequence representation is available as Supplementary Files S4 and S5.Several aspects of diversity analysis were carried out on variant data in FASTA format - Arachis genomes: A. duranensis, A. ipaënsis .Base-calls were also derived computationally for four sequenced A. duranensis and A. ipaënsis calls combined into one “synthetic-tetraploid” accession. Base calls that were absent in that accession were removed from the merged file, giving an alignment 10,278 bases wide, by 1,123 samples . Approximate genomic locations of SNPs were determined as: the location in the respective diploid chromosomes were present; otherwise, the location in Tifrunner; otherwise in Shitouqi; otherwise the location in Fuhuasheng, as shown in Supplementary File S6. Two reduced alignments were also generated (Supplementary Files S7 and S8), consisting of representative “centroid” sequences from clusters at the 98% and 99% identity levels, using the cluster_fast method in the vs.earch suite, version 2.4.3  with the The phylogenetic tree in To assess the genetic similarity among multiple samples from an accession, a list of all possible pairs of replicates per accession was calculated, giving “N choose 2” combinations for an accession with N samples: 3 combinations for an accession with 3 samples; 6 combinations for an accession with 4 samples, etc. For each possible combination, the sequence identity was calculated between the sequence pairs (using blastn), and then scored as “similar” if >= 98% identity and “dissimilar” otherwise. These results are shown in the “rep analysis” worksheet of Supplementary File S1.To define subpopulations based on genomic sequences, a structure analysis and PCA was performed on high confidence Axiom_Arachis2 SNP array variant data. Structure analysis was performed using a Bayesian inference algorithm implemented in fastSTRUCTURE  using an R package, Pophelper version 2.3  were used. The R package, SNPRelate  and relatedness analysis, only SNPs in approximate linkage equilibrium with each other , growth form, pod type, and country from which seed was originally collected.ST for selected accession groups defined as above under the Structure and PCA methods section. SNPs were first pruned to reduce SNPs in strong LD with one another, as described above. The FST analysis was performed using the R package Hierfstat, , and previous evidence of heterogeneity  has one expected pairing (A-B), while an accession with two replicates  has three expected pairings , and an accession with three replicates has six expected pairings. A missed pairing means that one or more samples for an accession are genetic outliers, and that the accession is not homogeneous. Among the 253 accessions with replicates, 218 accessions genotyped in replicate were selected at random from the core, and 35 accessions were selected as being potentially mixed or in which the seeds appeared to be visibly heterogeneous, based on assessment in Of the 428 expected pairings among replicates , 368 pairings were observed (86%). The observed pairings had an average identity of 94.4% and a median of 98.7%. The 60 instances of a sample that did not match to a replicate for that accession occurred among 42 accessions, meaning that some accessions had more than one “missing” match for a replicate.Of the 35 accessions selected as “probably mixed” based on seed color or identified in We describe one of the “probably mixed” accessions in some detail, as it appears to indicate substantial diversity within a single line. For accession PI 442786 (from Zimbabwe), five individuals were selected for genotyping, based on observed variation in seed phenotype. Seeds varied in both shape and color: darker or lighter red, and small/round or elongated. Sequence identity among these five replicates is at 87% or below, and the replicates appear in several intermediate positions in the phylogenetic tree and the network plot , these fOf the 218 accessions selected at random for replicate genotyping, most accessions  were NOT mixed genotypically: in these accessions, all replicates were close to identical (>=98%) across all replicates. Nevertheless, the high rate of apparent genotypic heterogeneity in accessions suggests that the core collection will require further subdivisions or selections to generate material that is well suited for analyses such as QTL and GWAS.The core collection contains considerable phenotypic diversity, but also displays high genotypic similarity among many accessions, as apparent in The phylogenetic tree of accession diversity shows four primary clades of accessions, numbered 1-4 in e.g., growth habit, botanical variety, market type, or pod type). Traits categories are shown superimposed on the clades, in the PCA plots in hypogaea, valencia, or mixed pods, but frequently having strong, linear reticulation, and including the aequatoriana botanical variety of subspecies fastigiata, as exemplified by PI 497426 from this clade (Supplementary File S14).A top-level summary of the cluster and trait correspondences demonstrates that most accessions, including all named cultivars, fall into three large clades , but those clades don’t correspond cleanly with typical peanut classifications .Next, we describe characteristics and compositions of the clades, working from the bottom of the tree (Clade 4) to the top (Clade 1).hypogaea, fastigiata, or mixed   also falls solidly within cluster 4, with 100% bootstrap support on several subtending branches in this clade.Clade 4 . The pod, 31.6%) . Growth stigiata , primarifastigiata botanical variety, upright growth habit, and fastigiata pod type . This is a transitional clade, with similarities to both Clades 2 and 3.Clade 3.2 . Both characteristics distinguish this group from Cluster 4. The most frequent South American accession origins for Cluster 3 are Bolivia, Argentina, and Brazil , with one each from Peru and Ecuador. The most frequent non-South American countries for cluster 3 are Zambia, Nigeria, and Zimbabwe .Clade 3 . The Clade 2 accessions also have the widest geographic spread. Also cosmopolitan in terms of country of origin. The most frequent South American countries for these accessions are Brazil, Argentina, Cuba, and Uruguay . Non-South American countries are the predominant sources for these accessions, however; Zambia, Zimbabwe, India, and Sudan are the most frequent sources . Because the highest-frequency countries of origin are Brazil in South America and Zambia, Zimbabwe and Sudan in Africa suggests early movement of this germplasm through the slave and other colonial trade.Clade 2 . Growth type varies widely, divided fairly evenly between erect, bunch, spreading-bunch, mixed, and prostrate). The most frequent market type is Virginia (64.2%). As with Cluster 2, the geographical spread is highly cosmopolitan  fall into the Bolivia-dominated Clade 4. This clade contains hypogaea and fastigiata varieties, including the uncommon aequatoriana variety, which is classified .rgentina , as wellA. duranensis and A. ipaensis are found close to Villa Montes, in the Province of Tarija : PI 336978, PI 442768, PI 210831, PI 497342, PI 331337, PI 471986, PI 288210, PI 221068, and PI 468280.The identification of southeastern Bolivia, as the center of origin of cultivated peanut relies on several lines of evidence. Both ancestral diploid species f Tarija . These sf Tarija . Also inll seeds . This coThe fastSTRUCTURE analysis resulted in five clusters (K = 5), which are shown in Overall, 240 accessions were exclusively assigned to a single group and 278 were assigned, in admixed proportions, to two or three groups: with 221 assigned to two, and 57 to three clusters. Of the 12 commercial cultivars genotyped, eight were assigned to cluster 4, along with Tifrunner. Of these, only Jupiter was exclusively assigned to a single cluster with the remaining seven, including Tifrunner, sharing admixed proportions with more than one cluster. Fuhuasheng and Shitouqi were assigned to cluster 2, same as cultivars Olin and Tamnut OL 06. The synthetic tetraploid sequence “duranensis_ipaensis” was assigned to cluster 5 - the only cluster without any commercial cultivars assigned.Clustering accessions via a phylogenetic analysis is overly simplistic as it suggests a one-dimensional source for sequence similarity or dissimilarity between a pair of accessions. Network analysis provides a more representative and explanatory relationship between given accessions. In ST: 0.74, 0.75, 0.67, 0.51). Clades 3 and 3.2 were not much different from each other (FST 0.22). Clade 3.2 was also not strongly distinct from Clade 2 (FST 0.3). Genetic clustering via PCA confirmed the main groups as distinct clusters , which together explained 59.75% of the genetic variation in the collection, differentiated between the two subspecies and corresponding botanical varieties. PC1 separated ssp. ia types .These results suggest a pattern, consistent with the biology of subspecies and botanical variety classification, as the most important correlates of the genetic diversity in the collection. Previous studies using a subset of this collection, the Mini Core, have suggested the presence of between four to five sub-populations  growth form. Several Virginia varieties are also classified as “decumbent”, for their intermediate growth form between “runner” and “bunch” . Since within-population diversity has been shown to affect FST as an estimate of genetic differentiation among populations , as did Valencia and Virginia (FST 0.4), and Valencia and Spanish (FST 0.3) (ST 0.027) compared to Valencia (FST 0.29), and Spanish types (FST 0.26)  . Indeed,ST 0.26) . ClassifST 0.55), “spreading” and “erect” (FST 0.39), “spreading-bunch” and “erect” (FST 0.28)  . This suST 0.28) .Collectively, these results suggest a level of stratification that is consistent with subspecies groups and botanical variety classification. Overall, we found accessions were similar within botanical varieties and subspecies groups, but genetic separation increased evidently between group comparisons. This carries important implications for studies using this collection for genetic associations. Treating the collection as a homogenous group may obscure association results and if not properly accounted for, population stratification may cause studies to fail due to lack of significant results or overwhelming false-positive signals.ST 0.02), as did Asian and African accessions (FST 0.01)  .We also found the country of seed origin to be a poor correlate of genetic structure, even though the core collection is predominated by accessions from South America and Africa, which together make up 74.6% of the entire collection. The peanuts collected from Bolivia and South America were not so distinct as to cluster around a recognizable pattern or separate from those collected from other continents. This may suggest that not many independent mutations have arisen in the different continental subgroups to cause significant genetic separation. It is also known that peanuts had completely differentiated into subspecies and botanical varieties prior to being dispersed from their center of origin by early explorers and traders , there was evidence of exchanges in at least one accession for 1,068 positions (9.8%). This is likely a highly conservative estimate, as many positions are ambiguous with respect to subgenome origin - for example, when the reference SNPs from the diploids may be from the other allele (not represented in the genome sequence).Our interpretation is that a substantial fraction (>10%) of alleles have arisen through subgenome exchanges; and further, that these exchanges appear to be ongoing, as there are numerous differences between accessions, in the subgenome allele status at a given locus.Genotype data for each accession in the U.S. peanut core collection will benefit peanut breeders in multiple ways: providing SNP data for use in marker-trait association studies to identify SNPs associated with important traits, describing the population structure of the core, and enabling breeders to work with smaller groups of accessions by selection through both phenotypic and genotypic characteristics. A probable ancestral genotypic group is identified, with most such accessions still coming from near the geographical origin of tetraploid peanut. The data also provides information about the ongoing rapid changes in the peanut genome through subgenome exchanges, and supports theories about the origin, early cultivation, and dispersion of peanut throughout the world.


Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic–phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals  regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37–53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory. Homo sapiens [Temperament is classically defined as those aspects of personality that express basic emotions like fear, anger, and disgust, and that are developmentally stable and heritable, rather than learned . However sapiens , 11–15.Early research assessing temperament focused on developmentally stable features of activity and affect, but some recent work has extended assessments of temperament to include aspects of attention and self-regulatory processes that emerged later in evolution and that develop in response to both individual experience and social norms , 2, 16. Studies of gene expression in response to associative conditioning in experimental animals have consistently documented the activation of specific molecular pathways that trigger synaptic plasticity, which is a fundamental basis for long-term memory , 32–34. Unfortunately prior genome-wide association studies (GWAS) of temperament that considered only the average effects of genes have identified few genes associated with personality and have specifically failed to uncover the genes associated with long-term memory whether the TCI or other personality inventories were used , 42. SucAs in our accompanying GWAS of human character , we haveSubjects and methods were the same as detailed in an accompanying paper , so esseOur discovery sample was the Young Finns Study, an epidemiological study of 2149 healthy Finnish children followed regularly from 1980 (ages 3–18 years) to 2012 (ages 35–50 years) . All FinWe replicated the results in two independent samples of healthy adults from Germany , 60 and All subjects completed the TCI to assess seven heritable dimensions of personality , 63. ThePeople at risk of unhealthy personality were identified as the bottom decile of the sum of TCI Self-directedness and Cooperativeness , a previWe also identified an empirical index of temperament  arrays . The Korp-value < 0.01 without Bonferroni correction) for possible association with temperament, taking gender and ethnicity into account as covariates of the individual SNPs, as detailed in an accompanying paper [After quality control, the PLINK software suite  was usedng paper . We accong paper .The cluster analyses used the Generalized Factorization Method –75 incluhttp://phop.ugr.es/fenogeno. The PGMRA method and algorithm are also summarized in Supplementary Sections Our web server application for Phenotype–Genotype Many-to-many Relations Analysis (PGMRA) in GWAS is published  and avaiReplicability of results was evaluated in the three independent samples for SNP sets, phenotypic sets, and genotypic–phenotypic relations using multi-objective optimization techniques , as deta902 Non-identical but possibly overlapping SNP sets were exhaustively identified by PGMRA in the Finnish sample without knowledge of the phenotype, as in our analysis of character . Among t118 Temperament sets were exhaustively identified by PGMRA in the Finnish sample using the 12 temperament subscales without knowledge of the genotype. These fine-grained sets were identified in clustering solutions with the possible number of sets ranging from 2 to 15. Hierarchically clustering these 118 fine-grained sets with PGMRA, we identified 3 temperament super-sets that minimized the Cophenetic Correlation Coefficient . SNP sets were labeled by a genotypic identification “G”, followed by 2 numbers indicating the maximum number of clusters and the order of their selection by the algorithm. For example, the SNP set G_13_3 has a p-value of 7.38E−14, whereas the best and average SNPs within this set have 1.46E−04 and 1.50E−01 p-values, respectively , which showed that SKAT did not inflate results.We computed the association of SNP sets with temperament in Finnish subjects. SKAT showed that the association of the empirical index of temperament with particular SNP sets was stronger than with the average effects of their constituent SNPs Table . We founThe 51 SNP sets associated with temperament are described in Table p < 1E−03, Table p < 4.6E−03).We found 44 of the 118 temperament sets were significantly associated with particular SNP sets , lncRNAs (14%), other RNA genes (5%), and targets of microRNAs (36%) as identified in the TRANSFAC® release 2017.1 database  of the 51 SNP sets associated with temperament in the Finnish sample were identified in one or both of the replication samples: 40 were identified in both the Korean and German samples, 5 in the Korean sample only, and 5 in the German sample only  and in the German (76%) study . In other words, the SNPs that comprise the different SNP sets strongly distinguished the temperament features of the subjects in each set, indicating that each individual SNP set contributed significantly to explain the total distributed heritability . Two SNP sets associated with sensitive profiles depended on the experience of tolerance and low income in childhood (p < 9.7E−04 to 4.7E−05). One SNP set associated with reliable profiles depended on high parental income throughout childhood (p < 1.5E−04).Furthermore, 12 novel associations between SNP sets and temperament sets were uncovered when environmental influences were used as mediators (Supplementary Table SNPs that map to 736 genes explained 48% of the variability in temperament in the Finnish sample, thereby accounting for nearly all the heritability of human temperament expected from twin studies. More specifically, most of the genes that we identified in a strictly data-driven manner are known to regulate synaptic plasticity, associative conditioning, and related processes of stress reactivity and neurotransmission. These findings confirm our hypothesis that the highly conserved molecular processes that regulate associative conditioning in experimental animals account substantially for the heritability of human temperament. Our findings are supported in independent replications by GWAS and by independent studies of gene expression during habit learning in experimental animals , 33.Most of the SNP sets associated with temperament were involved in the regulation of habit learning and synaptic plasticity in response to extracellular stimuli mediated mainly by the Ras-MEK-ERK and the PI3K-AKT-mTOR cascades Table , Fig. 2cThese findings about specific molecular pathways for human temperament have important implications. First, they confirm our hypothesis that the human temperament is based on the highly conserved mechanisms for habit learning. This supports a precise definition of temperament in terms of associative conditioning , 18. SecThese results should encourage widespread use of PGMRA for analysis of complex phenotypes in a variety of settings, including GWAS , 55 and The major strength of these findings is the strong replicability of the findings in three independent samples from different cultures and in independent studies of gene expression during behavioral conditioning of experimental animals. While it is true that cluster analysis is a hypothesis-generating method in which there is no unique solution to the number of clusters, which features are relevant for a cluster, or the degree of homogeneity to be demanded for each cluster, PGMRA included a practical and robust solution for each of these problems , 54.We were able to describe and replicate the complex genotypic–phenotypic risk architecture of temperament in three independent samples of people. Our unbiased data-driven findings confirm the hypothesis that temperament is based on associative conditioning and related processes, particularly stress reactivity in response to extracellular stimuli. We have found that different molecular and cognitive processes are associated with character , but heaSupplementary InformationSupplementary Figure S1ASupplementary Figure S1BCSupplementary Figure S1D-FSupplementary Figure S2Supplementary Figure S3Supplementary Figure S4Supplementary Figure S5Supplementary Figure S6Supplementary Figure S7Supplementary Figure S8Supplementary Figure S9Supplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Table S7Supplementary Table S8Supplementary Table S9Supplementary Table S10Supplementary Table S11Supplementary Table S12Supplementary Table S13Supplementary Table S14Supplementary Table S15Supplementary Table S16Supplementary Table S17Supplementary Figure S18Supplementary Table S19Supplementary Table 20Supplementary Table S21Supplementary Table S22


Populus euphratica in northwest China are divided into four clades exhibiting strong geographical distribution patterns. A total of 38 single nucleotide polymorphisms were associated with salinity tolerance, located in or near 82 genes.Natural populations of  Populus euphratica is a dominant tree species in desert riparian forests and possesses extraordinary adaptation to salinity stress. Exploration of its genomic variation and molecular underpinning of salinity tolerance is important for elucidating population evolution and identifying stress-related genes. Here, we identify approximately 3.15 million single nucleotide polymorphisms using whole-genome resequencing. The natural populations of P. euphratica in northwest China are divided into four distinct clades that exhibit strong geographical distribution patterns. Pleistocene climatic fluctuations and tectonic deformation jointly shaped the extant genetic patterns. A seed germination rate-based salinity tolerance index was used to evaluate seed salinity tolerance of P. euphratica and a genome-wide association study was implemented. A total of 38 single nucleotide polymorphisms were associated with seed salinity tolerance and were located within or near 82 genes. Expression profiles showed that most of these genes were regulated under salt stress, revealing the genetic complexity of seed salinity tolerance. Furthermore, DEAD-box ATP-dependent RNA helicase 57 and one undescribed gene (CCG029559) were demonstrated to improve the seed salinity tolerance in transgenic Arabidopsis. These results provide new insights into the demographic history and genetic architecture of seed salinity tolerance in desert poplar. Populus euphratica is a dominant tree species in desert riparian forests and plays an important role in maintaining local desert ecosystems . Based on these SNPs, we analysed population structure, genetic diversity, and demographic history. A seed germination rate-based salinity tolerance index was used to perform GWAS analysis to identify the genes associated with the seed salinity tolerance, and the candidate genes were genetically transformed into Arabidopsis for functional verification.Here, we report whole-genome resequencing of a collection of 252 P. euphratica individuals and 10 P. pruinosa individuals was sampled from natural populations in northwest China (JXB online). As the sister species of P. euphratica, P. purinosa was added as a reference or outgroup. Populus euphratica individuals were sampled from 27 populations spanning the main regions of distribution: 15 populations  in southern Xinjiang , seven populations  in northern Xinjiang , one population  in Inner Mongolia (IM), three populations  in Gansu Province (GS), and one population  in Qinghai Province (QH)  . AsexualA260/A280 ratio with a NanoDrop instrument . The DNA was sheared into ~500 bp fragments, which were used for library construction using the NEBNext DNA Library Prep Reagent Set for Illumina (BioLabs). Subsequently, the library was sequenced on the Illumina HiSeq 2500 platform at Beijing Novogene Bioinformation Technology Co. Ltd. All individuals were sequenced to a target depth of ×10. Finally, a total of 11.7 billion paired-end 125-base-pair reads were obtained.The total DNA of the leaves was extracted using the DNeasy Plant Mini Kit . The integrity of the DNA was estimated by 0.8% agarose gel electrophoresis, and its quality was measured using the P. euphratica version 1 (v1.0) genome  unidentified nucleotides ≥10%; (ii) more than 10 nucleotides aligned to the adaptor or mismatches >10%; (iii) more than 50% of the read bases with phred quality scores less than 5; and (iv) putative PCR duplicates generated in the library construction process. Then, the high-quality reads were aligned to the reference ) genome  with theP. euphratica reference genome annotation, variant loci were categorized in exonic regions (overlapping with coding exons), intronic regions (overlapping with introns), splicing sites (within introns and 2 bp of splicing junctions), upstream and downstream regions (within 1 kb upstream or downstream from the transcription start sites), and intergenic regions. SNPs in exonic regions were further grouped into synonymous SNPs, non-synonymous SNPs, stop gain, and stop loss.SNP annotation was carried out using the ANNOVAR package (frappe package (version 1.1)  was conducted to evaluate genetic structure using GCTA software (version 1.24.2) (p-distance using TreeBeST software (version 1.9.2) (W) were calculated using the ANGSD program (version 0.910) (We investigated genetic structure and estimated admixture proportions using the r2) between pairwise SNPs was calculated using Haploview software (version 4.2)  using the high-quality SNPs. To estimate LD decay, the degree of linkage disequilibrium coefficient (Ne) over historical time was estimated through pairwise sequentially Markovian coalescence (PSMC) method (version 0.6.4-r49) (–8 and generation time (g)=15 years were used to convert the scaled times and population sizes into real times and sizes (The variation in population sizes (6.4-r49) . Mutatiofastsimcoal26 software  and five NaCl concentrations  conditions. One hundred seeds of each half-sib family were plated on filter paper with 8 ml sterile salt solution in a 12 cm-diameter glass Petri plate. Three biological replicates (100 seeds in each replicate) were performed under different conditions. The plates were sealed using micropore surgical tape (3M) and placed in a controlled environment chamber . The cotyledons of the vast majority of seeds that were capable of germinating could open fully under control and NaCl conditions in 3 d. Thus, seeds were considered germinated when the two cotyledons protruded through the seed coat, and seed germination rates were counted after 3 d. Seed salinity tolerance was evaluated by a seed germination rate-based salinity tolerance index (GRI) with the equation: GRI=germination rate of salt-treated seeds/germination rate of seeds under control condition. GRIs and mean values were calculated using Microsoft Excel (version 2013), and the correlation analysis of GRIs was performed using SPSS Statistics (version 22.0).Freshly matured seeds were harvested from the y=Xα+Sβ+Kµ+e, where y represents the phenotype, X represents the genotype, S is the structure matrix, K is the relative kinship matrix, Xα and Sβ represent the fixed effects, and Kμ and e represent the random effects. The first three principal components were used for population structure correction.A total of 3 154 839 detected SNPs were used in the GWAS for the GRIs under different salinity concentrations. Mixed linear model analysis was performed using GEMMA (version 0.94.1)  and the Populus euphratica seeds were plated on glass Petri plates, and subjected to control (0 mmol l−1) or NaCl (150 mmol l−1) treatment for six time points . Three-month-old seedlings were water cultured using Hoagland’s nutrient solution. These seedlings were subjected to control (0 mmol l−1) or NaCl (250 mmol l−1) treatments for five time points . At the end of each time point, seeds or leaves were harvested and frozen immediately in liquid nitrogen for gene expression pattern analysis. Three biological replicates were performed.TM Premix Ex TaqTM II , 0.8 μl of each primer, 2 μl of cDNA template and 6.4 μl of ddH2O. qRT-PCR reactions were conducted on the LightCycler® 480 System according to the manufacturer’s instructions. The PeuEF1α gene was used as the reference gene for the salt treatment in P. euphratica, and the AtActin gene was used as the reference gene in Arabidopsis. The final threshold cycle (Ct) values were the average of four technical replicates and three biological replicates.Gene expression levels were determined using quantitative real-time PCR (qRT-PCR). Total RNA of samples was extracted using the RNeasy Plant Mini Kit , and first-strand cDNA synthesis was performed with ∼4 μg RNA using the SuperScript III reverse transcription kit , and the final product was diluted 40-fold as cDNA template. Primers were designed using Primer 3 software, and the primer sequences are listed in CaM 35S using the Gateway system (Thermo Fisher Scientific). The floral dip method was used for genetic transformation in Arabidopsis. After screening on medium containing 25 mg l−1 hygromycin (Thermo Fisher Scientific), more than 30 transgenic lines were obtained. Finally, three independent transgenic lines with high candidate gene abundance were used for further experiments.To verify the functions of the candidate genes, we performed gene transformation in Arabidopsis. The full-length coding sequence of the candidate genes was cloned into pDONR222.1 for sequencing, and the correct coding sequence was cloned into pMDC32 under the control of the 3 transgenic lines of Arabidopsis were used for salinity tolerance measurements. On the same day, the seeds were harvested and stored in a desiccator at room temperature. To minimize biological variation, we mechanically sorted the seeds and selected those that were 250–300 μm in size as described previously (−1 NaCl. Square Petri plates (10 cm×10 cm) were sealed with micropore surgical tape. Three independent transgenic lines and WT were plated per plate, with 64 seeds of one genotype. The plates were placed at 4 °C in the dark for 3 d and then transferred to a controlled environment chamber . Seeds were considered to have germinated when the radicle protruded through the seed coat. The seed germination rate and survival rate of transgenic plants were obtained by counting. Three biological replicates were performed in each experiment, and data were analysed by two-sample t test using SPSS Statistics (version 22.0).Seeds of wild-type (WT) and homozygous Thttp://bigd.big.ac.cn/) with accession number CRA002337.The raw data of the whole-genome resequencing have been deposited in the Genome Sequence Archive (GSA) in Beijing Institute of Genomics (BIG) Data Center  were uniquely found in either of the two species. Only the remaining 1.38% (43 705) of SNPs were fixed within each species, showing intraspecific homozygous polymorphisms  showed polymorphisms in both orphisms . To evalorphisms , indicatK=2 (the number of pre-defined genetic clades), all individuals were clearly subdivided into two species-specific clades of P. euphratica and P. pruinosa. Two dozen individuals were inferred to be a mixture of genetic components of P. euphratica and P. pruinosa, named the intermediate clade, implying the existence of gene flow in these two species  that exhibited strong geographical distribution patterns: (i) SX contained all of the southern Xinjiang individuals; (ii) NX included most of the northern Xinjiang individuals; (iii) IMGS contained Inner Mongolia and Gansu individuals, and seven northern Xinjiang individuals; (iv) QH comprised Qinghai individuals  was dominated by the variation among the four clades  all showed that distinct genetic differences existed between clades SX and NX, and IMGS was a mixture of their genetic components. Interestingly, Mulei individuals were interspersed among NX and IMGS clades, indicating that this region might be their genetic boundary because of its middle geographical position . The QH W values to measure the genetic diversity of four P. euphratica clades , followed by the SX clade  and the NX clade ; the QH clade had lowest nucleotide diversity . Overall, the genetic diversity (π=1.430×10−3) of P. euphratica in northwest China was lower than that of other poplar species, such as P. trichocarpa (π=4.100×10−3) (P. deltoides (π=1.700×10−3)  (FST=0.097), and between QH and other populations (FST from 0.110 to 0.160), whereas moderate levels of genetic differentiation occurred between IMGS and SX (FST=0.043), and between IMGS and NX (FST=0.044)  .r2) decreased with physical distance between SNPs in P. pruinosa and all clades of P. euphratica (P. pruinosa exhibits more extensive genome-wide LD than does P. euphratica (r2 threshold of 0.2) was observed in P. euphratica, which was comparable to the value for P. deltoides (1.4 kb) at same r2 threshold (LD (indicated by phratica . This reNe)  to ∼10 kya during the last glaciation. The four clades  of P. euphratica exhibited similar demographic trajectories until ∼0.1 Mya, and then a difference occurred: NX exhibited the slowest decline, followed by IMGS and QH, and SX showed the smallest Ne value.To explore the demographic history, we employed the pairwise sequentially Markovian coalescence (PSMC) method to investigate historical fluctuations in effective population size (Ne) . After afastsimcoal26 software. The best-supported model  and the control (0 mmol l−1) condition, and utilized GRIs to measure seed salinity tolerance ; the second group comprised 200 P. euphratica and intermediate individuals; and the third group comprised only 187 P. euphratica individuals. Manhattan plots and quantile–quantile plots for GRIs under five NaCl concentrations are shown in 10>6.0) on 18 scaffolds were significantly associated, including 18 SNPs located in the genic regions  and 20 SNPs located in the non-coding regions   . Among t region) .Candidate regions from −20 kb upstream to +20 kb downstream of the 18 signal peak positions were used to identify 82 candidate genes associated with GRIs at five salt concentrations. According to the expression profiles during seed germination and seedling growth under salinity stress, these 82 associated genes were classified into three patterns: pattern I comprised 28 genes that were induced dramatically during seed germination but were inhibited obviously in seedlings; pattern II comprised 19 genes most of which were induced significantly both during seed germination and seedling growth; and pattern III comprised 35 genes the majority of which were inhibited during seed germination but maintained high expression in seedlings . The resCCG016767 and CCG018138, respectively. The −log10 was 6.80 at scaffold 292.1:292299, which was higher than the value of 6.11 at scaffold 309.1:314223 , CCG012661 (three SNPs), CCG029559 (two SNPs), and CCG030115 (one SNP)  in all three sample groups  . Among te groups . This suCCG016767, which is annotated as DEAD-box RNA helicase 57 (DBRH57)  was mapped in the exon of DBRH57) , B. DBRHRH57 , B.stresses . We furtfunction . The seefunction . These rCCG029559 gene, generating two haplotypes AC and TT. The individuals with the alternate TT showed significantly higher GRI than those with the reference AC  were located in the rence AC , B. The T plants . Thus, w2, respectively, triggered two bottlenecks for P. euphratica. Expansion instead of shrinkage occurred for P. euphratica in the Xixiabangma glaciation and penultimate glaciation  have been characterized in some plant species  with significant alterations in their expression levels during seed germination under salinity conditions might contribute to salinity tolerance in P. euphratica, and further investigation is needed.Worldwide, more than 800 million hectares of land are subjected to salinization, accounting for approximately 6% of the world’s total land area (periment . The remcis-acting elements in promoter regions might influence the binding of upstream genes (in situ Hi-C followed by chromatin immunoprecipitation (HiChIP), and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) make it feasible to explore the three-dimensional genome architecture and chromatin accessibility, and these results have been incorporated into GWAS analysis to interpret the functional significance of the non-coding variants  through time inferred by PSMC for the four clusters of P. euphratica , P. pruinosa, and their intermediate cluster.Fig. S2. Changes in effective population size .Fig. S6. Manhattan plots and quantile–quantile plots for GWAS of the GRIs under five NaCl conditions using the first group that included 210 individuals .Table S13. Genome-wide association study for GRIs of 210 individuals (P. euphratica and intermediate individuals.Table S14. Genome-wide association study for GRIs of P. euphratica individuals.Table S15. Genome-wide association study for GRIs of Table S16. Comparison of GWAS results with three sample groups under five NaCl conditions.eraa172_suppl_Supplementary_Figures_S1-S12_Table_S1-S16Click here for additional data file.


Poultry feed constitutes the largest cost in poultry production, estimated to be up to 70% of the total cost. Moreover, there is pressure on the poultry industry to increase production to meet the protein demand of humans and simultaneously reduce emissions to protect the environment. Therefore, improving feed efficiency plays an important role to improve profits and the environmental footprint in broiler production. In this study, using imputed whole-genome sequencing data, genome-wide association analysis (GWAS) was performed to identify single-nucleotide polymorphisms (SNPs) and genes associated with residual feed intake (RFI) and its component traits. Furthermore, a transcriptomic analysis between the high-RFI and the low-RFI groups was performed to validate the candidate genes from GWAS. The results showed that the heritability estimates of average daily gain (ADG), average daily feed intake (ADFI), and RFI were 0.29 (0.004), 0.37 (0.005), and 0.38 (0.004), respectively. Using imputed sequence-based GWAS, we identified seven significant SNPs and five candidate genes  associated with RFI, 20 significant SNPs and one candidate gene (inositol polyphosphate multikinase) associated with ADG, and one significant SNP and one candidate gene  associated with ADFI. After performing a transcriptomic analysis between the high-RFI and the low-RFI groups, both 38 up-regulated and 26 down-regulated genes were identified in the high-RFI group. Furthermore, integrating regional conditional GWAS and transcriptome analysis, ras-related dexamethasone induced 1 was the only overlapped gene associated with RFI, which also suggested that the region (GGA14: 4767015–4882318) is a new quantitative trait locus associated with RFI. In conclusion, using imputed sequence-based GWAS is an efficient method to identify significant SNPs and candidate genes in chicken. Our results provide valuable insights into the genetic mechanisms of RFI and its component traits, which would further improve the genetic gain of feed efficiency rapidly and cost-effectively in the context of marker-assisted breeding selection. Poultry feed consistutes the largest cost of poultry production, estimated to be up to 70% of the total cost . MoreoveOver the past decade, the genome-wide association analysis (GWAS), based on single nucleotide polymorphism (SNP) chip data to identify the genetic mechanisms of FE, has been widely implemented in livestock, especially in cattle  and pig In this study, WGS data were obtained by a two-step imputation approach (from 55 to 600 K and then imputed to WGS) using a combined reference panel. Using imputed WGS data, GWAS was performed to identify SNPs and genes associated with RFI and its component traits. In addition, a transcriptomic analysis was performed to identify differentially expressed genes (DEGs) between high- and low-RFI groups to validate the candidate genes of RFI from the GWAS results and give biological evidence to candidate genes. The aims of this study were to pinpoint the associated loci and genes that contribute to the phenotypic variability in feed efficiency and provide valuable insights into the genetic mechanisms of RFI.ad libitum during all stages. Finally, the remaining 1,338 birds were slaughtered at 91 days of age for carcass trait recording. ADG and ADFI per individual were calculated for the period from 45 to 84 days. The RFI was calculated as follows:A chicken population derived from a yellow-feather dwarf broiler breed that was maintained for 25 generations by Wens Nanfang Poultry Breeding, Co., Ltd.  was used in this study. This population includes 1,600 birds  and was the third batch of the 25th generation of this chicken population. These birds came from a mixture of full-sib and half-sib families from mating 30 males and 360 females of the 24th generation. After hatching, all birds were maintained in a closed building under controlled environmental conditions and provided with a standard diet until they were 4 weeks of age. The chickens were then randomly assigned to six pens by gender  for growth performance testing from 5 to 13 weeks of age. They received food and water where b0 was the intercept, MMBW is mid-test body weight (MBW raised to the power of 0.75), and the MBW was the predicted body weight on day 21 of the test. b1 and b2 are the partial regression coefficients for MMBW and ADG, respectively. The descriptive statistics of the analyzed traits could be found in ® Axiom® HD genotyping array  method implemented in the software DMU v6.0 . The stay is a vector of phenotypic values of all individuals, b is the vector of fixed effects including batch effect, and g was the vector of the animal additive genetic effect [G is the marker-based genomic relationship matrix], e is the residual term [I was an identity matrix], and X and Z are incidence matrices relating the fixed effects and the additive genetic values to the phenotypic records; G is calculated using 600 K chip data as follows  for estimating the effective number of independent tests. Finally, the effective number of independent tests were set to 200,629, estimated by sampleM  and 2.49 × 10–7 , respectively. For evaluating the extent of the false positive signals of the GWAS results, a genomic inflation factor (λ) was calculated as the median of the resulting chi-squared test statistics divided by the expected median of the chi-squared distribution with one degree of freedom . Haploview 4.1 software (The Manhattan plot and quantile–quantile plot (QQ plot) were generated by the qqman package  in R. Th sampleM . Therefosoftware  was usedsoftware .P-value less than 0.05  and the absolute value of log2-transformed of fold change more than or equal to 1. Function and pathway enrichment was performed with the R language package [clusterProfiler  were downloaded from our previous study . The rawProfiler ]. Using  testing . An adjuThe identification of candidate genes of lead SNPs from GWAS results was performed basing on their corresponding genomic positions. The candidate gene regions were defined as extended 50-kb flanking regions both upstream and downstream of the lead SNP position. If there are no genes in the candidate gene regions, the nearest genes both upstream and downstream of the lead SNP position were selected as the candidate genes. To identify the causal genes or quantitative trait loci (QTLs), the overlap genes or regions between the candidate genes from sequence-based GWAS and DEGs were compared.To compare the results from sequence-based GWAS with reported QTLs, significant and suggestively significant SNPs were selected to compare with the QTLs. These QTLs, all of which affect ADG, ADFI, and RFI, were selected from the Animal QTLdb , respectP-values were less than 0.054, which indicated compliance with Gaussian distribution. The standard deviation (SD) of RFI, ADG, and ADFI were 8.83, 4.87, and 14.36, respectively. In addition, the coefficient of variation (CV) was 13.22% for ADFI and 16.82% for ADG. Therefore, these traits showed substantial phenotypic variation . These SNPs were located on these chromosomes . All significant SNPs were in high linkage disequilibrium . These SNPs were located on these chromosomes . The most significant SNPs were located at 1,147,989-bp position of GGA25. High linkage disequilibrium between SNPs of significant regions was found on GGA25  would decrease below the suggestive threshold . These significant SNPs were mainly located on these chromosomes . The most significant SNP was located at 4,779,635 bp on GGA14, which explained 5.46% of the phenotypic variance of RFI. Using SnpEff software, five candidate genes associated with RFI were identified and annotated  were selected to compare with the reported QTLs. These QTLs were collected from the Animal QTL database based on their physical locations. For RFI, only two QTLs located on GGA4 and GGAZ were extracted, and the nearest distance of QTL between significant SNPs was 24,435,812 bp on GGAZ  (P < 0.05)  . The oth < 0.05) . Moreove < 0.05) . Vaidyan < 0.05) . For ADG < 0.05) . IPMK is < 0.05) . For exa < 0.05) . For ADF < 0.05) . COPA muNowadays, the combination of GWAS and transcriptomic analysis is an efficient method to identify the causal mutations of complex traits in livestock. In cattle, a previous study integrated RNA-Seq data and sequence-based GWAS data to explore the genetic mechanisms of mastitis resistance and milk production . In swinComparing the GWAS results with the reported QTLs, we found no significant SNP located inside the reported QTL in this study . This isUsing imputed sequence-based GWAS is an efficient method to identify significant SNPs and candidate genes in chicken. In this study, using imputed sequence-based GWAS, we identified seven significant SNPs associated with RFI, 20 significant SNPs associated with ADG, and one significant SNP associated with ADFI. Furthermore, by combining regional conditional GWAS and transcriptome analysis between the high-RFI and the low-RFI groups, an overlapped gene (RASD1) was identified to be associated with RFI, which also suggested that a new QTL (GGA14: 4767015–4882318) was truly associated with RFI. Our results provide valuable insights into the genetic mechanisms of RFI and component traits in chickens.http://www.animalgenome.org/repository/pub/SCAU2016.0217/. The imputed WGS data of these 626 birds have been uploaded to the figshare repository (https://doi.org/10.6084/m9.figshare.10264913.v1).Raw reads of four samples (sample45561 and sample46307 with high RFI and sample45012 and sample46732 with low RFI) were downloaded from here: Animal care and experiments were conducted according to the Regulations for the Administration of Affairs Concerning Experimental Animals  and approved by the Animal Care and Use Committee of the South China Agricultural University, Guangzhou, China .SY, ZZ, and JL conceived the study, designed the project, and helped in preparing the draft. XZ provided the chickens’ dataset. SY and RZ finished the genotype imputation and performed GWAS. SY and Z-TC performed the transcriptomic analysis. Z-TC, RZ, SD, JT, XY, HZ, and ZC participated in the design and contributed to the manuscript. All the authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.