Many traits in free-breeding dogs (FBDs) may be under similar natural and sexual selection conditions to wild canids, while relaxation of sexual selection is expected in pure-breed dogs. We used a Bayesian approach with strict false-positive control criteria to identify FST-outlier SNPs between FBDs and either European or East Asian breeds, based on 167,989 autosomal SNPs. By identifying outlier SNPs located within coding genes, we found four candidate genes under diversifying selection shared by these two comparisons. Three of them are associated with the Hedgehog (HH) signaling pathway regulating vertebrate morphogenesis. A comparison between FBDs and East Asian breeds also revealed diversifying selection on the BBS6 gene, which was earlier shown to cause snout shortening and dental crowding via disrupted HH signaling. Our results suggest that relaxation of natural and sexual selection in pure-breed dogs as opposed to FBDs could have led to mild changes in regulation of the HH signaling pathway. HH inhibits adhesion and the migration of neural crest cells from the neural tube, and minor deficits of these cells during embryonic development have been proposed as the underlying cause of “domestication syndrome.” This suggests that the process of breed formation involved the same genetic and developmental pathways as the process of domestication.Domesticated species are often composed of distinct populations differing in the character and strength of artificial and natural selection pressures, providing a valuable model to study adaptation. In contrast to pure-breed dogs that constitute artificially maintained inbred lines, free-ranging dogs are typically free-breeding,  Although the time of dog domestication is still under debate , although the strength of this selection may vary between populations. Natural and sexual selection likely accounts for considerably smaller morphological variation between FBD populations when compared to morphological variation between dog breeds , available from our earlier study . The second dataset derived from the LUPA project (Table S2). All three datasets were generated using CanineHD BeadChip (Illumina), and therefore could be merged without reduction of the usable SNP set.We also used two datasets of SNP genotypes of pure-breed dogs. The first dataset (“UK dataset”) consisted of 88 pure-breed dogs collected from across the United Kingdom, available from our earlier study . Pruning for individuals with over 10% of missing data gave the final sample sizes of 29 individuals for Asian breeds and 356 for European breeds.From the combined dataset of pure-breed dogs, we selected the East Asian and Arctic breeds that branch from basal nodes in pure-breed dogs’ phylogenies , and individuals representing the same breed, independent of whether they originated from the UK or the LUPA dataset, clustered together in an individual-based dog phylogeny  and FBDs (N = 190), (b) modern European dog breeds (N = 356) and FBDs, and (c) East Asian and European dog breeds. We removed X chromosome SNPs from the dataset, because differences in the mode of inheritance of this chromosome compared with autosomal chromosomes could have led to biased results. We also pruned the datasets “a,” “b,” and “c” from SNPs with MAF < 0.01 and those with missing data for more than 10% of individuals, which reduced them to around 146K SNPs . Dataset pruning was carried out in Plink  East Asian dog breeds . However, Bed power . Therefoatistics .vs. FBDs) relative to the analysis involving the small dataset of East Asian breeds , and also identified the closest gene. This search was based on the CanFam3.1 dog genome assembly. The location of each gene identified this way was further confirmed via a search in the Ensembl Release 82 database. For SNPs located within protein-coding genes, we checked whether they are located within introns or exons. Ensembl was also used as a starting point to obtain information on gene function, followed by searches in the NCBI database and primary literature.We used the UCSC Genome Browser to search for the protein-coding genes within a predefined distance to the outlier SNPs detected in BayeScan analysis. We selected the distance of 100 kb following a study on genomic signatures of artificial selection during dog domestication (The gene ontology (GO) term enrichment analysis was carried out for genes located within 100 kb upstream and downstream of the outlier SNPs identified in the BP < 0.05) GO terms using the GOstat program . Although these SNPs can be located in sequences that have no functional role, some of them may be located in enhancers, matrix/scaffold attachment regions, miRNA target sites and other gene regulatory elements. Therefore, we analyzed the effect of nucleotide substitution in outlier SNP sites on transcription factor (TF) binding. The analysis of putative TF binding sites was carried out for sequences located in 5′-upstream, 3′-downstream, or intron sequences within 20 bp from the outlier SNPs identified in the BayeScan analysis. We identified putative TF binding sites using the LASAGNA2 program . To take into account the length of the putative promoter sequence in which a hit is found, an E-value is calculated according to the formula: E-value = P-value × (L − l + 1), where L is the length of the promoter sequence and l is the length of the putative binding site  did not identify any outlier SNPs. All the SNPs analyzed had q-values ranging from 0.9836 to 0.9901, α values ranging from −0.0078 to 0.0065, and FST values ranging from 5.20 to 5.42 × 10−6. This supports our interpretation that outliers found in the main analyses reflect real deviations from neutrality, and are not simply the results of analyzing large numbers of loci.BayeScan analysis for the main dataset identified a small number of FST-outlier SNPs , which was expected given the strict criteria we used to minimize the occurrence of false positives . Twelve SNPs, located on 10 different chromosomes, were identified as candidate loci under diversifying selection between East Asian breeds and FBDs (Table S3A). Importantly, all these SNPs were also identified as candidate loci under diversifying selection either between modern European breeds and FBDs or between East Asian and European breeds. Seven of these SNPs were located within genes, including one SNP located within an exon of the PKD1L1 gene and one within a 5′ UTR of the CALCB gene. One SNP was located in a 5′-upstream sequence at a distance of 191,715 bp from the start site of transcription. Four SNPs were located in a 3′-downstream sequence at a distance between 652–365,573 bp from the transcription end point.BTable S3A, and Table S4). From among the top 20 outlier SNPs with the lowest q-values (< 0.005), six were located within genes, including one SNP located within an exon of PKD1L1 gene and one within a 5′ UTR of the CALCB gene (Table S3B). Of the remaining 14 SNPs, eight were located in a 5′-upstream sequence at a distance between 18,237–437,119 bp from the start site of transcription, and six in a 3′-downstream sequence at a distance from 652–365,573 bp from the transcription end point (Table S3B).Sixty SNPs, located in 52 different chromosomal regions, were identified as candidate loci under diversifying selection between modern European dog breeds and FBDs , and European breeds with European FBDs only . Both analyses gave very consistent results with the earlier ones that included all FBDs, with 11 out of 12 outlier SNPs confirmed for the East Asian breeds vs. FBDs, and 17 out of the top 20 outlier SNPs confirmed for European breeds vs. FBDs; this included all the SNPs identified as shared outliers between the two sets. This high consistency further confirms that the outlier SNPs identified in this analysis accurately reflect the diversifying selection among the populations studied.The above results were obtained from the Bvs. East Asian breeds and FBDs vs. European breeds (PKD1L1) gene, one in an exon and another in an intron , also known as CRSP1  gene. In addition, another outlier SNP shared by the comparisons between FBDs and both groups of breeds was located 652 bp downstream from a gene homologous to a member of the vomeronasal 1 receptor (V1R) gene family, which has been annotated in several mammalian species (Table S3).Eight outlier SNPs were shared by the comparison between FBDs n breeds . Four ofPKD1L1 gene (BICF2G630842219) has been mapped to exon 54 of transcript PKD1L1-201 or exon 47 of transcript PKD1L1-202  has been mapped to a 5′ UTR, which is involved in the regulation of translation of a transcript . Heterozygous genotypes were also observed in all representatives of several dog breeds, which were genotyped in the LUPA project . None of these SNPs corresponded to the eight shared outlier SNPs for the analyses comparing FBDs with either East Asian or European breeds. However, four of these SNPs also occurred as outliers in the comparison between FBDs and East Asian breeds , associated with developmental anomaly syndromes in humans: McKusick–Kaufman and Bardet–Biedl syndromes (Table S7). In all four cases, the same allele was also fixed or occurring in high frequency in gray wolves, and was fixed in two other canid species, Eurasian golden jackal C. aureus and black-backed jackal C. mesomelas, suggesting that it represents an ancestral state for the wolf/dog lineage.We carried out a Bn breeds . One of P < 0.05) for comparisons between (1) East Asian breeds vs. FBDs and (2) modern European breeds vs. FBDs. Twenty significantly enriched GO terms were shared between these two analyses, including: metabolic process, developmental process, biological regulation, signaling, response to stimulus, synapse, and behavior (P < 0.05). Seventeen significantly enriched GO terms were shared between these two analyses, including: developmental process, biological regulation, signaling, response to stimulus, and behavior .The GO analysis carried out for all candidate genes located within 100 kb from outlier SNPs showed good consistency in enriched GO terms (significant at behavior . The GO Table S8). For six alleles no putative TF binding sites were identified. The other 44 alleles are located in putative binding sites for one  to five TFs . Nucleotide substitution in 19 out of 25 SNP sites had an effect on TF binding (Table S8).Twenty-five SNP sites with 50 alleles were identified in LASAGNA2 as located in putative TF binding sites (E < 0.001). This analysis showed that the alleles are located in putative binding sites of 44 transcription factors  a SNP site located 11 kb downstream of the PKD1L1 gene (BICF2S23454833) represented an A/C substitution changing the type of a transcription factor bound; (ii) a SNP site located 652 bp downstream from a gene homologous to a member of the V1R gene family (BICF2P1363919) represented an A/G substitution changing the type of TF bound; and (iii) a SNP site located –191 kb upstream from the matrix metalloproteinase 16 (MMP16) gene (TIGRP2P367127) represented an A/G substitution changing the DNA sequence from TF-binding to not TF-binding (Table S8).In three out of eight BPKD1L1 gene and one downstream of the SETBP1 gene  in pure-breed dogs relative to FBDs, or a combination of both mechanisms.The strict criteria applied to minimize the false discovery rate resulted in a small number of outlier SNPs between FBDs and both breed groups. Despite the small overall number of outlier SNPs, eight were shared between the comparisons of FBDs with either European and East Asian pure-breed dogs. This could result from artificial selection targeting the same traits in both breed formation events, relaxation of natural/sexual selection on particular traits  of a gene. The majority of SNPs within a genome occur in noncoding regions, and therefore the fact that 50% of common outlier SNPs fall within coding genes supports our interpretation that their outlier status truly reflects deviations from neutrality. This also provides an unambiguous link between the outlier SNPs detected in BIn addition, of the six shared outlier SNPs not located within exons, five were located within putative TF binding sites, and in three of them the nucleotide substitution had an effect on TF binding. Mutations in TF binding sites can be deleterious, as shown by their involvement in human disease. For instance, of 2931 disease-associated SNPs located within regulatory DNA, 93.2% fall within TF binding sites . Although only nine functional V1R genes have been identified in dogs and wolves , an isomorph of the better studied calcitonin-related polypeptide α gene (CALCA). Both genes are preferentially expressed in the brain and share similar functions . Similarly, displaying certain mating behaviors may be essential for reproductive success in FBDs, but nonessential in pure-breed dogs, where mating partners are selected by humans.The MARCH7 gene in the regulation of activated T lymphocytes  signaling pathway, one of the key regulators of development in all metazoans  pleiotropy. Similar questions may be posed regarding nonlethal genetic syndromes in humans, such as e.g., the ciliopathy disease spectrum. To our knowledge, a precise answer to this question is still unknown. However, the common involvement of several candidate genes in the HH pathway suggests that the diversifying selection between pure-breed dogs and FBDs did not act on each of these genes independently. The differentiation between dog breeds and FBDs involves multiple phenotypic traits, and therefore it may be expected that it has a common genetic and developmental mechanism.Multiple functions of CALCA in different body systems, involvement of MARCH7 in the regulation of neuronal stem cells, and the role of PKD1L1 in germ cell regulation are all consistent with the regulatory role of the HH pathway. However, these genes are highly pleiotropic and may also affect other pathways. For example, mutations in ciliary genes such as pathways . This lei.e., a distinctive set of heritable morphological, physiological, and behavioral traits typical of domesticated mammals  results from a common underlying mechanism. They argued that mild neural crest cell (NCC) deficits during embryonic development can account for most of these apparently unrelated traits  and HH pathway genes (PKD1L1 and SHH gene) were also found to be under diversifying selection between different breeds of dogs  and Bardet–Biedl syndrome  are likely selected against in free-ranging and free-breeding populations that are subject to natural and sexual selection.In the present study, we compared different groups of domestic dogs rather than comparing the dog with its wild ancestor. Therefore, it may be argued that all of the individuals studied should express the same set of traits related to domestication syndrome, which were acquired early during the domestication process. However, if genetic changes induced by domestication cause alterations in the process of embryonic development (such as mild NCC deficits; A recent study demonstrated that the sensitivity and accuracy of selective sweeps detection in domestic dogs based on SNP chip data varies between loci and depends on the method applied .The analysis of signatures of diversifying selection between FBDs and either European or East Asian breeds identified candidate genes shared between these two comparisons. The presence of such shared candidate genes suggests either selection for the same traits during the two breed formation events, relaxation of natural and sexual selection in pure-breed dogs as opposed to FBDs, or a combination of both these processes.e.g., The association of these candidate genes with the HH signaling pathway suggests that they could affect dog phenotypes through their influence on common developmental processes regulated by this pathway. HH inhibits the adhesion and migration of NCCs from the neural tube, and minor deficits of these cells during embryonic development have been proposed as the underlying cause of the domestication syndrome (


Brassica Infinium single nucleotide polymorphism (SNP) array in 520 oilseed rape accessions. A total of 11 peak SNPs significantly associated with GS content were detected in growing seasons of 2013 and 2014 and were located on B. napus chromosomes A08, A09, C03, and C09, respectively. Two associated regions of GS content covered by these markers were further verified, and three B. napus homologous genes involved in the biosynthesis and accumulation of GS were identified. These genes were multigene family members and were distributed on different chromosomes. Moreover, two genes (BnGRT2 and BnMYB28) associated with GS content were validated by the qRT-PCR analysis of their expression profiles. The further identification and functionalization of these genes will provide useful insight into the mechanism underlying GS biosynthesis and allocation in B. napus, and the associated SNPs markers could be helpful for molecular maker-assisted breeding for low seed GS in B. napus.Rapeseed contains glucosinolates, a toxic group of sulfur-containing glucosides, which play critical roles in defense against herbivores and microbes. However, the presence of glucosinolates in rapeseed reduces the value of the meal as feed for livestock. We performed association mapping of seed glucosinolate (GS) content using the 60K  Brassicaceae and provide protection against plant pest and pathogens. However, Glucosinolates also impart a pungent flavor and inhibit thyroid function, resulting in liver and kidney abnormalities if consumed in excess in higher animals [Brassica napus L., genome AACC, 2n = 38) is an important source of edible oil for humans and protein for live-stock [i.e., with seeds lacking erucic acid (another toxic compound produced by rapeseed) and containing only low levels of glucosinolate in seeds, has been an important objective of rapeseed breeding programs globally.Glucosinolates, a group of sulfur-containing glucosides, are secondary metabolites uniquely detected in  animals . TherefoB. napus [B. napus chromosomes N9, N12, and N19 [B. napus populations [To date, quantitative trait locus (QTL) mapping based on molecular markers is an effective method for identifying candidate alleles involved in a complex trait, by which method numerous markers associated with total GS content have been mapped using different populations of B. napus ,4,5,6,7. and N19 ,4,5,8,9. and N19 ,11,12,13 and N19 . Using a and N19 . Additioulations . Althougulations ,18,19, tBrassica Genome Project [Brassica genome using next-generation high-throughput DNA sequencing techniques and has developed the Brassica 60K SNP BeadChip Array with 52,157 Infinium Type II SNP loci . In this study, we used a panel of 520 rapeseed lines for association mapping of total GS content using the 60k Brassica Infinium SNP array. The objective of our study was to identify the consensus association peaked SNP markers, which could be used for molecular marker assisted breeding of low GS content. Meanwhile, we also conducted comparative association mapping with A. thaliana to identify candidate genes for GS content in Brassica species. This work would lay the foundation for understanding the biosynthesis and accumulation of seed glucosinolate in rapeseed.Recently, the multinational  Project  has made−1 with an average of 54.90 μmol·g−1, while in 2014, it ranged from 20.53 to 162.51 μmol·g−1 with an average of 52.12 μmol·g−1. The high coefficient of variation  < 0.05, with 34,103 polymorphic SNPs were used for the association analysis. The population structure of 520 rapeseed accessions were classified using by STRUCTURE 2.1 with the optimal value of K = 2 (Ln P (D) = −5,240,293.4  (Q + K) model, and the QQ plot as shown in ectively . Hence, B. napus genomic sequences between the adjacent peak SNPs were extracted. As a result, the associated regions with glucosinolate content on chromosome A09 and C09 included three Arabidopsis homologous genes  that were involved in the glucosinolate metabolic pathway (AT5G62680 (GRT2) and AT5G60890 (MYB34) were approximately 0.2 and 0.1 Mb away from the significant SNP, Bn-A09-p3116738 and Bn-A09-p3029767 on chromosome A09, respectively; and the homologous gene AT5G61420 (MYB28) was 0.3 Mb away from the significant SNPs, Bn-scaff_19783_1-p327775, on chromosome C09  studies can be used to identify rapeseed genes underlying seed glucosinolate content by comparing the genomics datasets of Brassica and Arabidopsis [In B. napus ,8,9. AssB. napus , and haswo years ,24,25,26bidopsis .Brassica 60 K SNP BeadChip Array. Of which, 11 showed high LD to this trait in both years which were distributed on chromosomes A08, A09, C03, and C09, respectively  and GTR2 (AT5G62680), which are essential for accumulation and allocation of glucosinolates in mature leaves [MYB34 (AT5G60890), MYB51, and MYB122 are reported to coordinately control the suite of enzymes that synthesize indolic glucosinolates [B. rapa [HAG1  has been reported to control aliphatic glucosinolate biosynthesis in A. thaliana [MYB28-1 (an isoform of MYB28) was involved in aliphatic glucosinolate biosynthesis in Brassica rapa (Chinese cabbage) seeds, and the glucosinolate content could be reduced by silencing orthologs of HAG1 in B. juncea [MYB28 and MYB34 have been reported to regulate glucosinolate biosynthesis [B. napus homologous of GRT2, MYB28 and MYB34 were identified according to the reference genome of B. napus [AT5G60890, MYB34 and AT5G62680, GRT2) were located within the 2.7–3.0 Mb physical region of B. napus chromosome A09, and AT5G60890 (MYB34) was 0.2 Mb away from the SNP Bn-A09-p3029767 and AT5G62680 was 20 kb away from the significant SNP Bn-A09-p3116738 on chromosome A09, respectively (AT5G61420 (referred to as MYB28 or HAG1) was located 0.3 Mb away from the significant SNP Bn-scaff_19783_1-p327775  method. The SNP analysis was performed in the National Key Laboratory of Crop Genetic Improvement, National Subcenter of Rapeseed Improvement in Wuhan, Huazhong Agricultural University, Wuhan, China, according to the manufacture’s protocol . The SNPBrassica 60K SNP BeadChip Array with 52,157 Infinium Type II SNP loci. The K-value (The putative number of genetic groups) best representing the data set was determined according to the method of Evanno et al. [K-value varying from K = 1 to K = 10, with the length of the burn-in period and the number of MCMC  repetitions after burn-in set to 100,000 and 1,000,000, for each of these. Then based on the rate of change in the log probability of data (Ln P (D)) between successive K-values, the most likely K-value was determined by the log probability of data (LnP(D)) and an ad hoc statistic ∆K as proposed by Evanno et al. [Population structure, which refers to the non-random distribution of genotypes among individuals within a population, is a key factor affecting the accuracy of association analysis . In thiso et al. . In shoro et al. . TASSEL o et al.  was usedo et al. .B. napus was performed using the mixed-linear model  implemented in TASSEL 5.2.1, as described by Yu et al. [p-value, which was determined by the formula p = 1/N  [The mixed-model method was developed to account for multiple levels of relatedness simultaneously as detected by random genetic markers , and theu et al. . The minn study) , was useB. napus “Darmor-Bzh” reference genome [B. napus genomic sequences centered by the peak SNPs were aligned to the Arabidopsis Information Resource [In order to identify the candidate genes, the flanking sequences between the adjacent peak SNPs were mapped onto a e genome  by a homResource , and the−ΔΔCt method using BnACTIN7  and BnUBC21  as internal controls [To confirm the association between candidate gene and seed GS contents, total RNA of leaves was extracted from B7, B52 and B95 with relative high GS content and B63, B91 and B342 with low GS content, respectively (see controls . All samB. napus, respectively. Three B. napus orthologs of the Arabidopsis thaliana genes involved in seed glucosinolate content were identified by comparison of the genomic sequences of adjacent peak SNPs, and BnGRT2 and BnMYB28 are the candidate genes involved in the glucosinolate biosynthesis. These results will help us to well understand the molecular mechanism of the glucosinolate biosynthesis and allocation in B. napus, and improve the breeding efficiency by molecular marker-assisted selection in the future.We used association mapping to identify SNP markers linked to glucosinolate content in a population of 520 rapeseed accessions. In total, 11 peak SNPs were repeatedly detected with two years of field data, including four significant association regions distributed on chromosome A08, A09, C03, and C09 of


Gallus gallus). We used next-generation sequencing technology to compare four transcriptomes pooled from 11 chickens, generated from reciprocally crossed families, to the DNA sequences of their parents. Candidate imprinted genes were then selected from these sequence alignments and subjected to verification experiments that excluded all but one SNP. Subsequent experiments performed with two new sets of reciprocally crossed families resulted in the exclusion of that candidate SNP as well. Attempts to find evidence of genomic imprinting from long non-coding RNAs yielded negative results. We therefore conclude that genomic imprinting is absent in the brains of 1-day-old chickens. However, due to the temporal and tissue specificity of imprinting, our results cannot be extended to all growth stages and tissue types.Genomic imprinting is a phenomenon characterized by parent-of-origin-specific gene expression. While widely documented in viviparous mammals and plants, imprinting in oviparous birds remains controversial. Because genomic imprinting is temporal- and tissue-specific, we investigated this phenomenon only in the brain tissues of 1-day-old chickens ( Genomic imprinting is an epigenetic phenomenon in which certain genes are expressed in a parent-of-origin-specific manner –3. In otThe parental conflict hypothesis is one of the most plausible explanations for genomic imprinting, offering an evolutionary advantage for this epigenetic mechanism. Sexual conflict between the parents results in the biased expression of genes that affect maternal resource allocation. Specifically, maternal genes are selected to conserve sufficient resources for both the mother and her progeny, while paternal genes are selected to use resources in a way that maximizes offspring growth at the expense of the mother ,5. UnderDrosophila [Genomic imprinting is very well documented in mammals and plants –14, althosophila ,16. Notaosophila . As yet,The recent advances in next-generation sequencing (NGS) technologies, including transcriptome sequencing (RNA-seq), now make answering such questions possible . In factGene imprinting is a special case of allele-specific expression , and preAnimal experiments were approved by the Animal Care and Use Committee of the China Agricultural University . Animals were fed and handled according to the regulations and guidelines established by this committee, and all efforts were made to minimize suffering.To determine whether genomic imprinting exists in 1-day-old chicken brains, we compared the allelic-bias of four transcriptome libraries pooled from 11 chickens, descended from two reciprocally crossed families, to the DNA sequences of their parents.Cornish: Cor and White Leghorn: WL) were chosen as the reciprocally crossed parents   . Eleven Whole-genome sequencing of parent genomes and RNA-seq of offspring genomes were performed using an Illumina HiSeq 2500 sequencer. Library construction and sequencing were performed following manufacturer protocols . Paired-end reads of 100 bps were produced. The insert sizes of the re-sequencing and RNA-seq libraries were 300 bps and 330 bps, respectively.Using our re-sequenced genome and the Galgal4 reference genome (from the Ensembl Genome Browser), we simulated Cor and WL genomes. We then used two programs, the Burrows-Wheeler Aligner’s Smith-Waterman Alignment tool  and the Next, we identified SNPs exhibiting parent-of-origin allelic expression in the reciprocal crosses with TopHat software , which aTo annotate the candidate SNPs and predict their function, we used the SnpEFF program , which cFinally, we identified any long non-coding RNAs that potentially exhibit imprinting. We assembled transcripts from our simulated genomes using the Cufflinks program . We founNGS techniques may occasionally provide false positives . Thus, tWe obtained cDNA from the RNA pools of offspring using reverse transcription polymerase chain reaction (RT-PCR). We then amplified any candidate genes of the offspring and parents that contained a potentially imprinted SNP. We generally used CAPS (cleaved amplified polymorphic sequences) assays for our analyses. However, in cases where the SNPs were not located at cleavage sites, we designed primers to introduce restriction enzyme sites and ran dCAPS (derived cleaved amplified polymorphic sequences) assays . AmplifiNext, we genotyped our amplified cDNA and DNA products using Sanger sequencing, to exclude the possibility of incomplete digestion.We aimed to detect imbalanced allelic expression in the offspring of our reciprocal crosses, through the use of pyrosequencing with Pyromark Q96 (Qiagen). One candidate SNP in particular warranted more detailed investigation, due to its unusual properties . For this SNP, we analyzed brain cDNA samples (obtained from PT-PCR), and extracted RNA from the liver, heart, and pectorals of each offspring separately. We also verified the SNP on two other reciprocally crossed families (cross III and cross IV), where the four parents’ genotypes are homozygous at that SNP locus. Similar to the previous four crosses, these parent genotypes were confirmed using Sanger sequencing.Escherichia coli for amplification and sequencing of the target DNA segments.Since some candidate SNPs are located in regions that are difficult to amplify and sequence directly, we performed monoclonal sequencing of the two candidate genes in which those SNPs are located. We connected a pMD19-T Vector to the target segment containing the candidate SNP. The vector was then transformed into We obtained a total of 412,605,954 reads (82.5 Gb) in the re-sequenced genome and 237,006,676 reads (47.4 Gb) in RNA-seq. The four RNA-seq libraries contained 10.2 Gb, 11.3 Gb, 11.8 Gb, and 14.1 Gb of reads. All libraries passed our quality control checks, and the base quality was very high, even at the end of reads  reads could be mapped to the Galgal4 reference genome. The mean sequence depth was 19.5×. We identified 726,884 SNPs and 422,693 SNPs under the loose and general standards, respectively. Under the general standard, SNPs that occurred between the two strains covered 4,960 genes, relative to a total of 17,108 genes in the reference genome.Of these SNPs, we found several that potentially exhibited parent-of-origin effects. The proportion of reads aligned to the maternal genome was 0.5 at most SNP loci . As imprinted genes may be expressed in a sex-specific pattern [Among the potential imprinted genes we identified, only two met the loose standards in both sexes . The results revealed that one of them (No.1 VHL) was a heterozygote across the parent strains. The other candidate gene (No. 2 XLOC_047216) contained six bases that were either inserted or deleted next to the SNP. These indels may be the reason why the sequencing methods we chose were unsuitable, potentially causing errors in the reads statistic and the SNP filter.Monoclonal sequencing was performed on two candidate genes (No. 1 ENSGALG00000000194) did appear to show expression patterns indicative of imprinting, but as it only exhibited parent-of-origin effects in females  located in this sex-specific gene, although it was excluded due to heterozygosity in the parents. Nonetheless, the results of our restriction endonuclease analysis on cross I DNA support the sex-specific characteristics of this gene; offspring only expressed the allele of their father  [Another notable factor that affects our experimental outcome involves the use of a 75% read alignment standard in filtering imprinted SNPs. We chose this strict standard because we have observed that typical imprinted genes in other species are very strongly expressed  –39. Givemammals) . Still omammals) . ClearlyOur current results suggest that genomic imprinting is absent in 1-day-old chicken brains. However, our findings are not generalizable across all growth stages and all tissues types, due to the temporal and tissue specificity of genomic imprinting, and the stricter standards we have chosen to impose in our analysis. Further studies in different tissues and at various developmental stages are required to confirm the existence of genomic imprinting in chickens and other oviparous organisms.S1 FileThis figure shows the overall sequencing quality, we randomly selected one of the re-sequencing libraries to be representative of all the other libraries . Quality of the randomly selected RNA-seq library, we found that the base quality was very high, even at the end of reads . This figure displays read alignment quality, the horizontal axis shows the proportion of reads aligned to the maternal genome, whereas the vertical axis shows the number of SNPs. Overall, the proportion of aligned reads at most SNP loci was 0.5 .(RAR)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Traits under study were abdominal circumference (AC), body height (BH), body length (BL), cannon bone circumference (CBC), chest depth (CD), chest width (CW), rump circumference (RC), rump width (RW), scapula width (SW), and waist width (WW). A total of 138 SNPs (the most significant being MARC0033464) on chromosome 7 were found to be associated with BH, BL, CBC, and RC . One SNP on chromosome 1 was found to be associated with CD at genome-wide significance levels. The percentage phenotypic variance of these significant SNPs ranged from 0.1–25.48%. Moreover, a conditional analysis revealed that the significant SNPs were derived from a single quantitative trait locus (QTL) and indicated additional chromosome-wide significant association for 25 SNPs on SSC4  and 9 SNPs on SSC7 (RC). Linkage analysis revealed two complete linkage disequilibrium haplotype blocks that contained seven and four SNPs, respectively. In block 1, the most significant SNP, MARC0033464, was present. Annotations from pig reference genome suggested six genes  in block 1 (495 kb), and one gene (SCUBE3) in block 3 (124 kb). Functional analysis indicated that HMGA1 and SCUBE3 genes are the potential genes controlling BH, BL, and RC in pigs, with an application in breeding programs. We screened several candidate intervals and genes based on SNP location and gene function, and predicted their function using bioinformatics analyses.As one of the main breeding selection criteria, external appearance has special economic importance in the hog industry. In this study, an Illumina Porcine SNP60 BeadChip was used to conduct a genome-wide association study (GWAS) in 605 pigs of the F External appearance is a major breeding selection criterion in the hog industry. Exterior traits such as body height (BH), body length (BL), cannon bone circumference (CBC), chest depth (CD), chest width (CW), and rump circumference (RC) are closely related to body growth, food intake capability, sow reproductive efficiency and longevity, and so on. Hence, these factors has attracted extensive attention in modern pig industry http://cn.animalgenome.org/cgi-bin/QTLdb/SS/index) In the past, advances in DNA-based marker technology made it possible to identify quantitative trait loci (QTLs) associated with complex economically important traits in pigs. To date, more than 762 QTLs for exterior traits have been identified via genome scans and included in the Pig QTLdb  array has been widely used and proved to be a powerful tool in identification of causal mutations associated with complex traits such as reproduction, disease susceptibility, and meat quality in livestock Sus scrofa genome, the present GWAS was performed to detect potential genetic variants associated with 10 exterior traits in a Large White × Minzhu pig resource population.In order to identify novel genes and novel quantitative trait nucleotides (QTN) related to exterior traits across the 2 individuals are presented in Summary statistics results of the phenotypes of the F2 individuals were used for the genome-wide association studies. The number of effective SNPs is 12309 and the significant threshold was 4.15E-06 (0.05/12039). None of the SNPs for AC, CW, RW, SW and WW reached a genome-wide significance level. One hundred and thirty-eight significant SNPs of BH, BL, CBC, and RC occurred in an enlarged region on SSC7 , and 25 SNPs were located within 24 known genes (http://david.abcc.ncifcrf.gov) solute carrier family 26 (SLC26A8), solute carrier family 22 member 7 (SLC22A7), primase, DNA, polypeptide 2 (PRIM2), and molybdenum cofactor synthesis 1 (MOCS1) and the associated GO terms included organic anion transport, 4 iron, 4 sulfur cluster binding, iron-sulfur cluster binding, and metal cluster binding. Only one genome-wide significant SNP, H3GA0005226, in c-abl oncogene 1, non-receptor tyrosine kinase (ABL1) gene was located on SSC1.Of the 138 SNPs on SSC7, 90, 115, 116, and 32 SNPs were significantly associated with BH, BL, CBC, and RC, respectively  and 9 SNPs on SSC7  showed chromosome-wide association with BL, CBC, and RC  haplotype block (block 1 and 3) (P<1E-5). The 495 kb haplotype included 6 annotated genes, which were GRM4 , HMGA1 (high mobility group AT-hook 1), NUDT3 (nudix-type motif 3), RPS10 , SPDEF (SAM pointed domain containing ETS transcription factor), and PACSIN1 (protein kinase C and casein kinase substrate in neurons 1). The GGAA  and AAGG  haplotypes of block 3 were also significantly associated with BH, BL, and RC (P<1E-5). This 124 kb haplotype included 1 annotated gene named signal peptide CUB EGF-like 3 (SCUBE3).Linkage analysis of a 4.76 Mb (from ALGA0039921 to H3GA0020849) region on SSC7 of significance  that con1 and 3) . Moreove1 and 3) . The AAAA GWAS using the PorcineSNP60 BeadChip was performed by Mix Model and Regression-Genomic Control (GRAMMAR-GC) methods. This pedigree-based method could exploit inter-family variation in addition to intra-family variation, and could rapidly analyze hundreds of thousands of markers Heavy linkage disequilibrium in intercross population is one of the well-known limitations in genetic studies, which makes it difficult to identify the causative gene and mutation. In order to decrease non-positive rates, Bonferroni corrections have been used for all multiple tests. Since the Bonferroni correction is a conservative method to determine significance thresholds, we calculated the number of effective SNPs, and used the number of effective SNPs for the correction to ensure that no QTL was missed. Anyhow, there are no SNP significant associated with AC, CW, RW, SW and WW in present study. This may suggest that single locus plays a minor role in AC, CW, RW, SW and WW variation or the variation of AC, CW, RW, SW and WW were caused by copy number variation and so on.In our research, the genome-wide significant association of SNPs on SSC7 disappeared after conditional analysis  protein species, HMGA1 serves as a IGF1 (modulator of insulin-like growth factor 1) activity HMGA1 protein is nearly disappeared in fully differentiated adult tissues IGF1 is usually present in small dog breeds, but nearly vanished in giant breeds, indicating that the variant plays important role in body size HMGA1 may be an ideal candidate as an anthropometric trait HMGA1 might be a prime candidate for BH and BL in pigs.Linkage analysis revealed two haplotype blocks in the significant region on SSC7 at complete linkage disequilibrium, which contained eight annotated genes of the pig reference genome. Of the 8 genes, SCUBE3  is also of particular interest. SCUBE proteins usually have three domains including multiple epidermal growth factor (EGF) domains, bone morphogenetic protein 1 (BMP1) C-terminal complement subcomponent (CUB) domain, and a huge spacer domain with several N-linked glycosylation sites SCUBE3 functions as an endogenous transforming growth factor (TGFβ) receptor ligand, increasing Smad2/3 phosphorylation, and thus up-regulates target genes such as hedgehog (Hh), TGFβ, and bone morphogenetic protein 2 (BMP2) SCUBE3 is usually very high SCUBE3 might also be an influencing factor in determining the size of bones and is a prime candidate for BH, BL, and RC in pigs.HMGA1 and SCUBE3 gene. Bioinformatics analysis indicated that HMGA1 and SCUBE3 genes might be prime candidates for some exterior traits in pigs.In summary, this study identified a total of 138 significant SNPs on SSC7 associated with one or more exterior traits and one SNP on SSC1 associated with CD. Conditional analysis indicated that significant SNPs originated from a single QTL. Furthermore, linkage analysis identified two complete linkage disequilibrium regions containing the All animals used in the study were housed and handled following the guidelines of experimental animals established by the Council of China. Animal experiments were approved by the Science Research Department of the Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS) .s among the traits were calculated to investigate whether they reflect the correlation among GWAS results. Pearson and genetic correlations among the traits were performed using SAS (version 9.1) and DMU (version 4.7), respectively.A 3-generation resource population constructed by intercrossing Large White boars and Minzhu sows between 2007 and 2011 was used in this study. In F1 generation, four Large White boars were mated with 16 Minzhu sows. Nine boars and 46 sows, all from the F1 population, were intercrossed to produce an F2 population consisted of 618 animals. All the animals were reared under the same nutritional and environmental conditions, and were housed at the experiment base of Institute of Animal Science, CAAS. All ten traits were measured by following standard procedures. Phenotypic and genetic correlationongissimus dorsi were collected when the pigs were dead, all the procedure need no anaesthesia or analgesia disposition. Genomic DNA was extracted from the ear or longissimus dorsi using the phenol-chloroform method. Quantification and qualification of DNA were performed using a NanoDrop 2000  with the standard: DNA concentration >50 ng/ul; 260/280 ratio between 1.8 and 2.1; 260/230 ratio >1.8. Genotyping was performed using the Illumina Porcine SNP60 BeadChip containing 62,163 SNPs across the whole genome. All F2 animals were genotyped using the BEADSTUDIO software . Before quality control, the maximum likelihood method was applied using the Cervus program P<10-6) were applied.A very small piece of ear tissue (about 50 mg) was collected when the ear number were coded using the ear tag pliers, and the lThe association analysis was conducted using the Mix Model and Regression - Genomic Control (GRAMMAR-GC) method In the first step, the residuals were analyzed using DMU software.y is the vector of phenotypes of all F2 individuals, b is the vector of fixed effects , w is the vector of body weights of the individuals (considered as a covariate), c is the vector of random effect , α is the vector of random additive genetic effects with α∼N , X, T and Z are incidence matrices relating records in y to fixed and random effects, p is the regression coefficient of body weight and e is the vector of residual errors with e∼N,where I is the identity matrix and σe2 is the residual variance.In this step, data were analyzed using the mixed model:*y is estimated as*b, *p, *c and *αare estimates and predictors for b, p, c and α, respectively.The vector of residuals th SNP Ti2 was calculated in the genomic control (GC) procedure in the R statistical environment using the GenABEL package Second, the residuals were used as the dependent traits for single locus regression analysis, and the unadjusted test statistic factor of the iP-value was divided by the number of effective SNPs. The number of genome-wide effective SNPs, which was 12,039  Using the R function eigen  to calculate the eigenvalues and 3) Inferring effective number of independent tests (Meff G) through PCA.The Bonferroni method was employed for the genome-wide significance threshold, in which the conventional The significant threshold was 4.15E-06 (0.05/12039). The Manhattan plot was constructed using the GAP package and Q-Q plot was constructed within the R statistical environment. Proportion of the explained variation of each SNP is calculated using Tassel software In the conditional GWAS, the most significant SNP for each trait was used as a additional fixed effect, and all the analysis were performed by following the state GWAS procedure. The most significant SNP, MARC0033464, was used for the conditional analysis of BH, BL, CBC, and RC, while the most significant SNP H3GA0005226 was used for conditional analysis of CD.http://www.ncbi.nlm.nih.gov/). Genes within the detected regions were retrieved from the Ensembl Genes 64 Database using the BioMart software (http://www.biomart.org). Gene ontology and KEGG pathway analyses were performed to extract the functional annotation clustering and the pathways involved using DAVID (http://david.abcc.ncifcrf.gov) with the threshold of P<0.05.All significant SNPs were BLASTed in the websites of Ensembl and NCBI  and the frequency of each haplotype (Hap-Freq) were calculated to test overall associations among haplotype blocks and traits.Association analysis of the detected haplotype blocks and traits of 594 F2 individuals were performed using the Haplo.Stats R package File S1Table S1. The correlations between each trait. Table S2. Distribution of SNPs and effective SNPs after quality control. Table S3. Genome – wide significant SNPs of BH, BL, CBC, and RC. Table S4. Percentage of phenotypic variance explanation of BH. Table S5. Percentage of phenotypic variance explanation of BL. Table S6. Percentage of phenotypic variance explanation of CBC. Table S7. Percentage of phenotypic variance explanation of RC. Table S8. Gene Ontology of 24 genes. Table S9. Chromosome-wide significant SNPs of BH, BL, CBC, and RC after conditional GWAS.(DOCX)Click here for additional data file.File S2Figure S1. The Manhattan plots for AC, CW, RW, SW and WW. Figure S2. The Q-Q plots for AC, CW, RW, SW and WW. Figure S3. The Manhattan plots for conditional GWAS for BH, BL, CBC, CD and RC.(DOCX)Click here for additional data file.


Drosophila lines with accumulating collections of “omics” data have proven especially valuable for the study of population genetics and complex trait genetics. Here we present a description of a resource collection of 84 strains of Drosophila melanogaster whose genome sequences were obtained after 12 generations of full-sib inbreeding. The initial rationale for this resource was to foster development of a systems biology platform for modeling metabolic regulation by the use of natural polymorphisms as perturbations. As reference lines, they are amenable to repeated phenotypic measurements, and already a large collection of metabolic traits have been assayed. Another key feature of these strains is their widespread geographic origin, coming from Beijing, Ithaca, Netherlands, Tasmania, and Zimbabwe. After obtaining 12.5× coverage of paired-end Illumina sequence reads, SNP and indel calls were made with the GATK platform. Thorough quality control was enabled by deep sequencing one line to >100×, and single-nucleotide polymorphisms and indels were validated using ddRAD-sequencing as an orthogonal platform. In addition, a series of preliminary population genetic tests were performed with these single-nucleotide polymorphism data for assessment of data quality. We found 83 segregating inversions among the lines, and as expected these were especially abundant in the African sample. We anticipate that this will make a useful addition to the set of reference D. melanogaster strains, thanks to its geographic structuring and unusually high level of genetic diversity.Reference collections of multiple  Drosophila melanogaster has long played a central role as an experimental genetic model system, as well as a preeminent model for understanding evolutionary and population genetic processes. As one of the first fully sequenced eukaryotic genomes, D. melanogaster offers a wealth of genomic and genetic resources, including increasing characterization of natural genetic variation found in extant populations  and small indel variation, as well as evidence for population structure . We have generated genome sequences for 84 lines to a depth of ~12.5× coverage. We have generated variant calls for SNPs, indels, and large inversions. Independent validation of all classes of variant calls has demonstrated that they are of high quality. A striking feature of inbred Drosophila. Despite the fact that the lines differ at a large number of nucleotides and insertions/deletions, those differences impose a correlation structure across lines that is informative for a systems biology approach to quantitative models of complex phenotypes. Those modeling papers will appear elsewhere, and it is our intention here to simply provide an introduction to this resource set of lines and to describe the basic attributes of their genetic differences.As expected, the African samples are the most diverse for all classes of mutation, but with significant heterogeneity remaining within the other four populations. By sampling from five highly dispersed regions, we anticipate that these lines will be of utility for studies that benefit from maximal genetic diversity, and in answering questions that entail aspects of interpopulation differences that may have been driven in part by local adaptation. The original intent in generating these lines was not for association mapping, but rather was to serve as a reference set of lines of high diversity for building and testing predictive models of metabolic regulation and other complex traits in Drosophila lines representing five global populations . Sequence reads were aligned with the Burrows-Wheeler Alignment Tool  to the reference D. melanogaster genome, and SNPs and small indel genetic variants were called using the Genome Analysis Tool Kit . Genetic variant calls were validated in two ways, by resequencing one line to 100× depth and by double-digest restriction-site associated . We noted that some subregions of the genome in most lines had an unexpectedly high frequency of heterozygous SNP genotypes that validated at high frequency, which we define as “heterozygous blocks” . Finally, we investigated the whole-genome sequence dataset for large chromosomal inversions using a custom bioinformatics pipeline (www.novocraft.com), Mosaik  across at least one of the inversion breakpoints, and the genotype of each line was determined by looking for reads supporting the presence of the inversion breakpoint and/or the reference sequence bridging the breakpoint . Alignment files and final genetic variant genotypes (vcf files), as well as companion files including 1) het blocks per line, 2) genome callability , and 3) regions of genetic identity by descent , are described in Table S1.We determined and vetted the whole genome sequences of 84 + 1 inbred , Mosaik ; File S1File S1, parts 13-16). To summarize, our divergence analyses were based on an updated five-species whole-genome alignment that we generated , within which we include the recently improved D. simulans assembly , and an average of 92% of the reads from each line mapped to the reference D. melanogaster genome. Both SNPs and small indels were called using a pipeline .After calling variants with GATK, we immediately observed that the distribution of heterozygous genotype calls was highly nonrandom across the genome of each line. The number of segregating sites within a line was expected to be low because of the 12 generations of sib-pair inbreeding for these lines, but we found contiguous stretches in the genome with a high frequency of heterozygous calls in many lines. These blocks of heterozygosity have also been found in the Figure S2). The agreement for heterozygous SNPs was lower (81%) overall and clearly different within “het blocks” (93%) compared with outside of “het blocks” (30%).To assess the false-positive rate for the variant calls, and to examine whether additional sites or calls should be filtered out, we validated the SNP and small indel calls in two ways. First, we resequenced a single line (ZW155) to 100× depth. SNPs and small indels were called from aligned reads using a simple read-count ratio to avoid systematic genotyping error. Variant sites were identified as sites with >100 reads for which >10% of reads supported an alternate allele from the reference genome; heterozygous sites were called when two different alleles each had support from >10% of reads. Overall, there was very high agreement between the “10×” GATK and “100×” read-count ratio genotype calls for ZW155 SNPs, greater than 99% for homozygous sites . The validation of SNP heterozygous sites was initially much lower at 62%, and did not improve within “het blocks.” This discrepancy is most easily reconciled by the fact that reduced representation libraries under-represented one chromosome as a result of variants that alter a restriction site or fragment size  but VQS did not (data not shown). Genotype calls with a greater GQ validated at a greater rate. We did find that, following variant quality score recalibration, the sites with the lowest VQS-recalibrated also had lower validation rates, especially heterozygous sites. We used a combination of GQ score, VQS recalibrated-flag, and genotype to filter out classes of genotype calls with validation rates below 90%, including all heterozygous calls outside of “het blocks.” This filtering reduced the number of nonreference SNP genotypes by 12% and the number of variant SNP sites by 5%. Finally, we masked SNP genotype calls if the SNP was within 5 nt of an indel call in the same line. There are more than 5.75 M euchromatic SNPs in the final set of variant sites (Table S3), with 97% of genotypes called across 84+1 lines.We investigated whether the variant quality score (VQS) per site or the genotype quality score (GQ) for individual genotype calls correlated with validation rate. Using the “100×” ZW155 validation set, we found that GQ clearly correlated with validation . Coverage of indels in the ddRAD dataset was too low and yielded uncertain representation of heterozygous sites to use for validation. Similar to the SNP calls, we used a combination of GQ score and genotype to filter out classes of genotype calls with validation rates below 75%, including all heterozygous calls outside of “het blocks.” A lower validation threshold was used for small indels because the only heterozygous calls retained  had a validation rate of ~75%, which set a maximum for homozygous calls as well to retain the same density of variant genotypes across lines. In total we identified nearly 1M euchromatic small indels (Table S3), with 80% of genotypes called across 84+1 lines.To validate small indel calls, we used the same “100×” ZW155 deep resequencing data . These patterns indicate the presence or absence, respectively, of large “het blocks.” The pattern for small indels is similar, including the reduction in heterozygous calls across all lines in the base recalibration step and the prevalence of heterozygous calls for certain lines and chromosomes indicating the presence of ‘het blocks’, although the filtering step reduced the number of calls more significantly than for SNPs .The impact of the variant call pipeline was quite different between SNPs and small indels, as well as within and outside of “het blocks.” The base quality score recalibration reduced the number of heterozygous calls similarly across all chromosomes and lines such that “het blocks” retained about half the number of preliminary heterozygous calls whereas regions outside of “het blocks” retained very few heterozygous genotype calls with lower GQ scores. In contrast, base quality score recalibration had minimal effect on homozygous calls. The number of heterozygous calls for a given line and chromosome is variable, with some lines having a comparable number of heterozygous and homozygous calls and others having very few heterozygous calls . These patterns of change in the distribution of variant annotations indicate the improved quality of the variant calls at each step of the pipeline.We next investigated the distribution of variant calls with respect to gene annotation. Variants with high predicted impact and located in exons were least likely to be retained in the variant call pipeline , indicatD. melanogaster population genomic data (Table S1).Investigation of genetic IBD revealed relatively few genomic regions containing signatures of recent common ancestry. As observed previously in D. melanogaster—especially in equatorial populations (heterozygous inversions.The identification of genomic regions with segregating variation within lines (“het blocks”) despite 12 generations of sib-pair inbreeding suggests that this heterozygosity is being actively maintained. One explanation would be the presence of large chromosomal inversions, known to be common in ulations . Large iWe developed a bioinformatic pipeline to detect candidate inversions from the mapped whole-genome sequencing reads. Candidate inversions were assayed by PCR across the predicted breakpoints and sequence-verified; this validation process indicated a high false-positive rate for inversion prediction, but also did confirm the presence of at least 10 large inversions across the Global Diversity Lines.D. melanogaster inversions with known molecular breakpoints .We independently identified and validated all of the eight large File S2). One of the inversions matches well the cytogenetic limits for In(3L)62D:68A ‘Ok,’ which has been described as a recurrent endemic inversion in Africa . Of the 83 heterozygous inversions, 81 (98%) are located within heterozygous blocks ; none of the 10 homozygous inversions are associated with heterozygous blocks. This strongly supports the hypothesis that the lingering blocks of heterozygosity result from the presence of recessive deleterious alleles in linkage with inversions.For most large inversions, a line often had reads indicating both the presence of the inversion chromosome and the reference chromosome, indicating both chromosomes are segregating in the line. In total we identified 83 instances of heterozygous inversions segregating in our lines and 10 instances of homozygous inversions  is quite surprising. After all, there are hundreds of inversions segregating in nogaster , includiFile S1 part 12). For five of six chromosomes tested, we found that homozygotes for either of the chromosomes segregating in the original stock were significantly underrepresented or entirely absent relative to heterozygous sibs.The presence of large inversions and associated heterozygous blocks in the inbred Global Diversity Lines implies that most individual flies in the stock may be heterozygous, because homozygous flies may have low chance of survival or be infertile. We developed PCR-based genotyping assays for inversions and for linked SNPs and confirmed that individual fly genotypes are typically heterozygous within inversion-associated heterozygous blocks (data not shown). Furthermore, to directly test whether heterozygous blocks contain recessive deleterious alleles, we designed a genetic approach to force selected chromosomes to be homozygous 62D:68A, is often homozygous in Zimbabwe line ZW155 , and that homozygous flies are fertile. The noninverted chromosome appears to harbor a recessive lethal allele, as it was never found homozygous in individual flies from this line. Furthermore, the inversion chromosome from this line generated close to the expected ratio of homozygous viable progeny in the genetic test, although derived homozygous stocks appear weak after a few generations. Surprisingly, given that flies homozygous for the In(3L)62D:68A chromosome in the ZW155 line appear viable and fertile, the In(3L)62D:68A inversion was sampled 10 times in the Zimbabwe lines and is never found homozygous despite 12 generations of sib-pair inbreeding. Thus we suspect that the In(3L)62D:68A inversion chromosome has sufficiently low fitness to avoid homozygosity.The quality of the sequence data indicates that it is suitable for molecular population genetic analyses. Our initial work highlights strong population differences among the five populations, as well as differences between autosomes and the X chromosome within populations. Patterns of nucleotide diversity and LD are consistent with Zimbabwe having the largest effective population size, while non-African populations are significantly less diverse and more similar to one another than to Zimbabwe. Notable differences in allele frequency exist among combinations of populations, but also at putatively functional sites that are monomorphic in all but one population, where a high frequency derived allele is found. In a companion paper we will investigate questions related to neutral and selective forces leading to population differentiation, as well as autosomal and sex chromosome differences.STF using the SNP data , with the four non-African populations more closely related to each other. These levels of STF are in general agreement with reports from smaller datasets from the same populations . Interestingly, after Zimbabwe, Beijing is the next most differentiated. A previous survey also showed that other Asian strains were among the more differentiated within world-wide samples , they provide additional evidence that Asia may harbor some of the more divergent D. melanogaster populations globally.As a first look into the extent of population structure between samples, we calculated genome-wide SNP data . ConsistPrincipal component analysis performed on the genome-wide “neutral” sites, which should best reflect demographic processes, reveals notable population structure among all populations . PlottinAnother data quality check examined the unfolded SFS for four SNP classes combined across the full dataset and partitioned into an X chromosome set and an autosomal set: (1) nonsynonymous, (2) synonymous, but not fourfold degenerate, (3) intergenic, and (4) a “neutral” class comprised of fourfold degenerate and small intronic SNPs . The numThe shapes of the SFS are consistent with the validation results (shown previously), showing the expected selection-driven enrichment of low frequency SNPs and characteristic decrease in abundance among more frequent variants. Among the rarest variants, there is a roughly 20% excess abundance of nonsynonymous SNPs (compared with synonymous SNPs), consistent with the role of purifying selection working to eliminate deleterious mutations from the population. The right tail of the SFS reflects the abundance of high frequency derived SNPs, and the excess  seen for all classes of SNPs may reflect SNPs that have risen to high frequency either neutrally or due to selection (either because of direct selection or linkage to selected variants), motivating a more complete follow-up analysis.Figure S9). For example, the larger effective population size of the Zimbabwe sample is apparent caused by the overall greater number of variant sites, both for the autosomes and the X chromosome. In particular, most of the contribution in the singleton class is provided by the Zimbabwe lines . In addition, the abundance of high frequency−derived alleles varies depending on both the class of site and the population being examined. These differences are not unexpected given the complex interactions of demographic and selective forces, and are currently being investigated.Partitioning the SFS into population-specific SFS further highlights population differences . These initial observations suggest a role for natural selection-driving patterns of differentiation and motivate a more thorough analysis of local adaptation detectable with these data.A major interest of the field is lineage-specific differentiation and the extent to which such differences might be driven by local adaptation. As a first look into this question we extracted all SNPs that were private to a single population and that also had a relatively high allele frequency (≥20%). As indicated by the SFS previously, significant heterogeneity in genome-wide nucleotide diversity is observed across all populations . Median D values (D) on the X vs. autosomes also consistently demonstrated significant differences between the chromosomes . In total this new data set is comprised of ~5.78 M SNPs and nearly 1 M small indels, providing a rich reservoir of naturally occurring variants with multiple mutations in every gene.Extensive effort has been invested into variant calling and empirical validation. The validation procedures that we have followed, involving the combination of orthogonally generated data, revealed notable context-dependent differences that do not allow for simple genome-wide variant call thresholding , which has been observed previously (The general agreement between our initial population genetic analysis and previous population genomic studies (D. melanogaster. The de novo generation and subsequent tolerance of inversions is increasingly recognized to be a major feature of this species’ genome (D. melanogaster in influencing the genetic basis of complex traits, it will be essential to characterize the population dynamics of inversions, including the age, geographic distribution, and impact of individual inversions and the modes of selection that maintain and modulate their frequency in natural populations. In any case, the wide geographic sampling of these lines makes them valuable for the investigation of global demography, inference of past action of diverse forms of natural selection, analysis of gene family evolution, and relating these patterns of variation and their inferred historical causes to measured phenotypic differences among the lines, and they serve as a complement to the already valuable sets of reference lines of D. melanogaster drawn from other natural populations.Our experimental and bioinformatics investigation of the relationship between heterozygous blocks and inversions has provided an important focus on the high frequency and chromosome-scale impact of large inversions in


Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear.n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes .This study compared patients with high and low FEP performances (P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance.The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion. Facial emotion perception (FEP) is a major indicator of social function -3. In scRecent reports show that the Met proto-oncogene (hepatocyte growth factor receptor) (MET) and v-akt murine thymoma viral oncogene homolog 1 (AKT1) genes have important roles in psychiatric conditions -8. For eAccumulating evidence obtained by polygenetic models reveal the joint effects of multiple SNPs in various diseases -21. CompThis study applied the particle swarm optimization (PSO) algorithm  to evaluThe SNP barcodes, i.e., the unique patterns of SNP combinations with their corresponding genotypes, were generated by a PSO algorithm . The PSOThe robustness of PSO  results pbest and gbest, and (4) updating the velocity and position of the particles. These procedures are repeated in successive iterations until the termination conditions are reached. The elements of the proposed PSO include (1) particle, (2) population, (3) particle velocity, (4) particle position, (5) inertia weight, (6) individual best value, (7) global best value, and (8) termination criteria. These elements are defined as follows.Figure 1. Particle: a problem solution consisting of several parameters.2. Population: the set of all particles.v): each particle has a velocity, and the velocity of the ith particle is vi = , where viD is the velocity of the ith particle with respect to the Dth parameter.3. Particle velocity (x): a vector consisting of available parameters in the practicable solution. The position of the ith particle is xi = , where xiD is the position of the ith particle with respect to the Dth parameter. Each parameter in the particle vector is defined by the number of selected SNPs and their corresponding genotypes for the associated SNPs.4. Particle position (w): the current velocity may be affected by the inertia weight, which determines whether the previous velocity increases/decreases the current velocity. Control factor w affects the trade-off between the exploration and exploitation capabilities of the particle.5. Inertia weight (pbest): the position of the ith particle with the highest fitness value (pbesti) is considered the best current solution for the ith particle.6. Individual best fitness value (gbest): the best particle in the population.7. Global best fitness value , and 1 and SNPm differ for each particle. If a given SNP is found to be repeated in a particle, the SNP is randomly generated among the available SNPs until it is different. For example, for a two-SNP particle with vector = , SNPs 1 and 4 and genotypes 2 and 3 are chosen. The SNP barcodes  are represented as  and .The above encoding schemes are used for randomly generating the particle population over the problem space. SNPs are selected based on a randomly generated value among available SNPs. Three genotypes are also randomly generated between 1 and 3 to represent ‘AA’, ‘Aa’, and ‘aa’. The SNPs selected between SNPAn objective function was used to compute the SNP barcode for the difference between the groups with high and low FEP values; the objective function was also used to compute the fitness value. The largest difference between the high and low groups was the highest fitness value. The equation was defined as follows:Xi represents the ith particle. The high-FEP group ∩ Xi was defined as the total number of intersections between the high-FEP group and the ith particle. The low-FEP group ∩ Xi was defined as the total number of intersections between the low-FEP group and the ith particle. For example, X = , i.e., SNP 1 with ‘aa’ and SNP 2 with ‘AA’, was used to evaluate the number of matching conditions in the two groups. The high-FEP group contained 30 samples, and the low-FEP group had 18 samples. According to Equation 1, the value was calculated by subtracting 18 from 30, leaving 12.where ‘high-FEP group’ and ‘low-FEP group’ are the total number of SNP interactions in the high-FEP group and the low-FEP group, respectively. The pbest) and the global best position (gbest) must be recorded for moving particles. If the current particle value is better than pbest, then the position and fitness value of pbest are updated with the current position and fitness value. If the fitness value of pbest is better than that of gbest, gbest is reset to pbest.The personal best position  needed to search for a better solution.The search direction of a particle is determined by the three different vectors, i.e., wLDW is the inertia weight, wmax is 0.9, wmin is 0.4, and Iterationmax is the maximum number of allowed iterations. The r1 and r2, which are the random functions in the range , are used to adjust the strength of the pbest and gbest vectors in a single generation. The c1 and c2 are acceleration constants that control the particle search direction (pbest or gbest). Velocities where w was set to 0.9, and the final value was set to 0.4 [c1 and c2 were both set to 2 [As suggested by , the PSOt to 0.4 . Accelerset to 2 .P value smaller than 0.05 was considered a statistically significant difference between the two groups. Finally, G*Power 3  [Statistical analysis was performed using SPSS version 19.0 . The OR with 95% confidence interval (CI) was used for performance measurements of the best SNP barcode; a Germany)  was usedhttp://bioinfo.kmu.edu.tw/All_genotypes_for_low_high_FEP.xlsx.The dataset used in this study was obtained from our previous study of associations with FEP  in a TaiThe objective of this study was to evaluate the use of PSO for identifying the best combinations of SNPs in specific genotypes. The subjects had an average FEP score of 90.66 ± 1.49 (mean ± SE). Based on their scores, the subjects were dichotomized into a high-FEP group (FEP score > 90.66) and a low-FEP group (FEP score ≤ 90.66). Accordingly, PSO-generating SNP barcodes were coupled with the phenotype (FEP) to analyze 89 subjects with high FEP scores and 93 subjects with low FEP scores.P value) of each of five SNPs of two related genes (MET and AKT1). The occurrence of the genotype for each SNP did not significantly differ between the high- and low-FEP groups (P > 0.05).Table http://bioinfo.kmu.edu.tw/All_2SNP_barcodes_for_FEP_scores.xlsx) along with all possible combinations of two SNPs with genotypes. The PSO was also used to obtain the SNP barcodes for the best-performing combinations of three to five SNPs .Table The left side of Table P value, OR, and 95% CI) of the best PSO-generated SNP barcodes in terms of FEP. The occurrence of the best SNP barcodes (combinations of two to five SNPs) significantly differed (P < 0.05) between the high-FEP group and the low-FEP group . The OR values of the best SNP barcodes for two to five SNPs ranged from 2.119 to 3.138, the 95% CI of OR ranged from 1.059 to 10.253, and the power ranged from 0.556 to 0.400.The right side of Table Single-SNP models are typically used to evaluate SNPs for associations with various diseases -44. For P < 0.05) in only one genotype (rs2237717) out of three MET genotypes. However, the genotypes were not analyzed in terms of associations with FEP scores or in terms of all potential SNP-SNP interactions.Our previous study  comparedP < 0.05)  Table . This suBy comparing the maximum difference between the high-FEP and low-FEP groups for each SNP barcode, PSO can be used to determine the relative contribution of an SNP in FEP performance. For example, the OR values between the high- and low-FEP groups for two-SNP barcodes was 2.119. Adding SNP 5 or SNP 5/SNP 3 for a three-SNP barcode and a four-SNP barcode slightly increased the OR values between the high- and low-FEP groups, which suggests that SNP 1 and SNP 4 have larger effects on FEP performance compared to SNP 5 and SNP 3. Similarly, compared to SNP2, SNP 5, and SNP 3 had larger effects on high-FEP performance. Accordingly, the relative strengths of the effects of individual SNPs on FEP performance are apparently as follows: SNP 1 (rs1130233)/SNP 4 (rs41735) > SNP 5 (rs42336)/SNP 3 (rs2237717) > SNP 2 (rs1858830).Although this study applied the PSO algorithm in a small sample size of SNPs, the algorithm has proven effective for large datasets ,54 such In conclusion, FEP is an essential component in social cognition and social skill not only in healthy individuals but also in patients with mental disorders such as schizophrenia -4. This In conclusion, the contribution of this study is a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as FEP performance. Although each SNP has a different impact on FEP, improved understanding of the joint effects of SNP-SNP interactions further elucidates the molecular mechanisms of facial emotion identification.The authors declare that they have no competing interests.CHY, and HWC wrote the manuscript. HYL and MTL designed the study. CHY, LYC, and YDL performed computation and conducted the statistical analysis. CHY and HWC interpreted the data. All authors read and approved the final manuscript.