Cattle include a large number of breeds that are characterized by marked phenotypic differences and thus constitute a valuable model to study genome evolution in response to processes such as selection and domestication. Detection of “signatures of selection” is a useful approach to study the evolutionary pressures experienced throughout history. In the present study, signatures of selection were investigated in five cattle breeds farmed in Italy using a multivariate approach.ST-based approach.A total of 4094 bulls from five breeds with different production aptitudes  were genotyped using the Illumina BovineSNP50 v.1 beadchip. Canonical discriminant analysis was carried out on the matrix of single nucleotide polymorphisms (SNP) genotyping data, separately for each chromosome. Scores for each canonical variable were calculated and then plotted in the canonical space to quantify the distance between breeds. SNPs for which the correlation with the canonical variable was in the 99th percentile for a specific chromosome were considered to be significantly associated with that variable. Results were compared with those obtained using an FMSTN, LCT, GHR, SCD, NCAPG, KIT, and ASIP. Moreover, new putative candidate genes were identified, such as GCK, B3GALNT1, MGAT1, GALNTL1, PRNP, and PRND. Similar results were obtained with the FST-based approach.Based on the results of the canonical discriminant analysis, a large number of signatures of selection were detected, among which several had strong signals in genomic regions that harbour genes known to have an impact on production and morphological bovine traits, including ST index.The use of canonical discriminant analysis on 50 K SNP genotypes allowed the extraction of new variables that maximize the separation between breeds. This approach is quite straightforward, it can compare more than two groups simultaneously, and relative distances between breeds can be visualized. The genes that were highlighted in the canonical discriminant analysis were in concordance with those obtained using the FThe online version of this article (doi:10.1186/s12711-016-0236-7) contains supplementary material, which is available to authorized users. During the long process of animal domestication that began approximately 8000 to 12,000 years ago, man and environment played a fundamental role in the evolution of livestock species –5.Studies on cattle are supported by the availability of whole-genome sequence data, of well-developed linkage maps, and of a pedigree structure that, for many breeds, allows for a precise dissection of the effects that underlie complex traits –8. High-ST), that was originally proposed by Wright [ST [Several approaches have been proposed to study signatures of selection in cattle that differ in the metrics and statistical inferences used , 14. They Wright . Studiesight [ST –20, althight [ST , 21 and ight [ST  have alsight [ST . Recentlight [ST .Genetic differences are often evaluated under the perspective of a single locus or an aggregate of a small number of loci because studies on signatures of selection are focused mainly on detection of single nucleotide polymorphisms (SNPs) that tag chromosomal positions where putative candidate genes may be located. However, since signatures of selection are the result of the interaction between selection pressure on a causative gene and LD with adjacent loci, analyzing the correlation structure between SNPs in a specific genomic region could represent an interesting starting point to quantify the existence of a signature of selection. Multivariate statistics offer a set of techniques to study the different aspects of correlation matrices, among which principal component analysis (PCA) has proven to be very efficient in extracting information from a set of multiple genetic markers and has been successfully used in several fields of genetics , 25, 26.ST) method was also performed.The aim of our study was to detect the presence of signatures of selection in cattle by comparing five Italian breeds with different production aptitudes  using CDA. In order to assess the reliability and power of the CDA-based approach for the detection of signatures of selection, a comparison with the fixation index  and minor allele frequency (<1 %). After filtering, 39,833 SNPs that were common among the five breeds were retained for further analyses.p markers measured on n animals from k breeds, the CDA derives linear combinations of SNPs that maximize the between-breed variation. The i-th CVA, can be written as:i are the SNP genotypes. The vector of coefficients B and W are the between- and within-group SNP (co)variance matrices, respectively [k and p minus 1. In the present work, five breeds were considered and therefore four CVA were extracted. The eigenvalue of each i-th CVA, i.e. the amount of variance explained by the CVA, is i2 is the i-th squared canonical correlation. The eigenvalue can be interpreted as the ratio of the between-breed to the pooled within-breed variation.Given the data matrix ectively . The dim® Software version 9.2, SAS Institute, Inc., Cary), separately for each bovine chromosome (BTA for Bos taurus chromosome). Thus, M was the data matrix of SNP genotypes  with n = 4094 rows, i.e. the number of bulls. The number of columns p varied from 2610 for BTA1 to 796 for BTA29, respectively. The CDA requires a full rank correlation matrix. However, the rank of a rectangular matrix is less or equal to the minimum value of the number of rows and columns [In our study, CDA was carried out using the CANDISC procedure of SAS 9.2  was calculated at each locus for all the pairwise (n = 10) between breed comparisons using the formula proposed by Nei [ST values were smoothed using a locally weighted scatterplot smoothing (LOWESS) regression, combined with a control chart approach [ST value exceeded the threshold of 3 standard deviations (σ) from the mean. Significant SNPs detected by the FST approach were compared with those identified by CDA.In order to compare the results of the CDA with a commonly applied method for detection of signatures of selection, the fixation index (Fd by Nei :\documenapproach . A SNP whttp://genome.ucsc.edu/) using the Bos taurus UMD 3.1 of the Tau 6 release. Intervals of 500 kb (250 kb upstream and 250 kb downstream of the significant SNP) were considered in both applied approaches.Annotated genes within the genomic regions that contained the relevant SNPs were obtained from the UCSC Genome Browser Gateway  of signatures of selection that were characterized by one or more SNPs (their number ranging from 1 to 8). The largest number of signatures of selection was detected on BTA1 (n = 45), whereas the smallest number was on BTA29 (n = 8). BTA4 had the largest number of relevant SNPs that were located in the same region. In particular, eight SNPs between 76.9 and 77.6 Mb were detected for CVA1 and five SNPs (between 34 and 35 Mb) for CVA2.ST analyses shows good agreement between the locations of the most relevant signatures of selection [see Additional file ST-based results for the two dairy breeds  and shows that both methods identify signatures of selection on BTA4, 6, 7, 14, 26, and 28. Similarly, both methods detected common signatures of selection in the dairy versus beef cattle comparison  gene. Moreover, the CVA2 that separated the Italian Brown Swiss from the other breeds had six significant SNPs in the region around 62 Mb, which contains the lactase (LCT) gene.More than 200 candidate genes that have a role in metabolic pathways of interest for the considered breeds were identified in the genomic regions flagged by the CDA [see Additional file PDK2, SPP1, MEPE, and ABCG2) and beef  traits, respectively. On the same chromosome, CVA3 revealed a gradual separation between breeds according to production aptitudes   gene.On BTA6, CVA2 showed that the Marchigiana breed was separated from the other breeds Fig. . The corse) Fig.  showed lgrowth hormone receptor (GHR) gene  locus; (2) on BTA18, CVA1 was correlated with three SNPs that were located between 14.3 and 14.5 Mb, where the melanocortin 1 receptor (MC1R) gene is positioned; and (3) on BTA13, CVA2 separates the Marchigiana breed from the other ones and was mainly correlated with SNPs that mapped close to the agouti signalling protein (ASIP) gene.Three well-known genes involved in the determinism of coat color in mammals were correlated with CDA: (1) on BTA6, CVA1 was correlated mainly with SNPs that were located close to the leptin receptor (LEPR) on BTA3, and the stearoyl-CoA deasturase (SCD) on BTA26 and the family of cathelicidins (CATHL) on BTA22, respectively.Other known genes that were identified in the discrimination between the five breeds were the glucokinase (hexokinase 4) (GCK), and the insulin like growth factor binding protein 3 and 1 (IGFBP1 and IGFBP3) located in the first signature of selection (between 77.7 and 77.9 Mb); and (2) the glutamate receptor metabotropic 3 (GRM3) in the second signature of selection. On BTA17, seven SNPs were identified within a region between 18.3 and 19.1 Mb that includes seven annotated genes . Several signatures of selection in regions that harbor genes involved in the metabolism of milk oligosaccharides (MO) were detected across the genome [see Additional file B3GALNT1 on BTA1 (two SNPs for CVA2), MGAT1 on BTA7 (one SNP for CVA3), GALNTL1 on BTA10 (one SNP for CVA3), POMT, ST6GAL2 and GALNT14 on BTA11 .The CDA analysis also identified several genomic regions that harbored genes, which have not been previously reported in studies on the detection of signatures of selection for cattle breeds. Two large signatures of selection were identified on BTA4 with (1) the prion protein (PRNP) between 47,400,392 and 47,418,507 bp and the prion protein 2 (dublet) (PRND) between 47,444,352 and 47,449,390 bp. We analyzed the polymorphisms at these significant SNPs and found four C/T SNPs , one G/T SNP (Hapmap39323-BTA-32823) and one A/C SNP (ARS-BFGL-NGS-3711). Allele frequencies at each SNP differed between breeds . On BTA21, CVA1 was correlated with a SNP located at 67.4 Mb, close to the maternally expressed gene 3 (MEG3). Finally, CVA3 for BTA28 was significantly associated with a SNP at 24.6 Mb, close to the sirtuin, type 1 (SIRT1) gene.Finally, three genes regulated by epigenetic mechanisms were detected. CVA1 for BTA3 was associated with two chromosome-wide significant SNPs located at around 36.5 Mb. The closest gene to these two SNPs is The use of canonical discriminant analysis on 50 K SNP genotypes allowed the extraction of new variables that were able to maximize the separation between breeds. Interpretation of the canonical structure led to the identification of a large number of signatures of selection. The CDA approach is quite straightforward, based on simple visual inspection of individual locations in the canonical spaces and on interpretation of the canonical structure. Analyses were carried out separately for each chromosome in order to mitigate the large unbalance between the number of animals and the number of SNPs. An alternative would be to select SNPs based on their ability to discriminate among populations. However, the use of selected SNP panels for the discrimination of individuals between breeds may give different results depending on the metric used . MoreoveST values had to be calculated in order to carry out the comparisons among the five breeds.A useful feature of CDA compared to other methods for detection of signatures of selection is that more than two groups can be compared in a single calculation step while other approaches may require repeated calculations. For example, in this study 10*39,833 FIn genetic diversity studies, a widely used multivariate method is principal component analysis. Figure ST approach. However, several of the identified regions did not contain genes, either because annotation of the bovine genome is incomplete or the SNP was positioned outside a coding region [ST analyses revealed 290 genes that were detected by both methods [see Additional file A large number of signatures of selection were detected and well-known or new candidate genes were identified. This result could be, at least partially, due to the structure of the sample analysed. Breeds with different production aptitudes and selection histories were considered. As a result, genes that are involved in specific biological functions or metabolic pathways of interest were detected [see Additional file g region , 40. In g region , or diffg region , 40, 42.PRPN and PRND) are located within the peak at 47–48 Mb. The PRPN and PRND genes encode the prion protein (PrP) and the doppel protein (Dpl), respectively. PrP is a transmembrane glycoprotein whose normal function is still unknown [PRPN locus [PRPN gene and its ubiquitous expression among mammals suggest several biological roles, such as regulation of the circadian rhythm, central nervous system development, neuronal survival, and maintenance of cellular Ca+2 homeostasis [PRPN and PRND loci [PRPN gene and milk production traits in cattle led to statistically significant results for milk fat content [CDP-diacylglycerol synthase (CDS2) gene, which is involved in the phospholipid biosynthetic process. This signature of selection may be an effect of artificial selection but the signal that we detected here, in the region that contains the PRPN and PRND genes, could be the result of natural selection. However, more studies are necessary on e.g. the distribution and frequency of genetic variants, linkage between PRPN alleles, recombination rate, and haplotype diversity within and between populations, in order to improve management of the disease  and possibly production performance.Strong signatures of selection were observed on BTA13. Two adjacent genes . The first biochemical characterization of these molecules was done in the early 1980s . RecentlPRMT6, MEG3 and SIRT1 are under epigenetic control and represent interesting candidate genes. Traditionally, for traits of economic interest, the relationship between genome and phenome has been investigated by considering that variability was the result of several combined genetic and environmental factors. Until a few years ago, epigenetics was neglected in livestock production. However, over the last 20 years, there have been numerous articles on this topic in humans, mouse and plants [In our genome-wide survey, three SNPs defined three signatures of selection on BTA3, 21 and 28, respectively. Among the identified genes, d plants , 72. Onld plants . Differed plants –76.MEG3 on BTA21 was previously reported to be polymorphic in cattle but the polymorphism was not associated with production traits [sirtuin1 (SIRT1) gene is located. SIRT1 is a nicotinamide adenine dinucleotide (NAD)-dependent deacetylase that is involved in a plethora of biological processes, including metabolic regulation, aging and stress response [n traits . In beefresponse , 77. In response  and obesresponse  and receresponse . Based oST or PCA) have led to conflicting results [In this work, five breeds of different geographical origin, selection histories, morphological appearance and production aptitudes were compared. A main issue of the between-breed comparisons that were carried out is the ascertainment bias caused by the method used to identify the SNPs. On the one hand, although SNP editing in the present work was performed across breeds, it should be recalled that the beadchip used was developed for other ascertainment groups . On the  results , 83. In  results . The mai results , althougmyostatin and GHR. Moreover, several interesting new candidate genes were identified, such as those involved with metabolism of milk oligosaccharides or those known to be regulated by epigenetic mechanisms.Our results suggest that canonical discriminant analysis can be a valid tool for detection of signatures of selection based on 50 K SNP beadchip data. The approach is quite straightforward, allows the comparison of more than two groups at the same time, and relative distances between breeds can be visually appreciated. A large number of signatures of selection were detected, within which, several well known candidate genes that affect meat or milk production traits were identified e.g.


Gossypium hirsutum L.) have recently been developed. This report presents genome-wide single nucleotide polymorphism (SNP) mining utilizing resequencing data with BAC-end sequences as a reference by alignment of 12 G. hirsutum L. lines, one G. barbadense L. line, and one G. longicalyx Hutch and Lee line. A total of 132,262 intraspecific SNPs have been developed for G. hirsutum, whereas 223,138 and 470,631 interspecific SNPs have been developed for G. barbadense and G. longicalyx, respectively. Using a set of interspecific SNPs, 11 randomly selected and 77 SNPs that are putatively associated with the homeologous chromosome pair 12 and 26, we mapped 77 SNPs into two linkage groups representing these chromosomes, spanning a total of 236.2 cM in an interspecific F2 population (G. barbadense 3-79 × G. hirsutum TM-1). The mapping results validated the approach for reliably producing large numbers of both intraspecific and interspecific SNPs aligned to BAC-ends. This will allow for future construction of high-density integrated physical and genetic maps for cotton and other complex polyploid genomes. The methods developed will allow for future Gossypium resequencing data to be automatically genotyped for identified SNPs along the BAC-end sequence reference for anchoring sequence assemblies and comparative studies.A bacterial artificial chromosome library and BAC-end sequences for cultivated cotton ( G. hirsutum and G. barbadense (G. hirsutum lines (Marker development in crop species has been an important aspect to facilitate genomics-based crop improvement. Single nucleotide polymorphisms (SNPs) are the most abundant form of markers in all organisms, because they have a possibility of occurring at every nucleotide position in the genome. This makes them ideal candidates for construction of high-density genetic maps, which can then be used for marker-based crop improvement and genetic analyses. In general, for marker-assisted selection, a large number of molecular markers are required in crop species as the majority of the traits of interest such as yield, drought and heat tolerance, nitrogen and water use efficiency, disease resistance, and fiber quality are complex and controlled by many genomic loci of small effect. Therefore, for marker-assisted breeding to be effective, markers for most of the regions controlling a trait need to be included in selection criteria , 2n = 4x = 52, genomic equation 2[AD]1   , and a diploid cotton species ; to experimentally validate in silico–derived SNPs; and to demonstrate mapping ability and utility of developed SNPs. This article reports on utilizing BESs as a high-quality reference for SNP discovery through resequencing. The new SNPs will be a resource for SNP-based integration of the physical and genetic maps, and the methodology can also serve as a useful model in resolving other complex plant genomes.Thus, the primary goals of the present study were: to develop large numbers of genomic-based SNPs for cultivated cotton, G. hirsutum genetic standard line Texas Marker-1 (TM-1) were used in this study. BAC DNA was isolated and sequenced by Sanger methods at USDA-ARS . A total of 179,209 BAC-end sequences were retained after quality trimming and filtering (LIBGSS_039228) .Two complementary BAC libraries (BstYI and HindIII) and a random sheared BAC library from G. hirsutum lines  and the G. barbadense genetic standard line, 3-79, were planted at Texas A&M University, University of California Davis, or the USDA-ARS facility in New Orleans, Louisiana. Young leaf tissues were sampled from each plant and used to isolate genomic DNA using the Qiagen DNeasy  plant extraction kit following manufacturer instructions, including RNase digestion. DNA concentrations and qualitative absorbance values were determined using the Nanodrop Spectrophotometer .The seed of 11 G. hirsutum TM-1; SRX668168, G. hirsutum Sealand 542; SRX668322, G. hirsutum PD-1; SRX668354, G. hirsutum Paymaster HS-26; SRX669467, G. hirsutum M-240 RNR; SRX669468, G. hirsutum Fibermax 832,; SRX669469, G. hirsutum Coker 312; SRX669470, G. hirsutum SureGrow 747; SRX669471, G. hirsutum Stoneville 474; SRX669472, G. hirsutum Tamcot Sphinx; SRX669473, G. hirsutum TX0231; SRX669474, G. barbadense 3-79). Raw reads for G. hirsutum Acala Maxxa and for G. longicalyx were obtained from the NCBI Small Read Archive under numbers SRR617482 and SRR617704.Double-stranded DNA was quantified with a PicoGreen assay on the Synergy HT plate reader . Libraries were prepared using an in-house protocol with individual barcoding. One and a half micrograms of each DNA sample was randomly sheared using a Bioruptor instrument , and then size selection was performed using AMPure XP beads to 300–500 bp. Fragments were end-repaired using NEBNext End Repair Module and then purified with AMPure beads. Next, an adenine was ligated at the end of the fragments, adapters were ligated, and the final products were purified with AMPure beads. Enrichment PCR was performed for 14 cycles, and then the PCR product was run on a 1.5% gel to confirm enrichment of product and size range. A final round of purification was performed using AMPure beads. The final libraries were assessed for quality with the Bioanalyzer  to determine final library size and concentration. Each sample was sequenced with paired-end sequencing (2×100 bp) on two Illumina HiSeq2500 lanes. Raw, paired-read sequence files were uploaded to NCBI under BioProject PRJNA257223 and SRA numbers , and the first 13 bases from each read were removed due to poor quality. The remaining reads were quality-trimmed, and adapters and any reads fewer than 40 bases were removed using the FastX toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). The files were assessed for final quality and concatenated into a single file to be utilized as single-end read data due to the size of the BESs being used as a reference. Once the sequences had been processed, they were imported into CLC Genomics Workbench v 6.0.2 . Reads from Sealand 542, PD-1, and 3-79 were aligned to the BAC-end sequence reference using different length and similarity fractions to determine optimal parameters. Iteration-1 utilized length fraction 0.70 and similarity fraction 0.99, whereas Iteration-2 utilized length fraction 0.99 and similarity fraction 0.98. SNPs were called using the Probabilistic variant caller in CLC Genomics Workbench using a minimum sequence depth of six, variant probability of 50.0, required presence in both forward and reverse reads, four maximum expected variants, and one standard genetic code. Theoretical homeo-SNP positions  were determined by aligning the TM-1 Illumina-based sample, which is the same genotype as the reference, to call SNPs using the same parameters as all other samples. These identified homeo-SNP positions were removed from the SNPs identified in the other samples.All raw sequence files were assessed for initial quality using FastQC  SNP assays using BatchPrimer3 . Primers were synthesized and diluted according to KASP developer  instructions. KASP assays were run on a “G. barbadense screening panel”  containing 12 samples including TM-1 (Stelly Lab), TM-1 (USDA), 3-79 (×2), F1 – 3-79×TM-1 (×2), RIL01-04 (3-79×TM-1), and water nontemplate control (×2) according to the manufacturer’s suggested PCR conditions. Plates were read using the Pherastar at 38, 44, and 50 cycles, and then they were analyzed using the KlusterCaller program. SNP assays were labeled as “good” if samples produced clean clusters that allowed for differentiation and scoring of the two parents and the F1 genotypes, or as “bad” if no definable clusters were produced or no amplification occurred. (These definitions of “good” and “bad” are used subsequently throughout the rest of the article.) Markers included in the “good” and “bad” categories for Iteration-1 and Iteration-2 were calculated  were removed, because regions with high coverage are likely to be associated with repetitive regions. Additional unique SNP positions identified as being homozygous in G. barbadense or G. longicalyx were added to the master file. Only positions with probability scores >0.98 were retained for G. barbadense and G. longicalyx. A hierarchical method was utilized to determine retention; SNPs were retained in the G. hirsutum file initially, then in G. barbadense, and then in G. longicalyx. Three nonredundant variant call format (VCF) files were obtained from each of the three species groups. These VCF files were uploaded to dbSNP in NCBI .All 12 https://www.broadinstitute.org/gatk/) that contained genotypes at all positions for all samples using the list of nonredundant SNP positions, binary alignment map (BAM) files exported from CLC Workbench for each individual sample, and BES FASTA file. The GATK UnifiedGenotyper command with default settings except for the GENOTYPE_GIVEN_ALLELES, EMIT_ALL_SITES and minimum base quality of 20 options were used to call genotypes for all previously identified SNP positions along the BES reference for the 12 G. hirsutum lines, G. barbadense, and G. longicalyx. Distributions of SNP types, number of missing calls per sample, and number of heterozygous calls per sample were calculated from the final VCF file using VCFtools (http://vcftools.sourceforge.net/). The number of homozygous differences between samples was determined using BCFtools command gtcheck.A final VCF file was derived using GATK software  reference genome (Gossypium arboreum (A2) reference genome  in Galaxy . An additional set of 96 markers developed from the 3-79 line were selected based on diploid (D5 genome) alignment information with inferred positioning on allotetraploid chromosomes 12 and 26 (G. barbadense screening panel (Table S1).A set of 48 randomly selected intraspecific SNP markers were selected for experimental screening to estimate average validation rate of G. longicalyx were selected and primers were designed. KASP assays were run per manufacturer’s instructions on a “G. longicalyx screening panel” that contained G. longicalyx (×2), G. hirsutum cv. TM-1 (×2), synthetic allotetraploid “FADD” (×2), 2 BC1F1 samples (FADD × TM-1), G. barbadense line 3-79, G. hirsutum accession TX0231, and two nontemplate controls (Table S3). Plates were cycled and analyzed as previously mentioned. SNP sequences and primers for all screened markers are listed in Table S4.A random set of 32 markers developed from G. barbadense SNPs were used to genotype 118 F2 (3-79×TM-1) individuals, two parents (G. hirsutum line TM-1 and G. barbadense line 3-79), and F1 (3-79×TM-1) as controls. KASP assays were run using the Fluidigm system in 96.96 dynamic array format, utilizing multiple arrays, and read using the Fluidigm BioMark HD . Clustering for genotyping was performed using Fluidigm SNP Genotyping Analysis software. Genotypes of the 118 F2s for successfully genotyped markers were imported into JoinMap 4.1 (Table S5) and linkage mapped using the maximum likelihood algorithm and Haldane’s mapping function with default parameters. Linkage groups were determined using LOD scores of 5.0 or more. Cytogenetic stocks including F1 hypo-aneuploids, each deficient for a known G. hirsutum chromosome, were also genotyped for the same markers.Good markers obtained in screening of the nMap 4.1 , and ideThe VCF file produced from GATK that included genotypes from all 14 samples was imported into R . The SNPG. hirsutum that occurred, on average, every 888 base pairs (bp). The distribution of base pairs between adjacent intraspecific SNPs found on the same BES is, on average, 76 bp  as the intraspecific SNPs, a larger number of SNPs was identified compared to intraspecific SNPs because they occurred on more BESs . However, G. longicalyx had a lower ratio of transition to transversion (1.63) compared to G. hirsutum (2.19) and G. barbadense (2.21) . High levels of missing data in G. longicalyx were expected because as a diploid, it would not contain loci from both A- and D-subgenomes of the tetraploid species, but rather would contain mostly only A-subgenome-like loci because it is closely related to the A-genome diploids  to 29,052 (TM-1 vs. Tamcot Sphinx). Differences between cultivated samples and the uncultivated G. hirsutum TX0231 were, in general, quite similar, as was the case between all G. hirsutum samples with G. barbadense and G. longicalyx.Types of SNPs were distributed similarly in all three species, with transitions being more abundant than transversions (e (2.21) . The amoe (2.21) . The perdiploids . Homozygdiploids . OverallG. hirsutum and G. barbadense, whereas a larger proportion was identified in G. longicalyx . The masked bases were largely composed of interspersed repeats and retroelements. The remaining bases were aligned to the Genbank nonredundant protein database, which resulted in 9.48% of all BESs having a hit to coding sequences. When all determined SNPs were considered, a similar proportion of genic SNPs (∼7%) was identified in ngicalyx .G. barbadense SNPs developed in silico were randomly and nonrandomly  selected for primer design and experimental screening. Initially, a random set of 32 G. barbadense markers was selected to determine optimal parameters for homeo-SNP removal. Screening produced a total of 13 SNPs (40.6%) that were found to have acceptable clustering patterns, of which seven (53.8%) were associated and six (46.2%) were not associated with the transcriptome, respectively , whereas Interation-2 produced a success rate of 84.6% (11/13); therefore, Iteration-2 mapping parameters (0.99 length fraction and 0.98 similarity fraction) were utilized for final mapping of all samples, except for G. longicalyx, as noted in the Materials and Methods (Table S6).ectively . Thus, nG. barbadense, following homeo-SNP removal, an additional 96 interspecific SNPs with successfully designed primers were selected for screening based on inferred positioning on chromosomes 12 and 26. A total of 77 (or 80.2%) markers were categorized as good markers (Table S7).Utilizing the SNPs identified in G. hirsutum samples and G. barbadense line 3-79, which produced 40 good markers or an 83.3% success rate (Table S8). To obtain primers for the 48 markers, 87 SNPs were randomly selected for primer design because primer design was successful in 56% of cases. Sample call rates ranged from 0.175 to 1.000. Excluding the two lowest samples, all samples had greater than 72.5% call rate. Marker call rates ranged from 0.545 to 0.970.A total of 48 intraspecific SNP markers were screened on 32 G. longicalyx interspecific SNP markers amenable to successful primer design were selected for screening. Primer design for this diploid species was significantly more difficult compared to the other species due to the elevated number of SNPs and their close proximity within a BES (Table S9). Two of the markers listed as good but exhibit very close clusters , the proximity of which may lead to difficulties for cluster separation with additional samples. So, conservatively, 29 would be regarded as good markers, i.e., a 90.6% success rate.A total of 32 in a BES . On screG. raimondii (D5) reference  draft genome (2 and D5 reference genomes were very similar for G. hirsutum (70.6%/40.9%) and G. barbadense (69.6%/41.1%), whereas the percentage was very different for G. longicalyx (55%/18.4%). A larger fraction of markers that can be aligned to the A2 genome is expected for G. longicalyx because it is an F-genome diploid that is closely related to A-genome diploids, including G. arboreum, and it is relatively distant from the D5 species, G. raimondii , which is expected because it is closely related to A-genome diploids.Utilizing the alignment information to both A- and D-diploid reference genomes, markers can theoretically be localized to either the A-subgenome or the D-subgenome in the tetraploid by considering whether the markers align uniquely to either the Aed to A2 . G. long5 reference sequence) screened SNPs identified from G. barbadense, genotypes were obtained for 88 markers that represent 67 unique BAC-end sequences. These 88 markers were screened against a population of 118 F2 (3-79 × TM-1) individuals and F1 hypo-aneuploid stocks. On linkage mapping of the F2s with JoinMap 4.1 software, two linkage groups representing a total of 236.2 cM were obtained (Table S10). The resulting two linkage groups were identified as allotetraploid homeologous chromosomes 12 and 26 by loss of heterozygosity in F1 hypo-aneuploid samples and by alignment information to the D5 reference genome.From the random and nonrandom . The analysis also showed a slight difference with cultivated G. hirsutum lines and the one wild G. hirsutum line TX0231. Similarly a dendrogram compiled with the SNPRelate program  and the individual dissimilarity, or difference between individuals, to be very low (close to 0).Principle component analysis with the SNPRelate program was able to successfully separate  program  was ableThe resulting linkage maps demonstrate the feasibility of integrating physical maps by utilizing markers associated with BAC-end sequences for genetic mapping. In this way, a large number of markers can be identified using resequencing data and can be genetically mapped with a moderately sized mapping population to obtain genetic maps associated with BAC resources. We demonstrate that even with an F2 mapping population of 118 individuals, we are able to obtain recombination events within the length of the BAC (average 120 kilobases) and are able to separate markers associated with the forward and reverse reads of a BAC in some instances. Additionally, multiple markers from the same BES were assayed on the population and linkage mapping places of all those markers except for two from GH_TBh104B19r in the same mapping position. The accuracy of mapping BES-associated SNPs makes it possible to utilize SNP mapping for ordering and orienting BACs and BAC contigs when SNPs are identifiable on both forward and reverse end sequences of single BACs, or when multiple SNPs are identified within a given BES.in silico–derived SNPs using this reference is also greatly enhanced. The flanking sequence based on the BES reference should theoretically be correct for TM-1, which can represent cultivated cotton with expectedly low levels of overall diversity and high levels of synteny; this hypothesis holds up to experimental testing as large number of SNPs are able to be successfully genotyped. This is likely due to the automatic inclusion of correct haplotype information for homeo-SNP alleles, which has been shown to increase percentage of co-dominant markers and overall success rates within polyploid cotton , so it is difficult to map large numbers of markers from intraspecific crosses. To date, this has led to use of interspecific crosses for the majority of mapping in allotetraploid cotton, such as the one utilized in this effort of G. hirsutum (line TM-1) by G. barbadense (line 3-79) (Table S8), most of the 32 tested G. hirsutum lines can be uniquely identified. Even the three TM-1 samples obtained from different labs had a small number of identified differences. The KASP assays were largely successful for most samples, except for Fibermax 966 and Deltapine 90, which likely had a much lower DNA concentration when measured via nanodrop, so it was not reaching a genotyping end-point consistently with the other samples and thus led to many uncalled genotypes. Call frequencies for individual markers showed greater variability than sample call frequencies, which is likely due to the need for optimization of PCR conditions for different marker sets. However, when using the Fluidigm system, this is difficult because all marker sets are run under the same conditions. It was found that while SNPs were identified in a relatively small number of samples from U.S. lines, polymorphisms were also transferrable to other germplasm sources, such as the three Australian samples  and one Indian line (MCU-5) included on the panel. This indicates that ascertainment bias may not be very large, and that sample lineage is likely more important than the country of germplasm origin.The availability of large numbers of intraspecific SNP markers is essential for marker-assisted breeding in G. hirsutum line TX0231 was markedly different from the cultivated samples, and the Tamcot Sphinx (a MAR program–derived sample) was the next most unique sample, which was expected due to the assumed diversity of those samples. Within the cultivated lines, when zoomed-in to a very small section of the plot, the samples appear to occupy three different clusters. Cluster 1 contains TM-1 and Fibermax 832, cluster 2 contains Acala Maxxa, Paymaster HS-26, M-240 RNR, and PD-1, and cluster 3 contains Sealand 542, SureGrow 747, Stoneville 474, and Coker 312. In G. hirsutum lines.Although only small differences exist between lines, the PCA and hierarchical clustering analyses were able to distinguish relationships among the samples. In the PCA, the wild The abundance of markers and accuracy of localization using SNPs associated with BAC-end sequences will be extremely helpful for integrating an allotetraploid cotton physical map with genetic maps. On finalization of an allotetraploid cotton physical map, BES-associated SNPs can also be utilized to integrate unplaced contigs and singletons to enhance the completion of a quality draft reference sequence. Fine- mapping utilizing BES-associated SNPs with a large population can also be used to correct ordering and localization of contigs. Integrating genetic maps with quantitative trait loci (QTL) mapping will allow for utilization of BAC resources for QTL cloning and fine-scale investigation of important regions in the cotton genome.The largest set of intraspecific and interspecific SNPs for cultivated cotton to date has been developed. These SNPs are associated with BACs and will serve as an interface between future physical and genetic maps. They were developed using genomic sequences from multiple lines and species aligned to BAC-end sequences generated by Sanger sequencing, which provided a high-quality reference. Experimental validation was highly successful and indicated that the SNPs will allow for future high-density mapping. Furthermore, additional lines can be resequenced and quickly genotyped for the identified SNP positions as shown here using the GATK software. The developed markers complement currently available genic-based SNPs and simple sequence repeat markers, and provide a largely evenly distributed set of markers for mapping the entire cotton genome with high-density. This will promote more extensive genomic-based studies and breeding of cotton.


SNPs to examine the factors that could be involved in stabilizing clines across a hybrid zone between two subspecies of the Atlantic killifish, Fundulus heteroclitus. Increased heterozygote deficit and cytonuclear disequilibrium in populations near the center of the mtDNA cline suggest that some form of reproductive isolation such as assortative mating or selection against hybrids may be acting in this hybrid zone. However, only a small number of loci exhibited these signatures, suggesting locus‐specific, rather than genomewide, factors. Fourteen of the 32 loci surveyed had cline widths inconsistent with neutral expectations, with two SNPs in the mitochondrial genome exhibiting the steepest clines. Seven of the 12 putatively non‐neutral nuclear clines were for SNPs in genes related to oxidative metabolism. Among these putatively non‐neutral nuclear clines, SNPs in two nuclear‐encoded mitochondrial genes (SLC25A3 and HDDC2), as well as SNPs in the myoglobin, 40S ribosomal protein S17, and actin‐binding LIM protein genes, had clines that were coincident and concordant with the mitochondrial clines. When hybrid index was calculated using this subset of loci, the frequency distribution of hybrid indices for a population located at the mtDNA cline center was non‐unimodal, suggesting selection against advanced‐generation hybrids, possibly due to effects on processes involved in oxidative metabolism.Steep genetic clines resulting from recent secondary contact between previously isolated taxa can either gradually erode over time or be stabilized by factors such as ecological selection or selection against hybrids. We used patterns of variation in 30 nuclear and two mitochondrial  When two previously isolated taxa come into contact, they may transiently form a hybrid zone, but in the absence of intrinsic or extrinsic mechanisms that maintain reproductive isolation the hybrid zone will gradually degrade as the taxa merge due to interbreeding  has been interpreted as evidence that exogenous environmental selection is acting in this hybrid zone. There is a moderately steep cline in Ldh‐B allele frequency, and amino acid differences at this locus have been shown to differ functionally in ways that are consistent with selection in response to environmental temperature variation  a cline in allele frequency of a SNP in the mitochondrial D‐loop and that the widths of these clines were narrower than what would be predicted in the absence of selection. Furthermore, individuals from a marsh located at the center of this hybrid zone exhibited a bimodal pattern of hybrid indices, again implicating selective forces in the maintenance of this zone . SNP genotypes for individuals from an additional eight locations outside of the putative contact zone . Nine of these SNPs failed to meet the quality control standards of the assay, and exhibited poor amplification, detection of multiple loci, or unreliable identification of hybrid individuals, and thus were dropped from the final SNP panel, which consisted only of those SNPs that could be scored with high accuracy (Table S1).Fish were genotyped at 30 nuclear SNPs and two mitochondrial SNPs selected from an existing panel of 458 genomewide SNPs from This Sequenom assay was used to genotype the 250 individuals collected for this study. Genotypes at these SNPs for an additional 235 individuals, also obtained using Sequenome, were taken from Williams et al.  resultinF. heteroclitus genome (www.fundulus.org). All SNP flanking sequences localized to only a single region of the genome, suggesting that our assay is robust against false positives due to detection of paralogs. The identities of the SNP‐containing loci were taken from the genome annotation tracks and were also manually curated by BLAST comparison against the NCBI nucleotide database  using the complete enumeration method, and test for heterozygote excess and deficiency for each SNP locus in each location. In addition, this program was used to test for cytonuclear disequilibrium among the SNPs. Genepop computes disequilibrium using a log‐likelihood ratio test (G‐statistic). Markov chain parameters were set to the defaults . The Black and Krafsur procedure, as implemented in Genetix 4.05.2, was used to test each pair of SNP loci among individuals from each population for evidence of linkage disequilibrium  of all 32 SNPs. For these analyses, the location of our most southern sampling location  was set to zero and the distance to each sampling location was measured using ArcGIS ArcMap 10  estimations were then used to calculate the number of generations that have elapsed under the assumption of a neutral cline using the equation: T represents the number of generations since contact under the assumption of a neutral cline, and σ represents dispersal per generation  using: z = hybrid index as calculated using the program introgress (Gompert and Buerkle z¯ and var (z) are the mean and variance of the hybrid index for these individuals, respectively, and n = the number of loci. The variance of allele frequency across the n loci was calculated as ar(p)=1/n∑(pi−p¯)2. The resulting value for D¯ was then substituted into: r = recombination rate (0.5) and w¯ = mean cline width. Equation σ.We also used the SNP data to calculate an estimate of dispersal as described by Barton and Gale . First, Cfit6  tested ranged from one to ten, and ten replicate analyses for each value of k were performed. The most likely number of clusters was then determined using Evanno et al.'s  with the identified most likely number of clusters . This finding is consistent with previous work on this gene in F. heteroclitus, which has detected significant recombination within the Ldh‐B gene  than negative Rij (Mann–Whitney U‐test P < 0.0001). However, there was no evidence of elevated LD within the putative contact zone, as there was no significant difference in the number of loci exhibiting positive Rij in hybrid zone populations compared to locations to the north or south of the zone .There was little evidence of significant linkage disequilibrium (LD) among loci (Table S2), with only the two most northern populations (Locations 1 and 2), demonstrating any evidence of pairwise linkage disequilibrium among loci following correction for false discovery rate , with the majority of the deviations (78%) occurring in populations within the borders of the contact zone (Table S5).Of the 30 nuclear SNPs genotyped, ten showed significant deviations from HWE following adjustment for false discovery rate for at least one of the 15 locations sampled, with a total of 24 significant deviations following FDR adjustment for all locus and location combinations. Of these 24 significant deviations, 23 were heterozygote deficits and all occurred among individuals from collection locations within the putative contact zone  because we found no evidence of genetic isolation by distance between marshes along the coast at this scale (data not shown). Our findings were similar to those of Brown and Chapman (w) of 614 km if diffusion is neutral, assuming that secondary contact occurred ~15,000 years ago, following the last glacial retreat  are shown in Figure  Abraham . We assu Chapman , suggestAs an alternative approach to estimating dispersal distance, we utilized tension zone theory to calculate a genetic estimate of dispersal distance using average pairwise linkage disequilibrium (see equations (AIC) approach to assess cline coincidence and concordance. A model in which all clines were constrained to have the same center and width did not fit the data as well as a model in which the cline centers and widths were allowed to vary independently , a pattern which is obvious in Figure We used an Akaike information criterion structure analysis of the 15 populations sampled spanning the entire range of the distribution of F. heteroclitus confirmed the presence of two hybridizing groups, with the Δk for k = 2 far exceeding those reported for the alternate numbers of clusters tested. 11.57% of the individuals sampled had admixture proportions >0.9 and the majority of these originated from the northerly located populations  greater than 0.1, indicating the presence of some admixed individuals. As individuals from more proximate populations are more likely to resemble individuals that are making a genetic contribution to individuals in the hybrid zone than are individuals from the extremes of the species distribution, we chose to use populations from the identified edges of the zone of secondary contact in the training dataset for the subsequent calculation of hybrid index using the program introgress. Thus, we used location 5  as the putative northern parental population (mean q = 0.569 compared to mean q = 0.906 for location 4). At southern end of the secondary contact zone, locations 11, 12 and 13 had mean q of 0.247, 0.236, and 0.167, respectively. We selected location 12  as the putative southern parental population because it is geographically proximate to the secondary contact zone and because we had the highest sample size for this location, allowing a more accurate estimation of parental allele frequencies. However, note that similar results are obtained when any of these populations are used as the southern parent. We calculated hybrid index using both the complete set of nuclear SNPs and the subset of five SNPs that had cline shapes that were coincident and concordant with the mtDNA clines , justifying the merging of the two groups. The combined data did not deviate from unimodality for hybrid indices calculated including all 30 nuclear SNPs . However, for hybrid indices calculated using the subset of five nuclear SNPs, the combined data revealed a trend toward bimodality .The results of the sis Fig.  clearly F. heteroclitus, we have demonstrated that: (1) populations from marshes located in the center of the putative contact zone between F. heteroclitus subspecies  exhibit higher levels of heterozygote deficit than do populations from marshes outside of the zone, (2) there is some evidence of cytonuclear disequilibrium within the contact zone, (3) there is a subset of very narrow clines in nuclear SNPs that are coincident and concordant with the mtDNA cline, and (4) multilocus analysis using this subset of markers suggests a non‐unimodal pattern of genotypic frequencies for these loci in the population at the center of the mtDNA cline, suggesting that contemporary forces such as exogenous selection, endogenous selection, and/or assortative mating may be operating in this hybrid zone. This latter observation is consistent with our previous findings of a bimodal distribution of hybrid indices calculated from microsatellite markers  of 2 km per generation. However, because of the assumptions inherent in clinal analyses, it is extremely challenging to determine whether these unusually steep clines are, in fact, being maintained by selection. In addition, the possibility that the observed clines are the result of neutral processes such as allele surfing as populations expanded into previously glaciated regions must also be considered  to 457 km (hemoglobin beta). Clines this steep would have to have been formed between approximately 225 and 1300 years ago in the absence of selection, assuming a dispersal distance , a locus which was not examined here, also exhibit an extremely steep cline that is coincident and concordant with the mtDNA cline in F. heteroclitus  is a glycolytic enzyme that is critical for supplying pyruvate to the mitochondrion. Variation in the expression of glyceraldehyde‐3‐phosphate dehydrogenase is associated with variation in cardiac metabolism between F. heteroclitus subspecies . The function of the GABAB receptor has received limited study in fishes, but it is expressed in the central nervous system and the available data suggest that it is involved in the control of locomotion, among other functions  all have functions associated with exercise and oxidative stress. In addition, locus 19, the mitochondrial phosphate transporter, has a narrow cline width and a cline center concordant with the mitochondrial DNA cline, and also exhibits cytonuclear disequilibrium, again indicating oxidative metabolism as a potential target of selection across this contact zone. The observation that no single locus exhibits cytonuclear disequilibrium across multiple populations within the contact zone also suggests that a process such as allele surfing is unlikely to be the cause of the clinal patterns observed in this species.Interestingly, there are a number of loci that demonstrate deviations from neutral expectations across multiple analyses. There are five loci with significant deviations from HWE that have high positive Fis values at 4 or more sites: 9, 12, 19, 20, and 29 (Table S5). Of these, three have narrow cline widths , and the cline in locus 12 is coincident and concordant with the cline in mitochondrial DNA. As discussed above, these loci (the GABALdh‐B), which has been the subject of intensive investigation in this species  or assortative mating based on a phenotype specified by a small number of loci would be predicted to have locus‐specific effects such as those observed here.A greater proportion of loci exhibited significant departures from Hardy–Weinberg equilibrium due to elevated levels of heterozygote deficit in marshes in the contact zone compared to those outside of the contact zone. Although departures from Hardy–Weinberg equilibrium can be caused by a variety of factors including population subdivision, natural selection, avoidance of heterospecific mating, or assortative mating, many of these processes would be expected to have effects on a whole‐genome scale. In F. heteroclitus differ in salinity tolerance, particularly during development  have functions that are likely associated with oxidative metabolism. As previously mentioned, HDDC2 is a nuclear‐encoded but mitochondrially localized protein of unknown function. Warm acclimation‐related protein is a hemopexin involved in coping with oxidative stress that is known to increase in expression in response to a variety of stressors (including temperature) in fish , when two collection seasons are combined. Inspection of Fig. Although some of the subunits of the complexes of the mitochondrially housed electron transport chain are encoded by the mitochondrial genome, the majority are transcribed from the nuclear genome and subsequently transported to the mitochondrion to form the multi‐subunit complexes of the electron transport chain. Thus, there is a functional interaction between nuclear and mitochondrially encoded genes  and data from Powers and Place (nd Place  shown inClick here for additional data file.Table S1. Sequence and SNP location information for loci used in this study.Click here for additional data file.Table S2. Summary of locus pairs in significant linkage disequilibrium after FDR‐adjustment at each location.Table S3. Number of loci showing deviations from HWE, LD or cytonuclear disequilibrium prior to FDR correction.Table S4. Summary of locus pairs in significant linkage disequilibrium at each location.Table S5. FIS values for loci that deviated significantly from HWE.Table S6. Four cline shape parameters as predicted by ClineFit, arranged by order of increasing centre (as in Fig Click here for additional data file.Table S7. Multilocus mitochondrial and nuclear SNP genotypes for all individuals used in this study.Click here for additional data file.


In recent years, with the rapid development of Chromosomal Micro-array Analysis (CMA), the resolution limit of 5Mb which was imposed by conventional cytogenetics, has been significantly lowered. Currently, array CGH is the most widely used CMA technology. With the inclusion of thousands to millions of probes, it allows the detection of small copy number variations (CNVs) of a few kb. SNP (Single Nuleotide Polymorphism) arrays can also be used for CMA. They too enable the detection of CNVs, but unlike array CGH, each probe is located at an SNP and can determine the genotype of the corresponding SNP.Here, we report on our 6 years experience of the use of SNP arrays in cytogenetic diagnosis on more than 3000 patients and we will focus on the main benefits of SNP arrays over array CGH. Because of their ability to perform SNP genotyping, SNP arrays can detect long contiguous stretches of heterozygosity (LCSH).LCSH have 2 main interests:1) they can detect uniparental isodisomies (UPD);2) they can detect genetic identity by descent.UPD can be responsible for imprinting disorders and both UPD and identity by descent is associated withpromote the occurrence of autosomal recessive disorders. Moreover LCSH analysis allows performing homozygosity mapping and helping guide sequencing of candidate genes responsible for recessive conditions. Because of an abnormal number of different alleles, SNP arrays also enable the detection of polyploïdy and chimerism. Besides, SNP arrays also are of interest in quality control: they can detect DNA contamination and false paternity.Thus, while array CGH is still a very efficient technique to detect CNVs, the inclusion of SNP probes in arrays is desirable when possible.


Drosophila melanogaster line maintained in constant dark conditions for more than 60 years. Our previous analysis identified 220,000 single nucleotide polymorphisms (SNPs) in the Dark-fly genome, but did not clarify which SNPs of Dark-fly are truly adaptive for living in the dark. We found here that Dark-fly dominated over the wild-type fly in a mixed population under dark conditions, and based on this domination we designed an experiment for genome reselection to identify adaptive genes of Dark-fly. For this experiment, large mixed populations of Dark-fly and the wild-type fly were maintained in light conditions or in dark conditions, and the frequencies of Dark-fly SNPs were compared between these populations across the whole genome. We thereby detected condition-dependent selections toward approximately 6% of the genome. In addition, we observed the time-course trajectory of SNP frequency in the mixed populations through generations 0, 22, and 49, which resulted in notable categorization of the selected SNPs into three types with different combinations of positive and negative selections. Our data provided a list of about 100 strong candidate genes associated with the adaptive traits of Dark-fly.Environmental adaptation is one of the most fundamental features of organisms. Modern genome science has identified some genes associated with adaptive traits of organisms, and has provided insights into environmental adaptation and evolution. However, how genes contribute to adaptive traits and how traits are selected under an environment in the course of evolution remain mostly unclear. To approach these issues, we utilize “Dark-fly”, a  The adaptive responses of organisms are thought to be a driving force for evolution. The characteristic traits of organisms, such as physiology, morphology, and behavior, impact organisms’ fitness in any particular environment, and are selected during the process of evolution. To understand the mechanisms of environmental adaptation, researchers have generally examined the traits of organisms that have evolved in nature. One of the best-known examples is the shape of the beaks of Darwin’s finches, which are related to the feeding habitats of each species  technology enables us to examine the whole genome as a means of studying environmental adaptation. Genome-wide association studies (GWAS) have revealed genome differences between two populations without any ulations . Althougulations .Drosophila revealed several important concepts of evolutionary biology, such as genetic assimilation , since 1954, by a laboratory at Kyoto University  and normal light–dark cycling (LD) conditions. We compared the population genome using an analogy of QTL analysis, and detected condition-dependent selection of the fly genome during the time course. The results obtained demonstrated the usefulness of genome reselection experiments for studying environmental adaptation, and provided us with a list of potential candidate genes involved in Dark-fly’s adaptive traits.D. melanogaster Dark-fly Oregon-R-S line (Dark-fly) has been reared in constant dark conditions at 25° with a minimal nutrient medium, Pearl’s medium (s medium  (termed s medium . We haveDiscosoma red fluorescent protein (DsRed) [P(GFP)Zasp66(ZCL0663) and P(DsRed)ap(PyR10)] were obtained from the Kyoto Stock Center (stocks #110740 and #109136). To make competitor lines for the fitness assay, both transgenic lines were backcrossed with the Oregon-R-S or Urbana-S line more than 10 times. We called these flies GFP-Oregon-R-S, DsRed-Oregon-R-S, GFP-Urbana-S, and DsRed-Urbana-S.The Oregon-R-S, Canton-S-iso3, and Urbana-S strains used for the experiments were obtained from the Bloomington Stock Center . The transgenic fly lines expressing Green Fluorescent Protein (GFP) and Results). We evaluated the fitness of tester parents based on the number of these classified progeny. Statistical tests for the proportions of offspring were performed using the Mann–Whitney U-test (wilcox.exact function of the R program: http://www.r-project.org/) and the Welch t-test . We also calculated the comparative fitness (CF) of the parental tester strain in the offspring population using the following equation:W is the number of White progeny , G is the number of Green progeny , R is the number of Red progeny , and Y is the number of Yellow progeny . Thus, CF = 50% means that the tester and competitor flies had equal fitness.For the fitness assay, we put four kinds of flies  into the same culture vial (about 30 ml volume), and kept them in DD or in 12-hour LD conditions for 3 d. Then, the parental flies were discarded and the number of progeny was counted after the adult offspring emerged. We observed the adult offspring under a fluorescence microscope (Olympus SZX16) and classified them according to their expression of GFP and DsRed .Canton-S-iso3 was used as a tester strain to examine the effects of distinct genetic backgrounds between tester and competitor strains.First, we prepared 16 vials each containing a total of 20 flies  as one replicate population. Three such replicate populations each were placed in LD and DD conditions. The parental flies were removed from the vials after 3 d and stored at −80° for use in the subsequent analyses. After the adult progeny emerged, we collected all flies from the 16 vials together into one vial, mixed them carefully, and divided them again into 16 fresh vials every generation with ice anesthesia, so that the flies reared in the 16 vials were kept as one population. One vial usually contained about 60 offspring flies, and thus one replicate population in 16 vials contained about 1000 flies. The mixed populations were maintained by transfer to fresh vials every 11–16 d. After generation 50, we started to use the modified fly food described above.To estimate the size of mixed populations, we measured the weight of frozen flies. Each value of weight was converted to number of flies using the mean weight of six populations at generation 0 (320 flies = 0.42 g). The statistical test of population size was performed using the one-way ANOVA of Microsoft Excel.To extract genomic DNA from the mixed populations, about 1000 frozen flies were crushed with liquid nitrogen and the fly powder was thoroughly mixed. The genomic DNA was extracted in quadruplicate from four small portions of the fly powder according to our previously reported standard method  using the aln and sampe functions (with default parameters) of BWA software (bwa-0.5.9rcl) , and theNonmetric multidimensional scaling (MDS) analysis was performed using the isoMDS function in the MASS package of R. Other analyses of overall SNP frequency were performed using standard functions of the R program.For Fisher’s exact test of SNP frequency, we first merged bam files of replicate populations with the merge function and mpileup function (with –B –d 9000) of samtools-0.1.19, and performed Fisher’s exact test (max-coverage 9000) using popoolation2 software  regions .To narrow down the candidate regions, we used the MULTIPOOL program, ver. 0.10.2 , which eTable S1. Each PCR reaction mixture  consisted of genomic DNA (1 ng/μl) and primers (0.3 μM each) in 1 × QuantiTect SYBR Green PCR master mix (Qiagen), except for the SNP at 2R:12478534. For this SNP, the PCR reaction mixture consisted of genomic DNA (1 ng/μl), primers (0.3 μM each), dNTP Mix (0.2 mM), Rox dye (Toyobo), EvaGreen dye (Biotium), and DNA polymerase Stoffel Fragment (Applied Biosystems) (1 unit/μl) in the Stoffel Buffer with 2.5 mM MgCl2. qPCR was performed using an ABI 7900HT. The data were normalized by the value of a hybrid line between Dark-fly and Oregon-R-S (Dark-fly SNP frequency: 0.5).To confirm the reliability of SNP frequency calculated from Illumina NGS data, we also measured SNP frequency by the quantitative PCR (qPCR)-based method , and then tested five combinations of competitor and tester . Even when the same strains were used as competitor and tester , the proportions of Yellow/Green/Red/White offspring were not equal . Conversely, the proportions of the White and Green progenies tended to be increased, though the differences were not statistically significant. These alterations of offspring proportion might have been due to different reproductive ability of Dark-fly in different light conditions (see Discussion). When we examined Dark-fly tester and Urbana-S competitor, the proportion of White progeny was significantly increased in the DD condition compared to the LD condition . These results indicate that regardless of which competitor line was used, Dark-fly exhibited a unique difference of its ability to reproduce in LD vs. DD conditions.We prepared two pairs of competitor strains by backcrossing with wild-type strains, Oregon-R-S or Urbana-S . The CF of Dark-fly vs. Oregon-R-S competitor showed a slight increase, from 54.1% in the LD to 55.6% in the DD condition (Table S2). Against Urbana-S competitor, the CF of Dark-fly was significantly increased, from 54.8% in the LD condition to 66.8% in DD . We did not detect such a condition-dependent advantage for other testers we examined. These results suggest that Dark-fly produces more progeny in the DD than in the LD condition and as a result dominates over the wild-type fly in the dark, and support the idea that Dark-fly is adapted to a dark environment.From the above data, we also calculated the CF of tester To identify genes associated with Dark-fly’s adaptive traits, we designed a genome reselection experiment under differing environments. That is, we maintained large mixed populations of Dark-fly and Oregon-R-S . Thus, the mixed populations were kept without showing any effect of the LD or DD conditions on population size.We examined the transition of population size during the first 60 generations by measuring the weight of the fly population . The estFigure S2). The remaining 73,644 SNPs did not show a frequency of around 0.5, and most of them showed a frequency of over 0.9, implying that these SNPs would be common between Dark-fly and Oregon-R-S. In previous analyses, we might have overlooked some SNPs, probably due to low depth of coverage (mean depth: 13.7). Therefore, we took the 143,685 SNPs with a frequency of 0.4 to 0.6 at generation 0 as Dark-fly SNPs  and used them in the following analyses.We extracted the genomic DNA of the mixed population at generations 0, 22, and 49  and performed whole-genome sequencing to measure the frequency of SNPs in each population’s genome. The average sequence coverage for the genome was 179 × in 13 populations , so that2 = 0.909, Figure S3), confirming the accuracy of our SNP frequency data.We then calculated the frequency of each Dark-fly SNP in the replicate populations at generations 22 and 49. To evaluate the reliability of these frequencies calculated from the NGS data, we measured the frequency of some SNPs by a qPCR-based method (Table S5), but was slightly higher in the DD-reared population than in the LD-reared population at generation 49 (0.588 vs. 0.560 for the average of three replicates). Plotting the SNP frequency along the chromosomal position  revealed that the frequency of SNPs increased or decreased in position-dependent manners, but was roughly conserved among populations. The plots of SNP frequency displayed overall a “belt-like” pattern, with the only exception being around the centromere of chromosome 2: the discontinuity of SNP frequency in this region might be due to structural variants of this chromosome (see Discussion). These observations of SNP frequency suggest that some of Dark-fly’s SNPs would be advantageous, and some would be disadvantageous, so that their frequencies would increase or decrease, respectively, during successive generations.We characterized the features of the overall SNP frequency in the populations, and observed that the SNP frequency became progressively more diverse during successive generations (comparing generations 0–22 and 22–49), as expected . The aveTo characterize the overall SNP frequency of successive populations reared under LD and DD conditions, we performed nonmetric MDS analysis of SNP frequencies. i.e., the difference of the frequency of SNPs increased with increasing generations . AFCs at generation 49 were slightly shifted in the plus-direction, indicating that the number of the Dark-fly’s SNPs showing higher frequency in the DD than in the LD condition was larger than that of the Dark-fly’s SNPs showing the opposite trend. This biased AFC distribution suggests that the Dark-fly genome carries a large fraction of SNPs positively selected in the DD condition or negatively selected in the LD condition.We next sought to identify SNPs showing different frequency in LD- and DD-reared populations. The mean frequency of each SNP in replicate populations was obtained for comparison in subsequent analyses. Then, allele frequency changes (AFCs) between conditions (frequency in DD − frequency in LD) were calculated for each SNP at generations 22 and 49. The distribution of AFCs gradually became broader during successive generations, x-axis) and DD condition (y-axis) . Among them, the number of SNPs showing higher frequency in DD than in LD was significantly larger than the number showing lower frequency , confirming that a large fraction of Dark-fly’s SNPs increased in frequency in DD conditions. We focused on SNPs that showed higher frequency in the DD than in the LD condition, and whose p-values were ranked in the top 5% , though this criteria when applied at generation 22  was not adequate for detecting such outliers, indicating that more SNPs had been selected in the population at generation 49 than at generation 22. At generation 49, 6015 such SNPs were grouped into three types according to their relative frequency in LD vs. DD conditions: type 1, 2, and 3 showed high, middle, and low relative frequency, respectively, in the DD compared to LD condition on average . These three different types might have resulted from different selection patterns of the respective SNPs by DD vs. LD conditions.Next, we examined how the SNP frequencies were affected by light condition. For this, we plotted the frequency of each SNP in the LD condition ((y-axis) . Many SNTo further characterize the patterns of selection of these SNPs, we integrated the data of generations 0, 22, and 49, and examined the trajectory of SNP frequency through successive generations . Type 1 Figure S4). However, we found that SNPs categorized into the same selection type were mapped to several separate loci on different chromosomes. That is, several loci shared the same trajectory of selection, and thus we speculate that several responsible SNPs (genes) at separate loci might function together for an adaptive trait of Dark-fly (see Discussion).We then examined the chromosomal positions of these selected SNPs, and found that the SNPs were accumulated in several distinct chromosomal regions . The accTable S6). In our previous genome analyses, we identified ROH regions in the Dark-fly genome (Table S6). This overlapping could not be explained merely by coincidence, because ROH regions cover only a much smaller fraction (3.5%) of the whole genome. Rather, the overlapping regions had been selected both in the history of Dark-fly and in the current experiment of selection in mixed populations.To search for a link between selected SNPs and Dark-fly’s traits, we further characterized the chromosomal loci carrying selected SNPs. We identified 28 such regions, which had accumulated selected SNPs and are separated from each other by at least 100 kb . The nonsynonymous SNPs and InDels located in the gene coding regions might alter the function and activity of gene products. Alternatively, the SNPs and InDels located in intergenic regions might affect the expression of genes. We consider these 1283 genes to be primary candidates selected in the mixed populations .Among the genes located in the 28 selected regions, we identified 1283 genes carrying SNPs or InDels in the Dark-fly genome . Using this threshold, we identified nine peaks of LOD scores in the data of generation 49 . We listed 84 genes carrying SNPs and InDels within these LOD peaks , and we need to examine the roles of these genes in future work.The 28 selected regions we identified constitute a total of 10,856,126 bases (6.4% of the genome), and include more than 1000 genes, so it would be laborious to test the role of each of these candidate genes for adaptive traits of Dark-fly. Thus, to narrow down the candidates, we utilized the MULTIPOOL program , which pation 49 . The LODation 49 . Among tOD peaks . For exaOD peaks . These 8Here we found that the relative fitness of Dark-fly under reproductive competition against other strains of flies was higher in the DD compared to the LD condition. Various wild-type lines we examined did not show such an advantage in the DD condition, indicating that Dark-fly is a unique line with regard to its display of reproductive success in a dark environment. Reproductive success could be increased via various traits, such as mating behaviors, egg-laying ability, egg-to-adult viability, and so on. From the results of our reproductive competition experiments between Dark-fly and the Oregon-R-S strain, we found that the proportion of Red progeny  was decreased in the DD condition, whereas the proportion of White progeny  tended to be increased in this condition. There are various possible explanations for this result. One possibility is that Dark-fly females might prefer Dark-fly males as partners, and avoid DsRed-Oregon-R-S males, in the dark. Regarding such a preference, Dark-fly females might detect some specific cues, such as odors or sounds, to discriminate partners in the absence of visual cues .When we performed the competition assay against Urbana-S competitors, we observed a different pattern of performance of Dark-fly. That is, the proportion of White progeny was increased in the DD condition, whereas that of Green progeny  was decreased. In this case, Dark-fly males might have a preference for Dark-fly females compared to Urbana-S females in the dark. Thus, Dark-fly exhibited different advantages when reared in the dark depending on the competitor line. Although we could not define particular traits contributing to Dark-fly’s fitness in the dark, we confirmed here that Dark-fly possesses some advantageous traits for reproduction in a dark environment, and these advantages suggest that Dark-fly could dominate over the wild-type fly in the dark.We designed a mixed population experiment to identify genes involved in the adaptation of Dark-fly. In many previous studies about environmental adaptation, QTL analyses revealed the genes associated with traits of organisms . Most ofGWAS is one of the approaches for analyzing population genomes with large genetic diversity in their background. Its statistical power is very high for detecting the consistency of genome alterations across large populations. Since our mixed population experiment started from two fly strains, the genome diversity in the population was quite low compared to that in GWAS. However, our experiment has the advantage that it uses a defined environment for the selection and makes it possible to observe the temporal pattern of the selection during successive generations. The mixed population experiment is thus a powerful strategy for identifying genome variations related to the fitness in a defined environment, and could be useful for application to other organisms.We analyzed the SNP frequency in the pooled genome of the populations. Analysis of pooled genomes has now become widely used because of its cost-effective high performance , which were identified as Dark-fly’s SNPs in our previous study . We might have overlooked these SNPs in the previous analysis, probably because of low coverage of sequencing. In addition to such hidden SNPs, we did not evaluate heterozygous SNPs of the original Dark-fly in this study. Although we detected a small number of heterozygous SNPs in the previous analyses of the Dark-fly genome, we might have overlooked more such SNPs. In this study, we used only confirmed homozygous Dark-fly SNPs to examine selections, although as a result we might have missed some SNPs actually selected. In order to minimize such an oversight, we will need to obtain more precise and comprehensive genome data for parental Dark-fly. Thus, improved accuracy of the sequence of the parental genome would be an important factor for ensuring the analytical scale and precision in the pooled genome analysis.For this population genome analysis, we used confirmed Dark-fly SNPs . This was probably not due to accidental extraction of some SNPs in a population, because replicate populations showed almost the same zigzag pattern of frequency changes along this chromosomal region. Rather, we speculate that there might be structural variants  of the Dark-fly or Oregon-RS genome in this region that might cause discontinuous changes of SNP frequency. Our analysis did not evaluate SNP selection precisely in this region, and we had to eliminate this region for identifying adaptive SNPs. Such structural variants of chromosomal regions would be a confounding issue for analyzing population genomes, and detailed information about the parental genome would be required for resolving the issues presented by such variants.The SNP frequency in the population would be affected by selective pressure and by recombination in the genome, and consequently the frequency of SNPs would change in a coordinated manner along chromosomal positions. However, we observed marked discontinuity of the SNP frequency in the region near the centromere of chromosome 2  that affect unique traits of organisms. First, these mutations might be located within the coding region of a gene and might generate nonsynonymous or frameshift mutations in the gene. In this case, the mutations would alter the activity of gene products. Second, the mutations might be located in an intergenic region, and might alter the expression of a gene(s). Both cases indeed have been documented in previous studies of evolved traits of organisms . One wayTable S6). We used the MULTIPOOL program, a program that has successfully identified QTL loci in previous studies, and that enabled us to narrow down the candidates to seven selected regions, and to identify 84 genes carrying SNPs or InDels in these regions of the Dark-fly genome. We suggest that these genes are potentially strong candidates for those involved in Dark-fly’s adaptation.As primary candidates for Dark-fly’s adaptive genes, here we identified 1283 genes that are located in selected chromosomal regions and that carry SNPs or InDels in the Dark-fly genome (Diptera species (Concerning the reproductive success of Dark-fly in a dark environment, as shown in Introduction, its rhythms of mating behavior and gametogenesis physiology have not been investigated. Some genes in the candidate list are indeed expressed in circadian oscillatory manners (Ace and Dip-B; We also considered another hypothesis, namely, that Dark-fly might possess abnormal circadian rhythm that results in higher fitness in the dark. While Dark-fly retains circadian locomotor rhythms  as noted