Gossypium spp.) is the largest producer of natural fibers for textile and is an important crop worldwide. Crop production is comprised primarily of G. hirsutum L., an allotetraploid. However, elite cultivars express very small amounts of variation due to the species monophyletic origin, domestication and further bottlenecks due to selection. Conversely, wild cotton species harbor extensive genetic diversity of prospective utility to improve many beneficial agronomic traits, fiber characteristics, and resistance to disease and drought. Introgression of traits from wild species can provide a natural way to incorporate advantageous traits through breeding to generate higher-producing cotton cultivars and more sustainable production systems. Interspecific introgression efforts by conventional methods are very time-consuming and costly, but can be expedited using marker-assisted selection.Cotton  markers for wild cotton species G. hirsutum will allow for the first high-density mapping of genes from five wild species that affect traits of interest, including beneficial agronomic and fiber characteristics. Upon mapping, the markers can be utilized for marker-assisted introgression of new germplasm into cultivated cotton and in subsequent breeding of agronomically adapted types, including cultivar development.This large set of 62,832 SNPs relative to cultivated The online version of this article (doi:10.1186/1471-2164-15-945) contains supplementary material, which is available to authorized users. Gossypium spp.) is the leading natural fiber crop worldwide and an important contributor to the economies of nearly 100 countries. The genus Gossypium is also an important model species for polyploidy and the biological processes of cell wall elongation and cellulose biosynthesis in fiber cells. This clade consists of approximately 45 diploid species and five allotetraploid species. Genomes of the allotetraploid species have 52 chromosomes (2n = 4x = 52) and are believed to have originated from a single polyploidization event between an A-genome diploid (n = 2x = 26) and a D-genome diploid (n = 2x = 26) approximately 1–2 million years ago [G. hirsutum genome ([AD]1) have been numbered according to their evolutionary origins and meiotic pairing relationships. Chromosomes 1–13 comprise the “A” sub-genome (AT) that originated from the extinct A-genome diploid ancestor and chromosomes 14–26 comprise the “D” sub-genome (DT) that originated from the extinct D-genome diploid ancestor. There are four major cultivated species wordwide, two diploids G. arboreum (A2 genome) and G. herbaceum (A1) and two allotetraploids G. hirsutum L. or Upland cotton and G. barbadense L. ([AD]2), extra long-staple Pima, Egyptian cotton or Sea Island cotton. Upland cotton cultivation represents over 95% of the fiber produced worldwide due to its high yield, but generally Pima cotton the next most cultivated cotton, exhibits longer, stronger, and finer fiber.Cotton  is the only member of the F-genome clade and is native to Africa. It has been shown to have resistance to pathogens, such as reniform nematode [G. armourianum Kearney (D2–1) belongs to the D-genome clade and is a wild species found in Mexico. It has been shown to exhibit resistance to the whitefly [Diploid cotton species have been shown to have many disease and insect resistance traits, as well as improved fiber characteristics. The diploid nematode , and to nematode . The dipwhitefly , which iwhitefly . The dipwhitefly , 9.G. tomentosum Nuttal ex Seeman originates from the Hawaiian islands and produces a small amount of short, reddish brown fiber. G. tomentosum has been found to show resistance against the cotton leaf hopper, Amrasca biguttula biguttula, and thrips, Frankliniella occidentalis[G. mustelinum Meers ex Watt is from Brazil and also produces a small amount of lint. Using HPLC analysis, G. mustelinum has been shown to have the highest leaf concentrations of terpenoid aldehydes that affect insect resistance [G. barbadense originates from South America and is a cultivated species which represents about five percent of the annual worldwide fiber crop. This tetraploid exhibits excellent fiber quality characteristics for fiber length, micronaire and high strength relative to G. hirsutum.While crossing cultivated tetraploid cotton directly with diploid species is difficult, allotetraploid species can be easily interbred and then backcrossed to move desired segments into cultivated material. The tetraploid identalis. The tetsistance . G. barbG. hirsutum × G. barbadense which offers a higher polymorphism rate than intraspecific crosses, and segregation for superior fiber quality characteristics. Moderate density linkage maps have been created using restriction fragment length polymorphisms (RFLPs) [Many of the mapping efforts in cotton have consisted of interspecific biparental populations of  (RFLPs) , 12, amp (RFLPs)  and simp (RFLPs) , 15. SSR (RFLPs) , 17. A c (RFLPs) . While s (RFLPs) , 19. SNP (RFLPs) .Gossypium diploid and tetraploid genomes [T and DT subgenomes, which was facilitated by recent publication of the reference genome sequence for G. raimondii (D5) [5 genome is regarded as the closest living diploid relative to the D-genome ancestor of current AD-allotetraploid species [G. hirsutum chromosomes will be useful for breeders to efficiently introgress quantitative trait loci (QTLs) and track alleles in marker-assisted selection (MAS) of beneficial traits from donor species.An aspect of polyploid genomes that creates difficulties during SNP development is that there are two indistinguishable types of SNPs in polyploid sequence data: homeologous sequence variants or “homeo-SNPs” and traditional SNPs or “allele-SNPs” . A catal genomes , 26. In dii (D5) . The D5  species . It has  species , 29. WhiG. hirsutum has thus far been constrained by the paucity of high-throughput genome-wide markers that would facilitate tracking of introgressed segments. The relative scarcity of SNPs in cultivated allotetraploid cotton reflects the difficulty of developing SNPs for its complex genome, comprised of large repetitive regions and homeologous content due to recent polyploidization. Here, we report a method utilizing the genomic reduction method of transcriptome sequencing to derive interspecific gene-associated SNPs between the genetic standard G. hirsutum TM-1, and five other species, including the genetic standard G. barbadense doubled haploid line 3–79, two allotetraploids G. tomentosum and G. mustelinum, and two diploids G. armourianum and G. longicalyx. These SNPs will be extremely beneficial for high-density interspecific mapping and will help revolutionize introgression breeding efforts by facilitating MAS-based introgression, genetic dissection, and gene utilization in cultivated cotton.Traditionally, interspecific introgression breeding efforts are extremely time-consuming and require large amounts of effort and funds. Interspecific genetic introgression into G. hirsutum (line TM-1) transcriptome assembly produced by Ashrafi et al. (in preparation) consisting of 72,450 contigs covering over 70 M bp with N50 of 1,100 bp, transcriptome sequence reads , G. mustelinum , G. armourianum , and G. longicalyx . Reads which mapped to multiple locations were randomly assigned to a single location to achieve higher mapping coverage with limited number of reads. Filtering included removal of theoretical homeo-SNP positions based on an index created by mapping back Illumina TM-1 reads to the assembly. All of the markers identified within a given species were classified according to surrounding polymorphisms for the same species. Marker classifications were based only on species-specific polymorphism data and determined independently for each species. “Class I” was a SNP in which no additional polymorphism was found to exist in the same contig. “Class II” was a SNP in which (an) additional polymorphism(s) was found within the same contig, but the additional polymorphism was outside of 50 base pairs (bp) of the marker. “Class III” was a SNP in which additional polymorphisms were found in the same contig and within 50 bp of the marker. The SNPs for G. barbadense, G. tomentosum, G. mustelinum, G. armourianum and G. longicalyx were classified according to these criteria  was low, e.g., only 3.3% or 367 markers of the G. hirsutum-G. barbadense SNPs were found to be redundant compared to the intraspecific SNPs. The overlap among the different species sets was plotted in a Venn Diagram, which revealed moderate levels of overlap among the three AD species . The overlap with intraspecific s Figure . FollowiGossypium raimondii (D5) diploid reference genome sequence. G. armourianum had the highest percentage of mapped markers, followed by G. longicalyx, and the three tetraploids G. barbadense, G. tomentosum, and G. mustelinum. Nearly all of the mapped markers (99.7%) aligned to one of the thirteen pseudo-chromosome scaffolds, and only 154 (0.3%) markers were aligned to unplaced scaffolds. A bimodal distribution across each D5-chromosome was observed when average density of markers was plotted  or 47,672 SNP markers in the final set could be aligned to the G. hirsutum and multiple species and was retained only once in the non-redundant list. The validation status of each marker for tested species is noted in columns B and C of Additional files Random sets of markers from the non-redundant final set of Class I and Class II SNPs were tested using KASP end-point assays  from each species. As random sets of markers for each species were selected for screening prior to development of the final non-redundant set, some markers may have been tested on species other than the ones with which they were associated in the non-redundant list of SNPs  and 286 (71%) of those were validated to have scoreable genotypes. Sets of markers were also screened which fall under the species-specific data sets, 466 were tested from the G. tomentosum set of which 252 were tested on the G. tomentosum screening panel which produced 168 (66.7%) successful markers. The other 214 markers were tested on other species and 141 (65.9%) were validated. A total of 138 were tested from the G. mustelinum data set, of which 90 were run on the G. mustelinum screening panel and 48 were run on other species screening panels. These tests resulted in 61 (67.8%) successful markers from the same-species tests and 27 (56.3%) successful markers from the other-species tests. For the G. armourianum data set, 214 markers were tested, of which only 5 were tested on species-specific panels. Overall, 146 out of the 209 (69.9%) markers tested on G. armourianum produced successful assays. A small set of 27 markers was tested from the G. longicalyx data set, of which 19 (70.4%) generated successful assays. A similar proportion of successful assays was obtained from SNPs generated for G. longicalyx using a different G. hirsutum assembly version that was abandoned because it yielded poor results when used to define SNPs in other species. Unique SNPs from this set were extracted and included in Additional file In the final non-redundant data sets, a total of 665 randomly selected markers were tested from the ” Figure  and 209 G. hirsutum line TM-1 × irradiated pollen of G. barbadense cv. 3–79 [G. raimondii draft genome sequence . Those e Figure  by alignG. barbadense markers used for WWRH mapping, based on Tukey’s boxplot method of outlier detection. These markers were Gb379_011066, Gb379_000467, UCcg10762_238, UCcg10614_274, c4_101926, and c4_44618.The number of WWRH deletions was significantly higher for only 6 (1.2%) of the 509 G. hirsutum reference. When translation was predicted using AUGUSTUS software  [Theobroma cacao as the model species, 52 out of the 116 translations were found to generate different amino acid sequences or non-synonymous substitutions, which corresponds to a Ka/Ks of 0.8125. As a method of comparison to the first set, a random set of 118 SNPs from the overall non-redundant Class I and II data set was chosen. These SNPs represented 118 different contigs. When the same analysis using AUGUSTUS was performed with this random set of contigs, 33 out of the 109 translations were found to generate different amino acid sequences, Ka/Ks of 0.4342.A total of 117 contigs containing 118 SNPs   and TheoG. barbadense into G. hirsutum along with superior fiber characteristics [G. barbadense with G. hirsutum has been a long-standing desire, because G. barbadense offers many superior fiber trait characteristics but does not produce the high lint yield of traditional G. hirsutum cultivars. The large number of G. barbadense SNPs identified in this study, 10,521, and particularly the 8,307 Class I and Class II markers will provide markers for a large number of genes in which differences exist between G. hirsutum and G. barbadense. A modest number, 509 markers, have been validated (from 594 tested) and the majority of these have been anchored to an allotetraploid chromosome by WWRH mapping, therefore relative physical location is known  of markers across individuals had a statistically significant number of deletions. This was a statistically insignificant number from the overall set. The relative abundance of these marker deletions suggests that the respective chromosomal segments have higher propensities for deletion and/or post-occurrence recovery of induced deletions. Some chromosomal segments may be more likely to be lost after pollen irradiation, and/or there may be significant differences in selection for/against loss of specific genes/alleles in these segments that correspond to the identified markers.5 reference sequence revealed most to be in non-pericentromeric and non-telomeric regions, i.e., similar to the pattern observed for individual markers  compared to uncultivated tetraploid species (~3-5), a much larger number of unique beneficial alleles may be found in diploid Gossypium species. Inventive methods of creating synthetic polyploids with diploid species have been devised to facilitate transfer of genetic material into G. hirsutum, as was the case for G. longicalyx, in order to create Upland cottons with strong resistance to reniform nematodes [Like ematodes . Two sisematodes . In geneG. hirsutum can be used to deduce the theoretical ancestral allele at a locus, as well as to suggest functional properties of a locus. The distribution of shared markers is depicted in Figure G. hirsutum differs, it can be inferred that the wild species share the ancestral allele and G. hirsutum contains an alternative allele. Such alleles are good candidates for being functionally important to domestication, cultivation or agronomic performance, or in linkage disequilibrium with such genes. The non-synonymous to synonymous rate was found to be much higher in the 118 SNPs shared between species (0.8125) than in 118 randomly selection SNPs from the final set (0.4342). This implies that there is a stronger positive selection upon the SNPs where G. hirsutum has an allele which is non-ancestral. This further supports the hypothesis that these loci are likely to be important in beneficial traits in G. hirsutum.Studying shared markers between the different species of varied genome composition relative to cultivated G. hirsutum lineage or are duplicated within a genome . Another possible explanation is that these hits relate to contigs that contain different isoforms of the same gene. When SNPs derived from multiple isoforms are mapped physically or by linkage, they will locate to a single locus. SNPs from the former case will occupy multiple locations in which different members of the gene family are found. We classified these SNPs separately as they may represent multiple loci throughout the genome and present another level of difficulty for genotyping. The diploid species were found to have many more SNPs exhibiting within-species redundancy, which are marker sequences that are identical but generated from multiple contigs in the assembly, than the tetraploid species. This result was expected because the tetraploid G. hirsutum that was utilized as a reference likely received a copy of each gene from ancestors of the A and D subgenomes during polyploidization. Thus relative to each diploid, the reference would have twice as many copies for each gene  and would lead to derivation of the same SNP sequence from each of the homeologous copies found in G. hirsutum when analyzing the diploid species.Some markers within the same species were identified from multiple scaffolds, but were identical in SNP and flanking sequence 2 genetic standard line 3–79, G. tomentosum (AD)3 plant number 19909036.05 from the Beasley Lab collection, G. mustelinum (AD)4 plant number 200508123.02 from the Beasley Lab collection, G. armourianum (D2–1) accession D2-1-6, and G. longicalyx (F1) plant number 200908137.04 from the Beasley Lab collection were planted at Texas A&M University. Young leaf tissues were sampled from each plant and used to isolate total RNA using the Qiagen RNeasy Mini Kit per manufacturer instructions. RNA isolates were quantified using NanoDrop spectrophotometry  and checked for quality by gel electrophoresis. PolyA RNA was extracted using double purification with oligo dT Dynal beads. Illumina RNA-Seq libraries were prepared using the manufacturer protocol . Libraries were normalized by denaturation and rehybridization in NaCl and TMAC (tetra-methyl-ammonium-chloride) buffers [G. barbadense, SRX472724 - G. tomentosum, SRX - 474879/SRR174699 G. mustelinum, SRX474240/SRR1174039 and SRR1174041- G. armourianum, and SRX474242/SRR1174179 and SRR1174182 - G. longicalyx).The seed of  buffers  and then buffers . The treWide-cross whole-genome radiation (WWRH) individuals  were plaG. hirsutum L. assembly created from genetic standard line, TM-1 , using CLC Genomics Workbench (V5.0). Reads which mapped to multiple locations were randomly assigned to a single location. Putative SNPs between TM-1 and each species were identified one accession at a time. The mapping data were exported as BAM files to a Linux server and SAMtools were used to call variants. Subsequent rounds of parameter tweaking resulted in the final pipeline used for SNP development. The resulting pileup files were filtered using the filter pileup Perl script in Galaxy (https://main.g2.bx.psu.edu/) [http://solgenomics.net/) intron finder tool. SNPs were further filtered to remove theoretical homeo-SNP positions based on allele-SNP calls generated when Illumina TM-1 reads were mapped back to the TM-1 reference.Reads from each species were trimmed for quality and then aligned to the su.edu/)  to remov9SNPs from each species were classified based on identification of additional SNPs. Class I was defined as SNPs from contigs with no other SNP residing within the contig. Class II was defined as SNPs from contigs that contained one or more additional SNP outside of the 50-bp flanking sequences (none within). Class III was defined as SNPs from contigs that contained one or more additional SNPs within the 50-bp flanking sequences.in silico-derived SNPs was used for BLAST analysis (v2.2.27) against a FASTA file containing all G. hirsutum markers from the Ashrafi et al. (in preparation) dataset. Markers were removed from the in silico set if BLAST analysis showed 100% identity over 100% length of the sequence to the G. hirsutum data set. The remaining set was then BLASTed against itself to determine identical markers within the set. Markers with hits in other species groups were removed in a hierarchical method, leaving markers in the highest set, based on the hierarchy G. barbadense, G. tomentosum, G. mustelinum, G. armourianum, and G. longicalyx. Markers with hits within the same species were separated into a data set containing “overlap markers” . Non-redundant Class III SNPs were compiled into Additional file A FASTA file containing all is v2.2.2 against G. raimondii (D5) reference genome sequence [5 genome scaffold alignment. Scaffold files were sorted by genome position. Densities of markers along the D5 genome scaffolds were plotted using densityPlot in the R program [The non-redundant SNPs and overlap markers were aligned to the sequence  using Busequence  using de program  over scaG. barbadense markers contained 2 G. hirsutum L. TM-1, 2 G. barbadense 3–79, 2 euploid F1 (G. hirsutum x G. barbadense), 4 RILs from a G. hirsutum × G. barbadense mapping population, and 2 non-template (negative) controls . Assay primers were developed using BatchPrimer3 with an optimal primer Tm of 57°C , optimal product size of 50 base pairs  and the default settings were used for the remaining parameters. Primers were mixed at the dilutions specified by LGC then used to perform KASP assays on small screening panels containing duplicates, e.g., the panel used to screen G. barbadense markers which clustered well from all rounds of development used for parameter tweaking until the final pipeline was reached were then run on a “full-panel” containing duplicates of the parental and F1 controls , G. hirsutum cultivar DP-90, G. barbadense cultivar Phytogen800, 131 wide-cross whole-genome radiation hybrid individuals, 47 F1 hypo-aneuploid lines, and 4 non-template negative controls. Genotypes were manually curated. All questionable genotypes were listed as unscored. A shift of genotype from the F1 heterozygote cluster to the homozygous 3–79 cluster was interpreted as a deletion in the respective interspecific WWRH or hypo-aneuploid F1 cytogenetic stock  was utilized to calculate the threshold for outliers greater than 1.5 times the IQR. Markers which had numbers of deletions beyond the threshold were determined to have a significantly different number of deletions than expected.G. hirsutum TM-1 for all five species from the reference were parsed into a FASTA file containing 117 contigs for 118 SNP. The reference FASTA file was modified to contain the alternate SNP allele(s) using an in house Perl script. Both the reference and alternate FASTA files were analyzed using AUGUSTUS [Theobroma cacao as the model species. Non-synonymous and synonymous changes between the reference and alternate files were calculated. Predicted amino acid coding sequences were then investigated using TBLASTX against NCBI’s non-redundant database. BLAST results were parsed to contain the top hit with covcutoff of 50 and e value cut off of 1e-8. The differences between predicted protein products were investigated. The same analysis was performed using a randomly selected set of 118 SNP from the final overall Class I and Class II data set.Contigs for which the SNPs represented identical differences from AUGUSTUS  to prediThe data sets supporting the results of this article are included within the article and its additional files.G. barbadensecv. 3–79 SNPs relative toG. hirsutumline TM-1. SNPs and 50-bp flanking sequences for in silico derived SNPs in G. barbadense cv. 3–79 relative to G. hirsutum line TM-1. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Columns 4 and 5: Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. Column 6 indicates overlap information for G. barbadense markers with G. tomentosum, G. mustelinum, G. armourianum, and G. longicalyx, where the Arabic numeral indicates number of shared species and subsequent abbreviations indicate with which of the above species the marker is shared . (XLSX 1023 KB)Additional file 1: Five species Class III SNPs. Class III SNPs forG. barbadensecv. 3–79,G. tomentosum, G. mustelinum, G. armourianum,andG. longicalyx. Class III are SNPs from contigs that contain one or more additional SNPs within the 50-bp flanking sequences. Class III SNP sequences are provided for reference, but have been shown to have issues during experimental screening using KASP assays. (XLSX 1 MB)Additional file 2: G. barbadensecv. 3–79 SNPs relative toG. hirsutumline TM-1 within-species overlapping markers. SNPs and 50-bp flanking sequences for in silico-derived SNPs of G. barbadense (cultivar 3–79) relative to G. hirsutum (genetic standard line TM-1) that have been found to be identical for SNP and flanking sequence within the G. barbadense data set. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. Column 6 indicates overlap information for G. barbadense markers with G. tomentosum, G. mustelinum, G. armourianum, and G. longicalyx, where the Arabic numeral indicates number of shared species and subsequent abbreviations indicate with which of the above species the marker is shared . (XLSX 101 KB)Additional file 3: G. tomentosumSNPs relative toG. hirsutumline TM-1. SNPs and 50-bp flanking sequences for in silico-derived SNPs of G. tomentosum relative to G. hirsutum line TM-1. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. Column 6 indicates overlap information for G. tomentosum markers with G. mustelinum, G. armourianum, and G. longicalyx, where the Arabic numeral indicates number of shared species and subsequent abbreviations indicate with which of the above species the marker is shared . (XLSX 487 KB)Additional file 4: G. tomentosumSNPs relative toG. hirsutumline TM-1 within species overlapping markers. SNPs and 50-bp flanking sequences for in silico-derived SNPs of G. tomentosum relative to G. hirsutum line TM-1 that have been found to be identical for SNP and flanking sequence within the G. tomentosum data set. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. Column 6 indicates overlap information for G. tomentosum markers with G. mustelinum, G. armourianum, and G. longicalyx, where the Arabic numeral indicates number of shared species and subsequent abbreviations indicate with which of the above species the marker is shared . (XLSX 60 KB)Additional file 5: Gossypium mustelinumSNPs relative toG. hirsutumline TM-1. SNPs and 50-bp flanking sequences for in silico-derived SNPs of G. mustelinum relative to G. hirsutum line TM-1. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. Column 6 indicates overlap information for G. mustelinum markers with G. armourianum, and G. longicalyx, where the Arabic numeral indicates number of shared species and subsequent abbreviations indicate with which of the above species the marker is shared . (XLSX 487 KB)Additional file 6: G. mustelinumSNPs relative toG. hirsutumline TM-1 within species overlapping markers. SNPs and 50-bp flanking sequences for in silico-derived SNPs of G. mustelinum relative to G. hirsutum line TM-1 that have been found to be identical for SNP and flanking sequence within the G. mustelinum data set. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. Column 6 indicates overlap information for G. mustelinum markers with G. armourianum, and G. longicalyx, where the Arabic numeral indicates number of shared species and subsequent abbreviations indicate with which of the above species the marker is shared . (XLSX 58 KB)Additional file 7: G. armourianumSNPs relative toG. hirsutumline TM-1. SNPs and 50-bp flanking sequences for in silico-derived SNPs of G. armourianum relative to G. hirsutum line TM-1. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. Column 6 indicates overlap between the two species G. armourianum markers with G. longicalyx. (XLSX 2 MB)Additional file 8: G. armourianumSNPs relative toG. hirsutumline TM-1 within species overlapping markers. SNPs and 50-bp flanking sequences for in silico-derived SNPs of G. armourianum relative to G. hirsutum line TM-1 that have been found to be identical for SNP and flanking sequence within the G. armourianum data set. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. Column 6 indicates overlap between the two species G. armourianum markers with G. longicalyx. (XLSX 220 KB)Additional file 9: G. longicalyxSNPs relative toG. hirsutumline TM-1. SNPs and 50-bp flanking sequences for in silico-derived SNPs of G. longicalyx relative to G. hirsutum line TM-1. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. (XLSX 2 MB)Additional file 10: G. longicalyxSNPs relative toG. hirsutumline TM-1 within species overlapping markers. SNPs and 50-bp flanking sequences for in silico-derived SNPs of G. armourianum relative to G. hirsutum line TM-1 that have been found to be identical for SNP and flanking sequence within the G. armourianum data set. SNPs have been classified into two categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Alignment position and scaffold information to G. raimondii (D5) draft genome sequence using BWA. (XLSX 295 KB)Additional file 11: AdditionalG. longicalyxSNPs . “Specific” G. longicalyx SNP markers, which were determined using an alternative version of the TM-1 assembly. Markers are classified into three categories, Class I are SNPs from contigs with no other SNP residing within the contig. Class II are SNPs from contigs that contain one or more additional SNP outside of the 50-bp flanking sequences (none within). Class III are SNPs from contigs that contain one or more additional SNPs within the 50-bp flanking sequences. (XLSX 5 MB)Additional file 12: G. barbadenseSNPs relative toG. hirsutumline TM-1 markers from different pipelines included in wide-cross whole-genome radiation hybrid mapping. SNPs and 50-bp flanking sequence for in silico derived SNPs in G. barbadense relative to G. hirsutum (genetic standard line TM-1). SNP and flanking sequence are listed for markers which did not overlap the set of SNPs produced using the final bioinformatic analyses to produce markers in Additional files Additional file 13: Singleton markers fromG. barbadensewide-cross whole-genome radiation hybrid mapping. List of markers which did not fall into any syntenic group from Carthagene analysis with G. barbadense markers used for whole-genome radiation hybrid mapping. (XLSX 9 KB)Additional file 14:


Natural selection and selective breeding for genetic improvement have left detectable signatures within the genome of a species. Identification of selection signatures is important in evolutionary biology and for detecting genes that facilitate to accelerate genetic improvement. However, selection signatures, including artificial selection and natural selection, have only been identified at the whole genome level in several genetically improved fish species. Tilapia is one of the most important genetically improved fish species in the world. Using next-generation sequencing, we sequenced the genomes of 47 tilapia individuals. We identified a total of 1.43 million high-quality SNPs and found that the LD block sizes ranged from 10–100 kb in tilapia. We detected over a hundred putative selective sweep regions in each line of tilapia. Most selection signatures were located in non-coding regions of the tilapia genome. The Wnt signaling, gonadotropin-releasing hormone receptor and integrin signaling pathways were under positive selection in all improved tilapia lines. Our study provides a genome-wide map of genetic variation and selection footprints in tilapia, which could be important for genetic studies and accelerating genetic improvement of tilapia. Genetic selection in which the best individuals are selected as parents of the next generation is the principal tool to improve crops and livestock568O. niloticus), Blue tilapia (O. aureus), Mozambique tilapia (O. mossambicus) and various hybrids including Red tilapia are the tilapia species/strains commonly produced across the worldO. aureus, O. niloticus20O. mossambicus x O. spp232122232727Tilapia is the common name of a group of cichlid fishes native to the Middle East and Africa, and includes over 70 speciesAdaptive differences can also be identified in genes subject to positive Darwinian selectionWe selected 47 samples collected from South Africa, Singapore and China to represent the wild tilapia, and the major genetically improved lines in Asia. These samples included 23 GIFT tilapia, eight Nile-c tilapia, 12 Mozambique tilapia (8 Mzb-F0 and 4 Mzb-F2) and four Red tilapia  of Nile tilapiaValidation of SNPs identified by whole genome resequencing was conducted by sequencing 96 randomly selected SNPs in 6 of the 47 samples with Sanger sequencing . The oveThe distributions of SNPs in the tilapia genome were calculated using the toolbox SnpEffHo) and expected heterozygosity (He). The total SNP number and population-specific SNP number are presented in Ho and He were in the Mzb-F2 population while the lowest Ho and He were seen in the GIFT population  and constructed a Neighbor-Joining (NJ) tree, based on the high-quality SNPs data. Using the first and second eigenvectors, the PCA clearly divided the 47 samples into three groups  . All theA NJ tree was used to cluster samples based on average genetic distances . The phy2) between pairs of SNPs using HaploviewLD is useful for determining the number of markers that are needed for an association study. To estimate the LD patterns in the different tilapia groups, we calculated squared correlations of allele frequencies against genome distance  of SNPs with extreme iHS values were found to be positioned in intergenic regions. Among them, 101 were located in introns, 22 in upstream and 26 in the downstream regions . Nonsens42In the other tilapia populations, most (more than 80%) SNPs under selection could be clustered into intergenic regions and introns. Only 3, 6 and 7 non-synonymous SNPs were detected in the Red, Mzb-F2 and Nile-c populations, respectively  and S6. http://www.pantherdb.org/geneListAnalysis.do). The candidate genes from each population were mapped into five gene ontology categories  that are thought to be important to tilapia performance , FGF signaling pathway (P00021), Gonadotropin releasing hormone receptor pathway (P06664), integrin signaling pathway (P00034) and Wnt signaling pathway (P00057) (Table S7). These pathways have been found to play important roles in animal growth, development and diseasesIn summary, we present the first comprehensive study for localizing signatures of selection in tilapia based on full re-sequencing data. A total of 1.4 million high-quality SNPs were identified by resequencing 47 tilapia samples, representing major cultured tilapia strains in Asia. Population structure and phylogenetic analysis using genome-wide SNPs clearly separated the GIFT, Red and Mozambique tilapia into three different groups. The selective sweep analysis revealed some interesting candidate genes and pathways under selection. The pathways affected by artificial or natural selection include the Wnt signaling, GnRH receptor and integrin signaling pathways. Most selection signatures were located in non-coding regions of the genome of tilapia. The data of our study could be important for speeding up genetic improvement of the tilapia populations and studying the mechanisms underlying phenotypic variations.Tilapia samples were collected from South Africa, China and Singapore. The detailed information about these samples is listed in http://cichlid.umd.edu/cgi-bin/gb2/gbrowse/Tilapia_broad_anchored_v1/?source=Tilapia_broad_anchored_v1). The software packages Bowtie 2Genomic DNA was extracted from each fish using a DNeasy Blood & Tissue Kit  according to the manufacturer’s instructions. DNA samples were sent for sequencing (101-bp pair-end reads) using Illumina HiSeq 2500 at Bejing Genome Institute . Paired-end data were then processed to filter out the low quality reads and adaptors using the software NGS QC Tool kitTo validate the SNPs that were identified from the NGS data, 96 SNP sites from the NGS dataset were randomly selected for validation of SNPs using Sanger sequencing. Primers  were desPrinciple component analysis (PCA) was conducted with the software EIGENSOFT (Ver. 5.0.1)http://tools.bioso.org/cgi-bin/amigo/slimmer) was used to examine if the selected genes were enriched for specific functional annotation clusters. Pathway analysis was conducted using the online tool Panther Classification System  with default parameters.The footprints of recent or ongoing selection were detected by the tool Rehh using EHH-related statistics with default settingHow to cite this article: Hong Xia, J. et al. Signatures of selection in tilapia revealed by whole genome resequencing. Sci. Rep.5, 14168; doi: 10.1038/srep14168 (2015).


Understanding the underlying pleiotropic relationships among quantitative traits is necessary in order to predict correlated responses to artificial selection. The availability of large-scale next-generation sequence data in cattle has provided an opportunity to examine whether pleiotropy is responsible for overlapping QTL in multiple economic traits. In the present study, we examined QTL affecting cattle stillbirth, calf size, and adult stature located in the same genomic region.A genome scan using imputed whole genome sequence variants revealed one QTL with large effects on the service sire calving index (SCI), and body conformation index (BCI) at the same location (~39 Mb) on chromosome 6 in Nordic Red cattle. The targeted region was analyzed for SCI and BCI component traits. The QTL peak included LCORL and NCAPG genes, which had been reported to influence fetal growth and adult stature in several species. The QTL exhibited large effects on calf size and stature in Nordic Red cattle. Two deviant haplotypes (HAP1 and HAP2) were resolved which increased calf size at birth, and affected adult body conformation. However, the haplotypes also resulted in increased calving difficulties and calf mortality due to increased calf size at birth. Haplotype locations overlapped, however linkage disequilibrium (LD) between the sites was low, suggesting that two independent mutations were responsible for similar effects. The difference in prevalence between the two haplotypes in Nordic Red subpopulations suggested independent origins in different populations.Results of our study identified QTL with large effects on body conformation and service sire calving traits on chromosome 6 in cattle. We present robust evidence that variation at the LCORL and NCAPG locus affects calf size at birth and adult stature. We suggest the two deviant haplotypes within the QTL were due to two independent mutations.The online version of this article (doi:10.1186/s12863-015-0202-3) contains supplementary material, which is available to authorized users. Genetic correlation between two complex (quantitative) traits may be due to pleiotropy, i.e. the same polymorphism affecting both traits, or linkage disequilibrium (LD) between loci affecting individual traits. Genome-wide association studies (GWAS) have identified many genetic loci that harbor variants associated with multiple traits, which highlights the traits sharing common genetic pathways, and emphasizes the relevance of pleiotropy in complex traits . TherefoIn a genome scan using whole genome sequence variants, we discovered QTL with a large effect on the service sire calving index (SCI), and body conformation index (BCI) at the same location (~39 Mb) on chromosome 6 (BTA6) in a Nordic Red cattle population  variants for Nordic Red cattle bulls was done in two steps. First, high density (HD) genotypes were imputed from 50 k genotypes using HD multi-breed reference genotype data. Then the imputed HD genotypes were further imputed to WGS variants using a multi-breed WGS reference. Genome-wide association analysis with the imputed WGS variants was done in two steps: 1) using a linear mixed model without considering the relationship among all individuals except half-sib family (sire-son) relationships and 2) followed by linear mixed model (LMM) analysis within the targeted region considering relationships among all of the bulls. A genome-wide scan with WGS variants using LMM considering the relationships among all individuals is computationally prohibitive. Haplotypes were constructed using top associated markers assuming they all had high linkage disequilibrium with the causal variant and haplotype based association was carried out using LMM analysis.http://www.nordicebv.info.No animal experiments were performed in this study, and, therefore, approval from the ethics committee was not required. The study was conducted in Nordic Red cattle (RDC) from Denmark (RDCDNK), Finland (RDCFIN), and Sweden (RDCSWE). The number of progeny tested bulls with breeding values for traits and genotypes is provided in Table BCI) is a compound index describing a sire’s genetic potential to produce desirable characteristics for various body measures in progeny. It is predicted as a linear combination of breeding values for stature, body depth, chest width, dairy form, top line, rump width, and rump angle. The definition of conformation traits used in this study was consistent with the International Committee for Animal Recording (ICAR) standards (http://www.icar.org/Documents/Rules%20and%20regulations/Guidelines/Guidelines_2012.pdf). The type traits were scored using a linear scale from 1 – 9 in Denmark, Sweden, and Finland. These records were corrected for calving age, lactation number, interval from calving, and optimum values fixed for each individual trait for each breed. In the joint Nordic evaluation for conformation traits, data from Finland, Sweden and Denmark were included.The indices for linear conformation traits describe different aspects of cow conformation. The linear traits were combined into trait groups describing the overall body form. The body conformation index (CI) is a compound index reflecting a sire’s additive genetic effects for calving ease and calf survival in progeny. SCI was predicted by combining service sire breeding values for the following: 1) stillbirth in first (SSBF) and later (SSBL) lactations; 2) calving ease in first (SCEF) and later (SCEL) lactations; and 3) calf size in first (SCSF) and later (SCSL) lactations. We followed Boelling et al. to estimate breeding values for calving traits in Denmark, Sweden, and Finland [Service sire calving index (S Finland . The her Finland .Accuracies (r) of de-regressed proofs used in this study were high. Average accuracies for DRPs ranged from 0.73 (chest width) to 0.89 (stature) for type traits. Average accuracies ranged from 0.57 (SCSL) to 0.75 (SSBF) for SCI component traits. For example, the DRP accuracy distribution for trait stature is presented in Additional file 2-test, 1 df, P < 0.00001) were excluded. The SNP number after quality control was 43,415 in the 50 k dataset. In addition, a multi-breed reference of 2,036 genotypes  using the Illumina BovineHD Genotyping BeadChip was available in-house, and from the EuroGenomics consortium [Genomic DNA was extracted from whole blood or semen. Bulls were genotyped using the Illumina Bovine SNP50 BeadChip  version 1 or 2. The SNP selection quality parameters were an 85% minimum call rate for individuals and 95% for loci. Marker loci without a known map position, with minor allele frequencies (MAFs) < 5%, and deviation from Hardy Weinberg proportions  < 0.90 were removed. The total marker number for chromosomes 1–29 was 8,938,927. The imputation accuracy for this data was reported earlier [The 242 dairy cattle whole genome sequences were from Aarhus University, and the 1000 bull genomes project. The reference included 132 Holstein, 42 Jersey, 52 Nordic Red, and 16 Brown-Swiss bulls . Markers10  = 8.25).A genome scan was conducted for BCI and SCI using a sire model without considering relationships among individuals other than between sire and son. The model is described in the Additional file j is the phenotype (de-regressed EBV) for individual j, μ is the overall mean, b is the allelic substitution effect, xj is the number of copies of an allele (with arbitrary labeling) of the SNP count in individual j , uj are random polygenic effects, рk is the effect of the k-th population , and ei is the random environmental deviate for individual j. The vector u = {uj} follows a multivariate normal distribution A is the additive relationship matrix, and e = {ej} follows a multivariate normal distribution W is the diagonal matrix containing weights of de-regressed estimated breeding values. The weight wj for individual j is estimated as rj2 is the reliability of the de-regressed EBV of individual j. Values of t-test against a null hypothesis of H0: b = 0, and the same Bonferroni multiple testing corrected significance threshold  = 8.25) was used as in the genome scan  was applied for association analyses of the BTA6 targeted region for the two indices (BCI and SCI), and the component traits Table . The asssoftware . DMU wasmentclass2pt{minimqh1j and qh2j are random effects of the two haplotypes carried by the j-th individual. Each haplotype effect is assumed independent and identically distributed (i.i.d), with a normal prior distribution 2 distribution with 1 df. The analysis was performed using the DMU software package [Haplotypes were constructed for markers selected from LMM analyses using BEAGLE software . We used package .Tests were conducted to examine if a significant candidate SNP or haplotype alone was sufficient to explain the variation resulting from a QTL region. Analyses were repeated with incorporation of the candidate SNP or haplotype as a fixed effect. If other SNPs or haplotypes remained significant in the presence of the new systematic effect, the original candidate alone was not sufficient to account for the entire effect of the QTL region.10  = 57.5, Figure The genome-wide associations scan using the sire model  was 89.4  = 57.9). This SNP was also the most significantly associated SNP for calf survival, i.e. stillbirth in first (SSBF) and later (SSBL) lactations. The SNP located at 38,956,499 bp showed the most significant association with calf size and calving ease. The allele substitution effect of this SNP on calf size and calving ease exhibited an inverse association, i.e. the allele increasing calf size at birth increased calving difficulty. The SNP most significantly associated with body conformation was at 38,914,033 bp  = 80.4). It was located 213,330 bp from the SNP most associated with SCI. The SNP most significantly associated with stature was at 38.13 Mb  = 81.9). However, a number of SNPs around 39.77 Mb showed a very strong association with stature  = 80.6).The Linear Mixed Model (LMM) analyses for conformation and calving indices and their component traits revealed overlapping peaks in the targeted region on BTA6 (~37.5-40.5 Mb). The most significant associated SNPs from LMM for calving, conformation and the component traits are presented in Table 10  at ~106  at ~80  for the targeted region among all traits analyzed was observed for SCSL  and later (SCEL) lactations, and calf size in first (SCSF) and later (SCSL) lactations. The second haplotype was constructed using the most significant markers for BCI  and in later (SSBL) lactations, and body-depth. These two haplotypes were fitted individually and jointly in the RHM to determine if one or both haplotypes were sufficient to explain the QTL variance observed in the targeted region for the traits analyzed.Two genomic regions were selected for haplotype based association analyses. The highest –logL Figure . The posL Figure , 3 and 4I Figure , 3 and 410 (P) = 106.9  between the two haplotype doses was 0.064, indicating very low LD between the two haplotype blocks.HAP1 and HAP2 were fitted as fixed effects in the mixed model to determine variance explained by these two haplotypes. A significant association for BCI Figure  and SCI In the present study, we reported QTL with a large effect on calf size and stature in Nordic Red cattle. We identified two deviant haplotypes that increased calf size at birth, and altered adult body conformation. However, the haplotypes also increased calving difficulties, and subsequently calf mortality due to the increase in calf size. Results indicated the haplotype locations overlapped. However, the LD between sites was low, suggesting two independent mutations responsible for similar effects. Several studies reported QTL in the same genomic region on chromosome 6 impacting birth weight, growth, and carcass traits in beef breeds and crossbreds -19. In dThe cattle population we used for association analysis had both population structure (three sub-populations) and family relationship . Therefore a linear model including the full relationship matrix is necessary to avoid false positive associations ,34. HoweWe observed reduced haplotype diversity at the QTL peak compared to the rest of the chromosome Figure . One of Soranzo et al.  studied The NCAPG and LCORL loci have been investigated in beef cattle populations for effects on carcass traits and body weight. A missense variant (rs109570900) at 38,777,311 bp within NCAPG, which induced a Ile-442-Met substitution in amino acid sequence was associated with carcass weight, the longissimus muscle area, and subcutaneous fat thickness in Japanese Black and Brown cattle . The samr = 0.62 in RDCDNK. This NCAPG gene missense variant was earlier reported from several cattle populations, e.g. the variant exhibited the strongest effect on birth weight in Holstein × Charolais [We observed two haplotypes within overlapping regions on BTA6, which affected SCI and BCI in Nordic Red cattle. Correlation between imputed haplotype doses for the two haplotype blocks was low, indicating low LDs. Haplotype distinctness was also supported by the model fit. The best model fit was obtained when HAP1 and HAP2 were included in the model. Marked frequency differences were observed in HAP1 and HAP2 from the three country RDC populations. HAP1 was common in RDCDNK (frequency = 0.42), but rare in RDCFIN 0.003) and RDCSWE (0.019). In contrast, HAP2 was rare in RDCDNK (frequency = 0.04), but common in RDCFIN (0.32) and RDCSWE (0.21). We checked the functional annotations for markers included in HAP1 and HAP2. All 15 markers from the LCORL gene, which form HAP1 were intronic variants  with similar effects on BCI and SCI. The two haplotype locations overlap, but LD between the sites was low. HAP1 frequency was common in RDCDNK, but rare in RDCFIN and RDCSWE, while HAP2 showed reverse frequencies, i.e. common in RDCFIN and RDCSWE, but rare in RDCDNK. Principal component analysis provided clear evidence RDCDNK was genetically divergent from RDCFIN and RDCSWE (results not shown). Therefore, the two haplotypes might have had independent origins in different populations. If the two haplotypes represent two distinct mutations on the same gene, then this would provide an explanation why they had the same effect on calf size at birth and adult stature.https://www.cdcb.us/eval/summary/trend.cfm?R_Menu=HO.sb#StartBody eval/summary/trend.cfm?R_Menu = HO.sb#StartBody, accessed 30 June 2014). In contrast, first lactation stature in Canadian Holstein has demonstrated steady genetic gain; average stature has increased from < 143 cm in 1991 to 147 cm in 2001 . The haplotypes identified in Nordic Red cattle increased adult body conformation. The present BCI breeding goal in Nordic Red cattle is for an optimum; however, the genetic trend for stature over time has been positive [Opposite trends for calving ease and stature had been reported during the last three decades. For example, the average breeding value (PTA) for daughter calving ease in US Holstein cattle have decreased from 9.3 in 1980 to 6.6 in 2009, while PTA for daughter stillbirths has decreased from 10.6 to 7.2 for the same period  responsible for the mutation(s) requires further study.ftp://ftp.ensembl.org/pub/release-73/fasta/bos_taurus/dna). All variations used in the mapping study have been submitted by the 1000 bull genomes project for inclusion in dbSNP (http://www.ncbi.nlm.nih.gov/SNP). Whole genome sequence data for the 234 individuals included in run2 of the 1000 bull genomes project are available at NCBI using SRA no. SRP039339 (http://www.ncbi.nlm.nih.gov/bioproject/PRJNA238491). All annotations were obtained from a publicly available source (http://www.ensembl.org) downloadable including through Variant Effect Predictor (http://www.ensembl.org/info/docs/tools/vep/script/index.html).All DNA sequences used were taken from a publicly available assembly. The assembly is available for download (


DNA sequencing is likely to become a standard typing method in forensics in the near future. We define a microhaplotype to be a locus with two or more single nucleotide polymorphisms (SNPs) that occur within a short segment of DNA  that can be covered by a single sequence run and collectively define a multiallelic locus. Microhaplotypes can be highly informative for many forensic questions, including detection of mixtures of two or more sources in a DNA sample, a common problem in forensic practice.e, converts the unequal allele frequencies at a locus into a value that is equivalent to some number of equally frequent alleles, allowing microhaplotype loci to be ranked. The expectations for the ability to qualitatively detect mixtures are given for different integer values of Ae, and the cumulative probabilities of detecting mixtures based on testing multiple microhaps are shown to exceed 95% with as few as five loci with average Ae values of even slightly greater than 3.0.When all alleles are equally frequent, the probability of detecting three or more alleles in a mixture is at maximum. The classical population genetics concept of effective number of alleles at a locus, termed Ae values of >3 will be exceedingly useful in ordinary forensic practice. Based on our studies, 3-SNP microhaplotypes will sometimes meet this criterion, but 4-SNP microhaplotypes can even exceed this criterion and have values >4.Microhaplotypes with AThe online version of this article (doi:10.1186/s13323-014-0018-3) contains supplementary material, which is available to authorized users. With the arrival of inexpensive DNA sequencing appropriate for forensic applications, we have reexamined the optimal type of genetic marker for forensic applications. It is clear that several relevant forensic questions cannot be answered by the existing or expanded set of short tandem repeat polymorphisms (STRPs) used by combined DNA index system (CODIS) ,2. The CSingle nucleotide polymorphisms (SNPs) can enhance the individual identification statistics beyond what the STRPs provide either as nucleotide variants within individual STRP alleles or as a separate set of markers -5. By thOur lab has long used minihaplotypes (minihaps) in anthropological studies -16. Thes−30 with fewer than 50 loci [In our initial studies ,18 of ma 50 loci ,19 and an + 1 alleles that can be generated by n accumulated mutations. However, we often see an allele (haplotype) that could only have arisen by a crossover among the SNPs within a microhap. In many cases, those appear to be single historical crossover products that have drifted to high frequency and are not recurring at a meaningful frequency of, say, >10−4. These considerations help us avoid pursuit of loci that may be hypervariable due to frequent mutation, frequent recombination, and/or errors in the datasets screened.Heterozygosity is the maximum possible when all alleles are equally frequent, and this clearly is optimal for individual identification for any specified number of alleles. For ancestry inference, high heterozygosity is less important than frequency variation among populations. However, in using large datasets generated from either chip-based genotyping or from whole-genome MPS, we have found that many of the loci that have the highest variation among populations represent either genotyping error or assembly errors in MPS analysis for the population(s) with more deviant frequencies. For lineage and ancestry inference, identity by descent (IBD) is important. Loci that may be hypervariable due to frequent mutation and/or recombination would complicate determining IBD among extended relatives or within a tribe of moderate size. Therefore, we have chosen to focus on loci that have most alleles at >5% (arbitrarily chosen); a frequency greater than frequent recombination is likely to generate and a frequency greater than typing errors. We could also limit selection to loci that have only the p + q + r)4 = (p + q + r)2 (p + q + r)2, which is the product of the Hardy-Weinberg genotype arrays for two random unrelated individuals. As shown in Figure p + q + r + s)4, the maximum occurs when all alleles are equally frequent. Conceptually, this maximum occurs at the center of a tetrahedron with each face identical to Figure Given the above consideration, how do we maximize the ability to detect mixtures? We are interested in qualitative determination that a mixture is present in a forensic sample. That occurs when three or more distinct alleles are observed. Obviously, an infinite number of alleles maximizes uniqueness of individuals and hence the difference between any two individuals. To consider the issue for a small finite number of alleles, we have started with the simplest case of three alleles. To evaluate the probability of a mixture having more than two alleles present, we have simply used a multinomial expansion to calculate the probability of a mixture of DNA from two independent individuals having at least three different alleles as a function of the allele frequencies. The function for three alleles is simply n. Table e from 3 to 5. Obviously, mixtures of loci with different Ae values will give intermediate results as will loci with non-integer Ae values between 3 and 5. We have not extended the table beyond 5 loci since the probability rapidly approaches certainty and we have yet to find a locus with a global average Ae of 5 or greater. Nor have we considered numerically the issue of a mixture of more than two individuals, but the logic applies to such cases as well and detecting five alleles indicates at least a three person mixture.The A 1-1–.6565n. Tablee for the 31 microhaps published [e, we decided to use Ae as an effective way to compare loci and to focus on microhaps with an Ae >3.0. Microhaps with four SNPs are an interesting subset to consider because they can have an Ae considerably greater than 3.0. While there are potential microhaps with more than four SNPs, we found that the pool of potential microhaps with SNP frequency data for all SNPs in the microhap, and with individual SNP heterozygosities above 18%, was very small. By focusing on 4-SNP microhaps, there still remained a large enough pool to be able to look at frequency distributions and the pairwise correlations between SNP frequencies in the microhaplotype. We developed empiric thresholds based on these distributions. In our search of the 1000 Genomes data using criteria described in Additional file e for each locus in each population using the haplotype frequencies calculated by PHASE [e for each of the 341 microhaps ranged from 1.79 to 6.96. The average Ae for each of the 20 populations ranged from 2.92 to 3.64.After identifying a potentially useful microhap in large public data sets, we used our set of DNA samples from over 2,500 individuals originating from 54 populations to confirm the pattern of variation and obtain statistics from a global sample of populations ,18. We sby PHASE . Considee values among the 2-SNP, the 3-SNP, and the 4-SNP microhaps that we have now tested on over 60 populations  may be unknown for a forensic analysis, by the global average Ae. However, it is important to recognize that by selecting for high average Ae to maximize mixture detection, we are tending to reduce large regional differences in allele frequencies and thus choose less ancestry informative loci. In future papers, we will discuss criteria for identifying and evaluating optimal microhaps for ancestry and lineage inference.Figures e of >3.0. While 17 of the 27 two-SNP microhaps we have already published [e >3.0, global averages are generally decreased especially due to Native American populations, where decreased genetic diversity means only 4 of the 2-SNP microhaps have Ae >3.0 in these populations. Microhap048 has all four possible haplotypes but the unequal frequencies result in a global average Ae of only 2.71, the second best of those we have examined. Microhap046 also has all four possible haplotypes and the highest Ae for the 2-SNP microhaps; the allele frequencies are seemingly more uneven but the global average Ae is slightly larger, 2.84. The 3-SNP microhaps allow for a maximum of eight microhaps, but our selection criteria have generally identified loci with fewer than eight alleles. Microhap049 is the second best with five of the eight possible haplotypes at common frequencies in all regions of the world with an additional microhap seen only in the Americas. The global average Ae of 2.89 is barely larger than the best of the 2-SNP microhaps. Microhap061 also has five haplotypes with similar frequencies among most populations except Native Americans and has a global average Ae of 3.12. The 4-SNP microhaps we have studied so far allow a much greater number of alleles and the two illustrated show considerable within-population variation. Microhap Tetrad180 has six haplotypes that occur essentially globally, and its Ae of 4.008 is the second best. Finally, microhap Tetrad315 has the most allelic complexity with 10 of the 16 possible haplotypes reaching frequencies >5% in many populations and a global average Ae of 4.76, the highest global average we have found to date.With two SNPs and a maximum of four haplotypes, we have yet to find a microhap with a global average Aublished  have indMicrohap Tetrad315 illustrates our concern about microhaps with frequent recombination for ancestry inference. We have not yet been able to document that most of the variation is old and not frequently regenerated although it is clear that certain combinations among the more common haplotypes are not seen at the frequencies that might occur if recombination were very frequent. Even a high recombination rate of 1% would still allow meaningful lineage inference with several loci such as this. However, that concern is not relevant to individual identification and mixture detection and deconvolution. Those rely on the allele/haplotype frequencies in the population, not on the ongoing origins of the alleles or identity by recent descent of the different alleles. This locus and the other 4-SNP microhap illustrated, Tetrad180, will be able to provide excellent mixture detection ability.The calculations we have made assume that the contributions from the two individuals can be detected. Issues of sensitivity enter when the contributions are not equal, as is almost always the case in forensics. Those issues are dependent on the method used and, since we assume sequencing will be used, on the depth of coverage, i.e., the number of reads per locus. With an average of >100 reads per allele, a minimal expectation for MPS, a third allele can be said to be present if several reads detect it. With experience, not yet available, the community can decide on the criteria necessary to overcome the stochastic aspect of number of reads of an allele. With a clear stochastic threshold, quantitative estimation of the relative contributions of individuals to a mixture is clearly possible. We note that these concerns are no different from those currently used for the CODIS STRPs. Considerable experience exists with these STRPs yet there are different criteria used in different labs to evaluate whether a low RFU peak represents chance or a real peak. With MPS, even very low levels of a second contributor to a mixture could result in multiple reads with a unique combination of SNP alleles (a unique haplotype) on single reads, giving multiple confirmation of the presence of more than one DNA. In contrast to many evaluations of mixtures using STRPs that require decisions surrounding stutter peaks and low RFU peaks in mixtures, MPS microhaps are clearly superior. We think that sequence will have less ambiguity at specific nucleotides and make low levels of mixture detectable, but actual experience is needed.e measures due to cryptic variation unseen by our pilot SNP-typing method. A rare SNP documented at 1% most likely subdivides some haplotype. Similarly, even the one-off variants already documented will provide what is likely a rare unique haplotype. From the available data, one cannot estimate how the combinations of these untested SNPs/variants with the tested SNPs will resolve into haplotypes but additional information will exist. For example, Microhap048, a 2-SNP microhap, has additional documented SNPs/variants within that 200 bp. Similarly, MicroTetrad315 has additional variants. The additional variation already documented for these two microhaps is summarized in Table To date, we have evaluated candidate microhaps using TaqMan assays to detect the individual SNPs that we know from our database screens will very likely yield useful loci. If we had sequencing data instead of genotype data, we would be able to show greater AIn the course of assembling a large number of microhap loci, we have come to recognize that a general nomenclature is advisable. Use of the specific SNPs involved is too cumbersome for use in ordinary text, and cross-referencing among publications from different laboratories will become difficult. Table e. Ranking loci by global average Ae values for loci we have already studied shows that a 4-SNP microhap can have very high values, but still less than five effective alleles. Even at an average Ae of 4, the probability of qualitative detection of a mixture is greater than 99% with just five such loci tested.While forensic researchers have demonstrated the utility of DNA analysis even in high-volume crimes , costs o


The multiple-SNP analysis has been studied by many researchers, in which the effects of multiple SNPs are simultaneously estimated and tested in a multiple linear regression. The multiple-SNP association analysis usually has higher power and lower false-positive rate for detecting causative SNP(s) than single marker analysis (SMA). Several methods have been proposed to simultaneously estimate and test multiple SNP effects. In this research, a fast method called MEML  was developed for simultaneously estimate of multiple SNP effects. An improved Lasso prior was assigned to SNP effects which were estimated by searching the maximum joint posterior mode. The residual polygenic effect was included in the model to absorb many tiny SNP effects, which is treated as missing data in our EM algorithm. A series of simulation experiments were conducted to validate the proposed method, and the results showed that compared with SMMA, the new method can dramatically decrease the false-positive rate. The new method was also applied to the 50k SNP-panel dataset for genome-wide association study of milk production traits in Chinese Holstein cattle. Totally, 39 significant SNPs and their nearby 25 genes were found. The number of significant SNPs is remarkably fewer than that by SMMA which found 105 significant SNPs. Among 39 significant SNPs, 8 were also found by SMMA and several well-known QTLs or genes were confirmed again; furthermore, we also got some positional candidate gene with potential function of effecting milk production traits. These novel findings in our research should be valuable for further investigation. Single marker analysis (SMA) is the most practical way for genome-wide association study (GWAS), in which each SNP is tested at a time along the genome. Assuming SNP is in linkage disequilibrium with a casual mutation or just a casual mutation, the association of a functional gene can be tested with its nearby SNP. Although the SMA provides a simple and fast way for genome-wide QTL mapping, it neglects the effects of other genes on the genome when only one SNP is tested. Because a single SNP only explains a very small portion of genetic variation, SMA may not be powerful for identifying weaker associations that may result from small allelic effects, low minor allele frequencies (MAF), or weak correlations with genotyped markers Compared with SMA, the multiple-SNP association study can simultaneously consider multiple QTL effects, and thus can reduce the estimate of error variance, and in turn increases the power to detect weaker associations and decrease the false-positive rate for QTL detection The genome-wide association study of economic important traits of domestic animals has been conducted by many researches In this research, we propose an Expectation-Maximization algorithm http://www.computationalgenetics.se/QTLMAS08/QTLMAS/DATA.html) p values in single-marker mixed-model analysis (SMMA), and then simultaneously analyzed these SNPs using our multiple-SNP method. It is a similar strategy adapted by The simulated dataset includes six chromosomes with a total length of 600 cM, on which.6,000 SNP markers are distributed with average marker interval of 0.1 cM. The pedigree spans four generations and includes 4,665 individuals with known genotypes and phenotypic values. The detailed descriptions of the dataset can be found from its official website  were shrunk to zero except several large-effect SNPs. Eleven SNPs were found to be significant out of the 15 simulated major-effect QTLs, and except several SNPs closely linked with the major-effect QTLs only 3 false -positive signals are showed.10p values from SMMA, where the threshold was determined with 1,000 permutations. It can be seen that almost all the simulated QTL were detected, but hundreds of false-positive signals were shown.To further validate the efficiency of the proposed method, we employed replicated simulation experiments to test the true-positive rate (power) and the false-positive rate. A population included two generations each had 2,000 individuals were simulated. The first generation included 50 full-sib populations each with 40 individuals; and the second generation included 20 full-sib populations each with 100 individuals. The genome and QTL (position and effect) were simulated according to QTL-MAS XII dataset. We totally simulated 6 chromosomes each contained 1,000 evenly spaced SNP markers with total length of 1M. The markers in disequilibrium were created using the gene-dropping method p-value of the Hardy-Weinberg Equilibrium (HWE) test less than 10−6. Eventually, 40,829 SNPs remained for the subsequent analyses. We selected 500 SNPs with the lowest p-values in SMMA, and then simultaneously analyzed them using the proposed method.The Chinese Holstein cattle population contained 14 sires and their 2,093 daughters, and the numbers of daughters of the 14 sires ranged from 83 to 358. The estimated breeding values (EBVs) of five milk production traits, including milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), and protein percentage (PP) were used as phenotypes in this study Each individual was genotyped for 54,001 SNP markers using the Illumina BovineSNP50 BeadChip  in these regions were found with MEML. In total, 39 SNPs were detected to be significantly associated with milk production traits; however, overall 105 significant SNPs were found with SMMA http://www.biomart.org/) was used to search the SNP nearby (±200 kb) these genes. The SNP ARS-BFGL-NGS-4939 which is located within DGAT1 (diacylglycerol O-acyltransferase 1) were found to have very large effects on all five milk production traits. The function of other genes associated each milk yield trait will be discussed later.a, b) with a and b being very small numbers to the hyper-parameter a,b) with a and b being small numbers are the key of MEML. Although the prior Gamma performed well in BIDE, it will not be meaningful in MEML, which can be seen from a and b to some other small numbers , but no obvious differences were shown.In this research, a new EM algorithm was developed to simultaneously estimate multiple-SNP effects, which uses an improved lasso prior for marker effects. Usually, the priors of coefficient is very important in multiple linear regression, which may bring special results for shrinking coefficient effects Yi and Banerjee proposed a hierarchical generalized linear model for multiple QTL mapping p-values in single-marker analysis for the multiple-SNP analysis. Compared with full-marker analysis , this strategy can save a lot of computational time, so one can use permutation test to ascertain the threshold value to declare the significance of a SNP. In both simulation study and real-data analysis, we selected 500 SNP markers for the analysis, and it performed well. We also varied the selected SNP numbers from 200 to 2000, and no clear differences were found (data not shown), which reflects MEML was not very sensitive to the selected SNP numbers.Following DGAT1 on chromosome 14 had the largest effects for all milk production traits. It has been concluded by many researches that a causative mutation in DGAT1 affects variation in milk production traits. On chromosome 20, a mutation in GHR (growth hormone receptor) has been identified to have a large effect on milk yield and composition. The SNP ARS-BFGL-NGS-118998 which is exactly located in this gene was detected to have large effect on fat percentage in our results, but we did not detect this SNP for milk yield. Moreover, Cohen-Zindel et al. identified mutations in the ABCG2 (ATP-binding cassette sub-family G member 2) gene on chromosome 6 which have an effect on protein percentage ABCG2 gene in a distance less than 100 kb. Compared with our previous research by SMMA In our results from the real data analysis, the SNP ARS-BFGL-NGS-4939 which is located within SLC40A1 (solute carrier family 40 member 1) gene on chromosome 2. SLC40A1 encodes ferroportin which is an iron transporter involved in iron export from cell, and it is essential for iron homeostasis. Moreover, Duan et al. found that the polymorphisms of this gene as well as TRF2 (transferrin receptor 2) were significantly associated with beef iron concentration JARID2  that encodes protein Jumonji (JMJ). JMJ also interacts physically with a transcriptional activator, ZNF496 (zinc finger protein 496), which inhibits the transcriptional repression of JMJ, which plays important roles in embryonic development. Golik et al found that a SNP in bovine ZNF496 gene displayed significant population-wide linkage disequilibrium with milk protein percentage in the Israeli Holstein population JARID2 also has value to be investigated in further research. SNP ARS-BFGL-NGS-111443 associated with both fat percentage and protein percentage is located between the DERA  gene and the SLC15A5  gene. SLC15A5 is also a member of the solute carrier family like SLC40A1 mentioned above, thus it of value to investigated whether it is a causal gene for milk composition.Besides those genes which already investigated in previous studies, in our result, we also detected other potential functional candidate genes nearby these associated SNPs. SNP ARS-BFGL-NGS-11319 associated with milk yield, is located nearby the We have presented the MEML method for multiple-SNP analysis, and it was found to be efficient for genome-wide association study both in simulation experiments and real-data analysis. The key feature of it is that it can dramatically reduce the false-positive SNPs number. Thus, in practice, one can first roughly select many suspicious markers using SMMA and then further confirm them using MEML.n individuals with p SNP markers being investigated, the multiple-SNP model can be expressed asiy is the phenotype of the ith individual; jth SNP marker of the ith individual, which is assigned 1, 0 and −1 for genotype AA, Aa and aa, respectively. jth SNP marker; ig is the residual polygenic effect for the ith individual, and ie is the residual error, which follows a normal distribution, Consider i.e.,a,b) with a and b being very small numbers, and here both a and b are taken as 10−6. The special prior was to have special characters in that it could estimate zero-effect marker effect very close to zero A is the additive genetic relationship matrix, which can be inferred from pedigree.The population mean follows a uniform prior, Let g cannot be derived explicitly, it is treated as missing data here and substituted with its posterior expectationSince the polygenic effect g is The posterior variance of The M-step maximizes the logarithm of the likelihood (6) Given the initial values for jth SNP can be expressed as jp is the allele frequency of the jth SNP. The heritability of the SNP can be written as The variance of the


Antioxidant defence system, a highly conserved biochemical mechanism, protects organisms from harmful effects of reactive oxygen species (ROS), a by-product of metabolism. Both invertebrates and vertebrates are unable to modify environmental physical factors such as photoperiod, temperature, salinity, humidity, oxygen content, and food availability as per their requirement. Therefore, they have evolved mechanisms to modulate their metabolic pathways to cope their physiology with changing environmental challenges for survival. Antioxidant defences are one of such biochemical mechanisms. At low concentration, ROS regulates several physiological processes, whereas at higher concentration they are toxic to organisms because they impair cellular functions by oxidizing biomolecules. Seasonal changes in antioxidant defences make species able to maintain their correct ROS titre to take various physiological functions such as hibernation, aestivation, migration, and reproduction against changing environmental physical parameters. In this paper, we have compiled information available in the literature on seasonal variation in antioxidant defence system in various species of invertebrates and vertebrates. The primary objective was to understand the relationship between varied biological phenomena seen in different animal species and conserved antioxidant defence system with respect to seasons. Types of seasons and their duration may vary from one ecological region to another and influence the physiology of the inhabiting flora and fauna . VariatiMetabolism is a direct reflection of cellular respiration where molecular oxygen is tetraelectronically reduced to water in electron transport chain of mitochondria to generate ATP to meet their energy requirement . NeverthThere are several reports in literature which have clearly demonstrated alterations in concentrations of various pollutants in air and water with respect to seasons , 15. GenBeing highly reactive and nonspecific in nature, ROS usually oxidize biomolecules such as lipids, carbohydrates, proteins, and DNA and, thereby, impair normal cellular functions. A shift in balance between oxidants to antioxidants in favour of oxidants is termed as “oxidative stress.” Oxidative stress is considered as cause or effects of several pathophysiological conditions, diseases, and aging processes .2•− to H2O2. Hydrogen peroxide is neutralized by two enzymes. One of them is catalase (CAT) and the other one is glutathione peroxidase (GPx) [2O2 to oxygen and water, whereas GPx reduces H2O2 and organic hydroperoxides by coupling them with oxidation of reduced glutathione (GSH). Glutathione reductase (GR) plays a major role in generating reduced glutathione from oxidized glutathione by oxidizing NADPH. Subsequently, NADPH is generated from NADP by the enzyme glucose-6-phosphate dehydrogenase (G6PDH) . Antioxidant enzymes are ubiquitous and highly conserved in their catalytic nature. Some of them are present in multiple forms. The first member of this cascade is superoxide dismutase (SOD) which dismutates Ose (GPx) . Catalas (G6PDH) . SOD is t stress . Nonenzyt stress .Animals have developed various physiological strategies such as hibernation, aestivation, and migration because they do not have control over the physical components of the environment such as food availability, temperature, and day length . Since aThe central focus of this mini review is to compile pieces of information available in literature on various parameters related to ROS and antioxidant defences in some vertebrates and invertebrates of different phyla with respect to seasons under one platform. Since components of antioxidant defence system are highly conservative, it will be interesting to find out how different species manipulate their antioxidant defences to adjust themselves to seasons.Metabolic activities and mitochondrial functions of invertebrates are regulated by several components of seasons such as photoperiod, temperature, humidity, and food availability –22. SincHowever, the major limitation in the use of antioxidant molecules or OS parameters as biomarkers to monitor environmental pollution is the lack of basic information on their seasonal variation. It is essential to ascertain the normal seasonal changes in antioxidant defence parameters before using them to monitor environment contamination. The lack of systematic information on the responses of antioxidant defences and OS markers of species to pollutants makes it difficult to use them as biomarkers to assess pollution. Laeonereis acuta, a polychaete, is commonly found in estuaries. The species is also used as biomarkers for pollution studies. Antioxidant enzymes such as SOD and CAT and OS parameter such as lipid peroxidation were investigated in the species with respect to seasons [Annelids (phylum Annelida) include ringworms, earthworms, and leeches. Annelids have adapted to various ecological zones which span aquatic (both marine and freshwater environments) to terrestrial environment. Seasonal variation of antioxidant defence system in species of this phylum is scanty. seasons . A highe seasons . It indi Mytilus edulis inhabiting same intertidal zone of France in two consecutive autumns were reported [ Mytilus to air was more (6 h/12 h) at high shore than that at low shore (2 h/12 h). The authors cautioned that for ecotoxicological studies due importance must be given on sampling sites [Molluscs (phylum Mollusca) encompass a large number of species that spread from aquatic to terrestrial ecosystem. Impacts of tidal heights on activities of antioxidant enzymes in gills and digestive glands ofreported . Resultsng sites . Mytilus galloprovincialis collected from an unpolluted site in Adriatic Sea of the species in October which was accompanied with an increase in CAT activity and LPx level [ Mytilus galloprovincialis were measured to monitor heavy metal pollution of the water of the Saronikos Gulf of Greece [It was reported that DNA strand fragmentation was elevated in tissues ofPx level . The autPx level . Activitf Greece . The autf Greece . A carefCrassostrea rhizophorae) also exhibits seasonal variation. Total oxygen radical scavenging capacity, level of LPx, and activity of GST in gills of immature adult mangrove oysters collected from polluted and nonpolluted sites of Brazilian coast were investigated in detail by Zanette et al. [Antioxidant defence system in oyster , small antioxidants (ascorbic acid and GSH), and OS markers (LPx and H2O2) were carried out in gills and digestive glands of natural population of green lipped mussel (Perna viridis) in Bambolim beach area of Goa, India [ P. viridis exhibited seasonal variation and, secondly, components of antioxidant capacity of tissues with respect to seasons varied from each other. Considering antioxidant defence parameters, both enzymatic and nonenzymatic components, the authors were of the opinion that changing environmental factors during seasons have crucial impact on regulating antioxidant defence mechanism in this species.Due to the sedentary nature, sessile-filter feeding, wide geographical distribution, and abundant availability,a, India . The autBalanus balanoides). They are dominant species in rocky shores. The authors noticed high levels of antioxidant enzymes and GST in digestive gland of the species in premonsoon period or summer followed by gradual decrease in their activities in monsoon with minimum levels in winter. Microsomal LPx exhibited a reverse trend of seasonal variation to that of antioxidant enzymes. Also the authors noticed a positive correlation between PAH and levels of antioxidant enzymes in the tissue.Most arthropod species are land dwellers and few are aquatic in nature. Niyogi et al.  studied Apis mellifera) revealed that levels of antioxidant enzymes and peroxides were higher in active season (August-September) in comparison to the other months [Scylla serrata) exhibited seasonal changes that are tissue as well as gender specific [A study on different antioxidant enzymes in honey bee  collected from creosote inlet of Elizabeth River were noticed from early to late summer [ Anguilla anguilla (European eel) and Mugil cephalus (stripped mullet), in Mediterranean lagoons in different seasons. Although responses of antioxidant defence parameters of fishes were influenced by temperature and reproductive cycle, the pattern of responses was species specific. Unlike striped mullet, antioxidant defence parameters of eels were independent of sexual maturation but were markedly influenced by high temperature of summer.Literature reveals that most of the studies related to seasonal variation of antioxidant defences in fish are investigated with respect to pollutants present in water. A decrease in GSH content and an increase in GR activity in hepatopancreas of kill fish population  from Southern Brazil suggested that changes in antioxidant enzymes with relation to season were not only species specific but also tissue specific [Barbus barbus) in spring than in summer but CAT activity was found to be in reverse order. It was reported that levels of CAT and GR enzymes in liver of Senegalese sole  exhibited seasonal variation [A comparative study on seasonal variation in antioxidant defence parameters and OS indices in tissues of four species of fish , a seasonal variation in OS in terms of generation of ROS was attributed to low availability of food [Hirundo rustica) by Raja-aho et al. [Birds are homeotherms. Some reports indicate occurrence of seasonal variation in their antioxidant defence system and OS status. Habitat and photoperiodism may be responsible for the observed seasonal variation of OS metabolism in their tissues. Poor habitat due to unavailability of food may induce intensive foraging activity which may increase generation of ROS in birds. In case of Seychelles warblers ( of food . A detaio et al. . It was o et al. .Mammals inhabiting temperate zone usually exhibit seasonal changes in their morphology, behavior, and physiology in order to cope with changing environment. Therefore, they can exhibit seasonal variation in their antioxidant defence system and OS status. Photoperiod has been shown to affect lipid droplets and LPx in bank vole . SeveralIn the present review an attempt is made to summarize information available in the literature on seasonal changes in antioxidant defence system and OS markers in invertebrates as well as in vertebrates. It is inferred that seasonal variation in antioxidant defence system may be an evolutionary strategy by animals for different adaptations against various physical aspects of environment. Also a far-fetched implication of changes in seasonal factors on global food chain cannot be denied as larval or embryonic forms of invertebrates constitute first line of primary productivity. It is apparent that various cues of seasons may regulate metabolic functions in tissues through appropriate receptors which in turn decide ROS and antioxidant defences in tissues and, thereby, govern various physiological activities in the animals .