CIITA (rs6498115), SMC6 (rs1834619), and KLHL29 (rs2288697) were most differentiated in the Amerindian-specific branch, while SNPs in the genes ADAMTS9 (rs7631391), DOCK2 (rs77594147), SLC28A1 (rs28649017), ARHGAP5 (rs7151991), and CIITA (rs45601437) were most highly differentiated in the Asian comparison. These genes are known to influence immune function, metabolic and anthropometry traits, and embryonic development. These analyses have identified candidate genes for selection within Amerindian ancestry, and by comparison of the two analyses, those for which the differentiation may have arisen during the migration from Asia to the Americas.Despite its population, geographic size, and emerging economic importance, disproportionately little genome-scale research exists into genetic factors that predispose Brazilians to disease, or the population genetics of risk. After identification of suitable proxy populations and careful analysis of tri-continental admixture in 1,538 North-Eastern Brazilians to estimate individual ancestry and ancestral allele frequencies, we computed 400,000 genome-wide locus-specific branch length (LSBL) Fst statistics of Brazilian Amerindian ancestry compared to European and African; and a similar set of differentiation statistics for their Amerindian component compared with the closest Asian 1000 Genomes population . After ranking SNPs by these statistics, we identified the top 10 highly differentiated SNPs in five genome regions in the LSBL tests of Brazilian Amerindian ancestry compared to European and African; and the top 10 SNPs in eight regions comparing their Amerindian component to the closest Asian 1000 Genomes population. We found SNPs within or proximal to the genes  In the last three decades, Brazil has undergone a rapid transition from low-middle income to a burgeoning high-income country, and this economic development has led to a public health paradox; the diseases of poverty such as infectious diseases and malnutrition, although in decline, still exist , but nowhttp:www.funai.gov.br; last accessed October 13, 2015). In the period after initial discovery by Europeans, migration gradually increased so that by 1,760, approximately 700 thousand Europeans had migrated to Brazil  substantially contribute to the variable ancestry within Brazil’s population. Although exact estimates differ, there were at least three million indigenous Amerindians in Brazil when Portuguese explorers first landed in Bahia in 1500, but their numbers dropped precipitously during the next centuries through enslavement and forced labor, conflict with the invading colonists, and European-borne disease epidemics, although their numbers have rebounded somewhat since the mid-20th century, to a 2010 census enumeration of 820 thousand   ancestral admixture component was most similar to the LWK population, we performed a supervised Africa-centric PCA and projected the remaining 1KG and Brazil samples on to genetic coordinates generated from the 1KG samples of sub-Saharan populations in Africa , but the mean ancestry in the Recodisa case control group (enrolled in six other cities across four North-Eastern states) differed from all five Fortaleza groups (P < 1 × 10−10). The participants in Recodisa had slightly higher mean European (61% vs. 52–55%) and African ancestry (24% vs. 21–22%) and were more variable in these components, while their mean Amerindian ancestry was lower (15% vs. 24–26%) and less variable.Having established the best 1KG proxy samples, we identified the optimal number of proxy samples to use for the supervised analysis to maximize the precision and minimize the bias of the estimates of ancestral proportions. We varied the equal number of ancestral samples from each proxy group in supervised ADMIXTURE analyses with bootstrap estimates of standard error of ancestry proportions, reasoning that the standard error and precision would decrease with the addition of more proxy samples until a minimum was reached after which addition of more samples would increase the standard error. In CIITA) gene, a positive regulator of class II major histocompatibility complex gene transcription, r2 > 0.8; r2 >  0.6) extend approximately 500 kb 5′ and 3′ to the leading SNP, extending the physical gene loci covered to EMP2 (5′) and the PRM1/RMI2 gene cluster (3′). SNP rs6498115 lies within prominent H3Kme1 and H3K27Ac marks defined by seven ENCODE cell lines  and in four of the seven, these marks lie in regions predicted to have strong enhancement . As shown in CIITA gene, from a secondary analysis in a case–control association study of ulcerative colitis in Europeans ; rs45601437 (CIITA intron); rs77979769 (within 10 kb 3′ of SOCS1); rs2021760 (within 10 kb 3′ of SOCS1); rs8054781 (within 10 kb 3′ of PRM1). The other four regions in SMC6 gene intron), chromosome 2 at 17.9 Megabases (Mb), shown in KLHL29 gene intron), chromosome 2 at 23.9 Mb, MEIS2 gene intron), chromosome 15 at 37.3 Mb, We used the method of locus-specific branch length  to compuuropeans , and rs6mericans . The othWe were interested in identifying SNPs that were most highly differentiated in the Amerindian ancestral component compared with the closest available Asian population, conjecturing that the most highly differentiated SNPs might be candidates for disease or trait loci under ancestral selection during the migration from Asia into the Americas, and could differentiate disease risk in Amerindians from ancestral Asian populations. Comparison of these SNPs to the Amerindian differentiated SNPs identified above could suggest whether the differentiation was more likely to have occurred during the Asia to Americas ancestral migration. We estimated the population Fst values for the Amerindian admixture component in Brazil, BRN2 (Amr), versus all Asian 1KG populations that were recruited from within Asia as shown in Similar to the LSBL test, the distribution of the Fst values was also exponential-like with mean, median, and 75% percentile Fst values of 0.083, 0.045, and 0.123 respectively.ADAMTS9) located on chromosome 3 at 64.5 Mb, DOCK2 gene introns, chromosome 5 at 169.2 Mb , rs4666032 , and rs117487308 . The genome interval annotation plots for CIITA, ADAMTS9, DOCK2, and ARHGAP5 displaying the most differentiated SNPs for both the Fst tests of ancestral locus branch length and differentiation from the Asian BEB population contain highly differentiated SNPs in both tracks.The top 10 most highly differentiated loci by Fst between the BRN2(Amr) component and the BEB population are shown in 169.2 Mb  online;  85.4 Mb  online;  32.6 Mb  online;  11.0 Mb  online; ADAMTS9 , ARHGAP5 , and CIITA  showed a trend from high Fst, low frequency in Africa with incremental Fst decreases and allele frequency increases in Asian populations, with the largest change occurring in the BRN2(Amr) component. The DOCK2 region  showed a low Fst in Africa  but higher Fst and frequency <0.10 in all other populations except Brazil Amerindian. The third pattern in SLC28A1  was an increasing Fst and increasing allele frequency from Africa to BEB and PJL and then a dramatic decrease in Fst and allele frequency in the rest of Asia and BRN2(Amr).To better understand the genetic history of the top differentiated SNPs and test for the possibility that the differentiation arose from founder effects within Asia, we computed the allele frequencies and locus-specific Fst values for ten of the 1KG populations: seven Asian populations resident within Asia countries; and Gujarati from Houston (GIH), LWK (Kenyans from Webuye), and TSI  as proxies for the ancestral African and European populations . TSI wasBrazil poses a complex methodological problem for genetic analysis due to extensive recent admixture between individuals of European, African, and Native American Indian descent, combined with a complex history of migration and forced slavery. This complexity is an advantage for disease gene mapping because it allows the interrogation of wider genetic variation and resulting clinical and biological effects. Our purpose in this study was to develop accurate estimates of ancestry from genome-wide SNP data and use the jointly fitted SNP allele frequencies in a genome-wide scan for the most highly differentiated loci in the Amerindian ancestry component. Although not proven, these loci are strong candidates for having been under selection pressure. We chose to focus on Amerindian locus differentiation within our Brazil population since less work has been possible on Native Amerindian population genetics due to community sensitivities and fewer publicly available large Amerindian genome-wide data sets.CIITA gene, 6 kb (kb) upstream of the start site of CIITA transcription, within prominent H3Kme1 and H3K27Ac epigenetic marks in a region of transcriptional enhancement, and is a strong positional and functional candidate as transcriptional regulator of CIITA expression. The LD region for this SNP extended from EMP2 (5′) to the PRM1/RMI2 gene cluster (3′). The second SNP was in an intron of the structural maintenance of chromosomes 6 gene SMC6 (rs1834619), a gene that is obligate for normal development and chromosome structure but without prior clinical research results or GWAS signals to suggest possible beneficial changes in associated human phenotypes. Other SNPs were located in an intron of kelch-like family member 29 (KLHL29) rs2288697, a gene desert region of chromosome 16  approx. 5 kb from a localized isolated genome region conserved in mammals and with a cluster of transcription factor sites identified by CHIP-Seq experiments, , and in an intron of Meis homeobox 2 (MEIS2) rs16964480.By ranking SNPs in the upper tail of the genome-wide distribution of Fst values from a locus-specific branch length (LSBL) test of reconstructed Amerindian ancestry in Brazilians, we found five genome regions containing the top 10 SNPs which were the most differentiated SNPs, and therefore candidates for selection . The LSBCIITA region, and as SMC6  also contained SNPs within the top 500 of the Amerindian vs. Asia analysis , DOCK2 , and ARHGAP5  regions also contained SNPs that were ranked in the top 500 of the LSBL test  contains a SNP that rose in Fst and allele frequency from Africa to Europe to Bangladesh and Pakistan but which subsequently drifted or experienced downward allele frequency selection pressure within Asia resulting in a similar frequency in Brazil Amerindian , we found evidence of extreme differentiation of the top 10 SNPs in eight regions in the reconstructed Amerindian branch compared with a single ancestral root population . By comperindian , but hadCIITA regulates MHC class II gene transcription and has been called “the master control factor” for expression of these genes. CIITA has been implicated in immune function through association with autoimmune diseases or very recently with leprosy ; 17th century ; and the 18th century (Mina Coast/Lower Guinea) . Before Inclusion of proxy samples for the components ancestries in a supervised ADMIXTURE analysis resulted in significantly different estimates of individual admixture. Similar results were found in one of the very few and limited genetic studies of North-Eastern Brazilians (768 SNPs), with the use of differing pseudo-ancestral populations . We founThe advantages of including proxies to identify components of admixture have described previously but using different methods to assess likely accuracy of ancestral reconstruction . AlthougPrevious studies of urban and regional Brazilian populations have shown that the predominant admixture components are European, African, and Amerindian with systematic variation in the proportions between the five major regions in Brazil, although the accuracy may have been limited by small SNP panels and unsupervised or joint admixture estimation . More reBased on the Fst genetic differentiation results and TREEMIX analyses, we found that the Bengali population is the closest proxy of the Asian 1KG populations for the source ancestral Asian population of migrants into the Americas, believed to be from North-Eastern Siberia . The TREIn conclusion, we have identified multiple differentiated regions in the Amerindian ancestral component of the North-Eastern Brazil population, drawn from six separate studies containing SNPs located in genes involved in immune function, metabolism, embryonic development, and other diseases and traits. We recognize that our results could be biased by the genetic panel used as the source of the SNP genotyping data, although the panel is informative for Latin American populations; that we have only investigated the ancestry derived from a single country, albeit one with a high degree of admixture; and that we have not proven that the most differentiated SNPs or genes are functionally under selection. Further work is needed to replicate these findings in other studies and to understand the health implications of the results.The genetic samples analyzed in this work were drawn from six cohorts/studies conducted on populations in North-Eastern Brazil and centered on Fortaleza, Ceará state. They have been previously described in detail and will only be reviewed briefly here. The Gonçalves Dias cohort was recruited in the Gonçalves Dias favela in Fortaleza between 1989 and 1993 to study the epidemiology, nutritional impact and causes of persistent diarrhea in early childhood . The MalThe Parque Universitário Zinc-Vitamin A clinical trial cohort enrolled 324 children between 2000 and 2006, and the Parque Universitário Zinc-Arginine clinical trial cohort enrolled 349 infants between 2006 and 2010, both from the Parque Universitário favela in Fortaleza . All famg for 10 min at 20 °C. After centrifugation, the syringes were removed and the DNA was manually extracted from 4.0 mL of Oragene DNA/saliva according to published vendor protocols. All samples were genotyped on the Affymetrix Axiom Latin America Array (LAT-1) with 818,154 SNPs and Indels specifically informative for Hispanic and Latin American populations. A total of 2,119 Brazil sample CEL files were processed using Affymetrix power tools (APT 1.16.0), applying the vendor’s best practices quality control (QC) criteria for samples and SNPs. More details are available in Saliva samples from all children were collected using Oragene DNA kit G-250 . Briefly, the sample collector was mixed gently and incubated at 50 °C for 1 h in a water bath. Unabsorbed liquid was transferred to a conical 15 mL centrifuge tube and the barrel of a 5 mL disposable syringe, containing collected sponges, was also placed inside the tube and centrifuged at 200 × ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502; last accessed January 30, 2015) and contained 2,504 samples from 26 populations. KING was used to identify residual relatedness up to degree 2, inferring eight parent-offspring, four full sibs and three 2nd degree relative pairs with one family of size 3. After dropping one of the related pairs and filtering SNPs > 1% MAF, the total data set was 2,490 samples × 30.7M variants. Intersection of the post-QC Brazil SNP (MAF ≥0.05) with the 1KG data resulted in 400,150 total autosomal SNPs for locus testing. The merged Brazil low LD data set and 1KG data resulted in 195,090 autosomal SNPs. This data set was used for the Brazil + 1KG joint PCA and supervised admixture analyses.The 1000 Genomes Project (1KG) phase3 release data (version date 2013/05/02) was downloaded  . PrincipFst. The second scenario identified the most highly genetically differentiated loci comparing the closest proxy Asian 1KG population, Bengalis in Bangladesh (BEB) to the Brazil Amerindian BRN2(Amr) component, using direct pairwise Fst, BRN2(Amr) vs. BEB). R version 3.0.3 was used for all other statistical analyses  of the three constrained total. The significance level was adjusted for 15 tests, . Genetic differentiation between ancestral admixture components and populations was measured using Hudson’s Fst . This wag(1−Fst)  substituanalyses .r2) between SNPs was estimated in LocusZoom using 1KG admixed American (AMR) samples .Genome regional plots of extreme Fst differentiation were created using LocusZoom . Linkagek − 1 events to identify the most parsimonious migration model at which further increase in the migration parameter led to insignificant improvement in the maximum likelihood.The relationship between the Amerindian component BRN(Amr) and 1KG Asian populations was analyzed using TREEMIX 1.12 . We inclMolecular Biology and Evolution online.Supplementary DataClick here for additional data file.


Eriobotrya japonica Lindl.) is one of the typical subtropical fruit trees, which was domesticated in China at least 2000 years ago. In the present study, we re-sequenced the genome of nine wild loquat accessions collected from wide geographical range and 10 representative cultivated loquat cultivars by using RAD-tag tacit to exploit the molecular footprints of domestication.The process of crop domestication has long been a major area of research to gain insights into the history of human civilization and to understand the process of evolution. Loquat  contains supplementary material, which is available to authorized users. The domestication of plant and animal is the most important development in the past 13,000 years of human history . In receEriobotrya japonica Lindl.) is one of the representative subtropical evergreen fruit trees [There are six to eight major origin centers of crops in the world and China is the most important one , 9. In ait trees . Loquat it trees , 15. Loqit trees , and alsit trees , 18.According to the “historical records”, one of the greatest books in the Chinese history, loquat trees had been cultivated in the royal gardens for the refreshment of king and princess in the Han Dynasty of China 2000 years ago. In twelfth century, loquat had been introduced into Japan, and then from China or Japan to the rest of the world . Today, Single nucleotide polymorphism (SNP) originated from single nucleotide substitute mutation or insertion/deletion, and it is the most abundant type of variation in the species or genomes , and havAs for non-model plant without reference genome, new ways of high throughout-sequencing have also been created to exploit SNPs at large scale, and the restriction-site associated DNA tags (RAD) is a famous one. The so-called “Restriction-site Associated DNA” (RAD) method was first described by Miller et al. . The conIn this study, we used RAD-tag tacit sequencing to genotype 10 representative loquat cultivars and nine wild loquat accessions, which collected from different natural habitats. The main aim was to detect the SNPs for whole loquat genome and to explore domestication event of cultivated loquat, such as the geographical origin of cultivated loquat, whether cultivated loquat population is originated from a single- or multi-domesticated events, and whether genetic bottleneck appeared as did in the other crops during domestication.EcoRI restriction enzyme sites had low content of GC sequence , which represent an error rate of 1 in 100, with a corresponding call accuracy of 99%, of sequencing data of all samples were more than 98%, indicating that the sequencing was of high quality, and the data quantity and quality fulfill the requirements for population genomics analysis. The mean GC content of sequencing data was 35.12%, which was slightly higher in wild group (35.35%) than the cultivated group (34.92%), suggesting that the sequences adjacent to t-test of independent samples on the frequency of total number of SNPs, homozygous SNPs and heterozygous SNPs exhibited a non-significant differences between cultivated and wild loquat groups (P > 0.05). We further obtained a SNP data set composed of 6406 SNPs with no gap from whole loquat population . These results showed that highly significant genetic differentiation happened between wild and cultivated loquat populations  populations have been found in Hubei, Sichuan, Yunnan, Guizhou, Guangxi and Guangdong provinces of China. Among them, Hubei and Guizhou provinces are the major distributing areas of wild loquat [According to the extensive survey, the wild loquat  for the frequency of total number of SNPs, homozygous SNPs and heterozygous SNPs. Above all results suggested that a non-significant genetic bottleneck appeared during the domestication of loquat. We deduced following major reasons for these results: 1) the domestication history of loquat is not so long; 2) loquat domestication is not yet complete, because there are non-significant differences for physiological and morphological traits between cultivated and wild loquat except that the fruit size of cultivated loquat is bigger than that of wild loquat.In the process of crop domestication, the genetic bottleneck is common, which indicate that the effective size of crops population were significantly fewer than its corresponding wild progenitor population . CompareDiscovering the origin center of a crop is not only an interesting scientific question, but also an important social science issue, because the ancient civilization of mankind is actually a farming civilization, and the action of crops domestication represents the height of the agricultural civilization to a certain extent. In fact, most crops were thought to have originated in the more developed areas of ancient civilizations , 9. The japonica rice [A crop that comes from a single domestication event or multiple domestication events is often present dispute based on the ancient book record and archaeology, which was used in corresponding research in the past –59. Howeica rice , and cucica rice . Multi-dica rice  and barlica rice .China has about 2000 years long history for loquat cultivation until the twelfth century. Loquat was introduced into Japan from China and then from China and Japan to other parts of the world . In the Here, we re-sequenced the genome of nine wild loquat accessions collected from wide geographical range and 10 representative cultivated loquat cultivars by using RAD-tag tacit to exploit the molecular footprints of domestication. The results showed that the SNP density and numbers were non-significantly higher in the wild loquat populations than that in the cultivated populations. All cultivars were clustered together by structure, phylogenetic and PCA analyses. The modern loquat cultivars have experienced a non-significant genetic bottleneck during domestication, and originated from a single domesticated event. Moreover, our results revealed that Hubei province of China might be the origin center of cultivated loquat.The leaves of ten cultivated loquat cultivars were sampled from Horticultural germplasm conversation center of South China Agricultural University (SCAU), where most of the major cultivated loquat cultivars of the world have been planted, and nine wild loquats were sampled from native wild loquat populations representing their natural distribution range Table ; Fig. 4.EcoRI digested about 1μg genomic DNA and joint P1 adapter  which contains sequences of primers P1-PCR (5 ′-AATGATACGGCCCACCGA-3 ′) for amplification, primer binding sites in Illumina Genome Analyzer and short tags to distinguish samples; (B) Samples with different adapters were mixed to be broken into segments of 300 ~ 700bp using physical method; (C) Joint P2 adapter , recovery; (D) PCR: 5-10 rounds of amplification target sequence (enrichment and sequencing primers for P1-PCR\P2-PCR).Mixed library of RAD-tag (restriction-site associated DNA tags) was constructed in this study by the protocols described by Etter et al. , and it Illumina Solexa sequencing was done by sequencer Hiseq 2000, and sequencing type was SE50. We make sure that clean data were at least more than 0.6 G (0. 8 × C) for each individual. After sequencing, the raw data were processed in three steps: first, we allocated the raw data to its own origin sample according to the labels (4 ~ 8bp) used for sequencing to distinguish samples (4 ~ 8bp); secondly, we filtered label sequences and the joint to exclude the pollution; thirdly, we discard those raw data with low quality base (Q ≤ 5 (E)) number accounted for more than half of the whole reads.http://catchenlab.life.illinois.edu/stacks). Software Stacks uses short-read sequence data to identify and genotype loci in a set of individuals either de novo or by comparing to a reference genome. From reduced representation Illumina sequence data, such as RAD-tags, Stacks can recover thousands of single nucleotide polymorphism (SNP) markers useful for the genetic analysis of crosses or populations [https://spss.en.softonic.com/) to execute the significance test of difference in average frequency of total SNPs, homozygous SNPs and heterozygous SNPs between wild and cultivated loquat by using T test of independent samples. For further analysis, we filtered the genotype data with the loss rate less than 50% (Additional file The rest clean and high quality raw data were used for further SNPs calling by software Stacks (ulations . We usedWe used Arliquin 3.11 software  to execu


To date, genome-scale analyses in the domestic horse have been limited by suboptimal single nucleotide polymorphism (SNP) density and uneven genomic coverage of the current SNP genotyping arrays. The recent availability of whole genome sequences has created the opportunity to develop a next generation, high-density equine SNP array.Using whole genome sequence from 153 individuals representing 24 distinct breeds collated by the equine genomics community, we cataloged over 23 million de novo discovered genetic variants. Leveraging genotype data from individuals with both whole genome sequence, and genotypes from lower-density, legacy SNP arrays, a subset of ~5 million high-quality, high-density array candidate SNPs were selected based on breed representation and uniform spacing across the genome. Considering probe design recommendations from a commercial vendor  a set of ~2 million SNPs were selected for a next-generation high-density SNP chip (MNEc2M). Genotype data were generated using the MNEc2M array from a cohort of 332 horses from 20 breeds and a lower-density array, consisting of ~670 thousand SNPs (MNEc670k), was designed for genotype imputation.Here, we document the steps taken to design both the MNEc2M and MNEc670k arrays, report genomic and technical properties of these genotyping platforms, and demonstrate the imputation capabilities of these tools for the domestic horse.The online version of this article (doi:10.1186/s12864-017-3943-8) contains supplementary material, which is available to authorized users. Soon after the horse reference genome from Twilight, a female Thoroughbred, was sequenced using Sanger technology , a genotHere we describe the steps taken, including careful and extensive variant filtering, to create both a 2 million (2M) SNP array and a 670 thousand (670k) SNP array from over 23 million variants discovered from whole genome sequence of 153 horses representing 24 breeds , paired-end Illumina HiSeq reads were generated for 153 horses (including Twilight), representing 24 distinct breeds, at a depth between 1.7X and 64X, with a median depth of 13X  by both WGS and 54k SNP chip were ranked by decreasing QUAL score. The proportion of genotype calls that agreed between 54K and WGS  was compared based on ranked QUAL score  . For eacConsidering 100% of variants detected by both technologies, genotypes had an overall precision of ~99% for 12X-called variants and 94-95% for 6X-called variants  SNPs on the legacy SNP50 chip; 2) WGS variants which were seen in four or more (4+) calling groups  were ranked either by decreasing QUAL or by decreasing VQSLOD score generated from each of the three gold standard training groups. Figure While an exact differentiation between the gold-standard training groups remains unclear See , recalibWe analyzed the 23M candidate SNP set with the goal of generating a target set of ~5 million high-confidence SNPs that would be compatible with array design. Following criteria provided by Affymetrix (now Thermo Fisher Scientific), 780 SNPs were filtered because they fell within repetitive regions; 9,342,733 SNPs were filtered because they were within 20 base pairs (bp) of another variant; and 4,858,273 SNPs were filtered for having a minor allele frequency below 1% in the 153 WGS horses. After filtering, approximately 10 million SNPs remained for further design selection  were filtered based on whether or not they were present in draft or pony variant calling groups to control for the fewer number of samples used in variant discovery. Previous SNP designs [To ensure backwards compatibility, we started with the ~74k SNPs that were present on the two legacy (54K/65K) arrays. To fill the remaining target of ~5 million probe design candidates, we added SNPs from the 10 M SNP set based on even genome distribution, informativeness among the variant calling groups  was divided into approximately 54,000 50 kb windows and a target of 37 SNPs per window was established. SNPs within each window were chosen for inclusion in the MNEc2M SNP set using a greedy algorithm. Briefly, the ~74,000 SNPs on previous generation arrays, as well as SNPs within the equine major histocompatibility complex (MHC) region (ECA20:28.7-33.6 Mb), were given VIP status and were automatically included in both forward and reverse strand directions. SNPs were added to windows until the target of 37 SNPs was met. If a window had more than 37 candidates, SNPs were selected based on by Affymetrix recommendation group (See  Gb was dn = 286) or hair roots (n = 61) (see A cohort of 347 horses were genotyped on the MNEc2M high-density array using DNA isolated from blood ( 61) see . The ~2M 61) see . For eacp ≤ 4.05e-04) and DNA concentration (p ≤ 2.77e-10) significantly influenced the probability of samples producing quality genotypes. Both factors also had substantial coefficients in the model indicating a large magnitude of effect. In the logistic regression, the factor indicating hair root had a strong negative model coefficient of −2.70 while DNA concentration had a positive coefficient of 1.71.  when re-hydrated (2.7 ± 2.9 ng/μL) than did DNA samples isolated from blood (43.1 ± 55.5 ng/μL). To determine if sample origin (blood versus hair roots) or DNA concentration, or both, were associated with failure to pass genotyping quality control metrics, a logistic regression for sample success on DNA concentration and blood/hair status was performed. , on average, at a rate of 80%. Yet, validation rates were much higher in SNPs discovered in multiple calling groups (>96%)  were identified using the software FastTagger [2 > 0.99. These SNPs were included on the MNEc670k imputation array  SNPs were within 17,128(63.5%) gene models. Likewise, of the 670,805 SNPs on the MNEc670k array, 192,681(28.7%) SNPs were within 14,758(54.7%) gene boundaries. In comparison, of the legacy 74,056 SNPs, 20,950(28%) were within 8249(30%) annotated gene models.Inter-SNP distance was calculated for the MNEc2M, as well as the MNEc670k, SNP sets at different levels of MAF see Fig.  to assesFrequency of alternate (non-reference) SNP alleles were calculated on both arrays using the full 485 sample dataset (WGS + SNP), genotypes derived from whole genome sequence only (WGS Only), and from samples genotyped on the 2M test array described above  . Using a test cohort of 485 horses, we identified “tag” SNPs that reconstructed haplotypes both across diverse breeds and within breeds, then selected a subset of ~670k SNPs for design of the commercially available MNEc670k array. The use of tagging SNPs in this commercial array ensures its utility as an imputation tool up to a SNP density of at least 2 million.Our goal was to provide a high-quality, standardized SNP array designed for imputation to overcome limitations in SNP density that under-power many genome mapping projects in the horse. To do this, we utilized whole genome sequencing data from 156 horses representing actively-researched and economically-important breeds that were collaboratively collected by 17 laboratories within the equine genetics community. These data, in turn, enabled a large scale, whole genome sequence mapping pipeline with Several successful GWAS in other agricultural animal species have been reported using this same Affymetrix® Axiom® HD genotyping array technology –37. HighInitial filtering for array compatible SNPs immediately reduced the pool from ~23 million SNPs that were discovered from de novo variant calling to 10 million SNPs compatible with array design. These purely technical constraints, such as removing all variants that were in close proximity to one another, substantially reduced the amount of variants considered here. While we thoroughly characterized the SNPs that were ultimately included on the genotyping array, the entire 23M SNP set has been submitted to dbSNP and the European Variation Archive. Additional analyses are being performed to further enhance knowledge of equine genetic diversity at the WGS level. This includes investigation of the >400,000 sites where all WGS alleles differed from the reference allele determined by Sanger sequencing of Twilight. If the discrepancies in these data are verified, this information will be useful for further annotation and error correcting of the current equine genome reference assembly as well as for future genome assemblies. Here, we focused on variants that were compatible with array design, however, we anticipate that improved genome annotation and finer scale haplotype maps will result from this larger SNP set.Quality control and evaluation of conversion rates on the previous arrays have shown that many of the SNPs assayed on the legacy arrays are non-informative or not polymorphic in many breeds  containing only 6% of the initial ~10 million array-compatible variants posed an opportunity to control for overall false discovery rate during SNP selection. Variant recalibration allowed successful identification of variants with high QUAL scores that were less discordant between WGS and SNP50 genotypes. In the absence of high-confidence training SNP sets , we defined several different “gold standard” SNP sets for variant recalibration. While variant recalibration performed better than QUAL scores alone, there was not a single training set that clearly out-performed the others Fig. . HoweverDNA samples genotyped on the MNEc2M array were primarily derived from blood, but also came from hair roots. Both DNA sources had failed samples, however, a substantially higher fraction of hair root samples produced poor genotyping rates. Hair root DNA also tended to have lower DNA concentrations when samples were re-hydrated. To maximize information, we chose to genotype all submitted samples, even though samples from hair roots did not meet minimal DNA quantity guidelines specified by Affymetrix, though samples were dropped if they did not meet genotyping quality control thresholds.To determine if sample origin (blood versus hair roots), DNA quantity, or both, were associated with failure to pass genotyping quality control metrics, a logistic regression for sample success on DNA concentration (Pico Green) and blood/hair status was performed  to a higher marker density (74k) using a reference population of 248 horses . AnotherWe masked variants genotyped on the MNEc2M array down to the MNEc670k SNP set in a test cohort of 485 horses and found a high overall imputation accuracy (96-99%) up to the 2M density, across several different breeds of horses  is feasible. A recent study using whole genome sequencing from 44 Franches-Montagnes and Warmbloods imputed SNPs from the legacy SNP50 genotypes to nearly 13 M variants with 95% accuracy. WGS breed representation in this study varied between 1 and 29 individuals. In the future, it will be critical to expand the number of WGS samples in the reference population described here in order to attain proper breed representation for reliable imputation to higher SNP densities.As sample size continues to plague the characterization of complex equine phenotypes, despite falling genotyping costs, the development of this affordable, backwards compatible, 670k imputation array will allow the inclusion of already genotyped individuals in higher-powered analysis, providing an economical trade-off for studies that must choose between more markers or more samples. We expect that imputation will complement future GWAS studies and mapping studies designed based on haplotype structure. In the future, improvements in imputation software as well as development of equine-specific imputation protocols that leverage breed information (e.g. recombination maps) will further increase accuracy of genotype imputation. Furthermore, as additional WGS are integrated into reference populations, imputation performance will continue to improve.Here we report, through a community effort, the leveraging of WGS data from 153 individuals with a 13X median coverage to achieve new variant discovery in the domestic horse. The use of WGS enabled us to generate orders of magnitude more data than previous technologies allowed. Empirical SNP properties were used to produce composite scores for each SNP based on machine learning approaches, allowing better detection of false positive variant calls that could not be achieved using generic QUAL scores alone. Thus, from a starting set of over 23 million SNPs, we identified a set of ~5 million SNPs which were considered for array design. With probe design recommendations from Affymetrix, we further filtered this list to 2M SNPs (MNEc2M), which adequately represented the breeds used in variant discovery, were evenly spaced across the genome, and had the highest chance of conversion in the array implementation.Using a test cohort of 332 horses, we used the MNEc2M SNP set to identify haplotype tagging SNPs both within and across breeds. Choices made in 2M array design, particularly with regard to variant filtering, resulted in greater than 92% of the SNPs on the 2M array (MNEc2M) returning high-quality genotypes. Filtering further, we designed the MNEc670k genotyping array to contain SNPs which allowed for accurate imputation. Together these genotyping platforms represent the next generation of genomic array technologies for the domestic horse.Paired-end Illumina Hi-Seq 100 base pair whole genome sequences were generated for 153 horses representing 24 distinct breeds  . These ahttps://github.com/schae234/pypeline.Programs within PALEOMIX are abstracted as nodes within the program to allow files to be generated within a temporary directory and only to be moved to the final directory upon successful completion. In addition to nodes for the read alignment mapping programs, additional nodes were created to run variant calling programs implemented by GATK  and SAMtThe PALEOMIX computational framework was further extended to process alignments and produce variant calls. Individuals were split into 16 different calling groups based on both breed and sequencing depth in order to minimize biases due to coverage and population stratification. Variant call files (.vcf) were produced for each group using both GATK Unified “Genotyper with” “–stand_call_conf” set to 30, “—stand_emit_conf” set to 10, and “–dcov” set to 200. Variants called by GATK were retained if they were independently called by SAMtools “mpileup” with default calling parameters. This allowed possible false negatives to be assessed downstream using other quality metrics. Of the 26,884,885 SNPs called by SAMtools and 31,506,364 SNPs called by GATK, 22,557,988 variants were called independently by both callers and retained for further analysis.Both GATK UnifiedGenotyper as well as SAMtools assign generic quality scores (QUAL) to each discovered variant, which is the posterior probability that a true variant exists given the pileup of reads at a given locus using base pair quality and expected allelic distribution of samples. Using GATK, additional metrics were generated for each variant including coverage (DP), quality of depth (QD), Fisher strand bias (FS), mapping quality rank sum (MQRankSum) and read position rank sum (ReadPosRankSum). Definitions from the GATK manual for each metric are below.Coverage (DP) – Total, unfiltered depth of coverage.Quality by Depth (QD) – Variant confidence (from QUAL field) divided by depth of non-reference samples.Fisher Strand Bias (FS) – Measure of strand bias, i.e. the variation seen on only the forward or reverse strand.Mapping Quality Rank Sum (MQRankSum) – The rank sum test for mapping qualities.Read Position Rank Sum (ReadPosRankSum) – The rank sum test for the distance of the variant from the end of reads.These parameters were used to train linear mixed models using GATK VariantRecalibrator which produces a composite variant quality metric called a VQSLOD score which can be summarized as using the formula VQSLOD ~ DP + QD + FS + MQRankSum + ReadPosRankSum. We defined three “gold standard” datasets: 1) SNPs previously genotyped on the 54K SNP chip, 2) variants called in four or more calling groups, 3) and variants within the 99th percentile of QUAL scores. Models were trained on chromosomes 2-31 and validated on chromosome 1 in both Morgan and Standardbred breed groups as a subset of each breed group had whole genome sequence data at target depths of 6X (12 Morgans and 6 Standardbreds) and 12X (6 Morgans and 4 Standardbreds) target coverage . These “gold standard” training SNPs were used to assess the precision of different quality metrics of de novo variant calling.2 value of over 0.90, priority was given to SNPs that were in called in Draft or Pony groups. If a SNP was in an equal number of priority groups, VQSLOD scores were used to break ties. After all filtering criteria were applied 5,443,950 SNPs remained.A preliminary list of approximately 5 million SNPs was prepared for probe design (conversion) recommendation by Affymetrix. VQSLOD scores were generated for all ~23 million initial bi-allelic candidate SNPs. SNPs were filtered out if they 1) fell within repetitive regions designated by RepeatMasker 3.3.0  . A recommended SNP has: a probability of conversion >0.60, no interfering polymorphisms within 24 bases, and unique flanking sequence. Non-recommended SNPs have: either duplicate flanking sequence, a probability of conversion <0.40, interfering polymorphisms within 21 bases, or more than 2 interfering polymorphisms within 24 bases. A ‘not possible’ designation is given to probes which probes cannot be created, and neutral recommendations cover all other cases. Furthermore, SNPs with alleles of A/T or C/G required two probes to differentiate between allele states and were tagged as allele-specific SNPs. Recommendations were generated for both forward and reverse strands based on the above criteria. Groups of SNPs were ranked within 50 kb windows and probe design criteria were chosen using a greedy algorithm until 37 SNPs were chosen per window using the following criteria: 1) VIP SNPs previously designed on 54/65k chip (regardless of recommendation or strand), 2) SNPs for known Mendelian traits such as coat color (regardless of recommendation or strand), 3) SNPs designated as ‘recommended’ by Affymetrix and designable with one probe, 4) SNPs with a ‘neutral’ recommendation from Affymetrix designable with one probe, 5) any SNP within the equine MHC region, 6) SNPs requiring multiple probes. If a SNP was equally designable in forward and reverse strand, the forward strand was chosen for interpretability. Using these criteria, 2,001,826 SNPs were chosen for array design.n = 286) using the Gentra PureGene Blood kit. DNA from hair roots (n = 61) was isolated using the Gentra Puregene Blood Kit with a modified version of the Gentra Puregene Mouse Tail purification protocol. The amount of Proteinase K was increased to 20 μL, isopropanol to 650 μL, and ethanol to 500 μL. The DNA hydration solution was reduced to 20-30 μL depending on size of DNA pellet. Quality control metrics were calculated on arrays grouped by tissue type (blood vs hair) as well as array batches, and arrays were dropped for various reasons at multiple QC steps. “DishQC” evaluates call rates between A/T probes and C/G probes of non-polymorphic sites to assess background probe contrast and was calculated using Affymetrix Power Tools. Samples with DQC scores below 0.60 were removed from further evaluation. Passing arrays were then genotyped using approximately 20,000 high-confidence probes provided by Affymetrix in their R1 release package . If the number of arrays per tissue/array group was above 96 samples, generic priors were used for probe-set genotyping; otherwise, SNP specific priors were used (provided in the R1 package released by Affymetrix). Sample arrays with 20 K call rates below 0.97 were removed from further analysis.Using probes designed according to the above criteria, 347 horses from 20 breeds were genotyped on the 2M array using DNA isolated from blood (n = 332) were genotyped on all probes using a similar approach. Generic or SNP specific priors (provided in R1 package) were used for groups containing more or less than 96 samples, respectively, and samples with call rates below 0.97 were removed from the analysis. Successfully genotypes samples were then grouped by genotyping plates to check for batch effects. Plates with pass rates below 95% in blood or 93% in hair were dropped.Remaining samples , a call rate below threshold, or a combination of poor performing cluster properties. SNPs with multiple probes were assigned a best probe based on the above high-quality conversion types with the added constraint that SNPs with no minor homozygote did not result in extreme Hardy-Weinberg values . 1,846,988 SNPs passed the above quality control metrics.Up to 4 probes were used to genotype each SNP across samples passing quality control criteria. Probe performance was calculated using the ‘n = 332) were combined with SNPs in horses called by whole genome sequencing (n = 153) resulting in a total of 485 horses genotyped at 1,846,988 SNPs. Tag SNPs were calculated using FastTagger [2.Genotype information derived from the horses on the 2M test array , samples were split into 15 tagging breed groups based on available sample size: Land Race, Arabian, Belgian, Draft, Franches-Montagne, Icelandic, Lusitano, Maremanno, Morgan, Pony, Quarter Horse, Standardbred, Thoroughbred, Trotter, and Warmblood  were included in the final commercial array if they tagged haplotypes in three or more of these diverse breed groups.Quarter Horses, Ponies, Morgans, and Standardbreds needed a much higher number of intra-breed specific SNPs to tag haplotypes, indicating they were the most diverse breeds. Additional tag SNPs (A reference population of the 485 horses with genotypes at ~2M SNPs (generated either by the MNEc2M array or whole-genome sequence) was used as a reference population for imputation. Horses from each different breed were used to test the imputation accuracy of the array. A random set of 1/3 of individuals were removed from each breed group and masked down to the MNEc670k SNP set as well as the SNP65 SNP set. Genotypes were then imputed using the remaining (non-masked) individuals as a reference population. Imputation was performed using Beagle 4.0 . BrieflyAdditional file 1: Table S1.Whole genome sequencing (WGS) samples. Whole genome sequencing samples with horse identification and read depth. 153 Individuals representing 24 breeds (Twilight is included as 4 entries). Table includes horse identifier, breed, contributing laboratory and coverage statistics for each individual in both the nuclear and mitochondrial genomes. Variant calling groups indicate which individuals were grouped together during variant discovery. (XLSX 21 kb)Additional file 2: Table S2.SNP50 breed informativeness. Impact of breed on SNP50 informativeness. Table shows informativeness (MAF ≥ 0.05) of the legacy, SNP50 SNP set for light, draft, and pony breeds described by McCue et al. , 2. MeanAdditional file 3: Table S3.SNP validation by discovery by variant calling group. SNPs discovered from whole genome sequence  from either whole genome sequence (WGS) or from the 2M test array (SNP) were assigned to one of 15 tagging breed groups based on their reported breed. (XLSX 21 kb)Sample breed and tagging breed groups. Samples Additional file 7:MNEc2M SNP information. Contains information for SNPs included on the MNEc2M array. Columns include MNEc identifiers, EquCab 2.0 coordinates, and fields indicating if the SNP was discovered in Draft and Pony groups Additional file 9:Figure S1. Alternate allele frequency for 23 M and 10 M SNP sets. Histograms show the alternative allele frequency for the ~23 million SNPs discovered by WGS and the ~10 million SNPs that were compatible with array design Additional file 11: Figure S3.MNEc2M Inter-SNP distance and informativeness by breed. Distance between SNPs on the MNEc2M array were calculated by breed group  were split into 15 tagging breed groups  and sample source as independent variables. (PNG 60 kb)


Crassostrea gigas to simulate sequencing data with different coverage, and we evaluate the influence of genotype calling rate and accuracy as a function of coverage. Having initially identified the appropriate parameter settings for filtering to ensure genotype accuracy, we used two different single-nucleotide polymorphism (SNP) calling pipelines, single-sample and multi-sample. We found that a coverage of 15× was suitable for obtaining sufficient numbers of SNPs with high accuracy. Our work provides guidelines for the selection of sequence coverage when using NGS to investigate species with a high degree of heterozygosity and rapid decay of linkage disequilibrium.Next-generation sequencing (NGS) technology is being applied to an increasing number of non-model species and has been used as the primary approach for accurate genotyping in genetic and evolutionary studies. However, inferring genotypes from sequencing data is challenging, particularly for organisms with a high degree of heterozygosity. This is because genotype calls from sequencing data are often inaccurate due to low sequencing coverage, and if this is not accounted for, genotype uncertainty can lead to serious bias in downstream analyses, such as quantitative trait locus mapping and genome-wide association studies. Here, we used high-coverage reference data sets from  Next-generation sequencing (NGS) technology is a powerful and cost-effective approach for large-scale DNA sequencing35For numerous non-model organisms, there exist genomic resources that can provide an important basis for the application of NGS technology. Through large-scale sequencing, it is not difficult to obtain NGS short-read data for these organisms. Analysis pipelines that deal with these NGS data include two steps, read mapping and genotype calling. The bioinformatics software related to these two steps are fully and widely used. Most read mapping algorithms for NGS data are based on either ‘hashing’ or an effective data compression algorithm called the ‘Burrows–Wheeler transform’ (BWT). The BWT-based aligners include, BWAIn research using NGS technologies, the most important consideration is to obtain single-nucleotide polymorphism (SNP) sites with accurate genotypes. For genome-wide association or QTL mapping studies for economic traits, in particular, the accuracy of genotyping SNP sites has a significant influence on the results obtained. If only a small fraction of SNP sites are incorrectly genotyped, this could lead to radically different conclusions. In order to obtain accurate genotypes for each individual in a sample, higher coverage sequencing depth for each individual is a possible solution. Twenty times or even higher coverage is possibly sufficient for accurate genotyping. However, for some non-model organisms, higher coverage sequencing for a large population would be expensive and beyond the means of many researchers. Another approach, low-coverage sequencing assisted by genotype imputation methods, is a possible solution for population studies of some organisms. This method has been very successfully applied to association studies of humans5However, the aforementioned methods have not been successfully applied to a large group of organisms with a high degree of heterozygosity and rapid decline in linkage disequilibrium (LD). This is because, compared with the low-coverage sequencing for an inbred line of rice, which could be considered as a diploid, it is very difficult to identify the genotypes for heterozygous SNP sites with very low covered reads. Moreover, genotype inference methods, which are based on the genotypes of contiguous SNP sites, may be problematic because of typing errors for heterozygous sites and lower linkage disequilibrium. Thus, genotyping based on higher sequencing depth is essential for organisms that have a high degree of heterozygosity and low LD values. The choice of appropriate sequencing coverage is accordingly an important consideration in the study of such organisms using NGS technology.Crassostrea gigas, is a marine bivalve belonging to the phylum Mollusca, which contains the largest number of described marine animal species. Oysters are important fishery and aquaculture species, as well as being models for studying neurobiology, biomineralisation, ocean acidification, and adaptation to coastal environments under climate change16C. gigas, which was first published in 2012, provides a basic resource for genetic and evolutionary studiesC. gigas has a modest genome size of less than 500 million bp (Mbp), a short generation time (1 year), and worldwide distribution, which makes it a suitable model molluscan species. Furthermore, the polymorphism rate for C. gigas is 1.3% and the linkage disequilibrium decays to less than 0.2 in approximately 20 bps, which makes C. gigas a suitable representative organism with high diversity and a rapid decay of LD for the study of sequencing coverage choiceThe Pacific oyster, C. gigas. Using two different SNP calling pipelines, we found that a coverage of 15× was suitable for NGS analysis to obtain a sufficient number of SNPs called with high accuracy. On the basis of our findings, we provide guidelines for the selection of sequencing coverage for NGS application to organisms with a high degree of heterozygosity.In this study, we used simulations to investigate genotyping accuracy as a function of sequence coverage and other filter parameters based on NGS sequencing data for C. gigas, we used three wild samples collected from Weifang, Rongcheng, and Lianyungang . Sequencing of these samples was performed using the HiSeq 2000 platform (Illumina) according to manufacturer’s standard protocols. We mapped the DNA sequence reads of the three samples to the oyster genome by using the software BWA. After read mapping and subsequent strict filtering, the average depth was approximately 20× for all three samples  and read depth (DP) are two commonly used filter parameters for determining the genotype for each SNP site. In this study, we fixed the parameter of GQ threshold as 20 and then evaluate the influence of different DP on the accuracy of SNP genotyping. Firstly, we chose SNP sites that had more than 20 reads covered in each sample as the benchmark SNP sets. We assumed that because these SNPs had sufficient read depth, the genotype inference for them should be accurate. Using the sampled data with coverage 5× and 10× for each individual, we then investigated the genotyping accuracy for the SNPs as a function of the DP parameter. The results showed that the accuracy was greater than 95% when the DP threshold was 5 and could reach 97% when DP threshold was larger than 10 . AccordiC. gigas samples, respectively.In order to evaluate the genotype calling rate and accuracy under different coverage, we simulated the sequencing data using the sampling method and then called the SNPs for each subgroup of sequencing data. Initially, we filtered the SNP sets obtained from the total sequencing data using the parameters GQ and DP (GQ ≥20 and DP ≥10). We then used these filtered SNPs as benchmarks because the genotype accuracy for them was high. We accordingly identified 3,909,589, 3,745,142, and 3,754,426 high quality SNPs for the three We subsequently investigated the calling rate and genotype accuracy as a function of coverage. We observed that the fraction of called SNPs increased sharply as the coverage increased from 5× to 18× . The fraOn the basis of the aforementioned results, we considered that a coverage of 10× or 15× would be suitable for individual SNP calling analysis for an organism with very high diversity. When the coverage was 10×, approximately 55% of the total SNPs could be called, whereas with a coverage of 15×, the fraction could be greater than 80%. Moreover, the genotype accuracy of the SNPs obtained using the two different coverages could be very high (>99%).C. gigas samples as the input for SAMtools, and called the SNP sites simultaneously. We also focused on the calling rate and accuracy of genotypes under different sequencing coverage. As benchmarks, we used only SNP sites for which genotypes were determined in all three samples (using the following parameters: GQ ≥20 and DP ≥10). In total, 5,022,415 SNP sites were found to be polymorphic in the three samples. We then investigated the calling rate and accuracy under two different levels of coverage, 10× and 15×. The results showed that the calling rate and accuracy were both a little lower than those obtained using the single-sample calling pipeline. Under 10× coverage, the calling rate was very low, with only approximately 37.4% of the total SNP sites being identified from the three samples. The accuracy of the genotypes determined was 97.2%. When the coverage was 15×, the calling rate could reach 75% and the genotype accuracy was greater than 98% . Thus, we considered that a coverage of 15× was a suitable choice for obtaining a sufficient number of accurately genotyped SNPs in an organism with a very high number of heterozygous sites.C. gigas is that it has a high degree of heterozygosity for polymorphic sites. We found almost 60% of the SNPs in C. gigas are heterozygous, which made the inference of genotypes very difficult. When the sequencing coverage was low, heterozygous sites are very likely to be inferred as homozygous. Moreover, such mistakes are very difficult to correct based on the sequencing data. Other widely used approaches for genotyping are the imputation-based methods51520212223Drosophila, and rice. Imputation-based methods take advantage of the pattern of LD at nearby sites to infer genotypes, and as a result, genotype calling accuracy is significantly improved and missing genotypes can be imputed. However, in oysters, the genotypes of nearby sites inferred from low coverage sequencing are not convincing, and the pattern of LD decayed rapidly, both of which make genotype imputation based on low coverage sequencing very difficult.Oysters belong to the class of bivalve molluscs, which has one of the highest diversities among animal phylaOne of the factors that influence the performance of single- and multi-sample calling pipelines is the non-uniform distribution of sequencing reads across a genome. We found that approximately 40% of the genome regions were covered less than 10× using the total sequencing data for each sample. Notably, approximately 20% of the regions did not have a single covered sequence read . We checWe also checked whether the genotype calling rates were different in different functional regions in the oyster genome. We found that calling rate was highest in the coding regions and lowest in the intergenic regions. The single sample calling rate of coding regions could reach approximately 70% when the sequencing coverage was 10×, and greater than 85% when coverage was 15× . Using mIn order to investigate what the genotyping calling rate and accuracy would be when the sequencing coverage was very high; we used the sequencing data in the work of the oyster genomeIn this study, we examined how the SNP filter parameters influence the genotype accuracy. The parameters choice suggestions could be used as a reference and be applicated to the genotyping work in other species with high level of heterozygous sites. However, the choice of sequence coverage is complicated and associated with several factors, e.g. the heterozygous sites density along the genome, the LD pattern and the genomic complexity. Thus, the recommended coverage was not suitable to be applied to other species directly. But we thought our study could provide a reference to the investigation in other species.One may argue that future studies will have increased coverage and that many of the problems highlighted herein will be solved; nevertheless, even with limited funding, we expect to be able to genotype a sufficient number SNP sites with high accuracy under suitable sequencing coverage. The insights gained here suggest how selection of an appropriate coverage for those organisms with high diversity can provide useful basic information for subsequent analyses.Crassostrea gigas, were collected from three Chinese cities, Weifang, Rongcheng, and Lianyungang. Genomic DNA was extracted from mantle tissue using a standard phenol-chloroform method. For each individual, 1–15 μg of DNA was sheared into fragments of 200–800 bp using the Covaris system (Life Technologies). DNA fragments were then treated according to the Illumina DNA sample preparation protocol. Fragments were end-repaired, A-tailed, ligated to paired-end adaptors, and PCR amplified with 300–500 bp inserts for library construction. Sequencing was performed to generate 100-bp paired-end reads using the HiSeq 2000 platform (Illumina) according to the manufacturer’s standard protocols.Three wild Pacific oysters, Filtered reads from all individuals were aligned to the oyster reference genomeAfter alignment, we performed SNP calling for the three samples using a Bayesian approach as implemented in the package SAMtools. The ‘mpileup’ command was used to identify SNPs with the parameters ‘-C 50 -t DP -t SP -ug’. Both the single-sample and multi-sample calling pipelines were used in the study. The SNPs with genotype quality ≥20 and coverage depth ≥10 were retained as the benchmark SNP sets for the subsequent analysis.In order to simulate the sequencing data under different coverage, the sampling method we used involved randomly selecting parts of the data in the high-quality SAM files. We generated a series of random numbers to represent the high-quality mapped reads and then sampled from the total mapped reads without replacement. We selected different coverage levels from 5× to 18×, with a 1× step between levels. The sampling processes were repeated five times to get the mean values for the calling rate and precision.We fixed the genotype quality as 20 and evaluated the genotype accuracy under different parameters of read depth (DP). We used the SNP sites with DP values larger than 20 called from the total high-quality mapped reads as the benchmark SNP sets. We then used two levels of lower coverage (5× and 10×) to evaluate the genotype accuracy under different thresholds of read depth. The depth was set at 5 to 15.How to cite this article: Song, K. et al. Coverage recommendation for genotyping analysis of highly heterologous species using next-generation sequencing technology. Sci. Rep.6, 35736; doi: 10.1038/srep35736 (2016).


Two and 1863 SNP-SNP interaction pairs detected stable across 12 and 11 environments, respectively, which account around 50% of total environments. Epistasis values and contribution rates of stable interaction (the SNP interaction pairs were detected in more than 2 environments) pairs were detected by the two way ANOVA test, the available interaction pairs were ranged 0.01 to 0.89 and from 0.01 to 0.85, respectively. Some of one side of the interaction pairs were identified with previously research as a major QTL without epistasis effects. The results of this study provide insights into the genetic architecture of soybean oil content and can serve as a basis for marker-assisted selection breeding.Soybean oil content is one of main quality traits. In this study, we used the multifactor dimensionality reduction (MDR) method and a soybean high-density genetic map including 5,308 markers to identify stable single nucleotide polymorphism (SNP)—SNP interactions controlling oil content in soybean across 23 environments. In total, 36,442,756 SNP-SNP interaction pairs were detected, 1865 of all interaction pairs associated with soybean oil content were identified under multiple environments by the Bonferroni correction with  Epistasis interaction is common and can cause cryptic genetic variation for quantitative traits [Soybean originates from China which is not only grain crop but also commercial crop. The content of protein accounts for 40% of soybean seeds, and the content of oil occupy 20%. Soybean is the largest oil seed crop in the world, which accounts for 57% of world oilseed production in 2015 , inspired by the combinatorial partitioning method, can effectively reduce genotype predictors from s to one –29. 2001s to one . In addis to one –27. Alths to one –31, most2:10–F2:20 population of 147 RILs was advanced by single-seed descent from crosses between two soybean cultivars: ‘Charleston’ (♀), (American cultivator) [An Ftivator) , and ‘DoThe plants were arranged in a randomized complete block experimental design involving single-row plots (1-m length and 0.5-m width), with two and three replicates in 2002–2007 and 2008–2012, respectively. The timing and frequency of cultural management procedures used for the trials were in accordance with normal production practices for the respective environments.Oil was analyzed from seeds obtained from five randomly harvested plants of each line per plot. The seed oil phenotypic data was analyzed using the FOSS Infratec TM1241 Grain Analyzer . The oil content of each RIL line was averaged from the data of two or three plot replicates. Two hundreds grams seeds of each replicate were used to analysis at a 13% moisture basis.SNP genotype data for the RIL population was detected based on the specific-locus amplified fragment sequencing (SLAF-seq) method following Qi et al.  and usedp < 0.001), with the selected optimization model based on the maximum Pearson chi-squared statistic combined with cross-validation [To identify SNP × SNP effects in this study, we used the MDR method –31, The lidation .The chi-square statistic measures the association between genotype (high-risk and low-risk group) and affection status (case and control group) in a two-way table. It is calculated as sum of the square of the difference between the observed and expected frequency in each combination, divided by the expected value, across all the combinations:The calculation was based on the value of chi-square. Average Pearson chi-square of 10 time 10-fold cross-validation were used to select optimization model . The p vOil content datas of the RIL population in 23 environments are shown in p <3.55×10−11 which was calculated based on the Bonferroni correction  with 5308 markers on 20 linkage groups and the map was 2294.43 cM in length, with an average distance of 0.43 cM between adjacent markers .Epistasis is a biochemically plausible feature of the genetic architecture of quantitative traits. So, epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects . In quanCurrently, many studies are based on multifactor dimensionality reduction (MDR) focus on the complex human diseases and animal-related traits. Guia et al.  reveals The present study has opened the door for further research on soybean quantitative trait integration analysis. On the one hand, the SNP-SNP interaction of hot region which were identical with some published major QTLs, however, those major QTLs were not shown the interaction each other previously. On the other hand, the candidate major region could be found as only one site of the pair was aligned with the published QTL. Based on this research, the markers of those hot region could be developed, and used for breeding selection process, furthermore, the candidate genes of hot region could be identified and to analysis the interaction of genes in next step. Future efforts based on the uncovered epistatic interaction values and their contribution to genetic values can be applied for the identification of key loci with higher phenotypic contributions, the prediction of phenotype and genotype design modules with epistatic values, and to provide direction for high-quality soybean breeding.S1 TableNS means no signification value in that environments, epistatic value indicates the epistatic effects value of the SNP—SNP pairs, contribution rates indicates the phenotypic variation of the SNP—SNP interaction pairs.(XLSX)Click here for additional data file.


While the genetic architecture of resistance appeared to be largely polygenic in nature, two regions on chromosome 18 showed suggestive association with both AGD resistance traits. Using a cross-validation approach, genomic prediction accuracy was up to 18% higher than that obtained using pedigree, and a reduction in marker density to ∼2,000 SNPs was sufficient to obtain accuracies similar to those obtained using the whole dataset. This study indicates that resistance to AGD is a suitable trait for genomic selection, and the addition of this trait to Atlantic salmon breeding programs can lead to more resistant stocks.Amoebic gill disease (AGD) is one of the largest threats to salmon aquaculture, causing serious economic and animal welfare burden. Treatments can be expensive and environmentally damaging, hence the need for alternative strategies. Breeding for disease resistance can contribute to prevention and control of AGD, providing long-term cumulative benefits in selected stocks. The use of genomic selection can expedite selection for disease resistance due to improved accuracy compared to pedigree-based approaches. The aim of this work was to quantify and characterize genetic variation in AGD resistance in salmon, the genetic architecture of the trait, and the potential of genomic selection to contribute to disease control. An AGD challenge was performed in ∼1,500 Atlantic salmon, using gill damage and amoebic load as indicator traits for host resistance. Both traits are heritable (h Salmo salar) has the highest production volume and value of all the salmonid species  have been widely and routinely applied  and chinook salmon (Oncorhynchus tshawytscha), and certain non-salmonid aquaculture species such as turbot , primarily caused by e.g., et al. 2016b). Harnessing this heritability for genetic improvement in selective breeding programs is a current goal. The high fecundity of aquaculture species, and resulting large full sibling family sizes, facilitates disease challenge testing of close relatives  to enable breeding value estimation in selection candidates. Selection is often more accurate when the relationship between individuals is obtained from genomic data (genomic selection) rather than the pedigree , but it depends on the architecture of the trait as well as other technical variables such as marker density . All phenotypic information is available in File S1.An AGD challenge experiment using 1,481 Atlantic post-smolt salmon  originating from a commercial breeding program  was conducted by distributing the fish equally into 2 × 4 m seawater tanks in the experimental facilities of Machrihanish . Seeder fish with a uniform level of AGD infection were produced by cohabitation with infected fish from an All animals were reared in accordance with relevant national and EU legislation concerning health and welfare. The challenge experiment was performed by the Marine Environmental Research Laboratory  under approval of the ethics review committee of the University of Stirling  and according to Home Office license requirements. Landcatch are accredited participants in the RSPCA Freedom Foods standard, the Scottish Salmon Producers Organization Code of Good Practice, and the EU Code-EFABAR Code of Good Practice for Farm Animal Breeding and Reproduction Organizations.N. perurans specific 18S sequence , and a 66 bp fragment of the Atlantic salmon Elongation Factor α 1 gene . DNA was normalized to 50 ng/µl, and 5 µl was combined into 50 µl QPCR duplex reactions comprising: 1X Taqman QPCR reaction mix , 300 nM N. perurans specific primers, 150 nM N. perurans specific probe, 150 nM ELFα primers, and 75 nm ELFα probe (Table S1). Ampifications were performed using a Biorad iCycler iQ QPCR Detection System. The thermal profile consisted of 95° for 10 min and 45 cycles of 15 s denaturation at 95°/30 s annealing/extension at 56°. Fluorescence in both FAM and HEX channels was acquired during the annealing/extension stage. Ct (threshold cycle) values were recorded and the level of N. perurans load was normalized against the ELF internal control by computing the ratio Equivalent Target Amount (ETA) N. perurans : ETA ELFα.Sampled whole gills were weighed and combined with an equal amount (wt/vol) 10 mM Tris, 1 mM EDTA, pH 8.0. Samples were then homogenized using a Qiagen TissueLyser II  following the manufacturers’ recommendations. Total DNA was extracted from 50 µl homogenate using Questgene 9600 DNA extraction kits  following manufacturers’ protocols. Amoebic load was determined via duplex qPCR reactions using primer/probe combinations targetting a 139 bp File S2), designed from a subset of SNPs from a higher density array (File S3) were filtered according to the following criteria: SNP call-rate < 0.9, individual call-rate < 0.9, FDR rate for high individual heterozygosity < 0.05, identity-by-state > 0.95 , Hardy-Weinberg equilibrium FDR p-value < 0.05, minor allele frequency < 0.05. After this filtering, a total of 1,430 fish and 7,168 SNPs remained for further analysis. The large number of SNPs removed by filtering is due to the lack of informativeness of the rainbow trout SNPs in these Atlantic salmon samples.DNA was extracted from fin tissue samples using the DNeasy 96 tissue DNA extraction kit  and samples were genotyped using an Illumina combined species Atlantic salmon and rainbow trout SNP array  and the pedigree (A-matrix). Heritabilities were estimated by ASReml 3.0  or N where σ2a is the additive (genetic) variance, G and A are the genomic and pedigree relationship matrices, respectively. X and Z are the corresponding incidence matrices for fixed and additive effects, respectively, and e is a vector of residuals. The genomic relationship matrix was constructed by the GenABEL R package  and tank (2 levels) as fixed effects and animal as a random effect. The additive effect was estimated using both the genomic kinship matrix . These windows overlap and therefore the significance threshold was determined using a Bonferroni correction using half the number of tested windows.A regional heritability mapping (RHM) analysis  / 2√h2y ]. Genomic best linear unbiased prediction (GBLUP) was applied to predict the masked phenotypes of the validation sets and the resulting prediction accuracy was compared to that of pedigree-based BLUP (PBLUP). The bias of the EBVs was estimated as the regression coefficient of the phenotypes on the predicted EBVs. Since medium-density SNP array genotyping can be expensive, we also evalutated the impact of reduced SNP density on prediction accuracy by using subsets of the SNP data for the GBLUP. To choose the SNPs for the (pseudo) low density panels we tried two different strategies: 1) we progressively increased the minimum allele frequency threshold in increments of 0.05  resulting in genotype datasets with progresively lower SNP density and progressively higher MAF; and 2) we iteratively removed the SNP showing the lowest mean distance to the previous and the next SNP on the genome, resulting in datasets of evenly spaced genotypes.The accuracy of genomic selection was estimated by five replicates of fivefold cross-validation analysis . The phenotypes recorded in the validation population were masked and breeding values were estimated using ASReml 3.0 using the linear mixed model described above. Prediction accuracy was calculated as the correlation between the predicted EBVs of the validation set and the actual phenotypes divided by the square root of the heritability estimated in the validation population [Table S1. Phenotypic data of the fish used in this study is available in File S1. Note that gill scores correspond to an experimental challenge, gill scores higher than 0.5-1 are rarely encountered in Landcatch commercial facilities. Markers included in the SNP array and their position in the Atlantic salmon genome can be found in File S2. Genotypes of the fish used in this study are available in File S3. The regional heritability mapping model is detailed in File S4.Primers and probes to perform amoebic load estimation by qPCR are provided in The means and standard deviations for AGD resistance traits were 2.79 ± 0.85 and 31.36 ± 3.24 for the gill score and qPCR amoebic load, respectively. Moderate heritability estimates were observed for both phenotypes, which ranged between 0.25 and 0.36 , and botA previous study on AGD disease resistance within the Tasmanian Atlantic salmon population found similar heritability estimates, ranging from 0.16 for gross gill score (similar to mean gill score here) to 0.35 for digital image gill score , and 0.1-0.3 for the Pacific sea louse  or are close , and these could also be QTL of moderate effect ; the significant SNP was the penultimate marker in chromosome 16. RHM uses information from several consecutive markers, and has been shown to have an advantage over single-SNP GWAS to explain part of the typical missing heritability of single-SNP association studies and to detect QTL of small effects which otherwise would not be detected using information from single SNPs  were consistent with the results of the single-SNP GWAS, with two regions in chromosome 18 showing the highest significance for both mean gill score and amoebic load . These rOur results point toward a polygenic architecture of resistance to AGD, but potentially including a few QTL explaining moderate levels of the genetic variation. Genotyping additional AGD-challenged and phenotyped samples would help provide evidence in support or against the existance of these QTL. Further, a higher SNP density could possibly identify additional QTL not in linkage disequilibrium with the SNPs in this study, help to fine map the ones reported here, and possibly increase the estimates of genetic variation explained by the QTL.et al. 2016) and Piscirickettsia salmonis  matrix was ∼18% higher than with the pedigree (A) matrix for both mean gill score and amoebic load, and GBLUP predictions showed practically no bias . Predicte.g., in ‘sib testing’ schemes), well designed low density genotyping panels may be useful in genomic selection. When SNP density was reduced either via progressive increase in MAF thresholds or selecting evenly-spaced sets of markers, accuracy remained relatively stable until 1,808 SNPs where a gradual drop off in accuracy was observed ; and recently different genomic selection models  have been shown to almost double the prediction accuracy for bacterial cold water disease in rainbow trout compared to pedigree-based estimates . As shown in the current study, genomic prediction accuracy is higher compared to pedigree-based prediction even when we use very low density genotyping (a few hundred SNPs). This is somewhat surprising given the size of the salmon genome  and can be measured using indicator traits such as gill score or amoebic load measured by qPCR. The genetic architecture of AGD resistance appears to be polygenic, but with two suggestive QTL explaining up to 11% of the genetic variance on chromosome 18, and other non-significant regions in other chromosomes accounting for a similar amount of variance. These possible QTL should be tested in independent populations, and may form the basis for identification of underlying causative genes. Genomic prediction accuracy was substantially higher (∼18%) when using genomic relationships rather than pedigree-based relationships with a ∼7K SNP panel, and remained so even when marker density substantially reduced. Since AGD is a large threat for salmon aquaculture in most major salmon production countries, genomic selection is likely to be an important component of breeding programs to help tackle this disease via genetic improvement of host resistance.Host resistance to AGD in Atlantic salmon is moderately heritable (hwww.g3journal.org/lookup/suppl/doi:10.1534/g3.118.200075/-/DC1Supplemental Material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Carcinus maenas) was initially introduced to eastern North America nearly 200 years ago where it expanded northwards to eastern Nova Scotia. A subsequent invasion to Nova Scotia from a northern European source allowed further range expansion, providing a unique opportunity to study the invasion genomics of a species with multiple invasions. Here, we use restriction‐site‐associated DNA sequencing‐derived SNPs to explore fine‐scale genomewide differentiation between these two invasions. We identified 9137 loci from green crab sampled from 11 locations along eastern North America and compared spatial variation to mitochondrial COI sequence variation used previously to characterize these invasions. Overall spatial divergence among invasions was high (pairwise FST ~0.001 to 0.15) and spread across many loci, with a mean FST ~0.052 and 52% of loci examined characterized by FST values >0.05. The majority of the most divergent loci  displayed latitudinal clines in allele frequency highlighting extensive genomic divergence among the invasions. Discriminant analysis of principal components  clearly resolved the two invasions spatially and was highly correlated with mitochondrial divergence. Our results reveal extensive cryptic intraspecific genomic diversity associated with differing patterns of colonization success and demonstrates clear utility for genomic approaches to delineating the distribution and colonization success of aquatic invasive species.Genomic studies of invasive species can reveal both invasive pathways and functional differences underpinning patterns of colonization success. The European green crab ( We predict (i) widespread genomic differentiation among these independent invasions based on existing work and the geographic isolation of the source populations and (ii) that larger numbers of loci used here will reveal finer‐scale population structure than has previously detected. We build directly on previous studies  gene was amplified using M13F (‐21)/M13R (‐27) tagged version of the primers described in Roman and Palumbi , 0.5 μm each primer, and 0.025 U AmpliTaq 360 (Life Technologies). Thermal cycling conditions consisted of 94°C for 3 min; 30 cycles of 94°C for 30 s, 50°C for 30 s, and 72°C for 30 s; 1 cycle of 72°C for 10 min. To verify amplification, 5 μl of the PCR product was electrophoresed on a 1% agarose gel, visualized using SYBR Safe (Life Technologies) and documented using a Gel Logic 200 (Kodak). Forty microliters of the remaining PCR product was purified using QIAquick PCR purification kit (Qiagen). The purified PCR products were quantified as described above and sequenced in both directions using M13F (‐21) and M13R (‐27) and BigDye Terminator v3.1 cycle sequencing kit (Life Technologies) per the manufacturer's instructions with the following modification, and 0.5 μl of the BigDye Terminator v3.1 Ready Reaction Mix (Life Technologies) and 3.8 μl of the 5× sequencing buffer (Life Technologies) was used per 20 μl reaction. Sequencing products were separated by size on a 36‐cm array containing POP7 polymer on an AB3130xl genetic analyzer (Life Technologies). The sequencing results from both directions were assembled using Vector NTI Advance v11 (Life Technologies). For identification, the resulting sequence for each individual was then aligned to reference sequences, haplotypes H1–H6 and H8–H10 from Darling et al. (FJ159008–FJ15016) using Vector NTI Advance v11 (Life Technologies).A 502‐base pair fragment of the mitochondrial cytochrome  Palumbi   was removed due to low sequence coverage, and so, we retained 241 individuals overall. We then used cstacks to construct a locus catalog based on sequence identity with 1 mismatch allowed between stacks. We used settings of M = 4 as the maximum distance between two loci to merge and n = 1 to allow two catalog loci to merge in case they are differently fixed versions of the same locus. All RAD tags were 80 bp in length. These were then filtered in the Stacks populations module to include only RAD tags present in each population and in ≥75% of individuals and a minor allele frequency (MAF) greater than 5%. This dataset was filtered for missing data and deviations from Hardy–Weinberg equilibrium using PLINK  and expected (He) heterozygosities were calculated in the package hierfstat . We first employed BayeScan v2.1 . We then conducted the DAPC using the defined clusters as prior information and retained the first 50 principal components to determine the best value of K for our data. The DAPC was conducted on the full SNP panel, as well as on the neutral and outlier datasets separately. Second, we used principal components analysis on the outlier and neutral SNP datasets to account for intrapopulation variance and compare with the results of our DAPC. Finally, Cavalli‐Sforza and Edwards’ (http://tree.bio.ed.ac.uk/software/figtree/). Geographic visualizations of FST similarities were built using GenGIS version 2.5 (PMID 23922841). Maps used in GenGIS were obtained from the Natural Earth project .Spatial structuring in neutral and outlier SNPs was explored using several methods. We conducted a discriminant analysis of principal components (DAPC) using adegenet 2.0.0 Jombart,  in R to Edwards’  chord diEdwards’ . NeighboFST values between each sampling location for the COI data and each of the three SNP panels. We then used a Mantel test with 1,000 permutations in the R package ade4  for the full SNP panel  . Average Ho for the neutral panel (0.254) was lower than that of the most divergent SNPs . The global unbiased FST for the full SNP panel was 0.052. Locus‐specific FST values ranged from 0.00001 to 0.551, and heterozygosities ranged from 0.095 to 0.564 for the full SNP panel . The nonhierarchical island model based method in Arlequin detected 548 SNPs under selection, while the hierarchical island model based method detected 332 SNPs; 281 loci were common to both of the models implemented in Arlequin. Among all three methods of outlier detection, there were 108  putatively adaptive loci , with a proportion of conserved variance of 0.779 explained by a single discriminant function and 100% correct assignment for both groups. In contrast, DAPC of outlier SNPs suggested a range of clusters from K = 2 to 3  and had a proportion of conserved variance of 0.925  intermediate along discriminant function 1 to the pure northern and southern clusters  and the COI data , Freamo, O'Reilly, Berg, Lien, & Boulding, Placopecten magellanicus), Van Wyngaarden et al., FST values were an order of magnitude larger across the genome than observed in native species [e.g., American lobster, Homarus americanus, Benestan et al. , respectively, where outlier SNPs showed the same population structure as neutral SNPs. Observations of similar spatial patterns in both outlier and neutral SNPs supports conclusions of significant genomewide divergence of the groups observed. For instance, both Moore et al.  detected the same level of population structuring as the full panel of SNPs, reinforcing the use of relatively few “diagnostic” markers for fine geographic scale population identification. Similar conclusions were reached by Moore et al.  and Cande et al.  and Cande et al.  focus one et al. . Our obs4.2FST values among sampling sites compared to the SNPs. The SNP panel clearly revealed this divergence was widespread across the genome and the outlier SNPs were better able to detect genetic differences between the northern and southern groups explaining significantly greater components of the spatial variance.Previous studies have used COI sequence variation to study green crab population structure and to trace the invasion history Darling, . Overall4.3The data presented here are consistent with previous descriptions of green crab invasion history . Genotype data, including filtered RAD‐seq data and aligned COI sequences are available from the Dryad Digital Repository at http://dx.doi.org/10.5061/dryad.619t0.DNA sequences: All raw COI mtDNA sequence data will be uploaded to NCBI and accession numbers provided. The RAD‐seq genotypes used in this study as well as all distance matrices and geographic least‐cost distances will be deposited in Dryad prior to final acceptance. All corresponding sample codes and locations are in Table None declared.IRB, R. Beiko, and CDB conceived of the study. JF, KM, CHM, R. Bernier, and CDB collected specimens for use in this study. LCH conducted DNA extraction and RADseq library preparation. NWJ, MVR, RES, PNR, R. Beiko conducted bioinformatics and statistical analyses of the data. All authors aided in the interpretation of the data and initial study design. All authors contributed to writing, revising, and approving the final draft of the manuscript. Click here for additional data file.