Exploring genetic differentiation and genomic variation is important for both the utilization of heterosis and the dissection of the genetic bases of complex traits.We integrated 1857 diverse maize accessions from America, Africa, Europe and Asia to investigatetheir genetic differentiation, genomic variation using 43,252 high-quality single-nucleotide polymorphisms(SNPs),combing GWAS and linkage analysis strategy to exploring the function of relevant genetic segments.EST of more than 0.2 were identified between the TS and temperate subpopulations. These regions comprised 79 % of the genetic variation, and most were significantly associated with adaptive traits. For example, the region containing the SNP tag PZE.108075114 was highly differentiated, and this region was significantly associated with flowering time (FT)-related traits, as supported by a genome-wide association study (GWAS) within the interval of FT-related quantitative trait loci (QTL). This region was also closely linked to zcn8 and vgt1, which were shown to be involved in maize adaptation. Most importantly, 197 highly differentiated regions between different subpopulation pairs were located within an FT- or plant architecture-related QTL.We uncovered many more subpopulations that recently or historically formed during the breeding process. These patterns are represented by the following lines: Mo17, GB, E28, Ye8112, HZS, Shen137, PHG39, B73, 207, A634, Oh43, Reid Yellow Dent, and the Tropical/subtropical (TS) germplasm. A total of 85 highly differentiated regions with a DHere we reported that 700–1000 SNPs were necessary needed to robustly estimate the genetic differentiation of a naturally diverse panel. In addition, 13 subpopulations were observed in maize germplasm, 85 genetic regions with higher differentiation between TS and temperate maize germplasm, 197 highly differentiated regions between different subpopulation pairs, which contained some FT- related QTNs/QTLs/genes supported by GWAS and linkage analysis, and these regions were expected to play important roles in maize adaptation.The online version of this article (doi:10.1186/s12870-015-0646-7) contains supplementary material, which is available to authorized users. Zea mays L.) is widely planted throughout the world, including in more than 70 countries across six continents /[n/(n-1)], where HSest is the observed gene diversity within subpopulation, HTest is the overall gene diversity, and n is the number of subpopulations [EST of all SNPs in LD was defined as the cutoff value to interpret the relevant differentiation level between subpopulations. Genomic regions with an average DEST exceeding the cutoff value were treated as cases of strong differentiation between subpopulations.Pair relatedness among the 1,857 accessions was estimated by definition as follows: Fectively . The GD,ectively , with heectively . The PICus study . This evus study . The DESulations . The sizP-values of SNPs were less than the cutoff value.The BLUP of FT-related traits, including the DTT, DTP and DTS for each accession across three environments, and 43,252 SNPs were selected to perform a phenotype-genotype GWAS, which was implemented in the GAPIT R package , using mAll data sets supporting the results of this article are included within the article.


Insertion and deletion (INDEL) is one of the main events contributing to genetic and phenotypic diversity, which receives less attention than SNP and large structural variation. To gain a better knowledge of INDEL variation in chicken genome, we applied next generation sequencing on 12 diverse chicken breeds at an average effective depth of 8.6. Over 1.3 million non-redundant short INDELs (1–49 bp) were obtained, the vast majority (92.48%) of which were novel. Follow-up validation assays confirmed that most (88.00%) of the randomly selected INDELs represent true variations. The majority (95.76%) of INDELs were less than 10 bp. Both the detected number and affected bases were larger for deletions than insertions. In total, INDELs covered 3.8 Mbp, corresponding to 0.36% of the chicken genome. The average genomic INDEL density was estimated as 0.49 per kb. INDELs were ubiquitous and distributed in a non-uniform fashion across chromosomes, with lower INDEL density in micro-chromosomes than in others, and some functional regions like exons and UTRs were prone to less INDELs than introns and intergenic regions. Nearly 620,253 INDELs fell in genic regions, 1,765 (0.28%) of which located in exons, spanning 1,358 (7.56%) unique Ensembl genes. Many of them are associated with economically important traits and some are the homologues of human disease-related genes. We demonstrate that sequencing multiple individuals at a medium depth offers a promising way for reliable identification of INDELs. The coding INDELs are valuable candidates for further elucidation of the association between genotypes and phenotypes. The chicken INDELs revealed by our study can be useful for future studies, including development of INDEL markers, construction of high density linkage map, INDEL arrays design, and hopefully, molecular breeding programs in chicken. Many common human diseases are frequently caused by INDELs, such as cystic fibrosis PMEL17 gene are causative mutations for plumage color  Chicken as one of the most important domestic animals not only provides essential proteins for human food industry, but also serves as an excellent biological model for many scientific researches ftp://ftp.ncbi.nih.gov/snp/organisms/chicken_9031/VCF/, updated in June 11, 2013). However, previous studies with human and other model organisms showed that INDELs accounted for 9–14% of all genetic polymorphisms With the rapid advance of sequencing technology, considerable progresses have been made in INDEL discovery in chicken genome. Wong et al. Recently, Fan et al. The whole blood samples were collected from brachial veins of chickens by standard venipuncture. The whole procedure was performed according to the protocol approved by the Animal Care and Use Committee of China Agricultural University.Twelve female birds from 12 different chicken breeds were used in this study. Seven breeds, Beijing You (BY), Dongxiang (DX), Luxi game (LX), Shouguang (SG), Silkie (SK), Tibetan (TB) and Wenchang (WC), were Chinese indigenous. Four were commercial breeds, i.e., Cornish (CS), Rhode Island Red (RIR), White Leghorn (WL) and White Plymouth Rock (WR). A Red Jungle Fowl (RJF), the wild ancestor of domestic chickens, was also used. Birds from these breeds exhibit significant differences in appearance  and production performance , and are believed to harbor extensive genetic diversity.Genomic DNAs were extracted from blood samples using standard phenol/chloroform extraction method. DNA concentration and purity were accessed on NanoDrop , and the qualified DNAs were used for library construction. Two paired-end libraries were constructed for each individual, with an intended 10-fold depth (5-fold for each library). Genomic DNAs were sheared to yield an average size of 500 bp and then ligated to Illumina paired-end adaptors. After PCR amplification and purification, the resulting libraries were sequenced on an Illumina Hiseq 2000 sequencer . Raw reads of 2×100 bp were generated for downstream analysis.http://hgdownload.soe.ucsc.edu/goldenPath/galGal4/bigZips/) Chicken genome assembly  was downloaded from UCSC Genome Browser website (Mapping reads to reference genome was performed using Burrows-Wheeler Alignment tool (BWA ver 0.6.2) ftp://ftp.ncbi.nih.gov/snp/organisms/chicken_9031/VCF/, updated in June 11, 2013) The chicken SNP information was downloaded from SNP database in NCBI  read depth between 5 and 31; b) quality by depth (QD)>5.0; c) ReadPosRankSum >−20.0 and d) FS<200.0. For SNPs, the criteria were: a) read depth between 5 and 31; b) QD>5.0; c) MQ>40.0; d) HaplotypeScore <13.0; e) MQRankSum >−12.5; f) ReadPosRankSum >−8.0 and g) FS<60.0. Besides, if more than 3 SNPs were clustered in a 10 bp window, they were all considered as false positives and removed http://www.premierbiosoft.com/primerdesign/) To evaluate the reliability of our data, we randomly selected a subset of putative variants for PCR validation in the positive chickens. PCR primers were designed using Primer Premier5  http://www.animalgenome.org/cgi-bin/QTLdb/GG/index, updated in July 8, 2013) P value (P<0.05) of a modified Fisher's exact test and Benjamini correction for multiple testing.The Ensembl chicken gene set (Ensembl release 74) was downloaded from the Ensembl website (All raw sequence data had been deposited in NCBI Sequence Read Achieve (SRA) under the Bioproject number PRJNA232548. The experiment numbers for the 12 chickens are SRX408161-SRX408172. The whole variant information was provided in the supplementary files.On average, about 140 million raw reads were generated for each individual, 90% of which were aligned to the reference genome . Here, wAlthough both INDELs and SNPs were identified in our study, we focused on INDELs for further analysis and discussion. In total, 1,766,724 and 1,759,849 raw INDELs were called by SAMtools and GATK, respectively. The concordant part contained 1,425,081 INDELs, accounting for 80.66% and 80.98% of the total number called by SAMtools and GATK, respectively . In termWe compared our results with the variants in SNP database  and found that the vast majority (92.48%) of our INELs were novel . The 101In order to evaluate the accuracy of our variant detection strategy, we randomly selected 57 INDELs for validation and 50 of them were successfully amplified and sequenced. These INDELs include 23 insertions and 27 deletions, with their sizes ranging from 1 to 31 bp . To addr (40/44) .The largest INDEL detected in this study was 49 bp , and theP<2e-16), with INDEL densities of macro-chromosomes (GGA1-5) and intermediate chromosomes (GGA6-10) significantly higher than that of micro-chromosomes (GGA11-28)   . The Z c 0.0018) . Micro-cTo explore the distribution of INDELs in genic regions, we annotated all detected INDELs using Ensembl gene set . For each INDEL, its genomic location  and functional role  were determined. In total, 620,253 (46.15%) INDELs were mapped to genic regions  . Among tP = 2.2e-16). A large number  of functionally important genes were covered by coding INDELs, many  of which contained two or more coding INDELs  were triplet (non-frameshifting), thus retaining the reading frame, and 23 (1.30%) caused gain or loss of stop codon. The remaining 1,022 (57.90%) were non-triplet INDELs, which were predicted to cause frameshift mutation, and this proportion was significantly lower compared with the genomic level  regions . AccordiGO and KEGG pathway analysis were performed on 1,593 genes that contained more than one hundred INDELs, which we assumed to be under high mutation load of INDELs. GO results showed 211 terms, 76 of which were significant after Benjamini correction. These genes were significantly enriched in the molecular functions of protein kinase activity, enzyme activator activity, molecule binding , GTPase regulator activity, channel activity, and substrate specific channel activity . The KEGIn this study, we performed NGS on 12 chicken individuals for INDEL discovery to gain a comprehensive understanding of INDEL variation in chicken genome. Although NGS technologies are routinely used to detect genome-wide variations To our knowledge, the number of INDELs identified in our study is the highest so far in chicken. Compared with the study in human, this number is smaller than the results from Mills et al. The INDEL density analyzed in 12 chickens was higher than that observed by Brandstrom and Ellegren THRSP encodes a small acidic protein that responds to thyroid hormone (TH) stimulation and is thought to play a role in growth. A 9 bp INDEL polymorphism and several SNPs in the exon1 of THRSP were found to be associated with abdominal fat content MUC6 (β-subset of ovomucin) is the homologue of human MUC6MUC6 and we suggested that these INDELs could be used as potential candidates for egg quality. In addition, quite many genes related to the development of chicken embryo or are the homologues of human disease-related genes. The results demonstrated that though strongly selected against, INDELs were common in some functionally important genes, arguing for their incorporation to elucidate the association between genes and traits.Like SNPs and CNVs, INDELs are of great importance for their ability to alter gene functions, especially those frameshifting INDELs locating in exons. In this study, lots of genes were affected by frameshifting INDELs. Some genes are associated with chicken performance traits. For instance, It is increasingly recognized that INDEL polymorphisms can be effectively used as genetic markers Microarrays are very powerful and essential tools in GWAS and genomic selection (GS). Up to date, medium and high density SNP arrays have been commercially available We performed whole genome sequencing on 12 diverse chicken breeds and identified the largest number of INDELs in chicken genome so far. Incorporating diverse chicken breeds for variant detection allowed for a larger collection of variants to be discovered. A large number of coding INDELs located in previously reported genes associated with chicken performance traits. We suggest that INDELs are crucial determinants causing genetic and phenotypic diversity and can be promising genetic markers. Our results can be used for a variety of studies in the future, including development of INDEL markers, construction of high density linkage map, INDEL arrays design, and hopefully, molecular breeding programs in chicken.Figure S1The number of raw variants called by SAMtools and GATK, respectively. A: INDELs. B: SNPs.(TIFF)Click here for additional data file.Table S1SNPs detected in 12 chickens.(DOCX)Click here for additional data file.Table S2Summary of validated INDELs.(XLSX)Click here for additional data file.Table S3Summary of validated SNPs.(XLSX)Click here for additional data file.Table S4Coding INDELs in each chromosome.(XLSX)Click here for additional data file.Table S5Distribution of INDELs in QTL regions.(XLSX)Click here for additional data file.Table S6Functional enrichment of genes under high mutation load of INDELs.(XLSX)Click here for additional data file.File S1The non-redundant set of detected INDELs in 12 chickens. To minimize the file size, only minimal essential information was listed, including CHR (chromosome), POS (position), REF , ALT , and BREEDS (chickens in which this INDEL was detected).(GZ)Click here for additional data file.File S2The non-redundant set of detected SNPs in 12 chickens. To minimize the file size, only minimal essential information was listed, including CHR (chromosome), POS (position), REF , ALT , and BREEDS (chickens in which this SNP was detected).(GZ)Click here for additional data file.


The extent of linkage disequilibrium (LD) between molecular markers impacts genome-wide association studies and implementation of genomic selection. The availability of high-density single nucleotide polymorphism (SNP) genotyping platforms makes it possible to investigate LD at an unprecedented resolution. In this work, we characterised LD decay in breeds of beef cattle of taurine, indicine and composite origins and explored its variation across autosomes and the X chromosome.2 was less than 0.2 for marker pairs separated by 50 kb. The LD decay curves clustered into three groups of similar LD decay that distinguished the three main cattle types. At short distances between markers (< 10 kb), taurine breeds showed higher LD (r2 = 0.45) than their indicine (r2 = 0.25) and composite (r2 = 0.32) counterparts. This higher LD in taurine breeds was attributed to a smaller effective population size and a stronger bottleneck during breed formation. Using all SNPs on only the X chromosome, the three cattle types could still be distinguished. However for taurine breeds, the LD decay on the X chromosome was much faster and the background level much lower than for indicine breeds and composite populations. When using only SNPs that were polymorphic in all breeds, the analysis of the X chromosome mimicked that of the autosomes.In each breed, LD decayed rapidly and rThe pattern of LD mirrored some aspects of the history of breed populations and showed a sharp decay with increasing physical distance between markers. We conclude that the availability of the HD chip can be used to detect association signals that remained hidden when using lower density genotyping platforms, since LD dropped below 0.2 at distances of 50 kb. Linkage disequilibrium (LD) between molecular markers reflects the correlation between genotypes of two markers or the degree of non-random association between their alleles. Previous studies that used single nucleotide polymorphisms (SNPs) to describe patterns of LD in cattle at the whole-genome level-6 have sThe extent of LD has implications for both GWAS and the delivery of accurate genomic predictions. However, its importance is often neglected despite the fact that it is known that it can introduce bias. Collecting and using SNP genotyping data have exploded for cattle in the last few years due in part to decreasing genotyping cost and to efforts to improve cattle breeding through genomic selection. Despite this, few studies have documented the behaviour of LD using the expanded set of 777 000 SNPs available on the BovineHD platform . One of the significant advances of this denser chip is that it allows for an accurate estimation of LD over short physical distances as it contains many more marker pairs separated by 10 kb or less.Bos taurus (taurine), Bos indicus (indicine) and a composite beef cattle group. The results were compared to an independent population to confirm and potentially generalize the findings. This report is intended to be used as an updated description of the extent of LD in beef cattle.Here, we present the LD decay curves for SNPs on bovine autosomes and the X chromosome for three genetic groups of cattle breeds: All analyses were performed using genotypes generated in previous work. Therefore, for this study, no animal ethics approval was requested because no new animals were sampled.Bos taurus breeds , Bos indicus (Brahman) and composite cattle . To confirm our findings, genotyping data from each cattle type  were sourced from the Bovine HapMap consortium[Animals used in this study Table  were parnsortium.http://www.illumina.com/documents/products/datasheets/datasheet_bovineHD.pdf) that includes 777 962 markers. Quality control and imputation of missing data in the Australian sample followed the pipeline described by Bolormaa et al.[2, which is less susceptible to bias due to differences in allelic frequency[All animals were genotyped using the BovineHD SNP chip  than their indicine and composite counterparts (Pn ~ 1.00 for both) (Table He) ranged from 0.25 (Brahman from the HapMap dataset and Shorthorn) to 0.35 . In general, the composite breeds showed higher He (0.34) than the taurine (0.28) and indicine breeds (0.26) because they originated from a mixture of both these types of cattle.A high proportion of polymorphic markers was observed across all breeds, with the taurine breeds showing a slightly lower proportion (2) for Brahman and Angus was 0.25 and 0.46, respectively  and the taurine breeds had the highest (r2 > 0.6). However, contrary to the pattern observed for autosomes, LD across BTAX decayed fastest in the taurine breeds, such that for marker pairs separated by 50 kb, the average LD was lower than that in either of the composite indicine populations  revealed a different pattern to that observed for autosomes Figure . The LD To assess whether the results obtained here were a specific feature of the Australian population, we repeated the analyses with an independent sample of Angus, Santa Gertrudis and Brahman animals from the Bovine HapMap dataset,10. ResuOur results expand on previous studies of genome-wide LD in bovine populations. By using larger samples and a much higher density of markers than before and by exploring variation across autosomes and the X chromosome, we obtained an exponential increase in pair-wise LD comparisons, which allowed us to produce robust results. Because LD dropped below 0.2 at marker distances above 50 kb, we conclude that the availability of the HD chip enables detection of association signals that remained hidden when using lower density genotyping platforms.The authors declare that they have no competing interests.LRPN, AR and JWK planned the experiment. LRPN ran the analyses. LRPN, AR and JWK drafted the manuscript. All authors read and approved the final version.2) decay on beef cattle autosomes from the Australian sample. AA = Angus, BB = Brahman, TC = Tropical Composite, SG = Santa Gertrudis, BR = Belmont Red, HH = Hereford, LL = Limousin, SS = Shorthorn. Plot of the linkage disequilibrium (r2) decay on beef cattle autosomes from the Australian sample colour-coded per breed. Figure S2. Linkage disequilibrium (r2) decay on the X chromosome of male beef cattle only. AA = Angus, BB = Brahman, TC = Tropical Composite, SG = Santa Gertrudis, BR = Belmont Red, HH = Hereford, LL = Limousin, SS = Shorthorn. Plot of the linkage disequilibrium (r2) decay on the X chromosome of male beef cattle colour-coded per breed and cattle type.Linkage disequilibrium (rClick here for file2) between unlinked markers. About ~7 K SNPs were randomly sampled and linkage disequilibrium was calculated for SNP pairs on different chromosomes.Average linkage disequilibrium (rClick here for file


Eighty loci were significantly associated with O/DM (p ≤ 10−4) and three key signals were found. We then evaluated the progeny of a Deli x AVROS breeding trial and a 4% higher O/DM was observed amongst those having the beneficial genotypes at two of the three key loci (p < 0.05). We have initiated MAS and large-scale planting of elite dura and pisifera parents to generate the new commercial tenera palms with higher O/DM potential.GWAS in out-crossing perennial crops is typically limited by insufficient marker density to account for population diversity and effects of population structure resulting in high false positive rates. The perennial crop oil palm is the most productive oil crop. We performed GWAS for oil-to-dry-mesocarp content (O/DM) on 2,045 genotyped  Elaeis guineensis Jacq.) as a model to address these limiting factors and to illustrate the potential of marker-assisted selection (MAS) in out-crossing breeding programmes. The oil palm with the highest yield per hectare of all oil crops, it accounts for a quarter of vegetable oil traded worldwide annually, despite occupying only 5% of the global oil planting acreageGenome-wide association study (GWAS) has emerged as a powerful method and commonly adopted, particularly in human populations to identify a broad range of complex diseases238In recent decades, molecular markers have been employed to identify quantitative trait loci (QTL) for traits of importance in oil palm. By determining the allelic variation present, palms that possess particular combinations of desired QTL can be selected at the nursery stage. Markers could significantly reduce the conventional phenotyping cycles and also enrich the best combinations of alleles in palms planted from each cross. Most marker discovery programmes in oil palm are still mainly based on controlled cross-based linkages using various marker systems with modest density, including restriction fragment length polymorphisms (RFLPs)12The publication of the oil palm genome consisting of 1.535 gigabase (Gb) of assembled sequencedura palms are a breeding population of restricted origin (BPRO) derived from the four original palms planted at Bogor Botanical Garden in 1848Ulu Remis and Johore Labis materialssh + and sh−dura (sh + sh+) to planting tenera (sh + sh−) derived from Deli x AVROS, realizing a 30% increment in oil yield/hectare22dura) and paternal (pisifera) breeding lines, followed by extensive progeny testing. Concerns of a narrow genetic base limiting future breeding progress has also led many oil palm breeders to evaluate other sources of dura germplasm. For this reason, the semi-wild Nigerian dura crossed with AVROS pisifera was also evaluated in Sime Darby.The Deli p < 0.001)   (Supplem< 0.001) . An incrr2)  decay rates of Deli x AVROS and Nigerian x AVROS were 25Kb and 20Kb at 0.12 and 0.15 of average pairwise correlation coefficients (r2) . The LD The O/DM of individual palms was measured over 7 years of field planting (summarized in 25p) at 10−4 and a Bonferroni cutoff at 10−7. The number of significant signals was smaller in latter group, possibly due to smaller population size at only half of Deli x AVROS group. Higher statistical power will be achievable with larger population size. We identified three significant SNPs i.e. SD_SNP_000010418 , SD_SNP_000019529  and SD_SNP_000002370  on Chromosome 5, which were commonly present in both groups and situated near gene models  and a progeny from Topi Deli dura x Yangambi pisifera . Interestingly, we also observed one of the QTLs located on Chromosome 6 between 41,545,028bp–41,557,789bp (Pyruvate kinase (PK) gene was found to be associated to O/DM. The expression of PK gene in oil palm was reported to be responsible for an increased flux through to pyruvate for fatty acid biosynthesis31In total, 62 and 18 significant association signals for the O/DM phenotype  were maie models . Compare57,789bp . The majdura maternal and shell-less pisifera paternal poolsdura and pisifera with good combining ability evaluated via progeny testing are crossed for thin-shelled tenera commercial production. Within the Deli x AVROS group , the homozygous G/G palms for SD_SNP_000019529 yielded a mean of 77.1% O/DM, which was significantly higher than the 75.8% observed amongst the heterozygous G/A palms  or family and individual selection (FIS), producing thick-shelled /A palms . The sam/A palms . The val/A palms . However/A palms . In addi/A palms .Dura-33 and Pisifera-12 cross . The O/DM mean for this progeny was 4% higher than the other two progeny   , while t alleles . Based oIn summary, we report the most comprehensive use of high density SNP genotyping in oil palm to date, the use of a GWAS approach to identify SNP variants associated with differences in the key oil-to-dry mesocarp yield trait, and confirmation of their action in an independent cross. Based on these results, we have implemented a MAS programme to breed new parental lines for commercial oil palm hybrid production. The reported study also lays the foundations for a genomic selection model for oil palm and will act as a model for other perennial tree crops.dura palms to a common Pisifera-12 (tenera) with their parents  were sampled. Total genomic DNA was isolated from young leaf tissue (frond 0) using the DNAeasy Plant Mini Kit (Qiagen).For re-sequencing, we sampled 132 palms belonging to 59 origins  maintainifera-12 . Three pE. oleifera and 746,092 SNPs based on coverage , genotype quality with minimal score of 8 and minor allele frequency (MAF < 0.05). The other filtering step removed 5,330,765 SNPs based on the technical requirements of Illumina, including the removal of pairs of SNPs with distance less than 60bp apart, ambiguous nucleotides, indels, non-biallelic and A/T or C/G types. We identified 593,888 quality SNPs. According to linkage disequilibrium with a r2 cut-off set at 0.3, 200,000 SNPs with an average density of one SNP per 11Kb were submitted to Illumina for design score calculation using Illumina’s Assay Design Tool for Infinium. The Infinium array, termed as OP200K was used to assay the GWAS discovery populations (~250 ng DNA/sample). The overnight amplified DNA samples were then fragmented by a controlled enzymatic process that did not require gel electrophoresis. The re-suspended DNA samples were hybridized to the BeadChips after an overnight incubation in the capillary flow-through chamber. The allele specific hybridizations were fluorescently labeled and detected by an Illumina BeadArray Reader. The raw reads were then analyzed using the GenomeStudio Data Analysis software for automated genotyping calling and quality control. To generate the genotypic dataset for GWAS, only the SNPs that had minor allele frequency (MAF) >0.01 and >90% call rate were accepted. The missing genotype of accepted SNPs was subsequently imputed based on the mean of each markerThe 132 samples were pooled based on an equal molar concentration of DNA from each sample to form the sequencing DNA pool. A library was prepared for re-sequencing using Illumina HiSeq 2000 to generate 100-bp pair-end reads to give a 35x genome coverage from 924,271,650 raw reads. The pair-end reads were trimmed, filtered and aligned to the published oil palm genomer2) dropped to the half of its maximum value. In this study, we calculated the pairwise r2 for all SNPs in a 1-Kb window and averaged across the whole genome based on the composite method in the R package SNPRelateA Neighbor-Joining (NJ) tree was used to infer the genetic stratification of the GWAS discovery populations. A Hamming’s pairwise distance matrix for all SNP sites was calculated to plot the NJ tree. The genome-wide LD decay rates in the Deli x AVROS and Nigerian x AVROS were important to anticipate the requirements for the suitable mapping resolution of the SNP array for GWAS. The rate is defined as the chromosomal distance at which the average pairwise correlation coefficient  is a direct measurement of crude palm oil (CPO) extracted from dry mesocarp tissue using a solvent. The individual palms were phenotyped to produce a reliable mean O/DM value for analysis as per standard industry practicep ≤ 10−4 were selected to measure the SNP effects on O/DM phenotype and further validated in a Deli x AVROS breeding trial separately. One-way ANOVA tests with multiple comparisons were performed to test the SNP genotypes against the O/DM trait variation. The same approach was applied to determine the SNP effects of the combination of significant SNPs based on the number of positive alleles carried by individual palms. All the statistical analyses were implemented in Minitab 14dura palms and the common pisifera. The profiled SNP effects were subsequently used to implement MAS on newly propagated dura and pisifera seedlings at the pre-nursery stage and followed by field planting.The common association signals in both groups based on All the SNPs used in the OP200K genotyping array have been deposited in dbSNP under the handle of SDTC_BB with NCBI submitted SNP (ss) accession numbers of 181006940–1810592638.How to cite this article: Teh, C.-K. et al. Genome-wide association study identifies three key loci for high mesocarp oil content in perennial crop oil palm. Sci. Rep.6, 19075; doi: 10.1038/srep19075 (2016).


STF, but these measures are not ideal for the task.High-throughput sequencing is a cost effective method for identifying genetic variation, and it is currently in use on a large scale across the field of biology, including ecology and population genetics. Correctly identifying variable sites and allele frequencies from sequencing data remains challenging, in large part due to artifacts and biases inherent in the sequencing process. Selecting variants that are diagnostic is commonly done using diversity statistics like Here, we develop a method that directly calculates the expected amount of information gained from observing each variant site. We then develop and implement a conservative estimator that takes into account uncertainity introduced by sampling bias and sequencing error. This estimator is applied to simulated and real sequencing data, and we discuss how it performs compared to the commonly used existing methods for identifying diagnostic polymorphisms.The expected information content gives an easy to interpret measure for the usefulness of variant sites. The results show that we achieve a clear separation between true variants and noise, allowing us to select candidate sites with a high degree of confidence. A large part of the genetic variation in a species come in the form of single nucleotide polymorphisms (SNPs) . Technolinformative for the task at hand. For instance, when selecting SNPs that are diagnostic , we would like to pick a small set of sites that provide the most information about the individual's group affiliation. Although one could achieve the same certainty (at a somewhat higher cost) using a larger set of individually less informative SNPs, this would also increase the risk of overfitting the model to the data. Careful selection of SNPs is therefore not just an issue of economy and expedience, but also of accuracy.In spite of improvements to technology, SNP analysis is still limited by genotyping cost and capacity. It therefore remains an important challenge to find a set of SNP markers that is as effective and efficient as possible. To be precise, we want to identify the minimal set of SNPs that must be examined in order to draw conclusions with an acceptable certainty - viz., the SNPs that are most In practice, diagnostic SNPs are usually identified and ranked or selected using some variation of the following procedure:First, samples are collected from individuals from the populations of interest, and DNA is extracted and sequenced to a depth deemed reasonable in terms of cost and benefit. Sequencing each sample individually is advantageous for reliably detecting rare alleles and to ensure a more complete SNP discovery , and is STF), or statistical confidence in allele frequency difference , and then ranked using the other . Sites can also be excluded based on coverage and more specific error estimates using base quality or mapping quality.The set of putative SNPs are then evaluated using some diversity statistic . Often In practice, some additional care is often taken in the selection of candidate sites. For instance, one might require a certain minimum distance between sites in the genome in order to avoid unwanted correlations, or exclude sites in regions with low average mapping quality.ST Fis perhaps most commonly used [There are many statistics that could be used to identify diagnostic SNPs  which culations .p-values for allele difference, usually calculated using Fisher's exact test, but other options are also possible [magnitude of the allele differences, this is only taken indirectly into account by p-values. Variation in sequencing coverage means that sites with high coverage will tend to have higher confidence, even if the actual allele difference is small [p-values if the coverage is sufficiently high. In addition, Fisher's exact test does not generally take into account the possibility of errors - the observation of a single allele will exclude an underlying frequency of zero for the observed allele, even if that may well be the case if the observation is an error.An alternative (or complimentary) approach is to use possible . One chais small . Even wia priori knowledge of allele frequency, we will then proceed to develop a method to make a conservative estimate for this statistic, taking into account sampling bias and uncertainty in the data. Finally, we provide an implementation, and discuss the results from applying it to both simulated and real data sets.In the following, we derive a method for calculating the expected information to be gained from genotyping a specific site, and argue that this is a more intuitive and useful measure for evaluating diagnostic SNPs than the commonly used alternatives. We will first describe how to calculate the expected informatino given ST Fdiscussed above, it is perhaps tempting to instead use some other measures, like nucleotide diversity or absolute difference in allele frequency. However, it is easy to see that nucleotide diversity per site  + (1 − p)q where p and q are the major allele frequencies in the two populations) fails to measure divergence when one of the populations has an allele frequency of 0.5 - substituting p = 0.5 in the formula above results in 0.5(1 − q) + (1 − 0.5)q, and it is easy to see that nucleotide diversity will be 0.5 regardless of q.Given the drawbacks to using |p − q|) is perhaps better, but consider populations where one allele's frequencies in the two populations are 0.4 and 0.6, respectively. Assigning an individual to a population based on observing this allele not inspire a lot of confidence in the result, they are roughly equally likely. Although the difference between allele frequency is the same for a site with allele frequencies 0.05 and 0.25, observing this allele is here five times as likely in one population as in the other, intuitively making this a much more useful site to observe.Absolute difference in allele frequencies , is known:For diagnostic SNP, what we really would like to know is the amount of N belonging to A, which after observing a is modified by a factor ofHere, P(A) is our prior probability of N to one of several populations , we are interested in the relative probabilities for the two hypotheses. In other words, we would like to know the odds for N belonging to one population or the other. Given the probabilities of P(a|A) and P(a|B), and initial odds P (A)/P(B), we getIn order to assign P(a), we find that the prior odds are modified by:Canceling out That is, the ratio of this allele's frequencies in each of the populations. For practical reasons, we take the logarithm of the odds. This gives us scores that are additive and symmetric (so that switching the two populations gives us the same score with the opposite sign). Specifically, base two logarithms will give us the score in bits.When observing a site, we may of course also encounter the alternative allele. By the same reasoning as above, we find that this allele modifies the prior odds byp and q  in the two populations, the expected added information from the site then becomes:Lacking any specific information about priors, we can consider each population equally likely, and the likelihood of observing a particular allele is the average of the likelihood in each population. The information gain from each possible allele is then averaged, weighted by this average likelihood. For a biallelic site with major allele frequencies i and with frequency vectors p and q in the two populations, this generalizes to:Note that we are here only interested in the amount of information gained, regardless of which hypothesis it favors, and thus we take the absolute values. For a site with multiple alleles enumerated by STF, measures of I is additive (assuming independence between sites), so the information gained from observing multiple sites is readily calculated, and observing with an ESI of 2.32 bits is equivalent to observing four sites with ESI 0.58.Returning to the example at the start of the section, we now find that a site with allele frequencies of 0.4 and 0.6 contributes 0.58 bits of expected information, while 0.05 and 0.25 contributes 2.32 bits. Unlike measures like It may also be instructive to compare this procedure to sequence alignment and position specific score matrices (PSSMs). In sequence alignment, a sequence of nucleotides or amino acids are scored by comparing its match to a target sequence to its match to some base model using log odds scores. The base model to compare against is often implicit , but more elaborate models is also possible . SimilaIn order to apply the above method in practice, we need to measure the allele frequencies in the population. This is problematic for two reasons: First, we do not have precise knowledge of the allele frequencies, we can only estimate them from our sample, which introduces a sampling bias. Second, the sequencing process introduces additional artifacts that add nose and bias to the data. For instance, sequencing errors often result in substitutions, which are observed as apparent alleles. In addition, sequences can be incorrectly mapped, contain contamination, the reference genome can contain collapsed repeats, and the chemistry of the sequencing process is usually also biased - for instance, coverage is often biased by GC content. These artifacts often give the false appearance of variant positions.One challenge with calculating site information from sequencing data , is that such errors in the data can vastly overestimate the information content. For instance, an allele that appears to be fixed in one population means that any other observed allele will assign the individual to the alternative population - regardless of any other alleles. It is easy to see that an allele frequency of zero results in the odds going either to zero or infinity, and thus the log odds will go to either positive or negative infinity.∈, corresponding to sequencing error rate. In the following, we will refer to the resulting measure as conservative site information, or CSI.For diagnostic SNP discovery, it is more important to ensure that identified SNPs are informative, than to precisely estimate the information content. Thus, we take a conservative approach and use upper and lower limits for the allele frequencies by calculating confidence intervals using the method by Agresti and Coull . In addiLepeophtheirus salmonis) draft genome assembly. Random substitutions were introduced at a rate of 1/200 bases to generate three different haplotypes, which where then admixed in proportions 1:2:3 and 3:2:1 to generate two population, P1 and P2 with variant allele frequencies of 0.17, 0.33 and 0.50, as shown in Table A set of simulated reads were generated using FlowSim , using aSimulated reads were then generated with genome coverages of 10x, 20x, and 40x from each of the populations, using substitution rates of 0.002, 0.01 and 0.02. To simplify analysis, the indel rate was held constant at 0.001.ST Fand p-values from Fisher's exact test.The reads were mapped to the reference genome using the BWA short read mapper , and anaIn Figure Figure p-values is shown in Figure p-values at higher coverage, indicating that Fisher's exact test is upwardly biased as coverage increases.The relationship between CSI and ST Fis explored. Especially, at low coverage, many sites show a very high ST Fvalue. As coverage increases, both the variation and expectation of ST Fis reduced, and also the correlation between CSI and ST Fimproves.In Figure ST Fvalues with p-values , and by replicate .Below, we examine pooled salmon sequencing data from rivers Flekkeelva and Suldalselva, and investigate the resulting CSI distributions. From each of the rivers, two pools were sequenced using Illumina HiSeq, resulting in datasets It can be seen from Figure A closer examination of the data reveals that many of the high scoring sites have a coverage substantially higher than the expected combined coverage of approximately 50x. This can be due to collapsed repeats in the genome assembly or other artifacts of the assembly, sequencing, or mapping processes. To investigate this, sites were filtered by coverage, retaining only sites with a total coverage of 50 ± 20 (which corresponds to roughly three standard deviations of a Poisson distribution). The results are shown in Figure We see from Figure p-values for the non-divergent sites increase with coverage. For instance, out of the 36000 non-divergent sites, we expect approximately 36 sites by chance to have a p-value less than 10−3. For 10x coverage, we find 9, for 20x, we find 35, and for 40x we find 70. This indicates that p-values are biased upwards with increasing coverage, and must be consequently be interpreted with care [ST Fsimilarly depends on coverage. In contrast, low coverage in combination with sequencing errors and incorrectly mapped reads here result in a large number of high-scoring non-divergent sites. Using a combination of these measures may be effective, but also effectively narrows the data set, much like a stringent filtering for coverage.It is striking that ith care . The expSimulated data is by definition a simplification of reality. For instance, here the data assumes uniform probability of reads across the genome, and unbiased and context independent sequencing errors. Also, divergent and non-divergent positions occur in similar numbers in the simulated data, in reality, there will be a continuous spectrum of allele frequencies, and it will depend globally on the degree of divergence between populations, and locally on selection and other non-random evolution pressures. Results from simulated data must, as always, be interpreted as optimistic. In practice, coverage will vary substantially across a sequenced genome. In general, high variance regions tend to have lower mapping , but othAlthough not commonly applied, information theoretic measures have been used previously in analyzing genetic variation. Expected site information is related to Kullback-Leibler divergence , but difBased on the assumption that most sequencing errors will be singletons, Achaz  developeexpected information content. As this is an additive measure, it is straightforward to sum over multiple sites to get the expected information for a set of SNPs. Since rare alleles yield more information than common ones, a natural extension might be to consider instead the minimum information content from a set of loci, ensuring that we can reach a conclusion even if we are unlucky with the actual alleles observed. Yet another option is to calculate a confidence interval for the information.Here, we have focused on the STF, these are indirect measurements, proxies for the actual information. In addition, we have seen that it and other commonly used statistics have intrinsic biases when applied to sequencing data, due to coverage variation, sequencing artifacts, and mapping errors.When selecting diagnostic SNPs, we want to find sites that provide the most information regarding our current problem. Although this is commonly measured using statistics like As an alternative, we have derived a direct calculation of the expected site information from allele frequencies, using a Bayesian framework. In addition to being a direct measurement of the value of interest, it has a clear interpretation, and desirable properties, like additivity. We have further developed a conservative estimator for this statistic, and provide an implementation.http://malde.org/~ketil/biohaskell/varan. Further information and documentation is available from http://biohaskell.org/Applications/Varan.The method as described above was implemented in a program, 'varan', which parses read alignments in the standard "mpileup" format as output by the samtools mpileup command. It can currently output several different statistics and estimators, including conservative expected site information (CSI). The software is distributed under the General Public License, and the source code can be downloaded from http://malde.org/~ketil/papers/varan. The salmon louse genome used to generate the simulated reads is available from http://sealouse.imr.no/.Simulation data, tables, and scripts used in this paper is available from The author declares that they have no competing interests.


Objective. Noninsulinoma pancreatogenous hypoglycemia syndrome (NIPHS) is a disorder of endogenous hyperinsulinemia that is clinically distinguishable from insulinoma, with a greater preponderance after Roux-en-Y gastric bypass (RYBG). Hyperinsulinemic hypoglycemia can predispose to attenuation of counterregulatory hormone responses to hypoglycemia, and consequent suppression of the hypothalamic–pituitary–adrenal (HPA) axis. This case series describes 3 individuals who were diagnosed with adrenal insufficiency (AI) after undergoing RYGB, complicated by NIPHS. Methods. A retrospective chart review was performed for each individual. Chart review applied particular attention to the onset of hyperinsulinemic hypoglycemia following bariatric surgery and the dynamic testing leading to the diagnoses of NIPHS and AI. Results. In each case, reactive hypoglycemia ensued within months to years after RYGB. Cosyntropin stimulation testing confirmed the diagnosis of AI. Hydrocortisone therapy reduced the frequency and severity of hypoglycemia and was continued until successful medical and/or surgical management of hyperinsulinism occurred. Follow-up testing of the HPA axis demonstrated resolution of AI. In all cases, hydrocortisone therapy was finally discontinued without incident. Conclusion. We speculate that transient AI is a potential complication in patients who experience recurrent hyperinsulinemic hypoglycemia after RYGB. The putative mechanism for this observation may be attenuation of the HPA axis after prolonged exposure to severe, recurrent hypoglycemia. We conclude that biochemical screening for AI should be considered in individuals who develop post-RYGB hyperinsulinemic hypoglycemia. If AI is diagnosed, supportive treatment should be maintained until hyperinsulinemic hypoglycemia has been managed effectively. Risk factors and mechanisms for the spontaneous development of NIPHS remain unclear. However, NIPHS appears to develop with increased frequency among patients who have undergone Roux-en-Y gastric bypass (RYGB).711 in infants with PHHI,13 and in diabetic patients treated with excess exogenous insulin.15 Insulinoma resection mitigates hyperinsulinemic hypoglycemic episodes and is associated with restoration of physiologic counterregulatory hormone response to hypoglycemia.711 We report 3 individuals who underwent RYGB complicated by NIPHS and subsequently developed adrenal insufficiency (AI). Supportive hydrocortisone therapy reduced severity and frequency of hypoglycemia and was continued until effective management of hyperinsulinism permitted recovery from pathologic hypoglycemia. Thereafter, endogenous glucocorticoid function recovered in all cases.Attenuated counterregulatory hormonal responses to severe hypoglycemia occur in patients with insulinoma,This retrospective case series was authorized by the institutional review board of Baylor College of Medicine and the Research and Development Committee at the Michael E. DeBakey Veterans Affairs Medical Center. Chart review applied particular attention to the onset of hyperinsulinemic hypoglycemia following bariatric surgery and the dynamic testing leading to the diagnoses of NIPHS and AI.A 53-year-old woman with morbid obesity underwent RYGB in March 2008. Two years later, she reported frequent postprandial hypoglycemic episodes. The patient underwent 75-g oral glucose tolerance test (OGTT). Ninety minutes after glucose load, plasma glucose decreased to 31 mg/dL, with concurrent serum insulin 41 µIU/mL, C-peptide 5.3 ng/mL, serum cortisol 10.5 µg/dL, and negative sulfonylurea screen . The patThis man presented at age 37 years with a history of obesity, hyperlipidemia, obstructive sleep apnea, gastroesophageal reflux disease, and lumbar radiculopathy. He underwent RYGB in 2008. One year later, he presented to endocrine clinic with symptomatic hypoglycemia. During a 72-hour fasting test, his blood glucose level decreased to 45 mg/dL after 24 hours. Concurrent labs included serum insulin of 87 µIU/mL, C-peptide of 9.7 ng/dL, cortisol of 14.2 µg/dL, and negative sulfonylurea screen . Serum c18 and hydrocortisone therapy was discontinued. She has remained clinically stable to date.This 39-year-old woman underwent RYGB in 2006. Three years later, she presented with postprandial hypoglycemic episodes. Diagnostic continuous glucose monitoring data revealed pattern of recurrent 1 to 3 hour postprandial interstitial (51 mg/dL and 47 mg/dL) and capillary (48 mg/dL and 59 mg/dL) glucose levels associated with hypoglycemic symptoms over 2 consecutive days. These results promoted dynamic testing with a 72-hour fast, which revealed no plasma glucose levels less than 70 mg/dL. However, due to clinical suspicion of NIPHS, she underwent selective arterial calcium stimulation test, which demonstrated hyperinsulinism localized to the veins draining the superior mesenteric, splenic, and gastroduodenal arteries. Despite undergoing an 80% pancreatectomy, which revealed normal pancreatic tissue, her hypoglycemic symptoms persisted. In January 2009, she was admitted for orthostatic hypotension, at which time she underwent a 250 µg cosyntropin stimulation test that revealed an inadequate cortisol response , and tre19 Although the cortisol responses observed during OGTT in case 1 and 72-hour fast in case 2 were considered inadequate and raised the suspicion for AI, the diagnosis of AI was confirmed by cosyntropin stimulation. In cases 1 and 2, physiologic hydrocortisone preceded medical and/or surgical therapies aimed at reducing endogenous hyperinsulinism, which ultimately permitted recovery from pathologic hypoglycemia. In case 3, however, persistence of hypoglycemia and orthostatic hypotension after pancreatectomy led to the diagnosis of AI. Although pancreatectomy may have eliminated the source of hyperinsulinism, the patient required hydrocortisone support until objective evidence of adrenal function recovery. In all 3 cases, supportive hydrocortisone therapy reduced the severity and frequency of hypoglycemia. Duration of physiologic steroid replacement for AI varied in each case. Follow-up dynamic testing with cosyntropin stimulation demonstrated recovery of the hypothalamic pituitary adrenal axis, mitigating the need for continued treatment.Two of the 3 patients with hyperinsulinemic hypoglycemia described in this report (Cases 1 and 2) developed post-RYGB AI prior to medical or surgical therapy of hypoglycemia, and the third patient was diagnosed with post-RYGB AI after a distal pancreatectomy. Histopathologic evidence of nesidioblastosis was identified in case 1, but not in case 3; however, the absence of nesidioblastosis does not preclude the diagnosis of NIPHS.20 NIPHS is a known complication of RYGB and can occur months to years after RYGB.2 This condition is diagnosed in approximately 0.21% of individuals who have undergone gastric bypass surgery, and it is more common in women.21 Medical therapy and/or gradient-guided surgical resection of the involved pancreas can attenuate the severity and frequency of hyperinsulinemic hypoglycemia in NIPHS.5Idiopathic NIPHS is considered a rare condition of unclear etiology. However, there appears to be increased risk to develop acquired NIPHS following RYGB. Bariatric surgery is associated with significant weight loss, improves glycemic control, and may decrease cardiovascular mortality as compared to medical therapy.715 Diabetic patients treated with intensive insulin therapy have a 3-fold increased risk of severe, recurrent hypoglycemia and demonstrate defective counterregulatory responses affecting cortisol, glucagon, GH, and catecholamines.15 In type 1 and advanced type 2 diabetic patients, iatrogenic hypoglycemia predisposes to hypoglycemia-associated autonomic failure, which is thought to arise from defective glucose counterregulation and reduced sympathoadrenal responses to subsequent hypoglycemia. The mechanism by which hypoglycemia shifts the glycemic thresholds for sympathoadrenal activation is unknown. What has been observed is that meticulous avoidance of hypoglycemia can reverse the pathophysiologic mechanisms responsible for hypoglycemia associated autonomic failure.16Historically, individuals at greatest risk for recurrent hyperinsulinemic hypoglycemia were patients with endogenous hyperinsulinism due to insulinoma or PHHI or exogenous hyperinsulinism in diabetic patients receiving excessive insulin therapy.7 Insulinoma resection and restoration of euglycemia raises the glycemic threshold for counterregulatory hormone response to hypoglycemia and increases the amplitude of that response.710Similarly, in a patient with chronic insulinoma-induced recurrent hypoglycemia, counterregulatory hormone responses to hypoglycemia were elicited at a lower glycemic threshold, and the absolute increase in counterregulatory hormone  levels was less than that observed in the same patient after insulinoma resection.22Putative mechanisms responsible for adaptive responses to severe recurrent hypoglycemia in this subset of patients include antecedent hypoglycemia-induced suppression of endogenous counterregulatory hormone responses to subsequent hypoglycemia and recurrent exposure of the HPA axis to hypoglycemia-induced hypercortisolism, which directly suppresses subsequent responses to severe hypoglycemia. Studies of type 1 and type 2 diabetics with hypoglycemia-associated autonomic failure identify various mechanisms for pathophysiologic responses to recurrent hypoglycemia, including loss of glucagon response, reduced sympathetic neural activation to subsequent hypoglycemia, and reduced sympathoadrenal responses to subsequent hypoglycemia.23 suggesting that CRH plays a role as a physiological mediator of adrenocorticotropic hormone (ACTH) secretion, when induced by hypoglycemic stress. Animal studies have confirmed a role for the CRH surge in counterregulatory hormone response to hypoglycemia, demonstrating that acute intracerebroventricular administration of CRH was associated with a concomitant activation of the HPA axis, while chronic administration resulted in an attenuated release of ACTH and cortisol,24 possibly via downregulation of the CRH receptor.25 Hypercortisolism itself, which is a response to acute hypoglycemia, can reduce autonomic neuroendocrine and neurogenic responses to subsequent hypoglycemic episodes.26 Antecedent hypoglycemia has been demonstrated in healthy controls to blunt glucagon, ACTH, and hepatic glucose production during subsequent hypoglycemia, suggesting that counterregulatory hypercortisolemic response to hypoglycemia may itself play a causative role in the subsequent inadequate cortisol responses to hypoglycemia.27In normal individuals who receive an exogenous bolus of insulin, the ensuing acute hypoglycemic response is associated with a significant rise in systemic concentrations of plasma corticotrophin releasing hormone (CRH),28This case series seeks to bring attention to a unique and compelling clinical observation that persistent endogenous hyperinsulinemic hypoglycemia after RYGB may contribute to the development of AI. The clinical hypothesis for the etiology behind this observation is that recurrent severe hypoglycemia and hypoglycemia-induced hypercortisolemic insults to the HPA axis results in downregulation of counterregulatory hormone responses, including glucocorticoid levels. In such patients, insulin tolerance testing may not be test of choice, due to an anticipated attenuation in physiologic response to acute insulin-induced hypoglycemia. Therefore, cosyntropin stimulation testing may be considered the preferred dynamic test. The choice between 1 µg and 250 µg cosyntropin stimulation remains challenging, given the operator variability that can occur with 1 µg dilution of cosyntropin. From a physiological perspective, it would appear that 1 µg cosyntropin stimulation test may be considered preferable, when the clinical suspicion supports a diagnosis of central AI.Limitations to this case series include the retrospective nature of the observation and the variability in the approach to diagnosis of reactive hypoglycemia and AI. Another limitation to this case series is the lack of additional biochemical evidence to support the clinical suspicion of central AI, such as adrencorticotropic hormone or growth hormone levels. Although the suggested hypothesis regarding the pathophysiological basis for development of AI and the reasons for recovery from AI in this series are grounded in animal and human studies of HPA physiological response to acute and chronic hypoglycemia and hypercortisolemic response to hypoglycemia, clinical studies within this particular subset of patients regarding the association of AI and post-RYBG have yet to be performed. Future research in post-RYGB patients in a controlled and prospective manner is warranted to validate the observations of this case series.To our knowledge, this is the first report suggesting that AI is a potential complication associated with NIPHS after RYGB. Physiologic hydrocortisone replacement therapy reduces the severity and frequency of hypoglycemia. Ultimately, medical and/or surgical amelioration of endogenous hyperinsulinemia-induced hypoglycemia appears therapeutically necessary. Thus, in cases of recurrent, severe hyperinsulinemic hypoglycemia after RYGB, AI may be suspected, and physiologic glucocorticoid replacement can be supportive until definitive management of hyperinsulinemic hypoglycemia has been achieved.


The aim of this study was to identify single nucleotide polymorphisms (SNPs) that could be associated with back fat thickness (BFT) in pigs. To achieve this goal, we evaluated the potential and limits of an experimental design that combined several methodologies. DNA samples from two groups of Italian Large White pigs with divergent estimating breeding value (EBV) for BFT were separately pooled and sequenced, after preparation of reduced representation libraries (RRLs), on the Ion Torrent technology. Taking advantage from SNAPE for SNPs calling in sequenced DNA pools, 39,165 SNPs were identified; 1/4 of them were novel variants not reported in dbSNP. Combining sequencing data with Illumina PorcineSNP60 BeadChip genotyping results on the same animals, 661 genomic positions overlapped with a good approximation of minor allele frequency estimation. A total of 54 SNPs showing enriched alleles in one or in the other RRLs might be potential markers associated with BFT. Some of these SNPs were close to genes involved in obesity related phenotypes. Sus scrofa) is the most relevant agricultural meat species as well as an important animal model for its numerous physiological and morphological similarities to the human [The pig  , it is nIn this study, with the final aim to identify SNPs that could be useful to evaluate the peculiarities of the Italian Large White heavy pig breed and explain, at least in part, the missed genetic variability for the BFT trait not completely captured by our previous association works, we tested the potential and limits of an experimental design in which we combined the Ion Torrent sequencing technology to sequence RRLs. Reduced representation libraries were obtained by enzymatically digest DNA pools constructed from divergent Italian Large White pigs with extreme estimated breeding value (EBV) for BFT. In addition, we used Illumina PorcineSNP60 BeadChip genotyping data already generated from the same animals to obtain a comparative analysis and validation of the sequencing information.A subset of the Italian Large White pigs that were previously used in a GWA study, carried out to identify markers associated with BFT EBV , were usGenomic DNA was extracted from blood using the Wizard Genomic DNA Purification kit . Extracted DNA was quantified using a NanoPhotometer P-330 instrument  and pooled at equimolar concentration to constitute two DNA pools, one including DNA from the 50 Italian Large White pigs with the lowest BFT EBV and a second including DNA from the 50 Italian Large White pigs with the highest BFT EBV.The investigated animals were previously genotyped with the Illumina PorcineSNP60 BeadChip , interrogating 62,163 SNPs . No filt HaeIII restriction enzyme and the digested products were loaded in a 0.8% agarose gel. HaeIII was selected as it did not produce visible patterns that could be ascribed to repetitive elements in the range of 500–700 bp (data not shown). DNA fragments from this range obtained from HaeIII digestion were purified from the agarose gel with the QIAquick Gel Extraction Kit  according to the manufacturer instructions. Obtained DNA was used for library preparation and sequencing with the Ion Torrent PGM .Ten micrograms of DNA from each of the two pools were digested overnight with 50 U ofSequencing of the two RRLs was obtained using 200 ng of DNA that was purified by agarose gel electrophoresis as described above, enzymatically sheared, end-repaired, and adapter-ligated using the Ion Xpress Plus Fragment Library Kit (Life Technologies). Obtained DNA material was size-selected using the e-gel system  and bands corresponding to 100 bp of inserts were collected and quantified by qPCR using a StepOnePlus Real-Time PCR System (Life Technologies). Selected fragments were clonally amplified, purified, and sequenced using the Ion One Touch 100 Template Kit and the Ion PGM Sequencing Kit with two Ion 318 chips (Life Technologies), for the two RRLs.θ) of 0.001, folded spectrum, and filtering by a posterior probability of segregation > 0.90. SNAPE input files (PILEUP format) were obtained using Samtools v.0.1.4 [http://www.ensembl.org/Sus_scrofa/Tools/VEP; [Obtained sequencing reads were filtered and trimmed using the Ion Torrent suite v.2.2 (Life Technologies) which (i) eliminated polyclonal sequences and sequences of low quality and (ii) trimmed adapters and low quality 3′-ends. Then data were inspected with FastQC v.0.11.22 . Sequenc v.0.1.4 , 22. SNA v.0.1.4 ). For ea v.0.1.4 , interro v.0.1.4 , 22. Varols/VEP; ) was useols/VEP;  was usedPFisher < 0.05 were also visually inspected with IGV (Integrative Genomics Viewer) software [In order to evaluate differences in allele frequency derived by the number of alternative reads between the two RRLs, Fisher's exact test was computed for each alternative genomic position covered by a minimum depth of 3x. All the positions with software .http://dx.doi.org/10.1155/2015/950737) reports the number of reads and nucleotides mapped on the different pig chromosomes. Sequence data obtained from the two RRLs have been submitted to the European Nucleotide Archive database  and are indexed with the accession number ERP009239.A total of 3,390,796 and 3,731,776 sequenced reads were obtained from the two RRLs produced using the positive and negative BFT EBV DNA pools, respectively . After c NUTM2D), and one was a stop-lost variation . Among the missense mutations, 37 were considered deleterious by SIFT (Table S2). Several genes with deleterious missense mutations  1, subcomplex unknown, 1, 6kDa (NDUFC1); parathyroid hormone 1 receptor (PTH1R); glycerol-3-phosphate acyltransferase 2, mitochondrial (GPAT2); and several olfactory receptor like genes) play important roles in different biochemical and physiological cellular mechanisms.Using sequencing data, a total of 39,165 putative SNPs were called with high confidence by SNAPE . Of thesTo validate some of the called SNPs we took advantage from the Illumina PorcineSNP60 BeadChip genotyping data obtained on the same animals used to construct the two RRLs. Considering SNP positions covered by a minimum of three reads, 661 out of 62,163 SNPs of the chip (1.1%) were identified from the 13,596,939 sequenced positions (0.45% of the porcine genome). SNAPE analysis over these positions reported that (i) 3 positions were discarded and 8 had read depth < 3 , (ii) 257 were identified as SNPs , and (iii) 375 positions showed only the sequence of the reference genome.Of the overlapping 653 positions (661 – 8 = 653), (i) for 28 of them the chip genotype data of the individual pigs were not possible to retrieve (probably due to problems in the design of the chip probes that could prevent the genotyping) and (ii) for 63 DNA positions having all individuals homozygous for only one genotype 59 of these base positions matched with the genotype inferred by NGS, whereas 2 were called as heterozygous and 2 were called as homozygous for a noncomplementary nucleotide by sequencing data (Table S3). If we go into more details for the 28 SNPs that failed to report reliable genotyping data from the PorcineSNP60 BeadChip, for 12 out of 28 both alleles were present in the NGS reads; 15 out of 28 showed only one allele and one was an erroneous SNP.↔ CG and AT ↔ TA, for each one of the remaining 258 SNPs, we compared the minor allele frequency (MAF) of the genotyping data against the frequency of the same allele derived by the sequencing. Results of the regression analysis are reported in In addition to these overlaps between NGS sequencing and genotyping data, we wanted to evaluate if the estimated allele frequencies derived by NGS in RRLs obtained from DNA pools could match the true allele frequencies at the same positions obtained by using the PorcineSNP60 BeadChip. Starting from 559 SNPs (derived by the subsequent filtering steps of the 661 SNPs reported above), 262  had the same type of substitution. Excluding the transversions GC PFisher < 0.05 comparing alternative reads observed in the two RRLs generated from DNA of pigs with extreme and divergent BFT EBV (Table S4). Only one of these SNPs showed a PFisher < 0.01. However, no one remained significant after Bonferroni correction. These SNPs were located in several autosomal chromosomes . These variants  were localized as follows: 63% were intergenic variants, 21% were in introns, 11% were upstream gene variants, and 5% were downstream gene variants. Intronic variants were located in four genes of which only two were annotated with a known function: (1) dysbindin (dystrobrevin binding protein 1) domain containing 1 (DBNDD1); (2) phosphatidic acid phosphatase type 2A (PPAP2A).For each of the two initial pileups we filtered out genomic positions having depth < 3x and then we used SNAPE to extract the allele frequency of each genomic position taking the advantage of the filters implemented in it. Polymorphic positions were compared among the 237,969 positions that were in common between the two RRLs . Among tP value < 0.05 in our previous study [PFisher for each of the identified regions is reported in P = 1.65E − 06) is located 72,572 bp downstream SNP SSC6:859837 (PFisher = 0.038) and 85,796 bp downstream the 6th top SNP SSC6:873061 (PFisher = 0.012) obtained from the list of the 54 SNPs. In this region there is the acyl-CoA synthetase family member 3 (ACSF3) gene that belongs to a family of enzymes that activate fatty acids. In the same region we previously showed that other markers  were associated with BFT in the same breed. Within P = 1.74E − 05 [PFisher = 0.029) identified in the present study  (DLL1), seems associated to type 1 diabetes in humans. For marker DRGA009307  there is no annotated gene in a ± 500 kbp region. DIAS0000309  is near the active breakpoint cluster-related (ABR) gene and ENSSSCG00000017808 gene, orthologous of the acyl-CoA-binding protein (DBI) gene. ABR gene is annotated with two interesting gene ontology (GO) terms: phospholipid binding and brain development. DBI gene functions as intracellular carrier of acyl-CoA esters and it seems that it could act as a neuropeptide modulating the action of the GABA receptor. It is annotated with the GO terms: long-chain fatty acyl-CoA binding, transport, phosphatidylcholine acyl-chain remodeling, and triglyceride metabolic process that might suggest a potential role in fat metabolism and deposition.In order to evaluate if the 54 SNPs that showed differences in number of alternative reads between the two RRLs were located in chromosome regions associated with BFT in Italian Large White pigs (listed in Table S4), we compared their positions on the basis of our previous GWA study carried out in the same breed . We consus study . The top74E − 05 ) is locant study . In thisNext generation sequencing is changing the way to identify markers associated with production traits in livestock species. Several applications and strategies have been designed mainly using Illumina platforms . To ourReduced representation libraries were generated as a simple strategy to reduce the complexity of mammalian genomes and to obtain information from a small part of it that can be sampled after restriction fragment digestion . SeveralAmong the 159 SNPs causing missense mutations, 37 were predicted to affect the function of the encoded protein (Table S2). These polymorphisms will be prioritized to evaluate their association with several production traits together with SNPs whose alleles were differentially enriched in the two RRLs  partial sequencing obtained with Ion Torrent technology of the pig genome from DNA pools by using RRLs; (ii) the application of SNP calling and MAF estimation on Ion Torrent low coverage sequencing data from DNA pools; (iii) the validation of SNP called in DNA pools using individual genotyping data from the same animals of the pools; (iv) the possibility to identify enriched alleles in the two sequenced RRLs representing two extremes for important phenotypes (BFT). All these approaches were implemented in a case study that tried to identify additional markers associated with BFT in the Italian Large White pig breed. The purpose was to set up a strategy that could reduce as much as possible the sequencing cost and that could produce data useful to identify novel markers for the targeted trait. Association studies will be carried out to evaluate the effects of the 54 selected markers.Ion Torrent can be successfully applied for SNP discovery even if its limited throughput reduced the possibilities to obtain reliable allele frequencies in the two DNA pools. Other reductionist approaches, like genotyping by sequencing or genotyping by genome reducing and sequencing , 36, migTable S1: Number of sequenced reads and nucleotides mapped on the different pig chromosomes.Table S2: List of SNPs included in transcribed regions.Table S3: Overlapping positions between sequencing and PorcineSNP60 Beadchip genotyping data.Table S4: List of the 54 SNPs showing significant differences (P<0.05) between the two libraries.Table S5: List of SNPs that showed differences in number of alternative reads compared with the genome wide association results.


No physical or genetic maps are required, and it is designed to work on unphased genotype data from heterozygous species. It exploits the fact that markers useful for imputation often are not physically close to the missing genotype but rather distributed throughout the genome. Using genotyping-by-sequencing data from diverse and heterozygous accessions of apples, grapes, and maize, we compare LD-kNNi with several genotype imputation methods and show that LD-kNNi is fast, comparable in accuracy to the best-existing methods, and exhibits the least bias in allele frequency estimates.Obtaining genome-wide genotype data from a set of individuals is the first step in many genomic studies, including genome-wide association and genomic selection. All genotyping methods suffer from some level of missing data, and genotype imputation can be used to fill in the missing data and improve the power of downstream analyses. Model organisms like human and cattle benefit from high-quality reference genomes and panels of reference genotypes that aid in imputation accuracy. In nonmodel organisms, however, genetic and physical maps often are either of poor quality or are completely absent, and there are no panels of reference genotypes available. There is therefore a need for imputation methods designed specifically for nonmodel organisms in which genomic resources are poorly developed and marker order is unreliable or unknown. Here we introduce LinkImpute, a software package based on a  Our understanding of human disease has been significantly accelerated through studies linking genotypes with disease phenotypes, primarily through the use of genome-wide association (GWA)  pass missing data thresholds  and cattle  method  first calculates a distance from the sample to every other sample. We use the taxicab distance, where the distance, nd  between any two samples s1 and s2 is given by the following equation:P is the set of all SNPs and g  is the genotype of sample s at position p. It is possible that either, or both, of g  or g  are unknown, in which case this SNP is ignored in the summation. To account for the fact that the distance between samples may therefore sum across a different number of samples, we include a normalizing term, 1/n, where n is the number of SNPs actually included in the summation. Distance measures other than the taxicab distance could be used, the most obvious alternative being the Euclidean distance. Using the Euclidean distance rather than the taxicab distance did not noticeably change performance (data not shown).To impute a genotype at SNP k nearest neighbors to is that have a known genotype at position p and then imputing the genotype, gi , as a weighted modal average of these genotypes  = a and 0 otherwise.The algorithm proceeds by picking the enotypes . That isk-nearest neighbor relies on the assumption that the most similar samples across the whole genome will be the best samples with which to impute any genotype. However, we reasoned that the best samples to use for imputation are those that share an evolutionary history at the SNP to be imputed, and that these samples may not be the most similar genome-wide. Genotype imputation methods like Beagle and fastPHASE use a similar reasoning: they use information from neighboring SNPs because these SNPs likely share a history with the SNP to be imputed due to physical linkage. Beagle and fastPHASE rely, however, on ordered markers and sufficiently dense genotype data to enable haplotype reconstruction. In the absence of a known marker order, we reasoned that the most informative samples for imputation may be those that are most similar at SNPs that are highly correlated with the SNP to be imputed. These highly correlated SNPs may be physically linked to the SNP of interest, but they may also be correlated without being physically linked. Regardless of the reason for the correlation, we reasoned that these SNPs would be most informative for choosing the nearest neighbors with which to perform imputation.Standard k-nearest neighbor imputation (LD-kNNi), where only the l SNPs most in LD with the SNP to be imputed are used to determine the nearest neighbor and the weightings to be used when imputing. The “LD” in our algorithm name, LD-kNNi, does not necessarily refer to physical linkage but rather to the correlation between any two SNPs in the data.We therefore introduce LD L(ip) is the set of l SNPs in strongest LD with the SNP to be imputed. The algorithm then continues as for kNNi with one minor exception. As we are now using a smaller number of SNPs to calculate distance, it is possible that there could be no genetic difference between a pair of samples. To avoid a distance of zero, which would result in Supporting Information, Table S1 and File S1).Thus, http://www.cultivatingdiversity.org/software under the GPL version 3 license). LinkImpute allows parameter values  to be either fixed or optimized. To optimize the parameter values, LinkImpute randomly samples 10,000 known genotypes. For each set of parameter values, each one of these genotypes is masked and then imputed to calculate accuracy. LinkImpute optimizes the parameters by assuming the parameter space is unimodal, bounding the search space and then searching the space for the optimal values.We implement LD-kNNi in our program LinkImpute . For kNNi, k = 8 was chosen. For LD-kNNi, k = 5 and l = 20 . When comparing imputation programs on different datasets, we allowed LinkImpute to choose optimal parameters.When comparing different imputation methods on the apple dataset, we used manually optimized parameter values  in separate GBS libraries and were sequenced using Illumina Hi-Sequation 2000 technology. Genotypes were called using a custom GBS pipeline described in Malus domestica reference genome version 1.0  of <0.01 were then discarded. Finally, the data were pruned to exclude clonal relationships: if two or more samples had IBD >0.9, they were considered clones and the sample with the least amount of missing data from the group was retained. This resulted in a dataset of 711 samples and 8404 SNPs.We collected GBS data from a collection of 1995 accessions from the genus sion 1.0 . GenotypTo test the accuracy of our imputation method we created a “masked” dataset by setting 10,000 random genotypes to missing. This created “truth known” genotypes to which our imputed genotype calls were compared. We limited our testing to 10,000 masked genotypes, which represents 0.17% of the genotype matrix, in order to maintain a dataset with a reasonable amount of missing data while providing enough masked genotypes to be able to estimate imputation accuracy.Figure S2). We masked and then imputed 20%  of the genotypes at random and compared the allele frequency estimates from the imputed data to the allele frequency estimates from the complete genotype matrix. As most imputation methods make use of other SNPs to aid imputation, we imputed using all 8404 SNPs in the dataset so as to provide more information to these methods. We then restrict our analysis to the 1001 complete SNPs.Biased allele frequency in imputed data has been shown to affect downstream analyses  and were sequenced using Illumina Hi-Sequation 2000 technology. We then used the 12X grape reference genome  with maxiter set to 10 and ntree to 100 and all other parameters set to default values. MF could not be run on the entire genome at once because the run time was prohibitive. Instead it was run one chromosome at a time (t a time .4.Beagle: Version r1230 using default settings (settings .5.fastPHASE: Version 1.4.0 using default settings except K (the number of clusters) is set to 200  than LD-kNNi (average distance: 1.8).Our results show that LD-kNNi performs slightly better than Beagle and fastPHASE, which have the greatest accuracy of all the other methods tested . MF perfeighbors . This filogy dl, , from thFigure S4). Although the apple reference genome is not used in LD-kNNi, we exploited it to investigate how often our nearest neighbor calculations used SNPs from chromosomes other than the chromosome on which the imputed SNP is located. To do this, we calculated the probability of being on the same chromosome as the imputed SNP for the 20 SNPs in greatest LD with the imputed SNP. Unsurprisingly, we found that the performance of LD-kNNi is dependent on the level of LD between the SNP to be imputed and the SNPs used to find the nearest neighbors. Where the average LD between the SNPs used and the imputed SNP is high, the imputation error is lower .Comparing the run time of the various imputation methods , we noteComparing the performance of LinkImpute and Beagle on multiple datasets  we note The allele bias observed in Figure S5). The two groups in the Mode plot are caused by the two different modal values for SNPs (0 or 1). The bottom left group is where the modal value is 0, the top right group is where it is 1 .LD-kNNi performs well compared with the most commonly used imputation methods. On our apple dataset it results in both superior imputation accuracy  and moreFigure S7 and Figure S8), which is particularly noteworthy because increasing sample size is critical to augmenting the statistical power of GWA studies , not just biparental crosses. Although we called SNPs using the apple reference genome, LD-kNNi makes no use of this information during imputation. Indeed A recently developed imputation algorithm was designed for heterozygous species without a reference genome and was applied to raspberry . This suggests that, as the SNP density of a dataset increases and more SNPs are in LD with one another, one can expect improvements in the imputation accuracy of LD-kNNi. One way of obtaining more SNPs would be to allow greater levels of missing genotypes, although the increase in missing data are likely to have a negative effect on imputation accuracy. Whether this negative effect is offset by the positive effect of increased SNP density is an area that warrants further study.We demonstrated that the performance of LD-kNNi improves as the LD between the imputed SNPs and the SNPs used to find the nearest neighbors increases . The relationship between imputation accuracy, allele frequency bias and their effects on downstream analyses warrants further investigation.Our results suggest that LD-kNNi produces more accurate allele frequency estimates at the cost of a slight decrease in imputation accuracy. Biased allele frequencies are known to adversely affect downstream analyses (Genotype imputation is a crucial step in many genomic studies as all existing genotyping methods result in some missing data. Most imputation algorithms rely on physical or genetic maps, either directly or in the generation of ordered SNPs, and are not suitable for use in non-model organisms with poor or underdeveloped genomic resources. Our novel genotype imputation method, LD-kNNi, does not rely on physical or genetic maps and imputes genotypes as accurately as the best existing methods that require ordered markers. In addition, it is fast and outperforms other methods in its ability to accurately estimate allele frequencies. Thus, LinkImpute is a valuable tool for improving genome-wide analyses in nonmodel organisms, especially for GWA and GS in highly diverse and heterozygous organisms.


A single locus was found to have a highly significant role in governing sea age at maturation in this species. The SNPs identified may be both used as markers to guide breeding for late maturity in salmon aquaculture and in monitoring programs of wild salmon. Interestingly, a SNP in proximity of the VGLL3 gene in humans (Homo sapiens), has previously been linked to age at puberty suggesting a conserved mechanism for timing of puberty in vertebrates.Wild and domesticated Atlantic salmon males display large variation for sea age at sexual maturation, which varies between 1–5 years. Previous studies have uncovered a genetic predisposition for variation of age at maturity with moderate heritability, thus suggesting a polygenic or complex nature of this trait. The aim of this study was to identify associated genetic loci, genes and ultimately specific sequence variants conferring sea age at maturity in salmon. We performed a genome wide association study (GWAS) using a pool sequencing approach  of male salmon returning to rivers as sexually mature either after one sea winter (2009) or three sea winters (2011) in six rivers in Norway. The study revealed one major selective sweep, which covered 76 significant SNPs in which 74 were found in a 370 kb region of chromosome 25. Genotyping other smolt year classes of wild and domesticated salmon confirmed this finding. Genotyping domesticated fish narrowed the haplotype region to four SNPs covering 2386 bp, containing the  For most species the factors that contribute to the genetic predisposition for age at maturity are currently unknown. In salmon aquaculture early maturation is negative for the growth, disease resistance and flesh quality. In addition, using populations of salmon selected to mature late may limit the genetic impact of aquaculture escapees, as these late maturing fish are more likely to die before they reach maturity. The aim of this study was to elucidate the genetic predisposition for salmon maturation. We determined the sequences of genomes from Atlantic salmon maturing early and late in six Norwegian rivers. This methodology enabled us to identify a short genomic region involved in determining the age at maturity in male Atlantic salmon. This region has also previously been linked to time of puberty in humans–supporting a general mechanism behind age at maturity in vertebrates. The results of this study may be used to breed salmon that are genetically predisposed to mature late which will improve welfare and production in aquaculture industry and aid in the management of escaped farmed salmon. Salmo salar L.) show large phenotypic variation for sea age at sexual maturity [Both wild and domesticated populations of Atlantic salmon  for age at sexual maturation [Salmonids in general display moderately high heritability  and chicken  [http://marineseq.imr.no/salmatsnp/.To find SNPs associated with age at maturation in salmon males, we sequenced 20 salmon per river and sea winter age (1SW and 3SW). This number of individuals in each pool has been shown to be sufficient to identify causative SNPs for a trait in nogaster . Mapping gallus) ,19. We hComparing 1SW and 3SW allele frequencies using the Cochran-Mantel-Haenszel (CMH) test in 4,326,591 SNPs revealed 138 SNPs that were significantly associated (0.1% FDR) with sea age at puberty . SeveralTo verify the GWAS findings and to ascertain whether genotypes of single individuals for SNPs are associated with sea age at maturity we designed a Sequenom assay for 11 of the most significant SNPs in the selective sweep found in Chr 25 . GenotypIn samples from the pool sequencing we identified haplotypes associated with sea age at maturity in the 2008 year class (year of migration to sea). These fish have possibly been exposed to similar environmental conditions during their early stay in the sea, therefore showing a selection for those conditions as postulated by several previous studies in salmon –27. To iSea age at maturity can be significantly altered in salmon by modulating both light and temperature ,28,29. Acharged multivesicular protein 2B (chmp2B), vestigial-like protein 3 (vgll3) and a-kinase anchor protein 11  54 and 323. The sea age at maturity trait was strongly associated to the genotype of these SNPs since 36%  and 33%  of the phenotypic variation could be explained by the genotype. The haplotypes associated with the 3SW trait encode a Thr and a Lys at these positions whereas the haplotype associated with 1SW encodes a Met and an Asp. Our analysis could not conclude whether these missense mutations are causative for the sea age at maturity trait, but since they occurred consistently together in the material we cannot rule out whether both or other non-coding variants at this locus are involved in age at maturity phenotype. It is also known from other studies that co-occurring amino acid changes can confer a phenotype [Salmo trutta L.), rainbow trout (Oncorhynchus mykiss) and arctic char  all have the 3SW variants of the amino acids. In addition, we ran an allelic discrimination assay on five individuals from the Swedish landlocked Atlantic salmon population, Gullspång , all carrying only the 3SW (Thr-Lys) amino acid variant. This indicates that the 3SW version of the Vgll3 is ancestral and that the 1SW (Met-Asp) is derived.Altogether the experiments clearly show that the selective sweep on Chr 25 significantly contributes to sea age at maturity both in wild and domesticated male salmon. Gene prediction in this area revealed three genes;  akap11, . From ths region . The 2.4henotype . The traVGLL3 locus has been linked to age at maturity or puberty by a SNP in close proximity of the gene [Mus musculus) [vgll3 transcripts have been associated with differential expression during the early stages of steroidogenesis in the embryonic testis [In humans the the gene , strengtusculus) . Changesusculus) –40. In sc testis , suggestvgll3 locus but two neighboring genes, chmp2B and akap11, also contain several significant SNPs . This aa AKAP11 is not located in any of the known functional domains related to PKA [Chmp2B did not contain any missense mutations but upon manual review of this region we detected a misplaced 16,885 bp region in the Chr 25 containing exon 1 and 2 of chmp2B. This region also carried many significant SNPs which were probably associated with the selective sweep. When this region was placed in proximity of the gene vgll3 lococytosis –46. It iocytosis  but whetvgll3 genotypes. Future studies will reveal if female puberty is also influenced by this locus. This study also shows that certain haplotypes significantly contribute to the sea age at puberty, and may therefore be implemented as markers in the management of wild salmon populations in the face of changing environmental conditions such as increased sea temperatures. Significantly, this study and a previous study in humans [Vgll3 protein in timing of puberty in vertebrates.In this study we performed a GWAS by genome re-sequencing with the aim to screen the genome of Atlantic salmon for loci regulating age at maturity in males. By investigating late and early maturing male fish from six rivers in Western Norway we demonstrated that the sea age at maturity trait was strongly associated with sequence variation at one locus on Chr 25. The haplotype associated with late maturity can be used for selective breeding on individuals predisposed for this trait, thereby possibly reducing the incidence of negative phenotypes associated with early maturation of males in salmon aquaculture. However, using only late maturing fish in aquaculture breeding will increase generation times in culture, thereby decreasing the speed at which other traits such as growth can be selected for. This potential conflict of interest could be solved by using photoperiod manipulation to shorten generation time of fish with a genetic predisposition for high age at maturity. In this study we did not investigate how female maturation is affected by n humans , suggesthttp://www.radgivende-biologer.no). Scales were taken from dead salmon fish that had been captured by anglers during the fishing season. In this manner, samples of wild salmon were acquired from six rivers in Western Norway; Eidselva, Gloppenelven, Flekkeelven, Årdalselva, Suldalslågen and Vormo , adipose fin clipped and PIT (passive integrated transponder) tagged. Fin clips, preserved on 95% ethanol, from a total of 97 fish maturing at different sea ages were included in this study. The four families consisted of 36, 24, 13 and 24 sibling fish per family. We used the parental information to avoid within family Mendelian errors and to phase decendent’s haplotypes.In addition to samples of wild salmon, we investigated age at maturity in four full sibling families of domesticated salmon from the Norwegian Mowi strain maturing at 1SW, 2SW or older. These fish were obtained from an ongoing study at the Matre Aquaculture Research station where they were reared in a common garden design in sea cages without the use of continuous light, i.e. under ambient light only. Before transfer to sea cages, fish were sedated  and 250 μM of each dNTP in a final volume of 20 μL. PCR products were visualized in 3% agarose gels.Total DNA from selected individuals was purified from 2 to 3 scales using Qiagen DNeasy Blood & Tissue Kit  according to the manufacturer´s recommendations. Sex of all samples used herein was validated by a PCR-based methodology aimed to detect the presence of the sdY gene ,49. IndisdY gene . PCR amphttps://www.sequencing.uio.no/, Oslo, Norway). In each sequencing lane we used pools of 10 fish from each sea age and river which made a total of 24 lanes sequenced in the whole experiment . Raw sequence data has been deposited at SRA with BioProject Accession number PRJNA293012. Library quality control was conducted to ensure that all the samples fulfilled the quality standards (FastQC—http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapter and quality trimming of FastQ format reads were carried out using Cutadapt [Following fluorometric quantification, equal amounts of DNA from ten males were pooled to generate paired-end libraries using the Genomic DNA Sample Preparation Kit  according to manufacturer’s instructions. Libraries were sequenced on the Illumina HiSeq2000 platform  at the Norwegian Sequencing center  this region existed as a single contig, and has presumably been inserted into the wrong chromosomal region in the most recent genome assembly. We corrected this by reverse-complementing the region and inserting it in the gap between the third exon of chmp2b and vgll3 in Chr 25 (position 28626249–28643134) placing the exons of chmp2b in coherent order. Genotype/phenotype association analysis was performed using Plink v1.8 [To improve the sensitivity to detect rare alleles, biological replicates in the dataset were bioinformatically fused using SAMtools merge, producing a single BAM file for each river and maturation stage . SNPs weink v1.8 ). WhenevThe salmon genome has undergone a recent whole genome duplication and is partly tetraploid giving rise to many highly similar duplicated regions . We havehttp://marineseq.imr.no/salmatsnp/.Sequenced pool material submitted to SRA (Bioproject number PRJNA293012). SNP data obtained has been deposited at th of June 2009, in force from 1st of January 2010. All personnel involved in the experiment had undergone training approved by the Norwegian Food Safety Authority. This training is mandatory for all personnel running experiments involving animals included in the Animal Welfare Act.Scale samples from wild salmon were collected by local anglers during the fishing season, thus no permits/licenses regarding the collection of these samples were required by the research team. Samples from domesticated fish were retrieved from an ongoing study at Matre Aquaculture Research station (IMR), where the experimental protocol (permit number 4268) had been approved by the Norwegian Animal Research Authority (NARA). Welfare and use of these experimental animals was performed in strict accordance with the Norwegian Animal Welfare Act of 19S1 Fig(A) Average read depth in all pooled samples (x-axis). The y-axis is showing average percent of the genome covered, with error bars. (B) Average number of uniquely mapped sequences (y-axis) on each chromosome (x-axis), with error bars. “Scaffolds” refers to unplaced contigs in the current genome assembly.(TIF)Click here for additional data file.S2 FigFrequencies of the late maturation allele in year class 2008 are shown for 1SW (green bars) and 3SW (blue bars) fish. The position of each SNP in Chr 25 is shown in the leftmost part. Above each bar the number genotyped fish is indicated. The y-axis shows the allele frequency between 0 and 1. Abbreviations; Ard—Årdalselven, Eid—Eidselven, Fle—Flekkeelven, Glo—Gloppenelven, Sul—Suldalslågen and Vor—Vormo.(TIF)Click here for additional data file.S3 FigFrequencies of the late maturation allele in Eidselva year class 1999 and Suldalslågen yearclass 2004 are shown for 1SW (green bars) and 3SW (blue bars) fish. The position of each SNP in Chr 25 is shown in the leftmost part. Above each bar the number genotyped fish is indicated. The y-axis shows the allele frequency between 0 and 1. Abbreviations; Eid—Eidselven, and Sul—Suldalslågen.(TIF)Click here for additional data file.S4 FigFrequencies of the late maturation allele in farmed salmon (Mowi strain) strains, maturing either after 1SW (green bars), 2SW (yellow bars) or after 3SW or more in sea water (blue bars). The position of each SNP in Chr 25 is shown in the leftmost part. Above each bar the number genotyped fish is indicated. The y-axis shows the allele frequency between 0 and 1.(TIF)Click here for additional data file.S1 TableTable of SNPs significantly associated to age at maturity. The table includes scaffold names, chromosome position, reference/alternative (Ref/Alt) Alleles, Average Coverage (+/-SE) and P-values.(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.