Metabolic syndrome is a risk factor for type 2 diabetes and cardiovascular disease. We identified common genetic variants that alter the risk for metabolic syndrome in the Korean population. To isolate these variants, we conducted a multiple-genotype and multiple-phenotype genome-wide association analysis using the family-based quasi-likelihood score (MFQLS) test. For this analysis, we used 7211 and 2838 genotyped study subjects for discovery and replication, respectively. We also performed a multiple-genotype and multiple-phenotype analysis of a gene-based single-nucleotide polymorphism (SNP) set.SIDT2 gene at 11q23.3. Both SNPs correlate with the expression of SIDT2 and TAGLN, whose products promote insulin secretion and lipid metabolism, respectively. This SNP pair showed statistical significance at the replication stage.We found an association between metabolic syndrome and an intronic SNP pair, rs7107152 and rs1242229, in Our findings provide insight into an underlying mechanism that contributes to metabolic syndrome.The online version of this article (10.1186/s40246-018-0180-4) contains supplementary material, which is available to authorized users. Metabolic syndrome is a cluster of metabolic risk factors for cardiovascular disease and type 2 diabetes that are attributable to both genetic and environmental factors –3. The Nhttp://healthstat.snu.ac.kr/software/mfqls/) test enables the estimation of the genetic relation matrix from population-based samples [Many genetic variants that are associated with metabolic syndrome have been identified by genome-wide association studies (GWASs). However, because these known genetic variants account for only a fraction of the heritability of metabolic syndrome, the genetic determinants of this condition remain undefined , 7. Many samples . From th samples , 14. ThiSIDT2, UBASH3B, and CUX2 were significant in the multi-SNP–multi-trait analysis but not significant in the single-SNP–multi-trait analysis. SIDT2 was not previously reported in the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/) [SIDT2 correlates with SIDT2 and TAGLN expression  [We conducted a multiple single nucleotide polymorphism (SNP)–multiple trait analysis to identify genetic variants associated with metabolic syndrome by utilizing 10,049 samples from Korean subjects. After the multiple-genotype and multiple-phenotype analysis , 27 SNP pairs were associated with metabolic syndrome in the discovery stage and successfully replicated. Of those pairs, from the joint analysis of a single genotype and multiple phenotypes , we found that three SNP pairs in the respective genes k/gwas/)  and was g/home/) . Our finP value threshold of P < 0.05 (P = 1.45 × 10−7) and were successfully replicated  had been previously reported to be associated with metabolic syndrome, as indicated by a keyword search of “metabolic syndrome” in the GWAS catalog , diastolic blood pressure (DBP), high-density lipoprotein (HDL), fasting plasma glucose (FPG), triglyceride, and waist circumference. Through the multi-SNP–multi-trait genome-wide analysis, adjusted for age and sex, 27 SNP pairs satisfied a Bonferroni-adjusted SIK3, SIDT2, UBASH3B, and CUX2, to metabolic syndrome was further examined by single-SNP–multi-trait analysis, adjusted for age and sex. Intronic SNPs on SIK3 showed genome-wide significance in single-SNP–multi-trait analysis . All three genes satisfied statistical significance in the meta-analysis. The number of SNPs in three genes ranges from 3 to 14. SNP sets in two genes (PAFAH1B2 and SIDT2) showed significant P values of < 3.45 × 10− 6 in the gene-based test and reached a nominal P value threshold of < 0.05 in the replication stage. Although the results of tests in which 14 SNPs were utilized in CUX2 were significant in the discovery stage, there was only a suggested P value of 0.06 in the replication analysis  project  [https://eqtl.onderzoek.io/), and RegulomeDB (http://www.regulomedb.org/) [SIDT2 and TAGLN expressions. The GTEx project showed that for rs7107152, the statistical significance of SIDT2 and TAGLN expression in whole blood was P = 3.89 × 10−14 and P = 3.59 × 10− 47, respectively  on the g/home/) , the NESdb.org/) . All thrhttps://www.broadinstitute.org/snap/snap) [r2 = 0.167 with rs508487  in the current study and nearby SNPs reported in the GWAS catalog , we conducted a pairwise linkage disequilibrium (LD) analysis utilizing the SNP Annotation and Proxy Search (SNAP) server (ap/snap) . The paiP = 5.87 × 10−5) and triglycerides (P = 1.67 × 10−9). Moreover, it showed a suggestive association with diastolic blood pressure (P = 0.07). But it did not correlate with waist circumference (P = 0.75), fasting plasma glucose (P = 0.55), systolic blood pressure (P = 0.29).A radar chart shows the results of the rs7107152 and rs1242229 SNP metabolic syndrome-component trait association pair analysis , indicating little correlation between these two SNPs.Utilizing multi-SNP–multi-trait analysis, we found an association between the PAFAH1B2, SIDT2, and CUX2 genes showed significant P values in the gene-based test . PAFAH1B2 and SIDT2 reached a replication P value of < 0.05. The SNP set on CUX2 suggested a P value of 0.06 in the replication stage . SNP sets on the SIDT2, correlated with the expression of SIDT2 and TAGLN  around SIDT2 and TAGLN  is a lysosomal integral membrane protein that promotes insulin secretion . Recentllyceride . TAGLN  final report guideline as metabolic syndrome definition . For waihttp://healthstat.snu.ac.kr/software/mfqls/), can be utilized for both quantitative and dichotomous phenotypes. It can be applied to large-scale, genome-wide association analysis as well as family-based samples. The empirical power test showed that it is statistically more efficient than existing methods. In addition, the genome-wide association analysis of 1,801 individuals with obesity showed that P values from the MFQLS were markedly less than those from other methods [H0: β11 = β12 = β21 = β22 = 0, where βij denotes the effect of association between the ith SNP and the jth trait. The effect of these genotypes and the statistical significance of this effect are greater when multiple genotypes and phenotypes are correlated. Of 351,983 SNPs, 344,677 SNP pairs with MAF > 0.01 were selected . Additional file r2 ≥ 0.8 by the use of Tagger (http://www.broad.mit.edu/mpg/tagger/) for the second set. Consequently, 14,475 SNP sets were selected for gene-based analysis . Table Previously, Won et al.  proposed methods . In our Two-stage analyses such as estimation of correlation between each SNP and calculation of statistics are used to run the MFQLS.The Fisher’s combined probability test was used to calculate the meta-analysis from discovery and replication results by application of MADAM in the R package.H0: β1 = β2 = β3 = 0 and H1: not H0, where βi denotes the effect of association between a SNP and the ith trait. Covariates such as age and sex were adjusted in the analysis.To determine whether a significant SNP pair identified in the multi-SNP–multi-trait analysis was still significant if a different approach was used, we performed a joint analysis between single genotype and multiple phenotypes. An SNP and multiple traits were incorporated into the statistics of a single test and three traits were given, To determine which metabolic syndrome-component traits are related to the SNP pair rs7107152 and rs1242229, we performed an association analysis between the SNP pair and each metabolic syndrome-component trait.To examine the expression pattern of each SNP of an identified SNP pair, we utilized three online data resources that provide eQTL information: the portal for GTEx , RegulomAdditional file 1:Figure S1. Screenshot of GTEx database for significant SNP pair rs7107152 and rs1242229. Figure S2. Screenshot of NESDA NTR Conditional eQTL Catalog for rs7107152 and rs1242229. Figure S3. Screenshot of RegulomeDB for rs1242229. Figure S4. Radar chart showing the result of the rs7107152 and rs1242229 SNP pair–single trait (metabolic syndrome-component trait) association analysis. Figure S5. Screenshot from the GTEx database of the eQTL SNP-enriched region around SIDT2 and TAGLN . Figure S6 Model for how significant SNP pair rs7107152/rs1242229 may affect metabolic syndrome risk. Table S1. Characteristics of gene-based SNPs set. Table S2. Minor allele frequency of identified SNPs. (PDF 975 kb)Supplementary information. Additional file 2:MFQLS results. (TXT 16795 kb)


Global Navigation Satellite System pseudorange biases are of great importance for precise positioning, timing and ionospheric modeling. The existence of BeiDou Navigation Satellite System (BDS) receiver-related pseudorange biases will lead to the loss of precision in the BDS satellite clock, differential code bias estimation, and other precise applications, especially when inhomogeneous receivers are used. In order to improve the performance of BDS precise applications, two ionosphere-free and geometry-free combinations and ionosphere-free pseudorange residuals are proposed to calibrate the raw receiver-related pseudorange biases of BDS on each frequency. Then, the BDS triple-frequency receiver-related pseudorange biases of seven different manufacturers and twelve receiver models are calibrated. Finally, the effects of receiver-related pseudorange bias are analyzed by BDS single-frequency single point positioning (SPP), single- and dual-frequency precise point positioning (PPP), wide-lane uncalibrated phase delay (UPD) estimation, and ambiguity resolution, respectively. The results show that the BDS SPP performance can be significantly improved by correcting the receiver-related pseudorange biases and the accuracy improvement is about 20% on average. Moreover, the accuracy of single- and dual-frequency PPP is improved mainly due to a faster convergence when the receiver-related pseudorange biases are corrected. On the other hand, the consistency of wide-lane UPD among different stations is improved significantly and the standard deviation of wide-lane UPD residuals is decreased from 0.195 to 0.061 cycles. The average success rate of wide-lane ambiguity resolution is improved about 42.10%. With the development of the Global Navigation Satellite System (GNSS), multi-frequency and multi-GNSS are the main characteristics of future satellite navigation. Multiple GNSS systems provide abundant observations for precise positioning, atmospheric modeling, and many other applications. However, it also brings great challenges for GNSS precise data processing when multi-frequency and multi-GNSS measurements are combined . For insThe topic of GNSS hardware biases has received a lot of attention in recent years, including satellite-related/receiver-related and code-related/phase-related biases of different GNSS systems ,4,5,6. AExcept GLObal NAvigation Satellite System (GLONASS), it is always assumed that the receiver hardware biases are the same for satellites belonging to the same GNSS system, which uses code division multiple access (CDMA). Correspondingly, in the satellite clock estimation with GNSS network, the general processing strategy is to estimate one receiver clock per station, which contains receiver hardware delay, and one satellite clock per satellite. However, this is not practical. Based on the data from different types of receivers, reference  analyzedThe problem of pseudorange biases has received great attention since the early days of Global Positioning System (GPS). The study has been triggered by signal anomaly of the GPS satellites Space Vehicle Number (SVN) 19 and SVN 49. It is demonstrated that the signal anomaly of SVN 19 and SVN 49 is caused by signal distortions and reflections of GPS satellites . The sigBy studying the characteristics of BDS pseudorange biases of different types of receivers, it is found that the BDS pseudorange biases are related to receiver manufacturers and receiver models, and they can reach up to about 3 ns among different types of receivers . In ordeIn order to further improve the performance of BDS precise data processing, it is essential to deeply study the characteristics of BDS signal delay biases. In this contribution, BDS receiver-related pseudorange biases of triple-frequency observations will be calibrated and validated. The observation model and method for receiver-related pseudorange bias calibration will be introduced in the second section. In the section on validation of BDS pseudorange biases, the bias corrections will be validated by BDS SPP, wide-lane uncalibrated phase delays (UPD), and ambiguity resolution. Finally, some conclusions and suggestions will be presented.f  from receiver r to satellite s , m is the number of satellites tracked by receiver r; c denotes the speed of radio waves in vacuum; rt and st are receiver and satellite clock error, respectively; Tz is the zenith tropospheric delay that can be converted to slant with the mapping function sα; In GNSS data processing, hardware delays are commonly partitioned into a sum of satellite- and receiver-specific biases. However, it is revealed that satellite-plus-receiver hardware delay cannot be rigorously split into a sum of two independent parts, there are pseudorange biases related to different receiver models or types, which are different between satellites at the receiver-end . Thus, cThe B1/B2 IF combination of receiver-related pseudorange biases are given and validated . In thisbination ,21 and PAs for MW combination, the integer wide-lane ambiguity can be removed by rounding . Then, wn is the number of stations that are equipped with the same receiver type, r ; k denotes the dimension of the matrix; For each receiver type, based on the biases calculated by different stations, the receiver-specific and receiver–satellite pair biases can be separated as: product . Since rOnce the receiver-related pseudorange biases of MW combination and pseudorange IFGF combination are determined, by combining the IF code bias corrections, the hardware delay of raw observations can be solved by the following linear equations:eference .In this section, observations from several GNSS networks are collected for BDS pseudorange bias calibration. With the observation model introduced in the above section, the receiver-related pseudorange biases , crustal movement observation network of China , the NatBased on the method introduced in eference , which cConcerning the consistency of estimated pseudorange bias, According to the analysis , there ihttps://www.researchgate.net/project/GNSS-Biases/update/5cd2cff83843b0b98251d8ed. Anyone can directly correct these biases to improve BDS data processing accuracy by any software.By combining the receiver-related code biases As discussed above, it was seen that BDS code biases were relevant to receiver types. In this section, to validate the code bias corrections proposed in this paper, several experiments including BDS single-frequency single point positioning (SF-SPP), single-frequency PPP (SF-PPP), dual-frequency PPP (DF-PPP) and wide-lane UPD estimation and ambiguity resolution at stations equipped with different types of receivers are carried out. A total of 70 stations with one-week observations from DOY 175 to 181, 2017 are used in this section. All these stations are located in China and their distributions are presented in In the processing of SPP, to achieve the highest positioning accuracy, users should implement the same conventions and models as those adopted by the International GNSS Service (IGS) products. Since BDS receiver-related code biases are ignored in the estimation of BDS satellite clock and DCB provided by GeoForschungsZentrum (GFZ) and Deutschen Zentrums für Luft- und Raumfahrt (DLR), these products should be recalculated with the consideration of BDS receiver-related code biases when evaluating the performances of positioning. Based on these newly estimated products, different positioning modes were carried out (named as the improved case). As a comparison, positioning results by using the satellite clock from GFZ and MGEX DLR DCB were also employed (named as the standard case). The details of the positioning model are listed in In this section, BDS SF-SPP by using B1, B2, and B3 signals is conducted to validate the corrections proposed. In this section, the convergence performances of SF-PPP by using B1, B2, and B3 signals and DF-PPP by using B1 and B2 ionosphere-free combination are analyzed and compared. In order to evaluate the performance of convergence time, the SRIF filter of SF-PPP and DF-PPP at each station was reset every 4 h for both the standard case and the improved case. All the stations from DOY 175 to 181, 2017 used in the evaluation of positioning performance are processed in this section. A total of 2940 independent PPP results were evaluated for convergence analysis.Comparing the SF-SPP, SF-PPP, and DF-PPP performance in terms of positioning accuracy and convergence time proves the effectiveness of code bias corrections in BDS precise positioning. It should be pointed out that the code biases must be corrected at the same time in the estimation of satellite clock and DCB and user terminal. Otherwise, the effect of the code bias corrections will be not significant.The BDS receiver-related pseudorange bias will greatly influence the effectiveness of wide-lane ambiguity resolution based on MW combination. In this section, wide-lane UPD estimation and ambiguity resolution are evaluated and compared for the standard case (without bias correction) and the improved case (with bias correction). The UPD consistency among float ambiguities of different stations plays an important role in un-difference ambiguity resolution and can reflect the quality of UPD estimation. fixn is the number of fixed ambiguities; alln is the number of all candidate ambiguities.Based on the UPDs estimated, the un-difference wide-lane ambiguity resolution was performed. The fixing decision was made according to the probability . Then, tThis study is the continuing work of BDS receiver-related code biases . It is oIn order to validate the availability and effectiveness of the BDS code bias corrections, precise positioning with different frequency observations from different receiver types are processed with and without code bias corrections, including SF-SPP, SF-PPP, DF-PPP, wide-lane UPD estimation, and ambiguity resolution. The SPP performance in different regions reveals that the positioning accuracy is not overall uniform as BDS-2 is still a regional navigation system, the geometric distribution of satellite is poorer in the northeastern and northwestern regions, and correspondingly the positioning accuracy will be worse. By correcting the receiver-related code biases, the positioning accuracy of SPP is improved about 20% on average. The positioning accuracy of single- and dual-frequency PPP at the initial time are also improved. It means the receiver-related code bias corrections play an important role in BDS precise positioning. Moreover, the consistency of wide-lane UPD is greatly improved and the success rate of wide-lane ambiguity resolution is improved 42.10% by correcting receiver-related pseudorange biases. It means the receiver-related code bias corrections play an important role in BDS precise data processing. Thus, experiments should be carried out to analyze the characteristics of pseudorange observations of other navigation systems in the future since receiver-related code bias will greatly affect GNSS data processing.The research has shown that the pseudorange biases of different BDS satellites at the receiver are not the same. Therefore, it is impractical to estimate one receiver DCB per station during ionospheric modeling and DCB estimation with BDS observations. It should be recalculated and compared with the DCB products from MGEX. Furthermore, in the future, the classification of different receivers into consistent groups for different satellite navigation systems should be further studied, which is of great importance in multi-GNSS precise data processing. Since the IGS bias working group has published SINEX_BIAS V1.00, which supports bias correction according to receiver group, IGS analysis centers should consider providing BDS receiver-related code bias corrections in their products with SINEX_BIAS format.


G) does not necessarily increase accuracy as the additive relationships can be accurately estimated using a much smaller number of markers. Due to these limitations, variant prioritization has become a necessity to improve accuracy. The fixation index (FST) as a measure of population differentiation has been used to identify genome segments and variants under selection pressure. Using prioritized variants has increased the accuracy of GS. Additionally, FST can be used to weight the relative contribution of prioritized SNPs in computing G. In this study, relative weights based on FST scores were developed and incorporated into the calculation of G and their impact on the estimation of variance components and accuracy was assessed. The results showed that prioritizing SNPs based on their FST scores resulted in an increase in the genetic similarity between training and validation animals and improved the accuracy of GS by more than 5%.A dramatic increase in the density of marker panels has been expected to increase the accuracy of genomic selection (GS), unfortunately, little to no improvement has been observed. By including all variants in the association model, the dimensionality of the problem should be dramatically increased, and it could undoubtedly reduce the statistical power. Using all Single nucleotide polymorphisms (SNPs) to compute the genomic relationship matrix ( Recent advances in high-throughput genotyping and sequencing techniques have led to the generation of dense marker panels and facilitated the genotyping of large numbers of individuals. Because of the availability of these cost-effective genotyping technologies and the increase in sequencing speed, large-scale genotyping for single-nucleotide polymorphisms (SNP) has become more affordable and accessible. Genomic data provide an unprecedented opportunity to dissect the genetic basis of complex traits and identify relevant functional associations. G) and thus the performance of mixed linear model-based approaches. There was no difference in accuracy between the 777K SNP and the 54K SNP panels [G when either 777K or 54K SNP panel were used was not that different. Due to these limitations, prioritization of variants to be included in the association model or to compute the genomic relationship matrix has become a necessity. Commercial livestock species are under heavy artificial selection. The effects of such selection on the genome can be traced through the changes in allele frequencies. The fixation index (FST) measures the rate of fixation through the increase in homozygosity and it has become an important tool to study population structure in humans, animals and plants. Chang et al. [ST which measures the allele differentiation among subpopulations to identify segments of the genome under selection pressure. There was an increased genomic similarity and improvement in the accuracy of genomic selection when the FST scores were used to prioritize SNP markers in high-density panels as compared with using BayesB [ST scores. From an animal breeding perspective, the use of genomic information results in a substantial reduction in generation interval and an increase in the accuracy of predicted breeding values, leading undoubtedly to an improvement in the genetic response ,3,4,5,6.P panels . This isg et al.  proposedg BayesB  and Bayeg BayesB  approachGenomic best linear unbiased prediction (GBLUP) assumes equal weight for all SNPs ,23. Sun ST scores-based prioritization method developed by our group  has bee0) was founded by 1000 males and 15,000 females randomly selected from the historical generation. A trait with heritability equal to 0.30 was simulated and all genetic variation was assumed to be due to the simulated QTL. The mating system was at random throughout up to generation G0. We also simulated an additional 15,000 animals for each of 7 additional generations (G1–G7). The parents were sampled on their estimated breeding values (EBVs), with a replacement rate of 50 and 20% for males and females, respectively. We assumed one progeny per mating and a sex ratio of 50%. Each simulation scenario was replicated 5 times. The average of the effective population size was equal to 323. Data from generation 6 (G6) was used as a training population and that of generation 7 (G7) was used to evaluate  the proposed method. All animals in the training and validation populations were genotyped with 400,000 SNP markers simulated to be uniformly distributed along 10 chromosomes of 100 cM in length each to approximate about 1.2 million SNP markers in the bovine genome. We sampled 200 biallelic QTL from a Gamma distribution with shape and scale parameters equal to 0.4 and 0.15 respectively. We did not allow any overlap between the SNP markers and QTL. The QMSim simulation software  was usedIσe2 where σe2 is the residual variance.Additionally, QTL were assumed not to be genotyped. The residual variance was scaled accordantly in each scenario of selected SNPs such that the heritability and phenotypic variance were constant at the values of 0.3 and 1, respectively. Trait phenotypes were generated as the sum of an overall mean, the random additive effects of QTL and their associated genotypes and the residual terms. The latter were sampled from a normal distribution with zero mean and variance-covariance matrices ST) have been used to measure the level of genetic differentiation between populations based on change in allele frequencies. The FST scores were calculated following Nei [6) were divided into three sub-populations based on the 5% and 95% quantiles (below the 5% quantile (S1), between 5% and 95% quantiles (S0), and above the 95% quantile (S2)). Genotypes of individuals (1500) in sub-populations S1 and S2 were used to calculate the FST scores. For each locus, the global FST estimator was defined as:Sip and Siq are the allele frequencies in subpopulation i, Sn1 and Sn2 are the number of individuals of the subpopulations S1 and S2, SH is the average heterozygosity of subpopulations, and TH is the heterozygosity based on the total population.Briefly, divergence between populations and subpopulations is often due to differential selection pressure. Wright’s fixation indexes (Fwing Nei  and Chanwing Nei . SpecifiG) is often calculated using the method proposed by VanRaden [G were observed with an increase in the number of SNPs used for its calculation. Current methods used to calculate G, generally, give the same weight to all the markers, and thus could not guarantee the optimality of genetic similarity between individuals at the QTL. For that purpose, contributions of the SNPs used to compute G have to be weighted according to their importance on the phenotype (strength of association with the phenotype). To maximize the functional genomic similarity between individuals, the SNPs have to be prioritized based on their ability to increase genetic or phenotypic similarity between individuals. Conversely, individuals with different genetic values or phenotypes are likely to have much lower genomic similarity at QTL than the expected or observed additive relationships. It is worth mentioning that FST is only a measure of population differentiation and an increase in functional similarity is achieved through an increase of the relative weight of prioritized markers.Several methods have been proposed to calculate the genomic relationships ,33,34,35VanRaden . It basiVanRaden ,37,38, nVanRaden ,39 have G. In this study, FST scores were used to prioritize and to assign relative weights to the SNP markers. The top 20K SNPs based on their FST scores were used either alone or with the remaining 380K SNPs to compute G with or without weighting. When only the top 20K SNPs were used to compute G, the following two scenarios were considered: 1) equal weights for all SNPs or 2) weights proportional to each SNP FST score. When all 400K SNP markers were used, the different weighting scenarios evaluated are presented in The challenge in maximizing the genomic similarities is finding the relative weights for the SNPs used in the calculation of i, ST score for SNP i and N is the total number of SNPs (400K or 20K).The relative weights were calculated using the following equation:6 and G7, respectively. For each scenario, the genomic relationship matrix was computed with the appropriate number of markers and the weighting factors and the analysis was carried out using the following mixed linear model:N × 1 column vector of phenotypes, X is a N × p known incidence matrix of the p predictor variables, b is a p × 1 column vector of fixed effects regression coefficients, Z is a N × q known incidence matrices with the appropriate dimensions for the q random effects, u is a q × 1 column vector of genomic breeding values, and e is a N × 1 column vector of random residuals. Additionally, it was assumed that For all scenarios, 10,000 and 5000 animals were randomly selected from GThe AIREMLF90 program  was usedST scores) accounted for 25% or less of the weights used to compute G. In all cases, the genetic variance was underestimated when no weights were used  was 0.690  in terms of quality-of-fit of the model . In factG, the genetic and residual variances were very similar to the estimates obtained for scenario one in We showed that only a portion of the genetic variance was recovered for the different scenarios. The inability to recover all the genetic variance is due to the large number of QTL with very small effects. In fact, 55% of QTL have a true effect smaller than one-tenth of one percent of the genetic variance and an additional 20% of QTL have an effect smaller than 0.5% of the total genetic variance. These small effect QTL are hard to track effectively when the LD is moderate to low. Across the different scenarios, there is an underestimation trend of the residual variance, although it does not seem to be any systematic bias. Heritability was clearly underestimated when the majority of the weight (≥90%) was allocated to the prioritized top 20K SNPs , thus favoring markers with low MAF. However, it is not weighed by the size of the marker effect. Consequently, after a certain number of SNP markers are included in the computation of G, little to no improvement is expected. Chang et al. [G with additional markers could be an indicator of the sufficiency of available SNPs in estimating the realized relationships. However, such sufficiency is not a guarantee of the optimality of such matrix for the implementation of association and genome selection analyses. In fact, as the number of randomly selected SNPs increased from 40K to 400K, the matrix G inched closer to the expected additive relationship matrix (A). Furthermore, they showed that a genomic relationship matrix computed based on a selected subset on 20K markers was markedly different from A. In this study, we further prove that within those selected 20K SNPs additional improvements could be achieved through appropriate weighting of the contribution of these SNPs in the calculation of G.Intrinsically, the contribution of a SNP marker to the estimation of g et al.  showed tST scores resulted in a 4.3% increase in accuracy as compared with the same weight scenario (G is not the best option. Weighting all markers with their relative FST scores seem to be an efficient prioritization tool to achieve such a goal, however, it should be noted that FST scores are only measures of fixation index. A combination of metrics of fixation index and index of genetic differentiation could lead to better representation of population partitioning [These results clearly indicate that additive relationships between individuals could be accurately estimated with a reasonably small number of well distributed SNP markers, however, that does not mean that the accuracy of genomic selection cannot be improved using high-density marker panels or even sequence data. To achieve that goal, the genomic matrix has to evolve from a measure of additive relationships to an optimum measure of genetic similarity at QTL between individuals. The Fitioning  and coulST scores and using the latter to compute relative weights has increased the genetic similarity between training and validation animals. Furthermore, it resulted in more than 5% improvement in accuracy. These results clearly indicate that additive relationships between individuals could be accurately estimated with a reasonably small number of well distributed SNP markers, however, that does not mean that accuracy of genomic selection cannot be improved using high-density marker panels. The genomic matrix should evolve from a measure of realized additive relationships to an optimum measure of genetic similarity between individuals. The current method used to calculate the genomic relationship matrix gives the same weight to all the markers and thus does not guarantee the optimality of genetic similarity at QTL.The dramatic increase in the number of identified common and rare variants due to advances in NGS was expected to significantly increase the accuracy of GWAS and GS. Unfortunately, little to no improvement in accuracy was observed using NGS or high-density marker data. In spite of the repeated argument that all needed information is already captured by the available marker panels, the results of this study clearly show that the lack of improvement in accuracy is due to the limitations of the methods used rather than the limited additional information in the high-density and sequence data. Prioritizing SNP markers based on their F


Littorina saxatilis. Patterns of diversity in the field and of recombination in controlled crosses provide strong evidence that at least the majority of these rearrangements are inversions. Most show clinal changes in frequency between habitats, suggestive of divergent selection, but only one appears to be fixed for different arrangements in the two habitats. Consistent with widespread evidence for balancing selection on inversion polymorphisms, we argue that a combination of heterosis and divergent selection can explain the observed patterns and should be considered in other systems spanning environmental gradients.Both classical and recent studies suggest that chromosomal inversion polymorphisms are important in adaptation and speciation. However, biases in discovery and reporting of inversions make it difficult to assess their prevalence and biological importance. Here, we use an approach based on linkage disequilibrium among markers genotyped for samples collected across a transect between contrasting habitats to detect chromosomal rearrangements de novo. We report 17 polymorphic rearrangements in a single locality for the coastal marine snail,  Remarkably, ~75% of these SNPs  were shown to be clustered in large genomic regions of high LD (12.5–29.5 cM) in three out of 17 linkage groups (putative chromosomes), suggesting large regions of low recombination compatible with the presence of chromosomal rearrangements  across a 152‐m transect along the shore and their positions in three dimensions was recorded Figure , a targeL. saxatilis  a r2.2In order to infer recombination in the Wave ecotype, juvenile virgin females were collected from a wave‐exposed habitat at Ängklåvebukten (north end) and kept in separate aquaria with running seawater. At the time of female maturity (9 months later), adult males were collected from the same area and paired with the females. Crosses resulted in four full‐sib families . Although a different female was used in each cross, the first three families shared the same father. Genotyping of one female parent failed so that only three families were available for analysis of female‐informative markers.www.anchoredphylogeny.com). Following qPCR and Bioanalyzer‐based quality control, libraries were sequenced on a partial Illumina 2,500 lane with paired‐end 150‐bp reads and 8‐bp indexing read.Targeted resequencing was performed using the same targeted‐capture sequencing approach but using about half of the probe set used in Westram et al. . We preftrimmomatic v. 0.36 , before SNP calling, which was performed using gatk unifiedgenotyper v3.7‐0  and only retained SNPs with coverage depth ≥8. The SNP filtering workflow was performed with vcftools . This set of SNPs was filtered using the criteria in Westram et al.  between all SNPs within each linkage group was generated for all individuals in the transect sample with the r package “genetics”  were also discarded. Whenever clusters obtained for the different values of φ shared SNPs, the one with the smaller number of SNPs (and higher median LD) was retained. The only two exceptions occurred when SNPs from two overlapping clusters became fused into a single larger cluster at higher φ, suggesting a common source of LD. In these cases, only the merged larger cluster was retained for downstream analyses. Although LDna allows detection of two different types of clusters, single‐outlier clusters (SOCs) and compound‐outlier clusters (COCs), the latter were disabled as they can be generated by different evolutionary forces acting simultaneously, making the interpretation of results difficult  on recombination, spatial population structure, or structure generated by local adaptation. Kemppainen et al. . Kemppainen et al.  to have high heterozygosity relative to the more extreme groups (homokaryotypes) on PC1 (and PC2 where there are 6 groups) and relative to collinear regions of the same linkage group. This pattern is expected to be particularly marked for SNPs with strong allele frequency differences between arrangements. We tested this prediction for each candidate inversion, by calculating observed heterozygosity  to assess the genetic variation present in each arrangement. For a young inversion, we expect one arrangement to have low π relative to the other arrangement and relative to collinear regions (outside LGCs). These differences should decrease with time due to mutation and gene flux  should increase. We calculated π and XYd to give a first view of the ages of inversions but note that other factors influence these statistics (see below). Nucleotide diversity was estimated for the two homokaryotypes of each LGC using vcftools in order to compare arrangements. π per site was estimated for each SNP and then averaged across all sites within the length of each probe region, including invariant sites (~120 bp). Pairwise divergence between the putative homokaryotypes (XYd) was estimated in the same way as π for each probe region, using the fact that tπ (for a group containing both homokaryotypes) is based on a mixture of comparisons between and within karyotypes. Specifically,Whereas xnand ynare the numbers of the two homokaryotypes, x + nyN = n, and xπ and yπ are nucleotide diversities for the two homokaryotypes separately. Differences in π and XYd between putatively inverted and collinear regions (as control) within each LGC were examined using Wilcoxon rank‐sum tests. Finally, differences in π between the homokaryotypes for each LGC were also tested for inverted and noninverted regions (as control) using Wilcoxon rank‐sum tests. All tests were adjusted for multiple comparisons using the sequential Bonferroni correction.where 2.6The presence of inversion polymorphism can be confirmed by their effects on patterns of recombination . Two effects may be observed in crosses: suppression of recombination in inverted regions where the informative parent is a heterokaryotype, and reversal of part of the genetic map when comparing parents that are homokaryotypes for opposite arrangements. To test these predictions, we re‐examined the Crab‐ecotype linkage map of Westram et al.  and we aFor the Wave families, we performed a PCA followed by kmeans clustering including all samples from the hybrid zone and laboratory crosses to infer the karyotype of each parent. By comparing parent and offspring genotypes, SNPs with genotyping errors as well as with extreme segregation distortion were removed. Moreover, apparent recombination events involving only single SNPs, or multiple consecutive SNPs within the same contig, were removed because genotyping errors cannot be excluded in these cases. Thus, the number of recombination events in our data is conservative, regardless of the region (inverted or not) where they were detected.We considered only male‐informative or female‐informative markers (not those heterozygous in both parents). For each parent separately, we then manually determined the parental haplotypes using informative SNPs and identified recombination events as positions where the haplotype switched in an offspring individual Figure . We test2.7r, function mle2, Bolker .In order to investigate the possibility of selection acting on the inversions, we considered their distribution along the transect from Crab to Wave habitat. After classifying individuals according to the number of copies of the alternative arrangement  for each inversion, based on the PCA clusters, changes in arrangement frequency across the transect were modelled as constant or clinal. Two cline models . The sy33.1The implementation of the LD analyses followed by our filtering criteria resulted in the detection of 17 LD clusters of loci (SOCs) identified as the candidate chromosomal rearrangements that were then characterized in downstream analyses Figure , Table 1r2 = 0.377 to r2 = 0.985) distributed over 15–99 different contigs  and they contained between 32 and 263 SNPs in relatively high LD  with intermediate genotypes between them absent or rare Figures . This par2 < 0.3), or the inversions detected on these LGs could extend further than our current estimates so that some SNPs currently included in the collinear regions are actually in the inversions. Alternatively, the grouping pattern could result from the influence of selection on markers that are not contained in a chromosomal rearrangement.In contrast, the collinear regions of the LGs containing LD clusters did not reveal obvious genotypic groups  or the numbers of contigs (r = 0.40) that they contained. This was presumably because the parental individuals used in generating the Crab map varied in karyotype such that recombination was suppressed in one or both parents for some inversions  between the PCA groups in order to confirm the expectation based on the interpretation of LD clusters as inversions. Putative heterokaryotypes  were expected to have higher heterozygosity than putative homokaryotypes. Heterokaryotypes were also expected to have higher heterozygosity than is observed in corresponding collinear regions. In most LGs, Hobs within the inverted regions of the putative heterokaryotypes was significantly higher than within the inverted regions of the putative homokaryotypes  revealed no significant differences with only one exception (LG12.1).The comparison within the same LGs revealed that XYd, was higher than control values from collinear regions , implying accumulation of genetic differences since the origin of the inversion  within the region covered by the LD cluster. For homokaryotypes with the alternative arrangement (relative to the Crab map reference) recombination patterns were predicted to be more consistent with the reversed gene order Figure .The identification of recombination events within four Wave families revealed no recombination event within the candidate inversions in 124 possible cases (parent–offspring combinations for each candidate inversion) in offspring genotyped from parents that were heterozygous for the different arrangements  Table . In contSince single crossovers are expected to be much more frequent than double crossovers or gene conversion, we classified the offspring haplotypes as more consistent with either the most frequent Crab gene order (R) or a gene order (A) that is reversed in the putatively inverted region, or as uninformative if they were equally consistent with both gene orders. All the informative haplotypes except one (99/100) were more consistent with the parent's karyotype (inferred from the PCA) than with the other gene order. Among these, 38 out of the 39 informative events were cases where the parent had the alternative gene order (AA) rather than with the order inferred for the Crab linkage map (RR). Altogether, recombination patterns compatible with the presence of inversions were observed for all LGCs except three, where difficulties in inferring the parent genotypes from the SNPs available (LGC7.2 and LGC14.3), as well as lack of informative events (LGC9.1), precluded inferences based on the observed recombination patterns  while others had one homokaryotype that was found mainly in the central part of the transect  or both homokaryotypes present across the entire transect  Figures .p = 1.854E‐13 and 1.284E‐35, respectively) among inversions  or high‐quality reference genomes are available, the candidate rearrangements detected by their LD signatures can be confirmed and their type  can also be identified. Detection of rearrangements using information from LD between markers, complemented by PCA and genetic diversity information, was proposed previously  will need to be fine‐tuned through exploratory analyses in order to make informed decisions concerning some parameters.L. saxatilis is high when compared with other systems  is a plausible explanation for the inversion clines in L. saxatilis. Clearly, further simulations and empirical observations will be needed to test the hypothesis of balancing and divergent selection and exclude alternative explanations. Direct estimates of selection might be possible, for example, using field transplants, but for arrangements on LG6, 14 and 17 there is already good evidence for a component of divergent selection from the analyses of SNP clines in Westram et al.  will allow the identification of candidate genes and functions that may play a role in adaptation and ecotype divergence. Association mapping in this hybrid zone has already revealed that a large proportion of the genetic variance observed for some key adaptive phenotypes may be explained by genetic variation within some of these inversions (Westram et al., Finally, complementary evidence to understand the link between inversions, adaption and selection can come from determining the genes present within inversions and the phenotypes that are associated with inversion polymorphisms. Although most of the outlier SNPs identified by Westram et al.  are locaWe have demonstrated the power of LD patterns to detect inversions. There may be some bias in this approach towards inversions associated with local adaptation. Nevertheless, in our study site, inversions apparently make a major contribution to adaptive divergence.R.F., R.K.B., K.J. and A.M.W. conceived the study; A.M.W., M.Rav, K.J., A.R.L., E.M.L., M.P. and R.K.B. collected data; R.F., A.M.W., P.C., T.L., H.M., M.Raf and R.K.B. analysed data; R.F. and R.K.B. wrote the first draft; and all authors contributed to subsequent versions of the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Adolescence is characterized by rapid growth and development with a significantly increased need for macro and micronutrients. However, there is little empirical evidence on the burden of anaemia among adolescent girls in developing countries such as Ethiopia. This study aims to address this gap by evaluating the magnitude of anaemia with an aim to guide design of intervention modalities to address anaemia among adolescent girls.The study employed a community based cross sectional design. The study was conducted on weekends to capture both in school and out of school adolescent girls. Data was collected from a total 1323 adolescent girls. From each district, we randomly selected villages and ensured that the sampled households had a range geographical spread  within the larger category of rural and urban. We performed anaemia testing using HemoCue B-Haemoglobin analyser. We applied a complex survey data analysis method to estimate the level of anaemia. The hemoglobin level was adjusted for altitude and smoking status. We ran a logistic regression model to evaluate predictors of anaemia.anaemia” was found to be protective against the risk of anaemia.The overall anaemia prevalence ranged from 24 to 38%, with an average rate of 29%. Less than half of the girls heard the term anaemia, and about one third knew the relationship between anaemia and the intake of iron rich foods. The risk of anaemia is higher among adolescent girls in their early adolescence period (10–14 years) (Adjusted Odds Ratio (AOR); 1.98; 95% CI; 1.03, 3.82] and among adolescent girls who lived in moderately food insecure households . However, knowing the term “The risk of anaemia was particularly high among adolescent girls in their early age and among those living in food insecure households. The prevalence of anaemia among adolescent girls is a moderate public health problem. According to the WHO set criteria, the districts could be candidates for intermittent iron and Folic acid supplementation program. The World Health Organization (WHO) defines adolescence as the period from 10 to 19 years of age , 3. EthiAdolescent girls are at risk of iron deficiency and anaemia due to various factors including high requirements for iron, poor dietary intake of iron, high rates of infection and worm infestation, as well as pregnancy . Female Inadequate dietary intake of iron-rich food independently determines anaemia among adolescent girls , 16. AdoIn nutshell, we have noted variation among studies on the reported magnitude as well as on the relative importance of factors associated with anaemia among adolescent girls. Of the studies that have investigated anaemia among adolescent girls, few of them provided estimates of the magnitude of anaemia for the entire adolescent period 10–19 years) and many of these studies are limited to school going adolescent girls which may potentially underestimate the magnitude of anaemia among adolescent girls. Failure to consider younger and out of school adolescent girls can also result in designing improper intervention modalities. Moreover, adolescent girls requires higher iron intake due to their rapid growth and menstrual losses [ years anThe WHO Ethiopia country office in collaboration with the government of Ethiopia launched the Accelerating Nutrition Improvement (ANI) project aiming at reducing iron-deficiency anaemia among adolescent girls in 10 selected districts in Ethiopia. This baseline survey was planned to establish the magnitude of anaemia among adolescent girls aged 10–19 years and identify possible delivery channels to provide anaemia control and prevention services to adolescent girls in the target districts. In the present work, we attempted to determine the burden of anaemia among adolescent girls. More specifically, we aimed to: (i) estimate the magnitude of anaemia among adolescent girls in the early, middle and late adolescence period (ii) evaluate adolescent girls’ perceptions on anaemia and preferred delivery channels for Iron and Folic Acid Supplementation, (iii) evaluate and recommend alternative platform for iron supplementation among adolescent girls. The findings from this study will provide insights about the burden of anaemia among the adolescent period and further help design appropriate implementation modalities for iron folic and acid supplementation among adolescent girls.We employed a community based cross-sectional study design. The study was representative of adolescent girls aged 10–19 years. The study was conducted between October and December 2015.The WHO country office is supporting the Accelerating Nutrition Improvement (ANI) project. The WHO ANI activities aimed, among others, to improve dietary habits and reducing iron deficiency anaemia among adolescent girls. The project was implemented in ten districts in the regions of Amhara, Oromia, and Southern Nations, Nationalities, and.Beyeda, West Belesa, Laygaynt, Wondogent, Gedebasasa, Derra, Debrelibanos, Angacha, Damotegale, and Boloso bombie. The baseline study was conducted in three districts  of the ten WHO Ethiopia country office supported districts.Peoples’ Region (SNNPR), in partnership with John Snow, Incorporated and under the overall coordination of the Federal Ministry of Health (FMoH) of Ethiopia. The ten implementation districts were In this baseline study, sample size determination considered any form of anaemia  were selected randomly among the 10 WHO ANI project districts. From each district, we randomly selected 6 rural and 1 urban kebeles . The selection of kebeles for the survey ensured that the sampled households had a balanced geographical spread  within the larger category of rural and urban. The total sample size in each district (n = 440) was allocated for the selected kebeles proportionally, based on the number of household in the kebeles.The 3 study districts .In the selected The first household was randomly selected from among the first 3 households. From then onwards, data collection teams visited every fourth household. If the selected household did not contain an adolescent girl age 10–19 years, the data collection team moved to the next household (the direction is decided a priori) and resumed their sampling once they identified an eligible household. In households in which there was more than one eligible adolescent girl, the team randomly chose one girl to interview.Amharic and afan oromo languages). The questionnaire included socio demographic characteristics of the adolescent girls and their respective parents such as education, schooling status, religion, marital status, parental occupation, and family size. Data on household characteristics such as ownership and size of land, type of house and construction materials; availability of fixed assets such as radio, television, phone, bed, and chair and other household items; possession of domestic animals; and access to utilities and infrastructure (sanitation facility and source of water) were collected.A questionnaire, adapted from the Ethiopian Demographic and Health Survey (EDHS) and relevant literature was developed. The questionnaire was translated into local languages  developed by Food and Nutrition Technical Assistance (FANTA) Project through the Academy for Educational Development . The geoDue to the lack of vital registration systems, we developed a local events calendar to estimate adolescent girls’ year of birth. All enumerators were required to apply the local events calendar to estimate adolescent girls’ age.The interviews were conducted during weekends i.e. on Saturdays and Sundays in order to capture both in school and out of school adolescent girls in the sample. Adults, preferably mothers or heads of household as appropriate, were interviewed about general household demographic and economic characteristics.Following the interviews, experienced nurses and lab personnel tested adolescent girls for anaemia using HemoCue B-Haemoglobin analyser. The HemoCue B-Haemoglobin analyser is a portable, rapid and accurate method of measuring haemoglobin. Results are displayed after 45 to 60 s in g/dl on an LCD display. Bio-safety measures such as use of sterile gloves; alcohol/clean water during collection of specimen as well as safe disposal system were employed .The enumerators and supervisors were trained for three days on general techniques of interviewing and supervision, administration of each item in the questionnaire, hemoglobin measurements, and instruction on ethical treatment of participants. In addition, the questionnaire was pretested in a village, not selected for the study, before the final study began to assess the performance of the study tools. Some revisions were made on the study instruments based on the feedback obtained from the pretest. Interviews were administered by 20 enumerators (experienced nurses and lab personnel) and supervised by three supervisors. The enumerators had a minimum of diploma education (experience in data collection preferable), fluently spoke the local languages, and were residents in the local area or vicinity. The supervisors had a minimum of a bachelor education and previous experience in supervising community based data collection. The supervisors addressed questions and queries of interviewers and corresponded with the investigators whenever necessary. In addition, 2–3 local residents were recruited from each of the districts to guide the data collectors through the villages and ease communication with the villagers. A field guide manual was also developed for use by the interviewers and supervisors.Three days long training was given for data collectors and supervisors. The focus of the training was on understanding the instrument and interviewing skills, appropriate use of HemoCue for anaemia testing, and GPS operation. Role plays and pretests were done before the actual data collection. The supervisors checked all filled questionnaires for completeness and consistency each day before turning them to the investigator.The HemoCue instrument is widely used to measure hemoglobin in anaemia surveys. Although the instrument is excellent on its own, data quality is dependent on good blood sample collection (capillary blood sample). The personnel were trained on the correct handling of the instrument and procedures. In order to get accurate and reliable hemoglobin values using HemoCue, standardization exercises were conducted multiple times during training until the performance standard was met. The performance standard is met when the difference in hemoglobin levels between data collectors and expert is less than 0.5 g/dl.We also checked the quality of the hemoglobin data in the sample by calculating its standard deviation (SD). A smaller SD (1.1–1.5) of hemoglobin is usually considered to denote a better data quality than a larger SD.Bio-safety measures such as use of sterile gloves and alcohol/water during collection of specimen as well as safe disposal system were employed. Materials like gloves and lancets were collected using safety boxes and were transported for safe disposal, either to be buried or incinerated. For adolescent girls who were anemic, counseling to take Iron and Folic Acid (IFA) supplement and referral to a nearby health facility was arranged.We used Epi Data Version 3.1 for data entry and Stata 14.0  for cleaning and further analysis.Descriptive analysis on the general characteristics of the adolescent girls such as age, schooling, marital status, and knowledge on anaemia as well as household characteristics such as household food insecurity, and household dietary diversity was done. In addition, data presentation using tables, graphs and appropriate summary figures were included.Household wealth: We applied a principal component analysis (PCA) to construct wealth index. In order to construct a relative household’s wealth index, a suite of several socio economic indicators were collected: land ownership, type of house and building materials, availability of fixed domestic assets , ownership of domestic animals, source of drinking and cooking water and availability and type of latrine. A relative socio-economic status was constructed by dividing the resulting score into quintiles that indicate poorest, poor, medium, rich and richest households.The household food insecurity (access) was derived from the HFIAS tool. The frequencies of affirmative responses to the HFIAS questions were used to classify households into one of the four categories of food insecurity i.e. food secured, mild, moderate and severe food insecurity. We generated household-level mean dietary diversity score using the sum of all foods (food groups) eaten in the respective household during the day and night prior to the date of the survey. We classified households into three levels  of dietary diversity; a household with a lowest dietary diversity score consumed three or less food groups, a household with a medium dietary diversity score consumed four or five food groups, while a household high dietary diversity consumed six or more food groups.The hemoglobin (Hb) level was adjusted for high altitude and smoking status before defining anaemia. The adjustment was done to account for a reduction in oxygen saturation of blood. We used the following formula for adjustment of hemoglobin for high altitude.Moreover, hemoglobin adjustments for smoking were done by subtracting 0.3 from individual’s observed hemoglobin level.Adolescent girls who had an Hb values below 12 g/dL were considered as anemic. Adolescent girls with Hemoglobin values of 11–11.9 g/dL, 8–10.9 g/dL, and < 8 g/dL were categorized as having mild, moderate, and severe anaemia, respectively.A complex survey data analysis was employed to calculate the district level anaemia prevalence, designating the survey’s primary sampling unit (villages) and strata . The variance was adjusted using Taylor linearized variance estimation method.Anaemia prevalence was also calculated for each of the districts; among the age groups such as early, middle and late adolescence period; urban and rural residence; and within the following categories: household wealth, food insecurity levels, dietary diversity status, schooling status, agro ecology, heard the term anaemia, and IFA and wealth status, as appropriate.We run a multivariate logistic regression model using the ‘svy’ command in STATA 14.0  to ensure that standard errors are adjusted for the complex survey design. This was done to identify factors that could potentially be associated with the occurrence of anaemia among adolescent girls. We selected theoretically relevant variables from the literature for the regression model including household, personal and diet related variable such as household food security, dietary diversity, socio-economic condition, place of residence, adolescent’s age, schooling, smoking and awareness of the term anaemia and IFA tablets.We included a total of 1323 adolescent girls aged 10–19 years. Only two adolescent girls refused to give blood for a test due to fear of needle prick.Table Table The point and interval estimates of the percentage of adolescents age 10–19 with anaemia, by selected background characteristics is depicted in Table A higher proportion of early adolescent girls, age 10–14 years (31.3%) and middle adolescence, age 15–17 years (28.1%) were anaemic than girls who are in their late adolescence period, age 18–19 years (17.2%).Debrelibanos district were anaemic  compared to those in urban areas (19%). Also, adolescent girls residing in the highlands had a relatively higher prevalence of anaemia (31.6%) than those girls residing in the lowlands (23.9%).We documented anaemia prevalence varied by area and residency. A higher proportion of adolescent girls in We asked adolescent girls if they have ever heard or know the term anaemia in their local languages. We found that four out of ten adolescent girls have heard about the term anaemia in their local language. We further asked those adolescent girls who heard about the term anaemia if they knew about any symptoms related to anaemia. Figure In addition to this, out of 576 adolescents who heard/knew about anaemia, 35.1% believed that deficiency of iron in foods is a major cause of anaemia. Following, menstrual bleeding (18.9%) and excessive loss of blood (17.4%) were reported as the next main causes of anaemia. Furthermore, we asked adolescents’ whether they knew about IFA supplement, their sources of information, supply and experiences about using them. We found that majority of adolescents did not know about (heard about) IFA. Only 11.2% of adolescent girls had heard about the IFA supplements. Health extension workers and health care providers such as nurses and health officers were primarily sources of information on IFA supplements. Out of the 153 adolescent girls who heard about IFA, only 20.3% had taken the IFA supplement. Most of the adolescent girls who took IFA got the supplements from heath posts (20 out of 31) or from health centers (9 out of 31). We asked these adolescent girls the reason they took IFA; most of them (26 out of 31) reported that they took the IFA because they believed that they were helpful to fill up blood.We evaluated adolescent girls’ willingness to use IFA and their preferred IFA delivery channel(s). The majority of them; (86.9%) reported that they will definitely be taking IFA to improve physical performance as well as learning and work capacity. Among those who are willing to take IFA, the channels that were most commonly chosen in order of preference were: (1) the health center (43.2%), (2) the health post (35.5%), (3) school clubs (12.0%), (4) at home (8.5%), (5) girls club (3.9%), and (6) the youth centers (1.9%).Debrelibanos (a highland) district. The odds of anaemia among adolescents who lived in Debrelibanos district were nearly 2 times than the odds among those who lived in Damotegale . The risk of anaemia also varied by the different age brackets during adolescence period. Girls in their early adolescence were more likely to be anemic as compared to adolescent in their late adolescence  of residency.In this study, we aimed to find out the magnitude and predictors of anaemia among adolescent girls, evaluate adolescent girls’ perceptions on anaemia and preferred delivery channels for Iron and Folic Acid Supplementation and recommend platform for iron supplementation among adolescent girls. The results of the survey indicated that anaemia rates ranged from 24 to 38%, with an average rate of 29%. Adolescent girls in their early adolescence period and those who lived in food insecure households had higher burden of anaemia. Less than half of the girls heard the term anaemia, and about one third knew of the relationship between anaemia and the intake of iron rich foods. The great majority of girls interviewed were willing to take iron-folic acid supplements to improve their health as well as their capacity to learn and to work. Most indicated they would prefer receiving supplements through the health system.Currently, the magnitude of anaemia among adolescent girls in the studied districts was alarmingly high. We calculated the sample size for this study based on a 13% expected prevalence of anaemia derived from an estimate made from the EDHS 2011. However, our result is more than double of what the EDHS reported in 2011 and from other studies . This vaA higher prevalence of anaemia in the early adolescent girl may be attributed to higher prevalence of puberty menorrhagia at the time around menarche. A similar higher prevalence of anaemia in the early adolescence period (10–14 years) than the late adolescence period (15–19 years) was reported in India , 22. ConWe used the dietary diversity score derived from the sum of food groups eaten by household members as proxy indicator for dietary intake and quality. The dietary score for the sampled adolescent girls was very low and the anaemia prevalence varied across these scores. Interestingly, the prevalence of anaemia was still considerably high for adolescents living in households with higher dietary diversity scores. This may be due to the fact that the consumption of iron rich food was generally low among the adolescent girls.Similar to a study conducted in Bangladesh , our finThe current study also revealed that the prevalence of any form of anaemia was higher (31.6%) in rural adolescent girls as compared to their counterpart in urban area (19%). This finding is analogous to studies conducted in India. The higher prevalence of anaemia among rural adolescent is due to higher possibility of food shortage and thus consumption of food poor in iron and other micronutrients. As anticipated, knowing (heard) the term anaemia was also found to be significantly associated with anaemia status among the adolescent girls. Therefore, nutrition education and counseling can be used to improve the nutrition knowledge of adolescent girls and make them realize healthy diet for healthy living.Debrelibanos, Damotegale and Laygaynt) can be classified under the category of moderate public health significance . According to the WHO set criteria for IFA supplementation for adolescents, the districts could be candidates for preventive iron supplementation program [According to the WHO classification of public health significance of anaemia in populations on the basis of prevalence estimated from blood levels of hemoglobin, all of the study districts ( program . We aimeThe fact that 12% of adolescents were out of school has programmatic implications on choosing the type of delivery channels to use. Programs relying solely on school based delivery channels might miss adolescents who are out of school. Alternative delivery channels including health centers and health posts as well as home to home delivery might be needed to reach of adolescents who are out of school. We recommend a formative assessment with an objective of in depth investigations to explore the appropriateness, feasibility and cost effectiveness of different delivery channels.In conclusion, the prevalence of anaemia among adolescent girls was found to be a moderate public health problem. According to the WHO set criteria for IFA supplementation for adolescents, and given the high rate of anaemia found among.adolescent girls, the districts could be candidates for intermittent iron and Folic acid supplementation program.


A key developmental transformation in the life of all vertebrates is the transition to sexual maturity, whereby individuals are capable of reproducing for the first time. In the farming of Atlantic salmon, early maturation prior to harvest size has serious negative production impacts.picalm) and membrane-associated guanylate kinase, WW and PDZ domain-containing protein 2 (magi2).We report genome wide association studies (GWAS) using fish measured for sexual maturation in freshwater or the marine environment. Genotypic data from a custom 50 K single nucleotide polymorphism (SNP) array was used to identify 13 significantly associated SNP for freshwater maturation with the most strongly associated on chromosomes 10 and 11. A higher number of associations (48) were detected for marine maturation, and the two peak loci were found to be the same for both traits. The number and broad distribution of GWAS hits confirmed a highly polygenetic nature, and GWAS performed separately within males and females revealed sex specific genetic behaviour for loci co-located with positional candidate genes phosphatidylinositol-binding clathrin assembly protein-like (The results extend earlier work and have implications for future applied breeding strategies to delay maturation in this important aquaculture species.The online version of this article (10.1186/s12864-019-5525-4) contains supplementary material, which is available to authorized users. Salmo salar) is a complex process informed by both genetic and environmental cues. It is highly variable, with extremes in age and size at maturation the result of adaptation to maximise fitness and reproductive success [The development of sexual maturation in Atlantic salmon ( success . The tra success . Life-hi success . Factors success .vgll3), which has a role in both regulation of adiposity and age at menarche in humans [The practise of salmon farming promotes high growth and adiposity through increasing food availability in comparison to wild stocks. The consequence is an elevated proportion of farmed animals entering maturation younger and at weights below harvest size, generating substantial production inefficiency. This can be partly addressed through modified management practise and selective breeding for delayed maturation, however the positive correlation with growth makes uncoupling positive and negative production impacts challenging. Existing estimates indicate the age and size of maturation are moderately heritable, ranging from 0.15 to 0.48 –10. The n humans .The objective of this study was to characterise the distribution of SNP effects that control variation in the timing of maturation, and identify the genes and genomic regions likely to regulate two maturation traits in salmon farmed in Tasmania. A custom SNP50 genotyping array was used to perform GWAS for 2721 fish with maturation data collected in the marine environment, while a parallel GWAS used 1846 related fish scored for maturation in freshwater. The results represent a detailed view of the genomic regions that explain variation in both traits, and provide key information for the design of selective breeding approaches to manage a substantial production challenge for the salmon farming industry.The GWAS population is from the Salmon Enterprises of Tasmania (SALTAS) selective breeding program which has been described previously –16. A susdY) [sdY assay returned a positive signal, and as female where no intensity data was observed from any sdY assay. Raw genotype calls were quality controlled to remove i) SNP with sample call rate < 90%; ii) SNP with minor allele frequency < 1% and iii) samples with missing genotypes in excess of 5%. A comprehensive pedigree check was performed by comparing the coefficients of the additive relationship matrix and the genomic relationship matrix (G matrix) calculated via the first method described in [G) explained by SNP with significant associations were established by fitting all significant SNP, the GRM used in the association analysis and a pedigree containing all relationships within the population in generalised linear mixed models in ASReml-R [r2 for SNP pairs on Ssa25. This was performed after setting the LD value threshold to 0.0 to allow the estimation of values across the full range from one to zero (−-ld-window-r2). To perform fine-mapping using dense SNP collections, haplotype phasing and imputation to sequence across chromosome Ssa11 was carried out using Eagle version 2.3.2 [R2 value produced by Minimac3. SNP having r2 < 0.4 were removed, leaving 10,940 SNP. The dataset was further filtered to a final set of 458 common SNP spanning the 3 Mb critical interval (Mb 80–83) on Ssa11. Association analysis was carried out using linear regression as implemented in PLINK v1.9 [Fin clips were used for the extraction of DNA by a commercial provider . Genotyping was performed using a custom 50 K Affymetrix SNP array by the same commercial provider, containing loci largely derived from an earlier custom 220 K SNP Affymetrix array developed by the Centre of Integrative Genetics (CIGENE) and AquaGen . Sex forsdY) . Probes ribed in . A totalribed in . To reduribed in . One thoASReml-R ) to faciASReml-R  was usedon 2.3.2  respection 2.3.2 . All refon 2.3.2 . The 50 INK v1.9 .2 transformed. Transformed FPKM data for candidate genes were obtained and plotted as heat map using the R package pheatmap (https://cran.r-project.org/web/packages/pheatmap/index.html).RNA-Seq datasets from eight Atlantic salmon tissues  were downloaded from Sequence Read Archive (SRA) database  were identified by visual assessment of secondary sexual characteristics such as the development of a kype and altered coloration. A significant sex bias was observed in early maturing animals, consistent with previous findings [χ2 = 141, p-value = 1.0 × 10− 32). The weight of mature animals was no different among females. However, males maturing in freshwater were significantly heavier . These biases were stronger for marine maturation (MMAT) which was measured in a separate group of 2721 animals from 554 families at 22 months of age, approximately 9 months after their transfer into sea cages. For example, only 10% of female fish presented as mature and they were significantly heavier than their immature female relatives . This is consistent with maturation in wild populations, where selection favours earlier maturation in males compared with females [h2 = 0.15 ± 0.1; MMAT h2 = 0.20 ± 0.2). The correlation of family means was estimated to determine if the traits should be considered as biologically independent. This revealed the traits are only moderately correlated (r2 = 0.76), prompting their subsequent treatment in GWAS as independent.The study population is from the SALTAS selective breeding program, which offers the opportunity to exploit large pedigrees and progeny trait data to perform GWAS. Maturation was assessed as two traits due to the life cycle of Atlantic salmon and the associated breeding program design , 169 Kb away from the magi2 gene. It explains a moderate proportion of genetic variance (17.7%) with an effect size larger than other strongly associated loci  is located within the phosphatidylinositol-binding clathrin assembly protein-like gene picalm. It explains a lower proportion of genetic variation and has a smaller effect size (Table magi2) or steroid hormones  represent plausible positional candidates, there is currently little known about their role in fish. The identity of the top 10 ranked SNP for freshwater maturation, along with physically co-located genes, are given in Table To commence GWAS, genotyping was performed using a custom 50 K SNP array before quality filters were applied to define a total of 46,500 SNP. Analysis of both traits was performed using mixed linear model association incorporating a genetic relationship matrix. This is important for populations with family substructure, to reduce the over-estimation of significance and the incidence of false positive association. The strength of SNP association, estimated using 1846 genotyped animals scored for FMAT, is given in Fig. p2rx5, cld4, tsk and LOC106612817). Of these, claudin-4  is a strong positional candidate as claudins are junction proteins and control electrolyte transportation across cell-cell junctions. Several claudins have roles in gonadal development such as claudin-3 which acts to control spermatocytes [The genomic distribution of associated SNP for marine maturation is also shown in Fig. atocytes , while catocytes . Human catocytes .Table 2Tpicalm gene on chromosome 11 as the associated SNP resides within the gene. The availability of whole genome sequence from 19 ancestors of the GWAS population [picalm with additional SNP. We imputed genotypes at 458 SNP in 4606 animals (1867 with MMAT and 2739 with FMAT trait records) across a 3 Mb region (Mb 80–83) spanning the Ssa11 peak SNP AX-87621437  were reconstituted in analysis using male fish but absent in the female specific analysis. For example, the proportion of genetic variation explained by the chromosome 10 SNP (AX-87354755) was 19.5% in males and non-significant in females despite its allele frequency being similar in both sexes  were again not significant using females alone  containing three non-synonymous mutations  were identified as putatively functional. Given sea age is associated with the timing of sexual maturity, we sought to determine if variation at either gene is associated with maturation in the Tasmanian breeding population. To commence, whole genome sequence from the same 19 animals used for imputation were used to estimate the allele frequency at each of the three putatively causal variants (Table vgll3 variant (Asn323Lys) [323Lys variant associated with delayed maturation and 3 sea winter animals. Both alleles were detected at the second vgll3 mutation (Met54Thr), with the frequency of the 3 sea winter allele (54Thr) much higher in the SALTAS genomes (0.78) than the frequency for the early maturing allele (0.23). In preparation for fine mapping the vgll3 region, we incorporated five SNP into the design of the Custom SNP50 chip spanning a 62 Kb interval (Mb position 28.659–28.721). Inspection of the resulting association signals revealed no evidence for variation at, or in the chromosomal regions surrounding vgll3, influencing freshwater or marine maturation , the second appears fixed for the allele associated with high maturation age (Asn323Lys), and GWAS clearly showed a lack of association in the region containing vgll3. This suggests Met54Thr is not the functional mutation, because if it exerted a phenotypic consequence it should generate an association signal which wasn’t present. Discounting Met54Thr as causal doesn’t mean Asn323Lys must therefore be responsible for controlling the phenotypic variation, however it adds to the weight of evidence in its favour. A more practical implication is there appears to be no benefit in using vgll3 DNA diagnostics to promote delayed maturation in the Tasmanian breeding program.A number of earlier studies have sought to identify genes and chromosomal regions controlling salmonid maturation , 11, 12.European . Earliervgll3 plays a major role as measured in the Tasmanian population. These results increase our understanding of the genetic basis of maturation and direct future strategies to delay maturation in this important aquaculture species.GWAS revealed a highly polygenetic nature for both maturation traits, with few common SNP suggesting they are likely controlled by largely distinct genetic mechanisms. Only two variants were significantly associated with both traits, and each display sex specific effects restricted to male fish. Neither GWAS suggest Additional file 1:p-values is compared to the observed values derived from linear regression (A) and the MLMA-LOCO approach for MMAT (B and C). (TIF 120 kb)Q-Q plots compared between GWAS methods. The expected distribution of Additional file 2:RNA-Seq data used for analysis of tissue specific expression. (DOCX 13 kb)Additional file 3:The SALTAS breeding program design. Animals are tagged and sampled for DNA testing at around 10 months of age, before the majority of animals are smolted for transfer to sea cages. The two maturation traits were collected 22 months after spawning. Freshwater progeny are maintained as candidates for the subsequent cycles of the breeding program. (TIF 145 kb)Additional file 4:All significant SNP identified for freshwater maturation, using both male and female fish. The table shows physically co-located genes and their distance from the associated loci (in bp). Gene identifiers, symbol and names are provided. (XLSX 10 kb)Additional file 5:All significant SNP identified for marine maturation, using both male and female fish. The table shows physically co-located genes and their distance from the associated loci (in bp). Gene identifiers, symbol and names are provided. (XLSX 13 kb)Additional file 6:picalm gene and their association to maturation. The table contains associations to both FMAT and MMAT. (DOCX 13 kb)Imputed chromosome Ssa11 SNP surrounding the Additional file 7:All significant SNP identified for freshwater maturation, using only male fish. The table shows physically co-located genes and their distance from the associated loci (in bp). Gene identifiers, symbol and names are provided. (XLSX 10 kb)Additional file 8:All significant SNP identified for freshwater maturation, using only female fish. The table shows physically co-located genes and their distance from the associated loci (in bp). Gene identifiers, symbol and names are provided. (XLSX 10 kb)Additional file 9:Sex specific behaviour at two SNP for both FMAT and MMAT. The table shows the strength of association, effect size and proportion of genetic variance explained in analysis using all animals, males or females alone. (DOCX 18 kb)Additional file 10:Genotype classes by maturation status for Ssa10 SNP AX-87354755. The distribution of genotype classes are shown separately within males and females in both matured and non-matured animals. (TIF 159 kb)Additional file 11:Genotype classes by maturation status for Ssa11 SNP AX-96411005. The distribution of genotype classes are shown separately within males and females in both matured and non-matured animals. (TIF 162 kb)Additional file 12:All significant SNP identified for marine maturation, using only male fish. The table shows physically co-located genes and their distance from the associated loci (in bp). Gene identifiers, symbol and names are provided. (XLSX 11 kb)Additional file 13:All significant SNP identified for marine maturation, using only female fish. The table shows physically co-located genes and their distance from the associated loci (in bp). Gene identifiers, symbol and names are provided. (XLSX 9 kb)Additional file 14:VGLL3 . No association peak was evident for either trait. Minor allele frequency (MAF) for SNP was plotted to search for evidence of a selection sweep for FMAT (C) and MMAT (D). No evidence was seen for decreased allele frequency in the region surrounding the gene. Together, this suggests the gene has no effect on maturation as measured in the SALTAS population. (TIF 226 kb)SNP association for maturation traits on chromosome 25. GWAS for FMAT (A) and MMAT (B) are shown spanning the region containing