Oreochromis niloticus). SNPs were identified from whole genome resequencing of 32 individuals from the commercial population of the Genomar strain, and were selected for the SNP-array based on polymorphic information content and physical distribution across the genome using the Orenil1.1 genome assembly as reference sequence. SNP-performance was evaluated by genotyping 4991 individuals, including 689 offspring belonging to 41 full-sib families, which revealed high-quality genotype data for 43,588 SNPs. A preliminary genetic linkage map was constructed using Lepmap2 which in turn was integrated with information from the O_niloticus_UMD1 genome assembly to produce an integrated physical and genetic linkage map comprising 40,186 SNPs distributed across 22 linkage groups (LGs). Around one-third of the LGs showed a different recombination rate between sexes, with the female being greater than the male map by a factor of 1.2 , with most LGs displaying a sigmoid recombination profile. Finally, the sex-determining locus was mapped to position 40.53 cM on LG23, in the vicinity of the anti-Müllerian hormone (amh) gene. These new resources has the potential to greatly influence and improve the genetic gain when applying genomic selection and surpass the difficulties of efficient selection for invasively measured traits in Nile tilapia.Despite being the second most important aquaculture species in the world accounting for 7.4% of global production in 2015, tilapia aquaculture has lacked genomic tools like SNP-arrays and high-density linkage maps to improve selection accuracy and accelerate genetic progress. In this paper, we describe the development of a genotyping array containing more than 58,000 SNPs for Nile tilapia ( Oreochromis niloticus) is an important fresh-water aquaculture species farmed in more than 100 countries including many developing countries in which the species is an essential source of dietary protein (®) strain which has undergone more than 25 generations of selection.Nile tilapia . Compared to livestock species, aquaculture has been slower to adopt genome-based selection tools largely due to a lack of genomic resources such as reference genomes, SNP arrays, and linkage maps. But in species like rainbow trout, salmon, and common carp where genomic selection is being practiced, priority of utilizing genomic information is on selection for disease and parasitic resistance. For example, resistance against Bacterial Coldwater Disease (BCWD) ,b, 2017bsalmonis  in rainbsalmonis  and resisalmonis  in Atlano salmon ; and juvo salmon . Similaro salmon , resistao salmon , sexual o salmon  and someo salmon .O. niloticus , which is the continuation of the widespread GIFT-strain.The first genome assembly for  of 2012 ) was bas of 2012 . Four li of 2012 , microsa of 2012 . The RAD of 2012 . In comp of 2012 . In this® strain of Nile tilapia used in this study originates from the original GIFT population . The graphical summary of the methodology is given in Figure 1.The GSTpulation . This stOreochromis niloticus: GCF_000188235.2_Orenil1.1_genomic), and reads were aligned to it using BWA-MEM algorithm in BWA 0.7.12  using Qiagen DNeasy columns . DNA quality was assessed by agarose gel electrophoresis and quantified using a Qubit fluorometer . After normalization, sequencing libraries were prepared and barcoded using TruSeq sample preparation kit and sequenced (2 × 125) across 10 lanes on an Illumina HiSeq 2500  by a commercial provider. At the time this work was carried out, Orenil1.1 Tilapia represented the highest quality reference genome available (NCBI Assembly A 0.7.12  with defA 0.7.12  with parP < 0.05) were removed.The initial set of putative SNPs was divided into three groups including SNPs located on scaffolds assigned to linkage groups (LGs) of the assembly, SNPs on unassigned scaffolds, and SNPs detected within the mitochondrial genome. SAMtools v1.2/bcftools  was thenn = 56,050) were submitted to in silico validation for Affymetrix Bioinformatic Service. The Affymetrix in silico probe set design and evaluation pipeline predicts the performance of SNPs and calculates a conversion probability value  using various criteria including: binding energy, GC content, and the expected degree of non-specific hybridization to multiple genomic regions. Based on the p-convert values, they classify the SNPs into different categories: recommended, neutral, not-recommended, etc. 46,877 SNPs under the categories recommended and/or neutral from probe scoring recommendations were retained and 24,349 extra SNPs were chosen from the regions were the SNPs were discarded. A total of 71,226 SNPs  were sent back to Affymetrix for in silico SNP validation. Finally, the best 58,466 SNPs were chosen to tile on the array based on their probe scoring recommendation . Upon its release, SNP positions were redefined based on the O_niloticus_UMD1 assembly  in Norway.Genomic DNA was isolated from ethanol-preserved fin clips collected from 1882 Nile tilapia samples using Qiagen 96 DNeasy Blood & Tissue Kits according to manufacturer’s instructions  for map construction. These samples were from different generations of GSTThe unfiltered dataset contained 58,466 SNPs, which were analyzed using the Best Practices Workflow with default settings  in the Axiom Analysis Suite software. Ten samples were excluded from analyzed dataset because of the low call rate. Then, the SNPs classified as PolyHighResolution or NoMinorHom [most informative categories from Best Practices Workflow in Axiom Analysis Suite software ] were sen = 1124) comprised individuals collected following the branching of the 20th generation, and were factorially crossed against each other after two generations. The experimental design for Group 1 is described in n = 748) were obtained from the 24th and 25th generations of GST® .The 1872 genotyped individuals could be divided into two groups based on the generations of the breeding population. Group 1  had SNP call rate >0.97, so this criteria was not used for filtration. Phenotypic sex was known for a subset of families (221 offspring + 33 parents) and was coded as 12 for males and 11 for females and included in the genotype file  before running Lepmap2  were further filtered for linkage map construction. All the retained SNPs ( Lepmap2  for link Lepmap2 . EventuaSeparateChromosomes (a module in Lepmap2) was run testing lodLimits from 1 to 50 and a sizeLimit = 100; a lodLimit of 10 resulted in 22 LGs, also with the lowest number of markers not assigned to any LG. JoinSingles was run to assign single markers to LG groups and tested with lodLimits from 1 to 15 and lodDifference = 2; a lodLimit of 4 was selected as this joined the highest number of single markers. OrderMarkers was used to order the markers within each LG. Each LG was ordered separately and replicated 5 times with commands: numThreads = 10, polishWindow = 30, filterWindow = 10, useKosambi = 1, minError = 0.15, and the order with highest likelihood was selected as the best order. For sex averaged map OrderMarkers was run similarly by adding sexAverage = 1.Sequence containing each SNP was used to find the physical position of the SNPs in the O_niloticus_UMD_NMBU assembly. Physical position information was used to adjust the order of the SNPs within respective LGs and Lepmap2 was rerun to produce the final linkage map.p-values, and clustering. SNPs could be divided into six different types on the basis of formation of clusters (i) “PolyHighResolution” – formation of three clusters with good resolution; (ii) “NoMinorHom” – formation of two clusters with no samples of one homozygous genotype; (iii) “MonoHighResolution” – a single cluster of a homozygous genotype; (iv) “OTV,” off-target variants – three good clusters, with a single additional off-target cluster caused by variants in the SNP flanking region; (v) “CallRateBelowThreshold” – the SNP call rate was below the threshold (0.970), but other cluster properties were above the threshold; and (vi) “Other” – the SNPs were not grouped into any of the previous categories.To get a more comprehensive overview about the array and SNP performance, 3119 additional Nile tilapia samples were genotyped using Onil50 array. The raw dataset of the 1872 samples which were used for linkage mapping was combined with the dataset of the 3119 samples and were analyzed together using the Best Practices Workflow with default parameters in Axiom Analysis Suite software . Four quSnpEffv4.3i  was used9 reads representing an average of 17.7× coverage per individual . After alignment, on average 98% of reads were mapped to the Orenil1.1 assembly yielding 12.78 million variants of which 10.5 million were putative SNPs. Rest 2.2 million were insertions, deletions, multi-nucleotide polymorphisms, etc. After performing multiple steps of filtering described in the section “Materials and Methods,” a subset of 2.76 million SNPs was retained and a final set of 58,466 SNPs were selected for assay design and printed on the Onil50 array.The sequencing of 32 Nile tilapia generated 4.22 × 10Table 1). Remapping revealed an increase in the number of SNPs mapping to LGs and a corresponding decrease in the number of SNPs on unmapped scaffolds. The average variant density per LG on the Orenil1.1 assembly is 12.5 ± 0.35 kb (12.1–13.7 kb). However, since the O_niloticus_UMD1 assembly includes an additional 87 Mb assigned to LGs the average variant density increased to 15.5 ± 4.06 kb (13.3–32.3 kb) (Table 1). Additional information about inter-SNP distance and standard deviation can be found in Supplementary Table 4. Physical size of LG03 increased by 2.4 times in the new assembly, thereby increasing the number of SNPs assigned to this LG by 2.3 times.Around 99.8% of the SNPs from the array were successfully re-mapped to the new O_niloticus_UMD1 assembly  were left after being subjected to sample call rate. Based on the cluster profile, over 74% of the 58,466 SNPs were classified as PolyHighResolution. More detailed information about the sample and SNP statistics are shown in Table 2. The majority of the SNPs are intronic (36.92%) or intergenic (20.80%) variants, while about 15% of are non-synonymous mutations. Since the SNP selection process specifically targeted variants with a potential functional effect, these variants are expected to have direct effect on traits of interest.Prediction of functional effects of the 58,340 remapped SNPs from the Onil50 array resulted, in most cases, in multiple annotations per variant. The effects with the highest putative impact are included for summary in n = 40,467) were ordered within the 22 LGs corresponding to the karyotype of Nile tilapia . Since, Build 1 linkage map is an intermediate step for the extension of the O_niloticus_UMD1 to the O_niloticus_UMD_NMBU genome assembly . This high correlation with the physical map demonstrates that the genetic map is of high quality and is highly accurate.A total of 40,549 SNPs were retained following quality filtering, and 99.78% of these . The average genetic distance across the LGs was 66.8 cM. The number of markers per LG varied from 1349 to 3391, with an average of 1827 markers per LG (Table 3). As a consequence of the SNP selection, which sought to position a SNP every 12 kb, the number of markers was mostly proportional to the size of the LG . A notable exception is LG03 where the inclusion of previously unassigned scaffolds has trippled the physical size without a corresponding tripling of SNP numbers. The SNP density (SNPs/Mb) varied across the genome from 19.68 to 56.83 .A total of 40,186 SNPs mapped to 22 LGs in Build 2 linkage map. The consensus (sex-averaged) map adds up to 1469.69 cM, with individual LG lengths ranging from 56.04 cM (LG19) to 96.68 cM (LG07) (Table 3). Around one-third of the LGs showed a different recombination rate between sexes , with male and female map lengths differing by a factor of 1.2 . Generally, female maps were found to be larger in all LGs with the exception of LG02, LG06, and LG22. Sigmoidal pattern of recombination, with no recombination at both ends of the LGs, was seen in almost all LGs .In this study, paternal and maternal informative markers were used to construct specific male and female maps (Supplementary Table 3). As a consequence, regions in the genome have poor SNP coverage. By stringently selecting SNPs with an even physical distribution in the genome the linkage map presented includes 10 times more SNPs and fewer gaps, compared to the most recent map  are seen in the initial and/or end regions of most of the LGs, generally up to 5 Mb and sometimes up to 10 Mb , indicating the presence of subtelo-acrocentric chromosomes. Because of these recombination deserts, most of the LGs, irrespective of the sexes, showed sigmoidal pattern, which is unusual when compared to other fish species. In channel catfish  in LG23 has been identified as the male sex determinant in Nile tilapia (amh gene have been mapped to around 35.4 Mb region of Nile tilapia genome (discussed below in section “Sex Locus Mapped in the Vicinity of amh Gene”), which is the same region where the unique recombination pattern is seen, suggesting limited recombination around the sex-determining genes in O. niloticus. Further, LG23 was formed by the fusion of two LGs during the evolution of cichlids . Around 9% of the SNPs were found to depart from HWE (p < 0.01), but it has to be noted that the population genotyped for the validation is the commercial strain that has undergone up to 25 generations of selection. Hence, these departures might be important as they could represent regions under selection, domestication and the outcome of assortative mating  and Mozambique tilapia (O. mossambicus) have the XY system of sex determination, where the males are heterogametic (XY) and females are homogametic (XX), so the sperm determines the sex of the offspring  as reported previously in several studies (amh) gene, previously characterized as sex determining gene in Nile tilapia  and maternal environmental effects (due to mouth brooding), making it difficult to detangle the various variance components accurately , which uWe present the first SNP-array, the Onil50-array, containing ca 58,000 SNPs for Nile tilapia, which was validated in close to 5000 individuals. Further, we constructed a high density integrated genetic and physical linkage map, with LGs showing sex-differentiated sigmoidal recombination patterns. These new resources has the potential to greatly influence and improve the genetic gain when applying genomic selection and surpass the difficulties of efficient selection for invasively measured traits in Nile tilapia.https://figshare.com/s/8427b97cf6e623173232.The assemblies used in this study can be found in NCBI using the following accessions: Orenil1.1 = GCF_000188235.2, O_niloticus_UMD1 = MKQE00000000, and O_niloticus_UMD_NMBU = MKQE02000000. The whole genome sequence data used for SNP detection has been deposited in the European Nucleotide Archive and can be found in EMBL-EBI_website using accession PRJEB28330. Linkage map generated from this study can be found in the Figshare: Sampling of DNA was done in accordance with the commercial practice and norms by Genomar Genetics AS.HG, AA, and MK conceived and designed the study. AA coordinated biological sampling. MK and MÁ were responsible for array design and MÁ performed lab work and initial analysis of results. RJ constructed the linkage map, while SL integrated the genetic and physical maps. RJ and MÁ prepared the draft manuscript which was reviewed and edited by HG, MK, AA, and SL. All authors read and approved the manuscript.Genomar Genetics AS employs one of the authors, AA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Each platform revealed equivalent numbers of robust bi-allelic SNPs . A small overlap of 464 SNPs was common to both platforms, indicating that the methodologies we used selectively access informative polymorphism in different portions of the barley genome. Approximately half of the GBS dataset was comprised of SNPs with minor allele frequencies (MAFs) below 1%, illustrating the power of GBS to detect rare alleles in diverse germplasm collections. While desired for certain applications, the highly robust calling of alleles at the same SNPs across multiple populations is an advantage of the SNP-array, allowing direct comparisons of data from related or unrelated studies. Overall MAFs and diversity statistics (π) were higher for the SNP-array data, potentially reflecting the conscious removal of markers with a low MAF in the ascertainment population. A comparison of similarity matrices revealed a positive correlation between both approaches, supporting the validity of using either for entire GenBank characterization. To explore the potential of each dataset for focused genetic analyses we explored the outcomes of their use in genome-wide association scans for row type, growth habit and non-adhering hull, and discriminant analysis of principal components for the drivers of sub-population differentiation. Interpretation of the results from both types of analysis yielded broadly similar conclusions indicating that choice of platform used for such analyses should be determined by the research question being asked, group preferences and their capabilities to extract and interpret the different types of output data easily and quickly. Access to the requisite infrastructure for running, processing, analyzing, querying, storing, and displaying either datatype is an additional consideration. Our investigations reveal that for barley the cost per genotyping assay is less for SNP-arrays than GBS, which translates to a cost per informative datapoint being significantly lower for the SNP-array.We compared the performance of two commonly used genotyping platforms, genotyping-by-sequencing (GBS) and single nucleotide polymorphism-arrays (SNP), to investigate the extent and pattern of genetic variation within a collection of 1,000 diverse barley genotypes selected from the German Federal  The detection of genome-wide sequence-defined single nucleotide polymorphism-arrays (SNPs) is key to addressing a wide range of biological and ecological questions, from describing and partitioning overall levels of biological diversity to cloning genes conferring phenotypic traits, and for practical exploitation in animal and crop breeding. Alternative SNP alleles can be detected using a range of technologies that can be broadly classified into two types, semi-open and closed, based on the nature of the data generated. Closed systems can be represented by hybridization-based commercial SNP-arrays  where thHordeum vulgare ssp. vulgare and ssp. spontaneum), a large genome (∼5 Gbp) inbreeding diploid (2n = 2x = 14) species that was one of the worlds’ first domesticated crop plants  and error checking is consistent and straightforward. However, SNP-arrays suffer from ascertainment issues  and these are dominated by the use of semi-open systems, mainly GBS. We wanted to understand and clarify the advantages and caveats associated with each platform and the resulting data types and ask whether the overall values afforded by one approach outweighed those of the other. We were motivated to do this by the recent genotypic characterization of the entire 22,000 genotypes in the IPK Gatersleben barley GenBank collection  locus in a bi-parental recombinant inbred line (RIL) population  with a requirement for 260/280 and 260/230 ratios to be > 1.8. DNA was then quantified using Picogreen  and 300 ng DNA per sample lyophilized and sent to Geneseek  for Illumina HTS processing and HiScan array imaging . SNP alleles were called using GenomeStudio Genotyping Module v2.0.2  and the resulting data investigated and analyzed as previously reported for 9K Illumina data . The phy3  was chosen, and 400 axes  retained for the discriminant analysis. The latter was run using the function dapc with all of the retained eigenvalues. Cluster assignation was used to create subpopulations. Fst estimates between subpopulations were calculated according to 10Fst values of individual markers plotted linearly along each chromosome according to physical position.Discriminant analysis of principal component (DAPC) was conducted as described in Among the 43,461 scorable assays on the 50K SNP-array in this experiment we extracted 42,300 (96%) robust and polymorphic SNPs after examination of genotype clusters in GenomeStudio. For comparison, the GBS genotype matrix was comprised of the 37,327 high quality polymorphic bi-allelic GBS SNPs described in Both datasets revealed a high number of markers in the distal regions of each chromosome decreasing markedly in the peri-centromeric/centromeric regions. As this could be biased due to the distribution of genes, we partitioned our analysis according to genic and intergenic markers. In accordance with the origin of the information for the 50K assay being from exome capture data we observed a large number of SNPs in genic regions, with 72%  associated with genes and present at higher density in the gene-rich distal regions of each chromosome . In contA striking difference between datasets was the high proportion of GBS markers associated with a low minor allele frequency (MAF) compared to the 50K dataset. The percentage of markers with a MAF < 1 or <5% was 1.2 and 7.6% for the 50K chip but reached 41.6 and 58.9% for GBS respectively . Across r = 0.62, p = 0.0001; 99 permutations, Mantel test).As the SNPs on the 50K chip were largely ascertained from the European domesticated gene pool (cultivars and landraces), we expected that its use across a wider spectrum of material may reveal biases associated with the origin of the data used in its design. We were therefore keen to investigate the degree of concordance between data generated by the ‘closed’ 50K platform with that from the semi-open GBS approach. To assess how each platform performs in diversity analyses we ran PCAs using the 39,733 (50K) and 37,327 (GBS) SNP marker datasets . We initOne obvious application of high throughput genetic characterization of germplasm collections is to explore the possibility of using the data for trait related gene identification using population genetic approaches. Here, we chose GWAS initially to investigate two of the traits analyzed by SIX ROWED SPIKE1 . As in HvVRS3 gene  gene, an ethylene response factor required to promote formation of the cementing layer. Using lemma adherence to the grain as a binary character we again performed a GWAS with each marker type. The most highly associated marker on the 50K SNP-array was some 44 kbp distal to NUD on chromosome 7H, compared to 1,831 kbp in the GBS marker set. However, the GBS marker set had more supporting markers and identified a second locus at the bottom of chromosome 6H. This locus was also above the threshold p-value in the 50K dataset, but below the adjusted FDR. This region along with two others on the top and bottom of 2H in both datasets and top of 5H in the 50K dataset may be worthy of further exploration as modifiers of the naked phenotype.Second, in both wild barley and the majority of domesticated types, the awn-bearing lemma is firmly bound to the grain through a lipid-based cementing layer between the caryopsis and lemma . Driven 10(p)∼8.0] in the 50K dataset some 29 kbp from the vernalization gene HvVRN1 (p) values of > 8 to < 20. Surprisingly none convincingly co-located with the HvVRN1 50K peak, with the closest of 8 markers distributed across 5H chromosome with −log10(p) > 8 some 50 Mbp from the VRNH1 locus. Based on this small dataset we conclude that both platforms, in general terms, perform equally well with any differences being trait-specific  using both marker types and a categorical classification of winter versus spring growth type. We observed a single well-supported  association on chromosome 5H  and may therefore have hidden genetic value. These alternatives are difficult to distinguish. To capture value from missing data, fast and accurate allele imputation programs such as FILLIN and FSFHap  have beeMissing data and bad calls are also observed on even the best SNP chips and can similarly be the result of technical or biological factors such as the presence of close sequence-related paralogs, sequence polymorphisms in the assay footprint outside of the queried SNP causing the assay to fail, borderline quality DNA or the presence of genuine PAVs. Runs of physically adjacent missing datapoints in an otherwise technically sound SNP chip assay or population-based quality filtering for SNaPs  can provWe used two approaches to explore the use of each marker dataset for identifying the location of genes underlying specific traits, GWAS and DAPC. Similar results were obtained for GWAS with both marker types, perhaps with the exception of growth habit using GBS data which generated several poorly supported but highly significant associations. We also explored DAPC because we had previously and successfully used this approach to investigate the discrimination between the genetically narrow spring and winter 2-row elite NW European genepools . The resThe use of SNP-arrays and GBS has already started to give way to (low coverage) whole genome shotgun sequencing in small genome models and crops . We attrBoth GBS and SNP-arrays efficiently sample the diversity present in the domesticated barley genepool but access different regions of the genome and reveal different characteristics. Choice of technology should be carefully considered according to desired applications and objectives, along with group preferences, available skills and infrastructure. Current commercial cost comparisons question the widely held view that GBS is considerably cheaper than SNP-arrays, in barley at least.RW, NS, JR, LR, and MMas conceived the study. BD, SM, PH, PS, and MMac designed and coordinated the experiments and conducted the data analysis. NS, MMas, and SM chose and provided the germplasm. MMac and PH performed the lab experiments. RW, BD, JR, NS, and MMas wrote the manuscript, while CH, DF, and PL critically reviewed it.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Puddled transplanted system of rice cultivation despite having several benefits, is a highly labor, water and energy intensive system. In the face of changing climatic conditions, a successful transition from puddled to dry direct seeded rice (DDSR) cultivation system looks must in future. Genome-wide association study was performed for traits including, roots and nutrient uptake (14 traits), plant-morphological (5 traits), lodging-resistance (4 traits) and yield and yield attributing traits (7 traits) with the aim to identify significant marker-trait associations (MTAs) for traits enhancing rice adaptability to dry direct-seeded rice (DDSR) system.− 4, 0.0330 to 1.25 × 10− 4, and 0.0534 to 4.60 × 10− 6 in 2015WS, 2016DS and combined analysis, respectively. The percent phenotypic variance (PV) explained by SNPs ranged from 9 to 92%. Among the identified significant MTAs, 15 MTAs associated with the traits including nodal root, root hair length, root length density, stem and culm diameter, plant height and grain yield were reported to be located in the proximity of earlier identified candidate gene. The significant positive correlation of grain-yield with seedling establishment traits, root morphological and nutrient-uptake related traits and grain yield attributing traits pointing towards combining target traits to increase rice yield and adaptability under DDSR. Seven promising progenies with better root morphology, nutrient-uptake and higher grain yield were identified that can further be used in genomics assisted breeding for DDSR varietal development.Study identified a total of 37 highly significant MTAs for 20 traits. The false discovery rate (FDR) ranged from 0.264 to 3.69 × 10Once validated, the identified MTAs and the SNPs linked with trait of interest could be of direct use in genomic assisted breeding (GAB) to improve grain yield and adaptability of rice under DDSR.The online version of this article (10.1186/s12864-019-5840-9) contains supplementary material, which is available to authorized users. Developing resource-water-labor-energy efficient, dry direct seeded adapted rice varieties under reducing water-labor availability in agriculture is necessary for the sustainability of world’s future food security . The con− 1, labor savings of 25 person-days ha− 1, energy savings of 1500 MJ ha− 1, a reduction of GHG emissions of 1500 kg CO2 equivalent ha− 1, yield increase of 0.5 t ha− 1 and increased net economic return of USD 50 ha− 1 with positive effect on soil heath in most of the rice-growing countries  and maximum root length (RL)  were recorded. The nodal roots were counted manually, and the maximum root length was measured using ruler measured in centimetre. The measurements on root hair length and density were recorded on six root samples following the procedure as described by Sandhu et al. .2SO4 (potassium sulphate) and concentrated H2SO4 (sulphuric acid) in presence of catalyst (fine powdered selenium). The NH3 (ammonia) produced was estimated by colorimetric method using Technicon autoanalyzer. The other nutrients phosphorus (P), Iron (Fe) and Zinc (Zn) were estimated using Nitric/Perchloric Acid digestion method using AIM 500 Digestion Block System.At booting stage, the Leaf Color Chart (LCC) developed by IRRI was used along with the chlorophyll meter (SPAD) (Minolta 502) to estimate and validate the relative accuracy of LCC in measuring greenness of fully expanded flag leaf nitrogen (N) concentration. The color of the flag leaf from the three randomly chosen plants was measured by placing the middle part of the middle lobe of the flag leaf in front of the color strip (LCC) for the comparison. The three phenotypically similar plants with approximately same value of LCC and SPAD were then uprooted for the total above-ground biomass measurement. The above ground fresh biomass was recorded immediately after uprooting, and then all the three plant samples were cut into small pieces and mixed properly, and a total of 200 g plant sample were taken for each progeny and then oven dried in 70 °C for 3 days and then weight for dry plant biomass and nutrient uptake measurements . In 2015− 2) were recorded. Stem and culm diameter were measured using vernier caliper. Stem strength at the breaking point was measured using the prostrate tester  of the internode. The dispacement in mm was measured as the stem strength at breaking point. The bending ability of internode was measured by bending the stem to point at which the stem break and the scale displacement (mm) due to bending ability was recorded.Randomly three plants per plot were chosen for the lodging resistance traits measurements. Traits related to lodging resistance including stem diameter , culm diameter , bending strength  and bending moment  was calculated according to Palamiswamy and Gomez [Five random plants were taken for recording of plant morphological, grain yield and yield-related traits. Days to 50% flowering (DTF) was recorded when ~ 50% of the plants in a plot showed panicle excertion. Plant height (PHT), panicle length (PL), flag leaf length (FLL) and flag leaf width (FLW) were measured using ruler in centimeter scale. The number of productive tillers per plant (NPT) were counted manually. The PHT was measured from the ground level to the tip of the highest panicle at the maturity stage. PL was measured from the node of the panicle to the tip of the panicle. The FLL and FLW was measured from the base to tip of flag leaf and at the middle part of the flag leaf, respectively. Flag leaf area . From the GBS data a total of 2,75,586 SNPs were called from the GBS genotyping. A stringent selection criterion to filter out the SNPs was used including missing percentage, MAF  and percent heterozygosity to select the panel of robust SNPs. SNP with call rate 80% and MAF  of > 5%  were filtered using Tassel 5. The 24,306 SNPs were used to estimate the genetic relationship, the building of neighbor Joining tree and were used for GWAS for emergence, root, grain yield and yield-related traits. To detect and correct for population structure, a PCA was carried out using 24,306 SNP markers.Samples comprised of the five parents, five checks and 300 progenies from the MAGIC population. The fresh leaf tissue samples from six plants per progeny were collected at 40 DAS. To increase the throughput and efficiency coupled with less chances of error, automated leaf sampling and high-throughput DNA extraction using the Brooks’ PlantTrak Hx rice leaf tissue sampler and LGC Genomics’ oKtopure systems was used. For genotyping by sequencing (GBS) a type II restriction endonuclease http://bbi.irri.org/products). The trial mean and trait mean for each season/years was computed using mixed model analysis considering replications, and block within replication as random effect and progenies as a fixed effect. The broad-sense heritability was computed using the equation, H = σ2G/(σ2G + σ2E/r).Analysis of variance (ANOVA) was computed using PBTools V 1.4.0  *100.indica and Aus parents. The population structure was estimated using STRUCTURE V.2.3.4. Using 24,306 SNPs, a series of models with K value ranging from 1 to 10 was run with a burn-in period to 50,000 and running length to 10,000 to give consistent results overruns. To verify the consistency and accuracy of the results, ten independent runs for each K was performed. The K value with maximum likelihood over the runs was considered as the most probable number of clusters [2 values were estimated using Tassel 5.0 and were averaged over the SNPs grouped based on stepwise increasing base-pair distance . The average LD over these base-pair distances was used to estimate the linkage disequilibrium (LD) decay in the MAGIC population. GAPIT (Genome Association and Prediction Integrated Tool) was used to conduct genome wide association studies on 2015WS, 2016DS and the combined data over seasons to identify the genomic regions associated with the traits of interest [The 300 progenies and five parents included in the present study include the progenies of clusters . The genclusters  and iterclusters , and an clusters . The pai.”The allelic effects of the significantly associated markers for root, nutrient uptake, and grain yield traits were determined by representing phenotypic data for the alleles as boxplots and the significant allelic variation for the associated traits was determined to perform Kruskal–Wallis test in “Rp-value benchmark, “Bonferroni Correction” the most stringent correction method was used. After the Bonferroni multiple test correction , the calculated threshold value was 4.1 × 10− 7. Only the marker-trait associations (MTA) that surpass the Bonferroni threshold and consistent over seasons for the recorded traits were reported. For the identified significant marker-trait associations, candidate gene prediction was performed within 120-kb region around i.e. 120 kb upstream and 120 kb downstream of the SNPs with significant association signals. Earlier reported QTL that co-localized with the present study SNPs/QTLs, literature searches were performed. Regions covered by SNPs were searched for candidate genes using the MSU v.7 rice genome browser (http://rice.plantbiology.msu.edu/cgi-bin/gbrowse/rice/#search) and QTARO database (http://qtaro.abr.affrc.go.jp).To correct the false positive in genome wide association analysis even keeping the stringent − 1 in 2015WS and 4472 kg ha− 1 in 2016DS. The parents Kali Aus and IR 87707–446-B-B-B showed better performance in term of grain yield and number of nodal roots in both wet and dry seasons, whereas NSICRc 222 and Vandana showed better grain yield performance under dry season , root morphological and nutrient uptake related traits  and grain yield attributing traits  in 2015WS  was significantly and positively correlated with nodal root, root length, root hair density, LCC, SPAD, number of productive tillers per plant, number of filled grains/panicle and grain yield Fig. d. The siaus and indica subpopulations of rice are adapted to different ecologies and geographies, and also harbor different traits of interest and alleles. The aus subpopulation is of interest as it has important source of alleles for drought tolerance and root traits while indica subpopulation harbors huge genetic variations. The kinship heatmap showed that most of the kinship value concentrated at *0.0 level indicating a very weak relatedness in the MAGIC population derived GWAS panel  was calculated between all 24,306 markers. The average value of r2 as a function of the inter-marker distance was used for LD decay calculation. The average r2 for close markers of 5 kbs starts at 0.4, and the LD (r2) dropped to half of its maximum value (0.2) at around 500 kb  of > 5%, a total of 24,306 polymorphic SNPs were retained for further GWAS. The information’s on p-values, FDR, SNP, SNP position and major and minor alleles are shown in Table To take advantage of identified marker-trait associations directly in MAS (marker-assisted selection), GWAS was performed directly on the MAGIC population. The high phenotypic variability existing in the MAGIC population coupled with high marker density across all chromosomes provided a strong base to the whole genome association mapping. About 25 and 12.5% of the genotypes in F− 4, 0.0330 to 1.25 × 10− 4, 0.0534 to 4.60 × 10− 6 was observed in 2015WS, 2016DS and combined seasons analysis respectively (Table http://qtaro.abr.affrc.go.jp) . A total of 12 SNPs reported being associated with root morphological and nutrient uptake traits whereas 5 SNPs reported to be associated with grain yield and yield contributing traits. Five SNPs observed to be associated with morphological traits . The plant height related SNP S1_38481437 on chromosome 1 colocalized with rice semi-dwarfing gene  gene which is known to be involved in secondary cell wall synthesis [lax [moc2 [The main objective of the present study was to identify the significant MTAs for the traits related to improving grain yield and adaptability of progenies under DDSR. A total of 37 significant trait-SNP associations were identified for 20 traits. The percent phenotypic variance (PV) explained by the SNP ranged from 9 to 92% for all the traits measured in the present study. The FDR ranged from 0.264 to 3.69 × 10ne  region. This region was reported to play an important role in improving grain yield in an Oryza sativa × Oryza rufipogon BC2F2 population under upland conditions [The SNP S2_31078747 , S5_14987295  and S5_15470880  observed to be significantly associated with more than one trait. For SNPs, S5_15470847 and S5_15470880 for root hair length and root hair density the colocalization was found with genes OsIPT3  and OsEX[OsEXPA3  which weu et al. . Three SA 0.483 Mb region on chromosome 5 found to be associated with N, P uptake, FLA, RHL and RHD and 0.489 Mb region on chromosome 11 with DTF, PHT, and grain yield. A 0.655 Mb region on chromosome 11 reported being associated with seedling establishment traits (DTFull and VVG). For most of the traits like DTFirst, DTFull, CD, GY and VVG, the identified significant multiple SNPs which were present in the full LD region on the same chromosome were having almost the same level of significance across seasons. Some SNPs associated with more than one trait such as S5_14987295 , and S5_15470880  were having the same level of significance in 2015WS, 2016DS and combined seasons. The colocation of SNPs related to nutrient uptake and root traits further confirmed by the significant positive correlation of root traits with nutrient uptake. The identification of progenies carrying multiple superior haplotype associated with various colocated traits would significantly aid to the development of rice varieties adapted to DDSR. Introgression of superior haplotypes that are responsible for improving grain yield and water-nutrient uptake under DDSR using haplotype-based breeding may open avenues for designing next generation DDSR adapted rice varieties.The identified significant marker-trait associations on the same chromosome within the 500 kb region, where the LD decays half of its maximum value were assigned as putative QTLs. The associated SNPs with very small interval ≤14 kb) can be considered as the same locus. The putative QTLs for RHL spanning 0.033 kb region on chromosome 5, for DTFull spanning 0.036 kb and for GY spanning 0.005 kb on chromosome 11, for SPAD spanning 177 kb, and for Fe uptake spanning 0.012 kb on chromosome 2, for VVG spanning 14 kb region on chromosome 1, and for DTFirst spanning 0.045 kb  gene, NARROW LEAF1 which regulates leaf and adventitious root development in rice [p-values of significant allelic variation for the number of nodal roots at 29DAS were 5.71 × 10− 5, 7.13 × 10− 6 and 6.267 × 10− 6 in 2015WS, 2016DS and combined season analysis.For the number of nodal roots at 29 DAS on chromosome 4: S4_31728342, ~ 115 kb upstream the positional match was identified as  in rice . QTLs fo in rice , 50 in u in rice  in the d− 5 to 8.287 × 10− 8 across seasons. The p-values representing allelic variation for nutrient uptake were 0.01184 (for S6_30887442), 0.04321 (for S6_30887442) and 0.02327 (for S5_14987295) for P uptake, 0.02449 (S5_14987295) for N uptake and 0.001039 (S7_26042045) for Zn uptake. Two SNPs S2_29221003 and S2_29221015 on chromosome 2 showed significant associations with iron (Fe) uptake under direct seeded cultivation conditions. Interestingly, the genes OsYSL15; a root tissue expressed iron-regulated gene for iron uptake at seedling development stage [OsCKI1; a rice casein kinase I gene associated with root development) [OsGPX3; mitochondrial glutathione peroxidase GPX3 essential in root-shoot development) [MAIF1; F-box protein gene promotes root growth in rice [OsIPT3 which was involved in cytokinin biosynthesis [qP5.2, qPU5.2 [qZn7.1; the QTL detected for grain zinc concentration in a rice MAGIC plus population [indica MAGIC population [The p-values of significant variations for all three significant SNPs associated with grain yield QTL ranged from 3.125 × 10nt stage , OsCKI1;lopment) , OsGPX3;lopment) , and MAI in rice  reportedynthesis  and QTL , qPU5.2 . The zinpulation  and withpulation . The colpulation  indicateThe allelic effects of the significantly associated markers for root, nutrient uptake and grain yield traits are shown in Fig. http://rice.plantbiology.msu.edu/cgi-bin/gbrowse/rice/#search) and the existing literature were searched. The identified candidate genes were in the vicinity of the reported SNPs/putative QTLs in the present study which warrant further investigation and validation. The detailed description of the candidate genes is presented in Additional file To identify the candidate genes underlying the significant marker-trait association/putative QTLs MSU v.7 rice genome browser . (XLSX 73 kb)Additional file 2:Figure S1. Average rainfall data (mm) collected during (a) 2015WS (b) 2016DS. Figure S2. The details on the seedling establishment traits, root and nutrient uptake traits, lodging resistance traits, Plant morphological, grain yield and yield attributing traits meausred under the present study. Figure S3. Prostrate tester quantifying stem strength at breaking point. Figure S4. Principal component analysis plot of phenotypic traits in (a) 2015WS (b) 2016DS and (c) combined season. (DOCX 1984 kb)


LF). Regions of differentiation between breeds were investigated using STF and the difference in allele frequencies between the two parental breeds (릌Δ) at each single-nucleotide polymorphism (SNP) position. A maximum likelihood approach was proposed to estimate allele frequencies in the parental populations. The average length of runs of homozygosity (ROH) across the genome was 3.91, 2.3, and 0.7 Mb for segments with at least 25, 15, and 5 SNPs, respectively. Average age to coalesce was 46, 414, and 388 years for segments with at least 25, 15, and 5 SNPs, respectively. The probability of autozygosity was not uniform along the crossbred genome, being higher at the center for most chromosomes. The correlation between autozygosity and distance to the closest telomere was positive and significant in most chromosomes, which could be attributed to the higher recombination rate near telomeres. We also report a relatively high negative correlation between probability of recombination (from a published map) and probability of autozygosity. It supports that structural characteristics of the chromosomes related to recombination rate determine autozygosity at each chromosomal position of the pig genome. The average is Δ across the genome was 0.17 (SD = 0.16). After testing for differences in allele frequencies between the parental breeds, there were 4,184 SNPs with a likelihood ratio test, LRT ≥ 32.02. The average STF across the genome was 0.038 (SD = 0.059). There were 2,949 SNPs with STF > 0.125. The correlation between estimates of LF and estimates of STF across the genome was -0.10 (SE = 0.006). Analysis of the gene content of the genomic regions with the 2000 SNPs with highest LRT for LF and high STF showed overrepresentation of genes with a regulatory function. Genes with biological functions associated with production, such as tissue development, anatomical structure, and animal organ development, were also overrepresented in regions with a high STF.Genomic information from crossbreds is routinely generated for genomic evaluations. The objective of this study is to investigate autozygosity and genetic differentiation in Landrace by Large-White breeds by using the genotypic information of SNP arrays in 1,173 crossbreds. A maximum likelihood approach was developed to estimate the probability of autozygosity ( STF of Wright), 2) changes in allele frequencies by  selection in one or both breeds, and 3) a combination of these two processes. Nevertheless, alleles are not necessarily segregating at a different frequency in the two breeds at all loci. For example, directional or stabilizing selection may be acting with the same intensity at the same allele  in the two breeds over time.Domestication of the pig took place some 9,000 years ago and occurred multiple times . After dThe presence of autozygosity in crossbreds can be revealed by runs of homozygosity (ROH), which are defined as continuous and uninterrupted stretches of homozygous DNA sequences in diploid state . VaryingSTF across the genome in the two breeds.The objective of this study is to investigate autozygosity and genetic differentiation in two parental breeds (Large White and Landrace) using the genotypic information from their crossbred progeny. The specific objectives were: 1) determining levels and regions of autozygosity and its relationship with recombination rate in crossbred progeny, 2) determining differences in allele frequency across the genome between the two parental breeds, and 3) determining genetic differentiation in Large White and Landrace by investigating the genomic distribution of Wright’s A method to estimate allele frequencies in the parental lines based on crossbred genotypes was developed because the genotypic information of the purebred parents was not available.1 Landrace x Large White crossbred barrows from two companies with 605 (company A) and 568 (company B) pigs, respectively, were used. Data were from six Porcine Reproductive and Respiratory Syndrome (PRRS) virus challenge trials with about 200 piglets each: PHGC17 (from 15 boars and 49 sows), PHGC18 (from 12 boars and 51 sows), PHGC21 (from 10 boars and 81 sows), PHGC23 (from 11 boars and 52 sows), PHGC24 (from 10 boars and 46 sows), and PHGC25 (from 9 boars and 72 sows). It was assumed that the parental populations from these companies are representative of the Landrace and Large White breeds. DNA was isolated from blood using a standard phenol/chloroform protocol and genotyped with either the Illumina Porcine SNP80 BeadChip or the SNP50 BeadChip and the Infinium HD Assay Ultra protocol (Illumina Inc.). All common SNPs of the two arrays were mapped to the Sscrofa11.1 assembly. Genotypes were called with GenomeStudio software (Illumina). Quality control of genotypes was performed using the following criteria: average call rate for a SNP >0.70; individuals with SNPs that had a genotype call rate <0.70 were assumed not genotyped for those SNPs; SNPs located on sex chromosomes and those not mapped in Sscrofa11.1 were also removed. After editing, the data consisted of genotypes for 32,659 SNPs on 1,173 crossbred pigs. There were 764 SNPs with frequency less than 0.05. In general, SNPs were evenly distributed across the swine genome. A program in Fortran 90 was written to identify ROH in the genome for each individual. The program searched contiguous and homozygous SNPs within each of the 18 autosomal chromosomes. For computing age to coalesce a minimum number of 5, 15, or 25 SNPs were required for declaring a stretch of DNA as a ROH. A fragment was not considered a ROH if there was one or more heterozygous SNPs in the stretch of DNA. The number of generations back to the common ancestor, g, of ROH fragments was estimated by solving for g the equation L = 100/2g, where L is the ROH length in cM , which was used by Kim et al. to compare cattle breeds. An alternative proposed in the present article is to compare LF with its expected distribution under the hypothesis that ROH status ih =1 or being autozygous is equally likely for all SNP positions within a chromosome. The likelihood of the number of animals with ROH status ih =1 at a given position under the null hypothesis is assumed to follow a binomial density with likelihood:where m is the number of animals with status ih =1 at a given SNP position. The maximum likelihood of m/N and the sampling variance of i-th individual is equal to the chromosomal inbreeding coefficient for that individual, nsnp being the number of SNPs genotyped on that chromosome. Under the alternative hypothesis, where A LOD score was constructed as:2 with one degree of freedom. In our analysis, a test was declared significant after using a Bonferroni correction to account for multiple testing with a genome-wide significance level of 0.001 (the threshold of the χ2 was 32.02). In addition, and to estimate sample size needed to detect departure from the null hypothesis, statistical power was computed with the package pwr in R (https://www.r-project.org/).Under the null hypothesis, LRT is distributed as a χAppendix). The method is based on putative departures from Hardy-Weinberg equilibrium conditions in the crossbreds. A computer simulation was carried out to validate the proposed method to estimate allele frequencies. Genotypes from 1,173 crossbred animals were generated by draws of the uniform distribution assigning alleles A or B with a probability corresponding to the simulated allele frequencies in each parental breed. For each replicate, estimates of allele frequency in the two populations and corresponding hypothesis testing by a Likelihood Ratio Test (LRT) were performed (see Appendix). In addition, the standard deviation (SD) of the estimates of the allele frequencies was obtained by both 1) using the standard deviation of the estimates of allele frequencies across the 10,000 simulated replicates and 2) taking the square of the estimates of variance accordingly to the formulae in the Appendix. Empirical power was computed using a significance level of 0.01, which corresponds to the 100th larger LRT result, obtained in the 10,000 replicates when simulating the null hypothesis . In our simulation experiment, this value was 5.66. Then, power was computed as the proportion of replicates that were higher than 5.66 for each of the simulation experiments: (1) Af = 0.1, Bf = 0.9; (2) Af = 0.3, Bf = 0.7; (3) Af = 0.35, Bf = 0.65; and (4) Af = 0.4, Bf = 0.6. The grid search method was used to solve the likelihood equations. In this method, the likelihood is evaluated at short intervals of the two allele frequencies throughout the entire range of possible values (0 to 1). As shown in the Appendix, there are two solutions to likelihood equations, which makes it difficult to assign each estimate of allele frequency to each parental population. To overcome this problem, the grid search method was performed starting the A allele frequency as in 0 and ending in 1. Then, grid search of the B allele frequency from 0 to 1 was performed within each of the A allele frequencies. The estimate of allele frequencies was the first maximum found by the grid search method. It allowed assigning allele frequencies to the two parental breeds when the difference in allele frequency is large, since the grid search method visits the same allele frequencies (A and B) in the same order in each replicate. For the null hypothesis, the estimates of the allele frequencies were pooled since both were 0.5.The estimation of genetic differentiation requires the availability of estimates of allele frequencies in the parental populations. Methods for the estimation of allele frequencies within a population are the direct method or the maximum likelihood for unrelated individuals , and theGenetic differentiation between the two parental breeds, A and B, was investigated by:Appendix. The sampling variance of the estimates of allele frequencies in the parental populations is also derived in the Appendix. Δ is a measure of how genetically distant the parental breeds are at a particular SNP location. Testing of the hypothesis was carried out using a likelihood ratio test as described in the Appendix. A difference in allele frequencies between parental breeds, together with dominant gene action, is the most accepted theory for heterosis (a) eterosis , which heterosis .STF method of Wright  The f Wright  for eachLF were generally observed in the central parts of each chromosome after genome-wide estimation of this statistic. There are several reasons why autozygosity may be higher at the center of a chromosome, including the region being near the centromere, where recombination is repressed. Because it was difficult to determine the exact map location of the centromere, the distance to the closest distal part of the chromosome was used for each SNP in each chromosome to investigate if the relationship between the distance to the closest telomere and the probability of autozygosity followed the same pattern across chromosomes. The relationship between STF and Δ with distal chromosomal distance was also investigated. A linear regression model was fitted using LF (or STF or Δ) at each SNP within chromosome as the dependent variable and the distance to the closest telomere as the independent variable. Correlations between LF (or STF or Δ) and the distance to the closest telomere per chromosome and genome-wide are reported.High values of Sus Scrofa; 2) sort the file according to chromosome, and position within chromosome; and (3) assign the recombination fraction of a given Mb from the map of STF. After this process, we were able to assign 29,315 SNPs with figures of recombination fraction to our estimates of LF and STF. A linear regression between recombination rate and LF (or STF) was carried out for each of the 18 autosomal chromosomes. A genome-wide linear regression between recombination rate and LF (or STF) was also carried out. In addition to these analyses, a linear regression was performed between estimates of LF and STF.The results of the analyses of this study suggested that variation in the recombination rate across the genome could be related to the local autozygosity. A literature review revealed that there is a published map of the recombination rate across the pig genome . In thisLRT, were selected to further investigate the biological functions of the genes located within those regions. The number of 2,000 SNPs was decided based on a trade-off between SNPs associated to high autozygosity and power to detect association with biological functions. Due to varying allele or haplotype frequencies within the same string of consecutive SNPs, not all SNPs were significant in a given autozygosity chromosomal region. To declare a chromosomal region as high or low autozygosity the following rules were applied after ordering each set of 2,000 SNPs: 1) the physical distance between two consecutive and significant SNPs was less than 2 Mb, 2) the value of LF for each of two consecutive SNPs was either higher (region of high autozygosity) or lower (region of low autozygosity) than expected for that chromosome under the null hypothesis http://www.ensembl.org/biomart/martview/) was used to identify putative genes located in regions of high or low autozygosity and high genetic differentiation. A test of overrepresentation for the GO complete biological processes was carried out using the PANTHER software (http://pantherdb.org/) to investigate the biological function of genes in high or low regions of autozygosity. The same approach was used for the 2,000 SNPs with the highest STF. The over-representation test applies a Bonferroni correction to account for multiple testing. Only significant biological processes with P values < 0.001 are presented for high and low autozygosity and only those with P values < 0.0001 for regions with high STF.The 2,000 SNPs with the highest LF = 0.16  from LF = 0.12 (null hypothesis) with a statistical power of 0.90 at a significance level of 0.01. Consequently, the sample size of our data was sufficient to detect deviations from the expected autozygosity under the null hypothesis. There were 32,659 SNPs genotyped for all individuals and the data analyses revealed a distribution of the size of ROH fragments (with 25 or more SNPs) as shown in LRT along chromosomal position) are depicted in LF, obtained in the data set of each of the two companies, was 0.50 (SE 0.005). This supports the hypothesis that the probability of autozygosity depends on the chromosome position and may be influenced by chromosomal physical characteristics or by the local recombination rate.The first step consisted of computing statistical power to evaluate if sample size was sufficient to detect autozygosity. Results showed that a sample size of 1,114 (two-side test) is required to detect a difference of Maximum likelihood estimates of allele frequencies in the parental populations (required to estimate differentiation) always yielded two solutions, corresponding to the two alternatives for assigning frequencies to the parental breeds. Standard errors of the estimates of allele frequencies estimated using the 1,173 crossbreds are depicted in STF and Δ , with estimates of allele frequencies in the parental populations. The mode of the distributions of STF and Δ showed that allele frequencies were not different in the two parental breeds for a large number of SNPs . There were 2,949 SNPs with an STF > 0.125, which illustrates regions of differentiation between the two breeds. STF and Δ was 0.73 (SE ∼ 0.004), suggesting similarities between both estimates of differentiation between the Landrace and Large White breeds.Differentiation between parental populations was investigated using  of SNPs Figure 7LF) with distance to the closest telomere was positive and highly significant for all chromosomes , negative  or close to zero . A genome-wide correlation between recombination rate and STF was close to zero and not significant. The correlation between estimates of LF and STF across the genome was -0.095 (SE = 0.006), which supports a significant negative relationship between these two measures. LF and STF per chromosome. The observed general trend is that SNPs with high LF have low STF and vice versa. However, there are no SNPs with high values for both LF and STF.LF were used to investigate their gene content using the Biomart software. This analysis revealed that loci with a high probability of autozygosity are clustered in regions that can expand up to 14 Mb in high autozygous regions  analysis of ROHs and autozygosity in crossbreds, and 2) analysis of Δ and An observed ROH in crossbreds indicates that the corresponding haplotype has persisted in the two breeds, in spite of opportunities to break it down by recombination. The number of generations back to the common ancestor was of about 46, 414, and 388 years when considering the distribution of ROHs with a minimum of 25, 15, and 5 SNPs, respectively. These results illustrate the large impact that the assumed number of contiguous and homozygous SNPs to declare a ROH has on estimating age to coalesce to a common ancestor. Historic records dated at the end of 19th century the creation of the parental breeds , which iSTF and Δ with distal chromosomal distance was not consistent across chromosomes, suggesting that genetic drift and/or selection may have acted differently in the different chromosomes.Our results show that the probability of autozygosity was very different between chromosomes and also along chromosome locations. This may be caused by chromosome structures that facilitate or obstruct recombination in different chromosomal regions. Our results identified positive correlations between the probability of autozygosity and distance to the closest distal part of the chromosome, although the size of this correlation varied greatly among chromosomes. This is consistent with previous work that observed larger recombination rates at the extremes than in the center of chromosomes in the pig . ChromosSTF statistic. Since both of these measures require estimates of allele frequencies, we developed a maximum likelihood approach to estimate allele frequencies and their sampling variance using genotype information from crossbreds. This method relies on departures from Hardy-Weinberg equilibrium conditions in crossbreds that occur when the allele frequencies at a SNP are different in the two parental breeds. Therefore, any factor that alters the allele frequencies among the gametes (when compared to the frequencies in the populations where they originated from) may affect estimation of allele frequencies with our method. This may include artificial and/or natural selection in the parental lines, the effects of which should be small unless selection is strong. The maximum likelihood method allows estimation of allele frequencies but is unable to distinguish which allele frequency corresponds to which parental population. Our interest was to investigate allele frequency differences between the parental breeds at each SNP position and neither Δ or STF requires the assignment of allele frequencies to the parental breeds. Contrary to the STF statistics, the difference in allele frequency between the two breeds, Δ, has the advantage that significance can be tested using a simple likelihood ratio test. This measure could also be used to test the dominance theory of heterosis. For example, SNPs with a high Δ could be tested for superiority of the heterozygotes in the parental breeds.The two measures of genetic differentiation that were used to investigate long-term changes in the genetic makeup of the two breeds at each SNP were: 1) estimates of the difference in allele frequency in the two breeds, and 2) the STF statistic has been widely used as a measure of genetic differentiation. This statistic has limitations for SNPs when the minor allele frequency is small. Jakobsson et al. examined the relationship between STF and the frequency of the most frequent allele, demonstrating that the range of values that STF can take is restricted considerably by the allele frequency distribution  or on different haplotypes (high STF) in the development of the two breeds. Our results show that high autozygosity in the crossbreds should correspond with low values of genetic differentiation (STF) across the swine genome. In fact, the correlation between STF and LF was negative and significant. Genes involved in biological functions, such as tissue development, anatomical structure, and animal organ development, were overrepresented in regions with high FST. This can be attributed to long-term effects of selection for those traits, which may have been different for the two breeds. Genes involved in sensory perception, response to stimulus, and neurological system processes were underrepresented for significant regions based on all statistics (autozygosity and genetic differentiation) investigated. Previous work has shown that genes associated with immune response and olfaction exhibit fast evolution  that may help to understand the evolution of the parental breeds. The application of this research to crosses between different species, such as the mule, may reveal the presence of ROH in the cross, which opens new avenues to investigate the life history of horse and donkey.Genetic analyses of crossbreds can reveal the location of common haplotypes (regions of autozygosity) and areas of genetic differentiation in the genomes of genetically distant breeds such as Landrace and Large White. Estimation of age of coalesce is influenced by the assumed minimum number of SNP in declared ROHs. Autozygosity is not uniform throughout the genome of crossbreds but more prominent in the central than in the distal part of chromosomes. Autozygosity is highly related to their chromosomal location which in turn depends on recombination hot and cold spots that can be tracked by the use of recombination maps. Genetic differentiation can be ascertained after estimation of allele frequencies in the parental lines, even when only crossbred genotype information is available. About 50% of the SNPs segregated at similar allele frequencies in the two parental breeds.The data that support the findings of this study are accessible from the PRRS Host Genetics Consortium, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data may be available from the authors upon reasonable request and with permission of the PRRS Host Genetics Consortium.All experimental protocols for these trials were approved by the Kansas State University Institutional Animal Care and Use Committee.LG-R developed methods, carried out analysis, and wrote the first version of the manuscript. JDe made major contributions in the writing of the manuscript and was involved in the generation of the data used in this article. JDu helped in the implementation of the methods and in the editing of the data. WR contributed in the analyses of data and in the writing of the manuscript. All authors read and approved the final manuscript.Generation of these data was supported by the USDA ARS and NIFA award 2013-68004-20362. WMR and LGR acknowledge financial support from the Salvador de Madariaga program of the Ministry of Education of Spain, and AGL2016-75942-R and ERA-NET SUSPIG projects.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


ROH), % of homozygosis (FSNP), and inbreeding coefficient (Fx). Further, Runs of Homozygosity (ROH) segments conserved among the animals were investigated to identify possible regions associated with the breed characteristics. Our results indicate that even the Indubrasil breed having a small effective population size, the levels of homozygosity (FROH = 0.046) are still small. This was possibly caused by the cross conducted among different breeds for its development. It suggests no immediate risks associated with loss of genetic variation. This information might be used in breeding programs, for the breed conservation and for the expansion of the Indubrasil breed.The Indubrasil breed was developed in the Brazilian region called Triângulo Mineiro as a result of a cross between zebu cattle. Initially, it was used as a terminal cross and currently it represents approximately 4.45% of all the Brazilian zebu cattle. Studies were conducted to estimate genetic parameters in the Indubrasil using pedigree information, however, until now, no study has been developed using large-scale genomic markers in this breed. Pedigree information are widely used to investigate population parameters; however, they can neglect some estimates when compared to the use of genomic markers. Therefore, the objective of this study was to investigate the population structure and the genetic diversity of Indubrasil cattle using a high-density Single Nucleotide Polymorphism (SNP) panel (Illumina BovineHD BeadChip 700k). Levels of genomic homozygosity were evaluated using three different approaches: Runs of homozygosity (F The Indubrasil breed was the first zebuine breed developed in Brazil, in the Triângulo Mineiro region. This breed originated from a cross between the Guzera, Gir, and Nelore breeds, with the objective to maximize the genetic gain of all zebus in only one breed . The breThe effective population size (Ne) of the Indubrasil breed has increased from 1956 to 1992, when it reached the highest peak of 292 individuals, followed by a reduction between 1998 and 2000, reaching only 26 individuals . To prevThe intensification in the use of genomic selection coupled with advanced reproductive technologies have facilitated the rapid spread of animals with higher breeding values. However, the constant use of these animals can drastically reduce the genetic variation among animals, and, therefore, increasing the probability of matings among related individuals . CarneirThis study was approved by the Embrapa Southeast Livestock Ethical Committee for Animal Use (CEUA-CPPSE) under protocol 02/2009. ® ND-2000 spectrophotometer . DNA samples with 260/280 relation between 1.85–1.95 and 260/230 between 2.0–2.3 were diluted in a final concentration of 500 μg and genotyped at Deoxi Biotectonology Ltd.a using the Illumina’s Bovine700k chip, which interrogates 777,962 SNPs. For the evaluation of levels of homozygosity, only autosome markers were included in the analyses  out of one breeding herd located in the state of Rio Grande do Sul were used. Animals from this herd were bought from different parts of Brazil; therefore, they represent a sample of the Brazilian Indubrasil population. Pedigree information from three generations were obtained by the Brazilian Association of Zebu Breeders public database. Blood samples were individually collected from each animal by the use of vacutainer tubes (with EDTA), and the DNA was extracted using a commercial kit from DNeasy Blood & Tissue Kits , following the manufacturer’s recommendations. The DNA quality was evaluated by NanoDropanalyses . Initial2 < 0.2, were used, and the results were plotted in a multidimensional graph (MDS) to facilitate their interpretation  levels were computed between all the individuals. Only markers with linkage disequilibrium (LD) levels, rretation . The proretation . The allretation .(1)SNP).The percentage of homozygous genotypes in relation to the total number of genotypes (F(2)x) using the frequency of the observed homozygous genotypes (HO) in relation to the expected homozygous genotypes (HE): Fx = [(HO − HE)/(HE)]).The inbreeding coefficient (F(3)ROH, the ROH were detected using the PLINK program with a sliding window of 50 SNPs with minimum length of 1000 kb, with a possibility to have 1 heterozygous SNP and 1 missing SNP within each window. For this, we used the following formula:Runs of homozygosis (ROH). For the verification of Fwhere “k” is the number of ROHs identified in each individual multiplied by the segment size average in Kb, and L is the total size of the cattle genome  [For the assessment of genomic homozygosis levels, three methodologies were used:genome 2,12,820 Kb2) and the number of regions of homozygosis (ROH) were estimated by chromosomes (The linkage disequilibrium levels (romosomes  in PLINKomosomes .n = 30) females and seven (n = 7) males with an average genotyping call rate of 99.26%. One female was excluded from the analysis for having more than 10% of missing genotypes. After the quality control, 735,293 SNPs were identified to be located on the autosomal chromosomes. From these, 13,699 SNPs were removed because they failed in more than 10% of the samples . After quality control, 721,594 autosomal SNPs and 37 animals were retained, 30  indicates a genomic heterogeneity among the animals studied  was the one with the largest amount of regions in homozygosity shared among the animals  in this population was 0.713 ± 0.02   was calculated by the excess of homozygous markers in an individual, through the PLINK, it was found an average of −0.032 ± 0.075, minimum value of −0.181, maximum value of 0.135 , we have identified an average of FROH = 0.04 ± 0.035, minimum of 0.0126 and maximum of 0.18   being FSof 0.135 . Males s of 0.18 . In malepulation .In the population studied, 5.4% of the animals presented recent inbreeding events with an average segments size between 3.5 and 4.0 Mb. Meanwhile, 18.93% of the animals presented ROH segments with average sizes between 2.5 and 3.5 MBs, and lastly, 75.67% presented ROH segments below 2.5 Mb, indicating older inbreeding mating.The estimation of the levels of homozygosity reveals important information about the genetic diversity of animals from different populations. In addition to that, this information can be used as a strategy to conduct mating in different selection programs . MaintaiROH is based on the segments of homozygosis of each animal, being a more specific evaluation and accurate than the estimates performed with pedigree data, which sometimes can be incomplete and contain identification errors [The increased levels of homozygosis may reflect what has occurred in the past or in a recent process with the intensification of the selection pressure among animals . While tn errors ,17. The n errors . Long ROn errors . Segmentn errors . From thVercesi Filho et al. (2002) studied the Indubrasil population evolution using the Wright’s inbreeding coefficient (FST) and verified an increase between 1938 and 1998 from 1% to 3.4% . In our x = 1.08% and 1.36% [x below 1%, indicating little loss of genetic variability.Using Wright’s Inbreeding Coefficient (1931) , Oliveirnd 1.36% . The lownd 1.36% . The resCommon regions of ROH shared among individuals are known as homozygous islands . In accoNCAPG gene expression, located in BTA6 and related with the development of the longissimus dorsi muscle [NCAPG were differentially expressed. These two genes, LCORL and DCAF16, have higher levels of expression in fetal muscle, suggesting their involvement in muscular development. In addition, the HCAP-G (NCAPG) and LCORL genes were suggested to be associated with production traits in beef cattle. These traits are: Weight of the carcass [An RNA sequencing study in the Qinchuan chinese cattle showed different levels of i muscle . In the  carcass , calving carcass ,29, heig carcass , feed in carcass . This reBTA6 related to milk production traits were also identified in the zebu breed [http://www.acgz.com.br/)).Studies with European breeds found QTLs in this chromosome 6 region associated with productivity, fat, and milk protein . Silva (bu breed . This suIn general, our results suggest that the inclusion of genomic data is of extreme importance to avoid possible problems related with the inbreeding depression, caused by the reduction of genetic variability in animals. In this way, it can be used as a criterion of selection and crossing of individuals in order to standardize the breed and to implement the selected traits in shorter time.In this work, parameters related to inbreeding in a sample population of Indubrasil animals were evaluated with the use of a high-density SNP panel. Animals in the present study have demonstrated low levels of genomics endogamy, not evidencing immediate risks related with inbreeding depression, possibly due to the previous crosses among different breeds made before the origin of the Indubrasil.


It is likely that mainly those patients with preexisting n-3 deficiency are those that benefit most from n-3 fatty acid supplementation. Therefore, for targeted interventions, a fast and reliable screening tool for n-3 PUFA intake is necessary. Thus, the aim of this project was to adapt and validate a food frequency questionnaire (FFQ) for n-3 PUFA intake in Switzerland while using as references the following: (1) 7-day food records (FR), and (2) n-3 fatty acid composition of red blood cells (RBC). We recruited 46 healthy adults for the first part of the study and 152 for the second. We used the dietary software EBISpro for the analysis of n-3 PUFA intake. RBC fatty acid composition was determined by gas chromatography mass spectrometry (GC-MS). Using correlation analysis, we found a moderate significant association between FFQ and FR for α-linolenic acid (ALA), eicosapentanoic acid (EPA), docosahexanoic acid (DHA), and total n-3 fatty acids . Bland Altman analysis further showed good agreement between the two methods and no proportional bias. Correlations between FFQ and RBC fatty acid composition were also moderate for EPA and DHA , but weaker for ALA and total n-3 . The efficacy of the FFQ to classify individuals into the same or adjacent quartile of RBC PUFA content ranged between 70% and 87% for the different fatty acids. In conclusion, we showed that the Swiss n-3 PUFA FFQ is a valid tool to assess dietary n-3 PUFA intake, especially DHA and EPA, to determine population groups at risk for low intake.Population-based data suggest that high intake of omega-3 ( Eskimos traditionally consume a diet very high in seafood and were found to have a low prevalence of several chronic diseases, including coronary heart disease, asthma, diabetes mellitus and multiple sclerosis [(n-6) to n-3 ratio towards a major excess in n-6 fatty acids. A relative deficiency in n-3 PUFAs with an estimated ratio of n-6 to n-3 PUFA of 15-20/1, as opposed to 1/1, has been the result of this change in dietary patters [The first evidence of a beneficial effect of omega-3  fatty acid composition reflects intakes over the past 120 days and plasma fatty acid composition is an indication for intakes over the past few days [n-3 PUFAs are largely exogenic, meaning they cannot be synthesized de novo by the human body, the major sources are intake via diet or supplements. Correlational analyses between dietary assessments and biomarkers yield good results [n-3 fatty acid intake in the human diet are relatively limited, and the use of an FFQ is therefore adequate in this case. The advantages of an FFQ are that the burden on the subjects is low and that it is therefore possible to include a large sample size. Furthermore, as it is a retrospective method, there is no risk of altered food habits for the recording period [With the growing burden of chronic, non-communicable disease in western societies, the importance of understanding the different influencing factors increases. In order to study intervention effects and relate intake to disease risk, however, having a rapid and reliable screening tool for few days . All of  results ,11,12. Dg period .n-3 PUFA intake from the United States for use in Switzerland using RBC n-3 fatty acid composition and 7-day food records (FR) as references.The aim of this study was to adapt and validate an existing FFQ for n-3 PUFA FFQ by Sublette et al. which was developed and validated to assess n-3 PUFA intake in the New York City area [n-3 PUFA FFQ is available as online We have adapted a self-report questionnaire based on the American ity area . The adan-3 PUFA composition. All participants were recruited from the student and staff population of ETH Zurich (both parts), staff of the University Hospital of Psychiatry Zurich (part 1) as well as staff of the Children’s Hospital Zurich (part 2). The study was approved by the Institutional Ethics Committee of the state of Zurich (BASEC Nr. 2017-01370) and was registered under clinicaltrials.gov (NCT03409445).The study was conducted in two steps: (1) validation of the FFQ against 7-day FR, and (2) validation of the FFQ against RBC n-3 PUFA FFQ. No health-related data from the participants were assessed. For part 2 we recruited 152 healthy male and female participants aged between 18 and 59 years. Participants were excluded if they suffered from a self-reported chronic disease of the gastrointestinal tract that might influence lipid absorption. For women, pregnancy and lactation were further exclusion criteria.For part 1, we recruited 46 healthy male and female participants aged between 19 and 53 years and willing to fill in a 7-day weighed FR and the adapted Swiss n-3 PUFA FFQ directly at the research institute. When they returned the completed FR, the study assistant reviewed all entries with the participants to rule out potential misunderstandings and misreporting. Data was collected between March and May 2017, and between February and March 2018.For part 1, interested volunteers met with the study assistant to receive detailed instructions for completion of the 7-day weighed FR. They were asked to record all food and drink consumed over a period of 7 consecutive days as precisely as possible. They were asked to weigh the foods consumed whenever possible and were provided with a kitchen scale if needed. If weighing was not possible for a certain meal, participants were asked to estimate portion sizes using the picture book from the National Nutrition Survey menuCH . Wheneven-3 PUFA FFQ directly at the research institute. All samples and data were collected between January and March 2018.For part 2 interested volunteers received the participant information sheet by e-mail. Those who were still interested in participating were invited to the Human Nutrition Laboratory of the ETH Zurich. The study procedure was again explained, and potential questions were answered before obtaining informed written consent. A venous blood sample of 4 mL was then collected into EDTA coated vacutainers and weight and height of the participants was measured. After blood sampling, the participants were asked to complete the Swiss n-3 PUFA FFQs were analyzed using the nutrition software EBISpro for Windows 2011 . The n-3 PUFAs of interest were total n-3 PUFAs, ALA, EPA, and DHA. Total n-3 PUFAs were calculated as the sum of ALA + EPA + DHA + docosapentanoic acid (DPA). DPA was included as part of total n-3 PUFA, even though its individual intake was not investigated. Missing data for n-3 PUFA content of the products was added either from Food Composition and Nutrition Tables by Souci, Fachmann, Kraut [n-3 PUFA FFQ were entered as a template in the software. For the analysis, only the frequency of consumption indicated by the participants had to be entered for each product. If the participants indicated having eaten several different species of fish, the recorded amount and frequency was divided by the number of species consumed. As very few participants who consumed n-3 PUFA containing dietary supplements could report the exact product name and amounts, we decided not to include the amount of n-3 PUFAs consumed from supplements in the analysis but divided the participants by supplement consumers (n = 14) and non-consumers.Both the 7-day FR and the Swiss n, Kraut  or from n, Kraut . The foon-3 PUFAs.For the analysis of the 7-day weighed FR, each product recorded was searched in the database and the indicated amount entered. The intake was averaged over the 7 days and this mean value was used for data analysis for the different g for 10 min at room temperature within 2 h of collection. The samples containing the RBC were stored at 4 °C for a maximum of seven days until further processing. The RBC were washed with 0.9%-NaCl solution and stored at −80 °C in Eppendorf tubes treated with 1%-BHT solution until analysis. The determination of fatty acids in RBC was carried out by gas chromatography coupled to a tandem mass spectrometer , according to the method of Moser et al. [Blood samples for fatty acid analysis in RBC were collected into EDTA coated vacutainers and plasma was separated by centrifugation at 2000 r et al. ,19. Brien-3 PUFA intake nor all RBC fatty acid concentrations were normally distributed, data analysis was done using non-parametric tests. The Wilcoxon signed-rank test was used to compare median intakes assessed by the 7-day FR and the FFQ. We further evaluated the strength and direction of the relationship between the two methods using Spearman’s correlation coefficients. In addition, we used Bland-Altman plots to visualize the agreement across the range of intake. To do this, we plotted the difference between the two methods against their respective average. The limits of agreement were determined as ±1.96 SD for each fatty acid. One sample t-tests on the difference were used to test whether the mean difference is constant across the range of means. To check for proportional bias, linear regression analysis was used with the difference as the dependent and the mean as the independent variable. To investigate the strength and direction of the relationship between n-3 PUFA intake assessed using the Swiss n-3 PUFA FFQ and RBC PUFA concentrations, we used Spearman’s correlation coefficients. To assess the importance of other factors, we used multiple linear regressions with the different RBC PUFAs as the dependent variable, PUFA intake from the Swiss n-3 PUFA FFQ, as well as gender, age, supplement intake, and fish consumption as the independent variables. Differences between RBC fatty acid compositions of different groups  were assessed using the Mann-Whitney U-test. Participants were classified into quartiles based on Swiss n-3 PUFA FFQ-estimated fatty acid intake and plasma concentrations. Efficacy of the FFQ to place participants in the same or adjacent quartile was further calculated, as well as the misclassification into extreme quartiles.Data analysis was done using IBM SPSS Statistics Version 24. All data was checked for normality using the Shapiro Wilks test. As neither the Characteristics of both study populations are shown in n-3 PUFA intake assessed by the FR and the Swiss n-3 PUFA FFQ, as well as results of the correlation analysis and the group comparisons, are shown in n-3 PUFA intake nor the individual PUFAs differed significantly between the assessment by FR and FFQ. Further, we determined significant correlations with a Spearman correlation coefficient between 0.5 and 0.6 between FR and FFQ for all PUFA’s investigated. The Bland Altman plots visualizing the agreement between the two methods for ALA, EPA, DHA, and total n-3 PUFA are shown in n-3 PUFA intake . Furthermore, the linear regressions produced unstandardized B values close to 0  with non-significant p-values , indicating no proportional bias.Median n-3 PUFA FFQ, RBC PUFA concentrations, and the results of the correlation analyses are shown in n-3 PUFA FFQ and RBC PUFA composition, especially for EPA (r = 0.430) and DHA (r = 0.605), and weaker, but still significant correlations for ALA (r = 0.314) and total n-3 PUFAs (r = 0.211). We used linear regression analysis to identify further predictors of RBC n-3 PUFA composition using gender, age, supplement intake, and fish consumption as independent variables besides FFQ PUFA. For all four analyses  the best model included all four additional factors, even though they did not all show a significant effect  as the coefficient was 0.Median PUFA intake assessed by the Swiss  compare . Equatiop < 0.001) and EPA (p = 0.029), but was not significant for DHA, total n-3 PUFA, n-3 index, or n-6/n-3 ratio . The proportion of ALA in RBC was higher in females, while the proportion of EPA was higher in males. PUFA intake assessed by FFQ differed between males and females for EPA (p = 0.009) and DHA (p = 0.006) and was not different for ALA and total n-3 (both p > 0.05). Again, EPA intake was higher in males, and so was DHA intake.The proportion of RBC PUFAs differed between males and females for ALA , with higher intakes and a lower n-6/n-3 ratio for supplement consumers. The intake assessed by the Swiss n-3 PUFA FFQ differed between supplement consumers and non-consumers for EPA (p < 0.001) and DHA (p < 0.001), but not for ALA and total n-3 intake (p > 0.05). Intakes were higher in supplement consumers compared to non-consumers.The proportion of RBC PUFA differed significantly between supplement consumers and non-consumers for all components assessed . The two validations were done in two separate groups of volunteers. We have been able to confirm good relative validity of the Swiss n-3 PUFA FFQ using both approaches.We have developed an adapted Swiss n the US . As FFQsr = 0.523 and r = 0.586) and similar to a previous validation study for an FFQ for n-3 PUFA intake in Australian children (between r = 0.684 and r = 0.691) [n-3 PUFA FFQ and FR was comparable, although the FFQ tended to estimate higher intake for EPA and DHA. This can be explained by the fact that the Swiss n-3 PUFA FFQ focuses on fish/seafood, as well as a few selected plant-based products containing high amounts of n-3 PUFA. While the main dietary sources for EPA and DHA in the human diet are fish and seafood, ALA sources  also include a variety of oils and other plant-based foods, which were not all represented in the Swiss n-3 PUFA FFQ. Thus, based on the recall period of six months, we may have a slightly skewed estimation of EPA and DHA using the Swiss n-3 PUFA FFQ, as some study participants may not have consumed fish or seafood in the 7-day FR period, but did within the past six months. On the other hand, the Swiss n-3 PUFA FFQ may not have captured all ALA and total n-3 PUFA sources, thus leading to a slight underestimation of those groups of food. Nevertheless, the median intakes did not differ significantly between the two methods for any of the PUFAs reported. It was reported previously that in general FFQs tend to overestimate dietary intake compared to other methods such as repeated 24-h recalls or food records [n-3 PUFA FFQ with FR, we found mean differences close to zero for all PUFAs investigated, indicating no systematic bias. As most of the points were further found to be within the calculated limits of agreement, those plots support the validity of our questionnaire. Nevertheless, it needs to be considered that the limits of agreement in the Bland-Altman plots are in the range of the mean values and are therefore rather large. Thus, the agreement between the two methods should be treated cautiously, even though there is no systematic bias.The strength of the correlations found between FFQ and FR was moderate (between = 0.691) . The ran records ,22,23. In-3 PUFA intake assessed using the Swiss n-3 PUFA FFQ and RBC n-3 PUFA composition was more variable compared to the above comparison between the Swiss n-3 PUFA FFQ and FR, and ranged from r = 0.211 for total n-3 to r = 0.605 for DHA. The correlation coefficients for ALA, EPA, and DHA found in the present study  were a little higher, but overall in a similar range as those found by Sublette et al. in their validation  [n-3 PUFA FFQ. The correlation found for EPA can still be considered good, if considerably lower than the one for DHA. However, in the case of EPA, it also has to be considered that physiological processes may influence the relationship between n-3 EPA intake and RBC EPA composition. One of those factors is the low conversion rate of ALA into EPA, which is strongly influenced by the inhibitory effects of n-6 PUFA intake [n-3 PUFA FFQ does not assess n-6 PUFA intake, as this would have been much more complex and time-consuming due to the many food items containing n-6 PUFAs. Thus, we were not able to control for this potential confounding effect. Furthermore, polymorphisms in the Δ-5 and Δ-6 fatty acid desaturase genes have been identified, which were shown to affect variations in EPA but not DHA [n-3 PUFA is an indication that the Swiss n-3 PUFA FFQ did not assess all sources of ALA. This agrees with other studies that also found weak or even non-significant correlations for ALA [The strength of the correlations between A intake . The Swi not DHA . Again,  for ALA ,27,28. A for ALA .We found significant gender differences in both RBC PUFA composition and PUFA intake. While RBC ALA was significantly higher in females, ALA intake did not differ between sexes. Even though this seems controversial at first sight, the agreement between the two methods to assess ALA was only limited, as not all ALA sources were assessed within the questionnaire. Thus, women may have consumed higher amounts of the non-detected ALA compared to men, which would explain the discrepancy in the findings. On the other hand, DHA intake was found to be higher in males, while there was no significant difference for RBC DHA content. This may be related to the more efficient conversion of ALA to DHA by women (up to 9%) compared to men (<1%) ,29,30. In-3 PUFA FFQ validated here is intended for use as a screening tool to identify individuals with low n-3 PUFA intakes in order to potentially improve their diet and select high risk groups with a low omega-3 status. It was thus important to investigate whether the tool can accurately identify consumers with low intakes. Using a quartile analysis, we have shown that the Swiss n-3 PUFA FFQ places participants into the same or adjacent quartile with an accuracy between 70% and 87% for the different fatty acids. The highest accuracy was found for DHA, followed by EPA, in agreement with the strength of correlations discussed above. These findings are in agreement with the study by Sublette et al. where the highest efficacy to accurately place participants in the same or adjacent quartile was with 83% also found for DHA [n-3 PUFAs with 22.4%. This is again in agreement with the explanation above that the FFQ was better able to capture DHA and EPA intakes rather than ALA and total n-3. Furthermore, total n-3 PUFA contain not only ALA, EPA, and DHA, but also DPA, which occurs in foods in much smaller amounts than the other three n-3 PUFAs. RBC or plasma DPA content is mainly derived from endogenous elongation of EPA and can also undergo retroconversion back to EPA, and thus is not expected to correlate well with dietary intake of DPA [n-3 PUFA and FFQ total n-3 PUFA. Despite the relatively high efficacy in placing participants in the same or adjacent quartiles, especially for DHA and EPA, misclassification does happen. Therefore, in order to have an accurate diagnosis of low n-3 PUFA status, a biochemical analysis should be favored whenever possible. However, the presented FFQ can be a valid screening tool, especially in population-based studies.The Swiss  for DHA . We foune of DPA . This fan-3 PUFAs. On one hand, the International Society for the Study of Fatty Acids and Lipids (ISSFAL) defined a minimum intake of total n-3 PUFA at 500 mg/day [n-3 PUFA while the European Food Safety Authority (EFSA) recommends a minimum EPA + DHA intake of 250 mg/day [n-3 PUFAs than non-affected healthy controls. In the validation of the Swiss n-3 PUFA FFQ against FR, total n-3 PUFA intake assessed by both methods was above both the ISSFAL and the AHANC recommendations, while in the validation of the FFQ against RBC PUFAs, n-3 PUFA assessed by the FFQ was slightly below the ISSFAL but above the AHANC recommendation. However, in none of the three assessments of dietary intake, the EFSA recommendation for EPA + DHA intake was reached. In general, intakes observed in our study were lower than what was found in most other European countries [n-3 PUFAs.Different recommendations exist for the intake of 0 mg/day . The Ame0 mg/day . To our ountries . The medountries . Thus, in-3 PUFA FFQ compared to FR and RBC PUFA composition, but did not test reproducibility or seasonal effects. In Switzerland, fish is an episodically consumed food. It is therefore possible that during the 7-day recall period of the FR, a person consumed unproportionally high or low levels of fish compared to their general diet, while the FFQ would have assessed the frequency over a longer term (six months). This may have limited the agreement between the two methods. Nevertheless, the fact that we used seven days for the recall period and not only three or four, would again have increased the reliability. Another limitation is the different periods of our measurements in general. While the FR was completed over seven days, the FFQ assessed consumption frequencies over six months and the RBC PUFA composition represents the consumption over about 120 days. For higher precision, it might have been desirable to repeat the FR and to adapt the recall period of the FFQ to four instead of six months to match the lifetime of RBCs. Also, the sample size for the validation of the FFQ against FR was quite small with 46 participants, which limits generalizability of the results. Further, we tested the questionnaire in healthy adults and can therefore not judge its validity in other population groups. As the Swiss n-3 PUFA FFQ, similar to any FFQ, is highly dependent on eating habits and food availability, changes in those may affect the validity of the questionnaire. Especially industry practices concerning the diet of livestock and farm-raised fish, but also economic changes and public perception can be important. Nevertheless, RBC DHA content was especially shown to be a reliable biomarker of DHA status, also reflecting a rapid reaction to an intervention similar to plasma DHA levels, which is a more short-term indicator [n-3 PUFA consumed in the form of supplements but could only correct for supplement intake as a binary variable in the regression analyses. As the number of supplement consumers was small (n = 14 out of 152), we do not expect this to have a major impact on the analysis. Nevertheless, when calculating n-3 PUFA intakes, we are likely underestimating the true values to some extent. Another point worth mentioning is that for evaluation of both the FFQ and the FR we depend on data from food composition tables. Especially because the fatty acid composition of fish varies largely depending on feed, the accuracy of those tables is always limited. As the same data was used for analysis of the FFQ and the FR, we cannot rule out a correlated error to a certain extent. Nevertheless, even though this may have biased the absolute intake of n-3 PUFA, it should not have had a huge impact on the agreement between the two methods.The present study has several limitations. We tested the validity of the Swiss ndicator . Lastly,n-3 PUFA FFQ is a valid tool to assess dietary n-3 PUFA intake, especially DHA and EPA intake, and that this intake is associated with RBC PUFA status. Therefore, the Swiss n-3 PUFA FFQ can be applied as a screening tool in population based studies, in particular to detect high risk populations with a severely insufficient n-3 intake that should then undergo confirmatory RBC n-3 PUFA analysis to determine if a dietary change or a supplementation with omega-3 fatty acids might be indicated.In conclusion, our study shows that the adapted Swiss