Brassica napus  is one of the world’s most important sources of vegetable oil for human nutrition and biofuel, and also a model species for studies investigating the evolutionary consequences of polyploidisation. Strong bottlenecks during its recent origin from interspecific hybridisation, and subsequently through intensive artificial selection, have severely depleted the genetic diversity available for breeding. On the other hand, high-throughput genome profiling technologies today provide unprecedented scope to identify, characterise and utilise genetic diversity in primary and secondary crop gene pools. Such methods also enable implementation of genomic selection strategies to accelerate breeding progress. The key prerequisite is availability of high-quality sequence data and identification of high-quality, genome-wide sequence polymorphisms representing relevant gene pools. We present comprehensive genome resequencing data from a panel of 52 highly diverse natural and synthetic B. napus accessions, along with a stringently selected panel of 4.3 million high-confidence, genome-wide SNPs. The data is of great interest for genomics-assisted breeding and for evolutionary studies on the origins and consequences in allopolyploidisation in plants. Ultra-high throughput, array-based genotyping platforms enable cheap and efficient whole-genome profiling in any species for which suitable DNA sequence polymorphism datasets are available. The recent publications of reference sequences for the allopolyploid genome of oilseed rapeB. napus, from de novo interspecific hybridisation between B. rapa and B. oleracea, provides the opportunity to capture completely novel B. napus variants to diversify the primary gene pool of cultivated oilseed forms4. Synthetic B. napus has also become an important model to study homoeologous chromosome pairing, inter-subgenomic chromosome exchanges and gene conversions resulting from de novo allopolyploidisation. Numerous examples demonstrate the influence and consequences of natural or artificial selection on particular chromosome exchanges for the expression of important agronomic traits5. To capture, recombine and utilise such variation for high-resolution trait dissection and genomics-based pre-breeding, we first established a highly diverse panel of 50 natural and synthetic B. napus accessions. These were subsequently used as founder lines for the development of large populations for nested association mapping (NAM6) and heterotic haplotype capture (HHC4). 30 of the founder accessions were selected to sample allelic variation as evenly as possible across the gene pools of diverse winter-type B. napus morphotypes, including fodder rapes, kales, and old European and Asian oilseed forms (B. napus ssp. napus), along with rutabaga/swede (B. napus ssp. napobrassica). The remaining 20 founder accessions comprise synthetic B. napus, generated through interspecific hybrids of genetically highly divergent representatives of the diploid A and C donor genomes of the allopolyploid B. napus. Two additional, modern breeding lines were used as recurrent parents for the generation of the NAM lines and HHC populations, respectively4.Furthermore, the unique ability to generate synthetic B. napus Darmor-bzh v4.2 reference genome. Using different, stringent variant calling strategies, a total of 4.3 million high-confidence, genome-wide single-nucleotide polymorphisms (SNPs) were detected across the 52 genomes.Whole-genome resequencing data was generated for the 50 accessions from the diversity panel, and for the two recurrent parents, using the Illumina HiSeq 2000 next-generation sequencing platform. Paired-end, 100 bp sequence reads were generated to an average depth of between 12x and 15x genome coverage. A total of 3.4 billion quality-trimmed paired reads (685 Gb) was utilized for the alignment to the Brassica crops, and to evolutionary biologists investigating polyploid evolution and the genome-scale consequences of natural or artificial selection. Furthermore, the data represent a foundation for development of new, ultrahigh-density variant screening arrays for population-level trait analysis and genomic selection.This paper reports these DNA sequence and SNP variant datasets in their entirety. These data are expected to be of considerable interest to breeders and geneticists working with B. napus accessions was selected from a species-wide pool of more than 500 highly diverse B. napus accessions described by7. By analysing the sequences of conserved genes across this entire diversity set we selected 30 homozygous inbred accessions that best represented the species-wide diversity present in this panel. The selected accessions include fodder rapes, kales, old European and Asian oilseed forms , along with rutabaga/swede forms (B. napus ssp. napobrassica). The genetic diversity of the 30 natural B. napus accessions was further expanded by adding 20 synthetic B. napus accessions developed by embryo rescue from interspecific hybridizations between highly diverse parental origins (B. napus accessions are described in detail by Girke8 and Jesske9. The Pre-BreedYield (PBY) collection was completed by two additional, modern breeding lines that were used as recurrent parents for the generation of the NAM lines and HHC populations, respectively4. Whole-genome shotgun sequencing (WGS) was performed for each of the 52 lines included in the PBY collection . The full sequence data for all 52 accessions is archived at the European Nucleotide Archive (www.ebi.ac.uk/ena) under the project numbers PRJEB5974 (Data Citation 1) and PRJEB6069 (Data Citation 2).The 52 FastQC software (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapters were removed using cutadapt (https://cutadapt.readthedocs.org/) and the reads were trimmed for low quality bases with the fastx quality trimmer algorithm (http://hannonlab.cshl.edu/fastx_toolkit/), using the settings −t 30 −l 90 for trimming bases with a Phred quality score below 30 and removing reads shorter than 90 nt. Paired reads were then synchronized using an in-house Perl script (Data Citation 3). General quality statistics of raw and trimmed reads were generated using fastx_quality_stats (http://hannonlab.cshl.edu/fastx_toolkit/). In total over 3.4 billion quality-trimmed, paired-end reads were used, providing a total pre-processed data volume of 685 Gbp. Average G/C content changed slightly from 36.34 to 35.35 percent after trimming of raw reads, whereby the diverse genotype collection showed a range of G/C content from 34.87 to 36.33%, emphasizing the high variability. These values are comparable to previous calculations of 35.4% in B. rapa and 36.0% in B. oleracea10. In consequence of the stringent quality trimming the average sequencing depth per genotype decreased to ~11.7 fold, with a Q1 quartile of ~10.0 fold and a Q3 quartile of ~12.6 fold. This provides a sufficient coverage for in-depth diversity study across all genotypes. The quality of the raw sequence data was validated using the B. napus1 is characterized by two highly similar sub-genomes. The A subgenome, derived from B. rapa, consists of 10 chromosomes, while the C subgenome contributed by B. oleracea has 9 chromosomes. The preprocessed sequence reads were mapped to the B. napus Darmor-bzh v4.2 reference genome using the SOAP2 software11 with the options −m 0, −x 500, −v 2. To achieve high confidence when aligning reads to the allopolyploid genome, we used an un-gapped alignment. This approach considers the close similarity of the two subgenomes, increasing the ratio of reads that were aligned to the correct subgenome. The SOAP2 output files with the positions of the aligned reads were converted to the Sequence Alignment/Map (SAM) format applying the 'soap2sam.pl' script (http://soap.genomics.org.cn/down/soap2sam.tar.gz), and then further converted into the binary version of the SAM format (BAM) using SAMtools view command12.The 1,130 Mb genome of the allopolyploid species B. napus during de novo allopolyploidisation [8]. Therefore we included only reference positions having a minimum of 4 reads aligned per reference position. The percentage of reference positions with sufficient read coverage (4) ranged between 51.3% (Q1) and 68.6% (Q3). Overall we discovered sufficient coverage for the natural B. napus accessions lines at 66.5% of all reference positions (Q1: 64.4% and Q3: 71.1%). In comparison, the synthetic B. napus accessions showed a significant overrepresentation of reference positions with missing data, with an average of only 48.4% (Q1: 41.1% and Q3: 57.5%) reference positions covered by at least 4 reads. Among the sequences with sufficient coverage in both groups, a higher sequence diversity compared to the B. napus Darmor-bzh reference sequence was observed in the synthetic accessions than in the adapted accessions.For the 52 different accessions an average of ~90 million reads per accession were aligned as proper read pairs, ranging from ~66.0 million reads at the first quartile (Q1) to ~102.6 million reads at the third quartile (Q3). A high average mapping quality was observed, with an average MAPQ value of ~69.1. The average insert sizes between corresponding read pairs ranged from 158 to 319 bp for the individual genotypes  and Sanger BES of an available BAC library. The construction of the final assembly involved careful investigation to fill the gaps between scaffolds and improve the sequences by error correction applied in the post-assembly phase. However, the final assembly still consists in 13.2% of the sequence base pairs of ‘N’s indicating an ambiguity for the respective nucleotides bases. The structure of the reference genome sequence is organized in 19 B. napus chromosomes. In addition, around 25% (204.9 Mbp) of the sequences of the Darmor-bzh v4.2 reference genome could not be assigned within the assembly process. These contigs were concatenated either in random chromosomes, where the contigs could be assigned to a chromosome, and in a random 'unknown' chromosome where no specific assignment was possible. Altogether, these reported characteristics of the reference genome indicate that the complexity of a genome and its repetitive regions even with today’s sequencing capacities is not an easy task, especially in an allopolyploid plant like B. napus.The B. napus Darmor-bzh v4.2 reference sequence, which appear extensively in data sequenced with the 454 technology15. This is consistent with the increased coverage rate monitored in the gapped alignment. Another possible cause may be the parameters used for the alignment. In this study we focused on the detection of high quality, position-specific SNPs. Therefore, we selected stringent options for the SOAP2 alignment that exclude reads which are mapping with the same probability to more than one position within the genome. As the two subgenomes in the adapted accessions show a similarity of around 95%, a high proportion of reads was excluded from the alignment. The resynthesized lines include accessions with different parental origin. Due to this diversity we expect them to have only a low similarity with the reference genome of the adapted accession Darmor-bzh. Therefore stringent alignment parameters, allowing only two mismatches and no gaps, also lead to a high rate of unaligned reads, potentially leading to a low portion of reference bases being covered. This is also concordant with the results from the less stringent gapped alignment, showing a significantly higher rate of sequence coverage. Finally, for >13% the genomic sequence is not known and therefore is excluded from the read alignment process. In consequence, the genome coverage calculation relies on very stringent and cautious estimation settings and thus might be an underestimation. Relaxing the number of aligned reads (>=4), that are required to be considered as covered positions, affects this calculation substantially. Reducing this requirement to a single read the proportion of the genome coverage is substantially increased. On average 75.6% of the B. napus genome is covered for adapted and 65.1% for synthetic lines when analyzing the stringent SOAP2 read alignment. This further is increased to 81.4% and 77.4, respectively, when the relaxed Bowtie2 alignment is used. In summary, we are confident that the established read alignment resources are of high quality.In our read alignments , a single sample SNP calling was utilized individually for each of the accessions. To reveal diversity with highest sensitivity we performed an approach using multiple variant-calling methods. Significant variability can be found in variant sites detected by different SNP calling methodsSAMtools ‘mpileup’ and together with the discovered variant positions these information is integrated by a custom script (Data Citation 3). For each variant position and genotype the putative allele(s) for the different variants are shown. For differentiation of homozygous and heterozygous positions all alternative alleles are required to succeed the minimal read depth >=4. Genotypes with insufficient or no read information (read depth <4) are presented as ‘NA’, while positions with no alternative allele call are presented with the reference allele. A variant position is defined as homozygous when the allele frequency (AF) in one of the variants is <10% or >90%. Homozygous positions are displayed by the corresponding nucleotides , heterozygous positions (10% <=AF <=90%) with both observed alleles. Per genotype we only present the bi-allelic relationship using the first two major alleles. For the minimum quality score per variant site a cut-off value of 100 was used. Due to the complex polyploid nature of the B. napus genome, heterozygous positions caused by mis-aligned reads from paralogues regions are common, hence we only report homozygous SNPs. The distribution of unique, genome-wide B. napus variant positions the 52 resequenced genomes were depicted graphically in a condensed circular layout constructed using the tool Circos20  before merging of the results from the multiple calling methods for construction of the final variant set. The independent application of multiple variant calling methods allowed us to use the variant caller count (VCC) as an additional confidence value for the prediction of variant positions. The VCC indicates how many variant calling methods predict a particular VP. All successful VP calls fulfil the following criteria in at least one of the genotypes: bi-allelic, SNP quality score >=100, homozygous, read depth >=4 and a VCC >=2. The final diversity set is published as a SNP matrix (Data Citation 4). To construct the SNP matrix the BAM files were converted into the PILEUP format using Circos20 . For claB. napus Darmor-bzh genome assembly contains 34.5% transposable elements1. To gain knowledge about the repeat constitution in our genotype collection we performed a k-mer analysis, using an adapted Kmasker21 method focused on the repeat constitution of sequences surrounding the detected VP sites. For each of the 52 resequenced accessions we used ~1-fold sequence data (down sampling) to construct individual k-mer indices . Each of these genotype-specific k-mer index was used to analyze the 100 bp flanking sequence (upstream and downstream) of a VP site. The combined and normalized results provided as data record represent a useful resource to estimate repeat occurrence (Data Citation 5). We found 545,846 VPs with increased k-mer values (>20) when calculating the average k-mer frequencies of flanking sequences. Within all these positions more than 40 genotypes (Q3) exhibited an increased k-mer frequency in either the left or the right flanking sequence. Validation in the B. napus Darmor-bzh reference genome demonstrated that, among the 4.3 million detected VPs, 693,004 (16.1%) were located in repeat regions and 3,610,807 (~83.9%) in non-repetitive reference positions. This analysis enables a more profound selection of VPs that are less likely to be affected by repeats.The Bowtie2. BAM files of the read alignments were converted into PILEUP files using SAMtools ‘mpileup’. These were further processed by BCFtools22 to screen for insertion and deletions using the setting ‘−V indels’. Stringent parameter setting required a minimal base quality of ‘−Q 30’ and a minimal read alignment quality of ‘−q 20’. A posterior filtering was performed subsequently that is prioritizing InDel sites with a minimal (8) and maximal read depth (50), as well as a stringent IMF (0.9) and IDV (8) setting to identify high quality and homozygous sites. The latter two parameter control the maximal fraction of reads (IMF) and set the maximum number of reads (IDV) that support an InDel. In total we detected 633,844 insertions and 469,860 deletions in the range between −20 and 20 bp, whereby the large majority (~90%) are <=3 bp in length , whereas for the oilseed forms in only 3 of the 32 accessions an exaggerated InDel number was observed. On the other hand 13 accessions were detected to have fewer InDels than the Q1 quartile , of which 10 are oilseed forms like the Darmor-bzh reference genotype. Accessions PBY047, which exhibited the highest number of insertions , and PBY0039, which exhibited the highest number of deletions , had more than 52.000 InDels, illustrating the high diversity within synthetic accessions. Analysis of InDels within coding regions of B. napus genes revealed a moderate enrichment of in-frame InDels (multiples of three nucleotides). This result is in accordance with previous findings in other species23.Calling of insertion-deletion polymorphisms (InDels) was performed independently from the SNP calling as a separate process, using a gapped alignment constructed with n length . The higCOOVAR24. For SNPs the class of non-synonymous SNPs (nsSNPs) was further differentiated into ‘radical’ SNPs, which can cause loss of function (LoF), and ‘moderate’ SNPs, which have lower potential to affect the gene function. The third class ‘low’ includes synonymous SNPs (synSNPs). These variations locate in coding regions, but do not affect the protein sequence. The fourth class ‘other’ includes non-coding SNPs (ncSNPs) that locate in intronic or intergenic regions. As expected, the majority of detected SNPs belong to the latter two classes, with 85.2% classified as ‘other’ and 7.8% as ‘low’. The complete listing of the distribution of SNPs within these 4 classes is given in B. napus. In total 39,446 different genes are affected in at least one of the studied genotypes by at least one radical SNP leading to a predicted LoF. A total of 5,121 genes are modified by InDels that lead to a predicted LoF. For InDels again, the classes ‘LoF’ and ‘moderate’ have minor proportions, with 1.32% and 0.48% respectively. With 98.2% the large majority of InDels is observed in intronic or intergenic regions (‘other’). The full set of gained functional annotation of SNPs and InDels are provided as a GVF file for each genotype (Data Citation 6).We investigated the functional effects of SNP and InDel variant positions (VPs), distinguishing between synonymous and non-synonymous variations using the tool B. napus gene models showed disruptions of the coding sequence by mutations . In total, we observed 488 highly diverse genes, affected in more than 32 genotypes. Furthermore, 11,818 genes were disrupted in a medium number of genotypes (>5 and <=32) and 24,708 genes in a low numbers of genotypes (<5=). Interestingly, a total of 61,594 (61.0%) gene models showed little or no nucleotide variability.A considerable proportion of the 101k BLASTN (version 2.2.30), SOAP2 (version 2.21), Bowtie2 (version 2.2.5), cutadapt (version 1.8), FastQC (version 0.11.2), FaSD (version downloaded January 2015), Freebayes (version 0.9.21), SAMtools (version 0.1.19), BCFtools (version 1.2) and e!DAL (version 2.3.9). Applied parameter settings are described in the corresponding sections.All custom scripts applied for data processing are deposited as DOI (Data Citation 3). The developed custom scripts are available without restrictions. Applied software including version details are 4. Functional annotation of SNPs and InDels are available as GVF files (Data Citation 6). Repeat investigations of sequences encapsulating the variant positions are available as DOI (Data Citation 5). Scripts applied for data processing are deposited as DOI (Data Citation 3). All DOIs were generated using the tool e!DAL25.The SNP matrix of the 4.3 million variant positions is published as DOI (Data Citation 4)B. napus Darmor-bzh reference sequence (v. 4.2), utilising a stringent BLASTN analysis27 with parameter ‘−perc_identity 98’, ‘−evalue 10’, ‘−word_size 11’. The majority of SNP markers are embedded in 201 bp . This information was used to set anchoring criteria requiring that 90% of the marker sequence is linked with 98% identity to the reference sequence. From 52,157 functional, polymorphic markers called by the array in the 52 genotypes, we assigned 35,534 different positions for 32,333 unique, high-confidence markers. Among these, 23,871 diverse reference positions  showed a direct overlap to a detected VP in the 52 accessions. In addition, we utilized a less stringent anchoring, using only the 50 bp oligonucleotide sequences of the SNP markers, and found an additional set of 6,371 SNPs overlapping the VPs that were identified in our study. In total, 30,242 diverse reference positions  could be directly validated by a marker from the 60k SNP array.As a measure of quality we compared the results of the VP calling method with the outcome from the Illumina 60k SNP Brassica Consortium Infinium genotyping array applied on the same samples. Flanking sequences from the SNP markers on the genotyping array were anchored to the To verify the correctness of variant calling we further validated the concordance of alleles at detected positions with the allele information of the genotyping assay. In 99.87% of the stringently assigned SNP markers (99.08% in the relaxed assignment) we found concordance between the two data sets, except for cases where missing marker calls from the Infinium assay can be attributed to excessively stringent filtering of SNPs in the variant calling process. Non-concordance can also result from incomplete anchoring of markers to the reference sequence. Nevertheless, with over 30,000 validated marker positions and a high allele concordance of over 99.76%, we conclude an exceptionally high quality of the SNP predictions.g.leckband@german-seed-alliance.de).Access to the plant materials described in this paper is possible for research purposes via a material transfer agreement with the material owners. Please contact Dr. Gunhild Leckband .


Sorghum bicolor) is one of the most important cereal crops globally and a potential energy plant for biofuel production. In order to explore genetic gain for a range of important quantitative traits, such as drought and heat tolerance, grain yield, stem sugar accumulation, and biomass production, via the use of molecular breeding and genomic selection strategies, knowledge of the available genetic variation and the underlying sequence polymorphisms, is required.Sorghum (Sorghum bicolor (v2.1) and the recently published sorghum re-sequencing data, ~62.9 M SNPs were identified among 48 sorghum accessions and included in a newly developed sorghum genome SNP database SorGSD (http://sorgsd.big.ac.cn). The diverse panel of 48 sorghum lines can be classified into four groups, improved varieties, landraces, wild and weedy sorghums, and a wild relative Sorghum propinquum. SorGSD has a web-based query interface to search or browse SNPs from individual accessions, or to compare SNPs among several lines. The query results can be visualized as text format in tables, or rendered as graphics in a genome browser. Users may find useful annotation from query results including type of SNPs such as synonymous or non-synonymous SNPs, start, stop of splice variants, chromosome locations, and links to the annotation on Phytozome (www.phytozome.net) sorghum genome database. In addition, general information related to sorghum research such as online sorghum resources and literature references can also be found on the website. All the SNP data and annotations can be freely download from the website.Based on the assembled and annotated genome sequences of 4 grasses.SorGSD is a comprehensive web-portal providing a database of large-scale genome variation across all racial types of cultivated sorghum and wild relatives. It can serve as a bioinformatics platform for a range of genomics and molecular breeding activities for sorghum and for other C Sorghum bicolor) originated from Africa and became an important cereal crop after a long period of domestication and selective breeding [http://faostat3.fao.org). In contrast to C3 crops such as rice and wheat, sorghum has the C4 photosynthetic pathway, which leads to higher photosynthetic efficiency under circumstances of intense light, high temperature and low water supply [Miscanthus and switch grass [4 grasses make sorghum a potential genetic model for the design of bioenergy crops [Sorghum  and simple  compared to other CSorghum species and subspecies.Recently, various high throughput strategies have been developed for genome re-sequencing –13, resuhttp://sorgsd.big.ac.cn). SorGSD contains ~62.9 million SNPs from a diverse panel of 48 sorghum accessions divided into four groups, including improved inbreds, landraces, wild/weedy sorghums, and accessions of the wild relative Sorghum propinquum. These SNP data have been annotated and an easy-to-use web interface has been designed for users to browse, search and analyze the SNPs efficiently. SorGSD allows users to query the SNP information and their relevant annotations for individual samples. The search results can be visualized graphically in a genome browser or displayed in formatted tables. Users can also compare SNP data between two and more sorghum accessions. The output of query results can be downloaded for further investigation, or users can bulk download the entire SNP dataset of 48 accessions. SorGSD also manages additional sorghum related information, such as general descriptions of sorghum and its genome, sorghum research institutions around the world, and lists of sorghum literature references.To enhance the utility of sorghum SNP data, we developed a web-based large-scale genome variation database  are separated into a distinct group since they are highly divergent from other S. bicolor races , for users to search, display and retrieve the SNPs and their annotations.The search function provides a user-friendly web interface to query SNP information. Users can search SNPs by specifying chromosomal co-ordinates or the locus ID. Users can also query SNPs based on their genotypes, and predicted variant effects. In addition, users can compare the SNPs between two and more sorghum lines. The query results can be shown as a formatted table which contains the information of ID, chromosome position, genomic location and predicted coding effects, 5′ and 3′ flanking sequences, reference and derived alleles, respectively. SNPs from the stringent set identified by both pipelines .The help menu provides a “How to” page, which gives a number of examples for users to learn how to search and compare target SNPs. For example, a step-by-step user-guide shows how to obtain non-synonymous SNPs in chromosome 1 of sweet sorghum E-Tian, and how to compare SNPs between sweet sorghum E-Tian and two grain sorghum Ji2731 and Keller. An FAQs page provides answers to a range of frequently asked questions not only about the content and usage of SorGSD but more broadly about sorghum genomics. Detailed information including software tools, parameters and data sources is presented in the “Pipeline” page. The “Statistics” page shows the SNP numbers distributed in different genomic regions  developed at Purdue University became available as a functional gene discovery platform.High coverage resequencing data from two previous sorghum studies , 16 wereWe will improve the SNP calling pipeline and the annotation procedure to obtain more accurate SNP data and upload them into the database. Furthermore, we will include additional types of genome variation data detected by newly developed pipelines, including INDELs and copy number variations (CNVs). At the same time, we will improve the web interface especially in the search function and give more examples in the user guide to help novice users to access the database easily. We will add more analytical functionalities so that users can perform more analyses such as Blast search, sequence alignment and phylogenetic analysis.SorGSD can serve as a bioinformatics platform to inform wet-lab experiments including biomarker development, allele mining and gene function assessment. In addition to the collaboration among research groups involving in this work, we will collaborate with other domestic and international laboratories in the sorghum research community to sequence and annotate more sorghum accessions in the future.We will update the database regularly and add SNP datasets with newly available re-sequenced sorghum accessions. We hope that the high density of these SNP data at genomic level collected from the major races of cultivated sorghum as well as other subspecies is a rich repository for a broader research community working in biomarker identification, genetic analysis and molecular breeding, especially for energy plant sweet sorghum cultivation.http://popgen.dk/angsd/index.php/RealSFS) and SOAPsnp [The construction of SorGSD was a multi-step process. Firstly, the sorghum re-sequencing paired-end raw reads reported in the previously published works were downloaded , 16. In  SOAPsnp  and anno SOAPsnp . With thThe raw reads of sequencing data were from three original datasets. The largest dataset  containshttp://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_Sbicolor) using the BWA program [S. propinquum accessions. The SAM tools package [http://picard.sourceforge.net) was applied to eliminate duplicated reads generated during the process of library construction.After trimming adapters, the clean reads were mapped to version 2.1 of the reference genome  and SOAPsnp [Subsequently, the GATK tools , 27 were SOAPsnp , describ SOAPsnp . Approxi SOAPsnp , which e SOAPsnp .The SNP data and their related annotations were formatted into tables and stored in SorGSD using the MySQL database management system (version 5). The web interface of SorGSD was designed by JAVA/JSP (JDK 1.6) under the Apache/Tomcat web server (version 2.0) running under a Linux operation system (CentOS 6). We installed the generic genome browser GBrowse  as a chr


While much attention has focused on the development of high-density single nucleotide polymorphism (SNP) assays, the costs of developing and running low-density assays have fallen dramatically. This makes it feasible to develop and apply SNP assays for agricultural species beyond the major livestock species. Although low-cost low-density assays may not have the accuracy of the high-density assays widely used in human and livestock species, we show that when combined with statistical analysis approaches that use quantitative instead of discrete genotypes, their utility may be improved. The data used in this study are from a 63-SNP marker Sequenom® iPLEX Platinum panel for the Black Tiger shrimp, for which high-density SNP assays are not currently available.For quantitative genotypes that could be estimated, in 5% of cases the most likely genotype for an individual at a SNP had a probability of less than 0.99. Matrix formulations of maximum likelihood equations for parentage assignment were developed for the quantitative genotypes and also for discrete genotypes perturbed by an assumed error term. Assignment rates that were based on maximum likelihood with quantitative genotypes were similar to those based on maximum likelihood with perturbed genotypes but, for more than 50% of cases, the two methods resulted in individuals being assigned to different families. Treating genotypes as quantitative values allows the same analysis framework to be used for pooled samples of DNA from multiple individuals. Resulting correlations between allele frequency estimates from pooled DNA and individual samples were consistently greater than 0.90, and as high as 0.97 for some pools. Estimates of family contributions to the pools based on quantitative genotypes in pooled DNA had a correlation of 0.85 with estimates of contributions from DNA-derived pedigree.Even with low numbers of SNPs of variable quality, parentage testing and family assignment from pooled samples are sufficiently accurate to provide useful information for a breeding program. Treating genotypes as quantitative values is an alternative to perturbing genotypes using an assumed error distribution, but can produce very different results. An understanding of the distribution of the error is required for SNP genotyping platforms.The online version of this article (doi:10.1186/s12711-014-0051-y) contains supplementary material, which is available to authorized users. High-density SNP (single nucleotide polymorphism) assays have become a standard tool in the study of human, animal and plant genetics, due to a rapid increase in SNP density for a constant or falling price per assay. Concurrently, progress has been achieved in technologies for low-density SNP assays with a substantially reduced price per assay. These developments in two directions i.e. maximum density per unit of cost and minimum cost per assay, have provided tools at different price points that are suited to different applications across a broad range of genetics studies. While much of the focus in the literature is on discoveries that arise from the application of the newest, most powerful high-density assays, in many applications, especially in species other than humans, reducing the price per assay is more important than increasing the number of SNPs in the assay. Parentage assignment in commercial livestock industries is an example. The benefit of knowing pedigree is real and quantifiable, but is relatively small per individual animal, and the cost of alternative means of assigning parentage puts a cap on the price at which genomic assays will be beneficial. In commercial aquaculture there are often no alternative means of assigning parentage unless families are reared in isolation, which is undesirable from the perspective of tank infrastructure. Consequently, there is much interest in the use of genomic assays for pedigree construction. In aquaculture, the value of individual animals is perceived to be lower than in many terrestrial livestock species, so systems to assign parentage may be even more sensitive to the price of assays. However, the opportunities are significant. DNA-based parentage removes the need for single-family rearing, allows physical tagging at a much later age, and offers the opportunity to link phenotypes from individuals grown in commercial ponds to broodstock grown in specialized pond or tank systems . In casePenaeus monodon breeding program, Sellars et al. [The shrimp industry is an example of an industry for which to date no high-density SNP assays have been developed, despite ongoing efforts. Establishment of Penaeid shrimp selective breeding programs over the last 20 years have underpinned global expansion of a sustainable shrimp aquaculture industry that is worth an estimated US 3 billion dollars in 2012 . More recently, breeding programs have adopted microsatellite-based DNA testing to enable accurate pedigree assignment, both to select for specific genetic traits that maximise production efficiency and to restrict inbreeding . When aps et al.  applied Microsatellites have been empirically compared to SNPs for parentage analysis in a number of studies e.g. –8 and paet al. [et al. [et al. [To date, comparisons of microsatellites and SNPs for parentage analysis have generally used the same methods and software for each assay platform, with little consideration of the properties of the method. Over the years, a number of statistical approaches to parentage analysis have been considered  from G9  breeding shrimp and a random sample of 202 G10 shrimp from the same shrimp breeding line were used. Pleopod tips were snap-frozen on dry ice for transport back to the laboratory. Genomic DNA (gDNA) was extracted from each individual using CSIRO standard industry protocols that apply QIAGEN’s DNeasy extraction methodology. gDNA was also extracted from 583 G1 and G2 individuals from an unrelated breeding line from the same farm. In addition to the individual samples, the 202 G10 shrimp were allocated at random to nine pools, each containing between 18 and 24 individuals, and the 311 G9 shrimp were allocated at random to 13 pools, each containing between 23 and 26 individuals. Pooled gDNA samples were then created by mixing 5 μL of gDNA from each individual in the pool. Subsequently, 60 μL from each pooled samples and 500 ng of gDNA from each individual shrimp were sent to GeneWorks® for genotyping using the 63-SNP marker Sequenom® iPLEX Platinum panel reported by Sellars et al., [Samples were available from multiple generations of  et al., . Of thesij, for sample i and SNP j, these consist of an area for each allele (a1ij and a2ij) and an uncertainty measure for each allele (u1ij and u2ij). We excluded assay results when the Sequenom software failed to call an allele. For all other assays, we converted Cartesian X-Y area measurements to polar coordinates [pij is in the μjAA, μjAB and μjBB) and standard deviations  for values pij for each genotype class  for each SNP. Unlike in the approach used to estimate B allele frequencies in [pij = 0.0, 0.5 and 1.0 corresponded to the means μjAA, μjAB and μjBB, respectively, we did not scale the pij values. As a measure of the power to discriminate between genotype classes, we used the Welch statistic  and (μjAB to μjBB), respectively.All 1096 G1, G2, G9 and G10 individual samples  were used to estimate SNP specific parameters. In addition to the standard genotype call for each SNP for each sample, GeneWorks® provided estimates of the X and Y areas that relate to the A and B alleles for each SNP, which are the raw output from the Sequenom genotyping platform. For assay rdinates  to derivncies in , where aion in R ). The WeParents from generation G9 were assigned to the G10 individuals in a two-step process.μjAA, μjAB, μjBB,σjAA, σjAB and σjBB) and the measures of allelic proportion pij. The latter were included regardless of whether the Sequenom software called a genotype, but were set to missing when a1ij - u1ij + a2ij - u2ij was less than 3.0. This cut-off was chosen after examining the values of a and u for those samples that were not assigned a genotype by the Sequenom software. For each pij, a quantitative ordered genotype probability matrix Gij was estimated as:ijAA is the height of the N distribution at x = pij, ΦijAB and ΦijBA are each half the height of the N distribution at x = pij, ΦijBB is the height of the N distribution at x = pij, and the sum ΣΦij is over genotypes AA, AB, BA and BB. The G matrices are symmetric, and have dimension (2 × 2) for biallelic SNPs. The vector of probabilities of allele transmission from a sire or dam i at marker j isFirst, the likelihoods of sire-dam-offspring trios were estimated. Instead of first assigning genotypes and then applying a likelihood method that incorporated an error term , G is symmetric. So here the equation for T could be simplified, but there are situations where G might not be symmetric , so we chose to leave the equation in this more general format. For a potential parent pair, sire (s) and dam (d), with transmission vectors Tsj and Tdj, the transmission matrix Goj, the likelihood of the trio  for marker j is:° is the Hadamard or entrywise product. To estimate the likelihood when marker data for one parent is missing, the genotype matrix for the missing parent was set to Fj is the (2 × 1) vector of allele frequencies for SNP j, estimated from all G9 animals. In the likelihood ratio Loj is the likelihood under the null hypothesis that the offspring is unrelated to the sire and dam, which is constructed by treating both parents as missing. The likelihood ratio across all markers is the product of the marker likelihood ratios, or the sum of the log-likelihood ratios. For consistency with earlier publications, we will refer to the summed log-likelihood as the log odds (LOD) score. We only retained LOD scores where, for at least 10 SNPs, none of the sire, dam or offspring genotypes were missing. This relatively relaxed threshold was used because the LOD scores were further processed in the second step, as described in a following paragraph. When the LOD for a sire-dam-offspring trio was less than the LOD for the sire-offspring pair with a missing dam, the sire-offspring LOD was used and the dam was assumed to be missing. Likewise, when the LOD for a sire-dam-offspring trio was less than the LOD for the dam-offspring pair with a missing sire, the dam-offspring LOD was used and the sire was assumed to be missing. The most likely pedigree was identified and will be referred to as the unrestricted pedigree.Multiplying by the vector of 1s produces the row averages P. monodon and hatchery records. The G10 offspring were produced by natural mating of the G9 parents in large mating groups, spawned over a three-week period. Under natural mating, females can only mate immediately following moult, which occurs in a cycle of approximately three weeks, and at each moult, females mate with at most one male. Given the width of the spawning window, it is unlikely, but not impossible, that females produced offspring from more than one male. After mating, male P. monodon take 7 to 12 days to re-develop their spermatophores [The second step in the parentage assignment was to impose constraints due to the reproductive biology of tophores ,37. So sj that comprised the maximums of the unordered genotypes in Gij (with the unordered heterozygous genotype being the sum of the two ordered heterozygous genotypes); and second, as:For comparison, we also conducted the analysis described above using an established maximum likelihood approach ,34,35 anThis second formulation allows for an underlying error rate regardless of the precision of the genotyping assay, which is similar to allowing a certain number of mismatches when using an exclusion method. We refer to Mij replaces the genotype probability matrix Gij in Equation Dij matrices are perturbed with an assumed error function. In forming the Dij matrices, we included only markers for which the most likely genotype in the quantitative ordered genotype probability matrix Gij was greater than 0.98 (or 0.49 for the two heterozygous genotypes).To assign parentage, the genotype probability matrix Dij is used instead of Mij to produce the transition matrices in Equation Counting mismatches for the exclusion method using the matrix formulation is equally straightforward. Matrix j), for each of the nine pools (subscript k) we assumed that the reaction had failed for assays for which akj1 + akj2 − ukj1 − ukj2 ≤ 3. For the remaining assays, allelic proportions pkj were estimated for each pooled sample using Equation et al. [μjAA, μjAB and μjBB) estimated from individual samples were used to adjust the allelic proportions to obtain estimates of allele frequencies For each SNP :As an estimate of the variation associated with Allele frequencies of pooled samples, m being the number of SNPs for each pool k, containing individuals from n families, matrices X (subscript k implied), y, and w were assembled as follows:Xjl contains estimates of the allele frequencies for SNP j and family l, given the estimated genotype probability matrices for the sire and dam described above. Missing values in X and y were replaced with 0.5, and missing values in w were replaced with 1.0. These X and y matrices are equivalent to those of Kinghorn et al. [w. These weights are based on the differences in resolution between genotype clusters for individual samples, which we assume is related to the variance of allele frequency estimates for pooled samples. We then used the pcls function in the R package mgcv [n × 1) vector of family contributions β, subject to the constraint min(β) > 0. Strictly, an additional constraint ∑ β = 1.0 applies, but we found that the sum of the solution vector β* returned by pcls was generally close to 1.0 , and we scaled β*  to produce a final estimated family contribution vector y using the means of allele frequencies for individual samples in the pool  for selected SNPs, based on X-Y areas unadjusted for area uncertainty on the left, and based on X-Y areas adjusted for area uncertainty on the right. For the adjusted plots, the means  and standard deviations  from values pij in each genotype class  are also provided. The first SNP (78443-0_3537) illustrates the value in subtracting the uncertainty value; although there was little change in the values of p for heterozygous individuals, the variation in the values of p for homozygous individuals was much reduced. The second SNP (550057-0_189) illustrates that the variation in heterozygous individuals could be much higher than that in homozygous individuals, and that the mean p for heterozygous individuals may not lie half way between the means for the homozygous individuals. For this SNP, most assays produced a genotype estimate. The third SNP (484475-0_438) exhibits even more variation, and the mean value of p for homozygous GG individuals was not close to 1.0. For this SNP, a genotype was not returned for some assays when p values did not clearly lie within the clusters. For the final SNP (753848-0_614), the within-genotype variation was smaller and clusters were quite distinct, with few uncalled genotypes, but the mean values of p were very different from 0.0, 0.5, and 1.0. The Welch statistics  for these four SNPs were , ,  and , respectively. For the second SNP, there was a large difference between τA, and τB, with a clearer distinction between the means of the AA and AG genotypes than between the AG and GG genotypes.Of the 64 538 assays , an estimated genotype was returned by the Sequenom software for 54 761 assays. There was a lot of variation in genotype call rates between SNPs. The reasons for this become evident when the raw data are examined. In Figure For three of the 61 SNPs, the number of genotype calls was not sufficient to estimate the SNP parameters, and, for the remaining 58 SNPs, quantitative genotype probability matrices (quantitative genotypes) could be estimated only for 43% of individuals. The estimated error rates For 87% of individuals, quantitative genotypes could be estimated on 50 or more SNPs, but for 10% of individuals, less than 20 SNPs provided quantitative genotypes. When quantitative genotypes could be estimated, in 95.4% of cases the most likely unordered genotype class had a genotype probability that exceeded 0.98, our threshold for declaring a genotype for use in the perturbed maximum likelihood and exclusion methods.In Figure G9 males (166) and females (124) recorded as potential parents of the G10 offspring were tested as parents of G10 progeny (210). Using the quantitative genotypes, of the 34 860 sire-offspring pairs, a LOD score was produced for only 28 566 pairs, the others having less than 10 SNPs for which both sire and offspring had an estimated allelic proportion. For dam-offspring pairs, 19 872 LOD scores were produced from a possible 26 040 pairs. Testing each sire by dam combination for the 207 offspring resulted in LOD scores for 2 741 922 sire-dam-offspring trios for which at least 10 SNPs in all three animals had a valid genotype. In Figure Differences in the distributions of LOD scores produced differences in the distribution of most likely pedigrees. Table In Figure The remaining results relate to the quantitative genotypes and likelihoods. Summaries of the impact of the pedigree constraints are in Table f * (P = 0.034). Generation (G9 or G10) only affected the correlation through its effect on the proportion of genotypes available on individuals; if this proportion was in the model then the generation effect on the correlation was not significant. There was also variation in the number of SNP genotypes available per pool, and this also had an effect on the correlation (P = 0.002). This suggests that for assays with fewer SNPs called, results for other SNPs were also more variable. The estimate of the Welch statistic ; the difference between f * tended to be smaller for estimates with high Welch statistics .Genotyping of three of the 22 pools failed and only results for the other 19 pools are presented, in Table p for individuals in the pool (i.e. using  f *) instead of from pooled DNA samples, correlations with pedigree-based estimates were 0.87, 0.65 and 0.60 for the full-sib, half-sib and unrestricted pedigrees, respectively. These correlations did not differ greatly from the correlations obtained from pooled DNA samples, which may be explained by the high correlation between contributions estimated from pooled and from individual samples . The most abundant family generally accounted for between one quarter and one third of the individuals in a pool . RatherThe differences in the pattern of area measurements between SNPs Figure  provide et al. [We tried to keep our approach to parentage assignment as clear as possible. The Mendelian transmission rules are simple and constant for diploid species, and are easily represented by matrix operations, regardless of whether genotypes are discrete or continuous. The error function we used for our quantitative approach assumed that area estimates produced by the genotyping platform have a normal distribution. Alternative maximum likelihood approaches make different assumptions about the distribution of the error, but the matrix algebra is the same. One characteristic of our error distribution is that, unlike (for example) Marshall et al. , we did We observed large differences between results for the quantitative approach and the perturbed error approach in our study, which highlights the importance of the error term in the maximum likelihood procedure. For some SNPs and some individuals, the quantitative approach sets the error term to a very low value, essentially zero, and therefore applies a strict exclusion criterion for these SNPs. For other SNPs and individuals, a more lenient error term is applied. Whether this is better or worse than SNP specific error rates, or the arbitrary error rate of 1% used for comparison with perturbation, depends on how well the alternatives match the true but unknown error distribution. For a SNP genotyping technology for which the probability of the relevant homozygous genotype is effectively equal to 0 whenever the signal for either X or Y is effectively 0, the quantitative method is preferred. If there is a probability that at random one of the X or Y signals totally drops out for a heterozygous individual, while the other signal remains strong, then the perturbed method is preferred. There is nothing in our dataset to help us to choose between these two alternatives. The assignment rates for the two approaches were both unacceptably low at around 20%, due either to a lack of power in the panel or missing genotypes on parents. However, because the families assigned using one error distribution had only a small overlap with the families assigned using other error distributions, it is clearly very important that we learn all we can about the true distribution of errors.Based on Figure P. monodon breeding program, although not desirable, this incomplete information is not an insurmountable problem. Individuals in such families, while missing one parent, are known to be full-sibs, and the potential mates of the identified parent are likely known and from a common genetic background. Thus, it is unlikely that breeding decisions would differ in a significant way if the missing information was available.A weakness of this study is the lack of knowledge on the true parentage of the G10 progeny, especially for families for which a parent was declared as missing. We do not know whether the true parent was not tested, or whether the true parent was tested but genotyping failed (which occurred in around 10% of cases), or whether the true parent was tested and wrongly excluded. However, for industrial application in the jA or τjB) related to the interval that contained the allele frequency estimate (pkj) from pooled DNA, and the absolute value of the difference between the allele frequency estimate from pooled DNA and from individual DNA (Figure Correlations between allele frequencies estimated from individual and from pooled samples were consistently greater than 0.9. Not surprisingly, given the variability between SNPs Figure , there wA Figure , panel BP. monodon multiplexed assays already available [The high correlations between allele frequency estimates from pooled and individual samples were reflected in the high correlations between estimates of family contributions to pools when using allele frequencies from the two sources (Figure vailable  or high-Treating SNP genotypes as continuous instead of as discrete values in parentage assignment poses no additional statistical problems. The maximum likelihood framework seamlessly incorporates quantitative allele probabilities; they only require an alternative formulation for the distribution of the error term. However, since the distribution of the error term can have a large impact on pedigree assignments, it is important to gain an understanding of this distribution. The likelihood equations can be conveniently formulated using matrix algebra, allowing easy implementation on any software platform that supports matrix operations. Using the Sequenom assay with only 63 SNPs, most individuals were assigned to full-sib families, as expected given the origin of the samples. Further refinement of the pedigree by constraining parents to only one mate reduced the number of families from 64 to 43, and probably results in a more accurate pedigree given the uncertainty in the parentage assignments from a low-density SNP panel. Estimates of family contributions in pooled samples of DNA obtained with 63 SNPs were of low accuracy, and for this application the use of multiple multiplexed low-density SNP assays would be beneficial.


Subsequently, pedigree and SNP-based genetic parameters were estimated and a genome-wide association study (GWAS) was conducted on EN, LR and AFE. The heritability estimates were similar between pedigree and SNP-based estimates varying from 0.17 to 0.36. In the GWA analysis, we identified nine genome-wide significant loci associated with EN of the laying periods from 21 to 26 weeks, 27 to 36 weeks and 37 to 72 weeks. Analysis of GTF2A1 and CLSPN suggested that they influenced the function of ovary and uterus, and may be considered as relevant candidates. The identified SNP rs314448799 for accumulative EN from 21 to 40 weeks on chromosome 5 created phenotypic differences of 6.86 eggs between two homozygous genotypes, which could be potentially applied to the molecular breeding for EN selection. Moreover, our finding showed that LR was a moderate polygenic trait. The suggestive significant region on chromosome 16 for AFE suggested the relationship between sex maturity and immune in the current population. The present study comprehensively evaluates the role of genetic variants in the development of egg laying. The findings will be helpful to investigation of causative genes function and future marker-assisted selection and genomic selection in chickens.Egg number (EN), egg laying rate (LR) and age at first egg (AFE) are important production traits related to egg production in poultry industry. To better understand the knowledge of genetic architecture of dynamic EN during the whole laying cycle and provide the precise positions of associated variants for EN, LR and AFE, laying records from 21 to 72 weeks of age were collected individually for 1,534 F Egg production traits, including egg number, egg mass and egg laying rate, have always been a focus of attention in the poultry breeding. Egg number (EN) and egg laying rate as the most meaningful traits in layers breeding program has procured considerable genetic progress in commercial egg-layer breeds through traditional selection for several decades, reaching a level at an egg on almost every day in highly efficient hens . SimilarGenetic variations in egg production traits can be dissected and quantified with the associated genetic markers. Thus, to identify genetic variants affecting egg production traits is one of primary goals in the poultry genetics for more than fifteen years. Numerous previous studies had been conducted to map or identify QTLs and SNPs that associated with EN. However, most of these candidates were cross-sectional in a specific laying period –5, which2 cross population design has obtained success in QTL mapping studies and GWA studies for target traits in chicken [2 offspring. Of the two breeds, White Leghorn (WL) is a world-wide standard layer breed with high egg production performance, while Dongxiang Blue-shelled (DBS) chicken is a Chinese local breed with relatively low laying performance [2 design population and chicken 600 K SNP arrays, the current study aim to identify the patterns of genetic control for EN at various laying period from 21 to 72 weeks of age and to unveil possible mutants and genes of interest. In addition, we expect to provide some promising candidate genes for egg laying rate and age at first egg.An F chicken , 8. Hereformance . Meanwhiformance  allows gthe Guidelines for the Care and Use of Experimental Animals established by the Ministry of Agriculture of China . The entire study was approved by the Animal Welfare Committee of China Agricultural University (permit number: SYXK 2007–0023).Blood samples were collected from brachial veins of chickens by standard venipuncture along with the regular quarantine inspection of the experimental station of China Agricultural University in accordance with 2 resource population was derived from reciprocal cross between two breeds that differed in egg production, i.e. White Leghorn (WL) originated from Shanghai Poultry Breeding Co., Ltd with selection on egg production and quality, and Dongxiang Blue-shelled chicken (DBS) which has been selected for egg production and egg-shell color since 1998 at the experimental farm in Jiangsu Institute of Poultry Science. Briefly, reciprocal mating of unrelated 6 WL(♂) × 133 DBS (♀) and unrelated 6 DBS(♂) × 80 WL (♀) was used to produce the F1 generation. Unrelated F1 chickens, involving 25 males and 406 females from WL/DBS pair and 24 males and 233 females from DBS/WL pair, were randomly selected to produce the F2 generation. In F2, a total of 3,749 F2 birds including 1,856 males and 1,893 females were produced from 590 half-sib families in a single hatch as described previously [A chicken Feviously . Hens weAt 17 weeks of age, birds were moved to the laying house and kept in the individual stair-step cages for one week adaption. For each bird, age at the first egg (AFE) was recorded. The number of eggs produced from AFE to 72 wks of age was daily recorded for each bird, and then egg numbers were divided into five parts based on the characterization of egg production curve , includi2 hens were genotyped for 580,961 markers using Affymetrix 600 K chicken high density genotyping array [http://affymetrix.com/) and the final average sample call rate was 99.2%. And then all autosomal SNPs from 1,512 qualified samples that met quality control criteria that set in PLINK [P-value < 1e-6) were used for imputation implemented in Beagle Version 4 software package based on localized haplotype clustering [Genomic DNA was isolated from whole blood samples using phenol-chloroform methods. A total of 1,534 Fng array  completein PLINK   distances were calculated between all individuals using the independent SNP markers. Finally, we calculated multidimensional scaling (MDS) components using the mds-plot option based on the IBS matrix, which was included as covariate in the subsequent GWAS analyses [Subsequently, the eligible SNPs and birds were used to evaluate the population structure by PLINK . Firstlyanalyses .y is the vector of traits value for all individuals; W is an matrix of covariates (fixed effects contain first MDS component and a column of 1s); α is a vector of the corresponding coefficients including the intercept; x is an vector of marker genotypes; β is the effect size of the marker; u is vector of individual random effects; ε is vector of errors. Wald test statistic was used as criteria to screen SNPs significantly associated with the investigated traits.Genome-wide association study analysis was performed using mixed models approach  implemenP-value threshold, simpleM method [−7  and 1.69×10−5 , respectively. Similarly, the chromosome-wide significant P-value threshold was adjusted based on the independent tests in each chromosome. The Manhattan and Q-Q plot were constructed for each trait by the GAP package (http://cran.r-project.org/web/packages/gap/index.html) within the R software [With respect to the genome-wide significant M method  was usedsoftware .haplo.score in the Haplo.Stats R-package, which calculated score statistics to evaluate the association of a trait with haplotype for ambiguous linkage phase [P-value was calculated to test overall associations among LD blocks and traits, and P-value for each haplotype was calculated to test significance between haplotype and traits. Score (Hap-Score) and frequency (Hap-Freq) for a particular haplotype were also provided in the results.Linkage disequilibrium (LD) analysis were performed for the chromosomal regions with many significant SNPs clustered using software Haploview version 4.2  with algge phase . Of the 1 generation, and available hens from the F2 generation. Among these animals, only F2 birds were phenotyped for egg production traits. On the other hand, estimation of the phenotypic variance explained by significantly associated SNPs and all SNPs (SNP-based heritability [Pedigree-based genetic parameters for egg production were estimated with the univariate and bivariate (two-trait) animal model implemented in DMU software  as follotability  and genetability ) were catability .Gallus gallus 4.0 genome version, and genes within 500,000 base pairs flanking the associated SNPs were chose for the further analysis. The positional annotation genes were extract from NCBI database using GetNeighGenes in the NCBI2R R-package (http://cran.r-project.org/web/packages/NCBI2R/index.html). Investigation of gene ontology (GO) and the relevant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for the genes within 1Mb of associated SNPs was performed to determine biological processes and pathways associated with traits using the Database for Annotation, Visualization and Integrated Discovery (DAVID) [SNP positions and information were obtained using annotation of  (DAVID) .The detailed information for all autosomal SNPs that passed the quality control and independent test for 28 autosomes and 2 linkage groups are respectively shown in Genetic parameters of egg number (EN1-5), egg laying rate (LR) and age at first egg (AFE) are presented in The Manhattan and Q-Q plot for egg number in the pre-peak, peak and persistent laying stages (EN1-3) are shown in P-value = 1.69e-5) in this region showed that 15 of these SNPs were clustered in three blocks with scale of 389 kb, 157 kb and 334 kb, respectively  and stonin 2 (STON2). It was notable that STON2 was an important paralog of GTF2A1. In addition to associations on GGA5, three associated genomic regions were located on GGA7, GGA16 and GGA23, respectively. The most significant SNP , explaining 2.61% (SE = 0.04) of phenotypic variance, situated in a 3 kb block on GGA23 (claspin (CLSPN) locating in the upstream 1.02 kb of the SNP at 4.19 Mb on GGA23.In the pre-peak laying stage (21–26 weeks), six loci located on genomic region spanning from 39.76 to 43.16 Mb on chromosome 5 (GGA5) significantly associated with egg number, which together explained 2.00% (SE = 0.02) of phenotypic variance. Linkage disequilibrium (LD) analysis for 19 SNPs that passed suggestive significant threshold  from NCBI database. These genes were used to perform gene ontology (GO) based on biological process and KEGG pathway analysis in DAVID (available at http://david.abcc.ncifcrf.gov/home.jsp). Twelve significant GO terms and two KEGG pathways were identified  and three genome-wide suggestive significance  SNPs in an genomic region from 7.47 to 7.54 Mb on GGA9 were related to egg number with negative effects for minor alleles. The most significant SNP, locating in 3’-UTR of phenylalanyl-tRNA synthetase, beta subunit (FARSB), explained 2.24% (SE = 0.03) of phenotypic variance. In addition to the associated hits on GGA9, five suggestive significant associations were situated on GGA1, GGA3, GGA5, GGA19 and GGA24. The positions and annotations for associated SNPs are provided in In GWA analysis of egg number in the peak laying period (EN2), one genome-wide significance . These SNPs were in strong LD status  between homozygous genotypes.To better show the effects of genome-wide significant SNP between individuals with different genotypes, a group of box plot was shown in P-value less than 8.43e-7  had not fell into these two blocks. The phenotypic difference between two homozygous genotypes (CC > TT) of this SNP was 6.86 at a significant level . Notably, loci on GGA23 and GGA9 that significantly associated with EN1 and EN2 were not significantly related to EN21-40, indicating that the genetic contributions to EN21-40 were mainly determined by significant genetic variants on GGA5 in the pre-peak laying period. As the accumulative egg number extended to 56 weeks (EN21-56), only several suggestive signals distributed on GGA1, 2, 3, 5 and 23 were detected, and the significant region on GGA5 for EN21-40 decreased to a suggestive significance for EN21-56. Moreover, one SNP on GGA1 was detected to be with a genome-wide suggestive significance for EN21-72. The difference of associated variants among EN21-40, EN21-56 and EN21-72 demonstrated that accumulative egg number was a complex trait varying with the laying age, and a proper molecular breeding program should be designed for the augment of accumulative egg number. The Manhattan and Q-Q plot for EN21-40, EN21-56 and EN21-72 are shown in Seventeen SNPs on GGA5 with  8.43e-7 , Table 6 8.43e-7 , howeverP-value, which was calculated from Bonferroni correction based on the independent tests of each autosome, as the criteria to screen associated SNPs. The identified 27 associations were located on GGA3, 5, 7, 9, 13, 17, 19, 23, 24, 27 and 28, which together explained 8.05% phenotypic variance of LR. The gene ontology analysis showed that 530 nearby genes were enriched in two significant  GO terms, including lipid localization and regulation of fatty acid biosynthetic process (No genome-wide significant SNP was detected for egg laying rate (LR), but a number of suggestive significant regions were identified in various chromosomes , indicat process . The infrs313725141, P-value = 1.36☓10−6) with a positive effect on AFE was in the downstream 0.75 kb of TAP2 ) on GGA16. Some other annotated genes encompassed MHC class IV antigen (B-G), guanine nucleotide binding protein (G protein), beta polypeptide 2-like 1 (GNB2L1), Transporter 2, and tripartite motif containing 27 (TRIM27) on GGA16, and stathmin 1 (STMN1), POU domain, class 3, transcription factor 1 (POU3F1) and glutamate receptor, ionotropic, kainate 3 (GRIK3) on GGA23.Two regions locating on GGA16 and GGA23 were detected to be associated with age at first egg (AFE) surpassing the suggestive genome-wise significance . The mos2 resource population to identify the genetic variants for egg number in a more comprehensive standpoint. The purpose of F2 design was to generate larger genetic variation through the DNA recombination. The most associated markers are therefore expected to reside in proximity of the causal variants, and then maximizing difference of interested trait values in the population [2 resource population was suitable for GWA analysis.In the current study, we conducted a GWA analysis in an Fpulation . A numbepulation , 29–31. pulation . Herein,For the heritability estimates, pedigree-based heritability for EN1 coincided with previously reported estimates , 32, whirs317449530 for EN1 was located in 3’-UTR of GTF2A1 gene on GGA5. GTF2A1 is a general transcription factor that interacts with the TFIID-promoter complex required for transcription initiation by RNA polymerase II [rs317510777 was situated nearby the CLSPN gene on GGA23, which encodes claspin protein involving in monitoring of DNA replication and sensoring of DNA damage in mammals [GTF2A1 and CLSPN were supposed to be associated with the function of ovary and uterus hence influenced the egg production. The significant associated SNP on GGA9 for EN2 was located in 3’ UTR of FARSB gene, which encodes a highly conserved enzyme that belongs to the aminoacyl-tRNA synthetase (ARSs) family (http://www.genecards.org/cgi-bin/carddisp.pl?gene=FARSB) [KIAA1549 gene on GGA1, which has been found to be fused to the BRAF oncogene in many cases of pilocytic astrocytoma in human (http://www.genecards.org/cgi-bin/carddisp.pl?gene=KIAA1549) [FARSB and KIAA1549 were undefined in chickens, we inferred that they interacted with central nervous system to regulate egg production based on the previous researches in human.Egg number has been studied in various manners for several decades. In the current study, we analyzed the genetic architecture for egg number in five separate laying periods and three accumulative egg number traits that commonly used in the selection programs. To our knowledge, it is the first time to conduct a GWA analysis for egg number in different laying stages that derived from the overall laying period from 21 to 72 weeks. Moreover, the study of longitudinal egg number has enabled the identification of genetic variants influencing trait values over time and may be a useful tool to capture age-independent variation . In the erase II . In humaerase II . Another mammals . Further mammals . Therefoe=FARSB) . The sigCALM1 for EN21-40 on GGA5 is a prototypical calcium sensor [CALM1 was found to be highly expressed in the ovary of the laying geese [CALM1 might be involved with the process of egg laying. With respect to the EN21-72, it was a new GWAS trait of accumulative egg number without any previous study. As no significant SNP was identified, and even regions with the genome-wide suggestive significance were few for EN21-72. Our findings suggested that accumulative egg numbers during 21 to 72 weeks of age may be more influenced by environment and marker assist selection or genomic selection for EN21-72 may be not a smart strategy in the egg production breeding.Regarding the accumulative egg number, the longer the laying period was, the fewer significant associations were detected for egg numbers. This indicated that the process of egg laying was intricate controlled by various factors involving genetic, physiological age, nervous, humoral regulation and environment. The SNPs that were associated with EN21-40 and EN21-56 did not coincide with previously reported QTL and SNP for similar traits [m sensor . Calciumm sensor , 48, whing geese  implied CALM1 might be involved with the process of egg laying hence influenced the egg laying rate from 25 to 40 weeks of age. Regarding other candidate genes with unknown functions in chickens, we found that a majority of them, such as TSN, THYN1, OPCML, SPPL2B and TCF3, were associated with immune and cytokines, which played essential modulatory roles in the regulation of ovarian function [No significant SNP was detected in the GWA analysis of LR. However, a sum of 27 chromosome-wide significant SNPs that located on different chromosomes was identified. The 27 chromosome-wide significant SNPs accounted for 37.08% of the total phenotypic variation that explained by entire genome SNP variants . Recently, SNPs on GGA13 [TAP2 may responsible for the appearance of the antigen presentation pathways in the primordial MHC [Age at first egg (AFE) is an important production trait that influences egg number and egg weight for egg-type chicken. The previous QTL mapping studies revealed AFE related QTLs located on GGA1, 2, 3, 4, 5, 7, 11, 13, 24 and Z .(TIF)Click here for additional data file.S2 FigP-value = 8.43e-7) and genome-wise suggestive significance threshold , respectively. GIF represents genomic inflation factor. (TIFF).Each dot represents a SNP in the dataset. Manhattan plot (left). EN4, egg numbers from 37 to 47 weeks of age; EN5, egg numbers from 48 to 72 weeks of age. The horizontal gray line and gray dashed line indicate the genome-wise significance threshold ((TIF)Click here for additional data file.S1 Table(DOCX).(DOCX)Click here for additional data file.S2 Table(DOCX).(DOCX)Click here for additional data file.S3 Table1: Chicken chromosome. 2: first listed marker is minor allele. 3: minor allele frequency. 4: allele substitution effect. 5: U and D indicates that the SNP is upstream and downstream of the gene; UTR indicates untranslated region. (XLSX).(XLSX)Click here for additional data file.S4 Table1: Chicken chromosome. 2: first listed marker is minor allele. 3: minor allele frequency. 4: allele substitution effect. 5: U and D indicates that the SNP is upstream and downstream of the gene; UTR indicates untranslated region. (XLSX).(XLSX)Click here for additional data file.S5 Table(DOCX).(DOCX)Click here for additional data file.S6 Table1: Chicken chromosome. 2: first listed marker is minor allele. 3: minor allele frequency. 4: allele substitution effect. 5: U and D indicates that the SNP is upstream and downstream of the gene. (XLSX).(XLSX)Click here for additional data file.S7 Table1: Chicken chromosome. 2: first listed marker is minor allele. 3: minor allele frequency. 4: allele substitution effect. 5: U and D indicates that the SNP is upstream and downstream of the gene. (XLSX).(XLSX)Click here for additional data file.


Despite the growing number of sequenced bovine genomes, the knowledge of the population-wide variation of sequences remains limited. In many studies, statistical methodology was not applied in order to relate findings in the sequenced samples to a population-wide level. Our goal was to assess the population-wide variation in DNA sequence based on whole-genome sequences of 32 Holstein–Friesian cows. The number of SNPs significantly varied across individuals. The number of identified SNPs increased with coverage, following a logarithmic curve. A total of 15,272,427 SNPs were identified, 99.16 % of them being bi-allelic. Missense SNPs were classified into three categories based on their genomic location: housekeeping genes, genes undergoing strong selection, and genes neutral to selection. The number of missense SNPs was significantly higher within genes neutral to selection than in the other two categories. The number of variants located within 3′UTR and 5′UTR regions was also significantly different across gene families. Moreover, the number of insertions and deletions differed significantly among cows varying between 261,712 and 330,103 insertions and from 271,398 to 343,649 deletions. Results not only demonstrate inter-individual variation in the number of SNPs and indels but also show that the number of missense SNPs differs across genes representing different functional backgrounds. Despite the fact that a continuously growing number of whole-genome sequences are available for cattle, the knowledge of the bovine genome is still limited . Libraries were generated from 1 ug of genomic DNA using the Illumina TruseqDNA PCR free sample prep kit following the manufacturer’s protocol and their evaluation was made with the Agilent Tape Station 2200. Fragments were quantified by Picogreen and then normalized to 10 nM as recommended by Illumina for clusters generation on the Hiseq2000. Libraries were denaturated and samples were run in a total of 32 lanes of Hiseq Flowcell. The Illumina Truseq PE cluster kit v3 was used to generate clusters on the grafted Illumina Flowcell and the hybridized libraries were sequenced on the Hiseq2000 with a 100 cycles of paired-end sequencing module using the Truseq SBS kit v3. All of the samples were sequenced on an IlluminaHiSeq2000 next-generation sequencing platform.http://broadinstitute.github.io/picard/). Variant calling was done using the latest version of FreeBayes with filters requiring a minimum alignment quality of 30 and a minimum base quality of 20 for allele calling.The total number of raw reads generated for a single animal varied between 164,984,147 and 472,265,620. Raw reads generated were filtered and trimmed using trimmomatic  were considered; (2) neutral to selection—representing genes which are supposed to remain relatively neutral to selection for modern dairy cattle according to ISAG–FAO recommendations; and (3) strongly selected—representing genes with documented strong effect on production traits in dairy cattle which are therefore supposed to be under strong unidirectional selection pressure over many generations. For calculating SNP number and density, exon regions (including UTR) within genes were considered, while introns were skipped, so that SNP density was expressed as the number of SNPs divided by the total exon length of a gene.Table Ni) defined as χ2 statistics were used: F tests for overall variability and post hoc t tests for assessing the differences between particular groups.In order to test hypotheses regarding the inter-individual variability in SNP/indel numbers (P < 10−20). Similar numbers of SNPs were reported by Jansen et al. . The vast majority of SNPs were bi-allelic (99.159 %). However, there were also monomorphic  SNPs (0.551 %), tri-allelic SNPs (0.289 %), and even a small fraction of four-allelic SNPs (0.001 %) Fig. . AlthougWe considered the distribution of SNPs identified among the 32 cows within coding regions, comparing genes belonging to three different categories: housekeeping genes (HK), genes undergoing strong selection in dairy cattle (SS), and genes neutral to selection (NS). Significant differences in SNP density for missense and stop codon removing SNPs were observed between gene categories and between individual genes, with the density for NS being the highest with 1.235 SNPs per Mbp, density of 0.261 SNP per Mbp being the intermediate for HK, and density of 0.059 SNP per Mbp for SS being the lowest. Also the raw number of missense SNPs was significantly higher within NS genes (2.33 SNPs per gene on average) than within HK (0.36 SNP) and SS (0.14 SNP) categories. The low number and density of SNPs identified within HK and SS was expected from the evolutionary perspective, both regarding natural and artificial selection, since potentially protein changing mutations have metabolic consequences ranging from a benign effect (58 % of nonsynonymous SNPs) to a damaging effect (24 % of nonsynonymous SNPs)—as predicted by Jansen et al. .Homo sapiens, Bos taurus, Mus sp) than the corresponding coding regions. This is, among other reasons, due to the presence of repetitive elements in the UTRs, which are not found in coding regions.Additionally, we considered polymorphisms located within the 3′-UTR and 5′-UTR regions marking the parts of genes that possibly affect the expression of exons. It was found that both their number and density varied significantly between gene categories as well as between genes. In particular, a significant difference in density was observed between the HK group with 0.621 SNPs per Mbp and the other two categories—NS with 1.491 SNPs and SS with 1.572 SNPs per Mbp. The highest average number of UTR SNPs per gene was equal to one for SS genes, followed by 0.34 for NS and 0.23 for HK, which is in contrast to the number of SNPs in coding regions of these genes. The discrepancy might at first glance seem surprising, but Larizza et al.  found thhttp://arxiv.org/abs/1207.3907), GATK , but a very high significant correlation of 0.996 was observed between the numbers of insertions and deletions identified for each animal. Correlations between the number of identified SNPs and mononucleotide deletions/insertions were somewhat lower, but also significant, amounting to 0.792 and 0.747, respectively.The number of insertions and deletions differed significantly among cows  and quantitative phenotypes, as well as environmental factors underlying phenotype expression, very deep pedigrees, and the relative ease of obtaining data from large groups of animals of predefined familial relationship structure, domestic cattle can play a role not only as production animals but also as model organisms. Using a dataset from 32 individuals, we were able to conduct an analysis of the genome mononucleotide variation in The importance of statistically supported analysis can be best visualized by comparing results for whole-genome sequence variation of a single Holstein cow  data has always been a high level of technological error underlying variant identification (Baes et al. Moreover, we also observed significant variation on a functional basis, by comparing the numbers and density of SNPs identified within genes representing different functional categories. The highest rate of SNPs within genes being neutral to selection in dairy cattle supports the hypothesis that natural selection tends to eliminate variation within housekeeping genes, whereas artificial selection for milk production pre-imposed on dairy cattle for many generations suppresses variation within genes responsible for the selected trait. We observed that the sequences of UTR appeared to be more variable than those of exons, which empirically proves the hypothesis that the predominant type of selection in non-coding sequences is a positive one, i.e., favoring sequence diversity, whereas in coding sequences, there is a very strong negative selection pressure that leads to elimination of new mutated alleles. Moreover, the most variable UTR sequence was observed in genes undergoing strong selection, which is in agreement with Hong et al. . This ph


NEDD8; rs11158609; raw p = 9.80 × 10−9, genomic control-adjusted p = 2.09 × 10−7) and 17  were associated with SBP including the genetic interaction with cigarette smoking. These two SNPs were not associated with SBP in a main genetic effect only model. This study advances knowledge in the area of main and joint effects of genetics and cigarette smoking on hypertension among African Americans and offers a model to the reader for assessing these risks. More research is required to determine how these genes play a role in expression of hypertension.Cigarette smoking has been shown to be a health hazard. In addition to being considered a negative lifestyle behavior, studies have shown that cigarette smoking has been linked to genetic underpinnings of hypertension. Because African Americans have the highest incidence and prevalence of hypertension, we examined the joint effect of genetics and cigarette smoking on health among this understudied population. The sample included African Americans from the genome wide association studies of HyperGEN  and GENOA , both part of the FBPP. Results suggested that 2 SNPs located on chromosomes 14 ( It has been widely accepted that environmental factors such as lifestyle behaviors that include excess tobacco usage can be hazardous to one’s health112335et al. applied the joint test in a genome-wide study, and identified joint effects of SNP and SNP-BMI interaction related to fasting glycemic traitsThe term gene-environment (G × E) interaction is used to define the biological or statistical interplay between genetic and environmental factors7Research in the area of genetics of cigarette smoking has expanded to include national consortiums1113−8) for hypertension and systolic and diastolic blood pressure, respectively20GWAS have paved the way for the examination of a multitude of single nucleotide polymorphisms (SNPs) that may contribute to development of various diseases such as hypertension15161718192, sex, BMI, current Smoking and top ten principal components from the GWAS data , we identified two SNPs located on chromosome 14 (rs11158609) and 17 (rs8078051) significantly associated with SBP including the genetic interaction with cigarette smoking. SNP rs11158609  is located in the intronic region of neural precursor cell expressed, developmentally down-regulated 8 (NEDD8). SNP rs8078051  is also located in the intronic region of gene tweety homolog 2 Drosophila (TTYH2). The QQ-plots  are summarized in −7) is located in an intergenic region on chromosome 11.We conducted the joint analysis of the main SNP effect and the SNP × Smoking interaction of 761,050 SNPs in 1,083 African American participants of HyperGEN. We identified fourteen SNPs having significant joint association with SBP using a threshold of false discovery rate (FDR) q-value less than 0.1 . Using aQQ-plots  of the p5 × 10−7 , which w−6 and 4.71 × 10−7 using method 2, and 6.09 × 10−7 and 5.17 × 10−7 using method 3.We examined the impact of alternative BP adjustment methods for antihypertensive use on the 2df SNP × Smoking interaction analysis in HyperGEN African Americans. In −8) in the combined test of HyperGEN and GENOA African American samples. Further illustration of these findings can be found in Using 1,427 African American participants from the GENOA study, we did not replicate the fourteen top associations (FDR less than 0.1 in HyperGEN) with SBP or DBP by testing the joint effects of SNP and SNP × Smoking interaction, at the alpha level of 0.05 . HoweverWe conducted additional tests for hypertension to compare the association results with SBP for the most significant SNPs.We further examined the association with hypertension status using the generalized estimating equation (GEE) models to adjust for the familial relatedness. Using the likelihood ratio test to compare the full model with SNP and SNPxSmoking interaction terms and the reduced model without these two terms, we examined the joint effects of top two SNPs, rs11158609 and rs8078051. In both cases, the SNP terms improve the fitting with p-values of 0.12 and 0.0078 for rs11158609 and rs8078051 respectively, which is consistent with the association with SBP with less degree of significance.NEDD8 and TTYH2 respectively. Interestingly, the protein products of NEDD8 and TTYH2 were both predicted to bind with neural precursor cell expressed, developmentally down-regulated 4-like, E3 ubiquitin protein ligase (NEDD4L) with moderate confidence scores of 0.589 (TTYH2) and 0.281 (NEDD8) using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 9.0The two most significant loci associated with SBP are located in two known genes, NEDD4L) was found to mediate ubiquitination of the tweety family members, in particular ion channelsNEDD4L gene encodes a member of the NEDD4 family of HECT domain E3 ubiquitin ligases, which target specific proteins for lysosomal degradation. The NEDD4L protein mediates the ubiquitination of multiple target substrates and plays a critical role in epithelial sodium transport by regulating the expression of the epithelial sodium channel. SNP in NEDD4L is suggested to be associated with essential hypertensionNEDD4L variants were associated with blood pressure response to diuretics3132NEDD4L is strongly associated with the serum level of a liver enzyme, gamma-glutamyl transferase  in a GWASTweety proteins are a family of chloride ion channels located extracellularly with the carboxyl-terminal domain located cytoplasmically25252728NEDD8 gene is located on chromosome 14 position 24688814, with SNP alleles A/G, the ancestral allele being A, and MAF is 0.108. The NEDD8 pathway has been shown to play an important part in regulating the actin cytoskeleton and cellular morphologyNEDD8, also known as neddylin, a ubiquitin-like protein that functions in cell cycle control and embryogenesis. NEDD8 is highly expressed in heart, skeletal muscle, spleen, thymus, prostate, testis, ovary, colon, and in leukocytes38394041Specifically in strokes, after toxic levels of ischemia, an accumulation of ubiquitinated proteins have been found in damaged brain regions, while under conditions of mild or reversible ischemia, the ubiquitinated proteins have been shown to play a protective role44454641−6, rs11041530 (CYB5R2) with p-value of 4.0 × 10−8 and rs17428471 (EVX1-HOXA) with p-value of 4.0 × 10−7. Although the SNP main effects of rs11041530 are similar (−1.67 in HyperGEN and -1.35 in COGENT), the association is not significant  and may be due to limited sample size. Using the SNAP database2 > 0.8 in YRI. For the top two associations from the joint test, rs11158609 (NEDD8) and rs8078051 (TTYH2), we looked up their main genetic effects in the COGENT BP meta-analysisOur analysis approach does not test a “perfect” or “pure” smoking-SNP interaction. Several statistical methods have been developed to assess gene-environment interactions on a genome-wide scale2) of SBP based on the partial F-test. In previous GWAS of blood pressure readings, the identified SNP associations have much smaller effect sizes. For example, the top 29 blood pressure-associated SNPs identified by the ICBP collectively explains only 0.9% of the phenotypic variance for SBP2 for the joint test, we have 22% power using the current AA sample, and only 1.6% power for detecting joint effect size of 1% R2. Thus, having larger sample size of consistently phenotyped AAs will be critical for discovering BP-associated SNPs and genes in the future, in addition to the implementation of complementary statistical approaches.Some limitations of the present study need to be considered. The top two SNPs have unusually large effect size. The joint SNP and SNP × Smoking interaction effects explain about 3% of variance Genomic control (GC) is an effective tool to control for type I error in GWAS. However, it tends to over-correct the most significant results and reduces power (i.e. increase type II error). Therefore, we presented both the unadjusted and GC-adjusted results to balance the strength and limitation of GC. To account for population stratification, we adjusted for the top 10 PCs in all regression models. No inflation was observed in the genome-wide SNP association analysis. Thus, the observed inflation is unlikely due to population stratification.We observed modest inflation of low p-values (inflation factor of 1.22 and 1.16 for SBP and DBP respectively) from the 2df joint test for SNP × Smoking interaction . The p-vGenome-wide association is a favored approach for genetic studies of common human diseases. However, localizing and identifying genes underlying environmental variations and the occurrence of high blood pressure poses a formidable challenge. The next step in studying African Americans and high blood pressure could use a similar research design incorporating a genome-wide association and other approaches in studying gene-cigarette smoking interactions on high blood pressure.TTYH2 and NEDD8 may play a role in the genetic architecture of cardiovascular disease, blood pressure regulation, and other disorders. Further research is indicated for a better understanding of how these genes play a role in expression of hypertension.Many of the genetic studies utilizing data to uncover genes or proteins responsible for underlying disease states utilize genome wide association studies with multivariate analysis and pairwise combinations of traits, specifically addressing genes that serve in a pleiotropic role52The parent study, the GENOA and HyperGEN both had HIC/IRB approval via University of Mississippi Medical Center, University of Michigan and Washington University in St Louis and the methods were carried out in accordance with the approved guidelines. Each study performed its own quality control procedures centrally, and shared the cleaned genotypic data for GWAS analysis. Therefore, the SNP inclusion/exclusion criteria between the HyperGEN and GENOA studies are not completely identical and details of both are provided.−6, and removed any genotypes with a non-Mendelian pattern of inheritance2122Genetic data for HyperGEN were completed utilizing the Affymetrix Genome-Wide Human SNP Arrays. Subjects with a call rate below 95% or with a sex mismatch were excluded. In HyperGEN, the majority of African Americans were genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0. We removed samples with sex-mismatch and pedigree errors. We also removed monomorphic SNPs and SNPs with missing rate >5% or Hardy-Weinberg p-value < 10® Genome-Wide Human SNP Array 6.0 platform. Participants were excluded if they had an overall SNP call rate <95% or sex mismatch between genotypic and phenotypic measurement. SNPs were excluded if they had unknown chromosomal location, a call rate less than 95% or a minor allele frequency (MAF) less than 0.05. After quality control filters, 650,367 SNPs were available for analysis in 1,427 GENOA African American participants who had both genotype and phenotype data.Participants in GENOA who self-reported their ethnicity as African-American were included in the analyses for the present study. Participant samples were genotyped for 906,602 SNPs using the Affymetrixet al. 2005A clinical visit was performed and each subject was interviewed by study personnel for data collection for all variables under study. Blood pressure was obtained using digitized blood pressure sphygmomanometers and was collected in accordance with JNC-7 guidelines. In the analyses, we corrected for anithypertension medications among the HyperGEN participants in accordance with designations outlined by HyperGEN investigators which can be found in full detail in Wu For GENOA participants taking antihypertensive medication, we corrected SBP and DBP values by adding 10 and 5 mmHg correspondingly5755In the GENOA study, the status of cigarette smoking was collected from a self-reported questionnaire as “smoked within the past year”, “not smoked within the past year”, “never smoked”. The variable “current smoker” was defined as an individual who smoked within the past yearAge, sex and other phenotypic data were collected from the physical examination and laboratory assessment at the time of the Phase I study visit in GENOA. The GENOA and HyperGEN studies were approved by the Institutional Review Boards of all participating institutions: University of Mississippi Medical Center, University of Michigan and Washington University in St Louis. Each participant gave written informed consent.2, BMI and top 10 principal components (PCs) of the GWAS data, as covariates. For participants who were taking antihypertensive medication we corrected the observed SBP and DBP values by adding the mean effect of each of the six major antihypertensive medication categories which include: angiotensin-converting enzyme (ACE) inhibitors, alpha1-blockers, cardioselective beta-blockers (beta1-blockers), calcium channel blockers, thiazide and thiazide-like diuretics, and loop diuretics. Data management, descriptive statistics for the covariates and outcome variables, and the regression analyses were conducted using the statistics software package R in version 2.11.1 (http://www.r-project.org/).We used linear mixed modelsSeveral methods to study Gene × Environment (G × E) interactions are available. In this study, we applied the joint analysis approachYi is the BP outcome (e.g. SBP and DBP) for person i, Zi is a vector of adjustment variables including age, age2, sex, BMI and the first 10 PCs of the common genome-wide SNPs, Smokingi is the smoking status of person i, and SNPi is the additive genetic effect of a SNP. β4 represents the effect of SNP × Smoking interaction.Where df) partial F-test to examine the null hypothesis H0: beta SNP = beta SNP×Smoking = 0. This joint test of SNP main effect and SNP × Smoking interaction allowed for screening the genetic effect conditioning on smoking, and the SNP × Smoking interaction effect simultaneously. In the discovery stage, we examined 761,050 SNPs  in 1,083 HyperGEN African American participants.The full model (1) included SNP, smoking and SNP × Smoking terms, and the reduced model (2) only included smoking. We used a 2 degree-of-freedom , which did not include the interaction term as in model (1). In all genetic analyses, appropriate methods will be used to account for multiple testing (see details below).pi is the p-value of the ith hypothesis test, and the chi-square distribution has 2m degrees-of-freedom, under the null hypothesis.Using 1,427 African Americans from GENOA study measured by Affymetrix 6.0 array, we examined the most significant joint associations of SNP and cigarette smoking with adjusted SBP and DBP to replicate the findings in HyperGEN African Americans. After applying the same static tests by contracting model (1) and (2), we combined the p-values of the same SNP from the two samples using Fisher’s product method: How to cite this article: Taylor, J. Y. et al. A Genome-wide study of blood pressure in African Americans accounting for gene-smoking interaction. Sci. Rep.6, 18812; doi: 10.1038/srep18812 (2016).