For adjusting multiple testing, phenotypic observations were permutated 10,000 times against the genotype and pedigree data to obtain the threshold for declaring genome-wide significant SNPs. Linkage disequilibrium (LD) in term of D' between significant SNPs was quantified and LD blocks were defined to indicate quantitative trait loci (QTL) regions.The mixed model based single locus regression analysis (MMRA) method was used to analyse the common simulated dataset of the 15th QTL-MAS workshop to detect potential significant association between single nucleotide polymorphisms (SNPs) and the simulated trait. A Wald chi-squared statistic with The estimated heritability of the simulated trait is approximately 0.30. 82 genome-wide significant SNPs (P < 0.05) on chromosomes 1, 2 and 3 were detected. Through the LD blocks of the significant SNPs, we confirmed 5 and 1 QTL regions on chromosomes 1 and 3, respectively. No block was detected on chromosome 2, and no significant SNP was detected on chromosomes 4 and 5.MMRA is a suitable method for detecting additive QTL and a fast method with feasibility of performing permutation test. Using LD blocks can effectively detect QTL regions. Recently, the high-density single nucleotide polymorphism (SNP) arrays have been developed for almost all domestic animals, which offer the prerequisite of genome-wide association study (GWAS), a more powerful approach for high-resolution mapping of loci controlling phenotypic traits in domestic animals . In GWASThe simulated population consisted of 3,220 individuals in two generations. The first generation consisted of 20 sires and 200 dams, which were assumed to be unrelated. Each sire mated with 10 dams and each dam produced 15 progenies, leading to a total of 3,000 individuals in the second generation. Of the 15 progenies of each dam, 10 were phenotyped for a continuous trait. All of the 3,220 individuals were genotyped for 9,990 SNP markers distributed on 5 chromosomes without missing. Each chromosome had a size of 1 Morgan (M) and carried 1,998 evenly distributed SNPs.We applied the software DMU   to estimy is the vector of phenotypes of the 2,000 phenotyped individuals, μ is the overall mean, a is the vector of the residual polygenic effect with A is the additive genetic relationship matrix and I is a unit matrix and Where We removed the 1,000 progenies without phenotypes off the genotype data, and we calculated the minor allele frequency (MAF) for each SNP for the remained 2,220 individuals . We found that 2,879 SNPs were homozygous (MAF = 0) for all the tested individuals and additionally 715 SNPs had a MAF less than 0.03. These SNPs were removed and 6,396 SNPs remained for the subsequent analyses.The mixed model based single locus analysis ,4 was pey is the vector of phenotypes of the 2000 phenotyped individuals, μ is the overall mean, × is the vector of the SNP genotype indicators which takes values 0, 1 or 2 corresponding to the three genotypes 11, 12 and 22 , b is the regression coefficient of phenotypes on SNP genotypes , a is the vector of the residual polygenic effect with Z is the incidence matrix of a, and e is the vector of residual errors with where df =1 was constructed to examine whether the SNP is associated with the trait.For each SNP, the estimate of b and the corresponding sampling variances For the analyses above, the permutation method was adopted to adjust for multiple testing from the number of SNP loci detected. In our method, the phenotypes were permuted 10,000 times against the genotype and pedigree data and the empirical distribution of the Wald chi-squared statistic under the null hypothesis (no association existed between any SNP and the trait in genome-wide level) was obtained using the largest Wald chi-squared statistic value across all SNPs from each permuted dataset. The threshold value for declaring a significant association was determined by choosing the 95th percentile of the empirical distribution, i.e., we declared a significant SNP at a 0.05 genome-wide significance level if its raw value of the Wald chi-squared statistic was larger than the empirical threshold value.D' between them was quantified using Haploview [For the significant SNPs, linkage disequilibrium (LD) in term of aploview  and the aploview  with defp values < 7.82E-6). However, by using permutation test, we detected 82 significant SNPs of 0.05 genome-wide significance level  for the simulated trait. The 82 significant SNPs are distributed on chromosomes 1, 2 and 3, i.e., 63 on chromosome 1, 3 on chromosome 2, and 16 on chromosome 3. The significant SNPs on chromosome 1 cover a large interval between 0.15cM and 15.30cM. The simulated SNP with the biggest effect is at 2.90cM (No.58), while the most significant SNP identified is at 3.55cM (No.71). The positions of the 3 significant SNPs on chromosome 2 are 81.90cM, 83.10cM and 95.80cM, respectively. Moreover, 13 of the 16 significant SNPs on chromosome 3 cover an interval between 4.25cM and 5.65cM, and the other 3 are at 3.70cM, 16.10cM and 26.75cM, respectively. No SNPs on chromosome 4 and 5 were found to be associated with the trait significantly. If we set the significant level at 0.01 for the permutation test, the number of significant SNPs reduces to 32, of which 3 are on chromosome 3 and all others on chromosome 1.The estimates of To further pinpoint the relationship among the detected SNPs, we analysed the LD levels in terms of D' between the significant SNPs Figures , 3 and 4D'>0.5) with it. This suggests that the simulated QTN may be surrogated by a suite of "ghost" QTNs nearby due to high LD level.On chromosome 1, there is one simulated QTN located at 2.85cM (No.57), which had the largest effect among all simulated QTNs. We detected 63 significant SNPs on this chromosome. However, the true QTN at 2.85cM has a MAF of 0 and was discarded after quality control, and the adjacent SNP at 2.90cM (No.58), which has the largest estimated effect among all significant SNPs, is accordingly considered as the putative QTN. Although a large number of pseudo significant SNPs were identified on this chromosome, the LD levels between the most effective SNP and other 62 significant SNPs  with the first one  and 93.75cM (No.3875), respectively. We detected 3 significant SNPs, the first is exactly at 81.90cM, and the second  is in strong LD (638 and 9D'), in which SNP No.4101 is just adjacent to the first true QTN. The second simulated QTN is 1.10cM away from the significant SNP (No.4322) and the LD level between them is strong (D'=0.93).On chromosome 3, there are two simulated QTN in repulsion linkage phase located at 5cM (No.4100) and 15cM (No.4300), respectively. However, the first simulated SNP on this chromosome also has a MAF of 0 and was discarded after quality control. Of the 16 significant SNPs detected, 10 are harboured in the LD block covering the interval between 4.75cM and 5.65cM with an average LD level of 0.97  reported in our another analysis on prediction of genomic breeding values for the same data set . Since tAll analyses were implemented through Fortran programs and performed on an octal-core Linux Server . The time needed was about 1.5 minutes for one permutation analysis. The 10,000 permutations were performed through 8 threads, each was assigned 1,250 permutations. So, the total computing time was about 31 hours. This shows that MMRA is a fast method with feasibility of performing a large number of permutations.Our results herein show that the MMRA method is suitable for detecting additive QTL, and it is a fast method with feasibility of performing permutation test. And the LD region on chromosome 3 can effectively integrate significant SNPs for QTL region detection. However, we detects only one true additive QTN (No.3638), two SNPs (No.58 and No.4101) close to two true additive QTNs (No.57 and No.4100) with many false positives, which remains to be further investigated and the MMRA method needs to be further improved to account for other non-additive effects.GWAS: Genome-Wide Association Study; MMRA: Mixed Model based Single Locus Regression Analysis; SNP: Single Nucleotide Polymorphisms; QTL: Quantitative Trait Locus; QTN: Quantitative Trait Nucleotide; LD: Linkage disequilibrium; M: Morgan; MAF: Minor Allele Frequency; TDT: Transmission Disequilibrium Test.The authors declare that they have no competing interests.WXF, CLW, XDD, ZZ, PPM and ZQW carried out the analyses and contributed the manuscript. JFL and QZ coordinated the analyses and drafted the write-up. All authors have read and contributed to the final text of the manuscript.


Single-nucleotide polymorphism typing found missing link between human strains in strain from deer. Escherichia coli (EHEC) O157 cause a spectrum of clinical signs that include diarrhea, bloody diarrhea, and hemolytic uremic syndrome. The current evolutionary model of EHEC O157:H7/H– consists of a stepwise evolution scenario proceeding from O55:H7 to a node  that then branches into sorbitol-fermenting (SF) O157:H– and non-SF (NSF) O157:H7. To identify this hypothetical intermediate, we performed single nucleotide polymorphism analysis by sequencing of 92 randomly distributed backbone genomic regions of 40 O157:H7/H– isolates. Overall, 111 single nucleotide polymorphisms were identified in 75/92 partial open reading frames after sequencing 51,041 nt/strain. The EHEC O157:H7 strain LSU-61 from deer occupied an intermediate position between O55:H7 and both O157 branches (SF and NSF O157), complementing the stepwise evolutionary model of EHEC O157:H7/H–. The animal origin of this intermediate emphasizes the value of nonhuman reservoirs in the clarification of the evolution of human pathogens.Highly pathogenic enterohemorrhagic  Escherichia coli (EHEC) belongs to the Shiga toxin–producing E. coli group and causes clinical signs ranging from watery to bloody diarrhea for most symptomatically infected patients  are the most frequently isolated from patients with severe EHEC-associated diseases, such as bloody diarrhea and hemolytic uremic syndrome. Infections caused by EHEC O157:H7/H– are major public health threats and require considerable resources for control and prevention  diverged  data  enabled precise reconstruction of this model  and had a distinct MLST sequence type  and serotyped by using antiserum against E. coli O antigens 1–181 and H antigens 1–56  was inoculated into a liquid culture of nutrient broth medium (Heipha) and incubated overnight at 37°C. The liquid culture was used to prepare DNA as described (fimH–locus) have been shown to be cluster specific , 1,460,599 , 2,370,797 , and 5,404,166  (E. coli MLST website (http://mlst.ucc.ie/mlst/dbs/Ecoli).As a first classification, we used MLST to determine the sequence type (ST)for all prototype strains of each subgroup and cluster by sequencing internal fragments of 7 housekeeping genes (http://frodo.wi.mit.edu/primer3), we developed 93 primer pairs that generated PCR products of backbone genomic regions ranging from 600 to 700 bp , ≈6 ng DNA, and 1.5 µL each forward and reverse primer, with a final concentration of 10 µmol/L. The cycling reaction conditions were initial denaturation (2 min at 94°C), 35 cycles of denaturation (45 s at 94°C), annealing (60 s at 60°C), and extension (90 s at 72°C), followed by a final extension (10 min at 72°C). PCR products were purified by using the exonuclease I  and shrimp alkaline phosphatase  according to methods modified from  and 2 µL (10 µmol/L) from the sequencing primer  and combined annealing and extension (4 min at 60°C). Finally, the sequencing reaction products were purified by using an alcohol precipitation method as recommended by the manufacturer and loaded onto a 3130xl Genetic Analyzer (Applied Biosystems) for capillary sequencing.For sequencing of both strands, 2 µL of the purified amplicons was mixed with 0.5 µL premix from the ABI Prism BigDye Terminator v3.1 Ready Reaction Cycle Sequencing Kit  plus 1.8 µL Tris-HCl-MgClstx-phage integration sites. We used the draft genome sequence of the O157 strain LSU-61 (GenBank accession no. AEUC00000000)  and corresponding Kyoto Encyclopedia of Genes and Genomes  assignments. Overall, genes were grouped into 3 functional categories: metabolism/housekeeping, putative metabolism/housekeeping, and hypothetical protein. If no KEGG phenotype assignment was found, a putative metabolism/housekeeping function was predicted on the basis of BLAST (http://blast.ncbi.mlm.nih.gov/Blast.cgi) results.Sequence trace files were analyzed and stored by using SeqSphere software version 0.9 beta ; a minimum-spanning tree was constructed with the integrated minimum-spanning tree algorithm. Gene functions were categorized by using the Pathosystems Resource Integration Center database  (The 50 strains of serotypes O157:H7/H genome) . Sequenc genome) . DeletioOf the 111 SNPs, 53 (47.7%) were synonymous SNPs (sSNPs) and 58 (52.3%) were nonsynonymous SNPs (nsSNPs); 78 (70.3%) SNPs were transitions, and 33 (29.7%) were transversions. Concatenation of all loci resulted in an average of 1 SNP every 460 bp; sSNPs occurred every 963 bp, and nsSNPs every 880 bp. On the level of analyzed partial ORFs, 1 SNP was found in 45 partial ORFs, 2 SNPs in 25 partial ORFs, 3 SNPs in 4 partial ORFs, and 4 SNPs in 1 partial ORF.To further elucidate the SNP distribution, we categorized the 92 loci into 3 functional groups. Most loci belonged to (putative) metabolism or housekeeping genes because these were chosen solely from backbone regions. If no KEGG assignment was possible, we estimated the function of the corresponding fragment on the basis of BLAST homologies. Defined annotation information regarding the function in metabolism or housekeeping was determined for 25 partial ORFs. A housekeeping/metabolism function was predicted for 58 loci. The remaining 9 loci were hypothetical proteins only .–– (subgroup B) or NSF O157:H7/H– . Strain LSU-61 interlinked all 3 subgroups, thereby substituting for the unknown intermediate  exhibited 8 SNP genotypes.On the basis of the 111 SNPs, the 50 strains were clustered into 27 SNP genotypes . The 111rmediate . Moreovestx-negative LSU-61 as a potential intermediate, we investigated each known potential stx insertion site in silico to determine the presence or absence of a Shiga toxin–carrying bacteriophage. We conducted BLAST searches within the recently published draft genome sequence of LSU-61 (stx1 (yehV) and stx2  were intact.To further validate the role of –, 2 O55:H7, and LSU-61) based on the 53 sSNPs and 22 nsSNP genotypes  based on the 58 nsSNPs. This excludes strong selection bias of the different loci.To investigate the effect of selective pressure on some loci and potential selecting biases, we analyzed sSNP and nsSNP types separately. In each scenario, the phylogenetic reconstruction resulted in comparable branching, with distinct lineages for SF and NSF O157 and strain LSU-61 as an intermediate. Only the number of SNP genotypes differed slightly: 19 sSNP genotypes  and enterohemolysin active  as white-tailed deer (North America), with comparable phenotypic and genotypic traits. Some of these were SF O157:H7 strains   shows a certain persistence of these O157:H7 clusters   and single nucleotide polymorphisms (SNPs)  of all 50 strains of Enterohemorrhagic Escherichia coli O157 investigated in this study.


The tassel-gbs pipeline successfully fulfills the following key design criteria: (1) Ability to run on the modest computing resources that are typically available to small breeding or ecological research programs, including desktop or laptop machines with only 8–16 GB of RAM, (2) Scalability from small to extremely large studies, where hundreds of thousands or even millions of SNPs can be scored in up to 100,000 individuals , and (3) Applicability in an accelerated breeding context, requiring rapid turnover from tissue collection to genotypes. Although a reference genome is required, the pipeline can also be run with an unfinished “pseudo-reference” consisting of numerous contigs. We describe the tassel-gbs pipeline in detail and benchmark it based upon a large scale, species wide analysis in maize (Zea mays), where the average error rate was reduced to 0.0042 through application of population genetic-based SNP filters. Overall, the GBS assay and the tassel-gbs pipeline provide robust tools for studying genomic diversity.Genotyping by sequencing (GBS) is a next generation sequencing based method that takes advantage of reduced representation to enable high throughput genotyping of large numbers of individuals at a large number of SNP markers. The relatively straightforward, robust, and cost-effective GBS protocol is currently being applied in numerous species by a large number of researchers. Herein we describe a bioinformatics pipeline,  The advent of next generation sequencing has elicited a revolution in biology buoyed by an advancing tidal wave of raw sequence datatassel-gbs pipeline is not limited to the specific restriction enzymes utilized in those protocols: it currently accepts 15 single restriction enzymes and 15 restriction enzyme pairs, and new enzymes are easily added. Furthermore, the tassel-gbs pipeline should work on nearly any restriction enzyme and barcoding approach . There were three primary motivations behind the development of this software: (1) Support the use of GBS in high-throughput, accelerated plant breeding, (2) Accommodate the high genomic diversity that is frequently encountered in species critical to agriculture and conservation, and (3) Provide an analysis platform that can be run in many contexts and with modest computational resources, such as those typically available in the developing world.Herein, we describe the good, barcoded read is a sequence read with a perfect match to one of the barcodes provided in a barcode key file and with no N's in the sequence following the barcode up to the trim length. Under the current implementation, reads are trimmed to 64 bp (not including the barcode).A tag refers to a unique sequence (excluding the barcode) up to a specified length (currently 64 bp) from one or more “good, barcoded reads”. A given tag is typically observed in numerous good, barcoded reads of identical sequence (up to the trim length).A taxon refers to a nameable entity from which one or more DNA samples can be taken.For our purposes, a tassel-gbs software, by its division into Discovery . For example, the most recent Discovery Build that we performed in maize (AllZeaGBSv2.6), comprising 31,978 samples , took 495 CPU-hours on 64 core Linux machine with 512GB of RAM (where each core was a 1.4 GHz AMD Opteron Processor 6272), plus additional time for staging of all of the input FASTQ files, etc. The Production Pipeline, in contrast, provides an avenue by which genotypes for the set of SNPs discovered in the most recent Discovery Build can be quickly generated for new samples. Running the Production Pipeline on a single FASTQ file containing sequence reads from 95 or 383 samples (plus a blank negative control) requires approximately 1 CPU-hour on a MacBook Pro with a 2.6 GHz Intel Core i7 processor and 16GB of RAM running OS X.tassel-gbs pipeline with low coverage data in mind. The resultant missing data and under-calling of heterozygotes can be compensated for by redundant coverage of haplotypes at high marker density, facilitating imputation.In a GBS assay, the tradeoff, for a given genome size, between number of SNPs genotyped and the depth of coverage at each SNP is controlled by the level of multiplexing and the choice of restriction enzyme(s) The likelihood of success of this imputation-based strategy depends on the number and length of homozygous, identical by descent (IBD) segments present in the study population. This, in turn, depends on the demographic history of the population. Imputation is least challenging in biparental populations consisting of RILs tassel-gbs pipeline is thus optimized for low sequencing depth (0.5 to 3×) over a large number of markers in a large sample of individuals. However, it is flexible enough for the analysis of higher coverage data either from a small genome species, or from a large genome species where a lower level of multiplexing, replicate runs of the same library preps, and/or a less frequently cutting restriction enzyme (or enzyme combination) are used.The tassel-gbs pipeline to be able to handle very large data structures (http://www.hdfgroup.org). We also implemented a rapid and efficient run length compression algorithm to further decrease the storage size. At low depth and with high genetic diversity (numerous sequence tags per locus), the TBT is a sparse data matrix consisting mostly of zeros; our run length compression algorithm takes advantage of this. The sequence tags themselves are stored in a binary format requiring only 16 bytes to hold 64 bases (2 bits per base), prior to compression. Thus, 64 base tags can be conveniently held in two “longs” in Java. The 64 base upper limit on tag lengths in the current implementation of tassel-gbs will be lifted in the near future, which will be helpful for study organisms with limited diversity.Our analysis strategy favoring large numbers of markers scored at low depth in a large sample of individuals requires the ructures . The lartassel-gbs Discovery and Production pipelines can be run on a Linux, Mac or Windows computer with 8–16 GB of RAM. The main demand with respect to the amount of RAM required is the number good, barcoded reads in a typical FASTQ file produced from GBS, which currently ranges from 200–300 million. The small memory footprint required by the tassel-gbs pipeline, in relation to its high capacity in terms of number of markers and individual samples, renders it useful to a broad array of users without access to sophisticated computing infrastructure, a target user group which may include breeders carrying out genomic selection experiments.GBS provides an unprecedented opportunity for genomic markers to be used by researchers working in numerous species, including researchers in the developing world. Many of these potential users do not have access to big memory computers or large clusters. The tassel-gbs pipeline first collapses all of the reads into a master tag list containing all of the sequence tags present at or above a user-specified minimum count, tallied across all of the samples in the Discovery Build . Illumina quality scores are ignored, and therefore do not need to be tracked throughout the pipeline.Quality scores produced by the Illumina base caller are strongly negatively correlated with position in the read. In most whole genome sequencing approaches the position of a particular SNP in different reads is essentially random. In contrast, a GBS SNP has a consistent position in each read, as all of the GBS tags from a particular genomic location (a “TagLocus”) that are used to discover and call SNPs originate from the same restriction enzyme cut site and have the same strand orientation. Consequently, the more distal SNPs in a GBS tag tend to have lower quality scores. If quality scores were used to filter GBS reads, these more distal SNPs might end up with lower depth of coverage. Furthermore, the quality scores frequently are not indicative of true quality tassel-gbs pipeline relies on population-genetic parameters such as the minor allele frequency (MAF) and, in particular, the inbreeding coefficient (or “index of panmixia”), FIT. Filtering based upon minimum MAF can remove spurious SNPs arising solely from sequencing error. Artifactual SNPs originating from paralogous tags will tend to be excessively heterozygous and can thus be distinguished on the basis of low FIT.Putative SNPs from GBS may be of low quality for multiple reasons. The sequencing error rate for a SNP may be high because of its distance from the read start and/or its immediate sequence context tassel-gbs pipeline allows the user to specify which samples are highly inbred, and uses this subset of inbreds to calculate FIT and apply the minimum FIT filter. Additional, related, filters can also be applied enforcing a minimum “inbred coverage” (proportion of the inbred samples to be non-missing at the SNP) and maximum “inbred heterozygosity score” .These population-genetic filters are most powerful if a substantial proportion of the samples consist of inbred lines. Among inbred samples, both error-prone SNPs and spurious SNPs originating from paralogous tags will appear to be excessively heterozygous. The Discovery SNP caller in the www.maizegenetics.net/tassel/docs/TasselPipelineGBS.pdf. Rather than describe each individual plugin, here we describe the main functions of the pipeline  being carried out by a TASSEL “plugin” that can be run from the TASSEL 4 Standalone command line interface. Detailed documentation on the function and usage of each individual plugin is available at pipeline  and theiIn order to have maximal power to discover and filter SNPs, we advocate running the Discovery Pipeline  at the species-wide level, with all samples sequenced to date, across multiple FASTQ files. Each FASTQ file contains GBS data from multiple samples distinguished by DNA barcodes at the beginning of each read tassel-gbs Discovery Pipeline first reads through each of the available FASTQ files and generates one output “TagCount” file per input FASTQ file  minimum number of reads across all of the FASTQ files in the experiment are retained in the output master tag list. The more times a particular tag has been observed, the less likely it contains a sequencing error. The minimum tag count controls the tradeoff between the amount of sequencing errors admitted into the analysis versus the minimum allele frequency of interest. We do not expect to eliminate all sequencing errors at this step. We are usually able to filter out or correct most of them in subsequent steps of the pipeline, or in further, downstream processing customized to the biology of the study population(s). Furthermore, tags containing one or more sequencing errors can still be useful to score SNPs at other, non-error positions.tassel-gbs pipeline and converted into a “TagsOnPhysicalMap” (TOPM) file that can be used for SNP calling.Alignment of each tag of interest in the master tag list to the reference genome is carried out with third party software. To facilitate this, the master tag list file is converted from TagCount format into FASTQ format . Currently, SAM format The TOPM contains all of the tags present in the master TagCount file and genomic positions for the subset of tags that align to a unique best position in the genome. Retention of all of the master tags in the TOPM, even those without unique best genomic positions, affords future incorporation of additional information regarding the positions of unplaced tags, either from alternative aligners or from genetic mapping evidence. The TOPM is sorted by tag sequence and functions as a key-value map where a tag sequence can be used as a key to retrieve its corresponding physical position. It is also possible to programmatically traverse the tags within a TOPM in their physical position order through the use of an in-memory, primitive treemap. At the SNP calling step of the Discovery Pipeline (see below), the alleles represented by each useful tag at each useful SNP (“variants”) are added to the TOPM. Three file formats of the TOPM are supported: text, binary, and HDF5.http://www.hdfgroup.org). The HDF5 format facilitates extremely fast read and write access to large data sets. To construct a TBT, the DNA sample of origin of each good, barcoded read in the set of input FASTQ files is determined based upon its barcode. If a good, barcoded read matches one of the tags in the master tag list, its depth in the appropriate taxon is incremented in the output TBT, up to a maximum depth of 127. Since the matrix is often extremely sparse, a custom run length encoding compression algorithm was implemented that provides a high level of compression, with minimal reduction in access speed.With a TOPM file available, the next ingredient needed to discover and call SNPs is a matrix that records the number of times each tag in the master tag list was observed in each DNA sample, which we refer to as a TagsByTaxa (TBT) file . In ordede novo multiple sequence alignment of all the tags in each TagLocus is performed using the BioJava 3.0 API SNP discovery  is perfoITF, , and q  =  minor allele frequency). Error-prone SNPs and spurious SNPs from paralogy often appear excessively heterozygous, with lower ITF than expected. If the user supplies a “pedigree file” that indicates the expected inbreeding coefficient (F) of each taxon, then only inbred taxa, with an expected inbreeding coefficient greater than or equal to the user-specified minimum coefficient of panmixia (minF parameter), are used in the calculation of ITF. Inbred samples, available in many crop species and model organisms, can greatly add to the power of this filter. If enough inbred samples are available, then, additional filtering of SNPs can then be optionally performed enforcing a minimum “inbred coverage” (proportion of the inbred samples to be non-missing at the SNP) and a maximum “inbred heterozygosity score” (defined above).After genotypes are obtained for a potential SNP, initial filtering is then performed based upon user settings for minimum minor allele frequency, and for a minimum coefficient of panmixia, or inbreeding relative to the entire population, 2-derived RIL families all with a common female parent, the inbred line B73. Error-prone SNPs can be identified in a biparental family through their tendency, when they are in fact not segregating, to appear to be weakly polymorphic, but with segregation ratios significantly deviating from the 1∶1 expectation. In contrast, non-segregating SNPs in a given family that display no spurious polymorphism are free of genotypic error in that family. The availability of the 25 biparental NAM families provides tremendous power to detect error-prone SNPs, and thus to examine the effectiveness of our filters.To illustrate the effectiveness of these population genetic-based SNP filters, as applied in our most recent maize Discovery Build , we focused on the subset of 5,254 samples from the maize Nested Association Mapping (NAM) population IT in inbred samples of 0.8, inbred coverage >0.15, and inbred heterozygosity score <0.21. The SNPs were discovered and filtered based upon all 31,978 maize samples (including the NAM RILs) and their allele frequencies were then separately calculated in each of the 25 NAM families. To minimize sampling error, allele frequency was only estimated for a particular SNP-NAM family combination if at least 19 RILs in that family had non-missing genotypes for the SNP (n > =  19). Thus, by “SNP-NAM family combination” we mean a particular SNP  in a particular NAM family  that has at least 19 non-missing genotypes, regardless of whether it is polymorphic or not within that family. Hence, for each SNP, allele frequencies were calculated in 25 or fewer families, depending on the amount of missing data in each family. NAM family-specific minor allele calls for a SNP were classified as errors if the family-specific MAF was greater than zero but less than 0.25, and the SNP significantly deviated from 1∶1 segregation in that family at p<0.001 . The overall error rate for a SNP was then estimated as the total number of these error calls divided by the total number of calls for that SNP in NAM families with n > =  19 where the SNP significantly deviated from 1∶1 segregation at p<0.001 (including the monomorphic SNPs). A pictorial explanation of this method of estimating error rates, using a single NAM family for illustrative purposes, is provided in Supplementary We used the 5,254 NAM RIL samples to estimate error rates for three alternative sets of chromosome 10 SNPs discovered in the full set of 31,978 maize samples comprising our AllZeaGBSv2.6 Discovery Build. The three sets of chromosome 10 SNPs were obtained after application of three different filtering regimes: (1) no filters other than MAF > =  0.001, (2) a minimal filter only for MAF > =  0.01, and (3) our “standard” maize Discovery Build filters of MAF > =  0.001, minimum FIT in inbred samples of 0.8, inbred coverage >0.15, inbred heterozygosity score <0.21) .Compared to non-filtered SNPs , applicae <0.21)  greatly e <0.21) , or no pe <0.21) . In conte <0.21)  was far e <0.21)  and redue <0.21)  relativee <0.21)  indicateError prone SNPs that are not removed by our standard filters . Depending on the genome size, the exact molecular protocol and restriction enzyme used, and the sequence depth obtained, missing data can be common and actual heterozygotes can be substantially under-called as homozygotes. Hence, imputation of missing data  is usually desirable. Since the optimal imputation approach depends greatly upon the biology of the species and the experimental design, this topic is beyond the scope of this paper. Numerous, general purpose imputation tools are already available tassel-gbs pipeline is part of the TASSEL package, which is written in Java, so it can be run on Linux, Mac, or Windows operating systems. A minimum of 8 GB of RAM is required (at least 16 GB is recommended). Detailed installation instructions are provided at www.maizegenetics.net/tassel as well as in the tassel-gbs pipeline documentation (www.maizegenetics.net/tassel/docs/TasselPipelineGBS.pdf). The source code is available from sourceforge.net/p/tassel/code/ci/master/tree, JUnit tests at sourceforge.net/p/tassel/maizegenetics4-test/ci/master/tree and a relatively small test data set at www.maizegenetics.net/tassel/GBSTestData.tar. The current version of tassel-gbs as described herein is implemented in TASSEL V4.3.5. Although we generally recommend using the latest version, users can revert to V4.3.5 or other versions by following the instructions posted in the following document www.maizegenetics.net/tassel/docs/UpdatingTasselStandaloneUsingGit.pdf, under the heading “To Update Packages to Older Releases…”. The tassel-gbs pipeline will soon be available in TASSEL 5; major improvements to the pipeline  will be implemented there.The tassel-gbs pipeline are the large number of markers potentially produced (depending on the biology of the species and the choice of restriction enzymes), low cost and minimal startup cost, and integration of SNP discovery with SNP calling.The strengths of GBS and the The potentially large number of markers available from GBS makes GWAS feasible in study populations where linkage disequilibrium (LD) extends far enough so that causative polymorphisms stand a reasonable chance of being in LD with one or more markers. Alternatively, the large number of markers facilitates accurate projection of haplotypes from a set of more densely genotyped reference haplotypes Compared to alternative high-density marker technologies such as SNP arrays, GBS is relatively inexpensive, particularly if low coverage data suffices for the purpose of your study. Startup costs for GBS are minimal, as startup involves only (1) testing that your one of your candidate restriction enzymes (or enzyme pairs) produces a suitable GBS library, and (2) optimization of the ratio of sample DNA to the PCR adapters tassel-gbs Discovery pipeline integrates SNP discovery with SNP calling, using all available samples to date, and thus avoids the ascertainment bias that would arise from a small discovery panel. This type of ascertainment bias will also be minimal for new samples run through the Production Pipeline, provided that their genetic diversity is well-represented among the samples included in the Discovery Build. However, there might still be some subtle biases in either pipeline, caused by factors such as null alleles IT.The common practice of using of a small panel of individuals to discover SNPs for inclusion in a SNP array introduces an ascertainment bias that can severely distort estimates of key population genetic parameters gauging genetic diversity, sub-population differentiation and relatedness among individuals The main weakness of the GBS assay, when conducted at low coverage, is the amount of missing data. However, numerous imputation approaches are currently available tassel-gbs pipeline for identifying and calling SNPs from next-generation, genotyping by sequencing data fulfills our design criteria better than any existing pipeline. It has a capacity for very large analyses involving tens of thousands of samples, yet can also be run at smaller scales. The pipeline permits rapid processing of the data, yet has a relatively modest memory footprint, allowing it to be run on desktop or laptop computers. This increases its usability by researchers in developing countries who may lack access to sophisticated computing resources. The separation of SNP discovery and genotyping into two phases reduces potential ascertainment biases and, more importantly, makes the tassel-gbs pipeline highly suitable for use in a genomics-assisted, accelerated breeding context, where rapid turnaround times from tissue collection to genotypes are essential. Furthermore, the high density of markers potentially available from the GBS assay should enable accurate genomic prediction over multiple generations.The Text S1Description of our binomial likelihood ratio method of quantitative SNP calling.(PDF)Click here for additional data file.Text S2Estimation of GBS SNP error rates using biparental RIL families.(PDF)Click here for additional data file.


Sheep chromosome 3 (Oar3) has the largest number of QTLs reported to be significantly associated with resistance to gastro-intestinal nematodes. This study aimed to identify single nucleotide polymorphisms (SNPs) within candidate genes located in sheep chromosome 3 as well as genes involved in major immune pathways. A total of 41 SNPs were identified across 38 candidate genes in a panel of unrelated sheep and genotyped in 713 animals belonging to 22 breeds across Asia, Europe and South America. The variations and evolution of immune pathway genes were assessed in sheep populations across these macro-environmental regions that significantly differ in the diversity and load of pathogens. The mean minor allele frequency (MAF) did not vary between Asian and European sheep reflecting the absence of ascertainment bias. Phylogenetic analysis revealed two major clusters with most of South Asian, South East Asian and South West Asian breeds clustering together while European and South American sheep breeds clustered together distinctly. Analysis of molecular variance revealed strong phylogeographic structure at loci located in immune pathway genes, unlike microsatellite and genome wide SNP markers. To understand the influence of natural selection processes, SNP loci located in chromosome 3 were utilized to reconstruct haplotypes, the diversity of which showed significant deviations from selective neutrality. Reduced Median network of reconstructed haplotypes showed balancing selection in force at these loci. Preliminary association of SNP genotypes with phenotypes recorded 42 days post challenge revealed significant differences (P<0.05) in fecal egg count, body weight change and packed cell volume at two, four and six SNP loci respectively. In conclusion, the present study reports strong phylogeographic structure and balancing selection operating at SNP loci located within immune pathway genes. Further, SNP loci identified in the study were found to have potential for future large scale association studies in naturally exposed sheep populations. Haemonchus contortus, Teledorsagia circumcincta, Trichostrongyles, Nematodirus sp. impose severe constraints on sheep and goat production especially those reared by marginal farmers under low external input system. These parasites incur heavy losses to farmers in terms of body weight loss, direct cost of anthelminthic drugs, loss due to mortality, etc. For example, annual treatment cost for Haemonchus contortus alone had been estimated to be 26 million USD in Kenya, 46 million USD in South Africa and 103 million USD in India Haemonchus contortus, Trichostrongylus colubriformis, Teledorsagia circumcincta and various Nematodirus species. Considerable variation has been reported among sheep breeds on their ability to resist gastro-intestinal nematodes (GIN). For example, indigenous sheep breeds like Red Maasai 2 = 0.149, Avikalin sheep 2 = 0.41, Armidale sheep Assessment of livestock health conditions in developing countries for identification of priority diseases to be targeted for control, revealed helminth infections as one of the most important problems in sheep and goat Exploration of genetic variation either within specific regions of genome or more specifically in candidate genes involved in innate and adaptive immune pathways may help to identify a set of DNA markers significantly associated with parasite resistance characteristics. The former approach in terms of quantitative trait locus (QTL) analysis is a powerful method to understand genotype-phenotype relationship. Several QTL studies on parasite resistance characteristics have been reported in sheep. A quick evaluation of Animal QTL database Haemonchus contortus parasite).One of the important objectives of QTL studies is to identify underlying causative gene polymorphisms associated with the trait. Different QTLs reported in chromosome 3 for parasite resistance characteristics were found to be distributed all over the chromosome with varying overlapping regions. Hence, different candidate genes within chromosome 3 along with genes involved in immune related KEGG pathways (KEGG-Kyoto Encyclopedia of Genes and Genomes) could be important targets for establishing underlying causative variations. It is expected that the potential causative polymorphisms within candidate genes are members of the same overarching KEGG pathway that lead to the phenotypic expression on parasite resistance characteristics in each population. Further, the extent of genetic diversity and population sub-structure at such polymorphic loci are critical for such a genotype-phenotype association study. Population stratification has been demonstrated to result in false positive associations in various species including humans Haemonchus contortus and blood samples were collected from jugular vein under the supervision of qualified veterinarians for extraction of DNA and assessment of blood parameters. 42 days post challenge, animals were dewormed to clear parasites from the gut after the experiment. The experimental challenge in both locations did not involve animals from any endangered or protected species/breeds. Blood sample collection for DNA extraction and genotyping from remaining breeds were performed by local veterinarians in respective countries following good animal practice.All procedures for artificial challenge experiment at different locations in Argentina and Indonesia were approved respectively by the Institutional Committee for Care and Use of Experimental Animals of the National Institute of Agricultural Technology (CICUAE-INTA), Argentina (protocol number 35/2010) and Institutional Research Animal Facility, Bogor Agricultural University, Indonesia following the guidelines described in their institutional manuals. Experimental animals were challenged with infective L3 larvae of in vitro conditions. All animals were maintained together during the entire trial in dry lot or under conditions of minimum additional parasite challenge. Body weight (BW), fecal egg count (FEC) and packed cell volume (PCV) were recorded at 0, 28, 35 and 42 days after artificial infection. 136 animals from Corriedale (66), Pampinta (34), Indonesian Fat Tail (17) and Indonesian Thin Tail breeds of sheep (19) were utilized for artificial challenge at experimental stations located in Argentina and Indonesia respectively.A total of 713 unrelated sheep from 22 different breeds/populations were utilized for diversity analysis in the present study. The sheep breeds/populations were distributed in three macro-environmental geographical locations including Asia (tropics), Europe (temperate) and South America (tropics) with the assumption that level of parasite load and infections vary significantly across these regions http://www.mbio. ncsu.edu/bioedit/bioedit.html). 44 novel SNPs were identified within the candidate genes under study for which competitive allele specific PCR (KASPar) assay based on FRET chemistry were developed for genotyping . Briefly, two forward primers one specific to each allele were designed with the respective proprietary tail sequence complementing the FAM or HEX fluorescence reporting system. A common reverse primer was designed for each genotyping assay. Thermal cycling parameters and recycling conditions were followed as per manufacturer's recommendations and are available on request. Endpoint allele discrimination module incorporated within the BioRad CFX96  was utilized for calling the genotypes based on fluorescent intensity recorded for each of the two alleles. The emission data of all the samples in the plate were plotted in X and Y axis respectively for each allele and the genotypes were called based on distinct clustering. Quality of allele calling was confirmed by comparing the genotypes derived from KASPar assay with the available sequence data on individuals from the panel of unrelated animals. 38 out of 44 assays passed quality control and were subsequently utilized for genotyping large number of animals. Additionally, ten toll like receptor (TLR) genes were selected for in silico mining of SNP variations from sequences available at NCBI-GenBank database. A total of 14 non-synonymous SNPs within coding DNA regions of TLR genes were identified for development of genotyping assays  using SPSS version 13.0. The first three principal components were used to draw the scattergram so as to understand underlying genetic structure and relationship of different breeds in three dimensional geometric space. The extent of population sub-structure was further explored using STRUCTURE with the assumption of different clusters, K = 1–15, 20, 25 and 30. Five replicate runs were performed for each K under admixture model without a priori population information. The number of burn in periods and MCMC repeats used for all the runs were 50000 and 100000 respectively. To identify the optimal ‘K’, the second order rate of change of L(K) with respect to ‘K’ was calculated by following the procedure reported elsewhere Basic diversity indices like allele frequency, genotype frequency, expected heterozygosity and test for Hardy Weinberg equilibrium were calculated using PEAS  www.stat.washington.edu/stephens) ST values using ARLEQUIN version 3.1 Thirteen SNP loci within candidate genes involved in different immune related KEGG pathways and located in chromosome 3 were used to reconstruct haplotypes from unphased genotypic data. Reconstruction of haplotypes and estimation of haplotype frequencies were performed using PHASE for Windows, version 2.1 , body weight change (BWC) and packed cell volume change (PCVC) measured 42 days post infective L3 larvae challenge. The data on FEC were subjected to log transformation before applying the program of least squares, LSMLMW A total of 243 sequences were generated by targeted re-sequencing of selected candidate genes  to 0.315 (Hamdani) while the mean expected heterozyosity varied from 0.237 (Pampinta) to 0.315 (Indonesian Thin Tail). Among different geographical regions, mean observed heterozygosity was highest in South West Asian sheep populations (0.309) followed by European populations [0.296] while South East Asian populations had the least mean observed heterozygosity (0.270). This is consistent with the fact that the diversity remains higher around the centre of domestication while decreasing with increasing geographic distance ST  to 0.469 (Pampinta/Mouflon). 21.3% of total genetic variation was found to be due to between breed differences while 77.3% was due to within breed differences. The values are much higher than that reported for European sheep (13.1%) ST values observed could be understood from the fact that the samples were derived from wide geographic locations in the present study . Further, phylogenetic analysis of pair-wise allele sharing distance revealed two major clusters with most of South Asian, South East Asian and South West Asian breeds clustering together while the European and South American sheep breeds clustered together separately  were plotted on a three dimensional scattergram to evaluate the genetic structure of sheep breeds  on 136 animals was used for least squares analysis under a complete fixed effect model. The effect of location of experimental stations  was not found to have significant influence on fecal egg count and packed cell volume while significant effect was observed with respect to body weight change (P<0.01). Although the locations are wide apart geographically, uniform protocol was followed across different experimental stations in terms of age of lambs selected for experiment, deworming and data recording schedule, however some differences did exist in terms of quality of pasture available for grazing, etc. Higher observed body weight change in animals challenged at Aguil Experimental Station (AES), Argentina was due to better growth performance of Pampinta lambs. Higher body weight achieved by Pampinta lambs were due to their genetic differences in growth rate (average pre-weaning weight gain of 295 g/day) and weight gain (average weaning weight of 33.4 kg) as compared to other breeds like Corriedale (218 g/day and 24.6 kg) To evaluate the potential utility of the SNP loci for future association study on a large number of samples, a pilot analysis was performed with the phenotypes generated in four breeds after artificial challenge with infective L3 larvae of In conclusion, the present study reports strong phylogeographic structure in sheep across Asia, Europe and South America and balancing selection operating at SNP loci located within immune pathway genes. Although the present association analysis is preliminary in nature, the SNP loci on chromosome 3 and those within immune pathway genes indicated their potential for future large scale association studies in naturally exposed populations.Figure S1http://www.animalgenome.org/cgi-bin/QTLdb/index).(a–c) QTLs related to gastro-intestinal nematode resistance in sheep (d) Chromosome-wise distribution of QTLs related to parasite resistance traits in sheep and number of SNP loci investigated in the present study (e) Quantitative trait loci (QTL) map of chromosome 3 related to parasite resistance traits in sheep Click here for additional data file.Figure S2Distribution of allele sharing distance (IBS) between pairs of individuals. Distance was plotted separately where pairs were drawn from within the same breed (blue bars) and from across the breeds (red bars).(TIF)Click here for additional data file.Figure S3Bayesian clustering of 713 sheep based on genotype data at (a) 18 non-neutral SNP loci (b) 23 neutral SNP loci under assumption of 2 to 6 clusters without a priori population information. The breed names are given below the box plot and the geographical origin indicated above the box plot with the individuals of different breeds separated by vertical black lines.(TIF)Click here for additional data file.Table S1Details of SNPs identified in silico within TLR genes of sheep.(DOCX)Click here for additional data file.Table S2ST (lower triangle) and allele sharing distance (upper triangle) among different sheep breeds.Pairwise F .(DOCX)Click here for additional data file.Table S3Global F-Statistics among different sheep populations at 41 SNP loci.(DOCX)Click here for additional data file.Table S4Results of Ewens Watterson Neutrality test at different SNP loci in various sheep breeds .(DOCX)Click here for additional data file.Table S5Analysis of molecular variance among different sheep breeds based on (right) genotypes at 18 non-neutral SNP loci and (left) genotypes at 23 neutral SNP loci.(DOCX)Click here for additional data file.


Phylogenetic trees are widely used for genetic and evolutionary studies in various organisms. Advanced sequencing technology has dramatically enriched data available for constructing phylogenetic trees based on single nucleotide polymorphisms (SNPs). However, massive SNP data makes it difficult to perform reliable analysis, and there has been no ready-to-use pipeline to generate phylogenetic trees from these data.We developed a new pipeline, SNPhylo, to construct phylogenetic trees based on large SNP datasets. The pipeline may enable users to construct a phylogenetic tree from three representative SNP data file formats. In addition, in order to increase reliability of a tree, the pipeline has steps such as removing low quality data and considering linkage disequilibrium. A maximum likelihood method for the inference of phylogeny is also adopted in generation of a tree in our pipeline.Using SNPhylo, users can easily produce a reliable phylogenetic tree from a large SNP data file. Thus, this pipeline can help a researcher focus more on interpretation of the results of analysis of voluminous data sets, rather than manipulations necessary to accomplish the analysis. Arabidopsis genome was completed [Since the ompleted , advanceompleted -4. Referp-distance from all SNP data between two samples, 2) making the p-distance matrix for all samples, 3) constructing a neighbor-joining tree with the matrix by a program such as ‘neighbor’ in the PHYLIP package [A variety of studies have begun to utilize and illustrate how to deal with extensive SNP data -10. Part package  and 4) d package . However package  providedWe developed a pipeline, SNPhylo  testing each SNP position and removing those positions which do not have sufficient numbers of qualified SNPs for all samples, 2) generating new GDS format files from the tested SNP data files, 3) reading the GDS file and extracting SNP data which meet criteria of ≥ MAF (Minor Allele Frequency) and ≤ missing rate threshold, and are in approximate linkage equilibrium with each other as determined by SNPRelate package , 4) ConcAll the steps are automated by one Bash shell script, snphylo.sh, though the pipeline includes additional components implemented in Python and R. Thus, by the script, users can obtain from a SNP data file a phylogenetic tree file and other informative files such as multiple alignment results file in PHYLIP format, which can be used for additional analysis such as a parallel bootstrap analysis by PhyML . The pipAs a demonstration of the use of SNPhylo, we determined a tree Figure A with puMost branches in the tree correspond to those inferred in the original report  though oAs another case study, we constructed a phylogenetic tree with rice SNP data that has 162,479 SNP loci determined by resequencing microarrays with 20 samples  Figure A. BecausThe tree constructed by SNPhylo had three evident clusters representing the three rice groups, japonica, indica and aus, and the results was consistent with the previous tree of the original report . InteresArabidopsis has been used as a model plant since its whole genome was sequenced [Arabidopsis genome data has been released by various re-sequencing projects. Thus, as an additional case study, we constructed a phylogenetic tree with SNP data . Because of the relatively high LD level [equenced  because a Figure  determinLD level , the phyArabidopsis genotypes which is likely caused by post-glaciation colonization routes [There are three major clusters in the phylogenetic tree Figure A. The acn routes .Arabidopsis SNP data for 2,595,179 SNP loci of 20 samples was 1,850 seconds. The result means that the pipeline can process about 1,402 SNP loci per second. However, it is not clear whether the number of SNP genotypes or the number of organism samples primarily determine the duration of the run. In order to address the question, we determined run times of the pipeline with various data sets generated from the Arabidopsis SNP data set in HapMap format : Linux, UNIX and OS XProgramming language: Python, R and BASHOther requirements: MUSCLE  and DNAMLicense: GNU GPLv2Any restrictions to use by non-academics: NoneLD: Linkage disequilibrium; VCF: Variant call format; GDS: Genomic data structure; MAF: Minor allele frequency.The authors declare that they have no competing interests.THL developed the pipeline and wrote the manuscript. HG, XW and CK provided advice and revised the manuscript. AHP provided substantial advice and guidance during all phases of the project. All authors read and approved the final manuscript.SNPhylo version 20140116. Description: This compressed file contains SNPhylo source codes and additional files such as setup script and instruction for installation. The latest version is available at SNPhylo homepage (http://chibba.pgml.uga.edu/snphylo/).Click here for file


There are many ways to perform adjustment for population structure. It remains unclear what the optimal approach is and whether the optimal approach varies by the type of samples and substructure present. The simplest and most straightforward approach is to adjust for the continuous principal components (PCs) that capture ancestry. Through simulation, we explored the issue of which ancestry informative PCs should be adjusted for in an association model to control for the confounding nature of population structure while maintaining maximum power. A thorough examination of selecting PCs for adjustment in a case-control study across the possible structure scenarios that could occur in a genome-wide association study has not been previously reported.We found that when the SNP and phenotype frequencies do not vary over the sub-populations, all methods of selection provided similar power and appropriate Type I error for association. When the SNP is not structured and the phenotype has large structure, then selection methods that do not select PCs for inclusion as covariates generally provide the most power. When there is a structured SNP and a non-structured phenotype, selection methods that include PCs in the model have greater power. When both the SNP and the phenotype are structured, all methods of selection have similar power.Standard practice is to include a fixed number of PCs in genome-wide association studies. Based on our findings, we conclude that if power is not a concern, then selecting the same set of top PCs for adjustment for all SNPs in logistic regression is a strategy that achieves appropriate Type I error. However, standard practice is not optimal in all scenarios and to optimize power for structured SNPs in the presence of unstructured phenotypes, PCs that are associated with the tested SNP should be included in the logistic model. The principal components (PCs) of genome-wide genotype data can be used to detect and adjust for population structure in genetic association analyses ,2. The pNumerous methods have been proposed to adjust for structure once PCs are computed Table . The sim1≠K2), while a non-structured phenotype has equal probably of being a case in the two sub-populations (K1 = K2). Likewise, a structured SNP (sSNP) has unequal risk allele frequency in the two sub-populations (p1≠p2), and a non-structured SNP (nsSNP) has equal risk allele frequency in the two sub-populations (p1 = p2). By testing the phenotype for association with the PCs, we can determine if the phenotype is structured :(1) No PC adjustment (None)(2) 10% Rule (10% Rule)(3) PCs significantly related to the outcome at significance level α = 0.001, 0.01, or 0.05 (4) PCs significantly related to the SNP at α = 0.05 or 0.01 (5) PCs significant according to the Tracy-Widom statistic at α = 0.05 (TW)(6) Top PCs (2 or 10) determined according to eigenvalue (7) Simulated true population i.e, Gold standard (Pop)We tested for association with the simulated case/control outcome using logistic regression and compared Type I error and power of associations between the outcome and SNP when adjusting for selected principal components of ancestry. Finally, to provide a practical example of the methods of PC selection, we performed all methods of PC selection using dichotomized height data from the Framingham Heart Study.j, j = 1,...,10000) from a uniform -distribution. We then created two sub-populations  of 500 individuals, each descending from the ancestral population according to Fst. We simulated the allele frequencies pij  in the two sub-populations according to a beta distribution [We simulated independent genome-wide SNPs by generating ancestral population allele frequencies for 10,000 SNPs  for each replicate. We varied the phenotypic and genotypic structure of the sub-populations, from having no structure to more extreme structure.To determine the effects of the PC selection methods when a sample is composed of a more complex structure, we simulated two populations that each diverged with as Fst of 0.01 is provided in Figure Type I error for the methods of selection with two subpopulations with Fst between the sub-populations was 0.1.Figure st (0.10), we expect many SNPs with greater than a 0.2 difference in allele frequency between sub-populations, and thus we believe it may be more common to observe large allele frequency differences between populations than large phenotypic differences between populations. We found that as the difference in the risk allele frequency between the two sub-populations increases, the difference in power between adjusting and not adjusting for PCs becomes greater  and the phenotype has large structure (phenotypic ratio = 4), we found slightly higher power when selecting PCs by the 10% rule compared to not selected any PCs, contrary to the findings presented in Figure We next expanded this simulation to larger differences in allele frequencies between the two sub-populations. With large Fst 0.0, we expst 0.0, we expFinally, we increased the sample size for the genome-wide SNPs simulation to 5,000 individuals from each sub-population. Increasing the sample size allowed us to determine if our observed results were affected by the simulated sample size of 500 cases and 500 controls. We found the same patterns with the larger sample size as we did when we used the 500 individuals from each sub-population (results not shown).In general, we observed similar patterns when the data consisted of four sub-populations as with the two sub-populations scenario already presented , selection methods that result in no PC adjustment  have optimal power. Conversely, when the phenotype is not structured and the SNP is structured, we achieve optimal power when PCs are included in the model, e.g., for the selection methods that include a fixed number of PCs or PCs associated with the SNP in the model . With logistic regression, when adjusting for non-confounding covariates (covariates only associated with the outcome) there is a loss in precision [Overall, we find that for some scenarios, the optimal choice of PCs to adjust for in a genome-wide association study using logistic regression is SNP-dependent Table . We defiWe explored the bias and standard error of our models to better understand the dependency of power on the SNP structure Figure . Biased Average adult height is taller in northern Europe than in southern Europe. By our definition, height is a structured phenotype, i.e., it varies by ancestry. Lactose intolerance also varies across Europe from North to South. The genetic polymorphism in the LCT (Lactase) gene that causes lactose intolerance, and the SNPs in LD with this polymorphism, appears to be associated with height in non-homogeneous samples of individuals of European descent . Observi• rs1042725 and rs6060369 : Two pos• rs2322659 : A struc• rs2290305: a non-structured SNP, not associated with PC1 .Table We performed a simulation study in which we generated multiple sets of genome-wide SNPs. The goal was to investigate Type I error and power of associations between case-control status and a SNP when adjusting for ancestry informative PCs selected by a variety of rules. A second aim of this study was to examine more critically the effects of the amount of phenotypic structure and genotypic structure on the association analysis, as well as investigate the bias and precision of the associations.We did not specifically address the issue of which SNPs to include in the PCA. Using all available SNPs in a PCA provides the maximal information to ancestry, but highly correlated SNPs or unusual chromosomal phenomena such as known inversion polymorphisms or genomic regions known to play a role in susceptibility to a disease can affect the results from a PCA . Under sAll simulations were performed using distinct sub-populations. Admixed individuals are commonly used in GWAS. While we did not explicitly simulate admixed individuals, we know based on previous work  that PCAWe focused our exploration on linear PC adjustment models. We did not investigate adjusting for clusters identified in the individual genotype data because previous work has suggested that linear adjustments are adequate for the population structure typical of European populations . We did Our findings suggest that to optimize power under certain scenarios, the choice of covariate PCs in a genome-wide association study using logistic regression with a dichotomous outcome should be SNP-dependent. Our findings only apply to case-control or dichotomous outcome analyses using logistic regression. These results may appear to conflict with Xing and Xing , who recFor linear regression using continuous phenotypes, one can check phenotypes for association with the PCs. If a top PC is significantly associated with the phenotype of interest then the trait-genotype association model should include PCs as covariates to adjust for population structure. Unlike logistic regression, adjusting for covariates associated with the trait in linear regression always improves the precision of the effect estimate by reducing the residual variance . Since tStandard practice is to include a fixed number of PCs in association models for GWAS. Here, we conclude that if power is not a concern, then selecting the same set of PCs for adjustment for all SNPs in logistic regression is a strategy that achieves appropriate Type I error. However, standard practice is not optimal in all scenarios and to optimize power for structured SNPs in the presence of unstructured phenotypes, PCs that are associated with the tested SNP should be included in the logistic model. The gain in power we observed in our simulations was an approximate 5%-percentage point increase for adjusting only when the SNP is structured over always adjusting for the ancestry informative PCs. We note that some of the differences in power may disappear if we correct for Type I error, but this is not done in practice. It may be easier and more intuitive to adjusting for the same set of PCs across all SNP associations.The authors declare that they have no competing interests.GMP and KLL conceived and designed the study, carried out statistical analyses, and interpreted the data. GMP drafted the manuscript. KLL critically revised the manuscript. All authors read and approved the final manuscript.Supplemental Figure 1. Empirical Type I error and power for increasing risk allele frequency differences.Click here for fileSupplemental Figure 2. Empirical Type I error and power with 4 sub-populations.Click here for fileSupplemental Figure 3. Power for 2 sub-populations and positive confounding.Click here for file


SLC2A9 gene having the largest effect identified so far. Gene-gene interactions (epistasis) are largely unexplored in these GWA studies. We performed a full pair-wise genome scan in the Italian MICROS population (n = 1201) to characterise epistasis signals in SUA levels. In the resultant epistasis profile, no SNP pairs reached the Bonferroni adjusted threshold for the pair-wise genome-wide significance. However, SLC2A9 was found interacting with multiple loci across the genome, with NFIA - SLC2A9 and SLC2A9 - ESRRAP2 being significant based on a threshold derived for interactions between GWA significant SNPs and the genome and jointly explaining 8.0% of the phenotypic variance in SUA levels (3.4% by interaction components). Epistasis signal replication in a CROATIAN population (n = 1772) was limited at the SNP level but improved dramatically at the gene ontology level. In addition, gene ontology terms enriched by the epistasis signals in each population support links between SUA levels and neurological disorders. We conclude that GWA epistasis analysis is useful despite relatively low power in small isolated populations.Genome-wide association (GWA) studies have identified a number of loci underlying variation in human serum uric acid (SUA) levels with the  Serum uric acid is the final oxidation product of purine metabolism in humans. High serum uric acid (SUA) levels can lead to gout and is associated with cardiovascular diseases and diabetes SLC2A9 gene are the most significant genetic risk factors associating with low fractional excretion of SUA and explain 5.3% of the SUA level variance in women and 1.7% of the SUA level variance in men in the VIS population SUA level is a complex trait that is affected by environmental (e.g. diet and excessive body weight) and genetic factors with heritability estimates of 60–87% One possible source of the unexplained variation in SUA levels is gene-gene interaction (epistasis) The Italian MICROS cohort was recruited from the villages in South Tyrol DNA samples were genotyped with Illumina Infinium HumanHap300v1/v2 or HumanCNV370v1 SNP bead microarrays and analyzed using the BeadStudio software. Quality control of the genotype data was performed for each cohort using the R/GenABEL package (Version 1.4.3) rntransform function that is implemented in the GenABEL package performing quantile normalisation of residuals from a generalized linear model analysis. The normalised SUA levels were then analysed using a linear mixed model to correct for polygenic effects and relatedness using the polygenic function in the GenABEL package and the resultant environmental residuals  were used as the trait to test for association In each individual cohort the raw SUA levels were corrected for age, sex and BMI and normalised using the mmscore function in the GenABEL package. The consensus GWA threshold of 7.3 (−log10(5.0E-08)) was applied to identify GWA significant SNPs 1SNP and 2SNP, the following genetic models are used to detect epistasis where genotypes of each SNP  were fitted as fixed factors y is the trait of interest, μ is the model constant, 1SNP (or 2SNP) is a fixed factor with three levels, 1*SNP2SNP is the interaction term, e is the random error term. The F ratio test of Model 1 against Model 3 is for the whole pair effect including interaction . The F ratio test of Model 1 against Model 2 is for the interaction between the two SNPs . SNP pairs with missing joint genotype classes  were not evaluated to reduced the risk of inflation of the type I error rate. P values were calculated based on the F distribution with relevant degrees of freedom and transformed in the −log10 scale . We applied the same −log10 scaled thresholds for both the pairF and intF tests to control the type I error rate A single SNP based GWA scan was performed in each population using a score test method (based on the additive model) implemented in the 10 scale) were derived based on Bonferroni correction for multiple tests, i.e. the 5% nominal P value corrected by the number of pair-wise tests. Considering 300,000 SNPs, a SNP-genome scan and a full pair-wise genome scan perform 3.0E+05 and 4.5E+10 association tests respectively, thus the genome-wide threshold is 11.95 (−log10(0.05/4.5E+10)) for the pair-wise genome scan. SNP-genome scans have been used to test epistasis for genome-wide significant signals specifically to increase the power of detection 10(0.05/3.0E+05/N) if there are N GWA significant SNPs.Genome-wide significance thresholds  were retained. The retained results were evaluated using the predefined thresholds to identify genome-wide significant epistatic signals. Each SNP in the retained results was annotated to the nearest gene within a window of 20 kilobases flanking the SNP based on the physical distances to either the start or end of transcription of genes (the distance is set to zero if the SNP is within a gene) without considering linkage disequilibrium (LD). A full pair-wise genome scan was also performed in the CROATIAN population as above to prepare input for GO enrichment analyses (see below).A full pair-wise genome scan was performed in the MICROS population and SNP pairs with a certain interaction signal  was used to claim significant replication for an epistatic pair (i.e. both replicated SNPs were exactly the same as the epistatic SNPs) because only one test was performed. When an epistatic pair was not replicated (e.g. due to missing genotype classes), we also tested the interactions between each of the adjacent SNPs of the first epistatic SNP and that of the second. In that case, the nominal threshold corrected by the actual number of tests was used to claim significant replication.polygenic function was also used to calculate the proportion of the phenotypic variance in SUA levels explained by epistatic pairs in the MICROS population. Because the quantile normalised SUA levels were based on the quantiles of the raw SUA levels, we used standardized SUA levels for variance calculation ). The standardized SUA levels were fitted into the full mixed model including polygenic effects and the identified SNP pairs. The difference of residual variance from a value of 1 is the proportion of phenotypic variance explained by the SNP pairs included.The 10Ppair>6.5 and −log10Pint>6.5) in each population and used the epistatic genes annotated from them as the target for the GO enrichment analysis to mine biological meanings from epistatic signals that were less significant. The GO terms enriched (P<1.0E-03) by the epistatic genes in each population were compared to identify replicated GO terms and then epistatic genes shared by each pair of replicated GO terms. The shared epistatic genes in the replicated GO terms were investigated further for a) associated biological functions or diseases and b) epistatic SNP pairs involved and their replication.A GO enrichment analysis was conducted for each of the MICROS and CROATIAN populations using the running mode of “Two unranked lists of genes” in GOrilla SLC2A9 gene. The GWA scan for the CROATIAN population also identified seven genome-wide significant SNPs which were exactly the same as those in MICROS. Thus the genome-wide threshold of 7.62 (−log10(0.05/3.0E+05/7) was used for SNP-genome scans. The inflation factor λ (computed by regression in a quantile-quantile (QQ) plot, The mean SUA level and phenotypic correlation between SUA level and BMI were similar across the MICROS, VIS and KORCULA cohorts . The polpairF and intF tests performed on chromosomes 3 and 4  to illustrate why the two tests are needed (pairF test (e.g. −log10Ppair>11.95 may pick up SNP pairs with very weak interactions (e.g. −log10Pint<3), whereas using only the intF test (e.g. −log10Pint>7.62) could pick up those with weak whole pair effects when both SNPs had small marginal effects (e.g. −log10Ppair<6)  had missing joint genotype classes and hence were ignored in this study. We plotted the P values of all the e needed . Using oPpair<6) . The QQ ir tests  showed ant tests  showed a10Ppair>4.7 and −log10Pint>3.2, 212933 in total) retained from the full pair-wise genome scan . However, we found a big cluster of SNP pairs with strong whole pair effects  but weak to high interactions . This cluster of SNP pairs all involved the SLC2A9 gene as one would expect. Two pairs: rs12130085 (NFIA) – rs737267 (SLC2A9) and rs737267 (SLC2A9) – rs9316212 (ESRRAP2) were significant based on the threshold of 7.62 for SNP-genome scans , in total 1326 SNP pairs involved 2063 unique SNPs of which 1148 (55.6%) were annotated to 910 unique genes, i.e. 1.5 pairs per gene. In contrast, 17 out of the 1326 SNP pairs involved the SLC2A9 gene including 4 GWA significant SNPs rs733175, rs737267, rs13131257 and rs13129697  and rs737267 – rs17064136 (adjacent to rs9316212) were 1.35 and 1.32 respectively. Combining the MICROS and CROATIAN data did not make the two significant pairs stronger (−log10Pint was 2.32 and 2.19 respectively) which is in line with the replication results. The two less significant SLC2A9 epistatic pairs also missed the SNP level replication in the CROATIAN population  in MICROS (910 genes from 1326 pairs) and CROATIAN (984 genes from 1260 pairs) were tested for GO enrichment. The GO terms enriched by epistatic genes in MICROS showed a complicated relationship among biological functions  and highlighted the importance of glutamate receptor activity  (SLC2A9 – LRRC16A (interaction between two SUA candidate genes) (SLC2A9) – rs4085921 (GPC6) and rs737267 (SLC2A9) – rs2302558 (CLEC16A). All the 49 epistatic gene pairs involved SLC2A9 and of these 5 involved interactions with other shared epistatic genes: ERC2, GPC6, CTNND2, CNTNAP2 and PTPRD. However, most of the 49 replicated gene pairs had a relatively weak interaction signal (−log10Pint<5).Epistatic genes annotated from the less significant SNP pairs  . Surprise genes) . Two genSLC2A9 gene may be an important epistatic locus interacting with multiple loci across the genome more frequently than expected by chance (SLC2A9 epistatic pairs (NFIA – SLC2A9 and SLC2A9 – ESRRAP2) were significant in SNP-genome scans and jointly explained 8.0% of the phenotypic variance in SUA levels where 3.4% was explained by their interaction components. However, caution should be taken in light of the billions of tests performed, the potential overestimation of the variance explained and the limited replication of the two pairs. The NFIA (nuclear factor I/A) gene is known as a cellular transcription DNA replication factor and its haploinsufficiency is associated with a central nervous system malformation syndrome and ureteral and renal defects NFIA was responsible for celiac disease ESRRAP2  is not yet known to have any related functions.Using a full pair-wise genome-wide association scan we generated a profile of epistasis in SUA levels in the Italian MICROS population. No epistatic SNP pairs reached the Bonferroni adjusted threshold for the pair-wise genome scan, which was not a surprise because of the relatively small population size. Nonetheless, we found that the y chance  and S1. SLC2A9 gene is known to interact with sex, age and dietary patterns SLC2A9 is a class II glucose/fructose transporter as well as high-capacity/low-affinity urate transporter with two isoforms expressed in basolateral and apical membranes of proximal renal tubular cells respectively SLC2A9 polymorphism was demonstrated further in two recent studies linking SUA levels with human cognitive aging SLC2A9 were involved in epistatic interactions, which adds another dimension of studying the SLC2A9 polymorphism and provides new clues of the genetic mechanism underlying SUA levels.The SLC2A9 interacting genes had clear neuronal influence  and/or membrane functions (SLC2A9 and genes that interacted with it (8 out of 910 unique genes in MICROS). The epistatic genes encoding glutamate receptors , schizophrenia and bipolar disorder , Alzheimer's disease , Parkinson's disease (DLG2), sclerosis , and diabetes .The impact of SUA levels on human cognitive dysfunction has been investigated in recent years unctions , S2, S3.Statistically detecting and replicating an epistatic pair of SNPs is far more challenging than for a single SNP signal for a number of reasons. Whereas a single SNP association is dependent on strong LD between the marker SNP and causative variant, detection of epistasis requires strong LD for both loci. Small allele frequency changes (e.g. 10%) between detection and replication populations can lead to a dramatic loss of power in replication. Such small allele frequency changes are not uncommon across the small and/or isolated populations. The issue of missing joint genotype class in replication populations  increases the difficulty of replication, especially when the epistatic pair for test has a rare genotype class  – rs4085921 (GPC6) was replicated at all three levels, rs737267 (SLC2A9) – rs2302558 (CLEC16A) at the SNP and gene levels but not the GO level as CLEC16A is not associated with an enriched GO term. Both GPC6 (glypican 6) and CLEC16A  are associated with multiple sclerosis The lack of statistical replication at the SNP level may be not too surprising considering that the epistatic signals detected in one isolated sample were to replicate in another isolated sample (CROATIAN) or a less isolated but smaller sample (i.e. SOCCS). Even when all the retained SNP pairs were considered regardless, we just found two SNP pairs (out of 212933) were replicated at the SNP level. When testing for replication at the GO level, we found 13 GO enriched terms (>50%) were replicated. Clearly replication improved dramatically as the level changes from SNP to GO annotation. A high level replication (i.e. gene or GO/pathway) does not guarantee a SNP level replication but is valuable to understand the biology of interest. Therefore, we recommend testing for replication of epistasis signals at all three levels. For example, rs737267 (10Pint of 6.5 used here) could be chosen as long as the big cluster of SLC2A9 pairs was mostly excluded Click here for additional data file.Figure S2The joint genotype – phenotype maps for two genome-wide significant epistatic pairs.(TIF)Click here for additional data file.Figure S3Gene component ontology terms enriched by epistatic genes in the MICROS population.(TIF)Click here for additional data file.Figure S4Gene component ontology terms enriched by epistatic genes in the CROATIAN populations.(TIF)Click here for additional data file.Table S1SLC2A910Ppair>6.5 and −log10Pint>6.5) in MICROS. involved epistatic pairs (−log(PDF)Click here for additional data file.Table S2Epistatic genes in the replicated GO terms shared by the MICROS and CROATIAN populations.(PDF)Click here for additional data file.Table S3Epistatic pairs with at least one shared GO gene and replicated in MICROS and CROATIAN.(PDF)Click here for additional data file.


Anopheles arabiensis, to further understanding of patterns of genetic diversity, population subdivision and linkage disequilibrium in this species. We found high levels of genetic diversity within the An. arabiensis genome, with ~800,000 high-confidence, single- nucleotide polymorphisms detected. However, levels of nucleotide diversity varied significantly both within and between chromosomes. We observed lower diversity on the X chromosome, within some inversions, and near centromeres. Population structure was absent at the local scale  but detected between distant populations (Cameroon vs. Tanzania) where differentiation was largely restricted to certain autosomal chromosomal inversions such as 2Rb. Overall, linkage disequilibrium within An. arabiensis decayed very rapidly (within 200 bp) across all chromosomes. However, elevated linkage disequilibrium was observed within some inversions, suggesting that recombination is reduced in those regions. The overall low levels of linkage disequilibrium suggests that association studies in this taxon will be very challenging for all but variants of large effect, and will require large sample sizes.Association mapping is a widely applied method for elucidating the genetic basis of phenotypic traits. However, factors such as linkage disequilibrium and levels of genetic diversity influence the power and resolution of this approach. Moreover, the presence of population subdivision among samples can result in spurious associations if not accounted for. As such, it is useful to have a detailed understanding of these factors before conducting association mapping experiments. Here we conducted whole-genome sequencing on 24 specimens of the malaria mosquito vector,  Genome-wide association studies have become a widely used and successful approach for identifying the genetic basis of traits in humans and model plant and animal species , Sagamaganga , and Minepa  in the Kilombero Valley, Tanzania in 2012, and Ourodoukoudje  in Cameroon in 2005. Samples were preserved in 80% ethanol. The head and thorax of each specimen was subsequently dissected and used for DNA extraction, which was performed with the QIAGEN Biosprint 96 system using QIAGEN blood and tissue kits . Anopheles arabiensis samples were distinguished from other An. gambiae s.l. species complex members with the Scott polymerase chain reaction assay , whereas the inverted arrangement is indicated by the letter alone .A number of polymorphic inversions have been identified among the species of croscope . InversiAn. arabiensis genome available was a transcriptome sequence. Therefore, we used the An. gambiae s.s. genome build AgamP3_15 as our reference. The An. gambiae s.s. genome was derived from the PEST strain that was fixed for the standard arrangement for all inversions polymorphic in An. gambiae s.s.At the time of analysis (January 2013), the only https://github.com/vsbuffalo/scythe) and conducted quality trimming using SICKLE . Reads were then aligned to the reference genome using BWA 0.6.1  but found the default parameters to be the most optimal with our dataset in terms of mapped reads. The An. gambiae s.s. genome includes a number of unmapped haplotype contigs; we excluded data aligned to these contigs. We marked duplicate reads using PICARD (http://picard.sourceforge.net) and realigned reads around indels with realigner target creator and indel realigner from the Genome Analysis Toolkit, GATK 2.4.3  hard filter recommendations , in addition to a quality score of >30 and combined depth of coverage >30×. The remaining biallelic SNPs constituted our high confidence SNP sites. We then repeated this process on a combined file of the 24 low-coverage samples, with the exception that the quality score threshold was reduced to 4 , and apparent multiallelic SNPs , we filtered SNPs as per the ced to 4 , and dep2. This analysis was not affected by phasing ambiguities because we calculated r2 directly from the genotypes rather than phased data , except FST, which was computed with a sliding window tool within VCFTOOLS. The software SNPEFF (www.gnuplot.info). For the sliding window plots, the approximate locations of the telomeres, centromeres, and known inversions were taken from coordinates derived from An. gambiae s.s. as detailed in vectorbase (www.vectorbase.org).Coverage across chromosomes was calculated with the depth of coverage tool in GATK. We used PLINK 1.07  to calcu similar . VCFTOOLAn. arabiensis, we conducted Illumina 100-bp paired end whole-genome sequencing on 24 samples at low coverage; 20 from three villages in Tanzania and four from a single village in Cameroon. In addition, three of these samples  were sequenced at greater coverage to create a reference of high-confidence SNP sites. The number of reads generated per sample varied between 8 and 19 million reads for the low-coverage sequencing and 47 and 62 million reads for the high coverage sequencing.To assess nucleotide diversity, population structure and LD, in An. gambiae s.s. genome. An. arabiensis and An. gambiae s.s. are closely related sister taxa that occasionally hybridize to produce fertile female offspring (An. gambiae s.s. and An. arabiensis differ by a number of inversion polymorphisms (An. gambiae is fixed for the Xag inversion (which is absent in An. arabiensis), whereas An. arabiensis is fixed for Xbcd which represents a complex of three fixed autapomorphic compound inversion arrangements. Furthermore, the 2La inversion which is fixed for the inverted arrangement in An. arabiensis (2La) is polymorphic in An. gambiae s.s. (a2La/+) and fixed for the standard arrangement (a/+a2L+) in the An. gambiae s.s. strain that was sequenced and used as a reference here.We aligned sequences to the Supporting information, Figure S1). Similar findings were observed for the low-coverage samples (data not shown). Lower coverage on the X chromosome was particularly associated with the Xag inversion . Coverage also decreased proximal to the centromere on all chromosomes.Following alignment to the reference, we assessed coverage after excluding duplicate, low-quality, and poorly mapping reads. On the autosomes, coverage was high with 73–80% of bases exhibiting ≥30× combined coverage for the three high coverage samples, with 97–99% of the genome showing coverage overall . On the An. arabiensis is polymorphic for a number of inversions on the 2R and 3R chromosomes. To assess the inversions present in the Kilombero Valley of Tanzania we conducted karyotyping on ovarian nurse cells extracted from 37 blood fed An. arabiensis females collected from Lupiro in 2011, as ovarian nurse cells were not available for the Tanzanian samples sequenced here . This revealed polymorphism for the 2Rb inversion , and 3Ra inversions  which is consistent with other studies of An. arabiensis in Tanzania , we excluded any SNPs that were: (1) not called in all 24 low-coverage samples ; (2) not present in the three high-coverage samples ; (3) where the nonreference allele was not observed in ≥5 samples ; and (4) any multiallelic SNPs, which includes SNPs biallelic within An. arabiensis but for two nonreference alleles . Of the 712,707 SNPs in our final data set, we found these to be predominately in intergenic (34%) or intronic (21%) regions or ±5 kb of a gene (38%), with only 6% occurring in coding regions.In total we identified 712,707 biallelic SNPs among our 24 low-coverage samples. This should be viewed as a conservative estimate because we applied a set of stringent filters to ensure a set of high-quality and high-confidence SNPs. Specifically, from the 2,574,223 SNPs remaining after standard quality filters were applied . It is noteworthy though, that during filtering we excluded all multiallelic SNPs , including SNPs where samples were biallelic for two non-reference alleles. As we had aligned our sequences to the An. gambiae s.s. genome, these biallelic nonreference SNPs were likely the most informative for identifying divergence within An. arabiensis. As such, we cannot exclude the possibility that some genetic sub-structuring exists between these locations, but on a finer scale than could be detected here with our conservative SNP dataset. Nonetheless, we did detect population structure between the Tanzania and Cameroon sites , which is consistent with expectations for restricted gene flow between populations separated by great distances  was higher in comparison to the other chromosome arms (FST = 0.016−0.051). Sliding window analyses showed that in contrast to other regions, divergence was elevated across most of the 3Ra inversion (mean 3Ra = 0.144), as well as on the 2Rc inversion  with physical distance for these samples is shown in 2) decayed rapidly to less than 0.2 within 200 bp in An. arabiensis  2Rb breakpoint, as well as the proximal breakpoint of the 3Ra inversion . SNP density on the X chromosome was too low for sliding window analyses.To assess genomic patterns of LD, we plotted LD for all SNPs separated by 1−10 kbp by genomic location . This shnversion . OtherwiAn. arabiensis. Diversity estimates  and An. arabiensis are fixed for alternative arrangements of the Xag inversion which covers ~60% of the X chromosome , which is highly diverged from the inverted arrangement (2La) which is fixed in An. arabiensis  to be lower than between Cameroonian populations of An. gambiae s.s.  , and detected two subpopulations. Given that our studies did not assess the same sites (except Lupiro), this may reflect sampling. Alternatively, as mentioned previously, our stringent filtering of variants was biased toward removing the SNPs most likely able to detect fine scale structure. Assessments involving larger sample sizes and more study sites would be useful in clarifying local population structure.Cryptic population structure can result in spurious associations if not accounted for (anzania) . However3Ra inversion, as well as the 2Rc inversion  and are under strong selection  among low overall genomic divergence (FST = 0.129) has also been reported in An. gambiae s.s.  were slightly lower than those reported for An. gambiae s.s. (above), but this could result from unbalanced representation of the inversions among our samples; something that we could not control or test for as our samples could not be karyotyped prior to sequencing . If as suggested by our data, chromosomal inversions contribute to population structure in An. arabiensis, it will be critical for this to be accounted for in association studies particularly given the potential for the inversions to cosegregate with the trait of interest (Our analyses detected great variability in differentiation across the genome, with greater levels found on the 3R, particularly the nversion . Few stus. e.g., . The valAn. arabiensis (A. gambiae s.s. (~6 Mb, 2Rb and 2Rc inversions and particularly at the proximal 2Rb breakpoint, whereas LD was only elevated at the proximal breakpoint of the larger ~24Mb 3Ra inversion (The extent of LD in a genome is a key factor influencing the feasibility of association mapping studies . Overallabiensis , but we abiensis . In part. ~6 Mb, , which enversion . This paAn. arabiensis is broadly consistent with the short LD reported for An. gambiae s.s. immunity genes (<1 kb, An. gambiae s.s. (<3 kbp, An. arabiensis than in An. gambiae s.s. (average spacing of SNPs ~3 kb, Arabidopsis thaliana and >100 kb in rice, Oryza sativa; Drosophila melanogaster, <30 bp; e.g., regions with selective sweeps, Drosophila melanogaster Genetic Reference Panel (An. arabiensis likely rests on the production of a similar type of resource which could be used to dis-entangle the genetic basis of traits that can be phenotyped in a laboratory setting.The rapid decay of LD in