PLEKHA7 (a known GWAS locus for blood pressure) and GPR180 (which plays a role in vascular remodelling), and also for triglycerides as local interactions within the 11q23.3 region (replicated significantly in NFBC1966), which notably harbours several loci  contributing to triglyceride levels. Tests of the local interactions within the 11q23.3 region conditional on the top GWAS signal suggested the presence of two independent functional variants, each with supportive evidence for their roles in gene regulation. Local interactions captured 9 additional GWAS loci identified in this study (3 significantly replicated) and 73 from previous GWAS (24 in the eight traits and 49 in related traits). We conclude that the detection of local interactions requires adequate SNP coverage of the genome and that such interactions are only likely to be detectable between SNPs in low linkage disequilibrium. Analysing local interactions is a potentially valuable complement to GWAS and can provide new insights into the biology underlying variation in complex traits.Local interactions between neighbouring SNPs are hypothesized to be able to capture variants missing from genome-wide association studies (GWAS) via haplotype effects but have not been thoroughly explored. We have used a new high-throughput analysis tool to probe this underexplored area through full pair-wise genome scans and conventional GWAS in diastolic and systolic blood pressure and six metabolic traits in the Northern Finland Birth Cohort 1966 (NFBC1966) and the Atherosclerosis Risk in Communities study cohort (ARIC). Genome-wide significant interactions were detected in ARIC for systolic blood pressure between  The study of gene-gene interactions (epistasis) in complex traits has seen rapid advances in recent years. The potential importance of epistasis in explaining the extent and basis of heritability has been emphasized in both model organisms New developments such as BiForce support high-throughput analysis of epistasis in GWAS data allowing full pair-wise interactions for multiple traits in multiple populations to be quickly computed Various approaches may be considered to increase detection power for epistasis. One reason for the low power issue is the use of stringent genome-wide significance thresholds derived from Bonferroni adjustment for often several billions of pair-wise tests of all SNP combinations. Several knowledge-driven methods select a subset of SNPs based on prior biological knowledge (e.g. genes and proteins in particular pathways) and only test pair-wise interactions between the selected SNPs so that a more relaxed threshold could be used to claim significance int) less than a threshold of 1.0E-05 derived from region based permutations . We used the r2 measure of LD throughout this study which is considered to be the best LD measure in studying epistasis and robust to the Hardy-Weinberg Equilibrium assumption Here we used the Atherosclerosis Risk in Communities study cohort (ARIC) and the Northern Finland Birth Cohort (NFBC1966) to explore the potential values of high throughput analyses of epistasis in the eight metabolic traits above. ARIC is one of the largest GWAS populations available and both its sample size and density of SNPs genotyped nearly double the counterparts in NFBC1966. After data scrutiny and quality control checks , we perfWe analyzed 514 662 and 323 697 SNPs in the ARIC and NFBC1966 cohorts respectively . For sinint of the SBP pair did not exceed but was close enough to the stringent threshold). The remaining four epistatic pairs each included one marginal SNP (int<1.9E-09 for TRI were genome-wide significant and the remaining two (for TRI and HDL respectively) were suggestive. Interestingly, the four epistatic SNP pairs identified for TRI were all local interactions between SNPs closely located (distance <45 kilobases) in the 11q23.3 region, which contains multiple genes associated with lipid traits (int of 3.9E-06. Replication of the six epistatic pairs was tested in the NFBC1966 cohort but only at the region level (int<0.05) but only the replication of the 11q23.3 local interaction pairs for TRI exceeded the significance threshold of 5.6E-04 derived from permutation.Pair-wise genome scans identified six epistatic pairs of SNPs carrying strong interaction signals in ARIC only . The firinal SNP , of whicd traits . The SNPon level  section int<5.0E-08 (i.e. the GWAS consensus threshold) in each trait in NFBC1966 were approximately 40% of the counterparts in ARIC, which is coincident with the difference of the SNP coverage in the two cohorts, i.e. (323 697 in NFBC1966)2/(514 662 in ARIC)2 = 0.4. The most striking differences were the numbers of local interaction pairs detected in each trait between NFBC1966 (<50) and ARIC (800 to 1000), suggesting that SNP coverage might be particularly important to detect local interactions.The power advantage in ARIC over NFBC1966 was clearly observed in every trait studied when sub-significant SNP pairs were considered together . For exaint<5.0E-08 (from 5684 to 5446) or the number of local interaction pairs (from 942 to 960), but did reduce the interaction signals of the top four pairs for TRI listed in int values reduced to 6.1E-07, 5.2E-06, 5.3E-06 and 1.7E-04 respectively, none remaining significant). However, a reduction by random sampling of the number of ARIC SNPs (with no changes to sample size) to 323 697 – the total number of SNPs in NFBC1966 – dramatically reduced the numbers of SNP pairs with Pint<5.0E-08 (to 2376) and local interaction pairs (to 332), including all the four SNP pairs for TRI  to 4873 – the sample size of NFBC1966  - did no for TRI . Clearlyint<1.0E-05 but which do not reach genome-wide significance it is clear that local interactions comprised only a small proportion (<0.1%) of the total number of SNP pairs retained per trait in both cohorts  show differentiated phenotypes which leads to an apparent statistical interaction signal in a contingency table based test whereas conventional GWAS can not detect the causal variant from the associations with either SNP. The model resembled the interaction between rs17119975 and rs10892020 in TRI  and only a few had r2>0.5 each with a generally moderate interaction signal (i.e. Pint<1.0E-06). Local interactions were distributed rather evenly across the ranges of distances between two epistatic SNPs and most Pint values in ARIC were less than 1.0E-06 with only 0.2% with Pint<1.0E-08  values of the four local interaction pairs for TRI in ARIC  but the interactions in four SNP pairs remained significant : rs3741298 – rs7396835 (or rs7396851) covering ZNF259 and APOA5, rs17092638 – rs3741298 and rs12799766 – rs4417316 covering BUD13 and ZNF259 (APOA5 and between the epistatic SNPs of the first two independent pairs (int of 1.5E-07 and 2.4E-07 respectively) to that SNP. In addition, we found two clusters of ENCODE regulatory elements BUD13 and ZNF259  of which the three pairs tagging LDLR and TOMM40 – APOE for LDL in ARIC and CETP for HDL in NFBC1966 respectively were significantly replicated.The ANNOVAR  for TRI  were the and TRI  in ARIC.G6PC2, ABCB11 and LRP2 associated with various metabolic traits and other biochemical traits ERAP1 and a haplotype of ERAP1 and ERAP2 (5q15) marked by local interactions in CRP were reported to be associated with Ankylosing Spondylitis where CRP levels are considered as one of the clinical indicators of inflammatory activities of patients CPS1 gene (2q34) marked by local interactions in DBP was previously found responsible for susceptibility to persistent pulmonary hypertension function KLKB1 (4q35.2) and ARL15 (5q11.2) in HDL and TRI; CD34 (1q32.2) and MYO16 (13q33.3) in DBP and SBP; VPS13C – C2CD4B (15q22.2), ZFAND6 (15q25.1) and WWOX (16q23.2-q23.1) in GLU and INS; SYCP2L – ELOVL2 (6p24.2-p24.1) in LDL and CRP; CCDC92 – ZNF664 (12q24.31) in LDL, CRP and DBP. In addition, we found the PCSK9 - USP24 (1p32.3-p32.2) region was marked by local interactions in LDL in both the ARIC and NFBC1966 cohorts.Local interactions also pointed to GWAS loci that were missed in our GWAS analyses, including 24 each associated with one of the eight traits studied here and 49 each associated with a related trait . These 7Compared to the great success in GWAS, high throughput analysis of epistasis is both in its infancy and substantially more challenging in detection as well as interpretation. Indeed, as shown in this study, conventional GWAS identified genome-wide significant SNPs in multiple loci in five out of the eight traits studied in both cohorts that were relatively easy to replicate , whereasPLEKHA7 and GPR180 for SBP (and DBP) may suggest an interesting model of blood pressure regulation, where PLEKHA7 is a GWAS locus associated with blood pressure GPR180 is a G protein-coupled receptor produced predominantly in vascular smooth muscle cells and may play an important role in the regulation of vascular remodelling PLEKHA7 codes adherens junction proteins binding paracingulin regulating RhoA and Rac1 activities GPR180.Low power of detection is the key issue in studying epistasis in single GWAS populations. We showed that in addition to sample size, SNP coverage was critical to power as it generates the detectable levels of LD required for epistasis detection and replication. The NFBC1966 cohort used a smaller sample size and a lower SNP coverage and this likely contributed to the many fewer epistatic SNP pairs than in ARIC based on the same criteria. Furthermore, in testing replication of the significant signals  in NFBC1Using a high density SNP chip for GWAS genotyping would mean even more stringent genome-wide significance thresholds based on Bonferroni adjustment and thus a further reduction of the power of detection of epistasis. Such Bonferroni adjusted thresholds can hardly remain practical as many more SNPs derived from the increasingly popular sequencing studies are used as input to future GWAS. There is a clear need for the community to define consensus genome-wide significance thresholds for future epistasis studies. A recent effort based on Illumina's HumanHap 550 bead SNP chip and Monte Carlo simulations has made a good progress towards this goal and suggests that an adjustment of 44% of the total number of pair-wise tests is appropriate to avoid using an overly stringent threshold Our local interaction results provide fresh evidence supporting the hypothesis that some genetic variations in complex traits may be captured by epistasis between neighbouring SNPs BUD13 and ZNF259 respectively. From a statistical viewpoint one may conclude that the top local interaction pair probably captured a marginal signal without interaction but the independent pairs could be real interactions. However, the mechanisms underlying these local interactions could be complicated. For example, the marginal signal captured by the top pair, if true, may also remove the interaction signals of two independent pairs overlapped the region marked by the top pair  were measured using standard enzymatic methods Subjects were excluded from the analysis of each individual trait if matching the phenotypic exclusion criteria defined in the original GWAS of NFBC1966 http://www.r-project.org/): individual call rate at 97%, SNP call rate at 95%, minor allele frequency at 2%, P value for deviation from Hardy-Weinberg equilibrium at 1.0E-10, false discovery rate for unacceptably high individual heterozygosity at 0.01. In addition, to control population stratification, individuals were excluded if they were outliers of one of the first three principal components  calculated  from the identity-by-state matrix constructed using the GenABEL ibs function. We analysed SNPs on the autosomal chromosomes only. After quality control, 514 662 and 323 697 SNPs were left in the ARIC and NFBC1966 cohorts respectively with various numbers of individuals in different traits  were used as the actual trait values to test for association In NFBC1966 each trait was adjusted for gender, oral contraception and pregnancy. In ARIC, each trait was adjusted for gender, age, oral contraception and field centre; the three lipid traits  were also adjusted for two additional effects: taking of cholesterol-lowering medication within two weeks of the visit and taking of medications that secondarily affect cholesterol. After adjustment for covariates, each trait was normalised using the GenABEL mmscore function and the consensus threshold (P = 5.0E-08) int) less than 1.0E-05. Bonferroni adjusted thresholds as previously defined N to be the total number of SNPs with K (K>0) marginal SNPs detected in the conventional GWAS, the 5% genome-wide thresholds were derived as P = 0.05/(N×(N–1)/2-(N–1)×K)) for a full pair-wise genome scan (i.e. 9.5E-13 and 3.8E-13 in NFBC1966 and ARIC respectively) and P = 0.05/((N–1)×K) for interactions involving at least one marginal SNP (i.e. 1.5E-07 and 9.7E-08 if K is 1 in NFBC1966 and ARIC respectively).Conventional GWAS analyses (i.e. assuming additive effects only) of each trait in each cohort were conducted using the GenABEL int value. If either SNP of an epistatic pair was not genotyped in the independent samples, the nearest SNP was chosen as its proxy to perform the region level replication tests for the pair.Identified epistatic SNP pairs were tested for replication in independent samples at the SNP and/or region levels following our previous protocol int value in each iteration to derive the 5% Pint value. We randomly sampled 200 SNPs from the genome and calculated the average of the derived 5% Pint values as the threshold as 1.3E-04, which was indeed less stringent than 6.1E-05 based on the Bonferroni adjustment (i.e. 0.05/(41*40/2)) or 1.0E-05 used to retain epistatic SNP pairs during the BiForce scans. The permutation derived threshold was based on one region at a time that was not adjusted for the total number of local interaction regions in the genome which is unknown in advance. For simplicity, we used 1.0E-05 as the threshold to declare local interactions in this study.The detection of local interactions may be substantially affected by LD patterns varying across the genome and thus require a different threshold to declare significance. We used permutation of the TRI trait in the ARIC cohort as an example to investigate such a threshold based on a window of 41 SNPs on the same chromosome (i.e. 20 upstream and 20 downstream of a SNP randomly sampled from the genome), which may not necessarily mark a region in exactly one million base pairs but capture the LD pattern within the region and with a fixed number of tests. For each randomly sampled SNP, we iteratively permuted the phenotypes and tested interactions of every pair-wise combination of SNPs within the window 100 times and recorded the lowest Pint<1.0E-05) and their LD values were calculated. The functional annotation tool ANNOVAR http://genome.ucsc.edu/) were used to search for regulatory elements published by the ENCODE project All SNP positions were based on the current human genome build (UCSC hg19/NCBI 37.3). Local interactions were extracted from the retained epistatic pairs if both SNPs located on the same chromosome and within a distance less than 1 Mb Click here for additional data file.Table S1Summary of the eight metabolic traits in ARIC and NFBC1966.(XLSX)Click here for additional data file.Table S2Genome-wide significant single SNPs in ARIC and NFBC1966.(XLSX)Click here for additional data file.Table S3Profiling epistatic SNP pairs detected in eight metabolic traits in ARIC and NFBC1966.(XLSX)Click here for additional data file.Table S4Local interactions tag GWAS loci identified from external studies of the same or highly related traits.(XLSX)Click here for additional data file.


Bos taurus bulls from distinct breeds for the purpose of identifying and annotating novel forms of genetic variation in cattle.One of the goals of livestock genomics research is to identify the genetic differences responsible for variation in phenotypic traits, particularly those of economic importance. Characterizing the genetic variation in livestock species is an important step towards linking genes or genomic regions with phenotypes. The completion of the bovine genome sequence and recent advances in DNA sequencing technology allow for in-depth characterization of the genetic variations present in cattle. Here we describe the whole-genome resequencing of two The genomes of a Black Angus bull and a Holstein bull were sequenced to 22-fold and 19-fold coverage, respectively, using the ABI SOLiD system. Comparisons of the sequences with the Btau4.0 reference assembly yielded 7 million single nucleotide polymorphisms (SNPs), 24% of which were identified in both animals. Of the total SNPs found in Holstein, Black Angus, and in both animals, 81%, 81%, and 75% respectively are novel. In-depth annotations of the data identified more than 16 thousand distinct non-synonymous SNPs (85% novel) between the two datasets. Alignments between the SNP-altered proteins and orthologues from numerous species indicate that many of the SNPs alter well-conserved amino acids. Several SNPs predicted to create or remove stop codons were also found. A comparison between the sequencing SNPs and genotyping results from the BovineHD high-density genotyping chip indicates a detection rate of 91% for homozygous SNPs and 81% for heterozygous SNPs. The false positive rate is estimated to be about 2% for both the Black Angus and Holstein SNP sets, based on follow-up genotyping of 422 and 427 SNPs, respectively. Comparisons of read depth between the two bulls along the reference assembly identified 790 putative copy-number variations (CNVs). Ten randomly selected CNVs, five genic and five non-genic, were successfully validated using quantitative real-time PCR. The CNVs are enriched for immune system genes and include genes that may contribute to lactation capacity. The majority of the CNVs (69%) were detected as regions with higher abundance in the Holstein bull.Substantial genetic differences exist between the Black Angus and Holstein animals sequenced in this work and the Hereford reference sequence, and some of this variation is predicted to affect evolutionarily conserved amino acids or gene copy number. The deeply annotated SNPs and CNVs identified in this resequencing study can serve as useful genetic tools, and as candidates in searches for phenotype-altering DNA differences. Cattle are an important source of meat, milk, and other goods in many parts of the world. Selective breeding has been used in conjunction with other approaches to increase the productivity of cattle and has contributed to dramatic changes in traits of interest. In dairy cattle, increases of 3,500 kg of milk, 130 kg of fat, and 100 kg of protein per cow per lactation have resulted from improvements in genetics, nutrition, and management during the past 20 years . More reA large catalogue of genetic variation, especially SNPs, exists for cattle in publicly accessible databases, thanks largely to the bovine HapMap project , the bovIn this work we describe the whole-genome resequencing of two individuals from distinct cattle breeds for the purpose of identifying DNA differences. One of the sequenced animals, Goldwyn, is a bull from the Holstein breed. Holstein cattle originated from the Friesian breed in Europe and were likely first imported to North America in 1795 . They arIn our analyses of the Holstein and Black Angus sequences we used assembly version Btau4.0  as a refGenomic DNA from a Black Angus bull and a Holstein bull were sequenced using the SOLiD system and a combination of fragment and mate pair libraries Table . The resTo estimate the rate at which SNPs were missed by sequencing , the SNP list for the Holstein animal was compared to the genotypes obtained using an array-based genotyping assay. Of 226,854 homozygous array calls that were different from the reference , 206,480 (91%) were identified as SNPs by sequencing, and 203,812 of the 226,854 (90%) SNPs showed concordant genotypes  were found to be true SNPs  are very close to the proportion of novel SNPs identified through the sequencing of a Fleckvieh bull genome (82%)   may be of particular interest. The SNPs that create stop codons can also be imagined to have important effects. This class is enriched for heterozygous SNPs . The annotation tool we used for this work (NGS-SNP) provides the names of protein features that overlap with SNP-altered residues (phosphorylation sites for example), as well as descriptions of protein function, gene names and identifiers, GO information, and known phenotypes in cattle or in humans linked to the SNP-affected gene or its human orthologue. This information, particularly in conjunction with QTL mapping or genome-wide association results, should be useful for future work aimed at better understanding the genetic mechanisms underlying phenotypic differences in cattle.Previous studies examining CNVs in cattle have employed array comparative genomic hybridization (aCGH) ,20 or SNA substantial number of the CNVs from this work (42%) are concordant with the CNVs previously identified in cattle using aCGH . This coGene Ontology enrichment analysis indicates that genes related to "response to stimulus," "immune system process," and "growth" are over-represented in the set of CNVs identified in this work Table . "ResponEach CNV is detected in this work as a gain of sequence dosage in one animal relative to the other. Some GO terms are enriched among the CNV gains in one animal but not among the CNV gains in the other. The GO term "locomotion" is enriched among the CNVs with higher copy number in Black Angus, while among the CNV gains in Holstein the terms "reproduction", "reproductive process", "membrane-enclosed lumen", and "enzyme regulator activity" are enriched. The term "locomotion" is defined as self-propelled movement of a cell or organism from one location to another and includes genes such as myotubularin related protein 9. Enrichment of this term could be imagined to reflect selection for lean muscle mass in beef cattle, while the enrichment of reproduction-related genes (which includes genes related to lactation) in the Holstein animal is consistent with the selection applied to the Holstein breed. At this point however there is no evidence linking the CNVs we detected to increased gene activity or to phenotypic differences.PLA2G2D gene were identified . Among the Black Angus animals tested by qPCR, copy number differences were observed relative to the Holstein calibrator , using a combination of fragment and mate-paired libraries. The libraries were prepared using the reagents and protocols provided by Applied Biosystems.Fragment libraries were generated by shearing 6 μg of genomic DNA into small fragments with a mean size of 110 bp using the Covaris S2 system  followed by end-repairing the DNA and ligating the P1 and P2 adaptors. The ligated, purified DNA was analyzed on an E-Gel 2% Size-Select gel  and 150-200 bp ligation products were collected, nick translated and amplified using library PCR primers 1 and 2. The PCR amplified samples were purified using the PureLink PCR purification kit .For mate-paired library preparation, 40-60 μg of DNA was sheared into 1.5 kb fragments for 2 × 50 bp libraries and 2.5 kb fragments for 2 × 25 bp libraries using a HydroShear . The fragmented DNA was end repaired using the End-It™ kit , and ligated to LMP CAP adapters for 2 × 50 bp libraries and EcoP151 for 2 × 25 bp libraries. The DNA was then size selected by electrophoresis on a 1% agarose gel, recovered from the gel using the PureLink Quick Gel Extraction Kit and the QIAquick Gel Extraction Kit  and circularized by ligation to a biotinylated internal adapter. The circularized DNA was isolated and digested before binding the library molecules to streptavidin beads. Before amplification of the library using PCR primers 1 and 2, the double-stranded P1 and P2 sequencing adapters were ligated to the end-repaired DNA. The amplified libraries were purified using the PureLink PCR Micro Kit .Average molecule sizes of all the libraries were confirmed by analysis with a Bioanalyzer and a DNA 1000 chip . The final concentrations of all the libraries were measured using the StepOnePlus Quantitation Kit  so that appropriate template volumes could be added to emulsion PCR reactions, which were performed using the SOLiD ePCR Kit . A portion of the beads was subjected to sequencing to assess the quality and to determine the volume of beads to be used for sequencing.Massively parallel DNA sequencing was performed using an Applied Biosystems SOLiD System (V3 chemistry) . The fraSequence reads were mapped to the Btau4.0 Bovine genome assembly using the Bioscope 1.0 software suite . A list of putative SNPs was generated for each animal from the mapped reads, using the diBayes SNP Detection module (with the "med-coverage" stringency setting) included with Bioscope. The lists were subjected to additional filtering to remove SNPs with particularly high read depth , and to remove SNPs that could not be unambiguously placed on the improved bovine genome assembly UMD3.1  using thTo estimate the completeness of the SNP lists obtained by sequencing, the Holstein bull was genotyped using the BovineHD BeadChip . Before comparing the genotyping results to the sequencing SNPs, BLAST was used to position each of the BovineHD SNPs on the Btau4.0 assembly. A total of 711,765 BovineHD SNPs could be unambiguously placed on the reference chromosomes used for read mapping (the 29 autosomes and chromosome X). These SNPs were used in subsequent comparisons with the sequencing SNPs. SNPs successfully genotyped using the BovineHD array and that were not homozygous for the reference alleles were compared to the sequence-derived SNPs. The false negative rate was calculated for homozygous sequence-derived SNPs as the percentage of non-reference-allele homozygous SNPs identified by genotyping that were not concordantly called as SNPs by sequencing . The false negative rate for heterozygous SNPs was calculated in a similar fashion, as the percentage of array-based heterozygous calls that were not concordantly called as SNPs by sequencing. SNPs identified by both sequencing and genotyping but which did not agree in terms of alleles present (discordant SNPs) were further classified based on the nature of the discrepancy.An Infinium iSelect HD Custom Beadchip  was used to genotype 427 SNPs and 422 SNPs selected from the Holstein and Black Angus SNP sets, respectively. DNA for genotyping was obtained from 1083 steers at the University of Guelph. GeneSeek  performed the genotyping and SNP calls were made using the GenomeStudio software package . The rate of false-positive discovery was calculated as the number of monomorphic SNPs returned, divided by the total number of SNPs.a) was calculated by comparing the reference amino acid and the non-reference amino acid to each orthologue. Briefly, the amino acid encoded by the variant  allele v is compared to each available orthologous amino acid o using a log-odds scoring matrix (BLOSUM62). Similarly, the amino acid encoded by the reference allele r is compared to the orthologues. The final score is the average score for the variant amino acid minus the average score for the reference amino acid (1). A positive value indicates that the variant amino acid is more similar to the orthologues than the reference amino acid, whereas a negative value indicates that the reference amino acid is more similar to the orthologues.NGS-SNP  was used2 threshold = 0.6) and a window size setting of 2, to generate a list of CNVs from the best-hit files. A "minimum-windows-required" setting of 10 was used to specify that ten consecutive sliding windows exhibiting a significant read depth difference were required for a region to be annotated as a CNV.Putative CNVs on the 29 bovine autosomes were identified using the CNV-seq program, which examines the mapped reads from two individuals and reports regions that exhibit statistically significant read depth differences . BrieflyQuantitative real-time PCR was performed for CNV validation using the StepOne Plus Real-Time PCR System and the SDS 2.2 software package . Primers and probes   for eachPS drafted the manuscript with assistance from the other authors, wrote software for SNP annotation, and determined the false-negative rate of SNP detection. JC identified CNVs, performed Gene Ontology analyses, and determined the false-positive rate of SNP detection. UB and YM performed library construction and DNA sequencing and UB assisted with validation of SNPs and CNVs. JMS performed genotype data interpretation and performed CNV validation. XL assisted with SNP annotation. SSM conceived the study and assisted with data interpretation. All authors have read and approved the final manuscript.Annotated Holstein SNPs. Tab-delimited text file of Holstein SNPs identified and annotated in this work. Intergenic and intronic SNPs were removed to reduce the size of the file.Click here for fileAnnotated Black Angus SNPs. Tab-delimited text file of Black Angus SNPs identified and annotated in this work. Intergenic and intronic SNPs were removed to reduce the size of the file.Click here for fileCNV details. Tab-delimited text file describing CNVs detected in this work.Click here for fileSNP annotation fields. PDF file containing a table of SNP annotation column descriptions.Click here for fileCNV validation primers and probes. PDF file containing a table of CNV validation primers and probes.Click here for file


Simple sequence repeat (SSR) and Single Nucleotide Polymorphic (SNP), the two most robust markers for identifying rice varieties were compared for assessment of genetic diversity and population structure. Total 375 varieties of rice from various regions of India archived at the Indian National GeneBank, NBPGR, New Delhi, were analyzed using thirty six genetic markers, each of hypervariable SSR (HvSSR) and SNP which were distributed across 12 rice chromosomes. A total of 80 alleles were amplified with the SSR markers with an average of 2.22 alleles per locus whereas, 72 alleles were amplified with SNP markers. Polymorphic information content (PIC) values for HvSSR ranged from 0.04 to 0.5 with an average of 0.25. In the case of SNP markers, PIC values ranged from 0.03 to 0.37 with an average of 0.23. Genetic relatedness among the varieties was studied; utilizing an unrooted tree all the genotypes were grouped into three major clusters with both SSR and SNP markers. Analysis of molecular variance (AMOVA) indicated that maximum diversity was partitioned between and within individual level but not between populations. Principal coordinate analysis (PCoA) with SSR markers showed that genotypes were uniformly distributed across the two axes with 13.33% of cumulative variation whereas, in case of SNP markers varieties were grouped into three broad groups across two axes with 45.20% of cumulative variation. Population structure were tested using K values from 1 to 20, but there was no clear population structure, therefore Ln(PD) derived Δk was plotted against the K to determine the number of populations. In case of SSR maximum Δk was at K=5 whereas, in case of SNP maximum Δk was found at K=15, suggesting that resolution of population was higher with SNP markers, but SSR were more efficient for diversity analysis.  Oryza sativa L.) is a staple food crop in the world and accounts for 21, 14 and 2% of global energy, protein and fat supply, respectively [indica, japonica and javanica constitute a large reservoir of rice germplasm including a variety of local landraces and cultivars [ Rice  got its preferential application in cultivar identification in many crops, such as grape , potato  studies ,12. The  studies , and tha studies , grapevi studies , melon [ studies  and rice studies .The present study was conducted to compare the two most important and preferred genetic markers, SSRs and SNPs for assessment of genetic variability and population structure among 375 rice varieties tested under distinct, uniform and stable (DUS-tested) system/ released and notified varieties (RV) by Indian system.Seed samples of 231 DUS-tested, 130 DUS-tested/released varieties and 14 released varieties of rice from different parts of India were received from Indian National Genebank, National Bureau of Plant Genetic Resources (NBPGR), New Delhi. The details of each variety along with passport data  number, State, local name, pedigree, regions), status (DUS/RV) and important traits are given in Six (6) to nine (9) seeds of each variety were dehusked and used for DNA isolation using QIAGEN DNeasy plant mini kit . Kernels were ground into fine powder using tissue lyser  with a tissue lyser adapter set (QIAGENq). DNA extraction procedure was as per manufacturer’s protocol.One hundred and twenty highly variable SSR (HvSSR) marker loci with repeat lengths of 51-70bp located across all twelve chromosomes of rice  were selGradient PCR was set for each primer with selected rice samples to standardize the temperature of amplification (Ta). Out of 120 HvSSR primers, 36 primers which were showing good amplification were selected for final study. 2, 0.2µl of 10mM dNTPs, 0.2 µl of each primer (10nmol), 0.2 µl of Taq DNA polymerase  and 5.6 µl distilled water. Amplification was performed in a thermocycler using following program; Initial denaturation at 94°C for 4 min followed by 36 cycles of 94°C for 30s, Ta for 45s, 72°C for 1 min and final extension at 72°C for 10 min. The amplified products were analyzed on 4% Metaphor agarose gel for 4 hrs at constant supply of 120V. Gel pictures were recorded using Gel Documentation System .Genomic DNA of all the 375 varieties was diluted to prepare working stocks of 10 ng/µl. PCR reaction was set in a total volume of 10µl containing 2µl genomic DNA (10ng/µl), 1µl of 10X buffer, 0.8 µl of 25 mM MgClThe Sequenom Mass ARRAY system  uses matrix assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometer for accurate detection of SNPs in a high-throughput manner (www. sequenom.com). Sequenom Mass ARRAY multiplex assays were designed for 36 SNPs (iPLEX gold chemistry), representing conserved single-copy rice genes , taking The SSR profiles were scored based on the size (bp) of fragments amplified across all the 375 varieties. The weak gel bands of negligible intensity and smeared bands were excluded from the final data analysis. In case of SNP, the Mass ARRAY Typer 3.4 Software was used for the visualization of SNPs and allele calling. The major allele frequency, gene diversity, heterozygosity and PIC for each locus were calculated for both SSR and SNP markers using Power Marker 3.5 . In addihttp://taylor0. biology.ucla.edu) to calculate final population structure. The proportion of the genome of an individual that belongs to each inferred population (admixture) was also estimated.Principle Coordinate Analysis (PCoA) and Analysis of Molecular Variance (AMOVA) were performed using software GenAlEx V6.5 . In caseindica rice varieties which included DUS tested as well as released and notified varieties from eighteen major rice growing states of India and varieties released and notified by Central Varietal Release and Notification Committee (CVRC) of India. These 375 varieties includes 5 landrace 369 modern varieties and one hybrid variety (KRH-2)representing five regions of India where rice is grown as a major crop (The present study was conducted on 375 jor crop . For comTemperature of amplification (Ta) for 36 HvSSR primers ranged from 51.9°C to 61.3°C, and used for generating amplification profiles of rice varieties. The number of alleles amplified per SSR primers varied from 2 to 4 . MaximumThe unlinked SNP markers located on the short arm, centromeric region and long arm of all 12 rice chromosomes were developed and used for diversity analysis. The chromosome number, primer ID and their physical position on rice chromosome are given in All the HvSSR amplicons generated across 375 varieties were assessed for genetic distance and the dissimilarity matrix was used for cluster development using the neighbour joining (NJ) method. In the Unrooted tree  and PhylSimilarly all 36 SNP loci were scored across all the tested rice varieties. Genetic distance was calculated and NJ tree was constructed using dissimilarity matrix. Unrooted tree  as well Analysis of Molecular Variance (AMOVA) with HvSSR showed that among regions only 1% diversity existed whereas at population level it was 4%. Maximum diversity has been observed at individual’s level (70 %) and within individual (25%) level . In casePrincipal Coordinate Analysis (PCoA) with SSR markers showed that large diversity existed in Indian rice varieties. Varieties exhibited uniform distribution across the two axes . The firAus' in West Bengal were compared with indica subpopulations and based on AMOVA, 1% variation  kept on increasing with increasing population number but there was no clear population structure, therefore Ln(PD) derived Δk was plotted against the K to determine the number of populations using a software “Structure harvester” available online. At K=5 maximum Δk was found  and thisSimilarly in case of SNP markers Δk values were plotted against the K to determine the number of populations and at K=15maximum Δk was found  Further,Aus and Indica subpopulation was also analysed with both SSR and SNP markers but no specific grouping of Aus was observed from indica subpopulation , gene diversity 0.30/0.28), heterozygosity (0.12/0.19) and PIC value (0.25/0.23), revealed that both marker systems generated almost similar information when equal number of locus (36 locus each) were studied. Earlier reports say that more SNP loci should be studied when comparing diversity generated by SSR . Further/0.28, heFurthermore, efficiency of both the markers was compared in assessing genetic relatedness among the varieties on the basis of their clustering pattern in the unrooted tree. Although the unrooted trees constructed using either SSR or SNP markers, grouped varieties into three major clusters, the numbers of varieties grouped into the clusters were different for both the trees. These findings were not surprising as broad pattern of grouping is expected to be more or less similar irrespective of type of markers used for genetic relatedness study. Similar, findings were also reported in European rice collection by Courtois et al. . Traits Indica rice has been subdivided into two subpopulations Aus and Indica [Aus did not show distinct grouping from indica subpopulation  and SNP (K=15) markers. The genetic structures of populations have been previously reported in rice -41. The In conclusion, though SSR/SNP are multi-allelic/bi-allelic nature and have different distribution pattern over genome even then in the present study equal number of locus (thirty-six each) amplified almost equal number of alleles in case of SSR (80) and SNP (72) markers. However, the unique features of SNP markers such as, abundance in the genome, ability to generate polymorphism due to variation at single base level and their development from the conserved single-copy rice genes , enabledTable S1List of rice varieties genotyped using SSR and SNP markers.(DOCX)Click here for additional data file.Table S2Details of rice samples showed trait based grouping with SSR markers.(DOCX)Click here for additional data file.Table S3a. AOMVA analysis between Indica rice population (345 varieties) and aus rice population (29 varieties) after removing hybrid rice (1 variety) sample based on SSR marker. b. F-statistics analysis between Indica rice population (345 varieties) and aus rice population (29 varieties) after removing hybrid rice (1 variety) sample based on SSR marker.(DOCX)Click here for additional data file.Table S4a. AOMVA analysis between Indica rice population (345 varieties) and aus rice population (29 varieties) after removing hybrid rice (1 variety) sample based on SNP marker. b. F-statistics analysis between Indica rice population (345 varieties) and aus rice population (29 varieties) after removing hybrid rice (1 variety) sample based on SNP marker.(DOCX)Click here for additional data file.Figure S1Indica rice population (345 varieties) and aus rice population (29 varieties) after removing hybrid rice (1 variety) based on (a) SSR data and (b) SNP data.Analysis of Molecular variance (AMOVA) between (DOCX)Click here for additional data file.


Imputation is a statistical process used to predict genotypes of loci not directly assayed in a sample of individuals. Our goal is to measure the performance of imputation in predicting the genotype of the best known gene polymorphisms involved in drug metabolism using a common SNP array genotyping platform generally exploited in genome wide association studies.Thirty-nine (39) individuals were genotyped with both Affymetrix Genome Wide Human SNP 6.0 (AFFY) and Affymetrix DMET Plus (DMET) platforms. AFFY and DMET contain nearly 900000 and 1931 markers respectively. We used a 1000 Genomes Pilot + HapMap 3 reference panel. Imputation was performed using the computer program Impute, version 2. SNPs contained in DMET, but not imputed, were analysed studying markers around their chromosome regions. The efficacy of the imputation was measured evaluating the number of successfully imputed SNPs (SSNPs).The imputation predicted the genotypes of 654 SNPs not present in the AFFY array, but contained in the DMET array. Approximately 1000 SNPs were not annotated in the reference panel and therefore they could not be directly imputed. After testing three different imputed genotype calling threshold (IGCT), we observed that imputation performs at its best for IGCT value equal to 50%, with rate of SSNPs (MAF > 0.05) equal to 85%.Most of the genes involved in drug metabolism can be imputed with high efficacy using standard genome-wide genotyping platforms and imputing procedures. The therapeutic efficacy of any given drug is influenced by a number of different variable factors including age, weight, concurrent drug use and fixed parameters, such as gender and genetic variation as well. Many of the enzymes involved in drug metabolism are genetically polymorphic. Consequently, their activity may differ depending upon an individual's genotype  and hencImputation can be used as a part of a genome-wide association (GWA) studies or for a fine-mapping. In a genome-wide association study (GWAS) a dense set of single-nucleotide polymorphisms (SNPs) across the genome is genotyped to survey the most common genetic variation for a role in disease or to identify the heritable quantitative traits that are risk factors for disease  and ofteBlood samples from thirtynine patients with Acute Myeloid Leukemia (AML) were collected by professor Giovanni Martinelli . Genomic DNA was extracted from peripheral blood samples with standard methods. A written informed consent was collected from each individual and the study was designed according to the ethical principles for medical research involving human subjects stated by the World Medical Association Declaration of Helsinki. The study was also approved by the Department of Hematology and Oncological Sciences "L. and A. Seràgnoli". The individuals were genotyped with the following two platforms: DMET™ Plus GeneChip array (DMET) and Genome Wide Human SNP 6.0 array . A brief description follows.The DMET™ Plus GeneChip array  contains 1931 SNPs and five Copy Number Variants (CNVs) distributed on 225 drug metabolizing enzymes and transporters genes. Amplified and non-amplified DNA samples were combined for the annealing and amplification steps, in which molecular inversion probes (MIP) technology was exploited to genotype all the genomic sites of interest in a single reaction. DNA samples were subsequently purified, fragmented, labeled and hybridized to the array to be scanned with the Gene Chip Scanner 3000 .DMET Console version 1.1  was used to perform genotype calls using standard parameters.DNA samples were genotyped using the Genome Wide Human SNP 6.0 array , according to the manufacturer's instructions and retrieving genotype information for ~ 906,000 loci. Genomic DNA samples were firstly digested with Nsp I and Sty I restriction enzymes and then adaptor-ligated and PCR amplified using a primer that recognizes the adaptor sequence. PCR products were subsequently purified, fragmented, labeled, denatured and hybridized to oligonucleotide probes attached to the surface of the array, followed by washing and staining procedures, as well as by scanning by means of the Gene Chip Scanner 3000 . Genotyping Console 3.0 package was used to perform genotype calls using standard parameters.The study was performed on 1860 of the 1931 markers of DMET because 71 markers were discarded for the following reasons:• 13 markers have been coded in PharmGKB , but hav• 5 markers have two different annotated positions;• 2 markers were duplicated;• 5 markers presented 3 alleles in the study sample;• 46 markers mapped on chromosome X .Before proceeding to the analysis, we performed some quality control checks on the data. First, we tested the concordance between the genetic and reported sex to check for errors in labeling the samples. Second, all subjects showing a genotype call rate < 95% would have been removed. Third, SNPs mapping on the regions of interest  were removed when showing a Hardy-Weinberg p-value inferior to 0.00001.The DMET SNPs investigated were grouped into 3 classes according to their presence in the AFFY platform and in the reference panel, as follows:• Shared: 205 markers present in both DMET and AFFY arrays .• Reference Panel Only (RPO): 654 markers in DMET and in the reference panel but not in AFFY .• Neither in AFFY nor in reference panel (NAR): 1001 markers in the DMET, but not in the AFFY or in reference panel. Therefore, we did not perform the imputation for this group of SNPs.Regardless of the SNP classes , markers were subdivided according to their minor allele frequency (MAF). We used the following 7 ranges: 0, 0-0.05, 0.05-0.10, 0.1-0.2, 0.2-0.3, 0.3-0.4 and 0.4-0.5.Imputation is a statistical process used to predict genotypes that are not directly assayed in a sample of individuals. The term often refers to the situation in which a reference panel of individuals genotyped at a dense set of SNPs is used to impute into a study sample of individuals that have been genotyped at a subset of the SNPs .1000 Genomes Pilot and HapMap 3 (release Jun 2010/Feb 2009) [Imputation was performed using the method implemented in the software IMPUTE 2 . IMPUTE eb 2009) .The imputation algorithm for large population genetic datasets, built starting from a model developed by Li and Stephens  to captuTo speed up the procedure and to reduce the computation load imputation was performed subdividing the chromosomal regions to be imputed in partially overlapping 2Mb-chunks that have been processed independently.We defined following parameters in order to evaluate the results of the imputation:• Discordance: the proportion of genotype calls for which the imputed genotype did not match the experimental genotype call, averaged over all SNPs.• Successful SNPs (SSNPs): in which the imputed genotype matched the experimental genotype for at least 37 of 39 subjects (that roughly corresponds to 5% of genotype error rate).• Genotype error rate: proportion of unmatched genotypes over the total genotypes.The discordance was evaluated at three different imputed genotype calling threshold (IGCT) of value. An imputed genotype was called if the corresponding posterior probability estimated by the imputation software (IMPUTE 2) was higher than investigated IGCT. Imputed genotypes below the IGCT were set as no-calls (i.e. missing genotypes). The IGCT were set on 50%, 70% and 90%.Thirty-nine (39) individuals have been genotyped with both DMET and AFFY array. Imputation was performed on the untyped markers of AFFY (study sample), as described in Materials and Methods.Table For 7995 genotypes , we observed 59 no-calls for DMET and 164 for AFFY for a corresponding genotype missing rate of 0.7% and 2%, respectively, and seventy-nine (79) discordant genotypes, corresponding to a mismatch rate of approximately 0.98%. Mismatches were randomly distributed among 40 different SNPs.Six-hundred and fifty-four (654) SNPs were imputed because present in the reference panel, but not in AFFY. Three different IGCT were used: 50%, 70% and 90%. Table The goal of the study is to measure the performance of imputation when predicting the genotype of SNPs involved in drug metabolism using a common SNP array genotyping platform generally used for genome wide association studies. High density SNP arrays have been projected to tag the most common SNPs along the genome. About 7 of the nearly 30 millions known SNPs have a MAF > 0.05 and only 1 million is actually contained in the standard SNP arrays. SNPs contained in the arrays were generally chosen with idea to select the minimum number of SNPs able to target the most common SNPs, exploiting the degree of linkage disequilibrium among them. Thus, SNPs in the array do not necessary belong to any class of genetic variants associated with a particular function or with the variability of any trait.Pharmacogenetics and pharmacogenomics are interested in the study of genetic variations that are associated with variable response to drugs in terms of therapeutic effect, as well as adverse effects. In oncology, for example, researchers are interested to study mutations affecting genes coding for liver enzymes responsible for drug metabolism and mutations in tumoral DNA leading to alteration in drug response. We investigated how a high density SNPs array combined with imputation methods might help in studying a large number of genetic polymorphisms involved in the drug metabolism. To do that 39 individuals with AML were genotyped with 2 different SNP arrays: the higly dense SNP AFFY with about 1 million SNPs and the DMET array containing the most known SNPs involved in the drug metabolism. Thereafter, we checked how many SNPs were in common between the 2 arrays and the number of SNPs whose genotypes could be directly imputed from AFFY to be then matched with the DMET experimental genotypes.A satisfactory match between experimental and imputed genotypes of a given SNP means that imputation was good enough to substitute the real genotyping for that SNPs. We measured the match between experimental and imputed genotypes looking at the overall concordance for a set of SNPs and the efficacy of imputation for any single SNP.Imputation was performed using reference panels from HapMap 3 and the novel 1000 Genomes project. We studied the genotypes of 1860 autosomal genetic variations of the DMET array. The 39 individuals resulted to be homozygotes for the most common allele for 1095 markers. Moreover 184 markers showed a MAF < 0.05. These large numbers of genetic variations at a low MAF was expected according to the MAF indicated by the manufacturer of the array and due to the fact that the DMET was projected to contain the genetic polymorphim involved in the drug metabolism, including rare variants (someone associated with strong or adverse drug reactions). For SNPs with low MAF we could not measure with certainty the efficacy of imputation because of the low number of observations. A large part of the DMET SNPs was not neither in the AFFY nor in the reference panel used for imputation. Most of them (983/1001) presented a MAF < 0.05 (952 showed a MAF = 0). The low MAF explains why they were not present in the reference panel or in the AFFY array. With the advance of the 1000 Genomes project it is likely that they will be included in the newly updated reference panels and that imputation might become accurate also for SNPs at a very low MAF.AFFY and DMET arrays share 205 SNPs. The genotype missing calls for DMET and AFFY were 59 (~0.7%) and 164 (~2%) respectively, indicating a higher sensibility of the DMET array. Moreover, 79 genotypes were discordant (~1%). Due to the fact that both experimentally determined and imputed genotypes are called with some degree of error, we cannot know which call (if either) is correct, so we report concordance rather than accuracy. We looked if imputation may help in detecting the array in which the genotype was badly called by selecting 2 SNPs, rs2108622 (2 mismatches) and rs1060463 (1 mismatch), presenting genotype mismatches. The genotypes were imputed after masking them in the AFFY array. Imputation suggests that the 2 rs2108622 heterozygotes called by the DMET array were 2 homozygotes as indicated by the AFFY array. On the other hand imputation and DMET were concordant to call rs1060463 homozygote in contrast with the AFFY call indicating a heterozygote (data not shown). This would suggest that the genotype calling is not totally accurate for both the arrays, as expected for the AFFY. However, since imputation suffers from a degree of uncertainty, we should perform the genotyping with an independent method to estimate the error rate of the 2 platforms and confirm the efficacy of imputation in these cases.Other 654 SNPs present in DMET, but not in AFFY 6.0, were imputed. The MAF observed for 107 SNPs was 0 and additional 124 SNPs showed a MAF < 0.05  of the ADH1A gene associated to drug response in AML patients . These 2This study indicates that imputation is a valid method, however it strongly depends on the array used for genotyping  and on the reference panel used. Future studies will investigate how more and more detailed reference panels impact on the efficacy of imputation, with particular attention on SNPs with rare alleles.AFFY: Genome Wide Human SNP 6.0 array ; DMET: DMET™ Plus GeneChip array ; IGCT: imputed genotype calling threshold; MAF: minor allele frequency; NAR: neither in Affy not in reference panel; RPO: reference panel only; SNP: Single nucleotide polymorphism; SSNP: successful SNP.The authors declare that they have no competing interests.MS and II carried out the molecular genetic studies. GMar, GMal and PFP participated in the design of the study. VM and LX performed the bioinformatics analysis. VM, GMal and AM performed the statistical analysis. GMal, GMar, LX, PFP and VM conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


Drosophila melanogaster, an ancestrally tropical insect that has spread to temperate regions and become cosmopolitan, offers a powerful opportunity for identifying the molecular polymorphisms underlying clinal adaptation. Here, we use genome-wide next-generation sequencing of DNA pools ('pool-seq') from three populations collected along the North American east coast to examine patterns of latitudinal differentiation. Comparing the genomes of these populations is particularly interesting since they exhibit clinal variation in a number of important life history traits. We find extensive latitudinal differentiation, with many of the most strongly differentiated genes involved in major functional pathways such as the insulin/TOR, ecdysone, torso, EGFR, TGFβ/BMP, JAK/STAT, immunity and circadian rhythm pathways. We observe particularly strong differentiation on chromosome 3R, especially within the cosmopolitan inversion In(3R)Payne, which contains a large number of clinally varying genes. While much of the differentiation might be driven by clinal differences in the frequency of In(3R)P, we also identify genes that are likely independent of this inversion. Our results provide genome-wide evidence consistent with pervasive spatially variable selection acting on numerous loci and pathways along the well-known North American cline, with many candidates implicated in life history regulation and exhibiting parallel differentiation along the previously investigated Australian cline.Understanding the genetic underpinnings of adaptive change is a fundamental but largely unresolved problem in evolutionary biology.  Unravelling the genetic basis of adaptive change in natural populations is one of the most fundamental aims of evolutionary biology . For exaOne common approach for identifying polymorphisms that might be targets of spatially variable selection is to search for alleles that show exceptionally strong differentiation among geographically distinct populations: strong outlier 'signals' are taken to be indicative of selection and local adaptation relative to background 'noise' caused by gene flow and drift , for insDrosophila melanogaster Payne to clinal variation and to identify polymorphisms likely independent of it. Finally, by comparing our results to those of Here, we aim to complement and extend these recent efforts by characterizing, for the first time, genome–sequence-based patterns of latitudinal differentiation along the well-known North American cline . Our firn = 39 isofemale lines; 07/2010); (ii) an intermediate population from Pennsylvania ; and (iii) a northern population from Maine   are intermediate with regard to climate and life history phenotypes, including the incidence of reproductive dormancy  a southern population from Florida  . Populatdormancy .n = 39 females; Pennsylvania, n = 102 females; Maine, n = 86 females), adjusted the ratio of homogenization buffer to the number of flies in a given pool and homogenized pools with an Ultra-Turrax T10 . Genomic DNA was extracted using the Qiagen DNeasy Blood and Tissue Kit . We fragmented genomic DNA using a Covaris S2 device  and prepared paired-end genomic libraries (using 5 μg of genomic DNA for each pool and library) with NEBNext DNA Sample Prep modules  following the manufacturer's instructions. Cluster amplification was performed using the TruSeq PE Cluster Kit v5 on a cluster station, and each sample was sequenced on one lane of a Genome Analyzer IIx using TruSeq SBS 36 Cycle Kits v5 . 101 bp paired-end reads were filtered for a minimum average base quality score of 18 and trimmed using PoPoolation . RepeatMasker was run using default sensitivity parameters including the search for simple repeats but without searching for bacterial insertion elements (option: -no_is). Mean coverage was highly consistent and uniform among populations and chromosomal arms ; mean sequencing quality (Phred score) was 37 for Maine and 38 for both Florida and Pennsylvania (details not shown). Raw sequencing data prior to trimming and mapping are available as FASTQ files at the Sequence Read Archive (SRA) of the European Nucleotide Archive (ENA) hosted by EMBL-EBI under accession ERP001535 (http://www.ebi.ac.uk/ena/data/view/ERP001535). A detailed description of our bioinformatic analysis pipeline can be found in Appendix S13 (Supporting information).We used 'pool-seq' to estimate allele frequencies and identify candidate single nucleotide polymorphisms (SNPs) . For eacD. melanogaster are known to exhibit low levels of LD, with most high-level LD occurring on a scale of < 200 bp . The SNP-based approach, however, might be more strongly affected by base-to-base variation in coverage and sequencing errors. Moreover, it assumes that SNPs are independent. While this assumption might be somewhat unrealistic, natural populations of X, 1,036,552-20,902,578; 2L, 844,225-19,946,732; 2R, 6,063,980-20,322,335; 3L, 447,386-18,392,988; 3R, 7,940,899-27,237,549 , because such sites might represent copy number variants rather than true SNPs. To minimize the impact of sequencing errors and maximize the probability of calling true SNPs, we pooled counts across all populations for each position and only considered those with a minimum allele count ≥ 6 (i.e. a minimum count of two per population on average) as polymorphic; we thus assume that alleles present at high number in at least one population or occurring at low number in multiple populations represent correctly called SNPs. For most analyses, we excluded gene-poor telomeric and centromeric regions with low or no recombination since such regions are expected to yield little insight into patterns of genic differentiation; we therefore focused on the following normally recombining regions of the genome: π, Watterson's θ, Tajima's D and FST  and variable site in the genome. To estimate π and θW, we assumed a minimum count of two and used unbiased estimators for pooled data that correct for pool size and coverage . For each population or pairwise comparison, we tested for significant variation in average π, θW and FST among chromosomal arms and among populations/pairs by using two-way anova on rank-transformed means of SNP-wise values. We did not fit the interaction term since anova applied to rank-transformed data is inappropriate for interpreting interactions , thereby conditioning FST outliers on statistical significance. We only considered biallelic SNPs for FET; for multiallelic SNPs, only the two most frequent alleles were used. Since we performed a large number of tests, likely resulting in many false positives, we obtained a false discovery rate (FDR) by calculating adjusted P-values   based on reference genome r5.40 and assigned to candidate genes (±1-kb up- and downstream). While our approach is likely to miss potentially interesting candidates   for all average FST across all polymorphic SNPs (i.e. implicitly accounting for LD) and only considered those falling into the upper 5% tail of the distribution to be truly differentiated. Based on this analysis, we calculated the percentage of overlap between SNP- and gene-defined lists of candidate genes for each pairwise comparison (i.e. the overlap of the number of SNP-defined and gene-defined candidate genes divided by the number of gene-defined candidate genes).To explore whether our set of candidates is robust, we also used an alternative method, similar to the window-based approach employed by 10(P)-values for all flanking SNPs (including noncandidate and candidate SNPs) occurring in 100-bp windows around each candidate SNP, covering a region of 100-kb up- and downstream of candidates. To visualize potential short-range effects, we repeated the same analysis by using a higher resolution, that is, using 10-bp windows and covering a region of 500-bp up- and downstream of candidates. Median −log10(P)-values were plotted as a function of the relative distance of flanking to candidate SNPs, with the position of each candidate SNP set to zero. Note that at position zero, the median −log10(P)-value of each candidate SNP was excluded. To generate a null expectation, we estimated P-value levels for a random set of noncandidate SNPs ≥ 500 kb away from candidate SNPs  and repeated the analysis described above.We also investigated the size of the genomic regions differentiated between populations. We reasoned that if there is strong differentiation in the vicinity of candidate SNPs, for example due to haplotype structure, we should see a marked increase in the statistical significance of SNPs flanking the candidate SNPs. In contrast, if windows of differentiation around candidates are relatively small, we would expect that significance levels of SNPs flanking candidate SNPs decay rapidly with increasing distance from the candidates. To test this prediction, we calculated median −logD. melanogaster genome annotations from FlyBase r.5.40 using snpEff v2.0.3. Genome positions were annotated as coding sequence , intron, 3′- and 5′- untranslated region (UTR), 1-kb downstream, 1-kb upstream, intergenic or 'other'. We calculated the proportion of features for (i) all SNPs in the normally recombining genome and (ii) all candidate SNPs. To test for over- or underrepresentation of candidate SNPs with given features, we used χ2 tests (α = 0.01) on SNP counts. For plots showing candidate SNPs for specific candidate genes, we included all SNPs 1 kb up- and downstream of the gene of interest to visualize SNPs located in putative regulatory regions.To investigate genic differentiation across genome features, we obtained http://code.google.com/p/gowinda/; D. melanogaster GO annotations from FlyBase r.5.40 and used the following parameters in Gowinda: 10 000 000 simulations; minimum significance = 1 and minimum number of genes per GO category = 5 (i.e. excluding GO categories with <5 annotated genes). SNPs within a region of 1-kb up- or downstream of a given gene were mapped as belonging to that gene, and overlapping genes were also considered.To analyse the biological function of candidates, we used Gene Ontology (GO) analysis (In(2L)t, In(2R)NS, In(3L)P and In(3R)P, based on their cytological breakpoints (http://flybase.org/static_pages/downloads/FB2011_09/map_conversion/genome-cyto-seq.txt).Since the major cosmopolitan inversions are thought to explain a substantial proportion of clinal variation , we examakpoints . To defiFST-value of a given inversion and that of the rest of the arm. In addition, we estimated among-population differences in the frequency of In(3R)P by examining four previously published markers: an 8-bp indel marker in hsr-omega  of frequency change across latitude between Florida (F) and Pennsylvania (P) (s1) and between Pennsylvania (P) and Maine (M) (s2) for each candidate SNP. We then subdivided each of these three sets  into three subsets based on the sign of the two slopes . Only candidate SNPs in the three ++ subsets  represent alleles whose frequencies increase consistently with latitude; we therefore combined these ++ subsets to obtain a core set containing all candidate SNPs whose frequencies change clinally across populations ('plus_plus' candidates). To characterize this core set, we performed GO analysis and plotted the frequency changes of candidate SNPs for these core clinal candidate genes against latitude.Since clinal changes in allele frequencies might reflect spatially varying selection, we analysed how frequencies of candidate SNPs change across latitude. We calculated SNP-wise allele frequencies for each population from the synchronized pileup file, conditioning for the allele that rises in frequency from South to North. For each pairwise comparison, we estimated the slopes  pattern, using the π and θW. Estimates of π and θW, when averaged over chromosomal arms, were overall higher in Florida  than in Pennsylvania and Maine  . We detected significant differences for both π and θW among chromosomal arms, averaged over populations, with the rank order being 2L > 2R = 3L > 3R > X . Our results for π are in good agreement with those for the Australian cline, with higher nucleotide diversity at lower latitude and the least amount of diversity on the X . Increasing base quality to a threshold of 30 and subsampling sequence reads to a uniform genome-wide coverage of 25 did not qualitatively change patterns of π and θW among populations (results not shown). θW remained overall higher in Florida than in Pennsylvania and Maine (not shown), suggesting that our analysis was not strongly influenced by variation in coverage or sequencing errors.When estimating D across the whole genome. For all three populations, D was negative, deviating from neutrality (D = 0). Average D differed significantly among all populations ; D was most negative for Florida, intermediate for Maine and least negative for Pennsylvania  and between Florida and Pennsylvania (FP) (mean FST = 0.043) was much larger than between Pennsylvania and Maine (PM) (mean FST = 0.027). While mean FST was not significantly different between FM and FP, the amount of differentiation differed markedly between FM/FP and PM. Major differentiation between Florida and the other populations was observed on a genome-wide level and for each chromosomal arm P (3R and In(3R)P in latitudinal differentiation. In contrast to the strong differentiation seen for FM and FP, FST values were much smaller and similarly sized for PM, with the X chromosome being the most differentiated P . This pay traits , yet thiD. melanogaster (In(2L)t, In(2R)NS, In(3L)P, In(3R)P) exhibit strongly clinal patterns, for example in North America (In(3R)Payne is thought to be a major driver of genetic and phenotypic differentiation along latitudinal clines (Polymorphic inversions are very common in nogaster . Previou America  and Austl clines .In(3R)P. First, we used four molecular markers to estimate the frequency of In(3R)P in each population and observed that it segregates at a frequency ≥ 0.5  in Florida but that it is almost absent in Pennsylvania (median < 0.05) and Maine (median = 0.05) (P = 0.021), our data qualitatively confirm that In(3R)P is much rarer (or possibly absent) at higher as compared to lower latitudes. Second, the region spanned by In(3R)P was significantly more differentiated than the rest of 3R for FM and FP but not for PM, as expected from our inversion frequency estimates . Thus, In(3R)P has a major impact on differentiation between Florida and Pennsylvania/Maine, a result that parallels the findings of In(3R)P, average π was significantly higher in Florida (π = 0.0077) as compared to Pennsylvania (π = 0.0061) and Maine (π = 0.0061) , whereas π did not differ between Pennsylvania and Maine (P = 0.94)  . While falso see .In(3R)P . For FM and FP, median FST values were significantly lower within In(2L)t and In(2R)NS and not different within In(3L)P as compared to the rest of the chromosomal arms. However, for PM the median FST within In(2L)t was significantly higher than for the rest of 2L. While we did not investigate the frequencies of major cosmopolitan inversions other than In(3R)P, the frequencies of In(2L)t, In(2R)NS and In(3L)P are also known to vary strongly clinally along the east coast of the United States . We identified 12 090 candidate SNPs in 3169 candidate genes across all three pairwise comparisons; for FM we found 6673 candidate SNPs in 2010 candidate genes, for FP 6892 in 2051 and for PM 1149 in 720 . FST scaled well with geographical distance between populations, with both average and maximum FST being highest for FM, slightly lower for FP and lowest for PM (In(3R)P, which harbours a large number of candidates, decreased substantially from Florida to Pennsylvania/Maine, whereas its frequency was very small (possibly zero) in Pennsylvania and Maine and practically indistinguishable between these two populations  but underrepresented on all other chromosomal arms as compared to genomic background (χ2 test: P < 0.001) P as compared to the rest of the arm , with more than 50% of all FM and FP candidate SNPs occurring in this inversion P in shaping latitudinal differentiation. Candidates for FM and FP were also overrepresented within the region of In(3L)P, an inversion whose frequency is negatively correlated with cold resistance (In(2L)t and In(2R)NS , whereas candidates were underrepresented on 2L, 2R and 3L . Notably, for PM, we failed to find enrichment of candidates on 3R and within In(3R)P as compared to the rest of 3R, with only 9% (107 SNPs in 82 genes) of all candidates occurring in this inversion P, in agreement with our inversion frequency estimates. Only 377 genes were differentiated between Pennsylvania and Maine, thus representing candidates that might be independent of In(3R)P . This small number of candidates might indicate that only few genes within the region of In(3R)P are locally adapted and that most of the elevated differentiation in this region is due to linkage within the inversion. Interestingly, candidates within the region spanned by In(2R)NS were underrepresented for all pairwise comparisons, suggesting a consistent deficiency of genes contributing to differentiation across populations in this inversion, although it is known to harbour several clinal loci  . Within nal loci .FST across all polymorphic sites within a given gene for each pairwise comparison and defined candidate genes as those with an average FST in the top 5% of the distribution. When applied to this set, our SNP-based approach detected 86% of all candidate genes for FM, 88% for FP and 22% for PM (details not shown), indicating that both methods yield largely similar results, at least for FM and FP. The rather small overlap between the methods for PM might reflect the small amount of differentiation between Pennsylvania and Maine; since effect sizes of allele frequency differences for PM were small, the SNP-based approach might be much more conservative when applied to PM than the gene-based approach which does not condition FST values on significant FET. Interestingly, when we excluded candidates on 3R, the overlap between the two approaches decreased to 63% (−25%) for FM and to 67% (−21%) for FP, but increased to 27% (+5%) for PM. In general, we favour using the SNP-based over the gene- or window-based approach, especially when differentiation between populations is not very large.Since our SNP-based candidate gene approach rests on the somewhat unrealistic assumption that SNPs are independent (no LD), we tested the robustness of our method by using a gene-based approach, similar to the window-based method used by 3R, −log10(P)-values rapidly dropped to random background levels within ~100 bp . In contrast, for FM and FP, −log10(P)-values decayed more slowly on 3R as compared to other chromosomal arms, resulting in a broad base extending over > 500 bp up- and downstream of candidates, with −log10(P)-values converging asymptotically to random background . To examine whether this was caused by In(3R)P, we asked whether the −log10(P)-value distributions differ between the inversion and the rest of 3R . Within the inversion, −log10(P)-values were on average higher, both for the baseline and random background, than outside the inversion . Our results thus suggest an excess of differentiated variants on this chromosomal arm. However, when averaging −log10(P)-values for each pairwise comparison across all autosomes and excluding 3R, −log10(P)-values still decayed much more slowly to background for FM and FP than for PM . Thus, Florida appears to exhibit generally more differentiation than the other two populations independent of 3R and In(3R)P.Next, we investigated the size of genomic regions differentiated between populations. We predicted that strong differentiation in the neighbourhood of candidate SNPs, for instance due to haplotype structure, would elevate the statistical significance of SNPs flanking candidate SNPs, resulting in broad peaks around candidates. In all pairwise comparisons, and for all chromosomal arms except χ2 test: P = 0.002) and FP  but not in PM (Helicase89B (Hel89B), which positively regulates expression of antimicrobial peptides , which is required in the gastrointestinal tract during host–microbe interactions . Nonsynonymous sites were overrepresented in both FM , it is noteworthy that the top three categories for FM and FP were all related to metabolism , whereas for PM the top three categories were all related to pathogen defence and immunity . Despite the lack of significance, these patterns are consistent with those reported by To further characterize candidates, we performed GO analysis with Gowinda  but failed to find significant enrichment of GO terms after FDR correction. Since Gowinda corrects for gene length bias by assuming complete linkage of SNPs within genes, power may be lower than for approaches that model the true underlying haplotype structure. For example, the top three GO categories with the lowest P < 0.0001; see Table S9, Supporting information). Thus, while we cannot rule out that some of our candidates are false positives, and although we cannot formally prove that these loci are under selection, the major overlap with previously and independently identified candidates strongly suggests that our candidate genes represent targets of spatially varying selection and that differentiation at these loci is unlikely due to demography alone. FST differentiation for candidate SNPs in six exemplary candidate genes; Appendix S1–S12 (Supporting information) show examples of candidate genes in major biological pathways, including hand-curated functional information from FlyBase and the literature. For a full list of candidate genes, see Table S5 (Supporting information).To supplement our analysis of candidates, we hand-curated functional information from FlyBase and the literature. Although candidates did not fall into significantly enriched GO categories, we identified hundreds of strongly differentiated genes in major functional pathways . Notably, our results not only identify numerous novel candidates but also confirm many genes and pathways previously implicated in latitudinal differentiation ; the insulin-like receptor (InR), previously found to vary clinally and affect life history traits in natural populations , previously linked to natural variation in reproductive dormancy (foxo downstream of IIS (target of rapamycin (Tor) . These findings are interesting in view of the fact that genetic manipulations of IIS/TOR are known to have major effects on life history traits in the laboratory , a gene that is expressed in several tissues including the ring gland , contains a large number of ecdysone response elements, varies both along the North American and Australian cline and underlies natural clinal variation in reproductive dormancy along the US east coast  were also found for the Australian cline  in an artificial selection experiment on body size  locus. Several nuclear hormone receptor and other endocrine genes, some of which are known to interact with ecdysone signalling (eclosion triggering hormone receptor (ETHR) . Interestingly, ETHR but found clinal variation at eclosion hormone (Eh), the gene encoding the ligand for this receptor. The fact that these endocrine pathways all have major metabolic functions is perhaps consistent with the observation that the top three GO categories for FM and FP are related to metabolism . In line with this, we also found several genes involved in lipid metabolism to be differentiated .We also detected at least 14 candidate genes involved in ecdysone signalling and production , a pathway important for regulating larval growth, body size, metamorphosis, ovarian development, reproductive dormancy, lifespan and immune function . The perst coast  Fig. 6)couch potody size . Moreovegnalling , were alpeptidoglycan recognition proteins (PGRPs), central signalling components, such as immune deficiency (imd) and Toll, and various antimicrobial peptides such as Diptericin (Dpt) and Drosocin (Dro). Irc and sick (sickie), which we also found. Our data thus indicate that latitudinal adaptation involves strong spatially varying selection on immunity, possibly due to variation in pathogen diversity and abundance across latitude . Again, we found differentiation in many candidates in these pathways also identified by A. gambiae (timeless (tim), timeout and cryptochrome (cry), which have all previously been found to vary clinally (clock (Clk) . Yet, unlike other studies (period (per) locus, presumably due to our rather stringent criteria for defining candidates . Differentiation in this pathway is noteworthy because it has been implicated in the photoperiodic regulation of reproductive dormancy (foraging (for), a cGMP-dependent protein kinase known to harbour a natural larval behavioural polymorphism  and Abdominal-B (Abd-B), two major Hox genes critically important for development, as well as fruitless (fru), a gene involved in determining sex-specific mating behaviour  show a constant increase across all populations; (ii) first drop in frequency between Florida and Pennsylvania and then increase between Pennsylvania and Maine and (iii) first increase in frequency between Florida and Pennsylvania and then drop between Pennsylvania and Maine. The latter two classes might not necessarily reflect clinal selection but might contain genes differentiated due to local adaptation or genetic drift. We therefore only considered SNPs showing a consistent increase in frequency across all populations and merged them into one clinal data set ('plus_plus' candidates), comprising 1974 candidate genes . For each of these candidate genes, we show plots of allele frequency against latitude in Appendix S14 (Supporting information). Importantly, numerous of these clinal candidates have also been found by P-values were 'RNA methylation' (FDR = 0.04) and, similar to our analyses for FM and FP above, 'metabolic process' (FDR = 0.12) as well as 'primary metabolic process' (FDR = 0.24) . We further refined our core set of clinal candidates by restricting the analysis to those SNPs whose 95% confidence intervals for allele frequencies did not overlap among populations; such SNPs show the steepest frequency change with latitude. This yielded a set of 173 significantly clinal SNPs located in 141 candidate genes . Since confidence limits were often quite large, and because we only had data for three populations along the cline, this is a relatively small set of clinal candidates. To our knowledge, only 13 genes (9.3%) of this set have previously been mentioned in the literature as varying clinally: CG5466; CG31320; CG31380; the cardioacceleratory peptide receptor CcapR; cpo; Eip63E; the gustatory receptor Gr36a; the transcription factor Ino80; InR; the lipophorin receptor LpR1; sick; the JAK/STAT transcription factor Stat92E; and the transglutaminase Tg , a gene with unknown molecular function and phenotypic effect; SNF4/AMP-activated protein kinase gamma subunit (SNF4Agamma), a gene involved in lipid metabolism and the response to starvation .Some interesting examples of clinal candidate genes and their allele frequency trajectories across latitude are shown in D. melanogaster along the well-known North American cline, a pattern thought to be caused by spatially varying selection Payne. This striking pattern might be consistent with the idea that In(3R)Payne represents a 'coadapted gene complex' or 'supergene' (Many previous studies have found major phenotypic and genetic differentiation in nogaster , it willnogaster , and ecdnogaster . Indeed,