DGAT1, SCD1 and FASN genes which are well-known to affect milk composition traits of dairy cattle. Combined with the previously reported QTL regions and the biological functions of the genes, 20 novel promising candidates for C10:0, C12:0, C14:0, C14:1, C14 index, C18:0, C18:1n9c, C18 index, SFA, UFA and SFA/UFA were found, which composed of HTR1B, CPM, PRKG1, MINPP1, LIPJ, LIPK, EHHADH, MOGAT1, ECHS1, STAT1, SORBS1, NFKB2, AGPAT3, CHUK, OSBPL8, PRLR, IGF1R, ACSL3, GHR and OXCT1. Our findings provide a groundwork for unraveling the key genes and causal mutations affecting milk fatty acid traits in dairy cattle.Detecting genes associated with milk fat composition could provide valuable insights into the complex genetic networks of genes underling variation in fatty acids synthesis and point towards opportunities for changing milk fat composition via selective breeding. In this study, we conducted a genome-wide association study (GWAS) for 22 milk fatty acids in 784 Chinese Holstein cows with the PLINK software. Genotypes were obtained with the Illumina BovineSNP50 Bead chip and a total of 40,604 informative, high-quality single nucleotide polymorphisms (SNPs) were used. Totally, 83 genome-wide significant SNPs and 314 suggestive significant SNPs associated with 18 milk fatty acid traits were detected. Chromosome regions that affect milk fatty acid traits were mainly observed on BTA1, 2, 5, 6, 7, 9, 13, 14, 18, 19, 20, 21, 23, 26 and 27. Of these, 146 SNPs were associated with more than one milk fatty acid trait; most of studied fatty acid traits were significant associated with multiple SNPs, especially C18:0 (105 SNPs), C18 index (93 SNPs), and C14 index (84 SNPs); Several SNPs are close to or within the  Fat is the major energy substance in milk and more than 50% milk total energy comes from milk fat, which accounts 3–5% of milk contents. Fat nutrition value depends on fatty acids. Monounsaturated fatty acids (MUFA) have a favourable effect on human health because of its cholesterol-declining properties. Polyunsaturated fatty acids (PUFA) of the n-6 and n-3 series are essential nutrients that exert an important influence on plasma lipids and serve cardiac and endothelial functions for prevention and treatment of coronary heart diseases DGAT1 p.Lys232Ala and SCD1 p.Ala293Val From the genetics point of view, milk fatty acids are complex traits influenced by non-genetic factors, such as breed, herd, stage of lactation, etc etc, in dairy and beef cattle. However, only studies have been carried out for fatty acids in Dutch dairy cattle At present, genome-wide association study (GWAS) has become a powerful strategy to identify genetic variants associated with complex traits. Since the first GWAS was published in 2005 The milk samples were collected along the regular quarantine inspection of the farms. The whole procedure for sample collection was carried out in strict accordance with the protocol approved by the Animal Welfare Committee of China Agricultural University (Permit Number: DK996).The Chinese Holstein population in this study comprised 784 cows, the daughters of 21 sire families. All cows in this study were from 18 farms of the Beijing Sanyuan Dairy Farm Center, where routine standard performance test, i.e. Dairy Herd Improvement system (DHI) have been carried out since 1999. A total of 50 ml milk sample was collected for each cow from the DHI laboratory of the Beijing Dairy Cattle Center during November to December, 2012. The procedure of milk sample collection was carried out corresponding to DHI sampling (dairy herd improvement). After DHI measure, the remaining milk samples were taken back to the laboratory within 4°C cooler and then stored at −20°C.http://www.mafic.ac.cn/intro/default.asp), which included SFA of C8:0, C10:0, C12:0, C14:0, C16:0, C18:0, C20:0, C22:0; MUFA of C14:1, C16:1, C18:1n9c; PUFA of CLA , C18:3n3, C18:3n6, C18:2n6c and C20:5n3. Before measuring, milk samples should be done with pretreatment. First, total milk fat were extracted from approximately 2 ml of each milk sample. The specific procedure was as follows: 2 ml milk was mixed with 4 ml solution of N-hexane/isopropyl alcohol (3∶2) and 2 ml solution of Na2SO4, and centrifuged at 3,000×g for 20 min. The upper layer was collected into 20 ml hydrolysis tube and 200 µl of C19:0 methyl ester as the internal standard was mixed, and then the extracted fat was dried under nitrogen. Methyl esters of fat were performed in the next step. 2 ml of NaOCH3/Methanol was put into the above hydrolysis tube for 15 min water bath under 50°C, and was mixed with 2 ml of hydrochloric acid/methanol solution (1∶10) for 1.5 h water bath under 80°C. After the temperature fell to room temperature level, 3 ml of water and 6 ml of n-hexane were put into above hydrolysis tube, mixed, vortexed, and stratified. The upper layer was collected and dried under nitrogen, and finally dissolved in 1 ml of n-hexane. 1 ml methyl esters of fatty acids were prepared to be determined by gas chromatography using a gas chromatograph  equipped with a flame-ionization detector and a high polar fused silica capillary column . About 1 µl sample was injected under the following gas chromatography conditions: Helium was used as the carrier gas at a flow of 45 ml/min. The split ratio was 100∶1. The oven temperature was programmed at 100°C and held for 10 min, then increased to 160°C at a rate of 6°C/min, held for 10 min, increased to 200°C with 5°C/min, held for 20 min, increased to 240°C at a rate of 4°C/min and held for 12 min. Both the injector temperature and the detector temperature were set on 260°C. Individual fatty acids were identified and quantified by comparing the methyl ester chromatograms of the milk fat samples with the chromatograms of pure fatty acids methyl ester standards (Supelco™ 37 Component FAME Mix), and were measured as the weight proportion of total fat weight (wt/wt%).Phenotypic values of 16 kinds of main milk fatty acids were measured by gas chromatography at the Ministry of Agriculture Feed Industry Centre of China , C14 index, C16 index and C18 index. The 3 indices were calculated as The descriptive statistics of these 22 fatty acid traits are presented in http://faculty.washington.edu/browning/beagle/beagle.html), the common SNP markers in both version chips were used in this study, as a result, the total number of SNPs in the panel was 52,340. The SNP positions were based on the bovinegenome assembly UMD_3.1.66 (http://www.ncbi.nlm.nih.gov/genome/guide/cow/).The cows were genotyped using the Illumina BovineSNP50 BeadChip , of which, some individuals were genotyped with the 54K chip version1 containing 54,001 SNPs, and others were genotyped with the 54K chip version 2 including 54,609 SNPs. After genotype imputation by BEAGLE software (−6). Eventually, 40,604 SNPs passed these quality control filters, which was 77.6% of the SNPs in the panel. The average distance between adjacent markers was quite constant among different chromosomes. The shortest average distance was 56 kb on BTA25, and the longest average distance was 75 kb on BTA5 (except for 198 kb on BTAX).The quality control procedure was as follows, 20 daughters were excluded due to low call rate (<90%), leading to 764 daughters remaining for the association analysis. On the other hand, 11,736 SNPs were removed for falling to meet the following requirements: 652 SNPs with <90% genotype call rate, 10,798 SNPs with a minor allele frequency (MAF) <0.05, 286 SNPs with extreme value of Hardy-Weinberg equilibrium statistics of the PLINK software (v1.07) The statistical tests followed a two-step analysis. For the first step, phenotypic values were corrected for fixed non-genetic effects by using SAS 9.1 general linear model (GLM) procedure. The statistical model was: http://www.r-project.org/).Manhattan plots of genome-wide association analyses were produced with R2.15.1 software <0.05/N, N is the number of SNP markers tested in analyses P-value as previously described Bonferroni correction was applied to adjust for multiple testing from the number of SNPs detected. A significant SNP at the genome-wise significance level was declared if a raw P-values for all SNPs of each trait was shown in Additional file 1. In total, 83 genome-wise significant SNPs (P<1.23E-06) and 314 suggestive significant SNPs (P<2.46E-05) were detected for 22 milk fatty acids on all chromosomes, ranged from 3 on BTA3, 4, 22 to 119 on BTA26 (P = 6.91E-17) and C14:1 (P = 7.08E-13) on BTA26. The top one common significant SNP (ARS-BFGL-NGS-4783) was associated with SFA, SFA/UFA and UFA. Besides, 146 SNPs were associated with multiple traits, especially ARS-BFGL-NGS-4939 on BTA14 for 9 traits. Further details on these associations are described as follows.The global view of on BTA26 –6. For mon BTA26 . The mosP = 5.89E-07), C12:0 (P = 3.94E-07), and C14:0 (P = 1.58E-07) were identified with BTB-01556197 on BTA9, BTA-76414-no-rs on BTA21 and Hapmap49848-BTA-106779 on BTA5, respectively. The SNP strongly associated with C10:0 (P = 8.54E-06), C12:0 (P = 1.16E-07) and C14:0 (P = 6.01E-06), ARS-BFGL-NGS-39328, is 58,172 bp close to the fatty acid synthase (FASN) gene on BTA19, which is well-known to affect fat composition of dairy cattle and beef.For C10:0, C12:0 and C14:0, 21, 22 and 27 SNPs were detected, respectively. Of these 70 SNPs, 10 were associated with two or three traits. The most significant association of C10:0 (P = 9.17E-09) on BTA12. The strongest association of C22:0 (P = 6.70E-07) was identified with the SNP (ARS-BFGL-NGS-109692) on BTA1. The SNP (ARS-BFGL-NGS-4939) associated with C16:0 (P = 9.04E-06) on BTA14 is located within the diacylglycerol O-acyltransferase 1(DGAT1) gene, the major gene with large effect on milk fat in dairy cattle.A total of 126 significant SNPs for LCFA were detected mainly on BTA1, 2, 8, 10 and 17, including 105 for C18:0, 18 for C16:0, 2 for C20:0 and one for C22:0. The top one significant SNP (ARS-BFGL-BAC-13788) was associated with C18:0 (stearoyl-CoA desaturase (SCD1) gene, and 7 SNPs fell in a 4.37 Mbp region (22.88∼27.25 Mbp). The SNP (BTB-00931481) on BTA26, 78,088 bp near to the SCD1 gene, showed the strongest effect (P = 7.08E-13). Though no SNPs for C16:1 and C18:1n9c reached genome-wise level, the second suggestive significant SNP (ARS-BFGL-NGS-4939) for C18:1n9c (P = 3.01E-06) is located within the DGAT1 gene on BTA14.A total of 93 and 13 Significant SNPs for MUFA and PUFA were detected, respectively. Of them, 67, 6 and 20 SNPs were associated with C14:1, C16:1 and C18:1n9c, respectively. For C14:1, 29 out of 34 genome-wise significant SNPs on BTA26 were clustered within three regions: 6 fell in a 6.40 Mbp region (10.39∼16.79 Mbp), 16 fell in a 1.75 Mbp region (20.36∼22.11 Mbp) containing the P = 6.87E-08) is located within the DGAT1 gene on BTA14, while the most significant SNP (BTA-50525-no-rs) for CLA only reached suggestive level (P = 1.16E-05).As for PUFA, 6 and 7 significant SNPs were detected for C18:2n6c and CLA, respectively. The most significant SNP (ARS-BFGL-NGS-4939) associated with C18:2n6c . In addition, 56 common SNPs for C14 index and C14:1, 4 common SNPs for C18 index and C18:1n9c, 28 common SNPs for C18 index and C18:0, and 4 common SNPs for C16 index and C16:1were identified.For indices of C14, C16 and C18, totally 84, 10 and 93 significant SNPs were detected, respectively. Forty-two SNPs associated with C14 index are located within a region of 16.89 Mbp on BTA26, which included four small segments: 6 in a 2.40 Mbp segment (9.86∼12.26 Mbp), 7 in a 4.84 Mbp segment (14.15∼18.99 Mbp), 18 in an 1.75 Mbp segment (20.36∼22.11 Mbp) containing the P = 6.07E-08), UFA (P = 4.01E-07) and SFA/UFA (P = 2.64E-07), respectively.A total of 108 significant associations mainly on BTA5, 10 and 20 with three sum of fatty acid traits were detected, which involved 52 distinct SNPs. Of them, 22 SNPs were simultaneously associated with three traits and 12 were common for two traits. The 0.96 Mbp region (72.69–73.65 Mbp) on BTA10 was associated with the three traits, in which the SNP (ARS-BFGL-NGS-4783) showed the strongest association for SFA  gene. As a bi-functional enzyme, EHHADH is part of the classical peroxisomal fatty acid β-oxidation pathway, which is highly inducible via peroxisome proliferator-activated receptor α (PPARα) activation 1-acylglycerol -3- phosphate O-acyltransferase 3 (AGPAT3) gene. AGPAT catalyzes the first step during de novo synthesis of triacylglycerol. AGPAT3 is a member of the acyltransferase family On BTA1, 23 SNPs associated with 9 fatty acids  were detected. The SNP associated with SFA and SFA/UFA is 38,733 bp away from the signal transducer and activator of transcription 1 (STAT1) gene, especially, one of them is the top 3 significant SNP for C18 index. STATs are transcription factors known to importance to cytokine signaling. STAT1 has a role in regulating the transcription of genes involved in milk protein synthesis and fat metabolism in Holstein acyl-CoA synthetase long-chain family member 3 (ACSL3) gene and the monoacylglycerol O-acyltransferase 1 (MOGAT1) gene, respectively. ACSL3 is an isozyme of the long chain fatty acids coenzyme A ligase family that convert free long chain fatty acids into fatty acyl-CoA esters and has a substrate preference for PUFA ACSL3 by RNAi causes a significant reduction in fatty acids uptake, thereby plays a key role in lipid biosynthesis and fatty acids degradation fatty acid binding protein 3 (FABP3) gene, respectively. FABP3 provides fatty acids for SCD, which is one of specific transporters for LCFA and one of the most abundant isoforms in bovine mammary tissue On BTA2, 21 SNPs showed associations with 7 traits . Two SNPs associated with C18:0 and C18 index are 0.50 Mbp away from the carboxypeptidase M (CPM) gene. Up-regulation of CPM in macrophages (MAs) is associated with increased lipid uptake CPM was detected in human adipocyte cell oxysterol binding protein-like 8 (OSBPL8) gene which encodes a member of the oxysterol-binding protein (OSBP) family, a group of intracellular lipid receptors. OSBPL8 has the capacity to modulate lipid homeostasis and SREBP activity probably through an indirect mechanism oxidized low density lipoprotein (lectin-like) receptor 1 (OLR1) gene which can bind and degrade oxidized low-density lipoprotein On BTA5, 17 SNPs showed association with 9 traits . The top one significant SNP for C14:0 is within the 5-hydroxytryptamine (serotonin) receptor 1B (HTR1B) gene, respectively. HTR1B is one of receptors for 5-hydroxytryptamine (serotonin). HTR1B gene knocked-out mice showed elevated aggression, higher food intake and impulsivity, indicating it possibly acts as a bridge between behavior and energy homeostasis HTR1B was also shown to affect milk production performance in Chinese Holstein On BTA9, 13 SNPs showed association with 9 traits . The top one significant SNP for C10:0 and C12:0 and the SNP for C18:0 are 0.40, 0.81 Mbp away from the growth hormone receptor (GHR) gene, the well-known major gene affecting milk fat trait 3-oxoacid CoA transferase 1 (OXCT1) gene which has a major function to utilize ketone bodies by mammary prolactin receptor (PRLR) gene which activates the STAT5A  expression On BTA20, 30 SNPs showed association with 11 traits . The SNP associated with C18:0 is located within the insulin-like growth factor 1 receptor (IGF1R) gene. Furthermore, the top one significant SNP for C12:0 is located within such region. IGF1R was found to affect milk composition traits lipin 1 (PLIN1) gene which plays a vital role in regulation on the expression of genes involved in milk fat synthesis On BTA21, 10 SNPs showed association with 8 traits . The 0.15 Mbp region (9.37∼9.52 Mbp) associated with C10:0 and C12:0 is 1.10 Mbp away from the SCD1 gene which encodes key enzyme responsible for the conversion of SFA to MUFA in mammalian adipocytes protein kinase, cGMP-dependent, type I (PRKG1) gene which is a key regulator of adipokine secretion and browning of white fat depots multiple inositol-polyphosphate phosphatase 1 (MINPP1) gene. MINPP1 encodes multiple inositol polyphosphate phosphatase which converts 2, 3 bisphosphoglycerate  to 2-phosphoglycerate lipase, family member J (LIPJ) gene and the lipase, family member K (LIPK) gene, respectively. The two genes belong to lipase family and take part in lipid catabolic process in human enoyl coenzyme A hydratase, short chain, 1(ECHS1) gene, respectively. ECHS1 takes part in fatty acid biosynthesis, elongation and metabolism and catalyzes the β-oxidation of fatty acid in human sorbin and SH3 domain containing 1 (SORBS1) gene. SORBS1 is an important protein in the insulin-signaling pathway in the adipose depots of human conserved helix-loop-helix ubiquitous kinase (CHUK) gene. CHUK takes part in mammary gland alveolus development, mammary gland epithelial cell proliferation nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (NFKB2) gene which is essential for normal development of the mammary gland On BTA26, 71 SNPs showed association with 8 traits . The nearest SNP is 32,576 bp close to the Apart from the seven chromosomes as mentioned above, other chromosome regions harboring several significant SNPs within or near to known genes involved in fatty acids synthesis were identified.DGAT1 gene, which has been confirmed to be the true QTL for milk fat composition in dairy cattle FASN, ACACA  and SREBF1 (Sterol regulatory element binding transcription factor 1), respectively. FASN is a multifunctional enzyme with a central role in the de novo lipogenesis in mammals SCD1 gene which is related to several genes of lipid metabolism peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PPARGC1A) gene which is involved in the regulation of fatty acids transcription and mammary gland metabolism acyl-CoA synthetase short-chain family member 2 (ACSS2) gene. ACSS2 provides activated acetate for de novo fatty acids synthesis Sphingosine kinase 2 (SPHK2) gene. As a lipid mediator with both intra- and extracellular functions, SPHK2 has diacylglycerol kinase activity and involves the sphingolipid synthesis butyrophilin, subfamily 1, member A1 (BTN1A1) gene, which is essential for milk lipid droplet formation PRL gene, which impacts milk fat composition through STAT5AOn BTA14, the SNP associated with 9 traits  is located within the acyl-CoA synthetase long-chain family member 1(ACSL1) gene. ACSL1 has a vital role in fatty acids activation for milk TAG 1-acylglycerol-3-phosphate O-acyltransferase 6 (AGPAT6) gene, a novel lipid biosynthetic gene required for triacylglycerol production in mammary epithelium, if AGPAT6 was knocked out, lactating mice failed to synthesize milk fat fatty acid desaturase 1(FADS1) gene which catalyzes the synthesis of LCFA The 0.03 Mbp region (16.39∼16.42 Mbp) on BTA27 associated with C12:0, SFA/UFA and UFA is 2.11 Mbp away from the No significant SNPs were detected with C8:0, C18:3n3, C18:3n6 and C20:5n3, probably because these four traits have special population requirements.HTR1B, CPM, PRKG1, MINPP1, LIPJ, LIPK, EHHADH, MOGAT1, ECHS1, STAT1, SORBS1, NFKB2, AGPAT3, CHUK, OSBPL8, PRLR, IGF1R, ACSL3, GHR and OXCT1. Our findings are helpful for follow-up studies to fine-mapping to unravel causal mutations for milk fatty acid traits in dairy cattle.The present genome-wide association study identified 83 genome-wide and 314 suggestive significant SNPs associated with 18 milk fatty acid traits. Some of these SNPs were located within or near to previously reported genes and QTL regions, while some of the SNPs were novel. Consequently, 20 novel promising candidate genes were identified for C10:0, C12:0, C14:0, C14:1, C14 index, C18:0, C18:1n9c, C18 index, SFA, UFA and SFA/UFA, such as Figure S1Manhattan plots for each studied milk fatty acids trait. BTAX is represented by BTA30), the first line represents genome-wise significant level (raw P<1.23E-06), and the second line represents suggestive significant level (raw P<2.46E-05).(PPTX)Click here for additional data file.


Barley and bread wheat show large differences in frequencies of Single Nucleotide Polymorphism (SNP) as determined from genome-wide studies. These frequencies have been estimated as 2.4-3 times higher in the entire barley genome than within each diploid genomes of wheat . However, barley SNPs within individual genes occur significantly more frequently than quoted. Differences between wheat and barley are based on the origin and evolutionary history of the species. Bread wheat contains rarer SNPs due to the double genetic ‘bottle-neck’ created by natural hybridisation and spontaneous polyploidisation. Furthermore, wheat has the lowest level of useful SNP-derived markers while barley is estimated to have the highest level of polymorphism.in silico’ search databases for assessing the potential value of SNPs have yet to be developed.Different strategies are required for the development of suitable molecular markers in these cereal species. For example, SNP markers based on high-throughput technology (Infinium or KASP) are very effective and useful in both barley and bread wheat. In contrast, Cleaved Amplified Polymorphic Sequences (CAPS) are more widely and successfully employed in small-scale experiments with highly polymorphic genetic regions containing multiple SNPs in barley, but not in wheat. However, preliminary ‘This mini-review summarises results supporting the development of different strategies for the application of effective SNP and CAPS markers in wheat and barley. Single Nucleotide Polymorphism (SNP), the substitution of a single nucleotide in any part of the genome as a result of natural mutation, has become one of the most powerful tools in molecular biology (Reviewed in ). Large-In plants, SNPs have a particularly important application as molecular markers reflecting both natural genetic variability and a genetic drift created by breeders during the course of crops improvement (Reviewed in –9). The The work of many researchers focuses on individual genes that are of significance to their area of study. Such Gene of Interest (GOI) can be sequenced and genetic polymorphism within the sequences can then be identified and analysed manually. However, large-scale Whole Genome Sequencing (WGS) is now achievable through robotics high-throughput technology. Thousands of SNP markers can be identified at a time and analyse hundreds of accessions. Initial applications of SNP markers were based on the high-throughput platform from the American company Illumina that was very suitable for plants . Our ownhttp://www.hutton.ac.uk/research/groups/information-and-computational-sciences/tools), SNP can be clearly visualised in the linear arrangement that allows a direct comparison of genotypes, as presented earlier [Many of the SNP markers identified by Illumina Infinium or shortgut WGS arrays have already been mapped out in the chromosomes. In conjunction with the easy to use and publicly available software FlapJack  and has been successfully used in plant biology , 20.However, if an SNP occurs within the recognition site of a restriction enzyme, it is much easier to use CAPS markers. The digestion of PCR products and subsequent separation of the fragments in agarose gel is a simple approach that can be carried out in any laboratory with basic molecular equipment, achieving accurate and clear results. The recent book  and reviTriticum aestivum L.). As mentioned above, 9K and 90K SNP arrays are now routinely employed for bread wheat analyses [The high-throughput technology of SNP markers is very effective in bread wheat , the reported number of SNP markers is quite variable: 1.5K [In cultivated barley . A occoides  and AegiD genome .Mla, with 12 different restriction enzymes. Presumably, the choice of restriction enzymes was based on their availability in the laboratory and was otherwise random. Two restriction enzymes (DraI and HpaII), with completely non-related recognition sites, revealed a polymorphism between resistant and sensitive barley plants. HpaII was chosen for CAPS marker development, eventually leading to its successful application [The difference in the expected frequencies in both crops can be illustrated by the rather unusual example of restriction enzyme selection. In this case, scientists working with a specific GOI in barley did not sequence the amplified PCR fragments at all. In the absence of a genetic sequence, Řepková et al.  digestedlication . This deThe differences in SNP and CAPS markers in barley and wheat are summarised in Table Bmy1 gene [Rrs2 gene [HvMATE1 [Isa gene [Rph7 gene [In barley, the SNP frequencies determined from genome-wide studies or at least multiple gene surveys record one SNP per 240 bp , 42, permy1 gene , per 42 rs2 gene , per 29 [HvMATE1 , per 27 Isa gene , and perph7 gene  . SNPs identified among homoeologous sequences in these three genomes are named ‘false SNP’  and reflGPC-B1 , and oneGPC-B1   is estimated to have taken place about 8-9 thousand years ago [Genetic differences between wheat and barley arise from the individual origins and evolutionary history of both species. Bread wheat contains rare SNPs as a result of the double genetic ‘bottle-neck’ created by the natural hybridisation and spontaneous polyploidisation that led to a significant reduction of genetic polymorphisms. Recent data has revealed that the initial hybridisation between progenitors of A and B genomes occurred between 0.52-0.82 million years ago , signifiears ago –61.The size of each separate genome, A, B or D, in bread wheat is comparable to the size of the genome in diploid barley. However, the percentage of non-coding genetic regions on the chromosomes with repetitive elements is dramatically different in bread wheat and barley, accounting for more than 85 % of the wheat genome . The domCultivated barley also experienced a genetic ‘bottle-neck’ through domestication, but the breeding pressure was less strong than in wheat resulting in more frequent polymorphisms . The majin silico’ in different databases following assessment of effective SNPs. Clear differences between barley and bread wheat are shown in the application of manually developed CAPS markers. In barley, the presence of highly polymorphic genetic regions containing multiple SNPs allows the simple development of CAPS in small-scale experiments. However, it is a much harder task to develop CAPS in bread wheat due to significantly lower occurrence of SNPs (5.2-87.6-fold in GOI).In summary, different strategies are required for the development of the most suitable molecular markers in the cereal species. High-throughput technology is very effective for SNP marker development in both bread wheat and barley despite considerable differences in the rate of their occurrence in the entire genomes: 2.4-3-fold more in barley than in each of three genomes of wheat. The potential SNPs for either Infinium or KASP high-throughput technology have to be initially searched ‘


Solanum tuberosum L.). The 156 F1 progeny and parents of a cross (MSL603) between “Jacqueline Lee” and “MSG227-2” were genotyped using the Infinium 8303 Potato Array. Furthermore, the progeny and parents were evaluated for foliar late blight reaction to isolates of the US-8 genotype of Phytophthora infestans (Mont.) de Bary and vine maturity. Linkage analyses and QTL mapping were performed using a novel approach that incorporates allele dosage information. The resulting genetic maps contained 1972 SNP markers with an average density of 1.36 marker per cM. QTL mapping identified the major source of late blight resistance in “Jacqueline Lee.” The best SNP marker mapped ∼0.54 Mb from a resistance hotspot on the long arm of chromosome 9. For vine maturity, the major-effect QTL was located on chromosome 5 with allelic effects from both parents. A candidate SNP marker for this trait mapped ∼0.25 Mb from the StCDF1 gene, which is a candidate gene for the maturity trait. The identification of markers for P. infestans resistance will enable the introgression of multiple sources of resistance through marker-assisted selection. Moreover, the discovery of a QTL for late blight resistance not linked to the QTL for vine maturity provides the opportunity to use marker-assisted selection for resistance independent of the selection for vine maturity classifications.The objective of this study was to construct a single nucleotide polymorphism (SNP)-based genetic map at the cultivated tetraploid level to locate quantitative trait loci (QTL) contributing to economically important traits in potato ( Solanum tuberosum ssp. tuberosum) because of complex genome structure, limiting linkage analysis to economically important traits. Cultivated autotetraploid potato (2n = 4x = 48) presents unique challenges for generating linkage maps. Population size and number of segregating markers required are both dictated by the complex nature of tetrasomic inheritance  have significantly increased the power of SNP data to detect recombination between loci and for interval mapping of QTL in autopolyploids (Marker-assisted selection (MAS) has been used in plant breeding but has lagged behind in certain crops such as potato (lyploids .Phytophthora infestans (Mont.) de Bary, is one of the most devastating diseases of cultivated potato. MAS can be one of the more efficient methods for the introgression of resistance into commercially desirable cultivars (R) genes/QTL have been identified in various Solanum species and used for MAS in cultivated potato  , quantified with the Quant-iT PicoGreen assay , and adjusted to a concentration of 50 ng·µLSolanum tuberosum group Phureja DM1-3 516 R44 (The SNP genomic positions are reported based on the PGSC Version 4.03 Pseudomolecules of the reference Potato P < 0.0001) . To call genotypes using allele dosage information, we ran an application of a test version of TetraploidMap software . This application fits mixture models to the theta scores distribution of the offspring and uses expected probabilities given the parental configurations as mixture proportions. It also provides the % variance of the theta score explained by configuration and shows maximum residuals to see if possible double reduction products are present . The best models are those with the lowest value for the SIC .A set of 7157 SNPs from the Infinium 8303 Potato Array maps to unique positions in the current PGSC Version 4.03 Pseudomolecules of the reference potato genome. After initial filtering to remove SNPs with missing theta values, and monomorphic markers, 3867 polymorphic SNPs remained for use in genotype calling. Nearly 95% of this set agrees with previously identified SNPs, from the Infinium 8303 Potato Array, having the ability to distinguish between the three heterozygous marker classes  in 221 tetraploid potato lines (P < 0.01) accounted for less than 6% of the 3867 segregating SNPs. Of the 145 distorted SNPs, 105 were duplex or higher dosage and 40 were simplex SNPs. Most of these SNPs were located on chromosomes 1, 2, 10, and 12 (Verticillium wilt resistance genes on the long arm (PGSC Version 4.03). The remaining distorted markers were scattered throughout the chromosomes or in small groups spanning ≤1 Mb in length. In tetrasomic inheritance, double reduction is one of the major biological causes of segregation distortion . Thus, with a large number of SNPs and a wide range of configurations, this dataset is likely to provide informative SNP pair combinations for the detection of linked loci and the estimation of recombination fractions , allele effects from both parents can be studied simultaneously , duplex , and double-simplex  totaled 65% (1278), whereas 35% (694) were between triplex  and higher dosages . The RAUDPC mean values obtained from 2002 and 2003 were significantly correlated with each other, with a Pearson’s correlation of 0.83 (P < 0.0001). Among the 156 progeny, 46 were classified as resistant , 41 as intermediate, and 67 as susceptible  by the Scott-Knott algorithm (2H) for both years showed statistically significant consistency, with a combined 2H estimate of 0.86. These results indicate a substantial genetic effect and suggest major gene effects for the trait.Plant response to foliar US-8 infection was consistent between the two years of observations (lgorithm . Two cloP < 0.001) on homologous chromosome 4 . The hotspot for resistance on the long arm of chromosome 9 is a relatively conserved region among Solanum species (Rpi-vnt1-like (NB-LRR) genes, the region harbors a cluster of close homologs of the Tospovirus resistance gene Sw-5 at the distal end  and is ∼0.54 Mb distal to a late blight resistance-hotspot region on the adjacent superscaffold PGSC0003DMB000000339. This region harbors Toll/Interleukin-1 Receptor (TIR)-Nucleotide Binding Site (NBS)-Leucine-Rich Repeat (LRR) genes and close homologs of the P. infestans in “Jacqueline Lee,” the cultivar “Tollocan”, was derived from the hexaploid Mexican species S. demissum and possibly from local varieties such as “Amarilla de Puebla” and “Leona” (R gene-based late blight resistance in “Jacqueline Lee” had not been determined (Solanum demissum is the source of 11 single dominant late blight resistance genes (R1-R11)  with the physical position (Mb) of each marker on chromosome 9, we assessed the concordance between the genetic and physical maps. The resulting graphs displayed the expected shape and fit well with chromosome structure  (FelcherTable S4). When the mean values were mapped, a significant association was found on chromosome 5. For year 2002, a LOD of 7.2 explained 14.7% of the phenotypic variance and for 2003, a LOD of 8.3 explained 16.1% of the trait variance. These LOD scores were above the upper bound of the permutation thresholds of 4.38 and 4.39 for 2002 and 2003, respectively. The peak LOD was detected at 21.0 cM in both years . The closest SNP with a duplex-simplex configuration (BBAA × BAAA) is the SNP solcap_snp_c2_22986 at 20.6 cM with the B allele associated with increase in lateness (P < 0.0001). Thus, progeny with two or three copies of the B allele  showed on average later maturity than progeny with none or a single copy . Regression of the maturity values on the genotype of this SNP further explained 19.0% of the phenotypic variance for 2002 and 16.0% for 2003.“Jacqueline Lee” is a selection from a cross between the late-maturing Mexican variety “Tollocan” and the early-maturing variety “Chaleur” (th years . ExploraATP-dependent helicase gene (PGSC0003DMG402030493) of the reference Potato (Solanum tuberosum group Phureja DM1-3 516 R44) PGSC v4.03 Pseudomolecules. This SNP (solcap_snp_c2_22986) is ∼0.25 Mb from the StCDF1  gene, which is a candidate gene for the plant maturity phenotype in potato . Combined, these results provide strong evidence for the location of genes controlling the trait. Further validation of solcap_snp_c2_22986 could potentially lead to a diagnostic marker for maturity.The SNP solcap_snp_c2_22986 maps to the distal end of the short arm of chromosome 5 at position 4.28 Mb on the P. infestans. It has exhibited consistently high levels of resistance not only to US-8 (The present study reports for the first time the major source of late blight resistance in “Jacqueline Lee.” This cultivar is a unique source of resistance to


Identification of genome-wide patterns of divergence provides insight on how genomes are influenced by selection and can reveal the potential for local adaptation in spatially structured populations. In Atlantic cod – historically a major marine resource – Northeast-Arctic- and Norwegian coastal cod are recognized by fundamental differences in migratory and non-migratory behavior, respectively. However, the genomic architecture underlying such behavioral ecotypes is unclear. Here, we have analyzed more than 8.000 polymorphic SNPs distributed throughout all 23 linkage groups and show that loci putatively under selection are localized within three distinct genomic regions, each of several megabases long, covering approximately 4% of the Atlantic cod genome. These regions likely represent genomic inversions. The frequency of these distinct regions differ markedly between the ecotypes, spawning in the vicinity of each other, which contrasts with the low level of divergence in the rest of the genome. The observed patterns strongly suggest that these chromosomal rearrangements are instrumental in local adaptation and separation of Atlantic cod populations, leaving footprints of large genomic regions under selection. Our findings demonstrate the power of using genomic information in further understanding the population dynamics and defining management units in one of the world’s most economically important marine resources. Genomic differentiation between populations can display complex patterns, involving selection of numerous genome wide loci1235127495412615Pan IAtlantic cod is one of the most studied and exploited marine species in the world. Despite this fact, the degree of population structuringPopulation divergence of Atlantic cod populations in northern Norway have so far been described by a small to moderate number of genetic markers204A total of 8.168 SNPs were analyzed in 141 individuals of Atlantic cod , Table 132q < 0.01), while FDIST2, implemented in LOSITAN, identified 479 outlier SNPs  as candidates for divergent selection in the NEAC/NCC comparison .FST values of the outlier SNPs (FST = 0.35053) were orders of magnitude larger than those of the neutral SNPs (FST = 0.00123) between the NEAC and NCC populations, and all FST values were significantly different from 0  were similar in all populations  to their presumed source populations . The misBayesian cluster analyses as implemented in STRUCTURE support a separation between all three populations using the full dataset and the outlier dataset, while little separation between the NEAC and the NCC populations was detected using the neutral dataset . The DAPR package inveRsion, the linked regions in LG1, 2, 7 and 12 were suggested as inversions  occur inter-chromosomally between SNPs on different LGs .Relatively high values of LD  which have been used to determine individuals as either stationary or migratory22Pan I locus .We collected 99 Atlantic cod specimens near the Lofoten Islands at different sampling times . SamplesPan I SNP) were genotyped on the MassARRAY (Sequenom Inc.), resulting in a final number of 8.168 SNPs with a minor allele frequency (MAF) >0.05 in any population. Out of these, 602 SNPs were close to selected candidate genes, 1.470 SNPs were non-synonymous coding SNPs while the remaining 5.857 were SNPs randomly distributed throughout the 23 different LGs  and normalized to 100 ng/μl measured on a NanoDrop DN1000 instrument (Thermo Scientific Inc.), accepting only DNA extractions with a 260/280 ratio >1.8 and a 260/230 ratio >2.0. All individual samples were genotyped using a 12 K Illumina SNP-chip. The 12 K SNP-chip was designed, based on re-sequencing and alignment to the Atlantic cod reference genomeHo) and expected heterozygosity (He) were calculated in ARLEQUIN 3.5.1.3q-value for each locus, using a q-value of 0.05 as a threshold for significance, using the QVALUE packageRWithin each population, estimates of observed (r2 > 0.5) were represented by tag SNPs, selected using PLINK v1.07FST and STRUCTURE analyses. Locus specific FST values and weighted average FST values between the three populations were calculated for the full-, the neutral- and the outlier datasets, using ARLEQUIN. For all comparisons, 10.000 permutations were used. We calculated the nucleotide diversity (π) within all populations and in all populations combined, using a sliding windows approach with a 50-SNP window and 10 SNPs per iteration in DnaSP 5.10SNPs were categorized as outliers or as neutral, based on the outlier analyses in the NEAC and the NCC material (see next section). To avoid bias in the datasets, neutral and outlier SNPs in LD with each other . Visualization and evaluation of the best K-value for the individual STRUCTURE runs was performed, using CLUMPAKR package ADEGENETWe used the program STRUCTURE v2.3.4r2 estimate, using PLINK. Different approaches were used to investigate rearrangement patterns in the data. We used the R package inveRsionR package invClustWe evaluated the presence of linkage disequilibrium (LD) in all three populations separately using all 8.168 SNPs, reporting both inter- and intra- chromosomal LD, quantified with the Assignment of all individuals to their presumed source populations was estimated using the Bayesian assignment method in GENECLASS2FST coefficients. To control for variation in the Bayes Factor (BF) distribution caused by randomness in each run of BAYESCAN, the median value of 10 independent runs were calculated for each SNP. We carried out the simulations, using stringent criteria, assuming selection to be 10%. The false discovery rate (FDR) was set to 0.01. We report both the median log10 value of the posterior odds (PO) as well as the q-value for each SNP in all pairwise comparisons. Second, we used the FDIST2 approach implemented in the software LOSITANFST values in relation to heterozygosity of individual loci, based on a neutral distribution. We carried out the simulations, under the Infinite Allele Method (IAM) with 1.000.000 simulations, a confidence interval of 0.99 with a FDR of 0.01; using the “neutral” mean FST option and forcing mean FST option. Based on the probabilities calculated in LOSITAN, q-values were calculated and reported for all pairwise comparisons, using the QVALUE package in R . To corroborate our choice of cutoff value, and to be able to use a consistent cutoff value in both the BAYESCAN and the LOSITAN analyses, we used both q-values AND cutoff values based on Log10 (PO) in determining the significance level in both outlier tests. The consistency of the different approaches for outlier detection and the strength of the identified outlier loci, strongly suggest that the majority of the identified loci and their associated genomic regions are subject to divergent selection. Nevertheless, some outlier loci are only detected by a single approach. This variation can either be caused by different underlying assumptions and detection rates of BAYESCAN and LOSITAN or slightly different cutoff criteria usedTwo independent methods were used to identify candidate loci under selection in all population pairs. First, we used a Bayesian regression approach implemented in BAYESCAN v2.1et al.www.ncbi.nlm.nih.gov/SNP/). All raw data are provided in All 8.168 SNPs used, were mapped to the published Atlantic cod genome (ATLCOD1C)How to cite this article: Berg, P. R. et al. Three chromosomal rearrangements promote genomic divergence between migratory and stationary ecotypes of Atlantic cod. Sci. Rep.6, 23246; doi: 10.1038/srep23246 (2016).


In this study, we show novel DNA motifs that promote single nucleotide polymorphism (SNP) formation and are conserved among exons, introns, and intergenic DNA from mice (Sanger Mouse Genomes Project), human genes (1000 Genomes), and tumor-specific somatic mutations (data from TCGA). We further characterize SNPs likely to be very recent in origin  and show enrichment for both synonymous and parallel DNA variants occurring under circumstances not attributable to purifying selection. The findings provide insight regarding SNP contextual bias and eukaryotic codon usage as strategies that favor long-term exonic stability. The study also furnishes new information concerning rates of murine genomic evolution and features of DNA mutagenesis (at the time of SNP formation) that should be viewed as “adaptive.” Mutations and specific constraints that govern sites of DNA polymorphism are not well understood. Although eukaryotic single nucleotide polymorphism (SNP) formation is viewed as stochastic, preferences in the nature and location of certain single base replacements (including C⇔T and A⇔G [transition] mutations) are described among metazoans . Isolatehttp://www.sanger.ac.uk/resources/mouse/genomes/, last accessed August, 2015), including deep sequence and polymorphism data, were examined for 17 strains to investigate SNP formation bias. The murine strains  have each been backcrossed and/or inbred for well over 50 filial generations to reach allelic fixation with respect to ancestral polymorphism  and acquired somatic mutations in human breast cancer . The results also establish a remarkable level of recent parallel evolution within the murine genome. Here we show regulatory patterns that underlie SNP formation, and provide a framework for investigating novel aspects of genomic diversification.Massive sequence compendia from inbred murine strains furnish a powerful tool for investigating the “randomness” of SNP accumulation. In the present analysis, full genomic files from the Sanger Institute Mouse Genomes Project  for 17 highly inbred Mus musculus strains  were queried and downloaded from the Sanger Mouse Genomes Project database  http://www.broadinstitute.org/igv/, last accessed August, 2015). Primary data from the Sanger Mouse Genomes Project  were downloaded and a 160–200 bp interval surrounding each SNP or random (non-SNP) position , and formally inspected for parameters associated with next generation sequencing (NGS) artifact including 1) diminished  Phred score, 2) low regional sequencing depth or map quality, 3) nearby short repeats , 4) DNA motifs linked previously to sequence error  and a random cohort of nucleotide positions without known polymorphism were evaluated using Interactive Genomics Viewer (IGV) software  indehttp://uswest.ensembl.org/Mus_musculus/Info/Index, last accessed August, 2015). DNA sequences 50 bp 5′ or 3′ to a position of interest were extracted from Ensembl so that reads surrounding each position could be compiled as output text files. The sequences were aligned at the time of acquisition and loaded into an Excel spreadsheet for analysis  with reverse complement SNPs combined . Base frequency at nucleotide positions relative to an SNP site (±50 bp) was monitored to obtain “bias (%)” . Phred score, depth cutoff, and so forth were held constant among all SNPs to maintain stringency. The analysis also was conducted for a comparable number of randomly chosen (nonpolymorphic) positions within murine chromosomes 1–4 (exonic) or 1–3 , as further control to assess context-dependent SNP location. Standard deviation for base representation was measured by bootstrapping individual samples from the data set over a series of 2,000 repeats.Neighboring nucleotides that flank SNP positions were retrieved from the Ensembl genome browser .For this study, dinucleotide quartets were defined as two base pairs upstream and two downstream of a polymorphic site or other nucleotide position being evaluated. The dinucleotide quartet frequency surrounding each SNP type was collected from 17 murine strains  and normalized for expected values among all 256 possible quartets  in areas of interest  as determined by a computer program that directly tallies occurrence of all possible quartets on murine chromosomes 1–3 using sequence data (.txt files) downloaded from Ensembl. The observed incidence for each quartet surrounding a particular SNP type was compared with a stochastic representation of SNP patterns (see Statistics). Quartets were considered “permissive” or “shielded” to polymorphism only if SNP representation was significantly different from expected; for example, for quartet context http://www.1000genomes.org, last accessed August, 2015) for 19 human genes of interest taken from a list extensively characterized by our laboratory for features, such as transition bias, genetic founder alleles, well-defined minor allelic frequency, intronic versus exonic SNP prevalence, haplotype block formation, synonymous versus nonsynonymous polymorphism, and conservation among multiple species  (SNP data were queried and downloaded from 1000 Genomes (chicken) . Intronihttps://tcga-data.nci.nih.gov/tcga/dataAccessMatrix.htm, last accessed August, 2015] for somatic mutations specific to human breast carcinoma ). Data were divided as above  and computer summation used to determine surrounding nucleotides for each somatic SNP. Site-specific base frequencies and dinucleotide quartet representation were calculated to determine over- and underrepresented contexts versus the incidence measured directly across greater than 300 million bases.SNP data from intronic regions were queried and downloaded from the TCGA database  and “no-hit” (no SNP) findings within 2 × 2 tables as magnitudes of these frequencies varied. However, chi-square using observed counts indicated that all assumptions for the test were satisfied. In particular, none of the four expected values in 2 × 2 contingency tables was less than 5 . Randomly selected (nonpolymorphic) bases corresponding to a given SNP type were obtained as a control and aligned to the ±50-bp region surrounding these sites . The program was run with Eclipse Integrated Development Environment (IDE) software . Results were used as a further control for comparison to relative nucleotide frequencies associated with random SNP locations (see Computer programs (written in “java”) were designed to perform the following tasks. Specific sequences ±50 bp for a given SNP site were retrieved from the Ensembl genome browser , therefore, the likelihood of sequence artifact or miscall was remote.To investigate positional bias contributing to SNP frequency and/or location, we studied genomic data from the Sanger compendium  stratifiData were divided into six categories based on the type of SNP identified , and a computer algorithm established to retrieve and align surrounding nucleotides for each polymorphic site. In A). This suggested a sequence bias in the immediate vicinity of A⇔G polymorphism. The same was observed prospectively for both intronic (C) and intergenic (E) murine SNPs . Bootstrapping indicated that standard deviations in all cases were small . The same was true for somatic SNPs differing from germ line in human breast carcinoma .A marked overrepresentation of cytosine (immediately 5′ to A⇔G SNP location) and underrepresentation of adenine (immediately 3′) were noted for exonic single base replacements A. This sintronic C and inttergenic E murine 1X2|X3X4; where vertical line represents SNP location). Data are shown for A⇔G polymorphisms . Each of 256 possible quartets was assembled and frequencies collated among homozygous SNPs for 17 murine strains in which 1) complete genomic sequence data were available and 2) multiple strains were homozygous for the minor allele at a specific position . Statistical analysis was performed by comparing incidence of each quartet surrounding a particular SNP versus the observed (non-SNP associated) occurrence . The results define a pronounced bias for SNP prevalence; ranking of quartets from among 256 possibilities for murine exonic A⇔G SNPs is shown in the far left column . Across murine exonic DNA , the 20 most frequent A⇔G SNP-associated quartets describe approximately 2,350 single nucleotide variants at preferential sites that would not have occurred on a random basis. For the 20 most shielding A⇔G contexts, approximately 1,840 SNPs expected at random were instead directed away from these specific motifs. Note that the quartets described here are not characteristic of stereotypic repetitive elements in human or murine DNA . Moreover, the same SNP promoting or shielding contexts were observed in both exonic DNA (where ancient transposable or repetitive elements rarely occur) and the noncoding compartment, indicating that purifying selection for improved protein folding or function does not account for the contextual bias described here.We next investigated sets of “dinucleotide quartets,” or groups of four bases immediately 5′ or 3′ to each SNP site , nor does methylation account for overrepresentation of thymine (or dramatic underrepresentation of adenine) immediately 3′ to A⇔G base replacement . Moreover, many of the 64 possible quartets with “C” preceding an A⇔G transition (5′-XC|XX-3′) showed no evidence of predisposition to SNP formation (The frequent observation of cytosine immediately 5′ to A⇔G polymorphism is likely attributable (at least in part) to DNA methylation on the complementary strand, followed by deamination (resulting in G:T mispairing). However, this cannot explain SNP-associated motifs in nontransition categories , these mice are widely viewed as homozygous at every position in the genome , authentic heterozygous positions are likely to reflect recent mutations on an otherwise highly inbred background . For example, in a murine line that has undergone 50 filial crossings, 99.998% of ancestral heterozygous loci should be fixed; that is, only approximately 2 in 100,000 of the originally heterozygous positions should remain heterozygous . This cae genome , barring−9 SNPs per base pair per generation (s) can be calculated as 4Neμ . For a haploid genomic region of 500 Mb, therefore, approximately 120  steady-state heterozygous positions would be expected per generation on murine chromosomes 1–3 for each inbred strain, or 1,900 heterozygous positions among 16 murine lines analyzed here.  This vWhen we tested heterozygosity within murine chromosomes 1–3  for 16 inbred strains, 18,558 heterozygous positions were observed. Note that Phred of 60 and depth of 30 represent very stringent benchmarks for SNP identification—the threshold is set to permit <<1 in a million miscalls from among approximately 18,500 heterosites. However, because approximately 120 heterosites should have been expected per murine line , one must also consider the possibility that NGS artifact has led to a significant burden of erroneous SNPs.Supplementary tables S5–S7, Supplementary Material online, describe assessment of erroneous SNP assignment by manual inspection. Polymorphisms with low-quality score, surrounded by inconsistent consensus sequence data, artifactually high “coverage” (pile-up) due to homologous sequences elsewhere in the genome, or clearly duplicated reads, for example, can often be dispatched by direct visualization of a specific genomic interval. Other features of SNP environment—such as location within a short dinucleotide repeat or nearby indel—are sometimes more difficult to evaluate, as these regions are known to be genomically unstable, and represent common sites of true SNP formation  based on robust Phred score and sequencing depth, 2) required to exhibit the minor allele in multiple distinct strains, and 3) found to occur at roughly the expected incidence of genomic variation among murine lines , the prior likelihood of error was very low (estimated at <10−22 per SNP). This assertion is borne out by the absence of misalignment, duplicate reads, indels, short local repeats, and so forth in the majority of homozygous SNPs . The pattern is similar to a randomly selected region of high-quality DNA sequence data  and indicates that the substantial majority of homosites reported here is authentic.supplementary table S6, Supplementary Material online). Nonetheless, a meaningful subset of representative heterosites (∼16%) fail to exhibit any evidence whatsoever of NGS artifact. These heterozygous SNPs exhibit high local mapping scores, depth of coverage, Phred, consistent surrounding sequence, and are without evidence of “pile-up,” local read duplication, homologues elsewhere in the genome, and so forth. The sampling analysis therefore suggests that from among 18,558 putative heterozygous positions, much smaller numbers  are likely to represent the authentic sites of recent mutation. This agrees well with expected heterozygosity calculated above based on population accumulation and the known murine mutation rate , particularly when one considers that de novo SNPs formed during early embryogenesis would further increase the total number of expected heterosites  above the value shown here  SNPs are most likely to occur in genomic DNA . One suce origin . We obsee origin  and 2, tP < 1.0 × 10−30; Materials and Methods). For proline, threonine, and alanine, the triplet containing CG is preferred at 10.3, 10.4, and 9.4%, respectively . Underrepresentation of CG dinucleotides within murine exons  has been reported previously , or within a CpG dinucleotide itself (5′-C|-3′). It is of interest that codon usage in mouse and human is underrepresented by CpG dinucleotides. For example, of the multiple nucleotide triplets available for serine, proline, threonine, and alanine , CG-containing codons are markedly underutilized. In mice, among six codons that designate serine, the triplet containing CG is preferred only 5.1% . A preference for transition substitutions (A⇔G and C⇔T), as well as transversion SNPs, was observed in the immediate vicinity of well-defined DNA quartets . The findings are not compatible with a “neutral”-type DNA evolutionary model, as SNP accumulation genome wide is strongly nonrandom, as evidenced by local context, predisposition toward synonymous alterations . Frame of reference for these studies was based on expected incidence for random  SNP accumulation.Our results demonstrate that nucleotide positions within murine and human DNA have distinct likelihoods of single base replacement that can be predicted in part from local sequence environment  to conventional evolutionary models in which SNP distribution is due to selective pressures that govern  the tendency for allelic fixation. The same quartet preferences observed in coding DNA were also noted in noncoding (regulatory) DNA in mouse and human  for de novo heterosites , such findings provide further support for the notion that significant numbers of heterosites have formed independently and in multiple strains  and subsequently become “fixed”; that is, detected as homozygous SNPs of very high quality judged by Phred score, depth, mapping quality, and other criteria described above. For the frequent parallel variants described here , SNP formation is approximately 9 orders of magnitude greater than expected on a stochastic basis. Quantitative estimates of murine evolutionary rate have not previously considered parallel SNP formation or the positional constraints that appear to underlie a very large subset of newly accumulating SNPs.This study also investigated ostensibly more recent genetic polymorphism among 16 murine lines. Because these strains are heavily inbred , homozygosity at virtually every position would be expected, and the likelihood of a heterozygous site due to anything other than recent mutation is remote . Such sterations ). This m22 based on sequencing depth and high Phred quality) and manual SNP assessment indicates authenticity of the homosites identified here. In addition, DNA contexts found by our studies to predict SNP location have not been associated with sequencing error in the past, and motifs shown previously to increase rates of sequence artifact . Manual analysis of local DNA environment did implicate NGS artifact as a primary source for most heterozygous calls, even at 30-fold coverage. Nonetheless, a meaningful subset of heterosites identified by Sanger appear authentic . Also, as shown in s) expected from germline mutation among 16 strains, we obtained a value of approximately 1,900 heterosites across 500 Mb (chromosomes 1–3), which is in reasonable agreement with a conservative estimate of approximately 3,000 authentic variants. Finally, even the most restrictive estimates indicate that meaningful numbers of heterozygous SNPs described by Sanger must exist. If all heterozygous positions are artifact, the present findings contradict known mutation rates in murine DNA, and debase a large number of past and ongoing genome-scale sequencing projects in multiple species, including human, that employ leading-edge data acquisition and analysis methods comparable to those used here. For the present interpretation of our findings to be discounted, therefore, one must assert that 1) parallel SNPs observed in our experiments are largely the result of NGS artifact ; 2) sequence artifact exhibits an inexplicable transition and synonymous bias, very similar to what occurs in living cells; 3) both heterozygous and homozygous calls are grossly in error ; and 4) not only are the identified heterozygous SNPs incorrect but also the true heterozygous positions  are missing—and not detectable by the best available DNA sequencing technology.Although it is expedient  to simply attribute an unexpectedly large number of parallel SNPs to NGS artifact, we stringently tested this possibility and were unable to arrive at such a conclusion. Care was taken to exclude sequence artifact as a confounding variable. As noted above, the likelihood that a homozygous SNP in 4 of 17 murine strains represents sequence error is minimal  were noAs expected, well-validated heterozygous SNPs were rare, yet a high degree of parallel occurrence with homosites (25–30%) and other heterosites (20%) was observed after just a few decades of laboratory breeding. We believe that still higher levels of concordance would be obtained were additional time allowed for SNPs to accumulate, or if it were possible to directly measure the number of recent SNPs that have subsequently undergone fixation. In either case, the extent of strain propagation in the present studies essentially precludes ancestral haplotype as an explanation for observed patterns of heterozygosity, and instead points to a robust positional bias for recent mutation. The finding of context dependent, parallel, and recent SNP formation (many orders of magnitude beyond that predicted on stochastic basis) has not been considered in previous studies of murine genomic mutation rate , evolutisupplementary fig. S3, Supplementary Material online). We also provide evidence that recently acquired (heterozygous) SNPs  nonetheless exhibit a strong synonymous predisposition . Contextual and other SNP preferences must, in part, reflect rates of nucleotide misincorporation, errors during proofreading, biased gene conversion, and/or failure to conduct mismatch-mediated repair with regard to certain DNA motifs. Findings presented here provide evidence that mutational fault tolerance has been adapted to spare eukaryotic reading frames. We also note that highly specialized sequence motifs favoring SNPs within promoters and other crucial regions of noncoding DNA  could serve to preferentially facilitate polymorphism and diversity in a manner that directs single base mutations to the regulatory compartment  , and suggested existence of adaptive mechanisms that regulate DNA mutation and serve to promote long-term genomic survival . Findingposition . In addipartment , while s online) . In eithSupplementary figures S1–S3 and tables S1–S7 are available at Genome Biology and Evolution online .


The genetic sequence variation of people from the Indian subcontinent who comprise one-quarter of the world's population, is not well described. We carried out whole genome sequencing of 168 South Asians, along with whole-exome sequencing of 147 South Asians to provide deeper characterisation of coding regions. We identify 12,962,155 autosomal sequence variants, including 2,946,861 new SNPs and 312,738 novel indels. This catalogue of SNPs and indels amongst South Asians provides the first comprehensive map of genetic variation in this major human population, and reveals evidence for selective pressures on genes involved in skin biology, metabolism, infection and immunity. Our results will accelerate the search for the genetic variants underlying susceptibility to disorders such as type-2 diabetes and cardiovascular disease which are highly prevalent amongst South Asians. The 1000 Genomes Project has now systematically completed mapping the genomes of >1000 Africans, Americans, East Asians, and Europeans for genetic variation We performed whole genome sequencing to a mean coverage of 4.3x (WGS-4x) amongst 168 South Asians. To provide detailed evaluation of coding regions, we then completed whole-exome sequencing (WES) amongst a further 147 South Asians. We report variant discovery based on these WGS-4x and WES data. We additionally carried out high-coverage WGS  to inform the accuracy of variant discovery and imputation in the low-coverage data. BAM files are available for download (European Nucleotide Archive: accession number PRJEB5476).Table S2 in File S1. Mean coverage was 4.3x, 28.4x and 20.6x for WGS-4x, WGS-28x and WES respectively . Realignment around indels and variant calling were done using the Genome Analysis Toolkit (GATK) Figure S3). The initial SNP Het/Hom ratio was 1.05±0.11 in WGS-4x, compared to 1.55±0.04 in WGS-28x, consistent with undercalling of heterozygotes in low-coverage sequencing. As expected, indels were enriched for variants that are multiples of 3 base-pairs long in coding regions  Sequencing was carried out using Illumina GAIIx and HiSeq2000 machines, with library preparation according to Illumina protocols. We performed sequence alignment and variant calling separately for WGS-4x, WGS-28x and WES. Short reads were aligned to reference genome (build 37) using BWA Figure S5), and the proportion of SNPs with a difference of >1% in allele frequency compared to microarray data fell from 29% in the unphased WGS-4x data to 4.3% in the phased WGS-4x data . After phasing and haplotype inference, concordance of SNP genotype calls with microarray data was comparable between the low coverage WGS-4x and the high coverage WES data . Positive predictive value for SNP calling was high for both phased WGS-4x and WES data, compared to results from genotyping by microarray .We next used BEAGLE Figure S10).Compared to Illumina microarray, mean sensitivity for detection of SNPs was 91.9±0.2% and 99.2±0.1% in WGS-4x and WES respectively. Results were similar for comparisons of WGS-4x to WGS-28x SNP genotypes. We estimate that we have identified >95% of SNPs with non-reference allele frequency ≥0.5% in the mapped exome, and >95% of variants with non-reference allele frequency ≥1.0% in the remainder of the mapped genome (Table S4 in File S1), enabling us to use our WGS-28x calls as a truth-set for the WGS-4x samples. Compared to indel genotypes called by WGS-28x, sensitivity and positive predictive value for indel genotypes called by WGS-4x were 58.4% and 96.0% respectively. Our findings are consistent with published data, and demonstrate the continued challenges posed by indel calling with low coverage data, even with state of the art approaches We evaluated the accuracy of indel calling in the WGS-28x samples by Sanger sequencing. We confirmed the presence of an indel at 100% of 35 positions tested . Overall we report 11,624,872 autosomal SNPs including 2,946,861 novel variants (Table S5 in File S1). We discovered 40,656 novel SNPs that are common (AF≥5%), 503,588 that are low frequency (AF≥1 and <5%) and 2,402,617 rare variants (AF<1%). In total we find 70,746 non-synonymous SNPs, 1,445 SNPs affecting stop codons, and 13,332 splice-site SNPs. Our discovery includes 30,914 novel nsSNPs, of which 2,960 are common or low frequency variants.We identify 11,538,889 autosomal SNPs by WGS-4x and 189,939 by WES, with average 3,120,893 SNPs per person by WGS-4x and 34,698 by WES (Table S6 in File S1). In addition we report a further 370,719 SNPs and 43,121 indels on the sex chromosomes, of which 117,197 and 17,201 are newly described (Table S5 and Table S6 in File S1).We report 1,337,283 autosomal indels by WGS-4x and 25,750 by WES, with an average 733,326 indels per person by WGS-4x and 10,217 by WES Table 1.Figure S11) even for low-frequency and rare variants.We carried out imputation of the phased WGS-4x data amongst South Asians, quantifying accuracy by comparison to i. whole genome microarray data , and ii. high-coverage WGS-28x data (N = 8). Imputation accuracy for common variants was high using phased WGS-4x South Asian haplotypes, and better than imputation using either unphased South Asian data or haplotypes from the 1000 Genomes Project Figure 2The genotypes identified in our study thus provide the most accurate available whole genome reference panel for imputation of both shared and South Asian specific genetic variants. Our resource will enable genetic association studies to accurately identify variants underlying differences in phenotype and disease susceptibility between South Asians and other populations.Figure S12). However, allele frequencies show substantial heterogeneity between populations ; this stratification may reflect founder effects, genetic drift or selective pressure. We used Wright's FST to identify alleles stratified between South Asian and the 1000 Genomes Project populations ST between South Asians and European, East Asian and African populations was 0.010, 0.028 and 0.040 respectively; lower levels of FST between South Asians and Europeans are consistent with more recent admixture between these populations  We find that the genetic variants with AF>5% amongst South Asians are almost universally shared with all three of the 1000 Genomes populations  confirming that most common genetic variants are cosmopolitan . The SNPs stratified between South Asians and Europeans at FST>0.10 are most enriched for non-synonymous, splice site, and 5′ UTR variants  with corresponding under-representation of intergenic SNPs . Our findings are consistent allelic stratification arising from selective pressure on functional genetic variants.We find that SNPs stratified between South Asians and Europeans are strongly enriched for variants in coding regions . Pathway analysis shows that the non-synonymous, splice-site, STOP and UTR SNPs most strongly differentiated between South Asians and Europeans  identify genes involved in the structure and function of the skin and eyes , in metabolism , and in infection and immunity .Allelic stratification shows regional clustering . We also find allelic stratification at genetic loci associated with central obesity, blood pressure, fasting glucose and triglyceride levels, core components of the metabolic syndrome of insulin resistance  that is 3–4 fold more prevalent common amongst South Asians than Europeans and considered to underlie their high cardiovascular disease risk We explored the potential biomedical relevance of allelic stratification. We find strong enrichment for differentiated alleles at genetic loci involved in susceptibility to skin cancers , including 40,656 variants that are common (AF≥5%), 503,588 that are low frequency (AF≥1 and <5%) and 2,402,617 rare variants (AF<1%). Novel genetic variants are less likely to be located in coding regions than expected under the null hypothesis, consistent with selection against sequence change in functional regions . As expected, selection is strongest against STOP and non-synonymous SNPs, and most evident at high allele frequencies . We find 568 coding-region SNPs specific to South Asians that are common (AF>5%). These are distributed between 502 genes; pathway analysis reveals these genes to be enriched for involvement in carbohydrate and lipid metabolism, including IGF1, IGF2, LDLR and LYN (P<0.01). Our results provide preliminary evidence for South Asian specific genetic variants in genes involved in energy storage and metabolism.We identify 2,946,861 novel SNPs , direct genotyping will be required for their evaluation; given the number of variants involved this will likely require a custom, South Asian specific microarray.Most novel variants in South Asians are low-frequency or rare (>95%). Rare and low-frequency variants are responsible for familial disorders showing Mendelian patterns of inheritances, and may also contribute to the unexplained heritability of common disease −14, Figure S17), whilst ASI have ∼30% more novel SNPs than ANI . These observations are consistent with previous reports identifying ASI as the earliest inhabitants of the South subcontinent, and most diverged from other populations Principal components analysis reveals a North-South gradient amongst South Asians, consistent with historic admixture of northern Indo-Aryan people  with predominantly southern Dravidian speakers . Sharing of genetic variants with European and African populations, is higher amongst South Asians with predominant ANI rather than ASI ancestry . Principal components analysis using whole genome SNP data (Illumina Hap610 microarray) available for participants confirmed that the South Asians sequenced were representative of South Asians living on the Indian subcontinent . The research was approved by the West London Research Ethics Committee (reference number: 07/H0712/150); all participants gave written informed consent.We sequenced 321 unrelated men of self-reported South Asian ancestry , participating in the London Life Sciences Population (LOLIPOP) study and living in the UK DNA libraries were prepared according to Illumina instructions. For WGS, 3–5 µg of genomic DNA were fragmented by nebulization with compressed nitrogen gas at 35 p.s.i. for 6 min. DNA fragments with overhangs were end-repaired using T4 and Klenow polymerases and T4 polynucleotide kinase with 10 mM dNTP's followed by addition of an “A” base at the ends using Klenow exo fragment (3′ to 5′-exo minus) and dATP (1 mM). Sequencing adaptors containing “T” overhangs were ligated to the DNA products followed by agarose (2%) gel electrophoresis. Fragments of about 400 bp were isolated from the gels (Qiagen Gel Extraction Kit) and the adaptor-modified DNA fragments were PCR enriched for 10-cycles using Phusion DNA polymerase (Finnzymes Oy) and primers PE 1.0 and PE 2.0 (Illumina). Enriched libraries were further purified using agarose (2%) gel electrophoresis as described above. The quality and concentration of the libraries was assessed using Nanodrop absorbance and by the Agilent 2100 Bioanalyzer using the DNA 1000 LabChip (Agilent).For WES, 1 mcg of DNA was sheared using the Covaris E-Series, and libraries prepared according to the Illumina TruSeq method manual, using a gel-free protocol. Samples were tagged using Illumina adapters before pooling. Samples were quantified using the Invitrogen Qubit QuantIT system and the Agilent Bioanalyzer HS DNA chip to check library sizing. Libraries were then further quantified using the Kapa SYBR Fast Library Quant Kit for Illumina GA (Anachem) to more accurately quantify adapter-ligated DNA present in each sample library. Sample libraries were pooled in groups of 6, all with differing tags, at 500 ng/sample, for a total DNA library mass of 3000 ng per pool. Exome capture was performed using the Illumina TruSeq Exome Enrichment kit, according to the provided method manual. Post-enrichment, samples were again quantified using Qubit and Bioanalyzer, and then again with the Kappa Library Quant Kit. Samples were diluted to 2 nM in preparation for sequencing.x instruments (WGS) or HiSeq 2000 (WES). Template DNA fragments were hybridized to the surface of the Illumina flow cells and amplified to form clusters using the Illumina cluster station. In brief, DNA (8–10 pM) was denatured followed by hybridization to grafted adaptors on the flow cell. Isothermal bridge amplification using Phusion polymerase was then followed by linearization of the bridged DNA, denaturation and blocking of 3′-ends and hybridization of the sequencing primer. Samples were sequenced using 2×101 cycles (WGS) or 2×100 cycles (WES) of incorporation and imaging. Imaging and analysis of the data was performed using the SCS 2.6 and RTA 1.6 software packages from Illumina, respectively. RTA analysis involved conversion of image data to base-calling in real-time.Paired-end sequencing-by-synthesis was performed on Illumina GAIIDefault parameters were used for all data processing and analysis stages unless otherwise specified. Each sample was assessed for sequence quality on the basis of per-base and per-sequence quality scores, GC content, duplication levels, kmer content and sequence over-representation using FastQC version 0.10.0. Low-quality sequence was trimmed from reads using the fastq_quality_trimmer component of the fastx toolkit version 0.0.13, removing bases from the 3′ end of reads with a quality score less than 15, and discarding reads which were shorter than 60 bp following quality trimming. Reads were also processed with cutadapt to remove any residual sequencing adapters. Duplicate reads resulting from both PCR and optical duplicates were identified using Picard's MarkDuplicates tool, using an optical pixel duplicate parameter of 100.Sequence reads were aligned to build 37 patch 5 of the human genome sequence using bwa Alignments were then sorted by co-ordinate and a BAM index generated using SAMtools. A read-group header (RG) was added to each bam file using Picard to allow differentiation of samples amongst pooled data sets. Individual alignments were then merged into pooled BAM format files. The alignments from the low-coverage genomes were then split into individual chromosomes, whereas the exome sequence alignments were left in a single merged file.http://genome.cshlp.org/content/21/6/830.long). The identified target loci were passed to the GATK IndelRealigner, with realignments carried out using the Smith-Waterman consensus determination model. The LOD threshold for cleaning was reduced to 3.0 in the case of the low-coverage genome samples, but left at the default of 5.0 for exomic or high-coverage sequences. Finally the GATK CountCovariates tool was used to assess a number of covariates , with known variant loci provided from dbSNP. The resulting recalibration data was applied to correct base quality scores using the TableRecalibration tool.Local re-alignments were carried out around indels in the aligned reads using the GATK The GATK Unified Genotyper was used for the identification of both SNPs and Indels, using dbSNP 135 for identification of known variant loci. The Unified Genotyper was run using ‘discovery’ genotyping mode , and alternately using ‘SNP’ and ‘INDEL’ genotype likelihood models. In the case of SNP identification, the default minimum confidence threshold for calling (30) was applied with exome sequences, but this was reduced to 4 for the low coverage genomes. Exome sequences and high-coverage genomes were analysed using a coverage threshold of 200, beyond which down-sampling was applied to the reads, whereas low-coverage genomes had a coverage threshold of 50 applied.Filtering of SNP calls was carried out using the GATK VariantRecalibrator using HapMap 3.3 as both truth and training sets (prior = 15), the 1000 Genomes genotypes from the Omni 2.5 chip as a training set (prior = 12) and dbSNP 135 for the known data set (prior = 8). Annotations used in the Gaussian mixture were DP , QD , Haplotype Score, Mapping quality rank sum test, read position rank sum test and Inbreeding coefficient. Only variants falling into the >99.9% truth-sensitivity tranche were retained for analysis. Recalibration of genomic indels was carried out using a similar process, but omitting the QD annotation from the recalibration process, and using the Mills-Devine/1000G Indel set for truth and training sets. Indel calls derived from exome sequencing were manually filtered using thresholds of QD<2.0, ReadPosRankSum<40.0, FS>200, InbreedingCoeffecient<−0.8. Despite variant recalibration, a high proportion of indel calls in the low-coverage WGS were false positive findings; we therefore only retained low-coverage indel calls at sites that were also identified as polymorphic in the high-coverage data.Unphased likelihoods from variants called from low-coverage genomes were prepared using the GATKs ProduceBeagleInput tool, and haplotype inference carried out using Beagle version 3.3.2 ST>0.15, N = 149). Direct genotyping was done amongst up to 2,638 South Asians. Our strategy thus provides a robust assessment of calling error rate and allele frequency estimates in conserved regions identified as showing the greatest evidence for departure from expectation. We defined sensitivity as the proportion of sites polymorphic in the reference set that were identified as polymorphic in the experimental (WGS-4x or WES) set. Positive predictive value was defined as the proportion of sites identified as polymorphic in the experimental (WGS-4x or WES) call set that were confirmed to be polymorphic in the reference set.We assessed the accuracy of SNP calling by the WGS-4x and WES data using three datasets: i. 537K SNP genotypes measured by the Illumina 610 microarray and ii. 252 SNPs identified by WGS or WES submitted to replication genotyping by KASPar, and iii. results from two samples analysed by both WGS-4x and WGS-28x. The 252 SNPs submitted for direct genotyping were specifically selected to be non-synonymous variants that were either i. novel (N = 103) or ii. known but showing evidence for stratification between South Asian and Europeans (FTable S13 in File S1). PCR was carried out with reaction volumes of 25 µl on a Bio Rad Peltier Thermal Cycler with BIOTAQ DNA Polymerase (Bioline). PCR products were purified for sequencing using MultiScreen PCRμ96 filtration plates (Millipore) and Sanger Sequenced on the ABI 3730 xl.We validated 35 indels using Sanger sequencing. The indel set comprised 18 known indels (Broad Institute set of Mills Devine and Thousand Genome indels) and 17 novel previously undescribed variants. Validation was carried out amongst eight individuals predicted to carry the indels by WGS-28x. PCR primers were designed using BatchPrimer3 (v1.0) or the Primer3Plus2 software (2) between predicted and observed genotypes doses, and was assessed separately in comparison to observed microarray and WGS-28x genotypes. Correlations were calculated for each genomic site predicted to be polymorphic by imputation, and results averaged by allele frequency (microarray) or allele count (WGS-28x).We carried out imputation amongst 6,557 South Asians previously genotyped using the Illumina 610 microarray and not included in the discovery experiment; this included the 8 South Asians with WGS-28x data. Imputation was done for chromosome 22 using IMPUTE2 ST as a measure of population differentiation that may arise from founder effects, genetic drift or through selective pressure ST were enriched for specific functional classes of variant, compared to distribution expected under the null hypothesis (χ2 test). We carried out pathway analysis for genes containing SNPs with high FST using Ingenuity Pathway Analysis To identify regions of the genome most closely associated with South Asian ancestry, we determined values for Wright's FWhere applicable, data sets for the analysis of the sequences were those distributed with version 1.5 of the Genome Analysis Toolkit (GATK) resource bundle. These include the Human Genome Reference Consortium build 37 patch 5 release of the Human genome, including unlocalised contigs and the rCRS MT sequence, dbSNP build 135, HapMap 3.3 and the Broad Institute curated Mills-Devine/1000 Genomes indel set. Comparison with variants identified by the 1000 Genomes project was made against the April 2012 Integrated Variant Set.Figure S1Principal components analysis of samples sequenced by WES or WGS-4x, compared to HapMap 2 (1A &1B) and HapMap 3 (1C and 1D) samples. Results are shown for all South Asians in the LOLIPOP study , and for South Asians sequenced .(TIF)Click here for additional data file.Figure S2Principal component analysis of genotype data for i. UK South Asian samples sequenced by WES or WGS in the current study, ii. HapMap CEU samples, iii. HapMap CHB/JPT samples, and iv. Indian samples studied by Reich et al (Nature 2009). The UK South Asian samples studied coincide with the Reich samples collected in India, confirming they are representative of non-migrant South Asians. The Reich samples include Aonaga and Nyishi peoples, small South Asian subpopulations from the north-east region of India, with strong East Asian influence.(TIF)Click here for additional data file.Figure S3Principal component analysis of genotype data for South Asian samples from the LOLIPOP study. Samples selected for WES or WGS in the current study are identified by red symbols.(TIF)Click here for additional data file.Figure S4Sequencing statistics for the WGS-4x (red) and WES (blue) samples.(TIF)Click here for additional data file.Figure S5Enrichment of indels for variants multiples of 3 bases long in coding regions.(TIF)Click here for additional data file.Figure S6SNP call metrics in unphased (red) and phased (blue) low-coverage WGS-4x data.(TIF)Click here for additional data file.Figure S7Allele frequencies in unphased (left panel) and phased (right panel) WGS-4x data compared to results from direct genotyping by microarray.(TIF)Click here for additional data file.Figure S8Difference in allele frequency between WGS-4x and microarray data, before and after phasing.(TIF)Click here for additional data file.Figure S9Concordance of genotype calls from unphased WGS-4x, phased WGS-4x or WES, with calls from microarray.(TIF)Click here for additional data file.Figure S10Allele frequency for 252 known and novel variants identified by WGS-4x or WES, compared to results from single variant genotyping in separate sample of up to 2,638 South Asians.(TIF)Click here for additional data file.Figure S11Power for discovery of SNPs at differing allele frequency by WGS-4x, by WES or in combination. Calculations are based on observed sensitivity for SNP detention of 91.9% by WGS-4x (N = 168) and 99.2% by WES (N = 147).(TIF)Click here for additional data file.Figure S122<0.5) with tag-SNPs on the llumina 610 microarray.Proportion of autosomal SNPs identified by WGS-4x that are in low LD (r(TIF)Click here for additional data file.Figure S13Sharing of South Asian SNPs identified by WGS-4x with 1000Genomes populations.(TIF)Click here for additional data file.Figure S14Allele frequencies for autosomal SNPs identified by WGS-4x amongst South Asians (x-axis), compared to 1000 Genomes populations (y-axis). Results shown only for SNPs shared between the respective populations.(TIF)Click here for additional data file.Figure S15ST values between South Asians and the 1000 Genomes populations for autosomal SNPs identified by WGS-4x.Distribution of F(TIF)Click here for additional data file.Figure S16ST values for autosomal coding SNPs amongst South Asians compared to Europeans on chromosomes 4–6.Manhattan plot showing F Regional plots are shown for SNPs with FST>0.30. In the regional plots the SNP with highest FST are identified by blue diamonds, while other SNPs are colour coded according to their LD with the sentinel .(TIF)Click here for additional data file.Figure S17Enrichment for SNP functional classes across a range of allele frequencies for South Asian specific SNPs (A) and for SNPs shared with other populations (3B). Enrichment is calculated compared to null hypothesis; P values are provided in Table S12.(TIF)Click here for additional data file.Figure S18Sharing of autosomal SNPs identified by WGS-4x amongst ANI and ASI with 1000Genomes populations.(TIF)Click here for additional data file.Figure S19Novel SNPs and Het-Hom ratio amongst ANI and ASI South Asians.(TIF)Click here for additional data file.Figure S20Relationship between ANI-ASI gradient and the number of novel autosomal SNPs identified by WGS-4x. ASI-ANI gradient quantified by principal components analysis (x axis) where lower PC indicates greater ASI content.(TIF)Click here for additional data file.File S1Table S1, Characteristics of participants.Table S2, Per sample sequencing metrics for WGS and WES. Results are mean (SD). Table S4, Results of indel validation by Sanger sequencing. Sanger sequencing of 35 indel (17 previously unreported) amongst 8 individuals predicted to carry the indels by WGS-28x. Sanger sequencing confirmed the presence of indels at all sites called by whole genome sequencing. For 33 of the 35 indels (94.3%) Sanger sequencing showed identical sequence to that predicted by WGS-28x. Sanger sequencing of the remaining two PCR amplicons confirmed presence of a complex indel within 20 nucleotides of the originally called indel; one fell within a repetitive region and the second was difficult to analyse. Both these indels were novel. ST between South Asians and the AFR, ASN or EUR populations. Table S8, P values for enrichment of functional classes amongst autosomal SNPs across the range of FST between South Asians and 1000 Genomes Project populations. Table S10, Pathway analysis  of genes with potentially functional SNPs stratified between South Asians and Europeans (FST>0.10). Table S11, Enrichment for stratified SNPs at genetic loci known to be associated with respective phenotype in GWA studies.Table S5, Functional class of SNPs identified by WGS-4x and WES. Table S6, Functional class of indels identified by WGS-4x and WES. Table S7, P values for enrichment of autosomal coding and intergenic SNPs across the range of F Observed: no of stratified SNPs (Fst>0.10) within 500 kb of the reported sentinel SNPs. Predicted: mean no of SNPs expected to fall within 500 kb of the sentinel SNPs under null hypothesis. Expectation based on permutation testing: 10,000 runs of SNP sets matched to the stratified SNPs based on allele frequency and gene proximity, but otherwise selected at random. Enrichment: observed/expected. P: exact probability of the observed based on the distribution of expected generated by permutation testing. Table S12, P values for enrichment of functional classes amongst autosomal SNPs across a range of allele frequencies.Table S13, PCR primers for validation of indel calling by Sanger sequencing.(DOCX)Click here for additional data file.File S2Table S3, Replication results for 252 SNPs genotyped by single variant tests amongst up to 2,638 South Asians. Dataset: sequence data SNP was selected from. AF: allele frequency. Table S9, Coding, splice and UTR SNPs showing greatest stratification between South Asians and Europeans. AF: allele frequency, SA: South Asians, AFR; Africans, ASN: East Asians; EUR: Europeans.(XLSX)Click here for additional data file.