Anopheles gambiae is the major malaria vector in Africa. Examining the molecular basis of A. gambiae traits requires knowledge of both genetic variation and genome-wide linkage disequilibrium (LD) map of wild A. gambiae populations from malaria-endemic areas. We sequenced the genomes of nine wild A. gambiae mosquitoes individually using next-generation sequencing technologies and detected 2,219,815 common single nucleotide polymorphisms (SNPs), 88% of which are novel. SNPs are not evenly distributed across A. gambiae chromosomes. The low SNP-frequency regions overlay heterochromatin and chromosome inversion domains, consistent with the lower recombinant rates at these regions. Nearly one million SNPs that were genotyped correctly in all individual mosquitoes with 99.6% confidence were extracted from these high-throughput sequencing data. Based on these SNP genotypes, we constructed a genome-wide LD map for wild A. gambiae from malaria-endemic areas in Kenya and made it available through a public Website. The average size of LD blocks is less than 40 bp, and several large LD blocks were also discovered clustered around the para gene, which is consistent with the effect of insecticide selective sweeps. The SNPs and the LD map will be valuable resources for scientific communities to dissect the A. gambiae genome. Plasmodium, leads to as many as 300 million clinical cases per year [ Anopheles gambiae is the most prevalent vector in Africa.Malaria, a mosquito-transmitted disease caused by parasites of the genusper year . Of thes P. falciparum infection [ A. gambiae populations from malaria-endemic areas. The currently available A. gambiae SNPs in the NCBI database dbSNP mainly derive from laboratory mosquito colonies [ A. gambiae populations. However, this result means neither that neighboring SNPs are not linked, nor that large LD blocks in the A. gambiae genome do not exist. Therefore, it is still critical to define the extent of genome-wide genetic variation and linkage information in A. gambiae populations from malaria-endemic areas. Recent advances in sequencing technologies and bioinformatics make it economically feasible for a single research laboratory to detect genome-wide SNPs and to construct an A. gambiae LD map using wild-derived mosquitoes. Depending on needs, the current available software such as Haploview [Genetic variation in mosquito populations affects the mosquitoes' susceptibility tonfection –4, insecnfection –8, and ocolonies , 10. Samcolonies  or SNPs colonies  in fieldaploview  allows t A. gambiae from Kenya, sequenced individual mosquitoes with Illumina sequencing technologies, developed novel pipelines to detect SNPs, constructed an A. gambiae LD map, and established a computer server to present the data to the public. Notably, the consistence between our data and experimental findings supports the accuracy of this resource and demonstrates the advantages of the SNPs and LD map.In this study, we collected wild A. gambiae populations, individual genomic DNA from nine randomly selected wild A. gambiae mosquitoes recovered from highland areas around Kisumu was studied. DNA from each mosquito was sequenced individually after the samples were confirmed to be A. gambiae. Each sequence read was 100 bp long. The average sequencing coverage of the whole genome for each individual was greater than 36.1-fold. These reads were mapped onto the A. gambiae reference genome [http://omics.ou.edu/AgHapMap). The results indicated a low error rate of less than 0.1%. Notably, as many as 87.6% of the newly discovered SNPs are novel, compared to SNPs in dbSNP  [ A. gambiae in the field. Among the novel common SNPs, 36,675 (1.9%) are nonsynonymous, 135,500 (7.0%) are synonymous, 371,417 (19.1%) are within intron regions, and the others  are at intergenic regions (To detect common SNPs (frequencies > 5%) in wilde genome . More the genome , 14. To ase 125)  that wer regions . The SNPa/2L+a and five were 2La/2La. The 2L chromosomal inversion region always had fewer SNPs than other genomic regions, regardless of mosquito karotypes. The consistence of the low recombinant rate at heterochromatic regions and chromosomal inversion to the SNP distribition partially validates the SNPs genome-widely.We identified that four large genomic regions, for example, 2R:57.6 MB-2L:4.0 MB, 2L:20.5–42.2 MB, X:17.6–24.4 MB, and 3R:52.0 MB-3L:0.4 MB, have much lower frequencies of SNPs, compared to other regions . As expeWe next extracted the SNP genotypes of individual mosquitoes based on the high-throughput short read sequences as described in the Methods section. Out of 2,219,815 common SNPs, 785,687 SNPs (one SNP per every 293 bp genome-wide) were reliably genotyped at 99.6% confidence in nine mosquitoes. The correlation coefficient among SNPs was calculated by using Haploview software . As show A. gambiae is very short ( para gene (AGAP004707) was at the center  of the large LD clusters  of our SNPs from wild-derivedisolates . FurtherAs mentioned above, it is well-known that the genetic variation and LD maps are important for association studies to analyze wild mosquitoes , 3, and  A. gambiae to investigate whether our sample contained a single karyotype form that causes the lower SNP-frequency at this region. Our results show that the lower SNP-frequency did not associate with any particular karyotype form of chromosomal inversion of 2La. Apparently, the consistence between SNP distribution and genetic data validates the detected SNPs genome-widely and demonstrates their usability.SNP distribution identified four large genomic regions harboring unusually low frequencies of SNPs, three of which localized to centromeres. Although this observation was expected, given that the genomic regions around centromeres have lower recombination rates than other loci and DNA recombinant rates and SNP density are positively correlated , we also A. gambiae LD map, a previous survey of a limited set of genes (n = 4) [n = 1,536) [ A. gambiae populations. Our data are consistent with those reports. However, here we extend these observations and show that the average LD size in A. gambiae populations in western Kenya is less than 40 bp. It is worth noting that our new results additionally provide a genome-wide LD map of nearly one million genetic markers. Furthermore, the LD map reveals a genomic locus on chromosome 2 (2R: 57.6 MB-2L: 5.1 MB) that is clustered with larger LD blocks. Analyses of the genes within this region identified that the para gene is at the center of this locus. The para gene encodes a voltage-gated sodium channel (VGSC) that is the target molecule of common insecticides such as pyrethroids [ kdr mutations, which change codon 1014 from leucine to serine or phenylalanine within the para gene coding region, confer insecticide resistance [n = 9) harbored the resistance allele (1014S) instead of the wild type allele (1014L). It is well known that the use of insecticides remains the traditional approach to combating the spread of malaria [ kdr mutation frequency and even fixation over the past decade when pyrethroid insecticides have been used extensively in Africa suggest the importance of this mechanism in the process of pyrethroid resistance. On the other hand, given the fixation of kdr mutations in many A. gambiae populations, metabolic detoxification is becoming an increasingly important resistance mechanism. Clearly, vector insecticide resistance is an outstanding issue in the control and prevention of vector-borne diseases as supported by our data and other reports [ para gene validates our LD map and demonstrates an application of using our LD map to detect genomic regions under selection pressure.Regarding our (n = 4)  or SNPs = 1,536)  suggesteethroids . The welsistance –27. Inde malaria . The mol malaria , 29. DDT malaria , and cau malaria , 31 wher reports , 33. Col A. gambiae mosquitoes from malaria-endemic areas in Kenya using next-generation sequencing technology and developed a pipeline to analyze SNPs and genotypes. More than 2 million common SNPs were identified in wild A. gambiae populations, and 785,687 SNPs were genotyped in nine mosquitoes. Using these data, we constructed the first genome-wide A. gambiae LD map, which will serve as a powerful and useful resource to dissect the mosquito genome. The consistence between our data and previous findings supports the accuracy of this resource.In conclusion, we collected and sequenced wild A. gambiae larvae were collected from natural habitats (>10 meters distance between any two habitats) in highland areas around the Kisumu district of Kenya where malaria is hyperendemic. More than half of mosquito larvae were successfully reared to adults in an insectary at the Kenya Medical Research Institute. The resulting 3–5-day post-emergence female mosquitoes were used for experiments. It is worth noting that only the female wild-derived mosquitoes that fed on human blood through membrane feeding were further analyzed in this study. Genomic DNA was extracted from 7-day post-blood-fed mosquitoes using DNAzol . The individual mosquito species was confirmed by the rDNA-PCR method [Collecting and rearing mosquitoes were performed as described previously . In brieR method . A. gambiae reference genome (assembly version AgamP3) using the short oligonucleotide analysis package (SOAP) [Genomic DNA from individual mosquitoes was sheared to construct a DNA library with fragment lengths of about 300 bp, and both sides of each DNA fragment were sequenced in lengths of 100 bp. These reads were mapped to thee (SOAP) . SOAP use (SOAP) . The nuc A. gambiae individual, a heterozygous genotype was assigned to that individual for that genome position. However, for each homozygous SNP genotype, the number of reads hitting that position was counted using the information obtained through the program “soapsnp” from the SOAP package [P value of missing a heterozygous allele is less than 0.004 based on the binomial distribution. The genotypes of these SNPs were applied to calculate the LD among SNPs by using the software Haploview [For each detected SNP, we checked all reads in each individual mosquito regardless of its sequencing quality score (Phred score) . If two  package . If a hoaploview . Two nei A. gambiae by PCR with primers shown in http://nucleobytes.com/), and the SNPs were read manually.We cloned two pairs of neighboring genes: AGAP006906 versus AGAP006914 and AGAP007031 versus AGAP007032 to verify the LD map. The sequences around nonsynonymous SNPs were cloned from 22 randomly selected female wild-derivedhttp://samtools.sourceforge.net/ was used to transform a data file from one format to another. The “samtool” was also used to import data into Gbrowse required databases. To generate these databases, the reference genome sequence was indexed , and then the data were imported into databases . The alignment files that contain short reads aligned to the reference sequence were sorted and indexed  sequentially. Finally, the sorted and indexed alignment files were imported into databases . SNP data were stored in a mysql database with a single table that was created with this command: “CREATE TABLE Agam_common_snps_position (snp varchar(10) NOT NULL, alleles varchar(4) NOT NULL, chr ENUM , pos int(10) unsigned NOT NULL default ‘0', Ag541 char(2), Ag544 char(2), Ag545 char(2), Ag551 char(2), Ag553 char(2), Ag564 char(2), Ag565 char(2), Ag566 char(2), Ag567 char(2), PRIMARY KEY , KEY chr (chr), KEY pos (pos));”. Each data set was displayed as a track  through Gbrowse. For instance, the genome annotation, including gene structure predictions in our internal databases (ReAno) [https://www.vectorbase.org/ [D′), logarithm of odds (LOD), and coefficient of determination (r2) for each pair of SNPs interactively. Based on a user's HapMap configuration (or default), the server calculates the colors and displays it as a plug-in track on the Web interface. Using downloaded Haploview software [The individual short reads, the sequence alignment to the reference genome, and the SNPs were compiled into databases as instructed by Gbrowse and displayed as tracks . In briep varchar NOT NULLase.org/ , was intsoftware , users c


The whole genome of Swarna was sequenced using Illumina’s paired-end technology, and the reads were mapped to the Nipponbare reference genome. Overall, 65,984 non-synonymous SNPs were identified in 20,350 genes, and in silico analysis predicted that 4,847 of them in 2,214 genes may have deleterious effect on protein functions. Polymorphisms were found in all the starch biosynthesis genes, except the gene for branching enzyme IIa. It was found that T/G SNP at position 246, ‘A’ at position 2,386, and ‘C’ at position 3,378 in the granule bound starch synthase I gene, and C/T SNP at position 1,188 in the glucose-6-phosphate translocator gene may contribute to the low GI phenotype in Swarna. All these variants were also found in the genome of another low GI indica rice variety from Columbia, Fedearroz 50. The whole genome analysis of Swarna helped to understand the genetic basis of GI in rice, which is a complex trait involving multiple factors.Swarna is a popular cultivated  Rice is the primary source of dietary energy for half of the World population, and 90% of them reside in low and middle income countries in Asia. In recent years, rice consumption is steadily increasing outside Asia also, especially in sub-Saharan African regions (www.iriri.org). The regular consumption of rice as a primary food increases the risk of developing type 2 diabetes by elevating the postprandial glucose level, a condition referred to as postprandial hyperglycemia (PPHG). PPHG stimulates deleterious structural changes in many tissues in the bodyindica rice varieties have lower GI than japonica rice varietiesindica rice variety from India, was having low GI4. This variety was developed in the year 1982 by crossing Vasista and Mahsuri rice varieties, and it is popularly cultivated in the southern states of India. Understanding the genetic basis of low GI in Swarna will help in breeding this trait in other rice varieties. Natural variations in the 18 starch biosynthesis related genes contribute directly or indirectly to the GI by altering the amylose and amylopectin content in riceGI of rice varieties is highly dependent on the composition of the carbohydrates in the seeds. In general, et al. (2011)O. sativa indica, O. sativa japonica and O. rufipogon. The breeding history of 20 rice varieties was revealed by comparing the genome sequence of landraces and modern rice varietiesjaponica groups10Whole genome sequencing using next-generation sequencing (NGS) technologies could identify millions of variants in a rapid, efficient, high throughput, and cost effective manner. These variants have been used as DNA markers that aid in marker-assisted breeding and quantitative trait locus analysis6et al. 20O. sativade novo assembled reads has also been provided. The results from this study will help in understanding the genetic basis of GI, and its utilization in developing rice varieties with anti-diabetic properties.To our knowledge, there has been no whole genome-based polymorphism study on rice varieties with low GI. Therefore, we have sequenced the whole genome of Swarna rice variety by Illumina’s paired-end sequencing, and identified single nucleotide polymorphisms (SNPs), insertions and deletions (InDels) by aligning it with the Nipponbare genome sequence as reference. In addition to the detailed analysis of the 18 starch biosynthesis related genes, an account of reference assembled reads as well as 6 reads with an average read length of 101 bp were obtained, and the average sequencing depth was 10×. We mapped 36.6 × 106 (91.0%) reads to the Oryza sativa L. cv. Nipponbare reference genome, and the unmapped reads were de novo assembled (described later). Out of the mapped reads, 80.3% (29.4 × 106) reads were uniquely mapped to the chromosomes, and the remaining reads were mapped to multiple locations on the reference genome  were identified using default parameters. Application of the filtering criteria yielded 1,253,861 quality filtered variants, which included 1,149,698 SNPs and 104,163 InDels . The ratio of homozygous to heterozygous variants was 27.4 for SNPs and 53.7 for InDels. Total number of variants was found to be the highest in chromosome 1 , and the lowest in chromosome 9 . Average number of variants per chromosome was 104,488 at the rate of one variant for every 297 bases. Genomic distribution of SNPs and InDels was determined by calculating the density of occurrence . These deleterious SNPs were located in 2,214 genes, and their number varied between 1 and 12 per gene. Among the 4,847 deleterious SNPs, 505 SNPs were predicted to be highly deleterious (tolerance index = 0.0). These highly deleterious SNPs were located in 452 genes, and their number varied between 1 and 4 per gene.http://oryzasnp.plantbiology.msu.edu/), 315 novel SNPs were identified in Swarna. Majority of the SNPs (403) and all the InDels were in introns and other non-coding regions. Coding regions contained 33 non-synonymous SNPs, which were validated by Sanger sequencing  may severely affect the protein stability.De novo assembly of the 3.6 × 106 unmapped reads yielded 2,392 contigs that ranged in size between 500 and 6,188 bp with an average of 769 bp. In BLASTn analysis, 1,784 contigs did not show significant similarity with any sequence in the non-redundant nucleotide database at NCBI. Among the remaining 608 contigs that showed similarity with the sequences in the database, 504 (83%) contigs were closely related to the sequences from Oryza species. Gene Ontology (GO) analysis of the annotated contigs related them to cellular components, biological and molecular processes  would be desirable because regular consumption of normal rice is associated with 11% increase in the risk of developing type 2 diabetesjaponica to indica as well as indica to indica1010Analysis of the mapped reads showed that the distribution of the SNPs in Swarna was not even, and there were many continuous regions with poor number of SNPs, called ‘SNP deserts’. ‘SNP deserts’ were reported in genome mapping of We have observed more transition SNPs than transversion SNPs in Swarna. This kind of ‘transition bias’ in rice genomes was reported before, and the ratio of transition SNPs to transversion SNPs varied narrowly between 2.0 and 2.531018182315A large number of non-synonyms SNP were found in Swarna but the major interest was to identify the SNPs that may affect the function of the proteins. Amino acid residues or motifs that are essential for the biological functions and stability of the proteins are likely to be more conserved than the others. Therefore, non-synonymous SNPs in the highly conserved regions may have profound effect on the protein functions. SIFT algorithm uses this phenomenon, and predicts the effect of non-synonymous SNPs as ‘tolerated’ or ‘deleterious’256 rice breeding lines have identified SNPs in the GPT1 and GBSSI genes to be highly associated with high amylose content and retrogradation The major carbohydrate in the rice seeds is starch, which is made of linear amylose and branched amylopectin. Digestive enzymes break down amylose more slowly than amylopectin. In addition, though retrogradation does not solely depend on starch content or qualityhttp://rice.plantbiology.msu.edu/), and we found that this gene was not annotated in the reference genome itself. Available mRNA sequence for the PUL gene from Nipponbare (Accession No. NM_001058664.1) contained only partial coding sequence (CDS). Therefore, we have used a 100% identical complete CDS from Taihunuo , and manually marked the genomic coordinates. Manual annotation of the variants revealed that the PUL gene in Swarna actually contained 52 variants. Therefore, it was found that all the SSRGs, except BEIIa gene, were polymorphic in Swarna. Roth et al. (2006)indica (this study) as well as japonica riceAnalysis of the variants in the 18 SSRGs in Swarna revealed the complete absence of variants in PUL and BEIIa genes. Manual verification using the genomic coordinates confirmed the absence of variants in BEIIa gene. Genomic coordinates for the PUL gene could not be obtained from the MSU7 rice genome database .Among the 475 variants that were identified in the SSRGs, 404 were in non-coding regions, and 71 were in the coding regions. One of the non-coding SNPs, T/G at position 246, was present at the intron/exon 1 junction site of GBSSI gene. Presence of ‘T’ in this position causes aberrant splicing of the first intron, and the aberrantly spliced mRNAs are not translated to GBSS1 protein. As a result, the normal and aberrantly spliced mRNAs were produced in the ratio of 1:5, and the amylose content was reducedIn silico investigation of the functional impact of the 33 non-synonymous revealed that the T/C SNP at position 6,422 in the SSIIIb gene (Glu643Gly) may be deleterious and would reduce the protein stability. Though lack of expression of starch synthase can increase the amylose content at the cost of chain elongation in amylopectin37in silico analysis should wait for the availability of the crystal structures for these proteins.Oryza sativa L. cv. Swarna were obtained from Tamil Nadu Agricultural University, Thirurkuppam, Tamil Nadu, India. The seeds were germinated under the controlled environment, and genomic DNA was isolated from the young leaves using cetyl trimethyl ammonium bromide (CTAB) methodSeeds of https://code.google.com/p/cutadapt/). The trimmed reads were filtered using sickle master (https://github.com/najoshi/sickle) by retaining the bases with minimum Phred quality score of 30, and truncation of the sequences with Ns. The quality filtered paired-end reads were aligned to the Oryza sativa L. cv. Nipponbare reference genome, the unified build release Os-Nipponbare-Reference-IRGSP-1.0 (IRGSP-1.0)Oryza sativa (http://sourceforge.net/projects/snpeff/files/databases/v3_6/ snpEff_v3_6_rice7.zip) and SnpEff V3.6 toolThe Illumina TruSeq DNA adapters in the paired-end reads were trimmed using ‘cutadapt’ (https://code.google.com/p/gasv/).Densities of SNPs and InDels in every 100 kb region were calculated for locating hyper- and hypo-varying regions. The chromosome regions were defined as hyper-varying regions if SNPs/kb > 5 or InDels/kb > 1, and hypo-varying regions if SNPs/kb < 0.1 or InDels/kb < 0.01. Copy number variation (CNV) analysis was done using GASV softwarehttp://sift.jcvi.org), which classifies the effect of the SNP as ‘tolerated’ or ‘deleterious’. The SIFT algorithm generates alignments with a large number of homologous sequences and assigns a tolerance index value of 0 to 1 to each residue. The SNPs are classified as tolerated (tolerance index > 0.05) or deleterious (tolerance index < 0.05)The impact of amino acid substitution on protein function and phenotype alteration were studied by using SIFT algorithm (http://sift.jcvi.org) Provean (http://provean.jcvi.org) and SNAP (https://rostlab.org/services/snap/). A non-synonyms SNP was designated as deleterious only if all the three algorithms categorized it as deleterious SNP. I-Mutant (version 2.0) (http://folding.uib.es/i-mutant/i-mutant2.0.html), a neural network based tool, was used to predict the effect of the non-synonymous SNPs on protein stability. I-Mutant estimates free energy change value , which is the difference in Gibb’s free energy value of the mutated and native protein . Based on the value of DDG, the SNPS were predicted to decrease (DDG < 0) or increase (DDG > 0) the protein stability.The functional effect of the non-synonymous SNPs in the 18 SSRGs were analyzed by using SIFT  against non-redundant nucleotide database. Functional classification of the contigs was done using Gene Ontology (GO) tool at TAIR (https://www.arabidopsis.org/). The contigs were classified under three GO categories, such as biological processes, molecular functions and cellular components.The unmapped reads from the BWA-MEM alignment file were extracted in to a BAM file using SAMtoolsHow to cite this article: Rathinasabapathi, P. et al. Whole genome sequencing and analysis of Swarna, a widely cultivated indica rice variety with low glycemic index. Sci. Rep.5, 11303; doi: 10.1038/srep11303 (2015).


Hypoglycemia is one of the most important adverse events in individuals with type 2 diabetes mellitus (T2DM). However, hypoglycemia-related events are usually overlooked and have been documented less in clinical practice.We evaluated the incidence, clinical characteristics, and medical expenses of hypoglycemia related events in T2DM patients based on the Korea National Diabetes Program (KNDP), which is the largest multi-center, prospective cohort in Korea . For accurate outcomes, the KNDP data were merged with claims data from the Health Insurance Review and Assessment Service (HIRA) of Korea.p < 0.001), had more hospital visits , had a longer hospital stays , and incurred greater medical costs  than subjects without hypoglycemia.During a median follow-up period of 3.23 years , 88 subjects (2.02%) were newly diagnosed with hypoglycemia, and the incidence of hypoglycemia was 6.44 cases per 1,000 person-years (PY). Individuals with hypoglycemia were significantly older (59.7±10.7 vs. 53.3±10.4 years, Hypoglycemia-related events were infrequently identified among the medical records of T2DM subjects. However, they were associated significantly with poor clinical outcomes, and thus, hypoglycemia could have a substantial burden on the Korean national healthcare system. Approximately 4 million 12.4%) have diabetes mellitus among Korean adults aged ≥30 years in 2013; 14.5% of males and 10.4% of females [2.4% haveHypoglycemia is one of the most important adverse events in individuals with type 2 diabetes mellitus (T2DM). Hypoglycemia occurs when blood glucose levels are < 70 mg/dl (3.9 mmol/L)  and can Thus, the aim of the current study is to investigate the prevalence of hypoglycemia and the associated healthcare costs in Korean subjects with T2DM using data from the Korea National Diabetes Program (KNDP) cohort. In previous studies, hypoglycemic events in diabetic patients were correlated with recurrent physical and psychological morbidity and mortality, such as cardiovascular disease and cognitive dysfunction –10. HoweThe KNDP cohort database was used for the present study. The KNDP is a nationwide, large-scale, prospective, multi-center cohort study comprising data from university hospitals in Korea for Korean type 2 diabetic patients and those at a high-risk for developing diabetes.A total of 4,350 subjects was recruited from 12 university hospitals  ; all pat.As described in detail previously , the firThe KNDP cohort data provided baseline clinical characteristics such as age, gender, blood pressure, body mass index (BMI), and habits of smoking and drinking, comorbidities such as hypertension, dyslipidemia, cardiovascular diseases, and cerebrovascular diseases, and histories of medication use, glucose levels, glycated hemoglobin (HbA1c) levels, liver function, and duration of T2DM. The baseline data mentioned above were collected from a survey performed at cohort registration and the patient’s medical records. The use of medication at baseline was defined as the initiation of a prescription within 3 months of enrollment and maintenance of the treatment for at least 2 months. The clinical and biochemical parameters were measured annually. The HIRA claim database provided the number of hospital visits, length of hospital stays, and the disease codes for hypoglycemia.th version of the Korean Standard Classification of Diseases are compatible with the International Classification of Diseases-10.Patients with hypoglycemia were identified based on a survey of the KNDP cohort and whether any of the claims codes from the HIRA database included ‘hypoglycemia’ as E10.63, E11.63, E12.63, E13.63, E14.63, E16.0, E16.1, or E16.2. These diagnostic codes of the 6t-tests and chi-square tests. An analysis of covariance (ANCOVA) model was used to adjust for the effects of covariates, such as age, BMI, triglyceride levels, alanine aminotransferase (ALT) levels, comorbidity of cerebrovascular diseases, administration of oral hypoglycemic agents, duration of diabetes, follow-up period, recuperation period, and medical expenditures. All statistical analyses were performed using R software, version 3.0.3 and p-values < 0.05 were considered to be statistically significant.The incidence rates of hypoglycemia were calculated per 1000 person-years (PY). Descriptive statistics are expressed as means± standard deviation (SD.) and the clinical characteristics of subjects with and without hypoglycemia were compared using Student’s www.ClinicalTrials.gov (NCT00474838) and were approved by the institutional review boards of each hospital .All subjects submitted written informed consent and received sufficient explanation from relevant medical staff prior to participation in the study. The cohort data were registered at .During the follow-up period, 88 subjects in the cohort experienced hypoglycemic events. The subjects with hypoglycemia were classified by age group, and the median follow-up period and incidence of hypoglycemia were documented. The median follow-up period for all enrolled subjects was 3.23 years . The incidence of hypoglycemia was 3.72, 4.26, 9.34, and 25.75 cases per 1000 PY in patients aged <50, 50–59, 60–69, and >70 years, respectively . At baseline, the age and mean disease duration were significantly higher in subjects who experienced hypoglycemia (p<0.001) compared with those who did not. There were no significant differences in BMI, waist circumference, systolic blood pressure, smoking or drinking habits, or comorbidities with hypertension, dyslipidemia, cardiovascular diseases, and cerebrovascular diseases between the two groups. Subjects who experienced hypoglycemia tended to use combination therapy for diabetes management more frequently as well as an increased proportion of renin-angiotensin system (RAS) blockade (p <0.001). Diastolic blood pressure, total cholesterol and low-density lipoprotein (LDL) cholesterol levels were significantly lower in hypoglycemic subjects. However, there were no significant differences in mean fasting glucose or HbA1c levels during the follow-up period between groups .The study subjects were classified according to presence of hypoglycemic episodes, and the differences between the groups were assessed p<0.05). (All costs presented in this paper are in US dollars (USD) using an exchange rate applicable at the end of the time of data collection, December 31, 2010, 1 USD = 1,120 Korean won)The subjects who experienced hypoglycemia tended to incur more medical expenses  than the subjects who did not experience hypoglycemia   levels, and ALT levels, hypoglycemia still contributed to a higher incidence of hospital visits, a longer duration of hospital stays and greater medical expenses  and longer hospital stays (16.13±29.21 days/PY) than subjects who did not experience hypoglycemia . Among subjects who experienced hypoglycemia, more medical expenses were incurred after hypoglycemic events  than before hypoglycemia events  (p = 0.18). And more hospital visits (147.62±250.77 days/PY) and longer hospital stays (21.25±39.73 days/PY) were incurred after hypoglycemia events than before hypoglycemia events (121.08±135.54 days/PY and 12.92±34.02 days/PY) (p = 0.25 and p = 0.04).Moreover, medical expenditures and recuperation periods before and after hypoglycemic events in the hypoglycemia-experienced group were analyzed in the present study The present study investigated the association between hypoglycemia and medical expenses using the KNDP cohort which is a nationwide, large-scale, prospective, and multi-center cohort study. To ensure accurate outcomes, nationally representative claims data from the HIRA Service of Korea was merged with the KNDP cohort. This facilitated a more precise assessment and more detailed outcomes of the cohort.In summary, Korean subjects who experienced hypoglycemia were older, had longer disease duration, and had an increased tendency for the use of medication. However, the mean HbA1c levels during the follow-up period did not significantly differ between the groups with and without hypoglycemia. After multiple adjustments using an ANCOVA model, hypoglycemia remained significantly associated with hospital visits and the duration of stays as well as medical costs. These adjustments also demonstrated that the experience of hypoglycemic episodes was significantly associated with increased medical expenses in Korean subjects.The incidence of hypoglycemia was 6.44 per 1000 PY among the 4,350 T2DM patients during a median follow-up period of 3.23 years. Hypoglycemic events tended to occur more frequently in older individuals and in those with a longer duration of T2DM. Hospital visits and longer stays were also more frequent in patients with hypoglycemia. Thus, hypoglycemia can potentially lead to excessive medical expenditure.A prior survey (n = 1848) conducted in Germany, France, and the United Kingdom reported that 600 of the diabetic patients were worried about developing hypoglycemia . In that€4.25 million (€14/PY). Therefore, hypoglycemia accounted for a significant portion of the total medical costs in that study [The expenditure related to hypoglycemic events in T2DM patients in Sweden was reported by Jonsson et al . Of the at study .Some studies investigated the medical costs associated with hypoglycemic events based on the proportion of insurance claims which were stratified according to the type of resource  ,15. InpaA study conducted in Germany reported that 95% of T2DM patients receiving emergency care for hypoglycemia, required hospitalization, and that the average length of hospitalization was 9.5 days .Hypoglycemia contributes to direct and indirect medical costs. For example, hypoglycemia decreases work efficiency and productivity and, thus, indirectly generates non-medical costs, because expense loss occurs when working hours are reduced ,17.€4.25 million annually [A Swedish study reported that the hiring rate of patients with hypoglycemia was relatively low . Compareannually .Several studies have assessed the burden of hypoglycemia in the United States and Europe ,18. SomeThere are also meta-analysis articles for economic impact of hypoglycemia which performed literature reviews searching MEDLINE database using PubMed ,20. Theyp = 0.04) in comparison to that before hypoglycemic events. This finding is meaningful because it decreases the QOL of diabetic patients and may eventually affect direct or indirect medical costs of diabetic patients who experienced hypoglycemic events. This point has not been investigated in previous studies and was supplemented in the paper as one of the novelties of this study.However, this is the first study to examine hypoglycemia-related medical costs in adult Korean patients with T2DM, involving a large population of 4,350 subjects. The present study calculated the mean cost of hypoglycemic events, the number of hospital visits, and the length of hospital stays. Notably, not only the length of hospitalization but also the recuperation period that refers to the total period of treatment including the duration of drug ingestion or a course of injections was documented. Although the subject  has been studied before, there have been relatively few studies in Asia. The evidences generated from this study can be of important value to other future studies on the topic. Moreover, this is the first study to compare medical expenditures and recuperation periods before and after hypoglycemic events in the hypoglycemia-experienced group. There was no significant difference in medical expenditures and recuperation periods before and after hypoglycemic events in the hypoglycemia-experienced group. Nevertheless, the length of hospital stays after hypoglycemic events increased substantially (Hypoglycemia-related events are infrequently observed among the medical records of T2DM subjects. However, hypoglycemia was significantly associated with poor clinical outcomes and, thus, could raise a substantial burden on the national healthcare system of Korea.The assessment of hypoglycemia using the diagnostic codes from the HIRA database did not include all patients with hypoglycemia. And it is possible that the present study has a limitation of underestimating hypoglycemic events. When less severe hypoglycemic events that were not registered by the HIRA diagnostic codes, are considered, the hypoglycemia burden could be much greater.Our investigation might be useful for the assessment of the economics of health care costs. And proper diabetic interventions could reduce hypoglycemic events and essentially the economic burden on the national healthcare system of Korea.


In this paper, we present an integrative analysis approach that leverages eQTL data collected from multiple population groups. In particular, our approach effectively identifies multiple independent cis-eQTL signals that are consistent across populations, accounting for population heterogeneity in allele frequencies and linkage disequilibrium patterns. Furthermore, by integrating genomic annotations, our analysis framework enables high-resolution functional analysis of eQTLs. We applied our statistical approach to analyze the GEUVADIS data consisting of samples from five population groups. From this analysis, we concluded that i) jointly analysis across population groups greatly improves the power of eQTL discovery and the resolution of fine mapping of causal eQTL ii) many genes harbor multiple independent eQTLs in their  Expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression phenotypes. Mapping eQTLs enables us to study the genetic basis of gene expression variation across individuals. In this study, we introduce a statistical framework for analyzing genotype-expression data collected from multiple population groups. We show that our approach is particularly effective in identifying multiple independent eQTL signals that are consistently presented across populations in the proximity of a gene. In addition, our analysis framework allows effective integration of genomic annotations into eQTL analysis, which is helpful in dissecting the functional basis of eQTLs. Expression quantitative trait loci, or eQTLs, are genetic variants that are associated with gene expression levels. Mapping eQTLs can help in dissecting the molecular mechanisms by which genetic variants impact organismal phenotypes. Recent studies –3 have rjointly address three outstanding issues in eQTL mapping. First, due to the high experimental cost, most available eQTL data sets typically have limited sample sizes. To improve power of eQTL discovery, it becomes necessary to aggregate evidence across multiple data sets. Second, because a gene is typically regulated by many regulatory elements, it is highly likely that there exist multiple independent eQTLs in its proximity . In this scenario, a multi-SNP analysis is required to uncover all relevant cis acting genetic factors involved in the gene regulation process 21] throuϕ2/(ϕ2 + ω2) from a comprehensive set H = {ϕ2/(ϕ2 + ω2) : 0.0, 0.1, 0.2, …, 0.9, 1.0}. We then estimated the probability weight on each grid value by pooling information across all genes using the EM algorithm implemented in eQTLBMA. The estimates show that the majority of the probability mass is concentrated in very low heterogeneity levels (ϕ2/(ϕ2 + ω2)|Y, G] = 0.060, i.e., on average, the eQTL effects are very consistent across populations. This result is also highly concordant to the findings in [Using the hierarchical model proposed in , we perfy levels , and thedings in  where eQcis-SNPs is negatively correlated with the gene-level local false discovery rate (lfdr). That is, on average, genes harboring more cis-SNPs tend to exhibit stronger gene-level association signals. The Spearman’s rank correlation between the two quantities is modest (-0.20), nonetheless statistically highly significant .Among other factors impacting eGene discoveries, we noted that the number of cis-eQTL per gene”, and considered the full range of alternative scenarios described by model  in the gene-level analysis. Unconditional on γj, the prior on βj is a type of “spike-and-slab”, where the “slab” is represented by a mixture of multivariate normal distribution, and the vast majority of the prior probability mass, In our multi-SNP analysis, we no longer assumed “one by model . To makeΓ value, a Bayes factor, BF(Γ), contrasting to the trivial null model, Γ ≡ 0, can be analytically approximated by applying the result discussed therein. With this result, the posterior probability, Pr, can be computed up to an unknown normalizing constant, i.e., Pr ∝ Pr(Γ)BF(Γ). We implemented a Metropolis-Hastings algorithm, similar to what is discussed in [p possible Γ values. In particular, we designed a novel proposal distribution that utilizes marginal and conditional analysis results to prioritize SNPs with strong marginal or conditional association signals. In practice, we observed that the resulting MCMC algorithm achieves fast mixing. The details of the algorithm is provided in Γ samples from the posterior distribution, from which we computed the PIP for each SNP by marginalization.The described linear model system including the prior specification , is a special case of the general system considered by . Given aussed in  for multcis-eQTLs and its variance for each gene are obtained by computing the sample mean and variance of the number of non-zero γj’s in each posterior model. Equivalently, the posterior expected number of cis-eQTLs can be computed by the sum of PIPs, i.e.,The posterior expected number of independent cis-SNPs, HLA-DRB1 , took 30 minutes on a computer with a single Intel Xeon 2.13GHz 8-core CPU. For average eGenes with ∼ 2,000 cis-SNPs the running time is approximately 3 to 4 minutes on the same machine. We ran our fine mapping analysis for GEUVADIS data with 8 parallel threads, the overall computation took about 30 hours.For the fine mapping analysis of the GEUVADIS data, we applied the MCMC algorithm for each identified eGene individually. We carried out 25,000 burnin steps and 50,000 repeats for each MCMC run. Taking advantage of parallel processing, we performed multiple-SNP analysis for multiple genes simultaneously in a distributed computing environment, which greatly reduced the overall computational time. The MCMC run for the eGene with the most Although the MCMC output conveys the full information of the fine mapping results, parsing the information manually to identify independent eQTL signal clusters can still be non-trivial. To tackle this problem, we designed and implemented a simple hierarchical clustering based algorithm that automatically parses the MCMC output and aides identifying independent eQTL signal clusters. The details of the algorithm are described in the cis-SNPs in each gene were assembled using the real genotype data from the GEUVADIS project. More specifically, we randomly selected 100 genomic regions each containing 25 consecutive SNPs, and directly took the observed genotypes of those 2,500 SNPs from each GEUVADIS population. Consequently, there are generally modest to high levels of LD within each region, and unremarkable levels of LD between regions. The resulting genotype data also maintain the variations in genotype frequency and local LD patterns (within each 25-SNP region) across populations. For each gene, we randomly assigned 1 to 4 population-consistent eQTL SNPs and generate the expression levels for all five populations using a system of linear models  built upon the single SNP meta-analysis procedure in 2We evaluated the three different mapping methods by their abilities in correctly identifying the (25-SNP) regions harboring the true causal variants. For the Bayesian approach, we computed a regional PIP by summing over the SNP-level PIPs of each region and selected the regions whose regional PIPs exceed a pre-defined probability threshold. For the single SNP analysis, we selected regions whose minimum p-values of the including SNPs are below a pre-defined p-value threshold. Finally, we performed the conditional analysis by a forward step-wise selection procedure using a pre-fixed p-value threshold. For both conditional and single SNP analyses, the SNP-level p-value was computed from the fixed effect meta-analysis model , respectively.Additionally, we examined the calibration of the reported regional PIPs using the simulated data set . The resTo test the enrichment of a particular genomic annotation in eQTL signals, we examine the associations between SNP level annotations of interest and the PIPs from our multi-SNP fine mapping approach computed under the null hypothesis of no enrichment.j in gene g . Our testing procedure starts by computing PIPs for all gene-SNP pairs using the proposed multi-SNP cis-eQTL mapping method with the exchangeable prior specification , and then test their associations with the annotations of interest. In comparison, the use of PIPs in our proposed method presents at least two obvious advantages: first, it naturally accounts for potentially multiple association signals within a single gene; and second, it fully accounts for the uncertainty in determining The commonly applied enrichment testing approach typically classifies the latent association status of each SNP  by the parametric function  (e.g. a flat prior), the quantities on the right hand side are individually straightforward to compute analytically. It is conceptually easy to modify our MCMC algorithm for the multi-SNP analysis to jointly sample . Nevertheless, due to the extremely high dimensionality of the target space , the convergence of the MCMC within a reasonable time frame may be in doubt. Furthermore, the computational resources, especially the memory usage, demanded by the MCMC algorithm may be too high to afford in a practical setting.Let α by treating  as missing data, and perform a final round of multi-SNP analysis (possibly only for eGenes) conditional on the resulting MLE t+1)-th iteration, in the Expectation step (E-step), we evaluate αt)(. In the Maximization step (M-step), we find αt+1) value, and iteratively performs multiple cis-eQTL mapping using the MCMC algorithm and maximization by fitting logistic regression models until convergence. The main computational benefit of the “MCMC-within-EM” algorithm is that the E-steps involving MCMC runs can be processed in parallel on a distributed computing system, hence the memory requirement is much relaxed.Alternatively, we consider an empirical Bayes approach which employs an EM algorithm to find the MLE of α, we simulated a set of eQTL data using the same scheme in the enrichment testing. In particular, we used a single binary annotation and set α1 = 1.50. We ran the MCMC-within-EM algorithm on this simulated data set with the starting point α1 = 0, and plotted the estimated α1 values in each iteration in α1 quickly converge to the close neighborhood of the true value after 6 to 7 iterations.To illustrate the procedure for estimating To reduce computation, in practice we analyzed the GEUVADIS data using only a single iteration of EM where all enrichment parameters are initialized at 0. As a consequence, our reported point estimates of the enrichment parameters are likely to be lower bounds of the true values. Nevertheless, as we have shown in the analysis of the GEUVADIS data, this simplified version of the algorithm still improves the resolutions of the fine mapping analysis by up-weighting the SNPs with relevant annotations.S1 Text(PDF)Click here for additional data file.S1 Dataset(TXT)Click here for additional data file.S2 Dataset(TXT)Click here for additional data file.S1 Figcis-eQTLs, respectively. In each panel, three competing methods are compared: the proposed Bayesian multi-SNP analysis method (brown line), a conditional meta-analysis method (dark green line) and a single SNP meta-analysis approach (navy blue line). Each plotted point on each panel represents the number of true positive findings versus the number of false positive findings of a given method at a particular threshold. In the simulation study, the numbers of genes containing 1, 2, 3 and 4 cis-eQTLs are 579, 457, 307 and 157, respectively. For genes that harbor only a single eQTL, all three methods yield similar performance. However, when there exist more eQTLs in a gene, the proposed Bayesian multi-SNP approach shows superior power over the two competing procedures.Panels A, B, C and D show simulation results for genes containing 1, 2, 3 and 4 (TIFF)Click here for additional data file.S2 FigUsing the simulated data set, we grouped the reported 1500 × 100 regional PIP values, ranging from 0.0 to 1.0, into 20 bins of width 0.05. For each bin, we plot the mean PIP value and the corresponding frequency of true causal regions represented in the bin. All points are closely located to the diagonal line, indicating that the reported PIPs are relatively well-calibrated.(TIFF)Click here for additional data file.S3 FigIn this simulated data set, the true enrichment parameter is set to 1.50 . The algorithm initiate the parameter value at 0 and quickly reaches to the close neighborhood of the true value.(TIFF)Click here for additional data file.S1 TableThe estimates are obtained from the hierarchical model described in , 21. For(PDF)Click here for additional data file.S2 Tablep-values in each gene, and classifies the top associated SNPs as the “causal” eQTLs if their p-values pass the significance threshold at FDR 5% level. Both methods (conservatively) control the type I errors at desired 5% level. The PIP based method show much improved powers comparing to the standard approach in all three alternative scenarios where the magnitude of enrichment ranges from small to modest.The Bayesian method utilizes PIPs from the multi-SNP eQTL fine mapping analysis. The approach in comparison ranks the SNPs by their single SNP testing (PDF)Click here for additional data file.


Amplicon re-sequencing based on the automated Sanger method remains popular for detection of single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (InDels) for a spectrum of genetics applications. However, existing software tools for detecting intra-individual SNPs and InDels in direct amplicon sequencing of diploid samples are insufficient in analyzing single traces and their accuracy is still limited.We developed a novel computation tool, named DiSNPindel, to improve the detection of intra-individual SNPs and InDels in direct amplicon sequencing of a diploid. Neither reference sequence nor additional sample was required. Using two real datasets, we demonstrated the usefulness of DiSNPindel in its ability to improve largely the true SNP and InDel discovery rates and reduce largely the missed and false positive rates as compared with existing detection methods.The software DiSNPindel presented here provides an efficient tool for intra-individual SNP and InDel detection in diploid amplicon sequencing. It will also be useful for identification of DNA variations in expressed sequence tag (EST) re-sequencing.The online version of this article (doi:10.1186/s12859-015-0790-y) contains supplementary material, which is available to authorized users. The learning steps are as follows.Values between 0 and 1 were randomly assigned for initial weights and thresholds assuming a maximum error ε = 0.1.kth sample, where v is the input vector, w is the weight vector, and b(1)2 is the threshold of neuron yl. The error is thus calculated as ek = dk − yk, where dk is the score calculated from the simulated data.Compute the BPNN output, e.g., N is the number of samples.Compute the sum of square errors V(w) < ε, turn to step 7 below. Otherwise, compute the Jacobian matrix J(w).If w using the LM equation as stated above.Compute ∆w(t + 1) = w(t) + Δw and compute the new sum of square errors V(w(t + 1)) similarly as in step 3 above. If V(w(t + 1)) < V(w(t)), set V(w(t)) = V(w(t + 1)), w(t) = w(t + 1) and μ = μ/β (β is a correction factor) and turn back to step 4; Otherwise, suppose μ = μ · β and turn back to step 5.Let Reach the optimal weights and end the training process.where Finally a true SNP was trained to a score ≥75, a vague SNP to a score smaller than 75 but no less than 60, a false SNP to a score smaller than 60 but no less than 20, and a strongly false SNP to a score <20. In addition, using another set of 147 simulated samples, the performance of the trained LM-BPNN was tested.A six-grade classification was established for the convenience of SNP diagnosis Fig. , that ism  bases to reach the maximal matchability and allowing for base transposition between top and secondary peaks at a potentially misleading position. The final m value indicates an InDel of the m bases, and its signal + or – represents the presence of an insertion or deletion as compared to the alternative sequence.A stepwise allelic base alignment algorithm was employed for intra-individual InDel detection. A maximal InDel size was set at 30 bases according to Bhangale et al. . The preEucalyptus EST amplicons  was reserved to display the base position and the peak features. Figure Totally 110 iterations were performed for LM-BPNN training. The mean squared error (MSE) decreased rapidly and reached a stably low level after about 13 iterations in the training and validation, indicating a strong convergence Fig. . In partWe compared our software with Mutation Surveyor, novoSNP and PolyPhred v6.18) in intra-individual SNP diagnosis. As Polyphred was limited in analyzing a single trace, it did not detect any SNPs for all the ranks (1–6) and was excluded from subsequent comparisons. Of the total of 66 CAPS-related SNPs validated experimentally, our software showed the highest rate of found SNPs and the lowest rate of missed SNPs even at the highest threshold when compared to the relaxed thresholds of novoSNP and Mutation Surveyor. For instance, the found SNP rate was 90.9 % (60/66) at grade 1 in DiSNPindel, much higher than that of the most relaxed threshold in novoSNP (37.9 % at score 1) or Mutation Surveyor  and PrimeIndel, except Polyphred that could not detect any InDels, DiSNPindel resulted in the highest true InDel rate 53.1 %) but the least missed 22.2 %) and false positive (0 %) rates, plus the highest rate (24.7 %) of size-correct but base-wrong InDels  is freely available to all readers at Project name: DiSNPindelProject home page:http://www.ritf.ac.cn/sitecn/FZBJKYCG/1377.htmlOperating system(s): Windows XP or higherProgramming language: MatlabLicense: noneAny restrictions to use by non-academics: none


Anopheles gambiae species complex are primary vectors of human malaria in Africa. Population heterogeneities for ecological and behavioral attributes expand and stabilize malaria transmission over space and time, and populations may change in response to vector control, urbanization and other factors. There is a need for approaches to comprehensively describe the structure and characteristics of a sympatric local mosquito population, because incomplete knowledge of vector population composition may hinder control efforts. To this end, we used a genome-wide custom SNP typing array to analyze a population collection from a single geographic region in West Africa. The combination of sample depth (n = 456) and marker density (n = 1536) unambiguously resolved population subgroups, which were also compared for their relative susceptibility to natural genotypes of Plasmodium falciparum malaria. The population subgroups display fluctuating patterns of differentiation or sharing across the genome. Analysis of linkage disequilibrium identified 19 new candidate genes for association with underlying population divergence between sister taxa, A. coluzzii (M-form) and A. gambiae (S-form).Members of the  Anopheles gambiae species complex are primary vectors of the human malaria parasite, Plasmodium falciparum, which is responsible for extensive human morbidity and mortality. Heterogeneity within the A. gambiae complex for ecological preference, feeding behavior, and Plasmodium susceptibility stabilize and expand the malaria vectorial system in nature . Th. ThA. conce kdr, . Similarnce kdr,  with no P. falciparum as compared to A. coluzzii and A. gambiae (p<1*10−4), consistent with previous observations [P. falciparum infection susceptibility between A. coluzzii and A. gambiae (p = 0.31), which is in accord with multiple published reports [The Goundry subgroup displays significantly higher susceptibility to infection with wild rvations  but here reports –39.A. coluzzii/gambiae-related elements such as SI [A. coluzzii and A. gambiae across most of the genome  has been previously implicated in A. gambiae/A. coluzzii differentiation [A. coluzzii and A gambiae populations at other sites where they are sympatric. In contrast to the above among-subgroup analysis, LD signals within population subgroups appeared as expected for the SNP marker density, detectable mainly at centromeres and segregating inversions  A. gambiae/A. coluzzii differentiation. Sequenom genotyping assays were developed and 80 individual samples were genotyped  karyotype of the 2La inversion, ii) genotype of the enotyped . GenotypWe sampled a local West African mosquito population over time and genotyped it with a large number of genome-wide markers selected for information content, but without regard to gene functional category. This approach yielded a comprehensive characterization of local population substructure, an important prerequisite for accurate assessment of vector control interventions, as well as for association studies linking measured phenotype to underlying genotype. The use of 800 markers in a ~280 Mb genome was more than sufficient to detect the level of population subdivision that, if left undetected, would likely lead to spurious results in a genome wide association study . As few Although whole-genome resequencing has become more accessible, nevertheless the analysis of >400 mosquitoes from one geographic site by resequencing for a single project would be costly. The SNP genotyping results obtained here have been used to identify small numbers of candidate ancestry-informative SNPs for different attributes . HoweverA. gambiae and A. coluzzii genome sequence. Some of these variants display under or over enrichment in Goundry and can be used for a partially-efficient probabilistic assay, but by definition the Goundry fixed differences that would be most informative tool cannot be identified from non-Goundry sequence, and must await whole genome sequences from Goundry mosquitoes.Regarding the Goundry form, the desirable SNPs for a diagnostic assay would be the fixed differences present in Goundry and absent from non-Goundry individuals. SNPs identified from the current study were ascertained from available A. coluzzii and A. gambiae are not known, but appear to be largely prezygotic because the species hybridize in the laboratory [A. coluzzii and A. gambiae are the SI in the centromeres [A. coluzzii and A. gambiae. None of these SNPs occur in the 2R non-centromeric island published by Turner et al. [Tep3, and a 100kb genomic region containing Tep3 was previously highlighted as differentiated between A. gambiae and A. coluzzii by White et al. [A. coluzzii and A. gambiae mosquitoes.The mechanisms of mating isolation and assortative mating between boratory , 47. Thetromeres , 32, butr et al. . Five ofe et al. . Thus, wA. coluzzii and A. gambiae mosquitoes that are proposed to underlie the production of different wingbeat harmonic frequencies, thus permitting mate discrimination by A. coluzzii and A. gambiae mosquitoes [A. coluzzii and A. gambiae for oviposition site choice, formation of mating swarms, or other phenotypes [A. coluzzii and A. gambiae. Because we analyzed sympatric mosquitoes collected from a single defined geographic region, geographic variables do not underlie the differentiation signal, although the results cannot necessarily be generalized to populations in other regions of West Africa without sampling and testing at other sympatric sites.Of the 19 newly-identified non-centromeric genes , one hassquitoes , 49. Twosquitoes –53), alosquitoes , 54, 55.enotypes , 23, 56.A. gambiae species complex mosquitoes were brought to the insectary in Ouagadougou where they were raised under standard laboratory rearing conditions to adulthood. Following emergence, 3 day old adults were challenged with wild P. falciparum by experimental infection. Feeding was done on an artificial membrane in a water-jacketed feeding device as described previously using gametocytemic blood obtained from study participants [Mosquitoes were sampled as larvae using the standard dipping method or as adults by aspirator catch, as previously described in detail . Mosquiticipants . Unfed mA. coluzzii and A. gambiae genome sequences available at Vector Base. At the time of the chip design, the A. coluzzii and A. gambiae genome assemblies were not available at VectorBase and raw sequence read data was used for SNP design. SNPs were identified by alignment of the A. coluzzii and A. gambiae sequence reads against the assembled genome of the PEST strain using BLAST. We summarized all high confidence alignments in a simple frequency table. For every position in the PEST genome we recorded the number of A,G,C,T nucleotides observed for that position. To be considered viable for inclusion on the chip, a SNP had to meet the following criteria: i) have a minimum read depth of 10, ii) be surrounded by ~200 bp of SNP free-sequence, iii) be variable across any set of samples used for SNP ascertainment, iv) have a minor allele frequency of at least 15%. We submitted to Illumina 5995 candidates, 4840 from shotgun sequence and 1155 from 3 independent deep re-sequencing projects. The final catalog of 1536 SNPs was selected from 3394 SNPs that passed Illumina design criteria, 1358 from shotgun sequence and 178 from deep sequencing projects. The complete set of SNPs typed on the Illumina chip and their primers is available in A. gambiae decays to uninformative levels on average within <500 bp [To design the custom SNP chip, polymorphism data were combined from individual sources , 55, 58  <500 bp . HybridiDue to the low quantity of DNA available from individual mosquitoes, all DNA samples were subjected to whole genome amplification  using supplied protocols. DNA was then ethanol precipitated, concentrations determined by the Picogreen method  and 500 Data were analyzed using the BeadStudio package (Illumina) following the manufacturer's guidelines . QualityA subset of samples (n = 24) were hybridized in duplicate, and over 99% of called genotypes were concordant. For additional validation of genotype calls using an independent technology and to test a set of SNPs with high informative value for molecular attributes, a subset of 21 SNPs were converted to Sequenom assays and 80 mosquito samples genotyped by this independent method. Across all 21 SNPs, the genotype concordance between Illumina and Sequenom averaged 95.5%, ranging from 89% to 99% . SequenoA. gambiae, A. coluzzii and the Goundry form.To test for differences in infection susceptibility across subgroups, analyses were carried out with infection as a blocking factor, and p-values were determined for each individual infection using the Chi Square test and combined p-values across infections via the method of R.A. Fisher . Most ofLinkage disequilibrium (LD), as analyzed and depicted in Figs A. coluzzii and A. gambiae mosquitoes. Centromeric regions were defined as +/-5Mb from the centromere for a total area of 10Mb, 5Mb on each chromosome arm. Initially, we determined the individual SNP that was in LD (r2>0.8) with the maximum number of other SNPs across the genome, imposing a SNP inclusion cutoff at minor allele frequency ≥10%. This SNP was on the X chromosome at position 23852135. This region of the X chromosome is the most informative for assignment of A. coluzzii and A. gambiae [2>0.5, minor allele frequency ≥10%. These SNPs, each individually highly correlated with the X.23852135, are presented in To identify SNP genetic correlation across chromosomes as shown in the centromeric regions boxes in , a selec gambiae . This SNP. falciparum gametocyte carriers for experimental membrane feeder infection of mosquitoes, the study protocol was reviewed and approved by the national health ethical review board IRB  of Burkina Faso, which issued ethical protocol N° 2006–032 for the described studies. The study procedures, benefits and risks were explained to subjects and their written informed consent was obtained. The consent procedure was approved by the IRB. Subjects who had given consent were brought to CNRFP the day of the experiment for gametocyte carrier screening. All children were followed and symptomatic subjects were treated with the combination of artemether-lumefantrine (Coartem) according to relevant regulations of the Burkina Faso Ministry of Health.For collection of blood from S1 FigA. coluzzii and A. gambiae (see SNPs indicated in red are the set of 66 that show greatest differentiation between biae see .(EPS)Click here for additional data file.S2 FigTwenty-one SNPs were typed on a set of 80 individual mosquito samples. Individual SNPs are shown on the x-axis, and concordance rates between genotype calls from the two distinct technologies are indicated on the y axis.(EPS)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.


In mammals the regulation of genomic instability plays a key role in tumor suppression and also controls genome plasticity, which is important for recombination during the processes of immunity and meiosis. Most studies to identify regulators of genomic instability have been performed in cells in culture or in systems that report on gross rearrangements of the genome, yet subtle differences in the level of genomic instability can contribute to whole organism phenotypes such as tumor predisposition. Here we performed a genome-wide association study in a population of 1379 outbred Crl:CFW(SW)-US_P08 mice to dissect the genetic landscape of micronucleus formation, a biomarker of chromosomal breaks, whole chromosome loss, and extranuclear DNA. Variation in micronucleus levels is a complex trait with a genome-wide heritability of 53.1%. We identify seven loci influencing micronucleus formation , and define candidate genes at each locus. Intriguingly at several loci we find evidence for sexual dimorphism in micronucleus formation, with a locus on chromosome 11 being specific to males. BRCA1, BRCA2, ATM, BUB1, and RAD51, are highly predisposed to tumorigenesis due to a significant increase in their basal somatic mutation rate -US_P08] were purchased from Charles River, Portage, at 4–7 wk of age and shipped to MRC Harwell, Oxfordshire, UK. Mice were housed in IVC cages (three per cage) on an P < 0.05) being included in a linear regression model from which residual measures were obtained. The linear model used for the micronucleus measure was the following: Micronucleus_level ∼1 + Sex + (1|Batch) + Year_of_measure. The residuals were quantile-normalized and used for genome-wide association testing. For the sex-specific genome-wide association testing, sex was omitted from the covariates in the modeling of the micronucleus measure.Analysis of micronucleus levels was performed using the R statistical analysis software package using purpose-written software available on request from the authors. Outliers, defined as data points that were more than three standard deviations from the mean, were excluded. The effects of covariates such as sex, body weight, and batch on micronucleus level were assessed by analysis of variance (ANOVA), with each explaining more than 1% of the variance at a significant level WtsiTrex1, tm1a(KOMP)WtsiNfkbil1, tm2a(EUCOMM)WtsiTrub2, and tm1a(KOMP)WtsiH2-Eb1) coming from the Sanger Mouse Genetics Project  with sites that were also polymorphic on a genotyping microarray (Details of QTL mapping are fully described in Putative SNPs were annotated using Annotate Variation (ANNOVAR) (http://sep2013.archive.ensembl.org/info/genome/compara/epo_anchors_info.html)]. A score for sequence constraint was also reported derived from stretches of the local, multiple alignment around each SNP .For each SNP position, a GERP sequence conservation score , noncoding SNPs were assessed for potential disruption of binding sites.Both coding and noncoding sequence nucleotide variants (SNVs) were analyzed using the Ensembl Variant Effect Predictor (VEP)  was usedA more detailed analysis to that performed for all the SNPs was performed for nonsynonymous SNPs predicted by ANNOVAR. GERP . In addiWith respect to protein-based analyses, VEP was configured, to report whether a coding SNP lay within a protein domain. As before, SIFT consequences  were repIn addition the effect of amino acid changes were assessed using the Grantham matrix . A SNP wThese UCSC gene identifiers were first used to link to Ensembl mouse transcript identifiers and then to Ensembl gene identifiers. The Ensembl gene identifiers were used to query the Ensembl Compara database for protein alignments across seven species; human, rat, dog, chicken, pig, cow, and platypus.For computational efficiency the Ensembl Compara software pipeline selects a single  transcript for each gene on which to compute homologies across species. Similarity is determined by aligning protein alignments of genes. Therefore, for genes that have multiple translations, features of interest can occur in transcripts from which it is not possible to determine their context in terms of homology via Ensembl. This is the case here for coding SNPs reported by ANNOVAR. The set of UCSC proteins used included proteins that were not considered the canonical transcript by Ensembl and upon which homologies were computed. Hence for ANNOVAR, predicted coding SNPs reported on a transcript not precomputed in Ensembl, these were reported as ‘UCSC_protein_not_used_for_homology_in_Ensembl.’ Also there can be examples where the gene in which the SNP lies has no sequence homology to another species; these were tagged as ‘no_mouse_sequence_in_Ensembl_homology.’For UCSC-based SNPs present in the Ensembl Compara database . Micronucleus levels were sexually dimorphic (c-/c-(C57BL/6-TyrC-Brd)] and 129S5SvEvBrd showing significantly lower micronucleus levels than male littermates .To determine whether genetic diversity drives differences in micronucleus levels in mice, we first analyzed males from four inbred strains  finding imorphic , with feFigure S1). Body weight and a full hematological profile were also measured at the time of blood collection. DNA was obtained from 1379 of these mice  and, following sparse whole-genome sequencing at an average coverage of 0.15X (range 0.06X to 0.51X), genotype probabilities were imputed at 7,073,398 SNPs segregating within this population  .To map QTL we used a subset of 359,559 SNPs tagging all other SNPs with a minor allele frequency (MAF) >0.1% at LD P-value of 13.05 , we found small but highly significant positive correlations between micronucleus level and several hematological measures; the red blood cells distribution width , the hemoglobin concentration distribution width , the mean cellular hemoglobin concentration , and the measure of blood hemoglobin . Elevation of RDW, which is a measure of the variance of red blood cell width, often occurs together with elevated HDW, which measures the variation in hemoglobin content of red blood cells. For example, iron deficiency may cause a reduction in hemoglobin production (elevated HDW), which causes smaller red blood cells (elevated RDW). Elevation of RDW and/or HDW is a characteristic of anemia, a condition that may be caused by genomic instability ]. We also mapped a micronucleus QTL at this locus, that contains only three genes within its 95% CI, including Slc7a1. Mice carrying a homozygous deleterious mutation of this gene die of anemia at birth with 50% fewer red blood cells and reduced hemoglobin levels  . The QTL on Chr 8 is present in both sexes while the QTL on chromosomes 2, 5, and 9 do not reach genome-wide significance (FDR < 5%) when males and females are tested separately, presumably due to a lack of power  were detected only in females and one, on Chr 11, only in males . We testof power .Table S1) with annotations associated with DNA repair: Wrn, Hspa1b, Hspa1a, and Hspa1l (GO 0006281). Other genes such as Aurora kinase B (Aurkb) have established roles in the regulation of processes such as the cell cycle (Gene ontology (GO) analysis revealed four genes under the micronucleus QTLs  at each QTL. From our set of 5,766,828 SNPs we annotated all variants in the 95% CI at each QTL provided they had a Wrn, Atrip, Trex1, and Aurkb genes, which, as noted above, have annotated roles in the regulation of genomic stability making these variants high-value candidates. For Aurkb the SNP identified (rs29417126) results in a F45S change, and falls into a highly conserved region of the protein . For many of the genes, phenotypes/functions associated with genomic instability have been assigned (Table S2). We then attempted to find genes associated with elevated levels of micronuclei. To do this we searched for live mice generated by the Wellcome Trust Sanger Mouse Genetics Project  available for testing using the micronucleus assay. Our analysis of potentially pathogenic variants within QTL  as a candidate variant. TREX1 is a major 3′-5′ DNA exonuclease linked to systemic lupus erythematosus and also Aicardi–Goutières syndrome .We next took advantage of both published and novel mouse knockout data to study genes within our QTL intervals. Over 1800 mammalian phenotype (MP) terms have been assigned to the 197 genes within the QTL, including some associated with genomic instability, spindle abnormalities, and defects in replication  in which chromatin (H2B) was labeled with mCherry in the context of TREX1 disruption, and in a matched control (TREX1 wild-type) line  line . Analysiin vivo genetic screen for mediators of micronucleus formation and identify seven loci that reach genome-wide significance. At these loci we mapped genes that have established roles in DNA repair or the regulation of the cell cycle, and we annotated variants at these loci to identify possible candidates associated with elevated micronucleus levels. One locus also affects two hematological measures and contains a gene (Slc7a1) causing severe anemia in the mouse. Further, functional evidence in vivo in a mouse mutant supports the role of Trex1 in the formation of extranuclear DNA.We report an Aurkb gene being male-specific. In the case of erythrocyte micronucleus levels it seems unlikely that this phenotype is mediated by sex hormones or anatomical differences between male and female mice, although these factors could possibly contribute. Interestingly, men have a higher incidence of and mortality rate from sex-unspecific cancers, a fact that is unexplained by known risk factors (We found evidence for a role of sex in the formation of micronuclei, with a locus on chromosome 11 containing the What might explain the differences in micronucleus levels in male and female mice that we observed? It has previously been shown that the regulatory genome is sexually dimorphic . In addiCollectively, this work reveals the landscape of micronucleus formation in the mouse and the value of studying traits in an outbred mouse population, and at the whole organism level.


ApeKI libraries using DNA from 48 genotypes each of two heterogeneous populations of tetraploid alfalfa (Medicago sativa spp. sativa): the synthetic cultivar Apica (ATF0) and a derived population (ATF5) obtained after five cycles of recurrent selection for superior tolerance to freezing (TF). Nearly 400 million reads were obtained from two lanes of an Illumina HiSeq 2000 sequencer and analyzed with the Universal Network-Enabled Analysis Kit (UNEAK) pipeline designed for species with no reference genome. Following the application of whole dataset-level filters, 11,694 single nucleotide polymorphism (SNP) loci were obtained. About 60% had a significant match on the Medicago truncatula syntenic genome. The accuracy of allelic ratios and genotype calls based on GBS data was directly assessed using 454 sequencing on a subset of SNP loci scored in eight plant samples. Sequencing depth in this study was not sufficient for accurate tetraploid allelic dosage, but reliable genotype calls based on diploid allelic dosage were obtained when using additional quality filtering. Principal Component Analysis of SNP loci in plant samples revealed that a small proportion (<5%) of the genetic variability assessed by GBS is able to differentiate ATF0 and ATF5. Our results confirm that analysis of GBS data using UNEAK is a reliable approach for genome-wide discovery of SNP loci in outcrossed polyploids.Genotyping-by-sequencing (GBS) is a relatively low-cost high throughput genotyping technology based on next generation sequencing and is applicable to orphan species with no reference genome. A combination of genome complexity reduction and multiplexing with DNA barcoding provides a simple and affordable way to resolve allelic variation between plant samples or populations. GBS was performed on  Medicago sativa spp.) is a perennial forage legume grown over 32 million ha worldwide [Alfalfa  technologies allow the characterization of genetic variation such as SNPs on a genome-wide scale. However, genome-wide sequencing of large and complex genomes of allogamous forage species is difficult to achieve even with NGS and requires complexity reduction . GenotypIn the present study, our objectives were to: 1- Use GBS for genome-wide SNP discovery in broad-based populations of alfalfa; 2- Assess the reliability of GBS genotype calls by comparing them with genotypes inferred after 454 resequencing of a subset of SNP loci and; 3- Compare the distribution of genotypes from a recurrently-selected TF population and its initial background in a multivariate space defined by genome-wide sampling of SNP loci.M. sativa ssp. sativa) cultivar Apica (ATF0) adapted to harsh winter conditions of eastern Canada [Forty-eight (48) genotypes each of the alfalfa (n Canada  and the n Canada ) were usn Canada . DNA conApeKI GBS library was prepared by the Plateforme d’analyses génomiques (Institut de Biologie Intégrative et des Systèmes (IBIS), Laval University, Quebec City, QC, Canada) using 100 ng each of the 96 DNA samples from the ATF0 and ATF5 populations essentially as previously described [A 96-plex escribed . Additioescribed . The resv3.0) [g) <0.1 (heterozygotes) were considered as missing (N).Illumina reads were processed using the Universal Network-Enabled Analysis Kit (UNEAK) for species without a reference genome implemented on TASSEL software (v3.0)  using dev3.0)  to removM. truncatula reference genome was evaluated using the Basic Local Alignment Search Tool (BLAST+ v2.2.28) using 1 x 10-8 as cut-off E-value. For SNP loci with multiple hits on the M. truncatula genome, the single highest score was retained. SNPs having multiple hits with identical score and SNPs having the two alleles mapping at different positions were discarded. BLAST searches were performed on the M. truncatula genome (v4.0) [The localization of each SNP locus on the e (v4.0) .The proportion of shared SNP loci captured by GBS in two different alfalfa germplasm was evaluated by performing a BLAST search of the 11,694 SNP loci identified in the current study (with cv. Apica) against the 301,258 SNP loci identified using a similar approach in an alfalfa mapping population .M. truncatula genome (v4.0) were sequenced on a 454 GS-FLX machine using the Titanium chemistry . To minimize ambiguities due to 454 sequencing errors, amplified regions were selected to contain a target SNP and no mononucleotide repeats greater than 5 bp in length in the first 100 bp. Specific primers were designed using the Primer3Plus software [M. truncatula (v4.0) genomic sequence as reference. Information on the location of the targeted regions on the M. truncatula genome (v4.0) [To assess the accuracy of the estimated frequency of alleles in individual samples based on GBS data, eleven amplicons of ~550 bp encompassing a total of 14 SNP loci that each mapped to a unique position on the software  and the e (v4.0) , the tar3130xl DNA Sequencer .PCR amplifications were initially performed on bulked DNA of the same 48 samples from each population (ATF0 and ATF5) to verify the specificity of the 11 amplicons. PCR amplifications were conducted in a total volume of 25 μL in 0.2 mL PCR strips containing 2.5 μL of 10X PCR buffer, 1 μL each of 5 μM primers, 0.5 μL of 10 mM dNTP , 0.5 μL of 5 PRIME GmbH Taq Polymerase 5 U/μL  and 5 μL of 10 ng/μL DNA (50 ng). PCR amplifications were performed on an Eppendorf Mastercycler ep System  as follows: 3 min of initial denaturation at 94°C; followed by 40 cycles at 94°C for 1 min, annealing at Tm for 1 min, extension at 72°C for 1 min; and final extension at 72°C for 7 min. The specificity of amplification was confirmed by running 20 μL of each reaction for 2–3 h at 70 V on a 2% agarose gel stained with ethidium bromide and products were visualized using a UVP BioDoc-It system . DNA fragments were recovered from agarose gels using the QIAquick gel extraction kit . A consensus sequence of each amplicon was obtained by bidirectional Sanger sequencing on an ABI Amplification of the 11 genomic regions was subsequently performed on a subset of eight plant samples previously analyzed with GBS under the conditions previously described, using PCR primers including universal M13 adaptors. PCR products were purified using the AxyPrep Mag Fragment Select-I Kit , following the manufacturer’s recommendations, and DNA concentration was determined on a Promega GloMax Multi Detection System using the Quantifluor dsDNA kit . For each plant sample, the eleven PCR products were pooled in equimolar amount. Pooled PCR products were barcoded for sample multiplexing prior to sequencing on a 454 GS-FLX Titanium system . All sequences were submitted to the National Center for Biotechnology Information Short Read Archive under the accession number #SRX1024927.http://www.geneious.com/). The number of 454 reads displaying 64-bp identity with one of the two alleles of the GBS SNP loci was determined. Read counts supporting each allele of GBS SNP loci were also used to calculate allelic dosage (RC of A1/RC of A1+A2) in each sample. Genotype calls based on 454 allelic ratios were compared with those obtained from GBS analysis using the following criteria: heterozygous for samples with 0.1 > allelic ratio < 0.9; homozygous for samples with allelic ratio ≤ 0.1 or ≥ 0.9. GBS alleles with only one read were considered as being absent. In each plant sample, all SNPs present within the first 400 bp of the 454 sequences (0.05 minimum frequency in at least one plant sample) were used to define haplotypes (417 bp were used for TP91313 to include the ApeKI restriction site which generates the GBS fragment). The number of 454 reads supporting each haplotype with one allele of the GBS SNP loci (64bp identity with the GBS sequences) and a 0.05 minimum frequency in at least one plant sample were used to determine haplotype counts for each amplicon. Sequences containing additional SNPs or indels in the 64 bp of the GBS sequence were analyzed separately using the same criteria.Reads were separated by barcode and aligned to the Sanger consensus sequence of each amplicon using the Geneious software .The plant sample distribution in the multivariate space defined by genotypes at SNP loci was inferred by Principal Component Analysis (PCA). PCA was performed with the covariance method (TASSEL v4.3.5) using the initial dataset obtained after the application of whole dataset-level filters and the subset of SNP loci retained after additional genotype-level filtration. Individual genotypes were converted into numerical data using TASSEL with the “Collapse Non Major Alleles” method. Missing data were imputed using unweighted Manhattan distance for samples with maximum missing data frequency set to 0.5 for both datasets. A graphical representation of the sample distribution along the first three axes of PCA was achieved using the PCA 3D Visualiser developed by Prism Training & Consultancy  showed that SNP loci were supported by a similar number of total reads per sample  or per locus  in each population . The freA total of 2,732 SNP loci were retained after the application of a genotype level-filter to the initial 11,694 SNP loci dataset . The pro-8) on the M. truncatula genome v4.0, including 5,952 SNP loci with a single hit and 1,110 SNP loci with multiple hits (M. truncatula chromosomes (Mt1 to Mt8) were covered with many hundred SNP loci (682 to 949). On average, a single-hit SNP loci was observed every 55 to 145 Kb. Finally, 88 single-hit SNP loci aligned to unmapped genome fragments of the M. truncatula genome.About 60% of the 11,694 SNP loci had significant matches  had a perfect sequence identity for both alleles, and 1,017 (18%) had an identical sequence for one allele and a variant sequence for the second one.About 50%  of the 11,694 SNP loci identified in the present study with the Apica germplasm were also found in the GBS analysis of a mapping population described  S1 Tabl, and 935M. sativa consensus sequences of these products with their respective M. truncatula reference sequences showed high similarity , confirming that the amplification products matched the targeted regions  sequences were retained for further analysis. Rejected sequences were mostly short reads (about 90% ≤ 200 bp) (data not shown). Similar sequencing depths were observed between samples  were detected in the first 400 bp of the retained sequences and were used to define haplotypes as illustrated in M. truncatula reference sequence is provided in About 60%  of the 454 sequences had a perfect 64-bp identity with one or the other allele of the 14 SNP loci used for validation. This proportion was highly variable depending on the considered SNP loci  .vs. 454) in more than 75% of cases  increased the proportion of concordant vs. discordant genotype calls between the two sequencing methods  RILs and doubled haploid barley (Hordeum vulgare) lines. Similarly, we observed considerable variation in the number of reads per sample even if no consistent explanation of this uneven coverage was identified in our study. However, samples with low read counts had a major impact on the number of SNP loci that were retained after filtering out loci with high number of missing genotypes. The exclusion of samples with less than 1 million reads resulted in a 30% increase in the number of SNP loci that were retained after filtration. This highlights the importance of read depth and homogenous sequencing among individuals for an optimal analysis of GBS data without a reference genome.Elshire et al.  observedHumulus lupulus L.) [The distribution of read counts per SNP locus was not uniform either, and a large proportion of genotype calls in both populations were supported by low read counts (10 or less). The distribution frequency of read counts supporting genotype calls in our populations was similar to the observation of highly variable genome coverage at distinct loci in RILs of maize  and genoulus L.) . The uneulus L.) .M. truncatula genome (v4.0) had a single match. This is in agreement with the observation that more than 85% of the SNP loci retained by UNEAK aligned with single locations in the maize genome [A large proportion of the ~640,000 tags that were found to differ by a single nucleotide from another tag were also able to form networks with additional tag variants, which was expected in a population of highly heterozygous tetraploid alfalfa. The main limitation of genetic analysis in complex polyploid genomes is the capacity to distinguish true alleles at a single locus from those arising from paralogs and duplicates . Using Ue genome .We used haplotype analysis based on 454 sequences to assess how the complexity behind GBS SNP loci such as repeats, paralogs and sequencing errors is handled by UNEAK in complex populations of tetraploid alfalfa. Although a large proportion of the 454 sequences (60%) showed a perfect match with one of the targeted SNP alleles, nearly 30% of the sequences imperfectly matched GBS alleles due to sequencing errors while 10% were either additional alleles or paralogs. This high proportion of sequence with an imperfect match is due to the combination of our requirement for a 64 bp identity with GBS alleles and 454 tendency to generate artefactual SNPs or indels particularly in regions with mono-nucleotide repeats. Notwithstanding, the elimination of sequences with an imperfect match had no major impact on the representation of haplotypes containing GBS alleles, except for TP32628 where the presence of a “CCYCC” motif generated more frequent 454 sequencing errors in allele 2 than in allele 1 .Analysis of 454 sequences revealed a large haplotypic diversity underlying GBS SNP loci, underscoring the fact that bi-allelic sampling is a simplified representation of genetic diversity in heterogeneous populations of tetraploid alfalfa. Up to eleven different haplotypes at a given targeted SNP loci were identified in eight plant samples. This either reflects allelic diversity at a single locus or the presence of paralogs. The fact that four haplotypes or less were present in all individual plant samples for most GBS loci (maximum of one haplotype per homologous chromosome) supports the efficiency of UNEAK to reduce false SNP calls from paralogs. We nevertheless observed cases where GBS alleles shared by paralogs could not be eliminated by UNEAK . This occurred when a SNP within an ApeKI restriction site prevented sampling of those haplotypes. For instance, mutations at positions 131 and 242 in the sequence covering TP7278 excluded haplotypes 1, 2, 3 and 5 from read counts for that TP. Although mutations of ApeKI restriction sites impacted allelic dosage at a tetraploid level, they seldom altered biallelic genotype calls.M. truncatula) to design locus-specific PCR primers for 454 re-sequencing of a tetraploid relative (M. sativa) could provide a partial representation of the existing diversity. For instance alternative alleles displaying polymorphisms in the annealing regions of the primers may not have been sampled or may have been less efficiently amplified.Conversely, there were instances of unambiguous heterozygous genotypes calls based on GBS that were not confirmed by 454 re-sequencing (e.g. TP46847). This shows the limitations of 454 re-sequencing approach as a validation for GBS analysis. It should be kept in mind that the use of a diploid genome  were validated using 454 sequences having a perfect match with the targeted SNP loci. Aside from the previously reported 454 limitations, the low concordance of allelic ratio estimates is mainly attributable to insufficient GBS read depth in an outcrossed polyploid, and to some extent to allele missampling. In heterogeneous cultivars of tetraploid potato, it has been estimated that a sequencing depth of 60-80x is required for reliable dosage . As mostM. truncatula genome at an E-value < 1 x 10-8. This proportion rose to 72% when considering the filtered set of 2,732 SNP loci. Using a less stringent homology requirement , as much as 83% of 3,769 SNP loci from GBS analysis of an alfalfa mapping population could be aligned with the M. truncatula reference genome [Populus, digestion with ApeKI captured loci in coding regions more frequently than expected from chance alone [M. sativa and M. truncatula genomes, which can be exploited for functional and comparative genomic studies [M. truncatula could be used as a pseudo-reference genome to further explore haplotype diversity in populations of alfalfa by exploiting GBS reads with multiple SNPs that were not retained by UNEAK.Sixty percent of the 11,694 SNP loci retained by UNEAK were found to align on the e genome . In Popuce alone , and conce alone . These r studies ,30,31. IIt should be noted that with a comparable sequencing depth, there was a 3-fold higher number of SNP loci  retained by UNEAK analysis of the narrow-based mapping population of  than thaAlfalfa is a highly heterozygous species with larger intra- than inter-varietal genetic diversity ,32. In oSolanum tuberosum cultivars uncovered by PCA of sequence variants was also associated with a limited proportion of the total genetic variance [Lolium perenne L.) were also differentiated by GBS analysis of allele frequencies at a large number of SNP loci across the genome [In spite of the simplification of genetic diversity, our study supports previous reports that UNEAK analysis of GBS is an efficient tool to track allele variability in single genotypes or complex populations ,13. We avariance . Populate genome . These aOur results confirm that GBS analysis with the UNEAK pipeline, developed for SNP discovery in diploid species without a reference genome, is suitable for the analysis of complex populations of autotetraploid alfalfa. Using direct validation of GBS by 454 sequencing, we have shown that although allele missampling and limited read depth does not allow accurate determination of tetraploid allelic dosage, accurate genotype calling simplified to a diploid state can be achieved. The reliability of genotype calls is strengthened by the combination of whole dataset- and genotype-level filters. We observed a clear differentiation between populations that was associated with a small portion of the overall genome-wide SNP variability. This suggests that a bulked DNA analysis of allelic imbalance could be an effective approach to identify genomic regions under selection pressure. This will be an important step towards the identification of polymorphisms associated to phenotypic variability in populations of open-pollinated alfalfa.S1 FigApeKI restriction sites, GBS fragment and 64 bp sequence obtained after UNEAK analysis are indicated.Location of PCR primers used for amplification, (PDF)Click here for additional data file.S2 FigApeKI restriction sites and PCR primer annealing regions are indicated.Sequences covering GBS SNP loci (64 bp sequence) are also aligned. SNPs used to define haplotypes, (PDF)Click here for additional data file.S3 FigA) GBS and 454 read counts of each allele (A1|A2); B) predicted tetraploid allelic ratios, with ratios identified as convergent in green and discordant in red; C) bi-allelic predicted genotype  before genotype-level filtration and D) after genotype-level filtration of GBS data for minimum read counts . Genotype calls showing concordance (green), discordance (red) with both sequencing methods or that are missing (white) before and after genotype-level filtration for minimum read counts.(PDF)Click here for additional data file.S1 TableM. truncatula (v4.0) genome and chromosome assignation on M. truncatula according to [M. sativa according to [Number of hits on rding to  and M. srding to  are prov(PDF)Click here for additional data file.S2 TableThe total number of good reads in ATF0 and ATF5 populations, and the maximum, mean and minimum counts of good reads per sample and SNP loci are reported. Homozygote, heterozygotes and missing genotypes frequencies were calculated for each population.(TIF)Click here for additional data file.S3 TableM. truncatula reference sequence. SNPs included in UNEAK TP are highlighted in bold. RC of GBS alleles (A1 and A2) and 454 sequences covering each haplotype in the eight genotyped plant samples are indicated. Cumulative number of A1-like, A2-like and total reads with perfect or imperfect match is also presented. Haplotypes with frequency <5% in all individual plant samples are not indicated but total read counts supporting those other haplotypes are reported.Haplotypes defined with 454 sequences with perfect and imperfect match with GBS 64bp sequence are listed separately. Position of SNPs is based on location on (PDF)Click here for additional data file.