Puccinia graminis f. sp. tritici (Pgt) race TTKSK (Ug99) poses a severe threat to global wheat production because of its broad virulence on several widely deployed resistance genes. Additional virulences have been detected in the Ug99 group of races, and the spread of this race group has been documented across wheat growing regions in Africa, the Middle East (Yemen), and West Asia (Iran). Other broadly virulent Pgt races, such as TRTTF and TKTTF, present further difficulties in maintaining abundant genetic resistance for their effective use in wheat breeding against this destructive fungal disease of wheat. In an effort to identify loci conferring resistance to these races, a genome-wide association study was carried out on a panel of 250 spring wheat breeding lines from the International Maize and Wheat Improvement Center (CIMMYT), six wheat breeding programs in the United States and three wheat breeding programs in Canada.The recently identified Sr2. Using the same model, 23, 86, and 111 SNP markers associated with seedling resistance against races TTKSK, TRTTF, and TKTTF were identified, respectively. These included markers linked to the genes Sr8a and Sr11 providing seedling resistance to races TRTTF and TKTTF, respectively. We also identified putatively novel Sr resistance genes on chromosomes 3B, 4D, 5A, 5B, 6A, 7A, and 7B.The lines included in this study were grouped into two major clusters, based on the results of principal component analysis using 23,976 SNP markers. Upon screening for adult plant resistance (APR) to Ug99 during 2013 and 2014 in artificial stem rust screening nurseries at Njoro, Kenya and at Debre Zeit, Ethiopia, several wheat lines were found to exhibit APR. The lines were also screened for resistance at the seedling stage against races TTKSK, TRTTF, and TKTTF at USDA-ARS Cereal Disease Laboratory in St. Paul, Minnesota; and only 9 of the 250 lines displayed seedling resistance to all the races. Using a mixed linear model, 27 SNP markers associated with APR against Ug99 were detected, including markers linked with the known APR gene Pgt races. Using the resistant lines and the SNP markers identified in this study, marker-assisted resistance breeding can assist in development of varieties with elevated levels of resistance to virulent stem rust races including TTKSK.Our results demonstrate that the North American wheat breeding lines have several resistance loci that provide APR and seedling resistance to highly virulent The online version of this article (doi:10.1186/s12870-015-0628-9) contains supplementary material, which is available to authorized users. Puccinia graminis Pers. f. sp. tritici (Pgt), is considered potentially the most damaging disease of wheat. Historical crop losses caused by stem rust epidemics have been recorded in all wheat growing regions of the world [Stem rust of wheat, caused by the fungal pathogen he world , 2. Thishe world –5. Howevhe world , and thrSr31 [Sr24 was defeated by race TTKST [Sr36 was defeated by race TTTSK [Sr9h was defeated by race TTKSF+ [The race TTKSK , first oSr31 , a widelce TTKST , Sr36 wace TTTSK , 9, and e TTKSF+ , 11. Thee TTKSF+ . Conseque TTKSF+ , 14. Thee TTKSF+ , 14, 15.Realizing the threat posed by the Ug99 race group, over 200,000 wheat lines including accessions from germplasm collections to breeding materials from wheat breeding programs throughout the world were screened for resistance to Ug99 in Kenya and Ethiopia . The resTwo main types of resistance strategies are used in wheat breeding against stem rust: 1) all-stage resistance (ASR) or seedling resistance, and 2) adult plant resistance (APR). ASR is usually characterized by a hypersensitive reaction upon fungal attack, and usually confers a high level of resistance that is effective in all stages of plant development . On the Association mapping (AM), or linkage disequilibrium (LD) mapping, is a powerful technique used to identify marker-trait associations, and has been used successfully in several crop species such as wheat, barley, soybean, and maize –22. The Pgt races TTKSK, TRTTF, and TKTTF.The objective of this study was to evaluate spring wheat lines from nine breeding programs in the United States and Canada, in addition to CIMMYT lines, for their field-based resistance to the Ug99 race group and conduct a GWAS to identify loci associated with resistance. We also made an attempt to distinguish between loci associated with APR and seedling resistance to exotic virulent www.triticeaecap.org). Elite lines were representative of the following wheat breeding programs in the United States: Montana State University (MSU), South Dakota State University (SDSU), University of California-Davis (UCD), University of Idaho (UI), University of Minnesota (UMN), and Washington State University (WSU); in Canada: Agriculture and Agri-Food Canada (Ag-Canada) Manitoba, Ag-Canada Saskatchewan, and Ag-Canada Alberta; and in Mexico: the International Maize and Wheat Improvement Center (CIMMYT). This germplasm panel was previously used to assess root morphology traits [Two-hundred and fifty spring wheat lines from wheat breeding programs in North America were assembled as part of the Triticeae Coordinated Agricultural Project , and the main-season (June to October 2013), and at Debre Zeit, Ethiopia during the off-season . These environments are referred in the text as KenOff13, KenMain13, EthOff13, and EthOff14, respectively. In all environments, phenotypic data was collected from plant heading stage up to grain maturing stage (i.e. between Zadoks 50 and 90) , when thSr24 virulence): ‘CCK’ (Canadian Cunningham Kennedy), ‘PBW343’, ‘Morocco’ and few susceptible CIMMYT lines. The disease was initiated by inoculating the spreader rows using a bulk inoculum of Pgt urediniospores collected at the Njoro field site. Wheat stem rust differential lines with known stem rust resistance genes indicated that the predominant, if not only, race present in the nursery since 2008 was race TTKST; [In Njoro, plots were arranged in an augmented design with the lines represented once and the susceptible check line ‘Red Bobs’ planted after every fifty entries. The lines were sown as 70 cm long twin rows, 20 cm apart, flat bed. Spreader rows were sown perpendicular to the twin rows, surrounding the field to initiate disease development and maintain uniform disease pressure in the nursery. The spreader rows comprised of a mixture of lines susceptible to race TTKST . The ure TTKST; ). The spSr31 and several races in the Ug99 race group are virulent to Sr31) and also collected from local fields. Inoculation was carried out in Debre Zeit as it was in the Njoro nursery.The nursery in Debre Zeit was set up similar to the Njoro nursery. Lines were planted in 1 m long twin rows, flanked by spreader rows comprised of a mixture of susceptible wheat varieties ‘PBW343’, ‘Morocco’, and ‘Local Red’. The spreader rows were artificially inoculated with a bulk of fresh urediniospores collected from PBW343 , race TRTTF (isolate 06YEM34-1), and race TKTTF (isolate 13ETH18-1). Race TRTTF was detected in Yemen and Ethiopia and characterized as broadly virulent to wheat stem rust resistance genes including Sr13 and Sr1RSAmigo [et al. [Pgt races. Two biological replicates of the seedling assays were performed for each Pgt race. Infection types (ITs) were recorded on a 0 to 4 scale according to Stakman et al. [et al. [Seedling assays were conducted at the United States Department of Agriculture, Agricultural Research Service (USDA-ARS) Cereal Disease Laboratory during the winter months between December and February starting December 2012 through February 2014. The 250 spring wheat lines were evaluated with three virulent races of  [et al.  for evaln et al. . ITs lesn et al. . In orde [et al.   for each line were predicted from the combined analysis model using SAS 9.1, from which final corrected trait values were obtained. After the mean values were normalized for each environment, trait values for each line in all environments were averaged, and used for genome-wide mapping. Heritability on an entry mean basis was calculated based on the method described by Holland et al. .As genotyping of these lines was carried out as a part of the TCAP, the genotype data was obtained from The Triticeae Toolbox. During data download, SNP markers with minor allele frequency less than 0.05 and no calls for more than 10 % of the lines were removed from the dataset. Lines that were genotyped at less than 20 % loci were also removed from the dataset. These filtering steps yielded 23,976 SNP markers and 241 lines that were used for genome-wide association analysis.The AM panel was genotyped at the USDA-ARS Cereal Crops Research Unit, Fargo, ND as part of the TCAP project for 90,000 gene-based SNPs using a custom Infinium iSelect bead chip assay following the manufacturer’s instructions  . Allele Principal component analysis (PCA) on the marker data of the lines was performed by using the Genomic Association and Prediction Integrated Tool (GAPIT) R package . For the2). The package ‘LDheatmap’ in R was used to calculate r2 in each of the A, B, and D genomes of common wheat. The LD decay between the marker pairs for each genome was estimated using the least squares regression function, and is represented by the exponential curve. Map positions of the SNPs were obtained from Wang et al. [Linkage disequilibrium between the markers was estimated as squared allele-frequency correlations . The extent of LD between marker pairs was visualized by fitting locally weighted polynomial regression (LOESS) curves into the scatter plot.Pgt resistance. The population parameters previously determined (P3D) model [Genome-wide association analysis investigating the marker-trait association was performed using the R package GAPIT , with thD) model  was usedp < 0.01) F-values from ANOVA results of the field data (results not shown). Distribution of rust severities in all environments indicated a quantitative mode of disease distribution with the Njoro main season (KenMain13) recording both higher severity and wider distribution of scores  between the field data from the four seasons was observed . However, the pair-wise correlations among the three races were low and not significant (data not shown). Most of the lines screened for seedling resistance against race TTKSK were susceptible, and only 15 (6 %) showed resistant reactions (IT 22+ or lower)  Fig. . Seedlin p < 0.00. However p < 0.00. HoweverTo investigate the population structure of the germplasm panel, the genotypes were analyzed for clustering based on principal component (PC) values. The groupings of lines belonging to each breeding program based on PC values are presented in Fig. et al. [In the A-genome, LD declined to 50 % of its original value at about 8 cM Fig. , whereaset al.  in theirp-value <0.001) with field resistance to Ug99  that were significantly associated  in either of the two replications of seedling screening, and with a complex score with low and high ITs (for example 2 + 3-) were also removed. This filtering step yielded a subset of 219 lines for the APR-specific genome-wide association analysis.2 values for the significant SNPs ranged from 0.1 % to 0.6 % (Table The APR-specific AM approach identified 26 SNP markers providing APR to Ug99 race group (Table IWA3120 (mapped to 1B) and IWB35697 (mapped to 6B) were common between the Ethiopia 2013 and 2014 environments. No SNP markers were common in all four environments.Significant SNP markers detected on the complete panel and by APR-specific mapping were cross-checked with the GWAS results for each of the four environments. While no significant SNPs were detected in the Kenya 2013 offseason environment, three, six, and nine SNPs were confirmed in the Ethiopia offseason 2013, Ethiopia offseason 2014, and Kenya main-season 2014, respectively , and 9 SNPs that were unmapped Table . AdditioWheat stem rust disease has been primarily controlled by the use of resistant genes discovered in hexaploid wheat and its related species. However, the Ug99 race group has defeated many of the widely deployed resistance genes, and thus poses a threat to wheat production globally. Moreover, several of the previously identified genes discovered in wild progenitors or landraces are not desirable for their use in resistance breeding because of linkage drag , 44. Theet al. [et al. [The map locations of significant SNP markers in our study, obtained from Wang et al. , were co [et al.  to obtaiIWA3120, IWB21176, IWB31027, IWB56771, IWB59663) were detected at position 90 cM on chromosome 1B, of which all except IWA3120 were also detected in Ethiopia 2013. The marker cfd48 reported by Pozniak et al. [Triticum durum Desf.) GWAS study is located 4 cM from the SNP markers we detected, and could represent the same locus. Bhavani et al. [et al. [wPt-1560 on 1BL to be associated with Ug99 resistance in separate spring wheat RIL populations. This marker, as well as Sr58, an APR gene for stem rust of wheat [et al. [gwm1045 to be significantly associated with Ug99 resistance in a durum wheat AM panel; and Haile et al. [gwm1198 on 2A that confers resistance to Ug99 in the durum wheat population Kristal/Sebatel. Neither of these markers was in proximity to the SNP markers detected in our study. We detected one significant marker, IWB8481, located at 9 cM on chromosome 2D. The only reported QTL on 2D that provides APR to Ug99 and its derivative races is in the CIMMYT biparental population PBW343/Kiritati [Sr genes – Sr32, and Sr46 have been mapped to the short arm of 2D [Sr32 has also been introgressed to 2A and 2B [Sr32 markers developed by Mago et al. [Sr46 has been developed, we are unable to distinguish between these two genes and the marker we found on 2D.Five significant SNP markers  in our study is very close (distance of 2.7 cM) to the marker wPt-8460, known to be linked to Sr9h in 1956 Rockefeller Foundation cultivar Gabo 56 (CI 14035) [et al. [Sr9h, previously temporarily designated as SrWeb, is derived from the Canadian wheat cultivar ‘Webster’ (RL6201) and confers ASR gene effective to TTKSK [et al. [Sr9h is present in 13 lines (5 %) in our panel  . The sam [et al.  in their [et al.  showed td on 2BL , 57, butd on 2BL .IWA8534, IWB23660, IWB25868, IWB69631, and IWB25869 located at 126 cM. Based on the consensus map published by Maccaferri et al. [wmc332, which is linked to Sr28 [wmc332 [Sr28 was present in our panel. While both markers have only been tested on a limited panel of lines and are not confirmed as diagnostic, we detected that up to 20 lines (8 %) could possess Sr28  on 6AS. Mapped at 66 cM, this SNP is located away (>100 cM) from the marker gwm617 reported by Pozniak et al. [Sr26#43 linked to Sr26, which provides resistance to the Ug99 and its derivative races [Sr26 is absent in the panel under study  to screen the panel for presence/absence of Sr42. Results indicated that at least 71 or more lines in the panel could carry this gene, yet the markers did not support each other  [Sr9h was confirmed by marker screening, as discussed above. Except for the likely presence of Sr9h, the position of the loci conferring Ug99 resistance in this study suggest that different genes than the ones discussed above could be present in our panel. We suspect that association mapping is limited by the low frequency of resistance loci (only 15 (6 %) of 250 lines resistant to TTKSK), leading to lack of detection of SNPs significantly associated with SrCad or SrTmp. Since no ASR genes effective to Ug99 are known to exist on chromosomes 3B and 5B, our findings indicate that the North American breeding germplasm might contain previously undiscovered important sources of resistance to the disease.We detected 23 SNPs in our germplasm panel that were significantly associated with race TTKSK resistance at the seedling stage. The SNP marker of races . The seeve races . Locatedcificity . SrTmp i42/SrCad . We usedd [cfd49  and a SNon type) . The preSr24 and Sr36 are resistant to the race TTKSK, yet no SNPs associated with resistance to this race were detected in the chromosomes containing these genes. Sr24, located on 3DL, is widely used in Mexico and the USA; Sr36, located on 2BS, is known to be present in wheat lines in the USA [Sr36 is not present in our panel; and only 7 lines (3 %) contain Sr24, confirming our assumptions.The genes Sr8a which is located on 6AS and is effective to the race TRTTF [IWB48466 located on the long arm of 7A (217 cM) is in the same region as the stem rust resistance gene Sr22. This gene was introgressed into 7AL of hexaploid wheat from its diploid relative Triticum boeoticum [et al. [Sr22 is not present in the panel. Sr31, while ineffective against TTKSK, is effective against TRTTF, and is located on 1BL [Sr31 in breeding programs, screening of lines with Sr31 with these markers is needed to determine if the markers are linked to Sr31, or if a novel source of resistance to TRTTF is located on 1BS.Of the 77 mapped SNPs significantly associated with resistance to TRTTF, 57 SNPs were located on the short arm of chromosome 6A (position range 2 cM – 26 cM). These markers are most likely linked to the gene ce TRTTF , 73. Simoeoticum , and is oeoticum . Marker  [et al.  confirmed on 1BL , 77. OurSr genes [Chromosome 1DS is known to harbor multiple Sr genes , and couSr11 which is effective to this race. Fifty-one significant SNPs were located on 4AL (142 cM – 164 cM) possibly indicative of resistance gene Sr7a (TKTTF is virulent to Sr7b). No ASR genes are known to be located on chromosome 5AL, and therefore the germplasm under study may possess a new source of resistance to the race TKTTF in this region. APR QTL providing resistance to the Ug99 and its derivative races have been detected in the 1AS region [Sr1RSAmigo is located on the 1RS.1AL rye chromosome arm translocation. Chromosome 7AS does not possess any known ASR genes, yet APR QTL effective to Ug99 and its derivative races have been detected in the region [One-hundred and nine SNPs associated with seedling resistance to the newly detected Ethiopian stem rust race TKTTF were detected on five chromosomes: 1AS, 4AL, 5AL, 6BL, and 7AS. The 52 6BL SNPs distributed in the positional range of 109 cM – 123 cM likely represent the gene S region , 55. Adde region , 68.None of the SNPs associated with seedling resistance for the three races were common, suggesting that none of the genes in this material are broadly effective. Further studies involving development of populations for fine mapping and allelism tests are required to elaborate and confirm the nature of the genetic mechanisms controlling the resistance to these three rust races.et al. [Sr marker associations. We are confident that the North American wheat breeding programs can fortify the stem rust resistance in their germplasm by capitalizing on the information provided in this study.One of the main advantages of conducting association mapping on a panel consisting of breeding germplasm is to explore the genetic composition of the lines, and estimate the effects of significantly associated loci with the trait(s) of interest. The discovery of significant SNPs can allow for tagging of lines that are enriched for alleles associated with the trait, and their use in gene introgression for resistance breeding. More importantly, as the lines used in this AM study are elite, they possess the desired agronomic traits, and are adapted to the desired regions. This helps in avoiding the problems that could otherwise arise from linkage drag, when more diverse germplasm is used to introgress alleles of interest. Singh et al.  have repPgt races. Several loci were found to be significant, which is an indication that despite the relatively narrow goals for germplasm development, enough genetic variation lies within the current North American breeding germplasm that can be utilized to breed for resistance against the virulent stem rust races, including the Ug99 race group. While only a small portion (6 %) of the germplasm showed seedling resistance, APR to Ug99 revealed several likely-novel genomic regions associated with resistance to Ug99. The lines that performed well at either or both growth stages (seedling and adult) could be used immediately to make crosses with elite lines to generate lines with improved rust resistance. Specific crosses could also be made to create mapping populations to fine map the regions of interest in an effort to identify diagnostic markers linked with the resistance loci. The discovery of such diagnostic markers will add great value to recurrent selection breeding programs as well as in identification of lines that carry the resistance loci. As such, further characterization and validation of the detected loci is necessary for effective utilization of these results. The availability of marker and trait data on the T3 webportal that were generated for this GWAS panel should enable interested groups to pursue these studies.In this study, we report the frequency and variability in seedling resistance and APR present in North American spring wheat breeding germplasm to virulent exotic The Triticeae Toolbox repository,https://triticeaetoolbox.org/wheat/display_genotype.php?trial_code=TCAP90K_SpringAM_panel). The phenotypic data collected on this GWAS panel and used in this article are available under the experiment set ‘USSpring_GWAS’ in The Triticeae Toolbox repository,https://triticeaetoolbox.org/wheat/view.php?table=experiment_set&uid=48). The data sets supporting the results of this article are included within the article and its additional files.The genotypic data generated on this GWAS panel and used in this article are available under the genotyping experiment ‘TCAP90K_SpringAM_panel’ in


SERPINA1 gene, found at 14q32.1. Mutations in the SERPINA1 gene can lead to AAT deficiency (AATD) which is associated with a substantially increased risk of lung and liver disease. The most common pathogenic AAT variant is Z (Glu342Lys) which causes AAT to misfold and polymerise within hepatocytes and other AAT-producing cells. A group of rare mutations causing AATD, termed Null or Q0, are characterised by a complete absence of AAT in the plasma. While ultra rare, these mutations confer a particularly high risk of emphysema.Alpha-1 antitrypsin (AAT) is the most abundant circulating antiprotease and is a member of the serine protease inhibitor (SERPIN) superfamily. The gene encoding AAT is the highly polymorphic SERPINA1 gene.We performed the determination of AAT serum levels by a rate immune nephelometric method or by immune turbidimetry. The phenotype was determined by isoelectric focusing analysis on agarose gel with specific immunological detection. DNA was isolated from whole peripheral blood or dried blood spot (DBS) samples using a commercial extraction kit. The new mutations were identified by sequencing all coding exons (II-V) of the SERPINA1 Null mutations, named: Q0cork, Q0perugia, Q0brescia, Q0torino, Q0cosenza, Q0pordenone, Q0lampedusa, and Q0dublin . Analysis of clinical characteristics revealed evidence of the recurrence of lung symptoms  and lung diseases  in M/Null subjects, over 45 years-old, irrespective of smoking.We have found eight previously unidentified SERPINA1 Null alleles. This study underlines that the laboratory diagnosis of AATD is not just a matter of degree, because the precise determination of the deficiency and Null alleles carried by an AATD individual may help to evaluate the risk for the lung disease.We have added eight more mutations to the list of The online version of this article (doi:10.1186/s13023-014-0172-y) contains supplementary material, which is available to authorized users. SERPINA1 gene on the long arm of chromosome 14 at 14q32.1. The gene is comprised of four coding exons , three untranslated exons  in the 5′ region and six introns. Following translation, the 24 amino acid signal peptide is removed and the mature polypeptide is a 394 amino acid, 52 kDa glycoprotein with three asparagine-linked carbohydrate side chains [Alpha-1 antitrypsin (AAT) is a serine protease inhibitor, encoded by the SERPINA1 gene and is characterised by decreased serum AAT concentrations. AATD is an under-diagnosed condition and the majority of cases remain undiagnosed. The World Health Organisation (WHO), the American Thoracic Society (ATS), and the European Respiratory Society (ERS) advocate a targeted screening approach for the detection of AATD in at risk populations, specifically chronic obstructive pulmonary disease (COPD), non-responsive asthma, cryptogenic liver disease and in first degree relatives of known AATD patients. Over 100 mutations leading to AAT deficiency have been identified to date and are associated with varying degrees of risk for lung and liver disease. AATD is associated with increased risk of cutaneous panniculitis [Alpha-1 antitrypsin deficiency (AATD) MIM # 613490) is an inherited condition caused by mutations within the polymorphic 13490 is iculitis , and Wegiculitis  with theiculitis ,12. HoweSERPINA1 mutations termed silent or Null are characterised by a complete absence of AAT in the plasma. Null  mutations are caused by a variety of different mechanisms including large gene deletions [The ultra rare family of eletions , intron eletions , nonsenseletions , and fraeletions . In someeletions . As Nulleletions . The exaSERPINA1 mutations in the Italian and Irish populations.We report here eight cases of previously unidentified Null The diagnostic algorithm for diagnosis of AATD was applied as previously reported . The proSERPINA1, RefSeq: NG_008290), as previously described [AAT measurements were performed by a rate immune nephelometric method  or by immune turbidimetry (Beckman Coulter AU5400). The phenotype was determined by isoelectric focusing analysis (IEF) on agarose gel with specific immunological detection . DNA wasescribed ,22, usincork, Q0perugia, Q0brescia, Q0torino, Q0cosenza, Q0pordenone, Q0lampedusa, and Q0dublin according to the birthplaces of the oldest subject carrying each mutation. Q0brescia, Q0torino and Q0cosenza consist of point mutations in the sequence of coding DNA that result in a premature stop codon (nonsense mutation). Q0cork, Q0perugia, Q0pordenone, Q0lampedusa and Q0dublin were caused by deletions, resulting in frameshift of the reading frame, and creating premature stop codons  background.The proband was a 43 year old female who presented with cough, dyspnoea and wheeze, and was subsequently diagnosed with asthma by methacholine challenge test , which causes a frameshift in the reading frame and the generation of a premature stop codon (241TGA). The novel mutation was also detected in a brother. Phenotype analysis and family pedigree revealed that this Null mutation arose on M1 background.The proband , the mother and an aunt.The proband was a 34 year old healthy male with a reported low concentration of AAT in plasma during a routine medical assessment , and in 4 relatives . The four families carrying this novel Null allele were not related, but all subjects carrying Q0pordenone identified so far were born in the North-East region of Italy.In index case 6.1 – IIB Table , a deletSERPINA1 in the index case  background. The same mutation was detected in heterozygosity in all three children.The proband was a 70 year old female who presented with bronchiectasis and a lower than expected AAT concentration given the apparent MM phenotype observed on IEF analysis . This substitution of a polar for a non-polar amino acid leads to folding impairment, with destruction of tertiary structure and therefore intracellular degradation [new hope and Nullnewport, were defined as Null on the basis of IEF and protein quantification in a period when molecular diagnosis was not widely available. A precedent for incorrect Null alleles does exist, and includes the well known Mheerlen, which was originally classified as PiQ0 on the basis of IEF and protein quantification [lowell, previously called Q0cardiff [Interestingly, Null mutations can also be induced by a simple amino acid substitution, like in Nullradation . In mostfication , and Plo0cardiff .SERPINA1 gene. Three  are nonsense mutations, the others  are frameshift mutations caused by deletion of one or two nucleotides.We describe here eight novel Null mutations in the coding regions of the SERPINA1 gene [brescia occurs in a portion of 27 nucleotides (nine amino acids) in exon III of the gene, where it is possible to find a conspicuous number of other mutations: Plowell /Pduarte/Ybarcelona at codon 256, Q0cairo and Mpisa [frankfurt at codon 255. Q0pordenone lies in another region of 27 nucleotides together with other Null  and normal  mutations. Q0lampedusa occurs in the region of 21 nucleotides where, in addition to Z, other deficient  and normal  mutations lie. Lastly, Q0dublin is only one nucleotide from Mheerlen and Mwurzburg mutations and two nucleotides from Etaurisano [It is worth noting most of the new mutations reported in this study occur close to other mutations, supporting the concept of mutational hot spots in the NA1 gene . In factnd Mpisa  at codonaurisano  deficienpordenone and Q0brescia in certain localized areas, without evidence of consanguinity, may indicate a relatively high prevalence of each Null allele in these geographic regions.While Null mutations are extremely rare, the recurrence of Q0Although a discussion of the clinical characteristics of the Null-bearing subjects presented herein is not the main purpose of this study, we can draw some interesting conclusions. Subjects with Null mutations should be considered a subgroup at particularly high risk of emphysema within the spectrum of AATD . In suppSERPINA1 gene and underlined the importance of the correct diagnosis of this group of mutations, because of the particularly high risk of lung disease.Our study has significantly expanded the list of Null alleles known to occur within the


Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations. In this study we performed association mapping for 19 agronomic traits including yield and yield components in a breeding population of elite irrigated tropical rice breeding lines so that the results would be more directly applicable to breeding than those from a diversity panel. The population was genotyped with 71,710 SNPs using genotyping-by-sequencing (GBS), and GWAS performed with the explicit goal of expediting selection in the breeding program. Using this breeding panel we identified 52 QTL for 11 agronomic traits, including large effect QTLs for flowering time and grain length/grain width/grain-length-breadth ratio. We also identified haplotypes that can be used to select plants in our population for short stature (plant height), early flowering time, and high yield, and thus demonstrate the utility of association mapping in breeding populations for informing breeding decisions. We conclude by exploring how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models. Developing new rice varieties that yield well with fewer inputs and under more stressful and unpredictable climatic conditions is essential for the future of food security, and is the major challenge for today's rice breeders ,2. FortuGenome wide association studies (GWAS) have been widely used to identify QTL underlying quantitative traits in humans and animals, and has recently also become a popular method of mapping QTL in plants. Association mapping identifies QTL based on the historic recombination in a panel of diverse germplasm via the presence of linkage disequilibrium (LD) between SNPs and QTL, i.e., the non-random association of alleles ,5,6. A hGWAS are most commonly performed in diversity panels, i.e., collections of unrelated diverse germplasm, in order to maximize the diversity of alleles and haplotypes ,11,12,13Genome-wide prediction, or genomic selection (GS), refers to the process of using genome-wide DNA markers to predict which individuals in a breeding population are most valuable as parents of the next generation of offspring. GS takes the same inputs as GWAS, a phenotype dataset and genotype dataset on a population of lines of interest to plant breeders ,15,16. AWe performed both GWAS and genomic selection on a population of elite breeding lines from the International Rice Research Institute (IRRI) irrigated rice breeding program in order to map QTL for agronomic, morphological and yield-related traits. In this paper, we report the association mapping results, which are leveraged for MAS and used to inform GS modeling on the same population.Genotyping-by-sequencing (GBS) was used to discover and genotype SNPs on 369 advanced breeding lines from the IRRI irrigated rice breeding program. Raw GBS data were imputed and the resulting matrix of SNP calls filtered on call rate and minor allele frequency (MAF) to obtain a set of 73,147 SNPs with call rates > = 90% (no MAF filter) and a set of 71,710 SNPs with call rates > = 75% and MAF > = 0.05. Six individuals with missing data > = 60% were removed from both SNP sets for a total of 363 genotyped lines. Data were transformed to numeric values and the minimal remaining missing data filled using the genotypic means of the lines  was performed using the 73,147 SNP dataset. In rice diversity panels, the first four principle components correspond to the five rice subpopulations. The first principle component separates indica/aus individuals from japonica/aromatic individuals, the second principle component separates aus and indica individuals, the third principle component separates temperate japonica individuals from tropical japonica individuals, and the fourth principle component separates the aromatic individuals from the japonica individuals [The majority of the 363 lines were classified ividuals .indica and tropical japonica. Based on a plot of the first two principle components, 13 individuals were identified as belonging to the japonica subpopulation and excluded from subsequent analyses , indicating that only 2 out of the 5 subpopulations were present: analyses , in theodmixture . After rAs a final check that we had adequately controlled for population structure in the dataset, we ran the GWAS both with and without the first principal component fit as a model covariate and confirmed that it did not have an effect on the results. The QQ plots for the final model indicate that we effectively control for subpopulation structure  . The four QTL identified for culm length in the dry season, for example, explained a total of ∼59% of the phenotypic variance. Collectively, the 10 QTL identified for grain length in the dry season explained only ∼31% of the phenotypic variance, the seven QTL for grain width in the dry season explained ∼28% of the variance, and the five QTL for lodging explained 26% of the variance in the wet season but only 13% in the dry season  Tables.Large effect QTL were, however, identified for some traits. A very large effect QTL for flowering time was identified on chromosome 3 that explained ∼43% of the variance for flowering time during the dry season and 45% during the wet season, and had the effect of decreasing flowering time by an average of six days. Another large effect QTL was identified for grain length, grain width, and length-breadth ratio on chromosome 7 that explained ∼12%, ∼16%, and ∼28% of the variance for grain length, grain width, and length-breadth ratio in the dry season, respectively, and which had the effect of decreasing grain length by an average of ∼0.3 mm, increasing grain width by an average of ∼0.1 mm, and decreasing the grain length-breadth ratio by an average of ∼0.4 mm  Tables.Only four out of the 52 identified QTL were significant for both the dry season and the wet season, three of which were the large effect QTL mentioned above for flowering time, grain length, and length-breadth ratio. The other QTL identified in both seasons was an additional QTL for length-breadth ratio on chromosome 2 (qLBR2-2) . A great2 = 0.71 for grain length and length-breadth ratio, r2 = −0.75 for grain width and length-breadth ratio, and r2 = −0.15 for grain width and grain length in the dry season) and shared the major QTL on chromosome 7 mentioned above . Grain length and length-breadth ratio also shared a QTL on chromosome 2 , and grain width and length-breadth ratio shared a QTL on chromosome 8 . Culm length and plant height were highly correlated in both seasons , and shared a QTL on chromosome 3 along with the major flowering time QTL , a QTL on chromosome 6 along with a lodging QTL  and a QTL on chromosome 8   Figs. . For suc2 = 0.24 for plant height and lodging, 0.02 for grain length and lodging, and −0.03 for length-breadth ratio and lodging in the dry season), yet in addition to the QTL described above, a single QTL for grain length and lodging was identified on chromosome 6  and a QTL for grain length-breadth ratio and lodging was identified on chromosome 12   Figs. . These rPerforming association mapping on a panel of adapted breeding lines rather than on a diversity panel provides the opportunity to apply the results directly to breeding programs. Unlike the results from studies using diversity panels, our association mapping results can be readily used to identify favorable or unfavorable haplotypes that are currently segregating in our elite breeding material. These haplotypes could be used to determine the most suitable parents for crossing in order to exploit transgressive segregation and/or to increase the frequency with which favorable haplotypes appear in the progeny. MAS for favorable haplotypes among the progeny would allow us to increase breeding efficiency and decrease cost by reducing the number of plants advanced to the next generation of breeding or that need to be phenotyped. In this way, we aim to increase the rate of genetic improvement by increasing gain from selection.2 value at or above which marker assisted selection would be more efficient than phenotypic selection, i.e., at or above this value, the increased efficiency of marker based selection would be expected to outweigh the drag on gain from selection that would result from mistakenly eliminating favorable individuals, or vice versa. Below this threshold, phenotypic selection would be preferable. Breeders generally expect marker assisted selection accuracy to be high (> = 95%) for traits with major genes or that are easy to phenotype. However, when a trait is laborious, time consuming, or technically difficult and/or expensive to phenotype, a lower accuracy ∼70% could be acceptable to breeders [MAS based on the haplotypes identified in an association mapping study such as ours necessitates a strong association between that haplotype and the phenotype-of-interest, and, ideally, a large effect of the haplotype on the phenotypic variation of the trait. When the association of haplotype and phenotype is non-perfect, as would be expected in many cases, there will be an rbreeders .We used PLINK to identify haplotypes that were associated with the significant peaks identified in our GWAS for three of our phenotypes that are routinely collected by breeders—flowering time, plant height, and yield  Tables. qFL-3. The haplotypes ranged from 2–124 SNPs in length, and spanned regions ranging from 233 bp—900.4 Kb , 12 were confirmed to carry the early H1 haplotype  was identified that co-localized with qPH-2, the plant height QTL identified in the WS GWAS. H1 was 3 SNPs in length, spanned 7 bp, and explained about 7% of the phenotypic variance . All 12 Two significant haplotypes were identified each for the dry season and the wet season for grain yield. In the dry season, both significant haplotypes were associated with lower yield and were located at the top of chromosome six, co-localizing with a GWAS peak that fell just below our significance threshold. The most significant of these two haplotypes, H2, was 8 SNPs long, spanned a region of 47.9 Kb, and explained ∼7% of the phenotypic variance . Of the The results are more interesting for the wet season. Two significant haplotypes (H1 and H3) were identified on chromosome 11, one of which, H1, was associated with higher yields. The H1 high-yield haplotype consisted of only 3 SNPs, spanned 38 bp, and explained ∼9% of the phenotypic variance . Of the qFL-3) corresponds to the location of the QTL Hd9 identified in early QTL mapping studies [qFL-3 (OsMADS50 gene (LOC_Os03g03100), an important activator of flowering in rice, and a homolog of Arabidopsis gene the SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) was identified as the best candidate. QTL for plant height (qPH-3) and culm length (qCulmL-3) also co-localized with qFL-3, suggesting that OsMADS50 may also play a role in the expression of these traits as well as for flowering time. Indeed, rice plants in which this gene was knocked down using RNAi exhibited elongated internodes, suggesting true pleiotropy may underlie these traits in rice [To identify candidate genes underlying the above QTLs/haplotypes of interest, we searched rice genome browsers and existing literature at regions that were either near the most significant GWAS SNPs reported in  studies . While ts [qFL-3 , the OsM in rice .OsMADS50 co-localizes with both Hd9 and our peak association marker , including Hd1, RFT1 (a.k.a. FT-3), and FT-2. Hd1, however, is ∼6Mb away from the peak QTL marker, and thus is not the best candidate  also corresponded to a previously mapped QTL, Hd5/DTH8, which encodes a HAP3 subunit of the CCAAT-box-binding transcription factor and has been shown to play an important role in early flowering [qFL-2, qFL-9) are shown in Several known, important, rice flowering time genes also underlie the flowering time QTL identified on chromosome 6 , as mentioned previously, although several other candidates were also identified , but several putative stress tolerance genes were identified in the vicinity of the significant haplotype which warrant further investigation, see No best single candidate gene was identified for the wet season yield QTL on chromosome 11 , first described by Meuwissen et al., in 2001 could solve this problem . InsteadGWAS have been used to identify many genes that underlie a variety of agronomic traits in rice ,13,35,36363 elite breeding lines were selected for genotyping from the International Rice Research Institute (IRRI) irrigated rice breeding program based on the planned inclusion of the lines in the 2011 Multi-Environment Testing Program and in the 2011 and 2012 Replicated Yield Trials (RYT) at IRRI (Los Baños). Approximately half of the lines were also included in the 2009–2010 RYTs at IRRI . The oth2 harvest area. All measurements were made on a quantitative scale, with the exception of lodging score and panicle exertion rate, which were nominal. Methods used followed IRRI’s standard evaluation system (SES) or other routinely used protocols. Predicted means and variance components were calculated using linear mixed models in Genstat v. 16. Entries were considered as fixed effects and replication and entry by replication interactions were considered as random effects. A detailed trait analysis will be reported elsewhere.A total of 19 agronomic, morphological, grain and yield-related traits were evaluated in the panel in 2012 (dry and wet seasons) as described in www.qiagen.com/HB/DNeasy96Plant).Young leaf tissue was collected from each of the 369 breeding lines from plants grown in Gutterman Greenhouse in Ithaca, NY. DNA was extracted using the Qiagen 96-plex DNeasy kit as per the Qiagen fresh leaf tissue 96-plex protocol  libraries were prepared using the protocol by Elshire al. 2011 , as descet al 2013 [SNPs were discovered and called from the raw 384-plex GBS data using the TASSEL3.0 GBS pipeline with physical alignment to the MSU version 6.0 Nipponbare rice reference genome using Bowtie2, as described in Spindel and Wright  al 2013 ,40  were removed from the dataset along with all monomorphic SNPs, for a total SNP set of 73,147 SNPs. A second genotype dataset was also obtained in which we removed all monomorphic SNPs, SNPs with call rates < 75%, and SNPs with minor allele frequencies (MAF) < 0.05 for a total of 71,710 SNPs. Individuals with more than 60% missing data were dropped from both datasets, which resulted in the removal of six individuals that failed sequencing for the total of 363 genotyped lines used throughout the study .Both final datasets were then transformed from nucleotide genotype coding  to numeric coding  in order to facilitate statistical analysis. The minimal remaining missing data were filled using the numeric genotype means of each line in order to perform PCA and GWAS .indica or indica-admixed subpopulation groups. In order to identify outlier individuals belonging to the japonica or japonica-admixed groups, principle components analysis (PCA) was performed in R  using the imputed 73,147 SNP dataset. The first principle component of high density SNP data in rice can separate the indica and japonica subgroups [japonica outliers were identified as pulled in a tight cluster apart from the rest of the 350 lines , with τ¯1 as the variance of the residual errors, λ the ratio between the two variance components, and K the m × m genomic relationship matrix. ε is ∼ MVNn where In is an n × n identity matrix [Association mapping was performed in R using the GEMMA implementation of the standard linear mixed model y = Wα + Xβ + u + ε, where y is a vector of phenotypes, W =  is an n × c matrix of covariates (fixed effect), α is a c-vector of the corresponding coefficients, β is the marker effect size, X is a matrix of allele dosages for the imputed variants, u is a vector of random effects and ε is a vector of errors. u is multivariate normal, ∼ MVNn as a covariate were compared to models that did not include the first PC using TASSEL v. 4.3.1, and were found not to differ significantly, therefore, we did not include the first PC as a covariate when building the GEMMA model. This makes sense given the small percent variance explained by any of the principle components after removal of outliers see . The QQ Haplotype blocks were calculated for flowering time, plant height, and yield for both seasons of data in PLINK (command—blocks). For all phenotypes except yield in the dry season, the input genotype files contained all SNPs that passed the GWAS significance threshold. For yield in the dry season, the input genotype file contained all SNPs with p-value < = .0001. The resulting PLINK blocks file was then used to calculate the haplotype frequencies in PLINK using the—hap-freq command. To test haplotypes for significant association with phenotype, we used the PLINK—hap-assoc command.For each trait, the most significant haplotype associated with either low phenotypic value or high phenotypic value (e.g. early flowering and late flowering) was identified. The proportion of either the individuals with the 5% lowest phenotypic values or the 5% highest phenotypic values that carried the most significant haplotypes associated with the respective high/low phenotype were calculated.http://archive.gramene.org/qtl/). To ascertain whether QTL had been cloned, literature searches were performed. Regions covered by significant haplotypes (http://rice.plantbiology.msu.edu/cgi-bin/gbrowse/rice/). All results are reported in Candidate genes were identified for the flowering time, plant height and yield QTLs, three of the most important traits to breeders. Previously identified QTL that co-localized with the QTL mapped in this study were identified using the Gramene QTL data archive  Initial principle components analysis (PCA) using 363 lines to identify 13 japonica outliers (purple). (B) PCA on remaining 350 lines after removing 13 outliers identified in A. An additional 18 outliers (purple) were subsequently identified and excluded.((EPS)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigDashed line shows the 0.1 FDR significance threshold. Plots with no dashed line did not have any SNPs that passed the significance threshold.(PDF)Click here for additional data file.S4 Fig384-plex GBS was used to discover and call SNPs on 369 elite inbred rice lines from the IRRI irrigated rice breeding program. SNPs were discovered and called from the raw GBS data using TASSEL3 with physical alignment to the MSU version 6 Nipponbare rice reference genome using Bowtie2 (yellow boxes). Imputation of missing data was then performed using the TASSEL3 fastimputationbitfixedwindow plugin . After imputation, custom python scripts (green boxes) were used to remove SNPs with call rates < 90% or <75% (two different datasets), remove monomorphic SNPs, remove SNPs with MAF < 0.05, drop individuals with more than 60% missing data, and finally, convert the ACTG nucleotide calls to numeric coding . After genotypes were converted to numeric format, remaining missing genotype values were filled using the numeric line mean.(EPS)Click here for additional data file.S1 TableTable shows up to the top five most significant SNPs identified for each season for each QTL, for each trait along with the major and minor alleles for the SNP and the allele effect of the major allele. rs = SNP identifier, p_wald = Wald Test p-value.(XLSX)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S3 Table(CSV)Click here for additional data file.S4 TableLocus = haplotype number assigned by PLINK, NANAL = number of individuals in analysis, BETA = regression coefficient, R2 = percent variance explained by haplotype, STAT = T test statistic, P = asymptotic p-value, pop_freq = frequency of haplotype in population, SNPs = SNPs that make up the haplotype. Negative BETA values indicate that the haplotype is associated with earlier flowering, positive BETA values indicate that the haplotype is associated with later flowering. The most significant haplotype associated with early flowering and the most significant haplotype associated with late flowering is highlighted for both seasons.(XLSX)Click here for additional data file.S5 TableLocus = haplotype number assigned by PLINK, NANAL = number of individuals in analysis, BETA = regression coefficient, R2 = percent variance explained by haplotype, STAT = T test statistic P = asymptotic p-value, pop_freq = frequency of haplotype in population, SNPs = SNPs that make up the haplotype. Negative BETA values indicate that the haplotype is associated with shorter stature, positive BETA values indicate that the haplotype is associated with taller stature. The most significant haplotype associated with short stature and the most significant haplotype associated with tall stature is highlighted for both seasons.(XLSX)Click here for additional data file.S6 TableLocus = haplotype number assigned by PLINK, NANAL = number of individuals in analysis, BETA = regression coefficient, R2 = percent variance explained by haplotype, STAT = T test statistic P = asymptotic p-value, pop_freq = frequency of haplotype in population, SNPs = SNPs that make up the haplotype. Negative BETA values indicate that the haplotype is associated with lower yield, positive BETA values indicate that the haplotype is associated with higher yield. The most significant haplotype associated with low yield in the dry season and the most significant haplotypes associated with low yield and high yield in the wet season are highlighted.(XLSX)Click here for additional data file.S7 Table(MSU 7.0) = position in bp from the MSU v 7.0 rice genome, pos (Gramene) = Ensemble/Gramene position for cloned genes and historic (reference) QTL. Near (marker or haplotype) = the significant SNP marker(s) or haplotype(s) identified in this study nearest the identified candidate gene. Reference QTL name = historic QTL name as recorded in the Gramene QTL archive, Gramene QTL ID = accession number of QTL in the Gramene QTL archive. Gene product description = description of gene product from MSU rice genome annotation project and/or Gramene. Highlighted rows represent particularly good candidate genes.QTL name = name of QTL from this study, gene name = published name of cloned gene, pos (XLSX)Click here for additional data file.S8 TableOne sample could not be tracked.(XLSX)Click here for additional data file.


Hepatitis C virus (HCV) remains a global problem, despite advances in treatment. The low cost and high benefit of vaccines have made them the backbone of modern public health strategies, and the fight against HCV will not be won without an effective vaccine. Achievement of this goal will benefit from a robust understanding of virus–host interactions and protective immunity in HCV infection. In this review, we summarize recent findings on HCV-specific antibody responses associated with chronic and spontaneously resolving human infection. In addition, we discuss specific epitopes within HCV’s envelope glycoproteins that are targeted by neutralizing antibodies. Understanding what prompts or prevents a successful immune response leading to viral clearance or persistence is essential to designing a successful vaccine. Flaviviridae family, is parenterally transmitted. HCV establishes a persistent infection in 60–80% of individuals infected. The treatment for HCV has long been pegylated interferon alpha co-administrated with ribavirin, but the response rates were unsatisfactory with only 50–60% of patients achieving a sustained virologic response (Between 130 and 185 million people worldwide are infected with hepatitis C virus (HCV) and are at risk of cirrhosis, hepatocellular carcinoma, and end-stage liver disease  are observed until 1–2 months after infection –18. Both in Ref. –23]. Vig in Ref. –27. In pIt was widely thought that the humoral immune response to HCV played only a peripheral role in HCV infection , 28, 29.Antibodies produced during acute HCV infection target epitopes within both structural and non-structural (NS) viral proteins. However, all known nAbs target epitopes within the HCV envelope glycoproteins E1 and E2, or the E1E2 heterodimer. The structural proteins core, E1, and E2 are released from the viral polyprotein by cellular signal peptidases. The viral particle contains the nucleocapsid, formed by the close interaction of the HCV RNA genome and core protein, surrounded by a lipid bilayer envelope into which the glycoproteins E1 and E2 are anchored. E1 and E2 form a heterodimer that mediates viral entry. Determining the structure of the E1E2 heterodimer has proven problematic. E2 is required for the correct folding of E1, so that E1’s structure is still uncertain , 36. It Most nAbs target E2. E2 plays a key role in HCV entry, directly interacting with two of the cellular proteins needed for viral entry, CD81 and scavenger receptor class B type I (SR-BI) , 39. CD8Flaviviridae, such as Tick-borne encephalitis virus or West Nile virus; instead, HCV E2 was found to be compact and globular  1, HVR2, and intergenotypic variable region. HVR1 contains dominant neutralizing epitopes, and its variation leads to immunological escape –50. HVR1in vivo  of patients spontaneously clear the infection depending on factors such as, race, sex, and genetics –95. It iThere is much evidence to support the theory that Abs have a limited impact on HCV disease outcome as HCV seroconversion is delayed , 101, nAIn contrast, there is striking evidence supporting a role for Abs in control of HCV infection and more interestingly in preventing reinfection. Early induction of cross-reactive nAbs during acute infection strongly correlates with the spontaneous clearance of HCV , 112. CoPassive immunization with nAbs can mediate protection: chimpanzees passively immunized with rabbit antisera specific for E1 and E2 were somewhat protected against HCV . RecentlVaccination has been the most-effective strategy used to control infections that have been a major public health concern. The hepatitis B virus (HBV) vaccine has proven a great success, greatly reducing the number of HBV infections worldwide . All sucNeutralizing antibodies are induced during HCV infection, which in some patients contribute to the spontaneous clearance of infection, yet the majority of infected patients progress to chronicity. How does HCV evade the humoral immune response to progress to chronicity? Several mechanisms may contribute to evasion of sterilizing Ab-mediated clearance. These include sequence changes, decoy epitopes, epitope masking, lipid shielding, induction of interfering antibodies, and the ability to move from one cell to another in a neutralization-resistant fashion Figure .12 new HCV virions are produced in the infected liver . . In vitrsmission . The impCompetition between interfering Abs and nAbs can disrupt virus neutralization by nAbs Figure F. InterfIdeally, an effective HCV vaccine will need to generate a broad and highly reactive immune response at the first signs of HCV infection, before the virus has the chance to unleash its many immune escape mechanisms. A vaccine would need to target multiple antigenic determinants, thus raising the genetic barrier for mutational escape.+ T helper cells that ultimately leads to the failure of the humoral immune response, and thus the failure to control the infection? Do the other immune cells remain silent or do they engage with B-cells? As we enter the big data era in science, we may be better positioned to answer some of these questions.The new DAAs will dramatically improve HCV outcome. However, the development of a prophylactic or therapeutic vaccine is needed to control the global HCV problem. Successful vaccine development is dependent on our understanding of the immune response to HCV infection. In particular, it is imperative that we understand why some patients clear the virus naturally and how they are protected from reinfection. The relativity low cost and high benefit of vaccines have made them the cornerstone of modern public health strategies. To date, all successful viral vaccines elicit nAbs . The burThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Current guidelines for spontaneous intracerebral hemorrhage (ICH) recommend maintaining cerebral perfusion pressure (CPP) between 50 and 70 mmHg, depending on the state of autoregulation. We continuously assessed dynamic cerebral autoregulation and the possibility of determination of an optimal CPP (CPPopt) in ICH patients. Associations between autoregulation, CPPopt and functional outcome were explored.Intracranial pressure (ICP), mean arterial pressure (MAP) and CPP were continuously recorded in 55 patients, with 38 patients included in the analysis. The pressure reactivity index (PRx) was calculated as moving correlation between MAP and ICP. CPPopt was defined as the CPP associated with the lowest PRx values. CPPopt was calculated using hourly updated of 4 hour windows. The modified Rankin Scale (mRS) was assessed at 3 months and associations between PRx, CPPopt and outcomes were explored using Pearson correlation and Fisher’s exact test. Multivariate stepwise logistic regression models were calculated including standard outcome predictors along with percentage of time with PRx >0.2 and percentage of time within the CPPopt range.n = 18). The mean PRx and the time spent with a PRx > 0.2 significantly correlated with mRS at 3 months . CPPopt was calculable during 57% of the monitoring time. The median CPP was 78 mmHg, the median CPPopt 83 mmHg. Mortality was lowest in the group of patients with a CPP close to their CPPopt. However, for none of the CPPopt variables a significant association to outcome was found. The percentage of time with impaired autoregulation and hemorrhage volume were independent predictors for acceptable outcome (mRS 1 to 4) at three months.An overall PRx indicating impairment of pressure reactivity was found in 47% of patients (Failure of pressure reactivity seems common following severe ICH and is associated with unfavorable outcome. Real-time assessment of CPPopt is feasible in ICH and might provide a tool for an autoregulation-oriented CPP management. A larger trial is needed to explore if a CPPopt management results in better functional outcomes. Of impo bedside . Studies bedside ,7 and su bedside  have sho bedside ,3,9,10 a bedside ,12 and,  bedside ,14, concHere, we (1) continuously assessed the state of cerebrovascular reactivity in patients with severe spontaneous ICH treated on a Neurocritical Care Unit (NCCU) and (2) investigated if the CPPopt concept is applicable for ICH patients. Furthermore, the association of impaired autoregulation and CPPopt to functional outcome as reported for TBI  and SAH aCO2) of 30 to 40 mmHg. ICH severity and neurological deficit on admission were assessed by the National Institutes of Health Stroke Scale (NIHSS). Hematoma volume was calculated from the first computed tomography (CT) scan using the axbxcx0.5 method [Between 2007 and 2009, 55 non-consecutive patients with spontaneous ICH admitted to the Heidelberg NCCU were prospectively included in our monitoring database. Inclusion criteria were: (1) spontaneous ICH and (2) the need for intracranial pressure (ICP) measurement. Our clinical standard protocol includes ICP measurement for all ICH patients who are analgosedated and mechanically ventilated. Furthermore, if patients have an indication for placement on an extraventricular drainage due to intraventricular extension of the hemorrhage, we usually aim to place a Raumedic NEUROVENT  probe, allowing ICP measurement during simultaneous cerebrospinal fluid (CSF) drainage (see below). All patients required intubation and mechanical ventilation and were continuously sedated using midazolam, sufentanil, ketamine and/or propofol. A total of 18 patients had been included in a previous study . All pat5 method . The amo5 method . The SAPThe study was part of a larger neuromonitoring project. The retrospective analysis of the monitoring data was approved by the local ethics committee . For retrospective data analysis, the need for informed consent was waived.Intracranial pressure was measured using a Raumedic NEUROVENT probe, providing a combined probe allowing ICP measurement during simultaneous CSF drainage. In six patients, ICP was measured by a standard external ventricular drain (EVD). These patients were excluded from the current analysis since ICP values are not correctly measured via an open EVD. Blood pressure was measured from the radial artery . ICP, systolic and diastolic blood pressure, mean arterial pressure (MAP), CPP and heart rate were synchronously recorded with a sampling frequency of 1 Hz using a commercially available software . The data were stored on a bedside computer.et al. [Data files were cleaned from epochs containing incomplete data recordings. Incomplete recordings were caused by disturbed interaction between the monitoring system and the recording software or by complete disconnection of the patient from the monitoring system . Next, data were re-sampled to obtain one value every 10 seconds. Then, PRx was calculated as a moving linear (Pearson) correlation between 30 consecutive values (=five-minute time window) of MAP and ICP as described by Aries et al. . For calet al. . For eacet al. [et al. [et al. [The optimal CPP (CPPopt) was calculated according to the algorithm published by Aries et al. . Shortly [et al. . Further [et al. .Spearman rank correlation was used in univariate analysis to assess the relationship among PRx, time spent within, below and above the CPPopt range and outcome at three months.In addition, patients were grouped according to their median difference between real CPP and CPPopt . The distribution of dichotomized outcome parameters  and favorable (mRS 1 to 3) outcome) was compared using Fisher’s exact test.Finally, separate forward stepwise logistic regression models were calculated to predict mortality, and favorable and acceptable outcome at three months. A stepwise model was chosen due to the explorative nature of the analysis. Due to the limited number of patients, only the known outcome predictors including age (years), initial hemorrhage volume (ml), presence of intraventricular blood in addition to percentage of time with PRx >0.2 and percentage of time within the CPPopt range were included in the model. To account for general disease severity in this special subset of ICU patients, the SAPS II score was also included in the model. There was no significant correlation between the included covariates. Statistical analysis was performed using the SPSS software package .In total, 55 patients with spontaneous ICH were prospectively monitored. Of these, four were excluded because of monitoring time <12 hours or CPP below 30 mmHg, one because of early withdrawal of care, one because of terminal stage malignoma and one because of generalized brain edema in a follow-up CCT. Additionally, the five patients with purely intraventricular hemorrhage were excluded as well as the patients who had standard EVDs without continuous ICP monitoring. All patients in whom full therapeutic measures initially were undertaken have been included in our analysis. This left 38 patients for the current analysis.During their ICU stay, in three patients it was decided not to escalate therapeutic measures , two of these died. The mean age was 58 years . Median NIHSS at admission was 30.5 . The median SAPS II on admission was 13 .Median hematoma size was 36 ml , 34 patients (89.5%) had intraventricular hemorrhage extension, the median Graeb score was 6.5 . A total of 32 patients (84.2%) had deep hemorrhage, 5 (13.2%) lobar and 1 brainstem and cerebellar hemorrhage (2.6%). With respect to etiology, 23 patients (60.5%) had hypertensive hemorrhage, 4 had hemorrhage associated with coagulopathy (10.5%), 5 had hemorrhage of other etiologies (13.2%) and for 6 patients no etiology could be determined (15.8%).Median total monitoring time was 78 hours . A total of 12 (31.6%) patients underwent hematoma evacuation, 2 (5.3%) had hemicraniectomy and 1 (2.6%) had hematoma evacuation and hemicraniectomy (for this patient only data before surgery were available).At three months, 3 patients (8.1%) attained a mRS of 2, 7 patients (18.9%) a mRS of 3, 11 patients (29.7%) a mRS of 4, 5 patients (13.5%) a mRS of 5. In-hospital mortality was 23.7% (n = 9), mortality at three months was 29.7% (n = 11). For one patient outcome at three months was not available.The mean PRx was suggestive for impaired pressure reactivity defined as PRx >0.2 in 47.4% (n = 18) of patients. The median time spent in PRx ranges >0.2 was 45.4% of the total monitoring time .P = 0.002 and r = 0.46, P = 0.004).The mean PRx and the time spent with a PRx >0.2 were significantly correlated to mRS at three months  or hemorrhage volume .In our cohort, autoregulatory failure was not correlated to ICH severity: neither the mean total PRx nor the mean PRx on the first day of monitoring were significantly correlated to admission status assessed by NIHSS . In a single patient, CPPopt was not calculable at all. The median of all continuously calculated CPPopt values was 83 mmHg . The median of all continuously calculated actual CPP values was 78 mmHg . The median CPPopt based on the CPP and PRx values of the entire monitoring time was 93 mmHg . The median CPP of the entire monitoring time was 79 mmHg . Median ICP was 10 mmHg .Patients spent 10.5% of their total monitoring time within the continuously updated CPPopt range . The percentage of time above the CPPopt range was 15%  and the time spent below the CPPopt range was 26.7%  in the median. The continuously calculated difference between CPP continuous CPPopt (Δ) was -5.6 mmHg  in the median.P = 0.791).There was no linear correlation between mRS at three months and the times spent within, above or below the continuously updated CPPopt. Furthermore, functional outcome was not correlated to total monitoring time , while it was almost equal in those more than 5 mmHg below (38.1%) or above (33.3%) CPPopt , compared to 47.6% of those below and 50% of those above CPPopt , compared to 20% of patients within the CPPopt range and 28.6% of those more than 5 mmHg below CPPopt .The distribution of the NIHSS scores or hemorrhage volume did not differ significantly among the three groups  was an independent predictor for acceptable outcome at three months. These results confirm the findings of a previous pilot study [patients . The medpatients ,14. Thiserfusion . In our et al. have found in their much larger TBI cohort that hypoperfusion was associated with mortality while hyperperfusion was correlated with severe disability. In contrast, in our cohort of ICH patients, both hypo- as well as hyperperfusion seemed harmful as could be concluded from the mortality rates in the different CPP groups. Mortality was lowest for patients close to their individual CPPopt. At the same time, patients in this group had the highest percentage of acceptable outcome. These findings, however, were not statistically significant. In addition, the results for favorable outcome remain inconclusive. In our cohort, the highest percentage of patients with favorable outcome was in the group of those with 5 mmHg or more above their individual CPPopt while at the same time patients with a CPP close to CPPopt had the least percentage of favorable outcomes. This directly contrasts the above mentioned findings with respect to mortality. These inconsistencies may be due to the small sample size and the total lack of patients with a delta CPP above +10 mmHg. Patient numbers in total and especially in the above CPPopt group (n = 6) were too small to draw any definitive conclusions. Furthermore, the variable ‘time spent within the CPPopt’ range was not an independent predictor for any of the outcome variables.Aries Our study has various important limitations. First, sample size was still relatively small and the study included a highly selected population of ICH patients. As stated above, especially the results of the outcome analyses comparing the different groups based on their proximity to CPPopt did not render any statistically significant results and have to be weighed critically. Only speculative conclusions can be drawn so far. Furthermore, the study was an explorative retrospective analysis of prospectively monitored, non-consecutive patients. Sample size was not predetermined. Second, the amount of available data differed greatly among different individuals, possibly adding further bias. However, reassuringly, total monitoring time was not correlated to outcomes, PRx or time of impaired PRx. Third, CPPopt was calculated retrospectively and the amount of time spent within the CPPopt range was relatively small . In addition, calculation of CPPopt based on the currently used automated algorithm was on average only possible during half of the monitoring time. This may be due to the fact that not all CPP values were covered by each patient, rendering identification of CPPopt impossible since it possibly lay outside the recorded values. This limits the generalizability of the association between the difference of ‘real CPP minus optimal CPP’ and outcome after ICH. However, as stated above, the same percentage (55%) was found in one other retrospective trial using continuous calculation of CPPopt in TBI patients. This number certainly has to be improved by carefully evaluating the reasons for failure of determination of CPPopt and possibilities to increase the numbers. This should be done in a prospective trial. Only after this issue has been addressed can the CPPopt concept be safely introduced for further clinical use. We are aware that a prospectively conducted study, trying to optimize cerebral perfusion based on an improved CPPopt algorithm may render entirely different results due to the availability of larger amounts of data and broader CPP variations. Finally, as a technical limitation, times during CSF drainage as well as patients following surgery and specifically two patients following hemicraniectomy have been included in our analysis. While the NEUROVENT probe allows reliable ICP measurement also during CSF drainage, it is unclear whether this will affect the reliability of PRx assessment. Calculation of PRx is based on the principle of a closed system, with ICP being a surrogate for changes in intracerebral blood volume. However, considering the fact that we clearly found positive correlations between MAP and ICP and hence positive PRx values, we argue that the system still reacts during times of drainage. The same holds true for the two patients following hemicraniectomy. A comparison of PRx values and CPPopt calculation before and after surgery suggests no systematic difference in these two patients (compare Additional file Despite these important limitations, the study encourages further research on autoregulation-based CPP management in ICH. Moreover, our finding of impaired dynamic autoregulation following ICH is corroborated by two other independent studies using the Doppler flow velocity-based index Mx ,9; one oDue to a lack of ICH specific data, current guidelines for CPP management in ICH recommend taking the functioning of cerebral autoregulation into account but are entirely based on data from TBI patients. Here we could show that cerebrovascular pressure reactivity is impaired in almost 50% of patients with severe ICH and impairment of pressure reactivity was related to poor functional outcome at three months. The CPPopt concept seems applicable to ICH patients: CPPopt could be determined in all but one patient and during 57% of the monitoring time. The median CPPopt was 83 mmHg and, therefore, considerably higher than the cutoff value of 70 mmHg recommended in the current guidelines. So far, however, a significant relationship between functional outcome and CPPopt could not be demonstrated in ICH patients. This may be due to sample size and the retrospective approach. Larger, prospective trials are needed to assess the validity of the CPPopt concept for severe ICH patients.● Cerebrovascular pressure autoregulation was impaired in half of the patients treated for severe ICH.● Impairment of pressure reactivity is independently associated with poor functional outcome at three months.● The CPPopt concept seems applicable in ICH: CPPopt was determinable in 57% of the total monitoring time.● The median CPPopt was 83 mmHg and thereby considerably higher than the guideline recommended value of 70 mmHg.● Mortality was lowest in the group of patients with a CPP close to their individual CPPopt.B: Regression coefficient; CI: Confidence interval; CPP: Cerebral perfusion pressure; CPPopt: Optimal cerebral perfusion pressure; CSF: Cerebrospinal fluid; CT: Computed tomography; EVD: External ventricular drain; ICH: Intracerebral haemorrhage; ICP: Intracranial pressure; IQR: Interquartile range; MAP: Mean arterial pressure; mRS: Modified Rankin Scale; NCCU: Neurocritical Care Unit; NIHSS: National Institutes of Health Stroke Scale; OR: Odds ratio; PaCO2: Arterial pressure of carbon dioxide; PRx: Pressure reactivity index; R: Correlation coefficient; SAH: Subarachnoid hemorrhage; SAPS II: Simplified Acute Physiology Score II; SE: Standard error; TBI: Traumatic brain injury; Δ: Delta.The authors declare that they have no competing interests.JD contributed to conception and design, data collection and analysis, manuscript writing and final approval of the manuscript. ES and SP contributed to data collection and analysis, critical revision and final approval of the manuscript. MS contributed to conception and design, data collection and analysis, critical revision and final approval of the manuscript. All authors read and approved the final manuscript.Comparison of PRx values and CPPopt assessment before and after surgery for the two patients who had hemicraniectomy.Click here for file


Picea glauca × P. engelmannii hybrid zone across a broad latitudinal (40–60 degrees) and elevational (350–3500 m) range in western North America. Our results revealed a wide and complex hybrid zone with broad ancestry levels and low interspecific heterozygosity, shaped by asymmetric advanced-generation introgression, and low reproductive barriers between parental species. The clinal variation based on geographic variables, lack of concordance in clines among loci and the width of the hybrid zone points towards the maintenance of species integrity through environmental selection. Congruency between geographic and genomic clines suggests that loci with narrow clines are under strong selection, favoring either one parental species  or their hybrids (overdominance) as a result of strong associations with climatic variables such as precipitation as snow and mean annual temperature. Cline movement due to past demographic events  may explain the asymmetry in introgression and predominance of P. engelmannii found in this study. These results provide insights into the genetic architecture and fine-scale patterns of admixture, and identify loci that may be involved in reproductive barriers between the species.Hybrid zones provide an opportunity to study the effects of selection and gene flow in natural settings. We employed nuclear microsatellites (single sequence repeat (SSR)) and candidate gene single-nucleotide polymorphism markers (SNPs) to characterize the genetic architecture and patterns of interspecific gene flow in the  The genetic architecture of hybrid zones provides the key to understand the sequence of genetic changes that accompany or facilitate speciation . In addiCline theory provides a rich and powerful conceptual framework for understanding the balance between selection and gene flow in the maintenance of hybrid zones, based on estimates of cline width and linkage disequilibrium  may resuWith the advent of genomic tools to analyze large numbers of loci, novel methods of clinal analysis (genomic clines) have been developed to analyze introgression of individual alleles and genotypes against average levels of genome-wide admixture , 2010. TPicea glauca Moench  is a transcontinental, boreal species that occurs naturally in almost all forested regions in Canada, with the exception of the Pacific Coast. It generally occurs at low elevations, although in Alaska it ranges from low elevations to alpine treeline. P. engelmannii Parry ex. Engelm (Engelmann spruce) extends from British Columbia and Alberta in the north, to New Mexico and Arizona in the south. Engelmann spruce grows at high elevations and is restricted to cold, humid habitats. It has a low tolerance of high temperatures and summer drought, but high tolerance of short growing seasons and deep snowpacks  of white spruce, Engelmann spruce and their hybrids were sampled for DNA extraction , Table 1M dNTP, 1 μl 10 × Paq5000 Reaction Buffer, 1 U Paq5000 DNA Polymerase, 0.5 pmol of M13 Infrared Label Primer and 1 pmol each of forward and reverse tailed primers. Samples were amplified using a modified version of Rungis' (2004) protocol, in which the annealing temperature varied according to each primer  in conjunction with the GoldenGate allele-specific assay in a 96-well, 768 SNP format. White and Engelmann spruce samples were tested using 1536 SNPs from a large panel of candidate genes putatively involved in cold hardiness (Q) was estimated from nuclear SSR data using the Bayesian clustering approach implemented in STRUCTURE version 2.2 (K) from 1 to 10 were tested using 50 000 iterations for the pre- and post-burn periods using the admixture model. Using a larger number of post-burn iterations (100 000) did not change the results obtained. The degree of admixture, α, was inferred from the data. Each run was repeated 20 times in order to estimate K using the method developed by Q>0.9 from STRUCTURE results for either species were used as reference for ‘pure species' in hybrid index calculations using the INTROGRESS 1.1  to evaluate which Kmax provided the best fit with the genetic data. On the basis of the results of the Evanno test, alignments of clusters for K=2 for STRUCTURE and TESS were optimized using the program CLUMPP 1.1.2b  in GENALEX. Principal coordinates 1 and 2 were plotted using R 3.0.3 . To test for isolation by distance, Pearson's product–moment correlations were estimated between populations using genetic and geographical distances in Mantel tests based on 1000 permutations, using the Vegan package in R 3.0.3 . Genetic distance was expressed as Fst/(1−Fst).Global and pairwise population F GENALEX . DiffereHybrid zones are often well described by a sigmoid curve showing the expected allele frequencies along geographic axes. Cline analysis can be used to estimate the shape, location (center) and width of the cline, and to test for concordance and coincidence in these parameters between markers (INTROGRESS 1.1 (P<0.0001). Evidence for directional selection for homozygous genotypes corresponds to an increase (+) or decrease (−) in the observed probability density in comparison with neutral expectations, resulting in genotypes EE−, EE+, WW+ or WW−. In the same way, an increase (+) or decrease (−) in the observed probability density of heterozygous genotypes will be observed as ‘overdominance' (WE+), or ‘underdominance' (WE−), respectively. A combination of increased density of one homozygous genotype, whereas the other homozygous and heterozygous genotypes remain neutral may be indicative of epistasis . The trait was assumed to have a constant heritability of 0.5 and fitness W for an individual with phenotype P in each local population; and it was modeled as stabilizing selection around the local optima as ω is the intensity of stabilizing selection. All simulations used ω=10. In addition to the quantitative traits, we also simulated data for 40 bi-allelic neutral loci. Each simulation was initiated with three populations fixed for alleles representative of each parental species  and the three remaining populations were colonized through migration from neighboring populations. After simulations had finished, the data were read into R and analyzed using the package INTROGRESS 1.1 , and between elevation and genetic distance ; however, the other parental species and hybrids showed no significant differences . Neither parentals nor hybrids showed differences in interspecific heterozygosity . When loci under selection represent 30–50% of the loci under study, the interspecific heterozygosity decreases  and the hybrid index  decreases in range. After 3000 generations following initial admixture, with 50% of loci under selection, the differentiation between parentals and hybrids is lost, and the hybrid zone becomes a hybrid swarm  and 1000  generations to those of our SNP data.In a second scenario, we simulated variation in ancestry and interspecific heterozygosity over time . In the In the third scenario, we ran simulations to assess the impact of the island model of migration on the levels of ancestry and interspecific heterozygosity. Migration was set to occur at the same rate as in the stepping stone simulations; however, migrant individuals could enter any patch regardless of spatial location in all simulations. When migration occurred only between adjacent populations (stepping stone model), the patterns of ancestry and interspecific heterozygosity varied little even after 3000 generations following initial contact. On the other hand, with migration under the island model, the hybrid zone tends to merge into a big hybrid swarm as early as 100 generations . When coOur results indicate the presence of an extensive hybrid zone between white and Engelmann spruce between 50 and 56°N and 600–1800 m in elevation in Western Canada and the United States. Both SSR and SNP clinal analyses revealed a complex hybrid zone, in which different loci introgress in different directions, with the center of individual clines shifted either toward Engelmann or white spruce. SSR clinal analysis pointed toward a unimodal hybrid zone distribution, whereas the SNP clinal analysis showed differentiation between parental and hybrids' habitats, pointing toward a trimodal hybrid zone distribution .SSR cline width varied from 50.1 to 401 km, with seven of the ten loci having cline widths between 50 and 59.82 km, and the remaining three loci with variable widths over 100 km . The cliP>0.0001). Overdominance  and directional selection  were the main types of selection explaining deviation of genomic clines away from neutral expectations  followed a bell-shaped distribution, with the more central Quesnel population having the highest SSR and SNP diversity. The peak in Na is shifted slightly toward Engelmann spruce (south from the average center of the hybrid zone) in both SSR and SNP clinal analyses  in the hybrid populations. Somewhat surprisingly, private alleles were only found in hybrid populations originating between 600 and 1600 m of elevation, and not in parental populations.Whereas all populations were highly diverse for the microsatellite and SNP markers assayed, those from the introgression zone at intermediate elevations and latitudes had, on average, more alleles and tended to have higher levels of heterozygosity and allelic richness . The numanalyses . The numHe=0.592) when analyzing SSR diversity, and in nearby Mount Robson (He=0.307) when analyzing SNP diversity. Showing a similar pattern to Na, the peak in heterozygosity was also shifted toward Engelmann spruce (south of average center of the hybrid zone) for SNP markers compared with SSRs .Heterozygosity was highest in the McGregor population .In most hybrid zones, clines are characterized by their narrow width, consistent shape and close concordance, suggesting that clines are maintained by dispersal and selection against heterozygotes (tension zones). In our study, however, the lack of concordance between markers, the width of the clines and the abundance of advanced-generation hybrids suggest that the Engelmann × white spruce hybrid zone is not a tension zone, and supports our recent studies suggesting the Engelmann × white spruce hybrid zone is maintained by a bounded hybrid superiority model  gene, part of the molecular circadian clock that provides the timing mechanism required for the response to seasonal changes in photoperiod. In Arabidopsis, mutation of this gene caused disruption of the circadian clock regulation of gene expression and caused flowering to occur independently of photoperiod  were associated with various important functions including carbohydrate and lipid metabolism, calcium signaling and transcription regulation . Most ofMorus  × white spruce hybrid zone is also asymmetric and appears to have moved from Sitka toward white spruce, in part due to the postglacial colonization sequence of the region (Lepus granatensis × L. europaeus (Thomomys actuosus × T.b ruidosae (Poecile carolinensis × P. atricapillus; Although temporal isolation and differences in population density cannot be ruled out as causes of asymmetric introgression, the ancient nature of the hybrid zone suggests that the explanation for asymmetry may come from past changes in demography of the hybridizing species. The shift in allelic richness and heterozygosity toward Engelmann spruce suggests that the asymmetry in introgression has likely been caused by past cline movement . Pollen e region . SimilarP. glauca × P. engelmannii hybrid zone in western North America. Our study has revealed the permeability of spruce genomes to introgression in this hybrid zone, in which different alleles are favored in different environments or genetic backgrounds and maintained by selection. By identifying populations with high-diversity levels and adaptations to particular environments, we set the ground for future studies aiming to predict the response of populations to climate change. This knowledge can then be translated into climate-based seed transfer recommendations to improve reforestation success, forest health and productivity for new climates.The work described here is the last in a series of studies exploring the genetic and genomic basis of local adaptation to climate in the http://doi.org/10.5061/dryad.473n5. SNP data in available at the Dryad Digital Repository: http://doi.org/10.5061/dryad.7h65f.Microsatellite data are available at the Dryad Digital Repository:


In the Methods section, a sentence from the DNA methylation at CpG islands subsection was inadvertently excluded. The complete, correct DNA methylation at CpG islands subsection should read as follows:"Methylation of the 5'-regulatory region of the genome was analyzed by Reduced Representation Bisulfite Sequencing (RRBS) using the ABI 37370  with a 48 cm capillary array (Cold Spring Harbor Laboratory [CSHL] Core Facility). RRBS experiments were sequenced at an average depth of 100 million reads per sample, resulting in coverage ranging from 2.4–4 million CpG sites per sample (mean of 3 million CpG sites) and 0.7 million overlapped CpG sites across all samples."http://www.cancer.gov/; grant number R01CA140487 to JLW and award number P20CA192994 to EL. Research was performed with additional assistance from CSHL Shared Resources, which are funded, in part, by the Cancer Center Support Grant 5P30CA045508. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Additionally, there is an error in the Funding section. The correct funding information is as follows: This study was supported by the National Institutes of Health—National Cancer Institute;