The domestication and subsequent selection by humans to create breeds and biological types of cattle undoubtedly altered the patterning of variation within their genomes. Strong selection to fix advantageous large-effect mutations underlying domesticability, breed characteristics or productivity created selective sweeps in which variation was lost in the chromosomal region flanking the selected allele. Selective sweeps have now been identified in the genomes of many animal species including humans, dogs, horses, and chickens. Here, we attempt to identify and characterise regions of the bovine genome that have been subjected to selective sweeps.Two datasets were used for the discovery and validation of selective sweeps via the fixation of alleles at a series of contiguous SNP loci. BovineSNP50 data were used to identify 28 putative sweep regions among 14 diverse cattle breeds. Affymetrix BOS 1 prescreening assay data for five breeds were used to identify 85 regions and validate 5 regions identified using the BovineSNP50 data. Many genes are located within these regions and the lack of sequence data for the analysed breeds precludes the nomination of selected genes or variants and limits the prediction of the selected phenotypes. However, phenotypes that we predict to have historically been under strong selection include horned-polled, coat colour, stature, ear morphology, and behaviour.The bias towards common SNPs in the design of the BovineSNP50 assay led to the identification of recent selective sweeps associated with breed formation and common to only a small number of breeds rather than ancient events associated with domestication which could potentially be common to all European taurines. The limited SNP density, or marker resolution, of the BovineSNP50 assay significantly impacted the rate of false discovery of selective sweeps, however, we found sweeps in common between breeds which were confirmed using an ultra-high-density assay scored in a small number of animals from a subset of the breeds. No sweep regions were shared between indicine and taurine breeds reflecting their divergent selection histories and the very different environmental habitats to which these sub-species have adapted. The transition from hunter-gather lifestyles to permanent dwelling societies was facilitated by both plant and animal domestication . The domStrong on-going selection for variants of large effect leads to a loss of variation within the chromosomal region flanking the selected variant and ultimately the complete fixation of the haplotype which harbours the variant. This phenomenon is known as the “hitch-hiking effect”  and a reBCDO2 which controls yellow and white skin colours, SEMA3A which plays a role in axonal path-finding important in brain development, and THSR which is postulated to derestrict the regulation of seasonal reproduction [TRYP1 which controls black coat colour in Large Munsterlanders and at FGFR3 in Dachshunds [FGFR3 mutations cause achondroplasia in humans and cattle. Other studies in dogs have identified a sweep surrounding IGF1 which is responsible for size variation [LCORL) that is associated with variation in human height [IGF2 has been implicated with selection for increased muscle mass and decreased fat deposition [NR6A1, PLAG1 and LCORL genes which are associated with an increased number of vertebrae and an elongation of the animal’s back [Several methods have been used to identify regions of the genome which have been subjected to selective sweeps, including those based on modeling allele frequency spectra, linkage disequilibrium and haplotype structure -12. Thesoduction . Selectichshunds . FGFR3 mariation  and in aariation . These sn height . A 28 kbposition . More rel’s back .Domestic animals have been demonstrated to be excellent models for genetic studies due to the availability of extensive pedigrees and because, as species, they are frequently more genetically diverse than human . HoweverBos taurus taurus) including Angus, Braunvieh, Charolais, Hanwoo, Hereford, Limousin, Salers, Shorthorn, Simmental, Brown Swiss, Finnish Ayrshire, Holstein, and Jersey, and the indicine (Bos taurus indicus) Brahman breed  allow the registration of crossbred cattle. This sampling strategy was employed to ensure that there would be minimal effects of recent introgression between the breeds following breed formation.All X-linked loci were removed from the analysis due to the greater number of assembly issues that are associated with this chromosome and also because the studied animals were male resulting in a halving of the number of chromosomes sampled for each breed which leads to a reduction in the precision of allele frequency estimation. The remaining BovineSNP50 genotypes were filtered on call rate ≥85% which left a total of 52,942 SNPs. Within the higher density AFFXB1P data, we required SNPs to have a call rate of ≥85% across all 5 breeds and a minimum call rate of 50% within each of the individual breeds. Following filtering 2,575,339 SNPs remained. These thresholds were based upon an empirical examination of the call rate distributions in the datasets (not shown) to retain the largest number of quality SNP in consideration of the within-breed sample size, which was small for the AFFXB1P genotyped individuals.N, assuming independence, and in testing 52,942 SNP on 29 autosomes we would expect to find 0.15N × ) regions in which N contiguous SNPs had fixed alleles. For N = 5 this corresponds to 4.0 false positives per breed but only 0.6 false positives per breed when N = 6. While increasing the number of contiguous monomorphic markers decreases the type I error rate, it simultaneously increases the size of the sweep region that can be detected to, on average, (N – 1) × 37 kb. To allow the identification of moderately sized sweeps, we chose an intermediate balance of these conflicting constraints based on the idea that any sweep identified in two or more breeds would almost certainly be real and likely share a common haplotype, while true sweeps found in only one breed would ultimately be independently validated by other studies. For example, assuming one false positive in each breed, there are 52,942 – 29 × (6 – 1) = 52,797 possible locations for a fixed 6 SNP haplotype in each breed and in the second breed there are 11 positions where the two haplotypes may overlap by at least one SNP (ignoring the centromeres and telomeres where the number is less). Thus the probability that the two false positive fixed haplotypes overlap anywhere in the genome is 52,797 ×  = 0.0002.The BovineSNP50 data were analysed by breed to identify putative selective sweeps. Because the number of variable loci differed within each breed primarily due to the breed of origin of SNP discovery in the design of the assay ,15, we r-1 where M was the number of individuals with genotypes for the SNP within the analysed breed. Among the variable SNPs, we further required that no more than 3 be contiguous. These thresholds were set to allow no more than one individual be heterozygous for a SNP within a selective sweep region in the event of genotyping errors and to allow for new variation to have been created within each region by mutation. These conditions again also allow for the possibility that the SNPs may not have been correctly ordered by the UMD3.1 assembly and that variable contigs may have erroneously been included within scaffolds containing a selective sweep. For regions containing 20 contiguous monomorphic SNPs, we would expect less than 10-10 false positives per breed if 15% of the SNPs were monomorphic within the breed. However, estimation of MAF for the loci on this assay was also influenced by the small sample size within each breed.We independently analysed the AFFXB1P data by breed requiring a putative selective sweep region to harbour at least 20 contiguous SNPs spanning at least 100 kb, with no more than 5% of the SNP having a MAF > (2M)Annotation of the genes present within all putative selective sweep regions was performed using the UCSC Genome Browser  and NCBITwenty eight genomic regions on 15 chromosomes were identified as putatively harbouring selective sweeps Table . SelectiThree of the five selective sweep regions detected in two or more breeds involved both beef and dairy breeds, whereas the 358 kb region on BTA12 is common to only the Angus, Salers, Shorthorn and Simmental beef breeds. None of the five selective sweeps shared by two or more breeds are phylogenetically congruent in the sense that we might have expected the sweep to have arisen in a recent common ancestor . While tth of 248 SNPs, whereas on BTA21 the variable allele was at the 22nd of 30 SNPs, indicating a conserved core at both loci. No sweeps were found in common between the four taurine breeds and the indicine Brahman.A total of 85 putative selective sweep regions spanning from 200 to 846 kb and averaging 321 ± 132 kb were identified on 28 of the 29 bovine autosomes in the five genotyped breeds and, of these regions, 20 were shared in two or more breeds  < α, for small α, we must choose N to be sufficiently large that it would be highly unlikely to observe N contiguous loci all with a MAF < α due to chance alone and yet sufficiently small that the targeted sweeps are not smaller than 37 × N-1) kb, where 37 kb represents the median intermarker interval on the BovineSNP50 assay. The design of the BovineSNP50 assay also led to lower average MAF and larger numbers of monomorphic loci in breeds such as Brahman, that are phylogenetically distant from the SNP discovery breeds . To adju kb, wherWe also employed the AFFXB1P assay which contained almost 2.8 million putative SNPs that were screened for variability in a small number of animals from several breeds prior to the design of the commercial BOS 1 Axiom assay. While we had many fewer animals genotyped with this assay which influenced the estimation of MAF, the AFFXB1P assay had over 50× the number of SNPs present on the BovineSNP50 assay which offered considerably greater power for identifying small sweeps and the application of this assay also suffers less from ascertainment bias. While loci that have been fixed in all domesticated cattle relative to their auroch forbears will still not appear on this assay due to the requirement that the putative SNP must have been predicted to have been variable in the sequence data for at least one breed, there was much less selection for SNPs with high MAF in numerous breeds in the design of this assay relative to the BovineSNP50. Consequently, we expected this assay to identify putative sweep regions that could not be identified by the application of the BovineSNP50 assay and to more precisely define the boundaries of sweeps that were detected by the BovineSNP50 assay and validated by the AFFXB1P assay.NBEA) and mab21-like 1 (MAB21L1) which have been implicated in human autism and psychiatric disorders, respectively [One of the main focuses of this study was to identify selective sweep regions for which a single haplotype was found in all breeds at the core region defined as the intersection of the overlapping sweep regions found in each of the breeds. The existence of these shared core regions dramatically reduces the potential for false positive discoveries since the likelihood of extended identical haplotypes being found in multiple breeds due to chance alone is close to nil. We identified a handful of regions in each dataset that met these criteria. The putative sweep shared among six breeds on BTA24 contains four pseudogenes, of which three are olfactory receptor-like. Whether any of these pseudogenes are expressed is unclear; however, olfactory receptor loci were detected as being recently duplicated within the bovine genome  suggestiectively -37. SincDGKZ) and several bovine ESTs. DGKZ has been implicated as a member of the downstream leptin signaling pathway and reduced expression or activity within the hypothalamus has been associated with obesity [POLL locus [Of the regions identified in both datasets, a selective sweep on BTA13 was detected in Holstein using the BovineSNP50 data and was also discovered in Wagyu using the AFFXB1P data. The region from 15.49-15.74 Mb contains diacylglycerol kinase zeta  in which mutations lead to the black coat colour in cattle [MC1R haplotype to cause it to fail to be detected as a sweep using the ultra-high-density data. Finally, of particular interest is the fact that no sweep was identified in this genomic region in Wagyu cattle suggesting that black coat colour in Angus and Wagyu cattle may not be allelic. Recently, a mutation within beta-defensin 103 (CBD103) has been shown to cause black coat colour in dogs [CBD103 maps to 4.89 Mb on BTA27 centromeric of the sweep that was detected in Wagyu cattle  gene which has previously been identified as a candidate for a QTL affecting ear floppiness and morphology in dogs [Bos taurus indicus breeds Guzerat, Nelore, Gir and Indu Brazil imported primarily from Brazil but all originating in India and the Bos taurus taurus Shorthorn and Hereford breeds [Among the other detected breed-specific sweep regions, there were several regions linked to known phenotype to genotype associations. A region on BTA14 specific to Angus cattle harbours several genes including n height , the stan height  and withn height . Angus hn height  perhaps n humans . Angus cn humans . Another in dogs ,55. Brahd breeds . There iDrosophila[An interesting observation relative to the identified breed-specific and common selective sweeps is the enrichment of genes related to immune function and response which have clearly been important to the adaptive evolution of the species . Strong rosophila,86 and hrosophila, as wellrosophila.The abundance of olfactory receptor (OR) genes and pseudogenes within sweep regions is intriguing and suggests that olfactory loci play a major role in the domesticability of species. Olfactory receptor genes have previously been found to have been under selection in cattle ,25 and mST statistics in the Bovine HapMap project [ST or iHS value. We found evidence for only one selective sweep region for which the Bovine HapMap project [ST statistics. Using the BovineSNP50 data, we found a putative sweep in Angus, Salers, Shorthorn and Simmental cattle on BTA12 in a region harbouring NBEA that was found to have differentiated among taurine breeds. The fact that only one such region was detected is not surprising since regions that have been strongly selected for a derived allele in one breed are likely to be selectively neutral in other breeds which do not possess this allele, leading to small differences in flanking SNP allele frequencies and modest FST statistics. Large FST statistics imply divergent selection for alternate alleles within different breeds suggesting that there may be several mutations in NBEA that have been strongly historically selected in some breeds and that are currently under selection in others. None of the putative sweep regions detected in the Holsteins were concordant with regions detected to be responding to recent selection in Israeli Holsteins [We found evidence for selective sweeps in genomic regions that were detected to have diverged between breeds using integrated haplotype scores (iHS) and F project . Putativ project  identifiolsteins . Rather olsteins , this moHIBCH, MGC128040, MSTN, PMS1, ORMDL1 and ASNSD1, of which MSTN is almost certainly the locus selected in Limousin due to its effects on muscling. This locus is unlikely to have been divergently selected in the West African populations. Likewise, the region on BTA7 contains several genes  and it is likely that different loci were under selection in U.S. Limousin and West African cattle. In comparison to a study of regions of differentiation among the genomes of three French dairy breeds [MC1R) locus on BTA18 and the region on BTA14 at 24.63 Mb (UMD3.1 position) harbouring PLAG1 which is associated with cattle stature [PDGFA) was identified as a potential candidate gene underlying the selective sweep at 42.2-42.8 Mb on BTA25 in Simmental, whereas, the receptor for this growth factor (PDGFRA) was identified as differentiated among the French dairy breeds. Our findings also demonstrate concordance with a study in dairy cattle where 1,600 out of 34,851 (4.59%) SNPs showed signatures of on-going selection via iHS test statistics [Two breed-specific sweep regions in Limousin on BTA2 at 5.97 Mb and BTA7 at 40.25 Mb (UMD3.1 positions) were also identified in a scan of West African cattle for loci underlying adaptive divergence between populations . The reg stature  were ideatistics . Howeveratistics  sought tBos taurus taurus breeds which likely reflects the recent admixture that occurred in the formation of the Brahman and the fact that the breed does not share a common phenotype such as coat colour with any of the taurine breeds. Furthermore, indicine cattle are more commonly found in the southern tier of the US where they are exposed to higher temperatures and humidities and lower pasture qualities and availability than are taurine cattle which are more frequently found in the northern US. Consequently, we would not expect these breeds to have been subjected to selection for common morphological or adaptive phenotypes. Additionally, no common sweeps were detected between the cattle sub-species possibly due to the more severe ascertainment bias on MAF for BovineSNP50 loci in Brahman cattle. While SNP discovery was performed in Brahman for the development of the AFFXB1P assay, the number of indicine breeds sequenced for SNP discovery was small relative to the number of sequenced taurine breeds leading to a bias towards SNPs common in taurine cattle being included on the assay. However, the density of SNPs on this assay is so great (~1 SNP/kb) that we did not expect the reliability of sweep regions identified in Brahman to be significantly less than those identified in taurine breeds. Our identification of a putative sweep region harbouring a previously identified QTL for ear length and floppiness in Brahman is consistent with the introduction of the undesirable allele from Indu Brazil cattle during breed formation and subsequent strong selection by breeders to remove the allele and fix a shorter ear type within the breed.No putative selective sweep regions were found in common between Brahman and any of the High-density assays, such as the BovineSNP50, have previously been shown to be adequate for the identification of runs of homozygosity (ROH) and for estimating inbreeding coefficients within cattle breeds . However-1 where M is the number of genotyped animals. We have previously found that the genotyping error rate of loci on the Affymetrix Axiom BOS 1 assay is very similar to that of the Illumina BovineSNP50 assay  and thus, we do not expect genotyping errors to explain this result, although it is certainly a possibility. It appears that the phenomenon is either due to errors in the genome assembly or mapping of probes for the AFFXB1P loci, or is simply due to type I errors. As a consequence, the reliability of declaration of a selective sweep is dramatically improved when sweeps are found to be common between breeds, particularly when the breeds are phylogenetically distant. We found several sweep regions that were common to two or more breeds and five sweeps predicted from the BovineSNP50 data were validated by the AFFXB1P data.Several putative selective sweeps identified using the BovineSNP50 data failed to be validated using the AFFXB1P data. While many fewer animals representing each breed were genotyped with the AFFXB1P assay, among the 50× additional SNPs within each such region, we found that at least 5% of the SNP had a MAF > (2M)Identifying the mutations that underlie these sweep regions will be paramount to more fully understanding the effects of human interaction on the genomes of domesticated cattle. Candidates will soon become available by sequencing the genomes of individuals that are homozygous for identical SNP haplotypes within a sweep region but where some originate from the breeds predicted to have undergone a selective sweep and the others from breeds in which no sweep was detected. However, even after these mutations have been identified, our understanding of the phenotype that was created and selected to complete fixation may still be limited. The functional analysis of genes within the selected regions sheds little light on this since each mutation within these genes may lead to unpredictable phenotypes. Finally, while our sampling of breeds was small, we found little evidence for the sharing of sweeps among phylogenetically closely related breeds. This further supports our conjecture that the design of the BovineSNP50 assay to include common variation makes it primarily suitable for the detection of sweeps that have occurred following breed formation.We identified selective sweeps that primarily appear to have occurred following breed formation events. Due to the constraint that SNPs be variable in multiple breeds which was imposed during the design of both of the utilized assays, we did not identify any sweeps that were common to all breeds within the study. There were also no sweep regions predicted to be in common between breeds of taurine and indicine descent probably reflecting the different environmental and demographic forces to which these sub-species have been exposed during breed formation. For several of the detected sweep regions we were able to identify the phenotypes and genes that were subjected to selection, or to propose these based upon the results of previous mapping studies. However, for many of these regions the selected gene and phenotype are unclear. The fact that so many of the detected sweep regions harbour genes associated with behavioural characteristics, immunity, reproductive processes, or embryonic development is probably not remarkable considering the fact that strong selection acts on these fitness traits and that the time required to achieve fixation of variants of modest effect may be considerably longer than the 200 years since breed formation during which strong human selection has acted.de novo sequence assemblies can accurately be created for each breed.We demonstrate that the resolution and SNP ascertainment bias inherent in the design of the assay used to detect selective sweeps is of paramount importance and that the BovineSNP50 assay is not generally suitable for this purpose due to the high type I error rates that are likely to be encountered. SNP ascertainment bias leads to lower MAF in breeds that are phylogenetically distant from the SNP discovery breeds and an increased rate of monomorphic SNPs within these breeds. As whole genome sequencing becomes less expensive, these problems will likely be ameliorated by sequencing a few distantly related individuals from each breed and this approach may also be used to identify the candidate mutations which underlie each sweep. However, the approach is reliant on the alignment of sequences to a draft Hereford reference assembly  which inThe identification of genes and variants underlying historical selective sweeps is of interest from the perspective of understanding how human interaction with cattle has influenced the patterning of variation within the bovine genome. Perhaps of more importance, the discovery of the selected variants will lead to the identification of large effect QTLs and ultimately a better understanding as to the phenotypes which are affected by variation within genes and regulatory elements.Genotypes are available to scientists interested in non-commercial research upon signing a Materials Transfer Agreement (MTA).The authors declare that they have no competing interests.JFT, JED and HRR designed the experiment. HRR, RDS and JFT analysed data. SDM, MMR and RDS extracted DNA. MMR and SDM prepared samples for genotyping, SDM ran the Illumina assay, and RDS genotyped samples and managed the genotype database. HRR and JFT wrote the manuscript. All authors read and approved the final manuscript.


PC1/PCSK1 region of the Z chromosome at 55.43-56.16 Mb was the most heavily selected region. This region had 26 SNP markers and seven genes, Mar-03, SLC12A2, FBN2, ERAP1, CAST, PC1/PCSK1 and ELL2, where PC1/PCSK1 are the chicken/human names for the same gene. The lean and fat lines had two main haplotypes with completely opposite SNP alleles for the 26 SNP markers and were virtually line-specific, and had a recombinant haplotype with nearly equal frequency (0.193 and 0.196) in both lines. Other haplotypes in this region had negligible frequencies. Nine other regions with selection signatures were PAH-IGF1, TRPC4, GJD4-CCNY, NDST4, NOVA1, GALNT9, the ESRP2-GALR1 region with five genes, the SYCP2-CADH4 with six genes, and the TULP1-KIF21B with 14 genes. Genome-wide association analysis showed that nearly all regions with evidence of selection signature had SNP effects with genome-wide significance (P<10–6) on abdominal fat weight and percentage. The results of this study provide specific gene targets for the control of chicken AFC and a potential model of AFC in human obesity.We conducted a selection signature analysis using the chicken 60k SNP chip in two chicken lines that had been divergently selected for abdominal fat content (AFC) for 11 generations. The selection signature analysis used multiple signals of selection, including long-range allele frequency differences between the lean and fat lines, long-range heterozygosity changes, linkage disequilibrium, haplotype frequencies, and extended haplotype homozygosity. Multiple signals of selection identified ten signatures on chromosomes 1, 2, 4, 5, 11, 15, 20, 26 and Z. The 0.73 Mb  Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates The chicken , and was approved by the Laboratory Animal Management Committee of Northeast Agricultural University.0 generation of NEAUHLF came from the same grandsire line originating from the Arbor Acres broiler, which was then divided into two lines according to their plasma VLDL concentration at 7 weeks of age. The G0 birds were mated (one sire: four dams) to produce 25 half-sib families for each line, with an average of 70 G1 offspring per family in two hatches. From G1 to G11, the birds of each line were raised in two hatches and housed in pens with five birds per cage. Plasma VLDL concentrations were measured for all male birds, which had free access to feed and water at 7 weeks, and the AFP of the male birds in the first hatch was measured after slaughter at 7 weeks. Sib birds from the families with lower (lean line) or higher (fat line) AFP than the average value for the population were selected as candidates for breeding, considering the plasma VLDL concentration and the body weights of male birds in the second hatch and the egg production of female birds in both hatches. These birds were kept under the same environmental conditions and had free access to feed and water. Commercial corn-soybean-based diets that met all National Research Council (NRC) requirements were provided. From hatch to 3 weeks of age, the birds received a starter feed  and from 4 weeks of age to slaughter the birds were fed a grower diet . A total of 475 individuals (203 from the fat line and 272 from the lean line) from generation 11 of NEAUHLF were used in this study. The AFP of the fat line was 3.75 times that of the lean line at generation 11  broiler lines divergently selected for AFC (NEAUHLF). The NEAUHLF lines have been selected since 1996 using abdominal fat percentage (AFP  =  AFW/body weight) and plasma very low-density lipoprotein (VLDL) concentration as selection criteria. The Gation 11 , and the2-coated tubes and stored at −20°C. Genotyping of the chicken 60k SNP chips from Illumina Inc. was performed by DNA LandMarks Inc., Quebec, Canada, using 75 μL of approximately 50 ng/μL genomic DNA.The 60k chip had a total of 57,636 SNPs, and 45,578 SNPs with a minor allele frequency (MAF) of 5% or greater and a call rate of 95% in the combined sample of the lean and fat lines were selected for use in this study. Individuals with pedigree error or 5% or more missing SNP genotypes were removed. Of the 45,578 SNPs, 45,005 had known chromosome locations and were distributed across 28 autosomes, the Z chromosome and two linkage groups (LGE22C19W28_E50C23 and LGE64). The number of SNPs per chromosome ranged from 2 to 7,135 with a mean distance of 22.51 kb between adjacent SNPs . GenomicThe selection signature analysis used a combination of various signals of selection, including long-range heterozygosity changes For long-range AFD and heterozygosity measures, we used 0.5 Mb sliding windows of SNP markers as the genome length. Two long-range heterozygosity measures were calculated following the method in 0 and q0 represent the allele frequencies of alleles 1 and 2 in the unselected population, and let pt and qt represent the allele frequencies for the same alleles after t generations of selection. Then, heterozygosity has no change at generation t if pt = q0 and qt = p0 (p-q sweep) assuming Hardy-Weinberg equilibrium. Therefore, the use of heterozygosity could miss significant allele frequency changes that result in a p-q sweep. For this reason, increases in heterozygosity (rather than decreases only) were also considered (in addition to AFD and Z_AFD) when identifying chromosome regions subjected to selection, because increases in heterozygosity could also be a result of substantial allele frequency changes when the initial population had extreme frequencies.The two AFD measures (AFD and Z_AFD) were used to compensate for the weakness of the three measures of heterozygosity (Z_lean and Z_fat) in cases of ‘p-q sweep’, where heterozygosity measures are expected to fail to detect genome changes due to selection. Let pP-value for declaring genome-wide significance was (0.05)/[(2)(2)]  = 2.74×10−7 = 10−6.56. The statistical model for PLINK was the same as that for the GLS-LS version of EPISNP, except that the family effect was treated as a fixed effect. Gene locations were based on Ensembl The significance testing of SNP effects on AFW and AFP used four methods, the GLS-LS version of EPISNP PC1/PCSK1, PAH-IGF1, TRPC4, GJD4-CCNY, NDST4, NOVA1, GALNT9, the ESRP2-GALR1 region with five genes, the SYCP2-CADH4 region with six genes, and the TULP1-KIF21B region with 14 genes. Multiple signals of selection identified ten selection signatures on chromosomes 1, 2, 4, 5, 11, 15, 20, 26 and Z as signatures of selection for high and low AFC in the 11 generations. The candidate genes and regions with multiple genes in the selection signatures were Long-range AFD between the lean and fat lines, Z_AFD, Z_lean, and Z_fat in 0.5 Mb sliding windows of SNP markers were used for initial genome-wide screening of selection signatures . LinkageMar-03, SLC12A2, FBN2, ERAP1, CAST, PC1, and ELL2, noting that only a downstream portion of Mar-03 and an upstream portion of ELL2 were in this region. Of these seven genes, only PC1 is known to have biological functions highly relevant to AFC. In humans, PC1 is also known as PCSK1 PCSK1 have been found to be associated with human obesity PC1/PCSK1 to obesity, we named this 0.73 Mb seven-gene region as the PC1/PCSK1 region.The most significant region in the 11 generations of divergent selection for high and low AFC was the 0.73 Mb region of 55.43–56.16 Mb of chromosome Z, with 26 intronic SNP markers. This region had the largest long-range AFD and the largest Z_AFD between the lean and fat lines for all chromosomes, with an average AFD greater than 0.70 and peak Z_AFD of 4.98 in 0.5 Mb sliding windows of SNP markers , Table 3The haplotype and LD analyses showed that the two lines had two main haplotypes and shared a common recombinant haplotype in two haplotype blocks separated by the recombination point at 55,736,592–55,808,443 bp, shown between markers 1,141 and 1,142 in 2 = 1 in both blocks for adjacent SNPs, except for one SNP pair in the first block and two SNP pairs in the second block, and had three fixed SNP markers in the first block , shown on the right of The LD and EHH results also showed that this 0.73 Mb region was potentially subject to selection, and that the fat line had stronger selection than the lean line. The fat line had strong LD values, rst block . The fatPAH and was about 150 kb upstream of IGF1, a gene known to be associated with fatness in chicken IGF1 but neither met the minor allele frequency (MAF) requirement (MAF >5%). Consequently, no SNP marker inside IGF1 was present in this study and it is unknown whether IGF1 was part of the PAH selection signature. However, four SNP markers that were immediately upstream of IGF1 were fixed, and another three SNPs were either fixed or nearly fixed with allele frequencies of 0.015, 1.0 and 0.983 in the fat line, while the AFD between the two lines for these seven markers were 0.14–0.53, indicating that IGF1 could have been selected for high AFC. The LD signals and EHH values in the region spanning IGF1 were considerably stronger in the fat line than in the lean line  and a large Z_AFD value (4.15) in the 12.48–12.80 Mb region with two genes, 8,966 bp . The EHHand CCNY . This reand CCNY . These rNDST4 was identified by two measures, the second largest AFD value of 0.50  and a large Z_AFD value (4.26). The LD and EEH values strongly suggested that selection in this region was in the lean line only  haplotype in the lean line did not exist in the fat line  was close to a group of SNP effects, and a human obesity gene, MTIF3TRPC4 region  at 50,714,780–50,717,850 bp. This molecule is on the leptin/melanocortin pathway, which is related to obesity IGFBP7-LEPROTL1 region. The SGCZ region downstream of the NDST4 selection signature had the most significant SNP effect and substantial AFD (0.35).The chromosome 4 signature at  effects . On the PC1/PCSK1 in the most significant 0.73 Mb region at 55.43–56.16 Mb of chromosome Z. This region clearly stood out as the region with the strongest evidence of selection in the entire chicken genome, in both the lean and fat lines in this study. In addition, the known function of PCSK1 in regulating insulin biosynthesis and its known association with human obesity PC1/PCSK1 the most likely candidate gene to have a causal effect on AFC. However, haplotype analysis showed that PC1/PCSK1 should not be the only gene with causal effect in this region, and that the evidence in this region was consistent with a two-locus model with two causal loci flanking the recombination point at 55,736,592–55,808,443 bp inside FBN2.The most apparent candidate gene was Mar-03, SLC12A2, and the upstream two-thirds of FBN2. None of these three genes has known biological functions specifically related to fat metabolism. However, based on chromosome positions, Mar-03 was on the left end of this region and should be least likely to have causal effects, because a substantial hitchhiking effect upstream of Mar-03 should have been observed if Mar-03 had been one of the most significant genes. This analysis leaves SLC12A2 and the upstream two-thirds of FBN2 to be the candidate genes for causal effects. Downstream of the recombination point were the downstream one-third of FBN, ERAP1, CAST, PC1/PCSK1 and part of ELL2. In this block, PC1/PCSK1 is the most apparent candidate gene but the downstream one-third of FBN, ERAP1, CAST and ELL2 could not be excluded from having causal effects. However, ELL2 is unlikely to be the target of selection given that AFD values downstream of ELL2 dropped rather sharply  was only 0.28 Mb downstream at 3.68–3.72 Mb, and a second human obesity gene (FTO) was less than 1.45 Mb downstream at 4.85–4.89 Mb, making this region an interesting region for candidate genes. Within this region, the most significant SNP was at 4.40 Mb for AFW and AFP  and abdominal fat percentage (AFP). Blue circles and red plus signs: SNP effects from EPISNP (PDF)Click here for additional data file.Figure S2AFD and Z values in 0.5 Mb sliding windows of SNP markers.(PDF)Click here for additional data file.Figure S3Linkage disequilibrium patterns of the selection signatures.(PDF)Click here for additional data file.Figure S4Extended haplotype homozygosity in selection signatures.(PDF)Click here for additional data file.Table S1Full gene names of candidate genes for chicken abdominal fat content. The genes in (DOC)Click here for additional data file.Table S2Human obesity genes on the chicken genome.(DOC)Click here for additional data file.Table S3Haplotype frequencies in selection signatures.(DOC)Click here for additional data file.


BCL11A gene and the HBS1L-MYB intergenic region, which are located outside the β-globin locus. For this reason, we developed two multiplex assays that allow the genotyping of SNPs at these two genomic regions which have been shown to be associated with variable HbF levels in different populations.B-thalassaemia and sickle cell disease (SCD) are two of the most common monogenic diseases that are found in many populations worldwide. In both disorders the clinical severity is highly variable, with the persistence of fetal haemoglobin (HbF) being one of the major ameliorating factors. HbF levels are affected by, amongst other factors, single nucleotide polymorphisms (SNPs) at the BCL11A gene and sixteen SNPs at HBS1L-MYB intergenic region which were shown to modify HbF levels. The different genotypes can be determined based on the position and the fluorescent colour of the peaks in a single electropherogram. DNA sequencing and restriction fragment length polymorphism (PCR-RFLP) assays were used to verify genotyping results obtained by SNaPshot minisequencing.Two multiplex assays based on the SNaPshot minisequencing approach were developed. The two assays can be used to simultaneous genotype twelve SNPs at the BCL11A gene and HBS1L-MYB intergenic region which have an effect on HbF levels. The assays can be easily applied for accurate, time and cost efficient genotyping of the selected SNPs in various populations.In summary, we propose two multiplex assays based on the SNaPshot minisequencing approach for the simultaneous identification of SNPs located at the  B-thalassaemia and SCD are common autosomally inherited disorders that are found in many populations around the world. B-thalassaemia is caused by mutations that lead to low or complete absence of expression of the β-globin gene while in SCD a point mutation in the β-globin gene causes changes in the protein structure ,2. In β-2γ2) production  (Applied Biosystems), 0.5X Terminator ready reaction mix v1.1 [2.5X] (Applied Biosystems), sequencing primer at a concentration of 0.5 μM  following the manufacturer’s instructions.To verify the genotyping of the selected BCL11A locus  and five SNPs at the HBS1L-MYB locus . Singleplex PCR reactions were performed in a final volume of 25 μl using 100 ng of genomic DNA, 1X Amplitaq Gold Buffer (Applied Biosystems), 200 mΜ of each dNTP (Invitrogen), 1.25 units of Amplitaq Gold DNA Polymerase (Applied Biosystems) and primers at 0.2 μM concentration as shown in Table BCL11A locus AvaII was used for rs7606173 SNP; AatII for rs6706648, and for HBS1L-MYB locus BstxI was used for rs28384513; RsaI: rs11759553; MspI: rs9376090; HinfI: rs4895440; and Hpy188III: rs4895441, all restriction enzymes were purchased from New England Biolabs) for 1 hour at 37°C and visualized in 2% w/v ethidium bromide-stained agarose gel to distinguish between the genotypes of each SNP allele on the basis of susceptibility to digestion by the enzymes.The PCR-RFLP method was applied for the detection of two SNPs at the BCL11A and sixteen HBS1L-MYB polymorphisms, respectively . Moreover, three unexpected non-specific peaks exist in the electropherogram  as well as the existence of any false positive or false negative peaks was examined. Our minisequencing results showed 100% agreement with the genotypes determined by sequencing and restriction fragment analysis. No false positive or false negative minisequencing results were found. These validation data prove that the new multiplex minisequencing assays developed are highly accurate and suitable for the detection of the twelve BCL11A and sixteen HBS1L-MYB polymorphisms.To evaluate the accuracy of the assays we screened the same cohort of 25 β-thalassaemia patients as above with sequencing and/or PCR-RFLP for all 12 BCL11A gene and the HBS1L-MYB intergenic region, respectively. These polymorphisms have been shown to have an effect on HbF levels, a major ameliorating factor of SCD and β-thalassaemia in various populations [In this study, we describe two simple multiplex assays for simultaneous detection of twelve and sixteen SNPs that are located in the ulations . The desulations ,38.BCL11A SNPs is a two-step strategy based on the multiplex amplification of nine fragments where the SNPs are located, in two separate multiplex PCR reactions, followed by one multiplex SNaPshot minisequencing reaction. Similarly, genotyping the selected SNPs in the HBS1L-MYB intergenic region is based on the multiplex amplification of thirteen fragments, in two separate multiplex PCR reactions, followed by one multiplex SNaPshot minisequencing reaction.The assay for genotyping the selected To validate the SNP genotyping results obtained by SNaPshot minisequencing we performed Sanger DNA sequencing or PCR-RFLP analyses of 25 DNA samples. The results of Sanger DNA sequencing or PCR-RFLP were always in agreement with those of the SNaPshot minisequencing.Using the SNaPshot minisequencing technique we were able to determine the genotypes of the samples based on the position and the fluorescent colour of the peaks on the electropherograms generated. The minor shifts observed in the position of the peaks compared to the expected size may be due to the different molecular weights of each of the four fluorescent ddNTPs which can be incorporated at the end of each primer and to the POP-7 polymer used. These shifts are consistent for all samples that were tested and did not interfere with the analysis of the polymorphisms. Neutral tails incorporated at the ends of the minisequencing primers ensured sufficient spacing of the peaks and accommodated any unexpected small shifts in peak positions.HBS1L-MYB intergenic region even though this SNP is known to be associated with elevated levels of HbF [A crucial step for the success of the minisequencing assay is the correct design of the multiplex minisequencing primers. Given that the 3′ end of the minisequencing primer must be immediately adjacent to the SNP of interest, there is little flexibility in the positioning of the primer, although parameters like the length of the primer and its orientation are more amenable to manipulation. In deciding the orientation of the primers, it is necessary to take into consideration the presence of any additional sequence variations flanking the SNP of interest as these may cause problems in the annealing and extension of the primer ,40. It is of HbF ,32. SpecBCL11A and HBS1L-MYB assays were well standardized and that no amplification problems were experienced for any of the samples that we genotyped, we concluded that it is possible to reduce the time necessary for the completion of the whole assay by omitting the agarose gel electrophoresis step for the detection and analysis of the initial multiplex PCR products. Moreover, the assay can be easily customized for the analysis of only a subset of the SNPs or the addition of more SNPs without major changes in the conditions for either assay.Based on our observations that the multiplex PCR reactions for both the BCL11A SNPs and thirteen sequencing reactions for the HBS1L-MYB SNPs compared to a single SNaPshot minisequencing reaction. Although it is possible to screen large scale sequences or SNPs with next generation sequencing and arrays, respectively, these high-throughput techniques are expensive and require specialized equipment which is not commonly available in most molecular biology laboratories.The advantage of the minisequencing technique as a means of screening for polymorphisms is that several selected polymorphisms can be detected simultaneously in a single reaction, with the results presented in a single electropherogram. The technique can be easily performed in any laboratory that is equipped with a thermocycler and a DNA sequencer. The SNaPshot minisequencing technique is highly accurate and sensitive and is suitable for the genotyping of large numbers of clinical samples relatively quickly. The possibility of using this technique for simultaneous identification of several polymorphisms at distant genetic locations in a single reaction makes it cost and time efficient compared to other commonly used genotyping techniques such as PCR-RFLP and Sanger DNA sequencing. Although PCR-RFLP is a simple technique, its use for genotyping the large numbers of SNPs selected for the two loci under investigation is time consuming and impractical because several PCR reactions must be carried out per locus. More importantly, PCR-RFLP is not applicable for all SNPs under investigation because of the absence of informative restriction enzyme recognition sites for all SNPs or the presence of additional SNPs within the recognition site. Similarly, Sanger DNA sequencing for the selected SNPs in our assays requires carrying out nine sequencing reactions for the In addition, the two described multiplex minisequencing assays can be combined with multiplex ligation-dependent probe amplification (MLPA) technique or Gap-PCR for rapid detection of any deletions or duplications of one or more sequences in or near the α–globin genes -43, sincBCL11A gene and in the HBS1L-MYB intergenic region that were shown in various studies to be associated with variable HbF levels in several populations. Multiplex SNaPshot minisequencing, a powerful method that can be used for analyzing SNPs distantly located to each other in a single reaction was chosen for this purpose. Taking the advantage of this method, two multiplex SNaPshot minisequencing assays were developed for simultaneous genotyping of twelve SNPs in the BCL11A gene and sixteen SNPs in the HBS1L-MYB intergenic region. The method was applied to screen 25 different samples and the results obtained were verified by DNA sequencing and PCR-RFLP. Concluding, the assays developed can be successfully applied for accurate, time and cost efficient simultaneous genotyping of SNPs associated with HbF levels in the BCL11A gene and in the HBS1L-MYB intergenic region in HPFH, β-thalassaemia, SCD or other hemoglobinopathy populations.The primary goal of this study was to develop a simple and rapid method to genotype SNPs in the The authors declare that they have no competing interests.BCL11A and HBS1L-MYB SNPs with the two SNaPshot Minisequencing Assays and provided advice on methodology development. PF designed the experiments, aided in the implementation of the experiments and drafted the manuscript. IK developed the laboratory protocols for the two SNaPshot Minisequencing assays to the level of standard screening procedure, performed the analysis of the data and drafted the manuscript. MP provided advice on methodology development and on manuscript writing. All authors read and approved the final manuscript.MK introduced the concept of the genotyping of


Identification of causal SNPs in most genome wide association studies relies on approaches that consider each SNP individually. However, there is a strong correlation structure among SNPs that needs to be taken into account. Hence, increasingly modern computationally expensive regression methods are employed for SNP selection that consider all markers simultaneously and thus incorporate dependencies among SNPs.We develop a novel multivariate algorithm for large scale SNP selection using CAR score regression, a promising new approach for prioritizing biomarkers. Specifically, we propose a computationally efficient procedure for shrinkage estimation of CAR scores from high-dimensional data. Subsequently, we conduct a comprehensive comparison study including five advanced regression approaches  and a univariate approach  to determine the effectiveness in finding true causal SNPs.http://strimmerlab.org/software/care/.Simultaneous SNP selection is a challenging task. We demonstrate that our CAR score-based algorithm consistently outperforms all competing approaches, both uni- and multivariate, in terms of correctly recovered causal SNPs and SNP ranking. An R package implementing the approach as well as R code to reproduce the complete study presented here is available from Genome-wide associations studies (GWAS) are now routinely conducted to search for genetic factors indicative of or even causally linked to disease. Typically, the aim of such a study is to identify a small subset of single nucleotide polymorphisms (SNPs) associated with a phenotype of interest. From an analysis point of view the screening for relevant biomarkers is best cast as a problem of statistical variable selection. In GWAS variable selection is very challenging as the full set of SNPs is often very large while both the effect of each potentially causal SNP as well as their number is very small e.g.-3)..3]).t-score, the ATT statistic[To date, most GWAS are based on single-SNP analyzes where each SNP is considered independently of all others and association with the phenotype is computed using a univariate test statistic such as variants of the tatistic or margitatistic. The advIn order to increase statistical efficiency and to exploit the correlation among predictive SNPs several authors have recently started to investigate simultaneous SNP selection using fully multivariate approaches. This was pioneered for GWAS in the seminal paper of that intt-score (CAT score) and the correlation-adjusted marginal correlation (CAR score), see[Recently, to address the problem of variable importance and selection under correlation in genomics, we have introduced two novel statistics, the correlation-adjusted re), see,9. TheseHere, we develop a novel multivariate algorithm for large scale SNP selection using CAR score regression. Specifically, we propose a computationally efficient procedure that allows for shrinkage estimation of CAR scores even for very high-dimensional data sets. Subsequently, we conduct a systematic comparison of state-of-the-art simultaneous SNP selection procedures using data from the GAW17 consortium. These dd predictors X = {X1,…,Xd} and a metric or binary response variable Y . In GWAS the covariates X are given by the genotype and the response Y  is the phenotype or trait of interest. The correlation matrix among the predicting variables has size d × d and is denoted by P . The vector of marginal correlations PXY = Tcontains the correlations between a metric response and each individual SNP. Similarly, for binary response the t-score vector τ = Tcontains the t-scores computed for each variable.The basic setup we consider here is a linear regression model for a set of P = Id) the t-scores τ provide an optimal ranking of SNPs in terms of predicting a binary Y [t-score, squared marginal correlation) are useful measures of variable importance, adding up to Hotelling’s T2 and the squared multiple correlation coefficient R2, respectively.If there is no correlation among SNPs  that is defined as To this end we have proposed a simple modification of the ions. In we have P−1/2 is the inverse of the matrix square-root of P. The vector τadj contains the adjusted t-scores which measure the influence of each predictor on Y  after simultaneously removing the effect of all other variables. The squared CAT score may thus be used as measure of variable importance. Unlike squared t-scores they sum up to Hotelling’s T2 even in the presence of correlation, where Correspondingly, in we invesThe squared CAR scores sum up to the squared multiple correlation coefficient  also known as coefficient of determination or proportion of variance explained. Because of this decomposition property CAT and CAR scores allow to assign importance not only to individual SNPs but also to groups of SNPs. Moreover, both CAT and CAR score share a grouping property that leads to similar scores for highly correlated SNPs. In addition they protect against antagonistic SNPs, i.e. if two SNPs are highly correlated and one has a protective and the other a risk effect, then both SNPs are assigned low scores.Y , we use a simple thresholding procedure with the critical threshold obtained by controlling local false discovery rates[For model selection using CAT and CAR scores, i.e. for identification of those SNPs that do not contribute to predict the response ry rates.In previous work we have shown for synthetic data as well as for data from metabolomic and gene expression experiments that CAT and CAR scores are effective multivariate criteria for obtaining compact yet highly predictive feature sets. Independently, in the study of it was ad the correlation matrix P becomes prohibitively large both to compute and to handle effectively. As a result, in high dimensions direct calculation of CAT and CAR scores using Eq. 1 and Eq. 2 is not possible. Thus, for application in high-dimensional data such as from GWAS an alternative means of computation must be developed.However, with increasing dimension n is smaller than the number of variables d we need to employ a regularized estimate for the correlation matrix P. A simple shrinkage estimator R for P is given by If the number of observations Rempirical is the empirical non-regularized correlation matrix and λ is a shrinkage intensity . Using ty UMUT where M is positive definite matrix of size m × m, U an orthonormal matrix of size d × m, and m = rank << d. This simplifies the shrinkage estimator to Using singular value decomposition the empirical correlation matrix can be written α-th matrix power of R using Following we then m × m matrix Im + M to obtain Rα. Moreover, for efficiently calculating CAT and CAR scores it is crucial to note that it is not at all necessary neither to store or to compute the full d × d sized matrix R−1/2 as This implies we only have to compute the matrix power of the d × m, and most are even smaller (d × 1 or m × 1), all without actually computing the shrinkage correlation matrix R.Consequently, Eq. 3 allows to obtain shrinkage estimates of CAT and CAR scores effectively even in high dimensions as none of the matrices employed in Eq. 3 is larger than We now compare the proposed CAR score approach to simultaneous SNP selection with competing methods and determine its effectiveness in finding true causal SNPs.http://www.gaworkshop.org/gaw17/). This data set is a combination of real sequence data and simulated synthetic phenotypes, where the true causal SNPs are known. In our study we investigate univariate ranking by marginal correlation and five multivariate approaches.For this purpose we use the mini-exome data set compiled for the GAW17 workshop held 13-16 October 2010 in Boston  prior,• MCP: regression with MCP penalty,• BOOST: boosting, and• LASSO: lasso regression.The corresponding software implementations are listed in Table• COR: univariate SNP ranking by marginal correlation, andRND: random ordering of all SNPs.fdrtool[All methods except CAR and COR combine regularization with variable selection. Thus, for determining model sizes for CAR scores and COR we adaptively estimated a threshold from the data using a local FDR cutoff of 0.5 as recommended in. In settfdrtool,28.λ = 0.1, for BOOST ν = 0.1, and for NEG λ = 85. For LASSO we used the built-in cross-validation routines.Generally, all software were run with default settings. The regularization parameters required by the NEG, MCP, BOOST and CAR approaches were set to fixed values optimizing the overall performance of each method. Specifically, for CAR and MCP we employed The aim of this study is to compare simultaneous SNP selection methods with regard to their ability to discover the true known SNPs. For this purpose we investigated the respective SNP rankings and the corresponding true positives, the size of the selected models, and the variability across the 200 repetitions.In FigureIn TableIn further investigation of these results we identified the actual true SNPs recovered by each SNP selection approach. Specifically, we counted which of the 38 respectively 71 true causal SNPs for Q1 and Q2 were found among the first 100 top ranking SNPs using the 200 repetitions available for each phenotype. The result is shown as a heatmap in FigureIn TableThe last column in TableFinally, in TableLarge scale simultaneous SNP selection is a statistically and computationally very challenging task. To this end, we have introduced here a novel algorithm based on CAR score regression that can be applied effectively in high dimensions. Subsequently, in a comparison study we have investigated five multivariate regression-based SNP selection approaches with regard to their ability to correctly recover causal SNPs and corresponding SNP rankings.As overall best method we recommend using CAR scores since this method was the only approach not only consistently outperforming the competing other multivariate SNP selection procedures in terms of identified true positives but also the only approach uniformly improving over simple univariate ranking by marginal correlation. In addition we have shown that CAR scores also are successful in detecting rare variants which recently have been recognize to be important indicators for human disease,30.The authors declare that they have no competing interests.VZ, PDS, and KS jointly developed the algorithm. VZ performed the analyzes. VZ and KS wrote the manuscript. All authors read and approved the manuscript.


Informing missing heritability for complex disease will likely require leveraging information across multiple SNPs within a gene region simultaneously to characterize gene and locus-level contributions to disease phenotypes. To this aim, we introduce a novel strategy, termed Mixed modeling of Meta-Analysis P-values (MixMAP), that draws on a principled statistical modeling framework and the vast array of summary data now available from genetic association studies, to test formally for locus level association. The primary inputs to this approach are: (a) single SNP level p-values for tests of association; and (b) the mapping of SNPs to genomic regions. The output of MixMAP is comprised of locus level estimates and tests of association. In application of MixMAP to summary data from the Global Lipids Gene Consortium, we suggest twelve new loci  for low-density lipoprotein cholesterol (LDL-C), a causal risk factor for cardiovascular disease and we also demonstrate the potential utility of MixMAP in small data settings. Overall, MixMAP offers novel and complementary information as compared to SNP-based analysis approaches and is straightforward to implement with existing open-source statistical software tools. Serum lipid levels are established determinants of cardiovascular disease morbidity with well-described heritability. Indeed, meta-analysis of data arising from recent genome-wide association studies has identified common genetic variants in at least 95 loci associated with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG) and total cholesterol (TC) Mixed modeling of Meta-Analysis P-values (MixMAP), draws strength from available information across all SNPs within a pre-defined region without requiring first-stage data reduction. That is, it is not necessary to eliminate redundancy among SNPs in high linkage disequilibrium (LD) within a locus. By incorporating all available information about SNPs within a locus, MixMAP results in increased sensitivity for identifying loci with multiple SNPs of moderate significance, as evidenced in the applications described below.In order to address this, we apply a mixed effects modeling paradigm that uses SNP-level meta-analysis p-values to arrive at formal analytic characterization of underlying locus-level effects on complex disease phenotypes. Through application of principled, well-vetted statistical concepts for valid and reliable inference, this approach, termed et al.et al. framework is based on fixed effects parameters (representing pathway effects) while MixMAP involves generating prediction intervals for latent (locus-level) effects.Applications of hierarchical linear and generalized linear models for analysis of data arising from genetic association studies have been described previously. Specifically, use of mixed effects models for family studies is common to account for correlation arising from familial level clustering where a random effect for family is included in the model (see for example http://cran.r-project.org/ and http://people.umass.edu/foulkes/software.html.).The purpose of this manuscript is to describe the MixMAP algorithm and to illustrate its application under a range of conditions. Application of MixMAP to two independent datasets of SNP associations with LDL-C, a causal risk factor for atherosclerotic cardiovascular disease (CVD), reveals that MixMAP offers novel and complementary information as compared to traditional analysis approaches. Notably, as an analytic tool that selects loci with multiple moderate to strong signals, and one that uses single SNP-level summary data  and the Penn Coronary Artery Calcification (PennCAC). These revealed that application of MixMAP: (1) supports published LDL-C loci; (2) suggests novel LD-CL genes and (3) complements a single-SNP testing approach, as described in detail below. Additionally, the results of simulation studies designed to characterize the performance of MixMAP under a range of conditions, also described below, support the concept that MixMAP serves as a complementary analysis strategy. To begin, we briefly outline the MixMAP algorithm and the available GLGC and PennCAC data. Further details are provided in the Material and Methods.MixMAP is a statistical framework that uses the results of single SNP-level analysis to test formally for locus-level association. The MixMAP approach is designed to identify loci involving multiple SNPs each with moderate effects on the trait that may not be detected by single SNP analysis. Loci that are detected by a single SNP approach may also be detected by MixMAP if multiple SNPs have modest association across the locus. The primary inputs to the MixMAP algorithm are: (a) p-values corresponding to single SNP tests of association with a trait (e.g. LDL-C); (b) a mapping of SNPs to genomic regions. Additional SNP and gene-level covariate information, such as gene size, number of SNPs per Kb, number of recombinant hotspots per Kb and average linkage disequilibrium can be incorporated.Step 1:] Fit a mixed effects model to inverse normally transformed (ranked) Step 2:] Predict random locus-specific effects using empirical Bayes estimation; [Step 3:] Calculate corresponding prediction intervals using a Bonferroni corrected threshold; and [Step 4:] Report a locus as statistically meaningful if the upper limit of the prediction interval corresponding to the locus effect is less than The MixMAP algorithm is summarized as follows, with additional detail provided below: [http://www.broadinstitute.org/mpg/pubs/lipids2010/); and (2) SNP level The current applied investigation focuses on loci for LDL-C, an important causal factor for CVD. Data arising from two independent data-sets are considered: (1) reported and publicly available meta-analysis SNP level A total of 31827 SNPs in 2960 genes that are common to both GLGC and PennCAC are used in the current investigation. As an illustrative example, we chose to focus on the set of SNPs included on the IBC array which was specifically designed to provide SNP coverage in putative candidate CVD genes as well as emerging loci at the time of design http://www.broadinstitute.org/mpg/snap/)  additional loci are supported by MixMAP that are not detected using the single SNP signal threshold in the GLGC data . While tHere we apply MixMAP to a small study (PennCAC) that generated IBC array SNP data. In terms of independent support, we treat published GLGC data as the gold standard to which PennCAC is compared. This analysis includes 31585  in 2944 (of 2960) genes that remain after filtering out those with MAFIn PennCAC, an additional hold of  . None ofTPR), defined as the proportion of true signal genes that are correctly identified; the false discovery rate (FDR), given by the proportion of selected genes that are not associated with the trait; and the false positive rate (FPR), defined as the proportion of truly uninformative genes that are incorrectly selected as significant. Explicit definitions of TPR, FDR and FPR are given in In order to evaluate the performance of MixMAP relative to single SNP analysis, we investigate the ability to detect informative loci and the likelihood of false findings as functions of gene level effect size (measured by the shift parameter in a two-component Gaussian mixture distribution), the number of informative genes  and coverage . Details of the simulation approach are described in Materials and Methods below. Simulation results are reported in terms of: the true positive rate . Third, although many loci for LDL-C have been identified through association studies at In GLGC data, MixMAP confirmed association for over A more detailed description of the 12 genes/loci detected by MixMAP alone is provided in Supplementary Materials. For many of these 12 loci  the GLGC single SNP test p-value provided suggestive evidence of association  using LDL-C as an example in part because the large GLGC dataset for LDL-C provides an external reference for any MixMAP findings. In PennCAC, no individual SNPs meet criteria for association with LDL-C using the conservative genome-wide Bonferroni correction . This is related in expectation to Based on this model formulation, the best linear unbiased predictor of the random effect for gene In general, we are interested in testing the null hypothesis of no association between a given gene and the trait. To this aim, we construct a one-sided prediction interval for the true gene-level effect, given by For all simulation studies, random gene level effects, Supporting Information S1Clinical Studies.(PDF)Click here for additional data file.Supporting Information S2Summary of suggested novel genes/loci identified in GLGC data by MixMAP.(PDF)Click here for additional data file.Table S1Empirical Bayes (EB) estimates and corresponding prediction intervals for MixMAP supported genes in GLGC. The Empirical Bayes estimate and corresponding prediction variance for each gene are used in the construction of an associated one-sided prediction interval. 1The upper limit of this interval is reported using a Bonferroni corrected α = 0.05/(2960). An upper limit that is less than 0 implies the gene's random effect  is significantly less than 0. Since negative transformed p-values that are large in absolute value correspond to small p-values on the original scale, this implies the gene as a whole has a significant effect on LDL-C. *Highlighted rows correspond to novel loci not identified using single SNP analysis.(PDF)Click here for additional data file.Table S2Empirical Bayes (EB) estimates and corresponding prediction intervals for MixMAP supported genes in PennCAC. The Empirical Bayes estimate and corresponding prediction variance for each gene are used in the construction of an associated one-sided prediction interval. 1The upper limit of this interval is reported using a Bonferroni corrected α = 0.05/(2944). An upper limit that is less than 0 implies the gene's random effect  is significantly less than 0. Since negative transformed p-values that are large in absolute value correspond to small p-values on the original scale, this implies the gene as a whole has a significant effect on LDL-C. None of these loci were identified using single SNP analysis.(PDF)Click here for additional data file.


Next-generation sequencing technologies are widely used for detection of millions of Single Nucleotide Polymorphisms (SNPs) and also provide a means of assessing their variation. This information is useful for composing subsets of highly informative SNPs for region-specific or genome-wide analysis and to identify mutations regulating phenotypic differences within or between populations. In this study, we investigated the sensitivity of SNP detection and introduced the flanking SNPs value (FSV) as a novel measure for predicting SNP-variability using ~5X genome resequencing with ABI SOLID and DNA pools from two chicken lines divergently selected for juvenile bodyweight.Genotyping with a 60 K SNP chip revealed polymorphisms within or between two divergently selected chicken lines for 31 363 SNPs, 48% of which were also detected using resequencing of DNA pools. SNP detection using resequencing was more powerful for positions with larger differences in allele frequency between the lines. About 50% of the SNPs with non-reference allele frequencies in the range 0.5-0.6 and 67% of those with frequencies > 0.9 could be detected. On average, ~3.7 SNPs/kb were detected by resequencing, with about 5% lower density on microchromosomes than on macrochromosomes. There was a positive correlation between the observed between-line SNP variation from the 60 K chip analysis and our proposed FSV score computed from the genome resequencing data. The strongest correlations on macrochromosomes and microchromosomes were observed when the FSV was calculated with total flanking regions of 62 kb (correlation 0.55) and 38 kb (correlation 0.45), respectively.Genome resequencing with limited coverage (~5X) using pooled DNA samples and three non-reference reads as a threshold for SNP detection, identified 50 - 67% of the 60 K SNPs with a non-reference allele frequency larger than 0.5. The SNP density was around 5% lower on the microchromosomes, most likely because of their higher gene content. Our proposed method to estimate the SNP variation (FSV) uses additional sequence information to better predict SNP informativity. The FSV scores showed higher correlations for SNPs with a larger difference in allele frequency between the populations. The correlation was strongest on macrochromosomes, probably due to a lower recombination rate. Next generation sequencing technologies and SNP-chip genotyping with genome-wide coverage are affordable high-throughput genomics tools that are now being used in many research projects. Thus, large amounts of data are being quickly generated that will increasingly require novel methods for efficient data mining and analysis. In many genetic studies aimed at identifying genes determining phenotypic variation in species or populations, two phenotypically divergent groups or populations are compared to search for underlying mutations, genes and pathways. Typically, initial mapping of contributing loci is done using either genome-wide association studies (GWAS) or genome scans for quantitative trait loci (QTL). The initial mapping usually highlights a relatively large genomic region containing one or several genes with a phenotypic effect on the trait and is thus often followed by fine-mapping strategies using sample material where the causative mutation(s) are present on shorter haplotype blocks due to more generations of recombination ,2. Fine Here, we have evaluated the sensitivity of informative SNP detection using genome resequencing. Pooled samples from two chicken lines that were divergently selected for 41 generations  have beeThe 60 K SNP chip genotyping of 20 individuals from each of the high and low lines sampled at generation 41 assayed 53 313 SNPs located on autosomes 1 to 28. A total of 51 894 SNPs (97%) had a call frequency > 0.95. Polymorphism within or between the high and low lines was detected for 31 363 SNPs (59%) and of these, 15 193 (48%) were also detected with the ABI SOLID resequencing. As expected, the fraction of 60 K SNPs detected with the resequencing increased with the frequency of the non-reference allele in the population represented in the DNA pool  from each of the lines, which corresponds to ~99.7% of the Red Jungle Fowl reference sequence from these chromosomes according to the May 2006 assembly of the chicken genome [-15) for the FSV interval with the strongest correlation (62 kb). The correlation was, however, highly dependent on the difference in allele frequency between the lines. There was no correlation for SNPs with low variation between lines and a clear correlation for SNPs with more variation between lines . As shown in Figure -15), whereas for the intermediate chromosomes (GGA6-10) and the microchromosomes (GGA11-28) the highest correlations were with 56 kb  and 38 kb intervals , respectively  was moderate . We have also demonstrated a positive correlation between FSV and the difference in allele frequency between lines from the 60 K SNP chip data. The correlation was strongest on the macrochromosomes when FSV was calculated with a 62 kb interval. For the microchromosomes the optimal interval size was considerably shorter (38 kb) and the correlation weaker, probably due to a higher recombination rate and/or a higher mutation rate ,12. HoweNo correlation was observed between FSV and difference in allele frequency between lines that were lower than 0.4. A plausible explanation is that the FSV is most affected by random sampling effects for the few sequencing reads covering the SNP, when the difference in allele frequencies is small. For example, if both lines show a high frequency for a non-reference allele there will be many cases where only one of the lines reaches over the SNP detection level, whereas the other line will be assumed to be fixed for the RJF allele. This would incorrectly increase the FSV and lower the correlation with the true SNP variation between lines. In other words, most random sampling effects among sequencing reads from a pool of DNA samples would increase the FSV and thereby reduce the correlation with SNP variation between lines if that variation is small. Another possible explanation is that large difference in allele frequencies between the two lines studied at a biallelic locus includes less sensitivity to such random sampling effects, because of one predominant allele in each line that is relatively rare or absent in the other line.In summary, our results show that the depth coverage and pooling of samples limits SNP detection sensitivity using resequencing. The described measure, however, makes better use of available sequence information than studies using only individual SNP positions and can thereby provide a valuable indication of the SNP variation-between resequenced populations, even when the depth coverage is quite low and pooled samples are sequenced. This could, for example, facilitate the selection of the most informative SNPs for a genotyping panel among millions of putative candidates detected in resequencing. In selecting SNPs it is also important to consider how many reads covered the SNP position and the individual SNP score indicating the fraction of reads in agreement with the reference. When there exist a very large number of SNPs to choose from it would be possible to stringently select SNPs with high FSV, large number of covering reads and a high proportion of non-reference reads.Genomic resequencing with limited coverage (~5X) using pooled DNA samples can be used to detect the majority of SNPs with an allele frequency difference larger than 0.5 between the pools. For any detected SNP, computation of the variation among flanking SNPs (FSV) based on the resequencing read scoring, can be used for estimating of the degree of polymorphism between the populations. This correlation is more accurate for chicken macrochromosomes than for microchromosomes, probably due to a higher recombination rate and more haplotype variation on the microchromosomes.The Virginia high and low body weight selected chicken lines were studied. The Virginia lines are chicken resource populations, which have been bred in a long-term, divergent selection experiment for body weight at 56 days of age. After 38 generations, the lines showed a near nine-fold difference in bodyweight at the selection age and large differences in a large number of correlated traits including fat deposition, appetite and immune response . Twenty Genome resequencing was carried out using the Applied Biosystems SOLID v2 technology according to the manufacturer's protocols, with one fragment library from each pool and 35 bases per read. The reads were aligned to the Red Jungle Fowl reference genome sequence  as descrWith resequencing SNP calling using the Corona Lite pipeline from Life Technologies, each detected SNP has a score indicating the proportion of reads that are in agreement with the Red Junglefowl (RJF) reference sequence. With very high depth coverage, this score could be used as an indicator of the allele frequency difference between lines. With low sequence coverage, however, the score results could be misleading as many SNPs that actually show variation within a line might appear to be fixed.To make better use of the available information from the resequencing, we propose a new method to estimate the informativity of a SNP based on the variation in SNPs flanking the tested position - the Flanking SNPs Value (FSV). This value is calculated as follows:i detected in the high line at position coordinate c. Likewise, j detected in the low line at position coordinate d.where H and NL are the total number of high line SNPs and low line SNPs, respectively, scored within the flanking regions.NTo evaluate how well FSV predicted SNP variability between lines from resequencing data, we studied the correlation between FSV and SNP variation between lines measured using SNP chip genotyping. All autosomal SNPs included on the 60 K chip that showed variation within or between the high and low lines were used. We included the chicken autosomes 1 to 28 because they were fully represented in both the 60 K and genome resequencing datasets. Most SNPs in the resequencing were detected in only one of the lines and then we assumed fixation for the reference Red Junglefowl (RJF) allele in the other line. SNPs where none of the reads in either line had the RJF allele were excluded from the calculation. Intervals with a SNP density higher than 1 SNP/kb for each line were considered to show sufficient information to be included in the correlation test. Previous estimates of 5 SNPs/kb for most chicken line comparisons  suggest SM developed the Python pipeline for analysis of the 60 K and ABI SOLID SNP detection data, analysed the data and did the major part of the manuscript preparation.SM and ÖC planned the work and regularly discussed the study including revisions of the manuscript.Python scripts used for computation of 60 K SNP allele frequencies and FSV.Click here for file


NURR1/NR4A2 is an orphan nuclear receptor that is critical for the development and maintenance of mesencephalic dopaminergic neurons and regulates transcription of genes involved in the function of dopaminergic neurons directly via specific NGFI-B response elements (NBRE).and substantial data support a possible role of Nurr1 in the pathogenesis of Parkinson's disease (PD). Here we show that Nurr1 is degraded by the ubiquitin-proteasome pathway and determined that N-terminal region (a.a 1–31) of Nurr1 is essential for an efficient targeting of Nurr1 to degradation in the cell. Nurr1 Δ1–31 has a much longer half-life, and as a consequence its steady-state protein levels were higher, than full-length Nurr1 in the cell. Nurr1 Δ1–31 was as potent as Nurr1 full length in transcriptional luciferase reporter assays after normalization with the corresponding steady-state protein expression levels, either in trans-activation of NBRE or trans-repression of iNOS (inducible NO synthase) reporters. These results suggest that Nurr1 Δ1–31, because of longer persistence in the cell, can be a good candidate for gene and cell therapies in the treatment of PD. NURR1  gene, a member of nuclear receptor superfamily The important role that Nurr1 plays in dopaminergic neurons has been underscored by the identification of several changes in its gene that are associated with Parkinson's disease (PD). Two monoallelic mutations in the 5′ region of Nurr1 gene (c.-291delinsT and c.-245T>G) have been shown to be associated with PD, those mutations reduced the expression of Nurr1 We have approached the study of the degradation pathway of Nurr1 because its importance in the maintenance of the dopaminergic phenotype, its implication in PD and its role as a protector for adult dopaminergic neurons. Previously it has been shown that Nurr1 is degraded by the ubiquitin proteasome pathway 5′-CGCACGGACAGTTAAAAAGGCCGGAGAGG-3′ and 3′Nurr1 1–337 5′-CCTCTCCGGCCTTTTTAACTGTCCGTGCG-3′. The constructs Nurr1 Δ163–187, Nurr1 Δ163–217 and Nurr1 Δ163–247 internal deletion mutants were obtained by PCR from mouse Nurr1 construct in pcDNA 3.1 by substituting the NdeI/XhoI cassette of Nurr1  with the products of amplification (digested with NdeI/XhoI) of mouse Nurr1 obtained with the following oligonucleotides:DNA constructs for expression of mouse Nurr1 and Flag-Nurr1 were generated from a mouse Nurr1 cDNAs and cloned into pcDNA3.1 (Zeo+) either untagged or Flag-tagged in the N-terminus. The construct Nurr1 1–337 was produced by PCR introducing a stop codon at position 338 of mouse Nurr1 sequence using the following oligonucleotides: 5′Nurr1 1–337 5′-GACGCATATGTCTAGCTGCCAGATGCGCTTCGAC-3′, 5′NdeI-Nurr1 Δ163–217 5′-GCGACATATGTTCGCCGTGCCCAACCC-3′, 5′NdeI-Nurr1 Δ163–247 5′-GCACGCATATGTCGCAGTTGCTTGACAC-3′ and a common reverse primer 3′XhoI-Nurr1 5′-AGCGCTCGAGTTAGAAAGGTAAGGTGTCCAGG-3′. The constructs Nurr1 Δ1–96, Nurr1 Δ1–161 and Nurr1 Δ1–262 N-terminal deletion mutants were obtained from mouse Nurr1 construct in pcDNA3.1 and the following oligonucleotides: 5′Nurr1 Δ1–96 5′-CGAAAGCTTATGCACAATACCAGCAACACAGCC-3′, 5′Nurr1 Δ1–161 5′-CCGAAGCTTATGATCGAGGCAGAGGAAGAC-3′, 5′Nurr1 Δ1–262 5′-GGCAAGCTTATGTGCGCTGTTTGCGGTGACAACG-3′ and the common reverse primer 3′XhoI-Nurr1. The constructs Nurr1 Δ1–80, Nurr1 Δ1–63, Nurr1 Δ1–43 and Nurr1 Δ1–31, N-terminal deletion mutants were obtained by PCR from mouse Nurr1 construct in pcDNA3.1 with the following oligonucleotides: 5′Nurr1 Δ1–80 5′-GCGTAGATCTATGCCCCTGTCCGGACAGC-3′, 5′Nurr1 Δ1–63 5′-GCGTAGATCTATGGACAACTACAGC-3′, ′Nurr1 Δ1–43 5′-GCGTAGATCTATGGACCTCACCAAC-3′, 5′Nurr1 Δ1–31 5′-GCCTAGATCTATGGATTTCTTAACTCC-3′ and the common reverse primer 3′XhoI-Nurr1. Introduction of the point mutation S125C into Nurr1 coding sequence was obtained by site-directed mutagenesis using the Stratagene “Quick-base change” method. Nurr1 triple Pro/Ala mutant (Nurr1 P2/12/17 A) was obtained by amplification from wild type Nurr1 with the following primers:: 5′Nurr1 P2/12/17A: 5′-GCGTAGATCTATGGCTTGTGTTCAGGCGCAGTATGGGTCCTCGGCTCAAGGAGCCAGCGCCGCTTCTCAGAGC-3′ and 3′ Xho reverse primer: 5′-AGCGCTCGAGTTAGAAAGGTAAGGTGTCCAGG-3′, fragment was digested with BglII/XhoI and ligated into pcDNA3.1 (Zeo+) digested with the same restriction enzymes. All constructs were completely sequenced by automatic DNA sequencing5′NdeI-Nurr1 Δ163–187 2 in P60 Petri dishes. Cells were treated with 25 µg/ml cycloheximide (CHX) in the absence or in the presence of 10 µM lactacystin for the times indicated up to 12 h. After the treatments, cells were washed in phosphate-buffered saline (PBS) and lysed in 200 µl of lysis buffer per well plate ; Cell extracts were sonicated for 10 min on ice, centrifuged at 14000×g for 30 min at 4°C and the supernatants were used to measure total protein concentration by BCA protein assay kit (Thermo Scientific-Pierce). Total proteins (50 µg) were separated onto 10% SDS-PAGE gels and transferred to nitrocellulose membrane for Western immunoblot analysis. Membranes were blocked with TTBS  with 3% BSA o.n. The blots were then probed with anti-Nurr1 antibodies 1∶1000  and anti-tubulin  as loading control. Signals from the primary antibodies were amplified using species-specific antibodies to rabbit or mouse IgG conjugated with horseradish peroxidase (Bio-Rad). Immunoblots were developed by direct capture of chemiluminescence with DNR MF-ChemiBIS 3.2 Bio-Imaging System and quantification with Totallab TL100 software. Results are expressed as mean ± s.e.m. for a minimal number of three independent experiments.Rat PC12 cells were grown in Dulbecco's modified Eagle's medium  supplemented with 10% horse serum (Gibco BRL), 5% foetal bovine serum (Gibco BRL) and 100 µg/mL gentamycin, at 37°C and 5% CO2. HeLa cells were plated at 3×105cells/well in 6-well plates and transfected with Lipofectamine (Invitrogen). Transfected cells were treated with the protein synthesis inhibitor CHX (25 µg/ml) for the times indicated; 10 µM Lactacystin, or 50 nM Leptomycin B was added where indicated. Transfected cells were processed as described above for PC12 cells and analyzed by immunoblot using the following primary antibodies: anti-Nurr1 , or anti-tubulin antibodies  as control for protein loading. Values reported are means ± s.e.m. from three independent experiments.HeLa cells were grown in Dulbecco's modified Eagle's medium  supplemented with 10% foetal bovine serum (Sigma-Aldrich) and 100 µg/mL gentamycin, at 37°C and 5% COFor the study of Nurr-1 ubiquitylation, cells were co-transfected with HA-tagged ubiquitin construct .plus Nurr1 full-length or Nurr1 Δ1–31 deletion construct. Transfected cells (36 h after transfection) were incubated in the presence or in the absence of 10 µM lactacystin for 12 h. Cells were lysed in 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.5% NP40, 20 µM leupeptin, 10 µg/ml pepstatin, 1 mM PMSF and the clear lysates were immunoprecipitated with anti HA-antibody (Roche) previously coupled to protein G-Sepharose . The immunoprecipitates were loaded onto 10% SDS-PAGE, transferred to nitrocellulose, and developed with anti-Nurr1 antibody .Renilla as a reporter control. Luciferase activity was assayed with the Dual-Luciferase reporter assay system from Promega according to the manufacturer's instructions. For the studies of Nurr-1 trans-repression, RAW 264.7 macrophage cell line was grown in RPMI plus 15% fetal bovine serum and transfected by nucleofection  as per manufacturer's protocol with pcDNA (mock), pcDNA Nurr1 or pcDNA Nurr1 Δ1–31, the reporter plasmid iNOS (inducible NO synthase) murine-luciferase piNOSm-luc Renilla luciferase.To study Nurr1 mediated transactivation, HeLa cells were transfected with pcDNA Nurr1 or pcDNA Nurr1 Δ1–31 and Δ1–80, the reporter plasmid 3xNBRE-tk-Luc containing 3 copies of the Nurr1-responsive element  and Cells were grown on coverslips and 36 h post-transfection coverslips were washed 3 times with cold PBS, fixed with 4% paraformaldehyde in PBS for 20 min and permeabilized and blocked with PBS, 1% Triton X-100 containing 3% BSA for 1 h at room temperature. Primary anti-Nurr1 antibody  was added in the blocking solution without Triton X-100, and incubated for 2 h at room temperature. After washing 5 times (each for 5 min) with PBS, coverslips were incubated with Alexa Fluor 488 conjugated anti-mouse antibody (1∶500 dilution) for 1 h, washed again 5 times with PBS. For nuclear visualization 4′,6-diamidino-2-phenylindole  was added in the first wash with PBS. Coverslips were finally mounted with ProLong for confocal microscopy observation in a laser scanning microscope (Carl Zeiss LSM-510). Images were captured with the same settings for each set of experiments. Controls, omission of primary or secondary antibodies, revealed no fluorescence.To begin the study of the mechanism of Nurr1 degradation, we analyzed the degradation of endogenous Nurr1 in PC12 cells by treatment of cells with CHX. As shown in The region of mouse Nurr1 from aminoacids 1–351 contains the AF-1 transactivation region of Nurr1, being the minimal transactivating region restricted to aminoacids 1–122 and the core transactivation sequence located between aminoacids 52–82 To check the ubiquitylation of Nurr1 and Nurr1 Δ1–31, HeLa cells were transiently co-transfected with those vectors and an HA-tagged ubiquitin expression vector. Ubiquitylated proteins were immunoprecipitated from solubilized cells using antibodies to the HA epitope and then separated by SDS-PAGE and immunoblotted with anti-Nurr1 antibodies. Results  showed tAAAGGTCA) that is present in the promoter region of genes that are regulated by this nuclear receptor Renilla luciferase as a control) and different doses of Nurr1 full length and two deletion mutants Nurr1 Δ1–80 and Nurr1 Δ1–31. These experiments showed that different DNA concentrations of Nurr1 and Nurr1 Δ1–31 produced a dose-dependent activation of NBRE luciferase reporter . We have shown that one main determinant for proteasomal degradation of Nurr1 is located at the N-terminal region comprising aminoacids 1–31 that has no structural motives and scores very poorly as a possible PEST region Nurr1 Δ1–31 is as potent as Nurr1 full-length in transactivation and trans-repression of luciferase reporter constructs, when corrected for the protein expression levels of both proteins, as Nurr1 Δ1–31 steady-state protein levels are higher than the levels of Nurr1 full length due to its longer half-life in the cell. These results are in agreement with the fact that the region 1–31 of Nurr1 is closed, but do not overlap, with the core region of the AF-1 transactivation domain of Nurr1, aminoacids 52–82 The report that Nurr1 phosphorylation by AKT at Ser347 promotes its degradation The presumptive role of Nurr1 in PD pathogenesis made us also to explore the possible effect of the mutation S125C that has been shown to be present in a PD patient The identification of the N-terminal region (a.a 1–31) in Nurr1 allowed us the production of a variant form of Nurr1 where that sequence was deleted and that was a rather stable protein in the cell and works very efficiently in promoting transactivation and trans-repression. Accordingly, the Nurr1 Δ1–31 becomes an ideal candidate to be used for direct gene transfer or transduction in cell-based therapies for the treatment of PD patients, because this construct keeps the main mechanisms of regulation of Nurr1 transactivation