dataesr/scientific-paragraphs-categorization huggingface dataset:
Global Diversity of RM Y-STR Haplotypes and Male Lineage Differentiation The RM Y-STR set applied here generated exceptionally high haplotype diversity in the worldwide sample set analyzed with 12,156 unique (i. e. , not matching any other individual in this dataset) haplotypes observed in 12,272 unrelated samples from 111 worldwide populations (Table  1 ). The global haplotype diversity estimate was 0. 9999985 (sampling variance = 2. 85 × 10 -8 ). Of the 102 haplotypes that were nonunique within this dataset (i. e. , matched other individuals in this dataset), 90 were shared between two males, 10 between three males, and two between four males. All nonunique haplotypes were shared between males of the same geographic region, and all but six were from the same sampling population. The six nonunique haplotypes shared between different populations involved males from Argentina (Chubut and Rio Negro regions), Greenland (from two separate samples of Inuit), Czech Republic (from two sample sets), Cologne and Warsaw, and Bhutan (the Lhokpu and Mönpa tribes), respectively.
There are four separate loci at the tetranucleotide DYF403S1a+b, three of which overlap in size. Differences in repeat sequences between the copies lead to the presence of partial (0. 1, 0. 2, and 0. 3) alleles, which can challenge interpretation even in single source samples due to a lack of single base resolution with POP-4 during capillary electrophoresis. To determine the effect of removing this potentially problematic marker from the RM Y-STR panel, the entire dataset was additionally analyzed without DYF403S1a+b. The global haplotype diversity slightly decreased from 0. 9999985 with the full 13-loci RM Y-STR set to 0. 9999981 with the 12-loci set. This translated to an increase in the number of nonunique haplotypes from 116 to 152 in the 12,272 samples-a decrease of zero to five haplotypes per regional population, with an average of 0.
32 fewer haplotypes within each of the 111 populations. Populations most affected by the omission of DYF403S1a+b were Aboriginal Australians (from 96 to 92 haplotypes among the 100 men tested), and Bhutan (from 56 to 51 haplotypes among the 78 men tested). However, the vast majority of the populations tested (90 of 111, 81. 1%) were not affected by the removal of DYF403S1a+b, with the haplotype diversities remaining the same. Therefore, and because of the extra care in quality control employed for this marker, all following data analyses were performed based on the complete 13-loci RM Y-STR set.
It is notable that populations from the same geographic group did not necessarily cluster together (Fig. 1 ). The differences in population sample size (Supp. Table  S4 ) did not greatly affect the dispersion of populationssimilar distributions were observed when the population sizes were restricted to 100/population (inset, Fig. 1 ), or 25/population (data not shown). This analysis, together with the AMOVA results, illustrates that no genetic-geographic structuring is detected with this set of RM Y-STR haplotypes, since their high mutation rates have likely removed signals of shared population history, and has driven the high number of unique haplotypes. The low between-population differentiation and lack of substructure could be expected as a result of the high within-population diversities, and the effect of mutation rates on the estimation of θ  [Meirmans and Hedrick, 2011].
As a practical consequence, no population substructure correction needs to be applied when using this RM Y-STR set in forensic or other applications, as is usually needed for other DNA marker systems. However, the lack of substructure correction required does not necessary infer a lack of structure in the distribution of  haplotypes across geographic regions. Nonrandom distributions, caused by shared population histories and common origins, would infer the need to generate and utilize regional or meta-populationspecific databases for frequency estimation. As such, the ability for RM Y-STRs to differentiate between male lineage within and between different populations and the relative distribution of haplotypes between regional groups was investigated.
Value of RM Y-STRs in Improving Male Lineage Differentiation When considering the utility of the RM Y-STR set for future applications in forensic, genealogical or anthropological genetic studies, it is informative to compare its properties to those of conventional Y-STRs, such as the 17 loci included in the Yfiler kit. Hence, RM YSTR and Yfiler haplotypes were compiled for 7,784 individuals across 65 populations as a subset of the global dataset presented here. In every diversity measure applied, the 13 RM YSTR set provided enhanced estimates relative to the 17-loci Yfiler set. Global haplotype diversity was increased from 0. 99995 with Yfiler to 0. 999997 with RM Y-STRs; the total number of haplotypes detected was increased from 6,975 to 7,714, and the number of singletons from 6,469 to 7,647.
Unlike the Yfiler network, the RM Y-STR network provides almost no clustering of haplotypes according to geographic regions of sampling (except some of the North African haplotypes), which underlines the lack of population substructure as also seen in the MDS and AMOVA of the complete data. Hence, for RM Y-STRs, the need for regional (metapopulation) reference databases for haplotype frequency estimation in forensic and other applications is strongly reduced compared with conventional Y-STRs such as those in the Yfiler kit. Combining the RM Y-STR set and the Yfiler set to generate 30-marker Y-STR haplotypes resulted in the individualization of 25 additional men of the 137 not already individualized with the RM Y-STR set alone. While this does represent a slight improvement on the global scale, from 7,714 to 7,737 haplotypes, it is clear that the vast majority of differentiation was achieved by the RM Y-STR set alone.
In support of the RM Y-STR set's ability to detect hidden paternal relationships, we indeed see significantly lower hap-lotype diversity and lower proportions of unique haplotypes in the endogamous group than in the rural group than in the urban group (diversity t 2 = 7,960, P = 1. 6 × 10 -8 , shared haplotypes t 2 = 1. 347, P = 0. 310) (Table  3 ). Although the sample sizes differ considerably between the three categories, this does not affect the conclusions as simulations performed with equalized sample numbers (n = 558 per category) showed that the RM Y-STRs still result in decreased diversity in endogamous and rural populations compared with urban populations (data not shown; diversity t 2 = 8936, P = 1. 3 × 10 -8 , shared haplotypes t = 1. 117, P = 0. 380). On the other hand, these data also demonstrate that even in populations with considerable hidden paternal relationships, such as in endogamous populations, the RM Y-STR set still allows the differentiation of a large number of men.
Value of RM Y-STRs for Male Relative Differentiation In addition, we tested the value of RM Y-STRs for differentiating male relatives. On a theoretical level, the rate of relative differentiation per meiosis can be calculated using the mutation rates of each locus within a genotyping panel (see Materials and Methods section). For the Yfiler set, it is estimated that a mutation at one or more of the 17 Y-STR loci will occur with a probability of 0. 047 (95% confidence interval 0. 038-0. 057) per meiosis, which for the RM Y-STR set is more than fourfold higher at 0. 195 (95% CI 0. 177-0. markers and four additional Y-STRs, this value is 0. 092 (95% CI 0. 077-0. 107), nearly twofold higher than for Yfiler, but more than twofold lower than for the RM Y-STR set studied here. To compare the theoretical expectation for the RM Y-STR and the Yfiler sets with empirical data, we genotyped 2,372 pairs of male relatives previously confirmed by autosomal DNA analysis (2,339 father-son pairs, 30 brother pairs, and three uncle-nephew pairs), confirmed the observed allelic differences by independent genotyping (and some by sequencing), and combined these new data with those from the 156 relative pairs separated by one to 20 paternal genetic transfers (or meioses) described previously  [Ballantyne et al.
, 2012]. In this combined dataset, the RM Y-STR set allows differentiation by at least one allelic difference (i. e. , mutation) in at least one locus in 742 (29%) of the total 2,528 pairs related by one to 20 generations, whereas Yfiler only allowed the separation of 118 (5. 5%) of a subset of 2,161 of these relative pairs (Fig. 5 ). In particular, the RM Y-STR set differentiated fathers from their sons in 26. 9% of the cases versus 4. 5% with Yfiler, and brothers from each other in 56. 3% of the cases versus 10. 0% with Yfiler. It should be noted, however, that only for the 2,378 father-son pairs investigated here, was the sample size large enough to allow reliable conclusions, whereas for more distantly related males, future studies need to deliver more data. However, as the chance of mutations increases with each meiosis, it is expected that the more distantly related men are, the higher the chance that they will have different RM Y-STR haplotype; a trend that can be seen in our data (Fig.
, 2007]. In the current dataset however, 3. 8% of the DNA-confirmed father-son pairs displayed three or four RM Y-STR mutations; as such, the number of mutations constituting exclusion should be increased when using the RM Y-STRs. As outlined elsewhere  (Ballantyne et al. 2012) , instead of an ad hoc cut-off, a dynamic threshold shall be used, estimated from the number of Y-STR loci genotyped and their locus-specific mutation rates. Because three mutations have also been observed occasionally among 17 Yfiler loci in DNAconfirmed father-son pairs  [Goedbloed et al. 2009] , this notion also applies to conventional Y-STR sets such as Yfiler, which due to their moderate mutation rates in principle are more appropriate for paternity and kinship testing than RM Y-STRs.
Conclusions The large worldwide dataset compiled, presented, and analyzed here demonstrates the exceptional value of the RM Y-STR panel for differentiating male lineages on a global and regional scale. Many of the current limitations of Y-chromosome applications are reduced with this RM Y-STR set, providing increased utility and effectiveness to the genetic analysis of male populations and lineages. In particular, this 13 RM Y-STR set provides near-complete paternal lineage differentiation in general populations as well as in populations with otherwise reduced Y-chromosome diversity, due to peculiarities in population history or cultural practices. The effects of this near-complete male individualization will be of great benefit to numerous fields using the Y-chromosome genetics to investigate male lineages, such as in genealogical studies (e.
On the other hand, however, the extreme degree of RM Y-STR haplotype diversity highlights the limitations of the current approach for placing a statistical weight on Y-STR haplotype matches by using empirically derived haplotype frequency estimates obtained from Y-STR haplotype reference databases. Although already noted with the currently used Y-STR sets such as Yfiler, the problem that an observed haplotype is not present in the large and growing reference database, and how to deal with rare haplotypes in obtaining frequency estimates and estimating match probabilities for which no consensus has been reached yet  [Krawczak, 2001; Brenner, 2010; Buckleton et al. , 2011; Willuweit et al. , 2011] , will become drastically increased when using RM Y-STRs, as can be seen from the data presented here.
Therefore, new statistical solutions shall be developed to estimate the weight of a RM Y-STR haplotype match with particular relevance for forensic applications. To assist future studies utilizing RM Y-STRs, complete dataset of RM Y-STR and Yfiler haplotypes obtained from the 12,272 individuals analyzed here is made available via Supp. Table  S6 , with allele frequencies for individual populations, regional groups, and the complete dataset summarized in Supp. Table  S7. Relative pair haplotypes for RM Y-STRs and Yfiler are available in Supp. Table  S8.
Acknowledgments The authors thank the following colleagues for their expert technical assistance:  Linda S. H. Gan  ( Singapore ) for the Singapore samples,  Minttu Hedman  ( Helsinki ) for the Finnish samples,  Helena Nilsson  (Linköping) for the Swedish samples,  Kevin Cloete  ( Cape Town ) for the South African samples,  Nancy Vanderheyden  ( Leuven ) for the Belgium samples,  Baowen Cheng  (Kunming) and  Yanan Sun  ( Kunming ) for the Kunming Chinese samples,  Juliana Januzzi  ( Rio de Janeiro ) for the Rio Brazil samples,  Maria Lourdes D. Honrado  ( Quezon City ) for the Filipino samples, and  Harald Niederstätter  ( Innsbruck ) for the Innsbruck Austrian samples as well as for his comments on the manuscript. Oscar Lao (Rotterdam)  is acknowledged for statistical advice and help in some calculations.
Acknowledgements RWJ gratefully acknowledges support received from the  Swiss National Research Foundation , the  Novartis Consumer Health Foundation and Unitec, University of Geneva. AK acknowledges financial support from the  National Institute for Health and Medical Research (INSERM). HK, AK and RWJ are members of the  COST Action   BM0904. Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Study populations We performed the study in two phases: first we analysed data from the BPC3 ER2 GWAS and second, for replication purposes, we used genotyping or existing data from selected breast cancer cases and controls collected by three different studies CPS-II, MCCS and the MARIE study. Individuals from CPS-II contributed cases and controls to both the initial GWAS and the replication phase, but there were no overlaps between sample sets used in the two phases of this study. The BPC3 has been described extensively elsewhere  [14]. It consists of cases and controls selected from large cohorts assembled in Europe, Australia and the United States that have both biological samples and extensive questionnaire information collected prospectively. Cases were women who were diagnosed with invasive BC after enrolment, the diagnosis was confirmed by tumor registries or by medical records.
SNPs selection (phase one) and genotyping The selection of the SNPs to be measured in phase one was done using the National Human Genome Research Institute's (NHGRI's) catalog of published GWA studies (http://www. genome. gov/gwastudies/) [4]. It contains summary information on polymorphic variants reported to be associated with a human disease, trait or phenotype in a GWA setting at the significance level of P,1. 0610 25. The data from the catalogue were downloaded in May 2012 and comprised 7986 SNPs. Approximately 60% (n = 5794) of the polymorphic variants reported in the catalogue had a P value higher than 5610 28 and were, therefore, excluded from further analysis. Of the remaining 3192 SNPs, 1688 (58%) were genotyped in the BPC3 scan. PLINK  [17]  was used to identify highly correlated (r 2.
0. 9 in Hapmap3 CEU) SNPs genotyped in the BPC3 GWAS for 452 variants (14. 2% of the total selected SNPs). Data for 939 SNPs were imputed: 901 (28. 3% of the total selected SNPs) from Hapmap 2 and 38 (1. 1% of the total selected SNPs) from Hapmap3. The remaining 113 (3. 6% of the total selected SNPs) variants were dropped from the analysis since no surrogate was found and it was not possible to impute data. Thus, data for 3079 out of 3192 catalogued SNPs (96. 4%) were used for this study. The 3079 remaining SNPs were looked up in the BPC3 GWAS ranking the P-value in decreasing order to check for their association. All already known breast cancer risk SNPs were excluded from the analysis.
Statistical analysis Logistic regression adjusted for five principal components, age (at diagnosis for cases and at selection for controls) and cohort was used to generate ORs, 95% CIs, and P values for each of the 3079 SNPs selected from the BPC3 ER negative GWA data set and for the 10 SNPs in the replication phase. The replication was performed using ER2 and ER+ breast cancer cases and the analysis was conducted using ER2 alone and in combination with ER+. Considering the fact that several ER2 SNPs are also associated with ER+ BC we included in the analysis ER+ and ER2 cases and then analyzed overall BC risk (ER+ and ER2) and ER2 specific (ER2 alone) to increase our power to find a true association. We had more than 90% power to replicate any of the associations observed in the discovery phase if considering all BC cases, and over 50% (53%-72%) power if considering only ER2 cases considering alpha of 0.
Alternatively, ER2 BC may share genetic risk factors with other common disease traits and phenotypes, but not with those we included in our analysis. The pleiotropic approach we used is necessarily limited by the number of disease traits and phenotypes that have been examined with enough statistical power to identify GWAS hits. It is possible that disease traits and phenotypes with biologic pathways similar to ER2 BC have not been examined adequately and are yet to be included in the NHGRI database. We are aware of several limitations that this work might present: first, we were not able to include all the SNPs from the catalogue because 113 (3. 6% of the total selected SNPs) variants were dropped from the analysis since no surrogate was obtained and it was not possible to impute data. Second, we replicated, as an exploratory analysis, only the 10 most significant SNP associations, thus we cannot exclude that a true positive association lies those SNPs that we did not attempt to replicate in the second phase, although due the complete lack of replication of the first ten SNPs this possibility seems unlikely.
Other hits, such as metabolic and catabolic proteins, or of the biosynthetic machinery were expected for any virus. However, the majority of hits had no prior connection to HPV16 infection. To extract information regarding critical processes in HPV entry, the list was subjected to rigorous bioinformatic analysis. An algorithm that combined function-and interaction-based information was used  [45]. When assigned to functional annotation clusters representing sets of proteins that share common annotations within public databases [DAVID;  [46] ], the hits were.
48 h after transfection, cells were infected with HSV-1-GFP or HPV16-GFP as above, and infection was scored at 6 h or 36 hpi, respectively, by automated microscopy and computational image analysis. Cell numbers and raw infection indices for each well were determined using a MATLAB-based infection scoring procedure described previously  [84]. The raw infection indices were normalized to AllStarNegative siRNA-transfected control cells to result in relative infection percentages. For the analysis of HPV16 infection kinetics upon knockdown of mitotic regulators, 2000 HeLa Kyoto cells were seeded into optical 96-well plates and reverse transfected with 5 nM siRNAs. 48 h after transfection, cells were infected with HPV16-GFP, and infection was scored at 24 h, 36 h and 48 hpi as above. For video microscopy of infected cells, HeLa H2B-mCherry cells were plated on optical 96-well plates (Nunc), or 8-well Labtek chambers (Nunc), and pretreated with aphidicolin for 6-16 h for synchronization.
Image analysis For image analysis of mitosis length, we used NIH ImageJ 1. 44j. Mitosis start and end frames were defined by visual detection of chromosome condensation and decondensation. GFP onset was defined as 10% signal over background. vDNA positive nuclei were quantified by thresholding of BrdU signals, with BrdU positivity $106the signal of uninfected cells. For quantification of L1-7 and BrdU signals, images were analyzed using the Imaris (v7. 2 and v7. 6. 4, respectively) Cell module. For segmentation and distance measurements, cell outlines were defined by F-actin and nuclear signals. L1-7 spot locations were segmented by thresholding, whereas signal intensity measurements occurred in arbitrary units compared to control. For quantification of nuclear import of IBB-GFP, Hoechst-stained nuclei were segmented by CellProfiler (v 2.
0,  [85, 86] ). IBB-GFP mean fluorescence intensities (fluorescence intensity/nuclear area) were measured as arbitrary units. For mitotic timing of L2-GFP expressing cells, dispersal of L2-GFP marked the onset of mitosis with NEB and chromosome decondensation during telophase the end of mitosis. L2-GFP expression levels were classified according to fluorescence intensity in interphase cells (mean intensity of nuclear L2-GFP at 25 min before NEB). Fluorescence intensities $20% above background (mock-transfected cells) were considered as L2-GFP expression. L2-GFP chromosomal association was assessed visually by accumulating signal on chromosomes. Colocalization of vDNA of EdU-HPV16 with host chromatin or LAMP1 and TGN46 was quantified by using the Imaris Colocalization module (v7. 6. 4). Cytosolic EdU-HPV16 was considered as virus that did not localize either to TGN46 or LAMP1.
Supporting Information Figure  S6  Mitotic indices of RNAi screen. The mitotic state of cells was determined according to nuclear morphologies at the end of the siRNA screen. Depicted is the distribution of the zscores of mitotic indices for all confirmed genes that enhanced or reduced HPV16 infection (see Table  S1   2A ) were evaluated as described for Figure  2B  and plotted according to GFP onset and mitosis duration. Given is a linear regression of the data. (B) The number of mitotic events in HeLa H2B-mCherry cells from time-lapse series (compare Figure  2A ) were assessed over a period of 48 h after infection with different amounts of HPV16-GFP and compared to uninfected cells imaged in parallel. Data was normalized to uninfected cells and is displayed as percent (of uninfected) 6 SD.
Depicted is the infection relative to AllStarNeg control in percent 6 SD of one quadruplicate experiment. (TIF) Table  S1  List of validated host factors involved in HPV16 infection. Each of the 162 identified host factors is annotated with a link to their corresponding NCBI Gene pages, as well as their entries from several common gene and protein annotation databases, including GO, and PANTHER. The 162 factors confirmed in the rescreen (Y) are marked in green, not confirmed (N) in black, and factors not included in the secondary screen are marked red. The information is separated according to the effect on HPV16 infection upon RNAi of the respective gene, i. e. enhanced and reduced infection (Tabs 1-2). (XLSX) Table  S2  DAVID functional annotation clustering of validated host cell genes involved in HPV16 infection.
Acknowledgments We would like to thank  K. Mench  for help with the preparation of RNAi plates,  Dr. A. Vonderheit  and  M. Stebler  ( LMC, RISC facility, ETH Zurich ) for technical assistance with  RNAi screening , and  C. Thompson  ( LCO, NIH ) for assistance in pseudovirus production. We are also grateful for the help of  C. Meyers  and  J. Biryukov  ( Penn State University ) to set up the production of raft-derived  HPV16.
i. d. )  or sorafenib at a starting dose of 400 mg b. i. d. Dose increases of axitinib based on individual tolerability and reductions or interruptions of axitinib and sorafenib for toxicities were previously described  (Rini et al, 2011; Motzer et al, 2013b). The trial was conducted in accordance with the Declaration of Helsinki, the International Conference on Harmonisation Guidelines on Good Clinical Practice, and applicable local regulatory requirements and laws. All patients provided written informed consent. The protocol, amendments, and informed consent forms were approved by an institutional review board or independent ethics committee at each study site. The trial is registered on ClinicalTrials. gov (identifier: NCT00678392; available at http:// clinicaltrials. gov/show/NCT00678392).