Populus deltoides. A total of 1,107 testcross markers were obtained, and the mapping population consisted of 376 progeny. Among these markers, 597 were from the mother, and were assigned into 19 linkage groups, spanning a total genetic distance of 1,940.3 cM. The remaining 519 markers were from the father, and were also were mapped into 19 linkage groups, covering 2,496.3 cM. The genome coverage of both maps was estimated as greater than 99.9% at 20 cM per marker, and the numbers of linkage groups of both maps were in accordance with the 19 haploid chromosomes in Populus. Marker segregation distortion was observed in large contiguous blocks on some of the linkage groups. Subsequently, we mapped the segregation distortion loci in this mapping pedigree. Altogether, eight segregation distortion loci with significant logarithm of odds supports were detected. Segregation distortion indicated the uneven transmission of the alternate alleles from the mapping parents. The corresponding genome regions might contain deleterious genes or be associated with hybridization incompatibility. In addition to the detection of segregation distortion loci, the established genetic maps will serve as a basic resource for mapping genetic loci controlling traits of interest in future studies.Based on a two-way pseudo-testcross strategy, high density and complete coverage linkage maps were constructed for the maternal and paternal parents of an intraspecific F2 pedigree of  Genetic linkage maps provide unique tools in breeding and genomic studies in a variety of ways. Based on genetic linkage maps, markers tightly linked to traits of interest are useful for quantitative trait locus (QTL) detection, marker-aided selection, and marker-assisted cloning –3. GenetSegregation distortion is a common phenomenon that has been observed in many mapping studies –13. The Populus has been widely used as a model system in tree genomic studies because of its fast growth, wide distribution, amenability for gene transformation, ease of vegetative propagation, and small genome size (ca. 480 Mb) [Populus species [Populus deltoides. Based on the established maps, we detected those SDLs that triggered segregation distortion of the mapped markers. In addition to SDL detection, the maps developed in this study will provide a useful resource to localize genes controlling traits of interest in future studies.The genus  480 Mb) –26. Sinc 480 Mb) , a numbe 480 Mb) –35. Thes 480 Mb) –38, comp 480 Mb) , 39, and 480 Mb) . Marker  species , 41, 42.2 cross, which comprised 3,186 offspring. The grandmother and the grandfather of this pedigree were P. deltoides clone “T-120” and “I-63”, respectively. “T-120” was introduced from Texas, USA, and “I-63” was introduced from Italy. The mother and father of this pedigree are siblings from “T-120”×“I-63”, they were named as “clone 2–2” and “clone 2–38”. The mapping population was maintained at the poplar plantation farm in Sihong county of Jiangsu Province, China. From this mapping pedigree, 384 progeny were randomly selected for the mapping study. Young leaves were collected from each individual, and genomic DNA was extracted by using the DNeasy plant kit . DNA quality and quantity were assessed using a NanoDrop 2000  and by electrophoresis on 1% agarose gels. The field studies did not involve any endangered or protected species, and a sample collection was authorized by the administration office of the poplar plantation farm in Sihong county of Jiangsu Province, China.The mapping pedigree in this study was an intraspecific FPopulus DNA was digested with restriction enzymes of EcoRI and MseI, followed by ligation of EcoRI and MseI adapters. The ligation mixture was diluted 1:10 with deionized water. Subsequently, pre-amplification was performed using an EcoRI (E) primer with no selective nucleotides and an MseI (M) primer with a “C” or “T” selective nucleotide. The reaction volume for pre-amplification was 25 μL, containing 3 μL diluted ligation mixture, 1.0 U Taq polymerase, 2.5 μL 10× buffer , 5 ng of the E-primer, 30 ng of the M-primer, 0.1 g/L BSA, and 0.2 mM dNTPs. PCR amplifications were performed for 20 cycles of 94°C for 30 s, 56°C for 30 s, and 72°C for 120 s. Finally, selective amplification was performed in a volume of 15 μL, containing 3 μL of 1:15 dilutions of the pre-amplification products, 3 ng of the E-primer with two selective nucleotides, and 10 ng of the M-primer with three selective nucleotides (5-FAM labeled), 1.0 U Taq polymerase, 1.5 μL 10× buffer , 0.2 mM dNTPs, 1% (v/v) deionized formamide, and 0.1 g/L BSA. PCR amplification was performed for 12 touchdown cycles of 94°C for 30 s, 65°C for 30 s (reduced by 0.7°C/cycle), and 72°C for 120 s; followed by 23 cycles of 94°C for 30 s, 56°C for 30 s, and 72°C for 120 s.AFLP genotyping was performed following the protocol described by Vos et al. . First, PCR products were analyzed on an ABI 3730 capillary sequencer  using the standard genotyping module and were scored with the GeneMapper v3.7 software . Fragments ranging from 50 to 500 base pairs were scored. The AFLP markers were named using a combination of letters and digital numbers: the first three letters were the selective nucleotides of the M-primer, the last two letters were the selective nucleotides of the E-primer, and the digital number was the allele size in base pairs . Genotyp2), and 0.1 g/L BSA. PCR reactions were performed using the following profile: 94°C for 4 min; 30 cycles of 94°C for 30 s, 56°C for 30 s, 72°C for 30 s; and a final extension of 94°C for 10 min. The PCR products were analyzed on an ABI 3730 capillary sequencer  using the standard genotyping module, following the same procedure as that for AFLP analysis.Anueploids and polyploids are occasionally observed in the hybrids of poplar species , which lThe linkage maps were constructed using MapMaker v3.0 , followiC = 1 − emd/Ge−2, where C is the coverage of the genome within d cM of a marker, m is the total number of markers, and Ge is the estimated genome length. In this study, the coverage estimation was performed with d = 20 cM.Genome coverage was estimated according to the function given by Lange and Boehnke , assumin2 test was performed for each marker to assess whether it significantly deviated from Mendelian segregation ratios (1:1). Segregation distorted markers were indicated with a “*” at a significance of P≤0.05, or a “**” at a significance of P≤0.01 (A χf P≤0.01 .λi) under the assumption of an even distribution of markers: λi = imL/∑iL where m was the total number of markers, and Li was the observed map length plus two times the size of the average interval of linkage group i. The probabilities that a linkage group contained more or fewer markers than the expected number (λi) were evaluated using a two-tailed cumulative Poisson calculator. Subsequently, the markers clustered or dispersed in regions within each linkage group were evaluated following the method described by Yin et al. [Marker distribution throughout different linkage groups was evaluated using the method described by Remington et al. , which en et al. .http://statgen.ucr.edu/download/software) [Based on the established genetic maps, SDL detection was performed using PROC QTL v2.0 (oftware) , with a http://dx.doi.org/10.6084/m9.figshare.1363641.The genotyping data generated in this study were deposited at website,  for this mapping pedigree. Among these, 587 (39.48%) were testcross markers from the female parent, 508 (34.16%) were testcross markers from the male parent, and 392 (26.36%) were intercross markers. The number of segregating markers varied among different primer combinations, ranging from 3 to 39 segregating bands per primer pair.The informativeness of the SSR primer pairs is listed in  for thiPoplars are outbreeding plants, and the heterozygosity or homozygosity of markers varied among different genetic loci of the poplar genomes. In this study, most of the segregating markers are testcross markers, which means they are heterozygous in one of the parents and are recessively homologous  in the other parent. Besides, a relatively high number of intercross markers (heterozygous in both parents) were also generated. However, the intercross markers provided limited information to estimate the recombination frequency precisely , and thuP≤0.025), and significantly less than the expected number of markers on linkage group II  were significantly distorted from the expected 1:1 segregation ratio (α ≤ 0.05), and most of the segregation distorted markers (86.30%) were observed on linkage groups I, IX, X, XII, and XIV . On thesSDLs analysis detected six and two SDLs with significant LOD support on the paternal and maternal maps, respectively , Fig 2. The AFLP technique generates multiple markers simultaneously. Referring to the presence of neighboring peaks, null alleles can be discriminated from failed amplification. Taking together the large number of progeny used for map construction, the quality of the established maps is highly ensured. In this study, a large number of testcross markers were generated for each of the mapping parents. As a result, we obtained male and female maps both with 19 linkage group numbers, which equals the number of haploid chromosomes in poplar. The established genetic maps were estimated to achieve nearly complete coverage of the poplar genome.EcoRI and MseI, or from recombination suppression in the corresponding genomic regions [Populus deltoides [Populus alba [Populus [P. deltoides, thus we propose that the difference in the genetic lengths of the male and female maps is associated with the sex-determining system of this poplar species. However, this hypothesis needs to be tested with more mapping data in different poplar species.High heterogeneities in marker density were revealed by examining the marker distribution, and substantial marker clusters were observed on both the paternal and maternal maps. These marker clusters might have resulted from either the overabundance of restriction sites of  regions , 54. Not regions , 55. How regions . Dioecy  regions , 58. Popeltoides  and Populus alba  revealed[Populus , 59. TheSegregation distorted markers have been frequently observed in mapping studies in many organisms, and are also a common in poplars , 33, 39.P. deltoides occurred through a ZW system in which the female plant was the heterogametic sex [Populus species revealed a preponderance of male plants, especially under the harsh environment [In this study, markers from the male parent had a higher segregation distortion ratio than those from the female parent. According to Haldane’s rule, the heterogametic sex is more likely to be sterile or rare in the hybrids . Sex detetic sex . Early eironment . Male suironment , 40, 59.QTL mapping based on phenotypic data provides an effective tool to dissect the genetic mechanisms underlying fitness traits –73. Howe2 full-sib family of P. deltoides. Large contiguous blocks of marker segregation distortion were observed on some of the linkage groups, and eight SDLs were detected. The detected SDLs provide essential information for further dissection of the biological factors underlying the uneven descent of gametes from the mapping parents. Meanwhile, an ongoing project using single nucleotide polymorphism markers to enhance the informativeness of the present maps will ultimately enable us to align the established maps to the poplar genome sequence. The maps developed in this study will serve as a basic platform to uncover genes controlling many other traits of interest in the future.In this study, we constructed highly dense genetic maps for the mapping parents of an FS1 FigP≤0.05, or “**” when P≤0.01.Note: Markers in bold and italic fonts are SSR markers; for marker ending with a letter of “R”, the corresponding marker is repulsion linkage phase; markers with significant segregation distortion are indicated with “*” when (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Tablei≥λi or mi≤λi) were evaluated by using a two-tailed cumulative Poisson calculator (P ≤ 0.025 is significant at α = 0.05).Note: “*” indicates significance at α = 0.05; “**” indicates significance at α = 0.01; the probabilities for clustering or dispersal of AFLP markers(m(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


We measure the DNA elongation at a range of constant stretching forces using optical tweezers, allowing direct characterization of the intercalation kinetics as well as the amount intercalated at equilibrium. Higher forces exponentially facilitate the intercalative binding, leading to a profound decrease in the binding site size that results in one ligand intercalated at almost every DNA base stack. The zero force Δ,Δ-P intercalation dK is 44 nM, 25-fold stronger than the analogous mono-nuclear ligand (Δ-P). The force-dependent kinetics analysis reveals a mechanism that requires DNA elongation of 0.33 nm for association, relaxation to an equilibrium elongation of 0.19 nm, and an additional elongation of 0.14 nm from the equilibrium state for dissociation. In cells, a molecule with binding properties similar to Δ,Δ-P may rapidly bind DNA destabilized by enzymes during replication or transcription, but upon enzyme dissociation it is predicted to remain intercalated for several hours, thereby interfering with essential biological processes.DNA intercalation by threading is expected to yield high affinity and slow dissociation, properties desirable for DNA-targeted therapeutics. To measure these properties, we utilize single molecule DNA stretching to quantify both the binding affinity and the force-dependent threading intercalation kinetics of the binuclear ruthenium complex Δ,Δ-[μ‐bidppz‐(phen) When synthesizing DNA-targeted drugs, high DNA binding affinity and slow DNA binding kinetics are considered essential aspects that enhance their therapeutic capability –5. Highe4Ru2]4+   Figure  exhibits) Figure ,12,19. E) Figure ,20. A re) Figure . Further) Figure ,21–23. Tet al. demonstrated the ability of such an approach to characterize the extremely slow process of DNA-Δ,Δ-P threading intercalation at a Δ,Δ-P concentration of 2 nM  were used to trap a streptavidin-coated ∼5.6 μm polystyrene bead with a typical optical trap stiffness of 80 pN/μm, while a second identical bead was held by a micropipette with a ∼1 μm tip. A single bacteriophage λ-DNA, labeled on opposite strands with biotin, was attached between the two beads and a piezoelectric positioner controlled the micropipette displacements (±10 nm) to maintain a fixed applied force (±1 pN) on the DNA molecule . After tLeq, (per bp)   Figure . The timal ,24:(1)\r AT-DNA ,19. HoweLsat(F). The equilibrium extensions per bp of the DNA-Δ,Δ-P complex, Leq, are presented in Figure Leq to the saturated extension at the same force, Lsat(F), (LDNA(F) is the DNA extension in the absence of ligand binding. Afterward, we use F). It follows from Equation  and Lsat (F) weighted with the fraction of each component, governed by Θ, according to the relationship:satL (F), obtained from Equation , ,24:(2)\ionship:\documentionship:\documentEquation , which iLeq curves calculated in this manner fit the equilibrium extensions obtained as a result of relaxation at each concentration supports the assumption that the intercalated DNA is indeed a mixture of the ligand-free and ligand-saturated DNA to a good approximation. It is important to note that, due to slow, force-dependent ligand binding, the equilibrium force-extension curve cannot be obtained directly, as this would require pulling on the timescale of many hours. To illustrate this point, a force-extension curve obtained after stretching the DNA to 60 pN at 50 nM, waiting at constant force until equilibrium is reached and then releasing at a rate of 200 nm/s shows very different elasticity due to the non-equilibrium nature of the stretching curve  is presented in Figure dK and the binding site size n, which are varied independently to fit the experimental Leq  curves at each force. The binding curves could not be fit well to a single value of n for all curves. The fitted dK(F) values decrease with force from 21±4 nM at 10 pN to 2.8±0.1 nM at 60 pN , decreases from 3.7±0.2 at 10 pN to 1.7±0.1 at 60 pN  values in terms of the expected exponential force dependence for ligand binding processes that elongate DNA (dK(0) of 44±2 nM. We also obtain a value of Δxeq = 0.19±0.01 nm, which represents the increase in DNA equilibrium extension due to one ligand binding event.The measured equilibrium length of the DNA-Δ,Δ-P complex entclass1pt{miniman Figure \documentn Figure \documentN Figure . Analyzin Figure \documentgate DNA ,25,34(5n(F), provides an independent estimate of Δxeq at each forcen(F) and ΔLsat(F) into Equation  to Equation , we can use this information to obtain the individual kon(F) and koff(F) rates at each constant force (kon(F) and koff(F) at each ligand concentration can be individually fitted to an exponential dependence on force, which provides a measurement of the zero-force rate, kon/off(0), and the change in DNA length between the unbound and the forward transition state, xon, and the intercalated and the reverse transition state, xoff, respectively,kon(F) and koff(F) are strongly facilitated by force, due to the significant complex elongations required for both ligand association (xon = 0.33±0.01 nm) and dissociation (xoff = 0.14±0.01 nm). Similar dynamic displacements are obtained from analyzing the kinetics at all other concentrations below saturated binding (data not shown). The difference between the measured complex elongations to the transition state in the forward and reverse directions provides another independent estimate of the equilibrium complex elongation upon a single Δ,Δ-P intercalation event \doct Figure , Δxeq = kon(F) for all measured forces show a linear dependence on ligand concentration, whereas the backward rates koff(F) are independent of concentration, as expected for a bimolecular process. For example, the forward rate kon at 30 pN ranges from ∼10−4 s−1 (τ ∼2 h) at 2 nM to ∼10−2 s−1 (τ ∼2 min) at 150 nM, as illustrated in Figure ka(F), as shown in Figure ak(0)∼104 M−1 s−1. This bimolecular association rate is ∼5 orders of magnitude slower than an association rate limited by diffusion.The forward rates xeq = 0.19 nm. Also, the DNA stretching force appears to relieve significant steric constraints against Δ,Δ-P threading, leading to saturated Δ,Δ-P-DNA intercalation every 1.7 bp at 60 pN force , also measured by DNA stretching with optical tweezers (F = 10 pN), the intercalative binding site size of Δ,Δ-P (3.7 bp) is somewhat larger than that of Δ-P (2.9 bp), as could be expected from the larger size of the former complex. However, increasing force has a much more dramatic effect on the binding site size of Δ,Δ-P, which decreases by 2 bp to 1.7 bp at 60 pN, relative to the binding site size of Δ-P, which decreases by only 0.4 bp  . Accordi1m = 10–26 kcal/mol  = 104 M−1s−1 is ∼5 orders of magnitude slower than the association rate limited by diffusion kdif ∼109 M−1s−1 (dK∼10 nM, such that the ligand dissociates from this non-intercalated state much faster than it threads. The overall forward intercalative rate difk and the probability of intercalation during non-specific binding, BTk, the creation of the helix/coil boundaries associated with local duplex disruption can easily introduce an additional 8–10 BTk (The force-facilitated Δ,Δ-P-dsDNA on rates in our experiments can be well described by a single exponential dependence on time Figure . This is9 M−1s−1 . The fac9 M−1s−1 ,20, is v8–10 kBT . The freIn vivo it is likely that the very slow threading of Δ,Δ-P into stable polymeric dsDNA would prevent this ligand from intercalating most chromosomal DNA if it reaches the nucleus. However, DNA undergoing high rates of transcription and replication, such as may be found preferentially in cancer cells, may already be destabilized by RNA polymerase or other motor proteins, facilitating rapid initial binding to these locations, followed by very slow dissociation. Therefore, the slow dissociation kinetics combined with its high DNA binding affinity highlights the potential therapeutic use of ligands with binding properties similar to Δ,Δ-P (to Δ,Δ-P ,44.


People give widely varying estimates for the amount of genetic overlap that exists between humans. While some laypeople believe that humans are highly genetically similar to one another, others believe that humans share very little genetic overlap. These studies examine how beliefs about genetic overlap affect neural and evaluative reactions to racially-ambiguous and biracial targets. In Study 1, we found that lower genetic overlap estimates predicted a stronger neural avoidance response to biracial compared to monoracial targets. In Study 2, we found that lower genetic overlap estimates predicted longer response times to classify biracial  faces into racial categories. In Study 3, we manipulated genetic overlap beliefs and found that participants in the low overlap condition explicitly rated biracial targets more negatively than those in the high overlap condition. Taken together, these data suggest that genetic overlap beliefs influence perceivers’ processing fluency and evaluation of biracial and racially-ambiguous individuals. An esse) traits . As such) traits .Surprisingly little research, however, has investigated how exactly people mentally represent the biological roots of essence. What—according to laypeople—creates a racial essence? A likely candidate is genes. In the present studies, we examined people’s underlying assumptions about the relationship between genetic variation and race.Recent evidence suggests that people generally consider between-group differences that are ascribable to genetic factors to be more significant and immutable than differences rooted in social or environmental factors . But whaa priori belief in discrete  categories predicted a stronger tendency to perceive a continuously-varying array of morphed faces as conforming to two discrete categories.While evidence from the Human Genome Project suggests that humans, regardless of race, share 99.9% of their genetic material , recent In the current work, we extend these findings by focussing specifically on how people react to faces in the middle of this continuous array—the blended, biracial faces that are most racially-ambiguous. Here, we aim to gain a more specific understanding of (a) whether beliefs about genetic overlap influence perceivers’ evaluations of ambiguous faces, and (b) what mechanisms may begin to explain any differences in evaluation. We do so by using a neural measure of avoidance (Study 1), a behavioral measure of processing disfluency (Study 2), and an explicit measure of trait judgments (Study 3).We theorized that biracial targets pose a unique perceptual and conceptual challenge because they are more likely than others to be racially-ambiguous, making them difficult to categorize discretely . We hypoOur approach extends previous studies that have investigated how beliefs about race influence judgments of mono- and biracial targets. For example, a priori beliefs on the perception of mono- and biracial targets. For example, Other researchers have focussed more specifically on the effects of perceivers’ All studies reported here were approved by the Office of Research Ethics at the University of Toronto.In Study 1, we examined whether genetic overlap beliefs would influence neural avoidance by using electroencephalography (EEG) to record participants’ neural responses while they viewed films in which faces slowly and continuously morphed from Black to White. We focused on frontal EEG asymmetry, which is thought by many to index the motivation to approach or avoid a given stimulus . GreaterParticipants were 25  undergraduate students .Figure 1. Creating the facial morphs in this way in this and the subsequent studies allows for more precise control over the “mixture” of the biracial faces than is possible with photographs of real biracial individuals. With facial morphs we can precisely control the percentage of facial features drawn from each of the “parent” faces, which is not possible with photographs of real people.We created a series of videos showing faces changing from one to another using 15 pairs of standardized and normed Black and White faces of undergraduate-age males . The facAfter providing informed consent, participants were connected to the EEG system. The 15 Black-to-White face morph videos were presented in random order for 60 s each. Participants watched the videos passively, without making any responses. After each video, participants were given the opportunity to take a short break. The video display and EEG recording were synchronized. Given EEG’s high temporal acuity, we could assess, in real time, variation in alpha asymmetry as a function of each face’s racial composition.Electroencephalography recording was stopped after the video task and participants were given time to wash their hair. After this break, participants completed additional, ostensibly unrelated questionnaires that were said to be provided by other researchers in the department. Embedded in these measures were two questions  designed to assess genetic-overlap beliefs:(1)If you were to choose two people at random from the entire world, what percentage of genetic material would they have in common?(2)If you were to choose two people at random who happened to be from the same racial background, what percentage of genetic material would they have in common?1.Question “1” has been used in previous research to index beliefs about genetic overlap between racial groups . This mewithin racial groups) was to put participants’ responses to Question “1” into context, allowing us to gain additional detail regarding how assumptions about overall genetic variation relate to assumptions about within-group variation in particular. Are these estimates positively correlated, negatively correlated, or unrelated? In other words, do people believe that high between-group difference implies low within-group difference . We included this item in light of recent findings indicating that higher conservatism predicts a stronger tendency to perceive biracial faces as Black . FinallyM = 71.34%, SD = 32.90%, range: 1–100%); Question “2” . The correlation between the two items was positive and high, r = 0.91, p < 0.001.As observed in previous research, participants’ responses to the genetic overlap questions varied widely: Question “1”  and digitized at 560 Hz using ASA acquisition hardware (Advanced Neuro Technology) with average-ear references and forehead ground. Impedances were kept below 5 kΩ and frequencies below 1 Hz and above 100 Hz were digitally filtered . Movement artifacts were detected with a -75 and +75 μV threshold. Two second epochs were extracted through a Hamming window (75% overlap). To examine alpha asymmetry we used a fast Fourier transform to extract power within the alpha band (8.00–13.00 Hz). We created three time windows  and calculated the relative left vs. right frontal activation asymmetry as the log of the alpha power at electrode site F4 (right) minus the log of the alpha power at electrode site F3 left; . Higher Figure 1. The overall mean frontal asymmetry values for each time period were as follows: time 1 (Black): M = 0.0501, SD = 0.0853; time 2 : M = -0.00767, SD = 0.137; time 3 (White): M = 0.0458, SD = 0.130.In order to examine genetic overlap beliefs continuously, we followed the recommendations of F = 6.01, p < 0.01, F = 3.40, p < 0.05, F = 6.61, p < 0.01, F = 6.37, p < 0.05, F = 12.44, p < 0.01, 2.This analysis revealed a significant main effect of time window, Figure 2. First, we examined estimates of general genetic overlap (Question “1”). At time 2, when the faces were most racially-ambiguous, lower estimates of genetic overlap between two randomly-selected people marginally predicted greater relative right frontal activation (avoidance), β = 0.35, t(23) = 1.80, p = 0.09. At time 1  and time 3 , estimates of genetic overlap did not significantly predict frontal asymmetry values, ps > 0.25. Thus, as predicted, a general assumption of clear, genetically-based distinctions between two randomly-drawn humans elicits a comparatively negative response toward people who are not readily classifiable.To probe these interactions, we regressed the frontal asymmetry values at each time window onto the genetic overlap estimates. The results for participants at 1 SD above and below the mean on the genetic overlap questions are displayed in t(23) = 2.23, p < 0.05. Estimates of genetic overlap within racial groups did not significantly predict frontal asymmetry values at time 1  or time 3 , ps > 0.65. This pattern suggests that people who give lower within-group overlap estimates expect individuals to differ visibly from one another – even when those individuals are from the same racial group. Visual evidence of a continuous blur from one individual to another violates the expectation of clear boundaries between individuals and thus elicits a relative avoidance response. Note that this neural avoidance pattern was no longer significant once the target face clearly belonged to another person (at time 3).Next, we examined estimates of within-race genetic overlap (Question “2”). Once again, at time 2, lower estimates of overlap predicted more relative right frontal activation (avoidance), β = 0.42, A priori, it might have been conceivable that high within-group estimates would predict lower “whole world” estimates . In these data, however, the two estimates were highly, positively correlated. Moreover, both estimates predicted EEG responding in a similar fashion. Thus, a general representation of low genetic overlap mattered more than the within- vs. between-group distinction.We hypothesize that a primary reason why people with low genetic overlap beliefs are inclined to avoid biracial faces is because such faces disrupt processing fluency—they are harder to classify. Study 1, however, contained no measures of processing fluency. Thus, in Study 2, we used a face-classification task to examine whether belief in lower genetic overlap would predict more difficulty in categorizing biracial faces compared to monoracial faces. =13.91) residents of the United States recruited via Qualtrics Panels in exchange for $4.20 compensation . Participants were randomly assigned to one of two classification conditions (described below) and all participants saw two types of faces, resulting in a 2  × 2  mixed design.Participants were 449  to assess beliefs about genetics. Three questions asked for estimates of genetic variation between two humans selected from: (1) the entire world, (2) the same race, and (3) different races. Four final multiple-choice questions were included as a quiz of knowledge of genetics .To obfuscate the connection between the genetics questions and the face classification task, participants also completed an anagram task. Participants were asked to rearrange the letters in a string of text  to produce a word , and were given 120 s to complete as many items as possible.Figure 3.Participants also completed the classification task reported by Participants in the race classification condition pressed the “A” key if the face was Caucasian or the “L” key if the face was Asian. Those in the control condition pressed the “A” key if the person was “feeling positive” or the “L” key if the person was “feeling negative” at the time the photograph was taken. The links between keyboard letters and response options were counter-balanced between participants. The inclusion of an emotion classification condition follows Faces were presented with an inter-trial interval of 5000 ms. If reaction times (RTs) exceeded 3000 ms, the program prompted participants to respond more quickly. Immediately after each classification, participants were asked to rate the attractiveness of the face just presented (1 = “not attractive”…9 = “very attractive”). Finally, participants completed a short demographics survey and were fully debriefed.The overall order of tasks was varied such that the genetics questions occurred before or after the classification task, and these two tasks were always separated by the anagrams task. The order of the genetics questions and classification task were randomized in this way to control for the possibility that responses on one task might influence responses on the next task, despite the interposed anagrams task.M = 51.28%, SD = 35.78%, range: 0–100%); Question “2” ; Question “3” . The correlations among the three items were positive and high, all rs > 0.74, all ps < 0.001.As observed in Study 1, participants’ responses to the human genetic overlap questions varied widely: Question “1”  or incoherent responding (n = 8), resulting in a final total of 393 participants.Participants with RTs greater than two SDs from the mean were removed (0.8%). All remaining RTs were log transformed. Additional participants’ data were removed due to failure to complete primary dependent measure items  face type, F = 16.20, p <0 .001, M = 1602.35 ms, SD = 434.74 ms, than to monoracial targets, M = 1500.01 ms, SD = 421.37 ms; (2) Question “1” , F = 8.19, p < 0.01, F = 5.50, p < 0.02, We examined the effects of genetic overlap beliefs on RTs to categorize biracial faces compared to monoracial faces with a repeated-measures full factorial ANCOVA. Face type  was entered as a within-subjects variable, classification condition [race classification vs. emotional (control) classification] was entered as a between-subjects variable, and responses to the two genetic overlap questions used in Study 1 (“whole world” and “same race”) were entered as covariates F = 4.99, p < 0.05, F = 11.83, p < 0.001, F = 7.65, p < 0.01, In addition, the following two-way interactions emerged: (1) face type × classification condition, F = 7.28, p < 0.01, F = 9.28, p < 0.01, The analysis also revealed the predicted three-way interactions for face type × classification condition × “whole world” general overlap estimate (Question “1”), Fs < 3.30, all ps > 0.07.To probe these interactions, we conducted analogous ANCOVAs within each classification condition. As expected, when the participants’ task was to classify the target’s emotional state, genetic overlap beliefs did not influence RTs, all F = 6.52, p = 0.01, t(182) = -2.05, p < 0.05. Thus, consistent with our hypothesis, belief in lower genetic overlap was associated with experiencing greater difficulty when classifying biracial  faces according to their race.However, when the task was to classify the target’s race, a face type × “whole world” general overlap estimate (Question “1”) interaction emerged, t(182) = -1.09, p > 0.25. Thus, when included simultaneously, beliefs about “whole world” genetic overlap appeared to be the better predictor of RTs to biracial vs. monoracial faces. Raw RT means and SDs are presented in Table 1.In this analysis, the face type × “same race” within-group estimate (Question “2”) interaction was no longer significant, β = -0.08, ps > 0.25). This is most likely because asking participants to explicitly compare the genetics of different racial groups activates a host of social desirability concerns, however, we believe that the “whole world” question may serve as an effective proxy for the “different race” question.Additional analyses that included the “different race” genetic overlap question as the predictor variable did not yield any significant effects  was entered as a within-subjects variable, classification condition [race classification vs. emotional (control) classification] was entered as a between-subjects variable, and responses to the “whole world” and “same race” genetic overlap questions were entered as covariates . This analysis revealed no significant main effects or interactions, all In sum, while we acknowledge that the observed effect sizes are small, Study 2 provides response time evidence that the lower the belief in genetic overlap between two humans, the greater the difficulty in racially classifying biracial  target faces. This supports the notion that the belief in different people as genetically distinct entities encourages dichotomous  racial classification . Study 2per se, but simply in order to avoid the feeling of confusion when faced with a difficult categorization task. Thus, in Study 3 we examined whether the differences observed in Studies 1 and 2 would translate into explicit negative evaluations of a biracial target. If so, this would suggest that low overlap believers’ response to biracial targets extends beyond their own feelings of confusion (“I don’t know how to categorize this person”) to the actual attribution of negative traits to the target (“he is disagreeable”).How does this difficulty with racial categorization translate into behavior toward a racially-ambiguous target? Based on Study 1, one answer to this question might be greater avoidance of biracial  targets. It is possible, however, that low overlap participants in Study 1 avoided biracial targets not because they evaluated them negatively In Study 3, we also sought to expand the methodologies employed in Studies 1 and 2 by manipulating beliefs about shared genetic material. Our attempt to manipulate genetic belief was motivated by two goals. First, if effects similar to those of Study 1 and 2 were found with manipulated beliefs, this would suggest that beliefs about genetic overlap help to cause different patterns of race perception. Second, if those randomly assigned to the high genetic overlap condition were to evaluate biracial individuals less negatively than those in a low genetic overlap condition, this would have practical implications for prejudice reduction programs.Why might one hypothesize that these beliefs can be manipulated? Most people have little intuitive knowledge about genomic science and few strong convictions about genetic variability. Thus, both the high and low overlap perspectives may seem believable to most people. Put differently, both perspectives may be cognitively available in long-term memory, but either may be made more accessible through persuasive primes e.g., . In prevParticipants were 171  residents of the United States recruited online via Amazon Mechanical Turk in exchange for $0.50 compensation . The mean age was 32.06 (SD = 10.51). The mean political orientation was 1.23 (SD = 2.62) on a scale from -5 (extremely conservative) to +5 .Stimuli in the face judgment task were nine grayscale images of male targets. Six photos of 100% Black and 100% White targets (three of each race) were drawn from the same normed and standardized photo set used to create the videos used in Study 1 . Three aAfter providing informed consent, participants began the first of what was described as two separate studies. “Study 1” was described as a reading comprehension task. The computer randomly presented one of two articles – high genetic overlap or low genetic overlap. After reading their assigned article, participants completed a manipulation check item: “In your estimation, if you drew two people at random from the entire world, what percentage of genetic material would they have in common?” Participants selected from five different values ranging from 1 (21.4%) to 5 (99.9%). In addition, participants were asked to describe the article in their own words.Next, in “Study 2,” the computer presented the following instructions: “Research in psychology has begun to show that people are surprisingly good at making judgments about a person just by looking at their face. We are interested in the accuracy of people’s facial judgments. We will ask you to make judgments about different people just by looking at their face. In addition, we will ask you to estimate how confident you are about your rating.”not at all…7 = extremely). The traits were: horrible, aggressive, dangerous, annoying, mean, crazy, wild, emotional, compassionate, polite, pleasant, nice, trustworthy, kind, calm, and rational. Participants also indicated how certain they were in their ratings (1 = extremely uncertain…5 = extremely certain).Next, participants saw a total of nine faces , presented individually in random order. Participants were asked to rate each face on a randomized series of positive and negative traits using a 1–7 rating scale , and a measure of the Big Five personality traits [the Big Five Aspects Scale (BFAS), Next, participants completed a battery of questionnaires, including demographics, political orientation (one item: -5 = Two coders rated responses to the suspicion check item on whether participants guessed that the study concerned the relationship between beliefs about genetics and perceptions of race. The coders (who achieved 99% agreement) identified 19 participants who identified the correct purpose of the study. These participants wrote statements such as, “This study was about what people think about genetics and how it affects racism.” Because knowledge of the hypothesized relationship between genetic overlap belief and racial perception could contaminate participants’ responses, these participants were removed from the analyses.M = 5.0, SD = 0.01, corresponding to a mean estimate of 99.9%) were significantly higher than those of low overlap participants , t = 49.55, p < 0.001.Of the remaining participants, seven failed the genetic belief manipulation check by leaving the item blank and six failed by summarizing the article in a non-sensical fashion. This left a total of 139 participants, 62 in the high overlap condition and 77 in the low overlap condition. Among these participants, the manipulation appeared to be successful: high overlap participants’ estimates of the percentage of genetic material people have in common  = 16.87, p < 0.001, F = 4.52, p < 0.05, .06.A factor analysis of the trait items revealed two significant factors . One factor – which included all items but “rational” – reflected overall valence (positive vs. negative traits). The second factor included only the traits “dangerous,” “crazy,” “emotional,” and “horrible.” These four traits (Cronbach’s α = 0.72), which appeared to cluster specifically around “primitive” or animal-like concepts, were combined into an evaluation index, our main dependent measure. To examine the effects of target race on participants’ evaluations, we conducted a 2 (genetic overlap belief: high vs. low) × 3  analysis of variance, with repeated measures on the second factor. This analysis revealed a significant effect for face type, Table 2, participants in the low genetic overlap condition rated the biracial targets more negatively on the four-item “primitivism” index  than did participants in the high overlap condition , F = 4.33, p < 05, d = 0.35. Low and high overlap participants did not differ in their ratings of 100% Black and 100% White targets (both Fs < 1.0).As reported in M = 2.78, SD = 0.82) less negatively on the four-item index than the 100% Black target , t = 2.18, p < 0.05, d = 0.33. In contrast, participants in the low overlap condition rated the biracial target  equivalently to the 100% Black target , t = 1.52, p = 0.13.Participants in the high overlap condition rated the biracial target  = 1.73, p = 0.18. Thus, it appears that genetic overlap beliefs were better predictors of participants’ ratings on traits that generally seem more “biological” and thus more susceptible to genetic influence. Indeed, these types of traits  are precisely those that feature prominently in stereotypes about the assumed “primitive” nature of people of African descent . However, the overall pattern is consistent with our primary hypothesis: low overlap participants rated biracial targets more negatively than did high overlap participants.F = 3.50, p < 0.05. Second, the fact that genetic overlap beliefs were manipulated renders an explanation due to individual differences in political orientation implausible. Moreover, the belief × face type interaction remained significant when each of the Big Five traits were added as covariates.Recent results indicate that political conservatism predicts the tendency to perceive biracial faces as Black . Might oTable 2, both high and low genetic overlap participants rated the 100% White targets significantly more negatively than both the biracial and Black targets, all Fs > 3.72, all ps < 0.05. Why might this be the case? We considered the possibility that participants may have judged the White faces as less attractive than the biracial or Black faces. However, a pilot study (N = 147) indicated no significant effect of face race on ratings of attractiveness, F = 0.55, p = 0.58. Another possibility may have to do with self-presentational concerns. Future researchers may consider examining whether, for example, extrinsic motivation to respond without prejudice  personal qualities to biracial people.causing different patterns of evaluation of biracial targets. As such, these findings shed light on a unique source of bias against biracial people, who tend to be racially-ambiguous, and constitute one of the fastest growing minority groups in the United States  would be inversely correlated . The data clearly indicated otherwise: the two genetic overlap questions were highly, positively correlated, and both yielded similar results. Thus, it appears that people with a low within-group overlap perspective expect even members of the same group to possess considerable genetic difference. The greater the assumed difference, the greater the avoidance of a face that blurs the distinction between the race of one person and the next. Taking both estimates together, the Study 1 data suggest that a generalized expectation of low genetic overlap leads perceivers to view ambiguous or difficult-to-classify individuals negatively.In both Study 1 and Study 2, we found that a belief in low genetic overlap predicted parallel results, regardless of whether the question was about people drawn at random from the whole world or people from the same race. It was conceivable One limitation of the current studies is that our experimental designs and participant samples make it impossible for us to examine the effects of participant race on genetic overlap estimates and perceptions. When we asked participants to provide genetic overlap estimates (Studies 1 and 2), or when we manipulated them in a general way without naming a specific group (Study 3), we have no way of knowing what groups participants have in mind when making judgments. In future research it would be helpful to examine variability in estimates depending on which racial groups participants are asked to think about. Participants may, for example, estimate higher overlap between their racial group and some out-groups, but lower overlap between their racial group and others. These estimates may vary based on the relative status of the out-group in question, or perhaps even on group-esteem. For example, individuals with high racial-group-esteem may perceive more overlap with high-status groups, whereas those with low group-esteem may perceive less overlap with high-status groups. Even when considering only racial out-groups, people likely perceive some groups as more similar and others as less similar . All of this variation may have important implications for attitudes about and interaction with members of racial in- and out-groups. Further, future research which specifically recruits large and equal samples of specific racial groups could shed more light on whether people draw on genetic overlap estimates differently when thinking about members of their own racial groups vs. members of racial out-groups.Throughout this paper, we have argued that a belief in low genetic overlap is associated with a variety of negative reactions to biracial individuals. In future research it will be important to investigate more systematically how differences in scientific literacy or even general IQ factor into how people respond to questions about genetic overlap. People who respond with lower estimates are more out of step with the scientific data, suggesting lower scientific or genetic literacy. More generally, scholars have long posited a negative relationship between cognitive ability and prejudice e.g., , and theIn addition, future researchers should investigate which exact emotions are associated with the negative responses suggested by these studies , as well as examine the impact of other types of genetic beliefs such as, for example, the amount of genetic overlap between men and women, normal weight and obese individuals, or individuals from different socioeconomic backgrounds. Finally, future studies should also capitalize on the finding in Study 3 that genetic beliefs can be manipulated as an avenue for prejudice-reduction interventions see also , Study 2The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1’s primary data format.To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.The online version of this article (doi:10.1186/s13742-015-0047-8) contains supplementary material, which is available to authorized users. Because of its broad functionality and efficient binary file format, PLINK is widely employed in data-processing pipelines that are established for gene-trait mapping and population-genetic studies. However, the five years since the final first-generation update (v1.07), however, have witnessed the introduction of new algorithms and analytical approaches, the growth in size of typical datasets, as well as wide deployment of multicore processors.In response, we have developed PLINK 1.9, a comprehensive performance, scaling, and usability update. Our data indicate that its speedups frequently exceed two, and sometimes even three, orders of magnitude for several commonly used operations. PLINK 1.9’s core functional domains are unchanged from that of its predecessor—data management, summary statistics, population stratification, association analysis, identity-by-descent estimation , IMPUTE2A major problem remains: PLINK’s core file format can only represent unphased, biallelic data; however we are developing a second update, PLINK 2.0, to address this.Modern ×86 processors are designed to operate on data in  machine word or (≥ 128-bit) vector chunks. The PLINK 1 binary file format supports this well: the format’s packed 2-bit data elements can, with the use of bit arithmetic, easily be processed 32 or 64 at a time. However, most existing programs fail to exploit opportunities for bit-level parallelism; instead their loops painstakingly extract and operate on a single data element at a time. Replacement of these loops with bit-parallel logic is, by itself, enough to speed up numerous operations by more than one order of magnitude.a1,a2,…,am} and {b1,b2,…,bm} where each ai and bi has a value in {0,1,2,ϕ}, representing either the number of copies of the major allele or (ϕ) the absence of genotype data. Also define an intersection set Ia,b:={i:ai≠ϕ and bi≠ϕ}. The “identity-by-state” measure computed by PLINK can then be expressed as Ia,b| denotes the size of set Ia,b, while |ai−bi| is the absolute value of ai minus bi. The old calculation proceeded roughly as follows:For example, when comparing two DNA segments, it is frequently useful to start by computing their Hamming distance. Formally, define two sequences {i∈{1,2,…,m}: ai=ϕ or bi=ϕ, skipIf ai=bi, increment IBS2otherwise, if ai=2 and bi=0), or (ai=0 and bi=2), increment IBS0otherwise, if diff := diff + popcount(F)obs := obs + popcount yields a bit vector with two ones for every marker where genotype data is present for both samples, and two 0 s elsewhere, so 2|Ia,b| is equal to the number of ones in that bit vector; while (({Ai}XOR {Bi})AND {Ci}AND {Di}) yields a bit vector with a 1 for every nucleotide difference. Refer to Additional file The idea is that , and der between a pair of genetic variants, where some data may be missing. Formally, let n be the number of samples in the dataset, and {x1,x2,…,xn} and {y1,y2,…,yn} contain genotype data for the two variants, where each xi and yi has a value in {0,1,2,ϕ}. In addition, define The applications of bit population count extend further than might be obvious at first glance. As another example, consider computation of the correlation coefficient The correlation coefficient of interest can then be expressed as Ix,y|, X and Y encode homozygote minor calls as 002, heterozygote and missing calls as 012, and homozygote major calls as 102, and then proceed as follows: Z := (XORY) AND01010101… 2Set XXORY) AND (10101010… 2 - Z)) ORZ),popcount2.n.Subtract the latter quantity from Given PLINK 1 binary data, |viwi term is in {−1,0,1}, and can still be represented in 2 bits in an addition-friendly manner. . The exact sequence of operations that we chose to evaluate the dot-product terms in a bitwise parallel fashion is somewhat arbitrary.The key insight behind this implementation is that each Ix,y| is invariant, y, and x. Thus, these five values would not need to be recomputed for each variant pair at O(m2n) total time cost; they could instead be precomputed outside the main loop at a total cost of O(mn) time and O(m) space. PLINK 1.9 optimizes this common case.We note that when computing a matrix of correlation coefficients between all pairs of variants, if no genotype data is absent, then |See popcount_longs in plink_common.c for our primary bit population count function, and plink_ld.c for several correlation coefficient evaluation functions.Modern x86 processors also contain increasing numbers of cores, and computational workloads in genetic studies tend to contain large “embarrassingly parallel” steps which can easily exploit additional cores. Therefore, PLINK 1.9 autodetects the number of cores present in the machine it is running on, and many of its heavy-duty operations default to employing roughly that number of threads.  Most of PLINK 1.9’s multithreaded computations use a simple set of cross-platform C functions and macros, which compile to pthread library idioms on Linux and OS X, and OS-specific idioms like _beginthreadex on Windows.PLINK 1.9 also contains improved support for distributed computation: the –parallel flag makes it easy to split large matrix computations across a cluster, while –write-var-ranges simplifies splitting of per-variant computations.Graphics processing units (GPUs) remain as a major unexploited computational resource. We have made the development of GPU-specific code a low priority since their installed base is much smaller than that of multicore processors, and the speedup factor over well-written multithreaded code running on similar-cost, less specialized hardware is usually less than 10x . HoweverTo make it possible for PLINK 1.9 to handle the huge datasets that benefit the most from these speed improvements, the program core no longer keeps the main genomic data matrix in memory; instead, most of its functions only load data for a single variant, or a small window of variants, at a time. Sample × sample matrix computations still normally require additional memory proportional to the square of the sample size, but –parallel gets around this:calculates 1/40th of the genomic relationship matrix per run, with correspondingly reduced memory requirements.Both genotypes are homozygous for the major allele.One is homozygous major, and the other is heterozygous.One is homozygous major, and the other is homozygous minor.Both are heterozygous.One is heterozygous, and the other is homozygous minor.Both are homozygous minor.At least one genotype is missing.Each entry of a weighted genomic distance matrix between pairs of individuals is a sum of per-marker terms. Given PLINK 1 binary data, for any specific marker, there are seven distinct cases at most: q is the minor allele frequency: 0; subtract 1 from the final denominator instead, in another loopFor example, the GCTA genomic relationship matrix is defined by the following per-marker increments, where Initialize all distance/relationship partial sums to zero.For each marker, calculate and save the seven possible increments in a lookup table, and then refer to the table when updating partial sums. This replaces several floating point adds/multiplies in the inner loop with a single addition operation.This suggests the following matrix calculation algorithm, as a first draft: We can substantially improve on this by handling multiple markers at a time. Since seven cases can be distinguished by three bits, we can compose a sequence of operations which maps a pair of padded 2-bit genotypes to seven different 3-bit values in the appropriate manner. On 64-bit machines, 20 3-bit values can be packed into a machine word—for example, let bits 0-2 describe the relation at marker #0, bits 3-5 describe the relation at marker #1, and so forth, all the way up to bits 57-59 describing the relation at marker #19—so this representation lets us instruct the processor to act on 20 markers simultaneously.xi’s are bit trios, and the fi’s map them to increments. This could be done with 20 table lookups and floating point addition operations. Or, the update could be restructured as x{0−4} denotes the lowest-order 15 bits, and f{0−4} maps them directly to f0(x0)+f1(x1)+f2(x2)+f3(x3)+f4(x4); similarly for f{5−9}, f{10−14}, and f{15−19}. In exchange for some precomputation—four tables with 215 entries each; total size 1 MB, which is not onerous for modern L2/L3 caches—this restructuring licenses the use of four table lookups and adds per update instead of twenty. See fill_weights_r and incr_dists_r in plink_calc.c for source code.Then, we need to perform the update p is the frequency of allele A1 and q=1−p is the frequency of allele A2 [Under some population genetic assumptions such as minimal inbreeding, genotype frequencies for a biallelic variant can be expected to follow the Hardy-Weinberg proportions mentclasspt{miniman diploid samples containing a total of n1 copies of allele A1 and n2 copies of allele A2 (so n1+n2=2n), there are n12 heterozygotes when n12 has the same parity as n1 and n2. Under Hardy-Weinberg equilibrium, each of these ways is equally likely. Thus, the ratio between the likelihoods of observing exactly n12=k+2 heterozygotes and exactly n12=k heterozygotes, under Hardy-Weinberg equilibrium and fixed n1 and n2, is PLINK 1.0 used the SNP-HWE algorithm in a paper by Wigginton et al.  to execuSNP-HWE also recognizes that it is unnecessary to start the computation with an accurate absolute likelihood for one table. Since the final p-value is computed as O(n) memory allocation  could be avoided by reordering the calculation; it is only necessary to track a few partial sums.Its size- O(n) to Since likelihoods decay super-geometrically as one moves away from the most probable table, only mentclass2pt{minimWhile studying the software, we made two additional observations: O(n) time; see Figure PLINK 1.0 also has association analysis and quality control routines which perform Fisher’s exact test on 2×2 and 2×3 tables, using the FEXACT network algorithm from Mehta et al. ,16. The p adjustments in biostatistics [p modes, and PLINK 1.9 exposes them.Standalone source code for early-termination SNP-HWE and Fisher’s 2×2/ 2×3 exact test is posted at . Due to atistics ,20, all We note that, while the Hardy-Weinberg equilibrium exact test is only of interest to geneticists, Fisher’s exact test has wider application. Thus, we are preparing another paper which discusses these algorithms in more detail, with proofs of numerical error bounds and a full explanation of how the Fisher’s exact test algorithm extends to larger tables.It can be useful to divide the genome into blocks of variants which appear to be inherited together most of the time, since observed recombination patterns are substantially more “blocklike” than would be expected under a model of uniform recombination . PLINK 1D′ disequilibrium statistic for pairs of variants. Depending on the confidence interval’s boundaries, a pair of variants is classified as “strong linkage disequilibrium (LD)”, “strong evidence for historical recombination”, or “inconclusive”; then, contiguous groups of variants where “strong LD” pairs outnumber “recombination” pairs by more than 19 to 1 are greedily selected, starting with the longest base-pair spans.This method is based on 90% confidence intervals  for LewD′ has been streamlined. Bit population counts are used to fill the contingency table; then we use the analytic solution to Hill’s diplotype frequency cubic equation [Estimation of diplotype frequencies and maximum-likelihood equation ,25 and oD′=0,D′=0.01,…,D′=1) and checking where the resulting cumulative distribution function (cdf) crossed 5% and 95%. However, the likelihood function rarely has more than one extreme point in  ; it is usually possible to exploit this unimodality to establish good bounds on key cdf values after evaluating just a few likelihoods. In particular, many confidence intervals can be classified as “recombination” after inspection of just two of the 101 points; see Figure 90% confidence intervals were originally estimated by computing relative likelihoods at 101 points Pahl et al.’s PERMORY algorithm for fast permutation testing ,Wan et al.’s BOOST software for fast epistasis testing ,Ueki, Cordell, and Howey’s –fast-epistasis variance correction and joint-effects test ,32,Taliun, Gamper, and Pattaro’s optimizations to Gabriel et al.’s haplotype block identification algorithm (discussed above) , andPascal Pons’s winning submission to the GWAS Speedup logistic regression crowdsourcing contest . , binary VCF (–bcf), and Oxford-format  files. However, since it cannot handle genotype likelihoods, phase information or variants with more than two alleles, the import process can be quite lossy. Specifically, Export to these formats is also possible, via –recode vcf and –recode oxford.When the –allow-extra-chr or –aec flag is used, PLINK 1.9 allows datasets to contain unplaced contigs or other arbitrary chromosome names, and most commands will handle them in a reasonable manner. Also, arbitrary nonhuman species (with haploid or diploid genomes) can now be specified with –chr-set.To improve the experience of using PLINK interactively, we have expanded the –help flag’s functionality. When invoked with no parameters, it now prints an entire mini-manual. Given keyword(s), it instead searches for and prints mini-manual entries associated with those keyword(s), and handles misspelled keywords and keyword prefixes in a reasonable manner.Most of our discussion has addressed computational issues. However, there is one methodological issue that deserves a brief comment. The online documentation of PLINK 1.07 weighed the pros and cons of its permutation procedure for within-family analysis of quantitative traits (QFAM) with respect to the standard quantitative transmission disequilibrium test (QTDT) . It poin“Mac-2” denotes a MacBook Pro with a 2.8 Ghz Intel Core 2 Duo processor and 4GB RAM running OS X 10.6.8.“Mac-12” denotes a Mac Pro with two 2.93 Ghz Intel 6-core Xeon processors and 64GB RAM running OS X 10.6.8.“Linux32-2” denotes a machine with a 2.4 Ghz Intel Core 2 Duo E6600 processor and 1GB RAM running 32-bit Ubuntu Linux.“Linux32-8” denotes a machine with a 3.4 Ghz Intel Core i7-3770 processor (8 cores) and 8GB RAM running 32-bit Ubuntu Linux.“Linux64-512” denotes a machine with sixty-four AMD 8-core Opteron 6282 SE processors and 512GB RAM running 64-bit Linux.“Win32-2” denotes a laptop with a 2.4 Ghz Intel Core i5-2430 M processor (2 cores) and 4GB RAM running 32-bit Windows 7 SP1.“Win64-2” denotes a machine with a 2.3 Ghz Intel Celeron G1610T processor (2 cores) and 8GB RAM running 64-bit Windows 8.“synth1” refers to a 1000 sample, 100000 variant synthetic dataset generated with HAPGEN2 , while ““synth2” refers to a 4000 case, 6000 control synthetic dataset with 88025 markers on chromosomes 19-22 generated by resampling HapMap and 1000 Genomes data with simuRare  and then“1000g” refers to the entire 1092 sample, 39637448 variant 1000 Genomes project phase 1 dataset [In the following tables, running times are collected from seven machines operating on three datasets. All times are in seconds. To reduce disk-caching variance, timing runs are preceded by “warmup” commands like plink –freq. PLINK 1.07 was run with the –noweb flag. “nomem” indicates that the program ran out of memory and there was no low-memory mode or other straightforward workaround. A tilde indicates that runtime was extrapolated from several smaller problem instances.Table O(n3) to O(n2 logn). , is possible in some cases [The PLINK 1.0 –cluster –matrix flag combination launches an identity-by-state matrix calculation and writes the result to disk, and then performs complete linkage clustering on the data; when –ppc is added, a pairwise population concordance constraint is applied to the clustering process. As discussed earlier, PLINK 1.9 employs an XOR/bit population count algorithm which speeds up the matrix calculation by a large constant factor; the computational complexity of the clustering algorithm has also been reduced, from me cases .)In Table  calculates the variance-standardized genomic relationship matrix used by many of its other commands. The latest implementation as of this writing (v1.24) is very fast, but only runs on 64-bit Linux, uses single- instead of double-precision arithmetic, and has a high memory requirement.PLINK 1.9’s implementation of this calculation is designed to compensate for GCTA 1.24’s limitations—it is cross-platform, works in low-memory environments, and uses double-precision arithmetic while remaining within a factor of 2-5 on speed. See Table r2 threshold for “synth2” was chosen to make the “synth1p” and “synth2p” pruned datasets contain similar number of SNPs, so Tables The PLINK 1.0 –indep-pairwise command is frequently used in preparation for analyses which assume approximate linkage equilibrium. In Table Table PLINK 1.0’s basic association analysis commands were quite flexible, but the powerful max(T) permutation test suffered from poor performance. PRESTO  and PERMDespite its computational advances, we recognize that PLINK 1.9 can ultimately still be an unsatisfactory tool for working with imputed genomic data, due to the limitations of the PLINK 1 binary file format. To address this, we designed a new core file format capable of representing most of the information emitted by modern imputation tools, which is the cornerstone of our plans for PLINK 2.0.all calls probabilistically, and introducing a few other extensions. Unfortunately, this would make PLINK 2.0’s representation of PLINK 1-format data so inefficient that it would amount to a serious downgrade from PLINK 1.9 for many purposes.As discussed earlier, PLINK 1 binary is inadequate in three ways: likelihoods strictly between 0 and 1 cannot be represented, phase information cannot be stored, and variants are limited to two alleles. This can be addressed by representing Therefore, our new format defines several data representations, one of which is equivalent to PLINK 1 binary, and allows different files, or even variants within a single file, to use different representations. To work with this, PLINK 2.0 will include a translation layer which allows individual functions to assume a specific representation is used. As with the rest of PLINK’s source code, this translation layer will be GPLv3-licensed open source; and unlike most of the other source code, we are explicitly designing it to be usable as a standalone library. PLINK 2.0 will also be able to convert files/variants from one data representation to another, making it practical for third-party tools lacking access to the library to demand a specific representation.The now-ubiquitous VCF file format requires reference alleles to be distinguished from alternate alleles, and an increasing number of software tools and pipelines do not tolerate scrambling of the two. This presents an interoperability problem for PLINK: while it was theoretically possible to handle binary data with PLINK 1.0 in a manner that preserved the reference vs. alternate allele distinction when it was originally present, with constant use of –keep-allele-order and related flags, doing so was inconvenient and error-prone, especially since the accompanying native.ped/.map and.tped/.tfam text formats had no place to store that information. PLINK 1.9’s –a2-allele flag, which can import that information from a VCF file, provides limited relief, but it is still necessary for users to fight against the program’s major/minor-allele based design.We aim to solve this problem for good in PLINK 2.0. The file format explicitly defines reference vs. alternate alleles, and this information will be preserved across runs by default. In addition, the file format will include a flag distinguishing provisional reference allele assignments from those derived from an actual reference genome. When PLINK 2.0 operates on.ped/.map or similar data lacking a reference vs. alternate distinction, it will treat a highest-frequency allele as the reference, while flagging it as a provisional assignment. When a file with flagged-as-provisional reference alleles is merged with another file with unflagged reference alleles, the unflagged reference allele assignments take precedence.  It will also be straightforward to import real reference allele assignments with an analogue of –a2-allele.PLINK 1.9 demonstrates the power of a weak form of compressive genomics : by usinTo do our part to make “strong” sub-linear compressive genomics a reality, the PLINK 2 file format will introduce support for “deviations from most common value” storage of low-MAF variants. For datasets containing many samples, this captures much of the storage efficiency benefit of having real reference genomes available, without the drawback of forcing all programs operating on the data to have access to a library of references. Thanks to PLINK 2.0’s translation layer and file conversion facilities, programmers will be able to ignore this feature during initial development of a tool, and then work to exploit it after basic functionality is in place.We note that LD-based compression of variant groups is also possible, and Sambo’s SNPack software  applies PLINK 2.0 is designed to meet the needs of tomorrow’s genome-wide association studies and population-genetics research; in both contexts, it is appropriate to apply a single genomic coordinate system across all samples, and preferred sample sizes are large enough to make computational efficiency a serious issue.Whole-exome and whole-genome sequencing also enables detailed study of structural variations which defy clean representation under a single coordinate system; and the number of individuals in such studies is typically much smaller than the tens or even hundreds of thousands which are sometimes required for effective GWAS. There are no plans to make PLINK suitable for this type of analysis; we strongly recommend the use of another software package, such as PLINK/SEQ , which iAn important consequence is that, despite its ability to import and export VCF files, PLINK should not be used for management of genomic data which will be subject to both types of analysis, because it discards all information which is not relevant for its preferred type. However, we will continue to extend PLINK’s ability to interpret VCF-like formats and interoperate with other popular software.Project name: Second-generation PLINKProject (source code) home page:https://www.cog-genomics.org/plink2/(https://github.com/chrchang/plink-ng)Operating systems: Linux (32/64-bit), OS X (64-bit Intel), Windows (32/64-bit)Programming language: C, C++Other requirements (when recompiling): GCC version 4, a few functions also require LAPACK 3.2License: GNU General Public License version 3.0 (GPLv3)Any restrictions to use by non-academics: noneGigaScience repository, GigaDB [The test data and the source code snapshots supporting the results of this article are available in the , GigaDB .


Entotheonella” (phylum “Tectomicrobia”) is a filamentous symbiont that produces almost all known bioactive compounds derived from the Lithistida sponge Theonella swinhoei. In contrast to the comprehensive knowledge of its secondary metabolism, knowledge of its lifestyle, resilience, and interaction with the sponge host and other symbionts remains rudimentary. In this study, we obtained two “Entotheonella” genomes from T. swinhoei from the South China Sea through metagenome binning, and used a RASTtk pipeline to achieve better genome annotation. The high average nucleotide index values suggested they were the same phylotypes as the two “Entotheonella” phylotypes from T. swinhoei from the Japan Sea. Genomic features related to utilization of various carbon sources, peptidase secretion, CO2 fixation, sulfate reduction, anaerobic respiration, and denitrification indicated the mixotrophic nature of “Entotheonella.” The endospore-forming potential along with metal- and antibiotic resistance indicated “Entotheonella” was highly resilient to harsh conditions. The potential for endospore formation also explained the widespread distribution of “Entotheonella” to some extent. The discovery of Type II  and Type VI secretion systems in “Entotheonella” suggested it could secrete extracellular hydrolases, form tight adhesion, act against phagocytes, and kill other prokaryotes. Overall, the newly discovered genomic features suggest “Entotheonella” is a highly competitive member of the symbiotic community of T. swinhoei.“ Candidatus Synechococcus spongiarum”  and candidate taxonomic status make its genome annotation very difficult. Additionally, the wide distribution of “Entotheonella” in sponges raises more questions about its genetic diversity and ecological roles  of T. swinhoei (yellow interior) were randomly sampled by scuba diving within a 15 m radius at approximately 10 m depth near Yongxing Island  in the South China Sea, and were morphologically identified by Prof. Jinhe Li at the Institute of Oceanology, Chinese Academy of Sciences. Sponge tissues were quickly rinsed with sterile artificial seawater then cut into small pieces thinner than 5 mm. Subsequently, the specimens were fixed in RNA Later®  at 4°C for 8 h. The fixed specimens were then transported to the lab on ice. Total DNA was extracted with a QIAGEN DNeasy Tissue Mini kit (Qiagen), following the manufacturer’s instructions. The integrity of DNA samples was inspected by 0.8% agarose gel electrophoresis. DNA samples of good quality  were pooled and used for metagenome sequencing.Individuals . In brief, a library with a 350 bp insert length was constructed and sequenced on a HiSeq 2000 , following the manufacturer’s instructions. Metagenome reads in FASTQ format (approximately 40 Gb) were trimmed using a minimum Phred score of 20 and a minimum length of 36, by Trimmomatic 0.32 , allowin1. The completeness of binned genomes was estimated based on the presence/absence of 106 essential bacterial genes, using the script HMM.essential.rb1.MetaBAT, an automatic metagenome binning software package, was used for metagenome binning . MetagenEntotheonella” genomes (AZHW01000000 and AZHX01000000) were annotated using a RASTtk pipeline based on the SEED subsystem (Entotheonella” genomes based on the EDGAR platform (Candidatus Entotheonella factor” TSY1 was used as reference owing to its high completeness. Scripts used for metagenome binning and genome annotation are provided in the Supplementary Information.The metagenome bins and publicly available “ubsystem . Primaryubsystem . Bidirecplatform . In bidiEntotheonella” phosphoenolpyruvate carboxylase (PEPC) sequences, with an e-value cutoff of 10-5, against the non-redundant protein database of NCBI  with accession numbers 93171.12 for v4.2 and 93171.13 for v4.3.The draft genomes of v4.2 and v4.3 were deposited at the European Nucleotide Archive (ENA) under accession PRJEB12598 and PRJEB12599, respectively. Additionally, the annotated genomes are available from the RAST guest accountEntotheonella” phylotypes/species, respectively (Candidatus Entotheonella gemina” TSY2 (hereafter TSY2) had a Z-Score of 0.995, while the Z-score between bin v4.3 and “Candidatus Entotheonella factor” TSY1 (hereafter TSY1) reached 0.996. A Z-score >0.989 suggested two strains were closely related. The ANI between v4.2 and TSY2 was 99.90%. Meanwhile, the ANI between v4.3 and TSY1 was 99.91%. ANI higher than 96% strongly indicates two genomes represent the same species  rates of v4.2 and v4.3 were both less than 4%. Overall, the quality of the two metagenome bins in this study met the requirement for reliable genome annotation and comparison with TSY1 and TSY2. Due to the high genome sequence similarities between genomes from the Japan Sea and the South China Sea, the functional annotation profiles of the genomes from the two geographical locations were reasonably coherent. Also, because of the considerable number of hypothetical proteins, the annotation profiles of the two “Entotheonella” phylotypes were largely similar. Hence, the subsequent analysis focused on the shared features of all four genomes, which comprised 62.4% of the total unique features . In this study, we re-annotated TSY1 and TSY2, which were previously annotated by a RefSeq pipeline , the pentose phosphate pathway, and oxidative phosphorylation could be identified from the four “Entotheonella” genomes, including v4.2 and v4.3 from the South China Sea, and TSY1 and TSY2 from the Japan Sea . The highly efficient uptake of dissolved and particulate organic matter by sponges could provide the microbial communities with various forms of carbon source  would also benefit the host and other symbiotic members by increasing the availability of more labile carbon sources. Similarly, the ability to degrade N-acetylglucosamine was found in another iconic phylum of sponge symbionts, “Poribacteria” , metallocarboxypeptidases (EC 3.4.17.-), omega peptidases (EC 3.4.19.-), serine endopeptidase (EC 3.4.21.-), and signal peptidase. Features related to peptide transportation, such as ABC transporters of oligopeptides (TC 3.A.1.5.1), dipeptides (TC 3.A.1.5.2), and branched-chain amino acids (TC 3.A.1.4.1) were also found in all “Entotheonella” genomes.Urea and other nitrogen-containing metabolic products are considered to be important nitrogen sources of sponge symbionts . Urease Entotheonella.” A near-complete Calvin–Benson cycle was reconstructed in this study . Some missing genes could be due to incompleteness of the current genomes. Carbonic anhydrase (EC 4.2.1.1) was also found in all “Entotheonella” genomes. This enzyme aids in concentrating CO2 by catalyzing CO2 hydration, a simple but physiologically relevant reaction in all kingdoms of life . The placement of “Entotheonella” PEPC was divergent from the Gammaproteobacteria and Viridiplantae, implying the unique evolutionary traits of “Entotheonella.”Interestingly, a set of genes related to crassulacean acid metabolism (CAM) and the Cf plants . For “En as well . The key is PEPC . BLAST aEntotheonella” belongs to the bacteria with large genomes (3). The mechanisms that support the large genomes of “Entotheonella” are worth mining. DNA replication is typically demanding with respect to nutrients and energy. For microbes inhabiting niches with scarce nutrients, genome streamlining reduces the minimum requirement for reproduction and thus promotes survival  is considered an adaptive trait of planktonic bacteria when facing limited nitrogen sources . With G+c matter . Hence, ymbionts  and the ymbionts .Entotheonella” genomes encode enzymes for sulfate reduction. A near complete dissimilatory sulfate reduction pathway was reconstructed from all “Entotheonella” genomes . The dissimilatory sulfate reduction potential may supply the energy that is needed in the Calvin–Benson cycle. Compared with the newly found sulfur-oxidizing bacterium in deep-sea glass sponge , nitric oxide reductase (EC 1.7.99.1), and the Wood–Ljungdahl pathway (anoxic CO2 fixation) have been found . Bacterial endospores are the most durable cells in nature , leucine-rich repeats (LRR), and tetratricopeptide repeats (TPR) were found .Eukaryotic-like proteins (ELPs) are well known as important factors involved in sponge–microbe interactions . In “EntFigure 6A). Extracellular proteins/enzymes are important for microbes to utilize the organic matter in a certain niche or interact with other organisms. Some extracellular proteolytic enzymes, mainly of the metalloprotease group, have been found to be virulence factors  and features related to the Widespread Colonization Island. T2SSs are found in both clinical pathogens and non-pathogens and usually require 12–15 Gsps to form the double-membrane-spanning structure . In our  factors . Only onroteases . Here wetad (tight adherence) locus for colonization of surfaces and biofilm formation . All the core components of T6SSs (Candidatus Entotheonella gemina” (TSY2 and v4.2), whereas the spike protein VgrG was missing in “Candidatus Entotheonella factor” (TSY1 and v4.3). A previous study of the sponge pathogen P. agarivorans NW4327 revealed the existence of 19 genes related to T6SSs , adaptation to the anaerobic environment, resistance to threats, and interaction with other organisms (Table 2).The uncultured status of “Entotheonella” strains from the two sea areas showed high intraspecies genomic coherence. Furthermore, future studies addressing the relationship between “Entotheonella” genetic diversity and host morphological features are essential for understanding the diversification of “Entotheonella.” The interior of T. swinhoei investigated by T. swinhoei with a white interior was reported to harbor a different phylotype, “Candidatus Entotheonella serta” . Thus, providing free amino acids would be crucial for cultivating “Entotheonella” due to its deficiency in synthesizing all proteinogenic amino acids. Thirdly, the anaerobic respiration reductases and sulfate-reduction capacity suggested “Entotheonella” faced variable oxygen levels in T. swinhoei. The shift between anaerobic and aerobic conditions might be important for the activity and metabolic regulation of “Entotheonella.”The genome information also provided clues for improving the strategy of cultivating “FL and ZL designed the study. FL undertook metagenome binning and genome analysis. JL finished the metagenome sequencing and assembling. GF sampled the sponges and extracted the DNA. All authors discussed and wrote the manuscript together.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.