The current paradigm of genomic studies of complex diseases is association and correlation analysis. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the identified genetic variants by GWAS can only explain a small proportion of the heritability of complex diseases. A large fraction of genetic variants is still hidden. Association analysis has limited power to unravel mechanisms of complex diseases. It is time to shift the paradigm of genomic analysis from association analysis to causal inference. Causal inference is an essential component for the discovery of mechanism of diseases. This paper will review the major platforms of the genomic analysis in the past and discuss the perspectives of causal inference as a general framework of genomic analysis. In genomic data analysis, we usually consider four types of associations: association of discrete variables (DNA variation) with continuous variables (phenotypes and gene expressions), association of continuous variables  with continuous variables , association of discrete variables (DNA variation) with binary trait (disease status) and association of continuous variables  with binary trait (disease status). In this paper, we will review algorithmic information theory as a general framework for causal discovery and the recent development of statistical methods for causal inference on discrete data, and discuss the possibility of extending the association analysis of discrete variable with disease to the causal analysis for discrete variable and disease. Many of these associated SNPs are non-coding variants  had reported significant association of 26,791 SNPs with more than 1,704 traits in 2,337 publications It is time to develop a new generation of genetic analysis to shift the current paradigm of genetic analysis from association analysis to causal inference. To make the shift feasible, we need (1) to develop novel causal inference methods for genome-wide and epigenome-wide causal studies of complex disease; (2) to develop unified frameworks for systematic casual analysis of integrated genomic, epigenomic, imaging and clinical phenotype data and to infer multilevel omics and image causal networks for the discovery of paths from genetic variants to diseases. The focus of this paper is to survey statistical methods for causal inference and explore the potential to use modern causal inference theory for developing statistics for genome-wide causal studies (GWCS) of complex diseases.The widespread view about causation in genetic epidemiology field is that only interventions to a system can discover causal relations. Many epidemiologists and statistical geneticists have doubt about the possibility of using observational data to identify disease-causing variants and “shied away” from causal inference based on observation data  where nodes in the DAG represent the random variables Xi and an arrow from node Xi to node Xj denotes a causal direction. Let P be a joint probability distribution of variables X1, …, Xn. The Markov condition describes the causal structure of the DAG G. The DAG can also be characterized by the concept of parent sets. Let pai and NDi be the set of parents of the node Xi, respectively. The Markov conditions can be formulated as the following three forms  can be factorized into the conditional distribution:The joint distribution P(Xi|pai) is the conditional probability of Xi, given the values of all parents of Xi.where Local markov condition:Every node is conditionally independent of its non-descendants, given its parents, i.e.,NDi denotes the non-descendent of the node i.where Global markov condition:X, Y and Z. If X and Y are d-separated by Z then we haveConsider three datasets X and Y are d-separated by a third set of variables Zif and only ifTo assess conditional independence in a more general case, we introduce a useful concept, d-separation which associates “correlation” with “connectedness” and independence with “separation”. Two sets of variables i → m → j where i ∈ X, m ∈ Z and j ∈ Y  as the Markov kernels. The set of Markov kernels defines a Markovian density. However, in general, the Markov conditions cannot uniquely determine causal graphs. Many different DAGs satisfy the same set of independence relations. For example, consider three simple DAGs: x → y → z, x ← y ← z and x ← y → z. Three variables x, y and zin all three DAGs satisfy the same conditional independent distribution: x and z are independent, giveny. Figure B⫫C|A and A⫫D|. These examples show that multiple graphs may satisfy the Markov conditions. Except for Markov conditions, we need other constraints for learning causal structure.In the DAG The faithfulness condition is another constraint that helps to infer causal structure from observational data. The faithfulness condition requires that conditional independences in the distribution correspond to the d-separation in the DAG one by one. In other words, faithfulness condition requires that every conditional independence in the distribution must correspond to the Markov condition that is applied to the DAG  as the length of the shortest program that generates x, denoted as x*, using universal prefix Turing machine  of a string x given another string y is defined as the length of the shortest program that can generate x from y. The joint Kolmogorov complexity K is defined as the complexity of concatenation x′y where x′ denotes a prefix code of x. Mutual information and Markov conditions can be extended to algorithmic mutual information and Algorithmic Markov conditions  test the association of single SNP with the disease. GWAS investigates dependence between two variables. Similar to GWAS, the genome-wide causation studies (GWCS) needs to discover the SNP that causes disease. We need to develop statistical methods for bivariate causal discovery. The DAG approach requires at least variables for causal inference. Therefore, the classical Bayesian networks and DAG-based causal inference methods cannot be applied to the GWCS. To overcome these limitations, some authors  of the response, given X is equal to the noise distribution PN(Y − f(X)). If X → Y then we should have . The support can be either infinite or finite. Consider an ANM from → Y:Consider two random variables f is a function f:ℤ → ℤ and N is a noise variable. Let n(l) = P(N = l). In (4), we further assume n(0) ≥ n(j) for all j ≠ 0. If there is also an ANM to fit the data, then the ANM is referred to as reversible.where mℤ as the set of remainders modulo m, i.e., ℤ/mℤ = {[0], [1], …, [m − 1]}.We first define the ring ℤ/m-cyclic random variable as the variable that takes values in ℤ/mℤ. Let X and Y be m and k-cyclic random variables, respectively. Define an ANM from X to Y:Now we consider random variables that can take values in a periodic domain. Formally, we define f is a function f:ℤ/mℤ → ℤ/kℤ, N is a k-cyclic noise. We assume n(0) ≥ n(j) for all j ≠ 0.where Y to :Similarly, we can define an ANM from g:ℤ/kℤ → ℤ/mℤ and Nx is a m-cyclic noise.where X → Y and ANM from Y to X hold then the ANM is called reversible. The selection of the integer ANM or cyclic ANM mainly depends on the target Y domain.If both the ANM from Y be a binary trait to indicate the disease status of the individual. Thus, Y is a 2-cyclic variable. Thepotential cause variable X denotes the genotype of the individual. Thus, X is a 3-cyclic variable.The cyclic models can be used for genetic studies of complex disease. Let Y satisfies the ANM from X to Y, but does not satisfy the ANM from Y to X, then we infer X to be the cause for Y, which is denoted by X → Y. In some cases, Y satisfies ANMs in two directions and hence causation does not exist. The question is under what conditions a joint distribution admits an ANM in at most one direction.In most cases, nature selects one causal direction. The causal inference principle states that if X → Y, then the conditional probability P(Y|X) does not depend on the cause X. Several examples to illustrate identifiability are given in Supplementary The causal inference principle implies that if It is well known that correlation (association) and causation are different concepts. Correlation does not imply causation and conversely, causation also does not imply correlation. Our experiences demonstrated that large proportions of causal loci cannot be discovered by association analysis. This explains why a large number of genetic variants still remain hidden. The observed association may be in part due to chance, bias and confounding. Furthermore, a recent study found that “association signals tend to be spread across most of the genome,” by the debatable omnigenic model, which again shows that association signals provide limited information on causes of disease, calling the future of the GWAS into question  and residuals can be formulated as a 2 × q contingence table  affects the effect variable additively, i.e.,Y is an arbitrary function of X and an exogenous variable E:The ANM can be extended to more general nonlinear model , then the exogenous variable Ẽ has higher Renyi entropy than that of E, i.e., H0(E) ≤ H0(Ẽ).In other words, if the model in the wrong causal direction: H0 is difficult to compute, we can replace Renyi entropy by Shannon entropy H1. An algorithm for inferring the true causal direction is to find the exogenous variable E with the smallest H1(E) such thatSince Renyi entropy H1(E). They make argument thatKocaoglu et al.  proposedfx(E) = f, then it follows from Equation (11) that we can use the conditional distribution P(Y|X) to calculate the distribution of E: P(Y = y|X = x) = P(fX(E) = y). Algorithm 1 in Kocaoglu et al. )2 × 2.Step 1. Input matrix e = [⊢].Step 2. Define M in decreasing order. Step 3. Sort each row of Step 4. Search minimum of maximum of each row: Step 5. While α > 0 doe ← ,pi(1) pi(1) − α, for all iRemove α from maximum of each row: M in decreasing order.Sort each row of updated Step 6. End whilee.Step 7. Output H1(E) and H1(Ẽ) as a DM in the ANM and compare H1(E) with H1(Ẽ). If H1(E) < H1(Ẽ) then X → Y; if H1(Ẽ) < H1(E) then Y → X.For the genotype data, we suggest to use Sharron entropy X → Y that the distribution P(X) of causal X is independent of the causal mechanism or conditional distribution P(Y|X) of the effect Y, given causal X. Now the question is how to assess their independence. Recently, distance correlation is proposed to measure dependence between random vectors which allows for both linear and nonlinear dependence  and P(Y|X), and viewing their discretized distributions as two vectors P(X) and P(Y|X), the distance correlation between P(X) and P(Y|X) can be used to assess causation between X and Y.Discretizing distributions p-dimensional vector X and q-dimensional vectorY. Let P(x) and P(y) be density functions of the vectors X and Y, respectively. Let P be the joint density function of X and Y. There are two ways to define independence between two vectors of variables: (1) density definition and (2) characteristic function definition. In other words, if X and Y are independent then eitherConsider two vectors of random variables: P = P(x)P(y) orfX, Y = fX(t)fY(s),X, Y), X and Y, respectively. Therefore, we can use both distances ||P − P(x)P(y)|| and ||fX, Y − fX(t)fY(s)|| to measure dependence between two vectors X and Y. Distance correlation , k = 1, …, n are sampled. Calculate the Euclidean distances:Assume that pairs of , variance Vn(X) and correlation Rn are defined asFinally, the sampling distance covariancerespectively.H0:X and Y are independent.Independence can be formally tested by statistics based on distance correlation. The null hypothesis is defined as We can use Equation (12) to define a test statistic:P-values. Specifically, we can permutate X and Ymillions of times. For each permutation, we compute test statistic TIND. Therefore, via permutations we can calculate the empirical distribution of TIND. Using an empirical distribution, we can calculate the P-value asSince distribution of test statistics is difficult to compute, we often use permutations to calculate TIND0 is the observed value of TIND in real data.where p -dimensional random vectorX and q -dimensional random vectorY. Let P be their joint distribution. Let P(X) and P(Y|X) be the density function of X and conditional density function of Y, givenX, respectively. Similarly, we can define P(Y) and P(X|Y). Unlike association analysis where dependence is measured between two random vectors, in causal analysis, dependence is measured between two distributions.Distance correlation can be used to test independence between causal and causal generating mechanisms  is a distance correlation measure between two vectors.where X and Y are discretized (or divided) into m and k groups, respectively. Let mi be the number of points X in the ith group and kij be the number of points  where X is in the ith group and Y is in the jth group. Then, Xi) is Suppose that SX → Y = a..b... Distance covariance is defined asLet SY → X can be similarly defined.Similarly, DefineThe null hypothesis for testing isH0: no causation between two vectors X and Y.X and Y is defined asThe statistic for testing the causation between two vectors TC is large, either ΔX→ Y > ΔY→ X which implies Ycauses X, or ΔY→ X > ΔX→ Y which implies that X causes Y. When TC ≈ 0, this indicates that no causal decision can be made.When We use simulation experiments that were presented in Liu and Chan  to compaX and residuals E are tested by entropy had the highest accuracies to infer cause-effect direction, the distance-correlation-based methods had the lowest accuracies, and the classical ANMs with discrete variables had the similar accuracies as that the entropy-based ANMs had. We observed that as sample sizes increased the accuracies increased and when the sample sizes are larger than 2,000 all three methods can accurately infer cause-effect directions.The accuracies of three methods for causation discovery in dataset 1 were shown in Figure Figure To further evaluate their performance, we calculated the type I error rates three statistics for testing causation using simulations. We randomly selected 10 SNPs across the genome from 1,000 Genome Project data. A total of 1,000 simulations were conducted. We consider two scenarios: (1) no association and no causation and 2) presence of association, but no causation. Tables  presenceTo give some recommendations on when and which methods should be used, we conducted power simulations using the data for type 1 error calculation. We assume that both association and causation exist as described in Supplementary TRIM16, two SNPs 15524749 and 15519576 in gene CDRT1 and other two SNPs were types in 1,707 individuals . The results were summarized in Table P-values of classical ANM test statistics and entropy-based ANM test statistics. The threshold for declaring significance of association test was 1.14 × 10−8. Since distance correlation test requires that the potential cause should take more than three values of states, in general, it cannot be used for causal genetic analysis. Table P-values of the classical ANM and entropy-based ANM were close, and that four SNPs in two genes which showed strong association also demonstrated causation. The literature reported that gene TRIM16 inhibited neuroblastoma cell proliferation through cell cycle regulation and dynamic nuclear localization and gene CDRT1 was involved in frontotemporal dementia . Two SNPs in genomic positions 15528889 and 15530350 in gene Association analysis has been used as a major tool for dissecting genetic architecture and unraveling mechanisms of complex diseases for more than a century Fisher, . AlthougPH perform data analysis, write paper. RJ perform data analysis; LJ design project. MX design project and write paper.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.


Daphnia pulex, a common zooplankton species, have invaded Japan from North America. Among these, a lineage named JPN1 is thought to have started colonization as a single genotype several hundred to thousand years ago and subsequently produced many genotypes in Japan. To examine the phenotypic variations due to ecological drivers diverging the genotypes in new habitats, we measured heritability and variation in 17 traits, including life history, morphology and digestive traits, and the genetic distance among the D. pulex JPN1 genotypes in Japan.Several genetic lineages of obligate parthenogenetic We found that most of the traits measured varied significantly among the genotypes and that heritability was highest in the morphological traits, followed by the digestive and life history traits. In addition, 93% of the variation in these traits was explained by the first three components in the principal component analysis, implying that variation of these heritable traits is not random but rather converged into a few directions. These relations among traits revealed the potential importance of predation pressures and food conditions as factors for diverging and selecting different genotypes. However, the magnitude of the difference in any single trait group did not correlate with the genetic distance.D. pulex JPN1 lineage without genetic recombination, since their ancestral clone invaded Japan. Large variations and covariations of the phenotypic traits, irrespective of the genetic distance among the genotypes, support the view that the invasive success of D. pulex JPN1 was promoted by a genetic architecture that allowed for large phenotypic variations with a limited number of functionally important mutations without recombination.Our findings show that the divergent traits evolved within The online version of this article (10.1186/s12862-019-1453-9) contains supplementary material, which is available to authorized users. Variations and relationships among heritable traits provide clues for identifying selective forces and thus understanding the evolution of species –3. SpeciDaphnia pulex [D. pulex JPN1 are obligate parthenogenetic animals, several genotypes (clones) were found within this lineage, suggesting that they genetically evolved without recombination after the initial invasion and likely adapted to new habitats in Japan [D. pulex that invaded Japan are a good model to explore whether the magnitude of the difference in the phenotypic traits is related to genetic distance among the genotypes and what environmental factors promoted evolution after Daphnia invasions into new habitats.Several genetic lineages of panarctic ia pulex , a commoia pulex . Althougin Japan . Since tin Japan , 9. If pDaphnia individuals is greatly influenced by a variety of environmental and biological factors, including interspecies interactions [Daphnia populations since it directly affects the survival rate [Daphnia populations frequently suffer from deficiencies in the quantity and quality of algal food [Daphnia species under different predation pressures [In nature, the abundance of ractions –12. Amonval rate –16 and ival rate , 18. In gal food –21. Therressures –24 and fressures –28. ThesTo maximize their fitness under given food scarcity conditions with or without the presence of predators, animals must efficiently gain energy and nutrients from their food. Recent studies have suggested that in addition to a herbivore’s feeding rate and digestive capacity, digestive enzyme activity is important for the animal’s ability to cope with nutritionally suboptimal foods and maximize its fitness , 30. ThuD. pulex, the variation and relationship among phenotypic traits have noticeable implications for their evolution [Daphnia [In obligate parthenogenetic organisms, such as asexual individuals of volution . The subvolution , 35. Accvolution , 37. If volution , then it[Daphnia  and how D. pulex JPN1 genotypes. The heritability is a metrics measuring the degree of genetic attribute to a phenotypic resemblance between parental and offspring individuals in sexual organisms [D. pulex lineage after invading Japan.To address these uncertainties, we examined the heritabilities of life history, morphology and digestive traits of rganisms . In the k and the body length at maturation and at day 5 only differed among genotypes, the intermoult duration before maturation differed between food levels, and the lipase activity and intermoult duration after maturation did not differ among genotypes or food levels (Table Both genotype (G) and food level (F) significantly affected 11 out of 17 traits  using best linear unbiased predictors (BLUPS). The first three components explained 93% of the variation in the traits examined. The eigenvectors of traits for each PCA showed that most of the life history traits contributed to the PC1, while most of the morphological traits contributed to the PC2. Digestive traits were not clustered together but distributed into different PCs . Most oo et al. . In addio et al.  used a ko et al.  and numbo et al. . AlthougD. pulex genotypes since narrow-sense heritability is meaningless for asexual organisms [Daphnia prey on change not only seasonally but also spatially depending on trophic conditions [According to Mousseau & Rolf , who exarganisms . Thus, crganisms . The lownditions . AccordiL∞) were positively correlated with each other. These results imply that clones that matured at an earlier age were of smaller size and produced a smaller number of eggs per clutch. These relationships suggest that maturation instar and size, egg number in the first several clutches and asymptotic size are determined solely by age when the Daphnia JPN1 clones mature. Thus, differences in these traits among clones may be a result of pleiotropic effects of mutation(s) that occurred in maturation-related genes, such as hormone genes and other endocrine genes [In this study, 93% of the variation in phenotypic traits among the JPN1 clones was summarized by the first three PCs in the principal component analysis, suggesting that the direction of these variations is not random but converged into a few directions. The phenotypic traits, maturation age, instar and size, egg numbers for the first several clutches, and asymptotic size  for maximizing their fitness in habitats with planktivorous fish. Heritable differences in maturation age and size suggest that habitat-specific variations in predation pressures may have acted as a selective force for the diverging JPN1 genotypes.Studies have shown that maturation size is a crucial factor in determining the colonization success of habitats , 47, 48.habitats , 50. Thehabitats , 50, 51.Daphnia individuals who rapidly attain larger sizes by postponing maturation have an advantage in reducing predation risk [Daphnia individuals to defend against predation by invertebrate predators [D. pulex JPN1 genotypes.Apart from life history traits, most morphological traits such as body size (length and weight) at day 5, neonate size, tail-spine length and growth coefficient (k) were positively related to the 2nd PC. These results imply that genotypes with higher growth rates produce larger neonates with longer tail-spines. In small lakes and ponds where planktivorous fish were not yet abundant or were absent due to temporal drying or summer or winter kills , inverteion risk , 50, 55.redators , 56. ThuD. pulex JPN1 genotypes may have experienced protein deficiencies in different manners. Although the cultured algae used in this study were sufficient in terms of nitrogen content relative to the demand of Daphnia [Daphnia often experience deficiencies in biologically important chemicals such as phosphorus and essential fatty acids [Among the enzyme activities measured, the alanine amino-peptidase activity was negatively related to PC2, indicating that genotypes with higher growth rates had reduced activity of the enzyme. Thus, the difference in the activity of this enzyme among genotypes may be a result of a decrease in activity associated with increasing body size. Nonetheless, this covariation is somewhat puzzling since animals with higher growth rates should require more nitrogen , 58. One Daphnia , algal e Daphnia , 59, 60.ty acids , 30. Undty acids –64. ThisIn this study, only beta-glucosidase activity was related to PC3. Since we could not rule out the possibility that this trait was directly or indirectly related to other traits that were not examined in this study, it was difficult to identify selective agents causing the genetic variation of this trait. However, the results showed that variation in this trait had evolved irrespective of the selective pressures posed by predators.Although a number of studies have examined how phenotypic differentiation is related to genetic differentiation in populations , 65, 66,D. pulex JPN1 evolved divergent traits without genetic recombination for several hundred to thousand years since their ancestral clone originally invaded Japan. Variations in heritable traits suggest that predation pressures and food environments have played roles in the divergence and selection of these clones. Moreover, the relationship among these traits support the view that large phenotypic differences among JPN1 genotypes likely occurred mainly by pleiotropic effects of a limited number of mutated genes, rather than an “adaptive character complex” that was created by natural selection favouring certain combinations of genetically independent traits through independent mutations and recombination [Daphnia pulex and a green alga Caulerpa racemosa have successfully invaded and expanded their distributions in Africa [D. pulex JPN1, which was established before invading Japan, may have produced various genotypes in the lineage that could adapt rapidly to a variety of novel lakes and ponds in Japan without genetic recombination by sexual reproduction. It is necessary to identify mutated genes among JPN1 clones to uncover the genetic architectures and linkages that promote such phenotypic divergence.This study showed that bination . Similarn Africa  and in tn Africa , respectn Africa . Howevern Africa , 73. Othn Africa  suggesteD. pulex are distributed in Japan [Daphnia pulex JPN1 lineage that were previously collected from ponds and small lakes in Japan and examined in So et al. [Four distinct genetic groups (JPN1–4) of obligate parthenogenetic in Japan . Among tin Japan . In thiso et al. . Genotypo et al. .Scenedesmus obliquus algae was cultured in a flow-through system with COMBO [Daphnia cultures. Before their use, algal cells were harvested, and their cell density was quantified under an optical microscope . According to the previously measured carbon content for an algal cell of S. obliquus (2.09 × 10− 8 mg C cell− 1), the appropriate amounts of algae were estimated for achieving a designed carbon food level and then used in experiments.th COMBO , a defin− 1 of S. obliquus in a controlled room : Dark(D) 14:10). The individuals were transferred to fresh medium every other day and fed daily. The individual abundance in the culture bottles was adjusted to less than 1 individual 20 ml− 1 in all the clones. Then, neonates born within 24 h (hr) were collected from the 3rd brood produced by the cultured individuals and used for the following experiments.Individuals in each genotype, taken from a single mother that originated from genotypes maintained for several years in our laboratory, were cultured in 900 ml bottles containing 600 ml of COMBO with 2.0 mg C LS. obliquus. Half of the Daphnia were grown at a food concentration of 2.0 mg C L− 1, and the other half were grown at 0.2 mg C L− 1. The animals were fed daily and moved to new medium every two days. After a 4-day cultivation, on day 5, 10 individuals of each treatment were randomly collected. At least 5 individuals were used for the measurements of body length and body weight, and the remaining individuals were used for measuring digestive enzyme activity.In each clone, 20 neonates were randomly chosen and individually placed into 50-ml stoppered bottles containing the growth medium COMBO with Daphnia were captured at a magnification of 40×, and their body length from the base of the tail-spine to the top of the head was subsequently measured using ImageJ software . Then, they were individually transferred into a pre-weighed aluminium boat, dried for 24 h at 60 °C, cooled for 12 h in a vacuum desiccator at room temperature, and weighed using a Mettler-Toledo UMX2 microbalance to the nearest 0.1 μg .For measurements of body length and weight, 5-day-old animals were rinsed twice in distilled water and placed under a microscope where media was removed until the animals were properly positioned. Using an Olympus DP20 camera  mounted on an Olympus SZH10 stereomicroscope , images of − 1, pH 8), and centrifuged for 10 min (min) at 14,000× gravitational acceleration (g) using a KUBOTA 1120 centrifuge . The supernatant of the centrifuged samples was treated as an extract of a single animal for the analysis of beta-glucosidase (EC no. 3.2.1.21), lipase (3.1.1.3 and others), alkaline phosphatase (3.1.3.1), arginine amino-peptidase (3.4.11.6) and alanine amino-peptidase (3.2.11.2). Assays were run at room temperature with 5 μl of substrate, 15 μl of sample extract and 230 μl of citrate-phosphate buffer  [− 1, pH 8) and other enzymes , according to the protocol developed by Knotz et al. [− 1). The concentration of these substrates in the assays was 100 μmol L− 1. Fluorescence was measured at 360 nm (excitation) and 450 nm (emission) every 5 min for 35 min with a Fluoroskan Ascent microplate fluorometer . Blanks were run in parallel. For preparing standard curves, 0~10 μmol L− 1 of 4-beta-methylubelliferone (MUF) and 7-amino-4-methylcoumarin (AMC) were used [For assaying the digestive enzyme activities, the animals were rinsed several times with distilled water, homogenized with a micropestle in 200 μl of ice-cold Tris/HCl buffer   for betaz et al. . The subere used .− 1, 100 mg L− 1) was used as the standard. For these measurements, 100 μL of supernatant from the centrifuged sample or BSA was mixed with 750 μL of the working reagent of the bicinchoninic acid kit (BCA1 SIGMA-ALDRICH) and incubated for 2 h at 55 °C. The assays were read at 562 nm using a UV-1600 spectrophotometer . Digestive enzyme activity was calculated corresponding to the average water-soluble protein content of each clone in a specific unit (nmol hr.− 1 mgprotein− 1).The water-soluble protein content of the animals was measured using a bicinchoninic acid assay (BCA) . Bovine Daphnia were fed daily.A growth experiment was performed at the two food concentrations mentioned above. For each food concentration, 10 neonates of each clone were individually grown in 50-ml stoppered bottles containing the growth medium COMBO with algal food as in the experiment for measuring digestive enzyme activities. In this study, the experiment lasted until they had produced the sixth brood. To ensure that the food particles were homogeneous in the suspension, the bottles were secured to a grazing wheel that rotated at a speed of 1 revolution per minute (rpm). The growth medium and algal food were changed every two days, and When the growth medium was changed, images of the animals were captured as above and used for measuring the body length from the base of the tail-spine to the top of the head, the tail-spine length and the number of eggs in the brood pouch. In this study, maturation was defined as the time when the eggs first appeared in the brood pouch. The frequency of moulting casts (exuviae) and the size of newly released neonates were also counted and removed from the bottle.L∞ and growth coefficient k as follows:Lt is the body length at age t (days), t0 is the hypothetical age at ‘0’ body length.The von Bertalanffy growth curve was applied for estimating the asymptotic body length Daphnia clones, we used genetic data acquired from our whole-genome sequencing as described below. Fifty to seventy individuals of each clone were used for DNA extraction conducted using a Maxwell(R) 16 instrument and Maxwell(R) 16 LEV Plant DNA Kit (Promega). Construction of the library and sample sequencing were performed at the Beijing Genomics Institute (BGI) JAPAN . The libraries were constructed by a unique method of BGI JAPAN (low input method) from more than 500 ng of DNA per sample. The libraries were sequenced on an Illumina Hiseq X™ Ten platform  using a paired-end 150 bp (PE150) strategy to obtain approximately 8 Gb data/sample (approximately 40x coverage). The data were filtered using MapReduce acceleration-supported (SOAPunke) software [D. pulex isolate PA42 [D. pulex JPN2 (HO01) was collected from Lake Hataya Ohnuma , JPN3 (AWA) from Lake Awaji nariai-ji  and JPN4 (SUM) from Sumiyoshi ike Pond , a genotype of D. pulex (LL05) was collected from a small lake in Manitoba, Canada (longitude/latitude unknown), and D. pulicaria (PUC01) was collected from Lake Biwa .To estimate the phylogenetic relationships and the genetic distances among software  with theate PA42  using buate PA42 . Removalate PA42 . The proate PA42 . In thisate PA42  other thp <  0.05, and α level was adjusted by a Bonferroni correction when multiple tests were performed. In these analyses, genotype and GxF were set as random factors, food level was set as a fixed factor, and variance components were estimated using the restricted maximum likelihood (REML) parameter. Before the analysis, a log-transformation was performed for beta-glucosidase, alkaline phosphatase, arginine amino-peptidase, maturation age, mean egg number of the first three clutches and body length at five days, and a two-step transformation algorithm [In this study, we measured a total of 17 phenotypic traits for each of the 5 clones (genotypes) that were grouped and categorized as enzyme activity (five traits), life history traits (seven traits) and morphological traits (five traits) (Table lgorithm  was perfH2) for each trait was calculated as follows:g is the genetic variance and VT is the total phenotypic variance [g was estimated by decomposing VT using two-way ANOVA according to Holland et al. [μ is the mean value of given phenotypic traits.Broad-sense heritability H for eachd et al. . Then, tTo detect the relationships among traits, we first estimated the best linear unbiased predictors (BLUPs) of genotype values  statistics version 21.0 , Armonk, USA) and R version 3.3.3 .Additional file 1:Figure S1. Phylogenetic relationships of five clones in JPN1 lineage and other clones. This is based on 5282 SNPs from whole genome sequence data. Using data with quality scores higher than 20, a phylogenetic tree of the clones by the maximum likelihood (ML) method were constructed using SNPhylo pipeline [D. pulex JPN1 clones. These are D. pulex JPN2 (HO01), JPN3 (AWA) and JPN4 (SUM) and a genotype of D. pulex (LL05) collected from a small lake in Manitoba, Canada, and D. pulicaria (PUC01) collected from Lake Biwa. (PDF 199 kb)pipeline  other thAdditional file 2:Table S1. Genetic distance measured by proportion of different sites (p-distance) among Daphnia pulex JPN1 clones . (PDF 46 kb)Additional file 3:Table S2. Distance of phenotypes in each category among Daphnia pulex JPN1 clones . The phenotypic distance was calculated using difference in BLUPs between clones. (PDF 51 kb)Additional file 4:Table S3. Distance of single phenotypes among Daphnia pulex JPN1 clones . The phenotypic distance was calculated using difference in BLUPs between clones. (PDF 68 kb)Additional file 5:Table S4. Relationship between genetic distance and phenotypic distance between clones in each of the single traits. (PDF 55 kb)Additional file 6:Table S5. BLUPs of each trait in Daphnia pulex JPN1 clones. (PDF 69 kb)


Telomere length (TL) is associated with numerous disease states and is affected by genetic and environmental factors. However, TL has been mostly studied in adult populations of European or Asian ancestry. These studies have identified 34 TL-associated genetic variants recently used as genetic proxies for TL. The generalizability of these associations to pediatric populations and racially diverse populations, specifically of African ancestry, remains unclear. Furthermore, six novel variants associated with TL in a population of European children have been identified but not validated. We measured TL from whole blood samples of 492 healthy African American youth (children and adolescents between 8 and 20 years old) and performed the first genome-wide association study of TL in this population. We were unable to replicate neither the 34 reported genetic associations found in adults nor the six genetic associations found in European children. However, we discovered a novel genome-wide significant association between TL and rs1483898 on chromosome 14. Our results underscore the importance of examining genetic associations with TL in diverse pediatric populations such as African Americans. Telomeres play a vital role in maintaining DNA stability and integrity, and are therefore, critical for preserving genomic information3. With each mitotic division, a portion of telomeric DNA is lost. The cell enters senescence upon reaching a critical telomere length (TL) threshold4. TL has thus become an important biomarker of aging and overall health8. A complex interaction between genetic9 and non-genetic factors10 affects TL. While heritability estimates of TL range from 36% to 82%11, much is still unknown about genetic factors leading to variation in TL13.Telomeres are DNA-protein structures composed of tandem hexamer repeat sequences  not previously discovered in adult telomere studies30. Replication of these six genetic variants has not yet been attempted. Given that adult TL appears determined prior to adulthood31, further research in diverse pediatric populations is necessary to validate the existence of genetic effects on TL early in life.Although epidemiological research in pediatric populations has linked TL to early life adversity32, yet there is evidence that TL varies by race/ethnicity34. African Americans have been shown to have longer telomeres throughout life36 and a greater rate of telomere attrition than populations of European ancestry37. Population-specific differences in genetic variants have previously been shown across the genome38. Thus, it is possible that population-specific variation of genetic factors contributing to TL influences the difference in TL observed between populations of African and European ancestries33.Previous genetic studies of TL have been done almost exclusively in populations of European ancestryTo further understand the relationship between genetic variants and TL, we performed the first large-scale genetic study of TL in African American children and adolescents (n = 492) from the Study of African Americans, Asthma, Genes and Environments (SAGE). Herein, we analyze genome-wide genetic data to attempt validation of previously reported genetic associations with TL and identify genetic variants influencing TL in African American children and adolescents.−4), there was no significant association of age or maternal education with TL (Supplementary Table Demographic information for the study population (n = 492) is presented in Table 30. There was no significant association between the GPS and TL in our study population of African American children and adolescents . Measurement of TL in our study population provided a snapshot of TL at a specific point in the life course. Longitudinal studies of TL are required to understand changes in TL over the life course. The major advantages of our study are that (1) it provides novel information about the genetic determinants of TL in a non-European pediatric population, and (2) our depth of phenotype data allowed us to adjust for social, environmental and genetic covariates  in common variants with a MAF of 0.05 or higher in our study population, we were not powered to detect weak effects (f2 < 0.02)51. It is possible that our inability to replicate the reported variants may be explained by limited statistical power; however, this would only be the case if these variants had effect sizes in our population that were significantly lower than the strong effects reported in previous studies. All but two of the variants of the 40 variants we attempted to replicate were common (MAF > 0.05) in both the populations in which they were discovered and in our study population.It is important to note that while our study was well powered to detect moderate to strong effects . SAGE is one of the largest ongoing gene-environment interaction studies of asthma in African American children and adolescents in the USA. SAGE includes detailed clinical, social, and environmental data on asthma and asthma-related conditions. Full details of the SAGE study protocols have been described in detail elsewhereAfter all quality control procedures relating to TL measurement, TL was computed for 596 healthy controls in SAGE from whole blood. There were 495 healthy controls with complete sex, age, African ancestry, maternal educational attainment and health insurance information. Three individuals showed extreme outlier measurements for TL (three times the interquartile range) and were thus removed.57. Maternal educational attainment was dichotomized based on whether a participant’s mother had pursued education beyond high school . Health insurance type was defined as private versus public insurance. The genetic ancestry of each participant was determined using the ADMIXTURE software package58 with the supervised learning mode assuming two ancestral populations (African and European) using HapMap Phase III data from the YRI and CEU populations as references59.Maternal educational attainment and health insurance type were used as proxies of SESa priori. We only tested genetic associations if the (i) published association reaches genome-wide significance (P ≤ 5 × 10−8) on NHGRI-EBI GWAS Catalog by October 26, 2017; (ii) variant used as genetic proxy of TL in at least one study; (iii) variant reaches suggestive genome-wide significance (P ≤ 5 × 10−5) in a novel GWAS of TL in children; (iv) variant has a minor allele frequency (MAF) of at least 1% in our study population. We identified variants from 11 studies30. Ten of the 11 studies were performed in adult populations and nine of the 11 studies were performed in populations of European descent, with the remaining two performed in Punjab Sikh24 and Han Chinese26 populations. In total, we identified 40 variants from the literature, of which 12 were genotyped and 28 were imputed. The 28 imputed SNPs had r2 (squared correlation between imputed and expected genotypes) ranging from 0.88 to 1.00.TL-associated variants were selected for replication using criteria set 60. Genotype call accuracy and Axiom array-specific quality control metrics were assessed and applied according to the protocol described in further detail in Online Resource 1. Data was submitted to the Michigan Imputation Server and phased using EAGLE v2.3 and imputed from the Haplotype Reference Consortium r1.1 reference panel using Minimac361. Imputed SNPs were included if they had an r2 higher than 0.3. Quality control inclusion criteria consisted of individual genotyping efficiency >95%, Hardy-Weinberg Equilibrium (HWE) P > 10−4, and MAF > 5%. Cryptic relatedness was also assessed to ensure that samples were effectively unrelated. Samples with an estimate of genetic relatedness greater than 0.025 were excluded. After quality control procedures, 7,519,176 imputed and genotyped SNPs were available for analysis.DNA was isolated from whole blood collected from SAGE participants at the time of study enrollment using the Wizard® Genomic DNA Purification kits . Samples were genotyped with the Affymetrix Axiom® LAT1 array , which covers 817,810 SNPs. This array was optimized to capture genetic variation in African-descent populations such as African Americans and LatinosGenomic DNA was isolated from whole blood according to manufacturer’s recommendation using Wizard® Genomic DNA Purification Kits . A NanoDrop® ND-1000 spectrophotometer (Thermo Scientific) was used to assess DNA quality and quantity. All samples assayed had absorbance ratios (260/280) between 1.8 and 2.0.et al., which quantified TL in terms of telomere/single copy gene (T/S) expression ratios62. This protocol was modified with regard to data processing and control samples as previously published by O’Callaghan et al. and described in further detail in Supplemental Methods63. In brief, relative TL for each sample was calculated using the delta-delta CT (2−∆∆Ct) formula62. Using this formula, the TL computed for each SAGE sample is proportional to the T/S ratio of that sample normalized to the T/S ratio of the PCR plate positive DNA control sample65. Inter- and intra-experimental coefficients of variation for our internal control (1301 cell line DNA) were 3% and 4.25%, respectively. Average amplification efficiency across plates was ≥90% for telomere and 36B4 assays. As TL was not normally distributed in our study population, we performed all parametric tests on a log-transformation of TL.Relative TL for each sample was determined using the quantitative real time PCR (qPCR) method first described by Cawthon Genotypes for all 40 previously published SNP’s in adults and children were tested for association with log-transformed TL in a multivariable linear regression analysis. Regression analyses were run separately for each SNP under an additive model to calculate the individual effect of the SNP on TL. Each regression analysis was adjusted for biological, environmental and social factors that may impact TL including sex, age, African ancestry, maternal education, and health insurance type. We adjusted for qPCR plate ID in all regression analyses to ensure that our results were not impacted by qPCR batch effects. To ensure direct comparison of results between previous studies and our current study we coded the effect alleles in our analysis to be the same as those used in previous studies.67. We therefore constructed a weighted GPS based on the six variants from Stathopoulou et al. to test their cumulative effect on TL30. We calculated each subject’s weighted GPS by summing the number of alleles  associated with longer telomeres after weighting the allele count by the reported β-coefficient from the literature. We assumed that an effect allele having a positive β-coefficient meant that each additional copy of that allele was positively associated with TL. We used the GPS as a predictor in a linear regression against log-transformed TL controlling for sex, age, genetic ancestry, maternal educational attainment, health insurance type and batch effects. We were unable to calculate a weighted GPS based on the 34 variants in adult studies because the effect size could not be standardized across the studies.Recent research suggests the cumulative effect of multiple genetic markers may be a stronger predictor of a quantitative phenotype than the individual markers−8. This number was derived by applying a Bonferroni correction for multiple testing to a dataset of one million independent markers/SNPs. However, in many cases, this threshold is overly conservative and can be inappropriate when (1) a smaller number of markers is genotyped, and (2) the assumption of independence of tests is violated.The standard GWAS threshold for statistical significance is 5 × 10et al.68. This method estimates the effective number of independent tests in a genetic dataset after accounting for linkage disequilibrium (LD) between SNPs using the LD pruning function in the PLINK 1.9 software package69. The following parameters were used in PLINK 1.9 as advised by the authors: 100 SNP sliding window, step size of 5 base pairs, and a variance inflation factor of 1.25. Applying this method on 7,519,176 genotyped and imputed SNPs yielded 431,896 independent tests, which was then used to calculate the genome-wide significance threshold . A suggestive threshold was set at 2.3 × 10−6 for association results based on the widely used formula: 1/(effective number of tests)70.In order to adjust the Bonferroni correction based on the actual number of independent test performed on our dataset, we determined the number of independent tests using the protocol published by Sobota 69. Manhattan plots  and LocusZoom72. Curated protein-protein interactions were extracted using the STRING database39. An integrated confidence score for the interaction ranges from 0.5 (medium confidence) to 1 (high confidence).We performed a genome-wide association study (GWAS) using 7,519,176 genotyped and imputed SNPs to assess the relationship between SNP genotype and log-transformed TL. The GWAS linear regression model adjusted for sex, age, African ancestry, maternal educational attainment, health insurance type and batch effects. All testing was performed using PLINK1.9ots Fig.  were genSupplementary Information


PSMB5, PRMT5, ACTB, PDE3A, NPC1, FSCN1, NR5A2, IQCG, LHX8, and DMRT1 were identified in these window regions for these five semen traits. Our findings provided a solid basis for further research into genetic mechanisms underlying semen quality traits, which may contribute to their accurate genomic prediction in Chinese Holstein population.Efficient production of high-quality semen is a crucial trait in the dairy cattle breeding due to the widespread use of artificial insemination. However, the genetic architecture  underlying such semen quality traits remains unclear. In this study, we performed genome-wide association studies to identify genes associated with five semen quality traits in Chinese Holstein population, including ejaculate volume, progressive sperm motility, sperm concentration, number of sperm, and number of progressive motile sperm. Our dataset consisted of 2,218 Holstein bulls in China with full pedigree information, representing 12 artificial insemination centers, with 1,508 genotyped using the Illumina BovineSNP50 BeadChip. We used a weighted single-step genome-wide association method with 10 adjacent Single nucleotide polymorphisms (SNPs) as sliding windows, which can make use of individuals without genotypes. We considered the top 10 genomic regions in terms of their explained genomic variants as candidate window regions for each trait. In total, we detected 36 window regions related to one or multiple semen traits across 19 chromosomes. Promising candidate genes of  The fertility of dairy cattle has decreased over recent decades due to highly intensive selection for milk production . Male feSemen traits such as progressive sperm motility (SM) and ejaculate volume (VE) are complex and affected by genetic factors  as well In our previous study, a single-SNP GWAS was performed for five semen traits in a population of 692 animals . Due to In order to study the genetic basis of the semen traits, a weighted single-step GWAS (WssGWAS) was performed in a larger Chinese Holstein bull population. Recent studies have shown that WssGWAS can improve precision in estimating SNP effects and perform better than traditional methods under certain conditions, including with small sample sizes with both genotypes and phenotypes where traits are regulated by loci with large effects . It alsoAll procedures pertaining to the handling of experimental animals were conducted in accordance with and approved by the Animal Welfare Committee of China Agricultural University (Permit Number: DK996). All efforts were made to minimize discomfort and suffering.9 spermatozoa per ml. Number of sperms (NSP) per ejaculate was calculated by multiplying VE and SC, and the number of motile sperm (NMSP) was obtained by multiplying NSP and SM. After removal of the error data , we yielded phenotypic records of 527,207 records of VE, 527,231 records of SC, 521,150 records of MS and NMSP, and 522,906 records of NSP. The means, standard deviations, and minimum and maximum values of these traits are shown in A total of 2218 Holstein bulls born between 1996 and 2016 were represented in semen samples and phenotypes, from 12 artificial insemination (AI) centers across China where routine semen testing has been carried out since 1995. Distributions of bulls’ birth year and location are given in DNA was extracted from 1508 semen samples using a standard phenol–chloroform method. Illumina BovineSNP50 BeadChip versions 1 and 2  were used to genotype these individuals. For filling the missing genotypes, genotype imputation was carried out using BEAGLE software version 3.3.1 , and 52,The complete pedigrees of all individuals included in this study comprised a total of 4,237 animals, which had 497 sires, 1,512 dams, and 2,695 bulls. The number of bulls with phenotypes was 2,218, and the number of bulls with both phenotypes and genotypes was 1,508.P value to evaluate the importance of SNPs, and the other is a Bayesian method that utilizes all SNPs simultaneously as random effect and output SNPs effect to evaluate their importance ; ais the vector of additive genetic effects with X, Z, and W are the design matrices of β, a, and p, respectively. I is an identity matrix, and H–1 is created as follows: A22 is the numerator relationship matrix for genotyped animals , D is a diagonal matrix of weights for SNP variances , and q is a weighting factor. The weighting factor can be derived based on SNP frequencies  to investigate the gene function based on orthologous genes of human.We also used the web of genecards . On BTA 8, a window region of 43.78–44.28 Mb was shared by NMSP and NSP, with five genes located there. Of these, DMRT1 was a cell-identifying gene in the testis cord and seminiferous tubules, according to LifeMap Discovery (https://discovery.lifemapsc.com/in-vivo-development/testis). On BTA 24, the window region 33.25–33.69 Mb was shared by VE, NMSP, and NSP, and nine genes were located there. Of these, the expression of NPC1 was related to the testis interstitium according to LifeMap Discovery (https://discovery.lifemapsc.com/in-vivo-development/testis). In total, we identified 36 window regions across 19 chromosomes, out of which 8 window regions were overlapped by at least two semen traits. On BTA 5, a window region of 113.47–113.87 Mb was shared by VE, NMSP, and NSP, and 12 genes were located in this region. Out of these 12 genes, P = 0.001), which is likely to be related to semen traits. Six genes were in this GO term, including CYB5R3, SLCO1B3, NPC1, SLCO1A2, SLCO1C1, and NR5A2. It is worth noting that the DMRT1 gene was in the GO term of male germ cell proliferation (GO: 000217) and spermatogenesis (GO: 0007283), indicating that DMRT1 is a promising candidate gene.The GO enrichment analysis of the 10 top regions for the five semen traits revealed 11 significant GO terms, including biological process, cellular component, and molecular function. The details of the significant GO terms are shown in Based on analysis above, we concluded 10 promising candidate genes for these five semen traits Table 3.In WssGWAS, different weights were given to SNPs according to their importance, and linkage disequilibrium made the consecutive SNP window method more effective at detecting Quantitative Trait Loci (QTL) compared with a single-SNP method . As a rePDE3A and SLCO1C1  on BAT 1 was detected in our study, which was also identified in GWAS by IQCG knockout mice can show total immobility and severe malformation of spermatozoa, due to disorganization of the sperm flagellum axoneme . LHX8, preferentially expressed in testicular germ cells  between NMSP and NSP were confirmed in our previous study , and ourrm cells , may plarm cells .NPC1 and NR5A2 were included in the GO term of steroid metabolic process (GO: 0008202) (P = 0.001), which is the most important to semen traits due to their spermatogenesis function being regulated by steroid hormones, the most important of which is testosterone. NPC intracellular cholesterol transporter 1 (NPC1) on BTA 24 was located in an overlapping window region among VE, NMSP, and NSP. Lack of a functional NPC1 protein can cause multiple defects in mouse sperm and result in sterility , expressed from NR5A2, is produced in human steroidogenic tissues and activates transcription of genes encoding steroidogenic enzymes  and spermatogenesis (GO: 0007283). DMRT1 is located in an overlapping window region between NMSP and NSP, and is required for spermatogonial stem cell replenishment and maintenance in mice  on BTA 25 was identified as a promising candidate gene for MS. ACTB is expressed in sperm and is distributed in the acrosomal and postacrosomal regions of ejaculated spermatozoa, where it is potentially involved in membrane changes during the acrosome reaction with important implications to sperm function (B-actin (ACTB) gene regulates the process from spermatogenesis to fertilization . All efforts were made to minimize discomfort and suffering.SZ and JL designed and supervised the study. HY and CZ contributed to genomic DNA extraction and conducted GWAS analysis, HY and CZ contributed to population construction and statistical analysis with help from JL and DS. SS contributed to genomic DNA extraction, phenotypes collection, and sample collection. HY drafted the manuscript, which was critically remarked by LF, JL, DS, and SZ. All authors read and approved the final manuscript.This work is supported by the 863 project (2013AA102504); the National Science and Technology Programs of China (2011BAD28B02); National Key Technologies R&D Program (2012BAD12B01); Beijing Dairy Industry Innovation Team, China Agricultural Research System (CARS-37); and Xinjiang Province Key Technology Integration and Demonstration Program (201230116). We are deeply grateful to all donors who participated in this program.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.The reviewer GM declared a past co-authorship with one of the authors LF to the handling editor.


Here, we hypothesized that maladaptive secondary sexual characteristics may reduce fitness of hybrids between two fish species. To test this, we evaluated the reproductive success of introduced brook trout , native white‐spotted charr (WSC: S. leucomaenis) and their hybrids in a natural stream in Hokkaido, Japan, where the two parental species show remarkably different male secondary sexual characteristics, such as elongated jaws and deeper bodies. We predicted that introgression from WSC is maladaptive for BT males because the BT male has more prominent secondary sexual characteristics. Our results suggest that both sexual selection and outbreeding depression in males and females significantly influence an individual's reproductive success. Our results also suggest that asymmetric introgression may increase the risks to persistence in the recipient species.Human‐mediated hybridization between introduced and native species is one of the most serious threats to native taxa. Although field studies have attempted to quantify the relative fitness or reproductive success of parental species and their hybrids Despite the potential importance of sexual selection, its role in postzygotic isolation has only been investigated in a few other cases, such as acoustic signals of the chorus frog . We focused on secondary sexual characteristics of hybrid males because male‐male competition often results in remarkable sexual dimorphisms in salmonid fishes , S. leucomaenis, occur in a boreal stream of Japan  and native westslope cutthroat trout (O. clarkii lewisi) has spread across watersheds in North America  and Siberian stone loach (Barbatula barbatula).Our field study was conducted in the Higashi‐Furebetsu stream, a tributary of the upper Nunobe stream, in central Hokkaido, Japan  of mature BT, WSC, and hybrid (HYB) males successfully genotyped were 181.9 ± 34.4 mm , 213.6 ± 25.6 mm , and 189.2 ± 50.4 mm , respectively. Additionally, individuals captured in 2013 that exceeded 45 mm in FL were implanted with 8 or 12 mm passive integrated transponder (PIT) tags for individual identification. We implanted 1,095 individuals with PIT tags in 2013 and 2014. Fin clips of all candidate parental and offspring fish were preserved in 99% ethanol for subsequent DNA analysis. Fin tissues were stored at −20°C until DNA extraction.The field survey in the Higashi‐Furebetsu stream was conducted in August, October, and December 2013, May, June, July, and October 2014, and July 2015. The survey was mainly concentrated on collecting adult individuals in 2013, age 0 + juveniles in 2014 and age 1 + juveniles in 2015 to conduct a subsequent parentage analysis for reproductive success. This sampling design evaluated detailed reproductive success of 2013‐year adult fish by collecting their offspring . We passed through each section two to four times using backpack electrofishing (200–300 V). Significant effort was made to collect all of the potential parents and their offspring inhabiting this study area to estimate reproductive success. Fish captured were anesthetized and measured for fork length (FL: nearest 1 mm), as an indicator of body size. The mean fork length  and digital camera . Nine morphological characters Figure , and sho2.22, and about 50–100 ng/µl of genomic DNA as a template. The PCR themocycling protocol was as follows: 2 min at 95°C, followed by 35 cycles of denaturation at 95°C for 30 s, annealing at 56°C for 30 s, and extension at 72°C for 30 s. The amplified products were analyzed on the genetic analyzer ABI 3,130 (Applied BioSystems) and allele sizes were scored using GeneMapper .Total genomic DNA was extracted from fin tissues using a PureGene DNA isolation kit (Applied Biosystems) following the manufacturer's protocol. The DNA was dissolved in 50 µl TE buffer. We used 13 microsatellite loci  for parentage analysis. The loci Sfo12, Ssa197, and MST‐85 were known to be diagnostic markers to identify BT, WSC, and HYB  was two, representing WSC and BT. Based on K = 2, we calculated a hybrid index as the proportion WSC ancestry: this ranges from 0.001 for pure BT  to 0.999 for pure WSC . First generation hybrids between BT and WSC have a hybrid index of 0.5 with the 13 microsatellite loci and heterozygous for alleles from each of the parental taxa at all the seven diagnostic loci , and the observed frequency of heterozygotes (Ho) were also calculated using CERVUS.We performed parentage analysis using CERVUS v3.0.7 , PC1 was expressed as the residuals from its liner regression against FL. The transformation of PC1 value individuals with remarkable secondary sexual characteristics, such as higher hump height than average for their size, had a large positive residual value. All statistical analyses were performed using the software R version 3.2.4  to model counts of offspring  were collected in October and December 2013, May, June, July, and October 2014 and July 2015. Because the total of 934 samples included individuals with multiple captures, data on 61 recaptured individuals were removed from subsequent analysis. Of the 873 samples, 637 offspring and 236 mature individuals  were successfully genotyped at a minimum of seven loci . Of the 236 successfully genotyped mature individuals , 176 were pure BT , 22 were pure WSC , and 38 were putative hybrids . The number of alleles per locus ranged from two to 24 (mean = 12.2), and expected heterozygosity ranged from 0.15 to 0.76 (mean = 0.41). This led to high combined exclusion probability for the whole set of loci in the parentage analysis . There was no significant difference among male types in PC2. The BT and WSC males were clearly distinguished by the secondary sexual characteristics , and that HYB and WSC males significantly differed from one another in PC1 (p < 0.0001), but that BT and HYB groups were not significant distinct from one another (p = 0.4322). The PC1 is based primarily on variation in secondary sexual characters such as hump height and lower jaw length  indicated that these groups differed significantly from each other along the first principal component  were assigned to a sampled father, and 367 (58%) were assigned to a sampled mother by CERVUS. Of the 112 mature males, 59 (53%) appeared to produce at least one offspring (maximum 82 offspring) with the median of 1. Of the 124 mature females, 89 (72%) appeared to produce at least one offspring (maximum 84 offspring) with the median of 2. The reproductive success was a count variable with many zeros as expected  estimated that reproductive success decreased by 30.6% and 25.0% for males and females, respectively, in BT with 20 percent proportion WSC ancestry  was reduced by the maladaptive phenotypes derived from WSC. This, in turn, suggests that native WSC may suffer from asymmetric introgression from non‐native BT, which have more remarkable secondary sexual characteristics. That is, hybrid WSC with more pronounced secondary sexual characteristics (via introgression with BT) might enjoy higher reproductive success compared to pure WSC. If this is the case, some genes of the non‐native species will persist and spread even when pure BT disappear  that have no record of non‐native species within 60 years, although a majority of single‐nucleotide polymorphism markers (65 of 68) showed no evidence of the spread beyond the region where non‐native species had been introduced. Further analysis using additional neutral and adaptive loci could improve our understanding of the risk in the spread of non‐native genes into WSC populations.Reduced hybrid reproductive success may inhibit the spread of non‐native alleles and the development of hybrid swarms. We used, however, only 13 microsatellite markers to assess the levels of introgression, and therefore, non‐native (BT) alleles at other loci, particularly those that might be advantageous, may have spread more widely across the WSC genome. For instance, Fitzpatrick et al.  document4.4While genomic data from next‐generation sequencing (NGS) has recently been accumulating, we used only 13 microsatellite loci in the present study. Consequently, the hybrid index should be less accurate compared to NGS data. Also, we cannot know how much introgression has been occurring in the native species, as well as the loci under selection. In addition, parents were not determined for 40%–50% of juveniles, indicating that we could have missed some mature adults. We tried to collect most of the adults in the study reaches by 2–4 passes of electrofishing until few fish were captured in the last pass. However, since breeding dispersal is common in stream salmonids .The data have been archived in Mendeley ( Click here for additional data file.


Copy number variations (CNVs) are structural variants consisting of large-scale insertions and deletions of genomic fragments. Exploring CNVs and estimating their effects on phenotypes are useful for genome selection but remain challenging in the livestock.P value < 0.05) associated with at least one out of 41 complex traits. Within them, 39 CNVRs were simultaneously associated with at least 2 complex traits. Notably, 24 CNVRs were markedly related to daughter pregnancy rate (DPR). For example, CNVR661 containing CYP4A11 and CNVR213 containing CTR9, respectively, were associated with DPR and other traits related to reproduction, production, and body conformation. CNVR758 was also significantly related to DPR, with a nearby gene CAPZA3, encoding one of F-actin-capping proteins which play a role in determining sperm architecture and male fertility. We corroborated these CNVRs by examining their overlapped quantitative trait loci and comparing with previously published CNV results.We identified 1043 CNV regions (CNVRs) from array comparative genomic hybridization (CGH) data of 47 Holstein bulls. Using a probe-based CNV association approach, we detected 87 CNVRs significantly  contains supplementary material, which is available to authorized users. Enhancement of dairy sector is required to meet the increasing demands of animal protein in the world. Since Holstein is the largest milk-producing dairy breed, improving its performance, e.g. production, reproduction, growth, and disease resistance, is crucial for the global agriculture. As one of the most important phenotypes, cattle fertility is affected by both genetic and environmental factors . For exaGenomic structural variants are comprised mainly of copy number variation (CNV) in the form of large-scale insertions and deletions, as well as inversions and translocations . CNVs haBos taurus and Bos indicus , body depth, rump angle, rump width, fore udder attachment (Fore_udder_att), rear udder height (rear_ud_height), udder depth, udder cleft, front teat placement (Front_teat_pla), teat length, Rear legs(rear view) [Rear_legs(rear)], feet/legs score (Feet_and_legs), rear teat placement (Rear_teat_pla). These PTA were predicted additive genetic effects after removing fixed non-genetic effects, and the reliabilities of the PTA were used to quantify the amount of information available for different individuals.apply.pca. Through clustering the PCA transformed data (first 3 components), a copy number genotype  was then assigned to animal for the association testing based on a standard regression approach [apply.pca function and phenotypes .Each CNV was analyzed for the association with each complex trait using the R package CNVtools separately, which implements a mixture model . Brieflyapproach . The CNVAdditional file 1:Figure S1. Characteristics of CNV distribution on each autosome. A. Distributions of CNV length per individual. B. Distributions of CNV count. Figure S2. Hierarchical clustering of 41 complex traits based on P values from association results between CNVRs and phenotypes. Pearson correlation was used to measure distances. Different colors represent various types of phenotype traits. (PDF 294 kb)Additional file 2:Table S1. CNVRs identified in this study and overlapping with QTL, OMIA, and CNVRs in previous studies. Table S2. Gene Ontology terms generated by PANTHER analysis. Table S3. Results of GWAS between CNVRs and 41 phenotypes of interest in 39 Holstein cattle. Table S4. Characterizes for significant CNVs associated to Holstein phenotypes. (XLSX 395 kb)


CPT1A, SOCS3 and ABCG1) using real data made available through the GAW20.Transgenerational epigenetic inheritance has been posited as a possible contributor to the observed heritability of metabolic syndrome (MetS). Yet the extent to which estimates of epigenetic inheritance for DNA methylation sites are inflated by environmental and genetic covariance within families is still unclear. We applied current methods to quantify the environmental and genetic contributors to the observed heritability and familial correlations of four previously associated MetS methylation sites at three genes (Our findings support the role of both shared environment and genetic variation in explaining the heritability of MetS and the four MetS cytosine-phosphate-guanine (CpG) sites, although the resulting heritability estimates were indistinguishable from one another. Familial correlations by type of relative pair generally followed our expectation based on relatedness, but in the case of sister and parent pairs we observed nonsignificant trends toward greater correlation than expected, as would be consistent with the role of shared environmental factors in the inflation of our estimated correlations.Our work provides an interesting and flexible statistical framework for testing models of epigenetic inheritance in the context of human family studies. Future work should endeavor to replicate our findings and advance these methods to more robustly describe epigenetic inheritance patterns in human populations. Metabolic syndrome (MetS) is a widespread problem in the United States, with 35% of U.S. adults having MetS in 2012 [Given the heritability that remains unexplained by established genetic variants for the subcomponents of MetS, transgenerational epigenetic inheritance has been posited as a possible contributor to the observed heritability . AlthougCPT1A, SOCS3, and ABCG1 have been associated with MetS, or its subcomponents  [To date, CpG methylation sites at , ABCG1) –12. The , ABCG1) , extende, ABCG1) , 14, andCPT1A, SOCS3, and ABCG1), adjusting for demographic, environmental factors and genetic variation in a stepwise fashion using both fixed and random effects. Then we estimated familial correlations of methylation profiles at these CpG sites, both with and without adjustments, and across relative pair types.We aimed to apply current methods  to quantify the environmental and genetic contributors to the observed similarity within families at four specific MetS CpG sites. To do this, we leveraged data on 1105 adults made available through the Genetic Analysis Workshop (GAW20) to estimate the heritability at CpG sites near 3 genes  study . Our anaCPT1A, SOCS3), or with Type 2 diabetes, lipids, and obesity-related traits  including: cg00574958 and cg17058475 near CPT1A [SOCS3 [ABCG1 [From the literature we selected 4 CpG sites that were previously associated with MetS . Then using the fixed effects identified in the reduced model above, we added household variance components to account for siblings and half-siblings within 15 years of each other, who were the relative pair type most likely to have shared an ‘early life’ environment at some point during their childhood or adolescence (Model 3a). Separately we added a variance component for parent pairs , who were the relative pair type most likely to have shared ‘later life’ environmental exposures (Model 3b). Lastly, in a fourth modeling strategy that included the same fixed effects from the reduced model (Models 1 and 2), we screened at P value < 0.05 local cis-acting genetic variants at each locus. To select these variants, we used publicly available 1000 Genomes phase 3 CEU (Northern Europeans from Utah) reference data to query two independent sets  [n = 8, 19, and 21 local and n = 6, 7, and 13 distant variants screened in heritability models for CpG sites at CPT1A, SOCS3, and ABCG1, respectively.No fixed effect covariates were included in our crude heritability models (Model 0). Further analyses accounted for an individual’s age and sex , quadratic age effects, and their interactions with sex (Model 1). In all subsequent models, environmental covariates were added into the models in the following sequence: center , followed by cigarette smoking status . We then screened all these demographic and environmental fixed effects, including only the effects that remained suggestively significant in the heritability models (on 1.07)  of geneton 1.07) . This reS.A.G.E. version 6.4 package (http://darwin.cwru.edu/sage/) within various pair types, representing a quasi-independent subset of the family pedigrees. We contrasted our correlations before and after creating a residual of methylation to account for the fixed effects identified in multiple reduced heritability models, and among a subset of unrelated individual pairs.The expected intra−/interclass correlation for each relative pair is a function of the pairs’ expected relatedness and the CpG site-specific heritability. We estimated weighted correlations using the FCORR module of the P value = 1E-5, n = 1103) in a model where fixed effects  explained 13% of the variation in MetS. Separately, we included variance components for early life shared environment , or later life shared environment . Although the addition of these terms influenced the magnitude of the heritability estimates , the resulting heritability estimates did not differ significantly.The prevalence of MetS at the baseline examination of GOLDN was 38.4% and its heritability was 0.47  were strongly associated with MetS and another site  was suggestively associated with MetS. Retaining these 3 sites in the polygenic model decreased the heritability estimate slightly  and increased the variance explained (VE) by all the fixed covariates to 18%. The addition of random effects of early life shared environment  decreased the heritability estimate , resulting in a nonsignificant MetS heritability estimate, whereas accounting for shared late life environment  increased the heritability only slightly .When we added fixed effects for the 4 MetS CpG sites into the model without shared environment-related variance components, two of the CpG sites  variant, rs17601808, was significantly associated with cg00574958  and cg17058475 heritability estimates  after accounting for fixed effects from the reduced model . For the SOCS3 site, one local (±250 kb) genetic variant, rs7220979, and two distant variants, rs9908993 and rs17736494, were significantly associated with cg18181703 heritability after accounting for fixed effects , resulting in similar heritability estimates  as in previous models. Including these 3 SNPs resulted in nonsignificant estimates for center and former smoking, and dropping these nonsignificant fixed covariates also yielded similar heritability estimates . At the ABCG1 site, 2 local  and 1 distant genetic variant  were associated with cg06500161 heritability  after accounting for fixed effects.For the two 2, sex, center, and current smoking, which were retained in more than 1 reduced heritability model , and were 0.01 to 0.15 greater than our expected correlation.We then estimated familial coefficients across a number of relationship pairings, before and after creating a residual adjusting for age, ageP value ≥0.3), the correlations estimated for sister pairs were the largest across all sites  positive correlations at 3 CpG sites among parent pairs (65 independent pairs), which were between 0.02 and 0.12 greater than expected. Among unrelated pairs, we observed correlations that were closer to our expectation of no correlation , which supports the upward bias of shared household environments on familial correlations. When we further paired this unrelated with respect to MetS status, the correlation at the 2 CPT1A CpG sites were biased upwards among concordant pairs, and downwards from the null among discordant pairs.Although not statistically significantly different from other pairings  across various types of relative pairs. We observed that correlations generally followed our expectation based on relatedness, but in the case of sister and parent pairs we observed nonsignificant trends toward greater correlation than expected. We posit that shared social and environmental factors may make particular relative pairs appear more similar than we would expect based on their relatedness alone, which could lead to further inflation of heritability and familial correlation estimates.Previous research has not been able to address the extent of inflation of epigenetic inheritance estimates by shared environmental effects, even though the sharing of social or environmental exposures within households may be a key driver of the observed similarity of methylation profiles within families , 13, 14.


Salix spp. are high-productivity crops potentially used for lignocellulosic biofuels such as bioethanol. In general, pretreatment is needed to facilitate the enzymatic depolymerization process. Biomass resistance to degradation, i.e., biomass recalcitrance, is a trait which can be assessed by measuring the sugar released after combined pretreatment and enzymatic hydrolysis. We have examined genetic parameters of enzymatic sugar release and other traits related to biorefinery use in a population of 286 natural Salix viminalis clones. Furthermore, we have evaluated phenotypic and genetic correlations between these traits and performed a genomewide association mapping analysis using a set of 19,411 markers.h2) of 0.23–0.29. Lignin syringyl/guaiacyl (S/G) ratio and wood density were the most heritable traits . Sugar release traits were positively correlated, phenotypically and genetically, with biomass yield and lignin S/G ratio. Association mapping revealed seven marker–trait associations below a suggestive significance threshold, including one marker associated with glucose release.Sugar release (glucose and xylose) after pretreatment and enzymatic saccharification proved highly variable with large genetic and phenotypic variations, and chip heritability estimates  contains supplementary material, which is available to authorized users. The bulcitrance .Biomass recalcitrance is a key barrier to the financial feasibility of lignocellulosic ethanol . RecalciAlthough biomass recalcitrance is known to be highly multifactorial, lignin composition is believed to play an important role. Hardwood lignin, while highly heterogeneous in structure, is composed mainly of sinapyl alcohol (S) and coniferyl alcohol (G) monolignols. The molar ratio of these subunits within the lignin polymer, commonly referred to as the S/G ratio, influences several properties of the lignin polymer including the content of labile ether bonds, molecular weight, and linearity, but the correlation between S/G ratio and recalcitrance is not consistently and unidirectionally reported in the literature . While dProducing plant varieties better suited for biorefining can be accomplished through different breeding methods and strategies, such as genetic modification (GM) or by recurrent selection for objective biorefining traits or for other traits genetically correlated to such traits. Due to reasons of legislation and public perception, the recurrent selection strategy may be desirable in many cases. To speed up the selection process, marker-assisted selection (MAS) can be used in the early stages of a breeding program. Increasingly, markers are generated using genomewide association studies (GWAS), where large numbers of molecular markers such as single-nucleotide polymorphisms (SNPs) and insertions or deletions (indels) are evaluated together with the traits of interest. GWAS methodology together with improvements in genetic sequencing outputs and costs have enabled genetic surveys of marker–trait associations in large populations of unrelated individuals, enabling identification of interesting alleles in natural populations, which harbor considerable genetic variation .Salix spp.), commonly grown in short-rotation coppice (SRC) systems as dedicated energy crops, represent a promising biorefinery feedstock. In an SRC system, the shoots are harvested after just a few years of growth, leaving the stools in the ground. In the following spring, new shoots emerge and the SRC cycle starts over. As willows are outcrossing, easily hybridized, readily clonally propagated through cuttings, and harbor considerable inter- and intraspecies genetic variabilities, they lend themselves well to breeding [S. viminalis is among the most common Salix species used for breeding, owing to its high biomass yield potential [Willows , and shoots that were deemed too small to accurately measure the densities of were excluded from such measurements (n = 1109 for density measurements).From each plant of the 291 vegetatively replicated accessions  from four blocks, the main shoot was harvested. Excluding dead and damaged plants, 1132 individual shoots remained for subsequent analyses, amounting to an average of 3.57 biological replicates per clone, with 286 clones remaining. Shoots that were too small to be chipped were further excluded from the sugar release assay  and wood density were measured. Whole plant fresh weight (FW) and number of shoots on the plant (NSh), which were recorded in 2017 for an earlier study , were alcombined release), the ranges were 0.05–0.15 and 0.11–0.37, respectively. Lignin S/G ratios, analyzed using py-MBMS, ranged from 1.6 to 2.5  was 0.06–0.23 g/g biomass Fig. . For xylh2) ranged from 0.23 for xylose release to 0.59 for wood density (Table a) for these traits were quite low. In contrast, classical biomass traits such as the number of shoots and plant fresh weight exhibited lower h2 estimates (0.29–0.38) but considerable CVa estimates (25.4% to 40.7%). For the sugar release traits, glucose release showed both the highest h2 and CVa.Chip heritability estimates  and chip-additive genetic correlation (ra), sugar release traits were positively correlated with several measures of biomass yield, especially main shoot weight and main shoot diameter , with slightly weaker correlations for plant fresh weight . In contrast, the number of shoots (Nsh) exhibited low and, in the genetic case, nonsignificant correlations with sugar release traits . Wood density was essentially uncorrelated with all other traits, however a weakly positive genetic correlation with xylose release was found (ra = 0.28).In terms of phenotypic correlations  with all studied traits except for xylose release . One notable association was between glucose release and the marker S1_306291149 . This particular marker was located on chromosome 15 at position 19051974 in a noncoding area of the genome and explained 2.4% of the phenotypic variation of glucose release. The effect of the genotype class homozygous for the rare C allele, found in five individuals with low coancestry (θ = 0.044–0.087), was a 27% increase in glucose release, whereas the other genotype classes of this SNP exhibited considerably smaller effects.One marker associated with number of shoots was identified below the Holm-adjusted < 0.05 significance level, while six other markers were associated at the suggestive .2 Table . All theθ > 0.6) and which all originated east of the Ural mountain range. Therefore, these particular associations should be interpreted with skepticism as they may be due to a specific population substructure that our analysis, despite our efforts, was unable to account for. This particular phenomenon was also seen in a previous GWAS on this population [In total, six marker–trait associations were identified for the non-sugar release traits, with three found for main shoot diameter, one for main shoot weight, and two for the number of shoots. No marker–trait associations were identified for lignin S/G ratio and wood density. Two of the markers, S1_193869984 and S1_90313329, associated with main shoot weight and main shoot diameter, respectively, exhibited a strong recessive effect heavily dependent on a particular group of five rare-allele homozygotes with uniquely high coancestry  confirming a certain degree of genetic control over the phenotypic variation. In terms of coefficients of genetic and residual variation (CVa and CVe), whole plant fresh weight and main shoot weight exhibited the greatest variation. On the other hand, wood density and S/G ratio showed the smallest coefficients of genetic variation  although their corresponding heritability estimates were the highest among the traits studied. This pattern of high h2 paired with a low CVa appears to be commonly reported for structural wood traits in several conifer species [a (6.2%) and high heritability (0.56) for density, calculated from genetic parameters estimated in a wood study in Populus trichocarpa [Salix varieties. These observations demonstrate the substantial variation found in natural Salix clones sampled across large parts of Europe, which, together with the moderate heritability estimates, constitute a valuable basis for selective breeding.All traits showed appreciable heritability estimates  were observed between main shoot weight and the sugar release traits, closely mirrored by main shoot diameters. The S/G ratio was also positively correlated with sugar release . Biomass yield is generally found to be positively correlated with cellulose and negatively with lignin content in both Salix [Populus [Populus nigra [Arabidopsis [Strong correlations  indicating that this trait could be improved by selecting clones homozygous for the rare C allele at this marker. The marker was located near the gene SapurV1A.0168s0180 according to the S. purpurea genome assembly. This gene encodes the protein CYP89A2, a member of the cytochrome P450 family of enzymes, a group of enzymes which catalyzes a wide range of redox reactions and are involved in virtually all secondary metabolic pathways including lignin, terpene, and plant hormone biosyntheses [Populus or Arabidopsis orthologs, and thus cannot speculate on the possible mechanism of action in the influence of this gene on saccharification yields.In terms of marker–trait associations, S1_306291149 was associated with glucose release at the suggestive significance level  and the lack of other significant associations it appears likely that biomass recalcitrance is a highly polygenic trait, especially given that heritability estimates for sugar release traits were still appreciable (h2 > 0.2). Biomass recalcitrance is indeed a complex phenomenon dependent on several gross compositional , chemical , as well as micro- and ultrastructural features affecting cellulose accessibility [Populus, Muchero and coworkers managed to link several SNP markers to sugar yields and cell wall chemistry [Given the limited percentage of phenotypic variance explained by the marker–trait association discussed above hemistry . FurtherSalix ideotype for optimal per-hectare sugar yields . Out of the four population clusters, this growth pattern was mainly observed in the Russian cluster  but to a certain extent also in the West European cluster , reflecting potential reservoirs of genetic variation for use in breeding programs. Furthermore, for glucose and total sugar release, population structure components exerted a significant effect indicating that clones belonging to the West European population cluster were associated with 9% higher glucose release, despite main shoot weight being only slightly elevated and S/G ratio being at the mean. Thus, the West European population cluster may harbor other recalcitrance factors not evaluated in this study.It is worth noting that only the main shoot of each plant was used for the sugar release assay, whereas whole plant sugar yields will depend on the sum of yields of all shoots. The finding that main shoot weights and diameters were positively correlated with sugar release suggests that accumulation of biomass in fewer, larger shoots may represent a component of the S. viminalis harbor considerable variation in sugar release traits, constituting a valuable resource for breeders looking to produce new Salix varieties for biorefinery use. This work represents the first large-scale survey of recalcitrance in this commercially relevant bioenergy crop species. Estimates of chip heritability and genetic coefficients of variation for these traits suggest that breeding toward this goal is feasible. Main shoot weights, shoot diameters, and lignin S/G ratios were positively correlated with sugar release and may thus represent indirect measurement traits to use in the selection and improvement of sugar release, although inconsistencies in the literature suggest that further validation of the correlation between S/G ratio and recalcitrance as a proxy trait needs to be examined further. Finally, one marker (S1_306291149) was indicated to be associated with glucose release, meriting further investigation.Natural populations of S. viminalis accessions. Each accession was vegetatively propagated  and planted in a completely randomized block design encompassing six blocks in Pustnäs, Uppsala, Sweden . Details of the population have been provided in earlier publications [The plant material used in this study originates from a previously established population of 323 genetically distinct ications , 36. In The plants were harvested winter 2016/17 having 2-year old shoots and 8-year old roots. Plants from four out of six blocks were used for this study. Number of shoots and fresh weight was recorded for each plant in the experiment. The highest shoot of each plant was selected and harvested separately, and the weight and diameter of the shoot was measured.From each individual shoot, an approximately 25 cm long piece from the bottom part of the shoot was cut and separated into two subsamples. The first subsample  was saved for chemical analyses, while the second subsample (approximately 5 cm long) was used for density measurements. Each such sample was debarked and dried in an oven at 103 ± 2 °C. After drying, the absolute dry weight of the sample was recorded and immediately the sample’s absolute dry volume measured by immersion in water. The two measurements were used to calculate the absolute dry density of the wood.After removing the bottom part of the shoot as described above, the remainder of the main shoot of each plant (including bark) was coarsely chipped using a compost grinder , and stored in air-tight bags at − 4 °C. Aliquots of approx. 5 g were dried for 6 h at 60 °C. After drying, samples were milled using a Wiley Mini-Mill  and passed through a 20-mesh sieve. The ground material was stored in antistatic sample bags at room temperature.10R0) of 3.6, calculated using the formula:t is the time in minutes and T is the temperature in °C. Each sample was pretreated in triplicate, and 24 samples were loaded on each 96-well plate. BESC standard poplar was used for the controls, and each plate contained eight controls and four blanks.A high-throughput pretreatment (HTP) assay was performed at the National Renewable Energy Laboratory (NREL) in Golden, CO, USA. This assay was conducted largely as described by Decker et al. , using 5For the enzymatic hydrolysis, Accellerase 1500  and Multifect xylanase  were used. The loadings were 80 mg and 20 mg/g biomass for the Accellerase and Multifect enzymes, respectively. Hydrolysis was conducted at 50 °C for 70 h, with 0.02% sodium azide in 140 mM pH 5.0 citrate buffer. Glucose and xylose release values were then measured using GOPOD and XDH enzymatic assays according to the manufacturer’s instructions .R0 = 3.4 and log R0 = 3.6, respectively. The results of this trial indicated that the pretreatment of log R0 = 3.6 and the 80/20 mixture was most appropriate  and protein loadings (20 and 70 mg protein/g biomass). Severities for these pretreatments were log m/z) values were collected over the range of 30–450. Each sample was analyzed in duplicate. Lignin S/G ratios were calculated relative to a standard with an S/G ratio of 1.7, as determined by thioacidolysis [Nonpretreated, unextracted, milled samples were analyzed using py-MBMS according to methods previously described , using hidolysis .2 was used as the nebulizing gas. The sugars were eluted using ultrapure water as a mobile phase at a constant flow rate of 0.5 ml/min and column temperature of 85 °C.For validation of Klason lignin estimates, a subset of 17 samples were chosen at random from the material used for density measurements. The samples were milled to pass a 40-mesh screen, and analyzed for acid soluble lignin (ASL), acid insoluble lignin (AIL), and monosaccharides according to the procedure of Sluiter et al. [Salix purpurea as a mapping reference . For the 291 clones for which DNA sampling, preparation, and genotyping were successful, 1,555,795 sequence sites showing polymorphisms  were identified and merged by TASSEL 4.3.7. The merged genotype data are stored at a publicly available repository [From clones of the study population, young leaves were sampled, and DNA was extracted according to a protocol described in . DNA extrea v1.0 , DOE-JGIpository .For putative polymorphic sites, diploid genotypes were called according to the maximum likelihood  requirinK) was estimated in accordance with [K. Based on this assessment, four population clusters—Swedish, Russian, East European, and West Europan—were identified  separately:b is the vector of fixed intercept and block effects, u is the random chip-additive genetic effects of each clone, and e is the random residual, and where the design matrices X and Z link the respective block and chip-additive genetic effects to their observations. All effects were assumed to be independent except for the chip-additive genetic effects which were assumed to follow the structure h2) and corresponding additive genetic and residual coefficients of variation (CVa and CVe) were then calculated by means of the R package heritability version 1.2 [µ represent the residual variance and the field trial mean, respectively. Effects of population structure on traits were evaluated using the mixed model above but including the population structure covariates (Fq) as fixed effects. The statistical significance of the structure covariates was assessed using a loglikelihood ratio test (χ2-distribution with 3 degrees of freedom), comparing the models with and without population structure covariates. Effects were calculated against the weighted mean for all population covariates.Statistical analyses were performed using R version 3.5.1 , and figsion 1.2  using thχ2-distribution with 1 degree of freedom), comparing a model where the chip-additive genetic variance–covariance matrix was considered to be unstructured (no components constrained) with a null-hypothesis model where the matrix was considered to be block-diagonal (covariance component locked at zero). Adjustments for multiple significance testing of genetic correlations were performed using the p.adjust function in R, using the method “holm” [Genetic correlations were calculated by fitting two traits simultaneously in bivariate mixed models but using the same linear model as previously described. Statistical significances of the genetic correlations were evaluated by a loglikelihood ratio test , heterozygotes (Aa), and rare-allele homozygotes (aa), respectively. All other effects were as described previously. All biological replicates were used in a single-step analysis . Adjustments for multiple significance testing of marker–trait association p-values were again performed using the p.adjust function in R, using the method “holm.” Markers with adjusted p values < 0.2 are reported herein. To calculate relative marker effects as percentages, effects as reported by TASSEL were first adjusted by subtracting the weighted mean of effects. The adjusted means were then divided by the population mean for the trait.For genomewide association analyses, all markers that were found to be biallelic, to be nonredundant, to have > 75% called genotypes and a MAF > 5% were included in the association analysis . Missing genotype calls were imputed using the LD-kNNi method as formulated in the software nkImpute  using thAdditional file 1: Figure S1. Estimates of Klason lignin from py-MBMS versus values obtained by wet chemistry. Figure S2. Results of initial test runs to assess optimal pretreatment severity and enzyme loading. Figure S3. Results of second test run to better assess optimal enzyme loading.