Heart failure (HF) is a rapidly growing global public health problem. Since HF results in high mortality and re-hospitalization, new effective treatments are desired. Although it remains controversial, omega 3 polyunsaturated fatty acids (n-3 PUFAs), such as the eicosapentaenoic acid and docosahexaenoic acid, have been widely recognized to have benefits for HF. In a large-scale clinical trial regarding secondary prevention of HF by n-3 PUFA , the supplementation of n-3 PUFA significantly reduced cardiovascular mortality and hospitalization. Other small clinical studies proposed that n-3 PUFA potentially suppresses the ventricular remodeling and myocardial fibrosis, which thereby improves the ventricular systolic and diastolic function both in ischemic and non-ischemic HF. Basic investigations have further supported our understanding regarding the cardioprotective mechanisms of n-3 PUFA against HF. In these reports, n-3 PUFA has protected hearts through (1) anti-inflammatory effects, (2) intervention of cardiac energy metabolism, (3) modification of cardiac ion channels, (4) improvement of vascular endothelial response, and (5) modulation of autonomic nervous system activity. To clarify the pros and cons of n-3 PUFA on HF, we summarized recent evidence regarding the beneficial effects of n-3 PUFA on HF both from the clinical and basic studies. Heart failure (HF) is a rapidly growing global public health problem both in developed and developing countries, with an estimated prevalence of over 37.7 million patients worldwide . DespiteEpidemiologic discovery first indicated that larger fish consumption afforded beneficial effects on cardiovascular disease (CVD). Observational and interventional studies, which were conducted from the 1980s to the early 2000s, indicated that marine-derived omega 3 polyunsaturated fatty acids  and docosahexaenoic acid (DHA)) are effective for the ischemic heart disease (IHD)  and ventSince multiple factors, such as the quality and quantity of n-3 PUFA supplementation and cohort profile , can affect clinical outcomes, it is difficult to accurately assess the beneficial effects of n-3 PUFA on a heterogenous clinical syndrome like HF. Furthermore, due to the multiple pleiotropic pharmacological effects of n-3 PUFAs in vivo, a complete understanding of the pathophysiological mechanisms of n-3 PUFA on HF remains elusive. In the current review, we overview results of clinical trials with regard to both dietary and therapeutic intervention of n-3 PUFA for the purpose of cardio-protection, mainly focusing on HF, and discuss the underlying basic molecular mechanism of these effects.3) or omega (n-) end of the chain provides the name of the PUFA, and n-3 PUFAs have a double bond at the site of the third carbon, whereas omega 6 PUFAs (n-6 PUFAs) have the bond at the sixth carbon from the methyl end of the chain  by the presence of two or more carbon-to-carbon double bonds within their molecular structure. The location of the first double bond from the methyl , or free fatty acids binding to the serum albumin. FAs are subsequently incorporated into the cellular phospholipid bilayer of the plasma cellular membrane of whole-body tissues. FAs can also provide large quantities of intracellular ATP, through β-oxidation and the Krebs cycle in mitochondria. In fact, the heart requires FAs as an essential energy source, which provides about 70% of the total amount of energy under normal conditions. Of note, it is well known that the bioactive eicosanoids derived from PUFA have different reactions in terms of pro- or anti-inflammation, thrombotic activity, and vasocontractile or vasodilate reaction, depending on their parent PUFA compounds .HF is a condition in which the heart is unable to pump enough blood to meet demand. Since HF is a multifactorial syndrome, its mortalities and progressions differ from their underling etiologies, such as IHD, valvular heart disease, hypertension, arrhythmia, cardiac myopathies, congenital heart disease, endocrine and metabolic diseases, infection, and certain drugs. Several prior prospective observational studies and randomized control trials proved that consumption of fish or fish oil containing n-3 PUFA decreased IHD mortality  and sudden cardiac death) in patients with or without pre-diagnosed CVD ,19,20. ITo date, a number of population-based prospective observational studies were conducted to reveal the effect of fish and fish oil for primary prevention of HF , and thep = 0.041) and the risk of hospitalization due to cardiovascular reasons by 8% (adjusted HR 0.92 (99% CI 0.849–0.999), p = 0.009) [Several clinical studies were conducted to elucidate the effect of n-3 PUFA on secondary prevention for HF . The GIS= 0.009) . Additio= 0.009) . Althougp < 0.001), and showed improvement in the peak VO2, exercise duration, and New York Heart Association (NYHA) functional class. In addition, HF hospitalization was significantly suppressed in the n-3 PUFA group  [Small-scale clinical trials among ischemic and/or non-ischemic HF patients, which studied the improvement of cardiac function mainly on the basis of echocardiography, have been reported ,36,37,38 0.0002) . Dose-de 0.0002) . In this 0.0002) .p = 0.017) and the non-infarct myocardial fibrosis  were significantly suppressed in the n-3 PUFA group compared to those in the placebo group. The serum biomarkers regarding systemic and vascular inflammation were significantly suppressed, which suggested that the suppression of myocardial fibrosis would be one of the mechanisms of n-3 PUFA-mediated protection on damaged myocardium. Cardiovascular event reduction in the GISSI-HF trial was mainly obtained by anti-arrhythmic effects [The Omega-3 Acid Ethyl Esters on the Left Ventricular Remodeling After Acute Myocardial Infarction (OMEGA-REMODEL) RCT, which was conducted with 358 patients with acute MI , reveale effects , and sma effects ,40. ThesTo date, the European Society of Cardiology guidelines give a class IIb recommendation (with level B evidence) regarding n-3 PUFA treatment for symptomatic HF patients to reduce the risk of cardiovascular hospitalization and cardiovascular death . The AmeThe effects of supplemental n-3 PUFA for the prevention of HF-related cardiac cachexia must also be clarified. A small randomized double-blind trial, which was conducted in a placebo-controlled study with 14 symptomatic HF patients (NYHA III or IV), revealed that a high-dose n-3 PUFA (8 g/day) intake for 18 weeks significantly decreased the lipopolysaccharide-induced tumor necrosis factor-alpha (TNF-α) released from patients’ peripheral blood mononuclear cells, as well as improving body weight, suggesting the potential benefit of n-3 PUFA against cardiac cachexia . FurtherAlthough it is well-known that HF preserved ejection fraction (HFpEF) accounts for up to half of all HF in developed nations , to the As mentioned above, heterogeneous CVD outcomes have been reported from PUFA clinical trials. These heterogenous outcomes in CVD may be derived from differences in the quality and quantity of n-3 PUFAs adopted in each clinical study, and other baseline clinical background factors, including genetic variance in terms of n-3 PUFA bioavailability. In this section we discuss the key points which can affect the results of PUFA clinical trials.Heterogeneity in the blood and tissue PUFA concentration: Although a previous meta-analysis reported no statistically significant relationship between n-3 PUFA consumption and CVD mortality, the variability in individual serum n-3 PUFA levels was not taken into consideration in the study . Superko®, GlaxoSmithKline (EPA/DHA ethyl-esters) and Vascepa®, Amarin Pharmaceuticals (EPA ethyl-esters)) and one free fatty acid ) forms of n-3 PUFA supplement. Thus, the different chemical forms and bioavailability of n-3 PUFA can affect the clinical outcome through absorption via the intestine. Regarding bioavailability, the free forms of n-3 PUFA seem to provide better CVD outcomes than the ethyl-ester forms [Effective dose and bioavailability of PUFA: Currently, few studies have provided an ideal n-3 PUFA dose, which can sufficiently correct the tissue PUFA concentration, and which may account for the heterogeneity of clinical outcomes. This unavailability of an effective dose of PUFA intake for HF prevention decreases the clinical evidence level. Thus, in order to use PUFA for HF prevention, we should first explore titration of the ideal n-3 PUFA dose by monitoring blood and/or tissue PUFA concentrations. In addition, the bioavailability of n-3 PUFA can be altered by its chemical form. In triglyceride or phospholipid forms , n-3 PUFA is mainly hydrolyzed by colipase-dependent pancreatic lipase. Meanwhile the bioavailability of the ethyl-ester form of n-3 PUFA is highly dependent on the presence of dietary fat, which can stimulate the release of bile salts into the small intestinal tract, because n-3 PUFA ethyl-esters requires digestion with bile salt-dependent lipase ,53. Currer forms ,55. TherGenetic variant in PUFA metabolism: It is likely that genetic polymorphism affects individual taste-perception, which decisively determines the intake of food containing n-3 PUFA . Prior familial aggregation analysis indicated the strong heritability of FA composition in the Red blood cells (RBC) membrane (up to 40%–70%) . The endDespite controversy regarding the benefits of n-3 PUFA for HF prevention in clinical trials, a significant amount of supportive evidence from basic and clinical translational research has been reported. Although such follow-up research tends to contain an inherent bias, by which positive data are likely to be reported, each study has well-explained pathophysiological mechanisms of preventive effects of PUFA against HF . In thisIt is well known that the inflammatory cytokines , which can reduce both systolic and diastolic cardiac function to advance cardiac remodeling through abnormal calcium handling in cardiomyocytes and enhancement of cardiac fibrosis via an activation of fibroblasts, are elevated both in the blood and local heart tissue of HF patients . In addi2), and prevented abnormal LV remodeling [Classically, eicosanoids originated from n-3 PUFA are known to enhance the production of anti-inflammatory cytokines at the site of inflammation, whereas essential n-6 PUFAs, such as AA, are considered as substrates of pro-inflammatory eicosanoids, which promote vascular permeability, leucocyte infiltration and activation, and pro-inflammatory cytokine release . The conmodeling . In addimodeling ,32,63,64modeling  and NLRPmodeling , or upremodeling . Meanwhimodeling . The immAdiponectin, a peptide hormone released from adipose tissue, is known to show a cardio-protective effect through anti-inflammatory reaction. PPARγ-dependent adiponectin secretion is thought to correlate with n-3 PUFA-mediated cardiac protection, and the administration of n-3 PUFA increased the circulation of adiponectin in a dose-dependent manner both in animal ,69 and hThe free FA specific membrane receptor family, a family of orphan G-protein coupled receptors (GPR), has been detected in the last 10 years. GPR120, also known as a free fatty acid receptor (FFR) 4, was detected as a receptor for n-3 PUFA, regulating downstream of intra-cellular signaling . Eclov eIn order to obtain enhanced benefits of n-3 PUFA-mediated cardiac protection, it would be better to identify the effective n-3 PUFA metabolites rather than to take a high dose of n-3 PUFA. Recently, the essential PUFA derived large class of cell signaling lipid mediators, named specialized pro-resolving mediators  were discovered and indicated to exert the resolution of inflammatory reactions at nanomolar levels of concentration ,75. HalaAlteration of energetic substrate utilization in myocardium and modification of its intra-cellular signaling pathway also play important roles for the pathogenesis and progression of HF . PreviouIn HF, lipotoxicity by excessive serum free FAs, which is caused by chronic adrenergic stimulation, is one of the critical pathophysiological mechanisms to exacerbate HF. Excessive free FA exposure to cardiomyocytes causes uncoupled mitochondrial respiration and reactive oxygen species (ROS) production , which tMitochondria are dynamic organelles, which can continuously alter their morphology to maintain a number of cellular processes, such as cell cycle, immunity, apoptosis, and mitochondrial quality control. Mitochondrial dynamics play key roles for the pathophysiology of HF ,91. Spec2+ wave, which was propagated from sarcoplasmic reticulum (SR) Ca2+ release by isoproterenol stimulation [Patients with HF are highly comorbid with arrhythmia, and the incidence of arrhythmia certainly worsens the rate of mortality of HF . n-3 PUFmulation , suggestAlthough the detailed mechanism remains unknown, n-3 PUFA may be able to improve the autonomic nervous system. An impaired autonomic tone is one risk-factor of a fatal arrhythmic event and sudden cardiac death in patients with dilated cardiomyopathy  and ischp < 0.01, diastolic BP (1.0–2.2), p < 0.01), and this anti-hypertensive effect of fish oil was obvious especially in the elderly and patients with hypertension [It is well known that hypertension is a critical factor in developing HF. Previous investigations revealed the beneficial effects of fish oil on hypertension. Morris et. al. reported that the intake of fish oils reduced blood pressure (BP) by 3.0 mmHg in systole and by 1.5 mmHg in diastole (95% CI: systolic BP (4.5–1.5), diastolic BP (2.2–0.8)) . Geleijnrtension . The n-3rtension  and in vrtension . In addirtension . Thus, nThe anti-thrombotic and anti-atherosclerotic effects by n-3 PUFA can contribute to HF prevention through the risk reduction of ischemic heart disease . The n-3In this review, we summarized the current knowledge regarding the relationship between n-3 PUFA and HF from both clinical and basic studies. Although there remains conflicting results regarding the effect of n-3 PUFA on primary HF prevention, this probably originates from the variety of HF subtypes, reliability of FFQs, participants’ genetic background for n-3 PUFA metabolism, and contamination of fish with pollutants that potentially increase cardiovascular disease risk. Regarding secondary HF prevention, the GISSI-HF showed that n-3 PUFA intervention slightly but significantly suppressed the mortality and hospitalization of HF patients. Considering the results of other small-scale studies regarding ischemic and/or non-ischemic HFrEF using detailed cardiac function assessments by imaging modalities , as well as the measurement of several inflammatory markers, n-3 PUFA supplementation can reduce ventricular remodeling and myocardial fibrosis, and improve ventricular systolic and diastolic function in a dose-dependent fashion. The differences of the quality and quantity of n-3 PUFAs adopted in each clinical study, and the cause and severity of HF in the study population, as well as other baseline clinical background factors, including genetic variance in terms of n-3 PUFA bioavailability, may be responsible for the inconsistent results of clinical trials of secondary prevention for HF. Basic experimental data support our understanding of the mechanisms of n-3 PUFA cardiac protection against HF, including: (1) Anti-inflammatory properties originated from classical eicosanoid production, as well as newly discovered highly bioactive PUFA metabolites, to reduce cardiac remodeling caused by excessive interstitial fibrosis and whole body inflammatory reaction leading to cardiac cachexia; (2) alteration of metabolic conditions of cardiomyocyte probably accompanied by mitochondrial functional modification; (3) direct and/or indirect functional modification of cardiac ion channels to reduce susceptibility for fatal arrhythmia; (4) anti-hypertensive effects originated from improvement of vascular endothelial response; and (5) modulation of autonomic nervous system activity.To ensure the benefits of n-3 PUFA for HF prevention, further investigation should aim to (1) titrate the ideal n-3 PUFA dose by monitoring blood and/or tissue PUFA concentrations, (2) define the bioavailability of n-3 PUFA that affords better CVD outcomes, and (3) understand the genetic variants that are physiological modifiers of the status of n-3 PUFA.


Arabidopsis halleri, a model species for metal homeostasis and adaptation to extreme environments, we identifid genes, gene variants, and pathways that are associated with soil properties and may thus contribute to adaptation to high concentrations of trace metal elements. We analysed whole-genome Pool-seq data from two metallicolous  and two non-metallicolous populations  and associated allele frequencies of the identified single-nucleotide polymorphisms (SNPs) with soil variables measured on site. Additionally, we accounted for polygenic adaptation by searching for gene pathways showing enrichment of signatures of selection. Out of >2.5 million SNPs, we identified 57 SNPs in 19 genes that were significantly associated with soil variables and are members of three enriched pathways. At least three of these candidate genes and pathways are involved in transmembrane transport and/or associated with responses to various stresses such as oxidative stress. We conclude that both allocation and detoxification processes play a crucial role in A. halleri for coping with these unfavourable conditions.When plants adapt to local environments, strong signatures of selection are expected in the genome, particularly in high-stress environments such as trace metal element enriched  soils. Using  As selection leaves distinct signatures in the genome, various landscape and population genomic approaches have been developed to (i) identify the regions in the genome that are putatively involved in local adaptation, (ii) find the environmental factors driving this process, but also (iii) pinpoint constraints decreasing local adaptation and convolute its detection3. When populations exhibit high levels of gene flow, strong selection pressure is required to maintain local adaptation. Hence, the interplay between gene flow and selection strength determines the genetic structure of populations as well as the possibility of detecting the genomic signature of adaptation3 and understanding its genomic basis.Local adaptation is a key evolutionary process allowing plants to cope with environmental changes and/or to colonize new and selective habitats. It is driven by natural selection acting on genetically controlled fitness traitsThlaspi caerulescens4, Biscutella laevigata5). Such high concentrations of TMEs can occur naturally, for example in rare serpentine soils6, or can result from anthropogenic activities (e.g. mining). Their toxic effect on growth, biochemistry, and physiology strongly depends on the bioavailability of these elements in the soil and on plant tolerance mechanisms7. At the cell level, TME stress can result from e.g. enzymatic dysfunction via TME binding to a functional domain and/or increased oxidative stress (OS8). Such reactions are amplified through the accumulation of some TMEs  within OS-prone organelles such as chloroplasts, causing injury to thylakoids and photosystems, consequently impairing photosynthesis10.Metalliferous (M) habitats exert a strong selection pressure on plant communities from high and potentially toxic concentrations of some trace metal elements (TMEs) in soils  soils by several orders of magnitude. These hyperaccumulator species20. At the species level, this large quantitative variation is commonly associated with different edaphic origins of populations. In general, populations on NM soils accumulate lower quantities of metals than populations on M soils. Yet, under experimentally controlled conditions, when all plants are exposed to the same elevated metal concentrations, non-metallicolous populations often accumulate higher amounts of TME compared to metallicolous populations, thus also reaching the threshold concentration for hyperaccumulation22. Genome scans23 and quantitative genetic studies25 have shown the involvement of genomic regions and genes that underlie processes of internal metal transport, homeostasis and/or detoxification in leaves of hyperaccumulating plants. Still, these studies are based on genomic information and categorical assignment (e.g. M or NM soils) only and lack associations with quantitative environmental variables that characterize e.g. soil metal content. Furthermore, the existing analyses and interpretations are often gene-focused and thus offer only limited insight into the genetic basis of adaptation to environmental stress, despite increasing evidence suggesting that such adaptation is polygenic26.Phenotypic studies on adaptation to calamine or serpentine soils using species that thrive on both M and NM soils, so called pseudometallophytes, have revealed considerable intraspecific variation in metal tolerance and hyperaccumulationArabidopsis halleri (L.) O’Kane and Al Shebaz. This outcrossing and also vegetatively reproducing Brassicaceae hypertolerates and hyperaccumulates Zn and cadmium (Cd)27. While hyperaccumulation of Zn in A. halleri is constitutive (i.e. species-wide), it appears to be population-specific for Cd29. Yet, a broad quantitative variation among populations has been observed for both traits under field and experimental conditions30. We used whole-genome re-sequencing of population pools (Pool-seq31) and mapped the obtained reads to a de-novo assembled draft reference genome of Swiss A. halleri. Next, we associated the allele frequencies of the identified single-nucleotide polymorphisms (SNPs) with environmental variables via environmental association analyses (EAA2). To complement this nucleotide-based approach and to account for a putatively polygenic nature of adaptation, we further used a gene-set enrichment analysis approach32. We present the SNPs, genes and pathways associated with different soil types and discuss our results in light of potential biological functions regarding  TME adaptation.Here, we explore the genetic basis of plant adaptation to high soil concentrations of TMEs by comparing two metallicolous (from M habitats) and two non-metallicolous (from NM habitats) populations Fig.  of the p34, we created a de novo assembly of two Swiss specimens. The Illumina sequencing of these two libraries led to 76*106 paired-end and 113*106 mate-pair reads. After quality trimming and filtering, 73% of the reads were used for the assembly process. Of those, 68% were incorporated in the final assembly. The draft reference genome  established in this study was 164.6 Mb in size and therefore accounts for 66% of the estimated genome size of A. halleri (250–255 Mb)33. It consisted of 40 345 scaffolds, included ca. 25 kb Ns, N50 was 82 799 bp, and the largest scaffold 774 kb. Of the predicted genes, 26 249 were larger than 67 amino acids and 16 088 could be functionally annotated. In order to assess the completeness of the genome assembly, we ran BUSCO v2.0.135, which revealed 1312 complete and single-copy (91.1%), 16 complete and duplicated (1.1%), 52 fragmented (3.6%) and 60 missing (4.2%) orthologs.To map our reads to a reference genome that is phylogenetically closer to the sampled populations than the available assembly of a recently published Japanese accessionA. halleri populations, we selected two anthropogenic M locations at low altitude (M_PL22 and M_PL27), one NM sub-alpine location (NM_PL35), and the only known NM lowland location in the study area  on abandoned farmland36, whereas M_PL27 is located in the area of an open-cast Zn and Pb ore mine (closed in 1912), with mining dating back to the 14th century .In order to cover the range of demographic clusters and the highly diverse ecological settings that characterise rea NM_PL4, Fig. 13,11 = 125, P = 4.7*10−7), extractable (surrogate for bioavailable) potassium (K) , extractable magnesium (Mg) , extractable lead (Pb) , and extractable Zn  content clearly differed between both site types  on climatic and soil variables. Metalliferous and NM locations were not clearly separated by their environmental conditions Fig. . However9 reads in total, 253*106 reads per population on average. We could map 89%  of reads to the reference genome, with an average (median) coverage of 155.1x (125x), 162.9x (131x), 122.8x (95x) and 165.3x (128x) for populations M_PL22, M_PL27, NM_PL14, NM_PL35, respectively .To identify the neutral genetic population structure, we performed a PCA with 500 000 random SNPs revealing a genetic distinction of metallicolous and non-metallicolous populations Fig.  along PC37) to identify the environment-driven genomic signatures of selection and found 12 927 SNPs , the latter value indicating a strong correlation with the environmental variable. In contrast, a random set of 12 000 SNPs, in the majority representing putatively neutral sequence variants, yielded a rather uniform distribution of the Z statistic in Bayenv2 , 26 459 (1763), 23 460 (1685), 24 469 (1648) and 22 982  solute carrier (SLC)-mediated transmembrane transport for Site-type, (2) translesion synthesis by POLH for all five soil variables, and (3) alpha-linolenic acid metabolism for all five soil variables  that are the most likely involved in local adaptation to the soil characteristics in the study area Table . In partD in at least one metallicolous population, indicating departure from neutral expectations by recent positive selection (likely less than 10 000 generations ago39). The alternative explanation of a recent population expansion can rather be excluded as the overall population-specific Tajima’s D values were slightly positive while the observed gene-specific Tajima’s D values were in the lower quantile distribution  had a negative Tajima’s D in both metallicolous populations  and close to 0 or positive in non-metallicolous populations. JMT and MTPA2  had negative Tajima’s D in the population originating from the most contaminated site M_PL27 .Three candidates had a negative Tajima’s on Table . In partJMT and RFC4) with a signature of positive selection , a single SNP out of a total of 36 and 100 SNPs, respectively, generates an intron variant associated with all soil variables. MTPA2 is the only identified transporter and had the lowest number of SNPs (five) identified, but four of these were associated with Site-type. One of these four SNPs is NS. We predicted the secondary structure of the MTPA2 protein to be an alpha helical transmembrane protein. The NS SNP associated with Site-type in MTPA2 causes replacement of Isoleucine at position 138 by Leucine  in the first transmembrane helix.To evaluate how the identified variants potentially affect the respective proteins, we assessed the predicted effects of the associated SNPs on the protein structure of the 19 genes. Most SNPs were either synonymous or intron variants. Only one of 15 Site-type associated candidate SNPs and nine of the 42 soil-associated SNPs were non-synonymous (NS) substitutions, respectively, corresponding to 20.7% of SNPs on average  by industry40. Using four populations to detect the signature of local adaptation is at the lower end of statistical power. However, we think that this is counterbalanced by the categorical sampling design (replicated populations of M and NM sites) and the strong selection pressure that such TME-enriched soils exert on plants, complemented by the genome-wide perspective. Furthermore, the selected populations exhibit a wide range of TME concentrations in plant shoots and represent the genetic diversity and recently refined population genetic structure of A. halleri in southern Poland21.In the present study, we searched for adaptive genetic changes that have evolved to allow the Brassicaceae 41 and should have largely reduced the number of false positives. Given the possible multi- to polygenic nature of TME adaptation29 (but see8) potentially including a high number of candidate genes, this combination of approaches further allowed reducing the number of candidate genes for follow-up molecular studies. Most of the identified genes are members of transmembrane transport and stress signalling pathways, which indicates that both allocation and detoxification of TMEs are important physiological processes in adaptation to M soils.By combining approaches targeting signatures of selection at the level of single nucleotides, genes and functional pathways, we identified 19 genes in three functional pathways Table  that areMTPA2 (AT3G58810), JMT (AT1G19640), and RFC4 (AT1G21690). Besides being associated to soil factors in EAAs and being members of gene pathways that are enriched for soil adaptation, these three candidates also have negative values of Tajima’s D in at least one metallicolous population. We consider these three genes to be our strongest candidates for metal stress adaptation. Below, we discuss them in the context of the enriched pathways and their physiological function.Since we were interested in variants that increased or decreased in frequency as a result of positive selection, we here focus on three candidate genes: metal tolerance protein A2  is involved in the transmembrane transport and vacuolar metal sequestration of divalent cations42. It is located in the vacuolar membrane (tonoplast), involved with Zn2+ homeostasis and confers tolerance to excess Zn42. This gene is a potential target of the Fe-deficiency induced transcription factor 1 (FIT1), a key regulator of Fe-deficiency responses43. However, there was no difference in the Fe soil content among our sites. Also, a recent study in A. halleri showed that FIT1 expression is not altered under Zn treatment28. Thus, the FIT1 transcription factor seems not to be involved in adaptation to the M locations investigated herein.The D) for MTPA2 only in population M_PL27. Interestingly, M_PL27 was the most contaminated site in our study and had more than twice as much extractable Zn compared to the other M site M_PL22 . Accordingly, the weaker and much younger (ca. 100 years40) selection pressure at M_PL22 seemed to be insufficient to create a strong selective sweep and thus resulted in a non-negative Tajima’s D in this population.It is remarkable that we found a signal of selection  in both metallicolous populations and absent or at low frequency in both non-metallicolous populations  gene, encoding Zn2+ transporters involved in vacuolar sequestration, is considered a key component of hypertolerance to elevated Zn concentrations in A. halleri47. Still, several studies have demonstrated that different evolutionary fates, some of them not concurring with increased Zn tolerance, are likely to take place for the up to five paralogs of the MTP1 in A. halleri49. Our study suggests that the related MTPA2 has also played a role in the adaptive evolution of Zn tolerance in the species.To date, no tertiary structure of the MTPA2 protein has been resolved, thus preventing positioning of the I138L substitution that we observed. However, using the ons Fig. . Our fincholine transporter-like 1 . This gene regulates the expression pattern of different ion transporters through the modulation of vesicle trafficking50 and is closely associated with auxin signaling in the control of plant developmental processes51. Although CTL1 was significantly associated with Site-type in our analysis, considering its positive Tajima’s D value it seems unlikely that this gene was under direct positive selection , are located at the beginning of the enriched alpha-linolenic acid metabolism pathway that leads to the synthesis of JA and are responsible for generating the substrate alpha-linolenic acid  and salicylic acid (SA) are two plant hormones that have widespread signalling roles with regard to many biotic and abiotic stresses which can cause oxidative damageors Fig.  and are D in at least one metallicolous population, is jasmonic acid carboxyl methyltransferase . Similar to MTPA2, its Tajima’s D was negative only for the population from medieval mining of natural Zn and Pb ore outcrops (M_PL27), suggesting that this is an ancient allele. On the one hand, the single associated SNP leading to an intron variant might not alter the protein or the protein’s expression. On the other hand, it is also possible that it creates a different splice variant or plays a role in the candidate’s regulation via different binding of transcription factors55. However, JMT has a direct connection to TME-induced stress. Methyl-JA has been shown to improve reactive oxygen species (ROS) scavenging through an enhanced antioxidant defence system56, linking it to OS response. It has also been shown to alleviate Cd-induced photosynthetic damage57, assigning a more prominent role to the molecule in plants growing on Cd-rich soils. Different to JA, however, methyl-JA does not control for lesion spread during cell death events triggered by OS58. Such lesions are a phenotypic hallmark of local TME adaptation and were shown to occur less often in adapted genotypes (from M sites in the Olkusz region) of another pseudometallophyte of the Brassicaceae family, Biscutella laevigata, compared to its non-tolerant genotypes (from NM habitats)5. Since JMT1 and another five of the 19 candidates with associated SNPs are part of the alpha-linolenic acid metabolic pathway that ultimately leads to JA synthesis, we hypothesize that the levels of JA are balanced differently in adapted plants via methyl-JA and JA levels.The second candidate that has a negative Tajima’s Replication factor C4 , is the only candidate with a negative Tajima’s D in both metallicolous populations. Despite this clear signal of selection, we identified only a single associated SNP that results in an intron variant (Supplementary Table ATPase family associated with various cellular activities (AAA)59. The other candidates that belong to the enriched ‘translesion synthesis by POLH’ pathway are involved in protein quality surveillance  or also in the replication machinery . Via translesion synthesis, POLH is specific to resolve pyrimidine dimers, which result from UV radiation60. Our current knowledge of POLH does not indicate any relation to high TME soil concentrations. However, the associated genes ensure protein quality and DNA repair and might relate to stress in general.The third candidate, de novo assembled draft reference genome  for mapping of the obtained reads. It represents a major improvement of the first A. halleri ssp. halleri draft genome used in Rellstab et al.61. Except for total assembly size, its assembly statistics are better than the first version of the assembly of the Japanese A. halleri ssp. gemmifera genome62, but inferior to its second version33, which was enhanced by adding long mate pair libraries of an inbred line. As Polish populations of A. halleri are genetically closer to Swiss than to Japanese populations34, and because the BUSCO analysis revealed a largely complete assembly (91.1%), we used the former as a reference.As a backbone of our bioinformatic analyses, we established our own A. thaliana orthologs, we found candidate genes that presumably play a key role in adaptation to TME-rich soils. As some of these genes have not previously been linked with adaptation to heavy metals, we can only hypothesize about the mechanism and role that these genes might have in the identified pathways. In silico data mining can only provide a list of the most promising genes that have to be investigated in more detail. To clarify whether the identified genes are indeed important for adaptation to M soils, further functional proof is needed. This could for example be achieved using knock-out lines or reciprocal transplant experiments to show that metallicolous and non-metallicolous genotypes and their respective alleles actually have a fitness advantage in their home environment. Such a transplant experiment that also involves our study sites and populations is currently ongoing.While the list of potential candidate genes in our study was limited to known A. halleri populations from southern Poland to high soil TME concentrations.Using whole-genome Pool-seq and environmental data, we found genomic signatures of adaptation to metalliferous soils by means of environmental association analyses. These associations were substantiated by identifying several gene networks, linked to transmembrane transport and response to stress, showing involvement in the local adaptation of A. halleri. For this, we used Illumina paired-end and 3 kb mate-pair libraries and plants from two Swiss populations (Aha11 and Aha18) described in Fischer et al.63. For details of the assembly process, see Supplementary Methods.To map our obtained sequences (see below) to a reference, we assembled and annotated our own draft reference genome of European A. halleri in southern Poland  chemical data from local soil profiles and (ii) climatic factors (precipitation and temperature) from existing databases. For details on soil chemical analysis and climate data processing see Supplementary Methods. In total, we measured 16 soil variables  that are averages of three independent topsoil samples  on linear models of the form ‘soil variable ~ population’ with the R-package MASS66. We tested for normal distribution of the residuals using the Shapiro test and further tested for homoscedasticity using the Breusch-Pagan test as implemented in the R package olsrr67. If those model assumptions were violated, we Box-Cox transformed the corresponding soil variable prior to ANOVA. Finally, we repeated the above described PCA only with the soil variables that were significantly different between M and NM sites.To describe the environment, we performed a PCA with all 31 variables using the R package 31 that has been shown to deliver accurate allele frequencies in the studied species61.DNA extraction, library preparation, and next-generation sequencing followed the description in Supplementary Methods. We used a pooled sequencing approach (Pool-seq)cutadapt 1.968. We then mapped the reads to our reference genome of A. halleri using Bwa-mem 0.7.1269 and Samtools 1.370, and filtered the resulting BAM files for reads of mapping quality ≥10 with Bamtools 2.4.171. After removing duplicate reads, adding read groups, and indexing the BAM files using Samtools and Bamaddrg (https://github.com/ekg/bamaddrg), we called genetic variants using Freebayes 1.0.172. We used the pooled discrete mode for Pool-seq data, set ploidy to 60, the minimum alternate fraction to 0.083, minimum coverage to four, and maximum coverage to 10 000.We trimmed off adapter sequences of the Illumina Pool-seq data using vcflib 1.0.1 (https://github.com/vcflib/vcflib) and for quality  >30, minimum mapping quality >40, read depth <1200, individual sample depth >60, quality/read depth >0.25, no missing data and minimum (4) and maximum (236) alternate allele counts using bcftools73 (https://github.com/samtools/bcftools). Additionally, we removed all non-variant sites using a custom Python3 script. The complete list of options and parameters can be found in Supplementary Table We filtered the resulting SNP list for bi-allelic loci using K) comprised our sample, was required to appropriately parameterize the subsequent EAA. To infer the neutral population structure, we randomly chose 500 000 SNPs from our data set and used their allele frequencies to perform a PCA with FactoMineR.Information on neutral genetic population structure, in particular how many genetic clusters  per environmental variable for K = 2–4 to account for neutral population structure. We used the categorical variable ‘Site-type’ (coded as 1 or 0), which denotes M or NM sites, respectively, and the soil variables (described above) as environmental variables. Following Lea2 instructions, we combined the results of the 12 runs using the median z score and calculated the genomic inflation factor (λ) for all Ks. For all six variables, the lowest λ was at K = 3, which is in concordance with the neutral genetic population structure (see Results). Therefore, we used the LFMM results for K = 3 and corrected the P values with the corresponding λ value. We set the false discovery rate to 1%.Using LFMMBayenv238 in pooled mode. To construct the variance–covariance matrix, we randomly sampled 20% of all SNPs and saw the matrix converging after 32 000 iterations. Subsequently, we performed three runs with different random seeds for the set of SNPs associated with Site-type in LFMM. Additionally, we randomly selected 12 000 SNPs, excluding SNPs that were significantly associated in LFMM, and performed three runs as a random control. This number of random SNPs was chosen to match the SNPs found to be associated in LFMM analyses (see Results). We then assessed the overlap of the Z statistic histograms of the SNPs associated in LFMM and random sets to estimate the performance of the LFMM analysis.We cross-evaluated the LFMM results using A. thaliana orthologs. Using “intersect” of Bedtools and Unix’ “awk” commands, we added the A. thaliana orthologue gene name to each SNP. Each annotated gene that contained at least one environmentally associated SNP was considered a candidate gene for adaptation to variation in soil TME concentrations.In order to be able to functionally interpret our gene-based analyses, we focused on functionally annotated Polysel pipeline for gene set enrichment analysis32. We obtained GeneIDs, BSID and the BSID to GeneID lists for A. thaliana from the repository at https://www.ncbi.nlm.nih.gov/biosystems/. We based our Polysel analyses on the maximum absolute LFMM z scores per gene . We added the NCBI Biosystems GeneID using a custom Python3 script. We applied the default settings of the pipeline and a post-pruning Q value cutoff of 10%. As we were primarily interested in adaptation to edaphic conditions, we performed this analysis for (i) Site-type and (ii) the five soil variables that significantly differed between M and NM sites.To identify signatures of putatively polygenic adaptation, we used the python3 scripts to calculate nucleotide diversity π and Watterson’s θW75 to estimate Tajima’s D76 for every gene in every population. A negative Tajima’s D value indicates an excess of rare alleles and is usually interpreted as either a signature of a selective sweep or a recent population expansion, e.g. after a bottleneck.We used custom SnpEff77. We added A. halleri to the SnpEff database following the developer’s instructions and annotated our final, stringently filtered vcf file. We used this annotated vcf file with our candidate bed files to extract the ‘Effect’  variant) and ‘Impact’  from the SnpEff annotation for the LFMM-derived SNPs. To identify whether a NS SNP has an influence on protein structure, we ran the online tool ‘Scratch Protein Predictor’ and used the SSpro (3 Class) and SSpro8 (8 Class) secondary structure predictor therein78.To infer the possible effect and impact of a single substitution, we used Supplementary methods and figuresSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Table S7Supplementary Table S8Supplementary Table S9Supplementary Table S10Supplementary Table S11Supplementary Table S12


A multitude of model and non-model species studies have now taken full advantage of powerful high-throughput genotyping advances such as SNP arrays and genotyping-by-sequencing (GBS) technology to investigate the genetic basis of trait variation. However, due to incomplete genome coverage by these technologies, the identified SNPs are likely in linkage disequilibrium (LD) with the causal polymorphisms, rather than be causal themselves. In addition, researchers could benefit from annotations for the identified candidate SNPs and, simultaneously, for all neighboring genes in genetic linkage. In such case, LD extent estimation surrounding the candidate SNPs is required to determine the regions encompassing genes of interest. We describe here an automated pipeline, “LD-annot,” designed to delineate specific regions of interest for a given experiment and candidate polymorphisms on the basis of LD extent, and furthermore, provide annotations for all genes within such regions. LD-annot uses standard file formats, bioinformatics tools, and languages to provide identifiers, coordinates, and annotations for genes in genetic linkage with each candidate polymorphism. Although the focus lies upon SNP arrays and GBS data as they are being routinely deployed, this pipeline can be applied to a variety of datasets as long as genotypic data are available for a high number of polymorphisms and formatted into a vcf file. A checkpoint procedure in the pipeline allows to test several threshold values for linkage without having to rerun the entire pipeline, thus saving the user computational time and resources. We applied this new pipeline to four different sample sets: two breeding populations GBS datasets, one within-pedigree SNP set coming from whole genome sequencing (WGS), and a very large multi-varieties SNP dataset obtained from WGS, representing variable sample sizes, and numbers of polymorphisms. LD-annot performed within minutes, even when very high numbers of polymorphisms are investigated and thus will efficiently assist research efforts aimed at identifying biologically meaningful genetic polymorphisms underlying phenotypic variation. LD-annot tool is available under a GPL license from  The progress in molecular technologies enabled the study of genetic variants at the genome level, in both model and non-model species, such as Genome-Wide Association Studies (GWAS) identifying genetic variants likely involved in variation of interesting quantitative traits or in adaptation to environmental stress. Among those molecular techniques, SNP genotyping chips and genotyping-by-sequencing (GBS) approaches [also addressing the related reduction site-associated DNA sequencing (RADseq) in this paper] are often deployed to efficiently screen genomes at the population level and test for relationships between genetic polymorphisms and either quantitative characteristics or environmental conditions i.e. ;. GBS isResearch projects based on either of these variant detection approaches often investigate the genomic basis of trait variations related to agronomic performance in cultivated plants or animals ;, the dir2) between two alleles at two different loci    or samplcy, MAF) . Despitecal link , either FST-based outlier analyses for instance, most often need to be further studied with additional approaches such as gene expression profiling among individuals with contrasting trait expression or genetic engineering for instance, to corroborate these variants’ involvement in trait variation  hundreds to thousands of outbred individuals from natural populations, or (2) the progeny of a controlled cross between two individuals differing widely (i.e. segregating) for the trait of interest. In the first approach, individuals are sampled and later phenotyped in controlled and uniform conditions to perform a GWAS identifying candidate polymorphisms. In the second approach, a progeny is also assessed in controlled and uniform conditions, and the co-segregation of alleles and trait values allows to identify candidate SNPs. Both approaches have different assumptions regarding the levels of LD; average LD is usually moderate to low in association tests while very high in F1 progenies where many candidate SNPs are found in complete or nearly complete LD. Here, we tested our annotation pipeline with four different datasets to investigate a wide range of expected LD levels, originating from: (1) a domesticated animal, (2) a domesticated plant, and (3) a wild insect. These sets also varied in sampling size, numbers of tested SNPs, and candidate SNPs, thus further allowing to evaluate the pipeline’s performance.Sus scrofa domesticus which is characterized by high LD levels due to hundreds of years of selection to improve performance. This GWAS tested GBS data for association with meat quality  and was based on the genotyping of 196 individuals coming from two different breeding companies selecting sires and dams after each generation to improve meat quality in the Duroc pig breed  was converted into a vcf file using bash commands and we tested both the entire set of SNPs and a set of SNPs with a minor allele frequency higher than 5%, yielding a total of 593,614 SNPs.Even though the main focus of the present study is on GBS and SNP-array datasets, we also tested a dataset of 14,374,088 SNPs obtained from whole genome sequencing of the plant model ed lines   and therefore can damage vast tree plantations and natural forests. The co-segregation of SNP alleles and flying capabilities was followed over four generations (F2–F5) in this line resulting from the mating between a fully flying individual and a flightless individual in this species complex  levels. The user must define an r2 threshold for limiting the region surrounding a candidate SNP in which annotations will be extracted, i.e. only polymorphisms linked to one candidate polymorphism with a LD value superior to this threshold will be considered to delineate the region of interest  that must be placed in the same folder. The command using vcf format input file is:python3 LD-annot.py geno.vcf annot.gff3 candidate\ type thr outputwhile the command using SNP-array input file is:python3 LD-annot.py PathToSnpFiles annot.gff3\ candidate type thr output SNP_Mapr2, and “SNP_Map” is a txt file providing chromosome and position identifiers for each SNP included on the SNP-array.where “type” is the feature , “thr” is the threshold for The LD-annot pipeline is based on three different inputs.r2 are then calculated using PLINK1.9  including the chromosome number/name in the first column, the feature in the third column , the starting and ending positions in respectively the fourth and fifth columns, and the annotation (= attributes) in the last column.Finally, the third file contains the list of candidate SNPs with chromosome name in the first column, position in the second column, and SNP_ID in the third column (not required).Note that the chromosome identification should be consistent among the various files; the number may often be prefixed with a “chr” or not. As this is the most likely source of errors and incompatibility, the format checking step generates error messages pointing at corrupt files and probable causes.r2 correlation score calculated using PLINK for genotyped SNPs located on the same chromosome in linkage for r2 > 0.4. This low threshold is defined as the lowest one that a user may select. The threshold defined by the user is used later in the pipeline when estimating an average distance in linkage with candidate SNPs according to this threshold, and during delineation of genomic regions in linkage with each candidate SNP for annotations extraction.Linkage disequilibrium is estimated using the r2 threshold set by the user, annotations from a .gff/.gff3/.gtf-type file are then gathered to create an annotation file for each candidate variant. A “.gff/.gff3/.gtf” file usually includes annotations for different features  which represents a hierarchical classification of the same genomic regions and thus results in some repetition of the information. According to the approaches deployed to annotate the reference genome, the level of its completeness or the biological question asked in the research, one might favor one over the other features. Thus, LD-annot offers an option to select the feature of interest and avoid redundancy of the information at the various levels , which also make it flexible to any feature that may be indicated in the annotations file.Based on the LD calculations previously computed and the r2 calculations, the python script gathers chromosome, position, and annotation for the designated feature. Afterward, it makes a dictionary of “candidate” regions  around candidate SNPs by using the position of the foremost upward and downward SNPs in linkage with each one of those candidates according to r2 threshold chosen by the user. However, a candidate SNP might not be surrounded by other genotyped SNPs because of true absence of polymorphisms (possibly in a specific sampling set) or low quality genotyping. In such cases, the average distance calculated earlier in the pipeline is used to delineate the region of interest around such candidates and an “alone” flag is added to the candidate SNP name in the output file. It should be noted that this average is a broad estimate and those results should be interpreted with caution given the r2 variability along the genome, and the possibility of the non-Gaussian distribution of distances between SNPs in LD.After input format checking and per se. According to the number of annotations overlapping the candidate region, a candidate SNP can be found several times in the output file.Finally, all annotated regions with the selected feature in .gff/.gff3/.gtf file that overlap the “candidate” region are included into an output file that provides: chromosome, candidate SNP position, region start and end positions, annotation start and end positions, and the annotation r2 thresholds. The goal being to make this procedure amenable to researchers without coding skills nor access to high-performance infrastructures, we ran the pipeline using a common laptop computer with 4CPU cores and 8 Gbytes of RAM.We assessed the performance of our tool through the analysis of the four datasets previously described and covering a large distribution in numbers of genotyped and candidate SNPs, and a variety of p < 2e-16; Medicago  typically harbor more annotation lines in the “CDS” feature than for “gene” or “mRNA.” As a result, the analyses were significantly longer when searching for “CDS” feature annotations  when there is no surrounding genotyped SNPs. This distance is a function of inbreeding as illustrated by our datasets where the higher the original effective population size, the shorter is the distance in LD. Even within the pig species, the pedigree denoted Sus1 generally presented shorter distances than Sus2 pedigree which was developed from a smaller effective population of sires and dams.The LD-annot pipeline calculates an average distance (in bp) separating two SNPs in LD according to the specified r2 values than more common SNPs  that were in fact more informative with regards to the trait of interest than the closest one . On the other hand, 40 candidate SNPs were genetically linked with two genes, and 62 annotated genes were linked to more than one candidate SNP  among the 250 candidate SNPs spread over 103 contigs. Despite the high level of LD and that all scaffolds harboring a candidate SNP were also encompassing one gene at the very least (2.39 genes in average), some candidate SNPs were not found in genetically linked with any gene. The distribution of recombination rates was not continuous as expected given the low number of individuals and generations, and LD breakpoints were observed along scaffolds. Thus, a SNP might be relatively close to a gene but still not representing it. Altogether, these results illustrate the need to evaluate the experiment-specific LD surrounding candidate SNPs when employing genes to annotate and prioritize these for further investigations, and understand the mechanisms underlying their association with trait variation.In the case of progenies study (gypsy moth case), the LD level is very high which resulted in blocks of several candidate SNPs genetically linked together, thus defining large regions possibly encompassing several genes. However, only 100 SNPs were in linkage with 64 genes , the Canadian Centre for Swine Improvement, FastGenetics Inc. and Olymel Inc. Asian gypsy moth sequence data originated from the BioSafe project financially supported by Genome Canada, Genome BC and Genome Quebec and the Canadian Food Inspection Agency.The authors declare that this study received funding from FastGenetics Inc. and Olymel Inc. which also provided meat quality data for the pigs’ use-cases. 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.


In forest trees, genetic markers have been used to understand the genetic architecture of natural populations, identify quantitative trait loci, infer gene function, and enhance tree breeding. Recently, new, efficient technologies for genotyping thousands to millions of single nucleotide polymorphisms (SNPs) have finally made large-scale use of genetic markers widely available. These methods will be exceedingly valuable for improving tree breeding and understanding the ecological genetics of Douglas-fir, one of the most economically and ecologically important trees in the world.Pseudotsuga menziesii var. menziesii) from Oregon and Washington, and 13 trees of interior Douglas-fir (P. menziesii var. glauca). As many as ~ 28 K SNPs were reliably genotyped and polymorphic, depending on the selected SNP call rate. To increase the number of SNPs and improve genome coverage, we developed protocols to ‘rescue’ SNPs that did not pass the default Affymetrix quality control criteria . Lowering the SNP call rate threshold from 97 to 60% increased the number of successful SNPs from 20,669 to 28,094. We used a subset of 395 unrelated trees to calculate SNP population genetic statistics for coastal Douglas-fir. Over a range of call rate thresholds (97 to 60%), the median call rate for SNPs in Hardy-Weinberg equilibrium ranged from 99.2 to 99.7%, and the median minor allele frequency ranged from 0.198 to 0.233. The successful SNPs also worked well on interior Douglas-fir.We designed SNP assays for 55,766 potential SNPs that were discovered from previous transcriptome sequencing projects. We tested the array on ~ 2300 related and unrelated coastal Douglas-fir trees (Based on the original transcriptome assemblies and comparisons to version 1.0 of the Douglas-fir reference genome, we conclude that these SNPs can be used to genotype about 10 K to 15 K loci. The Axiom genotyping array will serve as an excellent foundation for studying the population genomics of Douglas-fir and for implementing genomic selection. We are currently using the array to construct a linkage map and test genomic selection in a three-generation breeding program for coastal Douglas-fir. For most applications, single nucleotide polymorphisms (SNPs) have become the marker of choice for genetic studies in a wide array of organisms. In forest trees, they are being used to understand the genetic architecture of natural populations, identify quantitative trait loci in pedigrees or natural populations, infer gene function, and assist tree breeding via parental analysis or genomic selection –5. SNPs High-throughput SNP genotyping is typically accomplished using fixed-arrays , PCR-based methods, or genotyping-by-sequencing (GBS) , 7. AlthConifer genomes pose challenges for some aspects of SNP genotyping. First, conifers are genetically diverse; often with at least one SNP every 50 bp , 14. AltThe main goal of this project was to develop a large-scale SNP genotyping array for Douglas-fir; primarily for use in breeding programs. Key objectives were to develop a platform that would allow forest geneticists and tree breeders to (1) process samples commercially , (2) genotype thousands to tens of thousands of SNPs, and (3) use readily available software for SNP data analysis.N = 8067) using an Illumina Infinium genotyping array, resulting in 5847 successful SNPs  [Two widely used genotyping platforms that meet these objectives are the Illumina Infinium® and Affymetrix/Thermo Fisher Axiom® genotyping arrays. The Infinium array can be used to genotype up to 700 K custom SNPs (Infinium iSelect HTS) and comes with software for data analysis (Genome Studio® Genotyping Module). Its main disadvantages are cost and non-overlap in some SNPs across different manufacturing runs. We previously used transcriptome sequencing to identify 278,979 probable SNPs in ~ 20,000 Douglas-fir genes . We thenassayed) . The Infassayed) , 18–21.Here, we report the development of an Axiom array capable of genotyping about 28 K SNPs in Douglas-fir. We chose to develop this new, larger Axiom array to characterize geographic variation and practice genomic selection in Douglas-fir. Within the past few years, Axiom arrays have been developed for many agricultural and horticultural crops, including corn, strawberry, rose, rice, apple, soybean, wheat, peanut, and chickpea –30. AlthPseudotsuga menziesii var. menziesii), (3) characterize the SNPs in relation to the Douglas-fir reference genome sequence, and (4) conduct a preliminary test of the array on samples of interior Douglas-fir (P. menziesii var. glauca).The specific objectives of this study were to (1) design and test a large-scale Axiom genotyping array in Douglas-fir, (2) characterize the performance of the array and the population genetics of individual SNPs in two populations of coastal Douglas-fir  thresholds used for SNP genotyping affect the number of samples and SNPs for which data are obtained. Thus, we evaluated array performance using three QC approaches  and five final SNP call rates. The Default protocol used the default Affymetrix QC thresholds , 1694 samples (88.2%) were successfully genotyped using the Default QC protocol. Because the four Rescue protocols used the same sample-level and plate-level QC thresholds for Phase 1, the number of genotyped samples was the same. When we used the Modified protocol, the number of successfully genotyped samples increased to 1898 (98.9%). For the second coastal Douglas-fir population (C2), 348 of 384 samples (90.6%) were successfully genotyped using the Default and Rescue protocols, and 376 (97.9%) were successfully genotyped using the Modified protocol.Based on a combined analysis of the first coastal Douglas-fir population (C1) and the interior Douglas-fir population (I1) . Using the Default QC thresholds , we were able to genotype 16,177 SNPs in the C1/I1 set of samples and 18,932 SNPs in the C2 population. This is an average of 17,555 SNPs across both populations, and 31.5% of the 55,766 putative SNPs tested on the array . The inferred allele accuracy for this protocol was 99.2%, with 9.8% missing values. The highest genotyping accuracy was 99.3% for the Default protocol. The inferred allele accuracy for this protocol was 99.6%, with 2.5% missing values.To understand which factors affected probeset success, we first studied whether probeset success was associated with our array design variables Table . ProbeseBased on logistic regression, the best predictor of probeset success was the number of hits to scaffolds (a transcript ranking variable), followed by pConvert and the target SNP probability Table , Fig. 3 Affymetrix calculated a Repetitive variable  based on v0.5 of the Douglas-fir reference genome. We generally excluded repetitive probesets, except 969 probesets for SNPs that had been successfully genotyped using the Infinium array. Of these, 651 67.2%) were successfully genotyped. After filtering repetitive probesets, array design focused on the pConvert variable. The average pConvert score was slightly higher for the successful probesets (0.615) than for the unsuccessful probesets (0.595) . For these SNP-level analyses, the average genotyping success was 50.4%. SNP success was 52.5% for the OSU SNPs  and 14.6% for the UH SNPs (tested SNPs = 3188). For the top category of each BLAST variable, SNP success ranged from 50.9 to 61.0%  are reported in Fig. obs was 0.352 using a call rate threshold of 60%, but these values decreased to 0.198 and 0.314 using the Default QC protocol with a call rate of 97%  that passed the default Affymetrix QC thresholds. Across all Douglas-fir samples, as many as ~ 28 K SNPs were successfully genotyped and polymorphic, depending on the SNP call rate threshold. We worked with Affymetrix bioinformaticists to develop protocols to ‘rescue’ SNPs that did not pass the default Affymetrix QC criteria . Lowering the call rate threshold from 97 to 60% using the custom R scripts  confidence score threshold. Averaged across the two populations, 31 to 45% of SNPs were successfully genotyped, depending on the final call rate threshold. The three largest remaining classes  were Other, MonoHighResolution, and NoMinorHom.SNP calls were made for the Other class consisted of SNPs that could not be grouped into a few discrete clusters and did not fall into any of the other default classes. Using the default QC thresholds, 30% of SNPs were classified as Other. This is at least twice that reported in some species of plants and animals [Other category represented at least 30% of SNPs in lodgepole pine (Pinus contorta), loblolly pine (Pinus taeda), and interior spruce  . The large Other class probably results from the large, repetitive genomes of conifers, and the resulting challenges imposed on genome assembly and SNP discovery (see Introduction). Although we reduced this class from 30 to 23% of SNPs using the rescue protocols, this was still the largest class of SNPs that did not convert.The  animals –37, but Other class because of poor thermodynamics of the probe, non-target hybridization, and SNPs in the flanking region . Non-target hybridization can occur when there are other sequences in the genome similar to the probe. We attempted to avoid this by considering two Affymetrix variables during array design. For the most part, we excluded SNPs when the Affymetrix Repetitive variable was ‘T’ (true). That is, when the number of 16-mer hits between the SNP sequence and the reference genome (v0.5) exceeded 300 hits. We also used the Affymetrix pConvert score to help select SNPs. The pConvert value reflects the thermodynamics of the probe and the number of 16-mer matches in the genome. However, we calculated additional BLAST variables that also helped predict SNP success . The best predictor of SNP success was the difference in percent identity between the best hit and second-best hit. This difference, which was 16% for successful SNPs versus 11% for failed SNPs, could be used to increase the number of successful SNPs in new Douglas-fir arrays or other species. The numbers of hits to scaffolds, singletons, gene models, and transcripts of reference genome v1.0 were also significantly related to SNP success, but to a lesser degree. Finally, because the probability of flanking SNPs was significantly related to SNP success, this variable might also be used to reduce the number of SNPs in the Other class.SNPs may occur in the MonoHighResolution class (16% of SNPs) were monomorphic. These putative SNPs may have been transcriptome sequencing errors, or real SNPs found in the SNP discovery population [SNPs in the pulation  that werpulation , we inclNoMinorHom class (13% of SNPs) may consist of SNPs with particularly low MAFs, or SNPs with segregation distortion, such as SNPs linked to recessive genes with deleterious effects. In either case, we did not count these as successful SNPs because they would probably be excluded from most applications. Overall, it should be possible to reduce the proportion of MonoHighResolution and NoMinorHom SNPs by enhancing the detection of high-MAF SNPs during SNP discovery. For the final selected variables  to 2% using a final call rate of 60%.The OTV), are SNPs with unexpected, low-intensity clusters that probably resulted from mismatches between the array probe and genomic target sequence [OTV clusters are often miscalled as heterozygotes [OTV SNPs was only 1%.The final class of SNPs, the off-target variants . We also tested HWE using other P-values and multiple comparison adjustments, and these results are available in Data file S1 , 22,896 of which were judged to be in HWE  have high call rates, are well distributed across the genome, have high MAFs, and target expressed genes. The biggest hurdle for implementing genomic selection is the per-sample cost, which may exceed the cost of progeny testing by several fold. Thus, reducing the costs of SNP genotyping will be important. Smaller numbers of SNPs will probably be optimal for validating genotypes, assigning parentage, estimating coancestry, tracking inbreeding, analyzing mating systems, and estimating pollen contamination in seed orchards. We currently use simple-sequence repeat markers (SSRs) for these latter purposes , but swiOur results indicate that SNP pre-screening would be valuable for large genotyping projects . Although we demonstrated that improvements in SNP filtering can increase SNP success, the proportion of successful SNPs would increase dramatically by using pre-screening to exclude the unsuccessful SNPs on the final array. This step would be particularly desirable when the number of samples is much larger than the minimum order size, which is currently 480 samples for the Axiom array . For exaSNP genotyping was conducted on two populations of coastal Douglas-fir (C1 and C2) and one population of interior Douglas-fir (I1). C1 consisted of 1825 trees (1907 samples) from two breeding populations managed by the Northwest Tree Improvement Cooperative: Coos Bay Low and South Central Coast. Trees were selected using three cycles of breeding and testing. The first-cycle selections consisted of 61 trees from native stands in coastal southern Oregon, plus 7 of their progeny growing in first-cycle field tests . Next, we selected 609 trees from the second-cycle field tests; i.e., controlled-cross progeny of first-cycle selections. Finally, we genotyped 1033 progeny of the second-cycle selections. These trees came from 24 full-sib families that were growing in the greenhouse. Because some inter-generational crosses were used, the resulting pedigree was complex. In addition to these pedigreed trees, we genotyped 59 trees from a single woodsrun seedlot and 56 trees of uncertain parentage. Because the duplicated tree samples were handled separately , these samples were included in analyses designed to test array performance. However, we used 112 unrelated trees (one sample per tree) to calculate population genetic statistics. Some of the unrelated C1 trees were derived from crosses between trees collected from different geographic locations. C2 consisted of 384 coastal Douglas-fir trees from western Oregon and Washington (one sample per tree). We used these trees to help judge the performance of the Axiom array, and then selected 283 unrelated trees for calculating population genetic statistics. The I1 samples consisted of foliage collected from 13 trees in native stands. All of the genotyped trees described above came from the same broad areas that were sampled for SNP discovery , 32. For− 1 were used for SNP genotyping.Dry needles were pre-treated with liquid nitrogen, and then pulverized in a shaker with tungsten beads for two cycles of 60s at 20 Hz. DNA was isolated using the DNeasy-96 Plant Kit (Qiagen), with the addition of a proteinase-K treatment. DNA concentrations were measured using the Pico Green fluorescent dye (Invitrogen) and a Gemini XPS microplate reader (Molecular Devices). Samples with concentrations ≥ 20 ng·μlPS and PF are the p-values associated with a SNP being a true target SNP or a true variant in the SNP flanking region . These were calculated using the methods described by Wei et al. [PS) of 0.001  of 0.001. To this dataset, we added 13,410 buildable SNPs from the UH dataset that were chosen to target transcripts not already represented in the OSU dataset. To identify novel transcripts, we compared 141,626 UH assembled isotigs (excluding singletons [− 10. Isotigs are transcript variants assembled using the Newbler de novo assembler [SNPs tested on the Axiom array were derived from the Oregon State University (OSU) dataset described by Howe et al.  and the i et al. , using ai et al. . From thplatform . Overallngletons ) to the ssembler , 32. We Each candidate SNP sent to Affymetrix consisted of the target SNP plus two 35-nt flanking sequences . For each of the two flanking sequences per SNP (forward and reverse), Affymetrix calculated a Repetitive indicator variable, pConvert score, and a Recommendation. For the Repetitive variable , Affymetrix counts the number of 16-nt hits between the SNP sequence and the supplied reference genome. Any flanking sequence with more than 300 hits was classified as repetitive (T). Affymetrix used the v0.5 Douglas-fir reference sequence for this analysis , or (3) Affymetrix classifications of ‘not recommended’ or ‘not possible.’ However, we did not remove SNPs if they had already been successfully assayed using the Infinium platform [To design the array, we first removed candidate SNPs that had no acceptable probesets. Unacceptable probesets were those with (1) no corresponding 71-nt matches in the reference genome, (2) SNPs or indels in their target sequences , we selected two probesets per SNP to increase the number of transcripts with successful SNP assays. Thus, on the final array, most SNPs were interrogated by the single best forward or reverse probeset using the criteria described below. Ultimately, we included 58,350 probesets representing 55,766 SNPs on the array. The ranking criteria used in this process are described next.Picea glauca) unigenes [We ranked the OSU transcripts using three variables. The first variable was the number of BLAST hits between the SNP sequence and the reference genome averaged across all SNPs in the transcript. One BLAST hit was counted for each 65-nt match between the 71-nt SNP sequence and the reference genome. The rank order for this variable (best to worst) was 1, > 1, and 0 hits. The second variable was the transcript confidence score described by Howe et al.   to identify low-, medium- and high-confidence SNPs. For the UH dataset, we identified low-, medium- and high-confidence SNPs based on the number of programs used to call a SNP  or the number of programs that were used to call the target SNP .After ranking the transcripts, we ranked probesets-within-transcripts based on five criteria. First, SNPs successfully genotyped with the Infinium platform were ranked at the top (other SNPs had not been tested using Infinium). The second variable reflected the likelihood of having SNPs or indels (variants) in the flanking sequences. This was accomplished by accounting for variants in the flanking sequences at multiple probability levels. For the OSU target SNPs, we used flanking probabilities of 0.1, 0.01, and 0.001 (programs ). Next, OTV, Other, CallRateBelowThreshold, NoMinorHom, MonoHighResolution, and PolyHighResolution [PolyHighResolution class (polymorphic high-resolution) were considered successful Axiom SNPs. We also used four SNP rescue protocols that employed two phases of SNP filtering. We used the default thresholds in Phase 1, and then re-classified the SNPs in the Other and CallRateBelowThreshold categories into a Rescued category if they passed a (lowered) SNP call rate cut-off of 90, 80, 70, or 60%. In Phase 2, we used the Ps_CallAdjust SNPolisher function to lower the SNP Confidence Score threshold from the default of 0.15 to 0.10 [PolyHighResolution and Rescued classes were considered successful SNPs.DNA samples were processed by GeneSeek  using the standard protocol for the Affymetrix Axiom array. Samples from populations C1 and I1 were processed jointly in two batches and then analyzed together. Samples from population C2 were processed and analyzed separately from C1 and I1. Raw SNP data were analyzed using Axiom Analysis Suite v.1.1.0.616 and the Best Practices Workflow . We conducted three types of analyses  using two phases of quality control (QC) filtering. The Default protocol used the Affymetrix diploid (default) QC thresholds . SNPs wesolution . SNPs in to 0.10 . The mor to 0.10 . We then to 0.10 . For theobs, HETexp), polymorphic information content (PIC), and probabilities of deviation from HWE using a chi-square goodness-of-fit test. We excluded SNPs that were not in HWE (P <  0.01), calculated statistics separately for each population (C1 and C2), and then averaged the values across the two populations. These analyses were conducted using the Default and Rescue protocols .We selected 395 unrelated trees from the C1 and C2 populations, and then used the SAS ALLELE procedure  to calculate SNP call rate (CR), MAF, observed and expected heterozygosities  array design variables and (2) variables calculated using v1.0 of the Douglas-fir reference genome. As described above, some of the array design variables were calculated using v0.5 of the reference genome. After a new reference genome became available (v1.0), we calculated new BLAST variables using the same SNP sequences (71-mers) as queries. The draft reference genome (v0.5) had 18.5 M scaffolds, whereas the newer genome assembly (v1.0) had 2.8 M scaffolds .N = 58,350) and ten array design variables, including four transcript variables, five probeset-within-transcript variables, and one final rank variable. Although they were not used to select SNPs, we also analyzed the ‘recommended’ and ‘neutral’ classes of the Affymetrix Recommendation variable, which are bins of pConvert. Two variables that were approximately normally distributed (No. of SNPs per transcript and pConvert) were analyzed using a T-test and a Satterthwaite adjustment for heterogeneous variances. Two rank variables  were analyzed using a Wilcoxon rank test with Monte-Carlo estimation of p-values in SAS PROC NPAR1WAY. The remaining variables (see Results) were analyzed as categorical variables using a likelihood ratio chi-square test for independence. Probesets were considered a success if they resulted in polymorphic genotypes in either the C1/I1 or C2 population using the Rescue QC protocol and a CR of 60%.First, we tested for associations between probeset success  and seven variables calculated using v1.0 of the reference genome. We used BLASTN to compare SNP sequences (71-mers) to v1.0 scaffolds, singletons, gene models and transcripts using a percent identity (PID) cutoff of 80% for missing values . The first two variables consisted of the PID of the best scaffold hit and the PID of the second-best scaffold hit. Although these variables were continuous, many SNPs fell into two classes . Therefore, to test for differences in SNP success, we binned the SNPs into two groups (PID >  80% and PID ≤ 80%). We also evaluated a third variable that captured differences in PID between the top two scaffold hits. For these analyses, we compared SNP success among three SNP categories: (1) PID >  80% for the top two scaffold hits, (2) PID >  80% for the top scaffold hit and ≤ 80% for the second-best hit, and (3) PID ≤ 80% for the top two scaffold hits. A likelihood ratio chi-square test was used to compare the successful and failed SNPs for each variable. For the remaining variables, we counted the number of hits at a PID > 90%, binned these numbers into three categories , and then used a likelihood ratio chi-square test to compare the successful and failed SNPs. This analysis was conducted separately for scaffolds, singletons, gene models, and transcripts, resulting in four variables. SNPs were considered a success if they had a least one probeset that was successful using the criteria described above.Second, we tested for associations between SNP success . In the second model, we dropped the number of SNPs per transcript and replaced the two v0.5 reference genome variables with six v1.0 variables. We conducted these analyses using the SAS LOGISTIC procedure, stepwise model selection option, and cross-validation.Additional file 1: Table S1. Quality control (QC) thresholds used to identify successfully genotyped samples, plates, and SNPs (PDF). Table S2. Population genetic statistics for successful SNPs genotyped in two populations of Douglas-fir trees (PDF). Text file S1. Example R script used for the Rescue SNP genotyping protocols.Additional file 2: Data S1. Characteristics of Douglas-fir SNPs successfully genotyped using an Axiom genotyping array.


Pinus massoniana) accessions collected from Guangdong in China. A large number of 471,660 SNPs in the total collection were identified from 599,164 polymorphic SLAF tags. Population structure analysis showed that 149 masson pines could not be obviously divided into subpopulations. Two core sets, containing 29 masson pine accessions for increasing resin and wood yield respectively, were obtained from the total collection. Phenotypic analyses of five traits showed abundant variations, 25 suggestive and 9 significant SNPs were associated with the resin-yielding capacity (RYC’) and volume of wood (VW) using EMMAX and FaST-LMM; 22 suggestive and 11 significant SNPs were associated with RYC’ and VW using mrMLM and FASTmrMLM. Moreover, a large number of associated SNPs were detected in trait HT, DBH, RW and RYC using mrMLM, FASTmrMLM, FASTmrEMMA and ISIS EM-BLASSO. The core germplasm sets would be a valuable resource for masson pine improvement and breeding. In addition, the associated SNP markers would be meaningful for masson pine resource selection.Germplasm resource collection and utilization are important in forestry species breeding. High-through sequencing technologies have been playing increasing roles in forestry breeding. In this study, specific-locus amplified fragment sequencing (SLAF-seq) was employed to analyze 149 masson pine ( Pinus massoniana) is a native species that grows throughout central and southern China. Besides its wide uses in the wood, pulp and paper industries, this species has long been employed as the main source of resin, a hydrocarbon secretion of many plants that is widely used to produce resin and turpentine for the chemical industry1. Masson pine is the most important resin tapping tree species in China and should thus be preserved2. However, due to its high commercial value, this species has been subjected to over-exploitation during past decades, leading to a gradual decrease in genetic resources3. Protection and sustainable use of the preserved masson pine resource are urgent problems for researchers.Masson pine (9. Single nucleotide polymorphisms (SNPs) have been widely reported in recent years because they are the most abundant and stable type of genetic marker in most genomes10. Deep sequencing technology has been rapidly developed to exploit these advantages and has enabled the high-throughput identification of SNPs13, albeit with the disadvantage of becoming cost-prohibitive when the population is large. The genomes of conifer trees such as Pinus taeda are complex and fairly long14. To reduce time and labor costs, reduced-representation genome sequencing has been widely used in plant genome sequencing14. Considering that whole-genome deep sequencing is still expensive and usually unnecessary11, several simplified and cost-effective methods for SNP discovery and high-throughput genotyping have been developed, such as reduced representation library (RRL) sequencing15, restriction-site associated DNA sequencing (RAD)17, and two-enzyme genotyping by sequencing (GBS)18. In recent years, a new strategy for de novo SNP discovery and genotyping of large populations, referred to as specific-locus amplified fragment sequencing (SLAF-seq), has been employed19. SLAF-seq is a high throughput, highly fast, highly efficient and cost-effective method for developing large-scale SNP and InDel markers19. By using enzyme digestion techniques, an SLAF-seq library containing specific size fragments of DNA can be obtained. Then, we could identify a polymorphic specific SNP locus from all of the accessions through software alignment. This high-resolution method has been tested on many organisms, including crape myrtle20, cucumber21, rapeseed22, sesame23 and soybean24. Moreover, this method has been widely used in GWAS for important traits26, as well as in the development of core germplasm27.Genetic structure and diversity analyses could help to scientifically simplify the resources. Various types of molecular markers, including RAPD, SRAP, SSR, and ISSR, have been used to estimate genetic relationships and genetic distances in masson pineP. massoniana accessions in Guangdong province, we conduct a genome-wide SNP discovery based on the SLAF-seq method. The identified SNPs were used to examine the masson pine population structure. Then, we selected a core set of masson pine germplasm resources for improving resin-yielding capacity (RYC) and volume of wood (VW). Finally, a genome-wide association study (GWAS) strategy was used to identify the SNP locus associated with growth, wood and resin yield traits. The results would be of great value for masson pine selection and breeding.To better understand the genetic relationship and the genetic architecture of wood and resin yield traits of the By SLAF-seq, 1759.00 M reads were obtained from this experiment. The average Q3 value was 92.78%, and the average GC content was 37.95%  was found among the above mentioned SNPs ; only 3.0% displayed very high LD (0.51–1.00). Mean r2 values of conifers differ among different species30. The generality of LD distribution across the entire masson pine genome remains to be further analyzed, as only a relatively small part of the entire genome was studied here.Linkage disequilibrium (LD) is the non-random association of alleles at different loci and may indicate the genetic forces that structure the genomeBased on the analysis of 471,660 SNPs, a neighbor-joining tree was constructed using MEGA software Fig. . The 149Genetic distance was estimated to evaluate the genetic diversity in all the accessions. Masson pine accessions GW29 and GW28 had the highest genetic distance (0.292). GW37 and GW112 had the lowest genetic distance (0.008). Genetic distance and population structure were used to select core germplasms. In this study, core sets containing 29 accessions were screened out and combined with traits VW and RYC, respectively , diameter at breast height (DBH), resin weight (RW), volume of wood (VW), and resin-yielding capacity (RYC) were used for association analysis. In addition, 69 clonal lines were employed for RYC’ association analysis. The average value and standard deviation of six traits are listed in Supplementary Fig. −8  and 2.12 × 10−7 , respectively. Among the 69 accessions, the P-values were 2.10 × 10−8  and 2.10 × 10−7 . The GWAS analysis was carried out by the methods of MLM, FaST-LMM, EMMAX, mrMLM, FASTmrMLM, FASTmrEMMA, ISIS EM-BLASSO, pKWmEB and pLARmEB. The results showed that some SNPs were detected and associated with VW and RYC’ by multiple methods. A total of 15 RYC’ associated SNPs and 2 VW associated SNPs were developed using EMMAX method  associated with trait RYC’ and 3 significant SNPs  associated with trait VW  for trait VW; the core set of resin showed a high level of genetic gain expectation (40.75%) for trait RYC. The core germplasm sets, for the purpose of improving resin and wood yield, were scientifically simplified resources that would be useful for masson pine breeding.Molecular markers have been employed to develop core germplasm sets in multiple tree species, e.g. western white pine40. The decay of LD over physical distances in a population determines the density of the marker coverage needed to perform a GWAS41. The faster LD decays, the more markers are likely needed in GWAS analysis for complex traits. LD estimates in this study based on the specific length sequences indicated a very fast decay. Excavation of favorable markers is necessary for improving masson pine breeding efficiency using molecular assisted selection (MAS). GWAS offers increased opportunities for detecting susceptible loci for complex traits. Masson pine is an economic tree species for resin and wood. In the breeding project of masson pine, both resin and wood yields are important breeding targets. Therefore, discovering SNPs related to resin and wood producing capacity is important for improving masson pine breeding efficiency.The GWAS analyses of complex traits in forestry conifer trees, especially conifer trees with large genomes, require an enormous density of SNP markers42. Therefore, only those traits with high heritability can be stably detected. The traits in masson pines, especially RYC and VW, have been demonstrated to have high heritability43. Furthermore, more extensive linkage disequilibrium has been found in conifer trees45. In our study, the number of SNPs identified from 149 masson pine germplasm resources is large enough, and GWAS can be feasible in masson pine even though the genome may be generally large13. RYC and VW are important traits for representing masson pine producing capacity and economic value and have an important value in breeding43. In this study, five traits  were selected for GWAS analyses. All of the traits showed large phenotypic variation, supporting the suitability of GWAS for these traits. Thus, we presented GWAS analyses of these important traits in masson pine. In our study, the suggestive SNPs associated with traits RYC’ and VW were different from the SNPs identified using EMMAX and FaST-LMM. Only one common SNP (Marker279561) was developed in trait RYC’ and VW using these methods, which meant that different types of GWAS methods can provide complementary results with each other and provide us with more sufficient results. Moreover, no SNPs were developed in trait HT, DBH, RW and RYC using EMMAX and FaST-LMM, while a large number of SNPs were detected using multi-locus methods in mrMLM. The multi-locus GWAS methods in mrMLM.GUI provide more possibilities in detecting associated SNPs. Hence, a group of various types of GWAS methods should be applied in future studies.In the present study, we focused on the GWAS of quantitative traits, including growth traits and the resin and wood yield in masson pine. The phenotypes of complex traits often result from the combined actions of multiple genes and environmental factors, all of which can easily lead to lost heritability47. GWAS and GS can each compensate for the other’s deficiencies, and both approaches are likely to be useful in conifer breeding. The developed SNP markers in GWAS can be directly used for both MAS and GS, and both approaches are likely to be useful in conifer breeding. Genotyping based on reduced-representation genome sequencing (RRGS) has become popular in a wide range of plant species48. The various types of RRGS methods, among which SLAF-seq is also widely used, have overcome the cost problem and have simplified the problem of identifying a large number of DNA markers in conifer species with large genomes as well as the large number of samples in the scientific research of forestry breeding.High correlations between these traits were identified, and strong positive correlations existed among the traits DBH, RW, VW, and RYC  overhang was added to the digested fragments using dATP at 37 °C, and then duplex tag-labeled sequencing adapters were ligated to the A-tailed DNA with T4 DNA ligase. The PCR products were purified and pooled. The pooled samples were separated via electrophoresis on a 2% agarose gel. Fragments with indices and adaptors from 264 to 414 bp were excised and purified. Finally, the purified gel product was sequenced using the Illumina HiSeq2500 system  at the Biomarker Technologies Corporation in Beijing.Total masson pine genomic DNA was extracted using the DP320 DNA secure Plant Kit (TIANGEN China); the quality and quantity of DNA were then inspected using 0.8% gel electrophoresis. The quantified DNA was diluted to 20 μg·μL51. SNPs were removed if the integrity <0.8 and minor allele frequency (MAF) ≤0.05. After these steps, the remaining SNPs were developed to calculate genetic structure, and the relationships were retained for genome-wide association study (GWAS).After sequencing, reads with double ends were compared with similar sequences that could be labeled as candidate SLAFs to proceed with the next step. The SLAF tags were defined as the group with the most samples. The samples with the most tags were used as references, and GATK and SAMTOOLS were employed for SNP calling52. Negative kinship values between two accessions indicate a poorer relationship than expected, and this was corrected to 053. ADMIXTURE was employed to investigate population structure based on the maximum-likelihood method54. The predefined K, which indicates the number of groups in a population, varied from 1 to 10 in ADMIXTURE models. Cross-validation, delta K and marginal likelihood against K were used to select the most probable value of K55. A phylogenetic tree based on the neighbor-joining method was constructed in MEGA 6.0 using the developed SNPs56. A PCA with Cluster software was used to cluster the masson pine population57. Genetic distance and population structure were used to develop an initial core germplasm set by CoreHunter software58. The results combined with phenotypic data VW and RYC were used to confirm the final core set.SNPs were used to calculate pairwise kinship relationships among the 149 accessions by using SPAGeDi software1. Trees were sampled once per day by removal of a sliver of wood from the stem without the application of a stimulant. VW and RYC were calculated by the formulas given below.Phenotype data, including height (HT), diameter at breast height (DBH), resin weight (RW), volume of wood (VW) and resin-yielding capacity (RYC), of 122 lines from 605 clone individuals were measured in 2010. RYC’ data were collected and calculated from 69 plus trees. Firstly, we collected the phenotype data of individuals. Then, the average value of individuals from the same accession was used as the final phenotype data. Resins were collected on sunny days from July to October using the narrow face system as described by COPPEN and HONE43:The VW of an individual was calculated as follows43:The RYC of an individual was calculated as follows59, Factored Spectrally Transformed Linear Mixed Models (FaST-LMM) in FaST-LMM software60, Efficient Mixed-Model Association eXpedited (EMMAX) in EMMAX software61 and six methods, including multi-locus random effect mixed linear model (mrMLM)62, fast Multi-locus random effect mixed linear model (FASTmrMLM)63, fast multi-locus random-SNP-effect EMMA (FASTmrEMMA)64, iterative modified-sure independence screening Expectation-Maximization-Bayesian least absolute shrinkage and selection operator (ISIS EM-BLASSO)65, polygenic-background-control-based Kruskal-Wallis test with empirical Bayes (pKWmEB)66 and polygenic-background-control-based least angle regression plus empirical Bayes (pLARmEB)67 in mrMLM.GUI in R. For MLM, fixed effects were calculated with a Q (population structure) matrix, and random effects were calculated with a K (Kinship) matrix. The Q + K matrices were both considered in the MLM model. The Q matrix was calculated using the Admixture software package54, and the K matrix (the genetic relationship among 149 accessions) was predicted using SPAGeDi software. FaST-LMM uses a linear mixed modeling approach to test SNP association with quantitative traits. For EMMAX, independent SNPs were used to compute the centered relatedness matrix, and the significant P-value between SNPs and phenotypes was calculated. For methods mrMLM, FASTmrMLM, FASTmrEMMA, ISIS EM-BLASSO, pKWmEB and pLARmEB, the methodologies and procedures were processed according to the reports in recent years67. The result of these analyses can be obtained by using the R network .The GWAS analysis was performed by multiple methods, namely, the Mixed Linear Model (MLM) in TASSLE software68.We found the SLAF sequences that the suggestive SNPs located on and used the DNA sequences as queries to conduct BLASTN with the public database. Meanwhile, the SLAF sequences were used to make a conserved domain database analysis using NCBI’s Conserved Domain DatabaseSupplementary Information