ACTN2 gene, a crucial structural protein inside the cardiac sarcolemma . Genome-editing in human embryonic stem cell-derived cardiomyocytes confirms the influence of the identified regulatory region in the expression of ACTN2. Our findings extend our understanding of biological mechanisms underlying heart failure.Heart failure is a major public health problem affecting over 23 million people worldwide. In this study, we present the results of a large scale meta-analysis of heart failure GWAS and replication in a comparable sized cohort to identify one known and two novel loci associated with heart failure. Heart failure sub-phenotyping shows that a new locus in chromosome 1 is associated with left ventricular adverse remodeling and clinical heart failure, in response to different initial cardiac muscle insults. Functional characterization and fine-mapping of that locus reveal a putative causal variant in a cardiac muscle specific regulatory region activated during cardiomyocyte differentiation that binds to the  ABO and ACTN2 and show that deletion of a ACTN2 enhancer leads to reduced ACTN2 expression in differentiating cardiomyocytes.Heart failure has a heterogeneous etiology and the genetic underpinnings are not well understood. Here, Arvanitis et al. perform GWAS meta-analysis including 10,976 heart failure cases and 437,573 controls, identify new loci near  Although almost any disease that directly or indirectly affects myocardial function can lead to the eventual development of clinical heart failure, it is well-established that certain intrinsic homeostatic mechanisms like the renin–angiotensin–aldosterone axis and the sympathetic nervous system potentiate the effects of a variety of myocardial insults and cause adverse left ventricular remodeling3, suggesting that multiple cellular mechanisms that lead to the disease are shared regardless of the inciting condition.Heart failure is a highly prevalent disease4. However, large-scale genome-wide associations studies (GWAS) for heart failure have been unsuccessful to-date at uncovering a significant proportion of this estimated heritability underscoring a major unmet need in cardiovascular genetics. In fact, the largest published GWAS for heart failure until recently had only identified one genome-wide significant locus for all-comers with the disease that the investigators attribute to its overlap with atrial fibrillation5. A larger GWAS performed and published in parallel to our study increased the number of identified loci to 116. Even within this important work, however, many of the identified loci appear to be acting via heart failure risk factors and these loci have not yet been extensively functionally characterized, thereby limiting identification of actionable targets that predispose to heart failure development.The increasing appreciation of an underlying strong heritable component of clinical heart failure further strengthens the argument for shared, yet unidentified, disease mechanisms whose discovery could reveal novel targets for its treatment and prevention. Indeed, large recent pedigree studies estimate heart failure heritability to be 26–34%PITX2 gene and two novel loci near the ABO (chromosome 9) and ACTN2 (chromosome 1) genes. One of the novel loci near ABO was also detected in the aforementioned recently published GWAS6. Heart failure sub-phenotyping and multi-trait conditional analyses show that the novel chromosome 1 locus affects heart failure and left ventricular remodeling independently of known risk factors and in response to a variety of initial cardiac muscle insults. Detailed functional characterization of that locus using epigenomic, Hi-C, and transcriptomic datasets in differentiating cardiomyocytes reveals a cardiac muscle-specific regulatory element that is dynamic during cardiomyocyte differentiation and binds to the promoter of the ACTN2 gene, whereas genome-editing confirms that ACTN2 expression is significantly reduced in cardiomyocytes that carry a deletion of the identified novel regulatory element.In the current work, we perform a large-scale GWAS for heart failure and replicate our findings in a comparably sized independent cohort. We identify and replicate associations between heart failure and one known locus in chromosome 4 near the We performed a large-scale GWAS meta-analysis of five cohorts that study cardiovascular disease and two population genetics cohorts, all of European ancestry comprising a total of 10,976 heart failure cases and 437,573 controls. We used the 1000 Genomes phase 3 reference panel to impute variants from single nucleotide polymorphism (SNP) array data and analyzed a total of 13,066,955 unique genotyped or high-confidence imputed variants (INFO score > 0.7) with a minor allele frequency >1%. We analyzed each individual cohort using a logistic mixed model and meta-analyzed all studies with fixed effects inverse-variance meta-analysis.p-value < 5e-8)   Fig. . All ide-8) Fig. . We vali5) Table  and afte7 was 5.9% (SE 0.7%), much lower than the pedigree-based estimates of 26%, a discrepancy that has been observed for other complex traits8 and could be explained by multiple factors, including rare variants. The LD score regression intercept was 0.99, indicating no inflation beyond what can be accounted for by polygenicity. As expected, we saw significant genetic correlation between heart failure and known heart failure risk factors, such as hypertension, ischemic heart disease, adverse lipid profiles, diabetes, and atrial fibrillation. We also found a strong association between heart failure and pulmonary, musculoskeletal, and GI traits  score regression to estimate heart failure heritability driven by common variants and the genetic correlation between heart failure and other complex traits. Liability scale SNP heritability for the disease assuming a population prevalence of 1.8%rs1906615 is found in an intergenic region close to the PITX2 gene. This locus was previously identified as containing the strongest evidence for association with atrial fibrillation9 and has been reported as a significant locus in recent heart failure GWAS6. However, that association was thought to be mediated via the relative enrichment of the heart failure population in atrial fibrillation cases5. Indeed, via multi-trait conditional and joint analysis using summary statistics from GWAS of atrial fibrillation, we confirm that the effect of the PITX2 locus on heart failure is explained by its effect in atrial fibrillation (Table r2 < 0.001) genome-wide significant atrial fibrillation-associated variants, provides further evidence for a directional effect of atrial fibrillation on heart failure development (weighted mode MR effect size 0.21 (odds ratio 1.23), z-test p < 0.0001). Sensitivity analysis using the MR Egger and weighted median approach to account for potential pleiotropy and/or invalid instruments confounding our MR estimates is statistically significant and supports our hypothesis for a causal effect of atrial fibrillation on heart failure  does not result in a significant change in the effect of the ACTN2 locus on heart failure, suggesting that the association signal is not primarily mediated via these other diseases  from the ENCODE registry14. Of 111 strongly associated variants in almost perfect LD within the credible set for ACTN2, only seven overlapped regulatory elements in both Roadmap and ENCODE and were, therefore, used in downstream analyses  derived cardiomyocytes during different stages of differentiation. We showed that one of the seven target variants, rs535411   Fig. .Fig. 4ThACTN2 gene are known to be associated with cardiomyopathies, the credible set analysis does not support the coding region as being the primary driver of the GWAS signal. Moreover, conditioning on the sentinel variant eliminates the signal for association of the locus with heart failure, suggesting that the association is driven primarily by a single causal variant in high LD with the sentinel SNP  in a recent large-scale GWAS of echocardiographic traits18, which corroborates our hypothesis for a role of the locus in left ventricular remodeling. Beyond ACTN2, the data from our genetic correlation analysis  and metabolic traits   and tested the association between the locus sentinel SNP and heart failure (log(Odds Ratio) = 0.1027, score test p-value = 1.3e-4). Since CAD is only one of the many causes of heart failure, and individuals can have both CAD and heart failure from a different cause, the restricted analysis is conservative and consequently underpowered compared to our discovery GWAS , which even at the expected, unrestricted effect size inevitably makes the association non-significant at a genome-wide p-value threshold of 5e-8. Nevertheless, the restricted effect size on HF did remain similar to our unrestricted analysis and the association remained nominally significant. Taken together, this sensitivity analysis and the multi-trait conditional analysis suggest the possibility of a role of the ABO locus on heart failure independent of its established influence on coronary disease risk. However, definitive proof of this hypothesis will require further study.Lastly, the chromosome 9 locus tagged by the SNP ABO gene expression in eQTLGen and GTEx whole blood, consistent with our findings of active chromatin state overlap in hematopoietic lineage cells. In addition, the eQTL signal had strong evidence of colocalization with the GWAS signal for the same locus (posterior probability 96%)  associated with O-blood type via a frameshift mutation that is thought to inactivate ABO (LD r2 0.64 in 1000 Genome Europeans). However, the effects of our lead variants on the expression of ABO remain after stratifying by rs8176719 genotype , which may limit interpretation of this analysis as definitive evidence of an independent signal. Since our GWAS locus is intronic, we also examined whether it could affect splicing of the ABO gene using whole-blood RNA-seq data from GTEx v8. Indeed, although the variant’s effect on expression appears stronger than its splicing consequence, we found that the locus is also associated with splicing of ABO, promoting a splice variant that skips the exon on which rs8176719 is found, which provides additional evidence for a regulatory role not due to linkage disequilibrium with rs8176719  Fig. . Notablyype Fig. , suggestABO gene has been linked to cardiovascular disease and cardiovascular mortality21, the mechanisms underlying this association are not fully understood. Our finding that regulatory variation of the ABO gene’s expression is linked to the development of heart failure highlights the importance of ABO in cardiovascular disease and opens the door to further studies to decipher the cellular mechanisms involved.Although having non-O blood type via a structural coding variation in the ABO gene in cardiovascular disease and highlight broadly shared heritability between heart failure and musculoskeletal disorders.In summary, we performed a large-scale genome-wide association study for heart failure and replicated our findings in a similarly powered cohort. Our results validate the use of this approach to discover regulatory variants associated with heart failure predisposition in response to a variety of cardiac insults, reveal a new putative mechanism for the disease associated with the regulation of a structural cardiac muscle protein during differentiation, underscore the role of the phs000007.v29.p11, phs000287.v6.p1, phs000209.v13.p3, phs000280.v4.p1, phs000200.v11.p3, phs000888.v1.p1). Our work complies with all relevant ethical regulations for work with human participants. All individuals provided informed consent for participation in the individual cohorts. Our GWAS study was approved by the Johns Hopkins School of Medicine IRB (IRB #00163194). For each individual study we performed sample level filtering . We also excluded individuals that were not of European Ancestry and for every group of individuals that were related (identity by descent (IBD) > 0.125) we randomly selected one.We performed genome-wide association studies in five cohorts that study cardiovascular disease  and the eMERGE initiative. Genotype and phenotype raw data were downloaded from dbGAP  between the different arrays. For individuals that were genotyped in more than one genotyping array, we selected the array that had the most extensive genotyping. We proceeded with imputing and analyzing each array separately for every study.In addition, for each study SNP level filtering was performed to exclude SNPs that had significant deviations from Hardy–Weinberg equilibrium in heart failure controls, minor allele frequency <0.01, missing call rate >0.05 and differential missingness between heart failure cases and controls23 after pre-phasing with Eagle24 on the Michigan Imputation Server. Prior to imputation, we lifted all SNPs to the hg19 human genome build using the UCSC liftOver tool, aligned all SNPs to the positive strand and filtered out SNPs whose minor allele frequencies deviated by >0.2 compared to the reference panel’s MAF and SNPs A/T or G/C SNPs with MAF > 0.4 as those are prone to strand alignment errors. After imputation, we excluded all imputed SNPs with imputation r squared (INFO score) <0.7, SNPs with MAF < 0.01 and SNPs with Hardy–Weinberg p-value <1e-4. For the eMERGE cohort, imputation was performed independently prior to the start of this study with procedures detailed elsewhere25 and we subsequently applied the same post-imputation filters.We imputed each study to the 1000 Genomes phase 3 reference panel using Minimac3r2 < 0.2) genotyped or high-quality imputed SNPs (INFO score>0.9) in an unrelated population (IBD < 0.08) and the SNP loadings were subsequently used to calculate the eigenvectors for all individuals included in the analysis. In the eMERGE cohort, since the population was collected from multiple different hospitals across the United States, we included an additional multilevel categorical covariate denoting the sample source. All GWAS were performed using a linear mixed model with the saddlepoint approximation (SAIGE)26 to account for any residual relatedness structure in our analysis and for case–control imbalance, which is inherent in our phenotype of interest. For the UK BioBank cohort we used the summary statistics for all cause Heart Failure (PheCode 428) generated by analyzing the UK BioBank data in the SAIGE paper26.For each study, we performed a GWAS for heart failure controlling for age, sex and the first 10 genotype principal components (PCs). Heart failure definitions in the different cohorts are listed in Supplementary Table 27. We kept only SNPs that were present in at least three studies and 5000 individuals. The following tools were used for the GWAS: Python, R, Bcftools, PLINK28, SNPRelate29, SAIGE26, METAL27.We meta-analyzed the results of all our GWAS using fixed effects inverse-variance meta-analysis via the software METALr2 > 0.95) using an imputation panel that combined the 1000 Genomes Phase 3 panel with the UK10k panel. The variants were analyzed via logistic regression assuming an additive model with covariates for age, sex, the first five genotype PCs, and indicator variables to represent the genotyping platform. The p-values were adjusted for an LD score regression intercept of 1.043.We replicated our findings in an independent cohort of 24,829 Heart failure cases and 1,614,513 controls of European ancestry within the 23andMe research cohort. 23andMe participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical and Independent Review Services (E&I Review). Heart failure in the replication population was self-reported as an answer to the question “Have you ever been diagnosed with or treated for Heart failure?”. All three replication variants were imputed with high quality  <50%, and heart failure with preserved ejection fraction otherwise. Individuals that did not have information on myocardial infarction history or echocardiographic information were not included in the respective analyses. We also obtained continuous data of LVEF, left ventricular end diastolic diameter, and interventricular septum diameter from each individual’s most recent available echocardiogram. Each of our sentinel variants from the general heart failure GWA meta-analysis were tested for an effect in each of these variables using SAIGE for the categorical variables and linear regression assuming an additive genotype effect for the continuous variables with the same covariates as in our primary GWAS. The results cross-cohort were meta-analyzed using METAL.20, which contains data from 4155 GWAS across 2960 unique traits and the 1488 Electronic Health Record-Derived PheWAS codes from the Michigan Genomics Initiative26.To evaluate if our lead GWAS variants had associations with other phenotypes we queried the NHGRI-EBI GWAS catalog and also evaluated the GWAS atlas30 with the 1000 Genomes European reference LD to evaluate the liability scale heritability explained by the common variants in our GWAS assuming a population prevalence of 0.0187. We subsequently analyzed our GWAS together with summary statistics from GWAS studies from the UK biobank31 using the genetic correlation method of the LD score regression pipeline to quantify the shared heritability between our phenotype and other traits32. For the genetic correlation analysis we selected traits to analyze based on the following procedure:26, we first excluded the categories Injuries and poisonings (as it is unlikely to have a major heritable component), as well as symptoms and pregnancy complications .Among all summary statistics analyzed in the SAIGE paperWe excluded general disease bundles that include the work “other” or “NOS”  or are a sign/symptom  or medication .We reclassified all infections into the “Infectious Diseases” category and all congenital anomalies to their respective organ system.From every organ system or general disease category we selected the three diseases with the highest number of cases.For every selected disease, we excluded diseases and disorders that are subsets of the same disease or highly related .z-score of observed heritability calculated via LD score regression was <1.We excluded diseases whose We used LD score regression33 from the GCTA pipeline to evaluate the residual association signal within our genome-wide significant loci after conditioning on our sentinel variants or other variants of interest using as reference the LD of the eMERGE heart failure dataset.We used the COJO package34 from the GCTA pipeline to evaluate the effects of our variants conditioned to other heart failure risk factors  and conditions associated with our sentinel variants in PheWAS studies using the 1000 Genomes Europeans reference LD scores.We used the mtCOJO package35 to perform Mendelian Randomization analysis in order to evaluate the effect of atrial fibrillation on the development of heart failure using summary statistics from a large-scale GWAS meta-analysis of atrial fibrillation36. The polygenic risk score was constructed using independent variants (LD r2 < 0.001) at a genome-wide significance threshold (p < 5e-8) using as reference the LD of the 1000 Genomes European samples.We used the MR base package13 across all tested cell types and tissues to visualize our significant loci and identify broad patterns of active promoter or enhancer elements across tissues. We subsequently used CAVIAR12 with the 1000 Genomes European reference LD and the assumption of at most two causal variants per locus to generate a credible set of SNPs for each locus. The CAVIAR analysis for the ACTN2 locus included all SNPs in a radius of 100 kilobases around the locus sentinel SNP. Since that analysis identified a large number of SNPs (N = 183), we selected only the 111 SNPs in the set that had a GWAS p-value < 5e-7 for downstream fine-mapping. Then, we intersected the SNPs in that set with active enhancer or promoter elements predicted by Roadmap epigenomics for heart tissues . Finally, we intersected SNPs selected by the previous step with candidate cis-regulatory elements predicted by ENCODE14. Bedtools37 was used for all intersection tests. The WashU Epigenome browser was used for visualization of our loci in the ChromHMM context38.We followed a step-wise approach based on epigenomic annotations and LD structure for our variant fine-mapping efforts. We first used the Roadmap epigenomics ChromHMM 25-state modelACTN2 locus on cardiomyocyte function, we performed ATAC-seq, ChIP-seq of H3K4me3 and H3K27ac, RNA-seq and HiC experiments39, in an engineered H9 hESC (WiCell Research Institute) modified into H9 hESC MLC2v:H2B-GFP reporter transgenic line, which expresses H2B-GFP in differentiated ventricular cardiomyocytes40. This cell line was differentiated into cardiomyocytes using a well-established Wnt-based differentiation protocol41. Cardiomyocytes and their intermediate cell populations were collected and analyzed at different differentiation stages  and epigenomics, transcriptomics and three-dimensional chromatin conformation assays were performed on these cells39. We queried our fine-mapped variants by intersecting them with ATAC-seq, H3K4me3, and H3K27ac peaks in the cardiomyocyte differentiation model and we subsequently assessed the HiC contacts between the identified peaks and nearby genes. HiC contacts were generated at 5 kb resolution. Expected contacts for each bin are calculated as the genome-wide average of contacts of the same distance, as Hi-C contacts follow a distance-based decay. The observed/expected value for each bin shows the enrichment of HiC contacts relative to the background. We tested the significance of enrichment of observed contacts with respect to the expected contacts using an upper-tail Poisson test with x equals observed contacts and lambda equals expected contacts42. The UCSC human genome browser was used to visualize the sequencing peaks43.To further probe the effects of the 44 to normalize gene expression and perform cis-eQTL analyses. In brief, we filtered out genes with <6 reads or <0.1 counts per million (cpm) in >20% of participants per tissue, performed normalization of expression values between samples using TMM45 and for each gene, we normalized gene expression across samples by an inverse rank-based transform to the standard normal distribution. The effect of a variant in gene expression was analyzed using linear regression as implemented in MatrixEQTL46 using age, sex, RNA-seq platform, five genotype PCs and 60 probabilistic estimation of expression residuals (PEER) factors47 as covariates. Using the methods above, we tested the effect of the ACTN2 locus fine-mapped variants to the expression of genes within 1 megabase in left ventricle tissue and the effect of the ABO locus in whole blood. (2) To increase the power of detecting a cis-eQTL association in whole blood, we obtained cis-eQTL summary statistics from eQTLGen, which includes eQTL data from 31,684 samples48. We queried our ABO locus sentinel variant in the dataset.We assessed the effect of our genome-wide significant variants in gene expression of nearby genes using two databases: (1) Genotype Tissue Expression (GTEx): we obtained whole-genome sequencing and RNA sequencing data from GTEx version 8. We followed the standard pipeline proposed by GTEx v749 to estimate the posterior probability of an identical causal variant per locus between eQTL and GWAS. Colocalization Manhattan plots for Supplementary Fig. 50.For each identified significant eQTL result for our variants, we evaluated whether the eQTL and GWAS signals colocalize using a Bayesian colocalization method as implemented in colocACTN2 locus in human embryonic stem cells (hESCs), we used the CRISPR/Cas9 system. More specifically, we used CHOPCHOP v251 to find guide RNAs (gRNAs) that in combination with Cas9 will generate cuts within the ATAC-seq peak detected as causal in our epigenetic hESC-CM experiments. We then cloned both gRNAs and Cas9 in vectors carrying a puromycin resistant cassette and used the NEON electroporation system (ThermoFisher) to effectively transform hESCs . Cells were then plated in flasks coated with Gelltrex  and maintained in Essential 8 medium (Thermofisher). hESCs underwent electroporation using the Thermo Neon Transfection system and transformed hESCs were selected using Puromycin for 48 h. Cells were replated and colonies from single cells were manually picked and expanded. All colonies were subsequently screened using PCR with primers that bind outside the expected Cas9 cuts. DNA from colonies carrying the deletion generated a 490 bp PCR fragment confirming a ~2200 bp deletion in the target segment  for 48 h followed by 2.5 μM of IWR-1  in RPMI-B27 without insulin medium (Thermofisher). Spontaneous beating was noted at day 7 of differentiation. Cardiomyocytes were further selected using sodium lactate53. RNA was isolated from cardiomyocytes at day 15 using Trizol, complementary DNA (cDNA) was generated using the high-capacity cDNA reverse transcription kit (Thermofisher) and quantitative PCR was performed using Sybr select protocol54. Gene expression levels were normalized with GAPDH. Expression in edited cardiomyocytes and controls was assessed in four replicates and compared with a two-tailed t-test. Whole list of primers is provided in Supplementary Table To generate a deletion of the candidate causal region within the 55 to perform splice-QTL (sQTL) analysis for rs550057 . We obtained and normalized intron excision ratios from binary sequence alignment/map files for whole-blood tissue in GTEx v8 following the filtering and normalization steps provided with the LeafCutter software. We then used FastQTL56 to perform nominal sQTL analysis for rs550057 using as covariates five genotype PCs, ten PCs calculated based on the normalized intron excision ratios, along with sex, age, and whole-genome sequencing library construction methodology.We used LeafCutterFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Lolium perenne L.), cocksfoot (Dactylis glomerata L.), tall fescue (Festuca arundinacea Schreb.) and chicory (Cichorium intybus L.). The leaf temperature of each species was modelled by using leaf energy budget equation and validated with measured data. All species showed limited homeothermy with the slope of 0.88 (P < 0.05) suggesting that pasture plants can buffer temperature variations in their growing environment. The DairyMod biophysical model was used to simulate photosynthesis during each treatment, using both air and leaf temperatures, and the patterns were compared with measured data using a response ratio (effect size compared to the well-watered control). The effect size of moderate heat and well-watered treatment was very similar to the measured values (~0.65) when simulated using T leaf, while T air overestimated the consecutive heat stress impacts (0.4 and 0). These results were used to test the heat stress recovery function (Tsum) of perennial ryegrass in DairyMod, finding that recovery after heat stress was well reproduced when parameterized with T sum = 20, while T sum = 50 simulated a long lag phase. Long term pasture growth rate simulations under irrigated conditions in south eastern Australia using leaf temperatures predicted 6–34% and 14–126% higher pasture growth rates, respectively at Ellinbank and Dookie, during late spring and summer months compared to the simulations using air temperatures. This study demonstrated that the simulation of consecutive heat and/or drought stress impacts on pasture production, using DairyMod, can be improved by using leaf temperatures instead of air temperature.Despite evidence that leaf temperatures can differ by several degrees from the air, crop simulation models are generally parameterised with air temperatures. Leaf energy budget is a process-based approach that can be used to link climate and physiological processes of plants, but this approach has rarely been used in crop modelling studies. In this study, a controlled environment experiment was used to validate the use of the leaf energy budget approach to calculate leaf temperature for perennial pasture species, and a modelling approach was developed utilising leaf temperature instead of air temperature to achieve a better representation of heat stress impacts on pasture growth in a biophysical model. The controlled environment experiment assessed the impact of two combined seven-day heat  and drought stresses (with seven-day recovery period between stress periods) on perennial ryegrass ( Climate change projections for Australia indicate increasing frequency and magnitude of extreme climate events such as heat waves, droughts, extreme precipitation and frost occurrences in the coming decades , which aLolium perenne) and Kentucky blue grass (Poa pratensis) starts at temperatures above 25 °C [Dactylis glomerata), the optimum temperature range for maximum net canopy photosynthesis is between 19 and 22 °C and the values declining to lowest at 31 °C [The optimal temperature range for growth of temperate pasture species lies between 15 °C and 23 °C ,7. Beyonve 25 °C ,9. For cat 31 °C . Physiolat 31 °C ,12, reduat 31 °C ,14, reduat 31 °C ,17 and sat 31 °C . Since hat 31 °C ,14,19.Triticum aestivum L.) due to heat stress between 32 °C and 34 °C (daily maximum temperatures) [Accurate assessment of the effects of heat and drought stresses on crop processes is important to identify correct management and adaptation strategies in order to stabilize production. Many crop simulation models incorporate heat and drought stress impacts on growth and developmental processes. For example, the Agricultural Production Systems sIMulator (APSIM) simulates leaf senescence of wheat (ratures) ,21. Ecosratures) . Likewisratures) . Howeverratures) , dependiratures) ,26,27. Tratures) .Secale cereal L.) was 2 °C cooler than the air when irrigated and it was 7 °C warmer than the air under rainfed conditions [Ignoring this leaf-to-air temperature difference may cause uncertainties in assessing heat and drought stress impacts by crop simulation models . It may nditions . Based onditions ) was calnditions . Leaf energy balance provides a process-based approach to incorporate canopy temperature effects into crop simulation models ,25,29. IDairyMod is a mechanistic biophysical pasture model developed to predict grazing dynamics across a range of climates, soil types, forage species and management under conditions in Australian and New Zealand . In the The broad objective of this study was to evaluate whether the use of leaf temperature compared to air temperature can better simulate the impact and recovery of consecutive heat and drought stresses leading to improved prediction of heat and drought impacts in biophysical models. The specific objectives were: (1) to test the limited homeothermy hypothesis for four temperate pasture species commonly grown in SE Australia; (2) to validate the leaf energy budget equation using leaf temperature data measured under well-watered (WW) and water-stressed (WS) conditions at three temperature levels; (3) to parameterize the high temperature stress recovery function in DairyMod, and finally, (4) to model leaf temperature using the leaf energy budget equation and use these in simulations in DairyMod to estimate the uncertainty associated with use of air temperature to predict pasture growth at two sites in SE Australia.2 = 0.95). This slope was significantly less than 1 (P = 0.001) and greater than 0 (P < 0.001) (The slope of the relationship between leaf and air temperature for irrigated plants was 0.88 (R< 0.001) , therefoP = 0.05 level, except for the moderate heat stress on day 2. The difference between WW and WS plants was smaller on day 2 of the stress treatment and it increased as the combined heat and drought stresses progressed through time. For instance, the difference between WW and WS plants on day 2 was 1.1 °C under severe heat stress (35 °C), but increased to 2.3 °C on day 7 (P < 0.001).In general, leaf temperatures were cooler than air temperatures under well-watered (WW) conditions, while leaves were warmer than air temperatures under water-stressed (WS) conditions . The difb) and Variance Ratio (V) were above 0.9 for all the categories, indicating that there were only small deviations from 1:1 reference line and that variance in measured and modelled data were similar in all the categories. In general, the leaf energy budget equation predicted the leaf temperature more accurately for grasses , modelling efficiency (MEF), concordance correlation coefficient (CCC) and lower mean prediction error (MPE)) than chicory.The measured leaf temperature is compared with modelled temperatures using the leaf energy budget for each pasture species in Perennial ryegrass showed a significant decrease in the measured leaf photosynthesis rates at the end of consecutive WS treatments by 74% and 65%, respectively, compared to the WW plants at the control temperature (25 °C) a. Heat sP < 0.05) correlation between measured and modelled data in WS treatments, while no such significant pattern was observed in WW plants  of each temperature and watering treatment compared to the WW control was shown for measured and simulated data (using Tsum 20) in When applying a leaf energy budget to calculate leaf temperature, the modelled leaf temperatures of perennial ryegrass under irrigated conditions were generally cooler than air at both Ellinbank and Dookie sites above maximum air temperatures of 18 °C and 16 °C, respectively . Below tP < 0.05) increase in predicted pasture growth rates simulated using leaf temperature from November through to March at both sites, compared to air temperature. For instance, simulated perennial ryegrass production at Ellinbank in Nov, Dec, Jan, Feb and Mar increased by 6%, 10%, 22%, 34%, and 23%, respectively, while at Dookie the simulated pasture production increased by 14%, 52%, 88%, 126% and 60%, respectively, when simulated using leaf temperature compared to air temperature  and lower mean prediction error (~3%). Leaf temperatures better simulated the effects of moderate heat stress on photosynthetic rates of perennial ryegrass while simulations with air temperatures overestimated the impacts. The pattern of photosynthesis recovery after heat stress was well reproduced by DairyMod when Tsum = 20 was used while T sum = 50 simulated longer lag phase between stress and full recovery. When the modelled leaf temperature was used, both Dookie and Ellinbank sites simulated under irrigated conditions predicted higher pasture growth rates in late spring and summer periods compared to the simulations run with air temperatures. These results confirmed that uncertainty in simulating heat and drought stress on pasture growth in DairyMod can be reduced by using leaf temperature in the simulations and parameterizing high temperature stress recovery function with T sum = 20.The slope of WW plants   was 0.88Well-watered plants often maintained cooler canopies than air through transpirational cooling  at all tCompared to the WW plants, WS plants had warmer leaves at all temperature levels. This was mainly due to the gradual decrease in stomatal conductance and development of greater leaf to air vapor pressure deficits with time as the combined stresses progressed . This reLeaf temperatures calculated using leaf energy budget equation were in good agreement with measured values for grasses but, the equation did not work well for chicory. Chicory, being a dicot plant has stomata only in one side of the leaf (hypostomatic dicot) and in contrast, grasses possess stomata on both sides (amphistomatic monocots) . GrassesThe difference between air and leaf temperature in plants has been well known for many years ,44,45; hThe use of leaf temperature in simulations is increasingly important for regions like south eastern Australia, where maximum temperature during the late spring and summer months is likely to pass the onset of heat stress threshold of perennial ryegrass (30 °C) on some days, while soil moisture is still available for plant growth. Under such situations, transpirational cooling is likely to reduce leaf temperature and use of leaf temperatures would realistically simulate the high temperature response of pastures. Use of leaf temperature is equally important for simulating heat stress impacts of other crops. For example, in wheat, grain sterility due to heat stress occurs at 31 °C ,47, but The approach of using soil moisture stress index (GLF_water) to allocate stomatal conductance on each day for the energy budget developed in this study is similar to other studies that attempted to integrate canopy temperatures into crop models . For exaPhalaris aquatica L.) are more persistent than perennial species that lack summer dormancy [Based on the results, it is confirmed that the DairyMod model default values of high temperature stress recovery function (T sum = 100) for perennial ryegrass was too high because it takes more days to recover from high temperature stress than observed values . Both vadormancy . In Dairdormancy . It has dormancy ,53. TherComparison of DairyMod predicted pasture growth rates using air and leaf temperatures indicated that there was a large uncertainty in yields when the leaf temperatures were ignored particularly at the medium rainfall warm temperate climate at Dookie. Yield increase when simulating with leaf temperature ranged from 14–126%, compared to air temperatures at Dookie during late spring and summer months. This could be due to leaf temperatures getting closer to optimum for photosynthesis at higher temperatures and transpirational cooling avoiding the high temperature threshold of 30 °C in irrigated simulations. In a study comparing different sterility functions of rice models, van Oort et al.  reportedWhile many studies have shown that the air temperature is a poor predictor in terms of plant production ,56, mostLolium perenne cv. Base AR37), cocksfoot (Dactylis glomerata cv. Savvy), tall fescue (Festuca arundinacea cv. Quantum II Max P) and a broad leaf; chicory (Cichorium intybus cv. Puna II) were grown in poly vinyl chloride tubes (height 75 cm and diameter 10 cm) inside a glasshouse in the Faculty of Veterinary and Agricultural Sciences, The University of Melbourne. Plants were well-watered and fertilised during the initial growing stage in the glass house. After eight weeks of vegetative growth, plants were transferred into three separate growth chambers and allowed to adapt to conditions for two weeks. The first week before the treatments were imposed was considered as the pre-treatment period. Plants in each chamber were then exposed to consecutive seven-day heat and water stress treatments each followed by a seven-day recovery period. Three temperature levels were allocated to three growth chambers including control = 25/15 °C, day/night, moderate heat stress = 30/20 °C, day/night and severe heat stress = 35/25 °C, day/night. At each temperature level, a group of plants were fully irrigated daily to the field capacity throughout the experiment  and irrigation was arrested during consecutive seven-day temperature treatments in the water-stressed (WS) plants. All pots were well watered during the pre-treatment and the recovery periods to the field capacity. A controlled environment experiment was conducted to collect the information required to validate leaf energy budget equation and simulation approach used in this study. Full experimental details, including the experimental design, treatments, and pasture species, were provided in  and onlyEach growth chamber contained 40 plants (4 species × 5 replicates × 2 irrigation treatments) arranged in eight rows and five columns. Watering treatments (WW and WS) were allocated in alternative rows in each chamber. Species were randomised in both row- and column-wise directions.−2s−1 (range of 844–1030) using high pressure sodium lamps and incandescent lights. Maximum leaf widths of 10 leaves were recorded from each pasture species and averaged to calculate leaf characteristic dimension (d = 0.72 × maximum leaf width in the direction of wind flow).Diurnal variation of temperature was implemented inside growth chambers by gradually increasing and decreasing air temperature changing between night time minimum to day time maximum over a period of 3 hours. Temperature inside the chambers were recorded every minute by a data logger in each growth chamber. Relative humidity (RH%) inside the chambers were set at 70% and light intensity was maintained at 900 µmolm2 m−2 s−1) of the youngest fully expanded leaf was measured in perennial ryegrass three times per week throughout the experiment using Li-6400 portable gas exchange system  under the given lighting conditions of the chamber and CO2 concentration set at 400 ppm. Modulated chlorophyll fluorescence was also measured in alternative days throughout the experiment. Since the grasses have a narrow leaf, the whole leaf chamber was not covered by leaves. Therefore, every leaf inside the LI-COR leaf chamber was photographed using a digital camera  when taking photosynthesis measurements to compute the leaf area within the leaf chamber. The leaf area was analyzed using ImageJ software [th edition) taking rows and columns in each chamber as the random effects and temperature, watering treatment and time as fixed effects. Three-way interaction was significant (P < 0.05), however to reduce the complexity of the analysis, only the means between WW and WS plants at each temperature on each day are presented in this study  keeping the plants inside the chamber during the days 2, 4 and 7 of the second heat and WS treatment. In each thermal image, pixels of the pot and the background were eliminated by selecting the maximum and minimum temperatures within the plant canopy using a code written in MATLAB R 2014b . Pixels −1), −2), −2 S−l) and radiative  conductance, -1 C-1), Temperatures of pasture leaves were calculated using air temperature, radiation, leaf characteristic dimension ,59, windMonocot plants (grasses) have stomata on both sides of the leaf (amphistomatous), while dicots (chicory in this study) have stomata only on one side (hypostomatous). To account for this feature, vapour conductance was calculated for grasses and chicory separately in the energy budget equation. For chicory, only the first half of Equation (3) was used.found in . 2); Pearson’s correlation coefficient (r), mean prediction error (MPE) where <5% represents excellent model prediction, 5–10% represents very good, 10–20% represents moderate and more than 20% represents poor model prediction [A range of statistics were calculated to test the adequacy of the leaf energy budget equation to predict the leaf temperatures of four pasture species with sufficient accuracy, based on methods reported in Tedeschi . The staediction ; for the−2 s−1), maximum and minimum daily temperatures (°C); RH%, vapor pressure (kPa) and wind speed . One set of simulations were run using the measured climate data including the maximum daily air temperature. Another set of simulations were run substituting the maximum daily temperatures with the leaf temperatures calculated for each day using leaf energy budget equation. Maximum air temperatures inside each growth chamber and leaf temperatures for WW and WS simulations are shown in Net positive photosynthesis rates of perennial ryegrass were simulated using the DairyMod biophysical model during consecutive heat and water stresses to compare the pattern with the measured data. Data for perennial ryegrass was used in this comparison as it is the most commonly grown pasture species in SE Australia. Six simulations were built to represent the three temperature and two watering levels separately. To run the simulations, climate files required for DairyMod were prepared using the data recorded in each growth chamber including light intensity . The RR of the 25 °C, WW treatment in both measured and modelled data sets were always equal to one as the value of each day is divided by the same value. RR was calculated for both measured and modelled photosynthesis values separately and then used for the comparisons. Several regression model evaluation statistics were used, such as residual mean squared error (RMSE) and mean absolute error (MAE) to calculate the error rate of modelled data. Photosynthesis data measured only at controlled and moderate heat stress treatments were used in the analysis. Photosynthesis data measured at the severe heat stress treatment was not used for comparisons due to instrumental error occurred during measurement. However, the pattern of photosynthesis was modelled for all three temperature levels and photosynthesis pattern at severe temperature treatment  was visually compared with the pattern of heat and/or drought stress responses of other growth and physiological measurements such as maximum quantum yield of photosystem II, relative leaf water content and the leaf elongation rate described in [Since measured and modelled photosynthesis values were in different units, they were transformed to comparable scales using response ratio (RR). RR was calculated as Psisribed in .Perennial ryegrass in the DairyMod biophysical model experience high temperature stress (high temperature stress-onset) at 30 °C and the stress become maximum (high temperature stress-full) at 35 °C. Recovery of pasture species from high temperature stress is modelled using an empirical function called T-sum recovery. The model default T sum for perennial ryegrass is 100 . This me−2, maximum and minimum temperatures in °C, rainfall in mm, evaporation in mm and RH%) were obtained from the SILO database service [To evaluate the effects of leaf temperature on pasture production compared to air temperature, perennial ryegrass was simulated using both approaches (Tair and Tleaf) separately at two contrasting sites in south eastern (SE) Australia  and medium rainfall warm temperate site at Dookie ) spanning the period from 1960–2015. Climate data for each site  simulated using air temperature and leaf temperature were analyzed using two sample t-tests for both irrigated and rainfed conditions and uncertainty was computed as percentage difference of net positive growth rates compared to air temperature.During simulations, the effect of transpiration on pasture growth was incorporated using modelled leaf temperature in two-step process. First, the model was run using the described climate data (obtained from SILO database) and growth limiting factor water (GLF_W) values on each day over 1960-2015 were extracted from export file for each site. GLF_W was then used to incorporate the transpirational cooling effect by adjusting stomatal conductance in the leaf energy budget, using a similar approach to a previous study . GLF_W rThe four pasture species used in this study showed limited homeothermy under WW conditions, suggesting that pastures can buffer temperature variations in a range of ambient air temperatures. A leaf energy budget equation modelled temperatures of the grasses better than chicory under different temperatures and watering levels. The leaf temperature modelled using an energy budget equation better simulated the heat stress impacts on perennial ryegrass compared to the air temperature, suggesting that the uncertainty of using air temperature can be reduced if leaf temperatures were used in crop simulation models. Weather variables available from the meteorological stations  together with crop-specific variables  and the soil moisture stress index (GLF_water) allows for the approximation of leaf temperature using the leaf energy budget. When using this approach in DairyMod, simulations using leaf temperature under irrigated conditions showed increased pasture growth rates during the late spring and summer months at the Ellinbank and Dookie sites, with the highest increase predicted at the medium rainfall site at Dookie. This approach can be used to model pasture production in other temperate and Mediterranean climates of the world where high temperature stress is an ongoing problem. While further research is required to better represent the canopy temperature in crop simulation models using an energy budget approach, this study demonstrated that leaf temperature can better simulate pasture responses under consecutive combined heat and water stresses.


BRCA1, a highly penetrant breast cancer susceptibility gene, as an example. Only after careful consideration of study design and DNA methylation measurement will prospective performance of the incorporation of information regarding DNA methylation marks into risk prediction models be valid.Current risk prediction models estimate the probability of developing breast cancer over a defined period based on information such as family history, non-genetic breast cancer risk factors, genetic information from high and moderate risk breast cancer susceptibility genes and, over the past several years, polygenic risk scores (PRS) from more than 300 common variants. The inclusion of additional data such as PRS improves risk stratification, but it is anticipated that the inclusion of epigenetic marks could further improve model performance accuracy. Here, we present the case for including information on DNA methylation marks to improve the accuracy of these risk prediction models, and consider how this approach contrasts genetic information, as identifying DNA methylation marks associated with breast cancer risk differs inherently according to the source of DNA, approaches to the measurement of DNA methylation, and the timing of measurement. We highlight several DNA-methylation-specific challenges that should be considered when incorporating information on DNA methylation marks into risk prediction models, using  In contrast to BOADICEA, IBIS and BRCAPRO, which includes details about the ages of cancer diagnoses in the relatives as well as germline mutations in BRCA1/2, other breast cancer risk models like the commonly used Breast Cancer Risk Assessment Tool (BCRAT) generally consider family history based on the number of first-degree family relatives affected and/or ever/never family history. Most models now include non-genetic established breast cancer risk factors .Current cancer risk prediction models are largely grouped based on the extent of family history and genetic data in addition to information relating to non-genetic risk factors such as lifestyle and the environment. Pedigree-based models, which are often used for genetic counselling and risk assessment and for making decisions about chemoprevention and risk-reducing surgeries,9 Epigenetic alterations have been demonstrated to be associated with an increased risk of certain diseases, such as Fragile X syndrome, Prader–Willi syndrome, and various cancers, and are increasingly being measured in epidemiological studies.12 However, potential integration of epigenetic information into risk prediction models requires further prospective evidence that might be more challenging to collect than those studies incorporating genetic information.Increasingly large-scale studies support that many of the breast cancer predisposition genes can also influence risk when altered through epigenetic mechanisms, such as DNA methylation and histone modification, and that such mechanisms might occur more commonly than currently appreciated.BRCA1, the breast cancer susceptibility gene with the highest penetrance, to illustrate the gaps in our knowledge that need to be addressed to improve clinical risk assessment. We selected this gene because the integration of germline mutation status of BRCA1 has already been shown to improve risk assessment and knowledge of the germline BRCA1 status has already altered clinical practice in terms of chemoprevention, recommendations for risk-reducing surgeries, and screening frequency. We consider germline DNA methylation of BRCA1 in all women and not just in women with pathogenic variants in BRCA1. The points we raise would also relate to other epigenetic marks such as histone acetylation and histone methylation.Here, we outline the case for the integration of epigenetic measures, in the form of blood-based DNA methylation marks, into existing risk prediction models using the example of 3) group to the cytosine residue of a cytosine-guanidine pair in the DNA sequence, commonly referred to as a CpG dinucleotide. DNA methylation is an essential component in early development through a process known as epigenetic reprogramming.13 In adult cells, it has been shown to be extensively involved in the initiation and progression of cancer whereby aberrant DNA methylation can lead to silencing and loss of expression of tumour suppressor genes such as BRCA1, MLH1 and ATM17 and genomic instability.19 In breast cancer, aberrant DNA methylation levels across specific breast cancer susceptibility genes, such as BRCA1,22ATM,23PALB224 and Sat2,25 have been associated with risk of the disease in women with and without pathogenic variants in these genes.DNA methylation refers to the addition of a methyl , and the timing of the biological sample collection for DNA methylation measurement, all of which are discussed below using 27 have demonstrated the utility of these changes in further subtyping cancers and refining precision medicine,30 as well as proving valuable for predicting prognosis after cancer diagnosis.33 The use of DNA methylation marks for risk prediction, however, often requires the use of surrogate tissue and/or blood-based biomarkers  that can be reliably and repeatedly measured using non-invasive sampling. Here we would like to emphasise that a good predictive marker need not be measured from the potential site of carcinogenesis, e.g. measured in DNA sourced from breast tissue or breast milk to determine breast cancer risk—a good predictive marker needs only to be associated with the disease of interest and to be stable over repeated measurements.Early studies focused on identifying changes in DNA methylation marks in disease-affected tissues35 and, as such, methylation measured in blood-derived DNA is influenced by the proportion of cell types present in the blood sample. To address this, study designs often match case–control pairs by the source of DNA  and control for variation in blood sample cellularity as part of the analytic process using statistical methods such as that proposed by Houseman et al.36 Continued improvement of these statistical methods will further improve the accuracy of cell-type adjustment. Specifically, white blood cells, as a non-invasive source of DNA prior to disease onset, has been used for studies searching for DNA methylation marks that can be useful for understanding cancer susceptibility (the focus of this Perspective).Caution is rightly warranted when using blood-derived DNA modifications as biomarkers. DNA methylation displays cell-type-specific heterogeneityBRCA1 promoter methylation—that is, DNA methylation that is present in every cell of the body—is associated with a 3.5-fold increased risk  of developing early-onset breast cancer of a specific histological type in non BRCA1 mutation carriers.21 Another study conducted in Japan also reported that BRCA1 promoter methylation detected in peripheral blood cells is associated with an increased risk of developing breast cancer  .20 Methylation of the ATMmvp2a intergenic region and the Sat2 repetitive element have also been reported to be associated with an increased risk of breast cancer .25 Xu et al.10 identified 250 blood-based CpG dinucleotides that were differentially methylated  between cases and controls,10 and that five differentially methylated CpG dinucleotides had similar model discrimination as the BCRAT model that included nine GWAS common variants (area under the curve (AUC) 65.8%, 95% CI: 61.0, 70.5% versus 66.1%, 95% CI: 61.0, 71.3%).Using DNA derived from peripheral blood, we reported that constitutional 12 However, a 2019 meta-analysis of four EWAS did not find an association between blood-based DNA methylation and the risk of breast cancer;37 this result could perhaps be partly explained by the later age of onset in the affected women (further discussed below), or the different analytic approaches used by each study .Several epigenome-wide association studies (EWAS) have also demonstrated an association between global DNA methylation levels and breast cancer risk.34 We will not discuss these methods in detail except to outline in Laboratory methodologies vary and thus measures of DNA methylation cannot, in all instances, be directly compared or easily combined.Given the different considerations for each of the three methodologies, an approach that targets risk-associated DNA methylation regions, akin to the commonly used gene panel sequencing methods in the germline context, might prove the most useful in integrating new information into cancer risk models. For example, although identified GWAS variants may be used in the future clinically through an integration of a PRS, genetic information from sequencing studies, which include fuller genetic alterations than those included in GWAS, are already being used clinically through gene panel tests. In a similar way, we might expect that studies that include deeper investigation of epigenetic alterations in the same genes used in these gene panels may be clinically more beneficial than information from an EWAS, which only includes selected CpGs from these high and intermediate penetrant genes.BRCA1 promoter was more common in women diagnosed with breast cancer compared with unaffected women.38 A number of different study designs with different methylation markers, including methylation measured in blood-derived DNA and from histologically normal and malignant breast tissues, were included in this meta-analysis. BRCA1 promoter methylation was associated with an increased risk of developing breast cancer , advanced stage histopathology features, and triple-negative disease.38 When considering only the nine blood-based studies, BRCA1 methylation was associated with a 1.87-fold increased breast cancer risk .38A meta-analysis found that DNA methylation of the 34 published a comprehensive review of blood-derived DNA methylation marks associated with an increased risk of breast cancer. Although overall increased BRCA1 promoter methylation was observed in the blood-derived DNA of affected women, the breast cancer risk estimates of the meta-analysis showed considerable variation between studies.34 While the factors mentioned above  could have contributed to the observed heterogeneity, it is possible that different regions measured even within the same gene, as highlighted by Zhang and Long,38 could also provide some explanation for the variation.Tang et al.BRCA1 promoter region , some studies have measured DNA methylation at other loci, such as the CpG island, which is likely to have contributed to the different findings  is highly methylated in blood-derived DNA from both affected and unaffected women and lacks variability between individuals.42 Therefore, DNA methylation assessment of this invariant region is likely to be uninformative and, indeed, no differences in BRCA1 DNA methylation levels were observed in this region between cases and controls irrespective of their BRCA1 or BRCA2 mutation status.40 Ziller et al.43 found that DNA methylation levels across the intermediate and low CpG density promoters and transcription start sites, rather than the CpG islands, are dynamic and variable between individuals.43 These two observations strongly indicate that the region outside of the CpG island, rather than the CpG islands itself, could stand to be more informative for DNA methylation assessment for risk prediction.Although the majority of studies have assessed blood-derived DNA methylation in the same BRCA1 exon 1a  have been evaluated across most studies  and rs799906 (P = 0.017)),21 but the publication of few, if any, reports on genetic variants that are positively associated with BRCA1 promoter methylation rendered these results inconclusive until Evans et al.45 identified a dominantly inherited BRCA1 5’ untranslated region variant (NM_007294.3:c.−107 A > T) in two multiple-case breast and ovarian cancer families.45 Hemi-methylation of the BRCA1 promoter region was found soma-wide, including in leukocyte-derived DNA in heterozygote carriers with BRCA1 promoter methylation associated with the c.−107 A > T variant, explaining at least 1.25% of BRCA1 pathogenic variants in their multiple-case breast and ovarian cancer families . We previously identified two Box 1 Laboratory methods to assess DNA methylation46 Methylation-Specific PCR,47 EpiTYPERMassARRAY System48 and pyrosequencing.49 Although capable of detecting low levels of methylation (as low as 1%), these methods are laborious and low-throughput . Individual assays need to be designed for each region of interest, with each amplicon typically 100–200 base pairs (bp) in size to retain its sensitivity (500 bp for the MassARRAY). DNA of variable quality derived from blood, plasma, dried blood spots and formalin-fixed paraffin-embedded (FFPE) material can be applied to these methods.25 Loci-specific assays are cost-effective and their increased sensitivity makes them suitable for validation studies.Loci-specific methods include MethyLight Real-time PCR,54 although the sensitivity of the Illumina Methylation EPIC array seems to have improved markedly at these extreme DNA methylation levels.52 Arrays are suitable candidates for large epidemiology studies and have been instrumental in the success of a large number of EWAS.37 These arrays specifically assess the most informative regions of the epigenome such as gene promoters, CpG islands, enhancer and regulatory regions. Although relatively cost-effective in the context of throughput and number of evaluated CpG dinucleotides, further validation is often required due to their limited sensitivity. DNA derived from blood, plasma, dried blood spots, saliva, fresh-frozen and FFPE material have all been successfully assayed using this platform.56Array-based methods that measure DNA methylation at a large number of CpG dinucleotides across the genome are less sensitive than loci-specific methods particularly at the extremes of DNA methylation57 MethylC-seq,58 targeted bisulphite sequencing and whole-genome bisulphite sequencing (WGBS).59 These methods quantitatively detect methylation at single base resolution and, depending on the assay, can evaluate methylation at a specific region (e.g. targeted bisulphite sequencing), at CpG-dense areas (e.g. RRBS) or across the methylome(e.g. WGBS).The increased resolution of these techniques also enables the detection of technical variabilities not previously possible using lower resolution techniques. The community’s experience with WGBS is mixed: areas of variable coverage and bisulphite conversion inefficiencies within and between, samples have, in many instances, affected data interpretation. It should also be noted that, of the 28 million CpG dinucleotides across the methylome, 70–80% are stably methylated and therefore are uninformative across different cell types.43 Bisulphite sequencing methods are the most expensive of the laboratory methods and generally require high molecular weight DNA, which can be challenging for many epidemiology studies that only have access to limited quantities of often degraded DNA derived from field-collected bioresources. Additionally, as sample size increases, the common set of CpGs with quality data after an experiment may result in a matrix containing similar or fewer CpGs than an array-based approach.Bisulphite sequencing methods include reduced-representation bisulphite sequencing (RRBS),60) as well as environmental conditions later in life (e.g. air pollution62). Alterations in DNA methylation might hold great potential as surrogate markers for factors that are not easily measured by questionnaires—for instance, Boyne et al.62 and Johansson et al.63 have found that long-term hormonal exposure can be proxied through DNA methylation signatures.Measuring DNA methylation marks requires considerable attention to the hypothesis being tested to not only define the relevant target tissue, but to also ascertain the appropriate timing of the biological sample collection. Blood-based DNA methylation marks can be altered by intrauterine exposures  variants in the same gene.BRCA1 DNA methylation and breast cancer have more commonly been reported by studies that specifically examined BRCA1 promoter methylation in younger women (< 45 years) or stratified their findings by age at diagnosis.66 When we specifically sampled for early-onset breast cancer cases (< 40 years), the association with BRCA1 promoter methylation and breast cancer risk is much stronger .21 Wodjacz et al.67 measured BRCA1 methylation in post-menopausal women and failed to find any significant differences between cases and controls. Cho and colleagues68 assessed BRCA1 promoter methylation in women across a wide age range (< 45 to > 75 years old) but did not consider a sub-analysis of young women, which is particularly relevant as BRCA1 promoter methylation is more frequently detected in young women diagnosed with breast tumours and with a specific histological type.69 If the breast cancer risk associated with BRCA1 promoter methylation mimics germline pathogenic variants (where the incidence of breast cancer in BRCA1 mutation carriers rises rapidly after the age of 30 years70) the timing of DNA sampling and the case mix in terms of age and absolute risk in studies of DNA methylation will likely affect the relative risk estimates for methylation markers and breast cancer.Changes in DNA methylation over time can also affect the interpretation of data collected across different participant age groups. In Table 73 Using a prospective study design and three epigenetic clocks ,Kresovich  et al.77 found that a 5-year acceleration of epigenetic age  was associated with increased risk for breast cancer . The inclusion of data on epigenetic age acceleration might therefore be pertinent to increase the accuracy of breast cancer risk prediction models.Finally, epigenetic aging should also be taken into account  as it has been shown to be associated with risk of mortality and disease.BRCA1. We have outlined a number of issues for consideration before information about DNA methylation can be integrated into breast cancer risk prediction models. We used the example of BRCA1 to illustrate the challenges faced when considering DNA methylation changes in a breast cancer susceptibility gene, as well as to caution that even when considering single-gene studies, results can be heterogeneous depending on study selection, sampling and laboratory methods.Extensive efforts to identify DNA methylation marks associated with risk of breast cancer have so far identified a small number of potential DNA methylation marks of interest with the prime example being 78 and, globally, breast cancer remains the top cancer in terms of both incidence and mortality in women in most countries across continents79— additional methods are needed to identify these high-risk women. It is in this context that measures of DNA methylation, particularly for DNA methylation alterations in high and intermediate penetrant breast cancer susceptibility genes, combined with germline genetic testing of cancer susceptibility genes might identify young women who are at increased risk with more precision than current approaches and models. Women identified as being at high risk via these multi-omic approaches might benefit from supplemental screening modalities such as MRI. Incorporation of information regarding DNA methylation changes, including those that target high and intermediate penetrant breast cancer susceptibility genes, into risk assessment may therefore prove important in ages and settings where population-based screening by mammography is not possible.With management of the above concerns, this line of research has several strengths, including the prevalence of DNA methylation changes (which is often higher than that of genetic changes), the potential magnitude of the association between DNA methylation and cancer risk, and DNA methylation alterations reflecting in part changes in exposures and conditions during the course of life. These three factors combined suggest that overcoming the challenges in conducting studies and implementing measurements in the clinic might be outweighed by the gain in improved accuracy of risk prediction models and ultimately the more precise identification of individuals who can benefit most from early intervention and early detection. As an example, there is an increasing incidence of advanced breast cancer in young women under the age of 40 in the USA


Mycobacterium tuberculosis (Mtb) in patients during treatment may identify adaptations to antibiotic and immune pressure. Understanding the significance of genetic heteroresistance, and more specifically heterozygous resistance-associated variants (RAVs), is clinically important given increasing use of rapid molecular tests and whole genome sequencing (WGS).Studying within-host genetic diversity of We analyse data from six studies in KwaZulu-Natal, South Africa. Most patients (>75%) had baseline rifampicin resistance. Sputum was collected for culture at baseline and at between two and nine intervals until month six. Positive cultures underwent WGS. Mixed infections and reinfections were excluded from analysis.Mtb overall genetic diversity (at treatment initiation or major change to regimen) was associated with cavitary disease, not taking antiretroviral therapy if HIV infected, infection with lineage 2 strains and absence of second-line drug resistance on univariate analyses. Baseline genetic diversity was not associated with six-month outcome. Genetic diversity increased from baseline to weeks one and two before returning to previous levels. Baseline genetic heteroresistance was most common for bedaquiline (6/10 [60%] of isolates with RAVs) and fluoroquinolones (9/62 [13%]). Most patients with heterozygous RAVs on WGS with sequential isolates available demonstrated RAV persistence or fixation . New RAVs emerged in 9/286 (3%) patients during treatment. We could detect low-frequency RAVs preceding emergent resistance in only one case, although validation of deep sequencing to detect rare variants is required.Baseline Mtb infections, baseline within-host bacterial genetic diversity did not predict outcome but may reveal adaptations to host and drug pressures. Predicting emergent resistance from low-frequency RAVs requires further work to separate transient from consequential mutations.In this study of single-strain Wellcome Trust, NIH/NIAID Mycobacterium tuberculosis (Mtb) genetic diversity has been seen in tuberculosis (TB) patients who are failing treatment and in genes responsible for encoding bacterial cell wall lipids and for the response to antibiotics.High-throughput sequencing can identify genetic differences among bacteria infecting an individual patient (referred to as within-host genetic diversity). Greater The clinical implications of low-frequency resistance mutations are unclear. Engelthaler et al. identified low-frequency mutations pre-dating phenotypic resistance in around 40% of patients who acquired resistance to second-line drugs, while modelling by Vargas et al. suggested that resistance mutations at >19% frequency often increase to fixation but those at lower frequencies tend to disappear in sequential sampling. However, the dynamics of low-frequency resistance mutations have not been studied prospectively.Mtb strains and absence of second-line drug resistance. We identified a transient increase in genetic diversity in the initial weeks following treatment initiation or a major change to regimen that was caused by genes from a variety of functional categories. Heteroresistance conferred by resistance-associated variants at frequencies >30% persisted or became fixed in all but one case. Low-frequency resistance-associated variants at <5% were not associated with the identification of emergent resistance.We found that baseline overall genetic diversity was associated on univariate analysis with presence of lung cavities, not taking antiretroviral therapy (if HIV co-infected), infection with lineage 2 Studying genes responsible for within-host pathogen genetic diversity during treatment may help identify new mechanisms of adaptation to drug and immune pressure. Our data were consistent with work suggesting that low-frequency resistance-associated variants (e.g. <5%) are not clinically relevant while those at higher frequency (e.g. >30%) are. Larger studies with frequent sampling and clinical outcome data using a testing system validated to detect a range of allele mixtures are required to delineate a clear minimum variant frequency threshold and inform roll-out of molecular and sequencing technologies.Alt-text: Unlabelled box1Mycobacterium tuberculosis complex (MTBC), consisting of seven human-adapted lineages as well as several animal-adapted strains which may also cause human disease, is a major human pathogen. The most prominent species in the complex, Mycobacterium tuberculosis (Mtb), represents the infectious agent causing most deaths worldwide Mtb has traditionally been viewed as a genetically homogenous bacterium that has evolved into a specialised human pathogen with a lower mutation rate than most other bacteria and no accessory genome or potential for horizontal gene transfer Mtb genetic diversity which may result from mixed infection with multiple strains or within-host microevolution of a single infecting strain Mtb infections typically last months to years and within-host bacterial populations may peak at over one billion colony forming units. At one extreme, up to 50 consensus-level SNP differences having been reported to occur over the duration of infection in patients with advanced disease when sampling from multiple body sites The Mtb genomes originating from individual bacteria) within a population  are genetically distinct. It can be measured by nucleotide diversity (π), which is the mean number of nucleotide substitutions per site between any two randomly selected DNA sequences in a population, and takes a value between 0 and 1 Within-host genetic diversity reflects the extent to which genomes  seemed unaffected Understanding factors affecting within-host Mtb directly from sputum identifies more genetic diversity than sequencing from culture [However, when reanalysing data collected by other groups, it may be difficult to account for the role that pathogen culture and subsequent subculturing steps may have on genetic diversity through random loss or selection of culture-adapted subpopulations. We and others have shown that culture-independent sequencing of  culture ,16, whilMixed populations of wild-type alleles and RAVs confers heteroresistance, where populations of resistant and susceptible bacteria co-exist within the same host. This may occur as the result of differential drug penetration to spatially and pathologically distinct lung regions The limit of detection for heterozygous variants depends on several factors including the number of reads covering a site, the minimum number of supporting reads required to call a variant and error rates at each step of the sequencing pipeline. For example, in this study where we have included sequences with a minimum 55x mean genome coverage with four reads required to call a variant, the limit of detection would be an allele frequency of 7%. We also use deep sequencing to >3000x coverage, where using a minimum of 6 supporting reads gives resolution to identify variants down to 0.2% frequency. However, the important caveat to this is that as coverage depth increases and the limit of detection decreases, the potential false positive rate due to PCR or sequencing errors increases. Using a base quality score of >30 (an error of 1 in 1000) and published DNA polymerase error rates Mtb genetic diversity in single strain infections and assess whether this type of genetic diversity is associated with poor outcomes. We then investigated the outcomes of genetic heteroresistance and whether presence of microheteroresistance could predict the emergence of macroheteroresistance.In this study we used prospectively enrolled patients with TB to identify factors associated with within-host 22.1We analysed data from five observational cohort studies and a randomised controlled trial in KwaZulu-Natal, South Africa, as described in Mtb isolate with sufficient mean genome-wide coverage depth for the proposed analysis: 80x for evaluation of baseline diversity, 55x for evaluation of diversity and heteroresistance in sequential isolates. Minimum coverage thresholds were chosen to maximise sequencing coverage depth, and therefore ability to identify low level variation, while ensuring that sufficient numbers of samples fulfilled the criteria for inclusion. A lower threshold was used for the sequential analysis as some follow-up samples did not meet the minimum 80x coverage depth threshold. As described below, sequences for diversity analyses were downsampled to a standard mean coverage, while original coverage was used for heteroresistance. A list of isolates included in each analysis and associated accession numbers is available in the Supplementary Spreadsheet.Every patient from these cohorts was included if they were enrolled prior to February 2019 and were culture positive with a baseline 2.2Mtb culture. Positive cultures underwent phenotypic DST for first- and second-line drugs on 7H11 agar using the 1% agar proportion method All sputum samples were decontaminated, homogenised and inoculated into liquid mycobacterial growth indicator tube (MGIT) and on Middlebrook 7H11 solid agar for 2.3DNA was extracted for whole genome sequencing (WGS) from all positive MGIT cultures by mechanical ribolysis before purification with AMPure XP beads  (full description in Supplementary Methods). When MGIT cultures yielded insufficient DNA for sequencing or were contaminated, DNA extraction from the parallel 7H11 culture or a 7H9 subculture was attempted. Given that multiple subculturing steps have been shown to reduce diversity, at most a single subculture step was used if required. Sequencing libraries were prepared with NEBNext Ultra II DNA  according to the manufacturer's instructions, with between four and fourteen PCR cycles depending on input DNA concentration. Batches of 48 multiplexed isolates were sequenced on a NextSeq 500  with 300-cycle paired end runs using a mid-output kit.Mtb DNA from drug resistance-associated genes (Supplementary Table 1). Overlapping 120 base pair RNA baits were designed using SureDesign (Agilent) to cover the entire positive strand of over 800 Mtb genomes downloaded from NCBI Sequencing Read Archive selected to cover the global genetic diversity of Mtb. Bait design is freely available from the authors on request. Genomic DNA was fragmented to approximately 330 base pairs, which is longer than the standard sequencing read of 150 base pairs. Hybridisation and library preparation were performed with the SureSelectXT Low Input kit (Agilent) protocol according to the manufacturer's instructions, including 20–23 PCR cycles depending on input concentration. Genomic libraries were multiplexed with 96 isolates sequenced on a NextSeq 500  300-cycle paired end run using a mid-output kit.Where emergent RAVs were identified by WGS, targeted deep sequencing was performed retrospectively on stored surplus extracted DNA from preceding isolates. A custom-designed SureSelect bait set  covering genes associated with drug resistance comprising approximately 3% of the genome was designed and used for target capture of 2.4esx genes and those categorised as insertion sequences and phages Raw sequencing reads had adapters removed with Trim Galore v0.3.7 Two steps were used in order to identify mixed infections. First, isolates with evidence of heterozygous SNPs at established lineage-specific sites k-mers allocated to the genus Mycobacterium using the MiniKraken2 v2 database (updated 23/04/2019). To prevent differences in coverage depth potentially affecting measures of π, aligned sequences were downsampled using Picard Tools v2.20 Rv0678, Rv1979c and rplC were recategorised as antibiotic resistance genes), antigen, essential or non-essential as described previously The level of isolate contamination was estimated using Kraken2 Mycobacteria in the isolate affected within-patient genetic diversity, as each PCR cycle comes with a theoretical risk of introducing base errors. We also investigated the possibility that contaminating reads erroneously mapped to Mycobacteria may inflate the genetic diversity.We first aimed to determine if the number of PCR cycles in library preparation or proportion of reads assigned to Rv0678 and delamanid resistance-associated genes fbiA/B/C, fgd1 and ddn, where a wide array of different mutations leading to loss of function occur, we took a more sensitive approach and manually evaluated all non-synonymous SNPs and indels for potential to cause resistance. We assumed that all loss of function mutations (frameshift or premature stop codons) to be potentially causative of resistance, as well as any SNPs previously reported to be associated with resistance.For identification of RAVs, we used a curated list of resistance-conferring mutations adapted from published sources ,44 as li2.5Mtb culture negative at months 5 and 6) or unfavourable .Structured clinical record forms were completed at all visits by dedicated research nurses. Responses were taken from patients verbally as well as from clinic/hospital records. Chest radiography findings were recorded as documented by the treating clinician. Six-month outcome was assessed as favourable . Distributions of non-parametric data were compared with Wilcoxon rank-sum test. Binary outcomes were assessed by logistic regression and continuous variables with linear regression. The Benjamini-Hochberg procedure was used to control for multiple tests when evaluating change in median π between baseline and sequential timepoints with Q  of 0.1, which should yield less than one false positive test when performing seven tests.33.1Mtb culture for sequencing. Treatment data were available for 334/399 (84%) patients and are described in the next paragraph. In some of these 334 cases there was discordance between the initial clinical resistance profile upon which drug treatment decisions were based and the study WGS profile: 2/66 (3%) patients initially diagnosed with drug-susceptible TB (DS-TB) actually had multidrug-resistant TB (MDR-TB) at baseline, and 25/268 (9%) patients treated for MDR-TB did not have rifampicin resistance detected on WGS. This may have been due previous mixed infection with other strains not found in the study sample, false positive/negative DST results or lab error.A total of 399 from the 710 patients recruited into six different studies described in All patients with DS-TB were treated with the standard treatment regimen . In line with global developments, there were substantial changes in DR-TB treatment regimens over the course of this study (2016–2019). At the beginning of this period, long regimens (20 months+) based on a fluoroquinolone and kanamycin were standard. For MDR-TB, these were progressively replaced with the short (nine-month) regimen based on kanamycin and a fluoroquinolone, and later bedaquiline and a fluoroquinolone. Pre-extensively/extensively drug-resistant TB (pre-XDR/XDR-TB) treatment remained long individualised regimens, although use of bedaquiline and linezolid became more common. Among the 268 patients with DR-TB for whom we have treatment data, all except three had been treated with bedaquiline or kanamycin: 128 received bedaquiline, 118 kanamycin and 19 received both or switched from kanamycin to bedaquiline. All except two patients with DR-TB received a fluoroquinolone. Full baseline treatment information in included in the Supplementary Spreadsheet.There were 352 patients with sequences meeting the minimum coverage threshold of 80x included in the analysis of baseline isolates (collected at initiation or major change to treatment). We excluded 21/352 isolates (6.0%) that we characterised as mixed infection, and conducted detailed analyses on the remaining 331 single strain infections. We did not identify any patient or bacterial factors significantly associated with likelihood of mixed infection (Supplementary Table 2). Patients were only included in the analysis of sequential isolates if they had >1 successfully sequenced positive culture after excluding isolates with evidence of reinfection or superinfection since baseline (<15 SNP differences from baseline isolate) or mixed infection. There were 348 individual isolates from 118 patients with >1 timepoint at coverage above 55x included in the sequential analysis.3.2p = 0.72, equality of medians test, Supplementary Figure 1). There was also no evidence for an association between π and the proportion of reads assigned to Mycobacteria , suggesting that high stringency mapping with BBMap adequately prevented mapping of contaminating reads. Finally, we tested whether isolates grown on solid media or after a single subculture in 7H9 were less diverse than those sequenced from MGIT culture, and again found no evidence for a statistically significant difference in π between groups . We therefore concluded that the number of PCR cycles, isolate contamination and culture media did not significantly impact on within-host genetic diversity for the purposes of this study. As expected, mixed infections had significantly higher median π than single strain infections .Among single strain infections, there was no difference in median π when isolates were stratified by the number of PCR cycles , and while there was evidence for a linear trend of increase of π with higher TTP , it explained only a small proportion of the variance (R-squared 1.8%).As bacterial load was expected to affect π, we tested for association with two measures of bacterial load: sputum smear grading and time to positivity of the original MGIT isolate (TTP). Smear grading is expected to increase and TTP to decrease with higher bacterial burden. There was no linear trend between increasing smear grade and π , taking ART (if HIV positive) (p = 0.017) and who were taking TB treatment (p = 0.049) at the time of enrolment were independently associated with a lower π on univariate analysis. In addition to these patient factors, we also examined bacterial lineage and drug resistance profile (p<0.001). While there was no difference in π between patients with genetically DS-TB and TB genetically resistant to first line drugs , patients with genetic second-line drug resistance (pre-XDR/XDR-TB) had lower π than patients with DS-TB (p<0.001) and RR-TB (p<0.001).To investigate how π varied in patients starting/changing TB treatment, we tested its association with the following patient factors: age, sex, HIV status, CD4 count <200 ), cavitation on chest radiography, previous TB infection and if the patient was on TB treatment at the time of enrolment (for example for DS-TB prior to detection of drug resistance) . We foun profile . Lineage3.3We next tested if there was a correlation between baseline (initiation or major change to treatment) π and six-month outcome. Six-month outcomes were available for 297/331 (89.7%) patients with single strain infection, of whom 236/297 (79.5%) had a favourable six-month outcome. Unfavourable outcomes were due to death , loss to follow-up , and microbiological failure without acquired genetic resistance  or with acquired genetic drug resistance . We found that male sex, HIV coinfection and not taking ART therapy if HIV positive were associated with higher risk of unfavourable outcome (Supplementary Table 3). Interestingly, genetically MDR-TB infection was not associated with worse outcomes at month six than genetically DS-TB, although pre-XDR/XDR-TB infection was on univariate analysis . Mixed i3.4−7, and not significantly different  to the value of 9.48 × 10−7 from the 331 baseline isolates downsampled to 80x analysed above. When comparing median π amongst all isolates at each timepoint, week 1 and 2 isolates showed an increase in π to 1.14 × 10−6 and 1.15 × 10−6 respectively  were downsampled to mean 55x genome-wide coverage. There were 118 patients included in this analysis, of which 115 had baseline isolates that met inclusion criteria. Three patients were excluded as they did not have a baseline sequence meeting criteria. Median π in these 115 isolates was 8.67 × 10ectively , while t3.5Rv1319c, a putative adenylate cyclase responsible for regulation of cellular metabolism was the gene displaying the highest within-host genetic diversity.We then assessed within-isolate genetic diversity for all coding genes at baseline (initiation or major change to treatment). In addition to functional categories, two additional redundant categories of antibiotic resistance genes and antigen genes were generated as described in the methods. There was no difference in mean π between any gene or functional category in the 331 single strain baseline isolates downsampled to 80x coverage . The mosRv0678, a gene responsible for bedaquiline and clofazimine resistance, and which occurred exclusively in patients who were treated with bedaquiline and subsequently acquired clinical resistance.To establish which functional categories of genes were driving changes in π over time, we examined π in the sequential isolates from the 115 patients described above. Diversity increased across multiple functional categories at the week 1 timepoint, before decreasing but remaining more diverse than at baseline . At each3.6katG RAVs, 60.4%) as shown in We then assessed the prevalence of heteroresistance in isolates with a minimum coverage depth of 55x that were not mixed infections. There were 399 baseline isolates included in this analysis, with genetic resistance (presence of ≥1 heterozygous or fixed RAV) most frequently identified in baseline isolates for rifampicin (77.1%) and high-level isoniazid resistance (Rv0678 M146T mutation at 2.0% frequency (associated with bedaquiline resistance), one rpoB S450L mutation at 9.1% frequency (rifampicin resistance) and one embB D328Y mutation at 43.9% frequency (ethambutol resistance). In the five cases of rifampicin heteroresistance that became fixed or persisted, all patients actually had two separate hetRAVs, all of which displayed dynamics consistent with clonal interference of two resistant subpopulations  or heteroresistance persisting with at least one hetRAV   of patients from cohorts where follow-up samples were routinely collected  variants in preceding isolates, we performed deep sequencing of resistance-associated genes (Supplementary Table 1) with minimum and mean coverage of 2897x and 4072x respectively for seven of the nine patients with emergent resistance and sufficient surplus DNA after standard WGS. Deep sequencing revealed large numbers of low-frequency variants. For example, the mean number of 4Mtb isolates at the whole genome level, across gene classes and at specific resistance-associated sites in several South African TB cohorts. A particular strength of this study is that all cohorts were prospective, reducing the potential for bias in patient selection and by limiting the number of subculturing steps prior to and following isolate storage.In this study we have analysed within-host diversity of The 6% rate of mixed infection that we identified is not as high as the 10–20% that has been reported in other studies in high-burden settings ,52, espeWe sought to determine if technical factors such as subculturing in liquid or solid media and isolate contamination could affect within-host genetic diversity, and reassuringly none had a measurable effect. While subculturing has been shown to lead to a loss in genetic diversity, we limited subculturing to a single event, tried to transfer the maximum volume of liquid culture possible or scrape all colonies from solid plates. To prevent cross-mapping of contaminating reads artefactually inflating within-host genetic diversity, we used sequence read mapping software that allows stringent read-identity parameters to be set which appeared effective on this dataset.Mtb lineage 2 was correlated with greater diversity than lineage 4, supportive of the proposed higher mutation rate in lineage 2 [We expected diversity to be related to bacterial load in the original patient sample but the absence of a correlation with smear positivity or TTP may be because these are not ideal measures of true within-patient bacterial load, being inherently limited by the stochasticity of sputum sampling and potential culture-induced bias. Conversely other factors such as drug penetration or immune control may be more important drivers of within-host genetic diversity than bacterial population size. Supporting this, we did find greater genetic diversity in patients with cavitary disease which may lead to spatially separated bacterial subpopulations, likely exposed to a different microenvironment for example in respect to drug concentrations . We alsoineage 2 ,56. DS aOverall, clinical outcomes were excellent with similar six-month outcomes in DS and MDR-TB, backing up recent promising observational reports from MDR-TB programmes Rv0678 among patients who had acquired bedaquiline resistance.There was a significant increase in overall genomic π at weeks 1–2, which then reduced back to similar levels to baseline throughout the rest of treatment . There wpncA mutations remain disputed Rv0678 bedaquiline/clofazimine RAVs often persisted as heterozygous variants often throughout treatment, suggesting there is an as yet undetermined underlying biological explanation why they fail to reach fixation. A driving factor behind heteroresistance to other antimycobacterial drugs may be the existence of Mtb subpopulations in lung cavities, where some drugs may penetrate at subtherapeutic concentrations or not at all [Frequency of genetic heteroresistance varied considerably by drug and was most common for bedaquiline and fluoroquinolones and rare for isoniazid and rifampicin in line with previous descriptions t at all ,62. WhilWhile the majority of hetRAVs persisted or became fixed, some of those at lower frequencies disappeared in sequential sampling. The current allele frequency limit of detection of Xpert MTB/RIF for rifampicin heteroresistance by Xpert MTB/RIF is 20–80% Rv0678 variants conferring bedaquiline/clofazimine cross-resistance accounted for the majority of emergent resistance mutations, which is likely to reflect both the relative importance of these drugs to current regimens and also that it may be comparatively easy for Mtb to acquire resistance due to the large mutational target for bedaquiline/clofazimine resistance with any mutation disrupting the function of the Rv0678 gene removing repression of the MmpL5 efflux pump.Emergent resistance during treatment was rare in this study compared to reports from only a few years ago quoting acquired second-line drug resistance in >25% of patients with MDR-TB during treatment Mtb using a culture and sequencing pipeline that has been formally validated with in vitro allele mixtures of different frequencies covering the range of interest. However, as very few of the emergent RAVs were identified even among the multiple low-frequency variants that may represent standing variation or sequencing error While there have been several reports of microheteroresistance  RAVs pre-dating the emergence of macroheteroresistance  or fixed resistance, in this prospectively enrolled cohort we found a high degree of noise with large numbers of very low-frequency variants detected on retrospective deep sequencing of isolates collected preceding the emergence of resistance of WGS. This may reflect either high turnover of variants that are rapidly selected against and/or sequencing error, and effectively separating the two remains challenging without a gold standard to discriminate the two. SureSelect RNA bait enrichment has previously been shown to not to bias population structure of heterozygous alleles with frequency >1% Mtb, WGS was performed on cultured isolates that may not truly represent the true within-host diversity. Sequencing directly from sputum is now possible but had not been implemented on sufficient scale to utilise during this study period. Not all cohorts collected samples at all timepoints, with week 1 data sourced from two cohorts and week 2 data from one cohort. Some patients were already taking treatment at the time of study entry, and although to be enrolled the patient would have to still be culture positive and require addition of ≥2 new drugs, it is possible that pre-treatment may have affected genetic diversity. Additionally, we selected cohorts that primarily recruited patients with DR-TB as these patients were expected to be at higher risk of acquiring further drug resistance and have prolonged culture positivity compared to patients with DS-TB. Therefore, while the findings may be true for these cohorts which ultimately had good treatment outcomes and relatively rare acquisition of further resistance, they do not necessarily represent a true cross-section of patients with TB in South Africa or elsewhere in the world.Limitations of this study include that, as for the majority of studies of Mtb genetic diversity identifies changes in genetic diversity during treatment and is a potential tool to investigate the bacterial response to selective pressures. Further work is required to delineate allele frequency thresholds for identification of significant low-frequency RAVs, and frequent sampling may be required to identify them. The emergence of bedaquiline/clofazimine and delamanid resistance is concerning and highlights the need for rigorous antimicrobial stewardship and resistance monitoring.In conclusion, in this prospective study of South African patients starting TB treatment, most of whom had at least first line drug-resistance, there was no association between within-host genetic diversity and clinical outcome for patients infected with a single strain. Studying within-host