TCRB locus and, specifically for the functional TCR repertoire, variation in the major histocompatibility complex locus. Further, we identify specific variations in the genes encoding the Artemis protein and the TdT protein to be associated with biasing junctional nucleotide deletion and N-insertion, respectively. These results refine our understanding of genetically-determined TCR repertoire biases by confirming and extending previous studies on the genetic determinants of V(D)J gene usage and providing the first examples of trans genetic variants which are associated with modifying junctional diversity. Together, these insights lay the groundwork for further explorations into how immune responses vary between individuals.Every T cell receptor (TCR) repertoire is shaped by a complex probabilistic tangle of genetically determined biases and immune exposures. T cells combine a random V(D)J recombination process with a selection process to generate highly diverse and functional TCRs. The extent to which an individual’s genetic background is associated with their resulting TCR repertoire diversity has yet to be fully explored. Using a previously published repertoire sequencing dataset paired with high-resolution genome-wide genotyping from a large human cohort, we infer specific genetic loci associated with V(D)J recombination probabilities using genome-wide association inference. We show that V(D)J gene usage profiles are associated with variation in the  Receptor proteins on the surfaces of T cells are an essential component of the cell-mediated adaptive immune response in humans. Cells throughout the body regularly present protein fragments, known as antigens, on cell-surface molecules called major histocompatibility complex (MHC). Each T cell expresses a randomly-generated T cell receptor (TCR) which can bind the MHC-bound peptide and, if necessary, initiate an immune response. As part of this immune response, a T cell will proliferate and subsequent clones of that T cell will inherit the same antigen-specific TCR. Over time, the collection of all TCRs in an individual (the TCR repertoire) will dynamically summarize their previous immune exposures .TCRB) locus over a series of steps. First, the intervening chromosomal DNA between a randomly chosen D- and J-gene is removed to form a hairpin loop at the end of each gene J recombination with a selection process for proper expression and MHC recognition. Each TCR is composed of an ach gene . Next, tach gene . Dependiach gene . From heach gene . This nuach gene . Next, nach gene . Once thmolecule .TCR repertoires vary between individuals and are a complicated tangle of genetically determined biases and immune exposures. Disentangling these factors is essential for understanding how our diverse repertoires support a powerful immune response. Previous efforts to unravel the genetic and environmental determinants governing TCR repertoire diversity have been highly impactful despite lacking high-throughput TCR repertoire sequencing data  and/or hTCRB loci variations have an important role in determining the V(D)J gene usage profiles of each individual’s repertoire. At the junctions, we demonstrate that variations in the genes encoding the Artemis protein and the TdT protein are associated with biasing V- and J-gene nucleotide deletion and V-D and D-J-junction N-insertion, respectively.In this paper, we directly address the question of how an individual’s genetic background influences their V(D)J recombination probabilities using large human discovery and validation cohorts for which both TCR immunosequencing data  and genoWe worked with paired SNP array and TCRβ-immunosequencing data representing 398 individuals and over 35 million SNPs and/or indels . TCR seqIn the data cohort of 398 individuals, an average of 235,054 unique TCRβ-chain nucleotide sequences were sequenced per individual. Within each individual repertoire, roughly 18% of sequences were classified as ‘non-productive’. Thus, we can analyze the productive and non-productive sequences separately to distinguish between TCR generation and selection effects within each TCR repertoire. Specifically, we inferred the associations between genome-wide variation and V(D)J gene usage of each V-, D-, and J-gene, the extent of TCR nucleotide trimming, the number of TCR N-insertions, and the fraction of non-gene-trimmed TCRs containing P-nucleotides for both productive and non-productive sequences .To quantify the effect of SNPs on the expression of various V-, D-, and J-genes during V(D)J recombination, we designed a fixed effects model to assess the relationship between SNP genotype and gene frequency across all individuals. We fit this ‘simple model’ for each different V-, D-, and J-gene in our paired dataset.Because of the potential for population-substructure-related effects to inflate associations between each SNP and gene usage frequency, we incorporated ancestry-informative principal components  based onTCRB locus for both productive and non-productive TCRs. The TCRB gene locus encodes the variable V-, D-, and J-gene segments which are recombined during V(D)J recombination. In our dataset, there are 60 V-genes, 2 D-genes, and 14 J-genes uniquely expressed. As we would expect, we find that the expression of many of these genes is associated with variation in the TCRB locus  followed by the expression of TRBD2 (median effect size = 0.035) and the expression of TRBV28 (median effect size = 0.019) all in productive TCRs  with expression of the gene TRBV28 within both productive (TCRB locus and expression of the gene TRBV7-3 within productive TCRβ chains (232 significant associations) and the gene TRBJ1-2 within non-productive TCRβ chains (290 significant associations).With these methods, we consider the significance of associations at a Bonferroni-corrected whole-genome p-value significance threshold of ome-wide . Of thesRB locus . For theive TCRs . VariatiTCRB locus, we also identified 1242 significant SNP associations within the major histocompatibility complex (MHC) locus. MHC proteins act by presenting self and foreign peptides to TCRs for inspection. Because of this important role in the functionality of T cells, the TCR-MHC interaction is important for thymic selection. We observe the expression of 12.1% of V-genes for productive TCRs to be associated with variation in the MHC locus. For the significantly associated MHC locus SNPs, the median association effect magnitude was largest for the expression of TRBV4-1 (median effect size = –0.004) followed by the expression of TRBV10-3 (median effect size = 0.0033)  . This asous work .ZNF443 and ZNF709 loci on chromosome 19. Both of these zinc finger proteins contain KRAB-domains and, thus, likely act as transcriptional repressors J TCRB gene choice  will on average be trimmed to different extents due, in part, to differences in their terminal nucleotide sequences (and the same is true for D- and J-genes). Thus, to quantify the effect of SNPs on the extent of V-, D-, and J-gene trimming during V(D)J recombination, without confounding the extent of trimming with TCRB gene choice, we designed a linear fixed effects model to measure the correlation between each SNP and the number of nucleotide deletions, while conditioning out the effect mediated by gene choice. We fit this ‘gene-conditioned model’ for each of the four trimming types  on our paired data set. We performed the analysis, as above, incorporating ancestry-informative principal components in each model . Diagnostic statistics show that this correction for population-substructure-related biases is sufficient J-recombination-associated genes, may influence the extent of TCR nucleotide trimming at V(D)J e choice . In othefficient . Here, wDCLRE1C gene locus for both productive and non-productive TCRs when considered in the whole-genome context. For these significant DCLRE1C locus SNP associations, the magnitudes of the effects were greater for non-productive TCRs compared to productive TCRs for both V-gene trimming and J-gene trimming J recombination. Many of the SNPs responsible for these 66 significant associations within the DCLRE1C locus were shared between trimming and productivity types , we no longer observe these associations between SNPs within the TCRB gene locus and the extent of 5’ end D-gene trimming J recombination J recombination (Because P-nucleotides can be present at V(D)J junctions in the absence of nucleotide trimming , we hypobination . We obsebination .TCRB gene choice J e choice . Thus, te choice .DNTT gene locus (rs2273892 and rs12569756) were responsible for these associations. The DNTT gene encodes the terminal deoxynucleotidyl transferase (TdT) protein which is a specialized DNA polymerase responsible for adding non-templated (N) nucleotides to coding junctions during V(D)J recombination. When we restrict our analysis to TCRs which contain TRBJ1 genes and consequently eliminate potential D-gene assignment ambiguity, we continue to observe these DNTT associations  . Two SNPciations .DNTT locus to be weakly associated with the number of N-insertions at V(D)J gene junctions, we wanted to explore the locus further. Restricting the analysis to the extended DNTT locus reduced the multiple testing burden such that 232 significant associations emerged  across TCR repertoires by PCA cluster, we note that subjects from the ‘Asian’-associated PCA cluster have significantly fewer total N-insertions for productive  . The totrection) .To validate our results, we worked with paired ancestry-informative marker (AIM) SNP array and TCRα- and TCRβ-immunosequencing data representing 94 individuals and 2 SNPs (which overlap with the discovery dataset) from an independent validation cohort  within the DCLRE1C locus was present in both SNP sets. This SNP was one of the significant associations we observed for V-gene trimming  failed to reach statistical significance for all N-insertion types, but had the same direction of effect in the validation cohort as follows. Within the discovery cohort, rs3762093 genotype was weakly associated with the number of V-D N-insertions  confirmed and extended previous studies on the genetic determinants of TCR V-gene usage, (2) discovered associations between common genetic variants within the DCLRE1C and DNTT loci and V(D)J junctional trimming and N-insertions, respectively, (3) developed a method for quantifying the extent of the associations between genetic variations and junctional features, directly, without confounding gene choice effects, and (4) revealed differences in the extent of N-insertion by ancestry-informative PCA cluster.V(D)J recombination is a complex stochastic process that enables the generation of diverse TCR repertoires. Our results show that genetic variation in various V(D)J recombination genes has a key role in shaping the TCR repertoire through biasing V(D)J gene choice, nucleotide trimming, and N-insertion in a broad population sample. While we recognize that there may be a complicated entanglement between allelic variation and local TCRB locus and V(D)J gene usage biases for both productive and non-productive TCRs. Although previous reports have revealed similar patterns of association for productive TCRs (TCRB locus variation on V(D)J gene usage for non-productive TCRs. This highlights that locus variation is associated with TCR generation-related gene usage biases, in addition to potential thymic selection biases for productive TCRs. These TCR generation-related gene usage biases likely reflect local gene regulation and/or recombination efficiency effects. For example, one of the SNPs most significantly associated with TRBV28 expression (rs17213) is located within the recombination signal sequence at the 3’-end of the gene and, thus, could be involved directly in changing the recombination efficiency of TRBV28. Thus, different expression levels of various genes could be promoted by variation within non-coding regions such as promoters, 5’UTRs and leader sequences, introns, or recombination signal sequences. Polymorphisms within these regions have been suggested to influence V(D)J gene expression levels within B-cell receptor repertoires  is associated with the extent of V- and J-gene nucleotide trimming for both productive and non-productive TCRs. These associations are strongest for non-productive TCRs suggesting a TCR generation-related repertoire bias. It is well established that the Artemis protein, in complex with DNA-PKcs, functions as an endonuclease responsible for cutting the hairpin intermediate, and thus, potentially creating P-nucleotides prior to nucleotide trimming during V(D)J recombination  was one of the significant associations we observed for V- and J-gene nucleotide trimming in both the primary cohort and the independent validation cohort. Perhaps this mutation, or other linked non-synonymous DCLRE1C variation that was not studied here, is directly involved in the trimming changes we observe. We did not observe strong associations between variation in the DCLRE1C locus and the number of P-nucleotides or the fraction of non-gene-trimmed TCRs containing P-nucleotides, despite the established mutually exclusive relationship between P-addition and nucleotide trimming  and the number of N-insertions for both productive and non-productive TCRs. Because of the established, direct involvement of the TdT protein in the N-insertion mechanism, these DNTT locus variations could be influencing the function of the TdT protein. These significant associations were slightly stronger for non-productive TCRs perhaps suggesting that thymic selection may limit the mechanistic effects of locus variation. Interestingly, we noted that the extent of N-insertion varies by ancestry-informative PCA cluster. Specifically, we found that the ‘Asian’-associated PCA cluster had significantly fewer N-insertions for productive TCRs when compared to the population mean which is dominated by the ‘Caucasian’-associated PCA cluster. This finding is, perhaps, related to the influence of broad heritable factors biasing the extent of N-insertions.Further, we have identified associations between variation in the gene encoding the TdT protein ; however, experimental work will be required to determine whether these SNPs modify the expression of There are several key limitations of our approach which are intrinsic to the data used in this study. First, the lack of overlap between SNP sets for the discovery and validation cohorts limited our ability to directly validate our strongest inferences. Next, it is possible that the SNP array data used here does not capture all potential causal variation. As such, a significantly associated SNP present in our SNP array data could simply be in linkage disequilibrium with a causal SNP which was either poorly imputed or not tested here. Previous work has suggested that polymorphisms within the immunoglobulin V-gene region are not completely captured by existing SNP array technology, and have been underrepresented in previous genome-wide association studies . SNP covTCRA and TCRB loci. The TCRA and TCRB regions have been historically difficult to reliably map using short read sequencing due to their repetitive and complex nature. While recent work has identified many new TRBV alleles, many more undocumented TRBV alleles likely remain to be discovered (TCRB locus limited our ability to infer the correct V(D)J -gene allele for each final nucleotide sequence. Further, the TCR sequencing technology used here leverages relatively short-read sequencing which captures only a portion of the V-gene present in each sequence. Because many TRBV alleles are identical to other TRBV alleles for much of the V-gene region present in these sequences, it can be difficult to unambiguously assign V-gene usage to the final nucleotide sequences. D-gene usage assignment is also challenging due to the short length of the TRBD alleles (12–16 nucleotides before nucleotide trimming and N-insertion). We have found that controlling for D-gene assignment ambiguity in the nucleotide trimming and N-insertion analyses results in similar significant associations within the DNTT and DCLRE1C loci. Although we cannot rule out some effect of incorrect V(D)J -gene assignment bias for trans associations resulting from the signal being ‘masked’ by stronger TCRB locus signals, these biases seem to be mostly restricted to cis associations.Another key constraint is the challenge of inferring the V(D)J rearrangements from the final nucleotide sequences due to the poor characterization of the scovered . As suchTCRB genes is associated with variation in MHC and TCRB loci, the number of N-insertions is associated with DNTT variation, and the extent of nucleotide trimming is associated with DCLRE1C variation. Our results clearly demonstrate how variation in V(D)J recombination-related genes can bias TCR repertoire combinatorial and junctional diversity. In the case of B cells, genetically determined V(D)J gene usage biases within B-cell receptor repertoires have been linked to functional consequences for the overall immune response to specific antigens and, thus, an increased susceptibility to certain diseases  allele sequences . Details of the SNP array dataset, genotype imputation, and quality control have been described previously .Peripheral blood mononuclear cell (PBMC) samples were collected from 150 healthy subjects recruited at the Health Center Sócrates Flores Vivas (HCSFV) in Managua, Nicaragua . HealthyWith these samples, PBMCs were stained with CD3-PerCP eFluor710 (Thermo Cat. 46-0037-42), CD4-BV650 (BD Biosciences Cat. 563875), CD8-APC Fire750 (Biolegend Cat. 344746), and gdCy7 (Biolegend Cat. 331222). Briefly, after thawing from cryopreservation and plating in a 96-well round bottom plate, cells were spun down and resuspended in 50 μL of human Fc block (BD Biosciences Cat. 564220) in Dulbecco’s phosphate-buffered saline (DPBS) at 5 μL per test . Bulk repertoires for sorted CD4 and CD8 T cells were generated in accordance with the protocol developed by Genotypes for SNPs of interest corresponding to 94 of these subjects were pulled from Infinium Global Screening Array-24 v3.0 BeadChip results, which measures 654,027 SNP markers including multi-ethnic genome-wide content, curated clinical research variants, and quality control markers. High quality DNA was extracted using the Qiagen QIAamp DNA Mini Kit (Cat. 51306), and submitted to the St. Jude Hartwell Center for preparation and processing. Two SNPs, rs72640001 and rs72772435, were not included on this chip and were determined using Thermo Fisher TaqMan SNP Genotyping Assays  and TaqMan Genotyping Master Mix (Cat. 4371353) according to the kit manual.With these paired SNP array and TCR-immunosequencing for both the discovery and validation cohorts, we aimed to identify significant associations between these SNPs and various TCR repertoire features. Because we would expect a difference in these phenotypes depending on whether a TCR sequence is classified as productive or non-productive, we split the data based on this TCR productivity status and computed associations separately for the two groups.TCRB genes for each sequenced TCR does not report a D-gene. Instead, this D-gene (if it is indeed present) is reported as a V-J N-insertion. Because of this, we excluded these observations when fitting models for TCR features involving the D-gene .We also subset the SNP data further based on several quality control metrics. We filtered the SNP array data to use only SNPs with a minor allele frequency above 0.05 in our analyses which excluded SNPs for which all subjects had the same genotype. For the discovery cohort, this filtering procedure and previous quality control  left 6,4The discovery dataset contains observations for a total of We first describe what we call the ‘simple model’. We will describe more complex models, as well as each model with added correction for population-substructure-related effects, in the sections following.i. By condensing the data in this way, for each subject We began by calculating the average occurrence of the TCR feature of interest, i and SNP where To estimate each regression coefficient, we solved the least squares problem:lm in R. With each estimate of the j-th SNP effect on the TCR feature of interest, using the function  problem , we quanand compare TCRB gene choice. Thus, we can condition on this gene choice to quantify the direct association between each SNP and the amount of each TCR feature, without confounding gene choice effects. In this way, we condition on each gene type We noted that the amount of certain TCR features  vary by V(D)J TCRB gene allele group TCRB gene allele, we combined TCRB gene alleles which had identical CDR3 sequences and were of the same candidate TCRB gene into TCRB gene allele groups. As such, the number of observations per subject TCRB V-gene allele groups, 16 J-gene allele groups, and 3 D-gene allele groups. Thus, using the extent of nucleotide trimming as an example TCR feature within the discovery cohort, with this condensed formulation, for each SNP and productivity status, we have As in the previous section, we, again, want to reduce the number of data observations. For each subject Using this condensed dataset, for each SNP, TCR feature, and productivity status, we fit the following ‘gene-conditioned model’:i, SNP TCRB gene usage, we calculated the proportion of TCRs from each candidate TCRB gene allele group, where cription  in the pWith this, we solved the following weighted least squares problem for each SNP, TCR feature, and productivity status combination:lm function in R.using the i, these p-values may be inflated due to intra-subject observations being potentially correlated. Thus, to increase the accuracy of the p-value calculation, for each association p-value below a certain threshold (we chose With each estimate of the tatistic  to obtaiStructure within our SNP genotype data (such as population-substructure-related biases due to ancestry), if present, may produce false positive associations when quantifying the association strength between each SNP and our phenotype of interest. To account for this, we implemented principal component analysis as commonly applied to genome-wide genotype data for population substructure inference. Specifically, we used the PC-AiR algorithm  which idAs such, to quantify the association strength between each SNP and TCR feature without conditioning on gene usage as in p-th principal component, and p-th principal component for subject i as calculated above. To estimate each regression coefficient, we solved the following least squares problem for each SNP, TCR feature, and productivity status combination:where Furthermore, to quantify the association strength between each SNP and TCR feature, conditional on gene usage as in where With these estimates for the population-substructure-corrected TCRB locus, we noted that SNP genotypes were associated with TRBD2 allele genotype , we considered the significance of associations using a Bonferroni-corrected threshold. To establish each threshold, we corrected for each TCR feature subtype , the two TCR productivity types (productive and non-productive), and the total number of SNPs tested. When considering associations in the whole-genome context, we corrected for the approximately 6.5 million SNPs (remaining after filtering). When considering associations in a gene-level context, we corrected for the number of SNPs within 200 kb of the gene of interest. For the validation analysis, we considered associations in a SNP-level context and did not correct for multiple SNPs. However, for the validation analysis, we considered the significance of associations within both TCRα and TCRβ chains and, thus, corrected the significance threshold accordingly.We defined the genomic inflation factor d median . For eacwhere 0.456 is the median of a chi-squared distribution with one degree of freedom. If the GWAS analysis results follow the chi-squared distribution, the expected value of analysis .DNTT and DCLRE1C loci, we performed a stepwise series of nested regression analyses to test for independent SNP associations within each locus for N-insertion and nucleotide trimming, respectively. We used the same models and covariates as the primary analyses (‘simple model’ for associations between N-insertion and DNTT variation and the ‘gene-conditioned model’ for associations between nucleotide trimming and DCLRE1C variation) and included the most significant SNP within each locus as an additional covariate. We inferred the association between each SNP within each locus and the TCR feature of interest using this new conditional model and considered significant associations at a gene-level Bonferroni-corrected significance threshold for each locus. From here, we repeated this analysis (if necessary), identifying and adding additional SNPs one-by-one as a covariate to each successive model. Once the p-value of top SNP within the locus was no longer significant, we concluded the analysis. SNPs which were added as as additional covariates in the final conditional model were considered to be independent signals.Within the In order to correct for population-substructure-related biases due to ancestry in our GWAS analyses, we used ancestry-informative principal component analysis. The original genotyping dataset  containeTCRB locus as shown in TRBD2 allele genotype across all subjects. We used Pearson correlation to measure the correlation between the two genotypes.For each significantly associated SNP within the To quantify PCA cluster variation of TCR repertoire features , we first calculated an average of each TCR repertoire feature by subject and productivity status. We also calculated a population mean of each TCR repertoire feature by productivity status. Each subject was classified into one of six PCA clusters. Thus, we compared the mean of the TCR repertoire features within each PCA cluster to the population mean using a one-sample t-test to compute each P-value. We used Bonferroni multiple testing correction to adjust each p-value.We also calculated SNP minor allele frequencies for the whole population and for each PCA cluster individually such thatDNTT gene), we compared the minor allele frequencies calculated within PCA-clusters to the minor allele frequencies calculated for the entire population using a one-sample t-test to compute each P-value. Again, we used Bonferroni multiple testing correction to adjust each p-value.Here, t_test function from the rstatix package in R.For both of these analyses, we used the https://github.com/phbradley/tcr-gwas,  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Benny Chain as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Aleksandra Walczak as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: James M. Heather (Reviewer #2).Thank you for submitting your article "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:Please respond to the specific issues raised by reviewers 2 and 3.Reviewer #1 (Recommendations for the authors):The genetic association data seems mostly convincing.In addition to the Manhattan plots, it would be very informative to see the actual effects on variable gene freqeuncy, their magnitude and the extent to which different genes are regulated together – e.g. do some SNPs regulate several different Vs together? Similarly, more detail on the magnitude and details of the effects seen on basde pair deletion and addition would perhaps contribute to a better understnading of the importance and impact of the genetic regulation.Weaknesses of the study: 1. Limited additional understanding of the process of regulation of T cell receptors; limited validation of results in an independent data sets.eLife.Overall, the findings are sound, but of incrememental importance in understanding TCR repertoire generation. A more specialised human genetics journal may be a more appropriate place to publish these results, and the methodology for conditioning one outcome on another outcome, rather than the more general readership of Reviewer #2 (Recommendations for the authors):This manuscript is already extremely well put together, making it a genuine pleasure to read, so I have only a few recommendations.(1a) I think it may be useful to have the ZNF peaks on Fig 1 labelled in some way. They definitely stand out when you the paper, yet are not addressed in the narrative of the paper until a figure or two later.(1b) On a related note, unless I missed it, it's probably worth explicitly mentioning that the TRVB24-1 association with ZNF443 was also observed in the Sharon et al paper too.(2) Fig 3 seems to show some signal for a weaker but significant SNP association with trimming on chr23. This is another observation that leaps out to the reader, but one for which I couldn't find a mention of in the text at all.(3) I appreciate that I may be unwittingly stirring up a well-trodden semantics issue from a field not my own, but would not many of these associations be considered eQTL? At the very least those involving productive gene expression as shown in Fig 1. If so I think it might be helpful to include the term in the text somewhere, even if only buried in the methods, purely to make this paper more likely to be returned in relevant literature searches.(4) I'm not sure how practical a request this is, but given the discussion of SNP coverage in the public review section: is there any way for the authors to measure (or even speculate) as to what portion of known TCR loci polymorphisms are covered by the SNP arrays used in these datasets? It feels like a number that might be helpful for contextualising the results of this study.(5) Lines 156/157 report that various SNPs associated with the expression "of the V-genes TRBV24-1 and TRBV24/OR9-2". A very minor pedantic point, but this sentence does make it sound like the expression of both genes (or expression of recombinations using both genes) is going up. Seeing as TRBV24/OR9-2 resides on another chromosome, it's almost certain that in actuality it's just that TRBV24-1 TCRs are increasing and the orphon gene recombination levels are remaining steady around zero. Incidentally this is a good illustration of a case where the short Adaptive reads can't discriminate between two genes, even when the immunological importance differs widely.(6a) While I didn't have time to actually run the code, I did note that the first step in the README is a little vague ("Download data into the directory ..."). A little more instruction would be very useful here, as in my experience half the battle in getting other people's analyses running is matching input data formats. Obviously the manuscript lists the accessions of the data themselves, but explicit discussion of the pre-processing steps would be extremely helpful for anyone wanting to re-run or adapt these analyses. (6b) On a related note, I noticed that a few details relevant for repeatability have been omitted from the methods. In particular the versions and non-default parameters of software tools used , and the date of accession of databases (e.g. IMGT/GENE-DB) should be included. Given the nature of this manuscript and the frequency of changes to GENE-DB I would even recommend actually uploading the specific version(s) of the database that were used.(7) The lines of Fig 9B are very narrow, which makes it very hard to tell the difference between some of the groups . Perhaps the lines could be made wider, or some lines made dotted or dashed or something, so as to make the groups easier to distinguish.(8) The observation that the different ancestry-associated groups differ in some of their recombination parameters is very interesting: I can't recall seeing similar data before, beyond some general V gene expression level differences . However, having never performed such an ancestry-informative PCA before I have what may be naive concerns about it. For instance, of the ~400 people in the cohort, it seems that all are assigned to one of the groups, while I would presume that out of those 400 people there are likely some multiracial individuals or those who just fall out of the typical expected SNP distributions . Some more discussion of this issue may be instructive for those like myself who have never run such an analysis. For example, are those individuals who are further from the center of their respective clusters - or perhaps those whose clustered ancestry group is different to their self-reported one - enriched in the outliers in Figs 7 and 8? Similarly, I'm curious as to how many individuals in each group end up assigned to another, and to which. These considerations seem especially important given the different sizes of the groups , and the fact that the target clusters are themselves informed by the original input reported groupings. While obviously beyond the scope of this manuscript, it's interesting in itself to note that such an observation hasn't been made before. Presumably most TCRseq studies are not large or diverse enough to have detected such differences (even had people been looking)?Reviewer #3 (Recommendations for the authors):1. Regarding the datasets, full information regarding the donors from which the data have been used need to be summarized in a Table and couple of figures to identify possible (or no if this is the case) bias in terms of sex, age, ethnicity, influenza exposure in the days before the sample collection, CMV serotype, etc… Indeed, all these factors can influence the repertoire composition, and maybe some correction/normalization should be applied.2. On the source data table containing 9957 associations, 43 TRBV out the 66 mentioned showed significant associations with 1 to 508 SNPs. For several TRBV genes, the association with SNPs was significantly different according to the productive vs nonproductive origin of the TRBV in the dataset. Same for the 10 TRBJ (out of the 14 tested) showing significant associations with SNPs.First, the "significant association" column in Table 1 should reflect those results by indicating the number of V genes and J genes found to be associated with at least one SNP above the significance threshold.Second, in the data source table we can see some V gene usage (and J gene usage as well) from nonproductive and productive rearrangements are associated with various number of SNPs. What is the overlap of the SNPs associated with each V according to its nonproductive/productive origin? In addition, a general comment on the use of nonproductive rearrangement data. Such "part" of the TCR repertoire is believed to reflect TCR generation independently of TCR selection. However, when analysed from blood TCR repertoires (like in this study), it is still unclear how much the nonproductive "repertoire" is biased by the fact that it is directly dependent on the productive, and therefore centrally/peripherally selected. Therefore, the variations associated at the V, J usage (as well as the trimming) may be biased by the immune history of every individual. Author should control on this possible bias or disregard the differences between productive vs. nonproductive sequences. The only dataset that could help address this question would be thymic cells at the different stage of differentiation.Third, in the text it is indicated that "variation in the TCRB locus is most significantly associated with the expression of the gene TRBV28 for both the productive and nonproductive". Unless I missed something, this is also (maybe more) true for TRBV12-3, TRBV12-4  as well as TRBV24-1, accounting for respectively 353, 358 and 319 SNP associations compared with 424. Are those differences significant? Are you referring to TRBV28 for a particular reason (major overlap between SNPs associations between nonproductive and productive for instance or something else)? This should be clarified and detailed.3. On the association between HLADRB1 and TRBV10-3, the authors refers to two other studies that found such association however they omit to discuss the fact that on those studies they found in fact other TRBV gene usage to be much more associated with the HLADRB1, notably TRBV20-1  which is not found from the Emerson dataset. Are the differences associated with the ethnicity (as Gao paper is mainly done on Asian population)? Maybe to provide some functional relationship of the associations, it could be of interest to analyze data from patients with AIDs, such as RA, SLE, Sjogren syndrome known to be associated with HLADRB1. Data from the Rossetti paper Rosseti et al.,  as well as from the Liu et al. . For instance it could be of interest to determine whether in RA or SLE patients, a differential usage of TRBV10-3, TRBV20 compared to controls has been shown. Eventually, if DNA is still available, ensure the HLADRB1 genotype to correlate with the observations.4. Regarding the SNPs association with the gene trimming and N-insertion numbers, interestingly the genes showing SNPs association with this TCR repertoire feature are definitely biologically linked. However, although the author distinguish the impact of the gene and on the trimming versus N-insertion, since the resulting repertoire analyzed is a post-selection repertoire, the observation are still bias by the selection effect. Moreover, it is also well known that shorted TCRs are more frequent in general, than long ones. In other word, authors should control for these bias and provide more evidence on the actual SNPs identified between the discovery and the validation cohorts. Reviewer #1 (Recommendations for the authors):The genetic association data seems mostly convincing.In addition to the Manhattan plots, it would be very informative to see the actual effects on variable gene freqeuncy, their magnitude and the extent to which different genes are regulated together – e.g. do some SNPs regulate several different Vs together? Similarly, more detail on the magnitude and details of the effects seen on basde pair deletion and addition would perhaps contribute to a better understnading of the importance and impact of the genetic regulation.We have added details about the magnitude of the effects for the significant associations observed for gene-usage within the MHC and TCRb loci and for nucleotide trimming and N-insertion within the DCLRE1C and DNTT loci, respectively.For associations observed for gene-usage within the TCRb locus, we have added lines 139-142:"For the significantly associated TCRB locus SNPs, the median association effect magnitude was largest for the expression of TRBD1 (median effect size = -0.038) followed by the expression of TRBD2 (median effect size = 0.035) and the expression of TRBV28 (median effect size = 0.019) all in productive TCRs."For associations observed for gene-usage within the MHC locus, we have added lines 153-156:"For the significantly associated MHC locus SNPs, the median association effect magnitude was largest for the expression of TRBV4-1 (median effect size = -0.004) followed by the expression of TRBV10-3 (median effect size = 0.0033)."For associations observed for nucleotide trimming within the DCLRE1C locus, we have added lines 199-201:"For these significant DCLRE1C locus SNP associations, the magnitudes of the effects were greater for non-productive TCRs compared to productive TCRs for both V-gene trimming and J-gene trimming."For associations observed for N-insertion within the DNTT locus, we have added lines 271-273:"For these significant DNTT locus SNP associations, the magnitudes of the effects were greater for non-productive TCRs compared to productive TCRs for both V-D-gene junction N-insertion and D-J-gene junction N-insertion."We have also added corresponding supplementary figures .Weaknesses of the study: 1. Limited additional understanding of the process of regulation of T cell receptors; limited validation of results in an independent data sets.Overall, the findings are sound, but of incrememental importance in understanding TCR repertoire generation. A more specialised human genetics journal may be a more appropriate place to publish these results, and the methodology for conditioning one outcome on another outcome, rather than the more general readership of eLife.eLife because of its general readership and focus on open science. We provide the first examples of genetic variants which are associated with modifying the extent of nucleotide trimming and N-insertion at V(D)J-gene junctions, and because V(D)J-gene junctional diversity is a major source of the overall diversity within the TCR repertoire, we believe our results are important for continuing to understand the T cell receptor generation process. Association between N-insertions and genetic variants near TdT (which likely influence its expression level) explain previous findings of population-level variation in N-insertions  and fit with known effects of TdT expression level on N-insertions during development. Our finding that genetic variation in and around the DCLRE1C locus (which codes for Artemis) influences the extent of nucleotide trimming, is both exceptionally strong statistically , and intriguing from a mechanistic perspective.Thank you for you thoughts, however, we like According to an expert in V(D)J recombination, our current understanding of Artemis regulation would not predict that simple variation in its expression level would have a significant effect on nucleotide trimming . Thus, we believe that the very strong Artemis associations we find represent more than a simple and incremental extension of our current understanding of V(D)J recombination; they may have important mechanistic implications that lead to new insights.eLife readership.It's also worth pointing out that these cohorts (N=398 and N=94) are quite small from the GWAS perspective, which attests to the strength of the associations we discovered. As such, we believe our results will be interesting for a general immunology and genetics audience, which form part of the Reviewer #2 (Recommendations for the authors):This manuscript is already extremely well put together, making it a genuine pleasure to read, so I have only a few recommendations.(1a) I think it may be useful to have the ZNF peaks on Fig 1 labelled in some way. They definitely stand out when you the paper, yet are not addressed in the narrative of the paper until a figure or two later.We have added a ZNF label to Figure 1, thanks.(1b) On a related note, unless I missed it, it's probably worth explicitly mentioning that the TRVB24-1 association with ZNF443 was also observed in the Sharon et al paper too.We have added the following sentence (lines 169-171 in the manuscript) describing that significant association between variation near ZNF443 and TRVB24-1 expression was also observed in the Sharon et al paper, thanks:"Significant association between variation near the ZNF443 locus and expression of TRBV24-1 in productive TCRs was also noted previously ."(2) Fig 3 seems to show some signal for a weaker but significant SNP association with trimming on chr23. This is another observation that leaps out to the reader, but one for which I couldn't find a mention of in the text at all.Despite looking closely, we were unable to find any genes proximal to the significant SNPs on chromosome 23. Because of this, we have chosen to not discuss these associations in the text.(3) I appreciate that I may be unwittingly stirring up a well-trodden semantics issue from a field not my own, but would not many of these associations be considered eQTL? At the very least those involving productive gene expression as shown in Fig 1. If so I think it might be helpful to include the term in the text somewhere, even if only buried in the methods, purely to make this paper more likely to be returned in relevant literature searches.We have added the following sentence (lines 418-421 in the manuscript) to the discussion to suggest that further experimentation will be required to determine whether the significant SNPs associated with changing the extent of nucleotide trimming and N-insertion identified here could be acting as eQTLs:"The significant SNPs associated with changing the extent of nucleotide trimming and N-insertion identified here could be expression quantitative trait loci (eQTLs), however, experimental work will be required to determine whether these SNPs modify the expression of DCLRE1C and DNTT, respectively."(4) I'm not sure how practical a request this is, but given the discussion of SNP coverage in the public review section: is there any way for the authors to measure (or even speculate) as to what portion of known TCR loci polymorphisms are covered by the SNP arrays used in these datasets? It feels like a number that might be helpful for contextualising the results of this study.We agree that including a measure of the proportion of known TCR loci polymorphisms which are covered by the SNP array used here would be valuable. The SNP array used here (after imputation) includes 7304 SNPs within the TRB region (hg19:chr7:141950000-142550000), a fact that we have added to the discussion. Given the complexity of the TCRbeta locus, we expect that this is likely to represent only a small fraction of the actual variation present across diverse populations. Cross-referencing these SNPs with known allelic variation in TCRbeta genes is made challenging by inaccuracies in the gene annotations for the hg19 build; our efforts to 'liftover' the SNP dataset to the hg38 build using automated tools suffered from high dropout rates. For these reasons, we were not able to arrive at a quantitative estimate of SNP coverage in the TCRbeta locus. Anecdotally, we note that when looking for TRB SNPs whose gene usage associations were much more significant for productive than non-productive SNPs, we identified 6 of the 7 genes  flagged in the Dean et al. study as having both productive and non-productive alleles . This suggests that our SNPs likely cover many of the known TRB alleles, however since each allele can be associated with variation at multiple nucleotides, panel coverage could still be quite low when measured at the per-nucleotide level.(5) Lines 156/157 report that various SNPs associated with the expression "of the V-genes TRBV24-1 and TRBV24/OR9-2". A very minor pedantic point, but this sentence does make it sound like the expression of both genes (or expression of recombinations using both genes) is going up. Seeing as TRBV24/OR9-2 resides on another chromosome, it's almost certain that in actuality it's just that TRBV24-1 TCRs are increasing and the orphon gene recombination levels are remaining steady around zero. Incidentally this is a good illustration of a case where the short Adaptive reads can't discriminate between two genes, even when the immunological importance differs widely.This is a great point. For our gene usage associations, we have now restricted our analyses to non-orphan genes. We have included this change within the Methods section (lines 545-547), and changed the corresponding results accordingly.(6a) While I didn't have time to actually run the code, I did note that the first step in the README is a little vague ("Download data into the directory ..."). A little more instruction would be very useful here, as in my experience half the battle in getting other people's analyses running is matching input data formats. Obviously the manuscript lists the accessions of the data themselves, but explicit discussion of the pre-processing steps would be extremely helpful for anyone wanting to re-run or adapt these analyses. Thanks for this suggestion. We have included all parsed TCR repertoire data files and all required meta data files on Zenodo . Details regarding the pre-processing steps for these parsed TCR repertoire data files have been included within the GitHub README. We have also provided further details about accessing and downloading these data, in addition to details about accessing, downloading, and processing the SNP data from The database of Genotypes and Phenotypes (dbGaP), within the GitHub README.(6b) On a related note, I noticed that a few details relevant for repeatability have been omitted from the methods. In particular the versions and non-default parameters of software tools used , and the date of accession of databases (e.g. IMGT/GENE-DB) should be included. Given the nature of this manuscript and the frequency of changes to GENE-DB I would even recommend actually uploading the specific version(s) of the database that were used.This is a great suggestion. We have added details about the versions and non-default parameters for each software tool used for pre-processing the TCR repertoire data within the GitHub README. We have also uploaded the specific TRB and TRA genes and sequences used from IMGT to Zenodo.(7) The lines of Fig 9B are very narrow, which makes it very hard to tell the difference between some of the groups . Perhaps the lines could be made wider, or some lines made dotted or dashed or something, so as to make the groups easier to distinguish.We have made Figure 9B larger and increased the line widths, thanks.(8) The observation that the different ancestry-associated groups differ in some of their recombination parameters is very interesting: I can't recall seeing similar data before, beyond some general V gene expression level differences . However, having never performed such an ancestry-informative PCA before I have what may be naive concerns about it. For instance, of the ~400 people in the cohort, it seems that all are assigned to one of the groups, while I would presume that out of those 400 people there are likely some multiracial individuals or those who just fall out of the typical expected SNP distributions . Some more discussion of this issue may be instructive for those like myself who have never run such an analysis. For example, are those individuals who are further from the center of their respective clusters - or perhaps those whose clustered ancestry group is different to their self-reported one - enriched in the outliers in Figs 7 and 8? Similarly, I'm curious as to how many individuals in each group end up assigned to another, and to which. These considerations seem especially important given the different sizes of the groups , and the fact that the target clusters are themselves informed by the original input reported groupings. While obviously beyond the scope of this manuscript, it's interesting in itself to note that such an observation hasn't been made before. Presumably most TCRseq studies are not large or diverse enough to have detected such differences (even had people been looking)?Thank you for your detailed suggestion. We have included a supplementary table (Table 1 - source data 1) containing the mapping between the original self-identified ancestry groups to the ancestry-informative PCA clusters. Also, we explored whether the outliers within Figure 7 were individuals whose self-identified ancestry group was different from their ancestry-informative PCA cluster. We largely found that this was not the case. For example, our comparison of the mean total N-insertions by PCA cluster  resulted in 15 total outliers  which represented 10 individuals. Of these 10 individuals, six had matching self-identified ancestry and PCA cluster (both ``Caucasian''), one had self-identified American Black ancestry and was part of the ``African''-associated PCA cluster, one had self-identified Chinese ancestry and was part of the ``Asian''-associated PCA cluster, one had self-identified ``Caucasian'' ancestry and was part of the ``Hispanic''-associated PCA cluster, and one had an unknown self-identified ancestry and was part of the ``Caucasian''-associated PCA cluster. While it is possible that individuals with multiple-ancestries could fall out of the typical expected SNP distributions, we believe this is of little concern for our analyses shown in Figure 7. Also, to clarify our Figure 8 analyses, each data point within the figure depicts the minor allele frequency of a N-insertion associated SNP within the DNTT region, computed using only individuals within each PCA cluster. As such, the Figure 8 outliers represent SNPs, not individuals with potential multiple-ancestry.Reviewer #3 (Recommendations for the authors):1. Regarding the datasets, full information regarding the donors from which the data have been used need to be summarized in a Table and couple of figures to identify possible (or no if this is the case) bias in terms of sex, age, ethnicity, influenza exposure in the days before the sample collection, CMV serotype, etc… Indeed, all these factors can influence the repertoire composition, and maybe some correction/normalization should be applied.We have added Table 1 and Table 3 which contain cohort demographic information  for the discovery and validation cohorts, respectively. While all of these factors may influence the repertoire composition, we have focussed on correcting factors which are known to affect both repertoire composition and SNP frequency directly. As such, for all of our analyses, we have incorporated ancestry-informative principal components as covariates in each model to correct for potential population-substructure-related effects which could inflate associations between each SNP and each repertoire feature of interest. Diagnostic statistics  show that this bias correction is sufficient for all of our analyses. If additional confounding variables  were still present within our analyses, we would expect these diagnostic statistics to indicate additional P-value inflation even after correcting for population substructure-related effects. Because this is not the case, we do not see evidence for additional confounding factors .2. On the source data table containing 9957 associations, 43 TRBV out the 66 mentioned showed significant associations with 1 to 508 SNPs. For several TRBV genes, the association with SNPs was significantly different according to the productive vs nonproductive origin of the TRBV in the dataset. Same for the 10 TRBJ (out of the 14 tested) showing significant associations with SNPs.First, the "significant association" column in Table 1 should reflect those results by indicating the number of V genes and J genes found to be associated with at least one SNP above the significance threshold.We have updated Table 1 to include the number of V genes and J genes found to be associated with at least one SNP above the significance threshold, thanks.Second, in the data source table we can see some V gene usage (and J gene usage as well) from nonproductive and productive rearrangements are associated with various number of SNPs. What is the overlap of the SNPs associated with each V according to its nonproductive/productive origin? In addition, a general comment on the use of nonproductive rearrangement data. Such "part" of the TCR repertoire is believed to reflect TCR generation independently of TCR selection. However, when analysed from blood TCR repertoires (like in this study), it is still unclear how much the nonproductive "repertoire" is biased by the fact that it is directly dependent on the productive, and therefore centrally/peripherally selected. Therefore, the variations associated at the V, J usage (as well as the trimming) may be biased by the immune history of every individual. Author should control on this possible bias or disregard the differences between productive vs. nonproductive sequences. The only dataset that could help address this question would be thymic cells at the different stage of differentiation.Thank you for raising this concern. Since each non-productive rearrangement is sequenced due to the presence in the same T cell of a successful rearrangement that survived selection, we agree that it is possible that within-cell correlation between rearrangement events could imprint selection effects onto the non-productive repertoire.However, it is commonly assumed that independence between the productive and non-productive recombination events breaks this linkage on the allele level .Further, we are not aware of any evidence for a mechanism in which the two recombination events at the TCRbeta locus are strongly correlated. As evidence of independence, we find that many of the differences that we see between productive and non-productive sequences (e.g. absence of MHC associations for non-productive TCRs) are consistent with the interpretation of these sequences as "unselected". For example, all significant SNPs within the MHC region for V-gene usage in productive TCRs were not significant for non-productive TCRs, as expected. We found that many of the significant SNPs within the TRB region had similar association p-values between non-productive and productive TCRs. Notably, the majority of TRB region SNPs which were significant for productive TCRs and not significant for non-productive TCRs occurred for the usage of genes which have both productive and non-productive alleles; in these cases the SNP is likely a proxy for the productivity status of the allele on the same chromosome (see PMID 26596423). We have included a supplementary figure  detailing the concordance between nonproductive and productive gene usage associations. This new analysis adds to the paper and we thank the reviewer for suggesting it.Third, in the text it is indicated that "variation in the TCRB locus is most significantly associated with the expression of the gene TRBV28 for both the productive and nonproductive". Unless I missed something, this is also (maybe more) true for TRBV12-3, TRBV12-4  as well as TRBV24-1, accounting for respectively 353, 358 and 319 SNP associations compared with 424. Are those differences significant? Are you referring to TRBV28 for a particular reason (major overlap between SNPs associations between nonproductive and productive for instance or something else)? This should be clarified and detailed.We have mentioned that "variation in the TCRB locus is most significantly associated with the expression of the gene TRBV28" in an effort to give some intuition for the strongest associations. We have clarified the wording of this sentence to indicate that expression of TRBV28 had the smallest, most significant association P-values for variation in the TCRB locus for both productive and non-productive TCRbeta chains (lines 143145).We have also added the following sentence (lines 145-148) discussing which genes had the largest number of significant associations for variation in the TCRB locus:"We identified the largest number of significant associations between variation in the TCRB locus and expression of the gene TRBV7-3 within productive TCRð›½ chains (232 significant associations) and the gene TRBJ12 within non-productive TCRð›½ chains (290 significant associations)."3. On the association between HLADRB1 and TRBV10-3, the authors refers to two other studies that found such association however they omit to discuss the fact that on those studies they found in fact other TRBV gene usage to be much more associated with the HLADRB1, notably TRBV20-1  which is not found from the Emerson dataset. Are the differences associated with the ethnicity (as Gao paper is mainly done on Asian population)? Maybe to provide some functional relationship of the associations, it could be of interest to analyze data from patients with AIDs, such as RA, SLE, Sjogren syndrome known to be associated with HLADRB1. Data from the Rossetti paper Rosseti et al.,  as well as from the Liu et al. . For instance it could be of interest to determine whether in RA or SLE patients, a differential usage of TRBV10-3, TRBV20 compared to controls has been shown. Eventually, if DNA is still available, ensure the HLADRB1 genotype to correlate with the observations.eLife manuscript . In our view, the primary contribution of this manuscript is to connect the genome-wide genotyping data with TCR repertoire features. For example, this is the first description (to our knowledge) of SNPs in the TdT and Artemis loci that influence V(D)J recombination.We thank the reviewer for these insightful observations. We have added a few sentences (lines 371-378) to the Discussion section to highlight that the specific associations identified between MHC locus variation and V-gene usage differed between the two previous studies  and our work reported here. We agree that the associations between HLA alleles and V-genes, and their variation across populations and in different disease contexts, constitute a fascinating topic for further investigation. However, we feel that deeper examination of these questions is outside the scope of the present manuscript: the high-resolution class I and class II HLA typing data for the Emerson cohort has been available since our 2018 4. Regarding the SNPs association with the gene trimming and N-insertion numbers, interestingly the genes showing SNPs association with this TCR repertoire feature are definitely biologically linked. However, although the author distinguish the impact of the gene and on the trimming versus N-insertion, since the resulting repertoire analyzed is a post-selection repertoire, the observation are still bias by the selection effect. Moreover, it is also well known that shorted TCRs are more frequent in general, than long ones. In other word, authors should control for these bias and provide more evidence on the actual SNPs identified between the discovery and the validation cohorts.Thank you for raising these concerns. As described above, while each nonproductive rearrangement may indirectly undergo selection along with its productive rearrangement counterpart, we are not aware of any evidence for a mechanism in which the productive and nonproductive recombination events are correlated. As such, we are assuming that independence between two recombination events breaks any sort of indirect selection linkage for the nonproductive rearrangement on the allele level. In regards to the validation analysis, the overlap between the discovery cohort and the validation cohort consisted of just two significant SNPs, one within the gene encoding the Artemis protein (DCLRE1C) and the other within the gene encoding the TdT protein (DNTT). We have described details regarding these SNPs and their associations within both the discovery and validation cohorts within Table 4.


Several vaccines have been approved for use against coronavirus disease (COVID-19) and distributed globally in different regions. However, general community knowledge, attitudes and perceptions towards COVID-19 vaccinations are poorly understood. Thus, the study aimed to investigate community knowledge, attitudes and perceptions towards COVID-19 vaccinations in Bangladesh.An exploratory and anonymous population-based e-survey was conducted among 1658 general individuals . The survey was conducted using a semi-structured and self-reported questionnaire containing informed consent along with four sections . Multiple linear regression was performed to determine the variables predicting knowledge, and attitudes towards COVID-19 vaccinations.The mean scores of knowledge and attitudes were 2.83 ± 1.48 (out of 5) and 9.34 ± 2.39 (out of 12) respectively. About a quarter of participants thought that the COVID-19 vaccination available in Bangladesh is safe, only 60% will have the vaccination and about two-thirds will recommend it to family and friends. In the multiple regression model, higher SES, having university/ higher levels of education, having nuclear families and having previous history of essential vaccines uptake were associated with knowledge; whilst attitudes were significantly associated with being female and having previous history of essential vaccines uptake. Just over half of the participants thought that everyone should be vaccinated and 61% responded that health workers should be vaccinated first on priority basis. 95% of respondents believed the vaccine should be administered free of charge in Bangladesh and almost 90% believed that the COVID-19 vaccine used in Bangladesh may have side effects.The findings reflect inadequate knowledge but more positive attitudes towards COVID-19 vaccine among the general population in Bangladesh. In order to improve knowledge, immediate health education programs need to be initiated before mass vaccination are scheduled.The online version contains supplementary material available at 10.1186/s12889-021-11880-9. Coronavirus disease COVID-19) is a deadly disease which continues to affect many countries in the world. This is caused by the new coronavirus strain severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which has become a serious public health concern worldwide 9 is a de. The incVaccines are the most important public health measure and most effective strategy to protect the population from COVID-19, since SARS-CoV-2 is a highly contagious virus and affects populations globally. The competition for COVID-19 vaccine invention and development against the spread and catastrophic effects of the disease is ongoing , 8, and In order to implement the most effective vaccination strategy in Bangladesh, we need to know the knowledge, attitudes and perceptions of Bangladeshi people about COVID-19 vaccinations. This is because, people’s knowledge, attitudes and perceptions towards COVID-19 are crucial for Government and policy makers to address all barriers to vaccine distribution. To date, there has been no prior study among the general population of Bangladesh investigating their knowledge, attitudes and perceptions towards COVID-19 vaccine.n = 80) were submitted blank/incomplete survey forms and were excluded from the final analysis. The inclusion criteria of participants were i) being a Bangladeshi resident, ii) being an adult (≥ 18 years old), iii) having internet access, and iv) providing consent. The exclusion criteria included being under 18 years old and incomplete surveys. The study was conducted following the Checklist for Reporting Results of Internet ESurveys (CHERRIES) guidelines . Participants in the study were informed about the procedure and purpose of the study and confidentiality of information provided. All participants consented willingly to be a part of the study during the data collection periods. All data were collected anonymously and analyzed using the coding system.A total of 1658 complete surveys were included in the final analysis. Of them, 56% were males and the participants’ mean age was 23.17 years (SD = 6.05) ranging from 18 to 65 years. Most were unmarried (86%) and had university/ higher levels of education (83%)  of participants thought everyone should be vaccinated. This perception was significantly higher among females versus males . 61% responded that health workers should be vaccinated first, which was significantly higher among females versus males . Almost 95% of participants responded that the vaccine should be administered free of charge in Bangladesh, which was significantly higher among females versus males . In addition, most participants believed that the newly discovered COVID-19 vaccine may have side effects (89%). Over half of participants (56%) responded that if everyone in the society maintains the preventive measures, the COVID-19 pandemic can be eradicated without vaccination and about a third (35%) responded that they would not purchase the vaccine at their own expense if it was not provided free of charge by the government.The distribution of each perceptions item about COVID-19 vaccine is presented in Table In order to halt the ongoing pandemic, the COVID-19 vaccine has been framed as the ideal solution. Substantial numbers of vaccine candidates are being developed and several clinical trials have recently been released with positive results, leading to a number of countries approving specific vaccines for implementation in vaccination programs. In Bangladesh, the government has already started the COVID-19 vaccination roll-out , bringinThe knowledge regarding COVID-19 vaccinations was low among more than half of the population. In this study, knowledge was significantly associated with education, family type, monthly income of a family, and previous vaccine uptake experience. However, attitudes were significantly associated with only sex and earlier vaccine administration experience. Importantly, the majority of participants (78%) showed positive attitude towards COVID-19 vaccine. Knowledge regarding COVID-19 vaccinations was not significant in terms of participants’ sex. This finding is similar with other studies in Bangladesh demonstrating no significant sex differences in knowledge regarding COVID-19 , 27. ThiAccording to our study, participants with a higher level of education were found to have more knowledge about COVID-19 vaccinations, which is also supported by previous research. Similar scenarios were found in other earlier studies in Bangladesh, illustrating that individuals with a higher educational background showed more knowledge regarding COVID-19 , 31. It Significant associations were found between living in a nuclear family and knowledge regarding COVID-19 vaccinations. This is not supported by a preceding study in the same country regarding knowledge about cholera and oral cholera vaccine, where no significant association between knowledge and family type was found . People People who have received any vaccine earlier were found to have more knowledge regarding COVID-19 vaccinations in this study. A recent study in China evaluating COVID-19 vaccine acceptance found that people who were previously vaccinated against influenza were more likely to accept the COVID-19 vaccine , which wIn the present study, 78% of participants had more positive attitudes towards COVID-19 vaccine, which was significantly associated with being female. This association is in line with a previous study on attitudes towards dengue vaccination conducted in Indonesia  and attiOur study found that over half (52%) of the participants thought that everyone should get COVID-19 vaccine in Bangladesh. Furthermore, more than half of the participants (61%) thought health workers should get the vaccine first. This perception might be because health care workers are at the frontline to diagnose with COVID-19 as they have to deal with patients regularly. In contrast to the general population, the probability of reporting a positive COVID-19 test was higher for frontline health workers . MoreoveIn our study, 89% of participants assumed that the recently developed COVID-19 vaccine (the vaccine currently being used in Bangladesh) could have some side effects, which is similar to a study in the United States . This maThere are certain limitations that should be addressed in the interpretation of the findings from this study. Firstly, the study was cross-sectional, so the causality cannot be attributed to the findings in the regression models and the findings may not be generalizable over time. In this respect, a longitudinal study is important. Secondly, the study used an online self-reporting method that may be exposed to social acceptability and memory biases. Thirdly, the study utilized snowballing sampling technique that may result in selection bias. So, a further study with random sampling technique is warranted. However, the survey was performed prior to the mass vaccination period in Bangladesh, and its findings may vary after the vaccination program has been implemented. Nevertheless, this is the first study of community perceptions about COVID-19 vaccinations in Bangladesh and will be crucial for health policy makers and planners who are aiming to vaccinate the highest proportion of the population possible in order to mitigate the impacts of the pandemic.The COVID-19 pandemic continues to wreak global havoc on lives and livelihoods, and the COVID-19 vaccine represents a possible light of hope for the future. The present study revealed inadequate knowledge but more positive attitudes towards COVID-19 vaccinations in Bangladesh. The findings suggest immediate health education programs and more accurate information should be distributed and advertised by respective health authorities. Policy makers should take steps to ensure adequate knowledge, positive attitudes and perceptions towards COVID-19 vaccinations in order to reduce the vaccine hesitancy facilitated and encouraged by misinformation in the media.Additional file 1.


To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes. Due to climate change threatening crop productivity in sub-Saharan Africa (SSA), breeding for drought tolerance and yield stability across target environments is a high priority for the International Maize and Wheat Improvement Center (CIMMYT) tropical maize breeding program , 2019. TAt stage 1 or preliminary yield trials, several experimental hybrids are generated by crossing DH lines, or lines developed using the pedigree scheme, to a tester from a complementary heterotic group. The testcross hybrids are evaluated in 3–5 environments, where each environment is a combination of location and management (WS and WW), and the data are used to select the best 10–15 percent of the lines within or across the managements for advancement to stage 2 yield trials . EffectiThe current GS strategy relies on phenotyping 50 percent of a bi-parental population, observed across WW and WS environments, to predict the genetic merit of un-tested candidates for both WW and WS  in a tesSparse testing represents a promising approach to expand the number of lines tested when GS is used to advance lines directly into stage 2, and for stage 1 screening of lines in cases where the genetic merit of some new lines may not be accurately predicted due to low genetic relationship between new lines and previously evaluated genotypes in the historical dataset. In the case where GEBV of lines cannot be accurately predicted from historical data, sparse testing has been identified as an optimal GS strategy compared to the current CIMMYT GS strategy  that tests half of a full sib family to train genomic prediction models for full sibs that are not tested in stage 1 . Given tTo identify a scalable strategy that optimizes the representation of genetic space of the genotypes across environments leading to efficient use of information across the environments at the early yield testing stage, we evaluated two different breeding scenarios: (1) predicting the genetic merit of new bi-parental populations across environments  or, (2) predicting different subsets of a bi-parental population across environments. Here, coefficient of determination (CDmean) was used to split bi-parental populations across environments.The main objectives of this study were to: (1) determine an effective strategy to implement sparse testing within the CIMMYT tropical maize breeding program and, (2) determine the optimal method to incorporate genotype by environment interaction (GEI) into the GS model for early yield testing stage.2O5 per ha, as recommended for the area. Nitrogen was applied in a split dose at planting and 6 weeks after emergence. For the purposes of modeling genotype by environmental interactions (GEI), several combinations of factors  were used to classify environments as summarized in The datasets used in this study are described in detail in All DH lines were genotyped using repeat Amplification Sequencing (rAmpSeq) at Cornell Life Science Core Laboratory Center, Ithaca, NY, United States. The genotyping platform takes advantage of knowledge of whole-genome sequences and repetitive sequences to identify DNA sequence polymorphisms using novel bioinformatics tools  and diagonal elements represent the residual variance within each of v environments. Generally, the residual variance for multi-environment GS models can take two different forms explaining different model assumptions. For example, a uniform residual variance for all environments −1X′, G, X, and Z are the same as defined above and K is a matrix of contrast vectors with the sum of each contrast vector equal to zero such that 1′K = 0.In principle λ = Where n is the number of individuals in the populations. Therefore, one individual of a full-sib in a specific bi-parental population serves as a calibration set to estimate the reliability of predicting the remaining full-sibs. This was repeated N times enabling each g-th individual of a full-sib to serve as calibration set. Consequently, we obtain a CDmean value for each individual in a given bi-parental population and individuals  with the highest CDmean value represent an optimized calibration set. Theoretically, individuals with high CDmean value maximize the reliability of those with low CDmean value, thus full-sib families where split between environment by keeping high and low CDmean lines together in WW environments, respectively. In the WS environment, a portion of lines from each WW environment were used as the calibration set . A scripThe Avg_GRM is a raw estimate of the proportion of the genome shared between a potential training set and all individuals in a specific full-sib family. Based on the results from our previous study , CDmean gj is the genomic relationship between the g-th individual in a target full-sib family and the j-th line in the historical data and n is the size of target full-sib family.where GThe predictive ability of two cross-validation schemes was evaluated for possible implementation of a sparse testing GS strategy in the CIMMYT tropical maize breeding program. For even distribution of populations across environments, a bi-parental population with size ≤ 30 was dropped from 2017 dataset and the remaining 12 bi-parental populations were used for the analysis. The first cross-validation scheme (CV1) involved masking six random bi-parental populations of the twelve bi-parental populations in one WW environment with the remaining bi-parental populations masked in the other WW environment. In the WS environment, three random bi-parental populations from each WW environment were masked; this process was repeated 10 times . Predict+ 1) and WS (M + 2). The prediction accuracy was calculated as the Pearson correlation of the predicted GEBV and the BLUE estimates of DH lines in each environment, obtained using the complete dataset for each population, from the combined analysis. The mean across populations is reported.In the second cross-validation scheme (CV2), CDmean was used for splitting each bi-parental population equally across WW environments by masking 50 percent of a bi-parental population with lowest CDmean value in one environment and the remaining 50 percent masked in the other WW environment. For the WS environment, half of the individuals unmasked in the WW environments were masked . Due to Except for when the environment was classified as year by management by location , where the US model was responsive to the training set and did not consistently converge, the results for FA and US models were equivalent regardless of the cross-validation schemes (Result not shown). Thus, only results from FA model were presented. The genetic correlation between environments  in the CV1 ranges from 0.13 to 0.64 . A simil+ 1) and WS (M + 2), the genetic correlation was 0.60 and 0.68 for CV1 and CV2, respectively. Generally, the estimates of plot-level heritability for CV1 and CV2 were moderate.The genetic correlation between environments  varies across the cross-validation schemes, it ranges from −0.14 to 0.74 for CV1 and −0.02 to 0.79 for CV2. The plot level heritability for each environment across the cross-validation was modest. In analyses where management was defined across years , the genetic correlation between managements also ranged from negative to moderate correlation for CV1 . While iThe grouping of the environments into management consistently shows higher prediction accuracy compared to modeling of covariance between environments defined as a combination of location, management and year , 2. ThouThe sparse testing GS strategy in which the genetic merit of new lines is evaluated in different but genetically correlated environments has proven to increase prediction accuracy compared to the test-half-predict-half GS strategy and, provided that all new lines have phenotypic data, it is seemingly robust for developing historical training datasets . The evaAlthough the predictive ability of the two cross-validation schemes is comparable, the improved prediction accuracy of CV1 might be due to the close relationship  of all the populations. Previous studies  also indThe FA is a parsimonious model for fitting a relatively high number of environments in multi-environment trials utilizing latent factors which give rise to correlations between environments to capture the complexity of covariances among many environments . HoweverIn practice, the CIMMYT tropical maize breeding program advances lines to multi-location, multi-tester yield trials based on relative performance within or across managements (WW and WS), the observed improvement in prediction accuracy when environments were grouped into managements suggests that categorizing the environments into management did not sacrifice information on GEI. Assigning environments/locations into groups using prior information, such as management, as is the case in this study, can serve as a complexity reduction strategy for reducing the number of model parameters, providing a more parsimonious approach for modeling GEI. However, stage 1 yield testing is typified by a small number of environments, which is a limitation to the generalization of the results of this study across different phases of yield testing, in particular with a large number of environments. However, similar to the strategy employed in this study, using multi-environment data, Augmenting a given full-sib training set with an optimized set of 300 individuals from historical data using the Avg_GRM genetic optimization algorithm improved prediction accuracy compared to using all available historical records. The similar genetic covariance between managements, heritability, and prediction accuracy obtained when historical data is used to complement the full-sib training set, suggests that an increase in the training set size using historical data results in more stable estimates of model parameters when compared to using only the full-sib records as the training set. The results from this study corroborate our earlier study  indicatiGiven the similar prediction accuracies obtained in CV1 and CV2, decisions on which sparse testing experimental design will likely depend on cost and ease of implementation. While the prediction accuracy for the cross-validation schemes is equivalent, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, which would allow efficient use of information across environments and would be ideal for building a robust historical dataset. Further, the CV2 can be extended to resource demanding multi-environment, multi-tester advanced yield testing stages to save resources. In this study, grouping similar environments to model GEI information reduced computational challenges and achieved superior prediction accuracy. In general, including historical information in trial advancement decisions improved prediction accuracy, suggesting that the use of historical information in routine advancement decisions could improve accuracy. Furthermore, selecting historical information based on genetic connectedness with the breeding population proved more effective than including all historical information.The original contributions presented in the study are included in the article/MO, KR, and SA conceptualized the study. SA analyzed, interpreted the result, and drafted the manuscript. YB coordinated the field experiments. MG and KD were responsible for phenotypic and genotyping data management. JB, JC, RR, DD, PB, PT, ED, GO, and other authors contributed to the editing of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past co-authorship with several of the authors JC, YB, MG, JB, and PB.


Unlike past health crises that were more localized, the highly contagious coronavirus disease 2019 (COVID-19) crisis is impacting the world to an unprecedented extent. This is the first study examining how and whether the COVID-19 pandemic affects herding behavior in the Eastern European stock markets. Using samples from the stock markets of Russia, Poland, the Czech Republic, Hungary, Croatia, and Slovenia from January 1, 2010 to March 10, 2021, we demonstrate that the COVID-19 pandemic has increased herding behavior in all the sample stock markets. Our results show that the COVID-19 crisis reinforces the impact of global market returns on herding behavior in these specific stock markets. We find that COVID-19 strengthens the spillover effect of regional herding on herding behavior. Thus, financial authorities should monitor investors in the stock market to avoid the increase in herding behavior as well as the reinforcement of the global market returns and regional return dispersion on herding during the period of pandemic. The highly contagious coronavirus disease 2019 (COVID-19) pandemic first occurred in Wuhan, China in December 2019 and spread rapidly worldwide. By February 18, 2021, 1.1 billion people in 218 countries were infected with the virus. Among them, there have been 2.44 million deaths globally and over 80 million recovered patients. In Eastern Europe, the first confirmed case appeared in Russia on January 31, 2020, and subsequent related cases gradually appeared and spread to other countries such as Croatia on February 25, 2020, Czech Republic on March 1, 2020, Poland and Hungary on March 4, 2020, and Slovenia on March 5, 2020 see . The infThe past literature on the effects of pandemics on financial markets is limited. Among them, pandemics can affect financial systems through huge economic costs –3. Ali eSpecifically, pandemics can result in investor herding behaviors in the stock market. This issue requires further analysis because the official announcement of World Health Organization (WHO) categorizing the COVID-19 outbreak as a global pandemic has led to serious crashes in global stock markets. Herding behaviors in the stock market denote that investors face uncertain information, and thus, they tend to follow the stock-investing decisions and actions of others or depend too much on public information without heeding their own private information (CSAD) model to investigate the existence of herding behavior in Eastern European stock markets. Their results show the evidence of herding behavior of investors at the sector level for most stock markets, except Poland. Furthermore, the herding behavior exists especially during the period of market downturn. By using the CSAD model, Angela-Maria et al. , WIG 30 (Poland), PX (Czech Republic), Budapest SE (Hungary), CROBEX (Croatia), and Blue-Chip SBITOP (Slovenia). The individual stock prices of all firms listed on our six Eastern European stock markets are taken from Datastream dataset, which is developed by Thomson CompanyRi,t = /Pi, t−1.Our total sample includes 2,913 observations in the Moscow Stock Exchange (MSE), Warsaw Stock Exchange (WSE), Prague Stock Exchange (PSE), Budapest Stock Exchange (BSE), Zagreb Stock Exchange (ZSE), and Ljubljana Stock Exchange (LJSE), respectively. Because we use daily data to calculate investor herding behavior in the stock market, the daily stock returns are computed as LSV measure needs the trading data with specific stock, specific time, and number of investors, while the return dispersion model only needs the trading data with specific stock and specific time. The return dispersion model that needs less data dimension is more likely to calculate and compare investor herding behavior in many countries than the LSV measure. Chang et al.  on day t in a country-specific stock market. Our CSAD model in the country-specific stock market is the following:where Rm, t and CSADt are the same as in Equation (1). γ1 is expected to be significantly positive because the cross-sectional dispersion of returns is supposed to increase with the level of absolute market returns. If herding exists, this relationship between CSAD and market return may become less positive and linear and might even turn negative and non-linear. This phenomenon occurs because herding denotes a decrease in CSAD and an increase in extreme market returns. Thus, herding is assumed to exhibit a significantly negative γ2.where the definitions of Then, we add the squared market returns of the global stock prices to examine if the global stock returns produce the impacts on investor herding behavior in the country-specific stock market. The test is shown in Equation (3) as follows:RGm,t is the returns of the global stock prices, which is proxied by the MSCI stock index returns. The significantly negative value of γ3 in Equation (3) indicates that the country-specific stock market exhibits herding around the global market returns.where Next, we add the regional return dispersions in the Eastern European countries to examine the existence of the regional herding spillover from the regional stock market to the country-specific stock market. The examination is given in Equation (4) as4 in Equation (4) means that the regional return dispersions spread out to raise the herding behavior of country-specific stock markets. The CSAD model's extension fits in this study since the global stock returns can affect herding behavior in the country-specific stock market, and the spillover effect of the regional herding on herding behavior can occur.where DCOVID) to examine the effect of COVID-19 on investor herding in the regional stock markets. This is presented in Equation (5) asMore importantly, we add the COVID-19 dummy variable (DCOVID is the dummy variable measuring whether COVID-19 occurs. DCOVID = 1 since the date of the first confirmed COVID-19 case in each sample country (see 21 and γ22 in Equation (5) denote the existence of herding following (before) COVID-19. Similarly, the significantly negative values of γ31 and γ32 indicate that country-specific stock markets exhibit herding around the global market returns following (before) COVID-19. Moreover, the significantly positive values of γ41 and γ42 imply that the herding of country-specific stock markets increases by the regional herding spillover following (before) COVID-19.where ntry see  and zeroIn addition, we take the previous effect of COVID-19 on stock herding in Equation (5) as our general model to further consider the asymmetric effects that can affect herding behavior: up- and down-market returns –20. ThisCSAD measure, the average market return  calculated using the equal weights for each sample markets, the global market return  proxied by the MSCI index return, and the regional CSAD measure calculated using the regional return dispersions , WIG 30 (Poland), PX (Czech Republic), Budapest SE (Hungary), CROBEX (Croatia), and Blue-Chip SBITOP (Slovenia) from January 1, 2010 to March 10, 2021, we demonstrate that the COVID-19 pandemic raises herding behavior in all Eastern European stock markets.This study then investigates whether the global stock market affects herding in country-specific stock markets since the occurrence of COVID-19. Our results show that the COVID-19 crisis reinforces the impact of the global market returns on herding behavior of specific stock markets in most Eastern European countries. Next, we investigate whether the regional return dispersions spread out to affect herding in the specific stock markets since the COVID-19 crisis. We find that COVID-19 strengthens the spillover effect of the regional herding on herding behavior of specific stock markets in almost all the Eastern European countries.Finally, we examine the asymmetric effects of herding behavior by considering up-/down-market returns during the periods pre- and post-COVID-19. Our results on the asymmetric effects are still robust for all the above tests in most Eastern European countries. Considering the asymmetric effects, we reconfirm that COVID-19 increases herding behavior, reinforces the effect of the global market returns on herding behavior, and strengthens the spillover effect of regional herding on herding behavior in these countries. Accordingly, financial authorities should monitor investor behavior in the stock market to avoid the increase in their herding behavior as well as the reinforcement of the global market returns and regional return dispersion on herding during the pandemic period. Especially, financial authorities in developing countries should formulate policies quickly to reduce investors' fears in their health and the economy as a pandemic occurs. Because medical resources and financial developments are limited in developing countries, investors in these countries are more likely to follow the trading behavior of others in the stock market due to their fears in such a pandemic. Meanwhile, because the stock prices in developing countries are easily affected by the global stock indices and the regional stock dispersions, financial authorities in these countries should formulate policies fast to decrease the reinforcement of the global market returns and the regional stock herding on herding behavior in stock markets of these countries as facing a pandemic.The limitation of this study is that several stock markets in Eastern European countries are not selected in our sample countries. The reason is because there was not enough trading information for constituent stocks of the market index in these markets during our sample period. In the further research, researches could consider the following issues. First, by adding the global return dispersions into CSAD model, they can investigate the evidence of the global herding spillover from the global stock market to the country-specific stock market since the pandemic occurs. Second, they can examine the effects of the different stages of the pandemic on herding behavior in stock markets.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.HF: writing this manuscript. C-PC: providing datasets and discussing. Y-HL: running program. XY: finding literatures. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.