RNF5 expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes.The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of  The 4 Mb major histocompatibility (MHC) region on chromosome 6p21.3, which encodes the human leucocyte antigen (HLA) gene complex, is highly polymorphic, gene dense, and has strong linkage disequilibrium (LD) . Genome-RNF5, and previous studies have shown that inhibition of RNF5 results in rescue of F508del mutant cystic fibrosis transmembrane conductance regulator (CFTR) function  from 419 individuals to call full resolution (eight-digit) HLA types and RNA-seq data from 361 matched induced pluripotent stem cells (iPSCs) to create a comprehensive map of regulatory variants in the MHC region. We identified eight-digit HLA types that are strongly associated with expression differences of their cognate HLA gene, and conducted eQTL analyses using both single-variants and eight-digit HLA types, which represent haplotypes of coding and regulatory variants. We built upon the comprehensive MHC regulatory map by functionally exploring GWAS signals for 4083 complex traits, finding strong evidence of colocalization for 180 disease loci with eQTLs. Our data are a valuable resource for both the genetics and disease-focus communities, providing insights into the genetic architecture of the MHC region that have not previously been described. To demonstrate the utility of our resource, we examined the common Caucasian 8.1 ancestral haplotype (8.1AH), which spans the entire MHC region and is associated with many autoimmune disorders and other diseases , but is function . Based oWe used HLA-VBSeq  to estimHLA-H, HLA-T and HLA-K) and four genes in HipSci  fewer than 95% of individuals were HLA-typed. The fraction of heterozygous and homozygous HLA types across the 30 genes was highly similar in the iPSCORE and HipSci individuals (r = 0.92). These results show that we were able to obtain high recall rates for all 30 MHC region genes.For each HLA gene, we initially calculated recall rate, measured as the fraction of individuals that could be HLA-typed. We observed a high recall rate for all 30 genes , Mendelian inheritance concordance from 17 non-overlapping trios and HLA type concordance of the 231 fibroblast-iPSC pairs (defined as HipSci concordance). Overall, we found that the median concordance across for all 526 HLA alleles was very high; however, both the twin pairs (96.7%) and fibroblast-iPSC pairs (95.7%) had slightly higher concordance than the Mendelian inheritance 90.9%, . These r−6, To estimate the accuracy of the 526 called HLA alleles we tested for Hardy-Weinberg equilibrium (HWE) in the unrelated individuals and found that the vast majority  were in HWE  showed more than four-fold differences between the least expressed and the most expressed alleles. We examined the HLA types corresponding to the 8.1AH at eight-digit resolution (HLA-DQA1 and HLA-DQB1) were significantly expressed lower and one (HLA-DRB1) was not differentially expressed  to 4911 (HLA-DQB1). Many of the eGenes were also associated with primary eQTLs spanning relatively large distances (median = 2.1 Mb), including ZFP57, HLA-C, HLA-B, MICA and RNF5, whose associated primary eQTLs nearly spanned the entire 4 Mb MHC region, which is consistent with the strong LD of ancestral haplotypes. While genome-wide eQTL analyses usually test associations between gene expression and neighboring variants (within 500 kb or 1 Mb), our results show that gene expression in the MHC is associated with cis regulatory variants that span regions several times larger than those normally tested.We performed an eQTL analysis by testing each of the 146 expressed genes and the genotypes of 45,245 variants (MAF >1%) spanning the MHC region (±1 Mb flanking each side) using a linear mixed model. We detected associations between 56 eGenes and 72,473 eQTLs , and for 36 eGenes we identified a tertiary eQTL upon conditioning on the top two independent eQTLs . Overall−64, Mann-Whitney U test, Given the complexity of conducting genetic association studies in the MHC region, we set out to validate the eQTLs that we discovered by showing that they have characteristics of regulatory variants. We initially investigated the distance between lead eQTLs and the transcription start site (TSS) of their associated eGene and found that in general they were close , APOM, DXO, RNF5 and TAP1 were novel, that is had not been identified in GWAS. Interestingly, the association of RNF5 was consistent with its function as a regulator of the CFTR protein and suggested role in cystic fibrosis  associated with weight phenotypes. The two eGenes most strongly associated, GPANK1 and RNF5, had opposite associations. GPANK1 was only associated with fat-free mass, suggesting that the protein product is involved in muscle or bone mass regulation. Conversely, RNF5 was more strongly associated with fat mass than fat-free mass, suggesting that its altered expression can contribute to CF phenotypic variation, as these individuals are known to have bile and other digestive issues, including difficulties in assimilating fat  patients including pulmonary diseases , weight  in GWAS  and in t in GWAS . Howeverfibrosis . We nextting fat . FinallyHLA-A expression and HLA-A:01:01:01:01). We found that for all 24 expressed HLA genes, there were at least two alleles significantly associated  with its expression , resulting in HLA-type eQTLs largely clustering into four distinct genomic regions. Interestingly, RNF5 showed strong associations with multiple HLA types in clusters 1, 2 and 3, confirming the observation that it had long-range associations with eQTLs spanning the entire MHC region  and HLA class I pseudogenes; 2) chr6:31171745–31512238, including HLA class I genes , MICA and MICB; 3) chr6: 32361463–32789609, including HLA class II genes , TAP1 and TAP2; and 4) chr6:32916389–33115544, including HLA class II genes . We observed that different eGenes in each cluster were typically associated with the genotypes of different HLA types, suggesting that the observed clustering was not due to co-expression  of neighboring HLA genes  displayed moderate correlated expression (We investigated the molecular underpinnings of the clustering of the most significant HLA-type eQTLs within the four distinct genomic regions : 1) chr6LA genes . There wLA types . For thipression , despitepression . These fpression , the HLARNF5, and the 8.1AH to gain insight into the molecular mechanisms underlying CF phenotypic variation. The expression of RNF5 was negatively associated with eight-digit HLA types for all six genes composing the 8.1AH  spanning relatively large distances (median = 2.1 Mb), which is consistent with the strong LD in the interval. We also conducted conditional eQTL analyses and discovered that the majority of these eGenes were associated with multiple independent regulatory variants. Given the complexity of conducting genetic association studies in the MHC region, we examined the primary and conditional eQTLs and showed that they have characteristics of regulatory variants including proximity to the TSS of their associated eGene, enrichment in active chromatin marks, and association with allele-specific expression of their eGene. We then tested the eQTL signals  for all 56 eGenes against GWAS signals for 4083 complex traits from the UK Biobank and found strong evidence of colocalization with 180 disease loci. Given that the 8.1 ancestral haplotype (8.1AH) is an important genetic modifier of lung disease in Cystic Fibrosis (CF) patients , we examWe hypothesized that HLA types, which represent a haplotype of many regulatory and coding SNPs, could exert a greater effect on gene expression than single variants. We conducted an HLA-type eQTL analysis and identified 114 eGenes, showing that indeed taking HLA type haplotypes into account substantially increased power to identify eGenes over single-variant eQTL analysis. HLA-type eQTLs largely clustered into four distinct genomic regions. We showed that this clustering was due to the fact that HLA-type eQTLs capture regulatory variants that act over relatively short distances and tend to be associated with the expression of only one or a few neighboring genes. Unfortunately, we were not able to conduct a colocalization analysis of HLA-type eQTLs and GWAS signals, because GWAS for complex traits have not yet been conducted using eight-digit HLA types. However, in the past few years, novel methods have been developed to impute HLA types from variants assayed by SNP arrays , which mRNF5 expression was negatively associated with eight-digit HLA types for all six genes composing the 8.1AH. Interestingly, RNF5 was recently described as a potential novel drug target in CF, because, when RNF5 activity is decreased, the mutant CFTR protein becomes stabilized and its function as a cAMP-dependent chloride channel in the lung epithelium is partially rescued  . We show rescued . Impaire rescued . Hence, Taken together, our data provide insights into the genetic architecture of the MHC region and show that disease associations with HLA types cannot only be due to epitope differences but differential expression of HLA genes and associated non-HLA genes. Further, these data are a valuable resource for both the genetics and disease-focus communities that can be exploited to identify causal mechanisms underlying disease associations in the MHC region, and potentially novel drug targets.iPSCORE Resource: Whole genome sequencing (WGS) was performed for 273 individuals  at Human Longevity, Inc (HLI) as described in http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). All BAM files were processed using GATK best practices  and 231 associated iPSC lines. Fibroblasts obtained from each HipSci individual were reprogrammed and between one to two iPSC lines were derived. We downloaded CRAM files for the 377 samples (146 fibroblast samples and 231 iPSCs) from the European Nucleotide Archive  and transformed to BAM files using Samtools  by determining SNP density in consecutive 10 kb windows across the region. The SNP density in the WGS data varied widely: 1) for the 273 iPSCORE genomes from 1 SNP/10 kb to 900 SNPs/10 kb (mean = 149.2 SNPs/10 kb) compared with the average of 79.6 SNPs/10 kb across chromosome 6 ; and 2)  studies . To dete studies . We founhttps://www.gencodegenes.org/releases/19.html) and SAMtools version 1.2 (http://nagasakilab.csml.org/hla/). For each HLA gene, only the alleles with mean coverage ≥20% of the average coverage calculated over the whole genome were considered, and a target HLA genotype was determined as follows: 1) If no allele passed the threshold, then there were not enough reads aligned to correctly identify an HLA type, and hence no alleles were called; 2) If there was only one allele that passed the threshold, and the depth of coverage was two or more times greater than that of the threshold, then the HLA locus was considered to be homozygous for that HLA allele; 3) If there was only one allele that passed the threshold, and the depth of coverage was less than twice that of the threshold, then the allele was called heterozygous with the second allele not determined; 4) If there were two or more alleles that passed the threshold, the alleles were sorted based on the depth of coverage (from high to low), if the depth of coverage of the top allele was more than twice as that of the second one, then the HLA locus was called homozygous for the top allele; 5) If there were two or more alleles that passed the threshold, the alleles were sorted based on the depth of coverage (from high to low), if the depth of coverage of the top allele was less than twice that of the second one, then the two alleles with the highest coverage were selected as the HLA type. For HLA types at four-digit resolution  recall, the fraction of determined HLA types for each gene; 2) HLA type concordance in 25 monozygotic twin pairs in the iPSCORE resource; 3) Mendelian inheritance concordance across 17 trios in the iPSCORE resource; and 4) HLA type concordance in 231 fibroblast-iPSC pairs. The recall was calculated independently for the 273 iPSCORE samples and the 377 HipSci samples as the number of determined HLA types divided by the total number of samples. For each gene, we calculated concordance as the fraction of HLA types that matched in the 25 monozygotic twin pairs in iPSCORE or the 231 fibroblast-iPSC paired genomes from the same individual in the HipSci resource. We calculated Mendelian inheritance concordance for each gene as the percentage of HLA types segregating in non-overlapping trios . If one or more HLA types were undetermined for a given pair/trio, we excluded the pair/trio during the concordance calculation.−6 were considered as significantly deviating from HWE and flagged in the HLA-type QTL analysis.To investigate HWE, we performed a likelihood ratio test on the allele frequency of each HLA allele independently in 275 unrelated Caucasians in the iPSCORE and HipSci cohorts. We tested each HLA allele independently for several reasons: 1) calculating HWE on sites with tens or hundreds of alleles is highly computationally intensive and complicated ; and 2) In the Allele Frequency Net Database (AFND) , 3,556,3https://www.ebi.ac.uk/ena, study PRJEB15299) and processed using the same pipeline used for the iPSCORE iPSC RNA-seq data.For the expression studies, we used RNA-seq data for 446 iPSC samples from 361 individuals . We obtained 45,245 filtered variants  in the MHC region  using bcftools  locaTo detect QTLs conditional on the lead eQTL, for each gene we repeated the eQTL adding the genotype from the lead eQTL as a covariate . To detect eQTLs conditional on the top two independent eQTLs , we repeated eQTL detection using the genotypes from the lead eQTL and the genotype from the lead Conditional 1 eQTL as covariates.To test if eQTLs were enriched for overlapping iPSC regulatory elements, we used the following approach: 1) For all 45,245 tested variants, we determined the overlapping chromatin mark in each of five Roadmap iPSCs; 2) We defined effect size deciles for all tested eQTLs  and binned the eQTLs based on their effect size ; 3) For We obtained allele-specific expression (ASE) data from our previous genome-wide eQTL analysis on 215 iPSCORE iPSCs  and defihttp://www.nealelab.is/uk-biobank). We identified 40,998 variants that were both tested for eQTLs and tested in the UK BioBank. For each of the 56 eGenes and each GWAS experiment, we extracted all p-values associated with these variants and performed colocalization using the coloc.abf from the coloc package in R  independently to examine associations between expression levels of each HLA gene and its HLA types. To determine if HLA-type eQTLs were independent from single-variant eQTLs, we repeated the HLA-type eQTL detection by conditioning on: 1) the lead primary eQTL ; 2) the lead primary and lead conditional 1 eQTLs ; and 3) on the lead primary, conditional 1 and conditional two lead eQTLs .For each sample, the HLA types were assigned dosage values and converted to VCF file format. Dosage was assigned as follows: 0 = the sample did not harbor the analyzed allele; 0.5 = the sample was heterozygous for the analyzed allele; 1 = the sample was homozygous for the analyzed allele. We adjusted the expression levels of all 146 expressed using the same covariates described for the eQTL detection  and performed HLA-type eQTL analysis investigating associations between each single HLA type with allele frequency >1%  and expression levels of each of the 146 expressed genes using a linear mixed model (lmekin from the coxme package in R) with a kinship matrix calculated on the 90,081 common SNPs in linkage equilibrium. P-values were adjusted for FDR by using the p.adjust function in R with method = ‘BH’ (Benjamini-Hochberg) on all combinations between the 146 expressed genes by 283 HLA types present in the 361 individuals  with a threshold of 0.05. The 15 HLA types that were not in HWE .Whole-genome sequencing data of 273 individuals in the iPSCORE cohort  is publi In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The MHC is the region of the human genome associated with the greatest number of different human diseases. Despite this, unravelling the relationship between genetic variation in the region and the expression of HLA and non-HLA genes with relevance to disease is a significant challenge. Here the authors have used deep sequencing of stem cells from 419 individuals to create an extensive map of regulatory variation in the MHC region, exploring genetic associations for over 4,000 different disease and traits, and uncovering links between 180 such associations with variation in the expression of specific genes. This study provides insights into the genetic architecture of the MHC region and sheds new light on the relatively underappreciated concept that non-coding, regulatory genetic variation in the MHC region can also contribute to disease risk. Therefore, this work has important implications for understanding the role of the MHC in human disease, and will be of interest to geneticists, immunologists, molecular biologists and clinical scientists.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Mark McCarthy as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Vivian G Cheung (Reviewer #1); Heather Cordell (Reviewer #2).Thank you for submitting your article "In-depth genetic analysis of 6p21.3 reveals insights into associations between HLA types and complex traits and disease" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:In this manuscript whole genome sequencing is employed to obtain HLA genotypes, which are then used to in a gene expression analysis to identify the regulatory variants affecting HLA and non-HLA genes in the major histocompatibility complex region of human chromosome 6. The study implies that HLA types can predict the expression of non-HLA genes, and identifies potential clusters of co-expressed genes. As the HLA region is highly polymorphic and many of its variants have been associated with immune-based and other human diseases, an accurate approach for determining HLA genotypes from whole genome sequencing data, and an improved understanding of the impact of variants within the region on modulating gene expression, are important.The essential revisions detailed below provide the collated comments and queries from the reviewers that need to be addressed. The reviewers were in agreement with respect to the need to give the paper a more cohesive structure, defining one or a few key questions and focusing on the most novel findings. We think that the suggestions regarding the statistical methods and the employment of simulation-based approaches are helpful and should be carefully considered. All reviewers also commented on the need to provide further support for the eQTL findings. Although we do not consider additional experimental validation to be necessary, we would like an indication of whether the eQTLs found were shared with those reported in previous studies or other available data sets. Other types of evidence that can support the eQTLs, such as the localisation of relevant variants in regulatory elements or GWAS enrichment of the variants, should also be investigated and reported.Essential revisions:1) Ideas presented in the Introduction are not taken up in the conclusion. The Introduction refers to "altered self" and "mistaken identity" hypotheses. Although the latter is taken up in the proof of principle, these terms are not used again, making the Introduction and Discussion somewhat disjointed. More generally, the paper covers a broad array of issues, but lacks focus: it was unclear exactly what the main idea is. If the focus is on demonstrating the enrichment of sites with regulatory role, a concern is that with respect to comparisons across studies with power that is non-comparable. If the idea is to defend the "mistaken identity" hypothesis, this needs to be taken up more thoroughly in the Discussion, given that the idea is now built upon, almost exclusively, a case study of one locus and its putative HLA-eQTL.2) The history of HLA typing need to be amended. The text states that "the development of array-based technologies provided methods to estimate HLA types at four-digit resolution". Four-digit resolution greatly predates array technologies, which were brought in late in the history of HLA typing as a means to impute four-digit alleles, given appropriate reference samples and SNP density. Similarly, the possibility of "incorporating synonymous variants and non-coding regulatory variants" is attributed to NGS-based methods (supported by a string of citations between 2012 and 2017) but (Sanger) sequencing and PCR-based with allele-specific primers describing these levels of variation have long been around . The critical step of the cited papers was in fact adapting NGS pipelines for HLA typing.3) In Figure 4, there are 3 groups of genes (described in the subsection “Genes in the MHC region are enriched for SNP-eQTLs”): those who expression levels show association with 283 SNP alleles, those with 10 or fewer and those with more than 1,000 associations. The data shown in Figure 4 do not show the distributions of expression levels for each allele so it is not possible to tell whether the associations are driven by just a few samples. Can the authors replot some of the results with graphs where expression levels are on the y-axis, and the three genotype groups on the x-axis?4) An important aspect of the study is the identification of "four groups of genes that have alleles with highly correlated expression". This is largely supported by the results in Figure 5, which show sets of genes with significant correlations with HLA genotype . This is a potentially interesting finding. Why was this analysis carried out in the first place? What are prior results that motivate the expectation that HLA allele identity will be predictive of expression of neighboring genes? Is this a causal mechanism or are HLA genes marking a haplotype with other genetic signatures? If documenting co-expression is of interest, why not examine it directly, rather than mediated through associations to an HLA type?5) In the treatment of HLA-eQTLs, an interesting idea that yielded useful results, the authors chose not to examine the role of HLA genotypes on the expression of other HLA genes in the MHC region. This examination should either be expanded upon or a justification for the lack of a more extensive analysis needs to be provided.6) The entire discussion of how levels of variability at 2 and 8 digit relate occupies a fair portion of the paper. Although essentially correct, much of the pattern seen is naturally expected given the nesting of 8 digit definitions within 2 digits. Thus, large sections of the paper relating diversity, using correlation analyses  and discussions of findings could be omitted. The very definition of these levels makes the patterns found and described expected. This shows up again in the discussion of concordance, where the fact that the values are higher for 2 digits is presented a result. This is unavoidable, given the nesting, which constrain the results so that the 2-digit concordance can only be the same as or greater than that at 8-digits. This is a feature of the data, not a result.7) The paper dedicates considerable space to discussing variation among alleles. The usage of "allele-specific expression" as employed here does not match the usual definition, which refers to the "comparing the expression of the alleles in a heterozygous individual " (https://doi.org/10.1186/s12864-018-5181-0). What is being done is in fact a test for differences in expression among alleles, a different question. Figure 3 treats the data as "each point reporting one allele". However, in the text referring to this same analysis we read "We (…) compared the TPM distributions between all samples carrying a specific HLA allele and all the other samples." This is a different analysis, since the two alleles of heterozygous individuals are confounded, both contributing to the locus-level expression. Although sometimes employed, extrapolating an individual's expression to an allele which is carried is an approach that introduces an additional source of uncertainty, which can be dealt with by analyzing the allele-level expression . The paper needs to clarify what was done and justify the choices.8) Throughout the manuscript, the authors refer to the personalized component in the index used to estimate expression levels as "personalized transcripts". However, according to the Methods, cDNA sequences from the IMGT database were used instead of personalized isoform sequences. While this is a personalized index at the HLA level, it is not a personalized transcriptome-based index since transcriptomic diversity is not being assayed as in Gensterblum-Miller et al. (https://doi. org/10.4049/jimmunol.1701061).9) The paper fails to acknowledge previous work. The statement "The inability to genetically interrogate the MHC region" fails to acknowledge work such as Lee et al. (https://doi.org/10.1093/bioinformatics/bty), Jensen et al. (10.1101/gr.218891.116), Aguiar et al. . Similarly, the paper states that high resolution data for high depth data has not been provided across the region, but the above papers do address this issue. Later, in Results, when discussing the use of personalized transcripts to determine gene expression levels the authors argue that "the expression levels of genes in this region measured by RNA-seq may be inaccurate due to large numbers of mismatches between sequence reads and reference sequence" but do not have results on this, and fail to cite papers such as Aguiar et al., 2019, which explores exactly the relationship between mismatches and accuracy of expression through simulations or more general treatments such as Panousis et al., 2014.10) The paper presents several analyses to support the reliability of the HLA genotype calls. Initially, the study examines the SNP density interferes with alignment coverage. It then argues that values which are sequencing depths (not coverage) are comparable to other genome regions and thus claims this implies "high quality". While the info on coverage and depth is useful, it is not clear that these values alone can be treated as proxies of high quality.11) The paper also uses concordance among twins to "examine the accuracy of the high-resolution HLA types". While concordance is useful to document if there are no experimental errors, it does not guarantee accuracy. For example, if there is a systematic error in the alignment procedure, the same and incorrect sample could be obtained in each case. Thus, this may be indicated as an assay to measure experimental replicability, but not accuracy.12) The argument for an improvement in expression estimates using the personalized pipeline  is that class I estimates are higher and class II are lower, when the pipeline is used. It is unclear why this is support for greater accuracy. It would imply that the higher expression of class II with the standard reference index results in overestimates, but mismatches between that index and the individual's alleles don't make such a prediction. The fact that HLA class II genes have lower expression doesn't naturally lead to the prediction that the effect of a better index would be to lower expression. Accuracy would require some external measure of expression (which isn't available) or a simulation-based approach (as implemented in some previously cited studies).13) The paper spends some time correlating genotype and allele frequencies, including a report of negative correlation between number of alleles and number of homozygous genotypes. The standard way to explore this is to test for deviations from Hardy-Weinberg proportions, something that can be done using various existing software packages.14) The study also shows a correlation between allele frequencies in the two datasets. While this is comforting, it is a very coarse indicator, which at most could be reported as a supplementary result, but doesn't warrant inclusion in the body of the paper.15) For the eQTL mapping, the authors have adjusted the expression levels for different batches, ethnicities etc, but have not controlled for the effects of population structure on the genotype data . It would also be helpful to provide more details on how the samples were used in the analyses. More details on how the adjustments were made to ensure that ethnicity did not add unexpected structure to the data would be helpful. Additionally, only one of the MZ twins should be used – it is not clear that the twins were removed.16) The authors should justify the use of the overly conservative Bonferroni correction in the eQTL mapping instead of the widely-used FDR correction in genome-wide studies. By querying the MHC region alone, the definition of significance becomes different from genome-wide studies, making contrasts between these (including the proportion of eGenes) not directly comparable. Wouldn't it be preferable to carry out a genome-wide analysis, and then use the distribution of p-values to estimate corrected values under a chosen FDR? This may give readers a better ability to place these findings in the context of previous eQTL studies.17) The authors claim that there is an enrichment of eGenes on the MHC region by comparing with a previous genome-wide work which analyzed only a subset of the samples (215 of the 446 samples). Since sample size affects power to detect eGenes, the direct comparison is not appropriate. More generally, the notion that "in the MHC region a large fraction of expressed genes are eGenes" should be stated in that qualifies the identify of an eGene as contingent on the dataset. A gene can be an eGene in some studies and not others, as a function of sampling. Thus, the statement that "genes in the MHC region are significantly enriched for having their expression levels modulated by regulatory variants" is not supported by the analysis.18) Further support for the eQTL findings. For some of the regulatory variants, are there any samples that are heterozygous and show that the two alleles are indeed expressed at the different levels? Were the eQTL variants enriched for localisation in regulatory sites? Were they enriched for GWAS signals and have the eGenes in question been previous linked to disease? Essential revisions:1) Ideas presented in the Introduction are not taken up in the conclusion. The Introduction refers to "altered self" and "mistaken identity" hypotheses. Although the latter is taken up in the proof of principle, these terms are not used again, making the Introduction and Discussion somewhat disjointed. More generally, the paper covers a broad array of issues, but lacks focus: it was unclear exactly what the main idea is. If the focus is on demonstrating the enrichment of sites with regulatory role, a concern is that with respect to comparisons across studies with power that is non-comparable. If the idea is to defend the "mistaken identity" hypothesis, this needs to be taken up more thoroughly in the Discussion, given that the idea is now built upon, almost exclusively, a case study of one locus and its putative HLA-eQTL.We thank the reviewers for their thoughtful and insightful comments and agree that the manuscript lacked focus. For these reasons, we have substantially re-written the manuscript and have focused on the following topics:• Using whole genome sequence from 419 individuals to call eight-digit HLA types and RNA-seq data from 361 matched induced pluripotent stem cells (iPSCs) to create a comprehensive map of regulatory variants in the MHC region.• Building on this regulatory map, we explore GWAS signals for 4,083 complex traits, and finding strong evidence of colocalization for 180 disease loci with expression quantitative trait loci (eQTLs).• Showing that taking eight-digit HLA type haplotypes into account substantially increases power to identify eGenes over single-variant eQTL analysis.RNF5 expression is the likely causal mechanism.• Examining the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, and postulating that downregulation of 2) The history of HLA typing need to be amended. The text states that "the development of array-based technologies provided methods to estimate HLA types at four-digit resolution". Four-digit resolution greatly predates array technologies, which were brought in late in the history of HLA typing as a means to impute four-digit alleles, given appropriate reference samples and SNP density. Similarly, the possibility of "incorporating synonymous variants and non-coding regulatory variants" is attributed to NGS-based methods (supported by a string of citations between 2012 and 2017) but (Sanger) sequencing and PCR-based with allele-specific primers describing these levels of variation have long been around . The critical step of the cited papers was in fact adapting NGS pipelines for HLA typing.To focus the manuscript and Introduction, we have removed this entire section describing the different resolutions of HLA typing. Instead we have added a new figure  that describes current standard HLA nomenclature3) In Figure 4, there are 3 groups of genes (described in the subsection “Genes in the MHC region are enriched for SNP-eQTLs”): those who expression levels show association with 283 SNP alleles, those with 10 or fewer and those with more than 1,000 associations. The data shown in Figure 4 do not show the distributions of expression levels for each allele so it is not possible to tell whether the associations are driven by just a few samples. Can the authors replot some of the results with graphs where expression levels are on the y-axis, and the three genotype groups on the x-axis?We thank the reviewers for this comment: to show that the eQTL associations are not driven by just a few samples we added Figure 4—figure supplement 2, which shows boxplots of the expression levels for each of the 56 eGenes divided by the three genotypes  of the lead eQTL. These plots show that the eQTL signals are supported by many samples. We have also made the eQTL summary statistics publicly available(https://figshare.com/s/c8533530204e1822c6f4 and https://figshare.com/s/eab712af0e4baeb99251).4) An important aspect of the study is the identification of "four groups of genes that have alleles with highly correlated expression". This is largely supported by the results in Figure 5, which show sets of genes with significant correlations with HLA genotype . This is a potentially interesting finding. Why was this analysis carried out in the first place? What are prior results that motivate the expectation that HLA allele identity will be predictive of expression of neighboring genes? Is this a causal mechanism or are HLA genes marking a haplotype with other genetic signatures? If documenting co-expression is of interest, why not examine it directly, rather than mediated through associations to an HLA type?We thank the reviewers for this observations. We agree that the rationale for analyzing the four sets of genes with significant associations with HLA genotypes was not clearly explained.Why was this analysis carried out in the first place?We hypothesized that HLA types, which represent a haplotype of many regulatory and coding SNPs, could exert a greater effect on gene expression than single variants. This is now clearly stated in the Discussion and alluded to in the Results.What are prior results that motivate the expectation that HLA allele identity will be predictive of expression of neighboring genes?There were no prior results that motived us to hypothesize that this would be the case. However, we have since identified a previous study that conducted a similar analysis but on a considerably smaller scale. We have cited a recent paper by Lam et al., which describes eQTLs in the MHC region in 31 LCL samples, in the Discussion.Is this a causal mechanism or are HLA genes marking a haplotype with other genetic signatures?We believe that these findings suggest that the HLA-type eQTLs capture regulatory variants associated with the expression of one or a few neighboring genes. Furthermore, unlike the eQTLs from the single-variant analysis that tend to span long distances most likely due to the strong LD in the MHC region, the HLA-type eQTLs capture regulatory variants that act over relatively short distances.We have expanded the Results and Discussion to describe these associations in detail.If documenting co-expression is of interest, why not examine it directly, rather than mediated through associations to an HLA type?In response to this comment, we have added a new analysis section in the Results “HLA-type eQTLs capture regulatory variation associated with expression of neighboring genes” and in the Materials and methods section “Coexpression analysis”.“We investigated the molecular underpinnings of the clustering of the most significantHLA-type eQTLs within the four distinct genomic regions : 1)HLA-A, HLA-F and HLA-G) and HLA class I pseudogenes; 2) chr6:31171745-31512238, including HLA class I geneschr6:29640168-30152231, including HLA class I genes , MICA and MICB; 3) chr6: 32361463-32789609, including HLA class, TAP1 andII genes  chr6:32916389-33115544, including HLA class II genes . We observed that different eGenes in each cluster were typically associated with the genotypes of different HLA types, suggesting that the observed clustering was not due to co-expression  of neighboring HLA genes . There was one exception in cluster 3, with the expression of both HLA-DRB5 and HLA-DRB1 associated with many of the same HLA types . For this reason, we examined the co-expression of the eGenes in each of the four clusters. Only five eGenes in cluster 3  displayed moderate correlated expression , despite having their expression levels largely associated with different HLA types of neighboring genes . These findings suggest that the HLA-type eQTLs capture regulatory variants associated with the expression of one or a few neighboring genes. Furthermore, unlike the eQTLs from the single-variant analysis that tend to span long distances most likely due to the strong LD in the MHC region , the HLA-type eQTLs capture regulatory variants that act over relatively short distances.”5) In the treatment of HLA-eQTLs, an interesting idea that yielded useful results, the authors chose not to examine the role of HLA genotypes on the expression of other HLA genes in the MHC region. This examination should either be expanded upon or a justification for the lack of a more extensive analysis needs to be provided.HLA-A expression), but we investigated the associations between the HLA type of one HLA gene and the expression of other HLA genes (for example HLA-A:01:01:01:01 and HLA-B expression). In the new version of the manuscript, we have added the self-association analysis (for example HLA-A:01:01:01:01 and HLA-A expression) in the Results section “HLAtype eQTLs associated with the expression of 114 eGenes in the MHC region” and in Supplementary file 9. We have tried to make the text in the Results and Materials and methods describing this analysis clearer.We apologize for the confusion: in our original submission, we only removed “self” associations, i.e., we did not analyze the role of HLA genotypes on the same HLA gene (for example HLAA:01:01:01:01 and 6) The entire discussion of how levels of variability at 2 and 8 digit relate occupies a fair portion of the paper. Although essentially correct, much of the pattern seen is naturally expected given the nesting of 8 digit definitions within 2 digits. Thus, large sections of the paper relating diversity, using correlation analyses  and discussions of findings could be omitted. The very definition of these levels makes the patterns found and described expected. This shows up again in the discussion of concordance, where the fact that the values are higher for 2 digits is presented a result. This is unavoidable, given the nesting, which constrain the results so that the 2-digit concordance can only be the same as or greater than that at 8-digits. This is a feature of the data, not a result.We agree that the discussion about the differences between two-digit and eight-digit resolutions is superfluous and contributes to the lack of focus that the reviewers pointed out in Comment 1. Therefore, we have removed this analysis from the manuscript including the Results and Materials and methods, and all related figures.7) The paper dedicates considerable space to discussing variation among alleles. The usage of "allele-specific expression" as employed here does not match the usual definition, which refers to the "comparing the expression of the alleles in a heterozygous individual " (https://doi.org/10.1186/s12864-018-5181-0). What is being done is in fact a test for differences in expression among alleles, a different question. Figure 3 treats the data as "each point reporting one allele". However, in the text referring to this same analysis we read "We (…) compared the TPM distributions between all samples carrying a specific HLA allele and all the other samples." This is a different analysis, since the two alleles of heterozygous individuals are confounded, both contributing to the locus-level expression. Although sometimes employed, extrapolating an individual's expression to an allele which is carried is an approach that introduces an additional source of uncertainty, which can be dealt with by analyzing the allele-level expression . The paper needs to clarify what was done and justify the choices.We have greatly reduced the space in the text devoted to discussing expression differences among HLA types . We have eliminated all discussion of comparing alignments to reference versus cDNA sequences specific for the HLA types from the Results. We moved some of this to the supplementary figures.We apologize for the confusion in the description of the analysis about obtaining expression levels from alignments to cDNA sequences specific for the HLA types. We have edited the Materials and methods to clarify that we treated homozygous and heterozygous individuals in different ways:“The IMGT-IPD HLA database release 3.30.0, RSEM version 1.2.20 contained cDNA sequences corresponding to each eight-digit resolution allele of the 24 HLA genes expressed in iPSCs. […] For heterozygous individuals, the TPM associated with each cDNA sequence was considered as HLA type-specific expression, while for homozygous individuals, the TPM was divided by two to obtain HLA type-specific expression levels.”8) Throughout the manuscript, the authors refer to the personalized component in the index used to estimate expression levels as "personalized transcripts". However, according to the Materials and methods, cDNA sequences from the IMGT database were used instead of personalized isoform sequences. While this is a personalized index at the HLA level, it is not a personalized transcriptome-based index since transcriptomic diversity is not being assayed as in Gensterblum-Miller et al. (https://doi. org/10.4049/jimmunol.1701061).We agree that “personalized transcripts” is not the correct term to use. As discussed in response to comment 7, we have eliminated all discussion of comparing alignments to reference versus cDNA sequences specific for the HLA types from the Results. We referenced the paper by Gensterblum-Miller et al. in the Results section “Eight-digit HLA types associated with expression of cognate HLA gene”9) The paper fails to acknowledge previous work. The statement "The inability to genetically interrogate the MHC region" fails to acknowledge work such as Lee et al. (https://doi.org/10.1093/bioinformatics/bty), Jensen et al. (10.1101/gr.218891.116), Aguiar et al. . Similarly, the paper states that high resolution data for high depth data has not been provided across the region, but the above papers do address this issue. Later, in Results, when discussing the use of personalized transcripts to determine gene expression levels the authors argue that "the expression levels of genes in this region measured by RNA-seq may be inaccurate due to large numbers of mismatches between sequence reads and reference sequence" but do not have results on this, and fail to cite papers such as Aguiar et al., 2019, which explores exactly the relationship between mismatches and accuracy of expression through simulations or more general treatments such as Panousis et al., 2014.We thank the reviewers for these comments. As part of focusing the paper we have removed all sections to which comment 9 is concerning. Nevertheless we have included references for: Jensen et al., Lee et al., Panousis et al. and Aguiar et al..10) The paper presents several analyses to support the reliability of the HLA genotype calls. Initially, the study examines the SNP density interferes with alignment coverage. It then argues that values which are sequencing depths (not coverage) are comparable to other genome regions and thus claims this implies "high quality". While the info on coverage and depth is useful, it is not clear that these values alone can be treated as proxies of high quality.In our original manuscript, we had concluded that WGS samples were high-quality because the read depth in the MHC region is comparable to the rest of the genome and that the SNP density is comparable to previous studies. This was based on the assumptions that, if the alignment were low-quality, the read depth would be lower than the rest of the genome and the SNP density distribution would be noisy and would not have the same shape as was previously observed. We understand that these assumptions are not sufficient to claim that WGS had high quality.To address this comment, we have changed the title of the first section of the Results to “Eight-digit HLA typing” and we removed any reference to quality. We have modified the take-home message of this section to the fact that the alignment of all WGS samples is comparable to the rest of the genome and that the SNP density is similar to previous studies.11) The paper also uses concordance among twins to "examine the accuracy of the high-resolution HLA types". While concordance is useful to document if there are no experimental errors, it does not guarantee accuracy. For example, if there is a systematic error in the alignment procedure, the same and incorrect sample could be obtained in each case. Thus, this may be indicated as an assay to measure experimental replicability, but not accuracy.We agree that concordance is not a measure of accuracy. When we describe high concordance we no longer refer to this as “accuracy” but rather “replicability” and have changed the section title in the Results section to “HLA types have high recall rates and reproducibility”.12) The argument for an improvement in expression estimates using the personalized pipeline  is that class I estimates are higher and class II are lower, when the pipeline is used. It is unclear why this is support for greater accuracy. It would imply that the higher expression of class II with the standard reference index results in overestimates, but mismatches between that index and the individual's alleles don't make such a prediction. The fact that HLA class II genes have lower expression doesn't naturally lead to the prediction that the effect of a better index would be to lower expression. Accuracy would require some external measure of expression (which isn't available) or a simulation-based approach (as implemented in some previously cited studies).We have eliminated most of the discussion on personalized expression from the manuscript. See answers to comments 7 and 8 for a description as to how we modified the manuscript.13) The paper spends some time correlating genotype and allele frequencies, including a report of negative correlation between number of alleles and number of homozygous genotypes. The standard way to explore this is to test for deviations from Hardy-Weinberg proportions, something that can be done using various existing software packages.We agree with the reviewers and have removed all the analyses describing correlations between genotypes and allele frequencies.We have added the following to the Results:-6, Figure 2—figure supplement 1A-C), consistent with the reproducibility values in twin pairs (96.7%) and in fibroblast-iPSC pairs (95.7%). […] Overall, HLA-VBSeq was able to accurately detect eight-digit resolution HLA alleles in the 650 WGS samples as evidenced by high recall rates, reproducibility and allele frequencies consistent with Hardy-Weinberg law.”“To estimate the accuracy of the 526 called HLA alleles we tested for Hardy-Weinberg equilibrium (HWE) in the unrelated individuals and found that the vast majority  were in HWE -6 were considered as significantly deviating from HWE and flagged in the HLA-type QTL analysis.”To investigate HWE, we performed a likelihood ratio test on the allele frequency of each HLA allele independently in 275 unrelated Caucasians in the iPSCORE and HipSci cohorts. […] All associations with p-values < 1 x 1014) The study also shows a correlation between allele frequencies in the two datasets. While this is comforting, it is a very coarse indicator, which at most could be reported as a supplementary result, but doesn't warrant inclusion in the body of the paper.We agree with the reviewers and have removed the figure showing the correlation between allele frequencies in the two datasets from the manuscript.15) For the eQTL mapping, the authors have adjusted the expression levels for different batches, ethnicities etc, but have not controlled for the effects of population structure on the genotype data . It would also be helpful to provide more details on how the samples were used in the analyses. More details on how the adjustments were made to ensure that ethnicity did not add unexpected structure to the data would be helpful. Additionally, only one of the MZ twins should be used – it is not clear that the twins were removed.We agree that the use of a linear model (such as MatrixEQTL) to test for eQTLs is not appropriate when tested individuals are related. Therefore, we repeated the eQTL analysis (both on single variants and HLA types) using a linear mixed model that includes a kinship matrix to take population structure into account.We have substantially re-written the Materials and methods to describe these analyses:“eQTL detectionFor eQTL analyses, we used the WGS from 361 individuals  for which iPSC RNA-seq data were available. […] We performed eQTL analysis for the 146 expressed genes and the 45,245 variants using a linear mixed model (lmekin from the coxme package in R) with a kinship matrix  calculated on 90,081 common SNPs distributed across the genome and in linkage equilibrium.”We have changed Supplementary files 6, 7 and 9 with the new QTL results.16) The authors should justify the use of the overly conservative Bonferroni correction in the eQTL mapping instead of the widely-used FDR correction in genome-wide studies. By querying the MHC region alone, the definition of significance becomes different from genome-wide studies, making contrasts between these (including the proportion of eGenes) not directly comparable. Wouldn't it be preferable to carry out a genome-wide analysis, and then use the distribution of p-values to estimate corrected values under a chosen FDR? This may give readers a better ability to place these findings in the context of previous eQTL studies.We thank the reviewers for suggesting how we could improve the eQTL analysis. In response to Comment 15, we have changed the method to perform the eQTL analysis from a linear model to a linear mixed model to account for random genetic effects, which has allowed us to use all the iPSC samples  and therefore has improved our eQTL detection power.To address the reviewers’ comments, we improved the explanation of the eQTL detection method in the Materials and methods section “eQTL detection”.“In order to detect significant eQTLs for each eGene, and then detect eGenes at genomewide significance levels, we employed a three-step procedure to perform hierarchical multiple testing correction : 1) locally adjusted p-value at gene-level: nominal p-values were Bonferroni-corrected for the number of tests performed for each gene ; the variant with the minimum adjusted p-value was considered as lead eQTL (Supplementary file 7B); 2) genome-wide adjusted p-value of lead eQTLs: to FDRcorrect across all expressed genes, we further adjusted the p-values of the lead eQTLs using the p.adjust function in R with method = “BH” (Benjamini-Hochberg) and n = 20,595 (corresponding to the number of expressed genes genome-wide) with a threshold of 0.05; and 3) eQTLs were identified for each significant eGene as variants with a locally adjusted p-value from step 1 < 0.1.”17) The authors claim that there is an enrichment of eGenes on the MHC region by comparing with a previous genome-wide work which analyzed only a subset of the samples (215 of the 446 samples). Since sample size affects power to detect eGenes, the direct comparison is not appropriate. More generally, the notion that "in the MHC region a large fraction of expressed genes are eGenes" should be stated in that qualifies the identify of an eGene as contingent on the dataset. A gene can be an eGene in some studies and not others, as a function of sampling. Thus, the statement that "genes in the MHC region are significantly enriched for having their expression levels modulated by regulatory variants" is not supported by the analysis.We agree that it would be misleading to compare the number of eQTLs across studies, where many other factors  play important roles. Therefore, we have removed this statement from the text .18) Further support for the eQTL findings. For some of the regulatory variants, are there any samples that are heterozygous and show that the two alleles are indeed expressed at the different levels? Were the eQTL variants enriched for localisation in regulatory sites? Were they enriched for GWAS signals and have the eGenes in question been previous linked to disease?We thank the reviewers for these suggestions. To respond to this comment we performed three additional analyses (explained below).For some of the regulatory variants, are there any samples that are heterozygous and show that the two alleles are indeed expressed at the different levels?To respond to this comment, we performed enrichment analyses to determine if eQTLs were associated with allele-specific expression (ASE):-64, Mann-Whitney U test, Figure 4—figure supplement 4, Supplementary file 7].1) We tested if the 56 eGenes were more likely to have ASE than the other expressed genes in the MHC region. We obtained ASE data from our previous eQTL analysis on 215 iPSCORE iPSCs and tested the distributions of major allele frequencies calculated using MBASED on all samples between the 56 eGenes and the other 90 genes expressed in the MHC region. We found that eGenes were significantly more likely to have ASE [mean allelic imbalance fraction (AIF) = 67.3%, compared with 61.9% in the other genes, p = 4.38 x 102) Next, we investigated if eGenes in samples with a heterozygous lead eQTL had stronger AIF than samples homozygous for the eQTL using Mann-Whitney U test. We observed that eGenes in samples with heterozygous eQTLs  have stronger ASE compared with eGenes in samples homozygous for eQTLs .We describe these analyses in the Results (“eQTLs associated with the expression of 56 eGenes in the MHC region”), in the Materials and methods , Figure 4—figure supplements 4 and 5.Were the eQTL variants enriched for localisation in regulatory sites?We obtained 15 ChromHMM chromatin states in five iPSC lines from the Roadmap Epigenomics Consortium and tested if the effect size of eQTLs changed between states. Intuitively, variants in regulatory elements are more likely to be associated with gene expression, therefore have stronger effect size. To perform this analysis, we used the following approach:1) For all 45,245 tested variants, we determined the overlapping chromatin mark in each of five Roadmap iPSCs;2) We defined effect size deciles for all tested eQTLs  and binned the eQTLs based on their effect size ;3) For each Roadmap iPSC sample, we counted the number of eQTLs associated with each mark in each bin, creating a 10-by-15 matrix (10 bins and 15 chromatin marks);4) For normalization, we divided each column by its average across all bins and log2-transformed each value , in order to obtain enrichments centered around zero;5) We plotted these enrichment values for each chromatin mark .Figure 4—figure supplement 3 shows that, in all Roadmap iPSC samples, the eQTLs in the MHC region with the largest effect sizes were enriched for overlapping regulatory elements active in iPSCs including active transcription start sites (TSS), regions flanking active TSS, UTRs (defined as regions transcribed at 5’ and 3’) and transcribed regions.We added a description of this analysis in the Results section “eQTLs associated with the expression of 56 eGenes in the MHC region”, in the Materials and methods section “Enrichment of eQTLs for regulatory elements” and in Figure 4—figure supplement 3.Were they enriched for GWAS signals and have the eGenes in question been previous linked to disease?We changed our approach to determine associations between eQTLs and complex traits to determine whether eQTL signals in the MHC region, rather than single variants, were associated with any of the 4,083 traits described in the UK BioBank. Using this approach, we used a colocalization approach to test all the eQTL signals with each trait in the UK BioBank and observed 180 associations with PPA > 0.8 . These results demonstrate the utility of our data to serve as a resource to narrow down the location of causal regulatory variants underlying known associations between genes in the MHC region and complex traits, as well as to identify biologically meaningful novel associations.We have added a section in the Results (“Regulatory variants in the MHC region play important roles in complex traits”), in the Materials and methods (“Associations between eQTLs and GWAS”), Supplementary file 8 and Figure 5.


Therapeutic antibodies have shifted the paradigm of disease treatments from small molecules to biologics, especially in cancer therapy. Despite the increasing number of antibody candidates, much remains unknown about the antibody and how its various regions interact. Recent findings showed that the antibody constant region can govern localization effects that are useful in reducing side effects due to systemic circulation by the commonly used IgG isotypes. Given their localized mucosal effects, IgA antibodies are increasingly promising therapeutic biologics. While the antibody Fc effector cell activity has been a focus point, recent research showed that the Fc could also influence antigen binding, challenging the conventional idea of region-specific antibody functions. To investigate this, we analysed the IgA antibody constant region and its distal effects on the antigen binding regions using recombinant Pertuzumab IgA1 and IgA2 variants. We found that mutations in the C-region reduced Her2 binding experimentally, and computational structural analysis showed that allosteric communications were highly dependent on the antibody hinge, providing strong evidence that we should consider antibodies as whole proteins rather than a sum of functional regions. Antibodies, called the “magic bullet” by Paul Erhlich ,2,3, havIgA (IgA1 and IgA2) is the major immunoglobulin isotype in adaptive mucosal immunity ,13,14,15Fc manipulations have also been used to improve antibody half-lives  as well Similar to the case of IgG ,26, our Wild-type recombinant Pertuzumab IgA1 and IgA2 were synthesized and expressed as were previously described . The mut®, Fortebio, Fremont, CA, USA) of the antibodies to Her2 were carried out by pre-binding of HIS-tagged Her2  onto the Ni-NTA (NTA) biosensors  as previously described and performed [Binding kinetics  to sample conformational changes of the whole antibody structures. The simulations were performed with time steps (dt) gradually increased from 15 fs to 22 fs during the equilibrium to accommodate ion wild motions, then fixed at dt = 22 fs during the production stages (3 × 1 μs) with the Verlet algorithm. Periodic boundary condition was also applied to avoid the finite size effects while simulating in explicit solvent . Temperature and pressure coupling schemes were used with the velocity rescale  and the Parrinello–Rahman barostat. Our analyses used the data from the last 600 ns (×3) of the simulated trajectories that reflected stable simulations, resulting in 3 × 1000 conformations.The energy-minimized structures of the two variants (each including the wild type and mutant) were then subjected to coarse grain simulation (using Martini force field for proteins combined with residue) with respect to perturbations due to each of the events [Her2site) and other corresponding regions (Δgregion) by averaging all Δgresidue values of the involving residues.We first used the minimized structures of the wild-type variants IgA1 and IgA2 to quantify the allosteric effects in both the Her2-binding and mutation events  compared to the wild types (data not shown), implying the important role of the conserved disulphide-forming cysteine in the antibody stability . A higheonstants A.Her2site > 0) due to contact losses between the Her2-interacting residues and their neighbours  with a larger residue. Interestingly, the highly conserved disulphide-forming cysteine contributed to less destabilizing effects on the Her2 binding site . These rThe IgA1 isotype contains a longer hinge (connecting CH1 and the rest of the Fc region), the flexibility of which amplified with the mutation events (with We set out to investigate the mechanism of the allosteric effects that the antibody constant regions elicited on the antigen binding as suggested in our previous work . WorkingExperimentally, we found that the double mutants were produced at a significantly reduced rate in our transient expressions, and that there were compromises in terms of binding ability to Her2, albeit at different magnitudes, despite unmodified variable regions. This further demonstrated that the constant region had clear significant effects on antigen binding, agreeing with other such studies on IgGs ,22,24,40We postulated that the energy compensation at the Fabs of the IgAs variants to accommodate the flexibility changes upon balance at the Her2-binding site  to maintOur results here are also consistent with other previous studies ,23 that In conclusion, using both experimental and computational approaches, we were able to show that the constant region is important in the ability of the variable regions in binding antigens. We showed this in the less studied IgA, and that such effects were computationally found to be mediated by the hinge region of the antibody. This further illustrates the need to consider antibodies as a whole, rather than merely a sum of its regions.


Using a panel design, this study examined the prospective relationships between Internet addiction and life satisfaction as well as hopelessness in a representative sample of Hong Kong adolescents. Starting from 2009/10 academic year, 3328 Secondary 1 students in 28 secondary schools in Hong Kong participated in this longitudinal study . All participants responded to a questionnaire that includes the Internet Addiction Test, Life Satisfaction Scale, and Hopelessness Scale on a yearly basis. Cross-lagged analyses based on three waves of data collected during three junior adolescent years showed that Internet addiction measured at Time 1 predicted poor life satisfaction and hopelessness at Time 2, but not vice versa. Similarly, Internet addiction at Time 2 predicted low life satisfaction at Time 3, and the cross-lagged effects of life satisfaction and hopelessness on Internet addiction from Time 2 to Time 3 remained non-significant. The findings support the thesis that poor personal well-being in adolescents is the consequence rather than the cause of Internet addictive behaviors. To improve quality of life and prevent suicidality in adolescents, strategies that help reduce addictive behaviors related to the Internet should be considered. The world has entered an Internet era where connected electronic devices have been playing a gradually important role. From 1995 to 2016, the percentage of the world population who has access to the Internet has increased dramatically from less than 1% to approximately 46%  among a representative sample of Hong Kong adolescents over a three-year period. Several cross-lagged models that hypothesize different relationships between Internet addiction and personal well-being indicators over time would be examined after controlling for the possible effects of demographic factors  Internet addiction and personal well-being do not influence each other directly, but share variance caused by unmeasured factors ; 2) Personal well-being indicators have direct and longitudinal effects on Internet addiction; 3) Internet addiction has a direct effect on personal well-being, or 4) Internet addiction and personal well-being indicators demonstrate reciprocal and longitudinal effects. Findings of this pioneer study are expected to deepen our understanding about the causes and effects of Internet addiction in adolescents. The results would also shed light on the development of theoretical models on Internet addiction as well as prevention and intervention programs to promote young people’s personal well-being.The present study was part of a large survey project that traces the development of secondary school students in Hong Kong. Based on a list of all secondary schools in different districts of Hong Kong provided by the local Educational Bureau, 28 secondary schools were randomly selected to join the project, including 5 schools from Hong Kong Island, 7 schools from Kowloon, and 16 schools from New Territories. According to Shek et al. , the demn = 3328); Time 2: when students had spent one year in secondary school ; and, Time 3: when students would be graduated from junior secondary school . Across three waves, 2023 students were successfully matched with complete data, which included 1040 male students, 959 female students, and 24 students who did not indicate their gender. Basic demographic characteristics of participants were summarized in Table t = 2.99, p = .01. In terms of the variables under focus of the present study, no significant differences were identified in life satisfaction  and hopelessness , while participants who completed questionnaires at all waves reported higher scores on Internet addiction than participants only completed Wave 1 survey .The present study was based on three waves of data collected during the participants’ junior secondary years, i.e., Time 1: when students just entered into secondary school  has been translated into Chinese and validated in multiple samples of Hong Kong adolescents  or not. In Hong Kong, families receiving CSSA are generally considered having financial difficulties  with AMOS 23.0 software package was employed to examine the cross-lagged longitudinal model. First, the measurement models of the three latent variables, Internet addiction, life satisfaction, and hopelessness were tested at each wave. Second, four competing hypothesized structural models were tested using data collected at three time points when students were in Secondary 1, Secondary 2, and Secondary 3 to examine the proposed cross-lagged effects. The first model (M1) can be regarded as a stability model, which contains only the autoregressive effects of each latent variable across two waves, but did not contain any cross-lagged effects. The second model (M2) is a causal model which includes both the autoregressive effects as specified in M1 and cross-lagged effects from life satisfaction and hopelessness at an earlier time point (Time 1 and Time 2) to Internet addiction later (Time 2 and Time 3). The third model (M3) represents a reversed causal model including both the autoregressive effects and the cross-lagged effects from Internet addiction at earlier time points to life satisfaction and hopelessness later, i.e., reversed effects of the causal paths specified in M2. The fourth model (M4) is named as the reciprocal model combining M2 and M3, which assumes that there are reciprocal relationships between Internet addiction and the two personal well-being indicators over time. For each model, we allowed synchronous correlations among latent variables and covariation of the error terms of each indicator at Time 1 with the corresponding indicator at Time 2 and Time 3, as a common practice in longitudinal structural equation modeling , age, and family economic status  at Time 1 were included in the panel model as possible confounders as suggested by previous studies . As all structural models are nested models, they were compared by chi-square difference tests . Second, the reversed causal model (M3) with the cross-lagged effects of Internet addiction at an earlier time point (Time 1 and Time 2) on later life satisfaction and hopelessness (Time 2 and Time 3) provided a better fit to the data than the stability model . Third, while the reciprocal model (M4) fit the data better than M1 (stability model) and M2 , this model did not significantly improve the model fit as compared with M3, the reversed causal model . Therefore, M3 appeared to be the best fitting model in terms of parsimony, although M4 showed marginally significant improvement as compared to M3 (p = .04 using one-tailed test) which may also deserve attention. In other words, the data supported the hypothesis that Internet addiction causes low life satisfaction and high level of hopelessness in the future, but not vice versa  without cross-lagged paths was compared to the causal model (M2) that specifies the cross-lagged effects of life satisfaction and hopelessness at Time 1 and Time 2 on Internet addiction at Time 2 and Time 3, respectively. The results showed no significant improvement  and low family economic status (receiving CSSA) was negatively related to adolescents’ life satisfaction . Second, adolescents’ Internet addiction at Time 1 had a positive longitudinal cross-lagged effect on hopelessness at Time 2 , and a negative cross-lagged effect on their life satisfaction at Time 2 , after controlling for their autoregressive effects and the influence by demographic variables. Third, from Time 2 to Time 3, Internet addiction negatively predicted life satisfaction , while the prediction of hopelessness was not significant .Figure Most of the previous studies on the relationship between Internet addiction and personal well-being in young people have been based on cross-sectional design. As such, longitudinal data from a representative sample are necessary for researchers to understand whether poor well-being is a risk factor for youth Internet addiction or its consequence. The present study serves this purpose by examining the longitudinal relationships between Internet addiction and two personal well-being indicators, life satisfaction and hopelessness, in a large sample of Hong Kong adolescents.Based on a three-wave cross-lagged panel design, the results supported a reversed causal model such that Internet addiction caused decreased personal well-being after the baseline status and the effects of gender, age, and family economic status were controlled for. The reciprocal model that hypothesized mutual influences was not supported. These findings provide new insights into the direction of relationships between Internet addictive behaviors and youth personal well-being. In contrast to cross-sectional studies, the use of panel design and structural equation modeling is a more rigorous approach to examine the issues of causality and reciprocity.It was found that Internet addiction predicted adolescents’ low life satisfaction and high hopelessness longitudinally, but the cross-lagged effects of the two well-being indicators on Internet addictive behaviors were non-significant. While this finding confirms the negative relationship between Internet addiction and personal well-being, the direction of this association is only partially consistent with previously reported findings  and a decreased level of well-being. For example, sleeping problems have often been reported as a result of Internet addiction  and there could also be synchronic association between demographic factors and these constructs at later time points. Therefore, future studies may include these factors as covariates at each wave when examining the relationship between Internet addiction and personal well-being. Besides, there were findings showing that the negative association between problematic Internet use and well-being was stronger in female adults than in males. It would be interesting to investigate possible gender difference in such relationship in adolescents by using multi-group structural equation modeling approach.The final limitation is that the cross-lagged effects found in the present study were relatively weak, especially the effect of Internet addiction at Time 2 on hopelessness and life satisfaction at Time 3. One explanation may be that adolescents’ personal well-being in the last year of junior secondary school study is affected substantially by other factors than Internet addiction, such as stress of the entrance examination to senior secondary school. Therefore, the additional variance that can be explained by Internet addiction is limited. Also, the effects of Internet addiction on hopelessness and life satisfaction may be moderated by other factors, such as the students’ academic performance, which were not investigated in the present study. In future research, possible moderating effects of students’ academic results and perceived stress on the relationship between Internet addiction and personal well-being should be further examined. Although the effects found in the present study were moderate, the findings can be regarded as meaningful.


The current developments of the new biological drugs targeting interleukin 5 (IL-5) and IL-5 receptor allowed to expand the treatment options for severe hypereosinophilic asthma. Clinicians will then be able to choose between antibodies targeting either circulating IL-5 or its receptor expressed on eosinophils and basophils. The available clinical trials consistently reported favorable results about the reduction of exacerbations rate, improvement in quality of life, and sparing of the systemic steroid use, with a favorable safety profile. Two of these new drugs are administered subcutaneously, mepolizumab every 4 weeks and benralizumab every 8 weeks, whereas reslizumab is given intravenously monthly on a weigh-based dose. In the future, the research actions will be involved in the identification of a single biomarker or multiple biomarkers for the optimal choice of biological agents to be properly prescribed. The recent change in the definition of asthma, from a unique disease characterized by a reversible airway obstruction to a heterogeneous disease (encompassing numerous phenotypes), prompted the research to look for more detailed pathogenic aspects, as endotypic targets, especially in uncontrolled or severe patients. In this regard, two main different phenotypes/endotypes of asthma could be distinguished, based on their inflammatory characteristics, that are, T helper lymphocyte type 2 (TH2)-high and TH2-low, depending on the predominance of TH2 cytokines . The morThe subsequent available clinical trials have shown a good efficacy in the above mentioned selected patients, with a favorable safety profile, for all of the three drugs .β common chain family and are able to bind a receptor involving the interleukin-5Ra and the common β subunit βc [IL-5 is a 13-amino acid protein forming a 52-kDa homodimer, which has long been evaluated as a valuable therapeutic target , since ibunit βc –26. Whilbunit βc . Recentlbunit βc .The awareness that IL-5 is involved in development, maturation, and action of eosinophils prompted the research to evaluate this cytokine as a possible therapeutic target in severe uncontrolled hypereosinophilic asthma. Two different mechanisms of action were identified, the former acting directly on IL-5 and the latter directly on IL-5 receptor alpha (IL-5Ra). Two different drugs are currently available to block IL-5: mepolizumab  , 23 and NCT02452190) and the reduction of daily systemic steroids in the second (NCT02501629) [First clinical trials about these drugs evaluated the intravenous route of administration with the above mentioned results. After these trials a second route has been evaluated for all of these drugs, the subcutaneous. For mepolizumab and benralizumab, it was shown that both routes were equally effective, with a better safety profile and a more convenient use of the subcutaneous route. The same thing did not happen for reslizumab; indeed recently two phase III studies  did not meet the primary endpoint: the reduction of exacerbations in patients with severe uncontrolled hypereosinophilic asthma (blood eosinophils >300/mcL) in the first one  phase 3 trials. The drug (or placebo) was given at 3.0 mg/kg intravenously every 4 weeks for 1 year. The trial reported a significant reduction in asthma exacerbations in the active group . In addition, the time to first exacerbation was considerably longer in the active than in the placebo group .Similarly to mepolizumab and reslizumab, several studies with Benralizumab evaluated the exacerbation rate reduction as primary endpoint. The results of a phase II DBRPC showed a reduction of exacerbations. A significant reduction of exacerbations rate (49%) and exacerbations requiring hospitalization , was also reported in another trial . The SINCT02501629). Preliminary results of this trial have been recently published in an official note, showing the failure of the drug in order to reduce daily OCS dose [A special attention was recently devoted to steroid-dependent patients; this was due to the well-known burden of steroid related side effects  . In one OCS dose . The effOCS dose .1 and AQLQ [μL [In addition to exacerbations, lung function, and safety, the effects on QoL are also relevant when a new drug is evaluated. Within the above mentioned trials with mepolizumab, Haldar et al. evaluated the effect of the medication on QoL, measured by the Asthma Quality of Life Questionnaire (AQLQ). After treatment, AQLQ improved from 0.55 in the active group to 0.19 . On the and AQLQ . MUSCA iand AQLQ . One of and AQLQ . A more and AQLQ . In a siand AQLQ . ConcernAQLQ [μL . An imprAQLQ [μL   IL-5 antagonists  show a favorable cost-to-benefit profile, in addition to clinical efficacy and biological effects. When all drugs will be marketed, we could choose between different kinds of administration (intravenous or subcutaneous) and administration frequency (4 or 8 weeks). For administration route, it has been already said that reslizumab, at least at the moment, remains with the only intravenous way; regarding the frequency of administration it is interesting that benralizumab could be dispensed every 8 weeks. In published clinical trials and documents no pathophysiological motivation was provided to explain the possibility to doubling administration time; however it is evident that, with the same efficacy results shown in the CALIMA and in the SIROCCO study , 20 at 43 for Benralizumab and mepolizumab and 400 cells/mm3 for reslizumab). The fact that it has been proven that all three drugs are more effective in the same types of subjects (severe asthmatics with serum hypereosinophilia), once on the market, could be a problem. Indeed if we have three drugs with similar patient targets, and a very similar efficacy between the different molecules, it will be difficult to choose [Regardless of the route and frequency of administration, in the main clinical trials all the above mentioned IL-5 antagonists have proven to be more effective in the severe form of asthma with high levels of blood eosinophils  still perceived the only (and insufficient) predictive biomarkers to guide the prescription of such expensive drugs. The coexistence of chronic rhino sinusitis with nasal polyposis could be a criterion for the choice of one drug or other biological drugs. Also, the route of administration (intravenous or subcutaneous) and the possibility of adjusting the dosage would be possible suggestions for clinicians. In addition to IL-5 antagonists, other biological drugs such as anti IL-4 and IL-13  were proThe wide variety of anti-IL-5 antagonists or IL-5 receptor blockers allow to have alternative treatment options for patients with severe hypereosinophilic patients. All the three drugs herein reviewed displayed a good safety profile, and a favorable clinical efficacy in the selected patients. It remains true that we do not still have reliable predictive markers to detect which single patient will respond individually to each of such expensive treatments. Also, the different routes of administrations would provide clinicians with the opportunity to choose the drug according to drug characteristic and patient's needs. At present, the best biomarker in patient eligible for anti-IL-5 or IL-5ra is blood eosinophils, exhaled nitric oxide, and clinical phenotyping . Predictive biomarkers allowing a better prescription of a personalized medicine are needed, although the introduction in clinical practice of novel biologics targeted to severe asthma represents a step forward.


The Global Programme to Eliminate Lymphatic Filariasis has encouraged countries to follow a set of guidelines to help them assess the need for mass drug administration and evaluate its progress. Papua New Guinea (PNG) is one of the highest priority countries in the Western Pacific for lymphatic filariasis and the site of extensive research on lymphatic filariasis and surveys of its prevalence. However, different diagnostic tests have been used and thresholds for each test are unclear.We reviewed the prevalence of lymphatic filariasis reported in 295 surveys conducted in PNG between 1990 and 2014, of which 65 used more than one test. Results from different diagnostics were standardised using a set of criteria that included a model to predict antigen prevalence from microfilariae prevalence. We mapped the point location of each of these surveys and categorised their standardised prevalence estimates.Several predictive models were produced and investigated, including the effect of any mass drug administration and number of rounds prior to the surveys. One model was chosen based on goodness of fit parameters and used to predict antigen prevalence for surveys that tested only for microfilariae. Standardised prevalence values show that 72% of all surveys reported a prevalence above 0.05. High prevalence was situated on the coastal north, south and island regions, while the central highland area of Papua New Guinea shows low levels of prevalence.Our study is the first to provide an explicit predictive relationship between the prevalence values based on empirical results from antigen and microfilaria tests, taking into account the occurrence of mass drug administration. This is a crucial step to combine studies to develop risk maps of lymphatic filariasis for programme planning and evaluation, as shown in the case of Papua New Guinea.The online version of this article (10.1186/s41182-018-0123-8) contains supplementary material, which is available to authorized users. Wuchereria bancrofti) that can seriously damage lymphatic vessels [Lymphatic filariasis (LF) is a mosquito-transmitted disease caused by a parasitic nematode  by the World Health Assembly , the estNoteworthy are the efforts of the Pacific Program to Eliminate Lymphatic Filariasis (PacELF), a WHO initiative with island countries, territories and communities in the Western Pacific region, to collaboratively eliminate the disease from their populations . This elThis patchy survey effort extends to Papua New Guinea (PNG), which continues to struggle with the disease. For instance, original mapping reported PNG as potentially containing one of the highest prevalence of lymphatic filariasis in the world , but theOne of the most critical challenges in the Western Pacific region is to identify new strategies to scale up MDA in PNG , 21. LocAnopheles mosquitoes [Diagnostic tests used in PNG generally fall into two categories:(a) microfilaraemia (Mf) detection through blood slides and (b) antigen tests through either point-of-care immunochromatographic test cards (ICT) or laboratory analysis (Og4C3 ELISA test) . The firsquitoes . The twosquitoes . Effortssquitoes , 26 but Few studies have explored this association, generally concluding that these two variables lack a predictive relationship that could be used to combine results in studies . While eTo develop a predictive model between LF diagnostics tests, we re-examined previously reviewed data summarised only at the district level  and consWe reviewed a total of 312 survey results originally summarised by Graves et al. . Each ofWe developed a set of criteria to standardise different diagnostic results from surveys into one chosen type. Following PacELF monitoring strategies , we chosAs the first reported LF prevalence survey in PNG using an antigen test (i.e. Og4C3 ELISA) was in 1990, all surveys previous to this year were not included in our study.For surveys reporting prevalence values from either an ICT or an Og4C3 ELISA test, these values were considered without further modifications.For surveys reporting values from both Mf counts and one other antigen test, only values from the antigen diagnostics were considered for the final mapping, although these surveys were used to develop our predictive model (see point 6).For surveys that reported values from both ICT and Og4C3 ELISA tests together, the average was calculated.When a combination of all three diagnostics was used in a survey, only the average of the two antigen tests was considered.For surveys that reported prevalence value based on microfilariae detection only, we predicted antigen values from these based on a predictive model developed specifically for this study. This process is described in the following section.To standardise the results in favour of antigen prevalence values, we did the following:We collected prevalence values from surveys that reported both Mf and antigen prevalence results (surveys in points 3 and 5 above). While the majority of these 65 surveys have been published in the peer-reviewed literature, some were extracted from PhD theses and others from unpublished reports by the WHO or by PNG Department of Health. Data from 51 of these surveys were previously summarised and reported in detail by Graves et al. . The remConsidering previous indications that the relationship between Mf and antigen diagnostics changes with the presence of MDA –28, we bδ = 1 for post-MDA) and (b) number of MDA rounds. We also developed two sets of considerations in the analyses of these models based on two possible scenarios:(A)Differentiated Scenario models: This approach assumed that a different type of function had to be applied as a response to the interaction. Thus, the dataset was categorised into two groups: pre- and post-MDA surveys. Individual functions were tested, and the optimum models were determined separately for the pre- and post-MDA data (see Table (B)Combined Scenario models: This approach assumed that the function would be similar before and after MDA, with only changes to some of the parameters after MDA deployment. For these analyses, the entire dataset with both pre- and post-MDA cases was included, and the optimum models were determined simultaneously for pre- and post-MDA data  presence/absence (n = 49), and then, a scaling factor estimated in each model was re-estimated based on the post-MDA data (n = 16). In the case of models in the Combined Scenario B, the parameters were estimated from the combined 65 data points but considered the MDA interaction as an independent variable (i). We compared the RSE  and the AIC (Akaike information criterion) for all models to evaluate their goodness of fit. The AIC value, in particular, was used to select one optimum model from each scenario. For our standardisation process, we chose only one of these two final models to predict antigen prevalence from Mf-only surveys. This decision was based on which function (1) provided the best AIC, (2) was more consistent across scenarios and (3) was supported by the biggest sample size.The parameters for each of these models were estimated using a nonlinear least square method in R software version 3.4.4. The models in the Differentiated Scenario A considered the MDA interaction (either its presence/absence or the number of rounds) as an indicator, where the parameters were first estimated based on the pre-MDA data  and post-MDA . The median values of Mf prevalence were 0.22 and 0.04 for pre- and post-MDA, respectively (p < 0.001). The median values of antigen prevalence for pre- and post-MDA were 0.53 and 0.34, respectively (p < 0.01). This interactive effect of MDA on the relationship between tests validated our two modelling scenarios described above. Table We initially considered 65 surveys’ results with both Mf and antigen values to develop our model. ANOVA results showed that there was a significant difference between surveys conducted pre-MDA  changing from 0 to 1 under pre- and post-MDA conditions, respectively. In this case, the pre-MDA curve is always higher than the post-MDA curve, especially at low Mf levels (Mf < 0.5). This difference decreased as Mf prevalence increased, with both curves converging around a predicted antigen value of 0.8, similar to model A1.Table n = 71 Mf-only surveys). Differences in the predictions between models A4 and B1 were not as closely aligned, however, with up to a maximum of 4.5% difference at Mf levels between 0.2 and 0.3  and model B1 (n = 65) lead us to believe that the divergent prediction of model A4 may be a result of the small sample size used (n = 16), with no Mf values above 0.3 after MDA. Thus, considering the ample sample size and low AIC value of model B1  as part of our prevalence standardisation process. In addition, model B1 produced very reliable predictions at low prevalence levels (< 10%), a critical range for decisions to implement or cease MDA [We compared the predictions of antigen prevalence from Mf estimates made by models A1 and B1 and found that they were closely aligned with each other. Although model A1 predictions were slightly higher than the model B1 predictions, the difference was within 1% in all cases  was not consistent (nor consistently reported) between surveys. Secondly, the sensitivity and specificity of the ICT tests varied over the years, while the Og4C3 ELISA sensitivity and specificity vary according to whether serum or dried blood spots were used . ThirdlyBy successfully combining varied survey estimates, we were able to produce a more detailed depiction of LF distribution in PNG, which shows a more complex picture than previously reported. While some previous assessments claimed that PNG had one of the highest LF prevalences in the world , one thiAmong these insights, we can see that the central highland districts of the country show low to no endemicity of the disease—probably due to environmental factors affecting vector distribution, such as altitude and temperature —while hiOur study confirms the predictive nature of the relationship between antigen and Mf prevalence tests. The models produced included the effect of MDA in this relationship. Model B1 in particular showed a robust goodness of fit and illuminated different aspects related to the sensitivity of the tests and the effect of MDA at different prevalence levels. The predictive nature of model B1 allowed us to aggregate prevalence estimates from assorted LF surveys to more accurately assess the extent of the disease in PNG.The prevalence distribution of LF across the country is more complex than previously considered. While there are certain provinces and districts showing high levels of prevalence, many other regions of PNG have low to no prevalence that may exclude them from the need to implement MDA. With respect to the GPELF priorities for PNG, our up-to-date risk map based on aggregated surveys identifies high prevalence areas of the country that the programme should prioritise for MDA implementation. Our risk map also identifies underserved survey areas that the programme should further investigate to reach a more detailed understanding of LF distribution in PNG.Underserved areas, however, may also reflect logistically challenging regions where additional surveys may be unfeasible. Other alternatives to surveillance programs may be required. Further spatial analyses are in progress to develop predictive models based on environmental transmission factors of the disease that can lead to the identification of “hot spots” of potential high prevalence in the country, as well as areas that are very unlikely to be LF endemic, even in the absence of completed prevalence mapping. These spatial tools have been used in previous studies , 33 suggThe elimination of LF remains a global priority, especially in poor communities. Developing new tools and approaches to accurately inform programmes, partners and donors on possible best practices is essential. Our study is a step towards this goal, in the hopes that this information can help in the modelling efforts of LF prevalence around the world and in the identification of potential areas for future localised MDA implementation in PNG.Additional file 1:Table S1. Description of the types of changes made to update survey locations. Table S2 Estimated parameters—Differentiated Scenario A models. Table S3 Estimated parameters—Scenario B models. Figure S1 Comparison of optimum models from Scenario A and B. (a) Plot of the paired predicted antigen prevalence. (b) Difference of predicted antigen prevalence. (DOCX 88 kb)