Human genetic variation associates with infection by derived Ugandan M. tuberculosis lineage

Human genetic variation associates with infection by derived Ugandan M. tuberculosis lineage | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Human genetic variation associates with infection by derived Ugandan M. tuberculosis lineage Catherine M. Stein , Penelope Benchek , Lentlamatse Mantshoyane , Timothy Ciesielski , Michael L. McHenry , Himiede Wilson-Sesay , Moses Joloba , Eddie Wampande , Kimberly A. Dill Mc-Farland , Allison W. Roberts , Ben Polacco , Max Bennett , View ORCID Profile Nevan Krogan , W. Henry Boom , Jeffery S. Cox , Harriet Mayanja-Kizza , Thomas R. Hawn , Scott M. Williams doi: https://doi.org/10.1101/2025.10.31.25339263 Catherine M. Stein 1 Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, OH 2 Department of Medicine, Division of Infectious Diseases, Case Western Reserve University , Cleveland, OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: cmj7{at}case.edu Penelope Benchek 1 Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lentlamatse Mantshoyane 1 Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Timothy Ciesielski 1 Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael L. McHenry 1 Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Himiede Wilson-Sesay 1 Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Moses Joloba 3 School for Biomedical Sciences, College of Health Sciences, Makerere University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eddie Wampande 3 School for Biomedical Sciences, College of Health Sciences, Makerere University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kimberly A. Dill Mc-Farland 5 Department of Allergy and Infectious Diseases, School of Medicine, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Allison W. Roberts 6 Department of Molecular and Cell Biology, University of California , Berkeley, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ben Polacco 7 Quantitative Biosciences Institute, University of California , San Francisco, CA; USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Max Bennett 7 Quantitative Biosciences Institute, University of California , San Francisco, CA; USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nevan Krogan 7 Quantitative Biosciences Institute, University of California , San Francisco, CA; USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nevan Krogan W. Henry Boom 2 Department of Medicine, Division of Infectious Diseases, Case Western Reserve University , Cleveland, OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeffery S. Cox 6 Department of Molecular and Cell Biology, University of California , Berkeley, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Harriet Mayanja-Kizza 4 Department of Medicine, School of Medicine, Makerere University and Mulago Hospital , Kampala Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas R. Hawn 5 Department of Allergy and Infectious Diseases, School of Medicine, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Scott M. Williams 1 Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Several studies examined host and pathogen genetic influences on tuberculosis (TB) susceptibility separately, but relatively few studied their combined effects. However, host-pathogen interactions or co-evolution may explain the inability to replicate many reported human genetic effects across global populations and provide additional insight into TB risk. In this study, we address such possible interactions by focusing on the outcome of infection with L4-Uganda M. tuberculosis sub-lineage and human genetic variants as independent variables. This is possible because the L4-Uganda sub-lineage is both restricted to Uganda and nearby locations and is recent there, compared to other more ancestral L4 lineages. Our study consisted of 276 culture-confirmed adult TB cases from a long-standing household contact study. Multiple loci with results suggestive of association (p<10 -5 ) also demonstrated convergent relevant evidence for strain specific infection via: evidence of gene expression in relevant cells and lung tissue, signatures of natural selection, eQTL expression, and CRISPR screens for immunity-related genes. We also replicated previously published host-pathogen interaction effects, demonstrating that effects seen for other sub-lineages were also present for L4-Uganda. These results provide evidence for host-pathogen co-evolution in TB and indicate these interactions involve genes highly relevant to the host immune response to Mycobacterium infection. INTRODUCTION Tuberculosis (TB) remains a major public health problem globally and is the leading infectious disease killer globally 1 . Caused by Mycobacterium tuberculosis (Mtb), TB disease ultimately results from a host-pathogen interaction, where susceptibility to disease is influenced by both host and microbial genetic factors 2 – 5 . The human genetic variants associated with TB often vary by global population. Not only do human genetic associations vary by geography, but Mtb genotypes do as well, and can be organized into 8 major lineages that have distinct geographical distributions and timelines of exposure to human populations 6 , 7 . Within these 8 major lineages, there are also recently diverged sub-lineages. As ancient lineages are less likely to be virulent than the modern ones, this diversity of lineages and their historical coexistence with humans has led to the hypothesis that disrupted coevolution between the host and Mtb complex genes increases virulence 8 – 13 . Consistent with this hypothesis, several candidate gene and genome-wide studies have been conducted 4 , 14 – 23 , demonstrating host-pathogen interaction effects at the human DNA – Mtb lineage level. However, most of these studies have been done in East Asia and the presence of such interaction effects in Africa are not as thoroughly explored. The role of host-pathogen interactions in Africa is critical as it is thought TB originated on the continent 24 , thereby providing a unique opportunity to assess how interactions between the two species can affect disease risk in light of ancient versus recently diverged, sub-lineages. In our previous work 25 , 26 , we posited that the relatively recent introduction into the Ugandan population of the L4-Uganda sublineage could result in co-evolution with host genotypes that affected disease severity. This is a different question than asking whether human genetic variation affects susceptibility to disease caused by a specific Mtb lineage, which is the approach taken by the other above cited studies. Given that the L4-Uganda sub-lineage is recently diverged from the ancestral L4 lineage and is unique to Uganda and surrounding countries, it is possible to identify novel human genetic variants that convey TB risk caused by this newer L4-Uganda lineage compared to older lineages that co-existed with humans in Uganda for much longer. Thus in this work, in contrast to our prior work on severity, we sought to identify human genetic factors significantly associated with pulmonary TB due to infection by the L4-Uganda sub-lineage. In Kampala, Uganda, both L4-Uganda and L4-NonUganda are common, providing a unique natural experiment in which to test the hypothesis that human genetic variants associate with strain specific disease. METHODS Subject ascertainment and characterization As described previously 26 , this study includes TB cases ascertained through a household contact study in Kampala, Uganda from 2002-2012. Culture-confirmed adult TB cases with human genome-wide association (GWAS) data and Mtb lineage data, as described previously 27 , were included in these analyses. Subjects were grouped into two cohorts, based on which human genotyping chips were accessible at the time of genotyping. We refer to them as Cohort 1 and Cohort 2 (N=149 and N=127, respectively). The study protocol was approved by the National HIV/AIDS Research Committee of Makerere University and the institutional review board at University Hospitals Cleveland Medical Center. Final clearance was given by the Uganda National Council for Science and Technology. All participants provided written informed consent. Additional details about the original study protocol are described elsewhere 28 . The two cohorts differed in percentage of HIV positive individuals ( Table 1 ); therefore, HIV status was used as a covariate in all analyses. View this table: View inline View popup Download powerpoint Table 1. Sample characteristics Human genotyping and Lineage typing Genotyping methods have been described in detail elsewhere 26 , 29 . Briefly, Cohort 1 was genotyped on the Illumina Infinium MegaEX chip, and Cohort 2 used the Illumina HumanOmni5 microarray. Both cohorts were separately imputed to the TOPMED-r2 reference panel. Prior to imputation, only SNPs that had a call rate greater than 0.98, minor allele frequency (MAF) > 0.05, and did not show deviation from Hardy-Weinberg equilibrium (p 0.8) SNPs that overlapped between the two cohorts was 18,468,458 (7,925,870 at MAF > 5%). Principal components were computed using PCAiR 30 . Mtb was isolated from sputum of each of the subjects, and lineages were classified according to lineage-identifying SNPs using real-time PCR and validated with long sequence polymorphism (LSP) PCR as described previously 27 . Lineage was determined using three SNPs that accurately distinguish all lineages found in Uganda, L4.6 Uganda (aka L4-Uganda), L3, and L4-NonUganda lineages 6 , 7 . L4-Uganda is a sub-lineage that recently diverged from the parent L4 lineage that is only found in Uganda and countries immediately surrounding it 6 , 7 , 9 , 31 . GWAS analysis In our primary analysis, we assessed as outcomes disease due to L4-Uganda vs. all other L4 lineages; this analysis contrasts the recently evolved sub-lineage with the “older” sub-lineages of the same phylogenetic family 6 , 9 GWAS analyses were performed in the two separate cohorts on well-imputed (R 2 > 0.8) SNPs with a within cohort minor allele frequency (MAF) > 5% and a within cohort minor allele count (MAC) ≥ 15 using the penalized quasi-likelihood approximation to the generalized linear mixed model in the R, Genesis package. Covariate adjustment included age, sex, HIV status and the first two principal components. A fixed effects meta-analysis was run over the two GWAS utilizing the metafor package in R. As a sensitivity analysis, we contrasted L4-Uganda with other L4 lineages and the L3 lineage combined. Unlike our primary analysis, this analysis does not strictly capture recently evolved vs older lineages, as this also includes L3, which is present in other global populations and diverged from L4 much longer ago 6 , 32 . Identification of regions with signatures of selection We examined SNPs with association p<10 -5 for signatures of natural selection using the extended haplotype homozygosity (EHH) approach 33 . Under this model, variants under recent positive selection will demonstrate a large region of surrounding haplotype homozygosity. We leveraged a newer measure based on EHH that accounts for local recombination hotspots: nSL scores (number of segregating sites by length) 34 . We examined whole genome sequence data available in the 1000 Genomes database (phased 30x coverage build 38 data) 35 , using Selscan 2.0 to obtain 26 nSL scores for each SNP (one nSL score per 1000 Genome population). Then for each population, we normalized the nSL scores in 100 bins of similar frequency to adjust for the impact of allele frequency on mean EHH. Finally, we evaluated if any of these normalized |nSL| scores were in the top 1% of scores for the population they were measured in. SNPs with a top1% |nSL| score were considered to be under recent positive selection in that population. Bioinformatic annotation We further investigated our most significant findings using a number of bioinformatic annotation tools. We prioritized SNPs that were significant at p<10 -6 and any other suggestive SNPs that showed signatures of selection as identified above. First, we used HumanBase (formerly known as GIANT 36 ) to identify tissues and cell types where proximal genes were expressed and how these genes were connected in gene expression networks. Second, we used FAVOR 37 to identify whether associated SNPs had epigenetic features, transcription factor binding sites, showed conservation across species according to GERP scores, and potential gene function implications through CADD scores. Third, we queried the most significantly associated genes from each region in the GWAS catalog for evidence of pleiotropy 38 . Query of eQTLs and CRISPR screens In a previous study 39 we identified SNPs that were associated with differential gene expression in response to macrophage stimulation with M. tuberculosis in vitro . We queried the regions associated with Mtb lineage at the p<10 -5 level for evidence of such eQTLs ( Table 2 ). In addition, a recent genome-wide CRISPR knockout screen in cells 40 was conducted independently, to identify macrophage genes that influence the induction of TNF and iNOS upon infection with Mtb. We also queried genes attaining p<10 -6 , a gene identified with a selection signature, and the eQTLs ( Table 2 ) in this dataset. View this table: View inline View popup Download powerpoint Table 2. Evidence for loci with p < 10 -5 and at least one other point of supportive evidence Examination of loci reported in prior studies We first examined association with previously-identified TB candidate genes, as summarized in our previous review 2 . Second, we examined loci that have been implicated in host-pathogen interaction analyses in other studies 14 – 23 . Because these are hypothesis-driven queries, we did not apply any multiple testing correction. RESULTS The sample of TB cases was split into two independent cohorts, as described previously 26 ; the first cohort had 149 TB cases, and the second had 127 cases ( Table 1 ). The cohorts differed in their prevalence of HIV-infection and TB severity based on the Bandim TBscore, so HIV status was retained as a covariate in the GWAS. We did not adjust for disease severity as our previous work in this same dataset has shown there is no association between lineage and TBscore 25 , 26 . GWAS Results In the meta-analysis, ten SNPs associated with disease due to L4-Uganda infection at p < 10 -6 and an additional 81 SNPs at p < 10 -5 ( Figure 1 , Supplemental Table 1). Among the regions that attained significance at the p<10 -6 threshold, there were four SNPs on chromosome 1 (∼156 Mb) ( Figure 2A ) with an additional six SNPs in the region significant at p<10 -5 . On chromosome 2 ( Figure 2B ), there was one SNP significant at p<10 -6 (∼149 Mb) with one flanking SNP with p<10 -5 in this gene poor region. In addition, there were four SNPs on chromosome 16 (∼890 kb) with p<10 -6 with an additional 11 flanking SNPs with p<10 -5 ( Figure 2C ). Furthermore, one SNP on chromosome 9 had a SNP meeting the p < 10 -6 threshold but there were no other associated SNPs in that region at even the suggestive threshold (p<10 -5 ), so this region was not considered further. SNPs on chromosome 12 barely missed the p<10 -6 threshold, with most significant p=1.1×10 -6 and another with p=1.2×10 -6 ( Figure 2D ). Download figure Open in new tab Figure 1. Manhattan plot Download figure Open in new tab Figure 2. LocusZoom plots for most significant loci: A) Chromosome 1; B) Chromosome 2; C) Chromosome 16; D) Chromosome 12 We examined all of the SNPs yielding association p<10 -5 for selection signatures in each of the 1000 Genome populations independently. Four of the 81 SNPs demonstrated recent evidence of positive selection in at least one of the 26 populations. All these SNPS were located within the same region on chromosome 12 (within an intron of SLC8B1 ). This region was only under selection in African populations (6 of 7 African populations; Supplemental Table 2). The one African population that showed no evidence of selection in this region was the Mandinka of the Gambian Western Division. Bioinformatic annotation We focused our next analyses using bioinformatic annotation and eQTL queries on the three loci attaining significance at the p<10 -6 threshold plus the one locus showing both evidence of selection that had a suggestive association ( Table 2 ). The most significant SNP associations on chromosome 1 mapped to two genes with overlapping coding regions, PRCC and HDGF ( Figure 2A ). Gene expression profiles in HumanBase revealed that these genes are highly expressed in lung, leukocytes, and lymphocytes, and are expressed at a lower level in monocytes, and neutrophils, ( Figure 3A ). A network analysis ( Figure 3B ) revealed that these genes are co-expressed with several genes key to the TB immune response, including IL12B , IL12A , and TNF , along with SLC8B1 , the latter of which was the gene shown to have signatures of selective pressure in African populations (Supplemental Table 2, Figure 2D ). The GWAS catalog revealed that PRCC and HDGF are associated with neutrophil count and leukocyte quantity, and the GERP score indicates potential functional importance of this SNP. Lastly, our eQTL query revealed a significant eQTL in this region for RRNAD1 (aka METTL25B ) (Supplemental Table 3). This gene appears in our network diagram ( Figure 3B ). We further examined gene expression profiles in HumanBase, and found this gene was also highly expressed in lung and leukocytes ( Figure 3 ). Download figure Open in new tab Figure 3. Gene expression profiling from HumanBase for genes on chromosome 1: a) tissue and cell type expression, and b) network diagram depicting co-expression with other genes Another region, on chromosome 16, showed that the associated SNPs in closest proximity to LMF1 ( Figure 2C ); the most closely related phenotype in the GWAS catalog for this gene was diabetes ( Table 2 ). While this gene is not highly expressed in any tissues or cells of interest (data not shown), our eQTL query revealed an association with NARFL (aka CIAO3 ) (Supplemental Table 3). The associated gene also had a high GERP score (6.3), indicating functional importance. In addition, the CRSPR macrophage screen showed a tentative association with NARFL (p=0.0266, data not shown) 40 ; two of the four guides that targeted NARFL were enriched in the TNF+iNOS-population. These results did not attain significance after multiple testing correction. Query of previously studied TB candidate genes and host-pathogen interaction effects First, we examined whether any candidate genes previously associated with TB susceptibility 2 were associated with infection by L4-Uganda lineage (Supplemental Table 4). While some of these results fell within type I error rate (proportion of SNPs with p<0.05 was ∼5%), there were some candidate genes with well more than 5% of SNPs that reached a p threshold of 0.05. SLC11A1 , aka NRAMP1 , was associated with infection by L4-Uganda (most significant SNP p=0.0055; 19% of SNPs reached p < 0.05); interestingly, this gene was previously indicated in an interaction effect with L4-Uganda in its association with disease severity as measured by the Bandim TBscore 25 along with susceptibility to infection by Beijing lineage 21 . Similarly, SNPs in TLR2 were associated with infection with L4-Uganda (p=0.0043); this gene has previously been associated with infection with Beijing lineage in Vietnam 16 . Another highly significant association was the vitamin D receptor gene (most significant p=0.007). Second, we queried genes and regions that were previously implicated in similar host-Mtb lineage interaction studies (Supp Table 5). While many results approached significance at p<0.10, a few loci were notably more significant. Many of these have been previously associated with infection with the Beijing lineage, including CD53 (p=0.000003), HLA-B (p=0.0006), as well as the L2.2 lineage that is likely related to Beijing, including FSTL5 (p=0.0035), CSGAL/NCAT1 (p=0.0061), along with one gene associated with an L1 sub-lineage ( RIMS3 , p=0.0077). Sensitivity analysis including L3 lineage Lastly, we conducted a sensitivity analysis to evaluate the impact of including L3 lineage within the referent group of the outcome variable. There were too few subjects with L3 lineage for this to be considered a separate contrast ( Table 1 ). Thus, we examined the correlation of p-values from our primary analysis, with those found when L4-Uganda was contrasted with both L4-nonUganda and L3 (Supplemental Figure 1). As shown in the correlation plot, the p-values at the most significant loci detected by the L4-Uganda vs L4-NonUganda GWAS had p-values in the L4-Uganda vs L4-NonUganda plus L3 GWAS that were barely significant at p<0.001, orders of magnitude less significant that the initial finding for L4-Uganda versus L4. In other words, these loci would not have been detected at the “suggestive” p<10 -5 threshold if the L3 lineage was included in the reference category. This analysis reveals that inclusion of the historically-diverged primary lineage of L3 decreases power, and that any co-evolutionary effects may have been missed if the contrast was not between the recently evolved lineage and its parent phylogenetic branch. DISCUSSION Our objective in this analysis was to further examine the paradigm of host-pathogen interaction in TB susceptibility, by examining the human genome for variants associated with disease due to the recently evolved L4-Uganda lineage. Under the co-evolutionary model that we have previously proposed 25 , 26 , SNPs associated with L4-Uganda versus the older L4-NonUganda would provide evidence of disruptive co-evolution. Our genome-wide analysis revealed five loci attaining suggestive signals. Two of these are in loci that are eQTLs for genes exhibiting differential expression in response to M. tuberculosis stimulation in vitro , and a third shows signatures of recent natural selection. In addition, we replicated previously reported genes involved in host-pathogen interaction effects as well as TB susceptibility genes. Together, these results demonstrate that the effect of immune response genes on TB susceptibility depends on the context of the infecting Mtb lineage. A unique strength of this work is the availability of data on eQTLs that are specific to the in vitro response to M. tuberculosis stimulation, as well as the availability of results from a genome-wide CRISPR screen of macrophage response to Mtb, to evaluate the biologic plausibility of our findings. Another unique strength of this work is the ability to contrast a recently diverged lineage from its ancestral lineage, pointing to potential co-evolutionary effects. Overall, our results provide further support for the concept that the effect of host genes on TB susceptibility may depend on the genetic background of the pathogen and the historical concordance between human alleles and lineage. This work demonstrates that these interaction effects are most prominent in immune response genes. Results for chromosome 1 showed several associated SNPs – two of these map to genes PRCC and HDGF , and these SNPs are eQTLs for the nearby gene RRNAD1 . Interestingly, these genes are all expressed in lung and immune cells and are also connected in a co-expression network in dendritic cells along with other genes that are part of the immune response to TB. Both PRCC and HDGF have been associated with neutrophil count in prior GWAS studies; neutrophils and dendritic cells play key roles in the immune response to TB 41 , 42 . Thus, our results support the hypothesis that genes in this region have a role in disease susceptibility most likely acting through host-pathogen interactions. There is extensive linkage disequilibrium in this region, and network analysis indicates these genes are co-expressed, so it is difficult to discern which gene(s) are causal, but as most significant the SNP is an eQTL for RRNAD1, it is a strong candidate. Chromosome 16 also includes some promising candidate genes. NARFL was weakly associated in the CRISPR screen, indicating a role in regulating early innate immune responses during the initial interaction of macrophages and Mtb. These results, though tentative, indicate that decreased NARFL results in more TNF production, so NARFL may inhibit TNF. NARFL (aka CIAO3 ) codes for cytosolic iron-sulfur assembly component 3, and recent work showed that iron-sulfur cofactors are essential for intracellular adaption of Mtb 43 . The role of bioelements such as iron and sulfur is currently an active area of TB research, thus making this gene another promising candidate for further investigation. On the other hand, the connection between LMF1 and TB susceptibility is less clear. We also examined genes that have been previously associated with TB susceptibility as well as those previously implicated in host-pathogen interaction effects on disease risk and severity. Several of these genes appeared to be overrepresented in terms of the number of SNPs that associated with TB in our data, providing evidence for replication. Most notably, we found several immune genes that were associated with L4-Uganda infection, including NRAMP1 (SLC11A1), TLR2, HLA-B , and CD53 . As we noted in our prior work 25 , NRAMP1 is the most studied TB candidate gene, but association results have been inconsistent across world populations. Our current findings, along with others 21 , indicate that the effect of NRAMP1 is dependent on pathogen lineage, which varies globally. While there has been a great emphasis on NRAMP1 in prior literature, this current work reveals that other immune genes are also subject to host-pathogen effects. The idea that immunological responses to TB may be pathogen-dependent 44 , 45 is critical for understanding how to best design future vaccine and other therapeutic targets. A limitation of this study is the sample size. This restricted our ability to examine effects of the entire L4 lineage vs L3 and may have resulted in reduced power to replicate other previously-reported genetic effects. While our work focused on one recently diverged L4 sub-lineage, it is likely that similar effects might be seen for other Mtb sub-lineages elsewhere. In addition, our bacterial genetic data are limited to the lineage level, so these results cannot be translated to effects due to specific genetic variants in Mtb. In conclusion, this work provides further support for host genetic effects that are significant primarily in the context of specific Mtb genetic backgrounds. Both our novel findings, and replication of previously published findings, demonstrate that these host genes tend to have roles in immunity or other relevance to Mtb infection and progression to disease. Furthermore, the results indicate that replication of host genetic associations across global populations may not be expected when such co-evolutionary effects exist. Ancestry explains genetic differences in immunity-related genes 46 , and genetic variation in immunity-related genes has been shaped by natural selection. Finally, genetic epidemiological studies need to incorporate cross species genetic effects to inform functional investigation in the pursuit of better treatment outcomes for TB. Competing interests The authors have no conflicts of interest to report. Data availability statement Due to Ugandan IRB restrictions, these data cannot be deposited publicly. Requests for data can be made to the chair of the data access committee, Assoc Prof Erisa Sabakaki Mwaka, erisamwaka{at}gmail.com . Funding Statement Funding for this work was provided by U19AI162583, N01 AI95383, R01HL096811, T32HL007567, and R01AI124348. Supplemental Table 1 – results with p < 10 -5 Supplemental Table 2 – results from selection signature analysis Supplemental Table 3 – eQTL query Supplemental Table 4 – Query of TB susceptibility candidate genes from the literature Supplemental Table 5 – Query of genes previously identified in host-pathogen interaction studies of TB Supplemental Figure 1 – correlation of p-values for L4-Uganda / L4-NonUganda GWAS vs. L4-Uganda / all other lineages ACKNOWLEDGEMENTS We want to acknowledge the contributions made by senior physicians, medical officers, health visitors, laboratory and data personnel: Dr. Lorna Nshuti, Dr. Roy Mugerwa, Dr. Alphonse Okwera, Dr. Deo Mulindwa, Dr. Mary Nsereko, Denise Johnson, Dr. Allan Chiunda, Hussein Kisingo, Mark Breda, Dennis Dobbs, Mary Rutaro, Albert Muganda, Richard Bamuhimbisa, Yusuf Mulumba, Deborah Nsamba, Barbara Kyeyune, Faith Kintu, Gladys Mpalanyi, Janet Mukose, Grace Tumusiime, Pierre Peters, Annet Kawuma, Saidah Menya, Joan Nassuna, Keith Chervenak, Karen Morgan, Alfred Etwom, Micheal Angel Mugerwa, Emily Hellwig, and Lisa Kucharski. 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