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When undertaking studies to investigate human genetic adaptations to malaria or other diseases, it is important to account for any confounding effects of \u03b1-thalassaemia to rule out spurious associations.\n                  <\/ns3:p>\n                  <ns3:p>\n                    <ns3:bold>Methods:<\/ns3:bold>\n                    In this study, we have used direct \u03b1-thalassaemia genotyping to understand why GWAS data from a large malaria association study in Kilifi Kenya did not identify the \u03b1-thalassaemia signal. We then explored the potential use of a number of new approaches to using GWAS data for imputing \u03b1-thalassaemia as an alternative to direct genotyping by PCR.\n                  <\/ns3:p>\n                  <ns3:p>\n                    <ns3:bold>Results:<\/ns3:bold>\n                    We found very low linkage-disequilibrium of the directly typed data with the GWAS SNP markers around \u03b1-thalassaemia and across the haemoglobin-alpha (\n                    <ns3:italic>HBA<\/ns3:italic>\n                    ) gene region, which along with a complex haplotype structure, could explain the lack of an association signal from the GWAS SNP data. Some indirect typing methods gave results that were in broad agreement with those derived from direct genotyping and could identify an association signal, but none were sufficiently accurate to allow correct interpretation compared with direct typing, leading to confusing or erroneous results.\n                  <\/ns3:p>\n                  <ns3:p>\n                    <ns3:bold>Conclusions:<\/ns3:bold>\n                    We conclude that going forwards, direct typing methods such as PCR will still be required to account for \u03b1-thalassaemia in GWAS studies.\n                  <\/ns3:p>","DOI":"10.12688\/wellcomeopenres.16320.2","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T10:25:51Z","timestamp":1632306351000},"page":"287","update-policy":"https:\/\/doi.org\/10.12688\/wellcomeopenres.crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Haplotype heterogeneity and low linkage disequilibrium reduce reliable prediction of genotypes for the \u2011\u03b13.7I form of \u03b1-thalassaemia using genome-wide microarray data"],"prefix":"10.12688","volume":"5","author":[{"given":"Carolyne M.","family":"Ndila","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vysaul","family":"Nyirongo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5364-9610","authenticated-orcid":false,"given":"Alexander W.","family":"Macharia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6454-3093","authenticated-orcid":false,"given":"Anna E.","family":"Jeffreys","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kate","family":"Rowlands","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9576-9581","authenticated-orcid":false,"given":"Christina","family":"Hubbart","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George B. 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status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.21956\/wellcomeopenres.18983.r45982, Karen G Scheps, Departamento de Microbiolog\u00eda, Inmunolog\u00eda, Biotecnolog\u00eda y Gen\u00e9tica, C\u00e1tedra de Gen\u00e9tica, Facultad de Farmacia y Bioqu\u00edmica, Universidad de Buenos Aires, Buenos Aires, Argentina, 23 Sep 2021, version 2, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/5-287\/v2#referee-response-45982","order":0,"name":"referee-response-45982","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.17939.r44833, Manit Nuinoon, School of Allied Health Sciences, Walailak University, Nakhon Si Thammarat, Thailand, 28 Jul 2021, version 1, 1 approved, 1 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/5-287\/v1#referee-response-44833","order":1,"name":"referee-response-44833","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Kirk Rockett<\/b>; \n<i>Posted: 26 Aug 2021<\/i>; We thank the reviewers' for their positive and helpful feedback and are pleased to make revisions accordingly as per our comments to their questions below.  We have therefore added further detail to our manuscript with positional references to the published PDF version.   \n<b>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; According to the MRM and CART models (Table 6), is it possible to select the same ethnicity for calculating the predictive performance?<\/b>  Thank you.&nbsp; We did not separate the different ethnicities for the MRM and CART as these methodologies rely on the intensity pattern of the SNP features rather than the surrounding genetic structure and therefore should not be affected by a person's origin (unless it is sample related in some way).&nbsp; Rather the data should be affected more by experimental methodology, reagents, samples and the microarray.&nbsp; Moreover, one has to balance this with the situation where some ethnic groups may have insufficient numbers to make any methodology viable, particularly when using the data to generate a reference panel as well (as in our group labelled OTHER which is a mixture of 18 different self-reported groups each with too few individuals to ethically identify them or provide sufficient power alone.&nbsp; Obviously one has to review the results with that in mind).&nbsp; However for this very reason, amongst others, we have made all the data available to allow it to be tested by interested parties.  This comment also hints at the main problem with microarray data and that is its quality, whether due to the specificity of the individual array features (for example the feature for SNP rs2541670 [Extended Data section 4] and main text figure 4) may behave differently in WT chromosomes versus deletion chromosomes, or sample quality (this data set did have 762 sample excluded for various reasons during its initial QC [see methods]) and even the distribution of SNP intensities across the genome that can affect the genotype calling confidence (as alluded to in point 3 below and in 3 new added figures).&nbsp; &nbsp;  \n<b>2.&nbsp;&nbsp;&nbsp;&nbsp; ROC curve (AUC calculation) is recommended for depicting the predictive performance of single SNP or combined SNPs.<\/b>  Thank you and all this data is included in the Extended Data file and data package.  &nbsp; \n<b>3.&nbsp;&nbsp; Regarding the \u03b1-globin gene cluster is located close to the telomere, please put more information about how the telomeric region of this gene cluster affects a more complex intensity pattern in the discussion part.<\/b>  Thank you and this is clearly an omission on our part.&nbsp; We have now provided new figures in the Main and Extended Data (Main Figure 11 and Extended Data Figures 12-14) along with accompanying text in both the Main Text and Extended Data text. &nbsp; \n<b><i>Main Text: page 18, section; '\u03b1-thalassaemia genotype was correlated with intensity signals for SNPs within the -\u03b13.7I deletion'<\/i><\/b>  \n<i>&quot;Given that we were unable to predict \u03b1-thalassaemia genotypes with sufficient reliability through the use of LD or imputation methods, we next investigated the potential for an alternative approach \u2013 the use of intensity data at SNPs that lie within the deleted region (Underlying Data2 Table OO_intensity_summary). &nbsp;&nbsp;For the purpose of these analyses, we therefore summed the chip channel intensities (X + Y) to produce a single total intensity value for each individual across each SNP across chromosome 16. We first plotted these intensities averaged over 100kb bins for the whole chromosome (Figure 11A) showing that there was an end-of-chromosome reduction in intensities (from ~ 1.25 to ~ 0.75). At the individual SNP level the mean intensity for the 0-1Mb 5' telomere region where the HBA region lies (Figure 11B) was ~0.75-1.<\/i>  \n<i>NEW FIGURE 11 showing the SNPs intensities across the full chromosome and within the 5' telomere and HBA regions.<\/i>  \n<i>The HBA region lies between 200 and 235kb from the 5' telomeric end where the mean intensities were ~ 0.75 (Figure 11B and C, and Extended Data Figure 12-14). While this may be sufficient to have some impact on the predicative performance of the data we<\/i>\n<i> We hypothesized that intensity signals for such SNPs within the deletion region would still be reduced in \u03b1-thalassaemic subjects and that reductions would be dose-dependent in such a way that they would be greatest in homozygotes. We identified six features (rs2362744, rs2854120, rs4021971, rs4021965, rs11639532 and rs2858942) on the Illumina HumanOmni2.5-4 chip that lay within the -\u03b1<sup>3.7I<\/sup> deletion, along with three flanking SNPs that served as controls (Figure 12 and Underlying Data2 Table OO_intensity_ summary). For the purpose of these analyses, we summed the chip channel intensities (X + Y) to produce a single total intensity value for each individual at wach of the six SNPs within the deletion and three flanking SNPs. These Sum(X+Y) data for each individual and each of the 6 SNPs data were then plotted as means with 25 th and 75 th percentiles and outliers on the y-axis with stratification by \u03b1-thalassaemia genotype on the x-axis (Figure 12, Extended Data Figures 16 and as scatter plots Extended Data Figure 17). As anticipated, we saw significant step-wise reductions in intensities by \u03b1-thalassaemia genotype for all six features within the deletion (Kruskall-Wallis and Dunn's test for multiple comparisons<sup>21<\/sup> ; Extended Data Section 4, Underlying Data2 Tables PP_intensity_comparisons and QQ_intensity_summary), leading us to investigate a range of potential methods for predicting \u03b1-thalassaemia genotypes from such data.&quot;<\/i>    \n<b>Extended Data Section 4, new introductory paragraph; <\/b>\n<b>Illumina Chip Intensities<\/b>  \n<i>&quot;We first extracted the intensities for all features from the chromosome 16 vcf file for the 3036 samples used in this study and then plotted the mean of the Sum(X+Y) channels for these samples for each SNP against chromosomal position (Figure 12A).&nbsp;&nbsp; This showed that the feature average intensities across the chromosome varied from ~0.25 to 2.&nbsp; In the smoothed plot and 100kb windowed plots (Figure 12B, C and D) it can be seen that the general intensity across the chromosome is ~1.25 while at the telomeres (start-10Mb and 85Mb to end), the intensities drop gradually to ~0.75 and 1 respectively.&nbsp; This is not unusual for such microarray data as was noted in the MalariaGEN study<sup>3 <\/sup>that generated this GWAS data.&nbsp; Focussing in on the region of interest, Figure 13 shows the first 1Mb region at the 5' telomere and Figure 14 the region across the HBA gene cluster (200kb to 235kb).&nbsp; These plots show the variation between SNP intensities, which are all reduced (~0.75) in comparison with the central region (~1.0) of the chromosome intensities.&nbsp; Notwithstanding this, it is important to review the distribution of sample intensities for each SNP.&quot;<\/i>    \n<b>4.&nbsp;&nbsp;&nbsp;&nbsp; Regarding the main text and several figures, two formats of the 3.7 kb type 1 deletion are found in this article (-\u03b1<sup>3.7I<\/sup> deletion and \u03b1<sup>-3.7I<\/sup> deletion). Would you please use the same format throughout the manuscript (commonly-used format is -a<sup>3.7I<\/sup> deletion)?<\/b>  Thank you and we have checked, corrected, and standardised throughout the manuscript depending on the context used.&nbsp; When referring to the 3.7kb deletion collectively or where the type is unknown we use -\u03b1\n<sup>3.7<\/sup>, while the suffix I is added when specifically referring to the Type I variant (and similarly for Type II and Type III). These have been corrected accordingly through both the Main text and Extended_Data text. &nbsp;   \n<b>5.&nbsp;&nbsp;&nbsp;&nbsp; According to the title of Table 3, the number of normal individuals should be changed from 5 to 9?<\/b>  Thank you, this has been done.","URL":"https:\/\/wellcomeopenresearch.org\/articles\/5-287\/v2#referee-comment-4729","order":2,"name":"referee-comment-4729","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"The Malaria Genomic Epidemiology Network study of severe malaria was supported by Wellcome [077383] and the Bill & Melinda Gates Foundation through the Foundations of the National Institutes of Health [566] as part of the Grand Challenges in Global Health Initiative. The Resource Centre for Genomic Epidemiology of Malaria is supported by Wellcome [090770; 204911]. This research was supported by the Medical Research Council [G0600718; G0600230; MR\/M006212\/1]. Wellcome also provides core awards to the Wellcome Centre for Human Genetics [203141] and the Wellcome Sanger Institute [206194]. Sample collection and processing was further supported through a Programme Grant [092654] to the Kilifi Programme from Wellcome. TNW was funded through Senior Research Fellowships from Wellcome [202800; 091758]. CMN was supported by funds from the MalariaGEN consortium and from Wellcome [084538]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":3,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}