Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision.
Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision.
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DOI：
10.7554/elife.69698
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发表时间：
2021-07-06
期刊：
影响因子：
7.7
通讯作者：
White NJ
中科院分区：
文献类型：
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作者：
Watson JA;Ndila CM;Uyoga S;Macharia A;Nyutu G;Mohammed S;Ngetsa C;Mturi N;Peshu N;Tsofa B;Rockett K;Leopold S;Kingston H;George EC;Maitland K;Day NP;Dondorp AM;Bejon P;Williams TN;Holmes CC;White NJ
严重的恶性疟疾极大地影响了人类的进化。对临床定义的严重疟疾患者和匹配的人群对照进行的遗传关联研究有助于表征人类对严重疟疾的遗传易感性，但表型不精确性损害了发现的关联。在疟疾传播率高的地区，幼儿严重疟疾的诊断，特别是与细菌性败血症的区分并不精确。我们利用血小板和白细胞计数数据开发了严重疟疾的概率诊断模型。在此模型下，我们重新分析了 2220 名患有临床定义的严重疟疾的肯尼亚儿童和 3940 名对照人群的临床和遗传数据，并对表型错误标记进行了调整。我们的模型通过镰状特征的分布进行验证，估计大约三分之一的病例没有严重疟疾。我们提出了一种用于表型错误标记的病例对照研究的数据倾斜方法，并表明这可以降低错误发现率并提高全基因组关联研究的统计功效。在疟疾流行的撒哈拉以南非洲地区，大多数人经常接触携带疟疾寄生虫的蚊子叮咬，因此血液中常常含有疟疾寄生虫。幼儿尚未对疟疾建立强大的免疫力，经常会患上严重的疟疾，这是一种危及生命的疾病。目前尚不清楚为什么一些儿童会患上严重的疟疾并死亡，而其他血液中寄生虫数量较多的儿童却没有出现任何明显的症状。遗传易感性研究旨在通过将患有严重疟疾的个体（称为“病例”）与来自一般人群的个体（称为“对照”）进行比较来揭示为什么存在这种差异。但严重的疟疾可能是诊断的一个挑战。由于健康儿童体内存在大量疟疾寄生虫，因此有时很难确定这些寄生虫是否导致儿童患病，或者是否是巧合。因此，纳入这些研究的一些“病例”实际上可能患有不同的疾病，例如细菌性败血症。这最终会影响研究的解释方式，并给数据带来错误和不准确。沃森，恩迪拉等人。研究了测量患者的血液生物标志物（来自全血细胞计数，包括血小板计数和白细胞计数）是否可以提高疟疾诊断的准确性。他们开发了一种新的数学模型，其中包含血小板和白细胞计数。该模型估计，在 2,220 名被诊断患有严重疟疾的肯尼亚儿童中，大约三分之一的登记儿童实际上并未患有这种疾病。进一步分析表明，严重疟疾患者的血小板计数不太可能高于每微升 200,000 个。这定义了一个界限，研究人员可以使用它来避免在未来的研究中招募没有严重疟疾的患者。此外，更准确地诊断严重疟疾的能力可以更容易地发现和治疗血液中疟原虫数量较多的儿童中具有类似症状的其他疾病。 Watson、Ndila 等人的研究结果支持了所有疑似疟疾儿童均应给予广谱抗生素的建议，因为许多误诊的儿童可能患有细菌性败血症。它还表明，使用全血细胞计数可以提高未来严重疟疾临床研究的诊断准确性，全血细胞计数的获取成本低廉且在资源匮乏的环境中越来越容易获得。这最终可能会提高这些研究为这种危及生命的疾病找到新疗法的能力。
Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission, the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis are imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model, we re-analysed clinical and genetic data from 2220 Kenyan children with clinically defined severe malaria and 3940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one-third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies. In areas of sub-Saharan Africa where malaria is common, most people are frequently exposed to the bites of mosquitoes carrying malaria parasites, so they often have malaria parasites in their blood. Young children, who have not yet built up strong immunity against malaria, often fall ill with severe malaria, a life-threatening disease. It is unclear why some children develop severe malaria and die, while other children with high numbers of parasites in their blood do not develop any apparent symptoms. Genetic susceptibility studies are designed to uncover why such differences exist by comparing individuals with severe malaria (referred to as ‘cases’) with individuals drawn from the general population (known as ‘controls’). But severe malaria can be a challenge to diagnose. Since high numbers of malaria parasites can be found in healthy children, it is sometimes difficult to determine whether the parasites are making a child ill, or whether they are a coincidental finding. Consequently, some of the ‘cases’ recruited into these studies may actually have a different disease, such as bacterial sepsis. This ultimately affects how the studies are interpreted, and introduces error and inaccuracy into the data. Watson, Ndila et al. investigated whether measuring blood biomarkers in patients (derived from the complete blood count, including platelet counts and white blood cell counts) could improve the accuracy with which malaria is diagnosed. They developed a new mathematical model that incorporates platelet and white blood cell counts. This model estimates that in a large cohort of 2,220 Kenyan children diagnosed with severe malaria, around one third of enrolled children did not actually have this disease. Further analysis suggests that patients with severe malaria are highly unlikely to have platelet counts higher than 200,000 per microlitre. This defines a cut-off that researchers can use to avoid recruiting patients who do not have severe malaria in future studies. Additionally, the ability to diagnose severe malaria more accurately can make it easier to detect and treat other diseases with similar symptoms in children with high numbers of malaria parasites in their blood. Watson, Ndila et al.’s findings support the recommendation that all children with suspected malaria be given broad spectrum antibiotics, as many misdiagnosed children will likely have bacterial sepsis. It also suggests that using complete blood counts, which are cheap to obtain and increasingly available in low-resource settings, could improve diagnostic accuracy in future clinical studies of severe malaria. This could ultimately improve the ability of these studies to find new treatments for this life-threatening disease.