Hemoglobinopathies, including sickle cell disease (SCD) and thalassemia syndromes, represent the commonest monogenic diseases in the world. Although their pathogenesis is established, the diverse clinical manifestations and the varying degree of severity are less understood and are thought to be governed, in part, by genetic modifiers. Previous studies have demonstrated the role of genetic modifiers in different hemoglobinopathy phenotypes, with co-inheritance of α-thalassemia and higher levels of fetal hemoglobin (HbF) being the best characterized disease modifiers. Several genome-wide analyses have identified three major quantitative trait loci modulating HbF levels: a promoter variant on HBG2 (XmnI-rs7482144), the HBS1L-MYB intergenic region and BCL11A, which together explain up to 50% of the genetic variation affecting HbF.^{1, 2} More recently, studies have identified genetic modifiers associated with laboratory and clinical markers of disease complications.^{3, 4} However, few of these modifiers have reached a level of clinical utility.

Importantly, most association studies in SCD have been restricted to patients that have not received disease-modifying therapies. Given that the influence of genetic disease modifiers may change with treatment, identification of genetic modifiers requires high-quality clinical, laboratory and treatment data to allow accurate genotype/phenotype correlation. Furthermore, with the emergence of novel targeted therapies for hemoglobinopathies, such as gene therapy, genetic modifiers can facilitate patient stratification and, also, influence the response to these treatments.

Currently, the ITHANET portal⁵ manually curates around 800 disease-modifying variants in over 420 genomic locations. However, most of these variants have not been validated with confirmatory or large-scale studies, and across diverse ethnic populations. In addition, data from different studies are not frequently reproducible and their possible effect size remains unknown.⁶ Most importantly, with most studies having a sample size of less than 2000 patients, it is not possible to identify genetic modifiers with high confidence. As a result, the translation of these results into clinical practice has been limited. There are currently very few established polygenic risk scores related to disease complications, severity, or response to treatment, that can be used as an evidence base to stratify disease and offer patient specific treatment regimens in hemoglobinopathy patients.⁷

A validated standard for data collection and phenotypic definitions is crucial for the accurate comparison and pooling of data. The recently developed Sickle Cell Disease Ontology⁸ represents a positive step towards disease-specific standardization that can facilitate integration of datasets in the field. In parallel, other ongoing initiatives, such as patient registries by RADeep and SPARCO, are working on standardization of clinical data collection for hemoglobinopathies using well-established international standards, such as the Human Phenotype Ontology.⁹ Despite these efforts, a common understanding and discussion among different initiatives is necessary to allow integration of data for large-scale clinical and genomic studies. Furthermore, there is a limited amount of high-throughput or genome-wide data available for further research, despite several genetic studies in the field. A large, international disease-specific data repository, compliant with the FAIR data principles,¹⁰ would revolutionize research in the field of hemoglobinopathies towards evidence-based approaches that utilize data science and artificial intelligence.

The primary aim of INHERENT is to study the role of genetic modifiers in hemoglobinopathies through a large-scale, multi-ethnic genome-wide association study (GWAS). Therefore, INHERENT will address challenges of previous studies related to small sample sizes and low statistical power, while promoting participation of diverse populations worldwide. Specifically, INHERENT aims to: (a) discover new genetic modifiers of hemoglobinopathies, (b) validate previously reported genetic modifiers, (c) pool and analyze existing genomic data, (d) standardize phenotypic descriptions using established standards, aligned with international recommendations, (e) develop a case report form (CRF) to efficiently gather sufficient high-quality data across countries accounting for different resource capabilities, and (f) develop a research resource of disease-specific data generated in INHERENT, including genomic, phenotypic, and functional data.

All INHERENT members agreed on the participation criteria, which are aimed at being inclusive and straightforward. Hence, participation in INHERENT is open for any group that can submit a minimum of 30 DNA samples with their core phenotypic description. Additional members that can significantly contribute to specific network activities, such as bioinformatics and biostatistical analyses, data management, genotyping, and regulatory/ethical issues, have also been invited to join the network. As a result, the current INHERENT membership is both international and interdisciplinary and includes over 160 experts from 89 organizations, spanning 36 countries worldwide (Table S1). Notably, based on the current membership of participating consortia, the projected membership of INHERENT can reach 73 countries, as shown in Figure 1A.

The target sample size of INHERENT is at least 30 000 individuals with hemoglobinopathies from diverse ancestries and geographic locations. Both pediatric and adult patients will be enrolled to study disease complications across all ages. To investigate the feasibility of the network's primary goal to perform a large-scale, multi-ethnic GWAS, we performed a survey among all centers that participate in INHERENT. The survey requested the number of patients per center and their distribution per disease group, age, and ancestry group. A total of 81 participating centers (91% of all members) responded to the survey, with the results illustrated in Figure 1. Through its current membership, INHERENT has the potential to enroll over 73 200 individuals with hemoglobinopathies. Specifically, 53 100 people with SCD have been reported by the participating centers, with 54.8% being homozygous S/S, while other major disease groups (S/β and S/C) are being well-represented. In addition, around 15 600 people with β-thalassemia have been reported, with over 62% of them being transfusion-dependent, and 4400 people with α-thalassemia. Basic genetic data are available for fewer than 45% of the patients, highlighting the need for genomic research in the field. Importantly, the diverse spectrum of ancestry groups for different disease groups clearly reflects the existing knowledge about the global hemoglobinopathy epidemiology, thus further supporting the multi-ethnic nature of the project. Some of the requested information was not readily available in the centers, and for large centers without an existing electronic patient registry the responses were approximations. Nevertheless, the results of the survey clearly support that the minimum target sample size set by INHERENT is realistic, highlighting the network's potential to be the largest international network on hemoglobinopathies.

INHERENT is in a unique position to address limitations of previous GWAS studies in hemoglobinopathies by increasing the sample size and achieving sufficient power to detect associations at GWAS significant levels. The Figure S1 illustrates different hypothetical, yet realistic, scenarios for the analyses to be performed to estimate the power under different assumptions. Panels A, B, and C refer to the evaluation of binary phenotypes using logistic regression with a P value of 5 × 10⁻⁸. Based on the survey results, the assumed total sample size of 10 000 is realistic for both SCD and β-thalassemia, while the case rate of 0.3 is applicable for several hemoglobinopathy complications, such as the prevalence of stroke¹¹ in SCD, and osteoporosis in β-thalassemia.¹² We show that we have the statistical power to detect associations down to a minor allele frequency (MAF) of 0.05, with an Odds Ratio of 1.5, while detection of associations for less common disease complications is also possible. Similarly for the analyses using survival endpoints (Figure S1, panels D–F), we have sufficient power to detect associations even with 5000 samples, assuming an event rate of 30%, hazard ratio of 1.5, and MAF greater than 0.05.

INHERENT is coordinated by a Steering and Data Access Committee, which provides direction to the network and includes the working group (WG) chairs and one representative from each of the participating consortia. To achieve the main objectives, the activities of the network have been divided into five WGs: clinical, genotyping, data management and analysis, ethics, and knowledge translation.

In conclusion, by bringing together existing consortia and additional partners throughout the world, INHERENT avoids duplication of efforts and is focused on integration and consolidation of evidence as well as the generation and analysis of novel and large datasets. The increased sample size and the diversity in the studied populations can lead to novel discoveries and translational impact in the field of hemoglobinopathies. Moreover, the interdisciplinary and international nature of the network can result in synergistic research studies that promote innovative use of the collected and generated data as well as novel methodological approaches.

血红蛋白病，包括镰状细胞病 (SCD) 和地中海贫血综合征，是世界上最常见的单基因疾病。尽管其发病机制已确定，但其不同的临床表现和不同的严重程度尚不清楚，并且被认为部分受遗传修饰因素控制。先前的研究已经证明了遗传修饰因子在不同血红蛋白病表型中的作用，α-地中海贫血和较高水平的胎儿血红蛋白 (HbF) 的共同遗传是最有特征的疾病修饰因子。一些全基因组分析已经确定了调节 HbF 水平的三个主要数量性状位点： HBG2上的启动子变体(XmnI-rs7482144)、HBS1L-MYB基因间区域和BCL11A，它们共同解释了高达 50% 影响 HbF 的遗传变异。^{1, 2}最近，研究发现了与疾病并发症的实验室和临床标志物相关的基因修饰因子。^{3, 4}然而，这些修饰剂很少达到临床实用水平。

重要的是，大多数 SCD 关联研究仅限于未接受疾病缓解治疗的患者。鉴于遗传疾病修饰因素的影响可能会随着治疗而改变，遗传修饰因素的鉴定需要高质量的临床、实验室和治疗数据，以实现准确的基因型/表型相关性。此外，随着针对血红蛋白病的新型靶向疗法（例如基因疗法）的出现，基因修饰剂可以促进患者分层，并且还可以影响对这些治疗的反应。

目前，ITHANET 门户⁵在 420 多个基因组位置手动管理约 800 个疾病修饰变异。然而，这些变异中的大多数尚未经过验证性或大规模研究以及不同种族人群的验证。此外，不同研究的数据通常无法重复，其可能的影响大小仍然未知。⁶最重要的是，由于大多数研究的样本量少于 2000 名患者，因此不可能高可信度地识别遗传修饰因子。因此，这些结果转化为临床实践受到了限制。目前，与疾病并发症、严重程度或治疗反应相关的多基因风险评分很少，可以用作对血红蛋白病患者进行疾病分层和提供特定治疗方案的证据基础。⁷

经过验证的数据收集和表型定义标准对于准确比较和汇集数据至关重要。最近开发的镰状细胞病本体论⁸代表了朝着特定疾病标准化迈出的积极一步，可以促进现场数据集的整合。与此同时，其他正在进行的举措，例如 RADeep 和 SPARCO 的患者登记，正在利用完善的国际标准（例如人类表型本体论）致力于血红蛋白病临床数据收集的标准化。⁹尽管做出了这些努力，但不同举措之间有必要达成共识和讨论，以便整合大规模临床和基因组研究的数据。此外，尽管该领域进行了多项遗传学研究，但可用于进一步研究的高通量或全基因组数据数量有限。符合 FAIR 数据原则的大型国际特定疾病数据存储库¹⁰将彻底改变血红蛋白病领域的研究，转向利用数据科学和人工智能的循证方法。

INHERENT 的主要目的是通过大规模、多种族全基因组关联研究 (GWAS) 来研究遗传修饰剂在血红蛋白病中的作用。因此，INHERENT 将解决先前研究中样本量小和统计能力低的挑战，同时促进全球不同人群的参与。具体来说，INHERENT 的目标是：(a) 发现血红蛋白病的新遗传修饰因子，(b) 验证之前报道的遗传修饰因子，(c) 汇集并分析现有基因组数据，(d) 使用与国际建议一致的既定标准标准化表型描述， (e) 制定病例报告表 (CRF)，以根据不同的资源能力有效地收集各国足够的高质量数据，以及 (f) 开发 INHERENT 中生成的特定疾病数据的研究资源，包括基因组、表型和功能数据。

所有 INHERENT 成员都同意参与标准，其目的是包容和直接。因此，任何能够提交至少 30 个 DNA 样本及其核心表型描述的团体都可以参与 INHERENT。其他能够对特定网络活动做出重大贡献的成员，例如生物信息学和生物统计分析、数据管理、基因分型和监管/伦理问题，也被邀请加入该网络。因此，目前的 INHERENT 成员是国际性和跨学科的，包括来自 89 个组织、遍布全球 36 个国家的 160 多名专家（表 S1）。值得注意的是，根据目前参与联盟的成员数量，预计 INHERENT 的成员数量可以达到 73 个国家，如图 1A 所示。

INHERENT 的目标样本量是至少 30 000 名来自不同血统和地理位置的血红蛋白病患者。儿童和成人患者都将被纳入研究所有年龄段的疾病并发症。为了调查该网络执行大规模、多种族 GWAS 的主要目标的可行性，我们对参与 INHERENT 的所有中心进行了一项调查。该调查要求提供每个中心的患者数量以及每个疾病组、年龄和血统组的分布情况。共有 81 个参与中心（占所有会员的 91%）对调查做出了回应，结果如图 1 所示。通过其现有会员资格，INHERENT 有可能招募超过 73 200 名血红蛋白病患者。具体而言，参与中心已报告 53 100 名 SCD 患者，其中 54.8% 为纯合子 S/S，而其他主要疾病组（S/β 和 S/C）也有很好的代表性。此外，据报道约有 15 600 名β-地中海贫血患者，其中超过 62% 依赖输血，还有 4400 名 α-地中海贫血患者。只有不到 45% 的患者可以获得基本遗传数据，这凸显了该领域基因组研究的必要性。重要的是，不同疾病组的不同祖先群体清楚地反映了关于全球血红蛋白病流行病学的现有知识，从而进一步支持了该项目的多种族性质。一些所要求的信息在这些中心并不容易获得，对于没有现有电子患者登记的大型中心，答复是近似值。尽管如此，调查结果清楚地表明 INHERENT 设定的最小目标样本量是现实的，突显该网络有潜力成为最大的血红蛋白病国际网络。

INHERENT 处于独特的地位，可以通过增加样本量并获得足够的能力来检测 GWAS 显着水平的关联，从而解决之前血红蛋白病 GWAS 研究的局限性。图 S1 说明了不同的假设但现实的场景，用于进行分析以估计不同假设下的功率。A、B 和 C 组是指使用逻辑回归对二元表型进行评估，P值为 5 × 10 ^-8。根据调查结果，假设总样本量为 10 000，对于 SCD 和 β 地中海贫血都是现实的，而 0.3 的病例率适用于几种血红蛋白病并发症，例如 SCD 中的中风 11 患病率以及 SCD 中的骨质疏松^症。 β-地中海贫血。¹²我们表明，我们具有检测低至 0.05 的次要等位基因频率 (MAF) 关联的统计能力，优势比为 1.5，同时检测不太常见的疾病并发症的关联也是可能的。类似地，对于使用生存终点的分析（图 S1，面板 D-F），假设事件率为 30%、风险比为 1.5、MAF 大于 0.05，即使使用 5000 个样本，我们也有足够的能力检测关联。

INHERENT 由指导和数据访问委员会协调，该委员会为网络提供指导，并包括工作组 (WG) 主席和每个参与联盟的一名代表。为了实现主要目标，该网络的活动分为五个工作组：临床、基因分型、数据管理和分析、伦理和知识翻译。

总之，通过汇集世界各地现有的联盟和其他合作伙伴，INHERENT 避免了重复工作，并专注于证据的整合和巩固以及新颖的大型数据集的生成和分析。样本量的增加和研究人群的多样性可以在血红蛋白病领域带来新的发现和转化影响。此外，该网络的跨学科和国际性质可以产生协同研究，促进收集和生成的数据以及新颖方法的创新使用。