Hemoglobinopathies are among the most common monogenic disorders worldwide, with an estimated 7% of the global population carrying abnormal hemoglobin genes (1). These disorders include structural variants such as sickle cell hemoglobin (HbS, HbC, HbD, HbE) and quantitative abnormalities such as β-thalassemia and hereditary persistence of fetal hemoglobin (HPFH). The clinical manifestations range from asymptomatic carriers to severe transfusion-dependent anemias, and the burden is particularly high in developing countries where screening and genetic counseling programs are still evolving (2).

In India, hemoglobinopathies are a major public health problem due to the country’s ethnic heterogeneity and large tribal population. Ghosh et al. highlighted that HbS predominates in central and eastern India, particularly in tribal belts, while HbE is more frequent in the North-East (3). Community-based studies across states confirm that prevalence varies from 1–40%, depending on region and endogamous practices (4,5,6). Importantly, tribal populations remain disproportionately affected due to high rates of consanguinity, limited access to healthcare, and lack of awareness (7).

High-Performance Liquid Chromatography (HPLC) has emerged as the method of choice for hemoglobinopathy screening, offering sensitive, reproducible, and quantitative analysis of Hb fractions. Unlike conventional electrophoresis, HPLC allows accurate detection of HbA₂, HbF, HbS, HbE, and other variants, making it useful for both carrier detection and disease diagnosis (8,9). Studies across India have demonstrated the utility of HPLC in diagnosing β-thalassemia trait, sickle disorders, and compound heterozygotes, as well as its limitations in cases with overlapping retention times (10,13).

Hospital-based studies from Western India report relatively lower prevalence of hemoglobinopathies (8–9%), dominated by β-thalassemia trait (11,10). In contrast, Central and South-Central India show a higher burden of sickle disorders, with Patil et al. reporting 20.5% abnormal cases dominated by SCT in Madhya Pradesh (12), and Baig et al. noting that sickle syndromes comprised over 56% of their cohort (9). Large-scale national datasets such as that of Warghade et al., analyzing 65,779 samples, revealed an abnormal prevalence of 18.4%, with β-thalassemia trait (11.2%) as the commonest finding (13).

Given this heterogeneity, there is an urgent need for region-specific data, particularly in tribal-dominant districts such as Andhra Pradesh, Odisha, and Telangana, where hemoglobinopathies contribute substantially to morbidity and mortality (14,5,6). Community-linked, HPLC-based screening not only enables early detection and counseling but also informs public health planning under initiatives such as the National Sickle Cell Anemia Elimination Mission (NSCAEM).

AIM AND OBJECTIVES

Aim:
To determine the prevalence and spectrum of hemoglobinopathies among patients and community members screened in a tribal-dominant region of Andhra Pradesh using High Performance Liquid Chromatography (HPLC).

Objectives:

1. To assess the distribution of hemoglobinopathies with respect to gender, age, and community background in the study population.
2. To evaluate the diagnostic contribution of HPLC in identifying sickle disorders, β-thalassemia syndromes, and rare hemoglobin variants.

Study Design: A cross-sectional observational study conducted at a tertiary care hospital serving tribal and non-tribal populations of Andhra Pradesh. Study Period and Setting: Data were collected over a defined study period from hospital attendees, antenatal clinics, community referrals, and outreach screening programs conducted under the National Sickle Cell Anemia Elimination Mission (NSCAEM). Sample Collection: A total of 3725 EDTA blood samples were collected. Samples were obtained from patients with clinical suspicion of anemia or hemoglobinopathy, individuals with family history of hemoglobinopathies, antenatal mothers, and community members screened at primary health centers and sub-centers. Inclusion Criteria: • Patients of all ages with anemia and clinical suspicion of hemoglobinopathy. • Individuals with a positive sickling test or positive family history. • Pregnant women attending antenatal screening. • Individuals referred by community health workers under screening programs. Exclusion Criteria: • Patients who had received a blood transfusion in the preceding 3 months. Laboratory Methods: • HPLC Analysis: All samples were analyzed on the Bio-Rad D-10 analyzer using the short program. Hemoglobin fractions including HbA, HbA₂, HbF, HbS, HbE, HbD, and rare variants were quantified. • Peripheral smear and sickling tests were reviewed for correlation. • Presumptive identification of variants was based on retention time (RT) windows and peak area percentage, following manufacturer guidelines. Data Analysis: • Results were tabulated for age, gender, type of hemoglobinopathy, and community distribution. • Frequencies and percentages were calculated. • Comparative analysis was performed with previous Indian studies to highlight regional variations.

Gender-wise Distribution

Out of 3725 cases, females constituted 61.7% (n=2297), males 38.0% (n=1418), and a small fraction (0.1%) were unclassified.

Table 1. Gender-wise distribution of hemoglobinopathies (n = 3725)

*5 entries had non-standard gender codes.

Females were more frequently screened, likely due to antenatal programs and higher health-seeking behavior. The distribution of abnormal cases was proportionally similar across genders.

Figure 1. Gender-wise distribution of hemoglobinopathies

Age-wise Distribution

Children and adolescents formed the majority: ≤30 years accounted for 77% of cases, with the highest burden in the 0–10 year group (35.4%).

Table 2. Age-wise distribution of hemoglobinopathies (n = 3725)

The predominance of pediatric and adolescent cases reflects early onset and the effectiveness of school/antenatal screening. The sharp fall in older age groups may suggest underdiagnosis or early mortality in severe cases.

Figure 2. Age-wise distribution of hemoglobinopathies

Hemoglobinopathy Types

The most common abnormality was Sickle Cell Trait (51.7%), followed by Sickle Cell Anemia (27.5%). Normal studies comprised 17.2%. Thalassemia-related disorders together accounted for <2%.

Table 3. Distribution of hemoglobinopathy types (n = 3725)

High carrier frequency (SCT) suggests a significant genetic burden. The substantial number of SCA cases reflects considerable clinical load requiring long-term care.

Figure 3. Distribution of hemoglobinopathy types (n = 3725)

Rare Disorders

Although rare, certain conditions are clinically important.

Table 4. Rare and uncommon hemoglobinopathies (n = 3725)

Though rare, these cases highlight the role of HPLC in detecting mixed or less common variants, which require tailored counseling and management.

Figure 4. Distribution of rare hemoglobinopathies (n = 3725)

Screening Outcome

Abnormal patterns were detected in 82.8% of cases, confirming high yield in this high-risk population.

Table 5. Screening outcomes (n = 3725)

Targeted screening of suspected and high-risk individuals resulted in very high abnormal yield, supporting the value of community-based screening.

Figure 5. Screening outcomes of hemoglobinopathies.

Area-wise Distribution

A disproportionate burden was concentrated in specific mandals, with the Top 10 mandals contributing nearly half of all cases.

Table 6. Top 10 Area-wise Distribution of Hemoglobinopathies (n = 3725)

These Top 10 mandals contributed 2734 cases (73.4%) out of 3725. Hukumpeta, Koyyuru, Chinthapalli, Pedabayalu, and G.K. Veedhi form the core cluster, together accounting for nearly half the total cases. Arakuvally, Munchingputtu, Dumbriguda, G. Madugula, and Paderu are significant secondary contributors. The clustering pattern confirms high genetic load in tribal belts, compounded by consanguinity and limited healthcare access.

Figure 6. Top 10 Area-wise Distribution of Hemoglobinopathies (n = 3725)

Hemoglobinopathies constitute a significant health challenge in India, particularly in tribal-dominated regions where genetic clustering, consanguinity, and poor access to healthcare amplify disease prevalence. In our cohort of 3725 individuals screened using HPLC, we observed an exceptionally high burden of hemoglobinopathies, with 3084 cases (82.8%) showing abnormal patterns, while only 641 (17.2%) had a normal study. This abnormal yield is far higher than hospital-based prevalence figures (8–20%) reported from Western and Central India, underscoring the endemic concentration of hemoglobinopathies in tribal Andhra Pradesh. Spectrum of Hemoglobinopathies The spectrum of disorders in our study revealed Sickle Cell Trait (SCT) as the most common abnormality (51.7%), followed by Sickle Cell Anemia (SCA) (27.5%). Together, sickle-related disorders contributed to nearly four out of five abnormal cases (~79%), demonstrating the dominance of the sickle mutation in this tribal belt. In addition, Sickle+β-thalassemia (0.4%), β-thalassemia trait (1.1%), β-thalassemia major (0.4%), and other/rare variants (1.3%) were also detected. This pattern differs markedly from Western Indian and Pan-Indian datasets. For example, Bhalodia et al. (2015) reported only 8.6% abnormal cases, dominated by β-thalassemia trait (5.2%), whereas Warghade et al. (2018), in their analysis of 65,779 cases from a national referral laboratory, found 18.4% prevalence, with β-thal trait (11.2%) as the most common abnormality, and sickle cell disorders only contributing 2–3%. By contrast, our findings align more closely with tribal and Central Indian studies. Patil et al. (2024) from Jabalpur reported 20.5% abnormal cases, with SCT (69.3%) as the leading variant, while Baig et al. (2021) described a high burden of sickle syndromes (56.1%), including HbSS (29%) and compound heterozygotes (11.6%). These comparisons highlight that tribal populations carry a distinct hemoglobinopathy spectrum dominated by sickle variants, unlike hospital-based or national reference cohorts where β-thalassemia predominates. Gender Distribution In our study, females constituted 61.7% (2297 cases) compared to 38.0% males (1418 cases), with a small fraction of gender not recorded (0.3%). This female predominance is consistent with earlier studies such as Mukhopadhyay et al. (2015) from West Bengal (67.5% females) and Seema Rao et al. (2015) (67.6% females), where antenatal screening and premarital counseling programs contributed to recruitment bias. Similar female skew was also seen in Atla et al. (2021) (60.6% females), supporting the interpretation that public health-driven screening explains the gender imbalance rather than true biological difference. Table 7. Gender distribution in tribal hemoglobinopathy studies Study (Author, Year) Sample (n) Female % Male % Observation Seema Rao et al. (16) 800 67.6% 32.4% Antenatal referrals Mukhopadhyay et al., 2015 (15) 10,407 67.5% 32.5% Reference lab; ANC-driven Pathak V et al., 2017 (18 ) 226 61.5% 38.5% ANC + premarital mix Atla B et al., 2021(17) 66 60.6% 39.4% Mixed hospital referrals Present study (2025) 3725 61.7% (2297) 38.0% (1418) Community + hospital; ANC + premarital + clinical Age Distribution Age-wise analysis showed that the 0–10 year group carried the maximum burden (35.4%), followed by 11–20 years (23.6%) and 21–30 years (18.3%). Together, ≤30 years accounted for 77% of all cases, confirming that hemoglobinopathies are detected predominantly in children and adolescents. The decline in older age groups is consistent with patterns observed in Odisha (5) and Telangana (6), and is likely due to higher early mortality in untreated SCA, combined with under-diagnosis among adults with milder phenotypes such as SCT or thalassemia trait. Community and Geographic Clustering A striking feature of our study was the community-wise burden, with the Bagatha tribe (28.4%) and Valmiki tribe (27.0%) contributing more than half the abnormal cases, followed by Konda Dora (10.6%). This pattern resonates with earlier tribal studies: Kumar et al. (2017) reported highest prevalence among Koya Dora (17%) in Visakhapatnam Agency, Naik et al. (2019) found Gond and Kondha dominance in Odisha, and Basu et al. (2020) highlighted high frequencies in Lambada and Koya communities in Telangana. 4,5 , 6 . Our area-wise analysis further showed that the Top 10 mandals contributed 73.4% of all cases, with clustering in Hukumpeta, Koyyuru, Chinthapalli, Pedabayalu, and G.K. Veedhi, highlighting the combined influence of endogamy, geographic isolation, and lack of healthcare outreach. (3,7) Rare Disorders Although rare, our study detected 16 cases of thalassemia major (0.4%), 16 cases of SCT+thalassemia (0.4%), and 47 cases of other variants (1.3%). While numerically small, these variants are clinically significant due to their impact on transfusion requirements, prognosis, and genetic counseling. Comparable rare variants (HbD Punjab, Hb Lepore, Hb J, Hb Burke) have been reported from Western India (Ankur et al., 2021; Baig et al., 2021) and national datasets (13). Importantly, HPLC alone cannot always distinguish between HbS/β⁰-thalassemia, HbS/β⁺-thalassemia, and HbS/HPFH due to overlapping retention times (20,21), necessitating molecular confirmation for precise classification. Community-wise Distribution Our analysis revealed that the Bagatha (226 cases, 28.4%) and Valmiki (215 cases, 27.0%) tribes were most affected, followed by Konda Dora (84 cases, 10.6%), confirming community clustering. Comparable tribal hotspots have been documented in Visakhapatnam Agency (Koya Dora, Kumar et al., 2017), Odisha (Gond and Kondha, Naik et al., 2019), Telangana (Lambada, Basu et al., 2020), and other AP cohorts (Atla et al., 2021). Table 8. Community-wise distribution of hemoglobinopathies in tribal India Study (Author, Year) Region Communities Most affected Key observation Present study (2025) Paderu, Andhra Pradesh Bagatha, Valmiki, Konda Dora, Kammara Bagatha (226), Valmiki (215) Major clustering in Bagatha & Valmiki Kumar et al., 2017 (14) Visakhapatnam Agency, AP Konda Dora, Koya Dora, Valmiki, Bagatha Koya Dora HbS trait ~17% Naik et al., 2019 (5 ) Odisha Gond, Kondha, Paroja, Gadaba Gond, Kondha Highest SCD rates Basu et al., 2020 (6) Telangana Chenchu, Lambada, Yerukula, Koya Lambada, Koya Lambada: highest SCD; Yerukula: β-thal Atla B et al., 2021 (17) ASR Dist., AP Konda Dora, Koya Dora, Bagatha, Valmiki Konda Dora, Bagatha High sickle prevalence Comparison with Other Studies When compared systematically, our study’s abnormal yield (82.8%) is at the extreme upper end of the Indian spectrum, reflecting tribal-focused community screening rather than general hospital recruitment. Western Indian hospital-based cohorts (11,10) report <10% prevalence, dominated by β-thalassemia trait, while Central Indian datasets (12,9) are sickle-dominant, though still at lower levels than our Andhra cohort. The Pan-India reference study (13) showed only 2% SCT prevalence, highlighting the geographic and ethnic heterogeneity of hemoglobinopathies across India. Table 9. Comparative studies on hemoglobinopathies in India Author (Year) Location / Setting Sample size (n) Prevalence of abnormal cases Most common findings Other key observations Present study (2025) Tribal Andhra Pradesh (community + hospital) 3725 82.8% SCT (51.7%), SCA (27.5%) Rare: Thal major (0.4%), SCT+Thal (0.4%), others (1.3%); clustering in tribal mandals Bhalodia et al. (2015) (11) Western India (hospital-based, anemic pts) 500 8.6% β-thal trait (5.2%) Others: SCT (1.2%), high HbF (0.8%), HbD Punjab (0.4%) Shelke et al. (2025) (10) Maharashtra (tertiary hospital, Pune) 1455 8.8% β-thal trait leading HbE, HbS, HbD also present; mean age ~27 yrs; female predominance Patil et al. (2024) (12) Jabalpur, Madhya Pradesh (district hospital) 239 20.5% SCT (69.3%) SCA (12.2%), β-thal trait (8.1%), HPFH/δβ-thal (8.1%), HbJ (4.1%), Double het (2%) Baig et al. (2021) (9) Multi-institution, South-Central India 579 – (case mix only) Sickle syndromes 56.1% (HbSS 29%) HbS/β⁰-thal (6.5%), HbS/β⁺-thal (5.1%), β-thal trait (10.7%); pitfalls: <0.63 min peaks, HbH, ↑HbF in aplasia Warghade et al. (2018) (13) Pan-India (65,779 samples, reference lab) 65,779 18.4% β-thal trait (11.2%) SCT (2.0%), SCD (1.6%), HbE trait/disease (1.1%), HbD (0.5%); rare Hb variants reported Ghosh et al. (2015) (3) National review (tribal focus) – – HbS predominant in central/east tribes; HbE in NE India Urges newborn screening, premarital counseling, hydroxyurea therapy in SCD The strengths of our study include its large cohort (n=3725), integration of community and hospital screening, and use of standardized HPLC for quantification. This allowed us to generate one of the most comprehensive datasets from tribal Andhra Pradesh. However, the lack of molecular confirmation is a limitation, particularly for borderline HbA₂ cases and rare variants. As Baig et al. (2021) and Warghade et al. (2018) caution, HPLC interpretation can be confounded by atypical peaks, HbH disease, or elevated HbF in aplastic anemias, necessitating molecular validation. Future research should integrate DNA-based testing and newborn screening to ensure earlier detection and stratification. Our study reaffirms that sickle disorders dominate the hemoglobinopathy spectrum in tribal Andhra Pradesh, in contrast to β-thalassemia–dominant regions of Western India and HbE-dominant North-East India. The extreme abnormal yield (82.8%), strong community clustering (Bagatha and Valmiki tribes), and early age concentration (≤30 years: 77%) emphasize the need for targeted interventions such as premarital counseling, antenatal screening, hydroxyurea therapy for SCA, and strengthening of genetic counseling services in tribal belts. Spectrum of Hemoglobinopathies Our data showed a very high sickle burden (~86%), including SCT (57.8%), SCA (8.5%), and S β-thalassemia (20.2%). Only 11.8% had a normal pattern, while β-thal trait (1.3%), β-thal major (0.1%), and rare Hb variants (<1%) were uncommon. This is in sharp contrast with hospital-based cohorts, where normal patterns range from 40–66% and β-thal trait is more frequent. Table 10. Spectrum of hemoglobinopathies across different studies Hemoglobin Pattern Present study (Tribal, n=3725) Atla B et al., 2021 (n=151) (17) Ravi Shankar et al., 2004 (n=200) Solanki et al., 2015 (n=190) Normal pattern 17.2% (641) 56.3% (85) 40% (80) 66% (125) SCT 51.7% (1928) 23.8% (36) 4.5% (9) 9.4% (18) SCA 27.5% (1025) 9.9% (15) 17% (34) 12.1% (23) S β-thal 0.4% (16) 2.0% (3) 14.5% (29) 2.1% (4) β-thal trait 1.1% (40) 5.3% (8) 11.5% (23) 5.3% (10) HPFH / Other 1.3% (47) 2.6% (4) – 0.5% (1) Hb D + β-thal – (0) – – – Hb E + β-thal – (0) – 4% (8) 1.6% (3) Hb E trait – (0) – 0.5% (1) 1.5% (2) β-thal major 0.4% (16) – 7% (14) 0.5% (1)

This study demonstrates a very high prevalence of hemoglobinopathies (82.8%) in the tribal-dominant population of Andhra Pradesh, with sickle disorders (SCT and SCA) forming nearly 80% of all abnormalities. Community clustering in Bagatha and Valmiki tribes, predominance in younger age groups (≤30 years: 77%), and female skew (61.7%) were striking findings.

The results reinforce the role of HPLC as a robust tool for large-scale screening, though rare variants and compound heterozygotes require molecular confirmation. The data emphasize the urgent need for targeted prevention strategies, premarital/antenatal screening, and genetic counseling programs in tribal belts to reduce disease burden and intergenerational transmission.

LIMITATIONS OF THE STUDY

1. Molecular confirmation was not performed for borderline HbA₂ values, rare variants, or compound heterozygotes, which may lead to under- or misclassification.
2. Selection bias was present, as many samples were drawn from high-risk tribal groups through targeted screening, which may overestimate prevalence compared to general populations.
3. Clinical follow-up of abnormal cases was not systematically included; hence, phenotype–genotype correlation is limited.

Future Directions

1. Integration of molecular testing (e.g., ARMS-PCR, gene sequencing) for precise classification of β-thalassemia mutations and compound heterozygotes.
2. Expansion of newborn and premarital screening programs in tribal-dominant districts under NSCAEM.
3. Longitudinal follow-up studies to document morbidity, mortality, and treatment outcomes (hydroxyurea, transfusions, iron chelation).
4. Development of community-based genetic counseling services to improve awareness and reduce consanguineous marriages.
5. Establishment of a regional hemoglobinopathy registry to support public health planning and targeted interventions.

1. Weatherall, D. J. (2010). The inherited diseases of hemoglobin are an emerging global health burden. Blood, 115(22), 4331–4336.
2. Weatherall, D. J., & Clegg, J. B. (2001). Inherited haemoglobin disorders: An increasing global health problem. Bulletin of the World Health Organization, 79(8), 704–712.
3. Ghosh, K., Colah, R. B., & Mukherjee, M. B. (2015). Haemoglobinopathies in tribal populations of India. Indian Journal of Medical Research, 141(5), 505–515. https://doi.org/10.4103/0971-5916.159492
4. Mohanty, D., Colah, R. B., Gorakshakar, A. C., Patel, R. Z., Master, D. C., Mahanta, J., Sharma, S. K., Chaudhari, U., Ghosh, M., Das, S., Britt, R. P., Singh, S., Ross, C., Jagannnathan, L., Kaul, R., Shukla, D. K., & Muthuswamy, V. (2013). Prevalence of β-thalassemia and other hemoglobinopathies in six cities. Journal of Community Genetics, 4(1), 33–42.
5. Naik, D., Patra, P. K., Sarangi, S., Sahoo, D., Das, B., & Behera, P. (2019). Spectrum of hemoglobinopathies in Odisha: A hospital-based cross-sectional study. Journal of Clinical and Diagnostic Research, 13(6), EC01–EC05. https://doi.org/10.7860/JCDR/2019/40930.12926
6. Basu, S., Narayan, J., & Das, V. (2020). Hemoglobinopathies among Scheduled Tribes in Telangana: A community-based study. Asian Journal of Medical Sciences, 11(1), 45–49. https://doi.org/10.3126/ajms.v11i1.26523
7. Urade, B. P. (2012). Sickle cell gene scenario in tribal India. Journal of Health & Medical Informatics, 3, 1000114. https://doi.org/10.4172/2157-7420.1000114
8. Colah, R. B., Surve, R., Sawant, P., D’Souza, E., Italia, K., Phanasgaonkar, S., & Ghosh, K. (2010). HPLC studies in hemoglobinopathies. Indian Journal of Pediatrics, 77(8), 877–881. https://doi.org/10.1007/s12098-010-0124-4
9. Baig, M. A., Swamy, K. B., Baksh, A. D., Bahashwan, A., Moshrif, Y., Al Sawat, A., Al Mutairi, N., & Alharbi, N. (2021). Evaluation of role of HPLC (Merits & Pitfalls) in the diagnosis of various hemoglobinopathies & thalassemic syndromes. Indian Journal of Pathology and Microbiology, 64(3), 518–523. https://doi.org/10.4103/IJPM.IJPM_709_20
10. Shelke, P., Doshi, P., Nisal, A., Momin, A., & Nimbargi, R. (2025). Chromatographic insights into hemoglobinopathies: Spectrum analysis by high-performance liquid chromatography in a western Indian tertiary care hospital. Medical Laboratory Journal, 19(2), 1–5. https://doi.org/10.29252/mlj.19.2.1
11. Bhalodia, J. N., Oza, H. V., Modi, P. J., Shah, A. M., Patel, K. A., & Patel, H. B. (2015). Study of hemoglobinopathies in patients of anemia using high performance liquid chromatography in Western India. National Journal of Community Medicine, 6(1), 35–40.
12. Patil, A. Y., Choudhary, S. K., & Damkondwar, O. B. (2024). Detection of haemoglobinopathies in patients of anaemia using high performance liquid chromatography (HPLC) in Jabalpur District Hospital, Madhya Pradesh: A cross-sectional study. International Journal of Life Sciences, Biotechnology and Pharma Research, 13(2), 1–8.
13. Warghade, S., Britto, J., Haryan, R., Dalvi, T., Bendre, R., Chheda, P., & Surve, R. (2018). Prevalence of hemoglobin variants and hemoglobinopathies using cation-exchange HPLC in central reference laboratory of India: Report of 65,779 cases. Journal of Laboratory Physicians, 10(1), 73–79. https://doi.org/10.4103/JLP.JLP_57_17
14. Kumar, V., Raghavendra, S., & Reddy, Y. V. (2017). Prevalence of sickle cell trait in tribes of Visakhapatnam Agency Area. Indian Journal of Hematology and Blood Transfusion, 33(2), 223–227. https://doi.org/10.1007/s12288-016-0670-7
15. Mukhopadhyay, D., Saha, K., Sengupta, M., Mitra, S., Datta, C., & Mitra, P. K. (2015). Spectrum of hemoglobinopathies in West Bengal, India: A CE-HPLC study on 10,407 subjects. Indian Journal of Hematology and Blood Transfusion, 31(1), 98–103. https://doi.org/10.1007/s12288-013-0324-1
16. Rao, S., Vashist, R. P., & Sharma, A. (2015). Hemoglobinopathies in antenatal women: Need for screening. International Journal of Reproduction, Contraception, Obstetrics and Gynecology, 4(4), 1179–1182. https://doi.org/10.18203/2320-1770.ijrcog20150039
17. Atla, B., Botta, V. S. K., Balakrishnan, P., Lalam, N., Argi, A., & Natukula, K. (2021). A clinico-haematological study of hereditary hemoglobinopathies: A tertiary care centre experience. International Journal of Research in Medical Sciences, 9(8), 2309–2314. https://doi.org/10.18203/2320-6012.ijrms20213007
18. Pathak, V., Mehta, R., Choudhary, R., Singh, S., & Meena, A. (2017). Spectrum of hemoglobinopathies diagnosed on HPLC in a tertiary care teaching hospital of Rajasthan. Indian Journal of Pathology and Oncology, 4(3), 426–430.
19. Ankur, K., Gulati, S., Sharma, N., & Saini, P. K. (2021). Pattern analysis of hemoglobin variants in Western India by HPLC: Strategies and practical implications for pursuing rare hemoglobins. Asian Hematology Research Journal, 4(3), 232–240.
20. Old, J. M. (2003). Screening and genetic diagnosis of hemoglobin disorders. Blood Reviews, 17(1), 43–53. https://doi.org/10.1016/S0268-960X(02)00071-5
21. Thein, S. L. (2004). Genetic insights into the clinical diversity of β thalassaemia. British Journal of Haematology, 124(3), 264–274. https://doi.org/10.1046/j.1365-2141.2003.04761.x