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<article class="scholarly-article">
<h2>Introduction</h2>
<p>Rare variants (minor allele frequency < 1%) are increasingly recognized as key contributors to heritability of complex traits and diseases [14, 28]. However, their detection through imputation in genome-wide association studies (GWAS) remains challenging, particularly in non-European populations, due to the lack of diverse reference panels [2, 6]. Most reference panels, such as those from the 1000 Genomes Project, have limited representation of African, Hispanic/Latino, and Asian ancestries, leading to imputation inaccuracies and reduced statistical power for rare variant association tests [5, 7, 16].</p><p>The development of ethnicity-aware reference panels—panels specifically constructed to include haplotypes from genetically similar populations—has been proposed to mitigate this disparity [17, 13]. By leveraging large, population-specific sequencing data, these panels can capture population-specific haplotypes and rare variants that are absent in cosmopolitan panels [15]. Previous studies have shown improvements in imputation accuracy for common variants when using population-specific references [2, 6], but the impact on rare variants, especially in admixed populations, remains underexplored [5, 16].</p><p>In this study, we systematically evaluate the performance of ethnicity-aware reference panels for rare variant imputation across five major populations (African, East Asian, South Asian, European, and Hispanic/Latino). We use both simulated and real sequencing data to assess imputation accuracy, rare variant detection sensitivity, and phasing errors. Our goal is to provide guidelines for constructing optimal reference panels that maximize equitable genomic discovery.</p>
<h2>Literature Review</h2>
<p>Genotype imputation relies on reference panels of phased haplotypes to predict unobserved genotypes in target samples [15]. The accuracy of imputation depends on the genetic similarity between target and reference populations [6, 17]. Studies have demonstrated that using a reference panel from the same population as the target yields higher imputation accuracy [2]. For example, Nelson et al. [2] showed improved imputation in Hispanic/Latino populations when using a larger and more diverse reference panel. Similarly, Wang et al. [17] found that trans-ethnic fine mapping benefits from population-specific reference panels in Asian populations.</p><p>Rare variants pose additional challenges due to their low frequency and population-specific nature [7]. Raska and Zhu [7] highlighted the variation in rare variant density across populations, emphasizing the need for diverse reference panels. Moreover, imputation methods that leverage large haplotype reference panels have been shown to improve phasing and rare variant detection [15]. However, these methods have not been extensively validated in admixed populations [5, 16].</p><p>Several approaches have been proposed to enhance rare variant detection in diverse populations. Qin et al. [5] developed a method for identifying rare variant associations in admixed populations, but it relies on accurate imputation. Greco et al. [10] introduced a general approach for combining rare variant tests, which can be more robust across genetic architectures. Additionally, Deng and Pan [13] showed improved use of small reference panels for conditional and joint analysis. These advances highlight the critical role of reference panel composition.</p><p>Despite these efforts, a comprehensive assessment of ethnicity-aware reference panels for rare variant imputation across multiple diverse populations is lacking. Our study fills this gap by systematically comparing cosmopolitan, population-specific, and ethnicity-aware panels using both simulated and empirical data.</p>
<h2>Methodology</h2>
<h4>Data Sources and Reference Panel Construction</h4><p>We obtained sequencing data from five population groups: African (AFR), East Asian (EAS), South Asian (SAS), European (EUR), and Hispanic/Latino (HIS). For each group, we included 5,000 individuals with whole-genome sequencing at 30× coverage, randomly split into training (80%) and validation (20%) sets. We constructed three types of reference panels: (1) <em>Cosmopolitan</em> (COS) panel—a random sample of 10,000 haplotypes representing all populations proportional to global frequency; (2) <em>Population-specific</em> (POP) panels—4,000 haplotypes each from AFR, EAS, SAS, EUR, and HIS; (3) <em>Ethnicity-aware</em> (EA) panels—for each target population, we constructed a panel comprising 8,000 haplotypes from the same population and 2,000 haplotypes from genetically similar populations (e.g., for HIS: 6,000 from HIS, 2,000 from EUR, 2,000 from AFR).</p><h4>Imputation and Evaluation</h4><p>We phased all target genotypes using SHAPEIT4 [15] and imputed missing genotypes using IMPUTE5 with each reference panel. We focused on rare variants (MAF < 1% in the target population) that were present in the validation set. Imputation accuracy was measured using the squared correlation between imputed and true dosages (R²) and the allelic correlation (r). We also assessed sensitivity for rare variant detection at varying certainty thresholds (info score > 0.3, 0.5, 0.8). Additionally, we evaluated phasing errors by comparing the switch error rate between phased and validated haplotype estimates.</p><h4>Simulation Study</h4><p>To assess the impact of reference panel size and composition, we simulated rare variants under a demographic model reflecting population bottlenecks and recent admixture. We generated 100 replicates of a target population with 10,000 individuals (50% African, 50% European admixture) and varied the reference panel size (2,000 to 20,000 haplotypes) and composition (cosmopolitan, population-specific, ethnicity-aware). We then computed the same accuracy metrics.</p>
<h2>Results</h2>
<h4>Imputation Accuracy Across Populations</h4><p>Table 1 compares the average imputation R² for rare variants across the five populations using different reference panels. Ethnicity-aware panels consistently outperformed cosmopolitan panels, with the largest gains observed in African and Hispanic/Latino populations (15% and 22% improvement, respectively). Population-specific panels performed similarly to ethnicity-aware panels for homogenous populations (EUR, EAS) but were inferior for admixed populations (AFR, HIS).</p><figure class="table-figure"><table><thead><tr><th>Target Population</th><th>Cosmopolitan Panel</th><th>Population-Specific Panel</th><th>Ethnicity-Aware Panel</th></tr></thead><tbody><tr><td>African (AFR)</td><td>0.52 (0.03)</td><td>0.58 (0.04)</td><td>0.60 (0.03)</td></tr><tr><td>East Asian (EAS)</td><td>0.61 (0.02)</td><td>0.67 (0.02)</td><td>0.68 (0.02)</td></tr><tr><td>South Asian (SAS)</td><td>0.58 (0.03)</td><td>0.63 (0.03)</td><td>0.64 (0.03)</td></tr><tr><td>European (EUR)</td><td>0.72 (0.02)</td><td>0.76 (0.02)</td><td>0.76 (0.02)</td></tr><tr><td>Hispanic/Latino (HIS)</td><td>0.50 (0.04)</td><td>0.57 (0.05)</td><td>0.61 (0.04)</td></tr></tbody></table><figcaption>Table 1. Mean imputation R² (standard deviation) for rare variants (MAF < 1%) by target population and reference panel type.</figcaption></figure><p><figure class="article-figure"><figcaption>Figure 1. Bar chart comparing mean imputation R² for rare variants across five populations using three reference panel types (cosmopolitan, population-specific, ethnicity-aware). Error bars indicate standard deviation.</figcaption></figure></p><h4>Rare Variant Detection Sensitivity</h4><p>Figure 1 (placeholder) shows the sensitivity for detecting truly rare variants at an info score > 0.5. Ethnicity-aware panels achieved a sensitivity of 78% in AFR and 82% in HIS, compared to 60% and 58% for cosmopolitan panels, respectively. The gains were more modest for EUR (92% vs. 90%). Table 2 presents sensitivity across different info score thresholds.</p><figure class="table-figure"><table><thead><tr><th>Info Score Threshold</th><th>African</th><th>East Asian</th><th>South Asian</th><th>European</th><th>Hispanic/Latino</th></tr></thead><tbody><tr><td>> 0.3</td><td>85% (72%)</td><td>88% (80%)</td><td>86% (78%)</td><td>95% (92%)</td><td>81% (68%)</td></tr><tr><td>> 0.5</td><td>78% (60%)</td><td>82% (73%)</td><td>80% (70%)</td><td>92% (90%)</td><td>72% (55%)</td></tr><tr><td>> 0.8</td><td>55% (40%)</td><td>60% (50%)</td><td>58% (48%)</td><td>75% (72%)</td><td>50% (35%)</td></tr></tbody></table><figcaption>Table 2. Sensitivity for rare variant detection using ethnicity-aware panels (cosmopolitan panel in parentheses) across populations and info score thresholds.</figcaption></figure><p><figure class="article-figure"><figcaption>Figure 2. Line plot showing sensitivity for rare variant detection as a function of info score threshold for three reference panel types in Hispanic/Latino population.</figcaption></figure></p><h4>Phasing Error and Panel Size Effects</h4><p>Ethnicity-aware panels reduced switch error rates by an average of 12% across populations, with the greatest reduction in AFR (15%). Table 3 shows the regression analysis of phasing error on reference panel characteristics. Panel size and population match were significant predictors.</p><figure class="table-figure"><table><thead><tr><th>Predictor</th><th>Coefficient</th><th>Std. Error</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.042</td><td>0.003</td><td>< 0.001</td></tr><tr><td>Panel size (per 1,000 haplotypes)</td><td>-0.003</td><td>0.001</td><td>< 0.001</td></tr><tr><td>Population match (yes/no)</td><td>-0.015</td><td>0.004</td><td>< 0.001</td></tr><tr><td>Proportion of same-ancestry haplotypes</td><td>-0.021</td><td>0.005</td><td>< 0.001</td></tr></tbody></table><figcaption>Table 3. Linear regression results for phasing switch error rate (response variable) on reference panel characteristics.</figcaption></figure><p>In simulation studies, imputation accuracy plateaued once panel size exceeded 10,000 haplotypes, regardless of composition. However, ethnicity-aware panels reached the plateau at smaller sizes (8,000 vs. 12,000 for cosmopolitan).</p>
<h2>Discussion</h2>
<p>Our results demonstrate that ethnicity-aware reference panels significantly improve rare variant imputation in diverse populations, particularly those with recent admixture. The 22% increase in R² for Hispanic/Latino populations and 15% for African populations highlights the inadequacy of current cosmopolitan panels [2, 6]. These improvements translate into higher sensitivity for detecting truly rare variants, which is crucial for identifying causal variants in association studies [5, 10].</p><p>The benefit of ethnicity-aware panels is most pronounced for rare variants due to their population-specific distribution [7]. While population-specific panels offer some improvement, they lack the diversity needed for admixed populations, where haplotypes from multiple ancestries are present [16]. Our ethnicity-aware approach that includes genetically similar populations captures this admixture more effectively.</p><p>Phasing errors were also reduced, likely because ethnicity-aware panels provide more accurate haplotype templates [15]. The regression analysis confirms that panel size and ancestry composition are key determinants of phasing accuracy [13].</p><p>Our simulation study suggests that panel sizes beyond 10,000 haplotytes yield diminishing returns, but ethnicity-aware panels achieve optimal performance with fewer resources. This has practical implications for biobanks and large-scale sequencing projects aiming to maximize utility across demographics.</p><p>Limitations include the use of simulated data for some analyses and the absence of extremely rare variants (MAF < 0.1%), where imputation is more challenging. Additionally, our definition of ethnicity-aware panels relied on self-reported ancestry; genetic ancestry may provide better stratification [11].</p>
<h2>Conclusion</h2>
<p>Ethnicity-aware reference panels substantially enhance rare variant imputation in diverse populations, reducing bias and improving power for genomic discovery. We recommend that future reference panels be constructed with balanced representation from multiple ancestries and tailored to the target population's genetic composition. Such efforts will promote health equity and facilitate the identification of rare variants underlying complex diseases.</p><p>Our findings underscore the need for continued investment in diverse genomic resources and methods that account for population structure. As sequencing costs decline, building large, ethnicity-aware reference panels should become a priority for the genomics community.</p>
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