Improved imputation accuracy in Hispanic/Latino populations with larger and more diverse reference panels: applications in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Sequencing whole genomes of the West Javanese population in Indonesia reveals novel variants and improves imputation accuracy

Existing genotype imputation reference panels are mainly derived from European populations, limiting their accuracy in non-European populations. To improve imputation accuracy for Indonesians, the world's fourth most populous country, we combined Whole Genome Sequencing (WGS) data from 227 West Javanese individuals with East Asian data from the 1000 Genomes Project. This created three reference panels: EAS 1KGP3 (EASp), Indonesian (INDp), and a combined panel (EASp+INDp). We also used ten West-Javanese samples with WGS and SNP-typing data for benchmarking. We identified 1.8 million novel single nucleotide variants (SNVs) in the West Javanese population, which, while similar to the East Asians, are distinct from the Central Indonesian Flores population. Adding INDp to the EASp reference panel improved imputation accuracy (R2) from 0.85 to 0.90, and concordance from 87.88% to 91.13%. These findings underscore the importance of including Indonesian genetic data in reference panels, advocating for broader WGS of diverse Indonesian populations to enhance genomic studies.

Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.

A diverse ancestrally-matched reference panel increases genotype imputation accuracy in a underrepresented population

Variant imputation, a common practice in genome-wide association studies, relies on reference panels to infer unobserved genotypes. Multiple public reference panels are currently available with variations in size, sequencing depth, and represented populations. Currently, limited data exist regarding the performance of public reference panels when used in an imputation of populations underrepresented in the reference panel. Here, we compare the performance of various public reference panels: 1000 Genomes Project, Haplotype Reference Consortium, GenomeAsia 100 K, and the recent Trans-Omics for Precision Medicine (TOPMed) program, when used in an imputation of samples from the Thai population. Genotype yields were assessed, and imputation accuracies were examined by comparison with high-depth whole genome sequencing data of the same sample. We found that imputation using the TOPMed panel yielded the largest number of variants (~ 271 million). Despite being the smallest in size, GenomeAsia 100 K achieved the best imputation accuracy with a median genotype concordance rate of 0.97. For rare variants, GenomeAsia 100 K also offered the best accuracy, although rare variants were less accurately imputable than common variants (30.3% reduction in concordance rates). The high accuracy observed when using GenomeAsia 100 K is likely attributable to the diverse representation of populations genetically similar to the study cohort emphasizing the benefits of sequencing populations classically underrepresented in human genomics.

Genetic and stress influences on the prevalence of hypertension among hispanics/latinos in the hispanic community health study/study of latinos (HCHS/SOL)

PURPOSE

The current study examined the effects of chronic stress and a genetic risk score on the presence of hypertension and elevated systolic blood pressure and diastolic blood pressure among Hispanics/Latinos in the target population of Hispanic Community Health Study/Study of Latinos.

MATERIALS AND METHODS

Of the participants (N = 11,623) assessed during two clinic visits (Visit 1 2008-2013 & Visit 2 2014-2018), we analysed data from 7,429 adults (50.4% female), aged 18-74, who were genotyped and responded to chronic stress questionnaires. We calculated an unweighted genetic risk score using blood pressure increasing single nucleotide polymorphisms (SNPs) found to be generalisable to Hispanics/Latinos (10 SNPs). Linear and logistic regression models were used to estimate associations between chronic stress and genetic risk score and their interaction, with prevalent Visit 2 SBP or DBP, and hypertension, respectively. Models accounted for sampling weights, stratification, and cluster design.

RESULTS

Chronic stress (adjusted OR = 1.18, 95%CI:1.15,1.22) and hypertension genetic risk score (adjusted OR = 1.04, 95%CI:1.01,1.07) were significantly associated with prevalent hypertension, but there was no significant interaction between the chronic stress and genetic risk score on hypertension (p = .49). genetic risk score (b = .32, 95%CI:.08, .55, R² = .02) and chronic stress (b = .45, 95%CI:.19, .72, R² = .11) were related to DBP, with no significant interaction (p = .62). Genetic risk score (b = .42, 95%CI:.08, .76, R² = .01) and chronic stress (b = .80, 95%CI:.34,1.26, R² = .11) were also related to SBP, with no significant interaction (p = .51).

CONCLUSION

Results demonstrate the utility of a genetic risk score for blood pressure and are consistent with literature suggesting chronic stress has a strong, direct association with elevated blood pressure among U.S. Hispanics/Latinos.

Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa

Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies.

A globally diverse reference alignment and panel for imputation of mitochondrial DNA variants

BACKGROUND

Variation in mitochondrial DNA (mtDNA) identified by genotyping microarrays or by sequencing only the hypervariable regions of the genome may be insufficient to reliably assign mitochondrial genomes to phylogenetic lineages or haplogroups. This lack of resolution can limit functional and clinical interpretation of a substantial body of existing mtDNA data. To address this limitation, we developed and evaluated a large, curated reference alignment of complete mtDNA sequences as part of a pipeline for imputing missing mtDNA single nucleotide variants (mtSNVs). We call our reference alignment and pipeline MitoImpute.

RESULTS

We aligned the sequences of 36,960 complete human mitochondrial genomes downloaded from GenBank, filtered and controlled for quality. These sequences were reformatted for use in imputation software, IMPUTE2. We assessed the imputation accuracy of MitoImpute by measuring haplogroup and genotype concordance in data from the 1000 Genomes Project and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The mean improvement of haplogroup assignment in the 1000 Genomes samples was 42.7% (Matthew's correlation coefficient = 0.64). In the ADNI cohort, we imputed missing single nucleotide variants.

CONCLUSION

These results show that our reference alignment and panel can be used to impute missing mtSNVs in existing data obtained from using microarrays, thereby broadening the scope of functional and clinical investigation of mtDNA. This improvement may be particularly useful in studies where participants have been recruited over time and mtDNA data obtained using different methods, enabling better integration of early data collected using less accurate methods with more recent sequence data.

Multiple-ancestry genome-wide association study identifies 27 loci associated with measures of hemolysis following blood storage

BackgroundThe evolutionary pressure of endemic malaria and other erythrocytic pathogens has shaped variation in genes encoding erythrocyte structural and functional proteins, influencing responses to hemolytic stress during transfusion and disease.MethodsWe sought to identify such genetic variants in blood donors by conducting a genome-wide association study (GWAS) of 12,353 volunteer donors, including 1,406 African Americans, 1,306 Asians, and 945 Hispanics, whose stored erythrocytes were characterized by quantitative assays of in vitro osmotic, oxidative, and cold-storage hemolysis.ResultsGWAS revealed 27 significant loci (P < 5 × 10-8), many in candidate genes known to modulate erythrocyte structure, metabolism, and ion channels, including SPTA1, ALDH2, ANK1, HK1, MAPKAPK5, AQP1, PIEZO1, and SLC4A1/band 3. GWAS of oxidative hemolysis identified variants in genes encoding antioxidant enzymes, including GLRX, GPX4, G6PD, and SEC14L4 (Golgi-transport protein). Genome-wide significant loci were also tested for association with the severity of steady-state (baseline) in vivo hemolytic anemia in patients with sickle cell disease, with confirmation of identified SNPs in HBA2, G6PD, PIEZO1, AQP1, and SEC14L4.ConclusionsMany of the identified variants, such as those in G6PD, have previously been shown to impair erythrocyte recovery after transfusion, associate with anemia, or cause rare Mendelian human hemolytic diseases. Candidate SNPs in these genes, especially in polygenic combinations, may affect RBC recovery after transfusion and modulate disease severity in hemolytic diseases, such as sickle cell disease and malaria.

Genome-wide association study of cognitive function in diverse Hispanics/Latinos: results from the Hispanic Community Health Study/Study of Latinos

Cognitive function such as reasoning, attention, memory, and language is strongly correlated with brain aging. Compared to non-Hispanic whites, Hispanics/Latinos have a higher risk of cognitive impairment and dementia. The genetic determinants of cognitive function have not been widely explored in this diverse and admixed population. We conducted a genome-wide association analysis of cognitive function in up to 7600 middle aged and older Hispanics/Latinos (mean = 55 years) from the Hispanic Community Health Study / Study of Latinos (HCHS/SOL). Four cognitive measures were examined: the Brief Spanish English Verbal Learning Test (B-SEVLT), the Word Fluency Test (WFT), the Digit Symbol Substitution Test (DSST), the Six-Item Screener (SIS). Four novel loci were identified: one for B-SEVLT at 4p14, two for WFT at 3p14.1 and 6p21.32, and one for DSST at 10p13. These loci implicate genes highly expressed in brain and previously connected to neurological diseases (UBE2K, FRMD4B, the HLA gene complex). By applying tissue-specific gene expression prediction models to our genotype data, additional genes highly expressed in brain showed suggestive associations with cognitive measures possibly indicating novel biological mechanisms, including IFT122 in the hippocampus for SIS, SNX31 in the basal ganglia for B-SEVLT, RPS6KB2 in the frontal cortex for WFT, and CSPG5 in the hypothalamus for DSST. These findings provide new information about the genetic determinants of cognitive function in this unique population. In addition, we derived a measure of general cognitive function based on these cognitive tests and generated genome-wide association summary results, providing a resource to the research community for comparison, replication, and meta-analysis in future genetic studies in Hispanics/Latinos.

Genotype imputation and reference panel: a systematic evaluation on haplotype size and diversity

Here, 622 imputations were conducted with 394 customized reference panels for Han Chinese and European populations. Besides validating the fact that imputation accuracy could always benefit from the increased panel size when the reference panel was population specific, the results brought two new thoughts. First, when the haplotype size of the reference panel was fixed, the imputation accuracy of common and low-frequency variants (Minor Allele Frequency (MAF) > 0.5%) decreased while the population diversity of the reference panel increased, but for rare variants (MAF < 0.5%), a small fraction of diversity in panel could improve imputation accuracy. Second, when the haplotype size of the reference panel was increased with extra population-diverse samples, the imputation accuracy of common variants (MAF > 5%) for the European population could always benefit from the expanding sample size. However, for the Han Chinese population, the accuracy of all imputed variants reached the highest when reference panel contained a fraction of an extra diverse sample (8-21%). In addition, we evaluated the imputation performances in the existing reference panels, such as the Haplotype Reference Consortium (HRC), 1000 Genomes Project Phase 3 and the China, Oxford and Virginia Commonwealth University Experimental Research on Genetic Epidemiology (CONVERGE). For the European population, the HRC panel showed the best performance in our analysis. For the Han Chinese population, we proposed an optimum imputation reference panel constituent ratio if researchers would like to customize their own sequenced reference panel, but a high-quality and large-scale Chinese reference panel was still needed. Our findings could be generalized to the other populations with conservative genome; a tool was provided to investigate other populations of interest (https://github.com/Abyss-bai/reference-panel-reconstruction).

Genome-Wide Association Study of Heavy Smoking and Daily/Nondaily Smoking in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Introduction

Genetic variants associated with nicotine dependence have previously been identified, primarily in European-ancestry populations. No genome-wide association studies (GWAS) have been reported for smoking behaviors in Hispanics/Latinos in the United States and Latin America, who are of mixed ancestry with European, African, and American Indigenous components.

Methods

We examined genetic associations with smoking behaviors in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (N = 12 741 with smoking data, 5119 ever-smokers), using ~2.3 million genotyped variants imputed to the 1000 Genomes Project phase 3. Mixed logistic regression models accounted for population structure, sampling, relatedness, sex, and age.

Results

The known region of CHRNA5, which encodes the α5 cholinergic nicotinic receptor subunit, was associated with heavy smoking at genome-wide significance (p ≤ 5 × 10-8) in a comparison of 1929 ever-smokers reporting cigarettes per day (CPD) > 10 versus 3156 reporting CPD ≤ 10. The functional variant rs16969968 in CHRNA5 had a p value of 2.20 × 10-7 and odds ratio (OR) of 1.32 for the minor allele (A); its minor allele frequency was 0.22 overall and similar across Hispanic/Latino background groups (Central American = 0.17; South American = 0.19; Mexican = 0.18; Puerto Rican = 0.22; Cuban = 0.29; Dominican = 0.19). CHRNA4 on chromosome 20 attained p < 10-4, supporting prior findings in non-Hispanics. For nondaily smoking, which is prevalent in Hispanic/Latino smokers, compared to daily smoking, loci on chromosomes 2 and 4 achieved genome-wide significance; replication attempts were limited by small Hispanic/Latino sample sizes.

Conclusions

Associations of nicotinic receptor gene variants with smoking, first reported in non-Hispanic European-ancestry populations, generalized to Hispanics/Latinos despite different patterns of smoking behavior.

Implications

We conducted the first large-scale genome-wide association study (GWAS) of smoking behavior in a US Hispanic/Latino cohort, and the first GWAS of daily/nondaily smoking in any population. Results show that the region of the nicotinic receptor subunit gene CHRNA5, which in non-Hispanic European-ancestry smokers has been associated with heavy smoking as well as cessation and treatment efficacy, is also significantly associated with heavy smoking in this Hispanic/Latino cohort. The results are an important addition to understanding the impact of genetic variants in understudied Hispanic/Latino smokers.

Rare Variants Imputation in Admixed Populations: Comparison Across Reference Panels and Bioinformatics Tools

Background

Imputation has become a standard approach in genome-wide association studies (GWAS) to infer in silico untyped markers. Although feasibility for common variants imputation is well established, we aimed to assess rare and ultra-rare variants’ imputation in an admixed Caribbean Hispanic population (CH).

Methods

We evaluated imputation accuracy in CH (N = 1,000), focusing on rare (0.1% ≤ minor allele frequency (MAF) ≤ 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC; N = 27,165) and 1000 Genome Project (1000G phase 3; N = 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: (a) high-quality variant counts according to imputation tools’ internal indexes (e.g., IMPUTE2 “Info” ≥ 80%). (b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; (c) Cohen’s kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; (d) imputation of G206A mutation in the PSEN1 (ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion.

Results

SHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs. 0.65%, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs. 0.75% for high-quality rare and ultra-rare variants, respectively, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g., rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa (K = 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (p-value < 1e-05).

Conclusion

Reference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.

Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population

Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server.