Sequencing and Imputation in GWAS: Cost-Effective Strategies to Increase Power and Genomic Coverage Across Diverse Populations

Mapping Intimacies ◽

10.1101/548321 ◽

2019 ◽

Cited By ~ 2

Author(s):

Corbin Quick ◽

Pramod Anugu ◽

Solomon Musani ◽

Scott T. Weiss ◽

Esteban G. Burchard ◽

...

Keyword(s):

Genetic Variation ◽

Statistical Power ◽

Rare Variants ◽

Association Studies ◽

Cost Effective ◽

Genotype Imputation ◽

Reference Panel ◽

Genome Wide Association Studies ◽

Full Spectrum ◽

Study Participants

ABSTRACTA key aim for current genome-wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole-genome sequencing is the gold standard to capture the full spectrum of genetic variation, but remains prohibitively expensive for large samples. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture variation across a wider set of variants. However, imputation coverage and accuracy depend crucially on the reference panel size and genetic distance from the target population.Here, we consider a strategy in which a subset of study participants is sequenced and the rest array-genotyped and imputed using a reference panel that comprises the sequenced study participants and individuals from an external reference panel. We systematically assess how imputation quality and statistical power for association depend on the number of individuals sequenced and included in the reference panel for two admixed populations (African and Latino Americans) and two European population isolates (Sardinians and Finns). We develop a framework to identify powerful and cost-effective GWAS designs in these populations given current sequencing and array genotyping costs. For populations that are well-represented in current reference panels, we find that array genotyping alone is cost-effective and well-powered to detect both common- and rare-variant associations. For poorly represented populations, we find that sequencing a subset of study participants to improve imputation is often more cost-effective than array genotyping alone, and can substantially increase genomic coverage and power.

Advantages of genotype imputation with ethnically matched reference panel for rare variant association analyses

10.1101/579201 ◽

2019 ◽

Cited By ~ 4

Author(s):

Mart Kals ◽

Tiit Nikopensius ◽

Kristi Läll ◽

Kalle Pärn ◽

Timo Tõnis Sikka ◽

...

Keyword(s):

Rare Variant ◽

Rare Variants ◽

Association Studies ◽

Low Frequency ◽

Genotype Imputation ◽

Reference Panel ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Variant Analysis ◽

Coding Variants

AbstractGenotype imputation has become a standard procedure prior genome-wide association studies (GWASs). For common and low-frequency variants, genotype imputation can be performed sufficiently accurately with publicly available and ethnically heterogeneous reference datasets like 1000 Genomes Project (1000G) and Haplotype Reference Consortium panels. However, the imputation of rare variants has been shown to be significantly more accurate when ethnically matched reference panel is used. Even more, greater genetic similarity between reference panel and target samples facilitates the detection of rare (or even population-specific) causal variants. Notwithstanding, the genome-wide downstream consequences and differences of using ethnically mixed and matched reference panels have not been yet comprehensively explored.We determined and quantified these differences by performing several comparative evaluations of the discovery-driven analysis scenarios. A variant-wise GWAS was performed on seven complex diseases and body mass index by using genome-wide genotype data of ∼37,000 Estonians imputed with ethnically mixed 1000G and ethnically matched imputation reference panels. Although several previously reported common (minor allele frequency; MAF > 5%) variant associations were replicated in both resulting imputed datasets, no major differences were observed among the genome-wide significant findings or in the fine-mapping effort. In the analysis of rare (MAF < 1%) coding variants, 46 significantly associated genes were identified in the ethnically matched imputed data as compared to four genes in the 1000G panel based imputed data. All resulting genes were consequently studied in the UK Biobank data.These associations provide a solid example of how rare variants can be efficiently analysed to discover novel, potentially functional genetic variants in relevant phenotypes. Furthermore, our work serves as proof of a cost-efficient study design, demonstrating that the usage of ethnically matched imputation reference panels can enable substantially improved imputation of rare variants, facilitating novel high-confidence findings in rare variant GWAS scans.Author summaryOver the last decade, genome-wide association studies (GWASs) have been widely used for detecting genetic biomarkers in a wide range of traits. Typically, GWASs are carried out using chip-based genotyping data, which are then combined with a more densely genotyped reference panel to infer untyped genetic variants in chip-typed individuals. The latter method is called genotype imputation and its accuracy depends on multiple factors. Publicly available and ethnically heterogeneous imputation reference panels (IRPs) such as 1000 Genomes Project (1000G) are sufficiently accurate for imputation of common and low-frequency variants, but custom ethnically matched IRPs outperform these in case of rare variants. In this work, we systematically compare downstream association analysis effects on eight complex traits in ∼37,000 Estonians imputed with ethnically mixed and ethnically matched IRPs. We do not observe major differences in the single variant analysis, where both imputed datasets replicate previously reported significant loci. But in the gene-based analysis of rare protein-coding variants we show that ethnically matched panel clearly outperforms 1000G panel based imputation, providing 10-fold increase in significant gene-trait associations. Our study demonstrates empirically that imputed data based on ethnically matched panel is very promising for rare variant analysis – it captures more population-specific variants and makes it possible to efficiently identify novel findings.

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Nature ◽

10.1038/s41586-021-03205-y ◽

2021 ◽

Vol 590 (7845) ◽

pp. 290-299 ◽

Cited By ~ 22

Author(s):

Daniel Taliun ◽

◽

Daniel N. Harris ◽

Michael D. Kessler ◽

Jedidiah Carlson ◽

...

Keyword(s):

Rare Variants ◽

Sequence Data ◽

Association Studies ◽

Genotype Imputation ◽

Genome Wide Association Studies ◽

Phenotypic Data ◽

Treatment And Prevention ◽

Genome Wide ◽

Diverse Backgrounds ◽

Unmapped Reads

AbstractThe Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Imputation Performance in Latin American Populations: Improving Rare Variants Representation With the Inclusion of Native American Genomes

Frontiers in Genetics ◽

10.3389/fgene.2021.719791 ◽

2022 ◽

Vol 12 ◽

Author(s):

Andrés Jiménez-Kaufmann ◽

Amanda Y. Chong ◽

Adrián Cortés ◽

Consuelo D. Quinto-Cortés ◽

Selene L. Fernandez-Valverde ◽

...

Keyword(s):

Native American ◽

Latin American ◽

Statistical Power ◽

Association Studies ◽

Demographic History ◽

Genotype Imputation ◽

Genomic Research ◽

European Ancestry ◽

Local Source ◽

Genome Wide Association Studies

Current Genome-Wide Association Studies (GWAS) rely on genotype imputation to increase statistical power, improve fine-mapping of association signals, and facilitate meta-analyses. Due to the complex demographic history of Latin America and the lack of balanced representation of Native American genomes in current imputation panels, the discovery of locally relevant disease variants is likely to be missed, limiting the scope and impact of biomedical research in these populations. Therefore, the necessity of better diversity representation in genomic databases is a scientific imperative. Here, we expand the 1,000 Genomes reference panel (1KGP) with 134 Native American genomes (1KGP + NAT) to assess imputation performance in Latin American individuals of mixed ancestry. Our panel increased the number of SNPs above the GWAS quality threshold, thus improving statistical power for association studies in the region. It also increased imputation accuracy, particularly in low-frequency variants segregating in Native American ancestry tracts. The improvement is subtle but consistent across countries and proportional to the number of genomes added from local source populations. To project the potential improvement with a higher number of reference genomes, we performed simulations and found that at least 3,000 Native American genomes are needed to equal the imputation performance of variants in European ancestry tracts. This reflects the concerning imbalance of diversity in current references and highlights the contribution of our work to reducing it while complementing efforts to improve global equity in genomic research.

Deep genotype imputation captures virtually all heritability of autoimmune vitiligo

Human Molecular Genetics ◽

10.1093/hmg/ddaa005 ◽

2020 ◽

Vol 29 (5) ◽

pp. 859-863 ◽

Cited By ~ 3

Author(s):

Genevieve H L Roberts ◽

Stephanie A Santorico ◽

Richard A Spritz

Keyword(s):

Complex Disease ◽

Rare Variants ◽

Association Studies ◽

Genotype Imputation ◽

Genome Wide Association Studies ◽

Common Variants ◽

Genome Wide ◽

Autoimmune Vitiligo ◽

Family Based ◽

Project Data

Abstract Autoimmune vitiligo is a complex disease involving polygenic risk from at least 50 loci previously identified by genome-wide association studies. The objectives of this study were to estimate and compare vitiligo heritability in European-derived patients using both family-based and ‘deep imputation’ genotype-based approaches. We estimated family-based heritability (h2FAM) by vitiligo recurrence among a total 8034 first-degree relatives (3776 siblings, 4258 parents or offspring) of 2122 unrelated vitiligo probands. We estimated genotype-based heritability (h2SNP) by deep imputation to Haplotype Reference Consortium and the 1000 Genomes Project data in unrelated 2812 vitiligo cases and 37 079 controls genotyped genome wide, achieving high-quality imputation from markers with minor allele frequency (MAF) as low as 0.0001. Heritability estimated by both approaches was exceedingly high; h2FAM = 0.75–0.83 and h2SNP = 0.78. These estimates are statistically identical, indicating there is essentially no remaining ‘missing heritability’ for vitiligo. Overall, ~70% of h2SNP is represented by common variants (MAF > 0.01) and 30% by rare variants. These results demonstrate that essentially all vitiligo heritable risk is captured by array-based genotyping and deep imputation. These findings suggest that vitiligo may provide a particularly tractable model for investigation of complex disease genetic architecture and predictive aspects of personalized medicine.

Increasing the resolution and precision of psychiatric GWAS by re-imputing summary statistics using a large, diverse reference panel

10.1101/496570 ◽

2018 ◽

Author(s):

Chris Chatzinakos ◽

Donghyung Lee ◽

Na Cai ◽

Vladimir I. Vladimirov ◽

Bradley T. Webb ◽

...

Keyword(s):

Large Scale ◽

Association Studies ◽

Genome Project ◽

Genotype Imputation ◽

Reference Panel ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Common Variants ◽

Post Traumatic Stress ◽

Mixed Ancestry

ABSTRACTGenotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel e.g., ~2,500 subject coming from the 1000 Genome Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: i) uses a much larger and more diverse reference panel and ii) estimates weights of ethnic mixture based solely on Z-scores (when AFs are not available). We applied DISTMIX2 to GWAS summary statistics from the Psychiatric Genetic Consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder (PTSD) study found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most Psychiatric GWAS studies.

Leveraging TOPMed Imputation Server and Constructing a Cohort-Specific Imputation Reference Panel to Enhance Genotype Imputation among Cystic Fibrosis Patients

10.1101/2021.12.20.473535 ◽

2021 ◽

Author(s):

Quan Sun ◽

Weifang Liu ◽

Jonathan D Rosen ◽

Le Huang ◽

Rhonda G Pace ◽

...

Keyword(s):

Cystic Fibrosis ◽

Sample Size ◽

Association Studies ◽

Genetic Disorder ◽

Genome Project ◽

Genotype Imputation ◽

Reference Panel ◽

Effective Sample Size ◽

Polygenic Risk Score ◽

Genome Wide Association Studies

Cystic fibrosis (CF) is a severe genetic disorder that can cause multiple comorbidities affecting the lungs, the pancreas, the luminal digestive system and beyond. In our previous genome-wide association studies (GWAS), we genotyped ~8,000 CF samples using a mixture of different genotyping platforms. More recently, the Cystic Fibrosis Genome Project (CFGP) performed deep (~30x) whole genome sequencing (WGS) of 5,095 samples to better understand the genetic mechanisms underlying clinical heterogeneity among CF patients. For mixtures of GWAS array and WGS data, genotype imputation has proven effective in increasing effective sample size. Therefore, we first performed imputation for the ~8,000 CF samples with GWAS array genotype using the TOPMed freeze 8 reference panel. Our results demonstrate that TOPMed can provide high-quality imputation for CF patients, boosting genomic coverage from ~0.3 - 4.2 million genotyped markers to ~11 - 43 million well-imputed markers, and significantly improving Polygenic Risk Score (PRS) prediction accuracy. Furthermore, we built a CF-specific CFGP reference panel based on WGS data of CF patients. We demonstrate that despite having ~3% the sample size of TOPMed, our CFGP reference panel can still outperform TOPMed when imputing some CF disease-causing variants, likely due to allele and haplotype differences between CF patients and general populations. We anticipate our imputed data for 4,656 samples without WGS data will benefit our subsequent genetic association studies, and the CFGP reference panel built from CF WGS samples will benefit other investigators studying CF.

Integrative analysis of sequencing and array genotype data for discovering disease associations with rare mutations

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1406143112 ◽

2015 ◽

Vol 112 (4) ◽

pp. 1019-1024 ◽

Cited By ~ 11

Author(s):

Yi-Juan Hu ◽

Yun Li ◽

Paul L. Auer ◽

Dan-Yu Lin

Keyword(s):

Type I Error ◽

Rare Variants ◽

Extreme Values ◽

Association Studies ◽

Cost Effective ◽

Type I ◽

Genome Wide Association Studies ◽

Score Statistic ◽

Sequencing Data ◽

Association Tests

In the large cohorts that have been used for genome-wide association studies (GWAS), it is prohibitively expensive to sequence all cohort members. A cost-effective strategy is to sequence subjects with extreme values of quantitative traits or those with specific diseases. By imputing the sequencing data from the GWAS data for the cohort members who are not selected for sequencing, one can dramatically increase the number of subjects with information on rare variants. However, ignoring the uncertainties of imputed rare variants in downstream association analysis will inflate the type I error when sequenced subjects are not a random subset of the GWAS subjects. In this article, we provide a valid and efficient approach to combining observed and imputed data on rare variants. We consider commonly used gene-level association tests, all of which are constructed from the score statistic for assessing the effects of individual variants on the trait of interest. We show that the score statistic based on the observed genotypes for sequenced subjects and the imputed genotypes for nonsequenced subjects is unbiased. We derive a robust variance estimator that reflects the true variability of the score statistic regardless of the sampling scheme and imputation quality, such that the corresponding association tests always have correct type I error. We demonstrate through extensive simulation studies that the proposed tests are substantially more powerful than the use of accurately imputed variants only and the use of sequencing data alone. We provide an application to the Women’s Health Initiative. The relevant software is freely available.

Privacy-preserving genotype imputation with fully homomorphic encryption

10.1101/2020.05.29.124412 ◽

2020 ◽

Author(s):

Gamze Gürsoy ◽

Eduardo Chielle ◽

Charlotte M. Brannon ◽

Michail Maniatakos ◽

Mark Gerstein

Keyword(s):

Statistical Power ◽

Homomorphic Encryption ◽

Association Studies ◽

Scale Up ◽

Privacy Preserving ◽

Cloud Services ◽

Genotype Imputation ◽

Computational Time ◽

Genome Wide Association Studies ◽

Effective Manner

AbstractGenotype imputation is the statistical inference of unknown genotypes using known population haplotype structures observed in large genomic datasets, such as HapMap and 1000 genomes project. Genotype imputation can help further our understanding of the relationships between genotypes and traits, and is extremely useful for analyses such as genome-wide association studies and expression quantitative loci inference. Increasing the number of genotyped genomes will increase the statistical power for inferring genotype-phenotype relationships, but the amount of data required and the compute-intense nature of the genotype imputation problem overwhelms servers. Hence, many institutions are moving towards outsourcing cloud services to scale up research in a cost effective manner. This raises privacy concerns, which we propose to address via homomorphic encryption. Homomorphic encryption is a type of encryption that allows data analysis on cipher texts, and would thereby avoid the decryption of private genotypes in the cloud. Here we develop an efficient, privacy-preserving genotype imputation algorithm, p-Impute, using homomorphic encryption. Our results showed that the performance of p-Impute is equivalent to the state-of-the-art plaintext solutions, achieving up to 99% micro area under curve score, and requiring a scalable amount of memory and computational time.

A one penny imputed genome from next generation reference panels

10.1101/357806 ◽

2018 ◽

Cited By ~ 1

Author(s):

Brian L. Browning ◽

Ying Zhou ◽

Sharon R. Browning

Keyword(s):

Association Studies ◽

Computational Cost ◽

Computation Time ◽

Genotype Imputation ◽

Reference Panel ◽

Genome Wide Association Studies ◽

Panel Size ◽

Genome Wide ◽

New Genotype ◽

Reference Samples

AbstractGenotype imputation is commonly performed in genome-wide association studies because it greatly increases the number of markers that can be tested for association with a trait. In general, one should perform genotype imputation using the largest reference panel that is available because the number of accurately imputed variants increases with reference panel size. However, one impediment to using larger reference panels is the increased computational cost of imputation. We present a new genotype imputation method, Beagle 5.0, which greatly reduces the computational cost of imputation from large reference panels. We compare Beagle 5.0 with Beagle 4.1, Impute4, Minimac3, and Minimac4 using 1000 Genomes Project data, Haplotype Reference Consortium data, and simulated data for 10k, 100k, 1M, and 10M reference samples. All methods produce nearly identical accuracy, but Beagle 5.0 has the lowest computation time and the best scaling of computation time with increasing reference panel size. For 10k, 100k, 1M, and 10M reference samples and 1000 phased target samples, Beagle 5.0’s computation time is 3× (10k), 12× (100k), 43× (1M), and 533× (10M) faster than the fastest alternative method. Cost data from the Amazon Elastic Compute Cloud show that Beagle 5.0 can perform genome-wide imputation from 10M reference samples into 1000 phased target samples at a cost of less than one US cent per sample.Beagle 5.0 is freely available from https://faculty.washington.edu/browning/beagle/beagle.html.

Evaluation and application of summary statistic imputation to discover new height-associated loci

10.1101/204560 ◽

2017 ◽

Author(s):

Sina Rüeger ◽

Aaron McDaid ◽

Zoltán Kutalik

Keyword(s):

Genetic Variants ◽

Association Studies ◽

Low Frequency ◽

Cost Effective ◽

Genotype Imputation ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Uk Biobank ◽

Genome Wide ◽

The Uk

AbstractAs most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as summary statistics imputation. Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, while genotype imputation boasts a 2- to 5-fold lower root-mean-square error, summary statistics imputation better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded an increase in statistical power by 15, 10 and 3%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian randomisation or LD-score regression.Author summaryGenome-wide association studies (GWASs) quantify the effect of genetic variants and traits, such as height. Such estimates are called association summary statistics and are typically publicly shared through publication. Typically, GWASs are carried out by genotyping ~ 500′000 SNVs for each individual which are then combined with sequenced reference panels to infer untyped SNVs in each’ individuals genome. This process of genotype imputation is resource intensive and can therefore be a limitation when combining many GWASs. An alternative approach is to bypass the use of individual data and directly impute summary statistics. In our work we compare the performance of summary statistics imputation to genotype imputation. Although we observe a 2- to 5-fold lower RMSE for genotype imputation compared to summary statistics imputation, summary statistics imputation better distinguishes true associations from null results. Furthermore, we demonstrate the potential of summary statistics imputation by presenting 34 novel height-associated loci, 19 of which were confirmed in UK Biobank. Our study demonstrates that given current reference panels, summary statistics imputation is a very efficient and cost-effective way to identify common or low-frequency trait-associated loci.