A one penny imputed genome from next generation reference panels

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.

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Genotype imputation using the Positional Burrows Wheeler Transform

10.1101/797944 ◽

2019 ◽

Cited By ~ 7

Author(s):

Simone Rubinacci ◽

Olivier Delaneau ◽

Jonathan Marchini

Keyword(s):

Association Studies ◽

Computational Cost ◽

Computation Time ◽

Fold Increase ◽

Genotype Imputation ◽

Reference Panel ◽

Genome Wide Association Studies ◽

Panel Size ◽

Genome Wide ◽

Burrows Wheeler Transform

AbstractGenotype imputation is the process of predicting unobserved genotypes in a sample of individuals using a reference panel of haplotypes. In the last 10 years reference panels have increased in size by more than 100 fold. Increasing reference panel size improves accuracy of markers with low minor allele frequencies but poses ever increasing computational challenges for imputation methods.Here we present IMPUTE5, a genotype imputation method that can scale to reference panels with millions of samples. This method continues to refine the observation made in the IMPUTE2 method, that accuracy is optimized via use of a custom subset of haplotypes when imputing each individual. It achieves fast, accurate, and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT). By using the PBWT data structure at genotyped markers, IMPUTE5 identifies locally best matching haplotypes and long identical by state segments. The method then uses the selected haplotypes as conditioning states within the IMPUTE model.Using the HRC reference panel, which has ~65,000 haplotypes, we show that IMPUTE5 is up to 30x faster than MINIMAC4 and up to 3x faster than BEAGLE5.1, and uses less memory than both these methods. Using simulated reference panels we show that IMPUTE5 scales sub-linearly with reference panel size. For example, keeping the number of imputed markers constant, increasing the reference panel size from 10,000 to 1 million haplotypes requires less than twice the computation time. As the reference panel increases in size IMPUTE5 is able to utilize a smaller number of reference haplotypes, thus reducing computational cost.Author summaryGenome-wide association studies (GWAS) typically use microarray technology to measure genotypes at several hundred thousand positions in the genome. However reference panels of genetic variation consist of haplotype data at >100 fold more positions in the genome. Genotype imputation makes genotype predictions at all the reference panel sites using the GWAS data. Reference panels are continuing to grow in size and this improves accuracy of the predictions, however methods need to be able to scale to increased size. We have developed a new version of the popular IMPUTE software than can handle referenece panels with millions of haplotypes, and has better performance than other published approaches. A notable property of the new method is that it scales sub-linearly with reference panel size. Keeping the number of imputed markers constant, a 100 fold increase in reference panel size requires less than twice the computation time.

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Genotype imputation using the Positional Burrows Wheeler Transform

PLoS Genetics ◽

10.1371/journal.pgen.1009049 ◽

2020 ◽

Vol 16 (11) ◽

pp. e1009049

Author(s):

Simone Rubinacci ◽

Olivier Delaneau ◽

Jonathan Marchini

Keyword(s):

Computational Cost ◽

Computation Time ◽

Imputation Method ◽

Genotype Imputation ◽

Reference Panel ◽

Imputation Methods ◽

Panel Size ◽

Burrows Wheeler Transform ◽

Made In ◽

Memory Efficient

Genotype imputation is the process of predicting unobserved genotypes in a sample of individuals using a reference panel of haplotypes. In the last 10 years reference panels have increased in size by more than 100 fold. Increasing reference panel size improves accuracy of markers with low minor allele frequencies but poses ever increasing computational challenges for imputation methods. Here we present IMPUTE5, a genotype imputation method that can scale to reference panels with millions of samples. This method continues to refine the observation made in the IMPUTE2 method, that accuracy is optimized via use of a custom subset of haplotypes when imputing each individual. It achieves fast, accurate, and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT). By using the PBWT data structure at genotyped markers, IMPUTE5 identifies locally best matching haplotypes and long identical by state segments. The method then uses the selected haplotypes as conditioning states within the IMPUTE model. Using the HRC reference panel, which has ∼65,000 haplotypes, we show that IMPUTE5 is up to 30x faster than MINIMAC4 and up to 3x faster than BEAGLE5.1, and uses less memory than both these methods. Using simulated reference panels we show that IMPUTE5 scales sub-linearly with reference panel size. For example, keeping the number of imputed markers constant, increasing the reference panel size from 10,000 to 1 million haplotypes requires less than twice the computation time. As the reference panel increases in size IMPUTE5 is able to utilize a smaller number of reference haplotypes, thus reducing computational cost.

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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.

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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%.

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Genotype Imputation in Genome‐Wide Association Studies

Current Protocols in Human Genetics ◽

10.1002/cphg.84 ◽

2019 ◽

Vol 102 (1) ◽

Author(s):

Adam C. Naj

Keyword(s):

Association Studies ◽

Genotype Imputation ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Animal-ImputeDB: a comprehensive database with multiple animal reference panels for genotype imputation

Nucleic Acids Research ◽

10.1093/nar/gkz854 ◽

2019 ◽

Vol 48 (D1) ◽

pp. D659-D667 ◽

Cited By ~ 2

Author(s):

Wenqian Yang ◽

Yanbo Yang ◽

Cecheng Zhao ◽

Kun Yang ◽

Dongyang Wang ◽

...

Keyword(s):

Large Scale ◽

Association Studies ◽

Genotype Imputation ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

High Quality ◽

Single Nucleotide ◽

Genome Wide ◽

Whole Genome Resequencing ◽

Missing Genotypes

Abstract Animal-ImputeDB (http://gong_lab.hzau.edu.cn/Animal_ImputeDB/) is a public database with genomic reference panels of 13 animal species for online genotype imputation, genetic variant search, and free download. Genotype imputation is a process of estimating missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs) and thus can be widely used in large-scale genome-wide association studies (GWASs) using relatively inexpensive and low-density SNP arrays. However, most animals except humans lack high-quality reference panels, which greatly limits the application of genotype imputation in animals. To overcome this limitation, we developed Animal-ImputeDB, which is dedicated to collecting genotype data and whole-genome resequencing data of nonhuman animals from various studies and databases. A computational pipeline was developed to process different types of raw data to construct reference panels. Finally, 13 high-quality reference panels including ∼400 million SNPs from 2265 samples were constructed. In Animal-ImputeDB, an easy-to-use online tool consisting of two popular imputation tools was designed for the purpose of genotype imputation. Collectively, Animal-ImputeDB serves as an important resource for animal genotype imputation and will greatly facilitate research on animal genomic selection and genetic improvement.

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Structural variants and selective sweep foci contribute to insecticide resistance in the Drosophila melanogaster genetic reference panel

10.1101/301937 ◽

2018 ◽

Cited By ~ 1

Author(s):

Paul Battlay ◽

Llewellyn Green ◽

Pontus B. Leblanc ◽

Joshua M. Schmidt ◽

Alexandre Fournier-Level ◽

...

Keyword(s):

Drosophila Melanogaster ◽

Transposable Element ◽

Selective Sweep ◽

Association Studies ◽

Transcript Abundance ◽

Reference Panel ◽

Drosophila Genome ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Number Variation

AbstractPatterns of nucleotide polymorphism within populations of Drosophila melanogaster suggest that insecticides have been the selective agents driving the strongest recent bouts of positive selection. However, there is a need to explicitly link selective sweep loci to the particular insecticide phenotypes that could plausibly account for the drastic selective responses that are observed in these non-target insects. Here, we screen the Drosophila Genetic Reference Panel with two common insecticides; malathion (an organophosphate) and permethrin (a pyrethroid). Genome wide association studies map ‘survival-on-malathion’ to two of the largest sweeps in the D. melanogaster genome; Ace and Cyp6g1. Malathion survivorship also correlates with lines which have high levels of Cyp12d1 and Jheh1 and Jheh2 transcript abundance. Permethrin phenotypes map to the largest cluster of P450 genes in the Drosophila genome, however in contrast to a selective sweep driven by insecticide use, the derived state seems to be associated with susceptibility. These results underscore previous findings that highlight the importance of structural variation to insecticide phenotypes: Cyp6g1 exhibits copy number variation and transposable element insertions, Cyp12d1 is tandemly duplicated, the Jheh loci are associated with a Bari1 transposable element insertion, and a Cyp6a17 deletion is associated with susceptibility.

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Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

10.1101/133132 ◽

2017 ◽

Cited By ~ 8

Author(s):

Dominic Holland ◽

Oleksandr Frei ◽

Rahul Desikan ◽

Chun-Chieh Fan ◽

Alexey A. Shadrin ◽

...

Keyword(s):

Association Studies ◽

Causal Snps ◽

Reference Panel ◽

Causal Effects ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Common Variants ◽

Genome Wide ◽

Causal Variants

AbstractEstimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from ≃ 2 × 10−5to ≃ 4 × 10−3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.Author SummaryThere are ~10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype.Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ~11 million common variants – among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants – we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities – whose product allows for lower bound estimates of heritability – vary by orders of magnitude.

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Abstract P068: A Genome-wide Association Study for Diabetic Nephropathy Genes in the Japanese Population: The J-MICC Study.

Circulation ◽

10.1161/circ.137.suppl_1.p068 ◽

2018 ◽

Vol 137 (suppl_1) ◽

Author(s):

Yasuyuki Nakamura ◽

Akira Narita ◽

Seiko Ohno ◽

Naoyuki Takashima ◽

Kenji Wakai ◽

...

Keyword(s):

Diabetic Nephropathy ◽

Japanese Population ◽

Association Studies ◽

Linear Regression Analysis ◽

Genotype Imputation ◽

Genome Wide Association ◽

Diabetic Patients ◽

Genome Wide Association Studies ◽

Genome Wide ◽

A Genome

Background: Diabetic nephropathy is the most common cause of chronic kidney disease in the developed countries. Clinical characteristics do not fully predict development of diabetic nephropathy in diabetic patients. There have been few genome-wide association studies (GWAS). Methods: We conducted a GWAS to identify common genetic variations that affected renal function in a Japanese population of 1,117 patients with type 2 diabetes mellitus (T2D) extracted from 14,091 participants appropriate for GWAS as a part of the Japan Multi-Institutional Collaborative Cohort (J-MICC) study. Genotyping was performed at a central laboratory using a HumanOmniExpressExome-8 v1.2 BeadChip array. Genotype imputation was performed using SHAPEIT and Minimac3 software based on the 1000 Genomes reference panel (phase 3). Estimated glomerular filtration rate (eGFR) was calculated according to Matsuo et al. for each patient. The association for the imputed variants with eGFR was performed by a linear regression analysis adjusted for age and sex. Results: We found that rs869312667 at NBEA (β=1.23, P=1.03E-08) and rs8523 at ELOVL2 (β=24.4, P=1.64E-08) were significantly associated with eGFR. These genes have been reported to participate in several metabolic functions and were associated with some disease conditions. However, no previous reports have implied that these genes were related to diabetic nephropathy. Conclusions: rs869312667 at NBEA and rs8523 at ELOVL2 were significantly associated with eGFR in patients with T2D in Japanese.

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Practical Issues in Screening and Variable Selection in Genome-Wide Association Analysis

Cancer Informatics ◽

10.4137/cin.s16350 ◽

2014 ◽

Vol 13s7 ◽

pp. CIN.S16350 ◽

Cited By ~ 2

Author(s):

Sungyeon Hong ◽

Yongkang Kim ◽

Taesung Park

Keyword(s):

Variable Selection ◽

Association Studies ◽

Screening Method ◽

Computational Cost ◽

Penalized Regression ◽

Adaptive Lasso ◽

Genome Wide Association ◽

Snp Analysis ◽

Genome Wide Association Studies ◽

Genome Wide

Variable selection methods play an important role in high-dimensional statistical modeling and analysis. Computational cost and estimation accuracy are the two main concerns for statistical inference from ultrahigh-dimensional data. In particular, genome-wide association studies (GWAS), which focus on identifying single nucleotide polymorphisms (SNPs) associated with a disease of interest, have produced ultrahigh-dimensional data. Numerous methods have been proposed to handle GWAS data. Most statistical methods have adopted a two-stage approach: pre-screening for dimensional reduction and variable selection to identify causal SNPs. The pre-screening step selects SNPs in terms of their P-values or the absolute values of the regression coefficients in single SNP analysis. Penalized regressions, such as the ridge, lasso, adaptive lasso, and elastic-net regressions, are commonly used for the variable selection step. In this paper, we investigate which combination of pre-screening method and penalized regression performs best on a quantitative phenotype using two real GWAS datasets.

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