Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores

Abstract Background Inherited susceptibility to common, complex diseases may be caused by rare, pathogenic variants (“monogenic”) or by the cumulative effect of numerous common variants (“polygenic”). Comprehensive genome interpretation should enable assessment for both monogenic and polygenic components of inherited risk. The traditional approach requires two distinct genetic testing technologies—high coverage sequencing of known genes to detect monogenic variants and a genome-wide genotyping array followed by imputation to calculate genome-wide polygenic scores (GPSs). We assessed the feasibility and accuracy of using low coverage whole genome sequencing (lcWGS) as an alternative to genotyping arrays to calculate GPSs. Methods First, we performed downsampling and imputation of WGS data from ten individuals to assess concordance with known genotypes. Second, we assessed the correlation between GPSs for 3 common diseases—coronary artery disease (CAD), breast cancer (BC), and atrial fibrillation (AF)—calculated using lcWGS and genotyping array in 184 samples. Third, we assessed concordance of lcWGS-based genotype calls and GPS calculation in 120 individuals with known genotypes, selected to reflect diverse ancestral backgrounds. Fourth, we assessed the relationship between GPSs calculated using lcWGS and disease phenotypes in a cohort of 11,502 individuals of European ancestry. Results We found imputation accuracy r2 values of greater than 0.90 for all ten samples—including those of African and Ashkenazi Jewish ancestry—with lcWGS data at 0.5×. GPSs calculated using lcWGS and genotyping array followed by imputation in 184 individuals were highly correlated for each of the 3 common diseases (r2 = 0.93–0.97) with similar score distributions. Using lcWGS data from 120 individuals of diverse ancestral backgrounds, we found similar results with respect to imputation accuracy and GPS correlations. Finally, we calculated GPSs for CAD, BC, and AF using lcWGS in 11,502 individuals of European ancestry, confirming odds ratios per standard deviation increment ranging 1.28 to 1.59, consistent with previous studies. Conclusions lcWGS is an alternative technology to genotyping arrays for common genetic variant assessment and GPS calculation. lcWGS provides comparable imputation accuracy while also overcoming the ascertainment bias inherent to variant selection in genotyping array design.

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Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores

10.1101/716977 ◽

2019 ◽

Author(s):

Julian R. Homburger ◽

Cynthia L. Neben ◽

Gilad Mishne ◽

Alicia Y. Zhou ◽

Sekar Kathiresan ◽

...

Keyword(s):

Genetic Testing ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Imputation Accuracy ◽

Whole Genome ◽

Genotyping Array ◽

Pathogenic Variants ◽

Genome Wide ◽

Polygenic Scores ◽

Low Coverage

ABSTRACTBackgroundThe inherited susceptibility of common, complex diseases may be caused by rare, ‘monogenic’ pathogenic variants or by the cumulative effect of numerous common, ‘polygenic’ variants. As such, comprehensive genome interpretation could involve two distinct genetic testing technologies -- high coverage next generation sequencing for known genes to detect pathogenic variants and a genome-wide genotyping array followed by imputation to calculate genome-wide polygenic scores (GPSs). Here we assessed the feasibility and accuracy of using low coverage whole genome sequencing (lcWGS) as an alternative to genotyping arrays to calculate GPSs.MethodsFirst, we performed downsampling and imputation of WGS data from ten individuals to assess concordance with known genotypes. Second, we assessed the correlation between GPSs for three common diseases -- coronary artery disease (CAD), breast cancer (BC), and atrial fibrillation (AF) -- calculated using lcWGS and genotyping array in 184 samples. Third, we assessed concordance of lcWGS-based genotype calls and GPS calculation in 120 individuals with known genotypes, selected to reflect diverse ancestral backgrounds. Fourth, we assessed the relationship between GPSs calculated using lcWGS and disease phenotypes in 11,502 European individuals seeking genetic testing.ResultsWe found imputation accuracy r2 values of greater than 0.90 for all ten samples -- including those of African and Ashkenazi Jewish ancestry -- with lcWGS data at 0.5X. GPSs calculated using both lcWGS and genotyping array followed by imputation in 184 individuals were highly correlated for each of the three common diseases (r2 = 0.93 - 0.97) with similar score distributions. Using lcWGS data from 120 individuals of diverse ancestral backgrounds, including South Asian, East Asian, and Hispanic individuals, we found similar results with respect to imputation accuracy and GPS correlations. Finally, we calculated GPSs for CAD, BC, and AF using lcWGS in 11,502 European individuals, confirming odds ratios per standard deviation increment in GPSs ranging 1.28 to 1.59, consistent with previous studies.ConclusionsHere we show that lcWGS is an alternative approach to genotyping arrays for common genetic variant assessment and GPS calculation. lcWGS provides comparable imputation accuracy while also overcoming the ascertainment bias inherent to variant selection in genotyping array design.

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Optimizing Genomic Selection in Dezhou Donkey Using Low Coverage Whole Genome Sequencing

10.21203/rs.3.rs-607740/v1 ◽

2021 ◽

Author(s):

Changheng Zhao ◽

Jun Teng ◽

Xinhao Zhang ◽

Dan Wang ◽

Xinyi Zhang ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genomic Selection ◽

Genome Sequencing ◽

Sequence Data ◽

Low Cost ◽

Imputation Accuracy ◽

Genotype Imputation ◽

Whole Genome Sequence ◽

Whole Genome ◽

Low Coverage

Abstract Background Low coverage whole genome sequencing is a low-cost genotyping technology. Combining with genotype imputation approaches, it is likely to become a critical component of cost-efficient genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low coverage whole genome sequence data and genomic selection based on the imputed genotype data. The specific aims were: (i) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, MAFs, and imputation pipelines; and (ii) to assess the accuracy of genomic selection under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single- vs multi-trait models. Results We found that a high imputation accuracy (> 0.95) can be achieved for sequence data with sequencing depth as low as 1x and the number of sequenced individuals equal to 400. For genomic selection, the best performance was obtained by using a marker density of 410K and a G matrix constructed using marker dosage information. Multi-trait GBLUP performed better than single-trait GBLUP. Conclusions Our study demonstrates that low coverage whole genome sequencing would be a cost-effective method for genomic selection in Dezhou Donkey.

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Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits

Human Molecular Genetics ◽

10.1093/hmg/ddz314 ◽

2020 ◽

Vol 29 (3) ◽

pp. 515-526 ◽

Cited By ~ 4

Author(s):

Tianzhong Yang ◽

Chong Wu ◽

Peng Wei ◽

Wei Pan

Keyword(s):

Gene Expression ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Association Studies ◽

Low Frequency ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Common Variants ◽

Transcriptomic Data ◽

Genome Wide

Abstract Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and transcriptomic data to showcase their improved statistical power of identifying gene–trait associations while, importantly, offering further biological insights. TWAS have thus far focused on common variants as available from GWAS. Compared with common variants, the findings for or even applications to low-frequency variants are limited and their underlying role in regulating gene expression is less clear. To fill this gap, we extend TWAS to integrating whole genome sequencing data with transcriptomic data for low-frequency variants. Using the data from the Framingham Heart Study, we demonstrate that low-frequency variants play an important and universal role in predicting gene expression, which is not completely due to linkage disequilibrium with the nearby common variants. By including low-frequency variants, in addition to common variants, we increase the predictivity of gene expression for 79% of the examined genes. Incorporating this piece of functional genomic information, we perform association testing for five lipid traits in two UK10K whole genome sequencing cohorts, hypothesizing that cis-expression quantitative trait loci, including low-frequency variants, are more likely to be trait-associated. We discover that two genes, LDLR and TTC22, are genome-wide significantly associated with low-density lipoprotein cholesterol based on 3203 subjects and that the association signals are largely independent of common variants. We further demonstrate that a joint analysis of both common and low-frequency variants identifies association signals that would be missed by testing on either common variants or low-frequency variants alone.

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Genome-wide association analyses of multiple traits in Duroc pigs using low-coverage whole-genome sequencing strategy

10.1101/754671 ◽

2019 ◽

Author(s):

Ruifei Yang ◽

Xiaoli Guo ◽

Di Zhu ◽

Cheng Bian ◽

Yiqiang Zhao ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Association Analysis ◽

Genome Sequencing ◽

Association Studies ◽

Genotype Imputation ◽

Genome Wide Association ◽

Whole Genome ◽

Genome Wide Association Analysis ◽

Genome Wide ◽

Low Coverage

AbstractHigh-density markers discovered in large size samples are essential for mapping complex traits at the gene-level resolution for agricultural livestock and crops. However, the unavailability of large reference panels and array designs for a target population of agricultural species limits the improvement of array-based genotype imputation. Recent studies showed very low coverage sequencing (LCS) of a large number of individuals is a cost-effective approach to discover variations in much greater detail in association studies. Here, we performed cohort-wide whole-genome sequencing at an average depth of 0.73× and identified more than 11.3 M SNPs. We also evaluated the data set and performed genome-wide association analysis (GWAS) in 2885 Duroc boars. We compared two different pipelines and selected a proper method (BaseVar/STITCH) for LCS analyses and determined that sequencing of 1000 individuals with 0.2× depth is enough for identifying SNPs with high accuracy in this population. Of the seven association signals derived from the genome-wide association analysis of the LCS variants, which were associated with four economic traits, we found two QTLs with narrow intervals were possibly responsible for the teat number and back fat thickness traits and identified 7 missense variants in a single sequencing step. This strategy (BaseVar/STITCH) is generally applicable to any populations and any species which have no suitable reference panels. These findings show that the LCS strategy is a proper approach for the construction of new genetic resources to facilitate genome-wide association studies, fine mapping of QTLs, and genomic selection, and implicate that it can be widely used for agricultural animal breeding in the future.

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Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data

Frontiers in Genetics ◽

10.3389/fgene.2021.704118 ◽

2022 ◽

Vol 12 ◽

Author(s):

Tianyu Deng ◽

Pengfei Zhang ◽

Dorian Garrick ◽

Huijiang Gao ◽

Lixian Wang ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Demographic History ◽

Imputation Accuracy ◽

Reference Population ◽

Genotype Imputation ◽

Whole Genome ◽

Snp Chip ◽

A Genome ◽

Low Coverage

Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.

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Whole genome sequencing in the Middle Eastern Qatari population identifies genetic associations with 45 clinically relevant traits

Nature Communications ◽

10.1038/s41467-021-21381-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Gaurav Thareja ◽

◽

Yasser Al-Sarraj ◽

Aziz Belkadi ◽

Maryam Almotawa ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Genome Wide Association Study ◽

Clinical Laboratory ◽

Medical Decision ◽

Future Application ◽

Whole Genome ◽

Genome Wide ◽

A Genome ◽

Polygenic Scores

AbstractClinical laboratory tests play a pivotal role in medical decision making, but little is known about their genetic variability between populations. We report a genome-wide association study with 45 clinically relevant traits from the population of Qatar using a whole genome sequencing approach in a discovery set of 6218 individuals and replication in 7768 subjects. Trait heritability is more similar between Qatari and European populations (r = 0.81) than with Africans (r = 0.44). We identify 281 distinct variant-trait-associations at genome wide significance that replicate known associations. Allele frequencies for replicated loci show higher correlations with European (r = 0.94) than with African (r = 0.85) or Japanese (r = 0.80) populations. We find differences in linkage disequilibrium patterns and in effect sizes of the replicated loci compared to previous reports. We also report 17 novel and Qatari-predominate signals providing insights into the biological pathways regulating these traits. We observe that European-derived polygenic scores (PGS) have reduced predictive performance in the Qatari population which could have implications for the translation of PGS between populations and their future application in precision medicine.

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353 ASAS-EAAP Talk: Low-coverage whole-genome sequencing in local livestock breeds

Journal of Animal Science ◽

10.1093/jas/skaa278.149 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 81-82

Author(s):

Joaquim Casellas ◽

Melani Martín de Hijas-Villalba ◽

Marta Vázquez-Gómez ◽

Samir Id Lahoucine

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Error Rate ◽

Allele Frequencies ◽

Paternity Testing ◽

Sequencing Error ◽

Whole Genome ◽

Genomic Evaluation ◽

Sequencing Error Rate ◽

Low Coverage

Abstract Current European regulations for autochthonous livestock breeds put a special emphasis on pedigree completeness, which requires laboratory paternity testing by genetic markers in most cases. This entails significant economic expenditure for breed societies and precludes other investments in breeding programs, such as genomic evaluation. Within this context, we developed paternity testing through low-coverage whole-genome data in order to reuse these data for genomic evaluation at no cost. Simulations relied on diploid genomes composed by 30 chromosomes (100 cM each) with 3,000,000 SNP per chromosome. Each population evolved during 1,000 non-overlapping generations with effective size 100, mutation rate 10–4, and recombination by Kosambi’s function. Only those populations with 1,000,000 ± 10% polymorphic SNP per chromosome in generation 1,000 were retained for further analyses, and expanded to the required number of parents and offspring. Individuals were sequenced at 0.01, 0.05, 0.1, 0.5 and 1X depth, with 100, 500, 1,000 or 10,000 base-pair reads and by assuming a random sequencing error rate per SNP between 10–2 and 10–5. Assuming known allele frequencies in the population and sequencing error rate, 0.05X depth sufficed to corroborate the true father (85,0%) and to discard other candidates (96,3%). Those percentages increased up to 99,6% and 99,9% with 0,1X depth, respectively (read length = 10,000 bp; smaller read lengths slightly improved the results because they increase the number of sequenced SNP). Results were highly sensitive to biases in allele frequencies and robust to inaccuracies regarding sequencing error rate. Low-coverage whole-genome sequencing data could be subsequently integrated into genomic BLUP equations by appropriately constructing the genomic relationship matrix. This approach increased the correlation between simulated and predicted breeding values by 1.21% (h2 = 0.25; 100 parents and 900 offspring; 0.1X depth by 10,000 bp reads). Although small, this increase opens the door to genomic evaluation in local livestock breeds.

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Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken

Scientific Reports ◽

10.1038/s41598-018-34364-0 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 8

Author(s):

Gabriel Costa Monteiro Moreira ◽

Clarissa Boschiero ◽

Aline Silva Mello Cesar ◽

James M. Reecy ◽

Thaís Fernanda Godoy ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Association Studies ◽

Fat Deposition ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Genome Wide

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Detection and characterization of copy number variants based on whole-genome sequencing by DNBSEQ platforms

10.1101/786962 ◽

2019 ◽

Author(s):

Junhua Rao ◽

Lihua Peng ◽

Fang Chen ◽

Hui Jiang ◽

Chunyu Geng ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Copy Number Variants ◽

Copy Number Variant ◽

Whole Genome ◽

Genome Wide ◽

Wide Range ◽

Distribution Sensitivity ◽

Cnv Detection

AbstractBackgroundNext-generation sequence (NGS) has rapidly developed in past years which makes whole-genome sequencing (WGS) becoming a more cost- and time-efficient choice in wide range of biological researches. We usually focus on some variant detection via WGS data, such as detection of single nucleotide polymorphism (SNP), insertion and deletion (Indel) and copy number variant (CNV), which playing an important role in many human diseases. However, the feasibility of CNV detection based on WGS by DNBSEQ™ platforms was unclear. We systematically analysed the genome-wide CNV detection power of DNBSEQ™ platforms and Illumina platforms on NA12878 with five commonly used tools, respectively.ResultsDNBSEQ™ platforms showed stable ability to detect slighter more CNVs on genome-wide (average 1.24-fold than Illumina platforms). Then, CNVs based on DNBSEQ™ platforms and Illumina platforms were evaluated with two public benchmarks of NA12878, respectively. DNBSEQ™ and Illumina platforms showed similar sensitivities and precisions on both two benchmarks. Further, the difference between tools for CNV detection was analyzed, and indicated the selection of tool for CNV detection could affected the CNV performance, such as count, distribution, sensitivity and precision.ConclusionThe major contribution of this paper is providing a comprehensive guide for CNV detection based on WGS by DNBSEQ™ platforms for the first time.

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