Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays.

Low-pass sequencing (sequencing a genome to an average depth less than 1× coverage) combined with genotype imputation has been proposed as an alternative to genotyping arrays for trait mapping and calculation of polygenic scores. To empirically assess the relative performance of these technologies for different applications, we performed low-pass sequencing (targeting coverage levels of 0.5× and 1×) and array genotyping (using the Illumina Global Screening Array [GSA]) on 120 DNA samples derived from African- and European-ancestry individuals that are part of the 1000 Genomes Project. We then imputed both the sequencing data and the genotyping array data to the 1000 Genomes Phase 3 haplotype reference panel using a leave-one-out design. We evaluated overall imputation accuracy from these different assays as well as overall power for GWAS from imputed data and computed polygenic risk scores for coronary artery disease and breast cancer using previously derived weights. We conclude that low-pass sequencing plus imputation, in addition to providing a substantial increase in statistical power for genome-wide association studies, provides increased accuracy for polygenic risk prediction at effective coverages of ∼0.5× and higher compared to the Illumina GSA.

Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics.

BACKGROUND: Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. RESULTS: To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1x coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1x genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8x, 0.6x, and 0.4x coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4x coverage sequencing) to 99.2% (for 1x coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 0.90, which was comparable to the imputation r2 from 0.4x coverage sequencing, while the mean imputation r2 from 1x sequencing data was 0.96. CONCLUSIONS: These results indicate that low-pass sequencing to a depth above 0.4x coverage attains higher power for association studies when compared to the PMRA and should be considered as a competitive alternative to genotyping arrays for trait mapping in pharmacogenetics.

Time-dependent saddle-node bifurcation: Breaking time and the point of no return in a non-autonomous model of critical transitions.

There is a growing awareness that catastrophic phenomena in biology and medicine can be mathematically represented in terms of saddle-node bifurcations. In particular, the term "tipping", or critical transition has in recent years entered the discourse of the general public in relation to ecology, medicine, and public health. The saddle-node bifurcation and its associated theory of catastrophe as put forth by Thom and Zeeman has seen applications in a wide range of fields including molecular biophysics, mesoscopic physics, and climate science. In this paper, we investigate a simple model of a non-autonomous system with a time-dependent parameter p(τ) and its corresponding "dynamic" (time-dependent) saddle-node bifurcation by the modern theory of non-autonomous dynamical systems. We show that the actual point of no return for a system undergoing tipping can be significantly delayed in comparison to the breaking time τ ^ at which the corresponding autonomous system with a time-independent parameter p a = p ( τ ^ ) undergoes a bifurcation. A dimensionless parameter α = λ p 0 3 V - 2 is introduced, in which λ is the curvature of the autonomous saddle-node bifurcation according to parameter p(τ), which has an initial value of p 0 and a constant rate of change V. We find that the breaking time τ ^ is always less than the actual point of no return τ ∗ after which the critical transition is irreversible; specifically, the relation τ * - τ ^ ≃ 2.338 ( λ V ) - 1 3 is analytically obtained. For a system with a small λV, there exists a significant window of opportunity ( τ ^ , τ ∗) during which rapid reversal of the environment can save the system from catastrophe.

Low-pass sequencing plus imputation using avidity sequencing displays comparable imputation accuracy to sequencing by synthesis while reducing duplicates.

Low-pass sequencing with genotype imputation has been adopted as a cost-effective method for genotyping. The most widely used method of short-read sequencing uses sequencing by synthesis (SBS). Here we perform a study of a novel sequencing technology-avidity sequencing. In this short note, we compare the performance of imputation from low-pass libraries sequenced on an Element AVITI system (which utilizes avidity sequencing) to those sequenced on an Illumina NovaSeq 6000 (which utilizes SBS) with an SP flow cell for the same set of biological samples across a range of genetic ancestries. We observed dramatically lower optical duplication rates in the data deriving from the AVITI system compared to the NovaSeq 6000, resulting in higher effective coverage given a fixed number of sequenced bases, and comparable imputation accuracy performance between sequencing chemistries across ancestries. This study demonstrates that avidity sequencing is a viable alternative to the standard SBS chemistries for applications involving low-pass sequencing plus imputation.

Ancestry dynamics and trait selection in a designer cat breed.

The Bengal cat breed was developed from intercrosses between the Asian leopard cat, Prionailurus bengalensis, and the domestic cat, Felis catus, with a last common ancestor approximately 6 million years ago. Predicted to derive ∼94% of their genome from domestic cats, regions of the leopard cat genome are thought to account for the unique pelage traits and ornate color patterns of the Bengal breed, which are similar to those of ocelots and jaguars. We explore ancestry distribution and selection signatures in the Bengal breed by using reduced representation and whole-genome sequencing from 947 cats. The mean proportion of leopard cat DNA in the Bengal breed is 3.48%, lower than predicted from breed history, and is broadly distributed, covering 93% of the Bengal genome. Overall, leopard cat introgressions do not show strong signatures of selection across the Bengal breed. However, two popular color traits in Bengal cats, charcoal and pheomelanin intensity, are explained by selection of leopard cat genes whose expression is reduced in a domestic cat background, consistent with genetic incompatibility resulting from hybridization. We characterize several selective sweeps in the Bengal genome that harbor candidate genes for pelage and color pattern and that are associated with domestic, rather than leopard, cat haplotypes. We identify the molecular and phenotypic basis of one selective sweep as reduced expression of the Fgfr2 gene, which underlies glitter, a trait desired by breeders that affects hair texture and light reflectivity.

Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers.

Decreasing costs are making low coverage sequencing with imputation to a comprehensive reference panel an attractive alternative to obtain functional variant genotypes that can increase the accuracy of genomic prediction. To assess the potential of low-pass sequencing, genomic sequence of 77 steers sequenced to >10X coverage was downsampled to 1X and imputed to a reference of 946 cattle representing multiple Bos taurus and Bos indicus-influenced breeds. Genotypes for nearly 60 million variants detected in the reference were imputed from the downsampled sequence. The imputed genotypes strongly agreed with the SNP array genotypes (r¯=0.99) and the genotypes called from the transcript sequence (r¯=0.97). Effects of BovineSNP50 and GGP-F250 variants on birth weight, postweaning gain, and marbling were solved without the steers' phenotypes and genotypes, then applied to their genotypes, to predict the molecular breeding values (MBV). The steers' MBV were similar when using imputed and array genotypes. Replacing array variants with functional sequence variants might allow more robust MBV. Imputation from low coverage sequence offers a viable, low-cost approach to obtain functional variant genotypes that could improve genomic prediction.