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1. An FPGA Accelerator for Genome Variant Calling

   https://doi.org/10.1145/3595297  

   Xu, Tiancheng; Rixner, Scott; Cox, Alan L (December 2023, ACM Transactions on Reconfigurable Technology and Systems)

   In genome analysis, it is often important to identify variants from a reference genome. However, identifying variants that occur with low frequency can be challenging, as it is computationally intensive to do so accurately. LoFreq is a widely used program that is adept at identifying low-frequency variants. This article presents a design framework for an FPGA-based accelerator for LoFreq. In particular, this accelerator is targeted at virus analysis, which is particularly challenging, compared to human genome analysis, as the characteristics of the data to be analyzed are fundamentally different. Across the design space, this accelerator can achieve up to 120× speedups on the core computation of LoFreq and speedups of up to 51.7× across the entire program. 

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2. Sequential and Shared-Memory Parallel Algorithms for Partitioned Local Depths

   https://doi.org/10.1137/1.9781611977967.5  

   Devarakonda, Aditya; Ballard, Grey (March 2024, Proceedings of the 2024 SIAM Conference on Parallel Processing for Scientific Computing)

   In this work, we design, analyze, and optimize sequential and shared-memory parallel algorithms for partitioned local depths (PaLD). Given a set of data points and pairwise distances, PaLD is a method for identifying strength of pairwise relationships based on relative distances, enabling the identification of strong ties within dense and sparse communities even if their sizes and within-community absolute distances vary greatly. We design two algorithmic variants that perform community structure analysis through triplet comparisons of pairwise distances. We present theoretical analyses of computation and communication costs and prove that the sequential algorithms are communication optimal, up to constant factors. We introduce performance optimization strategies that yield sequential speedups of up to 29x over a baseline sequential implementation and parallel speedups of up to 26.2x over optimized sequential implementations using up to 32 threads on an Intel multicore CPU. 

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3. Sex differences in deleterious genetic variants in a haplodiploid social insect

   https://doi.org/10.1111/mec.17057  

   Miller, Sara E.; Sheehan, Michael J. (June 2023, Molecular Ecology)

   Abstract Deleterious variants are selected against but can linger in populations at low frequencies for long periods of time, decreasing fitness and contributing to disease burden in humans and other species. Deleterious variants occur at low frequency but distinguishing deleterious variants from low‐frequency neutral variation is challenging based on population genomics data alone. As a result, we have little sense of the number and identity of deleterious variants in wild populations. For haplodiploid species, it has been hypothesised that deleterious alleles will be directly exposed to selection in haploid males, but selection can be masked in diploid females when deleterious variants are recessive, resulting in more efficient purging of deleterious mutations in males. Therefore, comparisons of the differences between haploid and diploid genomes from the same population may be a useful method for inferring rare deleterious variants. This study provides the first formal test of this hypothesis. Using wild populations of Northern paper wasps (Polistes fuscatus), we find that males have fewer missense and nonsense variants per generation than females from the same population. Allele frequency differences are especially pronounced for rare missense and nonsense variants and these differences lead to a lower mutational load in males than females. Based on these data we infer that many highly deleterious mutations are segregating in the paper wasp population. Stronger selection against deleterious alleles in haploid males may have implications for adaptation in other haplodiploid insects and provides evidence that wild populations harbour abundant deleterious variants. 

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4. Leveraging base-pair mammalian constraint to understand genetic variation and human disease

   https://doi.org/10.1126/science.abn2937  

   Sullivan, Patrick F.; Meadows, Jennifer R.; Gazal, Steven; Phan, BaDoi N.; Li, Xue; Genereux, Diane P.; Dong, Michael X.; Bianchi, Matteo; Andrews, Gregory; Sakthikumar, Sharadha; et al (April 2023, Science)

   INTRODUCTION Thousands of genetic variants have been associated with human diseases and traits through genome-wide association studies (GWASs). Translating these discoveries into improved therapeutics requires discerning which variants among hundreds of candidates are causally related to disease risk. To date, only a handful of causal variants have been confirmed. Here, we leverage 100 million years of mammalian evolution to address this major challenge. RATIONALE We compared genomes from hundreds of mammals and identified bases with unusually few variants (evolutionarily constrained). Constraint is a measure of functional importance that is agnostic to cell type or developmental stage. It can be applied to investigate any heritable disease or trait and is complementary to resources using cell type– and time point–specific functional assays like Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTEx). RESULTS Using constraint calculated across placental mammals, 3.3% of bases in the human genome are significantly constrained, including 57.6% of coding bases. Most constrained bases (80.7%) are noncoding. Common variants (allele frequency ≥ 5%) and low-frequency variants (0.5% ≤ allele frequency < 5%) are depleted for constrained bases (1.85 versus 3.26% expected by chance, P < 2.2 × 10 −308 ). Pathogenic ClinVar variants are more constrained than benign variants ( P < 2.2 × 10 −16 ). The most constrained common variants are more enriched for disease single-nucleotide polymorphism (SNP)–heritability in 63 independent GWASs. The enrichment of SNP-heritability in constrained regions is greater (7.8-fold) than previously reported in mammals and is even higher in primates (11.1-fold). It exceeds the enrichment of SNP-heritability in nonsynonymous coding variants (7.2-fold) and fine-mapped expression quantitative trait loci (eQTL)–SNPs (4.8-fold). The enrichment peaks near constrained bases, with a log-linear decrease of SNP-heritability enrichment as a function of the distance to a constrained base. Zoonomia constraint scores improve functionally informed fine-mapping. Variants at sites constrained in mammals and primates have greater posterior inclusion probabilities and higher per-SNP contributions. In addition, using both constraint and functional annotations improves polygenic risk score accuracy across a range of traits. Finally, incorporating constraint information into the analysis of noncoding somatic variants in medulloblastomas identifies new candidate driver genes. CONCLUSION Genome-wide measures of evolutionary constraint can help discern which variants are functionally important. This information may accelerate the translation of genomic discoveries into the biological, clinical, and therapeutic knowledge that is required to understand and treat human disease. Using evolutionary constraint in genomic studies of human diseases. ( A ) Constraint was calculated across 240 mammal species, including 43 primates (teal line). ( B ) Pathogenic ClinVar variants ( N = 73,885) are more constrained across mammals than benign variants ( N = 231,642; P < 2.2 × 10 −16 ). ( C ) More-constrained bases are more enriched for trait-associated variants (63 GWASs). ( D ) Enrichment of heritability is higher in constrained regions than in functional annotations (left), even in a joint model with 106 annotations (right). ( E ) Fine-mapping (PolyFun) using a model that includes constraint scores identifies an experimentally validated association at rs1421085. Error bars represent 95% confidence intervals. BMI, body mass index; LF, low frequency; PIP, posterior inclusion probability. 

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5. RelocaTE2: a high resolution transposable element insertion site mapping tool for population resequencing

   https://doi.org/10.7717/peerj.2942  

   Chen, Jinfeng; Wrightsman, Travis R.; Wessler, Susan R.; Stajich, Jason E. (January 2017, PeerJ)

   Background Transposable element (TE) polymorphisms are important components of population genetic variation. The functional impacts of TEs in gene regulation and generating genetic diversity have been observed in multiple species, but the frequency and magnitude of TE variation is under appreciated. Inexpensive and deep sequencing technology has made it affordable to apply population genetic methods to whole genomes with methods that identify single nucleotide and insertion/deletion polymorphisms. However, identifying TE polymorphisms, particularly transposition events or non-reference insertion sites can be challenging due to the repetitive nature of these sequences, which hamper both the sensitivity and specificity of analysis tools. Methods We have developed the tool RelocaTE2 for identification of TE insertion sites at high sensitivity and specificity. RelocaTE2 searches for known TE sequences in whole genome sequencing reads from second generation sequencing platforms such as Illumina. These sequence reads are used as seeds to pinpoint chromosome locations where TEs have transposed. RelocaTE2 detects target site duplication (TSD) of TE insertions allowing it to report TE polymorphism loci with single base pair precision. Results and Discussion The performance of RelocaTE2 is evaluated using both simulated and real sequence data. RelocaTE2 demonstrate high level of sensitivity and specificity, particularly when the sequence coverage is not shallow. In comparison to other tools tested, RelocaTE2 achieves the best balance between sensitivity and specificity. In particular, RelocaTE2 performs best in prediction of TSDs for TE insertions. Even in highly repetitive regions, such as those tested on rice chromosome 4, RelocaTE2 is able to report up to 95% of simulated TE insertions with less than 0.1% false positive rate using 10-fold genome coverage resequencing data. RelocaTE2 provides a robust solution to identify TE insertion sites and can be incorporated into analysis workflows in support of describing the complete genotype from light coverage genome sequencing. 

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