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.

Variance component estimates, phenotypic characterization, and genetic evaluation of bovine congestive heart failure in commercial feeder cattle.

The increasing incidence of bovine congestive heart failure (BCHF) in feedlot cattle poses a significant challenge to the beef industry from economic loss, reduced performance, and reduced animal welfare attributed to cardiac insufficiency. Changes to cardiac morphology as well as abnormal pulmonary arterial pressure (PAP) in cattle of mostly Angus ancestry have been recently characterized. However, congestive heart failure affecting cattle late in the feeding period has been an increasing problem and tools are needed for the industry to address the rate of mortality in the feedlot for multiple breeds. At harvest, a population of 32,763 commercial fed cattle were phenotyped for cardiac morphology with associated production data collected from feedlot processing to harvest at a single feedlot and packing plant in the Pacific Northwest. A sub-population of 5,001 individuals were selected for low-pass genotyping to estimate variance components and genetic correlations between heart score and the production traits observed during the feeding period. At harvest, the incidence of a heart score of 4 or 5 in this population was approximately 4.14%, indicating a significant proportion of feeder cattle are at risk of cardiac mortality before harvest. Heart scores were also significantly and positively correlated with the percentage Angus ancestry observed by genomic breed percentage analysis. The heritability of heart score measured as a binary (scores 1 and 2 = 0, scores 4 and 5 = 1) trait was 0.356 in this population, which indicates development of a selection tool to reduce the risk of congestive heart failure as an EPD (expected progeny difference) is feasible. Genetic correlations of heart score with growth traits and feed intake were moderate and positive (0.289-0.460). Genetic correlations between heart score and backfat and marbling score were -0.120 and -0.108, respectively. Significant genetic correlation to traits of high economic importance in existing selection indexes explain the increased rate of congestive heart failure observed over time. These results indicate potential to implement heart score observed at harvest as a phenotype under selection in genetic evaluation in order to reduce feedlot mortality due to cardiac insufficiency and improve overall cardiopulmonary health in feeder cattle.

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.

Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations.

Genetic studies in underrepresented populations identify disproportionate numbers of novel associations. However, most genetic studies use genotyping arrays and sequenced reference panels that best capture variation most common in European ancestry populations. To compare data generation strategies best suited for underrepresented populations, we sequenced the whole genomes of 91 individuals to high coverage as part of the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study with participants from Ethiopia, Kenya, South Africa, and Uganda. We used a downsampling approach to evaluate the quality of two cost-effective data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole-genome sequencing data. We show that low-coverage sequencing at a depth of ≥4× captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1×) performed comparably to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation; 4× sequencing detects 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, effectively identify novel variation particularly in underrepresented populations, and present opportunities to enhance variant discovery at a cost similar to traditional approaches.

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.

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.

Population genetics of the coral Acropora millepora: Toward genomic prediction of bleaching.

Although reef-building corals are declining worldwide, responses to bleaching vary within and across species and are partly heritable. Toward predicting bleaching response from genomic data, we generated a chromosome-scale genome assembly for the coral Acropora millepora We obtained whole-genome sequences for 237 phenotyped samples collected at 12 reefs along the Great Barrier Reef, among which we inferred little population structure. Scanning the genome for evidence of local adaptation, we detected signatures of long-term balancing selection in the heat-shock co-chaperone sacsin We conducted a genome-wide association study of visual bleaching score for 213 samples, incorporating the polygenic score derived from it into a predictive model for bleaching in the wild. These results set the stage for genomics-based approaches in conservation strategies.

Correction: The population genetics of human disease: The case of recessive, lethal mutations.

[This corrects the article DOI: 10.1371/journal.pgen.1006915.].

Detecting Polygenic Adaptation in Admixture Graphs.

An open question in human evolution is the importance of polygenic adaptation: adaptive changes in the mean of a multifactorial trait due to shifts in allele frequencies across many loci. In recent years, several methods have been developed to detect polygenic adaptation using loci identified in genome-wide association studies (GWAS). Though powerful, these methods suffer from limited interpretability: they can detect which sets of populations have evidence for polygenic adaptation, but are unable to reveal where in the history of multiple populations these processes occurred. To address this, we created a method to detect polygenic adaptation in an admixture graph, which is a representation of the historical divergences and admixture events relating different populations through time. We developed a Markov chain Monte Carlo (MCMC) algorithm to infer branch-specific parameters reflecting the strength of selection in each branch of a graph. Additionally, we developed a set of summary statistics that are fast to compute and can indicate which branches are most likely to have experienced polygenic adaptation. We show via simulations that this method-which we call PolyGraph-has good power to detect polygenic adaptation, and applied it to human population genomic data from around the world. We also provide evidence that variants associated with several traits, including height, educational attainment, and self-reported unibrow, have been influenced by polygenic adaptation in different populations during human evolution.

The population genetics of human disease: The case of recessive, lethal mutations.

Do the frequencies of disease mutations in human populations reflect a simple balance between mutation and purifying selection? What other factors shape the prevalence of disease mutations? To begin to answer these questions, we focused on one of the simplest cases: recessive mutations that alone cause lethal diseases or complete sterility. To this end, we generated a hand-curated set of 417 Mendelian mutations in 32 genes reported to cause a recessive, lethal Mendelian disease. We then considered analytic models of mutation-selection balance in infinite and finite populations of constant sizes and simulations of purifying selection in a more realistic demographic setting, and tested how well these models fit allele frequencies estimated from 33,370 individuals of European ancestry. In doing so, we distinguished between CpG transitions, which occur at a substantially elevated rate, and three other mutation types. Intriguingly, the observed frequency for CpG transitions is slightly higher than expectation but close, whereas the frequencies observed for the three other mutation types are an order of magnitude higher than expected, with a bigger deviation from expectation seen for less mutable types. This discrepancy is even larger when subtle fitness effects in heterozygotes or lethal compound heterozygotes are taken into account. In principle, higher than expected frequencies of disease mutations could be due to widespread errors in reporting causal variants, compensation by other mutations, or balancing selection. It is unclear why these factors would have a greater impact on disease mutations that occur at lower rates, however. We argue instead that the unexpectedly high frequency of disease mutations and the relationship to the mutation rate likely reflect an ascertainment bias: of all the mutations that cause recessive lethal diseases, those that by chance have reached higher frequencies are more likely to have been identified and thus to have been included in this study. Beyond the specific application, this study highlights the parameters likely to be important in shaping the frequencies of Mendelian disease alleles.

Identifying genetic variants that affect viability in large cohorts.

A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10-6 for fathers and P~2.0 × 10-3 for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10-3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans.

Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.

The immune system plays a major role in human health and disease, and understanding genetic causes of interindividual variability of immune responses is vital. Here, we isolate monocytes from 134 genotyped individuals, stimulate these cells with three defined microbe-associated molecular patterns (LPS, MDP, and 5'-ppp-dsRNA), and profile the transcriptomes at three time points. Mapping expression quantitative trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with varying effects between conditions. We characterize the dynamics of genetic regulation on early and late immune response and observe an enrichment of reQTLs in distal cis-regulatory elements. In addition, reQTLs are enriched for recent positive selection with an evolutionary trend towards enhanced immune response. Finally, we uncover reQTL effects in multiple GWAS loci and show a stronger enrichment for response than constant eQTLs in GWAS signals of several autoimmune diseases. This demonstrates the importance of infectious stimuli in modifying genetic predisposition to disease.Insight into the genetic influence on the immune response is important for the understanding of interindividual variability in human pathologies. Here, the authors generate transcriptome data from human blood monocytes stimulated with various immune stimuli and provide a time-resolved response eQTL map.

Contrasting Determinants of Mutation Rates in Germline and Soma.

Recent studies of somatic and germline mutations have led to the identification of a number of factors that influence point mutation rates, including CpG methylation, expression levels, replication timing, and GC content. Intriguingly, some of the effects appear to differ between soma and germline: in particular, whereas mutation rates have been reported to decrease with expression levels in tumors, no clear effect has been detected in the germline. Distinct approaches were taken to analyze the data, however, so it is hard to know whether these apparent differences are real. To enable a cleaner comparison, we considered a statistical model in which the mutation rate of a coding region is predicted by GC content, expression levels, replication timing, and two histone repressive marks. We applied this model to both a set of germline mutations identified in exomes and to exonic somatic mutations in four types of tumors. Most determinants of mutations are shared: notably, we detected an effect of expression levels on both germline and somatic mutation rates. Moreover, in all tissues considered, higher expression levels are associated with greater strand asymmetry of mutations. However, mutation rates increase with expression levels in testis (and, more tentatively, in ovary), whereas they decrease with expression levels in somatic tissues. This contrast points to differences in damage or repair rates during transcription in soma and germline.

Case-control association mapping by proxy using family history of disease.

Collecting cases for case-control genetic association studies can be time-consuming and expensive. In some situations (such as studies of late-onset or rapidly lethal diseases), it may be more practical to identify family members of cases. In randomly ascertained cohorts, replacing cases with their first-degree relatives enables studies of diseases that are absent (or nearly absent) in the cohort. We refer to this approach as genome-wide association study by proxy (GWAX) and apply it to 12 common diseases in 116,196 individuals from the UK Biobank. Meta-analysis with published genome-wide association study summary statistics replicated established risk loci and yielded four newly associated loci for Alzheimer's disease, eight for coronary artery disease and five for type 2 diabetes. In addition to informing disease biology, our results demonstrate the utility of association mapping without directly observing cases. We anticipate that GWAX will prove useful in future genetic studies of complex traits in large population cohorts.

Genome-wide association study identifies 74 loci associated with educational attainment.

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

Detection and interpretation of shared genetic influences on 42 human traits.

We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at a false discovery rate of 10%) associated with multiple traits. Several loci are associated with multiple phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of the traits, including risk of schizophrenia (rs13107325: log-transformed odds ratio (log OR) = 0.15, P = 2 × 10(-12)) and Parkinson disease (log OR = -0.15, P = 1.6 × 10(-7)), among others. Second, we used these loci to identify traits that have multiple genetic causes in common. For example, variants associated with increased risk of schizophrenia also tended to be associated with increased risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased body mass index causally increases triglyceride levels.

Approximately independent linkage disequilibrium blocks in human populations.

UNLABELLED: We present a method to identify approximately independent blocks of linkage disequilibrium in the human genome. These blocks enable automated analysis of multiple genome-wide association studies. AVAILABILITY AND IMPLEMENTATION: code: http://bitbucket.org/nygcresearch/ldetect; data: http://bitbucket.org/nygcresearch/ldetect-data. CONTACT: tberisa@nygenome.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide patterns of selection in 230 ancient Eurasians.

Ancient DNA makes it possible to observe natural selection directly by analysing samples from populations before, during and after adaptation events. Here we report a genome-wide scan for selection using ancient DNA, capitalizing on the largest ancient DNA data set yet assembled: 230 West Eurasians who lived between 6500 and 300 bc, including 163 with newly reported data. The new samples include, to our knowledge, the first genome-wide ancient DNA from Anatolian Neolithic farmers, whose genetic material we obtained by extracting from petrous bones, and who we show were members of the population that was the source of Europe's first farmers. We also report a transect of the steppe region in Samara between 5600 and 300 bc, which allows us to identify admixture into the steppe from at least two external sources. We detect selection at loci associated with diet, pigmentation and immunity, and two independent episodes of selection on height.