Optimal sequencing strategies for identifying disease-associated singletons

Whole genome sequences of 70 indigenous Ethiopian cattle

Indigenous animal genetic resources play a crucial role in preserving global genetic diversity and supporting the livelihoods of millions of people. In Ethiopia, the majority of the cattle population consists of indigenous breeds. Understanding the genetic architecture of these cattle breeds is essential for effective management and conservation efforts. In this study, we sequenced DNA samples from 70 animals from seven indigenous cattle breeds, generating about two terabytes of pair-end reads with an average coverage of 14X. The sequencing data were pre-processed and mapped to the cattle reference genome (ARS-UCD1.2) with an alignment rate of 99.2%. Finally, the variant calling process produced approximately 35 million high-quality SNPs. These data provide a deeper understanding of the genetic landscape, facilitate the identification of causal mutations, and enable the exploration of evolutionary patterns to assist cattle improvement and sustainable utilization, particularly in the face of unpredictable climate changes.

DNA methylation changes underlie the long-term association between periodontitis and atherosclerotic cardiovascular disease

Periodontitis, the leading cause of adult tooth loss, has been identified as an independent risk factor for cardiovascular disease (CVD). Studies suggest that periodontitis, like other CVD risk factors, shows the persistence of increased CVD risk even after mitigation. We hypothesized that periodontitis induces epigenetic changes in hematopoietic stem cells in the bone marrow (BM), and such changes persist after the clinical elimination of the disease and underlie the increased CVD risk. We used a BM transplant approach to simulate the clinical elimination of periodontitis and the persistence of the hypothesized epigenetic reprogramming. Using the low-density lipoprotein receptor knockout (LDLR^o ) atherosclerosis mouse model, BM donor mice were fed a high-fat diet to induce atherosclerosis and orally inoculated with Porphyromonas gingivalis (Pg), a keystone periodontal pathogen; the second group was sham-inoculated. Naïve LDLR ^o mice were irradiated and transplanted with BM from one of the two donor groups. Recipients of BM from Pg-inoculated donors developed significantly more atherosclerosis, accompanied by cytokine/chemokines that suggested BM progenitor cell mobilization and were associated with atherosclerosis and/or PD. Using whole-genome bisulfite sequencing, 375 differentially methylated regions (DMRs) and global hypomethylation in recipients of BM from Pg-inoculated donors were observed. Some DMRs pointed to the involvement of enzymes with major roles in DNA methylation and demethylation. In validation assays, we found a significant increase in the activity of ten-eleven translocase-2 and a decrease in the activity of DNA methyltransferases. Plasma S-adenosylhomocysteine levels were significantly higher, and the S-adenosylmethionine to S-adenosylhomocysteine ratio was decreased, both of which have been associated with CVD. These changes may be related to increased oxidative stress as a result of Pg infection. These data suggest a novel and paradigm-shifting mechanism in the long-term association between periodontitis and atherosclerotic CVD.

Next-Generation Sequencing Advances the Genetic Diagnosis of Cerebral Cavernous Malformation (CCM)

Cerebral Cavernous Malformation (CCM) is a cerebrovascular disease of genetic origin that predisposes to seizures, focal neurological deficits and fatal intracerebral hemorrhage. It may occur sporadically or in familial forms, segregating as an autosomal dominant condition with incomplete penetrance and highly variable expressivity. Its pathogenesis has been associated with loss-of-function mutations in three genes, namely KRIT1 (CCM1), CCM2 and PDCD10 (CCM3), which are implicated in defense mechanisms against oxidative stress and inflammation. Herein, we screened 21 Italian CCM cases using clinical exome sequencing and found six cases (~29%) with pathogenic variants in CCM genes, including a large 145−256 kb genomic deletion spanning the KRIT1 gene and flanking regions, and the KRIT1 c.1664C>T variant, which we demonstrated to activate a donor splice site in exon 16. The segregation of this cryptic splicing mutation was studied in a large Italian family (five affected and seven unaffected cases), and showed a largely heterogeneous clinical presentation, suggesting the implication of genetic modifiers. Moreover, by analyzing ad hoc gene panels, including a virtual panel of 23 cerebrovascular disease-related genes (Cerebro panel), we found two variants in NOTCH3 and PTEN genes, which could contribute to the abnormal oxidative stress and inflammatory responses to date implicated in CCM disease pathogenesis.

Lower Exome Sequencing Coverage of Ancestrally African Patients in The Cancer Genome Atlas

BACKGROUND

In the United States, cancer disproportionately impacts Black and African American individuals. Identifying genetic factors underlying cancer disparities has been an important research focus and requires data that are equitable in both quantity and quality across racial groups. It is widely recognized that DNA databases quantitatively underrepresent minorities. However, the differences in data quality between racial groups have not been well studied.

METHODS

We compared the qualities of germline and tumor exomes between ancestrally African and European patients in The Cancer Genome Atlas of 7 cancers with at least 50 self-reported Black patients in the context of sequencing depth, tumor purity, and qualities of germline variants and somatic mutations.

RESULTS

Germline and tumor exomes from ancestrally African patients were sequenced at statistically significantly lower depth in 6 out of the 7 cancers. For 3 cancers, most ancestrally European exomes were sequenced in early sample batches at higher depth, whereas ancestrally African exomes were concentrated in later batches and sequenced at much lower depth. For the other 3 cancers, the reasons of lower sequencing coverage of ancestrally African exomes remain unknown. Furthermore, even when the sequencing depths were comparable, African exomes had disproportionally higher percentages of positions with insufficient coverage, likely because of the known European bias in the human reference genome that impacted exome capture kit design.

CONCLUSIONS

Overall and positional lower sequencing depths of ancestrally African exomes in The Cancer Genome Atlas led to underdetection and lower quality of variants, highlighting the need to consider epidemiological factors for future genomics studies.

Towards a Cost-Effective Implementation of Genomic Prediction Based on Low Coverage Whole Genome Sequencing in Dezhou Donkey

Low-coverage whole genome sequencing is a low-cost genotyping technology. Combined with genotype imputation approaches, it is likely to become a critical component of cost-effective 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 prediction based on the imputed genotype data. The specific aims were as follows: 1) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, minor allele frequency (MAF), and imputation pipelines and 2) to assess the accuracy of genomic prediction under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single-vs. multi-trait models. We found that a high imputation accuracy (>0.95) can be achieved for sequence data with a sequencing depth as low as 1x and the number of sequenced individuals ≥400. For genomic prediction, the best performance was obtained by using a marker density of 410K and a G matrix constructed using expected marker dosages. Multi-trait genomic best linear unbiased prediction (GBLUP) performed better than single-trait GBLUP. Our study demonstrates that low-coverage whole genome sequencing would be a cost-effective approach for genomic prediction in Dezhou donkey.

Comparison of sequencing data processing pipelines and application to underrepresented African human populations

Background

Population genetic studies of humans make increasing use of high-throughput sequencing in order to capture diversity in an unbiased way. There is an abundance of sequencing technologies, bioinformatic tools and the available genomes are increasing in number. Studies have evaluated and compared some of these technologies and tools, such as the Genome Analysis Toolkit (GATK) and its “Best Practices” bioinformatic pipelines. However, studies often focus on a few genomes of Eurasian origin in order to detect technical issues. We instead surveyed the use of the GATK tools and established a pipeline for processing high coverage full genomes from a diverse set of populations, including Sub-Saharan African groups, in order to reveal challenges from human diversity and stratification.

Results

We surveyed 29 studies using high-throughput sequencing data, and compared their strategies for data pre-processing and variant calling. We found that processing of data is very variable across studies and that the GATK “Best Practices” are seldom followed strictly. We then compared three versions of a GATK pipeline, differing in the inclusion of an indel realignment step and with a modification of the base quality score recalibration step. We applied the pipelines on a diverse set of 28 individuals. We compared the pipelines in terms of count of called variants and overlap of the callsets. We found that the pipelines resulted in similar callsets, in particular after callset filtering. We also ran one of the pipelines on a larger dataset of 179 individuals. We noted that including more individuals at the joint genotyping step resulted in different counts of variants. At the individual level, we observed that the average genome coverage was correlated to the number of variants called.

Conclusions

We conclude that applying the GATK “Best Practices” pipeline, including their recommended reference datasets, to underrepresented populations does not lead to a decrease in the number of called variants compared to alternative pipelines. We recommend to aim for coverage of > 30X if identifying most variants is important, and to work with large sample sizes at the variant calling stage, also for underrepresented individuals and populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04407-x.

More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics

While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains nontrivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resources to sequence a greater number of genomes or spending resources to sequence genomes with increased accuracy. Our goal is to find the optimal allocation of resources between quantity and quality. Optimizing resource allocation promises to reveal as many new variations in the genome as possible. We introduce a Bayesian nonparametric methodology to predict the number of new variants in a follow-up study based on a pilot study. When experimental conditions are kept constant between the pilot and follow-up, we find that our prediction is competitive with the best existing methods. Unlike current methods, though, our new method allows practitioners to change experimental conditions between the pilot and the follow-up. We demonstrate how this distinction allows our method to be used for more realistic predictions and for optimal allocation of a fixed budget between quality and quantity. We validate our method on cancer and human genomics data.

Whole genome sequencing of 10K patients with acute ischaemic stroke or transient ischaemic attack: design, methods and baseline patient characteristics

Background and purpose

Stroke is the second leading cause of death worldwide and the leading cause of mortality and long-term disability in China, but its underlying risk genes and pathways are far from being comprehensively understood. We here describe the design and methods of whole genome sequencing (WGS) for 10 914 patients with acute ischaemic stroke or transient ischaemic attack from the Third China National Stroke Registry (CNSR-III).

Methods

Baseline clinical characteristics of the included patients in this study were reported. DNA was extracted from white blood cells of participants. Libraries are constructed using qualified DNA, and WGS is conducted on BGISEQ-500 platform. The average depth is intended to be greater than 30× for each subject. Afterwards, Sentieon software is applied to process the sequencing data under the Genome Analysis Toolkit best practice guidance to call genotypes of single nucleotide variants (SNVs) and insertion-deletions. For each included subject, 21 fingerprint SNVs are genotyped by MassARRAY assays to verify that DNA sample and sequencing data originate from the same individual. The copy number variations and structural variations are also called for each patient. All of the genetic variants are annotated and predicted by bioinformatics software or by reviewing public databases.

Results

The average age of the included 10 914 patients was 62.2±11.3 years, and 31.4% patients were women. Most of the baseline clinical characteristics of the 10 914 and the excluded patients were balanced.

Conclusions

The WGS data together with abundant clinical and imaging data of CNSR-III could provide opportunity to elucidate the molecular mechanisms and discover novel therapeutic targets for stroke.

Initial whole-genome sequencing and analysis of the host genetic contribution to COVID-19 severity and susceptibility

The COVID-19 pandemic has accounted for millions of infections and hundreds of thousand deaths worldwide in a short-time period. The patients demonstrate a great diversity in clinical and laboratory manifestations and disease severity. Nonetheless, little is known about the host genetic contribution to the observed interindividual phenotypic variability. Here, we report the first host genetic study in the Chinese population by deeply sequencing and analyzing 332 COVID-19 patients categorized by varying levels of severity from the Shenzhen Third People's Hospital. Upon a total of 22.2 million genetic variants, we conducted both single-variant and gene-based association tests among five severity groups including asymptomatic, mild, moderate, severe, and critical ill patients after the correction of potential confounding factors. Pedigree analysis suggested a potential monogenic effect of loss of function variants in GOLGA3 and DPP7 for critically ill and asymptomatic disease demonstration. Genome-wide association study suggests the most significant gene locus associated with severity were located in TMEM189-UBE2V1 that involved in the IL-1 signaling pathway. The p.Val197Met missense variant that affects the stability of the TMPRSS2 protein displays a decreasing allele frequency among the severe patients compared to the mild and the general population. We identified that the HLA-A*11:01, B*51:01, and C*14:02 alleles significantly predispose the worst outcome of the patients. This initial genomic study of Chinese patients provides genetic insights into the phenotypic difference among the COVID-19 patient groups and highlighted genes and variants that may help guide targeted efforts in containing the outbreak. Limitations and advantages of the study were also reviewed to guide future international efforts on elucidating the genetic architecture of host-pathogen interaction for COVID-19 and other infectious and complex diseases.

The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals

Metabolic diseases are the most common and rapidly growing health issues worldwide. The massive population-based human genetics is crucial for the precise prevention and intervention of metabolic disorders. The China Metabolic Analytics Project (ChinaMAP) is based on cohort studies across diverse regions and ethnic groups with metabolic phenotypic data in China. Here, we describe the centralized analysis of the deep whole genome sequencing data and the genetic bases of metabolic traits in 10,588 individuals from the ChinaMAP. The frequency spectrum of variants, population structure, pathogenic variants and novel genomic characteristics were analyzed. The individual genetic evaluations of Mendelian diseases, nutrition and drug metabolism, and traits of blood glucose and BMI were integrated. Our study establishes a large-scale and deep resource for the genetics of East Asians and provides opportunities for novel genetic discoveries of metabolic characteristics and disorders.

Optimal sequencing depth design for whole genome re-sequencing in pigs

BACKGROUND

As whole-genome sequencing is becoming a routine technique, it is important to identify a cost-effective depth of sequencing for such studies. However, the relationship between sequencing depth and biological results from the aspects of whole-genome coverage, variant discovery power and the quality of variants is unclear, especially in pigs. We sequenced the genomes of three Yorkshire boars at an approximately 20X depth on the Illumina HiSeq X Ten platform and downloaded whole-genome sequencing data for three Duroc and three Landrace pigs with an approximately 20X depth for each individual. Then, we downsampled the deep genome data by extracting twelve different proportions of 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 paired reads from the original bam files to mimic the sequence data of the same individuals at sequencing depths of 1.09X, 2.18X, 3.26X, 4.35X, 6.53X, 8.70X, 10.88X, 13.05X, 15.22X, 17.40X, 19.57X and 21.75X to evaluate the influence of genome coverage, the variant discovery rate and genotyping accuracy as a function of sequencing depth. In addition, SNP chip data for Yorkshire pigs were used as a validation for the comparison of single-sample calling and multisample calling algorithms.

RESULTS

Our results indicated that 10X is an ideal practical depth for achieving plateau coverage and discovering accurate variants, which achieved greater than 99% genome coverage. The number of false-positive variants was increased dramatically at a depth of less than 4X, which covered 95% of the whole genome. In addition, the comparison of multi- and single-sample calling showed that multisample calling was more sensitive than single-sample calling, especially at lower depths. The number of variants discovered under multisample calling was 13-fold and 2-fold higher than that under single-sample calling at 1X and 22X, respectively. A large difference was observed when the depth was less than 4.38X. However, more false-positive variants were detected under multisample calling.

CONCLUSIONS

Our research will inform important study design decisions regarding whole-genome sequencing depth. Our results will be helpful for choosing the appropriate depth to achieve the same power for studies performed under limited budgets.

Extremely rare variants reveal patterns of germline mutation rate heterogeneity in humans

A detailed understanding of the genome-wide variability of single-nucleotide germline mutation rates is essential to studying human genome evolution. Here, we use ~36 million singleton variants from 3560 whole-genome sequences to infer fine-scale patterns of mutation rate heterogeneity. Mutability is jointly affected by adjacent nucleotide context and diverse genomic features of the surrounding region, including histone modifications, replication timing, and recombination rate, sometimes suggesting specific mutagenic mechanisms. Remarkably, GC content, DNase hypersensitivity, CpG islands, and H3K36 trimethylation are associated with both increased and decreased mutation rates depending on nucleotide context. We validate these estimated effects in an independent dataset of ~46,000 de novo mutations, and confirm our estimates are more accurate than previously published results based on ancestrally older variants without considering genomic features. Our results thus provide the most refined portrait to date of the factors contributing to genome-wide variability of the human germline mutation rate.

Rare-Variant Studies to Complement Genome-Wide Association Studies

Genome-wide association studies (GWASs) have revolutionized human disease genetics by discovering tens of thousands of associations between common variants and complex diseases. In parallel, huge technological advances in DNA sequencing have made it possible to measure and analyze rare variation in populations. This review considers these two stories and how they have come together. We first review the history of GWASs and sequencing. We then consider how to understand the biological mechanisms that drive signals of strong association in the absence of rare-variant studies. We describe how rare-variant studies complement these approaches and highlight both data generation and statistical challenges in their interpretation. Finally, we consider how certain special study designs, such as those for families and isolated populations, fit in this paradigm.

Association of Rare Predicted Loss-of-Function Variants in Cellular Pathways with Sub-Phenotypes in Age-Related Macular Degeneration

PURPOSE

To investigate the association of rare predicted loss-of-function (pLoF) variants within age-related macular degeneration (AMD) risk loci and AMD sub-phenotypes.

DESIGN

Case-control study.

PARTICIPANTS

Participants of AREDS, AREDS2, and Michigan Genomics Initiative.

METHODS

Whole genome sequencing data were analyzed for rare pLoF variants (frequency <0.1%) in the regions of previously identified 52 independent risk variants known to be associated with AMD. Frequency of the rare pLoF variants in cases with intermediate or advanced AMD was compared with controls. Variants were assigned to the complement, extracellular matrix (ECM), lipid, cell survival, immune system, metabolism, or unknown/other pathway. Associations of rare pLoF variant pathways with AMD sub-phenotypes were analyzed using logistic and linear regression, and Cox proportional hazards models.

MAIN OUTCOME MEASURES

Differences in rare pLoF variant pathway burden and association of rare pLoF variant pathways with sub-phenotypes within the population with AMD were evaluated.

RESULTS

Rare pLoF variants were found in 298 of 1689 cases (17.6%) and 237 of 1518 controls (15.6%) (odds ratio [OR], 1.11; 95% confidence interval [CI], 0.91-1.36; P = 0.310). An enrichment of rare pLoF variants in the complement pathway in cases versus controls (OR, 2.94; 95% CI, 1.49-5.79; P = 0.002) was observed. Within cases, associations between all rare pLoF variants and choroidal neovascularization (CNV) (OR, 1.34; 95% CI, 1.04-1.73; P = 0.023), calcified drusen (OR, 1.33; 95% CI, 1.04-1.72; P = 0.025), higher scores on the AREDS Extended AMD Severity Scale (Standardized Coefficient Beta (β)=0.346 [0.086-0.605], P = 0.009), and progression to advanced disease (hazard ratio, 1.25; 95% CI, 1.01-1.55; P = 0.042) were observed. At the pathway level, there were associations between the complement pathway and geographic atrophy (GA) (OR, 2.17; 95% CI, 1.12-4.24; P = 0.023), the complement pathway and calcified drusen (OR, 3.75; 95% CI, 1.79-7.86; P < 0.001), and the ECM pathway and more severe levels in the AREDS Extended AMD Severity Scale (β = 0.62; 95% CI, 0.04-1.20; P = 0.035).

CONCLUSIONS

Rare pLoF variants are associated with disease progression. Variants in the complement pathway modify the clinical course of AMD and increase the risk of developing specific sub-phenotypes.