Development of a comprehensive cardiovascular disease genetic risk assessment test.

Background: Despite monogenic and polygenic contributions to cardiovascular disease (CVD), genetic testing is not widely adopted, and current tests are limited by the breadth of surveyed conditions and interpretation burden. Methods: We developed a comprehensive clinical genome CVD test with semi-automated interpretation. Monogenic conditions and risk alleles were selected based on the strength of disease association and evidence for increased disease risk, respectively. Non-CVD secondary findings genes, pharmacogenomic (PGx) variants and CVD polygenic risk scores (PRS) were assessed for inclusion. Test performance was modeled using 2,594 genomes from the 1000 Genomes Project, and further investigated in 20 previously tested individuals. Results: The CVD genome test is composed of a panel of 215 CVD gene-disease pairs, 35 non-CVD secondary findings genes, 4 risk alleles or genotypes, 10 PGx genes and a PRS for coronary artery disease. Modeling of test performance using samples from the 1000 Genomes Project revealed ~6% of individuals with a monogenic finding in a CVD-associated gene, 6% with a risk allele finding, ~1% with a non-CVD secondary finding, and 93% with CVD-associated PGx variants. Assessment of blinded clinical samples showed complete concordance with prior testing. An average of 4 variants were reviewed per case, with interpretation and reporting time ranging from 9-96 min. Conclusions: A genome sequencing based CVD genetic risk assessment can provide comprehensive genetic disease and genetic risk information to patients with CVD. The semi-automated and limited interpretation burden suggest that this testing approach could be scaled to support population-level initiatives.

Effect of Whole-Genome Sequencing on the Clinical Management of Acutely Ill Infants With Suspected Genetic Disease: A Randomized Clinical Trial.

Importance: Whole-genome sequencing (WGS) shows promise as a first-line genetic test for acutely ill infants, but widespread adoption and implementation requires evidence of an effect on clinical management. Objective: To determine the effect of WGS on clinical management in a racially and ethnically diverse and geographically distributed population of acutely ill infants in the US. Design, Setting, and Participants: This randomized, time-delayed clinical trial enrolled participants from September 11, 2017, to April 30, 2019, with an observation period extending to July 2, 2019. The study was conducted at 5 US academic medical centers and affiliated children's hospitals. Participants included infants aged between 0 and 120 days who were admitted to an intensive care unit with a suspected genetic disease. Data were analyzed from January 14 to August 20, 2020. Interventions: Patients were randomized to receive clinical WGS results 15 days (early) or 60 days (delayed) after enrollment, with the observation period extending to 90 days. Usual care was continued throughout the study. Main Outcomes and Measures: The main outcome was the difference in the proportion of infants in the early and delayed groups who received a change of management (COM) 60 days after enrollment. Additional outcome measures included WGS diagnostic efficacy, within-group COM at 90 days, length of hospital stay, and mortality. Results: A total of 354 infants were randomized to the early (n = 176) or delayed (n = 178) arms. The mean participant age was 15 days (IQR, 7-32 days); 201 participants (56.8%) were boys; 19 (5.4%) were Asian; 47 (13.3%) were Black; 250 (70.6%) were White; and 38 (10.7%) were of other race. At 60 days, twice as many infants in the early group vs the delayed group received a COM (34 of 161 [21.1%; 95% CI, 15.1%-28.2%] vs 17 of 165 [10.3%; 95% CI, 6.1%-16.0%]; P = .009; odds ratio, 2.3; 95% CI, 1.22-4.32) and a molecular diagnosis (55 of 176 [31.0%; 95% CI, 24.5%-38.7%] vs 27 of 178 [15.0%; 95% CI, 10.2%-21.3%]; P < .001). At 90 days, the delayed group showed a doubling of COM (to 45 of 161 [28.0%; 95% CI, 21.2%-35.6%]) and diagnostic efficacy (to 56 of 178 [31.0%; 95% CI, 24.7%-38.8%]). The most frequent COMs across the observation window were subspecialty referrals (39 of 354; 11%), surgery or other invasive procedures (17 of 354; 4%), condition-specific medications (9 of 354; 2%), or other supportive alterations in medication (12 of 354; 3%). No differences in length of stay or survival were observed. Conclusions and Relevance: In this randomized clinical trial, for acutely ill infants in an intensive care unit, introduction of WGS was associated with a significant increase in focused clinical management compared with usual care. Access to first-line WGS may reduce health care disparities by enabling diagnostic equity. These data support WGS adoption and implementation in this population. Trail Registration: ClinicalTrials.gov Identifier: NCT03290469.

Clinical whole genome sequencing as a first-tier test at a resource-limited dysmorphology clinic in Mexico.

Patients with rare, undiagnosed, or genetic disease (RUGD) often undergo years of serial testing, commonly referred to as the "diagnostic odyssey". Patients in resource-limited areas face even greater challenges-a definitive diagnosis may never be reached due to difficulties in gaining access to clinicians, appropriate specialists, and diagnostic testing. Here, we report on a collaboration of the Illumina iHope Program with the Foundation for the Children of the Californias and Hospital Infantil de Las Californias, to enable deployment of clinical whole genome sequencing (cWGS) as first-tier test in a resource-limited dysmorphology clinic in northern Mexico. A total of 60 probands who were followed for a suspected genetic diagnosis and clinically unresolved after expert examination were tested with cWGS, and the ordering clinicians completed a semi-structured survey to investigate change in clinical management resulting from cWGS findings. Clinically significant genomic findings were identified in 68.3% (n = 41) of probands. No recurrent molecular diagnoses were observed. Copy number variants or gross chromosomal abnormalities accounted for 48.8% (n = 20) of the diagnosed cases, including a mosaic trisomy and suspected derivative chromosomes. A qualitative assessment of clinical management revealed 48.8% (n = 20) of those diagnosed had a change in clinical course based on their cWGS results, despite resource limitations. These data suggest that a cWGS first-tier testing approach can benefit patients with suspected genetic disorders.

Copy-number variants in clinical genome sequencing: deployment and interpretation for rare and undiagnosed disease.

PURPOSE: Current diagnostic testing for genetic disorders involves serial use of specialized assays spanning multiple technologies. In principle, genome sequencing (GS) can detect all genomic pathogenic variant types on a single platform. Here we evaluate copy-number variant (CNV) calling as part of a clinically accredited GS test. METHODS: We performed analytical validation of CNV calling on 17 reference samples, compared the sensitivity of GS-based variants with those from a clinical microarray, and set a bound on precision using orthogonal technologies. We developed a protocol for family-based analysis of GS-based CNV calls, and deployed this across a clinical cohort of 79 rare and undiagnosed cases. RESULTS: We found that CNV calls from GS are at least as sensitive as those from microarrays, while only creating a modest increase in the number of variants interpreted (~10 CNVs per case). We identified clinically significant CNVs in 15% of the first 79 cases analyzed, all of which were confirmed by an orthogonal approach. The pipeline also enabled discovery of a uniparental disomy (UPD) and a 50% mosaic trisomy 14. Directed analysis of select CNVs enabled breakpoint level resolution of genomic rearrangements and phasing of de novo CNVs. CONCLUSION: Robust identification of CNVs by GS is possible within a clinical testing environment.

Detection of long repeat expansions from PCR-free whole-genome sequence data.

Identifying large expansions of short tandem repeats (STRs), such as those that cause amyotrophic lateral sclerosis (ALS) and fragile X syndrome, is challenging for short-read whole-genome sequencing (WGS) data. A solution to this problem is an important step toward integrating WGS into precision medicine. We developed a software tool called ExpansionHunter that, using PCR-free WGS short-read data, can genotype repeats at the locus of interest, even if the expanded repeat is larger than the read length. We applied our algorithm to WGS data from 3001 ALS patients who have been tested for the presence of the C9orf72 repeat expansion with repeat-primed PCR (RP-PCR). Compared against this truth data, ExpansionHunter correctly classified all (212/212, 95% CI [0.98, 1.00]) of the expanded samples as either expansions (208) or potential expansions (4). Additionally, 99.9% (2786/2789, 95% CI [0.997, 1.00]) of the wild-type samples were correctly classified as wild type by this method with the remaining three samples identified as possible expansions. We further applied our algorithm to a set of 152 samples in which every sample had one of eight different pathogenic repeat expansions, including those associated with fragile X syndrome, Friedreich's ataxia, and Huntington's disease, and correctly flagged all but one of the known repeat expansions. Thus, ExpansionHunter can be used to accurately detect known pathogenic repeat expansions and provides researchers with a tool that can be used to identify new pathogenic repeat expansions.

Mutational signatures of de-differentiation in functional non-coding regions of melanoma genomes.

Much emphasis has been placed on the identification, functional characterization, and therapeutic potential of somatic variants in tumor genomes. However, the majority of somatic variants lie outside coding regions and their role in cancer progression remains to be determined. In order to establish a system to test the functional importance of non-coding somatic variants in cancer, we created a low-passage cell culture of a metastatic melanoma tumor sample. As a foundation for interpreting functional assays, we performed whole-genome sequencing and analysis of this cell culture, the metastatic tumor from which it was derived, and the patient-matched normal genomes. When comparing somatic mutations identified in the cell culture and tissue genomes, we observe concordance at the majority of single nucleotide variants, whereas copy number changes are more variable. To understand the functional impact of non-coding somatic variation, we leveraged functional data generated by the ENCODE Project Consortium. We analyzed regulatory regions derived from multiple different cell types and found that melanocyte-specific regions are among the most depleted for somatic mutation accumulation. Significant depletion in other cell types suggests the metastatic melanoma cells de-differentiated to a more basal regulatory state. Experimental identification of genome-wide regulatory sites in two different melanoma samples supports this observation. Together, these results show that mutation accumulation in metastatic melanoma is nonrandom across the genome and that a de-differentiated regulatory architecture is common among different samples. Our findings enable identification of the underlying genetic components of melanoma and define the differences between a tissue-derived tumor sample and the cell culture created from it. Such information helps establish a broader mechanistic understanding of the linkage between non-coding genomic variations and the cellular evolution of cancer.

Genome-wide DNA methylation profiles in hematopoietic stem and progenitor cells reveal overrepresentation of ETS transcription factor binding sites.

DNA methylation is an essential epigenetic mark that is required for normal development. Knockout of the DNA methyltransferase enzymes in the mouse hematopoietic compartment reveals that methylation is critical for hematopoietic differentiation. To better understand the role of DNA methylation in hematopoiesis, we characterized genome-wide DNA methylation in primary mouse hematopoietic stem cells (HSCs), common myeloid progenitors (CMPs), and erythroblasts (ERYs). Methyl binding domain protein 2 (MBD) enrichment of DNA followed by massively parallel sequencing (MBD-seq) was used to map genome-wide DNA methylation. Globally, DNA methylation was most abundant in HSCs, with a 40% reduction in CMPs, and a 67% reduction in ERYs. Only 3% of peaks arise during differentiation, demonstrating a genome-wide decline in DNA methylation during erythroid development. Analysis of genomic features revealed that 98% of promoter CpG islands are hypomethylated, while 20%-25% of non-promoter CpG islands are methylated. Proximal promoter sequences of expressed genes are hypomethylated in all cell types, while gene body methylation positively correlates with gene expression in HSCs and CMPs. Elevated genome-wide DNA methylation in HSCs and the positive association between methylation and gene expression demonstrates that DNA methylation is a mark of cellular plasticity in HSCs. Using de novo motif discovery, we identified overrepresented transcription factor consensus binding motifs in methylated sequences. Motifs for several ETS transcription factors, including GABPA and ELF1, are overrepresented in methylated regions. Our genome-wide survey demonstrates that DNA methylation is markedly altered during myeloid differentiation and identifies critical regions of the genome and transcription factor programs that contribute to hematopoiesis.

Genome-wide ChIP-Seq reveals a dramatic shift in the binding of the transcription factor erythroid Kruppel-like factor during erythrocyte differentiation.

Erythropoiesis is dependent on the activity of transcription factors, including the erythroid-specific erythroid Kruppel-like factor (EKLF). ChIP followed by massively parallel sequencing (ChIP-Seq) is a powerful, unbiased method to map trans-factor occupancy. We used ChIP-Seq to study the interactome of EKLF in mouse erythroid progenitor cells and more differentiated erythroblasts. We correlated these results with the nuclear distribution of EKLF, RNA-Seq analysis of the transcriptome, and the occupancy of other erythroid transcription factors. In progenitor cells, EKLF is found predominantly at the periphery of the nucleus, where EKLF primarily occupies the promoter regions of genes and acts as a transcriptional activator. In erythroblasts, EKLF is distributed throughout the nucleus, and erythroblast-specific EKLF occupancy is predominantly in intragenic regions. In progenitor cells, EKLF modulates general cell growth and cell cycle regulatory pathways, whereas in erythroblasts EKLF is associated with repression of these pathways. The EKLF interactome shows very little overlap with the interactomes of GATA1, GATA2, or TAL1, leading to a model in which EKLF directs programs that are independent of those regulated by the GATA factors or TAL1.

A bioinformatics approach for determining sample identity from different lanes of high-throughput sequencing data.

The ability to generate whole genome data is rapidly becoming commoditized. For example, a mammalian sized genome (∼3Gb) can now be sequenced using approximately ten lanes on an Illumina HiSeq 2000. Since lanes from different runs are often combined, verifying that each lane in a genome's build is from the same sample is an important quality control. We sought to address this issue in a post hoc bioinformatic manner, instead of using upstream sample or "barcode" modifications. We rely on the inherent small differences between any two individuals to show that genotype concordance rates can be effectively used to test if any two lanes of HiSeq 2000 data are from the same sample. As proof of principle, we use recent data from three different human samples generated on this platform. We show that the distributions of concordance rates are non-overlapping when comparing lanes from the same sample versus lanes from different samples. Our method proves to be robust even when different numbers of reads are analyzed. Finally, we provide a straightforward method for determining the gender of any given sample. Our results suggest that examining the concordance of detected genotypes from lanes purported to be from the same sample is a relatively simple approach for confirming that combined lanes of data are of the same identity and quality.

Accurate and comprehensive sequencing of personal genomes.

As whole-genome sequencing becomes commoditized and we begin to sequence and analyze personal genomes for clinical and diagnostic purposes, it is necessary to understand what constitutes a complete sequencing experiment for determining genotypes and detecting single-nucleotide variants. Here, we show that the current recommendation of ∼30× coverage is not adequate to produce genotype calls across a large fraction of the genome with acceptably low error rates. Our results are based on analyses of a clinical sample sequenced on two related Illumina platforms, GAII(x) and HiSeq 2000, to a very high depth (126×). We used these data to establish genotype-calling filters that dramatically increase accuracy. We also empirically determined how the callable portion of the genome varies as a function of the amount of sequence data used. These results help provide a "sequencing guide" for future whole-genome sequencing decisions and metrics by which coverage statistics should be reported.

Unified translation repression mechanism for microRNAs and upstream AUGs.

BACKGROUND: MicroRNAs (miRNAs) are endogenous small RNAs that modulate gene expression at the post-transcriptional level by binding complementary sites in the 3'-UTR. In a recent genome-wide study reporting a new miRNA target class (miBridge), we identified and validated interactions between 5'-UTRs and miRNAs. Separately, upstream AUGs (uAUGs) in 5'-UTRs are known to regulate genes translationally without affecting mRNA levels, one of the mechanisms for miRNA-mediated repression. RESULTS: Using sequence data from whole-genome cDNA alignments we identified 1418 uAUG sequences on the 5'-UTR that specifically interact with 3'-ends of conserved miRNAs. We computationally identified miRNAs that can target six genes through their uAUGs that were previously reported to suppress translation. We extended this meta-analysis by confirming expression of these miRNAs in cell-lines used in the uAUG studies. Similarly, seven members of the KLF family of genes containing uAUGs were computationally identified as interacting with several miRNAs. Using KLF9 as an example (whose protein expression is limited to brain tissue despite the mRNA being expressed ubiquitously), we show computationally that miRNAs expressed only in HeLa cells and not in neuroblastoma (N2A) cells can bind the uAUGs responsible for translation inhibition. Our computed results demonstrate that tissue- or cell-line specific repression of protein translation by uAUGs can be explained by the presence or absence of miRNAs that target these uAUG sequences. We propose that these uAUGs represent a subset of miRNA interaction sites on 5'-UTRs in miBridge, whereby a miRNA binding a uAUG hinders the progression of ribosome scanning the mRNA before it reaches the open reading frame (ORF). CONCLUSIONS: While both miRNAs and uAUGs are separately known to down-regulate protein expression, we show that they may be functionally related by identifying potential interactions through a sequence-specific binding mechanism. Using prior experimental evidence that shows uAUG effects on translation repression together with miRNA expression data specific to cell lines, we demonstrate through computational analysis that cell-specific down-regulation of protein expression (while maintaining mRNA levels) correlates well with the simultaneous presence of miRNA and target uAUG sequences in one cell type and not others, suggesting tissue-specific translation repression by miRNAs through uAUGs.

New class of microRNA targets containing simultaneous 5'-UTR and 3'-UTR interaction sites.

MicroRNAs (miRNAs) are known to post-transcriptionally regulate target mRNAs through the 3'-UTR, which interacts mainly with the 5'-end of miRNA in animals. Here we identify many endogenous motifs within human 5'-UTRs specific to the 3'-ends of miRNAs. The 3'-end of conserved miRNAs in particular has significant interaction sites in the human-enriched, less conserved 5'-UTR miRNA motifs, while human-specific miRNAs have significant interaction sites only in the conserved 5'-UTR motifs, implying both miRNA and 5'-UTR are actively evolving in response to each other. Additionally, many miRNAs with their 3'-end interaction sites in the 5'-UTRs turn out to simultaneously contain 5'-end interaction sites in the 3'-UTRs. Based on these findings we demonstrate combinatory interactions between a single miRNA and both end regions of an mRNA using model systems. We further show that genes exhibiting large-scale protein changes due to miRNA overexpression or deletion contain both UTR interaction sites predicted. We provide the predicted targets of this new miRNA target class, miBridge, as an efficient way to screen potential targets, especially for nonconserved miRNAs, since the target search space is reduced by an order of magnitude compared with the 3'-UTR alone. Efficacy is confirmed by showing SEC24D regulation with hsa-miR-605, a miRNA identified only in primate, opening the door to the study of nonconserved miRNAs. Finally, miRNAs (and associated proteins) involved in this new targeting class may prevent 40S ribosome scanning through the 5'-UTR and keep it from reaching the start-codon, preventing 60S association.

Discriminating single-base difference miRNA expressions using microarray Probe Design Guru (ProDeG).

MicroRNAs (miRNA) are endogenous tissue-specific short RNAs that regulate gene expression. Discriminating each let-7 family member expression is especially important due to let-7's abundance and connection with development and cancer. However, short lengths (22 nt) and similarities between multiple sequences have prevented identification of individual members. Here, we present ProDeG, a computational algorithm which designs imperfectly matched sequences (previously yielding only noise levels in microarray experiments) for genome-wide microarray "signal" probes to discriminate single nucleotide differences and to improve probe qualities. Our probes for the entire let-7 family are both homogeneous and specific, verified using microarray signals from fluorescent dye-tagged oligonucleotides corresponding to the let-7 family, demonstrating the power of our algorithm. In addition, false let-7c signals from conventional perfectly-matched probes were identified in lymphoblastoid cell-line samples through comparison with our probe-set signals, raising concerns about false let-7 family signals in conventional microarray platform.