PREIMPLANTATION GENETIC TESTING: Preimplantation genetic testing for polygenic disease risk.

Since its introduction to clinical practice, preimplantation genetic testing (PGT) has become a standard of care for couples at risk of having children with monogenic disease and for chromosomal aneuploidy to improve outcomes for patients with infertility. The primary objective of PGT is to reduce the risk of miscarriage and genetic disease and to improve the success of infertility treatment with the delivery of a healthy child. Until recently, the application of PGT to more common but complex polygenic disease was not possible, as the genetic contribution to polygenic disease has been difficult to determine, and the concept of embryo selection across multiple genetic loci has been difficult to comprehend. Several achievements, including the ability to obtain accurate, genome-wide genotypes of the human embryo and the development of population-level biobanks, have now made PGT for polygenic disease risk applicable in clinical practice. With the rapid advances in embryonic polygenic risk scoring, diverse considerations beyond technical capability have been introduced.

Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning.

BACKGROUND: Alzheimer's disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions. METHODS: We present a machine learning approach (SVM), based upon integrating gene expression data with human brain-specific gene network data, to discover the full spectrum of AD genes across the whole genome. RESULTS: We classified AD candidate genes with an accuracy and the area under the receiver operating characteristic (ROC) curve of 84.56% and 94%. Our approach provides a supplement for the spectrum of AD-associated genes extracted from more than 20,000 genes in a genome wide scale. CONCLUSIONS: In this study, we have elucidated the whole-genome spectrum of AD, using a machine learning approach. Through this method, we expect for the candidate gene catalogue to provide a more comprehensive annotation of AD for researchers.

Targeted next-generation sequencing analysis identifies novel mutations in families with severe familial exudative vitreoretinopathy.

PURPOSE: Familial exudative vitreoretinopathy (FEVR) is a genetically and clinically heterogeneous disease, characterized by failure of vascular development of the peripheral retina. The symptoms of FEVR vary widely among patients in the same family, and even between the two eyes of a given patient. This study was designed to identify the genetic defect in a patient cohort of ten Chinese families with a definitive diagnosis of FEVR. METHODS: To identify the causative gene, next-generation sequencing (NGS)-based target capture sequencing was performed. Segregation analysis of the candidate variant was performed in additional family members by using Sanger sequencing and quantitative real-time PCR (QPCR). RESULTS: Of the cohort of ten FEVR families, six pathogenic variants were identified, including four novel and two known heterozygous mutations. Of the variants identified, four were missense variants, and two were novel heterozygous deletion mutations [LRP5, c.4053 DelC (p.Ile1351IlefsX88); TSPAN12, EX8Del]. The two novel heterozygous deletion mutations were not observed in the control subjects and could give rise to a relatively severe FEVR phenotype, which could be explained by the protein function prediction. CONCLUSIONS: We identified two novel heterozygous deletion mutations [LRP5, c.4053 DelC (p.Ile1351IlefsX88); TSPAN12, EX8Del] using targeted NGS as a causative mutation for FEVR. These genetic deletion variations exhibit a severe form of FEVR, with tractional retinal detachments compared with other known point mutations. The data further enrich the mutation spectrum of FEVR and enhance our understanding of genotype-phenotype correlations to provide useful information for disease diagnosis, prognosis, and effective genetic counseling.

A short-read multiplex sequencing method for reliable, cost-effective and high-throughput genotyping in large-scale studies.

Accurate genotyping is important for genetic testing. Sanger sequencing-based typing is the gold standard for genotyping, but it has been underused, due to its high cost and low throughput. In contrast, short-read sequencing provides inexpensive and high-throughput sequencing, holding great promise for reaching the goal of cost-effective and high-throughput genotyping. However, the short-read length and the paucity of appropriate genotyping methods, pose a major challenge. Here, we present RCHSBT-reliable, cost-effective and high-throughput sequence based typing pipeline-which takes short sequence reads as input, but uses a unique variant calling, haploid sequence assembling algorithm, can accurately genotype with greater effective length per amplicon than even Sanger sequencing reads. The RCHSBT method was tested for the human MHC loci HLA-A, HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1, upon 96 samples using Illumina PE 150 reads. Amplicons as long as 950 bp were readily genotyped, achieving 100% typing concordance between RCHSBT-called genotypes and genotypes previously called by Sanger sequence. Genotyping throughput was increased over 10 times, and cost was reduced over five times, for RCHSBT as compared with Sanger sequence genotyping. We thus demonstrate RCHSBT to be a genotyping method comparable to Sanger sequencing-based typing in quality, while being more cost-effective, and higher throughput.

Polygenic Health Index, General Health, and Pleiotropy: Sibling Analysis and Disease Risk Reduction.

We construct a polygenic health index as a weighted sum of polygenic risk scores for 20 major disease conditions, including, e.g., coronary artery disease, type 1 and 2 diabetes, schizophrenia, etc. Individual weights are determined by population-level estimates of impact on life expectancy. We validate this index in odds ratios and selection experiments using unrelated individuals and siblings (pairs and trios) from the UK Biobank. Individuals with higher index scores have decreased disease risk across almost all 20 diseases (no significant risk increases), and longer calculated life expectancy. When estimated Disability Adjusted Life Years (DALYs) are used as the performance metric, the gain from selection among ten individuals (highest index score vs average) is found to be roughly 4 DALYs. We find no statistical evidence for antagonistic trade-offs in risk reduction across these diseases. Correlations between genetic disease risks are found to be mostly positive and generally mild. These results have important implications for public health and also for fundamental issues such as pleiotropy and genetic architecture of human disease conditions.

Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations.

Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more. PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of two individuals is at a lower disease risk, even when these two individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results. New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology. We review the advances described above and discuss related ethical considerations.

Genetic diversity, molecular phylogeny and selection evidence of the silkworm mitochondria implicated by complete resequencing of 41 genomes.

BACKGROUND: Mitochondria are a valuable resource for studying the evolutionary process and deducing phylogeny. A few mitochondria genomes have been sequenced, but a comprehensive picture of the domestication event for silkworm mitochondria remains to be established. In this study, we integrate the extant data, and perform a whole genome resequencing of Japanese wild silkworm to obtain breakthrough results in silkworm mitochondrial (mt) population, and finally use these to deduce a more comprehensive phylogeny of the Bombycidae. RESULTS: We identified 347 single nucleotide polymorphisms (SNPs) in the mt genome, but found no past recombination event to have occurred in the silkworm progenitor. A phylogeny inferred from these whole genome SNPs resulted in a well-classified tree, confirming that the domesticated silkworm, Bombyx mori, most recently diverged from the Chinese wild silkworm, rather than from the Japanese wild silkworm. We showed that the population sizes of the domesticated and Chinese wild silkworms both experience neither expansion nor contraction. We also discovered that one mt gene, named cytochrome b, shows a strong signal of positive selection in the domesticated clade. This gene is related to energy metabolism, and may have played an important role during silkworm domestication. CONCLUSIONS: We present a comparative analysis on 41 mt genomes of B. mori and B. mandarina from China and Japan. With these, we obtain a much clearer picture of the evolution history of the silkworm. The data and analyses presented here aid our understanding of the silkworm in general, and provide a crucial insight into silkworm phylogeny.

Preimplantation Genetic Testing for Polygenic Disease Relative Risk Reduction: Evaluation of Genomic Index Performance in 11,883 Adult Sibling Pairs.

Preimplantation genetic testing for polygenic disease risk (PGT-P) represents a new tool to aid in embryo selection. Previous studies demonstrated the ability to obtain necessary genotypes in the embryo with accuracy equivalent to in adults. When applied to select adult siblings with known type I diabetes status, a reduction in disease incidence of 45-72% compared to random selection was achieved. This study extends analysis to 11,883 sibling pairs to evaluate clinical utility of embryo selection with PGT-P. Results demonstrate simultaneous relative risk reduction of all diseases tested in parallel, which included diabetes, cancer, and heart disease, and indicate applicability beyond patients with a known family history of disease.

Author Correction: Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.

We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~0.58-0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of polygenic score, or PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.

Utility and First Clinical Application of Screening Embryos for Polygenic Disease Risk Reduction.

For over 2 decades preimplantation genetic testing (PGT) has been in clinical use to reduce the risk of miscarriage and genetic disease in patients with advanced maternal age and risk of transmitting disease. Recently developed methods of genome-wide genotyping and machine learning algorithms now offer the ability to genotype embryos for polygenic disease risk with accuracy equivalent to adults. In addition, contemporary studies on adults indicate the ability to predict polygenic disorders with risk equivalent to monogenic disorders. Existing biobanks provide opportunities to model the clinical utility of polygenic disease risk reduction among sibling adults. Here, we provide a mathematical model for the use of embryo screening to reduce the risk of type 1 diabetes. Results indicate a 45-72% reduced risk with blinded genetic selection of one sibling. The first clinical case of polygenic risk scoring in human preimplantation embryos from patients with a family history of complex disease is reported. In addition to these data, several common and accepted practices place PGT for polygenic disease risk in the applicable context of contemporary reproductive medicine. In addition, prediction of risk for PCOS, endometriosis, and aneuploidy are of particular interest and relevance to patients with infertility and represent an important focus of future research on polygenic risk scoring in embryos.

Validation of concurrent preimplantation genetic testing for polygenic and monogenic disorders, structural rearrangements, and whole and segmental chromosome aneuploidy with a single universal platform.

Preimplantation genetic testing (PGT) has been successfully applied to reduce the risk of miscarriage, improve IVF success rates, and prevent inheritance of monogenic disease and unbalanced translocations. The present study provides the first method capable of simultaneous testing of aneuploidy (PGT-A), structural rearrangements (PGT-SR), and monogenic (PGT-M) disorders using a single platform. Using positive controls to establish performance characteristics, accuracies of 97 to >99% for each type of testing were observed. In addition, this study expands PGT to include predicting the risk of polygenic disorders (PGT-P) for the first time. Performance was established for two common diseases, hypothyroidism and type 1 diabetes, based upon availability of positive control samples from commercially available repositories. Data from the UK Biobank, eMERGE, and T1DBASE were used to establish and validate SNP-based predictors of each disease (7,311 SNPs for hypothyroidism and 82 for type 1 diabetes). Area under the curve of disease status prediction from genotypes alone were 0.71 for hypothyroidism and 0.68 for type 1 diabetes. The availability of expanded PGT to evaluate the risk of polygenic disorders in the preimplantation embryo has the potential to lower the prevalence of common genetic disease in humans.

Accurate Genomic Prediction of Human Height.

We construct genomic predictors for heritable but extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). The constructed predictors explain, respectively, ∼40, 20, and 9% of total variance for the three traits, in data not used for training. For example, predicted heights correlate ∼0.65 with actual height; actual heights of most individuals in validation samples are within a few centimeters of the prediction. The proportion of variance explained for height is comparable to the estimated common SNP heritability from genome-wide complex trait analysis (GCTA), and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for SNPs. Thus, our results close the gap between prediction R-squared and common SNP heritability. The ∼20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common variants. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier genome-wide association studies (GWAS) for out-of-sample validation of our results.

A novel mutation in PRPF31, causative of autosomal dominant retinitis pigmentosa, using the BGISEQ-500 sequencer.

AIM: To study the genes responsible for retinitis pigmentosa. METHODS: A total of 15 Chinese families with retinitis pigmentosa, containing 94 sporadically afflicted cases, were recruited. The targeted sequences were captured using the Target_Eye_365_V3 chip and sequenced using the BGISEQ-500 sequencer, according to the manufacturer's instructions. Data were aligned to UCSC Genome Browser build hg19, using the Burroughs Wheeler Aligner MEM algorithm. Local realignment was performed with the Genome Analysis Toolkit (GATK v.3.3.0) IndelRealigner, and variants were called with the Genome Analysis Toolkit Haplotypecaller, without any use of imputation. Variants were filtered against a panel derived from 1000 Genomes Project, 1000G_ASN, ESP6500, ExAC and dbSNP138. In all members of Family ONE and Family TWO with available DNA samples, the genetic variant was validated using Sanger sequencing. RESULTS: A novel, pathogenic variant of retinitis pigmentosa, c.357_358delAA (p.Ser119SerfsX5) was identified in PRPF31 in 2 of 15 autosomal-dominant retinitis pigmentosa (ADRP) families, as well as in one, sporadic case. Sanger sequencing was performed upon probands, as well as upon other family members. This novel, pathogenic genotype co-segregated with retinitis pigmentosa phenotype in these two families. CONCLUSION: ADRP is a subtype of retinitis pigmentosa, defined by its genotype, which accounts for 20%-40% of the retinitis pigmentosa patients. Our study thus expands the spectrum of PRPF31 mutations known to occur in ADRP, and provides further demonstration of the applicability of the BGISEQ500 sequencer for genomics research.

Applying targeted next generation sequencing to dried blood spot specimens from suspicious cases identified by tandem mass spectrometry-based newborn screening.

BACKGROUND: Tandem mass spectrometry (TMS)-based newborn screening has been proven successful as one of the public healthcare programs, although the practicability has not yet been specifically addressed. METHODS: Sixty residual dried blood spot (DBS) specimens from confirmation/diagnosis-insufficient cases discovered by TMS screening were analyzed by targeted next generation sequencing (TNGS) assay. RESULTS: In total, 26, 11, 9, and 14 cases were diagnosed as positive, high risk, low risk, and negative, respectively. CONCLUSIONS: Applying the DBS-based TNGS assay for the accurate and rapid diagnosis of inborn errors of metabolism (IEMs) is feasible, competent, and advantageous, enabling a simplified TMS screening-based, TNGS assay-integrated newborn screening scheme highlighting an efficient, executable, and one-step screening-to-diagnosis workflow.

Second-generation PLINK: rising to the challenge of larger and richer datasets.

BACKGROUND: PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. FINDINGS: To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, [Formula: see text]-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). CONCLUSIONS: The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

De novo assembly of a haplotype-resolved human genome.

The human genome is diploid, and knowledge of the variants on each chromosome is important for the interpretation of genomic information. Here we report the assembly of a haplotype-resolved diploid genome without using a reference genome. Our pipeline relies on fosmid pooling together with whole-genome shotgun strategies, based solely on next-generation sequencing and hierarchical assembly methods. We applied our sequencing method to the genome of an Asian individual and generated a 5.15-Gb assembled genome with a haplotype N50 of 484 kb. Our analysis identified previously undetected indels and 7.49 Mb of novel coding sequences that could not be aligned to the human reference genome, which include at least six predicted genes. This haplotype-resolved genome represents the most complete de novo human genome assembly to date. Application of our approach to identify individual haplotype differences should aid in translating genotypes to phenotypes for the development of personalized medicine.