Efficient study design for next generation sequencing

Sub-multiplicative interaction between polygenic risk score and household coal use in relation to lung adenocarcinoma among never-smoking women in Asia

We previously identified 10 lung adenocarcinoma susceptibility loci in a genome-wide association study (GWAS) conducted in the Female Lung Cancer Consortium in Asia (FLCCA), the largest genomic study of lung cancer among never-smoking women to date. Furthermore, household coal use for cooking and heating has been linked to lung cancer in Asia, especially in Xuanwei, China. We investigated the potential interaction between genetic susceptibility and coal use in FLCCA. We analyzed GWAS-data from Taiwan, Shanghai, and Shenyang (1472 cases; 1497 controls), as well as a separate study conducted in Xuanwei (152 cases; 522 controls) for additional analyses. We summarized genetic susceptibility using a polygenic risk score (PRS), which was the weighted sum of the risk-alleles from the 10 previously identified loci. We estimated associations between a PRS, coal use (ever/never), and lung adenocarcinoma with multivariable logistic regression models, and evaluated potential gene-environment interactions using likelihood ratio tests. There was a strong association between continuous PRS and lung adenocarcinoma among never coal users (Odds Ratio (OR) = 1.69 (95% Confidence Interval (CI) = 1.53, 1.87), p=1 × 10-26). This effect was attenuated among ever coal users (OR = 1.24 (95% CI: 1.03, 1.50), p = 0.02, p-interaction = 6 × 10-3). We observed similar attenuation among coal users from Xuanwei. Our study provides evidence that genetic susceptibility to lung adenocarcinoma among never-smoking Asian women is weaker among coal users. These results suggest that lung cancer pathogenesis may differ, at least partially, depending on exposure to coal combustion products. Notably, these novel findings are among the few instances of sub-multiplicative gene-environment interactions in the cancer literature.

The effects of taxonomy, diet, and ecology on the microbiota of riverine macroinvertebrates

Freshwater macroinvertebrates play key ecological roles in riverine food webs, such as the transfer of nutrients to consumers and decomposition of organic matter. Although local habitat quality drives macroinvertebrate diversity and abundance, little is known about their microbiota. In most animals, the microbiota provides benefits, such as increasing the rate at which nutrients are metabolized, facilitating immune system development, and defending against pathogenic attack. Our objectives were to identify the bacteria within aquatic invertebrates and determine whether their composition varied with taxonomy, habitat, diet, and time of sample collection. In 2016 and 2017, we collected 264 aquatic invertebrates from the mainstem Saint John (Wolastoq) River in New Brunswick, Canada, representing 15 orders. We then amplified the V3-V4 hypervariable region of the 16S rRNA gene within each individual, which revealed nearly 20,000 bacterial operational taxonomic units (OTUs). The microbiota across all aquatic invertebrates were dominated by Proteobacteria (69.25% of the total sequence reads), but they differed significantly in beta diversity, both among host invertebrate taxa (genus-, family-, and order-levels) and temporally. In contrast to previous work, we observed no microbiota differences among functional feeding groups or traditional feeding habits, and neither water velocity nor microhabitat type structured microbiota variability. Our findings suggest that host invertebrate taxonomy was the most important factor in modulating the composition of the microbiota, likely through a combination of vertical and horizontal bacterial transmission, and evolutionary processes. This is one of the most comprehensive studies of freshwater invertebrate microbiota to date, and it underscores the need for future studies of invertebrate microbiota evolution and linkages to environmental bacteria and physico-chemical conditions.

Effects of gintonin-enriched fraction on hippocampal gene expressions

Background

Recently, gintonin and gintonin-enriched fraction (GEF) have been isolated from ginseng, a herbal medicine. Gintonin induces [Ca²⁺]_i transition in cultured hippocampal neurons and stimulates acetylcholine release through LPA receptor activation. Oral administration of GEF is linked to hippocampus-dependent cognitive enhancement and other neuroprotective effects; however, effects of its long-term administration on hippocampal gene expression remains unknown. Here, we used next-generation sequence (NGS) analysis to examine changes in hippocampal gene expressions after long-term oral administration of GEF.

Methods

C57BL/6 mice were divided into three groups: control group, GEF50 (GEF 50 mg/kg, p.o.), and GEF100 (GEF 100 mg/kg, p.o.). After 22 days, total RNA was extracted from mouse hippocampal tissues. NGS was used for gene expression profiling; quantitative-real-time PCR and western blot were performed to quantify the changes in specific genes and to confirm the protein expression levels in treatment groups.

Results

NGS analysis screened a total of 23,282 genes, analyzing 11-related categories. We focused on the neurogenesis category, which includes four genes for candidate markers: choline acetyltransferase (ChAT) gene, β₃-adrenergic receptor (Adrb3) gene, and corticotrophin-releasing hormone (Crh) gene, and tryptophan 2,3-dioxygenase (Tdo2) gene. Real-time PCR showed a marked overexpression of ChAT, Adrb3, and Crh genes, while reduced expression of Tdo2. Western blot analysis also confirmed increased ChAT and decreased Tdo2 protein levels.

Conclusion

We found that GEF affects mouse hippocampal gene expressions, associated with memory, cognitive, anti-stress and anti-anxiety functions, and neurodegeneration at differential degree, that might explain the genetic bases of GEF-mediated neuroprotective effects.

Targeted insertional mutagenesis libraries for deep domain insertion profiling

Domain recombination is a key principle in protein evolution and protein engineering, but inserting a donor domain into every position of a target protein is not easily experimentally accessible. Most contemporary domain insertion profiling approaches rely on DNA transposons, which are constrained by sequence bias. Here, we establish Saturated Programmable Insertion Engineering (SPINE), an unbiased, comprehensive, and targeted domain insertion library generation technique using oligo library synthesis and multi-step Golden Gate cloning. Through benchmarking to MuA transposon-mediated library generation on four ion channel genes, we demonstrate that SPINE-generated libraries are enriched for in-frame insertions, have drastically reduced sequence bias as well as near-complete and highly-redundant coverage. Unlike transposon-mediated domain insertion that was severely biased and sparse for some genes, SPINE generated high-quality libraries for all genes tested. Using the Inward Rectifier K⁺ channel Kir2.1, we validate the practical utility of SPINE by constructing and comparing domain insertion permissibility maps. SPINE is the first technology to enable saturated domain insertion profiling. SPINE could help explore the relationship between domain insertions and protein function, and how this relationship is shaped by evolutionary forces and can be engineered for biomedical applications.

Bioinformatics Strategies, Challenges, and Opportunities for Next Generation Sequencing-Based HLA Genotyping

The advent of next generation sequencing (NGS) has altered the face of genotyping the human leukocyte antigen (HLA) system in clinical, stem cell donor registry, and research contexts. NGS has led to a dramatically increased sequencing throughput at high accuracy, while being more time and cost efficient than precursor technologies. This has led to a broader and deeper profiling of the key genes in the human immunogenetic make-up. The rapid evolution of sequencing technologies is evidenced by the development of varied short-read sequencing platforms with differing read lengths and sequencing capacities to long-read sequencing platforms capable of profiling full genes without fragmentation. Concomitantly, there has been development of a diverse set of computational analyses and software tools developed to deal with the various strengths and limitations of the sequencing data generated by the different sequencing platforms. This review surveys the different modalities involved in generating NGS HLA profiling sequence data. It systematically describes various computational approaches that have been developed to achieve HLA genotyping to different degrees of resolution. At each stage, this review enumerates the drawbacks and advantages of each of the platforms and analysis approaches, thus providing a comprehensive picture of the current state of HLA genotyping technologies.

Structural Variant Prediction in Extended Pedigrees Through Sparse Negative Binomial Genome Signal Recovery

Structural variants (SVs) are rearrangements, such as deletions, insertions, duplications, inversions, and translocations, in an individual's genome relative to a reference. SV detection is often marred by high false positive rates due to errors in sequencing and mapping. In previous work, we proposed a maximum likelihood approach to SV prediction that incorporated low-coverage sequencing data and coverage distribution. In particular, we developed a negative binomial framework to reflect a more realistic representation DNA fragment distributions sampled from an individual's genome. In this paper, we leverage relationships between an off spring and both parents, in addition to the negative binomial framework, to improve SV identification accuracy. We present numerical results on both simulated genomes as well as two sequenced parent-child trios from the 1000 Genomes Project.

Selection Has Countered High Mutability to Preserve the Ancestral Copy Number of Y Chromosome Amplicons in Diverse Human Lineages

Amplicons-large, highly identical segmental duplications-are a prominent feature of mammalian Y chromosomes. Although they encode genes essential for fertility, these amplicons differ vastly between species, and little is known about the selective constraints acting on them. Here, we develop computational tools to detect amplicon copy number with unprecedented accuracy from high-throughput sequencing data. We find that one-sixth (16.9%) of 1,216 males from the 1000 Genomes Project have at least one deleted or duplicated amplicon. However, each amplicon's reference copy number is scrupulously maintained among divergent branches of the Y chromosome phylogeny, including the ancient branch A00, indicating that the reference copy number is ancestral to all modern human Y chromosomes. Using phylogenetic analyses and simulations, we demonstrate that this pattern of variation is incompatible with neutral evolution and instead displays hallmarks of mutation-selection balance. We also observe cases of amplicon rescue, in which deleted amplicons are restored through subsequent duplications. These results indicate that, contrary to the lack of constraint suggested by the differences between species, natural selection has suppressed amplicon copy number variation in diverse human lineages.

A method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals

Background

This paper describes a heuristic method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals. Low-coverage sequencing assembles high-coverage sequence information for every individual by accumulating data from the genome segments that they share with many other individuals into consensus haplotypes. Deriving the consensus haplotypes accurately is critical for achieving a high phasing and imputation accuracy. In order to enable accurate phasing and imputation of sequence information for the whole population, we allocate the available sequencing resources among individuals with existing phased genomic data by targeting the sequencing coverage of their haplotypes.

Results

Our method, called AlphaSeqOpt, prioritizes haplotypes using a score function that is based on the frequency of the haplotypes in the sequencing set relative to the target coverage. AlphaSeqOpt has two steps: (1) selection of an initial set of individuals by iteratively choosing the individuals that have the maximum score conditional on the current set, and (2) refinement of the set through several rounds of exchanges of individuals. AlphaSeqOpt is very effective for distributing a fixed amount of sequencing resources evenly across haplotypes, which results in a reduction of the proportion of haplotypes that are sequenced below the target coverage. AlphaSeqOpt can provide a greater proportion of haplotypes sequenced at the target coverage by sequencing less individuals, as compared with other methods that use a score function based on haplotype frequencies in the population. A refinement of the initially selected set can provide a larger more diverse set with more unique individuals, which is beneficial in the context of low-coverage sequencing. We extend the method with an approach for filtering rare haplotypes based on their flanking haplotypes, so that only those that are likely to derive from a recombination event are targeted.

Conclusions

We present a method for allocating sequencing resources so that a greater proportion of haplotypes are sequenced at a coverage that is sufficiently high for population-based imputation with low-coverage sequencing. The haplotype score function, the refinement step, and the new approach for filtering rare haplotypes make AlphaSeqOpt more effective for that purpose than previously reported methods for reducing sequencing redundancy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0353-y) contains supplementary material, which is available to authorized users.

Sensitive Detection and Simultaneous Discrimination of Influenza A and B Viruses in Nasopharyngeal Swabs in a Single Assay Using Next-Generation Sequencing-Based Diagnostics

Reassortment of 2009 (H1N1) pandemic influenza virus (pdH1N1) with other strains may produce more virulent and pathogenic forms, detection and their rapid characterization is critical. In this study, we reported a “one-size-fits-all” approach using a next-generation sequencing (NGS) detection platform to extensively identify influenza viral genomes for diagnosis and determination of novel virulence and drug resistance markers. A de novo module and other bioinformatics tools were used to generate contiguous sequence and identify influenza types/subtypes. Of 162 archived influenza-positive patient specimens, 161(99.4%) were positive for either influenza A or B viruses determined using the NGS assay. Among these, 135(83.3%) were A(H3N2), 14(8.6%) were A(pdH1N1), 2(1.2%) were A(H3N2) and A(pdH1N1) virus co-infections and 10(6.2%) were influenza B viruses. Of the influenza A viruses, 66.7% of A(H3N2) viruses tested had a E627K mutation in the PB2 protein, and 87.8% of the influenza A viruses contained the S31N mutation in the M2 protein. Further studies demonstrated that the NGS assay could achieve a high level of sensitivity and reveal adequate genetic information for final laboratory confirmation. The current diagnostic platform allows for simultaneous identification of a broad range of influenza viruses, monitoring emerging influenza strains with pandemic potential that facilitating diagnostics and antiviral treatment in the clinical setting and protection of the public health.

Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations

Background

The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program.

Results

The results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives.

Conclusions

Results of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2345-z) contains supplementary material, which is available to authorized users.

Potential of genotyping-by-sequencing for genomic selection in livestock populations

Background

Next-generation sequencing techniques, such as genotyping-by-sequencing (GBS), provide alternatives to single nucleotide polymorphism (SNP) arrays. The aim of this work was to evaluate the potential of GBS compared to SNP array genotyping for genomic selection in livestock populations.

Methods

The value of GBS was quantified by simulation analyses in which three parameters were varied: (i) genome-wide sequence read depth (x) per individual from 0.01x to 20x or using SNP array genotyping; (ii) number of genotyped markers from 3000 to 300 000; and (iii) size of training and prediction sets from 500 to 50 000 individuals. The latter was achieved by distributing the total available x of 1000x, 5000x, or 10 000x per genotyped locus among the varying number of individuals. With SNP arrays, genotypes were called from sequence data directly. With GBS, genotypes were called from sequence reads that varied between loci and individuals according to a Poisson distribution with mean equal to x. Simulated data were analyzed with ridge regression and the accuracy and bias of genomic predictions and response to selection were quantified under the different scenarios.

Results

Accuracies of genomic predictions using GBS data or SNP array data were comparable when large numbers of markers were used and x per individual was ~1x or higher. The bias of genomic predictions was very high at a very low x. When the total available x was distributed among the training individuals, the accuracy of prediction was maximized when a large number of individuals was used that had GBS data with low x for a large number of markers. Similarly, response to selection was maximized under the same conditions due to increasing both accuracy and selection intensity.

Conclusions

GBS offers great potential for developing genomic selection in livestock populations because it makes it possible to cover large fractions of the genome and to vary the sequence read depth per individual. Thus, the accuracy of predictions is improved by increasing the size of training populations and the intensity of selection is increased by genotyping a larger number of selection candidates.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0102-z) contains supplementary material, which is available to authorized users.

Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data

Statistical genetics is undergoing the same transition to big data that all branches of applied statistics are experiencing. With the advent of inexpensive DNA sequencing, the transition is only accelerating. This brief review highlights some modern techniques with recent successes in statistical genetics. These include: (a) lasso penalized regression and association mapping, (b) ethnic admixture estimation, (c) matrix completion for genotype and sequence data, (d) the fused lasso and copy number variation, (e) haplotyping, (f) estimation of relatedness, (g) variance components models, and (h) rare variant testing. For more than a century, genetics has been both a driver and beneficiary of statistical theory and practice. This symbiotic relationship will persist for the foreseeable future.

Identifying rare variants with optimal depth of coverage and cost-effective overlapping pool sequencing

Genome-wide association studies have identified hundreds of genetic variants associated with complex diseases although most variants identified so far explain only a small proportion of heritability, suggesting that rare variants are responsible for missing heritability. Identification of rare variants through large-scale resequencing becomes increasing important but still prohibitively expensive despite the rapid decline in the sequencing costs. Nevertheless, group testing based overlapping pool sequencing in which pooled rather than individual samples are sequenced will greatly reduces the efforts of sample preparation as well as the costs to screen for rare variants. Here, we proposed an overlapping pool sequencing to screen rare variants with optimal sequencing depth and a corresponding cost model. We formulated a model to compute the optimal depth for sufficient observations of variants in pooled sequencing. Utilizing shifted transversal design algorithm, appropriate parameters for overlapping pool sequencing could be selected to minimize cost and guarantee accuracy. Due to the mixing constraint and high depth for pooled sequencing, results showed that it was more cost-effective to divide a large population into smaller blocks which were tested using optimized strategies independently. Finally, we conducted an experiment to screen variant carriers with frequency equaled 1%. With simulated pools and publicly available human exome sequencing data, the experiment achieved 99.93% accuracy. Utilizing overlapping pool sequencing, the cost for screening variant carriers with frequency equaled 1% in 200 diploid individuals dropped to at least 66% at which target sequencing region was set to 30 Mb.

Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification

Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website.

As next-generation sequencing (NGS) costs continue to fall and genome-wide association study (GWAS) platform coverage improves, the human genetics community is positioned to identify potentially causal variants. However, current NGS or imputation-based studies of either the whole genome or regions previously identified by GWAS have not yet been very successful in identifying causal variants. A major hurdle is the development of methods to distinguish disease-causing variants from their highly-correlated proxies within an associated region. We show that various common factors, such as differential sequencing or imputation accuracy rates and linkage disequilibrium patterns, with or without GWAS-informed region selection, can substantially decrease the probability of identifying the correct causal SNP, often by more than half. We then describe a novel and easy-to-implement re-ranking procedure that can double the probability that the causal SNP is top-ranked in many settings. Application to the NCI Breast and Prostate Cancer (BPC3) Cohort Consortium aggressive prostate cancer data identified new top SNPs within two associated loci previously established via GWAS, as well as several additional possible causal SNPs that had been previously overlooked.

Haplotype kernel association test as a powerful method to identify chromosomal regions harboring uncommon causal variants

For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome-wide association studies is minor. Although the so-called "rare variants" (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the "missing heritability" because very few people may carry these rare variants. The genetic variants that are likely to fill in the "missing heritability" include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single-nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome-wide or exome-wide next-generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.

One thousand genomes imputation in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer genome-wide association study

BACKGROUND

Genotype imputation substantially increases available markers for analysis in genome-wide association studies (GWAS) by leveraging linkage disequilibrium from a reference panel. We sought to (i) investigate the performance of imputation from the August 2010 release of the 1000 Genomes Project (1000GP) in an existing GWAS of prostate cancer, (ii) look for novel associations with prostate cancer risk, (iii) fine-map known prostate cancer susceptibility regions using an approximate Bayesian framework and stepwise regression, and (iv) compare power and efficiency of imputation and de novo sequencing.

METHODS

We used 2,782 aggressive prostate cancer cases and 4,458 controls from the NCI Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer GWAS to infer 5.8 million well-imputed autosomal single nucleotide polymorphisms (SNPs).

RESULTS

Imputation quality, as measured by correlation between imputed and true allele counts, was higher among common variants than rare variants. We found no novel prostate cancer associations among a subset of 1.2 million well-imputed low-frequency variants. At a genome-wide sequencing cost of $2,500, imputation from SNP arrays is a more powerful strategy than sequencing for detecting disease associations of SNPs with minor allele frequencies (MAF) above 1%.

CONCLUSIONS

1000GP imputation provided dense coverage of previously identified prostate cancer susceptibility regions, highlighting its potential as an inexpensive first-pass approach to fine mapping in regions such as 5p15 and 8q24. Our study shows 1000GP imputation can accurately identify low-frequency variants and stresses the importance of large sample size when studying these variants.

AbCD: arbitrary coverage design for sequencing-based genetic studies

Recent advances in sequencing technologies have revolutionized genetic studies. Although high-coverage sequencing can uncover most variants present in the sequenced sample, low-coverage sequencing is appealing for its cost effectiveness. Here, we present AbCD (arbitrary coverage design) to aid the design of sequencing-based studies. AbCD is a user-friendly interface providing pre-estimated effective sample sizes, specific to each minor allele frequency category, for designs with arbitrary coverage (0.5-30×) and sample size (20-10 000), and for four major ethnic groups (Europeans, Africans, Asians and African Americans). In addition, we also present two software tools: ShotGun and DesignPlanner, which were used to generate the estimates behind AbCD. ShotGun is a flexible short-read simulator for arbitrary user-specified read length and average depth, allowing cycle-specific sequencing error rates and realistic read depth distributions. DesignPlanner is a full pipeline that uses ShotGun to generate sequence data and performs initial SNP discovery, uses our previously presented linkage disequilibrium-aware method to call genotypes, and, finally, provides minor allele frequency-specific effective sample sizes. ShotGun plus DesignPlanner can accommodate effective sample size estimate for any combination of high-depth and low-depth data (for example, whole-genome low-depth plus exonic high-depth) or combination of sequence and genotype data [for example, whole-exome sequencing plus genotyping from existing Genomewide Association Study (GWAS)].

Genotype imputation via matrix completion

Most current genotype imputation methods are model-based and computationally intensive, taking days to impute one chromosome pair on 1000 people. We describe an efficient genotype imputation method based on matrix completion. Our matrix completion method is implemented in MATLAB and tested on real data from HapMap 3, simulated pedigree data, and simulated low-coverage sequencing data derived from the 1000 Genomes Project. Compared with leading imputation programs, the matrix completion algorithm embodied in our program MENDEL-IMPUTE achieves comparable imputation accuracy while reducing run times significantly. Implementation in a lower-level language such as Fortran or C is apt to further improve computational efficiency.

Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units

Monogenic diseases are frequent causes of neonatal morbidity and mortality, and disease presentations are often undifferentiated at birth. More than 3500 monogenic diseases have been characterized, but clinical testing is available for only some of them and many feature clinical and genetic heterogeneity. Hence, an immense unmet need exists for improved molecular diagnosis in infants. Because disease progression is extremely rapid, albeit heterogeneous, in newborns, molecular diagnoses must occur quickly to be relevant for clinical decision-making. We describe 50-hour differential diagnosis of genetic disorders by whole-genome sequencing (WGS) that features automated bioinformatic analysis and is intended to be a prototype for use in neonatal intensive care units. Retrospective 50-hour WGS identified known molecular diagnoses in two children. Prospective WGS disclosed potential molecular diagnosis of a severe GJB2-related skin disease in one neonate; BRAT1-related lethal neonatal rigidity and multifocal seizure syndrome in another infant; identified BCL9L as a novel, recessive visceral heterotaxy gene (HTX6) in a pedigree; and ruled out known candidate genes in one infant. Sequencing of parents or affected siblings expedited the identification of disease genes in prospective cases. Thus, rapid WGS can potentially broaden and foreshorten differential diagnosis, resulting in fewer empirical treatments and faster progression to genetic and prognostic counseling.

Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data

Massively parallel sequencing (MPS), since its debut in 2005, has transformed the field of genomic studies. These new sequencing technologies have resulted in the successful identification of causal variants for several rare Mendelian disorders. They have also begun to deliver on their promise to explain some of the missing heritability from genome-wide association studies (GWAS) of complex traits. We anticipate a rapidly growing number of MPS-based studies for a diverse range of applications in the near future. One crucial and nearly inevitable step is to detect SNPs and call genotypes at the detected polymorphic sites from the sequencing data. Here, we review statistical methods that have been proposed in the past five years for this purpose. In addition, we discuss emerging issues and future directions related to SNP detection and genotype calling from MPS data.

Extremely low-coverage sequencing and imputation increases power for genome-wide association studies

Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.

GSTO1*E155del polymorphism associated with increased risk for late-onset Alzheimer's disease: association hypothesis for an uncommon genetic variant

Glutathione S-transferases are multifunctional enzymes involved in cellular detoxification. A genetic linkage was found between Alzheimer's Disease (AD) and the chromosome 10q, where the GSTO1 and GSTO2 genes are located, leading to the hypothesis that GST Omega class (GSTO) genes may be an AD risk factor. Since it is still controversial, we decided to explore GSTO polymorphisms in Italian cohorts. We analyzed 119 AD patients and 114 healthy controls for the GSTO gene polymorphisms. In particular we investigated two common polymorphisms (GSTO1*A140D, GSTO2*N142D) and two uncommon variants (GSTO1*E155del, GSTO1*E208K) to find loci associated with AD risk. Detection of GSTO1*A140D and GSTO2*N142D was performed by PCR-RFLP, while GSTO1*E155del and GSTO1*E208K were detected using confronting two-pair primer and allele specific PCR, respectively. While GSTO1*A140D, GSTO1*E208K and GSTO2*N142D polymorphisms did not show significant outcomes, the GSTO1*E155del polymorphism is associated with AD [P=0.003; adjusted OR=3.70 (1.57-8.75)]. Our results suggest that GSTO1-1 plays a role in AD since the GSTO1*del155 variant is involved in changes in GSTO1-1 activities decreasing in enzyme stability. Specifically, three hypotheses may explain the role of GSTO1-1 in the pathophysiology of AD: the antioxidant activity of GSTO1-1 may protect brain tissue against oxidative stress; GSTO1-1 activity regulate interleukin-1β activation and its genetic variation may act to modulate inflammation in AD; GSTO1-1 is involved in the arsenic biotransformation pathway and gene polymorphisms may be implicated in the modulation of arsenic neurotoxicity. In conclusion, we hypothesized that GSTO1*E155del is an uncommon genetic variant associated with AD risk.

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.