Common vs. rare allele hypotheses for complex diseases

Comparing the power of family-based association tests for sequence data with applications in the GAW18 simulated data

We apply a family-based extension of the sequence kernel association test (SKAT) to 93 trios extracted from the 20 pedigrees in the Genetic Analysis Workshop 18 simulated data. Each extracted trio includes a unique set of parents to ensure conditionally independent trios are sampled. We compare the empirical type I error and power between the family-based SKAT and the burden test under varying percentages of causal single-nucleotide polymorphisms included in the analysis. Our investigation using simulated data suggests that, under the setting used for Genetic Analysis Workshop 18 data, both the family-based SKAT and the burden test have limited power, and that there is no substantial impact of percentage of signal on the power of either test. The low power is partially a result of the small sample size. However, we find that both the family-based SKAT and the burden test are more powerful when we use only rare variants, rather than common variants, to test the association.

Genetic polymorphisms of ALOX5AP and CYP3A5 increase susceptibility to ischemic stroke and are associated with atherothrombotic events in stroke patients

BACKGROUND

The contributions of gene-gene interactions to pathogenesis of stroke remain largely elusive. The present study was designed to investigate the associations between genetic variations and ischemic stroke risk, the roles of gene-gene interactions in ischemic stroke, and their associations with atherothrombotic events.

METHODS

Among 396 patients with ischemic stroke and 378 controls, we examined 8 variants from 5 genes, including ALOX5AP-SG13S32 (rs9551963), SG13S42 (rs4769060), SG13S89 (rs4769874), SG13S114 (rs10507391), EPHX2 G860A (rs751141), CYP2C9*2 C430T (rs1799853), CYP2C9*3 A1075C (rs1057910), and CYP3A5 A6986G (rs776746), using matrix-assisted laser desorption/ionization time of flight mass spectrometry. Gene-gene interactions were determined by the generalized multifactor dimensionality reduction (GMDR) method. All ischemic stroke patients were followed up 12 months for atherothrombotic events, including recurrent ischemic stroke and other vascular events.

RESULTS

Single-gene variant analysis showed no significant differences in the genotype distributions of the 8 variants between the 2 groups. However, the GMDR analysis showed a significant interaction between rs10507391 and rs776746, in those cases carrying rs10507391 AA and rs776746 GG, the risk of ischemic stroke increased by 2.014 times (95% confidence interval [CI], 1.896-6.299; P = .006), and the atherothrombotic events occurred more frequently in those patients during follow-up period (P < .001). Multiple Cox regression analysis showed that the interaction between rs10507391 AA and rs776746 GG was an independent risk factor for atherothrombotic events (relative risk = 2.921; 95% CI, 1.118-7.012; P = .008).

CONCLUSIONS

The interaction between rs10507391 and rs776746 increases the susceptibility to ischemic stroke and is associated with atherothrombotic events in stroke patients.

Single nucleotide variations: biological impact and theoretical interpretation

Genome-wide association studies (GWAS) and whole-exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease-causing mutations are exonic non-synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.

Hierarchical Bayesian model for rare variant association analysis integrating genotype uncertainty in human sequence data

Next-generation sequencing (NGS) has led to the study of rare genetic variants, which possibly explain the missing heritability for complex diseases. Most existing methods for rare variant (RV) association detection do not account for the common presence of sequencing errors in NGS data. The errors can largely affect the power and perturb the accuracy of association tests due to rare observations of minor alleles. We developed a hierarchical Bayesian approach to estimate the association between RVs and complex diseases. Our integrated framework combines the misclassification probability with shrinkage-based Bayesian variable selection. It allows for flexibility in handling neutral and protective RVs with measurement error, and is robust enough for detecting causal RVs with a wide spectrum of minor allele frequency (MAF). Imputation uncertainty and MAF are incorporated into the integrated framework to achieve the optimal statistical power. We demonstrate that sequencing error does significantly affect the findings, and our proposed model can take advantage of it to improve statistical power in both simulated and real data. We further show that our model outperforms existing methods, such as sequence kernel association test (SKAT). Finally, we illustrate the behavior of the proposed method using a Finnish low-density lipoprotein cholesterol study, and show that it identifies an RV known as FH North Karelia in LDLR gene with three carriers in 1,155 individuals, which is missed by both SKAT and Granvil.

Mitochondrial DNA Sequence Variation Associated With Peripheral Nerve Function in the Elderly

BACKGROUND

Mitochondrial dysfunction is a prominent hallmark of many sensory neuropathies. The purpose of this study was to assess the influence of mitochondrial DNA sequence variation on peripheral nerve function in the population-based Health, Aging, and Body Composition Study.

METHODS

We investigated the role of common mitochondrial DNA variation (n = 1,580) and complete mitochondrial DNA sequences (n = 138) on peroneal motor nerve conduction velocity and amplitude, average vibration detection threshold, and monofilament sensitivity.

RESULTS

Nominal associations among common mitochondrial DNA variants and haplogroups were identified but were not statistically significant after adjustment for multiple comparisons. Sequence-based approaches were used to identify aggregate variant associations across the 16S rRNA (weighted-sum, p = 2E-05 and variable threshold, p = 9E-06) for nerve conduction velocity. Several of these rare 16S variants occurred at or near sites with earlier disease associations and are also in close proximity to the peptidyl transferase center, which is the catalytic center of the 16S rRNA CONCLUSIONS: These results suggest that sequence variation related to mitochondrial protein synthesis/assembly is associated with peripheral nerve function and may provide insight into targets for intervention or new clinical strategies to preserve nerve function in late life.

Exonic variants associated with development of aspirin exacerbated respiratory diseases

Aspirin-exacerbated respiratory disease (AERD) is one phenotype of asthma, often occurring in the form of a severe and sudden attack. Due to the time-consuming nature and difficulty of oral aspirin challenge (OAC) for AERD diagnosis, non-invasive biomarkers have been sought. The aim of this study was to identify AERD-associated exonic SNPs and examine the diagnostic potential of a combination of these candidate SNPs to predict AERD. DNA from 165 AERD patients, 397 subjects with aspirin-tolerant asthma (ATA), and 398 normal controls were subjected to an Exome BeadChip assay containing 240K SNPs. 1,023 models (2¹⁰-1) were generated from combinations of the top 10 SNPs, selected by the p-values in association with AERD. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was calculated for each model. SNP Function Portal and PolyPhen-2 were used to validate the functional significance of candidate SNPs. An exonic SNP, exm537513 in HLA-DPB1, showed the lowest p-value (p = 3.40×10⁻⁸) in its association with AERD risk. From the top 10 SNPs, a combination model of 7 SNPs (exm537513, exm83523, exm1884673, exm538564, exm2264237, exm396794, and exm791954) showed the best AUC of 0.75 (asymptotic p-value of 7.94×10⁻²¹), with 34% sensitivity and 93% specificity to discriminate AERD from ATA. Amino acid changes due to exm83523 in CHIA were predicted to be “probably damaging” to the structure and function of the protein, with a high score of ‘1’. A combination model of seven SNPs may provide a useful, non-invasive genetic marker combination for predicting AERD.

Case-only exome sequencing and complex disease susceptibility gene discovery: study design considerations

Whole exome sequencing (WES) provides an unprecedented opportunity to identify the potential aetiological role of rare functional variants in human complex diseases. Large-scale collaborations have generated germline WES data on patients with a number of diseases, especially cancer, but less often on healthy controls under the same sequencing procedures. These data can be a valuable resource for identifying new disease susceptibility loci if study designs are appropriately applied. This review describes suggested strategies and technical considerations when focusing on case-only study designs that use WES data in complex disease scenarios. These include variant filtering based on frequency and functionality, gene prioritisation, interrogation of different data types and targeted sequencing validation. We propose that if case-only WES designs were applied in an appropriate manner, new susceptibility genes containing rare variants for human complex diseases can be detected.

Two rare AKAP9 variants are associated with Alzheimer's disease in African Americans

BACKGROUND

Less is known about the genetic basis of Alzheimer's disease (AD) in African Americans (AAs) than in non-Hispanic whites.

METHODS

Whole exome sequencing (WES) was performed on seven AA AD cases. Disease association with potentially AD-related variants from WES was assessed in an AA discovery cohort of 422 cases and 394 controls. Replication was sought in an AA sample of 1037 cases and 1869 controls from the Alzheimer Disease Genetics Consortium (ADGC).

RESULTS

Forty-four single nucleotide polymorphisms (SNPs) from WES passed filtering criteria and were successfully genotyped. Nominally significant (P < .05) association to AD was observed with two African-descent specific AKAP9 SNPs in tight linkage disequilibrium: rs144662445 (P = .014) and rs149979685 (P = .037). These associations were replicated in the ADGC sample (rs144662445: P = .0022, odds ratio [OR] = 2.75; rs149979685: P = .0022, OR = 3.61).

CONCLUSIONS

Because AKAP9 was not previously linked to AD risk, this study indicates a potential new disease mechanism.

Generalized functional linear models for gene-based case-control association studies

By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses.

Gene-based rare allele analysis identified a risk gene of Alzheimer's disease

Alzheimer’s disease (AD) has a strong propensity to run in families. However, the known risk genes excluding APOE are not clinically useful. In various complex diseases, gene studies have targeted rare alleles for unsolved heritability. Our study aims to elucidate previously unknown risk genes for AD by targeting rare alleles. We used data from five publicly available genetic studies from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the database of Genotypes and Phenotypes (dbGaP). A total of 4,171 cases and 9,358 controls were included. The genotype information of rare alleles was imputed using 1,000 genomes. We performed gene-based analysis of rare alleles (minor allele frequency≤3%). The genome-wide significance level was defined as meta P<1.8×10^–6 (0.05/number of genes in human genome = 0.05/28,517). ZNF628, which is located at chromosome 19q13.42, showed a genome-wide significant association with AD. The association of ZNF628 with AD was not dependent on APOE ε4. APOE and TREM2 were also significantly associated with AD, although not at genome-wide significance levels. Other genes identified by targeting common alleles could not be replicated in our gene-based rare allele analysis. We identified that rare variants in ZNF628 are associated with AD. The protein encoded by ZNF628 is known as a transcription factor. Furthermore, the associations of APOE and TREM2 with AD were highly significant, even in gene-based rare allele analysis, which implies that further deep sequencing of these genes is required in AD heritability studies.

Cohen's h for detection of disease association with rare genetic variants

Background

The power of the genome wide association studies starts to go down when the minor allele frequency (MAF) is below 0.05. Here, we proposed the use of Cohen’s h in detecting disease associated rare variants. The variance stabilizing effect based on the arcsine square root transformation of MAFs to generate Cohen’s h contributed to the statistical power for rare variants analysis. We re-analyzed published datasets, one microarray and one sequencing based, and used simulation to compare the performance of Cohen’s h with the risk difference (RD) and odds ratio (OR).

Results

The analysis showed that the type 1 error rate of Cohen’s h was as expected and Cohen’s h and RD were both less biased and had higher power than OR. The advantage of Cohen’s h was more obvious when MAF was less than 0.01.

Conclusions

Cohen’s h can increase the power to find genetic association of rare variants and diseases, especially when MAF is less than 0.01.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-875) contains supplementary material, which is available to authorized users.

The humankind genome: from genetic diversity to the origin of human diseases

Genome-wide association studies have failed to establish common variant risk for the majority of common human diseases. The underlying reasons for this failure are explained by recent studies of resequencing and comparison of over 1200 human genomes and 10 000 exomes, together with the delineation of DNA methylation patterns (epigenome) and full characterization of coding and noncoding RNAs (transcriptome) being transcribed. These studies have provided the most comprehensive catalogues of functional elements and genetic variants that are now available for global integrative analysis and experimental validation in prospective cohort studies. With these datasets, researchers will have unparalleled opportunities for the alignment, mining, and testing of hypotheses for the roles of specific genetic variants, including copy number variations, single nucleotide polymorphisms, and indels as the cause of specific phenotypes and diseases. Through the use of next-generation sequencing technologies for genotyping and standardized ontological annotation to systematically analyze the effects of genomic variation on humans and model organism phenotypes, we will be able to find candidate genes and new clues for disease's etiology and treatment. This article describes essential concepts in genetics and genomic technologies as well as the emerging computational framework to comprehensively search websites and platforms available for the analysis and interpretation of genomic data.

Dynamic Bayesian testing of sets of variants in complex diseases

Rare genetic variants have recently been studied for genome-wide associations with human complex diseases. Existing rare variant methods are based on the hypothesis-testing framework that predefined variant sets need to be tested separately. The power of those methods is contingent upon accurate selection of variants for testing, and frequently, common variants are left out for separate testing. In this article, we present a novel Bayesian method for simultaneous testing of all genome-wide variants across the whole frequency range. The method allows for much more flexible grouping of variants and dynamically combines them for joint testing. The method accounts for correlation among variant sets, such that only direct associations with the disease are reported, whereas indirect associations due to linkage disequilibrium are not. Consequently, the method can obtain much improved power and flexibility and simultaneously pinpoint multiple disease variants with high resolution. Additional covariates of categorical, discrete, and continuous values can also be added. We compared our method with seven existing categories of approaches for rare variant mapping. We demonstrate that our method achieves similar power to the best methods available to date when testing very rare variants in small SNP sets. When moderately rare or common variants are included, or when testing a large collection of variants, however, our method significantly outperforms all existing methods evaluated in this study. We further demonstrate the power and the usage of our method in a whole-genome resequencing study of type 1 diabetes.

Identifying rare and common disease associated variants in genomic data using Parkinson's disease as a model

BACKGROUND

Genome-wide association studies have been successful in identifying common genetic variants for human diseases. However, much of the heritable variation associated with diseases such as Parkinson's disease remains unknown suggesting that many more risk loci are yet to be identified. Rare variants have become important in disease association studies for explaining missing heritability. Methods for detecting this type of association require prior knowledge on candidate genes and combining variants within the region. These methods may suffer from power loss in situations with many neutral variants or causal variants with opposite effects.

RESULTS

We propose a method capable of scanning genetic variants to identify the region most likely harbouring disease gene with rare and/or common causal variants. Our method assigns a score at each individual variant based on our scoring system. It uses aggregate scores to identify the region with disease association. We evaluate performance by simulation based on 1000 Genomes sequencing data and compare with three commonly used methods. We use a Parkinson's disease case-control dataset as a model to demonstrate the application of our method. Our method has better power than CMC and WSS and similar power to SKAT-O with well-controlled type I error under simulation based on 1000 Genomes sequencing data. In real data analysis, we confirm the association of α-synuclein gene (SNCA) with Parkinson's disease (p = 0.005). We further identify association with hyaluronan synthase 2 (HAS2, p = 0.028) and kringle containing transmembrane protein 1 (KREMEN1, p = 0.006). KREMEN1 is associated with Wnt signalling pathway which has been shown to play an important role for neurodegeneration in Parkinson's disease.

CONCLUSIONS

Our method is time efficient and less sensitive to inclusion of neutral variants and direction effect of causal variants. It can narrow down a genomic region or a chromosome to a disease associated region. Using Parkinson's disease as a model, our method not only confirms association for a known gene but also identifies two genes previously found by other studies. In spite of many existing methods, we conclude that our method serves as an efficient alternative for exploring genomic data containing both rare and common variants.

Novel rare variants in F-box protein 45 (FBXO45) in schizophrenia

The ubiquitin ligase F-box protein 45 (FBXO45) is critical for synaptogenesis, neuronal migration, and synaptic transmission. FBXO45 is included in the 3q29 microdeletion region that confers a significant risk for schizophrenia, as shown by rare structural variant studies. Thus, FBXO45 is considered a prominent candidate for mediating schizophrenia pathogenesis. Here, we investigated rare, deleterious single nucleotide variants (SNVs) as well as small insertions and deletions (INDELs) in FBXO45 that may contribute to schizophrenia susceptibility. Using Sanger sequencing, we performed mutation screening in FBXO45 exon regions in 337 schizophrenia patients. Novel missense or nonsense variants were followed up with a genetic association study in an independent sample set of 601 schizophrenia patients and 916 controls, a case report for assessing the clinical consequence of the mutations, a pedigree study for measuring mutation inheritance in the proband's family, bioinformatics analyses for evaluating mutation effect on protein structure and function, and mRNA expression analysis for examining mutation transcriptional influence on FBXO45 expression. One heterozygous, novel, and rare missense mutation (R108C) was identified in a single schizophrenia patient and in his healthy mother. At age 20, this patient was diagnosed with paranoid schizophrenia and carried some clinical features of 3q29 deletion phenotypes, including premorbid IQ decline. With follow-up genotyping, this mutation was not found in either the schizophrenia group (0/601) or the healthy control group (0/916). Bioinformatics analyses predicted that R108C probably pathologically impacted the structure and function of the FBXO45 protein. The relative expression of FBXO45 in SCZ case with R108C mutation was relatively low when compared to 50 schizophrenia patients and 52 healthy controls. The R108C mutation in FBXO45 is a rare variant with a modest effect on schizophrenia risk that may disrupt the structure and function of the FBXO45 protein. Our findings also suggest that FBXO45 may be a new attractive candidate gene for schizophrenia.

A new genotype imputation method with tolerance to high missing rate and rare variants

We report a novel algorithm, iBLUP, to impute missing genotypes by simultaneously and comprehensively using identity by descent and linkage disequilibrium information. The simulation studies showed that the algorithm exhibited drastically tolerance to high missing rate, especially for rare variants than other common imputation methods, e.g. BEAGLE and fastPHASE. At a missing rate of 70%, the accuracy of BEAGLE and fastPHASE dropped to 0.82 and 0.74 respectively while iBLUP retained an accuracy of 0.95. For minor allele, the accuracy of BEAGLE and fastPHASE decreased to -0.1 and 0.03, while iBLUP still had an accuracy of 0.61.We implemented the algorithm in a publicly available software package also named iBLUP. The application of iBLUP for processing real sequencing data in an outbred pig population was demonstrated.

A coding IRAK2 protein variant compromises Toll-like receptor (TLR) signaling and is associated with colorectal cancer survival

Within innate immune signaling pathways, interleukin-1 receptor-associated kinases (IRAKs) fulfill key roles downstream of multiple Toll-like receptors and the interleukin-1 receptor. Although human IRAK4 deficiency was shown to lead to severe immunodeficiency in response to pyogenic bacterial infection during childhood, little is known about the role of human IRAK2. We here identified a non-synonymous IRAK2 variant, rs35060588 (coding R214G), as hypofunctional in terms of NF-κB signaling and Toll-like receptor-mediated cytokine induction. This was due to reduced ubiquitination of TRAF6, a key step in signal transduction. IRAK2 rs35060588 occurs in 3-9% of individuals in different ethnic groups, and our studies suggested a genetic association of rs35060588 with colorectal cancer survival. This for the first time implicates human IRAK2 in a human disease and highlights the R214G IRAK2 variant as a potential novel and broadly applicable biomarker for disease or as a therapeutic intervention point.

Comparing logistic regression, support vector machines, and permanental classification methods in predicting hypertension

In this paper, we compare logistic regression and 2 other classification methods in predicting hypertension given the genotype information. We use logistic regression analysis in the first step to detect significant single-nucleotide polymorphisms (SNPs). In the second step, we use the significant SNPs with logistic regression, support vector machines (SVMs), and a newly developed permanental classification method for prediction purposes. We also detect rare variants and investigate their impact on prediction. Our results show that SVMs and permanental classification both outperform logistic regression, and they are comparable in predicting hypertension status.

Association analysis of whole genome sequencing data accounting for longitudinal and family designs

Using the whole genome sequencing data and the simulated longitudinal phenotypes for 849 pedigree-based individuals from Genetic Analysis Workshop 18, we investigated various approaches to detecting the association of rare and common variants with blood pressure traits. We compared three strategies for longitudinal data: (a) using the baseline measurement only, (b) using the average from multiple visits, and (c) using all individual measurements. We also compared the power of using all of the pedigree-based data and the unrelated subset. The analyses were performed without knowledge of the underlying simulating model.

Comparison of several sequence-based association methods in pedigrees

Genome-wide association studies are very powerful in determining the genetic variants affecting complex diseases. Most of the available methods are very useful in detecting association between common variants and complex diseases. Recently, methods to detect rare variants in association with complex diseases have been developed with the increasingly available sequencing data from next-generation sequencing. In this paper, we evaluate and compare several of these recent methods for performing statistical association using whole genome sequencing data in pedigrees. Specifically, functional principal component analysis (FPCA), extended combined multivariate and collapsing (CMC) method for families, a generalized T(2) method, and chi-square minimum approach were compared by analyzing all the genetic variants, common and rare, of both the real data set and the simulated data set provided as part of Genetic Analysis Workshop 18.

Evidence for the role of EPHX2 gene variants in anorexia nervosa

Anorexia nervosa (AN) and related eating disorders are complex, multifactorial neuropsychiatric conditions with likely rare and common genetic and environmental determinants. To identify genetic variants associated with AN, we pursued a series of sequencing and genotyping studies focusing on the coding regions and upstream sequence of 152 candidate genes in a total of 1205 AN cases and 1948 controls. We identified individual variant associations in the Estrogen Receptor-ß (ESR2) gene, as well as a set of rare and common variants in the Epoxide Hydrolase 2 (EPHX2) gene, in an initial sequencing study of 261 early-onset severe AN cases and 73 controls (P=0.0004). The association of EPHX2 variants was further delineated in: (1) a pooling-based replication study involving an additional 500 AN patients and 500 controls (replication set P=0.00000016); (2) single-locus studies in a cohort of 386 previously genotyped broadly defined AN cases and 295 female population controls from the Bogalusa Heart Study (BHS) and a cohort of 58 individuals with self-reported eating disturbances and 851 controls (combined smallest single locus P<0.01). As EPHX2 is known to influence cholesterol metabolism, and AN is often associated with elevated cholesterol levels, we also investigated the association of EPHX2 variants and longitudinal body mass index (BMI) and cholesterol in BHS female and male subjects (N=229) and found evidence for a modifying effect of a subset of variants on the relationship between cholesterol and BMI (P<0.01). These findings suggest a novel association of gene variants within EPHX2 to susceptibility to AN and provide a foundation for future study of this important yet poorly understood condition.

Managing incidental findings in exome sequencing for research

Exome sequencing for research has become available for broadly based genomic studies as well as smaller targeted investigations. New exome research projects being considered will intentionally process a large amount of common and rare DNA variation for the purpose of finding specific links between genotype and phenotype. However, the risks of uncovering a clinically relevant incidental finding are not uniform across projects but are highly dependent on the question being asked and exactly how it is intended to be answered.Factors that influence the possibility of revealing a clinically relevant incidental DNA variation include the following: The overall design of the study and the number of participants involved, the mode of inheritance of the phenotype including whether the phenotype is likely to have a monogenic or a complex inheritance, whether the study is assessing a known list of genes or not, and whether the causative DNA variation is likely to be rare or common. Importantly, differing bioinformatics DNA variant filtering strategies strongly influence the odds of discovering an incidental finding. This chapter provides a framework for understanding and assessing the likelihood of discovering clinically relevant, incidental DNA variations that are not directly related to the question being addressed in a particular exome research project. It also outlines DNA variant filtering and functional informatics approaches that can investigate specific genomic questions while minimizing the risks of uncovering an incidental finding.

The impact of population demography and selection on the genetic architecture of complex traits

Population genetic studies have found evidence for dramatic population growth in recent human history. It is unclear how this recent population growth, combined with the effects of negative natural selection, has affected patterns of deleterious variation, as well as the number, frequency, and effect sizes of mutations that contribute risk to complex traits. Because researchers are performing exome sequencing studies aimed at uncovering the role of low-frequency variants in the risk of complex traits, this topic is of critical importance. Here I use simulations under population genetic models where a proportion of the heritability of the trait is accounted for by mutations in a subset of the exome. I show that recent population growth increases the proportion of nonsynonymous variants segregating in the population, but does not affect the genetic load relative to a population that did not expand. Under a model where a mutation's effect on a trait is correlated with its effect on fitness, rare variants explain a greater portion of the additive genetic variance of the trait in a population that has recently expanded than in a population that did not recently expand. Further, when using a single-marker test, for a given false-positive rate and sample size, recent population growth decreases the expected number of significant associations with the trait relative to the number detected in a population that did not expand. However, in a model where there is no correlation between a mutation's effect on fitness and the effect on the trait, common variants account for much of the additive genetic variance, regardless of demography. Moreover, here demography does not affect the number of significant associations detected. These findings suggest recent population history may be an important factor influencing the power of association tests and in accounting for the missing heritability of certain complex traits.

Elevated blood pressure: Our family's fault? The genetics of essential hypertension

AIM

To provide an updated review on current genetic aspects possibly affecting essential hypertension (EH), and to further elucidate their role in EH.

METHODS

We searched for genetic and epigenetic factors in major studies associated with EH between Jan 2008-Oct 2013 using PubMed. We limited our search to reviews that discussed mostly human studies, and were accessible through the university online resource. We found 11 genome wide association studies (GWAS), as well as five methylation and three miRNA studies that fit our search criteria. A distinction was not made between genes with protective effects or negative effects, as this article is only meant to be a summary of genes associated with any aspect of EH.

RESULTS

We found 130 genes from the studies that met our inclusion/exclusion criteria. Of note, genes with multiple study references include: STK39, CYP17A1, MTHFR-NPPA, MTHFR-NPPB, ATP2B1, CSK, ZNF652, UMOD, CACNB2, PLEKHA7, SH2B3, TBX3-TBX5, ULK4, CSK-ULK3, CYP1A2, NT5C2, CYP171A, PLCD3, SH2B3, ATXN2, CACNB2, PLEKHA7, SH2B3, TBX3-TBX5, ULK4, and HFE. The following genes overlapped between the genetic studies and epigenetic studies: WNK4 and BDKRB2. Several of the identified genes were found to have functions associated with EH. Many epigenetic factors were also correlated with EH. Of the epigenetic factors, there were no articles discussing siRNA and its effects on EH that met the search criteria, thus the topic was not included in this review. Among the miRNA targets found to be associated with EH, many of the genes involved were also identified in the GWAS studies.

CONCLUSION

Genetic hypertension risk algorithms could be developed in the future but may be of limited benefit due to the multi-factorial nature of EH. With emerging technologies, like next-generation sequencing, more direct causal relationships between genetic and epigenetic factors affecting EH will likely be discovered creating a tremendous potential for personalized medicine using pharmacogenomics.

Functional linear models for association analysis of quantitative traits

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study.

Unraveling genetic origin of aging-related traits: evolving concepts

Discovering the genetic origin of aging-related traits could greatly advance strategies aiming to extend health span. The results of genome-wide association studies (GWAS) addressing this problem are controversial, and new genetic concepts have been fostered to advance the progress in the field. A limitation of GWAS and new genetic concepts is that they do not thoroughly address specifics of aging-related traits. Integration of theoretical concepts in genetics and aging research with empirical evidence from different disciplines highlights the conceptual problems in studies of genetic origin of aging-related traits. To address these problems, novel approaches of systemic nature are required. These approaches should adopt the non-deterministic nature of linkage of genes with aging-related traits and, consequently, reinforce research strategies for improving our understanding of mechanisms shaping genetic effects on these traits. Investigation of mechanisms will help determine conditions that activate specific genetic variants or profiles and explore to what extent these conditions that shape genetic effects are conserved across human lives and generations.

The application of network label propagation to rank biomarkers in genome-wide Alzheimer's data

Background

Ranking and identifying biomarkers that are associated with disease from genome-wide measurements holds significant promise for understanding the genetic basis of common diseases. The large number of single nucleotide polymorphisms (SNPs) in genome-wide studies (GWAS), however, makes this task computationally challenging when the ranking is to be done in a multivariate fashion. This paper evaluates the performance of a multivariate graph-based method called label propagation (LP) that efficiently ranks SNPs in genome-wide data.

Results

The performance of LP was evaluated on a synthetic dataset and two late onset Alzheimer’s disease (LOAD) genome-wide datasets, and the performance was compared to that of three control methods. The control methods included chi squared, which is a commonly used univariate method, as well as a Relief method called SWRF and a sparse logistic regression (SLR) method, which are both multivariate ranking methods. Performance was measured by evaluating the top-ranked SNPs in terms of classification performance, reproducibility between the two datasets, and prior evidence of being associated with LOAD.

On the synthetic data LP performed comparably to the control methods. On GWAS data, LP performed significantly better than chi squared and SWRF in classification performance in the range from 10 to 1000 top-ranked SNPs for both datasets, and not significantly different from SLR. LP also had greater ranking reproducibility than chi squared, SWRF, and SLR. Among the 25 top-ranked SNPs that were identified by LP, there were 14 SNPs in one dataset that had evidence in the literature of being associated with LOAD, and 10 SNPs in the other, which was higher than for the other methods.

Conclusion

LP performed considerably better in ranking SNPs in two high-dimensional genome-wide datasets when compared to three control methods. It had better performance in the evaluation measures we used, and is computationally efficient to be applied practically to data from genome-wide studies. These results provide support for including LP in the methods that are used to rank SNPs in genome-wide datasets.

A generalized genetic random field method for the genetic association analysis of sequencing data

With the advance of high-throughput sequencing technologies, it has become feasible to investigate the influence of the entire spectrum of sequencing variations on complex human diseases. Although association studies utilizing the new sequencing technologies hold great promise to unravel novel genetic variants, especially rare genetic variants that contribute to human diseases, the statistical analysis of high-dimensional sequencing data remains a challenge. Advanced analytical methods are in great need to facilitate high-dimensional sequencing data analyses. In this article, we propose a generalized genetic random field (GGRF) method for association analyses of sequencing data. Like other similarity-based methods (e.g., SIMreg and SKAT), the new method has the advantages of avoiding the need to specify thresholds for rare variants and allowing for testing multiple variants acting in different directions and magnitude of effects. The method is built on the generalized estimating equation framework and thus accommodates a variety of disease phenotypes (e.g., quantitative and binary phenotypes). Moreover, it has a nice asymptotic property, and can be applied to small-scale sequencing data without need for small-sample adjustment. Through simulations, we demonstrate that the proposed GGRF attains an improved or comparable power over a commonly used method, SKAT, under various disease scenarios, especially when rare variants play a significant role in disease etiology. We further illustrate GGRF with an application to a real dataset from the Dallas Heart Study. By using GGRF, we were able to detect the association of two candidate genes, ANGPTL3 and ANGPTL4, with serum triglyceride.

Targeted sequencing in candidate genes for atrial fibrillation: the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study

BACKGROUND

Genome-wide association studies (GWAS) have identified common genetic variants that predispose to atrial fibrillation (AF). It is unclear whether rare and low-frequency variants in genes implicated by such GWAS confer additional risk of AF.

OBJECTIVE

To study the association of genetic variants with AF at GWAS top loci.

METHODS

In the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study, we selected and sequenced 77 target gene regions from GWAS loci of complex diseases or traits, including 4 genes hypothesized to be related to AF (PRRX1, CAV1, CAV2, and ZFHX3). Sequencing was performed in participants with (n = 948) and without (n = 3330) AF from the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Massachusetts General Hospital.

RESULTS

One common variant (rs11265611; P = 1.70 × 10(-6)) intronic to IL6R (interleukin-6 receptor gene) was significantly associated with AF after Bonferroni correction (odds ratio 0.70; 95% confidence interval 0.58-0.85). The variant was not genotyped or imputed by prior GWAS, but it is in linkage disequilibrium (r(2) = .69) with the single-nucleotide polymorphism, with the strongest association with AF so far at this locus (rs4845625). In the rare variant joint analysis, damaging variants within the PRRX1 region showed significant association with AF after Bonferroni correction (P = .01).

CONCLUSIONS

We identified 1 common single-nucleotide polymorphism and 1 gene region that were significantly associated with AF. Future sequencing efforts with larger sample sizes and more comprehensive genome coverage are anticipated to identify additional AF-related variants.

Chip-based direct genotyping of coding variants in genome wide association studies: utility, issues and prospects

There is considerable debate about the most efficient way to interrogate rare coding variants in association studies. The options include direct genotyping of specific known coding variants in genes or, alternatively, sequencing across the entire exome to capture known as well as novel variants. Each strategy has advantages and disadvantages, but the availability of cost-efficient exome arrays has made the former appealing. Here we consider the utility of a direct genotyping chip, the Illumina HumanExome array (HE), by evaluating its content based on: 1. functionality; and 2. amenability to imputation. We explored these issues by genotyping a large, ethnically diverse cohort on the HumanOmniExpressExome array (HOEE) which combines the HE with content from the GWAS array (HOE). We find that the use of the HE is likely to be a cost-effective way of expanding GWAS, but does have some drawbacks that deserve consideration when planning studies.

Identifying rare variants associated with complex traits via sequencing

Although genome-wide association studies have been successful in detecting associations with common variants, there is currently an increasing interest in identifying low-frequency and rare variants associated with complex traits. Next-generation sequencing technologies make it feasible to survey the full spectrum of genetic variation in coding regions or the entire genome. The association analysis for rare variants is challenging, and traditional methods are ineffective, however, due to the low frequency of rare variants, coupled with allelic heterogeneity. Recently a battery of new statistical methods has been proposed for identifying rare variants associated with complex traits. These methods test for associations by aggregating multiple rare variants across a gene or a genomic region or among a group of variants in the genome. In this unit, we describe key concepts for rare variant association for complex traits, survey some of the recent methods, discuss their statistical power under various scenarios, and provide practical guidance on analyzing next-generation sequencing data for identifying rare variants associated with complex traits.

A method to incorporate prior information into score test for genetic association studies

Background

The interest of the scientific community in investigating the impact of rare variants on complex traits has stimulated the development of novel statistical methodologies for association studies. The fact that many of the recently proposed methods for association studies suffer from low power to identify a genetic association motivates the incorporation of prior knowledge into statistical tests.

Results

In this article we propose a methodology to incorporate prior information into the region-based score test. Within our framework prior information is used to partition variants within a region into several groups, following which asymptotically independent group statistics are constructed and then combined into a global test statistic. Under the null hypothesis the distribution of our test statistic has lower degrees of freedom compared with those of the region-based score statistic. Theoretical power comparison, population genetics simulations and results from analysis of the GAW17 sequencing data set suggest that under some scenarios our method may perform as well as or outperform the score test and other competing methods.

Conclusions

An approach which uses prior information to improve the power of the region-based score test is proposed. Theoretical power comparison, population genetics simulations and the results of GAW17 data analysis showed that for some scenarios power of our method is on the level with or higher than those of the score test and other methods.

Genomics, personalized medicine, and pediatrics

Genomic discoveries are advancing biomedicine at an ever-increasing pace. Pediatrics is near the epicenter of these discoveries, which are revising our understanding of the genome and its function. Since the completion of the Human Genome Project in 2003, dramatic reductions in the cost of genotyping, and more recently sequencing, have permitted the study of the genomes of a great number of species as well as humans. These studies have led to insights on gene regulation and the complex interplay of factors responsible for normal development and biology. Study of single-gene disorders has greatly benefited from the genomics revolution and tests are now available for well over 2000 Mendelian conditions; availability of these tests are changing screening and diagnosis paradigms for rare conditions. Genomics is also yielding an increased understanding of common conditions such as diabetes, obesity, asthma, cancers, and mental health conditions. Personalized medicine, an approach to care in which an individual's genomic information is used to help tailor interventions to maximize health outcomes, is rapidly becoming a reality for a variety of conditions. Though challenges remain in translating new genomic insights into improved patient health, today's pediatricians and their patients will increasingly benefit from this watershed moment in the biological sciences.

Systems analysis of high-throughput data

Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g.,  sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.

Association study of germline variants in CCNB1 and CDK1 with breast cancer susceptibility, progression, and survival among Chinese Han women

The CCNB1 and CDK1 genes encode the proteins of CyclinB1 and CDK1 respectively, which interact with each other and are involved in cell cycle regulation, centrosome duplication and chromosome segregation. This study aimed to investigate whether the genetic variants in these two genes may affect breast cancer (BC) susceptibility, progression, and survival in Chinese Han population using haplotype-based analysis. A total of ten tSNPs spanning from 2kb upstream to 2kb downstream of these genes were genotyped in 1204 cases and 1204 age-matched cancer-free controls. The haplotype blocks were determined according to our genotyping data and linkage disequilibrium (LD) status of these SNPs. For CCNB1, rs2069429 was significantly associated with increased BC susceptibility under recessive model (OR=2.352, 95%CI=1.480-3.737), so was the diplotype TAGT/TAGT (OR=1.947 95%CI=1.154-3.284, P=0.013). In addition, rs164390 was associated with Her2-negative BC. For CDK1, rs2448343 and rs1871446 were significantly associated with decreased BC risk under dominant models, so was the haplotype ATATT. These two SNPs also showed a dose-dependent effect on BC susceptibility. Using stratified association analysis, we found that women with the heterozygotes or minor allele homozygotes of rs2448343 had much less BC susceptibility among women with BMI<23. In CDK1, three closely located SNPs, rs2448343, rs3213048 and rs3213067, were significantly associated with tumor’s PR status: the heterozygotes of rs2448343 were associated with PR-positive tumors, while the minor allele homozygotes of rs3213048 and heterozygotes of rs3213067 were associated with PR-negative BC tumors. In survival analysis, rs1871446 was associated with unfavorable event-free survival under recessive model, so was the CDK1 diplotype ATATG/ATATG, which carried the minor allele homozygote of rs1871446. Our study indicates that genetic polymorphisms of CCNB1 and CDK1 are related to BC susceptibility, progression, and survival in Chinese Han women. Further studies need to be performed in other populations as an independent replication to verify these results.

Efficient clustering of identity-by-descent between multiple individuals

MOTIVATION

Most existing identity-by-descent (IBD) detection methods only consider haplotype pairs; less attention has been paid to considering multiple haplotypes simultaneously, even though IBD is an equivalence relation on haplotypes that partitions a set of haplotypes into IBD clusters. Multiple-haplotype IBD clusters may have advantages over pairwise IBD in some applications, such as IBD mapping. Existing methods for detecting multiple-haplotype IBD clusters are often computationally expensive and unable to handle large samples with thousands of haplotypes.

RESULTS

We present a clustering method, efficient multiple-IBD, which uses pairwise IBD segments to infer multiple-haplotype IBD clusters. It expands clusters from seed haplotypes by adding qualified neighbors and extends clusters across sliding windows in the genome. Our method is an order of magnitude faster than existing methods and has comparable performance with respect to the quality of clusters it uncovers. We further investigate the potential application of multiple-haplotype IBD clusters in association studies by testing for association between multiple-haplotype IBD clusters and low-density lipoprotein cholesterol in the Northern Finland Birth Cohort. Using our multiple-haplotype IBD cluster approach, we found an association with a genomic interval covering the PCSK9 gene in these data that is missed by standard single-marker association tests. Previously published studies confirm association of PCSK9 with low-density lipoprotein.

AVAILABILITY AND IMPLEMENTATION

Source code is available under the GNU Public License http://cs.au.dk/~qianyuxx/EMI/.

A powerful association test of multiple genetic variants using a random-effects model

There is an emerging interest in sequencing-based association studies of multiple rare variants. Most association tests suggested in the literature involve collapsing rare variants with or without weighting. Recently, a variance-component score test [sequence kernel association test (SKAT)] was proposed to address the limitations of collapsing method. Although SKAT was shown to outperform most of the alternative tests, its applications and power might be restricted and influenced by missing genotypes. In this paper, we suggest a new method based on testing whether the fraction of causal variants in a region is zero. The new association test, T REM , is derived from a random-effects model and allows for missing genotypes, and the choice of weighting function is not required when common and rare variants are analyzed simultaneously. We performed simulations to study the type I error rates and power of four competing tests under various conditions on the sample size, genotype missing rate, variant frequency, effect directionality, and the number of non-causal rare variant and/or causal common variant. The simulation results showed that T REM was a valid test and less sensitive to the inclusion of non-causal rare variants and/or low effect common variants or to the presence of missing genotypes. When the effects were more consistent in the same direction, T REM also had better power performance. Finally, an application to the Shanghai Breast Cancer Study showed that rare causal variants at the FGFR2 gene were detected by T REM and SKAT, but T REM produced more consistent results for different sets of rare and common variants.

Two-phase and family-based designs for next-generation sequencing studies

The cost of next-generation sequencing is now approaching that of early GWAS panels, but is still out of reach for large epidemiologic studies and the millions of rare variants expected poses challenges for distinguishing causal from non-causal variants. We review two types of designs for sequencing studies: two-phase designs for targeted follow-up of genomewide association studies using unrelated individuals; and family-based designs exploiting co-segregation for prioritizing variants and genes. Two-phase designs subsample subjects for sequencing from a larger case-control study jointly on the basis of their disease and carrier status; the discovered variants are then tested for association in the parent study. The analysis combines the full sequence data from the substudy with the more limited SNP data from the main study. We discuss various methods for selecting this subset of variants and describe the expected yield of true positive associations in the context of an on-going study of second breast cancers following radiotherapy. While the sharing of variants within families means that family-based designs are less efficient for discovery than sequencing unrelated individuals, the ability to exploit co-segregation of variants with disease within families helps distinguish causal from non-causal ones. Furthermore, by enriching for family history, the yield of causal variants can be improved and use of identity-by-descent information improves imputation of genotypes for other family members. We compare the relative efficiency of these designs with those using unrelated individuals for discovering and prioritizing variants or genes for testing association in larger studies. While associations can be tested with single variants, power is low for rare ones. Recent generalizations of burden or kernel tests for gene-level associations to family-based data are appealing. These approaches are illustrated in the context of a family-based study of colorectal cancer.

To control false positives in gene-gene interaction analysis: two novel conditional entropy-based approaches

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects.

Resequencing the untranslated regions of the lipoprotein lipase (LPL) gene reveals that variants in microRNA target sequences are associated with triglyceride levels

BACKGROUND

Rare variants in the protein coding regions of the lipoprotein lipase (LPL) gene have been shown to be important in the development of hypertriglyceridemia.

OBJECTIVES

The aim of this study was to determine whether rare variants in the 3' and 5' untranslated regions (UTRs) of the LPL gene are also associated with severe hypertriglyceridemia.

METHODS

The DNA sequences of the 3' and 5' UTRs of the LPL gene of 63 patients with triglycerides > 875 mg/dL (10 mmol) and 69 probands with triglycerides below the 25th percentile for age and sex were determined. The sequence at the 5' end was extended to include 2 further elements (-702 to -666 and -468 to -430) shown to be associated with the control of LPL expression.

RESULTS

No statistically significant difference was found in the occurrence of rare mutations in either the 3' or the 5' UTRs between the 2 groups. Sequence analysis allowed the genotyping of 47 single nucleotide polymorphisms (SNPs) in the 3' UTR and 11 in the 5' UTR. Two groups of SNPs in the 3' UTR, based on allelic association, were statistically significantly associated with plasma triglycerides. Each of these groups contained SNPs in the target sequences for microRNAs. These findings were replicated in independently formed groups.

CONCLUSION

We provide genetic evidence that microRNAs may play a role in the expression of LPL and thus plasma triglyceride levels.

Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m(2) and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk.

The next-generation sequencing revolution and its impact on genomics

Genomics is a relatively new scientific discipline, having DNA sequencing as its core technology. As technology has improved the cost and scale of genome characterization over sequencing's 40-year history, the scope of inquiry has commensurately broadened. Massively parallel sequencing has proven revolutionary, shifting the paradigm of genomics to address biological questions at a genome-wide scale. Sequencing now empowers clinical diagnostics and other aspects of medical care, including disease risk, therapeutic identification, and prenatal testing. This Review explores the current state of genomics in the massively parallel sequencing era.

An evaluation of power to detect low-frequency variant associations using allele-matching tests that account for uncertainty

There is growing interest in the role of rare variants in multifactorial disease etiology, and increasing evidence that rare variants are associated with complex traits. Single SNP tests are underpowered in rare variant association analyses, so locus-based tests must be used. Quality scores at both the SNP and genotype level are available for sequencing data and they are rarely accounted for. A locus-based method that has high power in the presence of rare variants is extended to incorporate such quality scores as weights, and its power is compared with the original method via a simulation study. Preliminary results suggest that taking uncertainty into account does not improve the power.