Evaluation of coverage variation of SNP chips for genome-wide association studies

Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward

Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.

Effect of single nucleotide polymorphisms on the structure of long noncoding RNAs and their interaction with RNA binding proteins

Long non-coding RNAs (lncRNA) are emerging as a new class of regulatory RNAs with remarkable potential to be utilized as therapeutic targets against many human diseases. Several genome-wide association studies (GWAS) have catalogued Single Nucleotide Polymorphisms (SNPs) present in the noncoding regions of the genome from where lncRNAs originate. In this study, we have selected 67 lncRNAs with GWAS-tagged SNPs and have also investigated their role in affecting the local secondary structures. Majority of the SNPs lead to changes in the secondary structure of lncRNAs to a different extent by altering the base pairing patterns. These structural changes in lncRNA are also manifested in form of alteration in the binding site for RNA binding proteins (RBPs) along with affecting their binding efficacies. Ultimately, these structural modifications may influence the transcriptional and post-transcriptional pathways of these RNAs, leading to the causation of diseases. Hence, it is important to understand the possible underlying mechanism of RBPs in association with GWAS-tagged SNPs in human diseases.

Current Status of Next-Generation Sequencing in Bone Genetic Diseases

The development of next-generation sequencing (NGS) has dramatically increased the speed and volume of genetic analysis. Furthermore, the range of applications of NGS is rapidly expanding to include genome, epigenome (such as DNA methylation), metagenome, and transcriptome analyses (such as RNA sequencing and single-cell RNA sequencing). NGS enables genetic research by offering various sequencing methods as well as combinations of methods. Bone tissue is the most important unit supporting the body and is a reservoir of calcium and phosphate ions, which are important for physical activity. Many genetic diseases affect bone tissues, possibly because metabolic mechanisms in bone tissue are complex. For instance, the presence of specialized immune cells called osteoclasts in the bone tissue, which absorb bone tissue and interact with osteoblasts in complex ways to support normal vital functions. Moreover, the many cell types in bones exhibit cell-specific proteins for their respective activities. Mutations in the genes encoding these proteins cause a variety of genetic disorders. The relationship between age-related bone tissue fragility (also called frailty) and genetic factors has recently attracted attention. Herein, we discuss the use of genomic, epigenomic, transcriptomic, and metagenomic analyses in bone genetic disorders.

In-silico mining to glean SNPs of pharmaco-clinical importance: an investigation with reference to the Indian populated SNPs

Drugs pharmacology is defined by pharmacokinetics and pharmacodynamics and both of them are affected by genetic variability. Genetic variability varies from population to population, and sometimes even within the population, it exists. Single nucleotide polymorphisms (SNPs) are one of the major genetic variability factors which are found to be associated with the pharmacokinetics and pharmacodynamics process of a drug and are responsible for variable drug response and clinical phenotypes. Studies of SNPs can help to perform genome-wide association studies for their association with pharmacological and clinical events, at the same time; their information can direct genome-wide association studies for their use as biomarkers. With the aim to mine and characterize Indian populated SNPs of pharmacological and clinical importance. Two hundred six candidate SNPs belonging to 43 genes were retrieved from Indian Genome Variation Database. The distribution pattern of considered SNPs was observed against all five world super-populations (AFR, AMR, EAS, EUR, and SAS). Further, their annotation was done through SNP-nexus by considering Human genome reference builds - hg38, pharmacological and clinical information was supplemented by PharmGKB and ClinVar database. At last, to find out the association between SNPs linkage disequilibrium was observed in terms of r2. Overall, the study reported 53 pharmaco-clinical active SNPs and found 24 SNP-pairs as potential markers, and recommended their clinical and experimental validation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00154-4.

Genetics and Epigenetics of Spontaneous Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) is a complex and heterogeneous disease, and there is no effective treatment. Spontaneous ICH represents the final manifestation of different types of cerebral small vessel disease, usually categorized as: lobar (mostly related to cerebral amyloid angiopathy) and nonlobar (hypertension-related vasculopathy) ICH. Accurate phenotyping aims to reflect these biological differences in the underlying mechanisms and has been demonstrated to be crucial to the success of genetic studies in this field. This review summarizes how current knowledge on genetics and epigenetics of this devastating stroke subtype are contributing to improve the understanding of ICH pathophysiology and their potential role in developing therapeutic strategies.

Assessment of Imputation Quality: Comparison of Phasing and Imputation Algorithms in Real Data

Despite the widespread use of genotype imputation tools and the availability of different approaches, late developments of currently used programs have not been compared comprehensively. We therefore assessed the performance of 35 combinations of phasing and imputation programs, including versions of SHAPEIT, Eagle, Beagle, minimac, PBWT, and IMPUTE, for genetic imputation of completely missing SNPs with a HRC reference panel regarding quality and speed. We used a data set comprising 1,149 fully sequenced individuals from the German population, subsetting the SNPs to approximate the Illumina Infinium-Omni5 array. Five hundred fifty-three thousand two hundred and thirty-four SNPs across two selected chromosomes were utilized for comparison between imputed and sequenced genotypes. We found that all tested programs with the exception of PBWT impute genotypes with very high accuracy (mean error rate < 0.005). PBTW hardly ever imputes the less frequent allele correctly (mean concordance for genotypes including the minor allele <0.0002). For all programs, imputation accuracy drops for rare alleles with a frequency <0.05. Even though overall concordance is high, concordance drops with genotype probability, indicating that low genotype probabilities are rare. The mean concordance of SNPs with a genotype probability <95% drops below 0.9, at which point disregarding imputed genotypes might prove favorable. For fast and accurate imputation, a combination of Eagle2.4.1 using a reference panel for phasing and Beagle5.1 for imputation performs best. Replacing Beagle5.1 with minimac3, minimac4, Beagle4.1, or IMPUTE4 results in a small gain in accuracy at a high cost of speed.

A survey of direct-to-consumer genotype data, and quality control tool (GenomePrep) for research

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Review of the offerings from commercial genotyping companies.

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Analysis of consumer genotype data SNP arrays.

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Quality control analysis of over 7000 open genomes.

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Open source tools and web service providing quality control report of genotype arrays.

Two major forces have contributed to the fast growth of human genetic data. One from medical research supported by governments and academic institutes; the other from direct-to-consumer (DTC) sequencing companies. While the former benefits from meticulously designed sequencing standards and quality control procedures, the latter comes in various formats and sequencing methods which are subject to changes over time and the particular needs of different companies. Thanks to the general public who shared their DNA data without constraint, here we provide a review for over 7000 genomes made public between 2011 and 2020, and produced by over six DTC sequencing companies. An open source tool-kit to systematically parse, quality check and filter genome files and statistically problematic alleles is provided to prepare consumer DNA datasets for research. The GenomePrep output is available in two common DNA datafile formats to enable further analysis with other tools. We also provide for download the combined output for all OpenSNP array genomes processed in this paper in a single data freeze file.

A comparison of genotyping arrays

Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study's requirements.

Global, pathway and gene coverage of three Illumina arrays with respect to inflammatory and immune-related pathways

Genome-wide association studies have led in the past to the discovery of susceptibility genes for many diseases including cancer and inflammatory conditions. However, a number of these studies did not realise their full potential. A first critical step in developing such large-scale studies is the choice of genotyping array with respect to the study goal. Coverage is the central criterion for array evaluation. We distinguish between estimates of global coverage across the genome, coverage for each chromosome, coverage for selected pathways and the coverage for genes of interest. Here, we focus on inflammatory and immunological pathways and genes relevant for haematopoietic stem cell transplantation. We compared three arrays: the Infinium Global Screening Array-24 v1.0, the Infinium OncoArray-500 K BeadChip and the Infinium PsychArray-24 v1.2 BeadChip. We employed the European population from the 1000 Genomes Project as reference genome. Global coverage was found to range between 12.2 and 14.2% whereas coverage for a selected pathway ranged from 6.2 to 13.2% and gene coverage ranged from 0 to 54.1%. The Global Screening Array outperformed both other arrays in terms of global coverage, for most chromosomes, most considered pathways and most genes. When selecting suitable arrays for a new study, the coverage of pathways or genes of interest should be considered in addition to global coverage. Local coverage should be regarded when discussing association findings inconsistent across studies and can be useful in data analysis and decision making for additional genotyping.

Mouse Models and Online Resources for Functional Analysis of Osteoporosis Genome-Wide Association Studies

Osteoporosis is a complex genetic disease in which the number of loci associated with the bone mineral density, a clinical risk factor for fracture, has increased at an exponential rate in the last decade. The identification of the causative variants and candidate genes underlying these loci has not been able to keep pace with the rate of locus discovery. A large number of tools and data resources have been built around the use of the mouse as model of human genetic disease. Herein, we describe resources available for functional validation of human Genome Wide Association Study (GWAS) loci using mouse models. We specifically focus on large-scale phenotyping efforts focused on bone relevant phenotypes and repositories of genotype-phenotype data that exist for transgenic and mutant mice, which can be readily mined as a first step toward more targeted efforts designed to deeply characterize the role of a gene in bone biology.

Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance via mean imputed r2 at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts vs. single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1%) by 0.5-3.1% for an array of one million sites and 0.7-7.1% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

What has GWAS done for HLA and disease associations?

The major histocompatibility complex (MHC) is located in chromosome 6p21 and contains crucial regulators of immune response, including human leucocyte antigen (HLA) genes, alongside other genes with nonimmunological roles. More recently, a repertoire of noncoding RNA genes, including expressed pseudogenes, has also been identified. The MHC is the most gene dense and most polymorphic part of the human genome. The region exhibits haplotype-specific linkage disequilibrium patterns, contains the strongest cis- and trans-eQTLs/meQTLs in the genome and is known as a hot spot for disease associations. Another layer of complexity is provided to the region by the extreme structural variation and copy number variations. While the HLA-B gene has the highest number of alleles, the HLA-DR/DQ subregion is structurally most variable and shows the highest number of disease associations. Reliance on a single reference sequence has complicated the design, execution and analysis of GWAS for the MHC region and not infrequently, the MHC region has even been excluded from the analysis of GWAS data. Here, we contrast features of the MHC region with the rest of the genome and highlight its complexities, including its functional polymorphisms beyond those determined by single nucleotide polymorphisms or single amino acid residues. One of the several issues with customary GWAS analysis is that it does not address this additional layer of polymorphisms unique to the MHC region. We highlight alternative approaches that may assist with the analysis of GWAS data from the MHC region and unravel associations with all functional polymorphisms beyond single SNPs. We suggest that despite already showing the highest number of disease associations, the true extent of the involvement of the MHC region in disease genetics may not have been uncovered.

Genome-Wide Association Studies

Genetic association studies have made a major contribution to our understanding of the genetics of complex disorders over the last 10 years through genome-wide association studies (GWAS). In this chapter, we review the key concepts that underlie the GWAS approach. We will describe the "common disease, common variant" theory, and will review how we finally afforded to capture the common variance in genome to make GWAS possible. Finally, we will go over technical aspects of GWAS such as genotype imputation, epidemiologic designs, analysis methods, and considerations such as genomic inflation, multiple testing, and replication.

Prioritising Causal Genes at Type 2 Diabetes Risk Loci

PURPOSE OF REVIEW

Genome-wide association studies (GWAS) for type 2 diabetes (T2D) risk have identified a large number of genetic loci associated with disease susceptibility. However, progress moving from association signals through causal genes to functional understanding has so far been slow, hindering clinical translation. This review discusses the benefits and limitations of emerging, unbiased approaches for prioritising causal genes at T2D risk loci.

RECENT FINDINGS

Candidate causal genes can be identified by a number of different strategies that rely on genetic data, genomic annotations, and functional screening of selected genes. To overcome the limitations of each particular method, integration of multiple data sets is proving essential for establishing confidence in the prioritised genes. Previous studies have also highlighted the need to support these efforts through identification of causal variants and disease-relevant tissues. Prioritisation of causal genes at T2D risk loci by integrating complementary lines of evidence promises to accelerate our understanding of disease pathology and promote translation into new therapeutics.

Using Zebrafish to Test the Genetic Basis of Human Craniofacial Diseases

Genome-wide association studies (GWASs) opened an innovative and productive avenue to investigate the molecular basis of human craniofacial disease. However, GWASs identify candidate genes only; they do not prove that any particular one is the functional villain underlying disease or just an unlucky genomic bystander. Genetic manipulation of animal models is the best approach to reveal which genetic loci identified from human GWASs are functionally related to specific diseases. The purpose of this review is to discuss the potential of zebrafish to resolve which candidate genetic loci are mechanistic drivers of craniofacial diseases. Many anatomic, embryonic, and genetic features of craniofacial development are conserved among zebrafish and mammals, making zebrafish a good model of craniofacial diseases. Also, the ability to manipulate gene function in zebrafish was greatly expanded over the past 20 y, enabling systems such as Gateway Tol2 and CRISPR-Cas9 to test gain- and loss-of-function alleles identified from human GWASs in coding and noncoding regions of DNA. With the optimization of genetic editing methods, large numbers of candidate genes can be efficiently interrogated. Finding the functional villains that underlie diseases will permit new treatments and prevention strategies and will increase understanding of how gene pathways operate during normal development.

Identifying tagging SNPs for African specific genetic variation from the African Diaspora Genome

A primary goal of The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to develop an 'African Diaspora Power Chip' (ADPC), a genotyping array consisting of tagging SNPs, useful in comprehensively identifying African specific genetic variation. This array is designed based on the novel variation identified in 642 CAAPA samples of African ancestry with high coverage whole genome sequence data (~30× depth). This novel variation extends the pattern of variation catalogued in the 1000 Genomes and Exome Sequencing Projects to a spectrum of populations representing the wide range of West African genomic diversity. These individuals from CAAPA also comprise a large swath of the African Diaspora population and incorporate historical genetic diversity covering nearly the entire Atlantic coast of the Americas. Here we show the results of designing and producing such a microchip array. This novel array covers African specific variation far better than other commercially available arrays, and will enable better GWAS analyses for researchers with individuals of African descent in their study populations. A recent study cataloging variation in continental African populations suggests this type of African-specific genotyping array is both necessary and valuable for facilitating large-scale GWAS in populations of African ancestry.

Novel gene-brain structure relationships in psychotic disorder revealed using parallel independent component analyses

BACKGROUND

Schizophrenia, schizoaffective disorder, and psychotic bipolar disorder overlap with regard to symptoms, structural and functional brain abnormalities, and genetic risk factors. Neurobiological pathways connecting genes to clinical phenotypes across the spectrum from schizophrenia to psychotic bipolar disorder remain largely unknown.

METHODS

We examined the relationship between structural brain changes and risk alleles across the psychosis spectrum in the multi-site Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) cohort. Regional MRI brain volumes were examined in 389 subjects with a psychotic disorder (139 schizophrenia, 90 schizoaffective disorder, and 160 psychotic bipolar disorder) and 123 healthy controls. 451,701 single-nucleotide polymorphisms were screened and processed using parallel independent component analysis (para-ICA) to assess associations between genes and structural brain abnormalities in probands.

RESULTS

482 subjects were included after quality control (364 individuals with psychotic disorder and 118 healthy controls). Para-ICA identified four genetic components including several risk genes already known to contribute to schizophrenia and bipolar disorder and revealed three structural components that showed overlapping relationships with the disease risk genes across the three psychotic disorders. Functional ontologies representing these gene clusters included physiological pathways involved in brain development, synaptic transmission, and ion channel activity.

CONCLUSIONS

Heritable brain structural findings such as reduced cortical thickness and surface area in probands across the psychosis spectrum were associated with somewhat distinct genes related to putative disease pathways implicated in psychotic disorders. This suggests that brain structural alterations might represent discrete psychosis intermediate phenotypes along common neurobiological pathways underlying disease expression across the psychosis spectrum.

Genomic copy number variation association study in Caucasian patients with nonsyndromic cryptorchidism

Background

Copy number variation (CNV) is a potential contributing factor to many genetic diseases. Here we investigated the potential association of CNV with nonsyndromic cryptorchidism, the most common male congenital genitourinary defect, in a Caucasian population.

Methods

Genome wide genotyping were performed in 559 cases and 1772 controls (Group 1) using Illumina HumanHap550 v1, HumanHap550 v3 or Human610-Quad platforms and in 353 cases and 1149 controls (Group 2) using the Illumina Human OmniExpress 12v1 or Human OmniExpress 12v1-1. Signal intensity data including log R ratio (LRR) and B allele frequency (BAF) for each single nucleotide polymorphism (SNP) were used for CNV detection using PennCNV software. After sample quality control, gene- and CNV-based association tests were performed using cleaned data from Group 1 (493 cases and 1586 controls) and Group 2 (307 cases and 1102 controls) using ParseCNV software. Meta-analysis was performed using gene-based test results as input to identify significant genes, and CNVs in or around significant genes were identified in CNV-based association test results. Called CNVs passing quality control and signal intensity visualization examination were considered for validation using TaqMan CNV assays and QuantStudio® 3D Digital PCR System.

Results

The meta-analysis identified 373 genome wide significant (p < 5X10⁻⁴) genes/loci including 49 genes/loci with deletions and 324 with duplications. Among them, 17 genes with deletion and 1 gene with duplication were identified in CNV-based association results in both Group 1 and Group 2. Only 2 genes (NUCB2 and UPF2) containing deletions passed CNV quality control in both groups and signal intensity visualization examination, but laboratory validation failed to verify these deletions.

Conclusions

Our data do not support that structural variation is a major cause of nonsyndromic cryptorchidism.

Electronic supplementary material

The online version of this article (doi:10.1186/s12894-016-0180-4) contains supplementary material, which is available to authorized users.

Evaluation of power of the Illumina HumanOmni5M-4v1 BeadChip to detect risk variants for human complex diseases

Although emerging sequencing technologies can characterize all genetic variants, the cost is still high. Illumina released the HumanOmni5M-4v1 (Omni5) genotype array with ~4.3M assayed SNPs, a much denser array compared with other available arrays. The Omni5 balances both cost and array density. In this article, we illustrate the power of Omni5 to detect genetic associations. The Omni5 includes variants with a wide range of minor allele frequencies down to <1%. The theoretical power calculation examples indicate the increased power of the Omni5 array compared with other arrays with lower density when evaluating associations with some known loci, although there are exceptions. We further evaluate the genetic associations between known loci and several quantitative traits in the Framingham Heart Study: femoral neck bone mineral density, lumbar spine bone mineral density and hippocampal volume. Finally, we search genome wide for novel associations using the Omni5 genotypes. We compare our association results from Affymetrix 500K+MIPS 50K arrays and two imputed data sets: (1) HapMap Phase II and (2) 1000 Genomes reference panel. We observed increased evidence for genotype-phenotype associations with smaller P-values for selected known loci using the Omni5 genotypes. With limited sample sizes, we identify novel variants with genome-wide significant P-values. Our observations support the notion that dense genotyping using the Omni5 can be powerful in detecting novel associated variants. Comparison with imputed data with higher density also suggests that imputation helps but cannot replace genotyping, especially when imputation quality is low.

Genome-wide association meta-analyses to identify common genetic variants associated with hallux valgus in Caucasian and African Americans

OBJECTIVE

Hallux valgus (HV) affects ∼36% of Caucasian adults. Although considered highly heritable, the underlying genetic determinants are unclear. We conducted the first genome-wide association study (GWAS) aimed to identify genetic variants associated with HV.

METHODS

HV was assessed in three Caucasian cohorts (n=2263, n=915 and n=1231 participants, respectively). In each cohort, a GWAS was conducted using 2.5 M imputed SNPs. Mixed-effect regression with the additive genetic model adjusted for age, sex, weight and within-family correlations was used for both sex-specific and combined analyses. To combine GWAS results across cohorts, fixed-effect inverse-variance meta-analyses were used. Following meta-analyses, top-associated findings were also examined in an African American cohort (n=327).

RESULTS

The proportion of HV variance explained by genome-wide genotyped SNPs was 50% in men and 48% in women. A higher proportion of genetic determinants of HV were sex specific. The most significantly associated SNP in men was rs9675316 located on chr17q23-a24 near the AXIN2 gene (p=0.000000546×10(-7)); the most significantly associated SNP in women was rs7996797 located on chr13q14.1-q14.2 near the ESD gene (p=0.000000721×10(-7)). Genome-wide significant SNP-by-sex interaction was found for SNP rs1563374 located on chr11p15.1 near the MRGPRX3 gene (interaction p value =0.0000000041×10(-9)). The association signals diminished when combining men and women.

CONCLUSIONS

The findings suggest that the potential pathophysiological mechanisms of HV are complex and strongly underlined by sex-specific interactions. The identified genetic variants imply contribution of biological pathways observed in osteoarthritis as well as new pathways, influencing skeletal development and inflammation.

Family-based genome scan for age at onset of late-onset Alzheimer's disease in whole exome sequencing data

Alzheimer's disease (AD) is a common and complex neurodegenerative disease. Age at onset (AAO) of AD is an important component phenotype with a genetic basis, and identification of genes in which variation affects AAO would contribute to identification of factors that affect timing of onset. Increase in AAO through prevention or therapeutic measures would have enormous benefits by delaying AD and its associated morbidities. In this paper, we performed a family-based genome-wide association study for AAO of late-onset AD in whole exome sequence data generated in multigenerational families with multiple AD cases. We conducted single marker and gene-based burden tests for common and rare variants, respectively. We combined association analyses with variance component linkage analysis, and with reference to prior studies, in order to enhance evidence of the identified genes. For variants and genes implicated by the association study, we performed a gene-set enrichment analysis to identify potential novel pathways associated with AAO of AD. We found statistically significant association with AAO for three genes (WRN, NTN4 and LAMC3) with common associated variants, and for four genes (SLC8A3, SLC19A3, MADD and LRRK2) with multiple rare-associated variants that have a plausible biological function related to AD. The genes we have identified are in pathways that are strong candidates for involvement in the development of AD pathology and may lead to a better understanding of AD pathogenesis.

[Genetic predisposition and Pediatric Acute Respiratory Distress Syndrome: New tools for genetic study]

Acute respiratory distress syndrome (ARDS) is the most severe form of respiratory failure. Theoretically, any acute lung condition can lead to ARDS, but only a small percentage of individuals actually develop the disease. On this basis, genetic factors have been implicated in the risk of developing ARDS. Based on the pathophysiology of this disease, many candidate genes have been evaluated as potential modifiers in patient, as well as in animal models, of ARDS. Recent experimental data and clinical studies suggest that variations of genes involved in key processes of tissue, cellular and molecular lung damage may influence susceptibility and prognosis of ARDS. However, the pathogenesis of pediatric ARDS is complex, and therefore, it can be expected that many genes might contribute. Genetic variations such as single nucleotide polymorphisms and copy-number variations are likely associated with susceptibility to ARDS in children with primary lung injury. Genome-wide association (GWA) studies can objectively examine these variations, and help identify important new genes and pathogenetic pathways for future analysis. This approach might also have diagnostic and therapeutic implications, such as predicting patient risk or developing a personalized therapeutic approach to this serious syndrome.

The development of genome-wide association studies and their application to complex diseases, including lupus

In this review, we explain the motivation for carrying out genome-wide association studies (GWAS), contrasting the achievements of linkage-based experiments for Mendelian traits with the difficulties found when applying that type of experiment to complex diseases. We explain the technical and organizational developments that were required to make GWAS feasible, as well as some of the theoretical concerns that were raised during the design of these studies. We describe the impressive achievements of GWAS in lupus, and compare them with the experiences in three other genetically complex disorders: rheumatoid arthritis, type 1 diabetes and coronary heart disease. GWAS have been successful in identifying many new susceptibility loci for these four diseases, and have provided the motivation for novel immunological work. We conclude by describing preliminary steps that have been taken towards translating the results of GWAS into improvements in patient care, explaining some of the difficulties involved, as well as successes that have already been achieved.

Current concepts and clinical applications of stroke genetics

Driven by innovative technologies, novel analytical methods, and collaborations unimaginable not long ago, our understanding of the role of genetic variation in stroke has advanced substantially in recent years. However, a vast amount of data have accumulated quickly, and increasingly complex methodologies used in studies make keeping up to date on relevant findings difficult. In addition to well known, highly penetrant rare mutations that cause mendelian disorders related to stroke, several common genetic variants have been associated with common stroke subtypes, some of which also affect disease severity and clinical outcome. Furthermore, common genetic variations in biological pathways that have an important role in the pathophysiology of cerebrovascular diseases-such as blood pressure and oxidative phosphorylation-have been implicated in stroke. Clinical and translational applications of these and future discoveries in stroke genetics include identification of novel targets for treatment and development of personalised approaches to stroke prevention and management.

Inflammation-related genetic variations and survival in patients with advanced non-small cell lung cancer receiving first-line chemotherapy

Background

accurate prognostic prediction is challenging for advanced-stage non-small cell lung cancer (NSCLC) patients.

Methods

we systematically investigated genetic variants within inflammation pathway as potential prognostic markers for advanced-stage NSCLC patients treated with first-line chemotherapy. A discovery phase in 502 patients and an internal validation in 335 patients were completed at MD Anderson Cancer Center. External validation was performed in 371 patients at Harvard University.

Results

a missense SNP (HLA-DOB:rs2071554) predicted to influence protein function was significantly associated with poor survival in the discovery (HR:1.46, 95% CI:1.02-2.09), internal validation (HR:1.51, 95% CI:1.02-2.25), and external validation (HR:1.52, 95% CI:1.01-2.29) populations. KLRK1:rs2900420 was associated with a reduced risk in the discovery (HR:0.76, 95% CI:0.60-0.96), internal validation (HR:0.77, 95% CI:0.61-0.99), and external validation (HR:0.80, 95% CI:0.63-1.02) populations. A strong cumulative effect was observed for these SNPs on overall survival.

Conclusions

Genetic variations in inflammation-related genes could have potential to complement prediction of prognosis.

Genome-wide association studies and the clinic: a focus on breast cancer

Breast cancer is the most frequently diagnosed cancer among women worldwide, and has long been considered to be a genetic disease. A wide range of genetic variants, both rare mutations and more common variants, have been shown to influence breast cancer risk. In particular, recent studies have identified a number of common genetic variants, or single nucleotide polymorphisms, that are associated with breast cancer risk. In this review, we will briefly present the genetic epidemiology of breast cancer, genome-wide association study technology and how this technology may influence breast cancer screening in the clinic.

Coverage and efficiency in current SNP chips

To answer the question as to which commercial high-density SNP chip covers most of the human genome given a fixed budget, we compared the performance of 12 chips of different sizes released by Affymetrix and Illumina for the European, Asian, and African populations. These include Affymetrix' relatively new population-optimized arrays, whose SNP sets are each tailored toward a specific ethnicity. Our evaluation of the chips included the use of two measures, efficiency and cost-benefit ratio, which we developed as supplements to genetic coverage. Unlike coverage, these measures factor in the price of a chip or its substitute size (number of SNPs on chip), allowing comparisons to be drawn between differently priced chips. In this fashion, we identified the Affymetrix population-optimized arrays as offering the most cost-effective coverage for the Asian and African population. For the European population, we established the Illumina Human Omni 2.5-8 as the preferred choice. Interestingly, the Affymetrix chip tailored toward an Eastern Asian subpopulation performed well for all three populations investigated. However, our coverage estimates calculated for all chips proved much lower than those advertised by the producers. All our analyses were based on the 1000 Genome Project as reference population.

Bioinformatics challenges in genome-wide association studies (GWAS)

Genome-wide association studies (GWAS) are a powerful tool for investigators to examine the human genome to detect genetic risk factors, reveal the genetic architecture of diseases and open up new opportunities for treatment and prevention. However, despite its successes, GWAS have not been able to identify genetic loci that are effective classifiers of disease, limiting their value for genetic testing. This chapter highlights the challenges that lie ahead for GWAS in better identifying disease risk predictors, and how we may address them. In this regard, we review basic concepts regarding GWAS, the technologies used for capturing genetic variation, the missing heritability problem, the need for efficient study design especially for replication efforts, reducing the bias introduced into a dataset, and how to utilize new resources available, such as electronic medical records. We also look to what lies ahead for the field, and the approaches that can be taken to realize the full potential of GWAS.

Genetic framework of type 2 diabetes mellitus

More than 100 genes associated with the risk of type 2 diabetes mellitus (T2DM) are now established. Most of them affect insulin secretion, adipogenesis and insulin resistance, but the exact molecular mechanisms determining their involvement in the pathogenesis of T2DM are not understood completely.

Clinical implications of shared genetics and pathogenesis in autoimmune diseases

Many endocrine diseases, including type 1 diabetes mellitus, Graves disease, Addison disease and Hashimoto disease, originate as an autoimmune reaction that affects disease-specific target organs. These autoimmune diseases are characterized by the development of specific autoantibodies and by the presence of autoreactive T cells. They are caused by a complex genetic predisposition that is attributable to multiple genetic variants, each with a moderate-to-low effect size. Most of the genetic variants associated with a particular autoimmune endocrine disease are shared between other systemic and organ-specific autoimmune and inflammatory diseases, such as rheumatoid arthritis, coeliac disease, systemic lupus erythematosus and psoriasis. Here, we review the shared and specific genetic background of autoimmune diseases, summarize their treatment options and discuss how identifying the genetic and environmental factors that predispose patients to an autoimmune disease can help in the diagnosis and monitoring of patients, as well as the design of new treatments.

Testing for modes of inheritance involving compound heterozygotes

Functional variants change the protein product or the expression of genes. Due to the latest advances in sequencing technology, most known functional variants can now be assayed in a cost-effective manner. However, to fully use the information from functional variants, researchers need to model the joint effect of these variants. In this article, we propose methods that model the action/interaction of loss-of-function (LOF) mutations, i.e., those mutations that eliminate the protein product of a gene. When multiple LOFs occur in the same causal gene/region, their effect on a phenotype might depend on whether these mutations lie on the same DNA strand/haplotype. When compared to LOFs occurring on the same strand, if these mutations lie on different strands, both copies of the gene are impaired and the impact on the relevant phenotypes is likely to be more severe. To use the information from LOF strand colocalization, we propose three methods that utilize the information from the estimated number of affected strands. We compare the performance of the proposed and competing methods by using simulations of common and rare LOF variants. Two of the proposed methods exhibited desirable power profiles, the first for both common and rare LOFs and the second only for common LOFs. One of the existing methods, collapsed double heterozygosity, exhibits good power to detect compound models for rare variants, especially when no haplotype harbors two or more rare alleles. Consequently, we recommend these three methods to be used for the analysis of functional variants coming from sequencing studies.

Comparison of the performance of two commercial genome-wide association study genotyping platforms in Han Chinese samples

Most genome-wide association studies to date have been performed in populations of European descent, but there is increasing interest in expanding these studies to other populations. The performance of genotyping chips in Asian populations is not well established. Therefore, we sought to test the performance of widely used fixed-marker, genome-wide association studies chips in the Han Chinese population. Non-HapMap Chinese samples (n = 396) were genotyped using the Illumina OmniExpress and Affymetrix 6.0 platforms, whereas a subset also were genotyped using the Immunochip. Genotyped markers from the Affymetrix 6.0 and Illumina OmniExpress were used for full genome imputation based on the HapMap 2 JPT+CHB (Japanese from Tokyo, Japan and Chinese from Beijing, China) reference panel. The concordance between markers genotypes for the three platforms was very high whether directly genotyped or genotyped and imputed single nucleotide polymorphisms (SNPs; >99.8% for directly genotyped and >99.5% for genotyped and imputed SNPs, respectively) were compared. The OmniExpress chip data enabled more SNPs to be imputed, particularly SNPs with minor allele frequency >5%. The OmniExpress chip achieved better coverage of HapMap SNPs than the Affymetrix 6.0 chip (73.6% vs. 65.9%, respectively, for minor allele frequency >5%). The Affymetrix 6.0 and Illumina OmniExpress chip have similar genotyping accuracy and provide similar accuracy of imputed SNPs. The OmniExpress chip however provides better coverage of Asian HapMap SNPs, although its coverage of HapMap SNPs is moderate.

Chapter 11: Genome-wide association studies

Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.

Current status on genome-metabolome-wide associations: an opportunity in nutrition research

Genome-wide association studies (GWASs) have become a very important tool to address the genetic origin of phenotypic variability, in particular associated with diseases. Nevertheless, these types of studies provide limited information about disease etiology and the molecular mechanisms involved. Recently, the incorporation of metabolomics into the analysis has offered novel opportunities for a better understanding of disease-related metabolic deregulation. The pattern emerging from this work is that gene-driven changes in metabolism are prevalent and that common genetic variations can have a profound impact on the homeostatic concentrations of specific metabolites. A particularly interesting aspect of this work takes into account interactions of environment and lifestyle with the genome and how this interaction translates into changes in the metabolome. For instance, the role of PYROXD2 in trimethylamine metabolism points to an interaction between host and microbiome genomes (host/microbiota). Often, these findings reveal metabolic deregulations, which could eventually be tuned with a nutritional intervention. Here we review the development of gene-metabolism association studies from a single-gene/single-metabolite to a genome-wide/metabolome-wide approach and highlight the conceptual changes associated with this ongoing transition. Moreover, we report some of our recent GWAS results on a cohort of 265 individuals from an ethnically diverse population that validate and refine previous findings on gene-urine metabolism interactions. Specifically, our results confirm the effect of PYROXD2 polymorphisms on trimethylamine metabolism and suggest that a previously reported association of N-acetylated compounds with the ALMS1/NAT8 locus is driven by SNPs in the ALMS1 gene.

Evaluating coverage of exons by HapMap SNPs

Genome-wide association (GWA) studies are currently one of the most powerful tools in identifying disease-associated genes or variants. In typical GWA studies, single-nucleotide polymorphisms (SNPs) are often used as genetic makers. Therefore, it is critical to estimate the percentage of genetic variations which can be covered by SNPs through linkage disequilibrium (LD). In this study, we use the concept of haplotype blocks to evaluate the coverage of five SNP sets including the HapMap and four commercial arrays, for every exon in the human genome. We show that although some Chips can reach similar coverage as the HapMap, only about 50% of exons are completely covered by haplotype blocks of HapMap SNPs. We suggest further high-resolution genotyping methods are required, to provide adequate genome-wide power for identifying variants.

Analysis of the DISC1 translocation partner (11q14.3) in genetic risk of schizophrenia

The Disrupted-in-Schizophrenia 1 (DISC1) locus on human chromosome 1 was identified as a consequence of its involvement in a balanced translocation (1;11)(q42.1;q14.3) segregating with major psychiatric disorders in a Scottish family. Recently a comprehensive meta-analysis of genome-wide association scan data found no evidence that common variants of DISC1 (1q42.1) are associated with schizophrenia. Our aim was to test for association of variants in the 11q14.3 translocation region with schizophrenia. The 11q14.3 region was examined by meta-analysis of genome-wide scan data made available by the Genetic Association Information Network (GAIN) and other investigators (non-GAIN) through dbGap. P-values were adjusted for multiple testing using the false discovery rate (FDR) approach. There were no single-nucleotide polymorphisms (SNPs) significant (P < 0.05) after correction for multiple testing in the combined schizophrenia dataset. However, one SNP (rs2509382) was significantly associated in the male-only analysis with P(FDR)  = 0.024. Whilst the relevance of the (1;11)(q42.1;q14.3) translocation to psychiatric disorders is currently specific to the Scottish family, genetic material in the chromosome 11 region may contain risk variants for psychiatric disorders in the wider population. The association found in this region does warrant follow-up analysis in further sample sets.

The limits of genome-wide methods for pharmacogenomic testing

OBJECTIVE

The goal of pharmacogenomics is the translation of genomic discoveries into individualized patient care. Recent advances in the means to survey human genetic variation are fundamentally transforming our understanding of the genetic basis of interindividual variation in therapeutic response. The goal of this study was to systematically evaluate high-throughput genotyping technologies for their ability to assay variation in pharmacogenetically important genes (pharmacogenes). These platforms are either being proposed for or are already being widely used for clinical implementation; therefore, knowledge of coverage of pharmacogenes on these platforms would serve to better evaluate current or proposed pharmacogenetic association studies.

METHOD

Among the genes included in our study are drug-metabolizing enzymes, transporters, receptors, and drug targets, of interest to the entire pharmacogenetic community. We considered absolute and linkage disequilibrium (LD)-informed coverage, minor allele frequency spectrum, and functional annotation for a Caucasian population. We also examined the effect of LD, effect size, and cohort size on the power to detect single nucleotide polymorphism associations.

RESULTS

In our analysis of 253 pharmacogenes, we found that no platform showed more than 85% coverage of these genes (after accounting for LD). Furthermore, the lack of coverage showed a marked increase at minor allele frequencies of less than 20%. Even after accounting for LD, only 30% of the missense polymorphisms (which are enriched for low-frequency alleles) were covered by HapMap, with still lower coverage on the other platforms.

CONCLUSION

We have conducted the first systematic evaluation of the Axiom Genomic Database, Omni 2.5 M, and the Drug Metabolizing Enzymes and Transporters chip. This study is the first to utilize the 1000 Genomes Project to present a comprehensive evaluative framework. Our results provide a much-needed assessment of microarray-based genotyping and next-generation sequencing technologies' ability to survey fully the variation in genes of particular interest to the pharmacogenetics community. Our findings demonstrate the limitations of genome-wide methods and the challenges of implementing pharmacogenomic tests into the clinical context.

Systematic evaluation of apoptotic pathway gene polymorphisms and lung cancer risk

We adopted a two-stage study design to screen 927 single nucleotide polymorphisms (SNPs) located in 73 apoptotic-pathway genes in a case-control study and then performed a fast-track validation of the significant SNPs in a replication population to identify sequence variations in the apoptotic pathway modulating lung cancer risk. Fifty-five SNPs showed significant associations in the discovery population comprised of 661 lung cancer cases and 959 controls. Six of these SNPs located in three genes (Bcl-2, CASP9 and ANKS1B) were validated in a replication population with 1154 cases and 1373 controls. Additive model was the best-fitting model for five SNPs (rs1462129 and rs255102 of Bcl-2, rs6685648 of CASP9 and rs1549102, rs11110099 of ANKS1B) and recessive model was the best fit for one SNP (rs10745877 of ANKS1B). In the analysis of joint effects with subjects carrying no unfavorable genotypes as the reference group, those carrying one, two, and three or more unfavorable genotypes had an odds ratio (OR) of 2.22 [95% confidence interval (CI) = 1.08-4.57, P = 0.03], 2.70 (95% CI = 1.33-5.49; P = 0.006) and 4.13 (95% CI = 2.00-8.57; P = 0.0001), respectively (P for trend = 6.05E-06). The joint effect of unfavorable genotypes was also validated in the replication population. The SNPs identified are located in or near key genes known to play important roles in apoptosis regulation, supporting the strong biological relevance of our findings. Future studies are needed to identify the causal SNPs and elucidate the underlying molecular mechanisms.

Genotype imputation for African Americans using data from HapMap phase II versus 1000 genomes projects

Genotype imputation provides imputation of untyped single nucleotide polymorphisms (SNPs) that are present on a reference panel such as those from the HapMap Project. It is popular for increasing statistical power and comparing results across studies using different platforms. Imputation for African American populations is challenging because their linkage disequilibrium blocks are shorter and also because no ideal reference panel is available due to admixture. In this paper, we evaluated three imputation strategies for African Americans. The intersection strategy used a combined panel consisting of SNPs polymorphic in both CEU and YRI. The union strategy used a panel consisting of SNPs polymorphic in either CEU or YRI. The merge strategy merged results from two separate imputations, one using CEU and the other using YRI. Because recent investigators are increasingly using the data from the 1000 Genomes (1KG) Project for genotype imputation, we evaluated both 1KG-based imputations and HapMap-based imputations. We used 23,707 SNPs from chromosomes 21 and 22 on Affymetrix SNP Array 6.0 genotyped for 1,075 HyperGEN African Americans. We found that 1KG-based imputations provided a substantially larger number of variants than HapMap-based imputations, about three times as many common variants and eight times as many rare and low-frequency variants. This higher yield is expected because the 1KG panel includes more SNPs. Accuracy rates using 1KG data were slightly lower than those using HapMap data before filtering, but slightly higher after filtering. The union strategy provided the highest imputation yield with next highest accuracy. The intersection strategy provided the lowest imputation yield but the highest accuracy. The merge strategy provided the lowest imputation accuracy. We observed that SNPs polymorphic only in CEU had much lower accuracy, reducing the accuracy of the union strategy. Our findings suggest that 1KG-based imputations can facilitate discovery of significant associations for SNPs across the whole MAF spectrum. Because the 1KG Project is still under way, we expect that later versions will provide better imputation performance.

Human genetics and genomics a decade after the release of the draft sequence of the human genome

Substantial progress has been made in human genetics and genomics research over the past ten years since the publication of the draft sequence of the human genome in 2001. Findings emanating directly from the Human Genome Project, together with those from follow-on studies, have had an enormous impact on our understanding of the architecture and function of the human genome. Major developments have been made in cataloguing genetic variation, the International HapMap Project, and with respect to advances in genotyping technologies. These developments are vital for the emergence of genome-wide association studies in the investigation of complex diseases and traits. In parallel, the advent of high-throughput sequencing technologies has ushered in the 'personal genome sequencing' era for both normal and cancer genomes, and made possible large-scale genome sequencing studies such as the 1000 Genomes Project and the International Cancer Genome Consortium. The high-throughput sequencing and sequence-capture technologies are also providing new opportunities to study Mendelian disorders through exome sequencing and whole-genome sequencing. This paper reviews these major developments in human genetics and genomics over the past decade.

Examining markers in 8q24 to explain differences in evidence for association with cleft lip with/without cleft palate between Asians and Europeans

In a recent genome-wide association study (GWAS) from an international consortium, evidence of linkage and association in chr8q24 was much stronger among nonsyndromic cleft lip/palate (CL/P) case-parent trios of European ancestry than among trios of Asian ancestry. We examined marker information content and haplotype diversity across 13 recruitment sites (from Europe, United States, and Asia) separately, and conducted principal components analysis (PCA) on parents. As expected, PCA revealed large genetic distances between Europeans and Asians, and a north-south cline from Korea to Singapore in Asia, with Filipino parents forming a somewhat distinct Southeast Asian cluster. Hierarchical clustering of SNP heterozygosity revealed two major clades consistent with PCA results. All genotyped SNPs giving P < 10(-6) in the allelic transmission disequilibrium test (TDT) showed higher heterozygosity in Europeans than Asians. On average, European ancestry parents had higher haplotype diversity than Asians. Imputing additional variants across chr8q24 increased the strength of statistical evidence among Europeans and also revealed a significant signal among Asians (although it did not reach genome-wide significance). Tests for SNP-population interaction were negative, indicating the lack of strong signal for 8q24 in families of Asian ancestry was not due to any distinct genetic effect, but could simply reflect low power due to lower allele frequencies in Asians.

Comprehensive evaluation of SNP identification with the Restriction Enzyme-based Reduced Representation Library (RRL) method

Background

Restriction Enzyme-based Reduced Representation Library (RRL) method represents a relatively feasible and flexible strategy used for Single Nucleotide Polymorphism (SNP) identification in different species. It has remarkable advantage of reducing the complexity of the genome by orders of magnitude. However, comprehensive evaluation for actual efficacy of SNP identification by this method is still unavailable.

Results

In order to evaluate the efficacy of Restriction Enzyme-based RRL method, we selected Tsp 45I enzyme which covers 266 Mb flanking region of the enzyme recognition site according to in silico simulation on human reference genome, then we sequenced YH RRL after Tsp 45I treatment and obtained reads of which 80.8% were mapped to target region with an 20-fold average coverage, about 96.8% of target region was covered by at least one read and 257 K SNPs were identified in the region using SOAPsnp software.

Compared with whole genome resequencing data, we observed false discovery rate (FDR) of 13.95% and false negative rate (FNR) of 25.90%. The concordance rate of homozygote loci was over 99.8%, but that of heterozygote were only 92.56%. Repeat sequences and bases quality were proved to have a great effect on the accuracy of SNP calling, SNPs in recognition sites contributed evidently to the high FNR and the low concordance rate of heterozygote. Our results indicated that repeat masking and high stringent filter criteria could significantly decrease both FDR and FNR.

Conclusions

This study demonstrates that Restriction Enzyme-based RRL method was effective for SNP identification. The results highlight the important role of bias and the method-derived defects represented in this method and emphasize the special attentions noteworthy.

Genetic bases of stuttering: the state of the art, 2011

OBJECTIVE

The literature on the genetics of stuttering is reviewed with special reference to the historical development from psychosocial explanations leading up to current biological research of gene identification.

SUMMARY

A gradual progression has been made from the early crude methods of counting percentages of stuttering probands who have relatives who stutter to recent studies using entire genomes of DNA collected from each participant. Despite the shortcomings of some early studies, investigators have accumulated a substantial body of data showing a large presence of familial stuttering. This encouraged more refined research in the form of twin studies. Concordance rates among twins were sufficiently high to lend additional support to the genetic perspective of stuttering. More sophisticated aggregation studies and segregation analyses followed, producing data that matched recognized genetic models, providing the final ‘go ahead’ to proceed from the behavior/statistical genetics into the sphere of biological genetics. Recent linkage and association studies have begun to reveal contributing genes to the disorder.

CONCLUSION

No definitive findings have been made regarding which transmission model, chromosomes, genes, or sex factors are involved in the expression of stuttering in the population at large. Future research and clinical implications are discussed.

Twin studies in autoimmune disease: genetics, gender and environment

Twin studies are powerful tools to discriminate whether a complex disease is due to genetic or environmental factors. High concordance rates among monozygotic (MZ) twins support genetic factors being predominantly involved, whilst low rates are suggestive of environmental factors. Twin studies have often been utilised in the study of systemic and organ specific autoimmune diseases. As an example, type I diabetes mellitus has been investigated to establish that that disease is largely affected by genetic factors, compared to rheumatoid arthritis or scleroderma, which have a weaker genetic association. However, large twin studies are scarce or virtually non-existent in other autoimmune diseases which have been limited to few sets of twins and individual case reports. In addition to the study of the genetic and environmental contributions to disease, it is likely that twin studies will also provide data in regards to the clinical course of disease, as well as risk for development in related individuals. More importantly, genome-wide association studies have thus far reported genomic variants that only account for a minority of autoimmunity cases, and cannot explain disease discordance in MZ twins. Future research is therefore encouraged not only in the analysis of twins with autoimmune disease, but also in regards to epigenetic factors or rare variants that may be discovered with next-generation sequencing. This review will examine the literature surrounding twin studies in autoimmune disease including discussions of genetics and gender.

Genome-wide assessment for genetic variants associated with ventricular dysfunction after primary coronary artery bypass graft surgery

Background

Postoperative ventricular dysfunction (VnD) occurs in 9–20% of coronary artery bypass graft (CABG) surgical patients and is associated with increased postoperative morbidity and mortality. Understanding genetic causes of postoperative VnD should enhance patient risk stratification and improve treatment and prevention strategies. We aimed to determine if genetic variants associate with occurrence of in-hospital VnD after CABG surgery.

Methods

A genome-wide association study identified single nucleotide polymorphisms (SNPs) associated with postoperative VnD in male subjects of European ancestry undergoing isolated primary CABG surgery with cardiopulmonary bypass. VnD was defined as the need for ≥2 inotropes or mechanical ventricular support after CABG surgery. Validated SNPs were assessed further in two replication CABG cohorts and meta-analysis was performed.

Results

Over 100 SNPs were associated with VnD (P<10⁻⁴), with one SNP (rs17691914) encoded at 3p22.3 reaching genome-wide significance (P_{additive model} = 2.14×10⁻⁸). Meta-analysis of validation and replication study data for 17 SNPs identified three SNPs associated with increased risk for developing postoperative VnD after adjusting for clinical risk factors. These SNPs are located at 3p22.3 (rs17691914, OR_{additive model} = 2.01, P = 0.0002), 3p14.2 (rs17061085, OR_{additive model} = 1.70, P = 0.0001) and 11q23.2 (rs12279572, OR_{recessive model} = 2.19, P = 0.001).

Conclusions

No SNPs were consistently associated with strong risk (OR_{additive model}>2.1) of developing in-hospital VnD after CABG surgery. However, three genetic loci identified by meta-analysis were more modestly associated with development of postoperative VnD. Studies of larger cohorts to assess these loci as well as to define other genetic mechanisms and related biology that link genetic variants to postoperative ventricular dysfunction are warranted.

Performance of genotype imputation for rare variants identified in exons and flanking regions of genes

Genotype imputation has the potential to assess human genetic variation at a lower cost than assaying the variants using laboratory techniques. The performance of imputation for rare variants has not been comprehensively studied. We utilized 8865 human samples with high depth resequencing data for the exons and flanking regions of 202 genes and Genome-Wide Association Study (GWAS) data to characterize the performance of genotype imputation for rare variants. We evaluated reference sets ranging from 100 to 3713 subjects for imputing into samples typed for the Affymetrix (500K and 6.0) and Illumina 550K GWAS panels. The proportion of variants that could be well imputed (true r(2)>0.7) with a reference panel of 3713 individuals was: 31% (Illumina 550K) or 25% (Affymetrix 500K) with MAF (Minor Allele Frequency) less than or equal 0.001, 48% or 35% with 0.0010.05. The performance for common SNPs (MAF>0.05) within exons and flanking regions is comparable to imputation of more uniformly distributed SNPs. The performance for rare SNPs (0.01 Collapse

Key Words Collapse

MESH Headings

• Exons/genetics
• Gene Frequency
• Genes/genetics
• Genetic Variation
• Genome-Wide Association Study
• Genotype
• Humans
• Polymorphism, Single Nucleotide/genetics

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Grants

• R01 HG004960 NHGRI NIH HHS
• R01 HG005701 NHGRI NIH HHS
• R01HG005701 NHGRI NIH HHS
• R01HG004960 NHGRI NIH HHS

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Affiliation(s)

Li Li Genetics, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, King of Prussia, Pennsylvania, United States of America, and Stevenage, United Kingdom
Yun Li Department of Genetics, Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
Sharon R. Browning Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Brian L. Browning Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, United States of America
Andrew J. Slater Genetics, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, King of Prussia, Pennsylvania, United States of America, and Stevenage, United Kingdom
Xiangyang Kong Genetics, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, King of Prussia, Pennsylvania, United States of America, and Stevenage, United Kingdom
Jennifer L. Aponte Genetics, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, King of Prussia, Pennsylvania, United States of America, and Stevenage, United Kingdom
Vincent E. Mooser Genetics, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, King of Prussia, Pennsylvania, United States of America, and Stevenage, United Kingdom
Stephanie L. Chissoe Genetics, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, King of Prussia, Pennsylvania, United States of America, and Stevenage, United Kingdom
John C. Whittaker Genetics, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, King of Prussia, Pennsylvania, United States of America, and Stevenage, United Kingdom
Matthew R. Nelson Genetics, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, King of Prussia, Pennsylvania, United States of America, and Stevenage, United Kingdom
Margaret Gelder Ehm Genetics, GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America, King of Prussia, Pennsylvania, United States of America, and Stevenage, United Kingdom

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