Medical sequencing at the extremes of human body mass

Low-frequency variants in genes involved in glutamic acid metabolism and γ-glutamyl cycle and risk of coronary artery disease in type 2 diabetes

BACKGROUND

A common genetic variant at the glutamate-ammonia ligase (GLUL) locus has been previously associated with an increased risk of coronary artery disease (CAD) as well as alterations of glutamic acid metabolism and the γ-glutamyl cycle in individuals with type 2 diabetes (T2D). Here we investigated whether less frequent variants in GLUL and 15 additional genes in these pathways are associated with differences in CAD risk in T2D.

METHODS

Coding sequences and regulatory elements of these genes were sequenced in 2,394 individuals with T2D from three CAD case/control sets.

RESULTS

Ninety-six variants with minor allele frequency [MAF]< 0.05 were identified as being nominally associated with CAD status. One of these variants (rs62447457, MAF 0.025), placed in a non-coding region flanking the γ-glutamylcyclotransferase (GGCT) gene, showed nominal evidence of replication in two other cases-control sets (n = 1,132), with summary OR of 0.54 (p = 2.5 × 10-4). Another variant (rs145322388, MAF = 0.039), flanking the dipeptidase 2 (DPEP2) gene, showed association with CAD status across discovery and replications sets (summary OR 0.61, p = 2.5 × 10-4). A third variant (rs1238275622, MAF 0.004), flanking the GLUL gene, was associated with increased risk of CAD (summary OR 1.84, p-value 2.1 × 10-3). Based on their Regulome scores (2b, 2a, and 3a, respectively), all three variants are very likely to have regulatory functions.

CONCLUSIONS

In summary, we have identified low-frequency variants associated with CAD in T2D at two loci involved in glutamic acid metabolism and the γ-glutamyl cycle. These findings provide further evidence for a role of these pathways in the link between T2D and CAD.

Rare variation in non-coding regions with evolutionary signatures contributes to autism spectrum disorder risk

Little is known about the role of non-coding regions in the etiology of autism spectrum disorder (ASD). We examined three classes of non-coding regions: human accelerated regions (HARs), which show signatures of positive selection in humans; experimentally validated neural VISTA enhancers (VEs); and conserved regions predicted to act as neural enhancers (CNEs). Targeted and whole-genome analysis of >16,600 samples and >4,900 ASD probands revealed that likely recessive, rare, inherited variants in HARs, VEs, and CNEs substantially contribute to ASD risk in probands whose parents share ancestry, which enriches for recessive contributions, but modestly contribute, if at all, in simplex family structures. We identified multiple patient variants in HARs near IL1RAPL1 and in VEs near OTX1 and SIM1 and showed that they change enhancer activity. Our results implicate both human-evolved and evolutionarily conserved non-coding regions in ASD risk and suggest potential mechanisms of how regulatory changes can modulate social behavior.

Genetic variant panel allows predicting both obesity risk, and efficacy of procedures and diet in weight loss

Purpose

Obesity is a multifactorial condition with a relevant genetic correlation. Recent advances in genomic research have identified several single nucleotide polymorphisms (SNPs) in genes such as FTO, MCM6, HLA, and MC4R, associated with obesity. This study aimed to evaluate the association of 102 SNPs with BMI and weight loss treatment response in a multi-ethnic population.

Methods

The study analyzed 9,372 patients for the correlation between SNPs and BMI (dataset A). The correlation between SNP and weight loss was accessed in 474 patients undergoing different treatments (dataset B). Patients in dataset B were further divided into 3 categories based on the type of intervention: dietary therapy, intragastric balloon procedures, or surgeries. SNP association analysis and multiple models of inheritance were performed.

Results

In dataset A, ten SNPs, including rs9939609 (FTO), rs4988235 (MCM6), and rs2395182 (HLA), were significantly associated with increased BMI. Additionally, other four SNPs, rs7903146 (TCF7L2), (rs6511720), rs5400 (SLC2A2), and rs7498665 (SH2B1), showed sex-specific correlation. For dataset B, SNPs rs2016520 (PPAR-Delta) and rs2419621 (ACSL5) demonstrated significant correlation with weight loss for all treatment types. In patients who adhered to dietary therapy, SNPs rs6544713 (ABCG8) and rs762551 (CYP1A2) were strongly correlated with weight loss. Patients undergoing surgical or endoscopic procedures exhibited differential correlations with several SNPs, including rs1801725 (CASR) and rs12970134 (MC4R), and weight loss.

Conclusion

This study provides valuable insights into the genetic factors influencing BMI and weight loss response to different treatments. The findings highlight the potential for personalized weight management approaches based on individual genetic profiles.

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Rare variation in noncoding regions with evolutionary signatures contributes to autism spectrum disorder risk

Little is known about the role of noncoding regions in the etiology of autism spectrum disorder (ASD). We examined three classes of noncoding regions: Human Accelerated Regions (HARs), which show signatures of positive selection in humans; experimentally validated neural Vista Enhancers (VEs); and conserved regions predicted to act as neural enhancers (CNEs). Targeted and whole genome analysis of >16,600 samples and >4900 ASD probands revealed that likely recessive, rare, inherited variants in HARs, VEs, and CNEs substantially contribute to ASD risk in probands whose parents share ancestry, which enriches for recessive contributions, but modestly, if at all, in simplex family structures. We identified multiple patient variants in HARs near IL1RAPL1 and in a VE near SIM1 and showed that they change enhancer activity. Our results implicate both human-evolved and evolutionarily conserved noncoding regions in ASD risk and suggest potential mechanisms of how changes in regulatory regions can modulate social behavior.

Genetics: A Starting Point for the Prevention and the Treatment of Obesity

Obesity is a common, serious, and costly disease. More than 1 billion people worldwide are obese-650 million adults, 340 million adolescents, and 39 million children. The WHO estimates that, by 2025, approximately 167 million people-adults and children-will become less healthy because they are overweight or obese. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer. These are among the leading causes of preventable, premature death. The estimated annual medical cost of obesity in the United States was nearly $173 billion in 2019 dollars. Obesity is considered the result of a complex interaction between genes and the environment. Both genes and the environment change in different populations. In fact, the prevalence changes as the result of eating habits, lifestyle, and expression of genes coding for factors involved in the regulation of body weight, food intake, and satiety. Expression of these genes involves different epigenetic processes, such as DNA methylation, histone modification, or non-coding micro-RNA synthesis, as well as variations in the gene sequence, which results in functional alterations. Evolutionary and non-evolutionary (i.e., genetic drift, migration, and founder's effect) factors have shaped the genetic predisposition or protection from obesity in modern human populations. Understanding and knowing the pathogenesis of obesity will lead to prevention and treatment strategies not only for obesity, but also for other related diseases.

The Role of Neural and Genetic Processes in Learning to Read and Specific Reading Disabilities: Implications for Instruction

To learn to read, the brain must repurpose neural systems for oral language and visual processing to mediate written language. We begin with a description of computational models for how alphabetic written language is processed. Next, we explain the roles of a dorsal sublexical system in the brain that relates print and speech, a ventral lexical system that develops the visual expertise for rapid orthographic processing at the word level, and the role of cognitive control networks that regulate attentional processes as children read. We then use studies of children, adult illiterates learning to read, and studies of poor readers involved in intervention, to demonstrate the plasticity of these neural networks in development and in relation to instruction. We provide a brief overview of the rapid increase in the field's understanding and technology for assessing genetic influence on reading. Family studies of twins have shown that reading skills are heritable, and molecular genetic studies have identified numerous regions of the genome that may harbor candidate genes for the heritability of reading. In selected families, reading impairment has been associated with major genetic effects, despite individual gene contributions across the broader population that appear to be small. Neural and genetic studies do not prescribe how children should be taught to read, but these studies have underscored the critical role of early intervention and ongoing support. These studies also have highlighted how structured instruction that facilitates access to the sublexical components of words is a critical part of training the brain to read.

Functionally Significant Variants in Genes Associated with Abdominal Obesity: A Review

The high prevalence of obesity and of its associated diseases is a major problem worldwide. Genetic predisposition and the influence of environmental factors contribute to the development of obesity. Changes in the structure and functional activity of genes encoding adipocytokines are involved in the predisposition to weight gain and obesity. In this review, variants in genes associated with adipocyte function are examined, as are variants in genes associated with metabolic aberrations and the accompanying disorders in visceral obesity.

In Silico Examination of Single Nucleotide Missense Mutations in NHLH2, a Gene Linked to Infertility and Obesity

Continual advances in our understanding of the human genome have led to exponential increases in known single nucleotide variants. The characterization of each of the variants lags behind. For researchers needing to study a single gene, or multiple genes in a pathway, there must be ways to narrow down pathogenic variants from those that are silent or pose less pathogenicity. In this study, we use the NHLH2 gene which encodes the nescient helix-loop-helix 2 (Nhlh2) transcription factor in a systematic analysis of all missense mutations to date in the gene. The NHLH2 gene was first described in 1992. Knockout mice created in 1997 indicated a role for this protein in body weight control, puberty, and fertility, as well as the motivation for sex and exercise. Only recently have human carriers of NHLH2 missense variants been characterized. Over 300 missense variants for the NHLH2 gene are listed in the NCBI single nucleotide polymorphism database (dbSNP). Using in silico tools, predicted pathogenicity of the variants narrowed the missense variants to 37 which were predicted to affect NHLH2 function. These 37 variants cluster around the basic-helix-loop-helix and DNA binding domains of the transcription factor, and further analysis using in silico tools provided 21 SNV resulting in 22 amino acid changes for future wet lab analysis. The tools used, findings, and predictions for the variants are discussed considering the known function of the NHLH2 transcription factor. Overall use of these in silico tools and analysis of these data contribute to our knowledge of a protein which is both involved in the human genetic syndrome, Prader-Willi syndrome, and in controlling genes involved in body weight control, fertility, puberty, and behavior in the general population, and may provide a systematic methodology for others to characterize variants for their gene of interest.

Inactivating NHLH2 variants cause idiopathic hypogonadotropic hypogonadism and obesity in humans

Metabolism has a role in determining the time of pubertal development and fertility. Nonetheless, molecular/cellular pathways linking metabolism/body weight to puberty/reproduction are unknown. The KNDy (Kisspeptin/Neurokinin B/Dynorphin) neurons in the arcuate nucleus of the hypothalamus constitute the GnRH (gonadotropin-releasing hormone) pulse generator. We previously created a mouse model with a whole-body targeted deletion of nescient helix-loop-helix 2 (Nhlh2; N2KO), a class II member of the basic helix-loop-helix family of transcription factors. As this mouse model features pubertal failure and late-onset obesity, we wanted to study whether NHLH2 represents a candidate molecule to link metabolism and puberty in the hypothalamus. Exome sequencing of a large Idiopathic Hypogonadotropic Hypogonadism cohort revealed obese patients with rare sequence variants in NHLH2, which were characterized by in-silico protein analysis, chromatin immunoprecipitation, and luciferase reporter assays. In vitro heterologous expression studies demonstrated that the variant p.R79C impairs Nhlh2 binding to the Mc4r promoter. Furthermore, p.R79C and other variants show impaired transactivation of the human KISS1 promoter. These are the first inactivating human variants that support NHLH2's critical role in human puberty and body weight control. Failure to carry out this function results in the absence of pubertal development and late-onset obesity in humans.

Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses

Exome association studies to date have generally been underpowered to systematically evaluate the phenotypic impact of very rare coding variants. We leveraged extensive haplotype sharing between 49,960 exome-sequenced UK Biobank participants and the remainder of the cohort (total N~500K) to impute exome-wide variants with accuracy (R²>0.5) down to minor allele frequency (MAF) ~0.00005. Association and fine-mapping analyses of 54 quantitative traits identified 1,189 significant associations (P<5 x 10⁻⁸) involving 675 distinct rare protein-altering variants (MAF<0.01) that passed stringent filters for likely causality. Across all traits, 49% of associations (578/1,189) occurred in genes with two or more hits; follow-up analyses of these genes identified allelic series containing up to 45 distinct likely-causal variants. Our results demonstrate the utility of within-cohort imputation in population-scale GWAS cohorts, provide a catalog of likely-causal, large-effect coding variant associations, and foreshadow the insights that will be revealed as genetic biobank studies continue to grow.

Signal pathway analysis of selected obesity-associated melanocortin-4 receptor class V mutants

Mutations in the melanocortin-4 receptor (MC4R) in humans are the single most common cause of rare monogenic 1severe obesity, and polymorphisms in this gene are also associated with obesity in the general population. The MC4R is a G-protein coupled receptor, and in vitro analysis suggests that MC4R can signal through several different G-protein subtypes. In vivo studies show complex outcomes, with different G-proteins in different cells responsible for different physiological responses linked to obesity. There is an emerging consensus that Gαq-linked signals in the paraventricular nucleus of the hypothalamus are essential for normal satiety and the control of feeding behavior. Many MC4R mutations have been analyzed for the molecular defect underlying their association with obesity, which has revealed a group - referred to as class V mutants - with no measurable change in receptor function. However, Gαq-linked signaling leading to Ca²⁺ release has only been examined for a few MC4R mutations. In this study, we have examined seven MC4R class V mutants, as well as two other well-characterized signal-defective mutants as controls, with respect to G-protein signaling coupled to cAMP production, mitogen-activated protein kinase (MAPK) activation, and Ca²⁺ release. These data confirm, with one exception (E308K), the expected pattern of cAMP and MAPK signaling for wild type and mutant MC4R. Our results also demonstrate normal MSH-induced Ca²⁺ signals for wild type as well as all the class V mutants, but not the signal-defective controls. Thus, the means by which class V MC4R mutations lead to obesity remains an open question.

Differential Signaling Profiles of MC4R Mutations with Three Different Ligands

The melanocortin 4 receptor (MC4R) is a key player in hypothalamic weight regulation and energy expenditure as part of the leptin–melanocortin pathway. Mutations in this G protein coupled receptor (GPCR) are the most common cause for monogenetic obesity, which appears to be mediated by changes in the anorectic action of MC4R via G_S-dependent cyclic adenosine-monophosphate (cAMP) signaling as well as other signaling pathways. To study potential bias in the effects of MC4R mutations between the different signaling pathways, we investigated three major MC4R mutations: a G_S loss-of-function (S127L) and a G_S gain-of-function mutant (H158R), as well as the most common European single nucleotide polymorphism (V103I). We tested signaling of all four major G protein families plus extracellular regulated kinase (ERK) phosphorylation and β-arrestin2 recruitment, using the two endogenous agonists, α- and β-melanocyte stimulating hormone (MSH), along with a synthetic peptide agonist (NDP-α-MSH). The S127L mutation led to a full loss-of-function in all investigated pathways, whereas V103I and H158R were clearly biased towards the G_q/11 pathway when challenged with the endogenous ligands. These results show that MC4R mutations can cause vastly different changes in the various MC4R signaling pathways and highlight the importance of a comprehensive characterization of receptor mutations.

Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis

BACKGROUNDS

Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants.

RESULTS

Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method.

CONCLUSION

In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/ .

Identifying Rare Variant Associations in Admixed Populations

An admixed population and its ancestral populations bear different burdens of a complex disease. The ancestral populations may have different haplotypes of deleterious alleles and thus ancestry-gene interaction can influence disease risk in the admixed population. Among admixed individuals, deleterious haplotypes and their ancestries are dependent and can provide non-redundant association information. Herein we propose a local ancestry boosted sum test (LABST) for identifying chromosomal blocks that harbor rare variants but have no ancestry switches. For such a stable ancestral block, our LABST exploits ancestry-gene interaction and the number of rare alleles therein. Under the null of no genetic association, the test statistic asymptotically follows a chi-square distribution with one degree of freedom (1-df). Our LABST properly controlled type I error rates under extensive simulations, suggesting that the asymptotic approximation was accurate for the null distribution of the test statistic. In terms of power for identifying rare variant associations, our LABST uniformly outperformed several famed methods under four important modes of disease genetics over a large range of relative risks. In conclusion, exploiting ancestry-gene interaction can boost statistical power for rare variant association mapping in admixed populations.

Identifying Multi-Omics Causers and Causal Pathways for Complex Traits

The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.

CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency

A wide range of human diseases result from haploinsufficiency, where the function of one of the two gene copies is lost. Here, we targeted the remaining functional copy of a haploinsufficient gene using CRISPR-mediated activation (CRISPRa) in Sim1 and Mc4r heterozygous mouse models to rescue their obesity phenotype. Transgenic-based CRISPRa targeting of the Sim1 promoter or its distant hypothalamic enhancer up-regulated its expression from the endogenous functional allele in a tissue-specific manner, rescuing the obesity phenotype in Sim1 heterozygous mice. To evaluate the therapeutic potential of CRISPRa, we injected CRISPRa-recombinant adeno-associated virus into the hypothalamus, which led to reversal of the obesity phenotype in Sim1 and Mc4r haploinsufficient mice. Our results suggest that endogenous gene up-regulation could be a potential strategy to treat altered gene dosage diseases.

Genetic variation in the SIM1 locus is associated with erectile dysfunction

Erectile dysfunction is a common condition of men in middle and older ages. Twin studies suggest that about one-third of the risk is due to genetic factors, independent of other known erectile dysfunction risk factors. However, studies that have searched for specific genetic contributors have been limited due to small sample sizes, candidate gene approaches, and weak phenotyping. As a result, there are no confirmed genetic risk factors for erectile dysfunction. This study finds a specific genetic cause for erectile dysfunction.

Erectile dysfunction affects millions of men worldwide. Twin studies support the role of genetic risk factors underlying erectile dysfunction, but no specific genetic variants have been identified. We conducted a large-scale genome-wide association study of erectile dysfunction in 36,649 men in the multiethnic Kaiser Permanente Northern California Genetic Epidemiology Research in Adult Health and Aging cohort. We also undertook replication analyses in 222,358 men from the UK Biobank. In the discovery cohort, we identified a single locus (rs17185536-T) on chromosome 6 near the single-minded family basic helix-loop-helix transcription factor 1 (SIM1) gene that was significantly associated with the risk of erectile dysfunction (odds ratio = 1.26, P = 3.4 × 10⁻²⁵). The association replicated in the UK Biobank sample (odds ratio = 1.25, P = 6.8 × 10⁻¹⁴), and the effect is independent of known erectile dysfunction risk factors, including body mass index (BMI). The risk locus resides on the same topologically associating domain as SIM1 and interacts with the SIM1 promoter, and the rs17185536-T risk allele showed differential enhancer activity. SIM1 is part of the leptin–melanocortin system, which has an established role in body weight homeostasis and sexual function. Because the variants associated with erectile dysfunction are not associated with differences in BMI, our findings suggest a mechanism that is specific to sexual function.

Comparative Genomics Approaches Accurately Predict Deleterious Variants in Plants

Recent advances in genome resequencing have led to increased interest in prediction of the functional consequences of genetic variants. Variants at phylogenetically conserved sites are of particular interest, because they are more likely than variants at phylogenetically variable sites to have deleterious effects on fitness and contribute to phenotypic variation. Numerous comparative genomic approaches have been developed to predict deleterious variants, but the approaches are nearly always assessed based on their ability to identify known disease-causing mutations in humans. Determining the accuracy of deleterious variant predictions in nonhuman species is important to understanding evolution, domestication, and potentially to improving crop quality and yield. To examine our ability to predict deleterious variants in plants we generated a curated database of 2,910 Arabidopsis thaliana mutants with known phenotypes. We evaluated seven approaches and found that while all performed well, their relative ranking differed from prior benchmarks in humans. We conclude that deleterious mutations can be reliably predicted in A. thaliana and likely other plant species, but that the relative performance of various approaches does not necessarily translate from one species to another.

Genetic Risk Prediction for Primary and Secondary Prevention of Atherosclerotic Cardiovascular Disease: an Update

PURPOSE OF REVIEW

This review aims to summarize the research on genetic risk scores and their ability to improve risk prediction in both a primary and a secondary prevention population.

RECENT FINDINGS

Several groups have examined the role of genetic scores in different patient populations. Recent studies have capitalized on the growing number of identified genetic variants to construct polygenic risk scores that include hundreds and sometimes thousands of SNPs. Also, recent studies have demonstrated that individuals with high genetic risk scores can attenuate their risk with lifestyle modifications and with statins, for which the benefit of treatment may be greater in those at highest genetic risk. Genetic risk scores when added to existing clinical models appear to improve risk prediction, particularly in the setting of incident cardiovascular disease and may provide actionable information to optimize prevention early in life. Future research will need to establish how to best use this genetic risk information either as a means to further individualize treatment decisions or to better identify high-risk populations.

TEX15: A DNA repair gene associated with prostate cancer risk in Han Chinese

BACKGROUND

Both common and rare genetic variants may contribute to risk of developing prostate cancer. Genome-wide association studies (GWASs) have identified ∼100 independent, common variants associated with prostate cancer risk. However, little is known about the association of rare variants (minor allele frequency [MAF] <1%) in the genome with prostate cancer risk.

METHODS

A two-stage study was used to test the association of rare, deleterious coding variants, annotated using predictive algorithms, with prostate cancer risk in Chinese men. Predicted rare, deleterious coding variants in the Illumina HumanExome-12 v1.1 beadchip were first evaluated in 1343 prostate cancer patients and 1008 controls. Significant variants were then validated in an additional 1816 prostate cancer patients and 1549 controls.

RESULTS

In the discovery stage, 14 predicted rare, deleterious coding variants were significantly associated with prostate cancer risk (P < 0.01). In the confirmation stage, Q1631H in TEX15 (rs142485241), a DNA repair gene, was significantly associated with prostate cancer risk (P = 0.0069). The estimated odds ratio (OR) of the variant in the combined analysis was 3.24 (95% Confidence Interval 1.85-6.06), P = 8.81 × 10^-5 . Additionally, rs28756990 (V741F) at MLH3 (P = 0.06) and rs2961144 (I126V) at OR2A5 (P = 0.065) were marginally associated with prostate cancer risk in the replication stage.

CONCLUSIONS

Our study provided preliminary evidence that the rare variant Q1631H in DNA repair gene TEX15 is associated with prostate cancer risk. This finding complements known common prostate cancer risk-associated variants and suggests the possible role of DNA repair genes in prostate cancer development.

Genetic interaction of DGAT2 and FAAH in the development of human obesity

PURPOSE

DGAT2 is the critical catalyzing enzyme for triglyceride biosynthesis, and excess triglyceride accumulation in fat tissues is a fundamental process for obesity. Mutations in DGAT2 or other genes interacting with DGAT2 associated with adiposity have not been reported in human to date.

METHODS

DGAT2 mutation was identified based on our in-home database-exome sequencing 227 young obese subjects (body-mass index (BMI), 35.1-61.7 kg/m²) and 219 lean controls (BMI, 17.5-23.0 kg/m²), further validated in 1190 lean subjects and the pedigree of the proband. The trios of the proband were further subjected to whole-exome sequencing to explore the candidate genes for obesity. The mutations in DGAT2 and FAAH were functionally evaluated in vitro.

RESULTS

We detected two rare variants in DGAT2 with no significant difference between obese and lean individuals. One novel heterozygous nonsense variant c.382C > T (p.R128*) was identified in one obese subject but not in 219 lean subjects and another 1190 lean subjects. Notably, in vitro study showed that R128* mutation severely damaged the TG-biosynthesis ability of DGAT2, and all other R128* carriers in the pedigree were lean. Thus, we further identified a loss-of-function variant c. 944G > T (p.R315I) in FAAH in the proband inheriting from his obese father. Importantly, FAAH overexpression inhibited DGAT2 expression and TG synthesis, while R315I mutant largely eliminated this inhibitory effect. We first report loss-of-function mutations in DGAT2 and FAAH in one obese subject, which may interact with each other to affect the adiposity penetrance, providing a model of genetic interaction associated with human obesity.

Identification and characterization of Aurora kinase B and C variants associated with maternal aneuploidy

STUDY QUESTION

Are single nucleotide variants (SNVs) in Aurora kinases B and C (AURKB, AURKC) associated with risk of aneuploid conception?

SUMMARY ANSWER

Two SNVs were found in patients with extreme aneuploid concepti rates with respect to their age; one variant, AURKC p.I79V, is benign, while another, AURKB p.L39P, is a potential gain-of-function mutant with increased efficiency in promoting chromosome alignment.

WHAT IS KNOWN ALREADY

Maternal age does not always predict aneuploidy risk, and rare gene variants can be drivers of disease. The AURKB and AURKC regulate chromosome segregation, and are associated with reproductive impairments in mouse and human.

STUDY DESIGN, SIZE, DURATION

An extreme phenotype sample selection scheme was performed for variant discovery. Ninety-six DNA samples were from young patients with higher than average embryonic aneuploidy rates and an additional 96 DNA samples were from older patients with lower than average aneuploidy rates.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Using the192 DNA samples, the coding regions of AURKB and AURKC were sequenced using next generation sequencing. To assess biological significance, we expressed complementary RNA encoding the human variants in mouse oocytes. Assays such as determining subcellular localization and assessing catalytic activity were performed to determine alterations in protein function during meiosis.

MAIN RESULTS AND THE ROLE OF CHANCE

Ten SNVs were identified using three independent variant-calling methods. Two of the SNVs (AURKB p.L39P and AURKC p.I79V) were non-synonymous and identified by at least two variant-identification methods. The variant encoding AURKC p.I79V, identified in a young woman with a higher than average rate of aneuploid embryos, showed wild-type localization pattern and catalytic activity. On the other hand, the variant encoding AURKB p.L39P, identified in an older woman with lower than average rates of aneuploid embryos, increased the protein's ability to regulate alignment of chromosomes at the metaphase plate. These experiments were repeated three independent times using 2-3 mice for each trial.

LARGE SCALE DATA

N/A.

LIMITATIONS, REASONS FOR CAUTION

Biological significance of the human variants was assessed in an in vitro mouse oocyte model where the variants are over-expressed. Therefore, the human protein may not function identically to the mouse homolog, or the same in mouse oocytes as in human oocytes. Furthermore, supraphysiological expression levels may not accurately reflect endogenous activity. Moreover, the evaluated variants were identified in one patient each, and no trial linking the SNV to pregnancy outcomes was conducted. Finally, the patient aneuploidy rates were established by performing comprehensive chromosome screening in blastocysts, and because of the link between female gamete aneuploidy giving rise to aneuploid embryos, we evaluate the role of the variants in Meiosis I. However, it is possible that the chromosome segregation mistake arose during Meiosis II or in mitosis in the preimplantation embryo. Their implications in human female meiosis and aneuploidy risk remain to be determined.

WIDER IMPLICATIONS OF THE FINDINGS

The data provide evidence that gene variants exist in reproductively younger or advanced aged women that are predictive of the risk of producing aneuploid concepti in humans. Furthermore, a single amino acid in the N-terminus of AURKB is a gain-of-function mutant that could be protective of euploidy.

STUDY FUNDING/COMPETING INTERESTS

This work was supported by a Research Grant from the American Society of Reproductive Medicine and support from the Charles and Johanna Busch Memorial Fund at Rutgers, the State University of NJ to K.S. and the Foundation for Embryonic Competence, Inc to N.T. The authors declare no conflicts of interest.

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.

Genetic disorders of nuclear receptors

Following the first isolation of nuclear receptor (NR) genes, genetic disorders caused by NR gene mutations were initially discovered by a candidate gene approach based on their known roles in endocrine pathways and physiologic processes. Subsequently, the identification of disorders has been informed by phenotypes associated with gene disruption in animal models or by genetic linkage studies. More recently, whole exome sequencing has associated pathogenic genetic variants with unexpected, often multisystem, human phenotypes. To date, defects in 20 of 48 human NR genes have been associated with human disorders, with different mutations mediating phenotypes of varying severity or several distinct conditions being associated with different changes in the same gene. Studies of individuals with deleterious genetic variants can elucidate novel roles of human NRs, validating them as targets for drug development or providing new insights into structure-function relationships. Importantly, human genetic discoveries enable definitive disease diagnosis and can provide opportunities to therapeutically manage affected individuals. Here we review germline changes in human NR genes associated with "monogenic" conditions, including a discussion of the structural basis of mutations that cause distinctive changes in NR function and the molecular mechanisms mediating pathogenesis.

Exploring the relationship between α-actinin-3 deficiency and obesity in mice and humans

Obesity is a worldwide health crisis, and the identification of genetic modifiers of weight gain is crucial in understanding this complex disorder. A common null polymorphism in the fast fiber-specific gene ACTN3 (R577X) is known to influence skeletal muscle function and metabolism. α-Actinin-3 deficiency occurs in an estimated 1.5 billion people worldwide, and results in reduced muscle strength and a shift towards a more efficient oxidative metabolism. The X-allele has undergone strong positive selection during recent human evolution, and in this study, we sought to determine whether ACTN3 genotype influences weight gain and obesity in mice and humans. An Actn3 KO mouse has been generated on two genetic backgrounds (129X1/SvJ and C57BL/6J) and fed a high-fat diet (HFD, 45% calories from fat). Anthropomorphic features (including body weight) were examined and show that Actn3 KO 129X1/SvJ mice gained less weight compared to WT. In addition, six independent human cohorts were genotyped for ACTN3 R577X (Rs1815739) and body mass index (BMI), waist-to-hip ratio-adjusted BMI (WHRadjBMI) and obesity-related traits were assessed. In humans, ACTN3 genotype alone does not contribute to alterations in BMI or obesity.

Detecting association of rare and common variants based on cross-validation prediction error

Despite the extensive discovery of disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants may explain additional disease risk or trait variability. Although sequencing technology provides a supreme opportunity to investigate the roles of rare variants in complex diseases, detection of these variants in sequencing-based association studies presents substantial challenges. In this article, we propose novel statistical tests to test the association between rare and common variants in a genomic region and a complex trait of interest based on cross-validation prediction error (PE). We first propose a PE method based on Ridge regression. Based on PE, we also propose another two tests PE-WS and PE-TOW by testing a weighted combination of variants with two different weighting schemes. PE-WS is the PE version of the test based on the weighted sum statistic (WS) and PE-TOW is the PE version of the test based on the optimally weighted combination of variants (TOW). Using extensive simulation studies, we are able to show that (1) PE-TOW and PE-WS are consistently more powerful than TOW and WS, respectively, and (2) PE is the most powerful test when causal variants contain both common and rare variants.

Detecting multiple variants associated with disease based on sequencing data of case-parent trios

With the advance of next-generation sequencing technology, the rare variants join the common ones in explaining more proportions of heritability. The coexistence of variants of common with rare, causal with neutral and deleterious with protective is a norm and should be appropriately addressed. Some existing methods suffer from low power when one or more forms of coexistence present, impeding their applications in practice. In this paper, for case-parent trios, pseudocontrols are constructed using the nontransmitted alleles of the parents. The Kullback-Leibler divergence is utilized to measure the difference between the distributions of variants in a genetic region for the affected children and pseudocontrols, and two nonparametric test statistics KLTT and cKLTT are proposed. Extensive simulations show that they are robust to the opposite directions of the causal variants and the amount of neutral variants, and have superiority over the existing methods when both rare and common variants are involved. Furthermore, their efficiency is demonstrated in the application to the data from Framingham Heart Study.

Pathway-based approach using hierarchical components of collapsed rare variants

MOTIVATION

To address 'missing heritability' issue, many statistical methods for pathway-based analyses using rare variants have been proposed to analyze pathways individually. However, neglecting correlations between multiple pathways can result in misleading solutions, and pathway-based analyses of large-scale genetic datasets require massive computational burden. We propose a Pathway-based approach using HierArchical components of collapsed RAre variants Of High-throughput sequencing data (PHARAOH) for the analysis of rare variants by constructing a single hierarchical model that consists of collapsed gene-level summaries and pathways and analyzes entire pathways simultaneously by imposing ridge-type penalties on both gene and pathway coefficient estimates; hence our method considers the correlation of pathways without constraint by a multiple testing problem.

RESULTS

Through simulation studies, the proposed method was shown to have higher statistical power than the existing pathway-based methods. In addition, our method was applied to the large-scale whole-exome sequencing data with levels of a liver enzyme using two well-known pathway databases Biocarta and KEGG. This application demonstrated that our method not only identified associated pathways but also successfully detected biologically plausible pathways for a phenotype of interest. These findings were successfully replicated by an independent large-scale exome chip study.

AVAILABILITY AND IMPLEMENTATION

An implementation of PHARAOH is available at http://statgen.snu.ac.kr/software/pharaoh/

CONTACT

tspark@stats.snu.ac.kr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Novelty Seeking and Drug Addiction in Humans and Animals: From Behavior to Molecules

Global treatment of drug addiction costs society billions of dollars annually, but current psychopharmacological therapies have not been successful at desired rates. The increasing number of individuals suffering from substance abuse has turned attention to what makes some people more vulnerable to drug addiction than others. One personality trait that stands out as a contributing factor is novelty seeking. Novelty seeking, affected by both genetic and environmental factors, is defined as the tendency to desire novel stimuli and environments. It can be measured in humans through questionnaires and in rodents using behavioral tasks. On the behavioral level, both human and rodent studies demonstrate that high novelty seeking can predict the initiation of drug use and a transition to compulsive drug use and create a propensity to relapse. These predictions are valid for several drugs of abuse, such as alcohol, nicotine, cocaine, amphetamine, and opiates. On the molecular level, both novelty seeking and addiction are modulated by the central reward system in the brain. Dopamine is the primary neurotransmitter involved in the overlapping neural substrates of both parameters. In sum, the novelty-seeking trait can be valuable for predicting individual vulnerability to drug addiction and for generating successful treatment for patients with substance abuse disorders.

Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level

Genetic association analyses of rare variants in next-generation sequencing (NGS) studies are fundamentally challenging due to the presence of a very large number of candidate variants at extremely low minor allele frequencies. Recent developments often focus on pooling multiple variants to provide association analysis at the gene instead of the locus level. Nonetheless, pinpointing individual variants is a critical goal for genomic researches as such information can facilitate the precise delineation of molecular mechanisms and functions of genetic factors on diseases. Due to the extreme rarity of mutations and high-dimensionality, significances of causal variants cannot easily stand out from those of noncausal ones. Consequently, standard false-positive control procedures, such as the Bonferroni and false discovery rate (FDR), are often impractical to apply, as a majority of the causal variants can only be identified along with a few but unknown number of noncausal variants. To provide informative analysis of individual variants in large-scale sequencing studies, we propose the Adaptive False-Negative Control (AFNC) procedure that can include a large proportion of causal variants with high confidence by introducing a novel statistical inquiry to determine those variants that can be confidently dispatched as noncausal. The AFNC provides a general framework that can accommodate for a variety of models and significance tests. The procedure is computationally efficient and can adapt to the underlying proportion of causal variants and quality of significance rankings. Extensive simulation studies across a plethora of scenarios demonstrate that the AFNC is advantageous for identifying individual rare variants, whereas the Bonferroni and FDR are exceedingly over-conservative for rare variants association studies. In the analyses of the CoLaus dataset, AFNC has identified individual variants most responsible for gene-level significances. Moreover, single-variant results using the AFNC have been successfully applied to infer related genes with annotation information.

Next-generation sequencing technologies have allowed genetic association studies of complex traits at the single base-pair resolution, where most genetic variants have extremely low mutation frequencies. These rare variants have been the focus of modern statistical-computational genomics due to their potential to explain missing disease heritability. The identification of individual rare variants associated with diseases can provide new biological insights and enable the precise delineation of disease mechanisms. However, due to the extreme rarity of mutations and large numbers of variants, significances of causative variants tend to be mixed inseparably with a few noncausative ones, and standard multiple testing procedures controlling for false positives fail to provide a meaningful way to include a large proportion of the causative variants. To address the challenge of detecting weak biological signals, we propose a novel statistical procedure, based on false-negative control, to provide a practical approach for variant inclusion in large-scale sequencing studies. By determining those variants that can be confidently dispatched as noncausative, the proposed procedure offers an objective selection of a modest number of potentially causative variants at the single-locus level. Results can be further prioritized or used to infer disease-associated genes with annotation information.

Poisson Approximation-Based Score Test for Detecting Association of Rare Variants

Genome-wide association study (GWAS) has achieved great success in identifying genetic variants, but the nature of GWAS has determined its inherent limitations. Under the common disease rare variants (CDRV) hypothesis, the traditional association analysis methods commonly used in GWAS for common variants do not have enough power for detecting rare variants with a limited sample size. As a solution to this problem, pooling rare variants by their functions provides an efficient way for identifying susceptible genes. Rare variant typically have low frequencies of minor alleles, and the distribution of the total number of minor alleles of the rare variants can be approximated by a Poisson distribution. Based on this fact, we propose a new test method, the Poisson Approximation-based Score Test (PAST), for association analysis of rare variants. Two testing methods, namely, ePAST and mPAST, are proposed based on different strategies of pooling rare variants. Simulation results and application to the CRESCENDO cohort data show that our methods are more powerful than the existing methods.

FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes

Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.

Statistical selection strategy for risk and protective rare variants associated with complex traits

In genetic association studies with deep sequencing data, it is a challenging statistical problem to precisely locate rare variants associated with complex diseases or traits due to the limited number of observed genetic mutations. In particular, both risk and protective rare variants can be present in the same gene or genetic region. There currently exist very few statistical methods to separate casual rare variants from noncausal variants within a disease/trait-related gene or a genetic region, while there are relatively many statistical tests to detect a phenotypic association of a group of rare variants such as a gene or a genetic region. In this article, we propose a new statistical selection strategy that is able to locate causal rare variants within the disease/trait-related gene or a genetic region. The proposed procedure is to linearly combine potential risk and protective variants in order to find the optimal combination of rare variants that can have the strongest association signal. It is also computationally very efficient since the procedure is based on forward selection. In simulation studies we demonstrate that the selection performance of the proposed procedure is more powerful than other existing methods when both risk and protective variants are present. We also applied it to the real sequencing data on the ANGPTL gene family from the Dallas Heart Study.

Individualized iterative phenotyping for genome-wide analysis of loss-of-function mutations

Next-generation sequencing provides the opportunity to practice predictive medicine based on identified variants. Putative loss-of-function (pLOF) variants are common in genomes and understanding their contribution to disease is critical for predictive medicine. To this end, we characterized the consequences of pLOF variants in an exome cohort by iterative phenotyping. Exome data were generated on 951 participants from the ClinSeq cohort and filtered for pLOF variants in genes likely to cause a phenotype in heterozygotes. 103 of 951 exomes had such a pLOF variant and 79 participants were evaluated. Of those 79, 34 had findings or family histories that could be attributed to the variant (28 variants in 18 genes), 2 had indeterminate findings (2 variants in 2 genes), and 43 had no findings or a negative family history for the trait (34 variants in 28 genes). The presence of a phenotype was correlated with two mutation attributes: prior report of pathogenicity for the variant (p = 0.0001) and prior report of other mutations in the same exon (p = 0.0001). We conclude that 1/30 unselected individuals harbor a pLOF mutation associated with a phenotype either in themselves or their family. This is more common than has been assumed and has implications for the setting of prior probabilities of affection status for predictive medicine.

A robust GWSS method to simultaneously detect rare and common variants for complex disease

The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical p-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-t) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants.

Whole-exome SNP array identifies 15 new susceptibility loci for psoriasis

Genome-wide association studies (GWASs) have reproducibly associated ∼40 susceptibility loci with psoriasis. However, the missing heritability is evident and the contributions of coding variants have not yet been systematically evaluated. Here, we present a large-scale whole-exome array analysis for psoriasis consisting of 42,760 individuals. We discover 16 SNPs within 15 new genes/loci associated with psoriasis, including C1orf141, ZNF683, TMC6, AIM2, IL1RL1, CASR, SON, ZFYVE16, MTHFR, CCDC129, ZNF143, AP5B1, SYNE2, IFNGR2 and 3q26.2-q27 (P<5.00 × 10(-08)). In addition, we also replicate four known susceptibility loci TNIP1, NFKBIA, IL12B and LCE3D-LCE3E. These susceptibility variants identified in the current study collectively account for 1.9% of the psoriasis heritability. The variant within AIM2 is predicted to impact protein structure. Our findings increase the number of genetic risk factors for psoriasis and highlight new and plausible biological pathways in psoriasis.

Obesity and genomics: role of technology in unraveling the complex genetic architecture of obesity

Obesity is a complex and multifactorial disease that occurs as a result of the interaction between "obesogenic" environmental factors and genetic components. Although the genetic component of obesity is clear from the heritability studies, the genetic basis remains largely elusive. Successes have been achieved in identifying the causal genes for monogenic obesity using animal models and linkage studies, but these approaches are not fruitful for polygenic obesity. The developments of genome-wide association approach have brought breakthrough discovery of genetic variants for polygenic obesity where tens of new susceptibility loci were identified. However, the common SNPs only accounted for a proportion of heritability. The arrival of NGS technologies and completion of 1000 Genomes Project have brought other new methods to dissect the genetic architecture of obesity, for example, the use of exome genotyping arrays and deep sequencing of candidate loci identified from GWAS to study rare variants. In this review, we summarize and discuss the developments of these genetic approaches in human obesity.

Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants

Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling.

Comparison of methods and sampling designs to test for association between rare variants and quantitative traits

Genome-wide association studies succeeded in finding genetic variants associated with various phenotypes, but a large portion of the predicted genetic contribution to many traits remains unknown. One plausible explanation is that some missing variation is due to rare variants. Latest sequencing technology facilitates the investigation of such rare variants, but their statistical analysis remains challenging. For quantitative traits, a commonly used approach is to contrast the frequency of putatively functional rare variants between subjects in the two tails of the trait distribution. The contrast is usually performed by Fisher's exact or similar test. These tests are conservative as they discard trait rank information and are most useful under the unrealistic homogeneity assumption (i.e., variants have similar effects). We propose, and investigate via simulations, various designs for resequencing studies and statistical methods that incorporate information about rank, predicted function and allow for heterogeneity of effects. We propose designs which accommodate heterogeneity by sequencing both tails and the middle of the trait and novel statistical tests for trend, for heterogeneity and for a combination of the two. The conclusions of the simulations are four fold: (1) sequencing both tails and the middle of the trait distributions is desirable when heterogeneity is suspected, (2) trend and heterogeneity statistics should be used alongside other methods, (3) using rank information improves power over Fisher's exact test when the number of rare variants is not very large and (4) due to high misclassification rates, incorporating current predictions of a variant's function does not improve power.

A weighted U-statistic for genetic association analyses of sequencing data

With advancements in next-generation sequencing technology, a massive amount of sequencing data is generated, which offers a great opportunity to comprehensively investigate the role of rare variants in the genetic etiology of complex diseases. Nevertheless, the high-dimensional sequencing data poses a great challenge for statistical analysis. The association analyses based on traditional statistical methods suffer substantial power loss because of the low frequency of genetic variants and the extremely high dimensionality of the data. We developed a Weighted U Sequencing test, referred to as WU-SEQ, for the high-dimensional association analysis of sequencing data. Based on a nonparametric U-statistic, WU-SEQ makes no assumption of the underlying disease model and phenotype distribution, and can be applied to a variety of phenotypes. Through simulation studies and an empirical study, we showed that WU-SEQ outperformed a commonly used sequence kernel association test (SKAT) method when the underlying assumptions were violated (e.g., the phenotype followed a heavy-tailed distribution). Even when the assumptions were satisfied, WU-SEQ still attained comparable performance to SKAT. Finally, we applied WU-SEQ to sequencing data from the Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very low density lipoprotein cholesterol.

Performance of statistical methods on CHARGE targeted sequencing data

Background

The CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Sequencing Project is a national, collaborative effort from 3 studies: Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), and Atherosclerosis Risk in Communities (ARIC). It uses a case-cohort design, whereby a random sample of study participants is enriched with participants in extremes of traits. Although statistical methods are available to investigate the role of rare variants, few have evaluated their performance in a case-cohort design.

Results

We evaluate several methods, including the sequence kernel association test (SKAT), Score-Seq, and weighted (Madsen and Browning) and unweighted burden tests. Using genotypes from the CHARGE targeted-sequencing project for FHS (n = 1096), we simulate phenotypes in a large population for 11 correlated traits and then sample individuals to mimic the CHARGE Sequencing study design. We evaluate type I error and power for 77 targeted regions.

Conclusions

We provide some guidelines on the performance of these aggregate-based tests to detect associations with rare variants when applied to case-cohort study designs, using CHARGE targeted sequencing data. Type I error is conservative when we consider variants with minor allele frequency (MAF) < 1%. Power is generally low, although it is relatively larger for Score-Seq. Greater numbers of causal variants and a greater proportion of variance improve the power, but it tends to be lower in the presence of bi-directionality of effects of causal genotypes, especially for Score-Seq.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-014-0104-9) contains supplementary material, which is available to authorized users.

The genetic architecture of alopecia areata

A major impetus to initiating the Human Genome Project was the belief that information encoded in the human genome would "accelerate progress in understanding disease pathogenesis and in developing new approaches to diagnosis, treatment, and prevention in many areas of medicine". Alopecia areata (AA) is a notable example of how understanding the genetic basis of a disease can have an impact on the care of patients in a relatively short time. Our first genome-wide association study in AA identified an initial set of common variants that increase risk of AA, some of which are shared with other autoimmune diseases. Thus, there has already been rapid progress in the translation of this information into new therapeutic strategies for patients, as drugs are already on the market for some of these disorders that can now be tested in AA. Informed by the progress achieved with genetic studies for mechanistically aligned autoimmune diseases, we are poised to carry this work forward and interrogate the underlying disease mechanisms in AA. Importantly, future genetic studies aimed at identifying additional susceptibility genes will further establish the foundation for the application of precision medicine in the care of AA patients.

Adiposity significantly modifies genetic risk for dyslipidemia

Recent genome-wide association studies have identified multiple loci robustly associated with plasma lipids, which also contribute to extreme lipid phenotypes. However, these common genetic variants explain <12% of variation in lipid traits. Adiposity is also an important determinant of plasma lipoproteins, particularly plasma TGs and HDL cholesterol (HDLc) concentrations. Thus, interactions between genes and clinical phenotypes may contribute to this unexplained heritability. We have applied a weighted genetic risk score (GRS) for both plasma TGs and HDLc in two large cohorts at the extremes of BMI. Both BMI and GRS were strongly associated with these lipid traits. A significant interaction between obese/lean status and GRS was noted for each of TG (P_Interaction = 2.87 × 10⁻⁴) and HDLc (P_Interaction = 1.05 × 10⁻³). These interactions were largely driven by SNPs tagging APOA5, glucokinase receptor (GCKR), and LPL for TG, and cholesteryl ester transfer protein (CETP), GalNAc-transferase (GALNT2), endothelial lipase (LIPG), and phospholipid transfer protein (PLTP) for HDLc. In contrast, the GRS_{LDL cholesterol} × adiposity interaction was not significant. Sexual dimorphism was evident for the GRS_HDL on HDLc in obese (P_Interaction = 0.016) but not lean subjects. SNP by BMI interactions may provide biological insight into specific genetic associations and missing heritability.

Adjusting family relatedness in data-driven burden test of rare variants

Family data represent a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM), and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control for the inflation of false positives. We compare WSMM with a nondata-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.

Methodology for the analysis of rare genetic variation in genome-wide association and re-sequencing studies of complex human traits

Genome-wide association studies have been successful in identifying common variants that impact complex human traits and diseases. However, despite this success, the joint effects of these variants explain only a small proportion of the genetic variance in these phenotypes, leading to speculation that rare genetic variation might account for much of the ‘missing heritability’. Consequently, there has been an exciting period of research and development into the methodology for the analysis of rare genetic variants, typically by considering their joint effects on complex traits within the same functional unit or genomic region. In this review, we describe a general framework for modelling the joint effects of rare genetic variants on complex traits in association studies of unrelated individuals. We summarise a range of widely used association tests that have been developed from this model and provide an overview of the relative performance of these approaches from published simulation studies.

On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis

Background

As the architecture of complex traits incorporates a widening spectrum of genetic variation, analyses integrating common and rare variation are needed. Body mass index (BMI) represents a model trait, since common variation shows robust association but accounts for a fraction of the heritability. A combined analysis of single nucleotide polymorphisms (SNP) and copy number variation (CNV) was performed using 1850 European and 498 African-Americans from the Study of Addiction: Genetics and Environment. Genetic risk sum scores (GRSS) were constructed using 32 BMI-validated SNPs and aggregate-risk methods were compared: count versus weighted and proxy versus imputation.

Results

The weighted SNP-GRSS constructed from imputed probabilities of risk alleles performed best and was highly associated with BMI (p = 4.3×10⁻¹⁶) accounting for 3% of the phenotypic variance. In addition to BMI-validated SNPs, common and rare BMI/obesity-associated CNVs were identified from the literature. Of the 84 CNVs previously reported, only 21-kilobase deletions on 16p12.3 showed evidence for association with BMI (p = 0.003, frequency = 16.9%), with two CNVs nominally associated with class II obesity, 1p36.1 duplications (OR = 3.1, p = 0.009, frequency 1.2%) and 5q13.2 deletions (OR = 1.5, p = 0.048, frequency 7.7%). All other CNVs, individually and in aggregate, were not associated with BMI or obesity. The combined model, including covariates, SNP-GRSS, and 16p12.3 deletion accounted for 11.5% of phenotypic variance in BMI (3.2% from genetic effects). Models significantly predicted obesity classification with maximum discriminative ability for morbid-obesity (p = 3.15×10⁻¹⁸).

Conclusion

Results show that incorporating validated effect sizes and allelic probabilities improve prediction algorithms. Although rare-CNVs did not account for significant phenotypic variation, results provide a framework for integrated analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-368) contains supplementary material, which is available to authorized users.