Common epilepsy variants from the general population are not associated with epilepsy among individuals with tuberous sclerosis complex

Common genetic variants identified in the general population have been found to increase phenotypic risks among individuals with certain genetic conditions. Up to 90% of individuals with tuberous sclerosis complex (TSC) are affected by some type of epilepsy, yet the common variants contributing to epilepsy risk in the general population have not been evaluated in the context of TSC-associated epilepsy. Such knowledge is important to help uncover the underlying pathogenesis of epilepsy in TSC which is not fully understood, and critical as uncontrolled epilepsy is a major problem in this population. To evaluate common genetic modifiers of epilepsy, our study pooled phenotypic and genotypic data from 369 individuals with TSC to evaluate known and novel epilepsy common variants. We did not find evidence of enhanced genetic penetrance for known epilepsy variants identified across the largest genome-wide association studies of epilepsy in the general population, but identified support for novel common epilepsy variants in the context of TSC. Specifically, we have identified a novel signal in SLC7A1 that may be functionally involved in pathways relevant to TSC and epilepsy. Our study highlights the need for further evaluation of genetic modifiers in TSC to aid in further understanding of epilepsy in TSC and improve outcomes.

Polygenic prediction of human longevity on the supposition of pervasive pleiotropy

The highly polygenic nature of human longevity renders cross-trait pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between the aging-related traits (ARTs), we sought to model the additive variance in lifespan as a function of cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores (iLGSs) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of i L G S , we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with i L G S highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.

Genome-wide meta-analyses identify five new risk loci for nonsyndromic orofacial clefts in the Chinese Han population

BACKGROUND

Nonsyndromic orofacial clefts (NSOFCs) are the most common craniofacial birth malformations in humans and are generally classified as nonsyndromic cleft lip with or without cleft palate (NSCL/P) and nonsyndromic cleft palate only (NSCPO). Genome-wide association studies (GWASs) of NSOFCs have demonstrated multiple risk loci and candidate genes; however, published risk factors are able to explain only a small fraction of the observed NSOFCs heritability.

METHODS

Here, we performed GWASs of 1615 NSCPO cases and 2340 controls, and then conducted genome-wide meta-analyses of NSOFCs, totaling 6812 NSCL/P cases, 2614 NSCPO cases, and 19,165 controls from the Chinese Han population.

RESULTS

We identify 47 risk loci with genome-wide pmeta -value <5.0 × 10-8 , 5 risk loci (1p32.1, 3p14.1, 3p14.3, 3p21.31, and 13q22.1) of which are new. All of the 47 susceptibility loci conjointly account for 44.12% of the NSOFCs' heritability in the Chinese Han population.

CONCLUSION

Our results improve the comprehending of genetic susceptibility to NSOFCs and provide new views into the genetic etiology of craniofacial anomalies.

A rarefaction approach for measuring population differences in rare and common variation

In studying allele-frequency variation across populations, it is often convenient to classify an allelic type as "rare," with nonzero frequency less than or equal to a specified threshold, "common," with a frequency above the threshold, or entirely unobserved in a population. When sample sizes differ across populations, however, especially if the threshold separating "rare" and "common" corresponds to a small number of observed copies of an allelic type, discreteness effects can lead a sample from one population to possess substantially more rare allelic types than a sample from another population, even if the two populations have extremely similar underlying allele-frequency distributions across loci. We introduce a rarefaction-based sample-size correction for use in comparing rare and common variation across multiple populations whose sample sizes potentially differ. We use our approach to examine rare and common variation in worldwide human populations, finding that the sample-size correction introduces subtle differences relative to analyses that use the full available sample sizes. We introduce several ways in which the rarefaction approach can be applied: we explore the dependence of allele classifications on subsample sizes, we permit more than two classes of allelic types of nonzero frequency, and we analyze rare and common variation in sliding windows along the genome. The results can assist in clarifying similarities and differences in allele-frequency patterns across populations.

New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications

Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.

Evaluating the association between DNM1L variants and Parkinson's disease in the Chinese population

Introduction

Parkinson's disease (PD) is a progressive movement disorder caused by a loss of dopaminergic neurons. Previous studies have highlighted the importance of mitochondria dynamics in the pathogenesis of PD. Dynamin-1-like (DNM1L) is a gene that encodes dynamin-related protein 1 (DRP1), a GTPase essential for proper mitochondria fission. In the present study, we evaluated the relationship between DNM1L variants and PD in the Chinese population.

Methods

A total of 3,879 patients with PD and 2,931 healthy controls were recruited and burden genetic analysis combined with high-throughput sequencing was applied.

Results

We identified 23 rare variants in the coding region of DNM1L, while no difference in variant burden was shown between the cases and controls. We also identified 201 common variants in the coding and flanking regions and found two significant SNPs, namely, rs10844308 and rs143794289 [odds ratio (OR) = 1.220 and 0.718, p = 0.025 and 0.036, respectively]. We also performed a meta-analysis to correlate the two SNPs with PD risk. However, none of the common variants was significant using logistic regression.

Conclusion

Despite the critical role of DRP1, our study did not support the relationship between DNM1L variants and PD risk in the Chinese population.

Exome-wide association analysis of CT imaging-derived hepatic fat in a medical biobank

Nonalcoholic fatty liver disease is common and highly heritable. Genetic studies of hepatic fat have not sufficiently addressed non-European and rare variants. In a medical biobank, we quantitate hepatic fat from clinical computed tomography (CT) scans via deep learning in 10,283 participants with whole-exome sequences available. We conduct exome-wide associations of single variants and rare predicted loss-of-function (pLOF) variants with CT-based hepatic fat and perform cross-modality replication in the UK Biobank (UKB) by linking whole-exome sequences to MRI-based hepatic fat. We confirm single variants previously associated with hepatic fat and identify several additional variants, including two (FGD5 H600Y and CITED2 S198_G199del) that replicated in UKB. A burden of rare pLOF variants in LMF2 is associated with increased hepatic fat and replicates in UKB. Quantitative phenotypes generated from clinical imaging studies and intersected with genomic data in medical biobanks have the potential to identify molecular pathways associated with human traits and disease.

Association between NOTCH3 gene and Parkinson's disease based on whole-exome sequencing

Objective

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a hereditary cerebral small vessel disease caused by mutations in the NOTCH3 gene. Previous studies have established a link between NOTCH3 variants and Parkinson's disease (PD) in terms of neuropathology and clinical characteristics. In this study, we aimed to explore the role of NOTCH3 gene in PD in a large Chinese cohort.

Methods

A total of 1,917 patients with early-onset or familial PD and 1,652 matched controls were included. All variants were divided into common or rare types by minor allele frequency (MAF) at a threshold of 0.01 (MAF > 0.01 into common variants and others into rare variants). Common variants were subjected to single-variant tests by PLINK, then gene-based analyses were used for rare variants with the optimized sequence kernel association test (SKAT-O). For genotype-phenotype correlation assessment, regression models were conducted to compare clinical features between the studied groups.

Results

Three common variants (rs1044006, rs1043997, and rs1043994) showed a nominal protective effect against PD. However, none of these SNPs survived Bonferroni correction. The results in the validation cohort revealed a significant but opposite association between these variants and PD. The gene-based analyses of rare variants showed no significant associations of NOTCH3 with PD. Although we did not find significant associations in the following genotype-phenotype analysis, the higher clinical scores of motor symptoms in NOTCH3-variant carriers were of interest.

Conclusion

Our results indicated that NOTCH3 gene may not play an important role in the early-onset or familial PD of Chinese population.

MicroRNA binding site variation is enriched in psychiatric disorders

Psychiatric disorders have a polygenic architecture, often associated with dozens or hundreds of independent genomic loci. Most associated loci impact noncoding regions of the genome, suggesting that the majority of disease heritability originates from the disruption of regulatory sequences. While most research has focused on variants that modify regulatory DNA elements, those affecting cis-acting RNA sequences, such as miRNA binding sites, are also likely to have a significant impact. We intersected genome-wide association study (GWAS) summary statistics with the dbMTS database of predictions for miRNA binding site variants (MBSVs). We compared the distributions of MBSV association statistics to non-MBSVs within brain-expressed 3'UTR regions. We aggregated GWAS p values at the gene, pathway, and miRNA family levels to investigate cellular functions and miRNA families strongly associated with each trait. We performed these analyses in several psychiatric disorders as well as nonpsychiatric traits for comparison. We observed significant enrichment of MBSVs in schizophrenia, depression, bipolar disorder, and anorexia nervosa, particularly in genes targeted by several miRNA families, including miR-335-5p, miR-21-5p/590-5p, miR-361-5p, and miR-557, and a nominally significant association between miR-323b-3p MBSVs and schizophrenia risk. We identified evidence for the association between MBSVs in synaptic gene sets in schizophrenia and bipolar disorder. We also observed a significant association of MBSVs in other complex traits including type 2 diabetes. These observations support the role of miRNA in the pathophysiology of psychiatric disorders and suggest that MBSVs are an important class of regulatory variants that have functional implications for many disorders, as well as other complex human traits.

Genetic Factors for Coronary Heart Disease and Their Mechanisms: A Meta-Analysis and Comprehensive Review of Common Variants from Genome-Wide Association Studies

Genome-wide association studies (GWAS) have discovered 163 loci related to coronary heart disease (CHD). Most GWAS have emphasized pathways related to single-nucleotide polymorphisms (SNPs) that reached genome-wide significance in their reports, while identification of CHD pathways based on the combination of all published GWAS involving various ethnicities has yet to be performed. We conducted a systematic search for articles with comprehensive GWAS data in the GWAS Catalog and PubMed, followed by a meta-analysis of the top recurring SNPs from ≥2 different articles using random or fixed-effect models according to Cochran Q and I² statistics, and pathway enrichment analysis. Meta-analyses showed significance for 265 of 309 recurring SNPs. Enrichment analysis returned 107 significant pathways, including lipoprotein and lipid metabolisms (rs7412, rs6511720, rs11591147, rs1412444, rs11172113, rs11057830, rs4299376), atherogenesis (rs7500448, rs6504218, rs3918226, rs7623687), shared cardiovascular pathways (rs72689147, rs1800449, rs7568458), diabetes-related pathways (rs200787930, rs12146487, rs6129767), hepatitis C virus infection/hepatocellular carcinoma (rs73045269/rs8108632, rs56062135, rs188378669, rs4845625, rs11838776), and miR-29b-3p pathways (rs116843064, rs11617955, rs146092501, rs11838776, rs73045269/rs8108632). In this meta-analysis, the identification of various genetic factors and their associated pathways associated with CHD denotes the complexity of the disease. This provides an opportunity for the future development of novel CHD genetic risk scores relevant to personalized and precision medicine.

The Role of Genetics, Epigenetics, and the Environment in ASD: A Mini Review

According to recent findings, variances in autism spectrum disorder (ASD) risk factors might be determined by several factors, including molecular genetic variants. Accumulated evidence has also revealed the important role of biological and chemical pathways in ASD aetiology. In this paper, we assess several reviews with regard to their quality of evidence and provide a brief outline of the presumed mechanisms of the genetic, epigenetic, and environmental risk factors of ASD. We also review some of the critical literature, which supports the basis of each factor in the underlying and specific risk patterns of ASD. Finally, we consider some of the implications of recent research regarding potential molecular targets for future investigations.

ALPL Genotypes in Patients With Atypical Femur Fractures or Other Biochemical and Clinical Signs of Hypophosphatasia

CONTEXT

Hypophosphatasia (HPP) is a rare metabolic disorder caused by deficiency of alkaline phosphatase (ALP) enzyme activity, leading to defective mineralization, due to pathogenic variants of the ALPL gene, encoding the tissue nonspecific alkaline phosphatase (TNSALP) enzyme. Inheritance can be autosomal recessive or autosomal dominant. An abnormal ALPL genetic test enables accurate diagnosis, avoiding the administration of contraindicated antiresorptive drugs that, in patients with HPP, substantially increase the risk of atypical femur fractures (AFFs) and worsen the fracture healing process that is usually already compromised in these patients.

OBJECTIVE

Performing ALPL genetic testing to identify rare variants in suspected adult patients with HPP. Comparing frequencies of ALPL common variants in individuals with biochemical and/or clinical signs suggestive of adult HPP and non-HPP controls, and among different clinical subgroups of patients with a clinical suspicion of adult HPP.

METHODS

Patients with suspected adult HPP were retrospectively selected for the genetic testing of the ALPL gene. Patients included were from 3 main European Bone Units (Florence, Naples, and Geneva); 106 patients with biochemical and/or clinical signs suggestive of a mild form of HPP were included.

RESULTS

Genetic testing led to the identification of a heterozygote rare variant in 2.8% of cases who were initially referred as suspected osteoporosis. The analysis of frequencies of ALPL common variants showed a high prevalence (30.8%) of homozygosity in subjects who developed an AFF, in association with normal serum total ALP activity.

CONCLUSION

The results suggest homozygosity of common ALPL variants as a possible genetic mark of risk for these fractures.

Middle Eastern Genetic Variation Improves Clinical Annotation of the Human Genome

Genetic variation in populations of Middle Eastern origin remains highly underrepresented in most comprehensive genomic databases. This underrepresentation hampers the functional annotation of the human genome and challenges accurate clinical variant interpretation. To highlight the importance of capturing genetic variation in the Middle East, we aggregated whole exome and genome sequencing data from 2116 individuals in the Middle East and established the Middle East Variation (MEV) database. Of the high-impact coding (missense and loss of function) variants in this database, 53% were absent from the most comprehensive Genome Aggregation Database (gnomAD), thus representing a unique Middle Eastern variation dataset which might directly impact clinical variant interpretation. We highlight 39 variants with minor allele frequency >1% in the MEV database that were previously reported as rare disease variants in ClinVar and the Human Gene Mutation Database (HGMD). Furthermore, the MEV database consisted of 281 putative homozygous loss of function (LoF) variants, or complete knockouts, of which 31.7% (89/281) were absent from gnomAD. This set represents either complete knockouts of 83 unique genes in reportedly healthy individuals, with implications regarding disease penetrance and expressivity, or might affect dispensable exons, thus refining the clinical annotation of those regions. Intriguingly, 24 of those genes have several clinically significant variants reported in ClinVar and/or HGMD. Our study shows that genetic variation in the Middle East improves functional annotation and clinical interpretation of the genome and emphasizes the need for expanding sequencing studies in the Middle East and other underrepresented populations.

Mutations in GBA, SNCA, and VPS35 are not associated with Alzheimer's disease in a Chinese population: a case-control study

SNCA, GBA, and VPS35 are three common genes associated with Parkinson’s disease. Previous studies have shown that these three genes may be associated with Alzheimer’s disease (AD). However, it is unclear whether these genes increase the risk of AD in Chinese populations. In this study, we used a targeted gene sequencing panel to screen all the exon regions and the nearby sequences of GBA, SNCA, and VPS35 in a cohort including 721 AD patients and 365 healthy controls from China. The results revealed that neither common variants nor rare variants of these three genes were associated with AD in a Chinese population. These findings suggest that the mutations in GBA, SNCA, and VPS35 are not likely to play an important role in the genetic susceptibility to AD in Chinese populations. The study was approved by the Ethics Committee of Xiangya Hospital, Central South University, China on March 9, 2016 (approval No. 201603198).

Gaining insight into metabolic diseases from human genetic discoveries

Human large-scale genetic association studies have identified sequence variations at thousands of genetic risk loci that are more common in patients with diverse metabolic disease compared with healthy controls. While these genetic associations have been replicated in multiple large cohorts and sometimes can explain up to 50% of heritability, the molecular and cellular mechanisms affected by common genetic variation associated with metabolic disease remains mostly unknown. A variety of new genome-wide data types, in conjunction with novel biostatistical and computational analytical methodologies and foundational experimental technologies, are paving the way for a principled approach to systematic variant-to-function (V2F) studies for metabolic diseases, turning associated regions into causal variants, cell types and states of action, effector genes, and cellular and physiological mechanisms. Identification of new target genes and cellular programs for metabolic risk loci will improve mechanistic understanding of disease biology and identification of novel therapeutic strategies.

The Role of Ion Channel-Related Genes in Autism Spectrum Disorder: A Study Using Next-Generation Sequencing

The clinical heterogeneity of autism spectrum disorder (ASD) is closely associated with the diversity of genes related to ASD pathogenesis. With their low effect size, it has been hard to define the role of common variants of genes in ASD phenotype. In this study, we reviewed genetic results and clinical scores widely used for ASD diagnosis to investigate the role of genes in ASD phenotype considering their functions in molecular pathways. Genetic data from next-generation sequencing (NGS) were collected from 94 participants with ASD. We analyzed enrichment of cellular processes and gene ontology using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We compared clinical characteristics according to genetic functional characteristics. We found 266 genes containing nonsense, frame shift, missense, and splice site mutations. Results from DAVID revealed significant enrichment for “ion channel” with an enrichment score of 8.84. Moreover, ASD participants carrying mutations in ion channel-related genes showed higher total IQ (p = 0.013) and lower repetitive, restricted behavior (RRB)-related scores (p = 0.003) and mannerism subscale of social responsiveness scale scores, compared to other participants. Individuals with variants in ion channel genes showed lower RRB scores, suggesting that ion channel genes might be relatively less associated with RRB pathogenesis. These results contribute to understanding of the role of common variants in ASD and could be important in the development of precision medicine of ASD.

The Emerging Genetic Landscape of Hirschsprung Disease and Its Potential Clinical Applications

Hirschsprung disease (HSCR) is the leading cause of neonatal functional intestinal obstruction. It is a rare congenital disease with an incidence of one in 3,500-5,000 live births. HSCR is characterized by the absence of enteric ganglia in the distal colon, plausibly due to genetic defects perturbing the normal migration, proliferation, differentiation, and/or survival of the enteric neural crest cells as well as impaired interaction with the enteric progenitor cell niche. Early linkage analyses in Mendelian and syndromic forms of HSCR uncovered variants with large effects in major HSCR genes including RET, EDNRB, and their interacting partners in the same biological pathways. With the advances in genome-wide genotyping and next-generation sequencing technologies, there has been a remarkable progress in understanding of the genetic basis of HSCR in the past few years, with common and rare variants with small to moderate effects being uncovered. The discovery of new HSCR genes such as neuregulin and BACE2 as well as the deeper understanding of the roles and mechanisms of known HSCR genes provided solid evidence that many HSCR cases are in the form of complex polygenic/oligogenic disorder where rare variants act in the sensitized background of HSCR-associated common variants. This review summarizes the roadmap of genetic discoveries of HSCR from the earlier family-based linkage analyses to the recent population-based genome-wide analyses coupled with functional genomics, and how these discoveries facilitated our understanding of the genetic architecture of this complex disease and provide the foundation of clinical translation for precision and stratified medicine.

Common deletion variants causing protocadherin-α deficiency contribute to the complex genetics of BAV and left-sided congenital heart disease

Bicuspid aortic valve (BAV) with ~1%-2% prevalence is the most common congenital heart defect (CHD). It frequently results in valve disease and aorta dilation and is a major cause of adult cardiac surgery. BAV is genetically linked to rare left-heart obstructions (left ventricular outflow tract obstructions [LVOTOs]), including hypoplastic left heart syndrome (HLHS) and coarctation of the aorta (CoA). Mouse and human studies indicate LVOTO is genetically heterogeneous with a complex genetic etiology. Homozygous mutation in the Pcdha protocadherin gene cluster in mice can cause BAV, and also HLHS and other LVOTO phenotypes when accompanied by a second mutation. Here we show two common deletion copy number variants (delCNVs) within the PCDHA gene cluster are associated with LVOTO. Analysis of 1,218 white individuals with LVOTO versus 463 disease-free local control individuals yielded odds ratios (ORs) at 1.47 (95% confidence interval [CI], 1.13-1.92; p = 4.2 × 10-3) for LVOTO, 1.47 (95% CI, 1.10-1.97; p = 0.01) for BAV, 6.13 (95% CI, 2.75-13.7; p = 9.7 × 10-6) for CoA, and 1.49 (95% CI, 1.07-2.08; p = 0.019) for HLHS. Increased OR was observed for all LVOTO phenotypes in homozygous or compound heterozygous PCDHA delCNV genotype comparison versus wild type. Analysis of an independent white cohort (381 affected individuals, 1,352 control individuals) replicated the PCDHA delCNV association with LVOTO. Generalizability of these findings is suggested by similar observations in Black and Chinese individuals with LVOTO. Analysis of Pcdha mutant mice showed reduced PCDHA expression at regions of cell-cell contact in aortic smooth muscle and cushion mesenchyme, suggesting potential mechanisms for BAV pathogenesis and aortopathy. Together, these findings indicate common variants causing PCDHA deficiency play a significant role in the genetic etiology of common and rare LVOTO-CHD.

Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases

Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.

MF-TOWmuT: Testing an optimally weighted combination of common and rare variants with multiple traits using family data

With rapid advancements of sequencing technologies and accumulations of electronic health records, a large number of genetic variants and multiple correlated human complex traits have become available in many genetic association studies. Thus, it becomes necessary and important to develop new methods that can jointly analyze the association between multiple genetic variants and multiple traits. Compared with methods that only use a single marker or trait, the joint analysis of multiple genetic variants and multiple traits is more powerful since such an analysis can fully incorporate the correlation structure of genetic variants and/or traits and their mutual dependence patterns. However, most of existing methods that simultaneously analyze multiple genetic variants and multiple traits are only applicable to unrelated samples. We develop a new method called MF-TOWmuT to detect association of multiple phenotypes and multiple genetic variants in a genomic region with family samples. MF-TOWmuT is based on an optimally weighted combination of variants. Our method can be applied to both rare and common variants and both qualitative and quantitative traits. Our simulation results show that (1) the type I error of MF-TOWmuT is preserved; (2) MF-TOWmuT outperforms two existing methods such as Multiple Family-based Quasi-Likelihood Score Test and Multivariate Family-based Rare Variant Association Test in terms of power. We also illustrate the usefulness of MF-TOWmuT by analyzing genotypic and phenotipic data from the Genetics of Kidneys in Diabetes study. R program is available at https://github.com/gaochengPRC/MF-TOWmuT.

Genome-Wide Meta-Analysis Identifies Three Novel Susceptibility Loci and Reveals Ethnic Heterogeneity of Genetic Susceptibility for IgA Nephropathy

BACKGROUND

Eighteen known susceptibility loci for IgAN account for only a small proportion of IgAN risk.

METHODS

Genome-wide meta-analysis was performed in 2628 patients and 11,563 controls of Chinese ancestry, and a replication analysis was conducted in 6879 patients and 9019 controls of Chinese descent and 1039 patients and 1289 controls of European ancestry. The data were used to assess the association of susceptibility loci with clinical phenotypes for IgAN, and to investigate genetic heterogeneity of IgAN susceptibility between the two populations. Imputation-based analysis of the MHC/HLA region extended the scrutiny.

RESULTS

Identification of three novel loci (rs6427389 on 1q23.1 [P=8.18×10^-9, OR=1.132], rs6942325 on 6p25.3 [P=1.62×10^-11, OR=1.165], and rs2240335 on 1p36.13 [P=5.10×10^-9, OR=1.114]), implicates FCRL3, DUSP22.IRF4, and PADI4 as susceptibility genes for IgAN. Rs2240335 is associated with the expression level of PADI4, and rs6427389 is in high linkage disequilibrium with rs11264799, which showed a strong expression quantitative trail loci effect on FCRL3. Of the 24 confirmed risk SNPs, six showed significant heterogeneity of genetic effects and DEFA showed clear evidence of allelic heterogeneity between the populations. Imputation-based analysis of the MHC region revealed significant associations at three HLA polymorphisms (HLA allele DPB1*02, AA_DRB1_140_32657458_T, and AA_DQA1_34_32717152) and two SNPs (rs9275464 and rs2295119).

CONCLUSIONS

A meta-analysis of GWAS data revealed three novel genetic risk loci for IgAN, and three HLA polymorphisms and two SNPs within the MHC region, and demonstrated the genetic heterogeneity of seven loci out of 24 confirmed risk SNPs.  These variants may explain susceptibility differences between Chinese and European populations.

Are Variants Causing Cardiac Arrhythmia Risk Factors in Sudden Unexpected Death in Epilepsy?

Sudden unexpected death in epilepsy (SUDEP) is the most common cause of premature mortality in individuals with epilepsy. Acute and adaptive changes in heart rhythm in epilepsy implicate cardiac dysfunction as a potential pathogenic mechanism in SUDEP. Furthermore, variants in genes associated with Long QT syndrome (LQTS) have been identified in patients with SUDEP. LQTS is a cardiac arrhythmia condition that causes sudden cardiac death with strong similarities to SUDEP. Here, we discuss the possibility of an additive risk of death due to the functional consequences of a pathogenic variant in an LQTS gene interacting with seizure-mediated changes in cardiac function. Extending this general concept, we propose a hypothesis that common variants in LQTS genes, which cause a subtle impact on channel function and would not normally be considered risk factors for cardiac disease, may increase the risk of sudden death when combined with epilepsy. A greater understanding of the interaction between epilepsy, cardiac arrhythmia, and SUDEP will inform our understanding of SUDEP risk and subsequent potential prophylactic treatment.

Genome-wide Analyses Identify a Novel Risk Locus for Nonsyndromic Cleft Palate

The 3 major subphenotypes observed in patients with nonsyndromic orofacial clefts (NSOFCs) are nonsyndromic cleft lip only (NSCLO), nonsyndromic cleft lip with palate (NSCLP), and nonsyndromic cleft palate only (NSCPO). However, the genetic architecture underlying NSCPO is largely unknown. Here we performed a 2-stage genome-wide association study (GWAS) on NSCPO and replication analyses of selected variants in other NSOFCs from the Chinese Han population. We identified a novel locus (15q24.3) and a known locus (1q32.2) where variants in or near the gene reached genome-wide significance (2.80 × 10^-13 < P < 1.72 × 10^-08) in a test for association with NSCPO in a case-control design. Although a variant from 15q24.3 was found to be significantly associated with both NSCPO and NSCLP, the direction of estimated effects on risk were opposite. Our functional annotation of the risk alleles within 15q24.3 coupled with previously established roles of the candidate genes within identified risk loci in periderm development, embryonic patterning, and/or regulation of cellular processes supports their involvement in palate development and the pathogenesis of cleft palate. Our study advances the understanding of the genetic basis of NSOFCs and provides novel insights into the pathogenesis of NSCPO.

Involvement of the 14-3-3 Gene Family in Autism Spectrum Disorder and Schizophrenia: Genetics, Transcriptomics and Functional Analyses

The 14-3-3 protein family are molecular chaperones involved in several biological functions and neurological diseases. We previously pinpointed YWHAZ (encoding 14-3-3ζ) as a candidate gene for autism spectrum disorder (ASD) through a whole-exome sequencing study, which identified a frameshift variant within the gene (c.659-660insT, p.L220Ffs*18). Here, we explored the contribution of the seven human 14-3-3 family members in ASD and other psychiatric disorders by investigating the: (i) functional impact of the 14-3-3ζ mutation p.L220Ffs*18 by assessing solubility, target binding and dimerization; (ii) contribution of common risk variants in 14-3-3 genes to ASD and additional psychiatric disorders; (iii) burden of rare variants in ASD and schizophrenia; and iv) 14-3-3 gene expression using ASD and schizophrenia transcriptomic data. We found that the mutant 14-3-3ζ protein had decreased solubility and lost its ability to form heterodimers and bind to its target tyrosine hydroxylase. Gene-based analyses using publicly available datasets revealed that common variants in YWHAE contribute to schizophrenia (p = 6.6 × 10-7), whereas ultra-rare variants were found enriched in ASD across the 14-3-3 genes (p = 0.017) and in schizophrenia for YWHAZ (meta-p = 0.017). Furthermore, expression of 14-3-3 genes was altered in post-mortem brains of ASD and schizophrenia patients. Our study supports a role for the 14-3-3 family in ASD and schizophrenia.

Next Generation Sequencing Analysis in Early Onset Dementia Patients

Background:

Early onset dementias (EOD) are rare neurodegenerative dementias that present before 65 years. Genetic factors have a substantially higher pathogenetic contribution in EOD patients than in late onset dementia.

Objective:

To identify known and/or novel rare variants in major candidate genes associated to EOD by high-throughput sequencing. Common-risk variants of apolipoprotein E (APOE) and prion protein (PRNP) genes were also assessed.

Methods:

We studied 22 EOD patients recruited in Memory Clinics, in the context of studies investigating genetic forms of dementia. Two methodological approaches were applied for the target-Next Generation Sequencing (NGS) analysis of these patients. In addition, we performed progranulin plasma dosage, C9Orf72 hexanucleotide repeat expansion analysis, and APOE genotyping.

Results:

We detected three rare known pathogenic mutations in the GRN and PSEN2 genes and eleven unknown-impact mutations in the GRN, VCP, MAPT, FUS, TREM2, and NOTCH3 genes. Six patients were carriers of only common risk variants (APOE and PRNP), and one did not show any risk mutation/variant. Overall, 69% (n = 9) of our early onset Alzheimer’s disease (EAOD) patients, compared with 34% (n = 13) of sporadic late onset Alzheimer’s disease (LOAD) patients and 27% (n = 73) of non-affected controls (ADNI, whole genome data), were carriers of at least two rare/common risk variants in the analyzed candidate genes panel, excluding the full penetrant mutations.

Conclusion:

This study suggests that EOD patients without full penetrant mutations are characterized by higher probability to carry polygenic risk alleles that patients with LOAD forms. This finding is in line with recently reported evidence, thus suggesting that the genetic risk factors identified in LOAD might modulate the risk also in EOAD.

Genetic Variation across Phenotypic Severity of Autism

It is still unclear how genetic factors of autism spectrum disorder (ASD) are implicated in the significant clinical heterogeneity ranging from intellectual disability (ID) to high-functioning profiles. Here, evidence from recent genetic studies encompassing common and rare variants are combined to suggest a genetic model that may explain the broad gradient of phenotypic severity observed in ASD.

Genetic variants in GRHL3 and risk for neural tube defects: A case-control and case-parent triad/control study

BACKGROUND

Neural tube defects (NTDs) are the most common severe birth defects with complex etiologies. Previous studies conducted on animals have suggested that the Grhl3 gene is essential for closure of the spinal neural tube, but little evidence from human studies on the variants of GRHL3 gene has been provided, especially the common genetic variants.

METHODS

To investigate the relationship between common genetic variants of GRHL3 and the risk for NTDs, we performed a case-control study and a case-parent triad/control study. Fast-target enrichment sequencing was performed to screen exon regions from 503 NTD cases, and three tag SNPs (single nucleotide polymorphisms, including rs12030057, rs2486668, and rs545809) were selected according to the sequencing results. Then, Sequenom MassARRAY genotyping was performed in 757 case parents and 519 controls to obtain genotype information of the target variant sites among all NTD triads and controls.

RESULTS

The genotype distributions of all SNPs were in accordance with Hardy-Weinberg Equilibrium (HWE) in the control population. In the case-control study, significant associations were found between C27G genetic variants on rs2486668 and risk for spina bifida and encephalocele, respectively, under different genetic models. Consistently, in the case-parent triad/control study, GG genotype on rs2486668 was associated with increased risk for spina bifida, with a RR of 2.15 (95% CI: 1.20-3.83). However, no parent-of-origin effect was found for any tag SNPs.

CONCLUSION

The GRHL3 C67G missense variant may increase the risk for spina bifida and encephalocele phenotypes.

Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples

The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.

A general statistic to test an optimally weighted combination of common and/or rare variants

Both genome-wide association study and next-generation sequencing data analyses are widely employed to identify disease susceptible common and/or rare genetic variants. Rare variants generally have large effects though they are hard to detect due to their low frequencies. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some adhoc assumptions (e.g., ignoring dependence between rare variants). In this study, we analytically derived optimal weights for both common and rare variants and proposed a general and novel approach to test association between an optimally weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. Additionally, we applied G-TOW and other competitive methods to test disease-associated genes in real data of schizophrenia. The G-TOW has successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic and the sequence kernel association test. Simulation study and real data analysis indicate that G-TOW is a powerful test.

Common Grounds for Family Maladies

Rare variants cause Mendelian family aggregation in subsets of common diseases, and common variants may contribute to rare diseases. In this issue of Neuron, Gormley et al. (2018) report that the common variant burden in familial migraine is larger than in migraine of the general population.

Genetic Determinants of IgA Nephropathy: Eastern Perspective

IgA nephropathy (IgAN) is one of the most common primary glomerulonephritides throughout the world and a major cause of end-stage renal disease among the East Asian population. It is widely considered that genetic factors play an important role in the pathogenesis of IgAN. This article summarizes the recent achievements in the genetic studies of IgAN, focusing mainly on studies performed in East Asia, from the early association studies of candidate genes and family based designs, to the recent genome-wide association studies. There have been five large genome-wide association studies performed that have identified multiple susceptibility loci for IgAN, especially some novel loci identified in the Chinese population. Genes within these loci have provided important insights into the potential biological mechanisms and pathways that influence genetic risk to IgAN. In susceptibility loci/genes, the study of genetic interaction and structural variants (such as copy number variation) was conducted to identify more variants associated with IgAN and disease progression. Genetic studies of IgAN from East Asia have made great achievements over the years. Most susceptibility loci discovered to date encode genes involved in the response to mucosal pathogens, suggesting that an intestinal-immune network for IgA production may be involved in the pathogenesis of IgAN. Although genetic studies of the complex diseases are challenging, for future genetic studies in IgAN, new genetic techniques and methods of analysis, especially next-generation sequencing, need to be applied to push the genetic studies forward.

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

Nonsyndromic cleft palate: An association study at GWAS candidate loci in a multiethnic sample

BACKGROUND

Nonsyndromic cleft palate only (nsCPO) is a common and multifactorial form of orofacial clefting. In contrast to successes achieved for the other common form of orofacial clefting, that is, nonsyndromic cleft lip with/without cleft palate (nsCL/P), genome wide association studies (GWAS) of nsCPO have identified only one genome wide significant locus. Aim of the present study was to investigate whether common variants contribute to nsCPO and, if so, to identify novel risk loci.

METHODS

We genotyped 33 SNPs at 27 candidate loci from 2 previously published nsCPO GWAS in an independent multiethnic sample. It included: (i) a family-based sample of European ancestry (n = 212); and (ii) two case/control samples of Central European (n = 94/339) and Arabian ancestry (n = 38/231), respectively. A separate association analysis was performed for each genotyped dataset, and meta-analyses were performed.

RESULTS

After association analysis and meta-analyses, none of the 33 SNPs showed genome-wide significance. Two variants showed nominally significant association in the imputed GWAS dataset and exhibited a further decrease in p-value in a European and an overall meta-analysis including imputed GWAS data, respectively (rs395572: P_MetaEU  = 3.16 × 10^-4 ; rs6809420: P_MetaAll  = 2.80 × 10^-4 ).

CONCLUSION

Our findings suggest that there is a limited contribution of common variants to nsCPO. However, the individual effect sizes might be too small for detection of further associations in the present sample sizes. Rare variants may play a more substantial role in nsCPO than in nsCL/P, for which GWAS of smaller sample sizes have identified genome-wide significant loci. Whole-exome/genome sequencing studies of nsCPO are now warranted.

Mapping the Schizophrenia Genes by Neuroimaging: The Opportunities and the Challenges

Schizophrenia (SZ) is a heritable brain disease originating from a complex interaction of genetic and environmental factors. The genes underpinning the neurobiology of SZ are largely unknown but recent data suggest strong evidence for genetic variations, such as single nucleotide polymorphisms, making the brain vulnerable to the risk of SZ. Structural and functional brain mapping of these genetic variations are essential for the development of agents and tools for better diagnosis, treatment and prevention of SZ. Addressing this, neuroimaging methods in combination with genetic analysis have been increasingly used for almost 20 years. So-called imaging genetics, the opportunities of this approach along with its limitations for SZ research will be outlined in this invited paper. While the problems such as reproducibility, genetic effect size, specificity and sensitivity exist, opportunities such as multivariate analysis, development of multisite consortia for large-scale data collection, emergence of non-candidate gene (hypothesis-free) approach of neuroimaging genetics are likely to contribute to a rapid progress for gene discovery besides to gene validation studies that are related to SZ.

Analysis of the Influence of microRNAs in Lithium Response in Bipolar Disorder

Bipolar disorder (BD) is a common, highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. Lithium is the best-established long-term treatment for BD, even though individual response is highly variable. Evidence suggests that some of this variability has a genetic basis. This is supported by the largest genome-wide association study (GWAS) of lithium response to date conducted by the International Consortium on Lithium Genetics (ConLiGen). Recently, we performed the first genome-wide analysis of the involvement of miRNAs in BD and identified nine BD-associated miRNAs. However, it is unknown whether these miRNAs are also associated with lithium response in BD. In the present study, we therefore tested whether common variants at these nine candidate miRNAs contribute to the variance in lithium response in BD. Furthermore, we systematically analyzed whether any other miRNA in the genome is implicated in the response to lithium. For this purpose, we performed gene-based tests for all known miRNA coding genes in the ConLiGen GWAS dataset (n = 2,563 patients) using a set-based testing approach adapted from the versatile gene-based test for GWAS (VEGAS2). In the candidate approach, miR-499a showed a nominally significant association with lithium response, providing some evidence for involvement in both development and treatment of BD. In the genome-wide miRNA analysis, 71 miRNAs showed nominally significant associations with the dichotomous phenotype and 106 with the continuous trait for treatment response. A total of 15 miRNAs revealed nominal significance in both phenotypes with miR-633 showing the strongest association with the continuous trait (p = 9.80E-04) and miR-607 with the dichotomous phenotype (p = 5.79E-04). No association between miRNAs and treatment response to lithium in BD in either of the tested conditions withstood multiple testing correction. Given the limited power of our study, the investigation of miRNAs in larger GWAS samples of BD and lithium response is warranted.

Polygenic determinants in extremes of high-density lipoprotein cholesterol

HDL cholesterol (HDL-C) remains a superior biochemical predictor of CVD risk, but its genetic basis is incompletely defined. In patients with extreme HDL-C concentrations, we concurrently evaluated the contributions of multiple large- and small-effect genetic variants. In a discovery cohort of 255 unrelated lipid clinic patients with extreme HDL-C levels, we used a targeted next-generation sequencing panel to evaluate rare variants in known HDL metabolism genes, simultaneously with common variants bundled into a polygenic trait score. Two additional cohorts were used for validation and included 1,746 individuals from the Montréal Heart Institute Biobank and 1,048 individuals from the University of Pennsylvania. Findings were consistent between cohorts: we found rare heterozygous large-effect variants in 18.7% and 10.9% of low- and high-HDL-C patients, respectively. We also found common variant accumulation, indicated by extreme polygenic trait scores, in an additional 12.8% and 19.3% of overall cases of low- and high-HDL-C extremes, respectively. Thus, the genetic basis of extreme HDL-C concentrations encountered clinically is frequently polygenic, with contributions from both rare large-effect and common small-effect variants. Multiple types of genetic variants should be considered as contributing factors in patients with extreme dyslipidemia.

Evaluation of the association of UBASH3A and SYNGR1 with rheumatoid arthritis and disease activity and severity in Han Chinese

Rheumatoid arthritis (RA) is a common complex autoimmune disorder. UBASH3A and SYNGR1 were identified recently as susceptibility genes for RA risk in Korean and European populations, but the genetic aetiology and pathogenesis of RA have not been fully elucidated. We designed a two-stage case-control study including 916 RA patients and 2,266 unrelated healthy controls to identify common genetic variants in UBASH3A and SYNGR1 that predispose Han Chinese individuals to RA. We also evaluated the role of associated variants in clinical manifestations of RA, which may provide clues to the mechanisms involved in the aetiology of RA. We successfully identified two SNPs, rs1893592 in UBASH3A and rs909685 in SYNGR1, as significantly associated with the disease status of RA using our two-stage strategy. The rs1893592 SNP in UBASH3A was related with DAS28, CRP level and bone erosion. In summary, our results indicate that genetic variants in UBASH3A and SYNGR1 may modify individual susceptibility to RA in the Han Chinese population and support the role of the UBASH3A gene in RA disease activity and severity.

Genetic modifiers of CHEK2*1100delC-associated breast cancer risk

PURPOSE

CHEK2*1100delC is a founder variant in European populations that confers a two- to threefold increased risk of breast cancer (BC). Epidemiologic and family studies have suggested that the risk associated with CHEK2*1100delC is modified by other genetic factors in a multiplicative fashion. We have investigated this empirically using data from the Breast Cancer Association Consortium (BCAC).

METHODS

Using genotype data from 39,139 (624 1100delC carriers) BC patients and 40,063 (224) healthy controls from 32 BCAC studies, we analyzed the combined risk effects of CHEK2*1100delC and 77 common variants in terms of a polygenic risk score (PRS) and pairwise interaction.

RESULTS

The PRS conferred odds ratios (OR) of 1.59 (95% CI: 1.21-2.09) per standard deviation for BC for CHEK2*1100delC carriers and 1.58 (1.55-1.62) for noncarriers. No evidence of deviation from the multiplicative model was found. The OR for the highest quintile of the PRS was 2.03 (0.86-4.78) for CHEK2*1100delC carriers, placing them in the high risk category according to UK NICE guidelines. The OR for the lowest quintile was 0.52 (0.16-1.74), indicating a lifetime risk close to the population average.

CONCLUSION

Our results confirm the multiplicative nature of risk effects conferred by CHEK2*1100delC and the common susceptibility variants. Furthermore, the PRS could identify carriers at a high lifetime risk for clinical actions.Genet Med advance online publication 06 October 2016.

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.

Associations of the Serum/Glucocorticoid Regulated Kinase Genes With BP Changes and Hypertension Incidence: The Gensalt Study

BACKGROUND

Single-marker and novel gene-based methods were employed to examine the associations of the serum/glucocorticoid regulated kinases (SGK) gene family with longitudinal blood pressure (BP) changes and hypertension incidence in a family-based cohort study.

METHODS

Totally, 1,768 Chinese participants from the Genetic Epidemiology Network of Salt Sensitivity (GenSalt) follow-up study were included in the current analyses. Nine BP measures were obtained at each of 3 visits during the GenSalt follow-up study. Mixed-model and Gene-based analyses were used to examine the associations of the SGK gene family with longitudinal BP phenotypes. Bonferroni correction was applied to account for multiple testing.

RESULTS

After an average 7.2-year follow-up, 32.2% (513) of participants free of hypertension at baseline developed hypertension. Four novel SNPs in the SGK1 gene were predictive of the longitudinal BP phenotypes. The major alleles of SGK1 rs1763498 and rs114414980 conferred 2.9- and 2.5-fold increased risks of hypertension development, respectively (P = 1.0×10^-4 and 6.0×10^-4, respectively). In addition, the major allele of SGK1 rs229133 was significantly associated with 0.4mm Hg larger annual increases in systolic BP (P = 4.2×10^-4), while the major allele of rs6924468 was significantly associated with 0.2mm Hg smaller annual increases in diastolic BP (P = 4.2×10^-4). Gene-based analyses revealed an association of the SGK1 gene with risk of hypertension development (P = 7.4×10^-3). No evidence for the SGK2 and SGK3 genes was found.

CONCLUSIONS

The findings of the current study suggest that the SGK1 gene may play a role in long-term BP regulation and hypertension incidence.

A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.

Two-stage additional evidence support association of common variants in the HDAC3 with the increasing risk of schizophrenia susceptibility

Schizophrenia (SCZ) is a complex neuropsychiatric disorder with high heritability. Abnormal gene methylation was found to play a key role in the development of SCZ, suggesting that histone deacetylases (HDACs) may increase the expression of several key genes in the brain. However, recent studies evaluating the association between SCZ and genetic polymorphisms in histone deacetylase 3 (encoded by HDAC3) have shown conflicting results. In this study, we designed a two-stage case-control study to investigate the association of the HDAC3 with SCZ. Fourteen tag single nucleotide polymorphisms (SNPs) entirely covering the region of HDAC3 were analyzed in the testing group of 1,421 patients and 2,823 healthy controls, and the SNP rs14251 was found to be significant (and rs2530223 to be nominally significant). The significant result of rs14251 was successfully replicated in the validation group consisting of 896 cases and 1,815 healthy controls (P = 0.009276, OR = 1.219), and also confirmed by haplotype based analyses (rs976552-rs14251, global P < 0.001). To sum up, our results provide additional evidence that HDAC3 confers the increasing risk of SCZ susceptibility in Han Chinese individuals, suggesting this gene as a potential genetic modifier for SCZ development. © 2016 Wiley Periodicals, Inc.

Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations.

Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also useful when generating hypotheses for experimental validation. However, commonly used methods for generating GWA gene scores are computationally inefficient, biased by gene length, imprecise, or have low true positive rate (TPR) at low false positive rates (FPR), leading to erroneous hypotheses for functional validation. Here we introduce a new method, PEGASUS, for analytically calculating gene scores. PEGASUS produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods. In simulation, PEGASUS outperforms existing methods, achieving up to 30% higher TPR when the FPR is fixed at 1%. We use gene scores from PEGASUS as input to HotNet2 to identify networks of interacting genes associated with multiple complex diseases and traits; this is the first application of HotNet2 to common variation. In ulcerative colitis and waist-hip ratio, we discover networks that include genes previously associated with these phenotypes, as well as novel candidate genes. In contrast, existing methods fail to identify these networks. We also identify networks for attention-deficit/hyperactivity disorder, in which GWA studies have yet to identify any significant SNPs.

Common variants in CACNA1C and MDD susceptibility: A comprehensive meta-analysis

Major depressive disorder (MDD) is one of the most common psychiatric disorders with a relatively high heritability (35-40%). Though rs1006737 in the CACNA1C gene showed significant association with MDD in a British large-scale candidate association study, most of the replication analyses with relatively small sample size reported negative association. Moreover, this locus has never been identified in previous genome-wide association studies (GWAS) for MDD. Here, we conducted a comprehensive meta-analysis of the association between CACNA1C variants and MDD risk by combining all published data. Genetic data from one European GWAS and five individual follow-up studies, which include up to 12,629 patients of MDD and 28,653 controls, that is, the largest sample size on CACNA1C to date, were collected. Rs1006737 showed significant association with MDD in the fixed-effect model (Z = 2.56, P = 0.011, OR = 1.08, 95%CI = 1.04-1.12) and the association remained after reanalyzing the data according to ethnicity. We additionally analyzed other 25 SNPs, genotyped in only one replication study, across the CACNA1C locus, and found that two SNPs, rs4765905 (P = 0.041, OR = 1.05, 95%CI 1.00-1.09) and rs4765937 (P = 0.025, OR = 1.05, 95%CI 1.01-1.09) showed nominal association with MDD, while rs2239073 (P = 0.002, OR = 1.07, 95%CI 1.02-1.11) exhibited significant association with MDD, which survived from multiple corrections. Our study provides support for positive association between CACNA1C and MDD; however, the current data suggest the necessity of replication analyses in a larger-scale sample. © 2016 Wiley Periodicals, Inc.

A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits

In association studies of complex traits, fixed-effect regression models are usually used to test for association between traits and major gene loci. In recent years, variance-component tests based on mixed models were developed for region-based genetic variant association tests. In the mixed models, the association is tested by a null hypothesis of zero variance via a sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C). Although there are some comparison studies to evaluate the performance of mixed and fixed models, there is no systematic analysis to determine when the mixed models perform better and when the fixed models perform better. Here we evaluated, based on extensive simulations, the performance of the fixed and mixed model statistics, using genetic variants located in 3, 6, 9, 12, and 15 kb simulated regions. We compared the performance of three models: (i) mixed models that lead to SKAT, SKAT-O, and SKAT-C, (ii) traditional fixed-effect additive models, and (iii) fixed-effect functional regression models. To evaluate the type I error rates of the tests of fixed models, we generated genotype data by two methods: (i) using all variants, (ii) using only rare variants. We found that the fixed-effect tests accurately control or have low false positive rates. We performed simulation analyses to compare power for two scenarios: (i) all causal variants are rare, (ii) some causal variants are rare and some are common. Either one or both of the fixed-effect models performed better than or similar to the mixed models except when (1) the region sizes are 12 and 15 kb and (2) effect sizes are small. Therefore, the assumption of mixed models could be satisfied and SKAT/SKAT-O/SKAT-C could perform better if the number of causal variants is large and each causal variant contributes a small amount to the traits (i.e., polygenes). In major gene association studies, we argue that the fixed-effect models perform better or similarly to mixed models in most cases because some variants should affect the traits relatively large. In practice, it makes sense to perform analysis by both the fixed and mixed effect models and to make a comparison, and this can be readily done using our R codes and the SKAT packages.

Genetic predisposition to ductal carcinoma in situ of the breast

BACKGROUND

Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer. It is often associated with invasive ductal carcinoma (IDC), and is considered to be a non-obligate precursor of IDC. It is not clear to what extent these two forms of cancer share low-risk susceptibility loci, or whether there are differences in the strength of association for shared loci.

METHODS

To identify genetic polymorphisms that predispose to DCIS, we pooled data from 38 studies comprising 5,067 cases of DCIS, 24,584 cases of IDC and 37,467 controls, all genotyped using the iCOGS chip.

RESULTS

Most (67 %) of the 76 known breast cancer predisposition loci showed an association with DCIS in the same direction as previously reported for invasive breast cancer. Case-only analysis showed no evidence for differences between associations for IDC and DCIS after considering multiple testing. Analysis by estrogen receptor (ER) status confirmed that loci associated with ER positive IDC were also associated with ER positive DCIS. Analysis of DCIS by grade suggested that two independent SNPs at 11q13.3 near CCND1 were specific to low/intermediate grade DCIS (rs75915166, rs554219). These associations with grade remained after adjusting for ER status and were also found in IDC. We found no novel DCIS-specific loci at a genome wide significance level of P < 5.0x10(-8).

CONCLUSION

In conclusion, this study provides the strongest evidence to date of a shared genetic susceptibility for IDC and DCIS. Studies with larger numbers of DCIS are needed to determine if IDC or DCIS specific loci exist.

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example.

Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models

We developed generalized functional linear models (GFLMs) to perform a meta-analysis of multiple case-control studies to evaluate the relationship of genetic data to dichotomous traits adjusting for covariates. Unlike the previously developed meta-analysis for sequence kernel association tests (MetaSKATs), which are based on mixed-effect models to make the contributions of major gene loci random, GFLMs are fixed models; i.e., genetic effects of multiple genetic variants are fixed. Based on GFLMs, we developed chi-squared-distributed Rao's efficient score test and likelihood-ratio test (LRT) statistics to test for an association between a complex dichotomous trait and multiple genetic variants. We then performed extensive simulations to evaluate the empirical type I error rates and power performance of the proposed tests. The Rao's efficient score test statistics of GFLMs are very conservative and have higher power than MetaSKATs when some causal variants are rare and some are common. When the causal variants are all rare [i.e., minor allele frequencies (MAF) < 0.03], the Rao's efficient score test statistics have similar or slightly lower power than MetaSKATs. The LRT statistics generate accurate type I error rates for homogeneous genetic-effect models and may inflate type I error rates for heterogeneous genetic-effect models owing to the large numbers of degrees of freedom and have similar or slightly higher power than the Rao's efficient score test statistics. GFLMs were applied to analyze genetic data of 22 gene regions of type 2 diabetes data from a meta-analysis of eight European studies and detected significant association for 18 genes (P < 3.10 × 10(-6)), tentative association for 2 genes (HHEX and HMGA2; P ≈ 10(-5)), and no association for 2 genes, while MetaSKATs detected none. In addition, the traditional additive-effect model detects association at gene HHEX. GFLMs and related tests can analyze rare or common variants or a combination of the two and can be useful in whole-genome and whole-exome association studies.

Genetics of coronary heart disease: towards causal mechanisms, novel drug targets and more personalized prevention

Coronary heart disease (CHD) is an archetypical multifactorial disorder that is influenced by genetic susceptibility as well as both modifiable and nonmodifiable risk factors, and their interactions. Advances during recent years in the field of multifactorial genetics, in particular genomewide association studies (GWASs) and their meta-analyses, have provided the statistical power to identify and replicate genetic variants in more than 50 risk loci for CHD and in several hundreds of loci for cardiometabolic risk factors for CHD such as blood lipids and lipoproteins. Although for a great majority of these loci both the causal variants and mechanisms remain unknown, progress in identifying the causal variants and underlying mechanisms has already been made for several genetic loci. Furthermore, identification of rare loss-of-function variants in genes such as PCSK9, NPC1L1, APOC3 and APOA5, which cause a markedly decreased risk of CHD and no adverse side effects, illustrates the power of translating genetic findings into novel mechanistic information and provides some optimism for the future of developing novel drugs, given the many genes associated with CHD in GWASs. Finally, Mendelian randomization can be used to reveal or exclude causal relationships between heritable biomarkers and CHD, and such approaches have already provided evidence of causal relationships between CHD and LDL cholesterol, triglycerides/remnant particles and lipoprotein(a), and indicated a lack of causality for HDL cholesterol, C-reactive protein and lipoprotein-associated phospholipase A2. Together, these genetic findings are beginning to lead to promising new drug targets and novel interventional strategies and thus have great potential to improve prevention, prediction and therapy of CHD.