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.