Human Disease Variation in the Light of Population Genomics.

Identifying the causes of similarities and differences in genetic disease prevalence among humans is central to understanding disease etiology. While present-day humans are not strongly differentiated, vast amounts of genomic data now make it possible to study subtle patterns of genetic variation. This allows us to trace our genomic history thousands of years into the past and its implications for the distribution of disease-associated variants today. Genomic analyses have shown that demographic processes shaped the distribution and frequency of disease-associated variants over time. Furthermore, local adaptation to new environmental conditions-including pathogens-has generated strong patterns of differentiation at particular loci. Researchers are also beginning to uncover the genetic architecture of complex diseases, affected by many variants of small effect. The field of population genomics thus holds great potential for providing further insights into the evolution of human disease.

The selection landscape and genetic legacy of ancient Eurasians.

The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in over 400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, whereas risk alleles for diabetes and Alzheimer's disease are enriched for Western hunter-gatherer ancestry. Our results indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.

Genetic correlations of polygenic disease traits: from theory to practice.

The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.

A comprehensive map of genetic relationships among diagnostic categories based on 48.6 million relative pairs from the Danish genealogy.

For more than half a century, Denmark has maintained population-wide demographic, health care, and socioeconomic registers that provide detailed information on the interaction between all residents and the extensive national social services system. We leverage this resource to reconstruct the genealogy of the entire nation based on all individuals legally residing in Denmark since 1968. We cross-reference 6,691,426 individuals with nationwide health care registers to estimate heritability and genetic correlations of 10 broad diagnostic categories involving all major organs and systems. Heritability estimates for mental disorders were consistently the highest across demographic cohorts (average h2 = 0.406, 95% CI = [0.403, 0.408]), whereas estimates for cancers were the lowest (average h2 = 0.130, 95% CI = [0.125, 0.134]). The average genetic correlation of each of the 10 diagnostic categories with the other nine was highest for gastrointestinal conditions (average rg = 0.567, 95% CI = [0.566, 0.567]) and lowest for urogenital conditions (average rg = 0.386, 95% CI = [0.385, 0.388]). Mental, pulmonary, gastrointestinal, and neurological conditions had similar genetic correlation profiles.

Association between Mental Disorders and Subsequent Medical Conditions.

BACKGROUND: Persons with mental disorders are at a higher risk than the general population for the subsequent development of certain medical conditions. METHODS: We used a population-based cohort from Danish national registries that included data on more than 5.9 million persons born in Denmark from 1900 through 2015 and followed them from 2000 through 2016, for a total of 83.9 million person-years. We assessed 10 broad types of mental disorders and 9 broad categories of medical conditions (which encompassed 31 specific conditions). We used Cox regression models to calculate overall hazard ratios and time-dependent hazard ratios for pairs of mental disorders and medical conditions, after adjustment for age, sex, calendar time, and previous mental disorders. Absolute risks were estimated with the use of competing-risks survival analyses. RESULTS: A total of 698,874 of 5,940,299 persons (11.8%) were identified as having a mental disorder. The median age of the total population was 32.1 years at entry into the cohort and 48.7 years at the time of the last follow-up. Persons with a mental disorder had a higher risk than those without such disorders with respect to 76 of 90 pairs of mental disorders and medical conditions. The median hazard ratio for an association between a mental disorder and a medical condition was 1.37. The lowest hazard ratio was 0.82 for organic mental disorders and the broad category of cancer (95% confidence interval [CI], 0.80 to 0.84), and the highest was 3.62 for eating disorders and urogenital conditions (95% CI, 3.11 to 4.22). Several specific pairs showed a reduced risk (e.g., schizophrenia and musculoskeletal conditions). Risks varied according to the time since the diagnosis of a mental disorder. The absolute risk of a medical condition within 15 years after a mental disorder was diagnosed varied from 0.6% for a urogenital condition among persons with a developmental disorder to 54.1% for a circulatory disorder among those with an organic mental disorder. CONCLUSIONS: Most mental disorders were associated with an increased risk of a subsequent medical condition; hazard ratios ranged from 0.82 to 3.62 and varied according to the time since the diagnosis of the mental disorder. (Funded by the Danish National Research Foundation and others; COMO-GMC ClinicalTrials.gov number, NCT03847753.).

Genetic factors underlying the bidirectional relationship between autoimmune and mental disorders - Findings from a Danish population-based study.

BACKGROUND: Previous studies have indicated the bidirectionality between autoimmune and mental disorders. However, genetic studies underpinning the co-occurrence of the two disorders have been lacking. In this study, we examined the potential genetic contribution to the association between autoimmune and mental disorders and investigated the genetic basis of overall autoimmune disease. METHODS: We used diagnostic information from patients with seven autoimmune diseases and six mental disorders from the Danish population-based case-cohort sample (iPSYCH2012). We explored the epidemiological association using survival analysis and modelled the effect of polygenic risk scores (PRSs) on autoimmune and mental diseases. Genetic factors were investigated using GWAS and imputed HLA alleles in the iPSYCH cohort. RESULTS: Of 64,039 individuals, a total of 43,902 (68.6%) were diagnosed with mental disorders and 1383 (2.2%) with autoimmune diseases. There was a significant comorbidity between the two disease classes (P = 2.67 × 10-7, OR = 1.38, 95%CI = 1.22-1.56), with an overall bidirectional association, wherein individuals with autoimmune diseases had an increased risk of subsequent mental disorders (HR = 1.13, 95%CI: 1.07-1.21, P = 7.95 × 10-5) and vice versa (HR = 1.27, 95%CI = 1.16-1.39, P = 8.77 × 10-15). Adding PRSs to these adjustment models did not have an impact on the associations. PRSs for autoimmune diseases were only slightly associated with increased risk of mental disorders (HR = 1.01, 95%CI: 1.00-1.02, p = 0.038), whereas PRSs for mental disorders were not associated with autoimmune diseases overall. Our GWAS highlighted 12 loci on chromosome 6 (minimum P = 2.74 × 10-36, OR = 1.80, 95% CI: 1.64-1.96), which were implicated in gene regulation through bioinformatic functional analyses, thereby identifying new candidate genes for overall autoimmune disease. Moreover, we observed 20 human leukocyte antigen (HLA) alleles strongly associated, either positively or negatively, with overall autoimmune disease, but we did not find significant evidence of their associations with overall mental disorders. A GWAS of a comorbid diagnosis of an autoimmune disease and a mental disorder identified a genome-wide significant locus on chromosome 7 as well (P = 1.43 × 10-8, OR = 10.65, 95%CI = 3.21-35.36). CONCLUSIONS: Our findings confirm the overall comorbidity and bidirectionality between autoimmune diseases and mental disorders and identify HLA genes which are significantly associated with overall autoimmune disease. Additionally, we identified several new candidate genes for overall autoimmune disease and ranked them based on their association with the investigated diseases.

Beyond heritability: improving discoverability in imaging genetics.

Structural neuroimaging measures based on magnetic resonance imaging have been at the forefront of imaging genetics. Global efforts to ensure homogeneity of measurements across study sites have enabled large-scale imaging genetic projects, accumulating nearly 50K samples for genome-wide association studies (GWAS). However, not many novel genetic variants have been identified by these GWAS, despite the high heritability of structural neuroimaging measures. Here, we discuss the limitations of using heritability as a guidance for assessing statistical power of GWAS, and highlight the importance of discoverability-which is the power to detect genetic variants for a given phenotype depending on its unique genomic architecture and GWAS sample size. Further, we present newly developed methods that boost genetic discovery in imaging genetics. By redefining imaging measures independent of traditional anatomical conventions, it is possible to improve discoverability, enabling identification of more genetic effects. Moreover, by leveraging enrichment priors from genomic annotations and independent GWAS of pleiotropic traits, we can better characterize effect size distributions, and identify reliable and replicable loci associated with structural neuroimaging measures. Statistical tools leveraging novel insights into the genetic discoverability of human traits, promises to accelerate the identification of genetic underpinnings underlying brain structural variation.

A large population-based investigation into the genetics of susceptibility to gastrointestinal infections and the link between gastrointestinal infections and mental illness.

Gastrointestinal infections can be life threatening, but not much is known about the host's genetic contribution to susceptibility to gastrointestinal infections or the latter's association with psychiatric disorders. We utilized iPSYCH, a genotyped population-based sample of individuals born between 1981 and 2005 comprising 65,534 unrelated Danish individuals (45,889 diagnosed with mental disorders and 19,645 controls from a random population sample) in which all individuals were linked utilizing nationwide population-based registers to estimate the genetic contribution to susceptibility to gastrointestinal infections, identify genetic variants associated with gastrointestinal infections, and examine the link between gastrointestinal infections and psychiatric and neurodevelopmental disorders. The SNP heritability of susceptibility to gastrointestinal infections ranged from 3.7% to 6.4% on the liability scale. Significant correlations were found between gastrointestinal infections and the combined group of mental disorders (OR = 2.09; 95% CI: 1.82-2.4, P = 1.87 × 10-25). Correlations with autism spectrum disorder, attention deficit hyperactivity disorder, and depression were also significant. We identified a genome-wide significant locus associated with susceptibility to gastrointestinal infections (OR = 1.13; 95% CI: 1.08-1.18, P = 2.9 × 10-8), where the top SNP was an eQTL for the ABO gene. The risk allele was associated with reduced ABO expression, providing, for the first time, genetic evidence to support previous studies linking the O blood group to gastrointestinal infections. This study also highlights the importance of integrative work in genetics, psychiatry, infection, and epidemiology on the road to translational medicine.

Identification of novel loci affecting circulating chromogranins and related peptides.

Chromogranins are pro-hormone secretory proteins released from neuroendocrine cells, with effects on control of blood pressure. We conducted a genome-wide association study for plasma catestatin, the catecholamine release inhibitory peptide derived from chromogranin A (CHGA), and other CHGA- or chromogranin B (CHGB)-related peptides, in 545 US and 1252 Australian subjects. This identified loci on chromosomes 4q35 and 5q34 affecting catestatin concentration (P = 3.40 × 10-30 for rs4253311 and 1.85 × 10-19 for rs2731672, respectively). Genes in these regions include the proteolytic enzymes kallikrein (KLKB1) and Factor XII (F12). In chromaffin cells, CHGA and KLKB1 proteins co-localized in catecholamine storage granules. In vitro, kallikrein cleaved recombinant human CHGA to catestatin, verified by mass spectrometry. The peptide identified from this digestion (CHGA360-373) selectively inhibited nicotinic cholinergic stimulated catecholamine release from chromaffin cells. A proteolytic cascade involving kallikrein and Factor XII cleaves chromogranins to active compounds both in vivo and in vitro.

Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS.

Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic ("z-score") of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a "relative enrichment score" for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3.

Identifying Novel Gene Variants in Coronary Artery Disease and Shared Genes With Several Cardiovascular Risk Factors.

RATIONALE: Coronary artery disease (CAD) is a critical determinant of morbidity and mortality. Previous studies have identified several cardiovascular disease risk factors, which may partly arise from a shared genetic basis with CAD, and thus be useful for discovery of CAD genes. OBJECTIVE: We aimed to improve discovery of CAD genes and inform the pathogenic relationship between CAD and several cardiovascular disease risk factors using a shared polygenic signal-informed statistical framework. METHODS AND RESULTS: Using genome-wide association studies summary statistics and shared polygenic pleiotropy-informed conditional and conjunctional false discovery rate methodology, we systematically investigated genetic overlap between CAD and 8 traits related to cardiovascular disease risk factors: low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. We found significant enrichment of single-nucleotide polymorphisms associated with CAD as a function of their association with low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. Applying the conditional false discovery rate method to the enriched phenotypes, we identified 67 novel loci associated with CAD (overall conditional false discovery rate <0.01). Furthermore, we identified 53 loci with significant effects in both CAD and at least 1 of low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, systolic blood pressure, and type 1 diabetes mellitus. CONCLUSIONS: The observed polygenic overlap between CAD and cardiometabolic risk factors indicates a pathogenic relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to CAD.

An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies.

Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn's disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the implications of pervasive small but replicating effects in CD and SZ on genomic control and power. Finally, we conclude that, despite having very similar estimates of variance explained by genotyped SNPs, CD and SZ have a broadly dissimilar genetic architecture, due to differing mean effect size and proportion of non-null loci.

A molecule-based genetic association approach implicates a range of voltage-gated calcium channels associated with schizophrenia.

Traditional genome-wide association studies (GWAS) have successfully detected genetic variants associated with schizophrenia. However, only a small fraction of heritability can be explained. Gene-set/pathway-based methods can overcome limitations arising from single nucleotide polymorphism (SNP)-based analysis, but most of them place constraints on size which may exclude highly specific and functional sets, like macromolecules. Voltage-gated calcium (Cav ) channels, belonging to macromolecules, are composed of several subunits whose encoding genes are located far away or even on different chromosomes. We combined information about such molecules with GWAS data to investigate how functional channels associated with schizophrenia. We defined a biologically meaningful SNP-set based on channel structure and performed an association study by using a validated method: SNP-set (sequence) kernel association test. We identified eight subtypes of Cav channels significantly associated with schizophrenia from a subsample of published data (N = 56,605), including the L-type channels (Cav 1.1, Cav 1.2, Cav 1.3), P-/Q-type Cav 2.1, N-type Cav 2.2, R-type Cav 2.3, T-type Cav 3.1, and Cav 3.3. Only genes from Cav 1.2 and Cav 3.3 have been implicated by the largest GWAS (N = 82,315). Each subtype of Cav channels showed relatively high chip heritability, proportional to the size of its constituent gene regions. The results suggest that abnormalities of Cav channels may play an important role in the pathophysiology of schizophrenia and these channels may represent appropriate drug targets for therapeutics. Analyzing subunit-encoding genes of a macromolecule in aggregate is a complementary way to identify more genetic variants of polygenic diseases. This study offers the potential of power for discovery the biological mechanisms of schizophrenia.

Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score.

BACKGROUND: Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction. METHODS AND FINDINGS: Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer's Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10-5). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer's Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer's Disease Center [NIA ADC], and Alzheimer's Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62-4.24, p = 1.0 × 10-22). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10-26) and longitudinal progression from normal aging to AD (NIA ADC, Cochran-Armitage trend test, p = 1.5 × 10-10), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10-6, and Consortium to Establish a Registry for Alzheimer's Disease score for neuritic plaques, p = 6.8 × 10-6) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10-6, and hippocampus, p = 7.9 × 10-5). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use. CONCLUSIONS: We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials.

Genetic architecture of sporadic frontotemporal dementia and overlap with Alzheimer's and Parkinson's diseases.

BACKGROUND: Clinical, pathological and genetic overlap between sporadic frontotemporal dementia (FTD), Alzheimer's disease (AD) and Parkinson's disease (PD) has been suggested; however, the relationship between these disorders is still not well understood. Here we evaluated genetic overlap between FTD, AD and PD to assess shared pathobiology and identify novel genetic variants associated with increased risk for FTD. METHODS: Summary statistics were obtained from the International FTD Genomics Consortium, International PD Genetics Consortium and International Genomics of AD Project (n>75 000 cases and controls). We used conjunction false discovery rate (FDR) to evaluate genetic pleiotropy and conditional FDR to identify novel FTD-associated SNPs. Relevant variants were further evaluated for expression quantitative loci. RESULTS: We observed SNPs within the HLA, MAPT and APOE regions jointly contributing to increased risk for FTD and AD or PD. By conditioning on polymorphisms associated with PD and AD, we found 11 loci associated with increased risk for FTD. Meta-analysis across two independent FTD cohorts revealed a genome-wide signal within the APOE region (rs6857, 3'-UTR=PVRL2, p=2.21×10-12), and a suggestive signal for rs1358071 within the MAPT region (intronic=CRHR1, p=4.91×10-7) with the effect allele tagging the H1 haplotype. Pleiotropic SNPs at the HLA and MAPT loci associated with expression changes in cis-genes supporting involvement of intracellular vesicular trafficking, immune response and endo/lysosomal processes. CONCLUSIONS: Our findings demonstrate genetic pleiotropy in these neurodegenerative diseases and indicate that sporadic FTD is a polygenic disorder where multiple pleiotropic loci with small effects contribute to increased disease risk.

Polygenic Overlap Between C-Reactive Protein, Plasma Lipids, and Alzheimer Disease.

BACKGROUND: Epidemiological findings suggest a relationship between Alzheimer disease (AD), inflammation, and dyslipidemia, although the nature of this relationship is not well understood. We investigated whether this phenotypic association arises from a shared genetic basis. METHODS AND RESULTS: Using summary statistics (P values and odds ratios) from genome-wide association studies of >200 000 individuals, we investigated overlap in single-nucleotide polymorphisms associated with clinically diagnosed AD and C-reactive protein (CRP), triglycerides, and high- and low-density lipoprotein levels. We found up to 50-fold enrichment of AD single-nucleotide polymorphisms for different levels of association with C-reactive protein, low-density lipoprotein, high-density lipoprotein, and triglyceride single-nucleotide polymorphisms using a false discovery rate threshold <0.05. By conditioning on polymorphisms associated with the 4 phenotypes, we identified 55 loci associated with increased AD risk. We then conducted a meta-analysis of these 55 variants across 4 independent AD cohorts (total: n=29 054 AD cases and 114 824 healthy controls) and discovered 2 genome-wide significant variants on chromosome 4 (rs13113697; closest gene, HS3ST1; odds ratio=1.07; 95% confidence interval=1.05-1.11; P=2.86×10(-8)) and chromosome 10 (rs7920721; closest gene, ECHDC3; odds ratio=1.07; 95% confidence interval=1.04-1.11; P=3.38×10(-8)). We also found that gene expression of HS3ST1 and ECHDC3 was altered in AD brains compared with control brains. CONCLUSIONS: We demonstrate genetic overlap between AD, C-reactive protein, and plasma lipids. By conditioning on the genetic association with the cardiovascular phenotypes, we identify novel AD susceptibility loci, including 2 genome-wide significant variants conferring increased risk for AD.

Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors.

Several lines of evidence suggest that genome-wide association studies (GWASs) have the potential to explain more of the "missing heritability" of common complex phenotypes. However, reliable methods for identifying a larger proportion of SNPs are currently lacking. Here, we present a genetic-pleiotropy-informed method for improving gene discovery with the use of GWAS summary-statistics data. We applied this methodology to identify additional loci associated with schizophrenia (SCZ), a highly heritable disorder with significant missing heritability. Epidemiological and clinical studies suggest comorbidity between SCZ and cardiovascular-disease (CVD) risk factors, including systolic blood pressure, triglycerides, low- and high-density lipoprotein, body mass index, waist-to-hip ratio, and type 2 diabetes. Using stratified quantile-quantile plots, we show enrichment of SNPs associated with SCZ as a function of the association with several CVD risk factors and a corresponding reduction in false discovery rate (FDR). We validate this "pleiotropic enrichment" by demonstrating increased replication rate across independent SCZ substudies. Applying the stratified FDR method, we identified 25 loci associated with SCZ at a conditional FDR level of 0.01. Of these, ten loci are associated with both SCZ and CVD risk factors, mainly triglycerides and low- and high-density lipoproteins but also waist-to-hip ratio, systolic blood pressure, and body mass index. Together, these findings suggest the feasibility of using genetic-pleiotropy-informed methods for improving gene discovery in SCZ and identifying potential mechanistic relationships with various CVD risk factors.

Genetic overlap between multiple sclerosis and several cardiovascular disease risk factors.

BACKGROUND: Epidemiological findings suggest a relationship between multiple sclerosis (MS) and cardiovascular disease (CVD) risk factors, although the nature of this relationship is not well understood. OBJECTIVE: We used genome-wide association study (GWAS) data to identify shared genetic factors (pleiotropy) between MS and CVD risk factors. METHODS: Using summary statistics from a large, recent GWAS (total n > 250,000 individuals), we investigated overlap in single nucleotide polymorphisms (SNPs) associated with MS and a number of CVD risk factors including triglycerides (TG), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, body mass index, waist-to-hip ratio, type 2 diabetes, systolic blood pressure, and C-reactive protein level. RESULTS AND CONCLUSION: Using conditional enrichment plots, we found 30-fold enrichment of MS SNPs for different levels of association with LDL and TG SNPs, with a corresponding reduction in conditional false discovery rate (FDR). We identified 133 pleiotropic loci outside the extended major histocompatibility complex with conditional FDR < 0.01, of which 65 are novel. These pleiotropic loci were located on 21 different chromosomes. Our findings point to overlapping pathobiology between clinically diagnosed MS and cardiovascular risk factors and identify novel common variants associated with increased MS risk.

Heritability of Biomarkers of Oxidized Lipoproteins: Twin Pair Study.

OBJECTIVE: To determine whether biomarkers of oxidized lipoproteins are genetically determined. Lipoprotein(a) (Lp[a]) is a heritable risk factor and carrier of oxidized phospholipids (OxPL). APPROACH AND RESULTS: We measured oxidized phospholipids on apolipoprotein B-containing lipoproteins (OxPL-apoB), Lp(a), IgG, and IgM autoantibodies to malondialdehyde-modified low-density lipoprotein, copper oxidized low-density lipoprotein, and apoB-immune complexes in 386 monozygotic and dizygotic twins to estimate trait heritability (h(2)) and determine specific genetic effects among traits. A genome-wide linkage study followed by genetic association was performed. The h(2) (scale: 0-1) for Lp(a) was 0.91±0.01 and for OxPL-apoB 0.87±0.02, which were higher than physiological, inflammatory, or lipid traits. h(2) of IgM malondialdehyde-modified low-density lipoprotein, copper oxidized low-density lipoprotein, and apoB-immune complexes were 0.69±0.04, 0.67±0.05, and 0.80±0.03, respectively, and for IgG malondialdehyde-modified low-density lipoprotein, copper oxidized low-density lipoprotein, and apoB-immune complexes 0.62±0.05, 0.52±0.06, and 0.53±0.06, respectively. There was an inverse correlation between the major apo(a) isoform and OxPL-apoB (R=-0.49; P<0.001) and Lp(a) (R=-0.48; P<0.001) and OxPL-apoB was modestly correlated with Lp(a) (ρ=0.57; P<0.0001). The correlation in major apo(a) isoform size was concordant (R=1.0; P<0.001) among monozygotic twins but not dizygotic twins (R=0.40; P=0.055). Lp(a) and OxPL-apoB shared genetic codetermination (genetic covariance, ρG=0.774±0.032; P=1.09×10(-38)), although not environmental determination (environmental covariance, ρE=0.081±0.15; P=0.15). In contrast, Lp(a) shared environmental but not genetic codetermination with autoantibodies to malondialdehyde-modified low-density lipoprotein and copper oxidized low-density lipoprotein, and apoB-immune complexes. Sib-pair genetic linkage of the Lp(a) trait revealed that single nucleotide polymorphism rs10455872 was significantly associated with OxPL-apoB after adjusting for Lp(a). CONCLUSIONS: OxPL-apoB and other biomarkers of oxidized lipoproteins are highly heritable cardiovascular risk factors that suggest novel genetic origins of atherothrombosis.