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.

100 ancient genomes show repeated population turnovers in Neolithic Denmark.

Major migration events in Holocene Eurasia have been characterized genetically at broad regional scales1-4. However, insights into the population dynamics in the contact zones are hampered by a lack of ancient genomic data sampled at high spatiotemporal resolution5-7. Here, to address this, we analysed shotgun-sequenced genomes from 100 skeletons spanning 7,300 years of the Mesolithic period, Neolithic period and Early Bronze Age in Denmark and integrated these with proxies for diet (13C and 15N content), mobility (87Sr/86Sr ratio) and vegetation cover (pollen). We observe that Danish Mesolithic individuals of the Maglemose, Kongemose and Ertebølle cultures form a distinct genetic cluster related to other Western European hunter-gatherers. Despite shifts in material culture they displayed genetic homogeneity from around 10,500 to 5,900 calibrated years before present, when Neolithic farmers with Anatolian-derived ancestry arrived. Although the Neolithic transition was delayed by more than a millennium relative to Central Europe, it was very abrupt and resulted in a population turnover with limited genetic contribution from local hunter-gatherers. The succeeding Neolithic population, associated with the Funnel Beaker culture, persisted for only about 1,000 years before immigrants with eastern Steppe-derived ancestry arrived. This second and equally rapid population replacement gave rise to the Single Grave culture with an ancestry profile more similar to present-day Danes. In our multiproxy dataset, these major demographic events are manifested as parallel shifts in genotype, phenotype, diet and land use.

Population genomics of post-glacial western Eurasia.

Western Eurasia witnessed several large-scale human migrations during the Holocene1-5. Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced 317 genomes-mainly from the Mesolithic and Neolithic periods-from across northern and western Eurasia. These were imputed alongside published data to obtain diploid genotypes from more than 1,600 ancient humans. Our analyses revealed a 'great divide' genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers were highly genetically differentiated east and west of this zone, and the effect of the neolithization was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacement of hunter-gatherers in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, whereas, east of the Urals, relatedness remained high until around 4,000 BP, consistent with the persistence of localized groups of hunter-gatherers. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 BP, resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive, but we show that hunter-gatherers from the Middle Don region contributed ancestry to them. Yamnaya groups later admixed with individuals associated with the Globular Amphora culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a 'Neolithic steppe' cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations.

Accounting for age of onset and family history improves power in genome-wide association studies.

Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age of onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here, we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), which jointly accounts for age of onset and sex as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields statistically significant power gains over LT-FH and large power gains over genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data and to mortality in the UK Biobank and found 20 genome-wide significant associations with LT-FH++, compared to ten for LT-FH and eight for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

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.

Genetics of suicide attempts in individuals with and without mental disorders: a population-based genome-wide association study.

Family studies have shown an aggregation of suicidal behavior in families. Yet, molecular studies are needed to identify loci accounting for genetic heritability. We conducted a genome-wide association study and estimated single nucleotide polymorphisms (SNP) heritability for a suicide attempt. In a case-cohort study, national data on all individuals born in Denmark after 1981 and diagnosed with severe mental disorders prior to 2013 (n = 57,377) and individuals from the general population (n = 30,000) were obtained. After quality control, the sample consisted of 6024 cases with an incidence of suicide attempt and 44,240 controls with no record of a suicide attempt. Suggestive associations between SNPs, rs6880062 (p-value: 5.4 × 10-8) and rs6880461 (p-value: 9.5 × 10-8), and suicide attempt were identified when adjusting for socio-demographics. Adjusting for mental disorders, three significant associations, all on chromosome 20, were identified: rs4809706 (p-value: 2.8 × 10-8), rs4810824 (p-value: 3.5 × 10-8), and rs6019297 (p-value: 4.7 × 108). Sub-group analysis of cases with affective disorders revealed SNPs associated with suicide attempts when compared to the general population for gene PDE4B. All SNPs explained 4.6% [CI-95: 2.9-6.3%] of the variation in suicide attempt. Controlling for mental disorders reduced the heritability to 1.9% [CI-95: 0.3-3.5%]. Affective and autism spectrum disorders exhibited a SNP heritability of 5.6% [CI-95: 1.9-9.3%] and 9.6% [CI-95: 1.1-18.1%], respectively. Using the largest sample to date, we identified significant SNP associations with suicide attempts and support for a genetic transmission of suicide attempt, which might not solely be explained by mental disorders.

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.

Modeling prior information of common genetic variants improves gene discovery for neuroticism.

Neuroticism reflects emotional instability, and is related to various mental and physical health issues. However, the majority of genetic variants associated with neuroticism remain unclear. Inconsistent genetic variants identified by different genome-wide association studies (GWAS) may be attributable to low statistical power. We proposed a novel framework to improve the power for gene discovery by incorporating prior information of single nucleotide polymorphisms (SNPs) and combining two relevant existing tools, relative enrichment score (RES) and conditional false discovery rate (FDR). Here, SNP's conditional FDR was estimated given its RES based on SNP prior information including linkage disequilibrium (LD)-weighted genic annotation scores, total LD scores and heterozygosity. A known significant locus in chromosome 8p was excluded before estimating FDR due to long-range LD structure. Only one significant LD-independent SNP was detected by analyses of unconditional FDR and traditional GWAS in the discovery sample (N = 59 225), and notably four additional SNPs by conditional FDR. Three of the five SNPs, all identified by conditional FDR, were replicated (P < 0.05) in an independent sample (N = 170 911). These three SNPs are located in intronic regions of CADM2, LINGO2 and EP300 which have been reported to be associated with autism, Parkinson's disease and schizophrenia, respectively. Our approach using a combination of RES and conditional FDR improved power of traditional GWAS for gene discovery providing a useful framework for the analysis of GWAS summary statistics by utilizing SNP prior information, and helping to elucidate the links between neuroticism and complex diseases from a genetic perspective.

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.

Conservation of Distinct Genetically-Mediated Human Cortical Pattern.

The many subcomponents of the human cortex are known to follow an anatomical pattern and functional relationship that appears to be highly conserved between individuals. This suggests that this pattern and the relationship among cortical regions are important for cortical function and likely shaped by genetic factors, although the degree to which genetic factors contribute to this pattern is unknown. We assessed the genetic relationships among 12 cortical surface areas using brain images and genotype information on 2,364 unrelated individuals, brain images on 466 twin pairs, and transcriptome data on 6 postmortem brains in order to determine whether a consistent and biologically meaningful pattern could be identified from these very different data sets. We find that the patterns revealed by each data set are highly consistent (p<10-3), and are biologically meaningful on several fronts. For example, close genetic relationships are seen in cortical regions within the same lobes and, the frontal lobe, a region showing great evolutionary expansion and functional complexity, has the most distant genetic relationship with other lobes. The frontal lobe also exhibits the most distinct expression pattern relative to the other regions, implicating a number of genes with known functions mediating immune and related processes. Our analyses reflect one of the first attempts to provide an assessment of the biological consistency of a genetic phenomenon involving the brain that leverages very different types of data, and therefore is not just statistical replication which purposefully use very similar data sets.

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.