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Fan, C. C.

Publications and source records attributed to Fan, C. C..

31 records · Page 2Linked to original sources

Linkage Disequilibrium and Heterozygosity Modulate the Genetic Architecture of Human Complex Phenotypes: Evidence of Natural Selection from GWAS Summary Statistics

We propose an extended Gaussian mixture model for the distribution of causal effects of common single nucleotide polymorphisms (SNPs) for human complex phenotypes that depends on linkage disequilibrium (LD) and heterozygosity (H), while also allowing for independent components for small and large effects. Using a precise methodology showing how genome-wide association studies (GWAS) summary statistics (z-scores) arise through LD with underlying causal SNPs, we applied the model to GWAS of multiple human phenotypes. Our findings indicated that causal effects are distributed with dependence on total LD and H, whereby SNPs with lower total LD and H are more likely to be causal with larger effects; this dependence is consistent with models of the influence of negative pressure from natural selection. Compared with the basic Gaussian mixture model it is built on, the extended model - primarily through quantification of selection pressure - reproduces with greater accuracy the empirical distributions of z-scores, thus providing better estimates of genetic quantities, such as polygenicity and heritability, that arise from the distribution of causal effects.

genomics↗

Polygenic Score of Intelligence is More Predictive of Crystallized than Fluid Performance Among Children

BackgroundFindings in adults have shown more culturally sensitive crystallized measures of intelligence have greater heritability, these results were not able to be shown in children. MethodsWith data from 8,518 participants, aged 9 to 11, from the Adolescent Brain Cognitive Development (ABCD) Study(R), we used polygenic predictors of intelligence test performance (based on genome-wide association meta-analyses of data from 269,867 individuals) and of educational attainment (based on data from 1.1 million individuals), associating these predictors with neurocognitive performance. We then assessed the extent of mediation of these associations by a measure of recreational reading. Resultsmore culturally sensitive crystallized measures were more strongly associated with the polygenic predictors than were less culturally sensitive fluid measures. This mirrored heritability differences reported previously in adults and suggests similar associations in children. Recreational reading more strongly statistically mediated the genetic associations with crystallized than those with fluid measures of cognition. ConclusionThis is consistent with a prominent role of gene-environment correlation in cognitive development measured by "crystallized" intelligence tests. Such experiential mediators may represent malleable targets for improving cognitive outcomes.

neuroscience↗

A genetic hazard score to personalize prostate cancer screening, applied to population data

BackgroundGenetic risk stratification may inform decisions of whether--and when--a man should undergo prostate cancer (PCa) screening. We previously validated a polygenic hazard score (PHS), a weighted sum of 54 single-nucleotide polymorphism genotypes, for accurate prediction of age of onset of aggressive PCa and improved screening performance. We now assess the potential impact of PHS-informed screening.\n\nMethodsUnited Kingdom population data were fit to a continuous model of age-specific PCa incidence. Using hazard ratios estimated from ProtecT trial data, age-specific incidence rates were calculated for percentiles of genetic risk. Incidence of higher-grade PCa (Gleason[≥]7) was estimated from age-specific data from the linked CAP trial. PHS and incidence data were combined to give a risk-equivalent age, when a man with a given PHS percentile will have risk of higher-grade PCa equivalent to that of a typical man at age 50 (50-years standard). Positive predictive value (PPV) of PSA testing was calculated using PHS-adjusted (PCa-risk-equivalent age) groups identified from ProtecT.\n\nResultsExpected age of onset of higher-grade PCa is modulated by 19 years between the 1st and 99th PHS percentiles. A man with PHS in the 99th percentile reaches 50-years-standard risk at age 41; conversely, a man in the 1st percentile reaches this risk at age 60. PPV of PSA was higher for men with higher PHS-adjusted age.\n\nConclusionsPHS informs PCa screening strategies with individualized estimates of risk-equivalent age for higher-grade PCa. Screening initiation could be adjusted according to a mans genetic hazard score, improving PPV of PSA screening.

genetics↗

Sex-dependent polygenic effects on the clinical progressions of Alzheimer’s disease

Sex differences in the manifestations of Alzheimers disease (AD) are under intense investigations 1,2. Despite the emerging importance of polygenic predictions for AD 3-8, the sex-dependent polygenic effects have not been demonstrated. Here, using a sex crossover analysis, we show that sex-dependent autosomal genetic effects on AD can be revealed by characterizing disease progress via the hazard function. We first performed sex-stratified genome-wide associations, and then applied derived sex-dependent weights to two independent cohorts. Sex-matched polygenic hazard scores (PHS) have significantly stronger associations with age-at-disease-onset, clinical progressions, amyloid depositions, neurofibrillary tangles, and composite neuropathological scores, than sex-mismatched PHS, independent of apolipoprotein E. Models without using hazard weights, i.e. polygenic risk scores (PRS), have lower predictive power than PHS and show no evidence for sex differences. Our results indicate revealing sex-dependent genetic architecture requires the consideration of temporal processes of AD. This has strong implications not only for the genetic underpinning of AD but also for how we estimate sex-dependent polygenic effects for clinical use.

genetics↗

Partitioning the genetic architecture of amyotrophic lateral sclerosis

The genetic basis of sporadic amyotrophic lateral sclerosis (ALS) is not well understood. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) associated with ALS conditional on genetic data from 65 different traits and diseases from >3 million people. We found strong genetic enrichment between ALS and a number of disparate traits including frontotemporal dementia, coronary artery disease, C-reactive protein, celiac disease and memory function. Beyond C9ORF72, we detected novel genetic signal within numerous loci including GIPC1, ELMO1 and COL16A and confirmed previously reported variants, such as ATXN2, KIF5A, UNC13A and MOBP. We found that ALS variants form a small-world co-expression network characterized by highly inter-connected hub genes. This network clustered into smaller sub-networks, each associated with a unique function. Altered gene expression of several sub-networks and hubs was over-represented in neuropathological samples from ALS patients and SOD1 G93A mice. Our collective findings indicate that the genetic architecture of ALS can be partitioned into distinct components where some genes are highly important for developing disease. These findings have implications for stratification and enrichment strategies for ALS clinical trials.

genetics↗

Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

Of signal interest in the genetics of human traits is estimating their polygenicity (the proportion of causally associated single nucleotide polymorphisms (SNPs)) and the discoverability (or effect size variance) of the causal SNPs. Narrow-sense heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics for SNPs with minor allele frequency >1%. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from [~=] 2 x 10-5 to [~=] 4 x 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation.

genetics↗

Lipid associated polygenic enrichment in Alzheimer’s disease

Cardiovascular (CV) and lifestyle associated risk factors (RFs) are increasingly recognized as important for Alzheimers disease (AD) pathogenesis. Beyond the [isin]4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV associated genes also increase risk for AD (genetic pleiotropy). Using large genome-wide association studies (GWASs) (total n > 500,000 cases and controls) and validated tools to quantify genetic pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR < 0.05 we identified 57 SNPs on 19 different chromosomes that were jointly associated with AD and CV outcomes including APOA4, ABCA1, ABCG5, LIPG, and MTCH2/SPI1. We found that common genetic variants influencing AD are associated with multiple CV RFs, at times with a different directionality of effect. Expression of these AD/CV pleiotropic genes was enriched for lipid metabolism processes, over-represented within astrocytes and vascular structures, highly co-expressed, and differentially altered within AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid associated RFs. Rather than a single causal link between genetic loci, RF and the outcome, we found that common genetic variants influencing AD are associated with multiple CV RFs. Our collective findings suggest that a network of genes involved in lipid biology also influence Alzheimers risk.

genetics↗

Spatial gene-by-environment mapping for schizophrenia reveals locale of upbringing effects beyond urban-rural differences

Identification of mechanisms underlying the incidence of psychiatric disorders has been hampered by the difficulty in discovering highly-predictive environmental risk factors. For example, prior efforts have failed to establish environmental effects predicting geospatial clustering of schizophrenia incidence beyond urban-rural differences. Here, we employ a novel statistical framework for decomposing the geospatial risk for schizophrenia based on locale of upbringing (place of residence, ages 0-7 years) and its synergistic effects with genetic liabilities (polygenic risk for schizophrenia). We use this statistical framework to analyze unprecedented geolocation and genotyping data in a case-cohort study of n=24,028 subjects, drawn from the 1.47 million Danish persons born between 1981 and 2005. Using this framework we estimate the effects of upbringing locale (E) and gene-by-locale interactions (GxE). After controlling for potential confounding variables, upbringing at high-risk locales increases the risk for schizophrenia on average by 122%, while GxE modulates genetic risk for schizophrenia on average by 78%. Within the boundaries of Copenhagen (the largest and most densely populated city of Denmark) specific locales vary substantially in their E and GxE effects, with hazard ratios ranging from 0.26 to 9.26 for E and from 0.20 to 5.95 for GxE. This study provides insight into the degree of geospatial clustering of schizophrenia risk, and our novel analytic procedure provides a framework for decomposing variation in geospatial risk into G, E, and GxE components.

epidemiology↗

Bivariate Gaussian Mixture Model of GWAS (BGMG)quantifies polygenic overlap between complex traitsbeyond genetic correlation

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. While current cross-trait analytical methods focus on genetic correlation between traits, we developed a novel statistical tool (MiXeR), which quantifies polygenic overlap independent of genetic correlation, using summary statistics from GWAS. MiXeR results can be presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that more than 9K variants causally influence schizophrenia, 7K influence bipolar disorder, and out of those variants 6.9K are shared between these two disorders, which have high genetic correlation. Further, MiXeR uncovers extensive polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, these traits share more than 9K causal variants, while 3K additional variants only influence educational attainment. By considering the polygenicity, heritability and discoverability of complex phenotypes, MiXeR provides a more complete quantification of shared genetic architecture than offered by other available tools.

genetics↗

Polygenic hazard score: an enrichment marker for Alzheimer’s associated amyloid and tau deposition

BackgroundThere is an urgent need for the early identification of nondemented individuals at the highest risk of progressing to Alzheimers disease (AD) dementia for early therapeutic interventions. Our goal was to evaluate whether a recently validated polygenic hazard score (PHS) can be integrated with known in vivo CSF or PET biomarkers of amyloid or tau pathology to prospectively predict cognitive decline and clinical progression to AD dementia in nondemented older individuals.\n\nMethodsWe evaluated 347 cognitive normal (CN) and 599 mild cognitively impaired (MCI) individuals. We first investigated whether PHS can predict CSF or PET amyloid and tau deposition. We evaluated differences in positive and negative predictive values of biomarker status, as a function of PHS risk. Next, we used linear mixed-effects (LME) to examine if PHS and biomarker status in conjunction, best predict longitudinal cognitive and clinical progression. Lastly, we used survival analysis to investigate whether a combination of PHS and biomarker positivity predicts progression to AD dementia better than using PHS or biomarker positivity alone.\n\nFindingsIn CN and MCI individuals, we found that amyloid and total tau positivity systematically varies as a function of PHS. For individuals in greater than the 50th percentile PHS, the positive predictive value for amyloid approached 100%. Similarly, for individuals in less than the 25th percentile PHS, the negative predictive value for total tau approached 85%. Beyond APOE, high PHS individuals with amyloid and tau pathology showed the fastest rate of longitudinal cognitive decline and time to AD dementia progression. Among the CN subgroup, we similarly found that PHS was strongly associated with amyloid positivity and the combination of PHS and biomarker status significantly predicted longitudinal clinical progression.\n\nInterpretationAmong asymptomatic and mildly symptomatic older individuals, PHS considerably improves the predictive value of CSF or PET amyloid and tau biomarkers. Beyond APOE, PHS may be useful for risk stratification and cohort enrichment for MCI and preclinical AD therapeutic trials.

genetics↗

Immune-related genetic enrichment in frontotemporal dementia

BackgroundConverging evidence suggests that immune-mediated dysfunction plays an important role in the pathogenesis of frontotemporal dementia (FTD). Although genetic studies have shown that immune-associated loci are associated with increased FTD risk, a systematic investigation of genetic overlap between immune-mediated diseases and the spectrum of FTD-related disorders has not been performed.\n\nMethods and findingsUsing large genome-wide association studies (GWAS) (total n = 192,886 cases and controls) and recently developed tools to quantify genetic overlap/pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with FTD-related disorders namely FTD, corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and amyotrophic lateral sclerosis (ALS) - and one or more immune-mediated diseases including Crohns disease (CD), ulcerative colitis (UC), rheumatoid arthritis (RA), type 1 diabetes (T1D), celiac disease (CeD), and psoriasis (PSOR). We found up to 270-fold genetic enrichment between FTD and RA and comparable enrichment between FTD and UC, T1D, and CeD. In contrast, we found only modest genetic enrichment between any of the immune-mediated diseases and CBD, PSP or ALS. At a conjunction false discovery rate (FDR) < 0.05, we identified numerous FTD-immune pleiotropic SNPs within the human leukocyte antigen (HLA) region on chromosome 6. By leveraging the immune diseases, we also found novel FTD susceptibility loci within LRRK2 (Leucine Rich Repeat Kinase 2), TBKBP1 (TANK-binding kinase 1 Binding Protein 1), and PGBD5 (PiggyBac Transposable Element Derived 5). Functionally, we found that expression of FTD-immune pleiotropic genes (particularly within the HLA region) is altered in postmortem brain tissue from patients with frontotemporal dementia and is enriched in microglia compared to other central nervous system (CNS) cell types.\n\nConclusionsWe show considerable immune-mediated genetic enrichment specifically in FTD, particularly within the HLA region. Our genetic results suggest that for a subset of patients, immune dysfunction may contribute to risk for FTD. These findings have potential implications for clinical trials targeting immune dysfunction in patients with FTD.

genetics↗

Polygenic hazard scores in preclinical Alzheimer’s disease

Identifying asymptomatic older individuals at elevated risk for developing Alzheimers disease (AD) is of clinical importance. Among 1,081 asymptomatic older adults, a recently validated polygenic hazard score (PHS) significantly predicted time to AD dementia and steeper longitudinal cognitive decline, even after controlling for APOE {varepsilon}4 carrier status. Older individuals in the highest PHS percentiles showed the highest AD incidence rates. PHS predicted longitudinal clinical decline among older individuals with moderate to high CERAD (amyloid) and Braak (tau) scores at autopsy, even among APOE {varepsilon}4 non-carriers. Beyond APOE, PHS may help identify asymptomatic individuals at highest risk for developing Alzheimers neurodegeneration.

genetics↗

A genetic risk score to guide age-specific, personalized prostate cancer screening

BackgroundProstate-specific-antigen (PSA) screening resulted in reduced prostate cancer (PCa) mortality in a large clinical trial, but due to a high false-positive rate, among other concerns, many guidelines do not endorse universal screening and instead recommend an individualized decision based on each patients risk. Genetic risk may provide key information to guide the decisions of whether and at what age to screen an individual man for PCa.\n\nMethodsGenotype, PCa status, and age from 34,444 men of European ancestry from the PRACTICAL consortium database were analyzed to select single-nucleotide polymorphisms (SNPs) associated with prostate cancer diagnosis. These SNPs were then incorporated into a survival analysis to estimate their effects on age at PCa diagnosis. The resulting polygenic hazard score (PHS) is an assessment of individual genetic risk. The final model was validated in an independent dataset comprised of 6,417 men with screening PSA and genotype data. PHS was calculated for these men to test for prediction of PCa-free survival. PHS was also combined with age-specific PCa incidence data from the U.S. population to generate a PCa-Risk (PCaR) age that relates a given mans risk to that of the population average. PHS and PCaR age were evaluated for prediction of positive predictive value (PPV) of PSA screening.\n\nFindingsPHS calculated from 54 SNPs was very highly predictive of age at PCa diagnosis for men in the validation set (p =10-53). PPV of PSA screening varied from 0.18 to 0.52 for men with low and high genetic risk, respectively. PHS modulates PCa-free survival curves by an estimated 20 years between men in the 1st or 99th percentiles of genetic risk.\n\nInterpretationPolygenic hazard scores give personalized genetic risk estimates and can inform the decisions of whether and at what age to screen a man for PCa.\n\nFundingDepartment of Defense #W81XWH-13-1-0391

genetics↗