scholarly journals Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS

2020 ◽  
Author(s):  
Wouter J. Peyrot ◽  
Alkes L. Price
Keyword(s):  
Psychiatric Disorders ◽  
Allele Frequency ◽  
Case Control ◽  
Genetic Differences ◽  
Allele Frequencies ◽  
Effective Control ◽  
Type I ◽  
Summary Statistics ◽  
Compulsive Disorder ◽  
Genome Wide

AbstractPsychiatric disorders are highly genetically correlated, and many studies have focused on their shared genetic components. However, little research has been conducted on the genetic differences between psychiatric disorders, because case-case comparisons of allele frequencies among cases currently require individual-level data from cases of both disorders. We developed a new method (CC-GWAS) to test for differences in allele frequency among cases of two different disorders using summary statistics from the respective case-control GWAS; CC-GWAS relies on analytical assessments of the genetic distance between cases and controls of each disorder. Simulations and analytical computations confirm that CC-GWAS is well-powered and attains effective control of type I error. In particular, CC-GWAS identifies and discards false positive associations that can arise due to differential tagging of a shared causal SNP (with the same allele frequency in cases of both disorders), e.g. due to subtle differences in ancestry between the input case-control studies. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder and major depressive disorder, and identified 116 independent genome-wide significant loci distinguishing these three disorders, including 21 CC-GWAS-specific loci that were not genome-wide significant in the input case-control summary statistics. Two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 from the Kruppel-like family of transcription factors; these genes have been linked to neurite outgrowth and axon regeneration. We performed a broader set of case-case comparisons by additionally analyzing ADHD, anorexia nervosa, autism, obsessive-compulsive disorder and Tourette’s Syndrome, yielding a total of 196 independent loci distinguishing eight psychiatric disorders, including 72 CC-GWAS-specific loci. We confirmed that loci identified by CC-GWAS replicated convincingly in applications to data sets for which independent replication data were available. In conclusion, CC-GWAS robustly identifies loci with different allele frequencies among cases of different disorders using results from the respective case-control GWAS, providing new insights into the genetic differences between eight psychiatric disorders.

scholarly journals GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jisu Shin ◽  
Sang Hong Lee
Keyword(s):  
Complex Traits ◽  
Error Rates ◽  
Type I ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Summary Statistics ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Scale Data ◽  
Variance Explained

AbstractGenetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

scholarly journals Allele frequency divergence reveals ubiquitous influence of positive selection in Drosophila

2021 ◽  
Author(s):  
Jason Bertram
Keyword(s):  
Allele Frequency ◽  
Evolutionary Biology ◽  
Polymorphic Locus ◽  
Purifying Selection ◽  
Allele Frequencies ◽  
Intermediate Allele ◽  
Genome Wide ◽  
Signature Of Selection ◽  
Basic Objective ◽  
Genomic Regions

Resolving the role of natural selection is a basic objective of evolutionary biology. It is generally difficult to detect the influence of selection because ubiquitous non-selective stochastic change in allele frequencies (genetic drift) degrades evidence of selection. As a result, selection scans typically only identify genomic regions that have undergone episodes of intense selection. Yet it seems likely such episodes are the exception; the norm is more likely to involve subtle, concurrent selective changes at a large number of loci. We develop a new theoretical approach that uncovers a previously undocumented genome-wide signature of selection in the collective divergence of allele frequencies over time. Applying our approach to temporally-resolved allele frequency measurements from laboratory and wild Drosophila populations, we quantify the selective contribution to allele frequency divergence and find that selection has substantial effects on much of the genome. We further quantify the magnitude of the total selection coefficient (a measure of the combined effects of direct and linked selection) at a typical polymorphic locus, and find this to be large (of order 1%) even though most mutations are not directly under selection. We find that selective allele frequency divergence is substantial at intermediate allele frequencies, which we argue is most parsimoniously explained by positive --- not purifying --- selection. Thus, in these populations most mutations are far from evolving neutrally in the short term (tens of generations), including mutations with neutral fitness effects, and the result cannot be explained simply as a purging of deleterious mutations.

Association between theNOTCH4gene rs3131296 polymorphism with schizophrenia risk in the Chinese Zhuang population and Chinese Han population

2014 ◽  
Vol 26 (4) ◽  
pp. 240-245 ◽  
Author(s):  
Li Su ◽  
Jianxiong Long ◽  
Baoyun Liang ◽  
Lian Gu ◽  
Runde Pan ◽  
...  
Keyword(s):  
Case Control Study ◽  
Case Control ◽  
Allele Frequencies ◽  
Gene Variant ◽  
Inherited Disease ◽  
Chinese Han ◽  
Genome Wide ◽  
Significant Difference ◽  
Different Populations ◽  
Control Study

BackgroundSchizophrenia (SZ) is a common severe psychiatric disorder and a complex polygenic inherited disease that has not yet been fully interpreted. Heredity was proven to play an important role in the development of SZ. The association between theNOTCH4gene rs3131296 polymorphism and SZ was reported to reach significance at the genome-wide level; therefore, it is necessary to replicate this association in other different populations.MethodsTo evaluate the association of theNOTCH4gene rs3131296 polymorphism with the risk for SZ, and to explore whether a significant association could be replicated in different ethnic groups of China, we conducted this case–control study on 282 SZ cases (188 Han and 94 Zhuang) and 282 controls (188 Han and 94 Zhuang) among the Chinese Zhuang and Han populations.ResultsThe results showed no statistically significant difference in the genotype or allele frequencies of theNOTCH4gene variant rs3131296 between SZ patients and healthy controls in either the Zhuang or Han samples (p> 0.05). In addition, no significant difference was found in genotype or allele frequencies of theNOTCH4gene variant rs3131296 between cases and controls in the combined samples including Zhuang and Han samples.ConclusionsOur study failed to replicate the significant association between theNOTCH4gene rs3131296 polymorphism and the risk for SZ.

scholarly journals Genetic characterization of buckwheat accessions through genome-wide allele frequency fingerprints / Genetska karakterizacija vzorcev ajde z odtisi frekvence alelov v genomu

2020 ◽  
Vol 61 (1) ◽  
pp. 17-23
Author(s):  
Michelle M. Nay ◽  
Stephen L. Byrne ◽  
Eduardo A. Pérez ◽  
Achim Walter ◽  
Bruno Studer
Keyword(s):  
Genetic Diversity ◽  
Single Nucleotide Polymorphisms ◽  
Allele Frequency ◽  
Genotyping By Sequencing ◽  
Allele Frequencies ◽  
Fagopyrum Esculentum ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Fagopyrum Esculentum Moench

Genomics-assisted breeding of buckwheat (Fagopyrum esculentum Moench) depends on robust genotyping methods. Genotyping by sequencing (GBS) has evolved as a flexible and cost-effective technique frequently used in plant breeding. Several GBS pipelines are available to genetically characterize single genotypes but these are not able to represent the genetic diversity of buckwheat accessions that are maintained as genetically heterogeneous, open-pollinating populations. Here we report the development of a GBS pipeline which, rather than reporting the state of bi-allelic single nucleotide polymorphisms (SNPs), resolves allele frequencies within populations on a genome-wide scale. These genome-wide allele frequency fingerprints (GWAFFs) from 100 pooled individual plants per accession were found to be highly reproducible and revealed the genetic similarity of 20 different buckwheat accessions analysed in our study. The GWAFFs cannot only be used as an efficient tool to precisely describe buckwheat breeding material, they also offer new opportunities to investigate the genetic diversity between different buckwheat accessions and establish variant databases for key material. Furthermore, GWAFFs provide the opportunity to associate allele frequencies to phenotypic traits and quality parameters that are most reliably described on population level. This is the key to practically implement powerful genomics-assisted breeding concepts such as marker-assisted selection and genomic selection in future breeding schemes of allogamous buckwheat. Key words: Buckwheat (Fagopyrum esculentum Moench), genotyping by sequencing (GBS), population genomics, genome-wide allele frequency fingerprints (GWAFFs)   Izvleček Genomsko podprto žlahtnjenje ajde (Fagopyrum esculentum Moench) je odvisno od robustnih metod genotipiziranja. Genotipiziranje s spremljanjem sekvenc (genotyping by sequencing, GBS) se je razvilo kot fleksibilna in razmeroma poceni metoda, ki se jo uporablja pri žlahtnjenju rastlin. Uporabnih je več virov GBS za genetsko karakterizacijo posamičnih genotipov, toda te metode niso primerne za predstavitev genetske raznolikosti vzorcev ajde, ki jih vzdržujemo v heterozigotni obliki, kar velja za odprto oplodne populacije. Tu poročamo o razvoju GBS metode, ki, namesto prikazovanja bi-alelnega polimorfizma posameznih nukleotidov (single nucleotide polymorphisms, SNPs), pokaže frekvence alelov v populaciji na nivoju genoma. Ta prikaz frekvence alelov na nivoju genoma (genome-wide allele frequency fingerprints, GWAFFs) z združenimi sto posameznimi rastlinami vsakega vzorca se je pokazal kot visoko ponovljiv in je prikazal genetsko podobnost 20 različnih vzorcev ajde, ki smo jih analizirali v naši raziskavi. Metoda GWAFFs ni uporabna samo kot učinkovito orodje za natančen opis materiala za žlahtnjenje ajde, ponuja tudi možnosti raziskave  genetskih razlik med različnimi vzorci ajde in omogoča zbirke podatkov. Nadalje, metoda GWAFFs omogoča povezovanje frekvenc alelov s fenotipskimi lastnostmi in kvalitativnih parametrov, ki so najbolj zanesljivo opisani na nivoju populacij. To je ključ za praktično uporabo z genomiko podprtega žlahtnjenja, kot je z genskimi markerji podprta selekcija in genomska selekcija z GWAFFs. Ključne besede: ajda (Fagopyrum esculentum Moench), genotipizacija s sekvenciranjem (GBS), populacijska genomika, GWAFFs

scholarly journals Region-based interaction detection in genome-wide case-control studies

2019 ◽  
Vol 12 (S7) ◽  
Author(s):  
Sen Zhang ◽  
Wei Jiang ◽  
Ronald CW Ma ◽  
Weichuan Yu
Keyword(s):  
Genome Wide Association Study ◽  
Detection Method ◽  
Building Blocks ◽  
Case Control ◽  
Error Rates ◽  
Detection Methods ◽  
Type I ◽  
Case Control Studies ◽  
Interaction Detection ◽  
Genome Wide

Abstract Background In genome-wide association study (GWAS), conventional interaction detection methods such as BOOST are mostly based on SNP-SNP interactions. Although single nucleotides are the building blocks of human genome, single nucleotide polymorphisms (SNPs) are not necessarily the smallest functional unit for complex phenotypes. Region-based strategies have been proved to be successful in studies aiming at marginal effects. Methods We propose a novel region-region interaction detection method named RRIntCC (region-region interaction detection for case-control studies). RRIntCC uses the correlations between individual SNP-SNP interactions based on linkage disequilibrium (LD) contrast test. Results Simulation experiments showed that our method can achieve a higher power than conventional SNP-based methods with similar type-I-error rates. When applied to two real datasets, RRIntCC was able to find several significant regions, while BOOST failed to identify any significant results. The source code and the sample data of RRIntCC are available at http://bioinformatics.ust.hk/RRIntCC.html. Conclusion In this paper, a new region-based interaction detection method with better performance than SNP-based interaction detection methods has been proposed.

scholarly journals Novel genome-wide associations for anhedonia, genetic correlation with psychiatric disorders, and polygenic association with brain structure

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Joey Ward ◽  
Laura M. Lyall ◽  
Richard A. I. Bethlehem ◽  
Amy Ferguson ◽  
Rona J. Strawbridge ◽  
...  
Keyword(s):  
White Matter ◽  
Psychiatric Disorders ◽  
Brain Structure ◽  
Genetic Correlations ◽  
Grey Matter Volume ◽  
Obsessive Compulsive ◽  
Major Depressive ◽  
Compulsive Disorder ◽  
Matter Volume ◽  
Genome Wide

AbstractAnhedonia is a core symptom of several psychiatric disorders but its biological underpinnings are poorly understood. We performed a genome-wide association study of state anhedonia in 375,275 UK Biobank participants and assessed for genetic correlation between anhedonia and neuropsychiatric conditions (major depressive disorder, schizophrenia, bipolar disorder, obsessive compulsive disorder and Parkinson’s Disease). We then used a polygenic risk score approach to test for association between genetic loading for anhedonia and both brain structure and brain function. This included: magnetic resonance imaging (MRI) assessments of total grey matter volume, white matter volume, cerebrospinal fluid volume, and 15 cortical/subcortical regions of interest; diffusion tensor imaging (DTI) measures of white matter tract integrity; and functional MRI activity during an emotion processing task. We identified 11 novel loci associated at genome-wide significance with anhedonia, with a SNP heritability estimate (h2SNP) of 5.6%. Strong positive genetic correlations were found between anhedonia and major depressive disorder, schizophrenia and bipolar disorder; but not with obsessive compulsive disorder or Parkinson’s Disease. Polygenic risk for anhedonia was associated with poorer brain white matter integrity, smaller total grey matter volume, and smaller volumes of brain regions linked to reward and pleasure processing, including orbito-frontal cortex. In summary, the identification of novel anhedonia-associated loci substantially expands our current understanding of the biological basis of state anhedonia and genetic correlations with several psychiatric disorders confirm the utility of this phenotype as a transdiagnostic marker of vulnerability to mental illness. We also provide the first evidence that genetic risk for state anhedonia influences brain structure, including in regions associated with reward and pleasure processing.

scholarly journals LD scores are associated with differences in allele frequencies between populations but LD score regression can still distinguish confounding from polygenicity

2019 ◽  
Author(s):  
Mason Alexander ◽  
David Curtis
Keyword(s):  
Allele Frequency ◽  
Population Stratification ◽  
Case Control ◽  
Regression Method ◽  
Allele Frequencies ◽  
Polygenic Effect ◽  
Chi Squared ◽  
Hapmap Populations ◽  
Large Case ◽  
Control Samples

SummaryThe LD score regression method tests whether there is an association between the LD score and allele frequency differences between cases and controls. It makes the assumption that there is no association between LD score and allele frequency differences between populations and hence that any observed association is due to a polygenic effect rather than population stratification. This assumption has not previously been tested. In comparisons between HapMap populations we observe that there is indeed an association between the LD score and allele frequency differences. However this effect is small and when we carry out simulations of large case-control samples the effect becomes negligible. We conclude that if the intercept is small then any increase in mean chi-squared does indeed reflect a polygenic effect rather than population stratification.

scholarly journals ProxECAT: Proxy External Controls Association Test. A new case-control gene region association test using allele frequencies from public controls

2018 ◽  
Author(s):  
AE Hendricks ◽  
S Billups ◽  
HNC Pike ◽  
IS Farooqi ◽  
E Zeggini ◽  
...  
Keyword(s):  
Genetic Resources ◽  
Critical Role ◽  
Case Control ◽  
Association Test ◽  
Allele Frequencies ◽  
Gene Region ◽  
Type I ◽  
Control Analysis ◽  
Genetic Associations ◽  
Case Control Analysis

AbstractA primary goal of the recent investment in sequencing is to detect novel genetic associations in health and disease improving the development of treatments and playing a critical role in precision medicine. While this investment has resulted in an enormous number of sequenced genomes, individual studies of complex traits are often smaller and underpowered to detect rare variant genetic associations. Existing genetic resources such as the Exome Aggregation Consortium (>60,000 exomes) and the Genome Aggregation Database (~140,000 sequenced samples) could be used as controls in these studies. Fully utilizing these and other existing sequencing resources has the potential to increase power and could be especially useful in studies where resources to sequence additional samples are limited. However, to date, these large, publicly available genetic resources remain underutilized, or even misused, in large part due to the lack of statistical methods that can appropriately use this summary level data. We present a new method to incorporate external controls in case-control analysis called ProxECAT (Proxy External Controls Association Test). ProxECAT estimates enrichment of rare variants within a gene region using internally sequenced cases and external controls. We evaluated ProxECAT in simulations and empirical analyses of obesity cases using both low-depth of coverage (7x) whole-genome sequenced controls and ExAC as controls. We find that ProxECAT maintains the expected type I error rate with increased power as the number of external controls increases. With an accompanying R package, ProxECAT enables the use of publicly available allele frequencies as external controls in case-control analysis.

scholarly journals Estimating the genome-wide contribution of selection to temporal allele frequency change

2019 ◽  
Author(s):  
Vince Buffalo ◽  
Graham Coop
Keyword(s):  
Allele Frequency ◽  
Genetic Drift ◽  
Genomic Data ◽  
Allele Frequencies ◽  
Frequency Change ◽  
Standing Variation ◽  
Genome Wide ◽  
Selection Experiments ◽  
Frequency Changes ◽  
Linked Selection

AbstractRapid phenotypic adaptation is often observed in natural populations and selection experiments. However, detecting the genome-wide impact of this selection is difficult, since adaptation often proceeds from standing variation and selection on polygenic traits, both of which may leave faint genomic signals indistinguishable from a noisy background of genetic drift. One promising signal comes from the genome-wide covariance between allele frequency changes observable from temporal genomic data, e.g. evolve-and-resequence studies. These temporal covariances reflect how heritable fitness variation in the population leads changes in allele frequencies at one timepoint to be predictive of the changes at later timepoints, as alleles are indirectly selected due to remaining associations with selected alleles. Since genetic drift does not lead to temporal covariance, we can use these covariances to estimate what fraction of the variation in allele frequency change through time is driven by linked selection. Here, we reanalyze three selection experiments to quantify the effects of linked selection over short timescales using covariance among time-points and across replicates. We estimate that at least 17% to 37% of allele frequency change is driven by selection in these experiments. Against this background of positive genome-wide temporal covariances we also identify signals of negative temporal covariance corresponding to reversals in the direction of selection for a reasonable proportion of loci over the time course of a selection experiment. Overall, we find that in the three studies we analyzed, linked selection has a large impact on short-term allele frequency dynamics that is readily distinguishable from genetic drift.Significance StatementA long-standing problem in evolutionary biology is to understand the processes that shape the genetic composition of populations. In a population without migration, the two processes that change allele frequencies are selection, which increases beneficial alleles and removes deleterious ones, and genetic drift which randomly changes frequencies as some parents contribute more or less alleles to the next generation. Previous efforts to disentangle these processes have used genomic samples from a single timepoint and models of how selection affects neighboring sites (linked selection). Here, we use genomic data taken through time to quantify the contributions of selection and drift to genome-wide frequency changes. We show selection acts over short timescales in three evolve-and-resequence studies and has a sizable genome-wide impact.

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