scholarly journals M-DATA: A Statistical Approach to Jointly Analyzing De Novo Mutations for Multiple Traits

2021 ◽  
Author(s):  
Yuhan Xie ◽  
Mo Li ◽  
Weilai Dong ◽  
Wei Jiang ◽  
Hongyu Zhao
Keyword(s):  
Statistical Power ◽  
De Novo ◽  
Expectation Maximization Algorithm ◽  
De Novo Mutation ◽  
Joint Analysis ◽  
De Novo Mutations ◽  
Multiple Traits ◽  
Disease Etiology ◽  
Degree Of Association ◽  
Shared Risk

Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DMNs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.

scholarly journals M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits

PLoS Genetics ◽  
2021 ◽  
Vol 17 (11) ◽  
pp. e1009849
Author(s):  
Yuhan Xie ◽  
Mo Li ◽  
Weilai Dong ◽  
Wei Jiang ◽  
Hongyu Zhao
Keyword(s):  
Statistical Power ◽  
De Novo ◽  
Expectation Maximization Algorithm ◽  
Joint Analysis ◽  
De Novo Mutations ◽  
Multiple Traits ◽  
Disease Etiology ◽  
Functional Annotations ◽  
Degree Of Association ◽  
Shared Risk

Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.

scholarly journals mTADA: a framework for analyzing de novo mutations in multiple traits

2018 ◽  
Author(s):  
Hoang T. Nguyen ◽  
Amanda Dobbyn ◽  
Joseph Buxbaum ◽  
Dalila Pinto ◽  
Shaun M Purcell ◽  
...  
Keyword(s):  
De Novo ◽  
Neuropsychiatric Disorders ◽  
Joint Analysis ◽  
Data Sets ◽  
Analysis Method ◽  
De Novo Mutations ◽  
Multiple Traits ◽  
Risk Genes ◽  
Trait Analysis ◽  
Chd Risk

AbstractJoint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. In addition, approaches that consider multiple traits can aid in the characterization of shared genetic etiology among those traits. In recent years, parent-offspring trio studies have reported an enrichment of de novo mutations (DNMs) in neuropsychiatric disorders. The analysis of DNM data in the context of neuropsychiatric disorders has implicated multiple putatively causal genes, and a number of reported genes are shared across disorders. However, a joint analysis method designed to integrate de novo mutation data from multiple studies has yet to be implemented. We here introduce multi pi e-trait TAD A (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. mTADA uses two single-trait analysis data sets to estimate the proportion of overlapping risk genes, and reports genes shared between and specific to the relevant disorders. We applied mTADA to >13,000 trios for six disorders: schizophrenia (SCZ), autism spectrum disorder (ASD), developmental disorders (DD), intellectual disability (ID), epilepsy (EPI), and congenital heart disease (CHD). We report the proportion of overlapping risk genes and the specific risk genes shared for each pair of disorders. A total of 153 genes were found to be shared in at least one pair of disorders. The largest percentages of shared risk genes were observed for pairs of DD, ID, ASD, and CHD (>20%) whereas SCZ, CHD, and EPI did not show strong overlaps In risk gene set between them. Furthermore, mTADA identified additional SCZ, EPI and CHD risk genes through integration with DD de novo mutation data. For CHD, using DD information, 31 risk genes with posterior probabilities > 0.8 were identified, and 20 of these 31 genes were not in the list of known CHD genes. We find evidence that most significant CHD risk genes are strongly expressed in prenatal stages of the human genes. Finally, we validated our findings for CHD and EPI in independent cohorts comprising 1241 CHD trios, 226 CHD singletons and 197 EPI trios. Multiple novel risk genes identified by mTADA also had de novo mutations in these independent data sets. The joint analysis method introduced here, mTADA, is able to identify risk genes shared by two traits as well as additional risk genes not found through single-trait analysis only. A number of risk genes reported by mTADA are identified only through joint analysis, specifically when ASD, DD, or ID are one of the two traits examined. This suggests that novel genes for the trait or a new trait might converge to a core gene list of the three traits.

scholarly journals SLC12A2 variants cause a neurodevelopmental disorder or cochleovestibular defect

Brain ◽  
2020 ◽  
Vol 143 (8) ◽  
pp. 2380-2387 ◽  
Author(s):  
Alisdair McNeill ◽  
Emanuela Iovino ◽  
Luke Mansard ◽  
Christel Vache ◽  
David Baux ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Sensorineural Hearing Loss ◽  
Developmental Disorders ◽  
De Novo ◽  
Neurodevelopmental Disorder ◽  
Sensorineural Deafness ◽  
Sensorineural Hearing ◽  
De Novo Mutation ◽  
Xenopus Laevis Oocytes ◽  
De Novo Mutations

Abstract The SLC12 gene family consists of SLC12A1–SLC12A9, encoding electroneutral cation-coupled chloride co-transporters. SCL12A2 has been shown to play a role in corticogenesis and therefore represents a strong candidate neurodevelopmental disorder gene. Through trio exome sequencing we identified de novo mutations in SLC12A2 in six children with neurodevelopmental disorders. All had developmental delay or intellectual disability ranging from mild to severe. Two had sensorineural deafness. We also identified SLC12A2 variants in three individuals with non-syndromic bilateral sensorineural hearing loss and vestibular areflexia. The SLC12A2 de novo mutation rate was demonstrated to be significantly elevated in the deciphering developmental disorders cohort. All tested variants were shown to reduce co-transporter function in Xenopus laevis oocytes. Analysis of SLC12A2 expression in foetal brain at 16–18 weeks post-conception revealed high expression in radial glial cells, compatible with a role in neurogenesis. Gene co-expression analysis in cells robustly expressing SLC12A2 at 16–18 weeks post-conception identified a transcriptomic programme associated with active neurogenesis. We identify SLC12A2 de novo mutations as the cause of a novel neurodevelopmental disorder and bilateral non-syndromic sensorineural hearing loss and provide further data supporting a role for this gene in human neurodevelopment.

Empirical investigation of mutation rate for herbicide resistance

Weed Science ◽  
2019 ◽  
Vol 67 (4) ◽  
pp. 361-368 ◽  
Author(s):  
Federico A. Casale ◽  
Darci A. Giacomini ◽  
Patrick J. Tranel
Keyword(s):  
Mutation Rate ◽  
Herbicide Resistance ◽  
De Novo ◽  
Selection Process ◽  
Theoretical Calculations ◽  
Acetolactate Synthase ◽  
De Novo Mutation ◽  
De Novo Mutations ◽  
Sources Of Resistance ◽  
Resistant Mutants

AbstractIn a predictable natural selection process, herbicides select for adaptive alleles that allow weed populations to survive. These resistance alleles may be available immediately from the standing genetic variation within the population or may arise from immigration via pollen or seeds from other populations. Moreover, because all populations are constantly generating new mutant genotypes by de novo mutations, resistant mutants may arise spontaneously in any herbicide-sensitive weed population. Recognizing that the relative contribution of each of these three sources of resistance alleles influences what strategies should be applied to counteract herbicide-resistance evolution, we aimed to add experimental information to the resistance evolutionary framework. Specifically, the objectives of this experiment were to determine the de novo mutation rate conferring herbicide resistance in a natural plant population and to test the hypothesis that the mutation rate increases when plants are stressed by sublethal herbicide exposure. We used grain amaranth (Amaranthus hypochondriacus L.) and resistance to acetolactate synthase (ALS)-inhibiting herbicides as a model system to discover spontaneous herbicide-resistant mutants. After screening 70.8 million plants, however, we detected no spontaneous resistant genotypes, indicating the probability of finding a spontaneous ALS-resistant mutant in a given sensitive population is lower than 1.4 × 10−8. This empirically determined upper limit is lower than expected from theoretical calculations based on previous studies. We found no evidence that herbicide stress increased the mutation rate, but were not able to robustly test this hypothesis. The results found in this study indicate that de novo mutations conferring herbicide resistance might occur at lower frequencies than previously expected.

Novel IRF6 Mutations Detected in Orofacial Cleft Patients by Targeted Massively Parallel Sequencing

2016 ◽  
Vol 96 (2) ◽  
pp. 179-185 ◽  
Author(s):  
K.D. Khandelwal ◽  
N. Ishorst ◽  
H. Zhou ◽  
K.U. Ludwig ◽  
H. Venselaar ◽  
...  
Keyword(s):  
Cleft Lip ◽  
Rare Variants ◽  
De Novo ◽  
Massively Parallel Sequencing ◽  
De Novo Mutation ◽  
De Novo Mutations ◽  
Orofacial Clefting ◽  
Unaffected Parent ◽  
Molecular Inversion Probes ◽  
Multiplex Sequencing

Common variants in interferon regulatory factor 6 ( IRF6) have been associated with nonsyndromic cleft lip with or without cleft palate (NSCL/P) as well as with tooth agenesis (TA). These variants contribute a small risk towards the 2 congenital conditions and explain only a small percentage of heritability. On the other hand, many IRF6 mutations are known to be a monogenic cause of disease for syndromic orofacial clefting (OFC). We hypothesize that IRF6 mutations in some rare instances could also cause nonsyndromic OFC. To find novel rare variants in IRF6 responsible for nonsyndromic OFC and TA, we performed targeted multiplex sequencing using molecular inversion probes (MIPs) in 1,072 OFC patients, 67 TA patients, and 706 controls. We identified 3 potentially pathogenic de novo mutations in OFC patients. In addition, 3 rare missense variants were identified, for which pathogenicity could not unequivocally be shown, as all variants were either inherited from an unaffected parent or the parental DNA was not available. Retrospective investigation of the patients with these variants revealed the presence of lip pits in one of the patients with a de novo mutation suggesting a Van der Woude syndrome (VWS) phenotype, whereas, in other patients, no lip pits were identified.

scholarly journals Soft sweeps predominate recent positive selection in bonobos (Pan paniscus) and chimpanzees (Pan troglodytes)

2020 ◽  
Author(s):  
Colin M Brand ◽  
Frances J White ◽  
Nelson Ting ◽  
Timothy H Webster
Keyword(s):  
Positive Selection ◽  
De Novo ◽  
Pan Paniscus ◽  
Novel Mutation ◽  
Simulated Data ◽  
Supervised Machine Learning ◽  
De Novo Mutation ◽  
De Novo Mutations ◽  
Recent Positive Selection ◽  
Neural Network Classifiers

Two modes of positive selection have been recognized: 1) hard sweeps that result in the rapid fixation of a beneficial allele typically from a de novo mutation and 2) soft sweeps that are characterized by intermediate frequencies of at least two haplotypes that stem from standing genetic variation or recurrent de novo mutations. While many populations exhibit both hard and soft sweeps throughout the genome, there is increasing evidence that soft sweeps, rather than hard sweeps, are the predominant mode of adaptation in many species, including humans. Here, we use a supervised machine learning approach to assess the extent of hard and soft sweeps in the closest living relatives of humans: bonobos and chimpanzees (genus Pan). We trained convolutional neural network classifiers using simulated data and applied these classifiers to population genomic data for 71 individuals representing all five extant Pan lineages, of which we successfully analyzed 60 individuals from four lineages. We found that recent adaptation in Pan is largely the result of soft sweeps, ranging from 73.1 to 97.7% of all identified sweeps. While few hard sweeps were shared among lineages, we found that between 19 and 267 soft sweep windows were shared by at least two lineages. We also identify novel candidate genes subject to recent positive selection. This study emphasizes the importance of shifts in the physical and social environment, rather than novel mutation, in shaping recent adaptations in bonobos and chimpanzees.

scholarly journals Association analysis of rare and common variants with multiple traits based on variable reduction method

2018 ◽  
Vol 100 ◽  
Author(s):  
LILI CHEN ◽  
YONG WANG ◽  
YAJING ZHOU
Keyword(s):  
Blood Pressure ◽  
Genetic Variants ◽  
Statistical Power ◽  
Reduction Method ◽  
Rare Variants ◽  
Complex Diseases ◽  
Common Variant ◽  
Joint Analysis ◽  
Multiple Traits ◽  
Variable Reduction

SummaryPleiotropy, the effect of one variant on multiple traits, is widespread in complex diseases. Joint analysis of multiple traits can improve statistical power to detect genetic variants and uncover the underlying genetic mechanism. Currently, a large number of existing methods target one common variant or only rare variants. Increasing evidence shows that complex diseases are caused by common and rare variants. Here we propose a region-based method to test both rare and common variant associated multiple traits based on variable reduction method (abbreviated as MULVR). However, in the presence of noise traits, the MULVR method may lose power, so we propose the MULVR-O method, which jointly analyses the optimal number of traits associated with genetic variants by the MULVR method, to guard against the effect of noise traits. Extensive simulation studies show that our proposed method (MULVR-O) is applied to not only multiple quantitative traits but also qualitative traits, and is more powerful than several other comparison methods in most scenarios. An application to the two genes (SHBG and CHRM3) and two phenotypes (systolic blood pressure and diastolic blood pressure) from the GAW19 dataset illustrates that our proposed methods (MULVR and MULVR-O) are feasible and efficient as a region-based method.

scholarly journals mTADA is a framework for identifying risk genes from de novo mutations in multiple traits

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Tan-Hoang Nguyen ◽  
Amanda Dobbyn ◽  
Ruth C. Brown ◽  
Brien P. Riley ◽  
Joseph D. Buxbaum ◽  
...  
Keyword(s):  
De Novo ◽  
De Novo Mutations ◽  
Multiple Traits ◽  
Risk Genes

scholarly journals Meta-GWAS for quantitative trait loci identification in soybean

2021 ◽  
Author(s):  
Johnathon M Shook ◽  
Jiaoping Zhang ◽  
Sarah E Jones ◽  
Arti Singh ◽  
Brian W Diers ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Seed Quality ◽  
De Novo ◽  
Agronomic Traits ◽  
Meta Analysis ◽  
Genomic Region ◽  
Multiple Traits ◽  
Robust Detection

Abstract We report a meta-Genome Wide Association Study involving 73 published studies in soybean (Glycine max L. [Merr.]) covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 peaks at 393 unique loci. Using stringent criteria to detect significant marker trait associations, 59 candidate genes were identified, including 17 agronomic traits loci, 19 for seed related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.

Expanding Phenotype of De Novo Mutations in GNAO1: Four New Cases and Review of Literature

2017 ◽  
Vol 48 (05) ◽  
pp. 371-377 ◽  
Author(s):  
Tobias Dietel ◽  
Christina Evers ◽  
Katrin Hinderhofer ◽  
Rudolf Korinthenberg ◽  
Daniel Ezzo ◽  
...  
Keyword(s):  
De Novo ◽  
Involuntary Movement ◽  
Epileptic Encephalopathy ◽  
De Novo Mutation ◽  
Guanine Nucleotide ◽  
Review Of Literature ◽  
De Novo Mutations ◽  
Guanine Nucleotide Binding Protein ◽  
Guanine Nucleotide Binding ◽  
Severe Epilepsy

AbstractMutations in GNAO1 (guanine nucleotide-binding protein, alpha-activating activity polypeptide O) were recently identified as being causative for early epileptic encephalopathy. Since then approximately 27 patients with severe developmental delay and different neurological phenotypes for epilepsy and involuntary movement disorder have been reported. We report four additional patients with mutations in GNAO1 including a report of siblings of different sex harboring the same de novo mutation (c.736G > A, p.Glu246Lys) but showing differences in phenotype with pronounced dystonia in the boy and epilepsy in his sister. Another de novo mutation in GNAO1 (c.607G > A, p.Gly203Arg) was identified in two unrelated girls with severe epilepsy. Both girls later also developed severe dystonia with severe nonepileptic spasms. An extensive review of published cases revealed that epilepsy was reported in only one male patient so far. Thus it appears possible that epilepsy is a sex-dependent phenotypic feature of GNAO1-related diseases.

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