Statistical Association Mapping of Population-Structured Genetic Data

Najafi, Amir Abbas; Janghorbani, Sepehr; Motahari, Seyed Abolfazl; Fatemizadeh, Emad

doi:10.1101/069658

Cited by 3 publications

(3 citation statements)

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“…PS was assessed by Bayesian cluster analysis using the Structure 2.3.4 software used 26517 SNPs. There were 34039 SNPs distributed on all 21 wheat chromosomes with r 2 <0.2 ( Najafi et al., 2019 ). LD was conducted with TASSEL 5.0 software using the expected versus observed allele frequencies of the SNPs ( Liu et al., 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of new resistance loci against wheat sharp eyespot through genome-wide association study

Wang

et al. 2022

Front. Plant Sci.

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IntroductionWheat sharp eyespot caused by Rhizoctonia cerealis is a serious pathogenic disease affecting plants. The effective strategy for controlling this disease is breeding resistant cultivar. However, to date, no wheat varieties are fully resistant to sharp eyespot, and only a few quantitative trait loci (QTLs) have been shown to be associated with sharp eyespot resistance.MethodsTo understand the genetic basis of this disease, a genome-wide association study (GWAS) of sharp eyespot resistance in 262 varieties from all China wheat regions was conducted.ResultsAfter cultivation for three years, only 6.5% of the varieties were resistant to sharp eyespot. Notably, the varieties from the middle and lower Yangtze River displayed higher sharp eyespot resistance than those from Huanghuai wheat zone. Only two varieties had the same resistance level to the control Shanhongmai. The results of GWAS showed that 5 single nucleotide polymorphism (SNP) loci were markedly related to sharp eyespot resistance in the three years repeatedly, and two QTLs, qSE-6A and qSE-7B, on chromosome 6A and 7B were identified. Based on the ‘CG’ haplotypes of significant SNPs, we found that the two QTLs exhibited additive effects on attenuating sharp eyespot resistance.DiscussionThese results provide novel insights into the genetic basis of sharp eyespot resistance in China wheat varieties. The SNPs related to sharp eyespot resistance can be applied for marker-assisted selection in plant breeding.

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Section: Methodsmentioning

confidence: 99%

Identification of new resistance loci against wheat sharp eyespot through genome-wide association study

Wang

et al. 2022

Front. Plant Sci.

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“…The role of population stratification in GWAS, an important application of BMMs in genetics research, is discussed in [30] and [38]. For more research on the employment of BMMs in GWA studies, see [39,40,41,42].…”

Section: Related Workmentioning

confidence: 99%

Reliable clustering of Bernoulli mixture models

Najafi¹,

Motahari²,

Rabiee³

2020

Bernoulli

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A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors with independent dimensions. The problem of clustering BMM data arises in a variety of real-world applications, ranging from population genetics to activity analysis in social networks. In this paper, we analyze the clusterability of BMMs from a theoretical perspective, when the number of clusters is unknown. In particular, we stipulate a set of conditions on the sample complexity and dimension of the model in order to guarantee the Probably Approximately Correct (PAC)-clusterability of a dataset. To the best of our knowledge, these findings are the first non-asymptotic bounds on the sample complexity of learning or clustering BMMs.

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“…Many algorithms have been proposed to solve the GWAS problem (see for instance [13]). Despite all of these progresses, addressing the problem from an abstract level where reliability in inference is the main objective is missing.…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Studies: Information Theoretic Limits of Reliable Learning

Tahmasebi

Maddah-Ali

Motahari

2018

2018 IEEE International Symposium on Information Theory (ISIT)

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In the problems of Genome-Wide Association Study (GWAS), the objective is to associate subsequences of individuals' genomes to the observable characteristics called phenotypes. The genome containing the biological information of an individual can be represented by a sequence of length G. Many observable characteristics of individuals can be related to a subsequence of a given length L called causal subsequence. The environmental affects make the relation between the causal subsequence and the observable characteristics a stochastic function. Our objective in this paper is to detect the causal subsequence of a specific phenotype using a dataset of N individuals and their observed characteristics. We introduce an abstract formulation of GWAS which allows us to investigate the problem from an information theoretic perspective. In particular, as the parameters N, G, and L grow, we observe a threshold effect at Gh(L/G) N , where h(.) is the binary entropy function. This effect allows us to define the capacity of recovering the causal subsequence by denoting the rate of the GWAS problem as Gh(L/G) N .We develop an achievable scheme and a matching converse for this problem, and thus characterize its capacity in two scenarios: the zero-error-rate and the −error-rate.

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Statistical Association Mapping of Population-Structured Genetic Data

Cited by 3 publications

References 50 publications

Identification of new resistance loci against wheat sharp eyespot through genome-wide association study

Identification of new resistance loci against wheat sharp eyespot through genome-wide association study

Reliable clustering of Bernoulli mixture models

Genome-Wide Association Studies: Information Theoretic Limits of Reliable Learning

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