Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Motivation: Clustering of individuals into populations on the basis of multilocus genotypes is informative in a variety of settings. In population-genetic clustering algorithms, such as BAPS, STRUCTURE and TESS, individual multilocus genotypes are partitioned over a set of clusters, often using unsupervised approaches that involve stochastic simulation. As a result, replicate cluster analyses of the same data may produce several distinct solutions for estimated cluster membership coefficients, even though the same initial conditions were used. Major differences among clustering solutions have two main sources: (1) ‘label switching’ of clusters across replicates, caused by the arbitrary way in which clusters in an unsupervised analysis are labeled, and (2) ‘genuine multimodality,’ truly distinct solutions across replicates. Results: To facilitate the interpretation of population-genetic clustering results, we describe three algorithms for aligning multiple replicate analyses of the same data set. We have implemented these algorithms in the computer program CLUMPP (CLUster Matching and Permutation Program). We illustrate the use of CLUMPP by aligning the cluster membership coefficients from 100 replicate cluster analyses of 600 chickens from 20 different breeds. Availability: CLUMPP is freely available at http://rosenberglab.

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CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure

Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.

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Efficient Control of Population Structure in Model Organism Association Mapping

Uncovering the genetic basis of agronomic traits in crop landraces that have adapted to various agro-climatic conditions is important to world food security. Here we have identified ∼ 3.6 million SNPs by sequencing 517 rice landraces and constructed a high-density haplotype map of the rice genome using a novel data-imputation method. We performed genome-wide association studies (GWAS) for 14 agronomic traits in the population of Oryza sativa indica subspecies. The loci identified through GWAS explained ∼ 36% of the phenotypic variance, on average. The peak signals at six loci were tied closely to previously identified genes. This study provides a fundamental resource for rice genetics research and breeding, and demonstrates that an approach integrating second-generation genome sequencing and GWAS can be used as a powerful complementary strategy to classical biparental cross-mapping for dissecting complex traits in rice.

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Genome-wide association studies of 14 agronomic traits in rice landraces

Although pioneered by human geneticists as a potential solution to the challenging problem of finding the genetic basis of common human diseases, genome-wide association (GWA) studies have, owing to advances in genotyping and sequencing technology, become an obvious general approach for studying the genetics of natural variation and traits of agricultural importance. They are particularly useful when inbred lines are available, because once these lines have been genotyped they can be phenotyped multiple times, making it possible (as well as extremely cost effective) to study many different traits in many different environments, while replicating the phenotypic measurements to reduce environmental noise. Here we demonstrate the power of this approach by carrying out a GWA study of 107 phenotypes in Arabidopsis thaliana, a widely distributed, predominantly self-fertilizing model plant known to harbour considerable genetic variation for many adaptively important traits. Our results are dramatically different from those of human GWA studies, in that we identify many common alleles of major effect, but they are also, in many cases, harder to interpret because confounding by complex genetics and population structure make it difficult to distinguish true associations from false. However, a-priori candidates are significantly over-represented among these associations as well, making many of them excellent candidates for follow-up experiments. Our study demonstrates the feasibility of GWA studies in A. thaliana and suggests that the approach will be appropriate for many other organisms.

/pdf/genome-wide-association-study-of-107-phenotypes-in-2m0qw4sdtk.pdf

Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines

We resequenced 876 short fragments in a sample of 96 individuals of Arabidopsis thaliana that included stock center accessions as well as a hierarchical sample from natural populations. Although A. thaliana is a selfing weed, the pattern of polymorphism in general agrees with what is expected for a widely distributed, sexually reproducing species. Linkage disequilibrium decays rapidly, within 50 kb. Variation is shared worldwide, although population structure and isolation by distance are evident. The data fail to fit standard neutral models in several ways. There is a genome-wide excess of rare alleles, at least partially due to selection. There is too much variation between genomic regions in the level of polymorphism. The local level of polymorphism is negatively correlated with gene density and positively correlated with segmental duplications. Because the data do not fit theoretical null distributions, attempts to infer natural selection from polymorphism data will require genome-wide surveys of polymorphism in order to identify anomalous regions. Despite this, our data support the utility of A. thaliana as a model for evolutionary functional genomics.

/pdf/the-pattern-of-polymorphism-in-arabidopsis-thaliana-4x8tcix3nq.pdf

The pattern of polymorphism in Arabidopsis thaliana.

An information value based approach to design procedure capture

Knowledge management has recently become the focus of public attention in business and engineering. Because knowledge acquisition is situated in the upstream of knowledge management, capturing knowledge is an important step for enterprises to achieve successful knowledge management. We focus on how engineers solve their design problems under given design contexts and propose a novel model and methods to capture knowledge from engineering design processes. Our goal is to acquire know-how knowledge without disrupting the normal design process. A three-layer design process model is introduced to represent generic design processes, and a grammar and extended dynamic programming (GEDP) method is developed based on the process model. GEDP adopts the grammar approach and EDP to automatically identify meaningful clusters, called operations, from primitive design events. Our approach is evaluated through a case study of designing a double-reduction gear system.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0890060402020073

Acquiring engineering knowledge from design processes

Estimate design intent: a multiple genetic programming and multivariate analysis based approach

Recently knowledge capturing in the design process using data mining techniques has attracted attentions from researchers. In this paper, we focus on how designers apply their knowledge and find ways to proceed with their design in given design situations. This design know-how is the knack for a design to be successful. In order to automatically capture the design know-how through design processes without overburdening designers, we 1) — introduced an object-oriented CAD system for data gathering, 2) — proposed a three-layer design process model to represent generic design processes, and 3) — developed a method, called Extended Dynamic Programming (EDP), to extract know-how knowledge from the gathered design process data. The effectiveness of the proposed approach has been demonstrated by a gear design prototype system Gear-CAD.

Yoko Ishino

Papers

The pattern of polymorphism in Arabidopsis thaliana.

An information value based approach to design procedure capture

Acquiring engineering knowledge from design processes

Estimate design intent: a multiple genetic programming and multivariate analysis based approach

Data mining for knowledge acquisition in engineering design