Publications by authors named "Aika Terada"

Genome-wide association studies have enabled the identification of important genetic factors in many trait studies. However, only a fraction of the heritability can be explained by known genetic factors, even in the most common diseases. Genetic loci combinations, or epistatic contributions expressed by combinations of single nucleotide polymorphisms (SNPs), have been argued to be one of the critical factors explaining some of the missing heritability, especially in oligogenic/polygenic diseases.

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Detecting combinatorial effects is important to various research areas, including biology, genomics, and medical sciences. However, this task was not only computationally nontrivial but also extremely difficult to achieve because of the necessity of a multiple testing procedure; hence few methods can comprehensively analyze high-order combinations. Recently, Limitless Arity Multiple-testing Procedure (LAMP) was introduced, allowing us to enumerate statistically significant combinations from a given dataset.

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Background: Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. However, some disease progression can be very unpredictable because the conventional approaches have failed to consider multiple-marker interactions.

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Summary: Exhaustive detection of multi-loci markers from genome-wide association study datasets is a computationally challenging problem. This paper presents a massively parallel algorithm for finding all significant combinations of alleles and introduces a software tool termed MP-LAMP that can be easily deployed in a cloud platform, such as Amazon Web Service, as well as in an in-house computer cluster. Multi-loci marker detection is an unbalanced tree search problem that cannot be parallelized by simple tree-splitting using generic parallel programming frameworks, such as Map-Reduce.

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Unlabelled: One of the major issues in genome-wide association studies is to solve the missing heritability problem. While considering epistatic interactions among multiple SNPs may contribute to solving this problem, existing software cannot detect statistically significant high-order interactions. We propose software named LAMPLINK, which employs a cutting-edge method to enumerate statistically significant SNP combinations from genome-wide case-control data.

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The habitats of polyploid species are generally distinct from their parental species. Stebbins described polyploids as 'general purpose genotypes', which can tolerate a wide range of environmental conditions. However, little is known about its molecular basis because of the complexity of polyploid genomes.

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Article Synopsis
  • More than three transcription factors can coordinate responses to signals, but identifying their combinations is complex due to multiple testing issues.
  • The "limitless arity multiple-testing procedure" (LAMP) is a new algorithm designed to efficiently count possible combinations and adjust testing thresholds to minimize error rates.
  • In studies on human breast cancer, LAMP successfully identified significant combinations of up to eight binding motifs, potentially revealing new regulatory pathways in complex biological data.
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