Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction

By Dan He, David N. Kuhn, Laxmi Parida. 2016

62 citations recorded by the source corpus.

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. Availability and implementation: The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. Contact: dhe@us.ibm.com

Subfields: Genetics, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Dacheng Tao
2. Qiang Yang
3. Yu Zhang

In Computational Biology & Bioinformatics

1. Yu Zhang
2. Dacheng Tao
3. Mingli Song
4. Qiang Yang
5. Yiding Yang
6. Xinchao Wang

Citations in the corpus

• An Overview of Multi-task Learning (2018, background): “populations [104], construction of personalized brain–computer interfaces [105], MHC-I binding prediction [106], splice-site prediction [106], protein subcellular location prediction [107], Alzheimer’s disease assessment scale cognitive subscale [108], prediction of cognitive outcomes from neuroimag”
• A Survey on Multi-Task Learning (2017, neutral): “In [194], several multi-task models corresponding to problems (4), (16) and (27) are employed for multiple genetic trait prediction.”
• Amalgamating Knowledge from Heterogeneous Graph Neural Networks (2021, background): “In the past few years, multi-task learning has been widely studied in various areas, such as bioinformatics [10, 20], ubiquitous computing [39, 40], natural language processing [4, 38] and computer vision [21, 54, 15, 14].”