NIRG: FARSPhase: a Flexible, widely Applicable, Robust, and Scalable phasing algorithm for human genetics

In computational genetics, phasing is the modelling of the underlying haploid structure of diploid genotypes. It is important for many genetic studies because inheritance actually takes place at the haploid level, even though we can only directly observe diploid genotypes with current mainstream technologies. In many applications haplotypes provide richer and more useful information than genotypes alone. Applications of haplotype phase include understanding the interplay of genetic variation and disease, enabling identity-by-descent models for use in heritability analysis, gene association studies and genomic prediction, imputation of un-typed genetic variation, prioritizing individuals for sequencing, calling genotypes, detecting genotype error, inferring human demographic history, inferring points of recombination, detecting recurrent mutation and signatures of selection, and modelling cis-regulation of gene expression.
Human genetics data sets that will likely be phased in the future can be categorised into: (i) huge populations of nominally unrelated individuals (e.g. 500,000 individuals, UK Biobank); (ii) smaller subsets of such populations (e.g. data collected in individual studies); (iii) large (e.g. 50,000 individuals) or small (e.g. 1,000 individuals) data sets collected from isolated populations with high degrees of relatedness within them (e.g. Orcades - Orkney, deCODE - Iceland, VIKING - Sweden); (iv) data sets with and without pedigree information; (v) data sets that combine several of these features (e.g. Generation Scotland); and (vi) data sets with different types of genomic information (e.g. single nucleotide polymorphisms, low- or high-coverage sequence, short or longer sequence reads, etc.).
There are many phasing methods for human genetics data and these can be broadly classified into two groups: (i) heuristic methods (e.g. Long-Range Phasing (LRP)); and (ii) probabilistic methods (e.g. Hidden Markov Models (HMM)). Phasing is computationally intensive and the size and features of different data sets make them more or less suited to particular methods. LRP is computationally fast in comparison to HMM, but is only applicable to situations where individuals share relatively recent ancestry (e.g. within 10 generations) and thus share relatively long haplotypes (e.g. 5 to 10 cM length). Isolated populations (e.g. as in Orcades, Orkney) are ideally suited to LRP but huge populations with hundreds of thousands of nominally unrelated individuals may also be suitable (e.g. UK Biobank). Application of current HMM to such huge populations is computationally intractable. However, HMM are more suited to subsets of such populations than LRP because HMM only require that individuals share short haplotypes (e.g. <1 cM) due to sharing very distant relatives (e.g. 50 to 100 generations ago).
LRP and HMM methods are complementary in many ways. One models long haplotypes, the other short haplotypes. HMM methods are more flexible and can better model uncertainty in the data. LRP methods are computationally much more efficient and are also more accurate in scenarios to which they are suited. LRP methods are also more amenable to incorporation of pedigree information. A combined algorithm could exploit this complementarity.
The objective of this proposal is to develop FARSPhase: a Flexible, widely Applicable, Robust, and Scalable, phasing algorithm for human genetics that combines the best features of LRP, other heuristics, and HMM methods into a single framework. As well as meeting the phasing needs for small data sets, if successful, this research will enable huge data sets be phased and thereby opening the possibility of more powerful analysis. The developed algorithm will be combined into a user friendly software package built using best practices in software engineering and its performance will be tested in a wide range of simulated and real data sets that reflect the likely future phasing scenarios for human genetics.

Phasing is the modelling of the underlying haploid structure of diploid genotypes. It is important because inheritance actually takes place at the haploid level, even though only diploid genotypes are observed. The many phasing methods for human data can be broadly classified into: (i) heuristic methods (e.g. Long-range phasing (LRP)); and (ii) probabilistic methods (e.g. Hidden Markov Models (HMM)). Phasing is computationally intensive and the size and features of data sets make them more or less suited to particular methods.
Future data sets could be: (i) huge populations of nominally unrelated individuals (e.g. 500,000 individuals, UK Biobank); (ii) smaller subsets of such populations; (iii) Isolated populations with high degrees of relatedness within them (e.g. Orkney, Iceland); (iv) Data sets with/without pedigree information; (v) data sets with several of these features (e.g. Generation Scotland); and (vi) Data sets with different types of genomic information (e.g. single nucleotide polymorphisms, low/high-coverage sequence, short/longer sequence reads).
Heuristic methods, such as LRP, are suited to isolated populations with/without pedigree information or, due to their computational efficiency, to large populations of nominally unrelated individuals. Small nominally unrelated populations may not comprise enough individuals LRP to work, but they are suited to HMM because they are small enough to be computationally tractable and individuals within them share short haplotypes that HMM can model. Some data sets may be best addressed with a combination of algorithms. A combined algorithm could exploit the complementarity that exists between heuristic and probabilistic algorithms and thus be more powerful than the component algorithms.
The objective of this proposal is to develop and test a Flexible, widely Applicable, Robust, and Scalable phasing algorithm that combines the best features of LRP, other heuristics methods, and HMM methods into a single framework.

This project will develop a practical tool enabling genotype phasing in a wide variety of human genetics scenarios opening up the potential for generating huge volumes of rich genomic information at low cost. It will develop fundamental scientific knowledge primarily in bioinformatics applied to genomics. The outcomes will be beneficial for:
(i) The academic community. Scientifically, the project constitutes a novel approach for combining heuristic and probabilistic phasing methods into a single scalable, flexible, and accurate algorithm that will be suited to a wide variety of scenarios in human genetics. In human genetics research applications of haplotype phase include understanding the interplay of genetic variation and disease, enabling identity-by-descent models for use in heritability analysis, gene association studies and genomic prediction, imputation of untyped genetic variation, prioritizing individuals for sequencing, calling genotypes in microarray and sequence data, detecting genotype error, inferring human demographic history, inferring points of recombination, detecting recurrent mutation and signatures of selection, and modelling cis-regulation of gene expression. In summary the method will enable richer analysis and creation of larger data sets.
(iii) Commercial sequence and genotype providers. Companies providing genotype data will be able to add value to the data that they generate.
(iv) Society. All members of society who depend on scientific research to unravel the biology of humans: healthy people, sick people, at risk people.
(v) UK science base. The proposed algorithm will provide a platform for increased R&D capabilities in the UK, maintaining its scientific reputation and associated institutions, with increased capability for a more healthy population.
(vi) Training. The proposed research will be embedded within training courses that the PI is regularly invited to give, and the post-doc working on the project will have the opportunity to be trained at a world-class institute in a cutting edge area of research.
(vii) Policy. Genomic data is expensive, but the research and practical benefits are potentially large. Therefore much investment will be made in genomic data by human geneticists, charities, and the government in the coming years. To maximise efficiency of investment a co-ordinated national and perhaps international effort may be needed. The method to be developed in this proposal could enhance and underpin such an effort.