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Human Genome Center

Division of Digital Genomics
デジタル・ゲノミクス分野
Professor

Natsuhiko Kumasaka, Ph.D.

教授

博士（理学）

熊坂夏彦

A genome-wide association study (GWAS) is a powerful approach for identifying
genetic variants and related genes involved in the molecular mechanisms ofcommon complex traits, such as diabetes and human height. As of April 2023, theGWAS Catalogue reports 496,341 genetic associations discovered for 56,399
common complex traits. The Division of Digital Genomics aims to identify thesegenetic associations of common complex traits and uncover their molecularmechanisms using cutting-edge molecular biology assays and integrated
mathematical and statistical approaches.

1.  Discovery of genetic determinants for childhealth and development
Natsuhiko Kumasaka
The Japan Environment and Children’s Study(JECS) is a nationwide birth cohort study launched in
2011. Its primary objective is to investigate the impact
of environmental exposures during pregnancy andchildhood on children’s health and development.
JECS collected a vast amount of data on parents andtheir children, including information on reproduction
and pregnancy complications, congenital anomalies,
neuropsychiatric disorders, immune system disorders, and metabolic and endocrine system disorders.
Genome-wide genotyping using SNP microarrays
was recently performed to identify common genetic
variants in the children of 100,000 participants. The
purpose of this genetic screening is to assess which
genetic variants affect child health and development
outcomes observed in JECS as confounding factors.
Furthermore, our aim is to improve child health bymapping gene-environment interactions, particularly
for those who belong to a genetically vulnerable
group exposed to specific environmental hazards.
My responsibilities involve maintaining whole-ge-nome genotyping data and conducting genome-wide
association studies on various child health and developmental outcomes. As of December 2023, a whole-genome imputation has been performed using a reference haplotype panel from the 1000 Genomes Project.
I have conducted Genome-Wide Association Studies(GWAS) for various disease states, including Kawasaki disease, food allergies, and congenital anomalies,
as well as developmental measures such as BMI andgross motor scores. The aim is to create a comprehensive list of genetic determinants for child health anddevelopment. The summary statistics from those
GWAS will be publicly available as part of the flagship paper entitled ‘Genome-wide association study
on longitudinal and cross-sectional traits of childhealth and development in a Japanese population’,
which will be prepared in 2024.
2.  Development of a novel in-vitro approach tovalidate and elucidate underlying molecularmechanisms of genetic associations discovered through GWAS
Kotoe Katayama1, Seiya Imoto1, Atsushi Fukuda2,Natsuhiko Kumasaka3
1
Division of Health Medical Intelligence, Human
Genome Center, IMSUT, 2School of Medicine, TokaiUniversity, 3Division of Digital Genomics, Human

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Genome Center, IMSUT
Although Genome-wide association studies(GWAS) have identified hundreds of thousands of genetic associations in common complex traits, morethan 90% of genetic variants discovered by GWAS (referred to as GWAS variants) are located in non-coding
regions. This poses a significant challenge in our efforts to identify putative causal variants and functional genes involved in a regulatory cascade of disease
onset and progression. In addition, the target celltype(s) and cellular states in which these GWAS variants affect gene expression often remain unknown,limiting our ability to identify molecular mechanisms
of GWAS susceptibility loci in follow-up studies.
Expression quantitative trait locus (eQTL) mapping is a powerful approach to gain insight into therole of non-coding variants in gene regulation. It allows us to discover genes that are regulated by these
GWAS variants and helps elucidate downstream consequences. In addition, the recent advances in single-cell genomics allow us to identify target cell types
and cellular states in which GWAS variants modulate
gene expression through eQTLs. However, the studyof eQTLs can often prove to be cost-ineffective andlabor-intensive, especially when sample collection ischallenging, such as in the case of in vivo brain samples collected from hundreds of patients undergoingneurosurgery, or in the case of differentiated neurons
derived from hundreds of human pluripotent stem
cell (hPSC) lines.
Recently, an alternative approach combining single-cell RNA-seq (scRNA-seq) with massively parallel CRISPR interference (CRISPRi) screening has been
proposed to map eQTLs. This approach relies on
CRISPR-mediated perturbations instead of natural
genetic variation and is theoretically feasible from asingle donor’s sample. However, even though this approach significantly reduces the cell culture and experimental burden, it has not yet been applied to any
brain cell type implicated in neuropsychiatric andneurodegenerative diseases.
Indeed, a combination of a flexible in vitro system
and a robust in silico approach is lacking. Although
the use of hPSC is a valuable tool to generate different
types of mature cells, it is not trivial to maintain a sufficient gRNA repertoire through cell expansion anddifferentiation processes due to the selection pressureof specific CRISPR-mediated perturbations (i.e., onlycells with specific gRNA expand faster and take overother cells). From a data analysis perspective, theidentification of dynamic genetic effects that manifest
only during specific phases of cellular transition has
historically been challenging, due to the absence ofrobust machine learning approaches until very recently.
We combine the unique expertise of machinelearning/bioinformatics and stem cell biology/gene
editing to develop a novel CRISPR perturbation system based on midbrain organoids established fromhPSC lines, coupled with a state-of-the-art machinelearning technique using Gaussian processes. We
have already established a comprehensive computational approach, GASPACHO (GAuSsian Processes
for Association mapping leveraging Cell HeterOgeneity), for mapping eQTLs along dynamic cellularstates, which can be readily applicable to CRISPR-mediated eQTL mapping as if presence/absence of agRNA in a cell were different genotypes at a natural
genetic variant. We also have established several
hPSC lines for CRISPR screening with inducible expression of dCas9-KRAB or dCas9-P300 by doxycycline (Dox), which ensures a more stable cell culture
and allows to introduce perturbations at any desired
time point of cell differentiation. In addition, we haveextensive experience in two-dimensional neuronaldifferentiation and development of brain organoids.
3.  Development of novel statistical approaches
to map genetic associations using GaussianProcesses
Natsuhiko Kumasaka
Gaussian process (GP) is a powerful approach formodelling nonlinear phenomena in scientific fields
such as genomics and genetics. This project focuses
on using GPs for genetic association mapping. The
goal is to identify genetic variants that affect gene regulation across continuous cellular states at the molecular level, as well as disease susceptibility over timeand space at the population level. I am currently developing robust and sensitive methods to map a dynamic genetic effect based on a continuous factor.
These methods can be applied to various outcomes,
including non-Gaussian outputs, with appropriate
statistical calibration.

98PublicationsIshitsuka K, Yamamoto-Hanada K, Mezawa H, YangL, Saito-Abe M, Nishizato M, Sato M, Miyaji Y, Kumasaka N, Ohya Y; Japan Environment, Children’sStudy Group: Teenage and young adult pregnancy
and depression: findings from the Japan environ-ment and children’s study. Arch Womens Ment
Health. 2023 Nov 22. doi: 10.1007/s00737-023-014006. Epub ahead of print. PMID: 37989798.
Kumasaka N, Genetic Association Mapping Leveraging Gaussian Processes, Applied Statistics, in press.