Mariko Segawa

Research Fellow, Albert Einstein College of Medicine

3 active projects

Leveraging health systems data to achieve health equity in the US

There are many people living in the US with different racial and ethnic backgrounds. Our knowledge accumulated through genomic researches, however, is biased toward European ancestry. The disease risk of an individuals is affected by their race, ethnicity and ancestry…

Scientific Questions Being Studied

There are many people living in the US with different racial and ethnic backgrounds. Our knowledge accumulated through genomic researches, however, is biased toward European ancestry. The disease risk of an individuals is affected by their race, ethnicity and ancestry due to shared genetic and environmental factors. Therefore, it is important to understand genetic diversity in the US to achieve the health equity. All Of Us contains genomic data of more than 160K individuals with diverse ancestry background. So we expect to identify fine scale population structure in the US and and detect genomic factors underlying the rare and common disease in various populations in the US.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We will analyze fine scale population structure by detecting Identity by descent (IBD) segments shared between every pair of individuals. Through this approach we can identify IBD clusters based on shared ancestry, which probably would include underrepresented populations in genomic researches so far. We can also see geographic distribution of each cluster using zip code data and the strength of founder event for each cluster. Then, we will examine association between each cluster and disease and detect variants associated with rare and common disease by IBD mapping.

Anticipated Findings

We anticipate that we can find many IBD clusters with different ancestry backgrounds across the US. Each cluster may have different disease risks. The frequency and variation of genetic factors may be different between clusters, too. These information would be helpful for the disease screening, diagnosis, and treatment. Our findings may be able to improve the health disparity.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Mariko Segawa - Research Fellow, Albert Einstein College of Medicine

Urban genetics

The world has witnessed on-going urbanization and globalization for decades. Currently, more than half of the world’s population lives in urban areas and the proportion is expected to go up to about 66% by 2050. The number of people living…

Scientific Questions Being Studied

The world has witnessed on-going urbanization and globalization for decades. Currently, more than half of the world’s population lives in urban areas and the proportion is expected to go up to about 66% by 2050. The number of people living outside their country of origin has also been increasing over the last two decades. This global trend is evident in largest cities in the world such as New York, suggesting rapid and extensive admixture of individuals with different ancestry inside the city. This admixture is different from the admixture we experienced before in many points. Thus, it is important to understand what would happen in our genome in the city from the perspective of population genetics.
Additionally, the environment in urban area is very unique: E.g. high density of people, heavy traffic, heavy air pollution, low level of physical activity and etc. These unique environment can drive evolution though gene environment interaction.

Project Purpose(s)

  • Ancestry

Scientific Approaches

To assess what genetic features in cities are different from those in rural areas, we will categorize All Of US participants by the level of urbanization based on zip code data and compare several statistics (e.g. genetic diversity). To understand what is happening in a large city, we will analyze population structure in New York City by PCA, ADMIXTURE, UMAP and IBD sharing network and compare with New York States. We will also infer local ancestry for each individual using MOSAIC and gnomix to compare ancestry composition in each individual's genome.

Anticipated Findings

By analyzing NYC participants, we will find several founder populations and many recently admixed individuals. By combining EHR data, we will be able to find population-specific disease and associated variants. This may improve our understanding of disease background for underrepresented populations such as admixed populations and small founder populations.
Higher genetic diversity and higher rate of assortative mating are anticipated in the city than rural area. Our findings may be helpful to create future frameworks of genomic study such as Genome-wide association study (GWAS) because some genetic features like assortative mating would contradict the assumption of current framework.
Inferring local ancestry is important for finding disease-associated variants in admixed individuals. We will provide the results to other researchers to enhance genetic researches in admixed populations.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Mariko Segawa - Research Fellow, Albert Einstein College of Medicine

Fine-scale ancestry mapping and disease associations in African Americans

African ancestry in individuals in the US often represents admixture among several African population groups, in addition to non-African ancestries such as European and Native American. The genomes of admixed individuals appear like a mosaic of ancestry-specific genomic tracts. Ascertaining…

Scientific Questions Being Studied

African ancestry in individuals in the US often represents admixture among several African population groups, in addition to non-African ancestries such as European and Native American. The genomes of admixed individuals appear like a mosaic of ancestry-specific genomic tracts. Ascertaining these tracts, measuring fine-scale local ancestry within admixed individuals, enables us to identify ancestry-specific disease associated loci, leading to comprehensive understanding of complex disease in all human beings. This is especially important as most genetic studies underrepresent admixed populations and have been conducted in European ancestry populations. Thus, in this study, we aim to construct fine-scale genetic ancestry in individuals with African descent and identify the relationship between ancestry and prevalence of chronic diseases; nearly half of the US population has at least one chronic disease and its prevalence and manifestation vary with individuals' ancestral backgrounds.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We will use the methods PCA, ADMIXTURE and fineSTRUCTURE to assess population structure of individuals of African descent from the All of Us dataset against a reference panel enriched for African ancestral populations. Then, we will infer local ancestry across genome using RFMix, ELAI, and MOSAIC. The accuracy of our local ancestry inference will be validated by comparing the global mean values with the results of ADMIXTURE. We will correlate local ancestry proportions with different complex diseases prevalence. We will perform association tests with the most correlated traits, adjusting for local ancestry.

Anticipated Findings

We hypothesized that different African ancestries can be identified in the genome, and that each of these ancestries show different correlations to complex disease outcomes. Fine-scale, local ancestry information will enable us to find population-specific associations and understand the contribution of ancestry to disease risk. Our findings will contribute to understanding the genetic basis of chronic disease in populations under-represented in genomic studies, such as African Americans.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Mariko Segawa - Research Fellow, Albert Einstein College of Medicine
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