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The Center for Admixture Science and Technology (CAST) is one of the renowned Centers of Genomic Sciences (CEGS) funded by the National Institutes of Health (NIH). CAST will use the largest genomic datasets of individuals with diverse ancestry, in combination with socioeconomic data, to better predict health and disease in admixed individuals.

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Содержание курса

Разделы в курсе «Center for Admixture Science and Technology (CAST)» 9 разделов Уроки в курсе «Center for Admixture Science and Technology (CAST)» 27 уроков Время прохождения курса «Center for Admixture Science and Technology (CAST)» 0 ч. Последнее обновление курса «Center for Admixture Science and Technology (CAST)» обн. 3 года назад

Introduction

5 уроков
1. What is CAST?
2. What can we learn from our genomes?
3. What are genetic variation and genetic diversity?
4. What is precision medicine?
5. What will we discuss in this course?

Ancestry

5 уроков
1. How did different human populations form?
2. How many present-day individuals are recently admixed?
3. What is global vs. local ancestry?
4. How do we quantify global ancestry?
5. How do we analyze local ancestry?

Complex Variants

5 уроков
1. What exactly is a "genome"?
2. How can genomes vary?
3. What are the types of repetitive DNA?
4. Why are human leukocyte antigen (HLA) regions important?
5. How do complex variants affect disease risk?

Predicting Risk from Genomes

3 урока
1. What is a polygenic risk score?
2. How well do current Polygenic Risk Scores perform?
3. How do we develop broadly-relevant Polygenic Risk Scores?

Social Determinants of Health

1 урок
1. How can we communicate about social determinants of health?

Building Disease Risk Models Across Biobanks

2 урока
1. How can we ensure the privacy and security of genomic data?
2. How can we minimize privacy risk when linking biomedical data?

Integrating Local Ancestry into Disease Risk Models

1 урок
1. How can we simulate and analyze haplotype data?

Conclusion

1 урок
1. NAME TBD

OLD MATERIALS

4 урока
1. What is DNA sequencing?
2. How do we detect mutations?
3. How do we find disease-associated genetic variants?
4. How can we integrate genotypes inferred by different tools?