Genetic genealogy | EPFL Graph Search

Genetic genealogy is the use of genealogical DNA tests, i.e., DNA profiling and DNA testing, in combination with traditional genealogical methods, to infer genetic relationships between individuals. This application of genetics came to be used by family historians in the 21st century, as DNA tests became affordable. The tests have been promoted by amateur groups, such as surname study groups or regional genealogical groups, as well as research projects such as the Genographic Project. about 30 million people had been tested. As the field developed, the aims of practitioners broadened, with many seeking knowledge of their ancestry beyond the recent centuries, for which traditional pedigrees can be constructed. The investigation of surnames in genetics can be said to go back to George Darwin, a son of Charles Darwin and Charles' first cousin Emma Darwin. In 1875, George Darwin used surnames to estimate the frequency of first-cousin marriages and calculated the expected incidence of marriage between people of the same surname (isonymy). He arrived at a figure of 1.5% for cousin-marriage in the population of London, higher (3%-3.5%) among the upper classes and lower (2.25%) among the general rural population. A famous study in 1998 examined the lineage of descendants of Thomas Jefferson's paternal line and male lineage descendants of the freed slave Sally Hemings. Bryan Sykes, a molecular biologist at Oxford University, tested the new methodology in general surname research. His study of the Sykes surname, published in 2000, obtained results by looking at four STR markers on the male chromosome. It pointed the way to genetics becoming a valuable assistant in the service of genealogy and history. Genealogical DNA testing In 2000, Family Tree DNA was the first company to provide direct-to-consumer genetic testing for genealogy research. It initially offered eleven-marker Y-chromosome STR tests and HVR1 mitochondrial DNA tests but not multi-generational genealogy tests.

The genetic architecture of complex human traits at the dawn of genomic medicine

Olivier Noël Marie Naret

The focus of the work presented in this thesis is the exploration of the genetic architecture of complex human traits - at the dawn of genomic medicine. The underlying mechanisms explaining the enormously polygenic nature of most human complex traits are still unknown. The first chapter explores a possible explanatory model in which variant effects are due to an indirect mechanism, namely competition among genes for shared intracellular resources such as ribosomes. Our findings show that under most reasonable assumptions, resource competition should not be expected to have much impact on either protein expression levels of individual genes or on complex trait outcomes. The prediction accuracy of polygenic scores (PGS) remains relatively modest compared to what is expected given the estimated heritability of traits. Traditionally, the construction of PGS uses a large number of genetic variations, most of which have weak additive effects. Recent machine learning methods could improve PGS by also aggregating epistatic effects. To evaluate these different methods, we conducted an experiment based on an innovative concept of crowdsourcing, detailed in the second chapter. We collaborated with opensnp.org, an open repository where people share their genotyping data and phenotypic information, and with crowdai.org, a platform that allowed us to create a public competition for the genomic prediction of height. The challenge lasted three months and attracted 138 participants. This was the first crowd-sourcing challenge based on publicly available genome-wide genotyping data. Due to the enormous number of potential combinations of variants, it is difficult to integrate epistatic effects into PGS. In the third chapter, we present a method where we limit the possible combinations to the boundaries of each topologically associated domain (TAD) independently. With the UK Biobank, for the height phenotype, we included 17,560 variants in an artificial neural network (ANN) and compared the variance explained (

R^2

) by the PGS with or without the knowledge of the TADs. We found that it brings a significant improvement with an average

R^2

going from 0.287 to 0.293 (with a p-value

=10E-5

for n=20). We concluded that it should be possible to build better PGS using ANNs and epistasis in TADs. The effect of genetic ancestry on phenotypes is not taken into account in PGS-based risk estimates. Doing so could accelerate the adoption of genomic medicine for underrepresented populations and mixed-race individuals. The fourth chapter presents a method for its integration through a secondary score derived from genome-wide genotyping data, the PC score (PCS). We compared two models, one using only the PGS and the other using both the PGS and the PCS. Using the UK Biobank, we found an improvement in genetic prediction for all phenotypes tested:

EPFL2021

The genetic architecture of complex human traits at the dawn of genomic medicine

Olivier Noël Marie Naret

R^2

) by the PGS with or without the knowledge of the TADs. We found that it brings a significant improvement with an average

R^2

going from 0.287 to 0.293 (with a p-value

=10E-5

EPFL2021