1. Collecting Family Medical History and Ancestry Data Yvette Conley, PhD
Professor of Nursing
and Human Genetics

2. Objectives
Describe how family medical history and ancestry data are collected and why they are important to precision health care
Interpret ancestry composition data

3. Family History vs Genome Testing

4. Neither family health histories nor genetic testing is perfect in predicting health risks, but they each have their strengths.
More importantly using them together improves upon the risk prediction by capitalizing on the situations where each method is at its strongest.
This paper describes a model to combine the two methods together to assess risk.
PLoS Genetics (2012) dx.doi.org/ /journal.pgen

5. Using quantitative genetic theory, this group of researchers develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)–based methods for assessing risk of polygenic disorders.
Family history is most useful for highly common, heritable conditions (e.g., coronary artery disease), where it explains roughly 20%–30% of disease heritability, on par with the most successful SNP models ofassociations discovered to date.
Diseases of moderate or low frequency (e.g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases.
For a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history–based counterparts, despite the large fraction of missing heritability that remains to be explained.
Their model illustrates the difficulty of using either family history or SNPs for standalone disease prediction.
However; unlike family history, SNP–based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis.

6. Figure 3. Proportion of heritability explained.
Do CB, Hinds DA, Francke U, Eriksson N (2012) Comparison of Family History and SNPs for Predicting Risk of Complex Disease. PLoS Genet 8(10): e doi: /journal.pgen

8. Family history should also include collection of ancestry information
If possible – ancestry of all 4 grandparents
The frequency of some phenotypes differ based on ancestry
Gene + Environment

9. Ancestry Composition

10. Calculating where your ancestors are from
Using principal component analysis (PCA) and linear regression — statistical tools for processing and visualizing large, complex datasets — researchers at 23andMe analyzed genetic data from customers who all had four grandparents from the same country of origin. (23andme is currently adding more analyses) When the results of the analysis are plotted on a two-dimensional graph, individuals of similar ancestry cluster together, and those clusters correspond closely to geographic locations.

11. A sample of 3,000 European individuals were genotyped at over half a million variable DNA sites in the human genome. Despite low average levels of genetic differentiation among Europeans, we find a close correspondence between genetic and geographic distances; indeed, a geographical map of Europe arises naturally as an efficient two-dimensional summary of genetic variation in Europeans. The results emphasize that when mapping the genetic basis of a disease phenotype, spurious associations can arise if genetic structure is not properly accounted for. In addition, an individual's DNA can be used to infer their geographic origin with surprising accuracy—often to within a few hundred kilometres.
Nature 456, doi: /nature07331

12. Accounting for population genetic substructure is important in reducing type 1 errors in genetic studies of complex disease. As efforts to understand complex genetic disease are expanded to different continental populations the understanding of genetic substructure within these continents will be useful in design and execution of association tests. For PCA, the first two PC's showed a pattern of relationships that closely followed the geographic distribution of the different East Asian populations. PCA showed substructure both between different East Asian groups and within the Han Chinese population. These studies have also identified a subset of East Asian substructure ancestry informative markers (EASTASAIMS) that may be useful for future complex genetic disease association studies in reducing type 1 errors and in identifying homogeneous groups that may increase the power of such studies.
PLoS ONE 3(12): e3862. doi: /journal.pone

13. Figure 1. Risk Allele Frequencies in Europeans and African Americans.
Haiman CA, Chen GK, Blot WJ, Strom SS, Berndt SI, et al. (2011) Characterizing Genetic Risk at Known Prostate Cancer Susceptibility Loci in African Americans. PLoS Genet 7(5): e doi: /journal.pgen

14. Neanderthal
60,000 years ago some humans migrated out of Africa and as they migrated through Eurasia, they encountered the Neanderthals and interbred. Because of this, a small amount of Neanderthal DNA was introduced into the modern human gene pool.
Everyone living outside of Africa today has a small amount of Neanderthal DNA.
It is estimated that most Europeans and Asians have between 1 to 4 percent Neanderthal DNA. Indigenous sub-Saharan Africans have no Neanderthal DNA because their ancestors did not migrate through Eurasia.

16. The genome-wide frequency of Neanderthal-like sites is approximately constant across all contemporary out-of-Africa populations
Genes involved in lipid catabolism contain more than threefold excess of such sites in contemporary humans of European descent
Evolutionally, these genes show significant association with signatures of recent positive selection in the contemporary European, but not Asian or African populations
Functionally, the excess of Neanderthal-like sites in lipid catabolism genes can be linked with a greater divergence of lipid concentrations and enzyme expression levels within this pathway, seen in contemporary Europeans, but not in the other populations

17. Mitochondria