Chapter 11: Genome-Wide Association Studies

<div><p>Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.</p> </div

Linkage and Linkage Disequilibrium.

<p>Within a family, linkage occurs when two genetic markers (points on a chromosome) remain linked on a chromosome rather than being broken apart by recombination events during meiosis, shown as red lines. In a population, contiguous stretches of founder chromosomes from the initial generation are sequentially reduced in size by recombination events. Over time, a pair of markers or points on a chromosome in the population move from linkage disequilibrium to linkage equilibrium, as recombination events eventually occur between every possible point on the chromosome.</p

Indirect Association.

<p>Genotyped SNPs often lie in a region of high linkage disequilibrium with an influential allele. The genotyped SNP will be statistically associated with disease as a surrogate for the disease SNP through an indirect association.</p

Spectrum of Disease Allele Effects.

<p>Disease associations are often conceptualized in two dimensions: allele frequency and effect size. Highly penetrant alleles for Mendelian disorders are extremely rare with large effect sizes (upper left), while most GWAS findings are associations of common SNPs with small effect sizes (lower right). The bulk of discovered genetic associations lie on the diagonal denoted by the dashed lines.</p

Accuracy of Administratively-Assigned Ancestry for Diverse Populations in an Electronic Medical Record-Linked Biobank

<div><p>Recently, the development of biobanks linked to electronic medical records has presented new opportunities for genetic and epidemiological research. Studies based on these resources, however, present unique challenges, including the accurate assignment of individual-level population ancestry. In this work we examine the accuracy of administratively-assigned race in diverse populations by comparing assigned races to genetically-defined ancestry estimates. Using 220 ancestry informative markers, we generated principal components for patients in our dataset, which were used to cluster patients into groups based on genetic ancestry. Consistent with other studies, we find a strong overall agreement (Kappa  = 0.872) between genetic ancestry and assigned race, with higher rates of agreement for African-descent and European-descent assignments, and reduced agreement for Hispanic, East Asian-descent, and South Asian-descent assignments. These results suggest caution when selecting study samples of non-African and non-European backgrounds when administratively-assigned race from biobanks is used.</p></div

Percentages of each administratively-assigned race assigned to each genetic ancestry group.

<p>Percentages reflect the proportion of individuals assigned to a genetic ancestry cluster for given administratively-assigned race.</p

Comparison of administratively-assigned race and genetic ancestry, based on principal component analysis.

<p>A) All pairwise combinations of principle components (PCs) 1 through 3, by administratively assigned race. B) All pairwise combinations of PCs 1 through 3, by cluster assignments corresponding to genetic ancestry. Comparison of Frames 1A and1B indicate individuals with administratively assigned race different than their genetically defined ancestry cluster. For example, the East Asian-descent cluster (1B; blue) contains individuals with administratively-assigned race (1A) of Caucasian (green), Hispanic (purple), and Other (orange).</p

Distribution of administratively-assigned race.

<p>Race categories listed are based on classification options originating from the SD. Our BioVU dataset contained no individuals labeled Other (O). Vanderbilt University Medical Center is located in Davidson County, TN. 2010 US census data is shown for Davidson County, Tennessee <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099161#pone.0099161-US1" target="_blank">[25]</a>. * For Davidson County, “Asian/Pacific” includes Asian (Non-Indian), Native Hawaiian, and Pacific Islander individuals, “Native American” includes Native American (American Indian) and Alaskan Native individuals, “Indian” includes Asian Indian individuals, and “Unknown” includes ‘some other race’ and individuals who reported two or more races for the census. ** “Hispanic” is not listed a race in the US Census; rather, Hispanic-origin is indicated and is not exclusive to any racial category. For example, 25,156 individuals in Davidson County who self-identified as ‘White’ also self-identified, separately, as Hispanic. Within Davidson County, 9.8% of individuals indicated Hispanic origin.</p

Agreement between genetic and assigned ancestry.

<p>Notation: Cohen's Kappa coefficient (standard error).</p><p>South Asian-descent includes individuals with Native American and Indian race codes in BioVU.</p><p>Samples with administratively-assigned race of “Unknown” were excluded from this analysis.</p

Percentage of SNPs (type 1 error) above HLOD thresholds using PedCut followed by two-point parametric linkage analyses assuming dominant and recessive models and nonparametric linkage analysis using the ‘all’ and ‘pairs’ statistics.

<p>Percentage of SNPs (type 1 error) above HLOD thresholds using PedCut followed by two-point parametric linkage analyses assuming dominant and recessive models and nonparametric linkage analysis using the ‘all’ and ‘pairs’ statistics.</p