Population Structure: A Key Concept for Understanding Genetic Variation

It is common for articles to claim that “the gene for” some trait or disease has been identified. Usually they actually mean that an association has been found between an uncommon genetic variant found in, or near, a gene and some trait or disease. These kinds of articles are becoming increasingly common because Genome-Wide Association Studies (GWAS) are becoming cheaper and more common. Though GWAS yield important insights their results can be misleading because ancestral relationships between individuals in the study can create signals that can be misinterpreted as association with the trait being studied. This phenomenon is very powerful and one reason why it is important to have a diverse group of individuals in any genetic study. Underlying ancestral relationships are known as “population structure” and serious thought is required to ensure that it doesn’t skew GWAS results. The paper below is a scientific review article (in an excellent journal with exceptional authors) and not exactly easy reading, but it was written for a broad audience and worth considering the next time you see an article discussing the identification of “the genes for” something or other, even if it appears in Genome-Media.

-RPR

Population Genetics: Why structure matters

Abstract

Great care is needed when interpreting claims about the genetic basis of human variation based on data from genome-wide association studies.

Main text

Human height is the classic example of a quantitative trait: its distribution is continuous, presumably because it is influenced by variation at a very large number of genes, most with a small effect (Fisher, 1918). Yet height is also strongly affected by the environment: average height in many countries increased during the last century and the children of immigrants are often taller than relatives in their country of origin – in both cases presumably due to changing diet and other environmental factors (Cavalli-Sforza and Bodmer, 1971; Grasgruber et al., 2016; NCD Risk Factor Collaboration, 2016). This makes it very difficult to determine the cause of geographic patterns for height, such as the ‘latitudinal cline’ seen in Europe (Figure 1).

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Disease risk estimates need more samples from more populations (Genome Biology)

Genetic disease risks can be misestimated across global populations

Michelle S. Kim, Kane P. Patel, Andrew K. Teng, Ali J. Berens, and Joseph Lachance

Genome Biology (Research article)

Accurate assessment of health disparities requires unbiased knowledge of genetic risks in different populations. Unfortunately, most genome-wide association studies use genotyping arrays and European samples. Here, we integrate whole genome sequence data from global populations, results from thousands of genome-wide association studies (GWAS), and extensive computer simulations to identify how genetic disease risks can be misestimated. In contrast to null expectations, we find that risk allele frequencies at known disease loci are significantly different for African populations compared to other continents. 

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Alzheimers insights from the desk of the NIH Director, Dr. Francis Collins

Largest-Ever Alzheimer’s Gene Study Brings New Answers

Predicting whether someone will get Alzheimer’s disease (AD) late in life, and how to use that information for prevention, has been an intense focus of biomedical research. The goal of this work is to learn not only about the genes involved in AD, but how they work together and with other complex biological, environmental, and lifestyle factors to drive this devastating neurological disease.

It’s good news to be able to report that an international team of researchers, partly funded by NIH, has made more progress in explaining the genetic component of AD. Their analysis, involving data from more than 35,000 individuals with late-onset AD, has identified variants in five new genes that put people at greater risk of AD [1]. It also points to molecular pathways involved in AD as possible avenues for prevention, and offers further confirmation of 20 other genes that had been implicated previously in AD.

The results of this largest-ever genomic study of AD suggests key roles for genes involved in the processing of beta-amyloid peptides, which form plaques in the brain recognized as an important early indicator of AD. They also offer the first evidence for a genetic link to proteins that bind tau, the protein responsible for telltale tangles in the AD brain that track closely with a person’s cognitive decline.

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Autism GWAS finds common risk variants

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci.

Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.

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Insomnia GWAS with 1.3 million individuals yields results

Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways

Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited.

Here, we use a large genetic association sample (n = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints.

We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets.