Genetic ancestry and brain morphology

Written by: Stephen Hsu

Primary Source:  Information Processing

Population structure — i.e., distribution of gene variants by ancestral group — is reflected in brain morphology, as measured using MRI. Brain morphology measurements can be used to predict ancestry. Strictly speaking, the data only show correlation, not genetic causation, but the most plausible interpretation is that genetic differences are causing morphological differences. One could check this easily by comparing individuals raised in different environments and cultures.

See also Heritability of brain structure, IQ prediction from structural MRI, and Metabolic costs of human brain development.

Most of the authors are associated with UCSD. From the paper:

PING (Pediatric Imaging, Neurocognition, and Genetics) was a multisite project recruiting children and adolescents from ages 3 to 21 at ten sites in the United States. All participants were screened for history of major developmental, psychiatric, and neurological disorders; brain injury; and other medical conditions that affect development. Participants then received neurodevelopmental assessments, standardized multi-modal neuroimaging, and genome-wide genotyping. The overall PING sample consisted of 1,493 participants; 1,152 individuals remained after quality control of the genotyping and neuroimaging data.

… Our data indicate that the unique folding patterns of gyri and sulci are closely aligned with genetic ancestry. The geometry robustly predicts each individual’s genetic background even though the population has been shaped by waves of migration and admixtures. A previous study, using only facial features, achieved 64% explained variance in YRI ancestry among African Americans [19]. Our 3D representation of cortical surface geometry performs similarly in predicting YRI ancestry and also performs well for the other three continental ancestries. As data in Table 1 show, the explanatory power is not due to the differences in total brain volumes, nor to the differences in areal expansion of the cortical surface. Instead, regional folding patterns characterize each ancestral lineage. …

Modeling the 3D Geometry of the Cortical Surface with Genetic Ancestry

Chun Chieh Fan, Hauke Bartsch, Andrew J. Schork, Chi-Hua Chen, Yunpeng Wang, Min-Tzu Lo, Timothy T. Brown, Joshua M. Kuperman, Donald J. Hagler Jr., Nicholas J. Schork, Terry L. Jernigan, Anders M. Dale, the Pediatric Imaging, Neurocognition, and Genetics Study

Cell: Current Biology 25, 1–5 August 3, 2015

•Geometry of the human cortical surface contains rich ancestral information
•The most informative features are regional patterns of cortical folding and gyrification
•This study provides insight on the influence of population structure on brain shape

Knowing how the human brain is shaped by migration and admixture is a critical step in studying human evolution [1, 2], as well as in preventing the bias of hidden population structure in brain research [3, 4]. Yet, the neuroanatomical differences engendered by population history are still poorly understood. Most of the inference relies on craniometric measurements, because morphology of the brain is presumed to be the neurocranium’s main shaping force before bones are fused and ossified [5]. Although studies have shown that the shape variations of cranial bones are consistent with population history [6–8], it is unknown how much human ancestry information is retained by the human cortical surface. In our group’s previous study, we found that area measures of cortical surface and total brain volumes of individuals of European descent in the United States correlate significantly with their ancestral geographic locations in Europe [9]. Here, we demonstrate that the three-dimensional geometry of cortical surface is highly predictive of individuals’ genetic ancestry in West Africa, Europe, East Asia, and America, even though their genetic background has been shaped by multiple waves of migratory and admixture events. The geometry of the cortical surface contains richer information about ancestry than the areal variability of the cortical surface, independent of total brain volumes. Besides explaining more ancestry variance than other brain imaging measurements, the 3D geometry of the cortical surface further characterizes distinct regional patterns in the folding and gyrification of the human brain associated with each ancestral lineage.

This figure shows ancestry admixture using PCA. The figure at top shows that brain morphological parameters can be used to build a reasonably good classifier.

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Stephen Hsu
Stephen Hsu is vice president for Research and Graduate Studies at Michigan State University. He also serves as scientific adviser to BGI (formerly Beijing Genomics Institute) and as a member of its Cognitive Genomics Lab. Hsu’s primary work has been in applications of quantum field theory, particularly to problems in quantum chromodynamics, dark energy, black holes, entropy bounds, and particle physics beyond the standard model. He has also made contributions to genomics and bioinformatics, the theory of modern finance, and in encryption and information security. Founder of two Silicon Valley companies—SafeWeb, a pioneer in SSL VPN (Secure Sockets Layer Virtual Private Networks) appliances, which was acquired by Symantec in 2003, and Robot Genius Inc., which developed anti-malware technologies—Hsu has given invited research seminars and colloquia at leading research universities and laboratories around the world.
Stephen Hsu

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