|Anna Di Rienzo
Humans originated in Africa and then dispersed all over the world to environments that differ in terms of climate, biodiversity, etc, which has brought selective pressures on different populations. At #AAASmtg in Washington DC on Saturday, Anna Di Rienzo presented her research on the how this dispersal has left signatures of adaptation to the pressures. Here are my notes from the talk.
The “Out of Africa” theory has it that humans left Africa 50 Kya and then Neolithic revolution happened 14 Kya. They shifted away from foraging subsistence to horticulture. We also know that levels of human skin pigmentation changed with latitude of populations. In addition, body size and proportions changed. For example, Inuit
have quite different proportions for the cold North.
Metabolic traits differ across human populations also, causing disease related traits to occur such as high blood pressure, high triglycerides, or high cholesterol. A prominent example is the rising prevalence of type 2 diabetes. “It’s been long proposed that it’s a [genetic] susceptibility to change in lifestyle and diet,” Di Rienzo said.
There is a prevalence of inter-ethnic differences in disease and traits. Environmental risk factors clearly play a role in shaping differences. There is a growing conseus that genetic factors also contribute. Is there evidence for genetic – in addition to cultural and physiological – adaptations? How much of the phenotypic diversity is adaptive? What is the contribution of local adaptive traits?
These question led to many studies on signals based on haplotype structure such as lactase persistence, which is common in Europe and in agropastoralist populations, but rare elsewhere. The ability to digest milk in adult life became advantages with the introduction of animal farming, Di Rienzo said. Another example is the FY allele that is fixed in most sub-saharan Africa and is virtually absent everywhere else. FY codes for a chemokine receptor (antimalarial).
Selection for polygenic traits is expected to generate subtle changes in allele frequency at multiple loci. Standard approaches are unlikely to capture these signals. The signature of selection is for monogenic (small shifts) versus polygenic traits.
Her approach is for search of information about environmental selective pressures. She searches for correlations between alledle frequency and environmental variables. She takes into account the geographic structure of human populations shaping distribution.
She used a large dataset of more than 642,000 autosomal SNPs. Environmental variables included climate, ecoregion, and subsistence. Climate includes seasons, ecoregion with temperature, humidity. The genome-wide evidence for environmental adaptations is that most of the genome doesn’t contain genes or variants that affect the function of the genes.
Natural selection acts only on variants that have both functional and phenotypic effects. Is there an excess of test SNPs relative to control SNPs among those with lowest minimum p-values?
The test SNPs used are enriched for SNPs with functional effects. Control SNPs are unlikely to have functional effects (e.g. far from genes).
In all cases, a significant excess of the test relative to control SNPs indicating that environmentals select pressures shaped the geographic distribution of variation in the human genome.
The results suggested genome-wide evidence for environmental adaptations,” Di Rienzo said.
Pancreatic lipase-related protein 2 hydrolyzes galactolipids, is the main component in plants. The truncated PLRP2 protein, which occurs at a higher frequency in those populations with higher consumption of cereal grains. PLRP2 is associated with cereal rich diet.
Two examples of patterns at individual SNPs are “foraging” and “nonforaging” and she shows a slide with patterns showing differences in Africa, Europe and other. In each geographic location, there is a shift in allele frequency that allude to differences in diet of the populations.
The shift in allele frequency is not dramatic, but small. The top signals are with categorical variables like roots & tubers, foraging, polar ecoregion, and a dry ecoregion. Top climate variables have to do with seasons.
“Selection doesn’t act on genes, it acts on phenotypes. The phenotypes are enriched with signals for environmental correlations,” Di Rienzo said.
Disease classes are influenced by environmental selective pressures. Climate influenced cancer, CVD, immune, infection. Subsistence influenced metabolic and reproduction phenotypes.
The overlaying signals of environmental correlations and genome-wide association studies show this flow:
SNP is affected by environmental correlations and GWAS, then selective pressures produce phenotype. It’s also known that pathogen diversity follows a gradient on climate factors, which can affect immune, autoimmune adaptations.
Di Rienzo made these conclusions from the data:
– Strong GWAS to climate, ecoregion and subsistence
– Signal of adaptation to environmental pressures are subtle, but consistent shifts in allele frequency
– Adaptation to local environ and common disease may have similar gene architecture
– Signals of climate correlations make a contribution to diseases of immune response and pigmentation traits
More information can be found at dbCLINE