Biology subjects

Lachance, J.

Publications and source records attributed to Lachance, J..

3 recordsLinked to original sources

bioRxiv2018

Adaptive eQTLs reveal the evolutionary impacts of pleiotropy and tissue-specificity, while contributing to health and disease in human populations

Regulatory DNA has the potential to be adaptive, and large numbers of expression quantitative trait loci (eQTLs) have recently been identified in humans. For the first time, a comprehensive study of adaptive eQTLs is possible. Many eQTLs have large allele frequency differences between populations, and these differences can be due to natural selection. Here, we combined population branch statistics with tissue-specific eQTL data to identify positively selected loci in human populations. Adaptive eQTLs tend to affect fewer tissues than non-adaptive eQTLs. Because the tissue breadth of an eQTL can be viewed as a measure of pleiotropy, these results suggest that pleiotropy can inhibit adaptation. The proportion of eQTLs that are adaptive varies by tissue, and we find that eQTLs that regulate expression in testis, thyroid, blood, or sun-exposed skin are enriched for adaptive outliers. By contrast, eQTLs that regulate expression in the cerebrum or female-specific tissues have a relative lack of adaptive outliers. These results reveal tissues that have been the targets of adaptation during the last 100,000 years of human evolution. The strongest adaptive signal in many regions of the human genome is an eQTL, including an eQTL associated with the Duffy blood group and malaria resistance. Scans of selection also reveal that many adaptive eQTLs are closely linked to disease-associated loci. Taken together, our results indicate that adaptive eQTLs have played an important role in human evolution.

genomics↗

bioRxiv2017

Ascertainment bias can create the illusion of genetic health disparities

BackgroundAccurate 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 GWAS, and extensive computer simulations to identify how genetic disease risks can be misestimated.\n\nResultsIn contrast to null expectations, we find that risk allele frequencies at known disease loci are significantly different for African populations compared to other continents. Strikingly, ancestral risk alleles are found at 9.51% higher frequency in Africa and derived risk alleles are found at 5.40% lower frequency in Africa. By simulating GWAS with different study populations, we find that non-African cohorts yield disease associations that have biased allele frequencies and that African cohorts yield disease associations that are relatively free of bias. We also find empirical evidence that genotyping arrays and SNP ascertainment bias contribute to continental differences in risk allele frequencies. Because of these causes, polygenic risk scores can be grossly misestimated for individuals of African descent. Importantly, continental differences in risk allele frequencies are only moderately reduced if GWAS use whole genome sequences and hundreds of thousands of cases and controls. Finally, comparisons between uncorrected and corrected genetic risk scores reveal the benefits of considering whether risk alleles are ancestral or derived.\n\nConclusionsOur results imply that caution must be taken when extrapolating GWAS results from one population to predict disease risks in another population.

genomics↗

bioRxiv2017

The Genomic Health Of Ancient Hominins

The genomes of ancient humans, Neandertals, and Denisovans contain many alleles that influence disease risks. Using genotypes at 3180 disease-associated loci, we estimated the disease burden of 147 ancient genomes. After correcting for missing data, genetic risk scores were generated for nine disease categories and the set of all combined diseases. These genetic risk scores were used to examine the effects of different types of subsistence, geography, and sample age on the number of risk alleles in each ancient genome. On a broad scale, hereditary disease risks are similar for ancient hominins and modern-day humans, and the GRS percentiles of ancient individuals span the full range of what is observed in present day individuals. In addition, there is evidence that ancient pastoralists may have had healthier genomes than hunter-gatherers and agriculturalists. We also observed a temporal trend whereby genomes from the recent past are more likely to be healthier than genomes from the deep past. This calls into question the idea that modern lifestyles have caused genetic load to increase over time. Focusing on individual genomes, we find that the overall genomic health of the Altai Neandertal is worse than 97% of present day humans and that Otzi the Tyrolean Iceman had a genetic predisposition to gastrointestinal and cardiovascular diseases. As demonstrated by this work, ancient genomes afford us new opportunities to diagnose past human health, which has previously been limited by the quality and completeness of remains.

genomics↗