Biology subjects

Nick J Patterson

Publications and source records attributed to Nick J Patterson.

3 recordsLinked to original sources

bioRxiv2016

Population structure of UK Biobank and ancient Eurasians reveals adaptation at genes influencing blood pressure

Analyzing genetic differences between closely related populations can be a powerful way to detect recent adaptation. The very large sample size of the UK Biobank is ideal for detecting selection using population differentiation, and enables an analysis of UK population structure at fine resolution. In analyses of 113,851 UK Biobank samples, population structure in the UK is dominated by 5 principal components (PCs) spanning 6 clusters: Northern Ireland, Scotland, northern England, southern England, and two Welsh clusters. Analyses with ancient Eurasians show that populations in the northern UK have higher levels of Steppe ancestry, and that UK population structure cannot be explained as a simple mixture of Celts and Saxons. A scan for unusual population differentiation along top PCs identified a genome-wide significant signal of selection at the coding variant rs601338 in FUT2 (p = 9.16 x 10-9). In addition, by combining evidence of unusual differentiation within the UK with evidence from ancient Eurasians, we identified new genome-wide significant (p < 5 x 10-8) signals of recent selection at two additional loci: CYP1A2/CSK and F12. We detected strong associations to diastolic blood pressure in the UK Biobank for the variants with new selection signals at CYP1A2/CSK (p = 1.10 x 10-19)) and for variants with ancient Eurasian selection signals in the ATXN2/SH2B3 locus (p = 8.00 x 10-33), implicating recent adaptation related to blood pressure.

Genetics↗

bioRxiv2015

Dating ancient human samples using the recombination clock

The study of human evolution has been revolutionized by inferences from ancient DNA analyses. Key to these is the reliable estimation of the age of ancient specimens. The current best practice is radiocarbon dating, which relies on characterizing the decay of radioactive carbon isotope (14C), and is applicable for dating up to 50,000-year-old samples. Here, we introduce a new genetic method that uses recombination clock for dating. The key idea is that an ancient genome has evolved less than the genomes of extant individuals. Thus, given a molecular clock provided by the steady accumulation of recombination events, one can infer the age of the ancient genome based on the number of missing years of evolution. To implement this idea, we take advantage of the shared history of Neanderthal gene flow into non-Africans that occurred around 50,000 years ago. Using the Neanderthal ancestry decay patterns, we estimate the Neanderthal admixture time for both ancient and extant samples. The difference in these admixture dates then provides an estimate of the age of the ancient genome. We show that our method provides reliable results in simulations. We apply our method to date five ancient Eurasian genomes with radiocarbon dates ranging between 12,000 to 45,000 years and recover consistent age estimates. Our method provides a complementary approach for dating ancient human samples and is applicable to ancient non-African genomes with Neanderthal ancestry. Extensions of this methodology that use older shared events may be able to date ancient genomes that fall beyond the radiocarbon frontier.\n\nSignificanceWe introduce a new genetic method for dating ancient human samples that uses the recombination clock. The main idea relies on the insight that an ancient genome lacks several thousand years of evolution compared to genomes of living individuals. To infer the age of ancient genomes, we take advantage of the shared history of Neanderthal gene flow into non-Africans that occurred around 50,000 years ago. By characterizing the dates of Neanderthal gene flow in ancient and extant genomes and quantifying the difference in these dates, we estimate the age of the ancient specimen. Our method is applicable for dating ancient samples more recent than the Neanderthal mixture event, so on par with radiocarbon dating, providing a complementary approach for dating.

Evolutionary Biology↗

bioRxiv2015

Fast principal components analysis reveals convergent evolution of ADH1B gene in Europe and East Asia

Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large data sets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at the ADH1B gene. The coding variant rs1229984*T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect new selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.

Bioinformatics↗