Publisher Correction: Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

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Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

Mendelian randomization (MR) is a valuable tool for detecting causal effects by using genetic variant associations. Opportunities to apply MR are growing rapidly with the increasing number of genome-wide association studies (GWAS). However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Correlated horizontal pleiotropy, which arises when variants affect both traits through a heritable shared factor, remains a particularly challenging problem. We propose a new MR method, Causal Analysis Using Summary Effect estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate, in simulations, that CAUSE avoids more false positives induced by correlated horizontal pleiotropy than other methods. Applied to traits studied in recent GWAS studies, we find that CAUSE detects causal relationships that have strong literature support and avoids identifying most unlikely relationships. Our results suggest that shared heritable factors are common and may lead to many false positives using alternative methods.

Genetic history from the Middle Neolithic to present on the Mediterranean island of Sardinia.

The island of Sardinia has been of particular interest to geneticists for decades. The current model for Sardinia's genetic history describes the island as harboring a founder population that was established largely from the Neolithic peoples of southern Europe and remained isolated from later Bronze Age expansions on the mainland. To evaluate this model, we generate genome-wide ancient DNA data for 70 individuals from 21 Sardinian archaeological sites spanning the Middle Neolithic through the Medieval period. The earliest individuals show a strong affinity to western Mediterranean Neolithic populations, followed by an extended period of genetic continuity on the island through the Nuragic period (second millennium BCE). Beginning with individuals from Phoenician/Punic sites (first millennium BCE), we observe spatially-varying signals of admixture with sources principally from the eastern and northern Mediterranean. Overall, our analysis sheds light on the genetic history of Sardinia, revealing how relationships to mainland populations shifted over time.

Genomic history of the Sardinian population.

The population of the Mediterranean island of Sardinia has made important contributions to genome-wide association studies of complex disease traits and, based on ancient DNA studies of mainland Europe, Sardinia is hypothesized to be a unique refuge for early Neolithic ancestry. To provide new insights on the genetic history of this flagship population, we analyzed 3,514 whole-genome sequenced individuals from Sardinia. Sardinian samples show elevated levels of shared ancestry with Basque individuals, especially samples from the more historically isolated regions of Sardinia. Our analysis also uniquely illuminates how levels of genetic similarity with mainland ancient DNA samples varies subtly across the island. Together, our results indicate that within-island substructure and sex-biased processes have substantially impacted the genetic history of Sardinia. These results give new insight into the demography of ancestral Sardinians and help further the understanding of sharing of disease risk alleles between Sardinia and mainland populations.

Visualizing the geography of genetic variants.

Summary: One of the key characteristics of any genetic variant is its geographic distribution. The geographic distribution can shed light on where an allele first arose, what populations it has spread to, and in turn on how migration, genetic drift, and natural selection have acted. The geographic distribution of a genetic variant can also be of great utility for medical/clinical geneticists and collectively many genetic variants can reveal population structure. Here we develop an interactive visualization tool for rapidly displaying the geographic distribution of genetic variants. Through a REST API and dynamic front-end, the Geography of Genetic Variants (GGV) browser ( http://popgen.uchicago.edu/ggv/ ) provides maps of allele frequencies in populations distributed across the globe. Availability and Implementation: GGV is implemented as a website ( http://popgen.uchicago.edu/ggv/ ) which employs an API to access frequency data ( http://popgen.uchicago.edu/freq_api/ ). Python and javascript source code for the website and the API are available at: http://github.com/NovembreLab/ggv/ and http://github.com/NovembreLab/ggv-api/ . Contact: jnovembre@uchicago.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Height-reducing variants and selection for short stature in Sardinia.

We report sequencing-based whole-genome association analyses to evaluate the impact of rare and founder variants on stature in 6,307 individuals on the island of Sardinia. We identify two variants with large effects. One variant, which introduces a stop codon in the GHR gene, is relatively frequent in Sardinia (0.87% versus <0.01% elsewhere) and in the homozygous state causes Laron syndrome involving short stature. We find that this variant reduces height in heterozygotes by an average of 4.2 cm (-0.64 s.d.). The other variant, in the imprinted KCNQ1 gene (minor allele frequency (MAF) = 7.7% in Sardinia versus <1% elsewhere) reduces height by an average of 1.83 cm (-0.31 s.d.) when maternally inherited. Additionally, polygenic scores indicate that known height-decreasing alleles are at systematically higher frequencies in Sardinians than would be expected by genetic drift. The findings are consistent with selection for shorter stature in Sardinia and a suggestive human example of the proposed 'island effect' reducing the size of large mammals.

A tortoise-hare pattern seen in adapting structured and unstructured populations suggests a rugged fitness landscape in bacteria.

In the context of Wright's adaptive landscape, genetic epistasis can yield a multipeaked or "rugged" topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to fix rapidly on one, which may not be the highest peak. In a spatially structured population, on the other hand, beneficial mutations take longer to spread. This slowdown allows distant parts of the population to explore the landscape semiindependently. Such a population can simultaneously discover multiple peaks, and the genotype at the highest discovered peak is expected to dominate eventually. Thus, structured populations sacrifice initial speed of adaptation for breadth of search. As in the fable of the tortoise and the hare, the structured population (tortoise) starts relatively slow but eventually surpasses the unstructured population (hare) in average fitness. In contrast, on single-peak landscapes that lack epistasis, all uphill paths converge. Given such "smooth" topography, breadth of search is devalued and a structured population only lags behind an unstructured population in average fitness (ultimately converging). Thus, the tortoise-hare pattern is an indicator of ruggedness. After verifying these predictions in simulated populations where ruggedness is manipulable, we explore average fitness in metapopulations of Escherichia coli. Consistent with a rugged landscape topography, we find a tortoise-hare pattern. Further, we find that structured populations accumulate more mutations, suggesting that distant peaks are higher. This approach can be used to unveil landscape topography in other systems, and we discuss its application for antibiotic resistance, engineering problems, and elements of Wright's shifting balance process.