Biology subjects

Mostafavi, H.

Publications and source records attributed to Mostafavi, H..

3 recordsLinked to original sources

bioRxiv2018

Measuring intolerance to mutation in human genetics

In numerous applications, from working with animal models to mapping the genetic basis of human disease susceptibility, it is useful to know whether a single disrupting mutation in a gene is likely to be deleterious1-4. With this goal in mind, a number of measures have been developed to identify genes in which protein-truncating variants (PTVs), or other types of mutations, are absent or kept at very low frequency in large population samples--genes that appear \"intolerant to mutation\"3,5-9. One measure in particular, pLI, has been widely adopted7. By contrasting the observed versus expected number of PTVs, it aims to classify genes into three categories, labelled \"null\", \"recessive\" and \"haploinsufficient\"7. Such population genetic approaches can be useful in many applications. As we clarify, however, these measures reflect the strength of selection acting on heterozygotes, and not dominance for fitness or haploinsufficiency for other phenotypes.

genetics↗

bioRxiv2018

Reduced signal for polygenic adaptation of height in UK Biobank

Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect-size estimates for height are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.

evolutionary biology↗

bioRxiv2016

Identifying genetic variants that affect viability in large cohorts

A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we find only a few common variants with large effects on age-specific mortality: tagging the APOE {varepsilon}4 allele and near CHRNA3. These results suggest that when large, even late onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence one of 42 traits, we detect a number of strong signals. In participants of the UK Biobank study of British ancestry, we find that variants that delay puberty timing are enriched in longer-lived parents (P~6x10-6 for fathers and P~2x10-3 for mothers), consistent with epidemiological studies. Similarly, in mothers, variants associated with later age at first birth are associated with a longer lifespan (P~1x10-3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease, body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. Moreover, we see marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of coronary artery disease and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical datasets can be used to learn about selection effects in contemporary humans.

evolutionary biology↗