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How well do we understand the basis of classic selective sweeps in humans?

Szpak M, Xue Y, Ayub Q, Tyler-Smith C

FEBS Lett., 2019 07;593(13):1431-1448.
PMID: 31116407 DOI: 10.1002/1873-3468.13447

Classic selective sweeps occur when positive selection increases a variant's frequency from low to high in a population, and underlie some long-studied human characteristics such as variation in skin, hair or eye colour. In such well-studied 'gold standard' examples, a known variant has been associated with a plausible phenotype and underlying selective force. Signatures of classic sweeps have more recently been detected in population-genetic data independently of any prior information about the corresponding phenotype or selective force, and usually without suggesting any insights into these. Motivated by the need to understand such candidates, we first review the gold standards and show that our understanding of them is often incomplete or unconvincing; only two of the examples we consider are compellingly explained. We assess approaches for large-scale association of classic sweep candidate variants to phenotypes and selective forces, test these on the gold standards, and discuss the standards of evidence needed to adequately understand a selective sweep.
Fulltext Prioritising positively selected variants in whole-genome sequencing data using FineMAV

Wahyudi F, Aghakhanian F, Rahman S, Teo YY, Szpak M, Dhaliwal J, et al.

BMC Bioinformatics, 2021 Dec 18;22(1):604.
PMID: 34922440 DOI: 10.1186/s12859-021-04506-9

BACKGROUND: In population genomics, polymorphisms that are highly differentiated between geographically separated populations are often suggestive of Darwinian positive selection. Genomic scans have highlighted several such regions in African and non-African populations, but only a handful of these have functional data that clearly associates candidate variations driving the selection process. Fine-Mapping of Adaptive Variation (FineMAV) was developed to address this in a high-throughput manner using population based whole-genome sequences generated by the 1000 Genomes Project. It pinpoints positively selected genetic variants in sequencing data by prioritizing high frequency, population-specific and functional derived alleles.
RESULTS: We developed a stand-alone software that implements the FineMAV statistic. To graphically visualise the FineMAV scores, it outputs the statistics as bigWig files, which is a common file format supported by many genome browsers. It is available as a command-line and graphical user interface. The software was tested by replicating the FineMAV scores obtained using 1000 Genomes Project African, European, East and South Asian populations and subsequently applied to whole-genome sequencing datasets from Singapore and China to highlight population specific variants that can be subsequently modelled. The software tool is publicly available at https://github.com/fadilla-wahyudi/finemav .
CONCLUSIONS: The software tool described here determines genome-wide FineMAV scores, using low or high-coverage whole-genome sequencing datasets, that can be used to prioritize a list of population specific, highly differentiated candidate variants for in vitro or in vivo functional screens. The tool displays these scores on the human genome browsers for easy visualisation, annotation and comparison between different genomic regions in worldwide human populations.
Fulltext FineMAV: prioritizing candidate genetic variants driving local adaptations in human populations

Szpak M, Mezzavilla M, Ayub Q, Chen Y, Xue Y, Tyler-Smith C

Genome Biol, 2018 Jan 17;19(1):5.
PMID: 29343290 DOI: 10.1186/s13059-017-1380-2

We present a new method, Fine-Mapping of Adaptive Variation (FineMAV), which combines population differentiation, derived allele frequency, and molecular functionality to prioritize positively selected candidate variants for functional follow-up. We calibrate and test FineMAV using eight experimentally validated "gold standard" positively selected variants and simulations. FineMAV has good sensitivity and a low false discovery rate. Applying FineMAV to the 1000 Genomes Project Phase 3 SNP dataset, we report many novel selected variants, including ones in TGM3 and PRSS53 associated with hair phenotypes that we validate using available independent data. FineMAV is widely applicable to sequence data from both human and other species.
Fulltext A Positively Selected MAGEE2 LoF Allele Is Associated with Sexual Dimorphism in Human Brain Size and Shows Similar Phenotypes in Magee2 Null Mice

Szpak M, Collins SC, Li Y, Liu X, Ayub Q, Fischer MC, et al.

Mol Biol Evol, 2021 Dec 09;38(12):5655-5663.
PMID: 34464968 DOI: 10.1093/molbev/msab243

A nonsense allele at rs1343879 in human MAGEE2 on chromosome X has previously been reported as a strong candidate for positive selection in East Asia. This premature stop codon causing ∼80% protein truncation is characterized by a striking geographical pattern of high population differentiation: common in Asia and the Americas (up to 84% in the 1000 Genomes Project East Asians) but rare elsewhere. Here, we generated a Magee2 mouse knockout mimicking the human loss-of-function mutation to study its functional consequences. The Magee2 null mice did not exhibit gross abnormalities apart from enlarged brain structures (13% increased total brain area, P = 0.0022) in hemizygous males. The area of the granular retrosplenial cortex responsible for memory, navigation, and spatial information processing was the most severely affected, exhibiting an enlargement of 34% (P = 3.4×10-6). The brain size in homozygous females showed the opposite trend of reduced brain size, although this did not reach statistical significance. With these insights, we performed human association analyses between brain size measurements and rs1343879 genotypes in 141 Chinese volunteers with brain MRI scans, replicating the sexual dimorphism seen in the knockout mouse model. The derived stop gain allele was significantly associated with a larger volume of gray matter in males (P = 0.00094), and smaller volumes of gray (P = 0.00021) and white (P = 0.0015) matter in females. It is unclear whether or not the observed neuroanatomical phenotypes affect behavior or cognition, but it might have been the driving force underlying the positive selection in humans.