On the Apportionment of Population Structure

Population history modulates the fitness effects of Copy Number Variation in the Roma

We provide the first whole genome Copy Number Variant (CNV) study addressing Roma, along with reference populations from South Asia, the Middle East and Europe. Using CNV calling software for short-read sequence data, we identified 3171 deletions and 489 duplications. Taking into account the known population history of the Roma, as inferred from whole genome nucleotide variation, we could discern how this history has shaped CNV variation. As expected, patterns of deletion variation, but not duplication, in the Roma followed those obtained from single nucleotide polymorphisms (SNPs). Reduced effective population size resulting in slightly relaxed natural selection may explain our observation of an increase in intronic (but not exonic) deletions within Loss of Function (LoF)-intolerant genes. Over-representation analysis for LoF-intolerant gene sets hosting intronic deletions highlights a substantial accumulation of shared biological processes in Roma, intriguingly related to signaling, nervous system and development features, which may be related to the known profile of private disease in the population. Finally, we show the link between deletions and known trait-related SNPs reported in the genome-wide association study (GWAS) catalog, which exhibited even frequency distributions among the studied populations. This suggests that, in general human populations, the strong association between deletions and SNPs associated to biomedical conditions and traits could be widespread across continental populations, reflecting a common background of potentially disease/trait-related CNVs.

Network-based hierarchical population structure analysis for large genomic data sets

Analysis of population structure in natural populations using genetic data is a common practice in ecological and evolutionary studies. With large genomic data sets of populations now appearing more frequently across the taxonomic spectrum, it is becoming increasingly possible to reveal many hierarchical levels of structure, including fine-scale genetic clusters. To analyze these data sets, methods need to be appropriately suited to the challenges of extracting multilevel structure from whole-genome data. Here, we present a network-based approach for constructing population structure representations from genetic data. The use of community-detection algorithms from network theory generates a natural hierarchical perspective on the representation that the method produces. The method is computationally efficient, and it requires relatively few assumptions regarding the biological processes that underlie the data. We show the approach by analyzing population structure in the model plant species Arabidopsis thaliana and in human populations. These examples illustrate how network-based approaches for population structure analysis are well-suited to extracting valuable ecological and evolutionary information in the era of large genomic data sets.

APOL1 Nephropathy: A Population Genetics and Evolutionary Medicine Detective Story

Common DNA sequence variants rarely have a high-risk association with a common disease. When such associations do occur, evolutionary forces must be sought, such as in the association of apolipoprotein L1 (APOL1) gene risk variants with nondiabetic kidney diseases in populations of African ancestry. The variants originated in West Africa and provided pathogenic resistance in the heterozygous state that led to high allele frequencies owing to an adaptive evolutionary selective sweep. However, the homozygous state is disadvantageous and is associated with a markedly increased risk of a spectrum of kidney diseases encompassing hypertension-attributed kidney disease, focal segmental glomerulosclerosis, human immunodeficiency virus nephropathy, sickle cell nephropathy, and progressive lupus nephritis. This scientific success story emerged with the help of the tools developed over the past 2 decades in human genome sequencing and population genomic databases. In this introductory article to a timely issue dedicated to illuminating progress in this area, we describe this unique population genetics and evolutionary medicine detective story. We emphasize the paradox of the inheritance mode, the missing heritability, and unresolved associations, including cardiovascular risk and diabetic nephropathy. We also highlight how genetic epidemiology elucidates mechanisms and how the principles of evolution can be used to unravel conserved pathways affected by APOL1 that may lead to novel therapies. The APOL1 gene provides a compelling example of a common variant association with common forms of nondiabetic kidney disease occurring in a continental population isolate with subsequent global admixture. Scientific collaboration using multiple experimental model systems and approaches should further clarify pathomechanisms further, leading to novel therapies.

From typical sequences to typical genotypes

We demonstrate an application of a core notion of information theory, typical sequences and their related properties, to analysis of population genetic data. Based on the asymptotic equipartition property (AEP) for nonstationary discrete-time sources producing independent symbols, we introduce the concepts of typical genotypes and population entropy and cross entropy rate. We analyze three perspectives on typical genotypes: a set perspective on the interplay of typical sets of genotypes from two populations, a geometric perspective on their structure in high dimensional space, and a statistical learning perspective on the prospects of constructing typical-set based classifiers. In particular, we show that such classifiers have a surprising resilience to noise originating from small population samples, and highlight the potential for further links between inference and communication.