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Biology subjects

Ceballos, F. C.

Publications and source records attributed to Ceballos, F. C..

3 recordsLinked to original sources

Human inbreeding has decreased in time through the Holocene

The history of human inbreeding is controversial. The development of sedentary agricultural societies may have had opposite influences on inbreeding levels. On the one hand, agriculture and food surplus may have diminished inbreeding by increasing population sizes and lowering endogamy, i.e. inbreeding due to population isolation. On the other hand, increased sedentism, as well as the advent of private property may have promoted inbreeding through the emergence of consanguineous marriage customs or via ethnic and caste endogamy. The net impact is unknown, and to date, no systematic study on the temporal frequency of inbreeding in human societies has been conducted. Here we present a new approach for reliable estimation of runs of homozygosity (ROH) in genomes with [≥]3x mean coverage across >1 million SNPs, and apply this to 440 ancient Eurasian genomes from the last 15,000 years. We show that the frequency of inbreeding, as measured by ROH, has decreased over time. The strongest effect is associated with the Neolithic transition, but the trend has since continued, indicating a population size effect on inbreeding prevalence. We further show that most inbreeding in our historical sample can be attributed to endogamy, although singular cases of high consanguinity can also be found in the archaeogenomic record. HighlightsA study of 440 ancient genomes shows inbreeding decreased over time. The decrease appears linked with population size increase due to farming. Extreme consanguineous matings did occur among farmers, but rarely.

genomics↗

Runs of Homozygosity in sub-Saharan African populations provide insights into a complex demographic and health history

The study of runs of homozygosity (ROH), contiguous regions in the genome where an individual is homozygous across all sites, can shed light on the demographic history and cultural practices. We present a fine-scale ROH analysis of 1679 individuals from 28 sub-Saharan African (SSA) populations along with 1384 individuals from 17 world-wide populations. Using high-density SNP coverage, we could accurately obtain ROH as low as 300Kb using PLINK software. The analyses showed a heterogeneous distribution of autozygosity across SSA, revealing a complex demographic history. They highlight differences between African groups and can differentiate between the impact of consanguineous practices (e.g. among the Somali) and endogamy (e.g. among several Khoe-San groups1). The genomic distribution of ROH was analysed through the identification of ROH islands and regions of heterozygosity (RHZ). These homozygosity cold and hotspots harbour multiple protein coding genes. Studying ROH therefore not only sheds light on population history, but can also be used to study genetic variation related to the health of extant populations.

genomics↗

Assessing Runs of Homozygosity: A comparison of SNP Array and Whole Genome Sequence low coverage data

Runs of Homozygosity (ROH) are sequences that arise when identical haplotypes are inherited from each parent. Since their first detection due to technological advances in the late 1990s, ROHs have been shedding light on human population history and deciphering the genetic basis of monogenic and complex traits and diseases. ROH studies have predominantly exploited SNP array data, but are gradually moving to whole genome sequence (WGS) data as it becomes available. WGS data, covering more genetic variability, can add value to ROH studies, but require additional considerations during analysis. Using SNP array and low coverage WGS data from 1885 individuals from 20 world populations, our aims were to compare ROH from the two datasets and to establish software conditions to get comparable results, thus providing guidelines for combining disparate datasets in joint ROH analyses. Using the PLINK Homozygosity functions, we found that by allowing 3 heterozygous SNPs per window when dealing with WGS low coverage data, it is possible to establish meaningful comparisons between data using the two technologies.

genomics↗