Congratulations to the winners of the Editors’ Choice Awards for outstanding articles published in GENETICS in 2021! The journal’s Editorial Board considered a diverse range of articles, finding many papers worthy of recognition. After much deliberation, they settled on one exceptional article for each of the three award categories: molecular genetics, population and evolutionary genetics, and quantitative genetics. Check out some of the best GENETICS had to offer in 2021, and be sure to browse the full Spotlight collection.

EDITORS’ CHOICE AWARD IN MOLECULAR GENETICS

Neurogenesis in the adult Drosophila brain

Kassi L Crocker, Khailee Marischuk, Stacey A Rimkus, Hong Zhou, Jerry C P Yin, Grace Boekhoff-Falk

GENETICS Oct 2021, 219(2), iyab092, https://doi.org/10.1093/genetics/iyab092

Crocker et al. describe the Drosophila central brain as a new model in which to investigate adult neurogenesis. The authors observe a significant increase in the number of proliferating cells following injury; they detect new glia, new neurons, and the formation of new axon tracts that target appropriate brain regions. The authors anticipate that this paradigm will facilitate the dissection of the mechanisms of neural regeneration and that these processes will be relevant to human brain repair.

EDITORS’ CHOICE AWARD IN POPULATION AND EVOLUTIONARY GENETICS

The timing of human adaptation from Neanderthal introgression

Sivan Yair, Kristin M Lee, Graham Coop

GENETICS May 2021, 218(1), iyab052, https://doi.org/10.1093/genetics/iyab052

Some Neanderthal-introgressed alleles in modern human populations were adaptive; however, the context in which they provided a fitness advantage is unknown. Yair, Lee, and Coop develop a population genetic method that uses ancient DNA and the hitchhiking effect to determine when natural selection favored the spread of Neanderthal-introgressed alleles. They identify regions of the genome in which Neanderthal alleles were immediately adaptive and others in which there was a significant time lag between admixture and the allele’s rise in frequency.

EDITORS’ CHOICE AWARD IN QUANTITATIVE GENETICS

Why genetic selection to reduce the prevalence of infectious diseases is way more promising than currently believed

Andries D Hulst, Mart C M de Jong, Piter Bijma

GENETICS April 2021, 217(4), iyab024, https://doi.org/10.1093/genetics/iyab024

Quantitative genetic analyses of binary disease status indicate low heritability for most infectious diseases, suggesting that the potential response to selection in disease prevalence is limited. By integration of quantitative genetics with epidemiological models, Hulst, de Jong, and Bijma show that the typical low heritability values of disease status correspond to a substantial genetic variation in disease susceptibility and to a large potential response to selection. Positive feedback mechanisms occurring in disease transmission are crucial for this response and even make eradication of infectious diseases possible. However, current quantitative genetic models ignore these feedback effects and thereby underestimate response to selection in disease prevalence.

Machine learning and access to ever-expanding databases improves genomic prediction of human traits.

In theory, a scientist could predict your height using just your genome sequence. In practice, though, this is still the stuff of science fiction. It’s not only your genes that affect height—environment also plays a role—but the larger problem is that height is affected by tens of thousands of individual genetic variations. This is also true of other complex traits, such as susceptibility to particular diseases. To get closer to accurate genomic prediction of human traits, geneticists are using new approaches to harness the vast amounts of sequence data becoming available. In GENETICS, Lello et al. describe a machine learning approach to the problem that allowed them to make predictions within a few centimeters of reality.

“To me, genomic prediction is the actual decoding of the genome,” says senior author Stephen Hsu from Michigan State University. A theoretical physicist by training, Hsu explains that his lab became interested in the problem of genomic prediction several years ago as the cost of genotyping continued to drop and more datasets became available. They had previously argued that they could predict complex traits, like height, if they only had enough data.The release of nearly 500,000 UK Biobank genotypes allowed them an opportunity to test this hypothesis.

A genomic prediction approach is quite different from the more familiar genome-wide association study (GWAS). GWAS methods test each SNP one at a time, looking for statistically significant contributions to the phenotype. In contrast, genomic prediction makes use of all SNPs at once in trying to build the best possible predictors.

The authors took the Biobank genotype and phenotype data and used a type of regression to identify the combination of SNPs that, taken together, best correlate with the trait of interest. Since only a subset of SNPs influence each trait—even the thousands of loci that control height are only a tiny fraction of the total number of SNPs identified —they also introduced a penalization factor that prevents the model from including unneeded SNPs. They were essentially trying to solve an optimization problem: identify the fewest number of variables (i.e. SNPs) that will allow for the best prediction about the outcome (i.e. trait).

Having generated their algorithm, the authors then put it to the test. They constructed models for height, heel bone density, and educational attainment, and they found that their algorithm worked well, particularly for height. For example, it produced a nearly 0.65 correlation with actual height, and predicted heights were usually within a few centimeters of actual heights. “Our predictor actually captures almost all the heritability that we could expect to find,” says Hsu.

With enough data, Hsu believes, accurate genomic prediction for complex traits will no longer be sci-fi. As more and more genotypes are obtained, Hsu predicts that this kind of prediction could be applied for most traits in as little as five years.

CITATION:

Accurate Genomic Prediction of Human Height

Louis Lello, Steven G. Avery, Laurent Tellier, Ana I. Vazquez, Gustavo de los Campos, Stephen D. H. Hsu

Genetics October 2018 210: 477-497; https://doi.org/10.1534/genetics.118.301267

http://www.genetics.org/content/210/2/477

Study of huge Ancestry.com pedigree suggests assortative mating may have inflated previous estimates of life span heritability.

Although long life tends to run in families, genetics has far less influence on life span than previously estimated, according to a new analysis published in GENETICS.  Ruby et al. used a data set of over 400 million historical persons obtained from public pedigrees on Ancestry.com to estimate the heritability of life span, finding it to be well below 10%.

“We can potentially learn many things about the biology of aging from human genetics, but if the heritability of life span is low it tempers our expectations about what types of things we can learn and how easy it will be,” says lead author Graham Ruby (Calico Life Sciences). “It helps contextualize the questions that scientists studying aging can effectively ask.”

Calico Life Sciences is a research and development company whose mission is to understand the fundamental science of aging. So how did Calico get involved with Ancestry, the online genealogy resource?

“We wanted to get a sense for the contribution of genetics to life span, and that’s something you can study using pedigrees,” says Ruby. With millions of members, Ancestry has no shortage of pedigrees.

Fortuitously, researchers at Calico and Ancestry were connected from their time in academic basic research. Calico’s Chief Scientific Officer David Botstein and Ancestry’s Chief Scientific Officer Catherine Ball (senior author on the GENETICS paper) both have backgrounds in yeast research. They were involved in the Saccharomyces Genome Database project during their times at Stanford University and published a number of papers together.

So researchers from both companies teamed up to use publicly available pedigree data from Ancestry.com to approach the problem of figuring out the genetic contributions to human longevity.

“Partnering with Ancestry allowed this new study to gain deeper insights by using a much larger data set than any previous studies of longevity,” says Ball.

The heritability of life span has been well investigated in the literature, with previous estimates ranging around 15-30%.

But some of these studies found that it wasn’t just blood relatives who shared similar life spans—so did spouses. This suggested that the heritability estimates might have been confounded by shared environments or assortative mating (the tendency to choose mates who have similar traits to ourselves).

The new study had the power to investigate these possibilities in more detail because of the large size and high quality of the data. The data set, called the SAP for “set of aggregated and anonymized pedigrees,” was constructed from Ancestry.com pedigrees with the help of source references like birth certificates.

Starting from 54 million subscriber-generated public family trees representing six billion ancestors, Ancestry removed redundant entries and those from people who were still living, stitching the remaining pedigrees together. Before sharing the data with the Calico research team, Ancestry stripped away all identifiable information from the pedigrees, leaving only the year of birth, year of death, place of birth (to the resolution of state within the US and country outside the US), and familial connections that make up the tree structure itself.

The SAP included almost 500 million individuals (with a single pedigree accounting for over 400 million people), largely Americans of European descent, each connected to another by either a parent-child or a spouse-spouse relationship. The scale of the data allowed the researchers to get accurate heritability estimates across different contexts; they could stratify the data by birth cohort or by sex or by other variables without losing the power needed for their analyses. They employed structural equation modeling—a technique that hasn’t often been applied to this problem due to the amount of data required for it to be productive—to calculate life span correlations and heritability across the giant pedigree.

Running the numbers, the team initially found heritability estimates to be between 15-30%—similar to the reported literature.

“But then I did correlations between first cousins-in-law, and their life spans didn’t correlate as much—but it was close,” says Ruby.

When a trait correlates between in-laws similarly to blood relatives, that can mean that something besides genetics is being shared across households. The term heritability describes the proportion of trait variability in a population that can be attributed to genetic differences. But genetics aren’t the only thing that can be passed down between generations: sociocultural factors can also influence certain traits, and these too can be inherited. The combination of genetic heritability and sociocultural heritability is the total transferred variance, that is, the total amount of variability in a trait that can be explained by inheritance.

“The comparison between in-law relatives is something that hadn’t been as thoroughly explored in the prior literature. With this large dataset, we could look at in-law relatives at a large scale and be confident that they weren’t that far off from blood relatives,” says Ruby.

The scale of the data allowed Ruby and colleagues to look not only at siblings-in-law and first cousins-in-law but also to examine correlation in both types of co-siblings-in-law (your sibling’s spouse’s sibling or your spouse’s sibling’s spouse). None of these relationship types generally share household environments, and yet their life spans showed correlation.

If they don’t share genetic information and they don’t share household environment, what accounts for the similarity in life span between individuals within these relationship types? Going back to their impressive dataset, the researchers were able to perform analyses that detected assortative mating.

“What assortative mating means here is that the factors that are important for life span tend to be very similar between mates,” says Ruby. In other words, people tend to select partners with traits like their own—in this case, how long they live.

Of course, you can’t easily guess the longevity of a potential mate. “Generally, people get married before either one of them has died,” jokes Ruby. Because you can’t tell someone’s life span in advance, assortative mating in humans must be based on other characteristics.

The basis of this mate choice could be genetic or sociocultural—or both. For a non-genetic example, if income influences life span, and wealthy people tend to marry other wealthy people, that would lead to correlated longevity. The same would occur for traits more controlled by genetics: if, for example, tall people prefer tall spouses, and height is correlated in some way with how long you live, this would also inflate estimates of life span heritability.

By correcting for these effects of assortative mating, the new analysis found life span heritability is likely no more than seven percent, perhaps even lower.

The upshot? How long you live has less to do with your genes than you might think.

CITATION

Estimates of the Heritability of Human Longevity Are Substantially Inflated due to Assortative Mating
J. Graham Ruby, Kevin M. Wright, Kristin A. Rand, Amir Kermany, Keith Noto, Don Curtis, Neal Varner, Daniel Garrigan, Dmitri Slinkov, Ilya Dorfman, Julie M. Granka, Jake Byrnes, Natalie Myres, and Catherine Ball.
GENETICS November 2018. 210(3): 1109-1124.
http://www.genetics.org/content/210/3/1109
DOI: 10.1534/genetics.118.301613

Watch presentations from the conference, including talks from Katie Peichel and Jonathan Pritchard.

Now that the dust has settled from the whirlwind of the first ever standalone GSA Population, Evolutionary, and Quantitative Genetics Conference (PEQG18), we’re delighted to be able to share the audio and synched slides from the Keynote and Crow Award sessions.

We’re gratified too that attendees got so much of value from the conference. Many have approached GSA staff and the conference organizers with rave reviews of their experience, and, despite the usual growing pains of a new conference, the results from the attendee survey have also been overwhelmingly positive.

We’re excited to incorporate some of the lessons we’ve learned into planning the next PEQG. It will be held April 22–26, 2020 in the metro Washington, DC, area at The Allied Genetics Conference (TAGC20). PEQG will join the C. elegans, Drosophila, mouse, Xenopus, yeast, and zebrafish research communities for a mix of community-specific and cross-community sessions.

Stay tuned for more announcements on the upcoming conference and for several more PEQG18 blog reports in the coming weeks. Enjoy the talks below!

PEQG18 Keynotes

Jonathan Pritchard Stanford University/HHMI

Omnigenic Architecture of Human Complex Traits

Catherine Peichel University of Bern

Genetics of Adaptation in Sticklebacks

Trudy Mackay North Carolina State University

Context-Dependent Effects of Alleles Affecting Genetic Variation of Quantitative Traits COMING SOON

Finalists for the 2018 Crow Award for Early Career Researchers

Amy Goldberg UC Berkeley

A mechanistic model of assortative mating in a hybrid population

Emily Josephs UC Davis

Detecting polygenic adaptation in maize

Jeremy Berg Columbia University 

Population genetic models for highly polygenic disease

Katherine Xue University of Washington 

Evolutionary dynamics of influenza across spatiotemporal scales

Alison Feder Stanford University 

Intra-patient evolutionary dynamics of HIV drug resistance evolution in time and space

Emily Moore North Carolina State University 

Genetic variation at a conserved non-coding element contributes to microhabitat-associated behavioral differentiation in Malawi African cichlid fishes

Videos

Jonathan Pritchard 

[youtube https://youtu.be/H18k55ruCOY&w=500&rel=0]

Catherine Peichel

[youtube https://youtu.be/QRCcLixjUtc&w=500&rel=0]

Amy Goldberg 

[youtube https://youtu.be/kccUNkF7SgY&w=500&rel=0]

Emily Josephs 

[youtube https://youtu.be/CxQOrK9h6D4&w=500&rel=0]

Jeremy Berg

[youtube https://youtu.be/HqA1H24LPZc&w=500&rel=0]

Katherine Xue

[youtube https://youtu.be/fTdaAwqdt0k&w=500&rel=0]

Alison Feder

[youtube https://youtu.be/ntM0448h2lA&w=500&rel=0]

Emily Moore

[youtube https://youtu.be/aX4_HS0K1kA&w=500&rel=0]

Archaeologists have long known how to extract millennia-old stories from a single tooth buried in an ancient ruin—and now geneticists have the tools to join them. Advances made in the last several years have enabled researchers to sequence tiny amounts of DNA preserved in very old specimens, such as the material inside a tooth from the Stone Age. But this ancient DNA (aDNA) is often severely degraded, limiting its use. In GENETICS, Joshua Schraiber describes a new statistical approach to getting the most from these old samples and reports how he used the method to uncover secrets about the relationships between ancient humans and modern ones.

A major obstacle to understanding humans’ recent evolutionary history has been the inability to infer much about it using genetic data from people living today. If restricted to data from modern people, we would be locked out of information of great scientific and cultural relevance. The genetic relationships between ancient and modern populations can provide clues about migrations that occurred thousands or tens of thousands of years ago and help us better understand our histories. For example, researchers recently found that many people living in South America today are in part descended from an ancient North American group called the Clovis people.

Solving these kinds of puzzles is where aDNA shines—if you know how to use it. Schraiber found that the best way to determine genetic relationships among modern and ancient populations using degraded samples of aDNA is to sequence multiple ancient samples at low coverage rather than fewer samples at high coverage. After applying his new method to existing genetic data from 230 West Eurasian people who lived 8500–2300 years ago, Schraiber discovered that none of them came from populations that are direct ancestors of any modern European populations tested.

Schraiber’s analysis also suggests many ancient European people separated into small populations with little gene flow among them, and that most of these local groups died out, leaving a limited genetic legacy in modern European people. His results further imply that the oldest populations were the smallest, although this must be tested further because of the complicated nature of drift time, one of the parameters used in the analysis. Since drift time also complicates other types of analysis, Schraiber anticipates that methods to fully account for the troublesome variable will allow development of a fuller picture of these results.

If validated, the increase in effective population size over time predicted by Schraiber’s method would be interesting to compare to archaeological information about ancient humans, especially because many current ideas hinge on a link between agriculture and the rise of larger, more interconnected societies. Perhaps new scientific evidence would put to rest some debates about ancient humans’ lives—but it would surely spawn even more questions, too.

CITATION:

Joshua G. Schraiber. Assessing the Relationship of Ancient and Modern Populations.
GENETICS, 208(1), 383-398.
DOI: 10.1534/genetics.117.300448
http://www.genetics.org/content/208/1/383

Over three centuries, as many as a million enslaved people were shipped to the Colombian port of Cartagena. From this hub of the slave trade, European colonists took Africans to labor in many places across the Americas, including the gold mines of the Chocó region. Today, people from Chocó often proudly identify as Afro-Colombian, while those from the nearby city of Medellín largely identify with their European ancestry. Despite the differences between these groups, research by Conley et al. in the October issue of G3 highlights a subtle underlying unity.

Like much of Latin America, Colombian people have a blend of African, European, and Native American ancestry. Previously, studies on Colombian genetic ancestry have focused on the Native American and European roots with less evaluation of African heritage, despite the fact that Colombia has the second greatest population of Afro-descendants in Latin America. Conley et al. set out to provide a detailed genetic picture of the Chocó region’s ancestry—and to compare it to other admixed Colombian populations.

The authors recruited 100 individuals from Chocó and compared their DNA to known African, European, and Native American reference populations. They also compared the Chocó to people from Medellín.

Chocó genetic ancestry is largely African (76%) with roughly equal amounts of European (13%) and Native American (11%) heritage. In contrast, Medellín genetic ancestry is predominately European (75%), with 18% Native American and 7% African contributions. These results largely agreed with the self-identification of these groups and with the known history of the areas, although Chocó individuals tended to assume a higher percentage of African ancestry than genetics suggested while those from Medellín assumed a higher percentage of European ancestry.

But while the continental spread of genetic ancestry was distinct between the two groups, when each contribution was broken down to the subcontinental level, the groups were much more similar. Both share European ancestry that is largely Spanish and Native American ancestry that appears most related to the Embera, Waunana, Arhuaco, Kogi, and Wayuu populations. The Chocó are slightly more similar to the modern-day Yoruba, while the Medellín are more West African; still, the overall subcontinental African identities are comparable. In fact, at the subcontinental level, the two Colombian populations are far more similar to each other than to any of the other admixed American populations that were analyzed. Conley et al. emphasize that this shared genetic legacy “underscores the biological reality of a common, unifying identity that binds the country.”

CITATION

A Comparative Analysis of Genetic Ancestry and Admixture in the Colombian Populations of Chocó and Medellín

Andrew B. Conley, Lavanya Rishishwar, Emily T. Norris, Augusto Valderrama-Aguirre, Leonardo Mariño-Ramírez, Miguel A. Medina-Rivas, I. King Jordan

G3: Genes, Genomes, Genetics October 2017 7: 3435-3447; https://doi.org/10.1534/g3.117.1118

http://www.g3journal.org/content/7/10/3435

Despite being covered by a massive, permanent ice sheet, Greenland has been continuously inhabited by humans for over a thousand years. Most modern Greenlanders are Inuit whose ancestors migrated eastward from Canada around 1000 AD, bringing technology like kayaks and dogsleds. They eventually settled on the coasts of the world’s largest island, hunting whales and seals. As well as their cultural and historical contributions, the people of Greenland carry important information in their genes. A study by Pedersen and colleagues published in the February issue of GENETICS examines variation in whole exome sequences of 18 Greenlandic Inuit individuals, showing the power this unique population could have for identifying rare genetic variants linked to diseases.

The patterns of genetic variation in any group of organisms, including humans, are closely tied to its size and history. The larger a population, the more genetically diverse it should be, and a migrant population will be less diverse than its source since its genetic diversity is a subset of the larger source population. Human populations from Africa are the most genetically diverse of all because their ancestors were the source for all other groups that migrated out of Africa to populate the rest of the world.

The ancestors of the Greenlandic Inuit journeyed most of the way around the world to get from equatorial Africa to the fringes of the Arctic Circle in Greenland: through Asia and across the Bering Strait to North America, and then across vast northern Canada to Greenland. This epic migration and settlement history is reflected in their genomes. Pedersen and colleagues show that the Greenlandic Inuit population has recently undergone a prolonged bottleneck of around 20,000 years, making it one of the historically smallest and most isolated human populations. When compared to much larger populations, the patterns of variation differ in specific ways.

The study found that Greenlandic Inuit have fewer genetic variants overall than other human populations tested so far, but the variants they do carry occur at higher frequencies. This might reflect the prediction that very small populations see increases in deleterious variation since natural selection is less effective in small groups. To explore this idea, the authors estimated how much of the genetic variation in the Greenlandic population is likely to alter protein function, including potential loss-of-function alleles and variants that alter the amino acid sequence of a protein. However, the evidence was conflicting, and the results depended strongly on the type of model used.

Nevertheless, the increased frequency of certain rare variants compared to other groups could prove a boon for disease association mapping by increasing the statistical power to detect links between gene variants and human diseases. One example is a single nucleotide change in the gene TBC1D4, which has been previously linked to type 2 diabetes. This particular allele is found at a much higher frequency in the Greenlandic Inuit population than other surveyed populations, even though type 2 diabetes is not reported to be more common in Greenland. The authors speculate this difference may be due to compensatory variation elsewhere in the genome since type 2 diabetes is a multigenic trait with many underlying contributing factors. In contrast, sucrase-isomaltase deficiency, a metabolic disease that prevents infants from digesting a certain sugar, is very rare in most populations but affects about 5–10% of people in Greenland. The exome data suggests this is due to the relatively high frequency of a particular frameshift mutation in the SI gene.

By any measure, Greenland is an isolated place. The people who first settled there used their creativity and ingenuity to flourish in a difficult environment, the same way humans have all over the Earth. Though genetic patterns differ subtly between groups, the deeper similarities show how closely humanity is connected. Thanks to this shared heritage, small differences between populations can serve as powerful tools for unearthing discoveries that will help us all.    

Pedersen, C. E. T., Lohmueller, K. E., Grarup, N., Bjerregaard, P., Hansen, T., Siegismund, H. R., Moltke, I., & Albrechtsen, A. (2016). The Effect of an Extreme and Prolonged Population Bottleneck on Patterns of Deleterious Variation: Insights from the Greenlandic Inuit. GENETICS, 205 (2): 787-801. DOI:10.1534/genetics.116.193821

http://www.genetics.org/content/205/2/787

Well over 15,000 years ago, a man and a bear died in a cave in the Jura Mountains in modern-day Switzerland. That was the end of the story for millennia—until their remains were discovered in 1954 by researchers investigating the cave. Further work in the 1990s uncovered the fact that the man had, in fact, shot the bear with an arrow. This established their bond beyond a coincidentally shared grave, identifying the man as a hunter-gatherer. Now, thousands upon thousands of years after he lived, geneticists are developing new methods to analyze this hunter-gatherer’s DNA in an effort to better understand genetic diversity in ancient humans—and how that compares to our diversity today.

In a report published this month in GENETICS, Kousathanas et al. describe a method to infer the level of genetic diversity from sequence data that doesn’t fit the bill for more common methods. This method improves our ability to analyze genomic sequences from ancient DNA samples, as well as other datasets with less than ideal sequence data.

Heterozygosity—that is, presence of two different bases or alleles at a single site—is a marker of genetic diversity, and the degree of heterozygosity present throughout a particular genome segment gives insight into that region’s evolutionary history. Identifying—or “calling”—heterozygosity is fairly straightforward with high-quality DNA and current sequencing technologies, which produce very high depths of coverage. Depth of coverage refers to the number of times a specific base in the genome is represented in the sequencing data; current next-generation sequencing methods can produce 30-40X coverage, sometimes higher. Existing analysis pipelines can easily call heterozygosity from high coverage data.

Calling heterozygosity from low coverage data is much harder because sequencing machine errors can be mistaken for true genetic variation. Low coverage sequence data is often all that can be derived from the tiny amounts of degraded DNA typically recovered from ancient tissue samples. To make things worse, ancient samples are also affected by postmortem DNA damage, which can dramatically increase the number of sequencing errors in the data. Determining levels of genetic diversity from prehistoric human DNA is a challenge, and it’s one that Kousathanas et al. attempt to solve by creating a method that uses a probabilistic framework to infer heterozygosity.

In broad terms, the method involves three steps: 1) estimate parameters of models that describe post-mortem DNA damage since that damage causes signature base substitutions, particularly at the beginning of sequencing reads, 2) recalibrate the quality scores given by sequencing machines by assuming a section of the sequence is monomorphic (e.g. the X chromosome in human males), which allows for better determination of the base-specific error rates present in the data 3) infer heterozygosity while accounting for the inferred DNA damage profiles and the recalibrated quality scores. Their method allows them to produce very accurate estimates of heterozygosity for regions about one megabase in size, which the scientists demonstrate first by analyzing a simulated chromosome.

To compare genetic diversity in ancient and modern humans, Kousathanas et al. analyzed genomic data from four prehistoric individuals from Europe and Africa—including the hunter-gatherer found in Switzerland—and several male individuals from the 1000 Genomes Project.

The researchers found that both ancient and modern African samples show much greater genetic diversity than European individuals, and diversity inferred from the ancient European samples was lower than that found in modern samples, which they think is due to smaller paleolithic population sizes.

The ability to more accurately analyze genomic diversity in “difficult” DNA samples may provide a more detailed look into the past, allowing a better understanding of the evolutionary processes that have shaped genetic variation.

CITATION

Kousathanas, A., Leuenberger, C., Link, V., Sell, C., Burger, J., Wegmann, D. 2017. Inferring Heterozygosity from Ancient and Low Coverage Genomes. GENETICS 205(1): 317-322. doi: 10.1534/genetics.116.189985

http://www.genetics.org/content/205/1/317

Geography and ecology are key factors that have influenced the genetic makeup of human groups in southern Africa, according to new research discussed in the journal GENETICS, a publication of the Genetics Society of America. By investigating the ancestries of twenty-two KhoeSan groups, including new samples from the Nama and the ≠Khomani, researchers conclude that the genetic clustering of southern African populations is closely tied to the ecogeography of the Kalahari Desert region.

A Nama man holding whip outside his tent while herding sheep and goats in the Richtersveld, South Africa. Photo courtesy of Justin Myrick.

The name KhoeSan refers to several indigenous populations in southern Africa; KhoeSan people speak “click” languages and include both hunter-gatherer groups and pastoralists. They are genetically distinct and strikingly isolated from all other African populations, suggesting they were among the first groups to diverge from the ancestors of all humans. Much scientific interest has focused on the KhoeSan as researchers try to reconstruct this early divergence; however, little genetic material was collected until the past decade.

Brenna Henn, of Stony Brook University in New York, has been studying southern African population genetics for over a decade. She notes that there is a tendency to lump all indigenous southern Africans into a single group – often called “Bushmen” – but in fact, the KhoeSan includes many distinct populations. She and her team set out to explore genetic diversity in the area and to better understand the differences between these KhoeSan groups.

“For the last twenty years or so, there has been a lot of interest in understanding how genetic patterns are determined by geography in addition to language,” says Henn. The genetic differences between human populations are strongly correlated with their linguistic histories, and both of these factors are also linked with geography. Henn argues that ecology and geography together are likely a better explanation for the genetic differentiation between groups than either linguistic differences or method of subsistence (i.e. hunting/gathering or farming). However, much of the research on southern African populations had previously focused on linguistics and subsistence, with little attention paid to ecogeography.

Henn and her colleagues analyzed genetic information from the KhoeSan. They collected genome-wide data from three south African populations: the Nama, the ≠Khomani San, and the South African Coloured (SAC) group. Their analysis also included samples from 19 other southern African populations. It quickly became apparent that the geography of the Kalahari Desert was closely tied to the population structure that they uncovered. The outer rim of the Kalahari Desert presented a barrier to genetic mixing, while populations that live within the Kalahari basin mixed more freely.

Their findings suggest a more complex history for the KhoeSan populations than originally predicted. Previous work argued for a northern vs. southern divergence pattern among the human groups, but this new work identifies five primary ancestries in the region, which points to a geographically complex set of migration events responsible for the heterogeneity observed in the region.

Henn points out that there are more KhoeSan populations who were not sampled. Sampling in the area is a significant challenge for a number of reasons, including the complex politics of the region in the post-Apartheid era. Most populations in South Africa and Zimbabwe no longer identify as KhoeSan and have been absorbed into other populations over the past 500 years. Still, their findings add to the body of knowledge surrounding the history of southern African populations – while also complicating them.

“There are a lot of threads of information to bring together – linguistics, subsistence, geography, genetics, archaeology. They don’t always reconcile easily,” says Henn.

View of arid mountains at dusk in the Richtersveld Community Conservancy, South Africa.

The challenge continues to fascinate Henn and her colleagues. She established a field site in 2005 and has maintained and expanded it over the years as she continues to research ancestry in the KhoeSan. She emphasizes that it is extremely important for investigators doing research in developing countries to work closely with local collaborators as they try to understand the genetic diversity of the region.

“The first author on this paper, Caitlin Uren, is a South African student. I’m very proud of our collaboration and her excellent work,” says Henn.

Much work remains to be done in understanding and uncovering the factors that contributed to the formation of southern African population structure.

“There is a huge amount of diversity in southern Africa populations. These groups speak differently, look distinct, and have divergent genetic histories. They are not homogenous people, and the historic and prehistoric factors that led to their divergence are still being explored. It’s amazing how much work there is to do.”

CITATION

Uren, C., Kim, M., Martin, A.R., Bobo, D., Gignoux, C.R., van Helden, P.D., Möller, M., Hoal, E.G., Henn, B.M. 2016. Fine-Scale Human Population Structure in Southern Africa Reflects Ecogeographic Boundaries. GENETICS, 204(1): 303-314. doi: 10.1534/genetics.116.187369 http://www.genetics.org/content/204/1/303

The Neanderthal genome included harmful mutations that made the hominids around 40% less reproductively fit than modern humans, according to estimates published in the latest issue of GENETICS. Non-African humans inherited some of this genetic burden when they interbred with Neanderthals, though much of it has been lost over time. The results suggest that these harmful gene variants continue to reduce the fitness of some populations today. The study also has implications for management of endangered species.

“Neanderthals are fascinating to geneticists because they provide an opportunity to study what happens when two groups of humans evolve independently for a long time–and then come back together,” says study leader Kelley Harris, of Stanford University. “Our results suggest that inheriting Neanderthal DNA came at a cost.”

Previous studies of DNA extracted from Neanderthal remains revealed that these Eurasian hominids were much more inbred and less genetically diverse than modern humans. For thousands of years, the Neanderthal population size remained small, and mating among close relatives seems to have been common.

Then, 50,000-100,000 years ago, groups of anatomically modern humans left Africa and moved to the homelands of their distant Neanderthal cousins. The two groups interbred, mingling their previously distinct genomes. But though a small fraction of the genome of non-African populations today is Neanderthal, their genetic contribution is uneven. Neanderthal sequences are concentrated in certain parts of the human genome, but missing from other regions.

“Whenever geneticists find a non-random arrangement like that, we look for the evolutionary forces that caused it,” says Harris.

Harris and her colleague Rasmus Nielsen (University of California, Berkeley / University of Copenhagen) hypothesized that the force in question was natural selection. In small populations, like the Neanderthals’, natural selection is less effective and chance has an outsized influence. This allows weakly harmful mutations to persist, rather than being weeded out over the generations. But once such mutations are introduced back into a larger population, such as modern humans, they would be exposed to the surveillance of natural selection and eventually lost.

To quantify this effect, Harris and Nielsen used computer programs to simulate mutation accumulation during Neanderthal evolution and to estimate how humans were affected by the influx of neanderthal genetic variants. The simulations incorporated data on the mutation rates, genome properties, and population dynamics of hominids.

The results suggest that Neanderthals carried many mutations with mild, but harmful effects. The combined effect of these weak mutations would have made Neanderthals at least 40% less fit than humans in evolutionary terms–that is, they were 40% less likely to reproduce and pass on their genes to the next generation.

Related conclusions were reached in an independent study that used very different methods, led by Ivan Juric at the University of California, Davis. This work is currently being peer reviewed and is available at the pre-publication preprint server bioRxiv.

Harris and Nielsen’s simulations also suggest that humans and Neanderthals mixed much more freely than originally thought. Today, Neanderthal sequences make up approximately 2% of the genome in people from non-African populations. But Harris and Nielsen estimate that at the time of interbreeding, closer to 10% of the human migrants’ genome would have been Neanderthal. Because there were around ten times more humans than Neanderthals, this number is consistent with the two groups acting as as single population that interbred at random. Recent DNA evidence has confirmed that the Neanderthal contribution to Eurasian genomes was higher in the past.

Although most of the harmful mutations bequeathed by our Neanderthal ancestors would have been lost within a few generations, a small fraction likely persists in people today. Harris and Nielsen estimate that non-Africans may have historically had approximately 1% lower reproductive fitness due to their Neanderthal heritage. This is in spite of the small number of Neanderthal gene variants thought to be beneficial today, including genes related to immunity and skin color.

The results also have implications for conserving endangered species. Many vulnerable populations in fragmented habitats face similar genetic problems to the Neanderthals: inbreeding, low genetic diversity, and accumulation of harmful mutations. One management strategy for overcoming these problems is genetic rescue–improving the health of an inbred population by outcrossing it with other populations.

“Genetic rescue is designed to move gene variants from an outbred population to an inbred population,” says Harris. “Our results suggest managers must ensure that this movement only goes one way; otherwise harmful mutations from the inbred population may lower the fitness of the outbred group.”

CITATION

The Genetic Cost of Neanderthal Introgression
Kelley Harris, Rasmus Nielsen
GENETICS June, 2016 Vol. 203, no. 2 881-891;
DOI: 10.1534/genetics.116.186890
http://www.genetics.org/content/203/2/881

FUNDING

This work was supported by an NIH Ruth L. Kirschstein National Research Service Award (NRSA) to Kelley Harris, award number F32GM116381 and by National Science Foundation IR01GM109454-01 (Rasmus Nielsen)