Comparative Population Genetics in the Human Gut Microbiome

We are delighted to share our latest preprint on:

Comparative Population Genetics in the Human Gut Microbiome

William Shoemaker, Daisy Chen, and Nandita Garud

The genetic variation in the human gut microbiome is responsible for conferring a number of crucial phenotypes like the ability to digest food and metabolize drugs. Yet, our understanding of how this variation arises and is maintained remains relatively poor. Thus, the microbiome remains a largely untapped resource, as the large number of co-existing species in this microbiome presents a unique opportunity to compare and contrast evolutionary processes across species to identify universal trends and deviations. Here we outline features of the human gut microbiome that, while not unique in isolation, as an assemblage make it a system with unparalleled potential for comparative population genomics studies. We consciously take a broad view of comparative population genetics, emphasizing how sampling a large number of species allows researchers to identify universal evolutionary dynamics in addition to new genes, which can then be leveraged to identify exceptional species that deviate from general patterns. To highlight the potential power of comparative population genetics in the microbiome, we re-analyzed patterns of purifying selection across ~40 prevalent species in the human gut microbiome to identify intriguing trends which highlight functional categories in the microbiome that may be under more or less constraint.


Welcome rotation students!

We are delighted to be working with rotation students Helen Huang, Nicole Zeltser, Jon Mah, Alejandro Espinoza, and Albert Xue this year!

Summer lab meetings

This summer we have a full house with many interns joining us, as well as new incoming PhD students including Albert Xue, Jon Mah, and Mariana Harris!  Here’s our group meeting over Zoom!


Welcome BIG students!

This summer we are delighted to welcome four new BIG (Bruins in Genomics) summer interns to our group: Shavonna Jackson, Etan Dieppa, Kevin Delao, and Maya Singh!


Etan Dieppa

Maya Singh

Shavonna Jackson

Kevin Delao


New paper on BioRxiv: Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data

Check out our latest paper on BioRxiv:

Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population of Drosophila melanogaster (DGRP) allowed us to rediscover three known cases of adaptation at the loci Ace, Cyp6g1, and CHKov1 known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harris et al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim, confusingly, that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps in D. melanogaster are first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that the demographic models employed in such analyses are generally necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require fitting a very large number of free parameters.

Demographic models tested in this paper

Leah’s talk at QCB!

Today Leah Briscoe will speak about her work on batch correction of microbiome data for improved phenotype prediction at the QCBio seminar series!

Resources for working from home during COVID-19

Nandita had the opportunity to join the Women in Science group at USC for a virtual coffee hour where she and others had a chance to brainstorm ideas for working at home during the COVID-19 quarantine.

Some resources if you want to work on your computational chops at home:

CS50: Introduction to Computer Science (Harvard)
CS229: Machine Learning (Stanford)
Learning Bioinformatics at Home (Harvard)

The Rosalind Project (Bioinformatics)

SLiM self-guided tutorial (population genetics simulation software)

Writing Rocks: a daily routine for writing

Welcome Will and Ricky!

We are delighted to welcome William Shoemaker and Ricky Wolff to our growing group!

Will is an NSF-funded postdoctoral scholar and joining us from his PhD lab in Indiana University.

Ricky is an MS student in the EEB department, recently graduated from Columbia University.

Thrilled to have you both join us!

Genetic Adaptation in New York City Rats

Our latest collaboration on studying the Genetic Adaptation in NYC Rats is now on BioRxiv! Here we apply the G12 statistic, which can detect hard and soft sweeps from unphased data, to 29 whole genomes from NYC rats. We find intriguing signals of adaptation to urbanized environments in this population. Check out this perspective published in Nature on our work!

Figure replicated from our paper

Longitudinal linked read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment

We are happy to share our latest collaboration examining the evolutionary dynamics in a single indivudual with high-res longitudinal data generated with long-read sequencing technology. Here, we show that genetic compositons within species can change in response to antibiotic perturbations even when the species relative abundance does not change significantly with time (see figure from the paper below). This indicates that the genetic changes observed within microbiomes play an important role in responding to selective pressures.