What an amazing Olympics that was, despite everything! Here are some (track) highlights from my perspective, with video clips. My performers of the meet: Sifan Hassan and Karsten Warholm.
In case you missed it, the Dutch woman Sifan Hassan won two golds (5+10) and bronze (1500). It's hard to put into words how awesome her range is +her huge kick. Here she falls in a qualifying round of the 1500, gets up to win (and wins the 5000 that night)
The M 400 hurdles with Karsten Warholm (Norway) and Rai Benjamin (US) was always going to be epic. Warholm had recently broken the 29 year world record, and Benjamin was nearly as fast at the US trials. Their race in Tokyo was one for the history books
The W 400 hurdles was also awesome. Dalilah Muhammed and Sydney McLaughlin have had the greatest current rivalry in track. Dalilah was the 2016 champ and WR holder, but Sydney has been gaining ground, grabbing Delilah's WR at the Trials: Now this:
Speaking of world records, there's also the amazing triple jump record from Venezuelan Yulimar Rojas. 15.67. Try measuring out 15 meters across your house
A real fan-pleaser was Uganda's great Joshua Cheptegei, world record holder at 5000 and 10,000. After a slightly disappointing silver here in the 10, he dropped a last mile of 3:58 in sweltering heat to win a famous 5000 victory:
W 800m: There has been an inevitability about Athing Mu all year. The 19 year-old Texas A&M freshman dominated the US Trials against a loaded field. Here she made it look oh-so-easy with a win and US record. Surely she will be an all-time great:
Elaine Thompson Herah stamps herself as an all-time sprinting great with a sensational second double 100-200 gold. Everyone's favorite running epidemiologist, Gabby Thomas, took 3rd. I've always enjoyed how much pleasure Gabby seems to take in racing
A brief footnote: Thompson Herah's 100 was slower only than the busted-wind reading 100 by FloJo, from way back in 1988. This was the subject of my first ever paper:
jstor.org/stable/29775326
Always my favorite races, the 1500s. Faith Kipyegon joins the great Seb Coe as a double Olympic champ with a huge kick in an Oly record time. Laura Muir finally gets that long-deserved medal and a British record as well, so well done Laura:
I first saw Jakob Ingebrigtsen in 2018 when, as a 17-year old, he beat the Rio Olympic champ, Matt Centrowitz, to win an evening race at Stanford. When I watched that Stanford race, at first I was confused about who he was. Nobody is confused any more:
and a longer clip of the 1500 til it gets taken down. Something tells me Norway enjoyed their Olympics. Also well done to Britain's Josh Kerr:
Stanford grad Valarie Allman takes a remarkable victory in the discus with a huge first round throw right before a crazy rainstorm unsettled the competition. In other throwing news, my wife's former stats TA, Mackenzie Little takes 8th in the Javelin
It's an all-Carribean M 400 as Steven Gardiner (Bahamas) storms away to win. Steve Solomon who once house-sat for me beats the PR he set in London 2012 to reach the semis
Last for now, but not least, awards for courage:
Courtney Frerichs in the steeplechase was surely nobody's favorite to win, but she made a huge move with 4 laps to go and very nearly pulled out a famous upset (here coming in an inspirational 2nd):
Many people thought the US women's marathon squad was weak, but Molly Seidel upset the form charts in sweltering weather to take a brilliant 3rd:
there were many more fantastic races that I haven't covered here, but it's time for me to get out for my daily plod around before the men's marathon starts. And hopefully get some work done next week, now that the Olympics are almost over

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More from @jkpritch

23 Apr 20
Yesterday I ran a thread about our new paper using UK Biobank GWAS to identify core genes for several traits:


Today I want to expand on some fascinating things that we learned about sex differences in testosterone.
We and other groups have recently noticed strong sex differences between males and females in testosterone genetics (our analysis led by Sahin Naqvi @snaqvi1990 and Nasa Sinnott-Armstrong). Lead hits for testosterone share ~no overlap, and highlight different types of genes Image
Another way to look at this is by comparing effect sizes for hit SNPs in females vs males. No correlation. The insets show two other traits that are more typical (urate, SHBG). The genomewide genetic correlation by LDSR is also ~0. Image
Read 15 tweets
22 Apr 20
Our latest: "GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background": biorxiv.org/content/10.110…
We use urate, IGF-1, and testosterone as model molecular traits to learn general principles about the architecture of complex traits.
Most importantly, this project is thanks to wonderful work by Nasa Sinnott-Armstrong and Sahin Naqvi @snaqvi1990 in my lab; thanks also @manuelrivascruz whose work with Nasa doing GWAS of UK Biobank biomarkers led to this project.
We picked urate, IGF-1 and testosterone as models for complex trait architecture.

All 3 traits show strong enrichment of genes involved in the relevant synthesis, transport or signaling pathways, depending on the trait. However, most h^2 comes from a huge polygenic background.
Read 18 tweets
7 Nov 19
Delighted to share our latest work, on ancient DNA of individuals from in and around Rome, spanning the last 12,000 years. At its peak Rome was the largest city of the ancient world with 1M inhabitants, controlling an empire of 70M people
science.sciencemag.org/content/366/64…
This was a wonderful collaboration with Ron Pinhasi and Alfredo Coppa; work was led by amazing people in my lab: @antmarge, @ziyue_gao, @mootspoints and other fantastic collaborators: @gaspi60 @ersilia_maria @diegoisworking @daniMfernandes @SerenaAneli @danjcotter @bigskybioarch
We sequenced 127 genomes, from 29 archaeological sites in Rome and central Italy. These span from the Mesolithic to the Medieval period. We see two major population transitions, corresponding to influxes of Neolithic and Steppe ancestry, as seen elsewhere in Europe.
Read 12 tweets

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